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Posts Tagged ‘Metabolomics’


Functional magnetic resonance imaging

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Demystifying BOLD fMRI Data

What does blood oxygen level–dependent functional magnetic resonance imaging actually tell us about brain activity?

By Tim Vernimmen | February 17, 2016   http://www.the-scientist.com/?articles.view/articleNo/45366/title/Demystifying-BOLD-fMRI-Data

BOLD signal in no task (“resting state”) fMRI YOUTUBE, ZEUS CHIRIPA
http://www.the-scientist.com/images/News/February2016/yC7leMG%20-%20Imgur.gif

he relevance and reliability of blood oxygen level-dependent functional magnetic resonance imaging (BOLD fMRI) data have been hotly debated for years, not least because it is still unclear what aspects of brain activity the technique is picking up. “In many ways, this would seem to be an unacceptable method for neuroscience,” said Ed Bullmore from the University of Cambridge, at a Royal Society-organizedgathering of neuroscients late last month. “But if you’re interested in humans, there isn’t much of a choice.” Bullmore and colleagues had convened in Buckinghamshire, U.K., to discuss what, exactly, BOLD fMRI results can tell us.

“What we do know, of course, is what MRI measures,” said Robert Turner, director emeritus of the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany. MRI measures the magnetization of hydrogen protons in water molecules excited by pulses of radio waves that lead their spins to temporarily align. “Over the next few tens of milliseconds,” Turner noted, “their orientations fan out again, and the magnetization we measure will quickly decrease.”

But what can this tell us about brain activity?

When hemoglobins—the iron-rich oxygen-carrying proteins in our blood—run out of oxygen, Turner explained, “they become paramagnetic,” disturbing the local magnetic field. This makes the protons spin out of phase more rapidly.” One might think this means BOLD fMRI highlights oxygen consumption by active neurons, but in reality, such activity is rarely measured.

What BOLD does reveal is what usually happens next: fresh blood rushes into the area, flushing out paramagnetic deoxyhemoglobin and replacing it with new, oxygenated hemoglobin. Since this does not interfere with the proton spins, the result is a larger fMRI signal. So BOLD fMRI reflects a combination of changes in blood flow and oxygen consumption within the brain—not neuronal activity itself.

“This means that if BOLD shows you a large blob of activity, that doesn’t necessarily mean that all the neurons in that region are spiking,” said David Attwell of University College London, one of the meeting’s organizers. “So what we really need to know is how neurons are influencing bloodflow.”

To find out, Attwell and his colleagues are studying postmortem slices of rodent brain to better understand the interactions between neurons, blood vessels, and supporting cells such as astrocytes and pericytes. These cells wrap around the vasculature and likely affect its response to local neural activity.

Research on living animals, on the other hand, has suggested that endothelial cells lining the brain’s blood vessels may also play an active role in coordinating such responses, as they are known to do elsewhere in the body. “The wave of vessel dilation resulting in increased bloodflow travels much faster and farther than could be explained by astrocytes and pericytes alone,” said Elizabeth Hillman of Columbia University in New York City, whose lab has developed an optical method to look into rat brains directly. “Moreover, if we disable parts of the endothelium, we can see that wave come to a halt.”

More recently, the Hillman lab unexpectedly uncovered what seems to be a convincing link between neural and vascular activity. “While trying to disprove that resting state activity in the brain could teach us about neural connections we have actually been able to observe seemingly spontaneous neural activity that correlates with bloodflow quite tightly,” Hillman told The Scientist, “which would be hard to show with the very precise single-neuron measurements many neuroscientists prefer, but when you zoom out and look at the larger picture, the synchrony is hard to deny—and believe me, we’ve tried very hard to explain these results away.”

If these unpublished findings stand up to the scrutiny of Hillman’s colleagues,  this would be reassuring news for neuroscientists using BOLD fMRI to study neural activity.

But in some brains, BOLD may not work at all, Hillman cautioned. “In the developing brain of young animals, for example, we find that BOLD activity is very unusual,” she said. “Initially, the bloodflow response doesn’t seem to be attuned to neural activity at all, so fMRI may be as good as blind.”

Diseased brains can also skew results. “Pathology may affect the BOLD signal in the absence of any changes in neurons themselves,” said Bojana Stefanovic of Toronto’s Sunnybrook Research Institute. In patients who suffered a stroke, for example, the amount of water may be reduced where cells have died, and increased by oedema in some of the surrounding tissues. The brain’s bloodflow may also be altered by disruptions to the vasculature, for example, or the formation of scar tissue.

The best way to deal with this depends on the research question, Stefanovic told The Scientist. “There’s this idea that if we can link BOLD to neuronal activity—that would be nirvana,” she said. “Clinicians, however, are looking for measures with a clear link to symptoms. And, fortunately, there is no shortage of disease effects BOLD can sense.”

Cognitive neuroscientist Geraint Rees of University College London sounded a similar note. “If whatever BOLD is measuring reproducibly correlates to the behavior I’m interested in, such as attention or consciousness, I am less worried about the physiological details behind it,” he said. “Which does not mean, of course, I don’t consider them interesting—otherwise, I wouldn’t be here.”

Meanwhile, researchers are developing methods to measure human neural activity more directly, learning more about BOLD fMRI data along the way. “Thanks to over 30 Parkinson’s patients who agreed to play an investment game while undergoing surgery for the placement of a deep-brain stimulation probe, we were able to directly measure the striatal dopamine response we only knew from rodents and human BOLD,” said Read Montague of the Virginia Tech Carilion Research Institute. “Surprisingly, we found that while BOLD responds to expected reward and actual outcome separately, the dopamine response integrates them into one ‘better or worse’ signal.”Montague’s team would next like to explore whether the same is true for people without Parkinson’s disease, which is known to affect dopaminergic neurons.

For now, however, the researchers’ results demonstrate the benefits of applying other techniques in parallel with BOLD fMRI. Not only might this approach reveal insights BOLD cannot, it might also help neuroscientists better understand the results of past fMRI experiments.

Interpreting BOLD: a dialogue between cognitive and cellular neuroscience

Kavli Royal Society Centre, Chicheley Hall, Newport Pagnell, Buckinghamshire, MK16 9JJ

Overview

Theo Murphy international scientific meeting organised by Dr Anusha Mishra, Professor David Attwell FRS, Dr Zebulun Kurth-Nelson, Dr Catherine N. Hall and Dr Clare Howarth

Functional imaging reveals statistical patterns of coordination between brain areas. (Copyright: Crossley N.A. et al., PNAS 2013 110:11583-8.)

Cognitive neuroscientists use BOLD signals to non-invasively study brain activity, although the neurophysiological underpinnings of these signals are poorly understood. By bringing together scientists using BOLD/fMRI as a tool with those studying the underlying neurovascular coupling mechanisms, the aim of this meeting was to create a novel dialogue to understand how BOLD relates to brain activity and inform future neurovascular and cognitive research.

 

Using an achiasmic human visual system to quantify the relationship between the fMRI BOLD signal and neural response

 

Achiasma in humans causes gross mis-wiring of the retinal-fugal projection, resulting in overlapped cortical representations of left and right visual hemifields. We show that in areas V1-V3 this overlap is due to two co-located but non-interacting populations of neurons, each with a receptive field serving only one hemifield. Importantly, the two populations share the same local vascular control, resulting in a unique organization useful for quantifying the relationship between neural and fMRI BOLD responses without direct measurement of neural activity. Specifically, we can non-invasively double local neural responses by stimulating both neuronal populations with identical stimuli presented symmetrically across the vertical meridian to both visual hemifields, versus one population by stimulating in one hemifield. Measurements from a series of such doubling experiments show that the amplitude of BOLD response is proportional to approximately 0.5 power of the underlying neural response. Reanalyzing published data shows that this inferred relationship is general.

DOI: http://dx.doi.org/10.7554/eLife.09600.001

 

eLife digest

When a part of the brain becomes active, more oxygen-rich blood flows to it to keep its neurons supplied with energy. This flow of blood can be measured using a technique called functional magnetic resonance imaging (fMRI). Yet, it was not known exactly how the magnitude of the signal recorded from the oxygenated blood flow – dubbed the BOLD (blood oxygenation level dependent) signal – relates to the level of neural activity.

In most people, the brain area that processes fundamental visual information – called the visual cortex – receives signals from both eyes, sent via the optic nerves. The two eyes’ optic nerves are bridged together with a structure called the optic chiasm, which ensures that each side of the brain gets input from both eyes for one side of the visual field. However, in rare cases, a person may lack an optic chiasm, and instead each side of the brain processes information about both sides of the visual field seen by one eye. This condition is known as achiasma.

Bao et al. have now used fMRI and behavioral experiments to study the brain activity of a volunteer who lacks an optic chiasm. This revealed that each half of the visual field stimulates different neurons in the same brain hemisphere of an achiasmic visual cortex. The two sets of neurons do not interact with each other, but they do share the same local blood supply. Moreover, these sets of neurons are organized in such a way as to preserve normal vision, and can be controlled independently using visual stimulation.

If both sets of neurons are stimulated with the same visual input at the same time, they together trigger twice as much neural activity as when just one set is stimulated. This also causes an increased BOLD signal as more blood flows to that region of the brain. Bao et al. were therefore able to infer a mathematical relationship between neural activity and the BOLD signal. This revealed that the magnitude of the BOLD signal is proportional to the square root of the underlying neural activity. Reanalyzing previously published BOLD data from other fMRI studies of healthy humans and monkeys supports this conclusion.

Bao et al.’s study provides scientists with a human model for noninvasively studying the origins and neural underpinnings of fMRI measurements, which may change how we analyze and interpret brain-imaging results in the future. The biggest challenge that researchers will likely face is in recruiting individuals with this rare condition of achiasma.

DOI:http://dx.doi.org/10.7554/eLife.09600.002

 

Functional magnetic resonance imaging (fMRI) based on the blood oxygenation level dependent (BOLD) signal has provided unprecedented insights into the workings of the human brain. The quantitative relationship between neural signals and the fMRI BOLD response is not precisely known and remains an active area of investigation. Most studies using the BOLD signal to infer brain activity rely on analytical methods (e.g., the general linear model) that assume a linear relationship between the BOLD signal and neural response, despite noticeable deviations from linearity (Boynton et al., 1996).

The BOLD signal is indirectly related to local neural response through mechanisms associated with oxygen metabolism and blood flow (Davis et al., 1998; Hoge et al., 1999; Thompson et al., 2003;Griffeth and Buxton, 2011). The neural response that is associated with information processing is itself multi-faceted. It comprises several interacting components, including subthreshold and suprathreshold electrical activities, the transport, release and reuptake of neurotransmitters, and various maintenance activities. Each of these components has its own metabolic and hemodynamic consequences. The common extracellular measurements of neural response include single- and multi-unit spiking activities and local field potential (LFP). While seminal studies have demonstrated a close relationship between the BOLD signal and these extracellular measurements of neural response (Logothetis et al., 2001; Mukamel et al., 2005), the quantitative nature of this relationship has not been sufficiently characterized. More importantly, since the relationship between these extracellular measurements and the intracellular components of neural activity is complex, the measured relationship between the BOLD signal to any specific extracellular components (e.g., power in the gamma band of LFP) may not reflect the relationship between the BOLD signal and the totality of neural response.

Most applications of fMRI, particularly in human neuroscience, sidestep any need for explicitly estimating neural activity and instead rely on establishing a direct relationship between the BOLD response and the stimulus condition. The general approach is to assume the BOLD responses evoked at different times and in different stimulus conditions sum linearly. Boynton and colleagues (1996) studied how the BOLD signal varied with the contrast and duration of stimulus presentation in the striate cortex and found that the system is approximately linear, in the sense that the BOLD response evoked by a 12 s stimulus was well approximated by summing the responses from two consecutive 6-s stimulations, even though predictions based on stimulations of much shorter durations (e.g., 3 s) failed to accurately predict the long-duration stimulus response. While this and similar studies (Cohen, 1997; Dale and Buckner, 1997; Heckman et al., 2007) have clearly noted the lack of linearity, their general message of an approximately linear system has nevertheless been used to justify the broad application of the general linear model (GLM) in fMRI data analyses. While the neural response is not explicitly involved in this type of analysis, it is always in the background — any nonlinearity observed in the BOLD response, e.g., in surround suppression or adaptation (Grill-Spector and Malach, 2001; Kourtzi and Huberle, 2005; Larsson and Smith, 2012) is often attributed to the underlying nonlinear neural response. The implicit assumption in common practice is that the relationship between the BOLD response and the neural response is essentially linear, a view that is widespread (Logothetis and Wandell, 2004) but under-examined.

An extensive set of biophysical models has been proposed to express either the steady-states (Davis et al., 1998; Griffeth and Buxton, 2011) or the dynamics of the BOLD response (Buxton et al., 1998;Mandeville et al., 1999; Feng et al., 2001; Toronov et al., 2003; Blockley et al., 2009; Kim and Ress, 2016) in terms of more basic physiological components, such as blood flow, blood volume, oxygen saturation, and oxygen extraction fraction in different vascular compartments. These biophysical models are foundational in our understanding of the BOLD signal, yet they do not provide any explicit and quantitative linkage between the neural response and the physiological components that are the inputs to these models. Friston et al. (2000) (see also Stephan et al., 2007), proposed a linkage between the evoked neural response and the blood-flow parameter of the Balloon model by Buxton et al. (1998). While the resulting model is a powerful tool for inferring effective connectivity between brain regions from the BOLD signal, direct empirical support for this specific linkage is limited.

How could we empirically determine the quantitative relationship between the BOLD signal and the neural response, and do so when the constituents of the neural response are not comprehensively defined? A condition known as achiasma or non-decussating retinal-fugal fibre syndrome may provide an excellent model system for this purpose. This congenital condition prevents the normal crossing of optic nerve fibers from the nasal hemi-retina to the brain hemisphere contralateral to the eye (Apkarian et al., 1994; 1995). The result is a full representation of the entire visual field (as opposed to only half the visual field) in each cerebral hemisphere (Williams et al., 1994; Victor et al., 2000; Hoffmann et al., 2012; Davies-Thompson et al., 2013; Kaule et al., 2014). Specifically, the representations of the two visual hemifields are superimposed in the low-level visual areas (V1-V3) ipsilateral to each eye, such that two points in the visual field located symmetrically across the vertical meridian are mapped to the same point on the cortex (Hoffmann et al., 2012). In other words, there are two pRFs for every point on this person’s low-level visual cortex. The two pRFs are symmetrically located across the vertical meridian. Prior to the current study, it was not known if these pRFs were represented by one or two neural populations, or if these neural populations interacted.

In the current study, we found that the two pRFs are each represented by an independent population of neurons. The result is an in-vivo system with two independent populations of spatially intermingled neurons that share the same local control of blood vasculature. Because their population receptive fields (pRFs) do not overlap, an experimenter can independently stimulate each population by presenting a stimulus to its respective receptive field. Such a system is ideal for characterizing the relationship between neural and BOLD responses. Even though we may not know the constituents of the neural response, it will be reasonable to assume that the local neural response evoked by presenting identical stimuli to both pRFs, thereby activating both neuronal populations equally, is twice the neural response evoked by presenting the stimulus to just one of the pRFs. Measuring BOLD responses under these conditions allows us to not only directly test for linearity between the BOLD signal and neural response but also quantify the relationship between them, up to an arbitrary scaling factor. This approach does not require us to know the constituents of neural activity, and it is non-invasive.

To determine the relationship between neural response and the corresponding fMRI BOLD signal, we measured BOLD responses in the cortical areas V1-V3 of our achiasmic subject to luminance-defined stimuli. We presented stimuli of different contrasts to either one or both of the pRFs. From this data set, we used a model-free non-parametric method to infer the quantitative relationship between the BOLD signal (B) and neural response (Z). We found that the resulting B vs. Z function is well approximated by a power function with an exponent close to 0.5. The exponent stayed the same for short and long stimulus durations. We successfully cross-validated this result by comparing the inferred neural responses from this and twelve other fMRI studies to the single-unit responses obtained from non-human primates in similar contrast-response experiments.

 

Article

…..

Figure 4.fMRI BOLD signal as a function of neural response.

(A) Five pairs of BOLD response amplitudes evoked in V1-V3 with the single- and double-sided stimulations, each with two stimulus durations, 6-s (left column) and 1-s (right column). If the neural response to a single-sided stimulus isZi, then the neural response to the corresponding double-sided stimulus will be 2Zi, given our empirical determinations of co-localization and independence of the neuronal populations in an achiasmic visual cortex. (B) The BOLD vs. neural response (BvZ) functions for V1-V3 as inferred by the stitching procedure for the two stimulus durations. The inferred functions can be well fitted with power-law functions (i.e. straight lines in log-log coordinates). These functions are nonlinear, with a log-log slope significantly shallower than unity (the background gray lines). (C) The exponents (γ) of the power-law fit of the BvZ functions for V1-V3. Error bars denote 95% CI. The red line indicatesγ = 0.5. γ estimated from V2 and V3 (γ ~ 0.5) were not significantly different, while that obtained from V1 was biased upward, due to a violation of the co-localization assumption (see Discussion) required for inferring the BvZ function using the summation experiment. We thus inferred the (true) BvZ function of V1-V3 using the average γ estimated from V2 and V3 only.

DOI: http://dx.doi.org/10.7554/eLife.09600.011

…..

Comparing BOLD amplitude and spiking activity

Spike rate is one of the most common measures of neural response, and the BOLD response has been related to spike rate (Heeger et al., 2000; Heeger and Ress, 2002; Logothetis and Wandell, 2004). To cross-validate our finding and to make contact with the broader literature, we used the inferred BvZ function (with γ inferred from V2 and V3) to estimate the neural response Z from the BOLD amplitude data of the single-sided conditions in the BOLD summation experiment, which were typical contrast response measurements. The inferred neural activity in V1 for both the 6-s and 1-s stimuli matched extremely well with the average primate V1 contrast response function measured in terms of single-unit spiking activity by Albrecht (1995) (Figure 5A). Contrary to earlier reports based on the same single-unit data (Heeger et al., 2000), linearly scaling our BOLD amplitude data does not fit the single-unit spiking data. The nonlinearity in our data cannot be attributed to anticipatory and other endogenous responses that might be induced by the task structure (Sirotin and Das, 2009) (Figure 3—figure supplement 3). This is because our subject was engaged in a demanding central fixation task (orientation discrimination) that was asynchronous with the blocked contrast stimuli.
Figure 5.Comparisons between neural response inferred from the BvZ function (B = kZγ) and single-unit spiking activity.  http://dx.doi.org/10.7554/eLife.09600.014

…….

We found that the fMRI BOLD response amplitude is proportional to the local neural response raised to a power of about 0.5. We reached this conclusion by measuring, in the visual cortex of an achiasmic subject, fMRI BOLD amplitudes at five levels of neural activity and also at twice those levels. Our ability to double the local neural response relies on the presence of two co-localized but independent populations of neurons in the visual cortex of the achiasmic subject. The two neuronal populations are equally excitable, and each population has a distinct and non-overlapping population receptive field. We used fMRI retinotopy and localized stimulation to demonstrate co-localization and equal excitability. We used a sensitive contrast detection task and a long-duration fMRI adaptation task to demonstrate independence. Taken together, our results demonstrate that the achiasmic human visual cortex provides a versatile in vivo model for investigating the relationship between evoked neural response and the associated fMRI BOLD signal.

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Metabolic Genomics and Pharmaceutics, Vol. 1 of BioMed Series D available on Amazon Kindle


Metabolic Genomics and Pharmaceutics, Vol. 1 of BioMed Series D available on Amazon Kindle

Reporter: Stephen S Williams, PhD

 

Leaders in Pharmaceutical Business Intelligence would like to announce the First volume of their BioMedical E-Book Series D:

Metabolic Genomics & Pharmaceutics, Vol. I

SACHS FLYER 2014 Metabolomics SeriesDindividualred-page2

which is now available on Amazon Kindle at

http://www.amazon.com/dp/B012BB0ZF0.

This e-Book is a comprehensive review of recent Original Research on  METABOLOMICS and related opportunities for Targeted Therapy written by Experts, Authors, Writers. This is the first volume of the Series D: e-Books on BioMedicine – Metabolomics, Immunology, Infectious Diseases.  It is written for comprehension at the third year medical student level, or as a reference for licensing board exams, but it is also written for the education of a first time baccalaureate degree reader in the biological sciences.  Hopefully, it can be read with great interest by the undergraduate student who is undecided in the choice of a career. The results of Original Research are gaining value added for the e-Reader by the Methodology of Curation. The e-Book’s articles have been published on the Open Access Online Scientific Journal, since April 2012.  All new articles on this subject, will continue to be incorporated, as published with periodical updates.

We invite e-Readers to write an Article Reviews on Amazon for this e-Book on Amazon.

All forthcoming BioMed e-Book Titles can be viewed at:

https://pharmaceuticalintelligence.com/biomed-e-books/

Leaders in Pharmaceutical Business Intelligence, launched in April 2012 an Open Access Online Scientific Journal is a scientific, medical and business multi expert authoring environment in several domains of  life sciences, pharmaceutical, healthcare & medicine industries. The venture operates as an online scientific intellectual exchange at their website http://pharmaceuticalintelligence.com and for curation and reporting on frontiers in biomedical, biological sciences, healthcare economics, pharmacology, pharmaceuticals & medicine. In addition the venture publishes a Medical E-book Series available on Amazon’s Kindle platform.

Analyzing and sharing the vast and rapidly expanding volume of scientific knowledge has never been so crucial to innovation in the medical field. WE are addressing need of overcoming this scientific information overload by:

  • delivering curation and summary interpretations of latest findings and innovations on an open-access, Web 2.0 platform with future goals of providing primarily concept-driven search in the near future
  • providing a social platform for scientists and clinicians to enter into discussion using social media
  • compiling recent discoveries and issues in yearly-updated Medical E-book Series on Amazon’s mobile Kindle platform

This curation offers better organization and visibility to the critical information useful for the next innovations in academic, clinical, and industrial research by providing these hybrid networks.

Table of Contents for Metabolic Genomics & Pharmaceutics, Vol. I

Chapter 1: Metabolic Pathways

Chapter 2: Lipid Metabolism

Chapter 3: Cell Signaling

Chapter 4: Protein Synthesis and Degradation

Chapter 5: Sub-cellular Structure

Chapter 6: Proteomics

Chapter 7: Metabolomics

Chapter 8:  Impairments in Pathological States: Endocrine Disorders; Stress

                   Hypermetabolism and Cancer

Chapter 9: Genomic Expression in Health and Disease 

 

Summary 

Epilogue

 

 

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Cancer Biology and Genomics for Disease Diagnosis (Vol. I) Now Available for Amazon Kindle


Cancer Biology and Genomics for Disease Diagnosis (Vol. I) Now Available for Amazon Kindle

Reporter: Stephen J Williams, PhD

Leaders in Pharmaceutical Business Intelligence would like to announce the First volume of their BioMedical E-Book Series C: e-Books on Cancer & Oncology

Volume One: Cancer Biology and Genomics for Disease Diagnosis

CancerandOncologyseriesCcoverwhich is now available on Amazon Kindle at                          http://www.amazon.com/dp/B013RVYR2K.

This e-Book is a comprehensive review of recent Original Research on Cancer & Genomics including related opportunities for Targeted Therapy written by Experts, Authors, Writers. This ebook highlights some of the recent trends and discoveries in cancer research and cancer treatment, with particular attention how new technological and informatics advancements have ushered in paradigm shifts in how we think about, diagnose, and treat cancer. The results of Original Research are gaining value added for the e-Reader by the Methodology of Curation. The e-Book’s articles have been published on the Open Access Online Scientific Journal, since April 2012.  All new articles on this subject, will continue to be incorporated, as published with periodical updates.

We invite e-Readers to write an Article Reviews on Amazon for this e-Book on Amazon. All forthcoming BioMed e-Book Titles can be viewed at:

https://pharmaceuticalintelligence.com/biomed-e-books/

Leaders in Pharmaceutical Business Intelligence, launched in April 2012 an Open Access Online Scientific Journal is a scientific, medical and business multi expert authoring environment in several domains of  life sciences, pharmaceutical, healthcare & medicine industries. The venture operates as an online scientific intellectual exchange at their website http://pharmaceuticalintelligence.com and for curation and reporting on frontiers in biomedical, biological sciences, healthcare economics, pharmacology, pharmaceuticals & medicine. In addition the venture publishes a Medical E-book Series available on Amazon’s Kindle platform.

Analyzing and sharing the vast and rapidly expanding volume of scientific knowledge has never been so crucial to innovation in the medical field. WE are addressing need of overcoming this scientific information overload by:

  • delivering curation and summary interpretations of latest findings and innovations
  • on an open-access, Web 2.0 platform with future goals of providing primarily concept-driven search in the near future
  • providing a social platform for scientists and clinicians to enter into discussion using social media
  • compiling recent discoveries and issues in yearly-updated Medical E-book Series on Amazon’s mobile Kindle platform

This curation offers better organization and visibility to the critical information useful for the next innovations in academic, clinical, and industrial research by providing these hybrid networks.

Table of Contents for Cancer Biology and Genomics for Disease Diagnosis

Preface

Introduction  The evolution of cancer therapy and cancer research: How we got here?

Part I. Historical Perspective of Cancer Demographics, Etiology, and Progress in Research

Chapter 1:  The Occurrence of Cancer in World Populations

Chapter 2.  Rapid Scientific Advances Changes Our View on How Cancer Forms

Chapter 3:  A Genetic Basis and Genetic Complexity of Cancer Emerge

Chapter 4: How Epigenetic and Metabolic Factors Affect Tumor Growth

Chapter 5: Advances in Breast and Gastrointestinal Cancer Research Supports Hope for Cure

Part II. Advent of Translational Medicine, “omics”, and Personalized Medicine Ushers in New Paradigms in Cancer Treatment and Advances in Drug Development

Chapter 6:  Treatment Strategies

Chapter 7:  Personalized Medicine and Targeted Therapy

Part III.Translational Medicine, Genomics, and New Technologies Converge to Improve Early Detection

Chapter 8:  Diagnosis                                     

Chapter 9:  Detection

Chapter 10:  Biomarkers

Chapter 11:  Imaging In Cancer

Chapter 12: Nanotechnology Imparts New Advances in Cancer Treatment, Detection, &  Imaging                                 

Epilogue by Larry H. Bernstein, MD, FACP: Envisioning New Insights in Cancer Translational Biology

 

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Pathway Specific Targeting in Anticancer Therapies

Writer and Curator: Larry H. Bernstein, MD, FCAP 

 

7.7 Pathway specific targeting in anticancer therapies

7.7.1 Structural basis for the allosteric inhibitory mechanism of human kidney-type glutaminase (KGA) and its regulation by Raf-Mek-Erk signaling in cancer cell metabolism

7.7.2 Sonic hedgehog (Shh) signaling promotes tumorigenicity and stemness via activation of epithelial-to-mesenchymal transition (EMT) in bladder cancer.

7.7.3 Differential activation of NF-κB signaling is associated with platinum and taxane resistance in MyD88 deficient epithelial ovarian cancer cells

7.7.4 Activation of apoptosis by caspase-3-dependent specific RelB cleavage in anticancer agent-treated cancer cells

7.7.5 Identification of Liver Cancer Progenitors Whose Malignant Progression Depends on Autocrine IL-6 Signaling

7.7.6 Acetylation Stabilizes ATP-Citrate Lyase to Promote Lipid Biosynthesis and Tumor Growth

7.7.7 Monoacylglycerol Lipase Regulates a Fatty Acid Network that Promotes Cancer Pathogenesis

7.7.8 Pirin regulates epithelial to mesenchymal transition and down-regulates EAF/U19 signaling in prostate cancer cells

7.7.9 O-GlcNAcylation at promoters, nutrient sensors, and transcriptional regulation

 

7.7.1 Structural basis for the allosteric inhibitory mechanism of human kidney-type glutaminase (KGA) and its regulation by Raf-Mek-Erk signaling in cancer cell metabolism

Thangavelua, CQ Pana, …, BC Lowa, and J. Sivaramana
Proc Nat Acad Sci 2012; 109(20):7705–7710
http://dx.doi.org:/10.1073/pnas.1116573109

Besides thriving on altered glucose metabolism, cancer cells undergo glutaminolysis to meet their energy demands. As the first enzyme in catalyzing glutaminolysis, human kidney-type glutaminase isoform (KGA) is becoming an attractive target for small molecules such as BPTES [bis-2-(5 phenylacetamido-1, 2, 4-thiadiazol-2-yl) ethyl sulfide], although the regulatory mechanism of KGA remains unknown. On the basis of crystal structures, we reveal that BPTES binds to an allosteric pocket at the dimer interface of KGA, triggering a dramatic conformational change of the key loop (Glu312-Pro329) near the catalytic site and rendering it inactive. The binding mode of BPTES on the hydrophobic pocket explains its specificity to KGA. Interestingly, KGA activity in cells is stimulated by EGF, and KGA associates with all three kinase components of the Raf-1/Mek2/Erk signaling module. However, the enhanced activity is abrogated by kinase-dead, dominant negative mutants of Raf-1 (Raf-1-K375M) and Mek2 (Mek2-K101A), protein phosphatase PP2A, and Mek-inhibitor U0126, indicative of phosphorylation-dependent regulation. Furthermore, treating cells that coexpressed Mek2-K101A and KGA with suboptimal level of BPTES leads to synergistic inhibition on cell proliferation. Consequently, mutating the crucial hydrophobic residues at this key loop abrogates KGA activity and cell proliferation, despite the binding of constitutive active Mek2-S222/226D. These studies therefore offer insights into (i) allosteric inhibition of KGA by BPTES, revealing the dynamic nature of KGA’s active and inhibitory sites, and (ii) cross-talk and regulation of KGA activities by EGF-mediated Raf-Mek-Erk signaling. These findings will help in the design of better inhibitors and strategies for the treatment of cancers addicted with glutamine metabolism.

The Warburg effect in cancer biology describes the tendency of cancer cells to take up more glucose than most normal cells, despite the availability of oxygen (12). In addition to altered glucose metabolism, glutaminolysis (catabolism of glutamine to ATP and lactate) is another hallmark of cancer cells (23). In glutaminolysis, mitochondrial glutaminase catalyzes the conversion of glutamine to glutamate (4), which is further catabolized in the Krebs cycle for the production of ATP, nucleotides, certain amino acids, lipids, and glutathione (25).

Humans express two glutaminase isoforms: KGA (kidney-type) and LGA (liver-type) from two closely related genes (6). Although KGA is important for promoting growth, nothing is known about the precise mechanism of its activation or inhibition and how its functions are regulated under physiological or pathophysiological conditions. Inhibition of rat KGA activity by antisense mRNA results in decreased growth and tumorigenicity of Ehrlich ascites tumor cells (7), reduced level of glutathione, and induced apoptosis (8), whereas Myc, an oncogenic transcription factor, stimulates KGA expression and glutamine metabolism (5). Interestingly, direct suppression of miR23a and miR23b (9) or activation of TGF-β (10) enhances KGA expression. Similarly, Rho GTPase that controls cytoskeleton and cell division also up-regulates KGA expression in an NF-κB–dependent manner (11). In addition, KGA is a substrate for the ubiquitin ligase anaphase-promoting complex/cyclosome (APC/C)-Cdh1, linking glutaminolysis to cell cycle progression (12). In comparison, function and regulation of LGA is not well studied, although it was recently shown to be linked to p53 pathway (1314). Although intense efforts are being made to develop a specific KGA inhibitor such as BPTES [bis-2-(5-phenylacetamido-1, 2, 4-thiadiazol-2-yl) ethyl sulfide] (15), its mechanism of inhibition and selectivity is not yet understood. Equally important is to understand how KGA function is regulated in normal and cancer cells so that a better treatment strategy can be considered.

The previous crystal structures of microbial (Mglu) and Escherichia coli glutaminases show a conserved catalytic domain of KGA (1617). However, detailed structural information and regulation are not available for human glutaminases especially the KGA, and this has hindered our strategies to develop inhibitors. Here we report the crystal structure of the catalytic domain of human apo KGA and its complexes with substrate (L-glutamine), product (L-glutamate), BPTES, and its derived inhibitors. Further, Raf-Mek-Erk module is identified as the regulator of KGA activity. Although BPTES is not recognized in the active site, its binding confers a drastic conformational change of a key loop (Glu312-Pro329), which is essential in stabilizing the catalytic pocket. Significantly, EGF activates KGA activity, which can be abolished by the kinase-dead, dominant negative mutants of Mek2 (Mek2-K101A) or its upstream activator Raf-1 (Raf-1-K375M), which are the kinase components of the growth-promoting Raf-Mek2-Erk signaling node. Furthermore, coexpression of phosphatase PP2A and treatment with Mek-specific inhibitor or alkaline phosphatase all abolished enhanced KGA activity inside the cells and in vitro, indicating that stimulation of KGA is phosphorylation dependent. Our results therefore provide mechanistic insights into KGA inhibition by BPTES and its regulation by EGF-mediated Raf-Mek-Erk module in cell growth and possibly cancer manifestation.

Structures of cKGA and Its Complexes with L-Glutamine and L-Glutamate.
The human KGA consists of 669 amino acids. We refer to Ile221-Leu533 as the catalytic domain of KGA (cKGA) (Fig. 1A). The crystal structures of the apo cKGA and in complex with L-glutamine or L-glutamate were determined (Table S1). The structure of cKGA has two domains with the active site located at the interface. Domain I comprises (Ile221-Pro281 and Cys424 -Leu533) of a five-stranded anti-parallel β-sheet (β2↓β1↑β5↓β4↑β3↓) surrounded by six α-helices and several loops. The domain II (Phe282-Thr423) mainly consists of seven α-helices. L-Glutamine/L-glutamate is bound in the active site cleft (Fig. 1B and Fig. S1B). Overall the active site is highly basic, and the bound ligand makes several hydrogen-bonding contacts to Gln285, Ser286, Asn335, Glu381, Asn388, Tyr414, Tyr466, and Val484 (Fig. 1C and Fig. S1C), and these residues are highly conserved among KGA homologs (Fig. S1D). Notably, the putative serine-lysine catalytic dyad (286-SCVK-289), corresponding to the SXXK motif of class D β-lactamase (17), is located in close proximity to the bound ligand. In the apo structure, two water molecules were located in the active site, one of them being displaced by glutamine in the substrate complex. The substrate side chain is within hydrogen-bonding distance (2.9 Å) to the active site Ser286. Other key residues involved in catalysis, such as Lys289, Tyr414, and Tyr466, are in the vicinity of the active site. Lys289 is within hydrogen-bonding distance to Ser286 (3.1 Å) and acts as a general base for the nucleophilic attack by accepting the proton from Ser286. Tyr466, which is close to Ser286 and in hydrogen-bonding contact (3.2 Å) with glutamine, is involved in proton transfer during catalysis. Moreover, the carbonyl oxygen of the glutamine is hydrogen-bonded with the main chain amino groups of Ser286 and Val484, forming the oxyanion hole. Thus, we propose that in addition to the putative catalytic dyad (Ser286 XX Lys289), Tyr466 could play an important role in the catalysis (Fig. 1Cand Fig. S2).

structure of the cKGA-L-glutamine complex

structure of the cKGA-L-glutamine complex

http://www.pnas.org/content/109/20/7705/F1.medium.gif

Fig. 1.  Schematic view and structure of the cKGA-L-glutamine complex. (A) Human KGA domains and signature motifs (refer to Fig. S1A for details). (B) Structure of the of cKGA and bound substrate (L-glutamine) is shown as a cyan stick. (C) Fourier 2Fo-Fc electron density map (contoured at 1 σ) for L-glutamine, that makes hydrogen bonds with active site residues are shown.

Allosteric Binding Pocket for BPTES. The chemical structure of BPTES has an internal symmetry, with two exactly equivalent parts including a thiadiazole, amide, and a phenyl group (Fig. S3A), and it equally interacts with each monomer. The thiadiazole group and the aliphatic linker are well buried in a hydrophobic cluster that consists of Leu321, Phe322, Leu323, and Tyr394 from both monomers, which forms the allosteric pocket (Fig. 2 B–E). The side chain of Phe322 is found at the bottom of the allosteric pocket. The phenyl-acetamido moiety of BPTES is partially exposed on the loop (Asn324-Glu325), where it interacts with Phe318, Asn324, and the aliphatic part of the Glu325 side chain. On the basis of our observations we synthesized a series of BPTES-derived inhibitors (compounds2–5) (Fig. S3 AF and SI Results) and solved their cocrystal structure of compounds 2–4. Similar to BPTES, compounds 24 all resides within the hydrophobic cluster of the allosteric pocket (Fig. S3 CF).

Fig. 2. Structure of cKGA: BPTES complex and the allosteric binding mode of BPTES.

Allosteric Binding of BPTES Triggers Major Conformational Change in the Key Loop Near the Active Site.  The overall structure of these inhibitor complexes superimposes well with apo cKGA. However, a major conformational change at the Glu312 to Pro329 loop was observed in the BPTES complex (Fig. 2F). The most conformational changes of the backbone atoms that moved away from the active site region are found at the center of the loop (Leu316-Lys320). The backbone of the residues Phe318 and Asn319 is moved ≈9 Å and ≈7 Å, respectively, compared with the apo structure, whereas the side chain of these residues moved ≈14 Å and ≈12 Å, respectively. This loop rearrangement in turn brings Phe318 closer to the phenyl group of the inhibitor and forms the inhibitor binding pocket, whereas in the apo structure the same loop region (Leu316-Lys320) was found to be adjacent to the active site and forms a closed conformation of the active site.

Binding of BPTES Stabilizes the Inactive Tetramers of cKGA.  To understand the role of oligomerization in KGA function, dimers and tetramers of cKGA were generated using the symmetry-related monomers (Fig. 2 A–E and Fig. S4 D and E). The dimer interface in the cKGA: BPTES complex is formed by residues from the helix Asp386-Lys398 of both monomers and involves hydrogen bonding, salt bridges, and hydrophobic interactions (Phe389, Ala390, Tyr393, and Tyr394), besides two sulfate ions located in the interface (Fig. 2E). The dimers are further stabilized by binding of BPTES, where it binds to loop residues (Glu312-Pro329) and Tyr394 from both monomers (Fig. 2 D and E). Similarly, residues from Lys311-Asn319 loop and Arg454, His461, Gln471, and Asn529-Leu533 are involved in the interface with neighboring monomers to form the tetramer in the BPTES complex.

BPTES Induces Allosteric Conformational Changes That Destabilize Catalytic Function of KGA

Fig. 3A shows that 293T cells overexpressing KGA produced higher level of glutamate compared with the vector control cells. Most significantly, all of these mutants, except Phe322Ala, greatly diminished the KGA activity.

Fig. 3. Mutations at allosteric loop and BPTES binding pocket abrogate KGA activity and BPTES sensitivity.

Raf-Mek-Erk Signaling Module Regulates KGA Activity. Because KGA supports cell growth and proliferation, we first validated that treatment of cells with BPTES indeed inhibits KGA activity and cell proliferation (Fig. S5 A–D and SI Results). Next, as cells respond to various physiological stimuli to regulate their metabolism, with many of the metabolic enzymes being the primary targets of modulation (18), we examined whether KGA activity can be regulated by physiological stimuli, in particular EGF, which is important for cell growth and proliferation. Cells overexpressing KGA were made quiescent and then stimulated with EGF for various time points. Fig. 4A shows that the basal KGA activity remained unchanged 30 min after EGF stimulation, but the activity was substantially enhanced after 1 h and then gradually returned to the basal level after 4 h. Because EGF activates the Raf-Mek-Erk signaling module (19), treatment of cells with Mek-specific inhibitor U0126 could block the enhanced KGA activity with parallel inhibition of Erk phosphorylation (Fig. 4A). Interestingly, such Mek-induced KGA activity is specific to EGF and lysophosphatidic acid (LPA) but not with other growth factors, such as PDGF, TGF-β, and basic FGF (bFGF), despite activation of Mek-Erk by bFGF (Fig. S6A).

The results show that KGA could interact equally well with the wild-type or mutant forms of Raf-1 and Mek2 (Fig. 4C). Importantly, endogenous Raf-1 or Erk1/2, including the phosphorylated Erk1/2 (Fig. 4 C and D), could be detected in the KGA complex. Taken together, these results indicate that the activity of KGA is directly regulated by Raf-Mek-Erk downstream of EGF receptor. To further show that Mek2-enhanced KGA activity requires both the kinase activity of Mek2 and the core residues for KGA catalysis, wild-type or triple mutant (Leu321Ala/Phe322Ala/Leu323Ala) of KGA was coexpressed with dominant negative Mek2-KA or the constitutive active Mek2-SD and their KGA activities measured. The result shows that the presence of Mek2-KA blocks KGA activity, whereas the triple mutant still remains inert even in the presence of the constitutively active Mek2 (Fig. 4E), and despite Mek2 binding to the KGA triple mutant (Fig. S7B). Consequently, expressing triple mutant did not support cell proliferation as well as the wild-type control (Fig. S7C).

Fig. 4. EGFR-Raf-Mek-Erk signaling stimulates KGA activity.

When cells expressing both KGA and Mek2-K101A were treated with subthreshold levels of BPTES, there was a synergistic reduction in cell proliferation (Fig. S6C and SI Results). Lastly, to determine whether regulation of KGA by Raf-Mek-Erk depends on its phosphorylation status, cells were transfected with KGA with or without the protein phosphatase PP2A and assayed for the KGA activity. PP2A is a ubiquitous and conserved serine/threonine phosphatase with broad substrate specificity. The results indicate that KGA activity was reduced down to the basal level in the presence of PP2A (Fig. 5A). Coimmunoprecipitation study also revealed that KGA interacts with PP2A (Fig. 5B), suggesting a negative feedback regulation by this protein phosphatase. Furthermore, treatment of immunoprecipitated and purified KGA with calf-intestine alkaline phosphatase (CIAP) almost completely abolished the KGA activity in vitro (Fig. S6D). Taken together, these results indicate that KGA activity is regulated by Raf-Mek2, and KGA activation by EGF could be part of the EGF-stimulated Raf-Mek-Erk signaling program in controlling cell growth and proliferation (Fig. 5C).

KGA activity is regulated by phosphorylation

KGA activity is regulated by phosphorylation

http://www.pnas.org/content/109/20/7705/F5.medium.gif

Fig. 5. KGA activity is regulated by phosphorylation. (C) Schematic model depicting the synergistic cross-talk between KGA-mediated glutaminolysis and EGF-activated Raf-Mek-Erk signaling. Exogenous glutamine can be transported across the membrane and converted to glutamate by glutaminase (KGA), thus feeding the metabolite to the ATP-producing tricarboxylic acid (TCA) cycle. This process can be stimulated by EGF receptor-mediated Raf-Mek-Erk signaling via their phosphorylation-dependent pathway, as evidenced by the inhibition of KGA activity by the kinase-dead and dominant negative mutants of Raf-1 (Raf-1-K375M) and Mek2 (Mek2-K101A), protein phosphatase PP2A, and Mek-specific inhibitor U0126. Consequently, inhibiting KGA with BPTES and blocking Raf-Mek pathway with Mek2-K101A provide a synergistic inhibition on cell proliferation.

Small-molecule inhibitors that target glutaminase activity in cancer cells are under development. Earlier efforts targeting glutaminase using glutamine analogs have been unsuccessful owing to their toxicities (2). BPTES has attracted much attention as a selective, nontoxic inhibitor of KGA (15), and preclinical testing of BPTES toward human cancers has just begun (20). BPTES selectively suppresses the growth of glioma cells (21) and inhibits the growth of lymphoma tumor growth in animal model studies (22). Wang et al. (11) reported a small molecule that targets glutaminase activity and oncogenic transformation. Despite extensive studies, nothing is known about the structural and molecular basis for KGA inhibitory mechanisms and how their function is regulated during normal and cancer cell metabolism. Such limited information impedes our effort in producing better generations of inhibitors for better treatment regimens.

Comparison of the complex structures with apo cKGA structure, which has well-defined electron density for the key loop, we provide the atomic view of an allosteric binding pocket for BPTES and elucidate the inhibitory mechanism of KGA by BPTES. The key residues of the loop (Glu312-Pro329) undergo major conformational changes upon binding of BPTES. In addition, structure-based mutagenesis studies suggest that this loop is essential for stabilizing the active site. Therefore, by binding in an allosteric pocket, BPTES inhibits the enzymatic activity of KGA through (i) triggering a major conformational change on the key residues that would normally be involved in stabilizing the active sites and regulating its enzymatic activity; and (ii) forming a stable inactive tetrameric KGA form. Our findings are further supported by two very recent reports on KGA isoform (GAC) (2324), although these studies lack full details owing to limitation of their electron density maps. BPTES is specific to KGA but not to LGA (15). Sequence comparison of KGA with LGA (Fig. S8A) reveals two unique residues on KGA, Phe318 and Phe322, which upon mutation to LGA counterparts, become resistant to BPTES. Thus, our study provides the molecular basis of BPTES specificity.

7.7.2 Sonic hedgehog (Shh) signaling promotes tumorigenicity and stemness via activation of epithelial-to-mesenchymal transition (EMT) in bladder cancer.

Islam SS, Mokhtari RB, Noman AS, …, van der Kwast T, Yeger H, Farhat WA.
Molec Carcinogenesis mar 2015; 54(5). http://dx.doi.org:/10.1002/mc.22300

shh sonic hedgehog signaling pathway nri2151-f1

shh sonic hedgehog signaling pathway nri2151-f1

Activation of the sonic hedgehog (Shh) signaling pathway controls tumorigenesis in a variety of cancers. Here, we show a role for Shh signaling in the promotion of epithelial-to-mesenchymal transition (EMT), tumorigenicity, and stemness in the bladder cancer. EMT induction was assessed by the decreased expression of E-cadherin and ZO-1 and increased expression of N-cadherin. The induced EMT was associated with increased cell motility, invasiveness, and clonogenicity. These progression relevant behaviors were attenuated by treatment with Hh inhibitors cyclopamine and GDC-0449, and after knockdown by Shh-siRNA, and led to reversal of the EMT phenotype. The results with HTB-9 were confirmed using a second bladder cancer cell line, BFTC905 (DM). In a xenograft mouse model TGF-β1 treated HTB-9 cells exhibited enhanced tumor growth. Although normal bladder epithelial cells could also undergo EMT and upregulate Shh with TGF-β1 they did not exhibit tumorigenicity. The TGF-β1 treated HTB-9 xenografts showed strong evidence for a switch to a more stem cell like phenotype, with functional activation of CD133, Sox2, Nanog, and Oct4. The bladder cancer specific stem cell markers CK5 and CK14 were upregulated in the TGF-β1 treated xenograft tumor samples, while CD44 remained unchanged in both treated and untreated tumors. Immunohistochemical analysis of 22 primary human bladder tumors indicated that Shh expression was positively correlated with tumor grade and stage. Elevated expression of Ki-67, Shh, Gli2, and N-cadherin were observed in the high grade and stage human bladder tumor samples, and conversely, the downregulation of these genes were observed in the low grade and stage tumor samples. Collectively, this study indicates that TGF-β1-induced Shh may regulate EMT and tumorigenicity in bladder cancer. Our studies reveal that the TGF-β1 induction of EMT and Shh is cell type context dependent. Thus, targeting the Shh pathway could be clinically beneficial in the ability to reverse the EMT phenotype of tumor cells and potentially inhibit bladder cancer progression and metastasis

Sonic_hedgehog_pathway

Sonic_hedgehog_pathway

7.7.3 Differential activation of NF-κB signaling is associated with platinum and taxane resistance in MyD88 deficient epithelial ovarian cancer cells

Gaikwad SM, Thakur B, Sakpal A, Singh RK, Ray P.
Int J Biochem Cell Biol. 2015 Apr; 61:90-102
http://dx.doi.org:/10.1016/j.biocel.2015.02.001

Development of chemoresistance is a major impediment to successful treatment of patients suffering from epithelial ovarian carcinoma (EOC). Among various molecular factors, presence of MyD88, a component of TLR-4/MyD88 mediated NF-κB signaling in EOC tumors is reported to cause intrinsic paclitaxel resistance and poor survival. However, 50-60% of EOC patients do not express MyD88 and one-third of these patients finally relapses and dies due to disease burden. The status and role of NF-κB signaling in this chemoresistant MyD88(negative) population has not been investigated so far. Using isogenic cellular matrices of cisplatin, paclitaxel and platinum-taxol resistant MyD88(negative) A2780 ovarian cancer cells expressing a NF-κB reporter sensor, we showed that enhanced NF-κB activity was required for cisplatin but not for paclitaxel resistance. Immunofluorescence and gel mobility shift assay demonstrated enhanced nuclear localization of NF-κB and subsequent binding to NF-κB response element in cisplatin resistant cells. The enhanced NF-κB activity was measurable from in vivo tumor xenografts by dual bioluminescence imaging. In contrast, paclitaxel and the platinum-taxol resistant cells showed down regulation in NF-κB activity. Intriguingly, silencing of MyD88 in cisplatin resistant and MyD88(positive) TOV21G and SKOV3 cells showed enhanced NF-κB activity after cisplatin but not after paclitaxel or platinum-taxol treatments. Our data thus suggest that NF-κB signaling is important for maintenance of cisplatin resistance but not for taxol or platinum-taxol resistance in absence of an active TLR-4/MyD88 receptor mediated cell survival pathway in epithelial ovarian carcinoma.

7.7.4 Activation of apoptosis by caspase-3-dependent specific RelB cleavage in anticancer agent-treated cancer cells

Kuboki MIto ASimizu SUmezawa K.
Biochem Biophys Res Commun. 2015 Jan 16; 456(3):810-4
http://dx.doi.org:/10.1016/j.bbrc.2014.12.024

Activation of caspase 3 and caspase-dependent apoptosis  nrmicro2071-f1

Activation of caspase 3 and caspase-dependent apoptosis nrmicro2071-f1

Highlights

  • We have prepared RelB mutants that are resistant to caspase 3-induced scission.
  • Vinblastine induced caspase 3-dependent site-specific RelB cleavage in cancer cells.
  • Cancer cells expressing cleavage-resistant RelB showed less sensitivity to vinblastine.
  • Caspase 3-induced RelB cleavage may provide positive feedback mechanism in apoptosis.

DTCM-glutarimide (DTCM-G) is a newly found anti-inflammatory agent. In the course of experiments with lymphoma cells, we found that DTCM-G induced specific RelB cleavage. Anticancer agent vinblastine also induced the specific RelB cleavage in human fibrosarcoma HT1080 cells. The site-directed mutagenesis analysis revealed that the Asp205 site in RelB was specifically cleaved possibly by caspase-3 in vinblastine-treated HT1080 cells. Moreover, the cells stably overexpressing RelB Asp205Ala were resistant to vinblastine-induced apoptosis. Thus, the specific Asp205 cleavage of RelB by caspase-3 would be involved in the apoptosis induction by anticancer agents, which would provide the positive feedback mechanism.

apoptotic-caspases-control-microglia-activation-cdd2011107f3

apoptotic-caspases-control-microglia-activation-cdd2011107f3

 

 

7.7.5 Identification of Liver Cancer Progenitors Whose Malignant Progression Depends on Autocrine IL-6 Signaling

He GDhar DNakagawa HFont-Burgada JOgata HJiang Y, et al.
Cell. 2013 Oct 10; 155(2):384-96
http://dx.doi.org/10.1016%2Fj.cell.2013.09.031

Il-6 signaling in cancer cells

Il-6 signaling in cancer cells

Hepatocellular carcinoma (HCC) is a slowly developing malignancy postulated to evolve from pre-malignant lesions in chronically damaged livers. However, it was never established that premalignant lesions actually contain tumor progenitors that give rise to cancer. Here, we describe isolation and characterization of HCC progenitor cells (HcPCs) from different mouse HCC models. Unlike fully malignant HCC, HcPCs give rise to cancer only when introduced into a liver undergoing chronic damage and compensatory proliferation. Although HcPCs exhibit a similar transcriptomic profile to bipotential hepatobiliary progenitors, the latter do not give rise to tumors. Cells resembling HcPCs reside within dysplastic lesions that appear several months before HCC nodules. Unlike early hepatocarcinogenesis, which depends on paracrine IL-6 production by inflammatory cells, due to upregulation of LIN28 expression, HcPCs had acquired autocrine IL-6 signaling that stimulates their in vivo growth and malignant progression. This may be a general mechanism that drives other IL-6-producing malignancies.

Clonal evolution and selective pressure may cause some descendants of the initial progenitor to cross the bridge of no return and form a premalignant lesion. Cancer genome sequencing indicates that most cancers require at least five genetic changes to evolve (Wood et al., 2007). It has been difficult to isolate and propagate cancer progenitors prior to detection of tumor masses. Further, it is not clear whether cancer progenitors are the precursors for the  cancer stem cells (CSCs)isolated from cancers. An answer to these critical questions depends on identification and isolation of cancer progenitors, which may also enable definition of molecular markers and signaling pathways suitable for early detection and treatment.

Hepatocellular carcinoma (HCC), the end product of chronic liver diseases, requires several decades to evolve (El-Serag, 2011). It is the third most deadly and fifth most common cancer worldwide, and in the United States its incidence has doubled in the past two decades. Furthermore, 8% of the world’s population are chronically infected with hepatitis B or C viruses (HBV and HCV) and are at a high risk of new HCC development (El-Serag, 2011). Up to 5% of HCV patients will develop HCC in their lifetime, and the yearly HCC incidence in patients with cirrhosis is 3%–5%. These tumors may arise from premalignant lesions, ranging from dysplastic foci to dysplastic hepatocyte nodules that are often seen in damaged and cirrhotic livers and are more proliferative than the surrounding parenchyma (Hytiroglou et al., 2007). There is no effective treatment for HCC and, upon diagnosis, most patients with advanced disease have a remaining lifespan of 4–6 months. Premalignant lesions, called foci of altered hepatocytes (FAH), were described in chemically induced HCC models (Pitot, 1990), but it was questioned whether these lesions harbor tumor progenitors or result from compensatory proliferation (Sell and Leffert, 2008). The aim of this study was to determine whether HCC progenitor cells (HcPCs) exist and if so, to isolate these cells and identify some of the signaling networks that are involved in their maintenance and progression.

We now describe HcPC isolation from mice treated with the procarcinogen diethyl nitrosamine (DEN), which induces poorly differentiated HCC nodules within 8 to 9 months (Verna et al., 1996). The use of a chemical carcinogen is justified because the finding of up to 121 mutations per HCC genome suggests that carcinogens may be responsible for human HCC induction (Guichard et al., 2012). Furthermore, 20%–30% of HCC, especially in HBV-infected individuals, evolve in noncirrhotic livers (El-Serag, 2011). Nonetheless, we also isolated HcPCs fromTak1Δhep mice, which develop spontaneous HCC as a result of progressive liver damage, inflammation, and fibrosis caused by ablation of TAK1 (Inokuchi et al., 2010). Although the etiology of each model is distinct, both contain HcPCs that express marker genes and signaling pathways previously identified in human HCC stem cells (Marquardt and Thorgeirsson, 2010) long before visible tumors are detected. Furthermore, DEN-induced premalignant lesions and HcPCs exhibit autocrine IL-6 production that is critical for tumorigenic progression. Circulating IL-6 is a risk indicator in several human pathologies and is strongly correlated with adverse prognosis in HCC and cholangiocarcinoma (Porta et al., 2008Soresi et al., 2006). IL-6 produced by in-vitro-induced CSCs was suggested to be important for their maintenance (Iliopoulos et al., 2009). Little is known about the source of IL-6 in HCC.

DEN-Induced Collagenase-Resistant Aggregates of HCC Progenitors

A single intraperitoneal (i.p.) injection of DEN into 15-day-old BL/6 mice induces HCC nodules first detected 8 to 9 months later. However, hepatocytes prepared from macroscopically normal livers 3 months after DEN administration already contain cells that progress to HCC when transplanted into the permissive liver environment of MUP-uPA mice (He et al., 2010), which express urokinase plasminogen activator (uPA) from a mouse liver-specific major urinary protein (MUP) promoter and undergo chronic liver damage and compensatory proliferation (Rhim et al., 1994). HCC markers such as α fetoprotein (AFP), glypican 3 (Gpc3), and Ly6D, whose expression in mouse liver cancer was reported (Meyer et al., 2003), were upregulated in aggregates from DEN-treated livers, but not in nonaggregated hepatocytes or aggregates from control livers (Figure S1A). Using 70 μm and 40 μm sieves, we separated aggregated from nonaggregated hepatocytes (Figure 1A) and tested their tumorigenic potential by transplantation into MUP-uPA mice (Figure 1B). To facilitate transplantation, the aggregates were mechanically dispersed and suspended in Dulbecco’s modified Eagle’s medium (DMEM). Five months after intrasplenic (i.s.) injection of 104 viable cells, mice receiving cells from aggregates developed about 18 liver tumors per mouse, whereas mice receiving nonaggregated hepatocytes developed less than 1 tumor each (Figure 1B). The tumors exhibited typical trabecular HCC morphology and contained cells that abundantly express AFP (Figure S1B).

Only liver tumors were formed by the transplanted cells. Other organs, including the spleen into which the cells were injected, remained tumor free (Figure 1B), suggesting that HcPCs progress to cancer only in the proper microenvironment. Indeed, no tumors appeared after HcPC transplantation into normal BL/6 mice. But, if BL/6 mice were first treated with retrorsine (a chemical that permanently inhibits hepatocyte proliferation [Laconi et al., 1998]), intrasplenically transplanted with HcPC-containing aggregates, and challenged with CCl4 to induce liver injury and compensatory proliferation (Guo et al., 2002), HCCs readily appeared (Figure 1C). CCl4 omission prevented tumor development. Notably, MUP-uPA or CCl4-treated livers are fragile, rendering direct intrahepatic transplantation difficult. CCl4-induced liver damage, especially within a male liver, generates a microenvironment that drives HcPC proliferation and malignant progression. To examine this point, we transplanted GFP-labeled HcPC-containing aggregates into retrorsine-treated BL/6 mice and examined their ability to proliferate with or without subsequent CCl4 treatment. Indeed, the GFP+ cells formed clusters that grew in size only in CCl4-treated host livers (Figure S1E). Omission of CC14 prevented their expansion.

Because CD44 is expressed by HCC stem cells (Yang et al., 2008Zhu et al., 2010), we dispersed the aggregates and separated CD44+ from CD44 cells and transplanted both into MUP-uPA mice. Whereas as few as 103 CD44+ cells gave rise to HCCs in 100% of recipients, no tumors were detected after transplantation of CD44 cells (Figure 1E). Remarkably, 50% of recipients developed at least one HCC after receiving as few as 102 CD44+ cells.

HcPC-Containing Aggregates in Tak1Δhep Mice

We applied the same HcPC isolation protocol to Tak1Δhep mice, which develop HCC of different etiology from DEN-induced HCC. Importantly, Tak1Δhep mice develop HCC as a consequence of chronic liver injury and fibrosis without carcinogen or toxicant exposure (Inokuchi et al., 2010). Indeed, whole-tumor exome sequencing revealed that DEN-induced HCC contained about 24 mutations per 106 bases (Mb) sequenced, with B-RafV637E being the most recurrent, whereas 1.4 mutations per Mb were detected inTak1Δhep HCC’s exome (Table S1). By contrast, Tak1Δhep HCC exhibited gene copy number changes. HCC developed in 75% of MUP-uPA mice that received dispersed Tak1Δhep aggregates, but no tumors appeared in mice receiving nonaggregated Tak1Δhep or totalTak1f/f hepatocytes (Figure 2B). bile duct ligation (BDL) or feeding with 3,5-dicarbethoxy-1,4-dihydrocollidine (DDC), treatments that cause cholestatic liver injuries and oval cell expansion (Dorrell et al., 2011), did increase the number of small hepatocytic cell aggregates (Figure S2A). Nonetheless, no tumors were observed 5 months after injection of such aggregates into MUP-uPA mice (Figure S2B). Thus, not all hepatocytic aggregates contain HcPCs, and HcPCs only appear under tumorigenic conditions.

The HcPC Transcriptome Is Similar to that of HCC and Oval Cells

To determine the relationship between DEN-induced HcPCs, normal hepatocytes, and fully transformed HCC cells, we analyzed the transcriptomes of aggregated and nonaggregated hepatocytes from male littermates 5 months after DEN administration, HCC epithelial cells from DEN-induced tumors, and normal hepatocytes from age- and gender-matched littermate controls. Clustering analysis distinguished the HCC samples from other samples and revealed that the aggregated hepatocyte samples did not cluster with each other but rather with nonaggregated hepatocytes derived from the same mouse (Figure S3A). 57% (583/1,020) of genes differentially expressed in aggregated relative to nonaggregated hepatocytes are also differentially expressed in HCC relative to normal hepatocytes (Figure 3B, top), a value that is highly significant (p < 7.13 × 10−243). More specifically, 85% (494/583) of these genes are overexpressed in both HCC and HcPC-containing aggregates (Figure 3B, bottom table). Thus, hepatocyte aggregates isolated 5 months after DEN injection contain cells that are related in their gene expression profile to HCC cells isolated from fully developed tumor nodules.

Figure 3 Aggregated Hepatocytes Exhibit an Altered Transcriptome Similar to that of HCC Cells

We examined which biological processes or cellular compartments were significantly overrepresented in the induced or repressed genes in both pairwise comparisons (Gene Ontology Analysis). As expected, processes and compartments that were enriched in aggregated hepatocytes relative to nonaggregated hepatocytes were almost identical to those that were enriched in HCC relative to normal hepatocytes (Figure 3C). Several human HCC markers, including AFP, Gpc3 and H19, were upregulated in aggregated hepatocytes (Figures 3D and 3E). Aggregated hepatocytes also expressed more Tetraspanin 8 (Tspan8), a cell-surface glycoprotein that complexes with integrins and is overexpressed in human carcinomas (Zöller, 2009). Another cell-surface molecule highly expressed in aggregated cells is Ly6D (Figures 3D and 3E). Immunofluorescence (IF) analysis revealed that Ly6D was undetectable in normal liver but was elevated in FAH and ubiquitously expressed in most HCC cells (Figure S3C). A fluorescent-labeled Ly6D antibody injected into HCC-bearing mice specifically stained tumor nodules (Figure S3D). Other cell-surface molecules that were upregulated in aggregated cells included syndecan 3 (Sdc3), integrin α 9 (Itga9), claudin 5 (Cldn5), and cadherin 5 (Cdh5) (Figure 3D). Aggregated hepatocytes also exhibited elevated expression of extracellular matrix proteins (TIF3 and Reln1) and a serine protease inhibitor (Spink3). Elevated expression of such proteins may explain aggregate formation. Aggregated hepatocytes also expressed progenitor cell markers, including the epithelial cell adhesion molecule (EpCAM) (Figure 3E) and Dlk1 (Figure 3D). We compared the HcPC and HCC (Figure 3A) to the transcriptome of DDC-induced oval cells (Shin et al., 2011). This analysis revealed a striking similarity between the HCC, HcPC, and the oval cell transcriptomes (Figure S3B). Despite these similarities, some genes that were upregulated in HcPC-containing aggregates and HCC were not upregulated in oval cells. Such genes may account for the tumorigenic properties of HcPC and HCC.

Figure 4  DEN-Induced HcPC Aggregates Express Pathways and Markers Characteristic of HCC and Hepatobiliary Stem Cells

We examined the aggregates for signaling pathways and transcription factors involved in hepatocarcinogenesis. Many aggregated cells were positive for phosphorylated c-Jun and STAT3 (Figure 4A), transcription factors involved in DEN-induced hepatocarcinogenesis (Eferl et al., 2003He et al., 2010). Sox9, a transcription factor that marks hepatobiliary progenitors (Dorrell et al., 2011), was also expressed by many of the aggregated cells, which were also positive for phosphorylated c-Met (Figure 4A), a receptor tyrosine kinase that is activated by hepatocyte growth factor (HGF) and is essential for liver development (Bladt et al., 1995) and hepatocarcinogenesis (Wang et al., 2001). Few of the nonaggregated hepatocytes exhibited activation of these signaling pathways. Despite different etiology, HcPC-containing aggregates from Tak1Δhep mice exhibit upregulation of many of the same markers and pathways that are upregulated in DEN-induced HcPC-containing aggregates. Flow cytometry confirmed enrichment of CD44+ cells as well as CD44+/CD90+ and CD44+/EpCAM+ double-positive cells in the HcPC-containing aggregates from either DEN-treated or Tak1Δhep livers (Figure S4B).

HcPC-Containing Aggregates Originate from Premalignant Dysplastic Lesions

FAH are dysplastic lesions occurring in rodent livers exposed to hepatic carcinogens (Su et al., 1990). Similar lesions are present in premalignant human livers (Su et al., 1997). Yet, it is still debated whether FAH correspond to premalignant lesions or are a reaction to liver injury that does not lead to cancer (Sell and Leffert, 2008). In DEN-treated males, FAH were detected as early as 3 months after DEN administration (Figure 5A), concomitant with the time at which HcPC-containing aggregates were detected. In females, FAH development was delayed. FAH contained cells positive for the same progenitor cell markers and activated signaling pathways present in HcPC-containing aggregates, including AFP, CD44, and EpCAM (Figure 5C). FAH also contained cells positive for activated STAT3, c-Jun, and PCNA (Figure 5C).

HcPCs Exhibit Autocrine IL-6 Expression Necessary for HCC Progression

In situ hybridization (ISH) and immunohistochemistry (IHC) revealed that DEN-induced FAH contained IL-6-expressing cells (Figures 6A, 6B, and S5), and freshly isolated DEN-induced aggregates contained more IL-6 messenger RNA (mRNA) than nonaggregated hepatocytes (Figure 6C). We examined several factors that control IL-6 expression and found that LIN28A and B were significantly upregulated in HcPCs and HCC (Figures 6D and 6E). LIN28-expressing cells were also detected within FAH (Figure 6F). As reported (Iliopoulos et al., 2009), knockdown of LIN28B in cultured HcPC or HCC cell lines decreased IL-6 expression (Figure 6G). LIN28 exerts its effects through downregulation of the microRNA (miRNA) Let-7 (Iliopoulos et al., 2009).

Figure 6  Liver Premalignant Lesions and HcPCs Exhibit Elevated IL-6 and LIN28 Expression

Figure 7  HCC Growth Depends on Autocrine IL-6 Production

The isolation and characterization of cells that can give rise to HCC only after transplantation into an appropriate host liver undergoing chronic injury demonstrates that cancer arises from progenitor cells that are yet to become fully malignant. Importantly, unlike fully malignant HCC cells, the HcPCs we isolated cannot form s.c. tumors or even liver tumors when introduced into a nondamaged liver. Liver damage induced by uPA expression or CCl4 treatment provides HcPCs with the proper cytokine and growth factor milieu needed for their proliferation. Although HcPCs produce IL-6, they may also depend on other cytokines such as TNF, which is produced by macrophages that are recruited to the damaged liver. In addition, uPA expression and CCl4 treatment may enhance HcPC growth and progression through their fibrogenic effect on hepatic stellate cells. Although HCC and other cancers have been suspected to arise from premalignant/dysplastic lesions (Hruban et al., 2007Hytiroglou et al., 2007), a direct demonstration that such lesions progress into malignant tumors has been lacking. Based on expression of common markers—EpCAM, CD44, AFP, activated STAT3, and IL-6—that are not expressed in normal hepatocytes, we postulate that HcPCs originate from FAH or dysplastic foci, which are first observed in male mice within 3 months of DEN exposure.

7.7.6 Acetylation Stabilizes ATP-Citrate Lyase to Promote Lipid Biosynthesis and Tumor Growth

Lin R1Tao RGao XLi TZhou XGuan KLXiong YLei QY.
Mol Cell. 2013 Aug 22; 51(4):506-18
http://dx.doi.org:/10.1016/j.molcel.2013.07.002

Increased fatty acid synthesis is required to meet the demand for membrane expansion of rapidly growing cells. ATP-citrate lyase (ACLY) is upregulated or activated in several types of cancer, and inhibition of ACLY arrests proliferation of cancer cells. Here we show that ACLY is acetylated at lysine residues 540, 546, and 554 (3K). Acetylation at these three lysine residues is stimulated by P300/calcium-binding protein (CBP)-associated factor (PCAF) acetyltransferase under high glucose and increases ACLY stability by blocking its ubiquitylation and degradation. Conversely, the protein deacetylase sirtuin 2 (SIRT2) deacetylates and destabilizes ACLY. Substitution of 3K abolishes ACLY ubiquitylation and promotes de novo lipid synthesis, cell proliferation, and tumor growth. Importantly, 3K acetylation of ACLY is increased in human lung cancers. Our study reveals a crosstalk between acetylation and ubiquitylation by competing for the same lysine residues in the regulation of fatty acid synthesis and cell growth in response to glucose.

Fatty acid synthesis occurs at low rates in most nondividing cells of normal tissues that primarily uptake lipids from circulation. In contrast, increased lipogenesis, especially de novo lipid synthesis, is a key characteristic of cancer cells. Many studies have demonstrated that in cancer cells, fatty acids are preferred to be derived from de novo synthesis instead of extracellular lipid supply (Medes et al., 1953Menendez and Lupu, 2007;Ookhtens et al., 1984Sabine et al., 1967). Fatty acids are key building blocks for membrane biogenesis, and glucose serves as a major carbon source for de novo fatty acid synthesis (Kuhajda, 2000McAndrew, 1986;Swinnen et al., 2006). In rapidly proliferating cells, citrate generated by the tricarboxylic acid (TCA) cycle, either from glucose by glycolysis or glutamine by anaplerosis, is preferentially exported from mitochondria to cytosol and then cleaved by ATP citrate lyase (ACLY) (Icard et al., 2012) to produce cytosolic acetyl coenzyme A (acetyl-CoA), which is the building block for de novo lipid synthesis. As such, ACLY couples energy metabolism with fatty acids synthesis and plays a critical role in supporting cell growth. The function of ACLY in cell growth is supported by the observation that inhibition of ACLY by chemical inhibitors or RNAi dramatically suppresses tumor cell proliferation and induces differentiation in vitro and in vivo (Bauer et al., 2005Hatzivassiliou et al., 2005). In addition, ACLY activity may link metabolic status to histone acetylation by providing acetyl-CoA and, therefore, gene expression (Wellen et al., 2009).

While ACLY is transcriptionally regulated by sterol regulatory element-binding protein 1 (SREBP-1) (Kim et al., 2010), ACLY activity is regulated by the phosphatidylinositol 3-kinase (PI3K)/Akt pathway (Berwick et al., 2002Migita et al., 2008Pierce et al., 1982). Akt can directly phosphorylate and activate ACLY (Bauer et al., 2005Berwick et al., 2002Migita et al., 2008Potapova et al., 2000). Covalent lysine acetylation has recently been found to play a broad and critical role in the regulation of multiple metabolic enzymes (Choudhary et al., 2009Zhao et al., 2010). In this study, we demonstrate that ACLY protein is acetylated on multiple lysine residues in response to high glucose. Acetylation of ACLY blocks its ubiquitinylation and degradation, thus leading to ACLY accumulation and increased fatty acid synthesis. Our observations reveal a crosstalk between protein acetylation and ubiquitylation in the regulation of fatty acid synthesis and cell growth.

Acetylation of ACLY at Lysines 540, 546, and 554

Recent mass spectrometry-based proteomic analyses have potentially identified a large number of acetylated proteins, including ACLY (Figure S1A available online; Choudhary et al., 2009Zhao et al., 2010). We detected the acetylation level of ectopically expressed ACLY followed by western blot using pan-specific anti-acetylated lysine antibody. ACLY was indeed acetylated, and its acetylation was increased by nearly 3-fold after treatment with nicotinamide (NAM), an inhibitor of the SIRT family deacetylases, and trichostatin A (TSA), an inhibitor of histone deacetylase (HDAC) class I and class II (Figure 1A). Experiments with endogenous ACLY also showed that TSA and NAM treatment enhanced ACLY acetylation (Figure 1B).

Figure 1  ACLY Is Acetylated at Lysines 540, 546, and 554

Ten putative acetylation sites were identified by mass spectrometry analyses (Table S1). We singly mutated each lysine to either a glutamine (Q) or an arginine (R) and found that no single mutation resulted in a significant reduction of ACLY acetylation (data not shown), indicating that ACLY may be acetylated at multiple lysine residues. Three lysine residues, K540, K546, and K554, received high scores in the acetylation proteomic screen and are evolutionarily conserved from C. elegans to mammals (Figure S1A). We generated triple Q and R mutants of K540, K546, and K554 (3KQ and 3KR) and found that both 3KQ and 3KR mutations resulted in a significant (~60%) decrease in ACLY acetylation (Figure 1C), indicating that 3K are the major acetylation sites of ACLY.  Further, we found that the acetylation of endogenous ACLY is clearly increased after treatment of cells with NAM and TSA (Figure 1D). These results demonstrate that ACLY is acetylated at K540, K546, and K554.

Glucose Promotes ACLY Acetylation to Stabilize ACLY

In mammalian cells, glucose is the main carbon source for de novo lipid synthesis. We found that ACLY levels increased with increasing glucose concentration, which also correlated with increased ACLY 3K acetylation (Figure 1E). Furthermore, to confirm whether the glucose level affects ACLY protein stability in vivo, we intraperitoneally injected glucose in BALB/c mice and found that high glucose resulted in a significant increase of ACLY protein levels (Figure 1F).

To determine whether ACLY acetylation affects its protein levels, we treated HeLa and Chang liver cells with NAM and TSA and found an increase in ACLY protein levels (Figure S1G, upper panel). ACLY mRNA levels were not significantly changed by the treatment of NAM and TSA (Figure S1G, lower panel), indicating that this upregulation of ACLY is mostly achieved at the posttranscriptional level. Indeed, ACLY protein was also accumulated in cells treated with the proteasome inhibitor MG132, indicating that ACLY stability could be regulated by the ubiquitin-proteasome pathway (Figure 1G). Blocking deacetylase activity stabilized ACLY (Figure S1H). The stabilization of ACLY induced by high glucose was associated with an increase of ACLY acetylation at K540, K546, and K554. Together, these data support a notion that high glucose induces both ACLY acetylation and protein stabilization and prompted us to ask whether acetylation directly regulates ACLY stability. We then generated ACLYWT, ACLY3KQ, and ACLY3KRstable cells after knocking down the endogenous ACLY. We found that the ACLY3KR or ACLY3KQmutant was more stable than the ACLYWT (Figures 1I and S1I). Collectively, our results suggest that glucose induces acetylation at K540, 546, and 554 to stabilize ACLY.

Acetylation Stabilizes ACLY by Inhibiting Ubiquitylation

To determine the mechanism underlying the acetylation and ACLY protein stability, we first examined ACLY ubiquitylation and found that it was actively ubiquitylated (Figure 2A). Previous proteomic analyses have identified K546 in ACLY as a ubiquitylation site (Wagner et al., 2011). In order to identify the ubiquitylation sites, we tested the ubiquitylation levels of double mutants 540R–546R and 546–554R (Figure S2A). We found that the ubiquitylation of the 540R-546R and 546R-554R mutants is partially decreased, while mutation of K540, K546, and K554 (3KR), which changes all three putative acetylation lysine residues of ACLY to arginine residues, dramatically reduced the ACLY ubiquitylation level (Figures 2B and S2A), indicating that 3K lysines might also be the ubiquitylation target residues. Moreover, inhibition of deacetylases by NAM and TSA decreased ubiquitylation of WT but not 3KQ or 3KR mutant ACLY (Figure 2C). These results implicate an antagonizing role of the acetylation towards the ubiquitylation of ACLY at these three lysine residues.

Figure 2  Acetylation Protects ACLY from Proteasome Degradation by Inhibiting Ubiquitylation

We found that ACLY acetylation was only detected in the nonubiquitylated, but not the ubiquitylated (high-molecular-weight), ACLY species. This result indicates that ACLY acetylation and ubiquitylation are mutually exclusive and is consistent with the model that K540, K546, and K554 are the sites of both ubiquitylation and acetylation. Therefore, acetylation of these lysines would block ubiquitylation.

We also found that glucose upregulates ACLY acetylation at 3K and decreases its ubiquitylation (Figure S2B). High glucose (25 mM) effectively decreased ACLY ubiquitylation, while inhibition of deacetylases clearly diminished its ubiquitylation (Figure 2E). We conclude that acetylation and ubiquitylation occur mutually exclusively at K540, K546, and K554 and that high-glucose-induced acetylation at these three sites blocks ACLY ubiquitylation and degradation.

UBR4 Targets ACLY for Degradation

UBR4 was identified as a putative ACLY-interacting protein by affinity purification coupled with mass spectrometry analysis (data not shown). To address if UBR4 is a potential ACLY E3 ligase, we determined the interaction between ACLY and UBR4 and found that ACLY interacted with the E3 ligase domain of UBR4; this interaction was enhanced by MG132 treatment (Figure 3A). UBR4 knockdown in A549 cells resulted in an increase of endogenous ACLY protein level (Figure 3C). Moreover, UBR4 knockdown significantly stabilized ACLY (Figure 3D) and decreased ACLY ubiquitylation (Figure 3E). Taken together, these results indicate that UBR4 is an ACLY E3 ligase that responds to glucose regulation.

Figure 3  UBR4 Is the E3 Ligase of ACLY

PCAF Acetylates ACLY

PCAF knockdown significantly reduced acetylation of 3K, indicating that PCAF is a potential 3K acetyltransferase in vivo (Figure 4C, upper panel). Furthermore, PCAF knockdown decreased the steady-state level of endogenous ACLY, but not ACLY mRNA (Figure 4C, middle and lower panels). Moreover, we found that PCAF knockdown destabilized ACLY (Figure 4D). In addition, overexpression of PCAF decreases ACLY ubiquitylation (Figure 4E), while PCAF inhibition increases the interaction between UBR4 E3 ligase domain and wild-type ACLY, but not 3KR (Figure 4F). Together, our results indicate that PCAF increases ACLY protein level, possibly via acetylating ACLY at 3K.

Figure 4  PCAF Is the Acetylase of ACLY

SIRT2 Deacetylates ACLY

Figure 5  SIRT2 Decreases ACLY Acetylation and Increases Its Protein Levels In Vivo

Acetylation of ACLY Promotes Cell Proliferation and De Novo Lipid Synthesis

The protein levels of ACLY 3KQ and 3KR were accumulated to a level higher than the wild-type cells upon extended culture in low-glucose medium (Figure S6A, right panel), indicating a growth advantage conferred by ACLY stabilization resulting from the disruption of both acetylation and ubiquitylation at K540, K546, and K554. Cellular acetyl-CoA assay showed that cells expressing 3KQ or 3KR mutant ACLY produce more acetyl-CoA than cells expressing the wild-type ACLY under low glucose (Figures 6B and S6B), further supporting the conclusion that 3KQ or 3KR mutation stabilizes ACLY.

Figure 6  Acetylation of ACLY at 3K Promotes Lipogenesis and Tumor Cell Proliferation

ACLY is a key enzyme in de novo lipid synthesis. Silencing ACLY inhibited the proliferation of multiple cancer cell lines, and this inhibition can be partially rescued by adding extra fatty acids or cholesterol into the culture media (Zaidi et al., 2012). This prompted us to measure extracellular lipid incorporation in A549 cells after knockdown and ectopic expression of ACLY. We found that when cultured in low glucose (2.5 mM), cells expressing wild-type ACLY uptake significantly more phospholipids compared to cells expressing 3KQ or 3KR mutant ACLY (Figures 6C, 6D, and S6D). When cultured in the presence of high glucose (25 mM), however, cells expressing either the wild-type, 3KQ, or 3KR mutant ACLY all have reduced, but similar, uptake of extracellular phospholipids (Figures 6C, 6D, and S6D). The above results are consistent with a model that acetylation of ACLY induced by high glucose increases its stability and stimulates de novo lipid synthesis.

3K Acetylation of ACLY Is Increased in Lung Cancer

ACLY is reported to be upregulated in human lung cancer (Migita et al., 2008). Many small chemicals targeting ACLY have been designed for cancer treatment (Zu et al., 2012). The finding that 3KQ or 3KR mutant increased the ability of ACLY to support A549 lung cancer cell proliferation prompted us to examine 3K acetylation in human lung cancers. We collected a total of 54 pairs of primary human lung cancer samples with adjacent normal lung tissues and performed immunoblotting for ACLY protein levels. This analysis revealed that, when compared to the matched normal lung tissues, 29 pairs showed a significant increase of total ACLY protein using b-actin as a loading control (Figures 7A and S7A). The tumor sample analyses demonstrate that ACLY protein levels are elevated in lung cancers, and 3K acetylation positively correlates with the elevated ACLY protein. These data also indicate that ACLY with 3K acetylation may be potential biomarker for lung cancer diagnosis.

Figure 7
  Acetylation of ACLY at 3K Is Upregulated in Human Lung Carcinoma

Dysregulation of cellular metabolism is a hallmark of cancer (Hanahan and Weinberg, 2011Vander Heiden et al., 2009). Besides elevated glycolysis, increased lipogenesis, especially de novo lipid synthesis, also plays an important role in tumor growth. Because most carbon sources for fatty acid synthesis are from glucose in mammalian cells (Wellen et al., 2009), the channeling of carbon into de novo lipid synthesis as building blocks for tumor cell growth is primarily linked to acetyl-CoA production by ACLY. Moreover, the ACLY-catalyzed reaction consumes ATP. Therefore, as the key cellular energy and carbon source, one may expect a role for glucose in ACLY regulation. In the present study, we have uncovered a mechanism of ACLY regulation by glucose that increases ACLY protein level to meet the enhanced demand of lipogenesis in growing cells, such as tumor cells (Figure 7C). Glucose increases ACLY protein levels by stimulating its acetylation.

Upregulation of ACLY is common in many cancers (Kuhajda, 2000Milgraum et al., 1997Swinnen et al., 2004Yahagi et al., 2005). This is in part due to the transcriptional activation by SREBP-1 resulting from the activation of the PI3K/AKT pathway in cancers (Kim et al., 2010Nadler et al., 2001Wang and Dey, 2006). In this study, we report a mechanism of ACLY regulation at the posttranscriptional level. We propose that acetylation modulated by glucose status plays a crucial role in coordinating the intracellular level of ACLY, hence fatty acid synthesis, and glucose availability. When glucose is sufficient, lipogenesis is enhanced. This can be achieved, at least in part, by the glucose-induced stabilization of ACLY. High glucose increases ACLY acetylation, which inhibits its ubiquitylation and degradation, leading to the accumulation of ACLY and enhanced lipogenesis. In contrast, when glucose is limited, ACLY is not acetylated and thus can be ubiquitylated, leading to ACLY degradation and reduced lipogenesis. Moreover, our data indicate that acetylation and ubiquitylation in ACLY may compete with each other by targeting the same lysine residues at K540, K546, and K554. Consistently, previous proteomic analyses have identified K546 in ACLY as a ubiquitylation site (Wagner et al., 2011). Similar models of different modifications on the same lysine residues have been reported in the regulation of other proteins (Grönroos et al., 2002Li et al., 20022012). We propose that acetylation and ubiquitylation have opposing effects in the regulation of ACLY by competitively modifying the same lysine residues. The acetylation-mimetic 3KQ and the acetylation-deficient 3KR mutants behaved indistinguishably in most biochemical and functional assays, mainly due to the fact that these mutations disrupt lysine ubiquitylation that primarily occurs on these three residues.

ACLY is increased in lung cancer tissues compared to adjacent tissues. Consistently, ACLY acetylation at 3K is also significantly increased in lung cancer tissues. These observations not only confirm ACLY acetylation in vivo, but also suggest that ACLY 3K acetylation may play a role in lung cancer development. Our study reveals a mechanism of ACLY regulation in response to glucose signals.

 

7.7.7 Monoacylglycerol Lipase Regulates a Fatty Acid Network that Promotes Cancer Pathogenesis

Nomura DK1Long JZNiessen SHoover HSNg SWCravatt BF.
Cell. 2010 Jan 8; 140(1):49-61
http://dx.doi.org/10.1016.2Fj.cell.2009.11.027

Highlights

  • Monoacylglycerol lipase (MAGL) is elevated in aggressive human cancer cells
  • Loss of MAGL lowers fatty acid levels in cancer cells and impairs pathogenicity
  • MAGL controls a signaling network enriched in protumorigenic lipids
  • A high-fat diet can restore the growth of tumors lacking MAGL in vivo
monoacylglycerol-lipase-magl-is-highly-expressed-in-aggressive-human-cancer-cells-and-primary-tumors

monoacylglycerol-lipase-magl-is-highly-expressed-in-aggressive-human-cancer-cells-and-primary-tumors

http://www.cell.com/cms/attachment/1082768/7977146/fx1.jpg

Tumor cells display progressive changes in metabolism that correlate with malignancy, including development of a lipogenic phenotype. How stored fats are liberated and remodeled to support cancer pathogenesis, however, remains unknown. Here, we show that the enzyme monoacylglycerol lipase (MAGL) is highly expressed in aggressive human cancer cells and primary tumors, where it regulates a fatty acid network enriched in oncogenic signaling lipids that promotes migration, invasion, survival, and in vivo tumor growth. Overexpression of MAGL in nonaggressive cancer cells recapitulates this fatty acid network and increases their pathogenicity—phenotypes that are reversed by an MAGL inhibitor. Impairments in MAGL-dependent tumor growth are rescued by a high-fat diet, indicating that exogenous sources of fatty acids can contribute to malignancy in cancers lacking MAGL activity. Together, these findings reveal how cancer cells can co-opt a lipolytic enzyme to translate their lipogenic state into an array of protumorigenic signals.

We show that the enzyme monoacylglycerol lipase (MAGL) is highly expressed in aggressive human cancer cells and primary tumors, where it regulates a fatty acid network enriched in oncogenic signaling lipids that promotes migration, invasion, survival, and in vivo tumor growth. Overexpression of MAGL in non-aggressive cancer cells recapitulates this fatty acid network and increases their pathogenicity — phenotypes that are reversed by an MAGL inhibitor. Interestingly, impairments in MAGL-dependent tumor growth are rescued by a high-fat diet, indicating that exogenous sources of fatty acids can contribute to malignancy in cancers lacking MAGL activity. Together, these findings reveal how cancer cells can co-opt a lipolytic enzyme to translate their lipogenic state into an array of pro-tumorigenic signals.

The conversion of cells from a normal to cancerous state is accompanied by reprogramming of metabolic pathways (Deberardinis et al., 2008Jones and Thompson, 2009Kroemer and Pouyssegur, 2008), including those that regulate glycolysis (Christofk et al., 2008Gatenby and Gillies, 2004), glutamine-dependent anaplerosis (DeBerardinis et al., 2008DeBerardinis et al., 2007Wise et al., 2008), and the production of lipids (DeBerardinis et al., 2008Menendez and Lupu, 2007). Despite a growing appreciation that dysregulated metabolism is a defining feature of cancer, it remains unclear, in many instances, how such biochemical changes occur and whether they play crucial roles in disease progression and malignancy.

Among dysregulated metabolic pathways, heightened de novo lipid biosynthesis, or the development a “lipogenic” phenotype (Menendez and Lupu, 2007), has been posited to play a major role in cancer. For instance, elevated levels of fatty acid synthase (FAS), the enzyme responsible for fatty acid biosynthesis from acetate and malonyl CoA, are correlated with poor prognosis in breast cancer patients, and inhibition of FAS results in decreased cell proliferation, loss of cell viability, and decreased tumor growth in vivo (Kuhajda et al., 2000Menendez and Lupu, 2007Zhou et al., 2007). FAS may support cancer growth, at least in part, by providing metabolic substrates for energy production (via fatty acid oxidation) (Buzzai et al., 2005Buzzai et al., 2007Liu, 2006). Many other features of lipid biochemistry, however, are also critical for supporting the malignancy of cancer cells, including:

Prominent examples of lipid messengers that contribute to cancer include:

Here, we use functional proteomic methods to discover a lipolytic enzyme, monoacylglycerol lipase (MAGL), that is highly elevated in aggressive cancer cells from multiple tissues of origin. We show that MAGL, through hydrolysis of monoacylglycerols (MAGs), controls free fatty acid (FFA) levels in cancer cells. The resulting MAGL-FFA pathway feeds into a diverse lipid network enriched in pro-tumorigenic signaling molecules and promotes migration, survival, and in vivo tumor growth. Aggressive cancer cells thus pair lipogenesis with high lipolytic activity to generate an array of pro-tumorigenic signals that support their malignant behavior.

Activity-Based Proteomic Analysis of Hydrolytic Enzymes in Human Cancer Cells

To identify enzyme activities that contribute to cancer pathogenesis, we conducted a functional proteomic analysis of a panel of aggressive and non-aggressive human cancer cell lines from multiple tumors of origin, including melanoma [aggressive (C8161, MUM2B), non-aggressive (MUM2C)], ovarian [aggressive (SKOV3), non-aggressive (OVCAR3)], and breast [aggressive (231MFP), non-aggressive (MCF7)] cancer. Aggressive cancer lines were confirmed to display much greater in vitro migration and in vivo tumor-growth rates compared to their non-aggressive counterparts (Figure S1), as previously shown (Jessani et al., 2004;Jessani et al., 2002Seftor et al., 2002Welch et al., 1991). Proteomes from these cancer lines were screened by activity-based protein profiling (ABPP) using serine hydrolase-directed fluorophosphonate (FP) activity-based probes (Jessani et al., 2002Patricelli et al., 2001). Serine hydrolases are one of the largest and most diverse enzyme classes in the human proteome (representing ~ 1–1.5% of all human proteins) and play important roles in many biochemical processes of potential relevance to cancer, such as proteolysis (McMahon and Kwaan, 2008Puustinen et al., 2009), signal transduction (Puustinen et al., 2009), and lipid metabolism (Menendez and Lupu, 2007Zechner et al., 2005). The goal of this study was to identify hydrolytic enzyme activities that were consistently altered in aggressive versus non-aggressive cancer lines, working under the hypothesis that these conserved enzymatic changes would have a high probability of contributing to the pathogenic state of cancer cells.

Among the more than 50 serine hydrolases detected in this analysis (Tables S13), two enzymes, KIAA1363 and MAGL, were found to be consistently elevated in aggressive cancer cells relative to their non-aggressive counterparts, as judged by spectral counting (Jessani et al., 2005Liu et al., 2004). We confirmed elevations in KIAA1363 and MAGL in aggressive cancer cells by gel-based ABPP, where proteomes are treated with a rhodamine-tagged FP probe and resolved by 1D-SDS-PAGE and in-gel fluorescence scanning (Figure 1A). In both cases, two forms of each enzyme were detected (Figure 1A), due to differential glycoslyation for KIAA1363 (Jessani et al., 2002), and possibly alternative splicing for MAGL (Karlsson et al., 2001). We have previously shown that KIAA1363 plays a role in regulating ether lipid signaling pathways in aggressive cancer cells (Chiang et al., 2006). On the other hand, very little was known about the function of MAGL in cancer.

Figure 1  MAGL is elevated in aggressive cancer cells, where the enzyme regulates monoacylgycerol (MAG) and free fatty acid (FFA) levels

The heightened activity of MAGL in aggressive cancer cells was confirmed using the substrate C20:4 MAG (Figure 1B). Since several enzymes have been shown to display MAG hydrolytic activity (Blankman et al., 2007), we confirmed the contribution that MAGL makes to this process in cancer cells using the potent and selective MAGL inhibitor JZL184 (Long et al., 2009a).

MAGL Regulates Free Fatty Acid Levels in Aggressive Cancer Cells

MAGL is perhaps best recognized for its role in degrading the endogenous cannabinoid 2-arachidonoylglycerol (2-AG, C20:4 MAG), as well as other MAGs, in brain and peripheral tissues (Dinh et al., 2002Long et al., 2009aLong et al., 2009bNomura et al., 2008). Consistent with this established function, blockade of MAGL by JZL184 (1 μM, 4 hr) produced significant elevations in the levels of several MAGs, including 2-AG, in each of the aggressive cancer cell lines (Figure 1C and Figure S2). Interestingly, however, MAGL inhibition also caused significant reductions in the levels of FFAs in aggressive cancer cells (Figure 1D and Figure S2). This surprising finding contrasts with the function of MAGL in normal tissues, where the enzyme does not, in general, control the levels of FFAs (Long et al., 2009aLong et al., 2009b;Nomura et al., 2008).

Metabolic labeling studies using the non-natural C17:0-MAG confirmed that MAGs are converted to LPC and LPE by aggressive cancer cells, and that this metabolic transformation is significantly enhanced by treatment with JZL184 (Figure S1). Finally, JZL184 treatment did not affect the levels of MAGs and FFAs in non-aggressive cancer lines (Figure 1C, D), consistent with the negligible expression of MAGL in these cells (Figure 1A, B).

We next stably knocked down MAGL expression by RNA interference technology using two independent shRNA probes (shMAGL1, shMAGL2), both of which reduced MAGL activity by 70–80% in aggressive cancer lines (Figure 2A, D and Figure S2). Other serine hydrolase activities were unaffected by shMAGL probes (Figure 2A, D and Figures S2), confirming the specificity of these reagents. Both shMAGL probes caused significant elevations in MAGs and corresponding reductions in FFAs in aggressive melanoma (Figure 2B, C), ovarian (Figure 2E, F), and breast cancer cells (Figure S2).

Figure 2  Stable shRNA-mediated knockdown of MAGL lowers FFA levels in aggressive cancer cells.

Together, these data demonstrate that both acute (pharmacological) and stable (shRNA) blockade of MAGL cause elevations in MAGs and reductions in FFAs in aggressive cancer cells. These intriguing findings indicate that MAGL is the principal regulator of FFA levels in aggressive cancer cells. Finally, we confirmed that MAGL activity (Figure 3A, B) and FFA levels (Figure 3C) are also elevated in high-grade primary human ovarian tumors compared to benign or low-grade tumors. Thus, heightened expression of the MAGL-FFA pathway is a prominent feature of both aggressive human cancer cell lines and primary tumors.

Figure 3  High-grade primary human ovarian tumors possess elevated MAGL activity and FFAs compared to benign tumors.

Disruption of MAGL Expression and Activity Impairs Cancer Pathogenicity

shMAGL cancer lines were next examined for alterations in pathogenicity using a set of in vitro and in vivo assays. shMAGL-melanoma (C8161), ovarian (SKOV3), and breast (231MFP) cancer cells exhibited significantly reduced in vitro migration (Figure 4A, F and Figure S2), invasion (Figure 4B, G and Figure S2), and cell survival under serum-starvation conditions (Figure 4C, H and Figure S2). Acute pharmacological blockade of MAGL by JZL184 also decreased cancer cell migration (Figure S2), but not survival, possibly indicating that maximal impairments in cancer aggressiveness require sustained inhibition of MAGL.

Figure 4  shRNA-mediated knockdown and pharmacological inhibition of MAGL impair cancer aggressiveness.

MAGL Overexpression Increases FFAs and the Aggressiveness of Cancer Cells

Stable MAGL-overexpressing (MAGL-OE) and control [expressing an empty vector or a catalytically inactive version of MAGL, where the serine nucleophile was mutated to alanine (S122A)] variants of MUM2C and OVCAR3 cells were generated by retroviral infection and evaluated for their respective MAGL activities by ABPP and C20:4 MAG substrate assays. Both assays confirmed that MAGL-OE cells possess greater than 10-fold elevations in MAGL activity compared to control cells (Figure 5A and Figure S4). MAGL-OE cells also showed significant reductions in MAGs (Figure 5B andFigure S4) and elevated FFAs (Figure 5C and Figure S4). This altered metabolic profile was accompanied by increased migration (Figure 5D and Figure S4), invasion (Figure 5E and Figure S4), and survival (Figure S4) in MAGL-OE cells. None of these effects were observed in cancer cells expressing the S122A MAGL mutant, indicating that they require MAGL activity.  MAGL-OE MUM2C cells also showed enhanced tumor growth in vivo compared to control cells (Figure 5F). Notably, the increased tumor growth rate of MAGL-OE MUM2C cells nearly matched that of aggressive C8161 cells (Figure S4). These data indicate that the ectopic expression of MAGL in non-aggressive cancer cells is sufficient to elevate their FFA levels and promote pathogenicity both in vitro and in vivo.

Figure 5 Ectopic expression of MAGL elevates FFA levels and enhances the in vitro and in vivo pathogenicity of MUM2C melanoma cells.

Metabolic Rescue of Impaired Pathogenicity in MAGL-Disrupted Cancer Cells

MAGL could support the aggressiveness of cancer cells by either reducing the levels of its MAG substrates, elevating the levels of its FFA products, or both. Among MAGs, the principal signaling molecule is the endocannabinoid 2-AG, which activates the CB1 and CB2 receptors (Ahn et al., 2008Mackie and Stella, 2006). The endocannabinoid system has been implicated previously in cancer progression and, depending on the specific study, shown to promote (Sarnataro et al., 2006Zhao et al., 2005) or suppress (Endsley et al., 2007Wang et al., 2008) cancer pathogenesis. Neither a CB1 or CB2 antagonist rescued the migratory defects of shMAGL cancer cells (Figure S5). CB1 and CB2 antagonists also did not affect the levels of MAGs or FFAs in cancer cells (Figure S5).

We then determined whether increased FFA delivery could rectify the tumor growth defect observed for shMAGL cells in vivo. Immune-deficient mice were fed either a normal chow or high-fat diet throughout the duration of a xenograft tumor growth experiment. Notably, the impaired tumor growth rate of shMAGL-C8161 cells was completely rescued in mice fed a high-fat diet. In contrast, shControl-C8161 cells showed equivalent tumor growth rates on a normal versus high-fat diet. The recovery in tumor growth for shMAGL-C8161 cells in the high-fat diet group correlated with significantly increases levels of FFAs in excised tumors (Figure 6D). Collectively, these results indicate that MAGL supports the pathogenic properties of cancer cells by maintaining tonically elevated levels of FFAs.

Figure 6  Recovery of the pathogenic properties of shMAGL cancer cells by treatment with exogenous fatty acids.

MAGL Regulates a Fatty Acid Network Enriched in Pro-Tumorigenic Signals

Studies revealed that neither

  • the MAGL-FFA pathway might serve as a means to regenerate NAD+ (via continual fatty acyl glyceride/FFA recycling) to fuel glycolysis, or
  • increased lipolysis could be to generate FFA substrates for β-oxidation, which may serve as an important energy source for cancer cells (Buzzai et al., 2005), or
  • CPT1 blockade (reduced expression of CPT1 in aggressive cancer cells (data not shown) has been reported previously (Deberardinis et al., 2006))

providing evidence against a role for β-oxidation as a downstream mediator of the pathogenic effects of the MAGL-fatty acid pathway.

Considering that FFAs are fundamental building blocks for the production and remodeling of membrane structures and signaling molecules, perturbations in MAGL might be expected to affect several lipid-dependent biochemical networks important for malignancy. To test this hypothesis, we performed lipidomic analyses of cancer cell models with altered MAGL activity, including comparisons of:

  1. MAGL-OE versus control cancer cells (OVCAR3, MUM2C), and
  2. shMAGL versus shControl cancer cells (SKOV3, C8161).

Complementing these global profiles, we also conducted targeted measurements of specific bioactive lipids (e.g., prostaglandins) that are too low in abundance for detection by standard lipidomic methods. The resulting data sets were then mined to identify a common signature of lipid metabolites regulated by MAGL, which we defined as metabolites that were significantly increased or reduced in MAGL–OE cells and showed the opposite change in shMAGL cells relative to their respective control groups (Figure 7A, B and Table S4).

Figure 7  MAGL regulates a lipid network enriched in pro-tumorigenic signaling molecules.

Most of the lipids in the MAGL-fatty acid network, including several lysophospholipids (LPC, LPA, LPE), ether lipids (MAGE, alkyl LPE), phosphatidic acid (PA), and prostaglandin E2 (PGE2), displayed similar profiles to FFAs, being consistently elevated and reduced in MAGL-OE and shMAGL cells, respectively. Only MAGs were found to show the opposite profile (elevated and reduced in shMAGL and MAGL-OE cells, respectively). Interestingly, virtually this entire lipidomic signature was also observed in aggressive cancer cells when compared to their non-aggressive counterparts (e.g., C8161 versus MUM2C and SKOV3 versus OVCAR3, respectively; Table S4). These findings demonstrate that MAGL regulates a lipid network in aggressive cancer cells that consists of not only FFAs and MAGs, but also a host of secondary lipid metabolites. Increases (rather than decreases) in LPCs and LPEs were observed in JZL184-treated cells (Figure S1 and Table S4). These data indicate that acute and chronic blockade of MAGL generate distinct metabolomic effects in cancer cells, likely reflecting the differential outcomes of short- versus long-term depletion of FFAs.

Within the MAGL-fatty acid network are several pro-tumorigenic lipid messengers, including LPA and PGE2, that have been reported to promote the aggressiveness of cancer cells (Gupta et al., 2007Mills and Moolenaar, 2003). Metabolic labeling studies confirmed that aggressive cancer cells can convert both MAGs and FFAs (Figure S1) to LPA and PGE2 and, for MAGs, this conversion was blocked by JZL184 (Figure S1). Interestingly, treatment with either LPA or PGE2 (100 nM, 4 hr) rescued the impaired migration of shMAGL cancer cells at concentrations that did not affect the migration of shControl cells (Figure 7E).

Heightened lipogenesis is an established early hallmark of dysregulated metabolism and pathogenicity in cancer (Menendez and Lupu, 2007). Cancer lipogenesis appears to be driven principally by FAS, which is elevated in most transformed cells and important for survival and proliferation (De Schrijver et al., 2003;Kuhajda et al., 2000Vazquez-Martin et al., 2008). It is not yet clear how FAS supports cancer growth, but most of the proposed mechanisms invoke pro-tumorigenic functions for the enzyme s fatty acid products and their lipid derivatives (Menendez and Lupu, 2007). This creates a conundrum, since the fatty acid molecules produced by FAS are thought to be rapidly incorporated into neutral- and phospho-lipids, pointing to the need for complementary lipolytic pathways in cancer cells to release stored fatty acids for metabolic and signaling purposes (Prentki and Madiraju, 2008Przybytkowski et al., 2007). Consistent with this hypothesis, we found that acute treatment with the FAS inhibitor C75 (40 μM, 4 h) did not reduce FFA levels in cancer cells (data not shown). Furthermore, aggressive and non-aggressive cancer cells exhibited similar levels of FAS (data not shown), indicating that lipogenesis in the absence of paired lipolysis may be insufficient to confer high levels of malignancy.

Here we show that aggressive cancer cells do indeed acquire the ability to liberate FFAs from neutral lipid stores as a consequence of heightened expression of MAGL. MAGL and its FFA products were found to be elevated in aggressive human cancer cell lines from multiple tissues of origin, as well as in high-grade primary human ovarian tumors. These data suggest that the MAGL-FFA pathway may be a conserved feature of advanced forms of many types of cancer. Further evidence in support of this premise originates from gene expression profiling studies, which have identified increased levels of MAGL in primary human ductal breast tumors compared to less malignant medullary breast tumors (Gjerstorff et al., 2006). The key role that MAGL plays in regulating FFA levels in aggressive cancer cells contrasts with the function of this enzyme in normal tissues, where it mainly controls the levels of MAGs, but not FFAs (Long et al., 2009b). These data thus provide a striking example of the co-opting of an enzyme by cancer cells to serve a distinct metabolic purpose that supports their pathogenic behavior.

Taken together, our results indicate that MAGL serves as key metabolic hub in aggressive cancer cells, where the enzyme regulates a fatty acid network that feeds into a number of pro-tumorigenic signaling pathways.

 

7.7.8 Pirin regulates epithelial to mesenchymal transition and down-regulates EAF/U19 signaling in prostate cancer cells

7.7.8.1  Pirin regulates epithelial to mesenchymal transition independently of Bcl3-Slug signaling

Komai K1Niwa Y1Sasazawa Y1Simizu S2.
FEBS Lett. 2015 Mar 12; 589(6):738-43
http://dx.doi.org:/10.1016/j.febslet.2015.01.040

Highlights

  • Pirin decreases E-cadherin expression and induces EMT.
  • The induction of EMT by Pirin is achieved through a Bcl3 independent pathway.
  • Pirin may be a novel target for cancer therapy.

Epithelial to mesenchymal transition (EMT) is an important mechanism for the initial step of metastasis. Proteomic analysis indicates that Pirin is involved in metastasis. However, there are no reports demonstrating its direct contribution. Here we investigated the involvement of Pirin in EMT. In HeLa cells, Pirin suppressed E-cadherin expression and regulated the expression of other EMT markers. Furthermore, cells expressing Pirin exhibited a spindle-like morphology, which is reminiscent of EMT. A Pirin mutant defective for Bcl3 binding decreased E-cadherin expression similar to wild-type, suggesting that Pirin regulates E-cadherin independently of Bcl3-Slug signaling. These data provide direct evidence that Pirin contributes to cancer metastasis.

Pirin regulates the expression of E-cadherin and EMT markers

In melanoma, Pirin enhances NF-jB activity and increases Slug expression by binding Bcl3 [31], and it may also be involved in adenoid cystic tumor metastasis [23]. Since Slug suppresses E-cadherin transcription and is recognized as a major EMT inducer, we hypothesized that Pirin may regulate EMT through inducing Slug expression. To investigate whether Pirin regulates EMT, we measured E-cadherin expression following Pirin knockdown. As shown in Fig. 1A and B, E-cadherin expression was significantly increased following Pirin knockdown indicating that it may promote EMT. To confirm this, we established Pirin-expressing HeLa cells (Fig. 1C), which inhibited the expression of E-cadherin (Fig. 1D). Additionally, the expression of Occludin, an epithelial marker, was decreased, and several mesenchymal markers, including Fibronectin, N-cadherin, and Vimentin, were increased by Pirin expression (Fig. 1D). These data suggest that Pirin promotes EMT.

Pirin induces EMT-associated cell morphological changes

As mentioned above, cells undergo morphological changes during EMT. Therefore, we next analyzed whether Pirin expression affects cell morphology. Quantitative analysis of morphological changes was based on cell circularity, {4p(area)/(perimeter)2}100, which decreases during EMT-associated morphological changes [34–36]. Indeed, TGF-b or TNF-a exposure induced EMTassociated cell morphological changes in HeLa cells (data not shown). Employing this parameter of circularity, we compared the morphology of our established HeLa/Pirin-GFP cells with control HeLa/GFP cells. Although the control HeLa/GFP cells displayed a cobblestone-like morphology, HeLa/Pirin-GFP cells were elongated in shape (Fig. 2A). Indeed, compared with control cells, the circularity of HeLa/Pirin-GFP cells was significantly decreased (Fig. 2B). To confirm that these observations were dependent on Pirin expression, HeLa/Pirin-GFP cells were treated with an siRNA targeting Pirin. HeLa/Pirin-GFP cells recovered a cobblestone-like morphology (Fig. 2C) and circularity (Fig. 2D) when treated with Pirin siRNA indicating that Pirin expression induces EMT.

Pirin induces cell migration

During EMT cells acquire migratory capabilities. Therefore, we analyzed whether Pirin affects cell migration. HeLa cells were treated with an siRNA targeting Pirin and migration was assessed using a wound healing assay. Although Pirin knockdown had no effect on cell proliferation (data not shown), wound repair was inhibited in Pirin-depleted HeLa cells (Fig. 3A and B) suggesting that Pirin promoted cell migration. Furthermore, camptothecin treatment of HeLa/GFP cells caused decreased cell viability in a dose-dependent manner, whereas HeLa/Pirin-GFP cells were more resistantto drugtreatment (datanot shown).These results suggest that Pirin induces EMT-like phenotypes, such as cell migration and anticancer drug resistance.
Pirin regulates EMT independently of Bcl3-Slug signaling

To investigate whether Pirin controls E-cadherin expression at the transcriptional level, we measured E-cadherin promoter activity with a reporter assay. Indeed, the luciferase reporter analysis indicated that Pirin inhibited E-cadherin promoter activity (Fig. 4A and B). To determine if Bcl3 is involved in Pirin-induced EMT, we tested whether a Pirin mutant defective in Bcl3 binding could inhibit E-cadherin expression. We generated a mutation in the metal-binding cavity of Pirin(E103A) and confirmed that it disrupted Bcl3 binding. In vitro GST pull-down analysis using recombinant Pirin and Bcl3/ARD demonstrated that the Pirin mutant was defective for Bcl3 binding compared to wild-type (Fig. 5A). Interestingly, expression of both wild-type Pirin and the mutant defective in Bcl3 binding reduced E-cadherin gene and protein expression (Fig. 5B and C). Taken together these results indicate that Pirin decreases E-cadherin expression without binding Bcl3, and suggest that Pirin regulates EMT independently of Bcl3-Slug signaling.

Discussion

A characteristic feature of EMT is the disruption of epithelial cell–cell contact, which is achieved by reduced E-cadherin expression. Therefore, revealing the regulatory pathways controlling E-cadherin expression may elucidate the mechanisms of EMT. Several transcription factors regulate E-cadherin transcription. For instance,Snail,Slug,Twist,and Zebact as mastertranscriptional regulators that bind the consensus E-box sequence in the E-cadherin gene promoter and decrease the transcriptional activity [38]. Since Pirin regulates the transcription of Slug [31], we hypothesized that Pirin may also regulate EMT. In this study we demonstrated that Pirin decreases E-cadherin expression, and induces EMT and cancer malignant phenotypes. Since EMT is an initial step of metastasis, Pirin may contribute to cancer progression. We next examined whether the regulation of EMT by Pirin is attributed to Bcl3 binding and the induction of Slug. To this end, we generated a Pirin mutant (E103A) defective for Bcl3 binding (Fig. 5A). Single Fe2+ ion chelating is coordinated by His56, His58, His101, and Glu103 of Pirin, and the N-terminal domain containing these residues is highly conserved between mammals, plants, fungi, and prokaryotic organisms [15,27]. Therefore, it has been predicted that this N-terminal domain containing the metal-binding cavity is important for Pirin function [20,26,31]. Indeed, TPh A inserts into the metal-binding cavity and inhibits binding to Bcl3 suggesting that the interaction occurs with the metal-binding cavity of Pirin [31]. In contrast, Hai Pang suggests that a Pirin–Bcl3– (p50)2 complex forms between acidic regions of the N-terminal Pirin domain at residues 77–82, 97–103 and 124–128 with a basic patch of Bcl3 [27]. In this study, we mutated Glutamic acid 103, a residue common between Hai Pang’s model and Pirin’s metalbinding cavity. Pull-down analysis indicated that an E103A mutant is defectiveinfor Bcl3binding(Fig.5A). Thisis the firstexperimental demonstration showing that Glu103 of Pirin is important Bcl3 binding. However, expression of the E103A mutant suppressed Ecadherin gene expression similarly to wild-type Pirin (Fig. 5B and C). Although the Bcl3–(p50)2 complex participates in oncogene addiction in cervical cells [39,40], expression of Pirin in HeLa cells did not increase Slug expression (data not shown). Therefore, we concludethatPirindecreasesE-cadherinexpressionindependently of Bcl3-Slug signaling. To understand how Pirin suppresses E-cadherin gene expression, we analyzed E-cadherin promoter activity (Fig. 4). Since Pirin decreased the activity of the E-cadherin promoter (995+1), we constructed a series of promoter deletion mutants (795+1, 565+1, 365+1, 175+1) to identify a region important for Pirin-mediated regulation. Expression of Pirin decreased the transcriptional activity of all constructs (Supplementary Fig. S1A), suggesting that Pirin may suppress E-cadherin expression through element(s) in region 175+1. Yan-Nan Liu and colleagues proposed that this region contains four Sp1-binding sites and two E-boxes that regulate E-cadherin expression.

Fig. 1. Pirin regulates E-cadherin gene expression. (A, B) HeLa cells were transfected with siRNA targeting Pirin (siPirin#1 or #2) or control siRNA (siCTRL). Forty-eight hours after transfection, cDNA was used for PCR using primer sets specific against Pirin, E-cadherin and GAPDH (A). Forty-eight hours after transfection, HeLa cells were lysed and the lysates were analyzed by Western blot with the indicated antibodies (B). (C) Lysates from HeLa/Pirin-GFP and HeLa/GFP cells were analyzed by Western blot with the indicated antibodies. (D) cDNA from HeLa/GFP or HeLa/Pirin-GFP cells was used for PCR to determine the effect of Pirin on the expression of EMT marker genes.

Fig. 2. Pirin induces cell morphological changes associated with EMT. (A) Phase contrast and fluorescence microscopic images were taken of HeLa/GFP and HeLa/Pirin-GFP cells. (B) Cell circularity was defined as form factor, {4p(area)/(perimeter)2}100 [%], and calculated using Image J software. A random selection of 100 cells from each condition was measured. (C, D) Phase contrast and fluorescence microscopic images were taken of siRNA-treated HeLa/GFP and HeLa/Pirin-GFP cells. Each cell line was transfected with siPirin#2 or siCTRL. Cells were observed by microscopy 48 h after transfection (C) and circularity was measured (D). Data shown are means ± s.d. ⁄P <0.05, bars 100lm.

Fig. 3. Pirin knockdown suppresses cell migration. (A, B) HeLa cells were transfected with siPirin#2 or siCTRL. An artificial wound was created with a tip 24h after transfection and cells were cultured for an additional 12 h. For quantification, the cells were photographed after 12h of incubation (A) and the area covered by cells was measured using Image J and normalized to control cells (B).

Fig. 4. Pirin regulates E-cadherin promoter activity.(A). HeLacells were transfected with siPirin#2 or siGFP (control) and cultured for 24 h. The E-cadherin promoter construct (995+1) and phRL-TK vectorwere transfected and cellswere cultured for an additional 24 h. Luciferase activities were measured and normalized to Renilla luciferase activity. (B) HeLa cells were transfected with the promoter construct (995+1), phRL-TK vector, and a Pirin expression vector. After 24 h, luciferase activities were measured and normalized to Renilla luciferase activity. Data are the mean ± s.d. ⁄P < 0.05.

Fig. 5. Pirin decreases E-cadherin expression in a Bcl3-independent manner. (A) Purified His6-Pirin and His6-Pirin(E103A) were incubated with Glutathione-Sepharose beads conjugated to GST or GST-Bcl3/ARD. The samples were analyzed by Western blot. (B, C) HeLa cells were transfected with vectors encoding GFP, Pirin-GFP, or Pirin(E103A)GFP. Cells were lysed 48 h after transfection and lysates were analyzed by Western blot (B). RNA collected at 48h was used for RT-PCR with the specified primer sets for each gene (C).

7.7.8.2 1324 PIRIN DOWN-REGULATES THE EAF2/U19 SIGNALING AND RETARDS THE GROWTH INHIBITION INDUCED BY EAF2/U19 IN PROSTATE CANCER CELLS

Zhongjie Qiao, Dan Wang, Zhou Wang
The Journal of Urology Apr 2013; 189(4), Supplement: e541
http://dx.doi.org/10.1016/j.juro.2013.02.2678
EAF2/U19, as the tumor suppressor, has been reported to induce apoptosis of LNCaP cells and suppress AT6.1 xenograft prostate tumor growth in vivo, and its expression level is down-regulated in advanced human prostate cancer. EAF2/U19 is also a putative transcription factor with a transactivation domain and capability of sequence-specific DNA binding. Identification and characterization of the binding partners and regulators of EAF2/U19 is essential to understand its function in regulating apoptosis/survival of prostate cancer cells.

7.7.8.3 Pirin Inhibits Cellular Senescence in Melanocytic Cells

Cellular senescence has been widely recognized as a tumor suppressing mechanism that acts as a barrier to cancer development after oncogenic stimuli. A prominent in vivo model of the senescence barrier is represented by nevi, which are composed of melanocytes that, after an initial phase of proliferation induced by activated oncogenes (most commonly BRAF), are blocked in a state of cellular senescence. Transformation to melanoma occurs when genes involved in controlling senescence are mutated or silenced and cells reacquire the capacity to proliferate. Pirin (PIR) is a highly conserved nuclear protein that likely functions as a transcriptional regulator whose expression levels are altered in different types of tumors. We analyzed the expression pattern of PIR in adult human tissues and found that it is expressed in melanocytes and has a complex pattern of regulation in nevi and melanoma: it is rarely detected in mature nevi, but is expressed at high levels in a subset of melanomas. Loss of function and overexpression experiments in normal and transformed melanocytic cells revealed that PIR is involved in the negative control of cellular senescence and that its expression is necessary to overcome the senescence barrier. Our results suggest that PIR may have a relevant role in melanoma progression

Cellular senescence is a physiological process through which normal somatic cells lose their ability to divide and enter an irreversible state of cell cycle arrest, although they remain viable and metabolically active.1,2The specific molecular circuitry underlying the onset of cellular senescence is dependent on the type of stimulus and on the cellular context. A central role is held by the activation of the tumor suppressor proteins p53 and retinoblastoma susceptibility protein (pRB),3–5 which act by interfering with the transcriptional program of the cell and ultimately arresting cell cycle progression.

In the last decade, senescence has been recognized as a major barrier against the development of tumors in mammals.6–8 One of the most prominent in vivo examples is represented by nevi, in which cells proliferate after oncogene activation and then become senescent. Melanoma is a highly aggressive form of neoplasm often observed to derive from nevi, and the transition implies suppression of the mechanisms that sustain the onset and maintenance of senescence.9 In fact, many of the melanoma-associated tumor suppressor genes identified to date are themselves involved in control of senescence, including BRAF (encoding serine/threonine-protein kinase B-raf), CKD4 (cyclin-dependent kinase 4), and CDKN2A (encoding cyclin-dependent kinase inhibitor 2A isoforms p16INK4a and p19ARF).3,10

Nevi frequently harbor oncogenic mutations of the tyrosine kinase BRAF gene, particularly V600E,11 andBRAFV600E is also found in approximately 70% of cutaneous melanomas.12 Expression of BRAFV600E in human melanocytes leads to oncogene-induced senescence,8 which can be considered as a mechanism that protects from malignant progression. In time, some cells may eventually escape senescence, probably through the acquisition of additional genetic abnormalities, thus favoring transformation to melanoma.13

Pirin (PIR) is a highly conserved nuclear protein belonging to the Cupin superfamily14 whose function is, to date, poorly characterized. It has been described as a putative transcriptional regulator on the basis of its physical association with the nuclear I/CCAAT box transcription factor NFI/CTF115 and with the B-cell lymphoma protein, BCL-3, a regulator of NF-κB/Rel activity. A recent report shows that PIR controls melanoma cell migration through the transcriptional regulation of snail homolog 2, SNAI2 (previously SLUG).16 Other reports described quercetinase enzymatic activity,17 and regulation of apoptosis18,19 and stress response, unveiling a high degree of cell-type and species specificity in PIR function.

There is evidence of variations in PIR expression levels in different types of malignancies, but a systematic analysis of PIR expression in human tumors has been lacking. We analyzed PIR expression pattern in a collection of normal and neoplastic human tissues and found that it is expressed in scattered melanocytes, virtually absent in more mature regions of nevi, and present at high levels in a subset of melanomas. Functional studies performed in normal and transformed melanocytic cells revealed that PIR ablation results in cellular senescence, and that PIR levels decrease in response to senescence stimuli. Our results suggest that PIR may be a relevant player in the negative control of cellular senescence in PIR-expressing melanomas.

PIR overexpression in melanoma

Figure 3  PIR overexpression in PIR melanoma cells has no effect on proliferation.
PIR Expression Is Down-Regulated by BRAF Activation and Camptothecin Treatment

BRAF mutations are frequent in nevi, and are directly linked to the induction of oncogene-induced senescence. Variations in PIR expression levels were therefore investigated in an experimental model of senescence induced by oncogenic BRAF. Human diploid fibroblasts (TIG3–hTERT) expressing a conditional form of constitutively activated BRAF fused to the ligand-binding domain of the estrogen receptor (ER) rapidly undergo oncogene-induced senescence on treatment with 4-hydroxytamoxifen (OHT).28,29 PIR protein and mRNA levels were measured in TIG3-BRAF-ER cells at different time points of treatment with 800 nmol/L OHT. PIR expression was significantly repressed both at the mRNA and at the protein level after BRAF activation (Figure 6A), and remained at low levels after 120 hours, suggesting that a significant reduction of PIR expression is associated with the establishment of oncogene-induced senescence in different cell types.

7.7.9 O-GlcNAcylation at promoters, nutrient sensors, and transcriptional regulation

Brian A. Lewis
Biochim et Biophys Acta (BBA) – Gene Regulatory Mechanisms Nov 2013; 1829(11): 1202–1206
http://dx.doi.org/10.1016/j.bbagrm.2013.09.003

Highlights

  • This review article discusses recent advances in the links between O-GlcNAc and transcriptional regulation.
  • Discusses several systems to illustrate O-GlcNAc dynamics: Tet proteins, MLL complexes, circadian clock proteins and RNA pol II.
  • Suggests that promoters are nutrient sensors.

Post-translational modifications play important roles in transcriptional regulation. Among the less understood PTMs is O-GlcNAcylation. Nevertheless, O-GlcNAcylation in the nucleus is found on hundreds of transcription factors and coactivators and is often found in a mutually exclusive ying–yang relationship with phosphorylation. O-GlcNAcylation also links cellular metabolism directly to the proteome, serving as a conduit of metabolic information to the nucleus. This review serves as a brief introduction to O-GlcNAcylation, emphasizing its important thematic roles in transcriptional regulation, and highlights several recent and important additions to the literature that illustrate the connections between O-GlcNAc and transcription.

links between O-GlcNAc and transcriptional regulation.

links between O-GlcNAc and transcriptional regulation.

http://ars.els-cdn.com/content/image/1-s2.0-S1874939913001351-gr1.sml
links between O-GlcNAc and transcriptional regulation.

systems to illustrate O-GlcNAc dynamics

systems to illustrate O-GlcNAc dynamics

http://ars.els-cdn.com/content/image/1-s2.0-S1874939913001351-gr2.sml
systems to illustrate O-GlcNAc dynamics

7.7.10 O-GlcNAcylation in cellular functions and human diseases

Yang YR1Suh PG2.
Adv Biol Regul. 2014 Jan; 54:68-73
http://dx.doi.org:/10.1016/j.jbior.2013.09.007

O-GlcNAcylation is dynamic and a ubiquitous post-translational modification. O-GlcNAcylated proteins influence fundamental functions of proteins such as protein-protein interactions, altering protein stability, and changing protein activity. Thus, aberrant regulation of O-GlcNAcylation contributes to the etiology of chronic diseases of aging, including cancer, cardiovascular disease, metabolic disorders, and Alzheimer’s disease. Diverse cellular signaling systems are involved in pathogenesis of these diseases. O-GlcNAcylated proteins occur in many different tissues and cellular compartments and affect specific cell signaling. This review focuses on the O-GlcNAcylation in basic cellular functions and human diseases.

O-GlcNAcylated proteins influence protein phosphorylation and protein-protein interactions

O-GlcNAcylated proteins influence protein phosphorylation and protein-protein interactions

http://ars.els-cdn.com/content/image/1-s2.0-S2212492613000717-gr2.sml
O-GlcNAcylated proteins influence protein phosphorylation and protein-protein interactions

aberrant regulation of O-GlcNAcylation in disease

aberrant regulation of O-GlcNAcylation in disease

http://ars.els-cdn.com/content/image/1-s2.0-S2212492613000717-gr3.sml
aberrant regulation of O-GlcNAcylation in disease

 Comment:

Body of review in energetic metabolic pathways in malignant T cells

Antigen stimulation of T cell receptor (TCR) signaling to nuclear factor (NF)-B is required for T cell proliferation and differentiation of effector cells.
The TCR-to-NF-B pathway is generally viewed as a linear sequence of events in which TCR engagement triggers a cytoplasmic cascade of protein-protein interactions and post-translational modifications, ultimately culminating in the nuclear translocation of NF-B.
Activation of effect or T cells leads to increased glucose uptake, glycolysis, and lipid synthesis to support growth and proliferation.
Activated T cells were identified with CD7, CD5, CD3, CD2, CD4, CD8 and CD45RO. Simultaneously, the expression of CD95 and its ligand causes apoptotic cells death by paracrine or autocrine mechanism, and during inflammation, IL1-β and interferon-1α. The receptor glucose, Glut 1, is expressed at a low level in naive T cells, and rapidly induced by Myc following T cell receptor (TCR) activation. Glut1 trafficking is also highly regulated, with Glut1 protein remaining in intracellular vesicles until T cell activation.

Dr. Aurel,
Targu Jiu

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The Metabolic View of Epigenetic Expression

Writer and Curator: Larry H Bernstein, MD, FCAP

Introduction

This is the fifth contribution to a series of articles on cancer, genomics, and metabolism.   I begin this after reading an article by Stephen Williams “War on Cancer May Need to Refocus Says Cancer Expert on NPR”, and after listening to NPR “On the Media”. This is an unplanned experience, perhaps partly related to an Op-Ed in the New York Times two days before by Angelina Jolie Pittman.  Taking her article prior to pre-emptive breast surgery for the BRCA1 mutation two years ago and her salpingo-oophorectomy at age 39 years with her family history, and her adoption of several children even prior to her marriage to Brad Pitt, reveals an unusual self-knowledge as well as perspective on the disease risk balanced with her maternal instincts.  I sense (but don’t know) that she had a good knowledge not stated about the estrogen sensitivity of breast cancer for some years, and balanced that knowledge in her life decisions.

Tracing the history of cancer and the Lyndon Johnson initiated “War on Cancer” the initiative is presented as misguided.  Moreover, the imbalance is posed aas focused overly on genomics, and there is an imbalnced in the attention to the types of cancer, bladder cancer (urothelial) receiving too little attention. However, the events that drive this are complex, and not surprising.  The funding is driven partly by media attention (a film star or President’s wife) and not to be overlooked, watch where the money flows.  People who have the ability to donate and also have a family experience will give, regardless of the statistics because it is 100 percent in their eyes.

Insofar as the scientific endeavor goes, young scientists are committed to a successful research career, and they also need funding, so they have to balance the risk of success and failure in the choice of problems they choose to work on.  But until the 20th century, the biological sciences were largely descriptive. The emergence of a “Molecular Biology” is a unique 20th century development. The work of Pathology – pioneered by Rokitansky, Virchow, and to an extent also the anatomist/surgeon John Harvey – was observational science.  The description of Hodgkin’s lymphoma was observational, and it was a breakthrough in medicine.

With the emergence of genomics from biochemistry and genetics in molecular biology (biology at the subcellular level), a part of medicine that was well founded became an afterthought.  After all, after many years of the history of medicine and pathology, it is well known that cancers are not only a dysmetabolism of cellular replication and cellular regulation, but cancers have a natural history related to organ system, tissue specificity, sex, and age of occurrence. This should be well known to the experienced practitioner, but not necessarily to the basic researcher with no little clinical exposure.  Consequently, it was quite remarkable to me to find that the truly amazing biochemist who gave a “Harvey Lecture” at Harvard on the pyridine nucleotide transhydrogenases, and who shared in the discovery of Coenzyme A, had made the observation that organs that are primarily involved with synthetic activity -adrenal, pituitary, and thyroid, testis, ovary, breast (most notably) – have a more benign course than those of stomach, colon, pancreas, melanoma, hematopoietic, and sarcomas. The liver is highly synthetic, but doesn’t fit so nicely because of the role in detoxification and the large role in glucose and fat catabolism.  Further, this was at a time that we knew nothing about the cell death pathway and cellular repair, and how is it in concert with cell proliferation.

The first important reasoning about cancer metabolism was opened by Otto Warburg in the late 1920s.  I have  little reason to doubt his influence on Nathan Kaplan, who used the terms DPN(+/H) and TPN(+/H), disregarding the terms NAD(+/H) and NADP(+/H), although I was told it was because of the synthesis of the pyridine nucleotide adducts for study (APDPN, etc.).

In a recent article, I had an interesting response from Jose ES Rosalino:

In mRNA Translation and Energy Metabolism in Cancer

Topisirovic and N. Sonenberg – Cold Spring Harbor Symposia on Quantitative Biology, Volume LXXVI – http://dx.doi.org:/10.1101/sqb.2011.76.010785

“A prominent feature of cancer cells is the use of aerobic glycolysis under conditions in which oxygen levels are sufficient to support energy production in the mitochondria (Jones and Thompson 2009; Cairns et al. 2010). This phenomenon, named the “Warburg effect,” after its discoverer Otto Warburg, is thought to fuel the biosynthetic requirements of the neoplastic growth (Warburg 1956; Koppenol et al. 2011) and has recently been acknowledged as one of the hallmarks of cancer (Hanahan and Weinberg 2011). mRNA translation is the most energy-demanding process in the cell (Buttgereit and Brand 1995). Again, the use of aerobic glycolysis expression has being twisted.”

To understand my critical observation consider this: Aerobic glycolysis is the carbon flow that goes from Glucose to CO2 and water (includes Krebs cycle and respiratory chain for the restoration of NAD, FAD etc.

Anerobic glyclysis is the carbon flow that goes from glucose to lactate. It uses conversion of pyruvate to lactate to regenerate NAD.

“Pasteur effect” is an expression coined by Warburg it refers to the reduction in the carbon flow from glucose when oxygen is offered to yeasts. The major reason for that is in general terms, derived from the fact that carbon flow is regulated by several cell requirements but majorly by the ATP needs of the cell. Therefore, as ATP is generated 10 more efficiently in aerobiosis than under anaerobiosis, less carbon flow is required under aerobiosis than under anaerobiosis to maintain ATP levels. Warburg, after searching for the same regulatory mechanism in normal and cancer cells for comparison found that transformed cell continued their large flow of glucose carbons to lactate despite of the presence of oxygen.

So, it is wrong to describe that aerobic glycolysis continues in the presence of oxygen. It is what it is expected to occur. The wrong thing is that anaerobic glycolysis continues under aerobiosis.

In our discussion of transcription and cell regulatory processes, we have already encountered a substantial amount of “enzymology” that drives what is referred to as “epigenetics”.  Enzymatic reactions are involved almost everywhere we look at the processes involved in RNA nontranscriptional affairs.

Enzyme catalysis

Pyruvate carboxylase is critical for non–small-cell lung cancer proliferation
K Sellers,…, TW-M Fan
J Clin Invest. Jan 2015; xx
http://dx.doi.org:/10.1172/JCI72873

Anabolic biosynthesis requires precursors supplied by the Krebs cycle, which in turn requires anaplerosis to replenish precursor intermediates. The major anaplerotic sources are pyruvate and glutamine, which require the activity of pyruvate carboxylase (PC) and glutaminase 1 (GLS1), respectively. Due to their rapid proliferation, cancer cells have increased anabolic and energy demands; however, different cancer cell types exhibit differential requirements for PC- and GLS-mediated pathways for anaplerosis and cell proliferation. Here, we infused patients with early-stage non–small-cell lung cancer (NSCLC) with uniformly 13C-labeled glucose before tissue resection and determined that the cancerous tissues in these patients had enhanced PC activity. Freshly resected paired lung tissue slices cultured in 13C6-glucose or 13C5, 15N2-glutamine tracers confirmed selective activation of PC over GLS in NSCLC. Compared with noncancerous tissues, PC expression was greatly enhanced in cancerous tissues, whereas GLS1 expression showed no trend. Moreover, immunohistochemical analysis of paired lung tissues showed PC overexpression in cancer cells rather than in stromal cells of tumor tissues. PC knockdown induced multinucleation, decreased cell proliferation and colony formation in human NSCLC cells, and reduced tumor growth in a mouse xenograft model. Growth inhibition was accompanied by perturbed Krebs cycle activity, inhibition of lipid and nucleotide biosynthesis, and altered glutathione homeostasis. These findings indicate that PC-mediated anaplerosis in early stage NSCLC is required for tumor survival and proliferation.

Accelerated glycolysis under aerobic conditions (the “Warburg effect”) has been a hallmark of cancer for many decades (1). It is now recognized that cancer cells must undergo many other metabolic reprograming changes (2) to meet the increased anabolic and energetic demands of proliferation (3, 4). It is also becoming clear that different cancer types may utilize a variety of metabolic adaptations that are context dependent, commensurate with the notion that altered metabolism is a hallmark of cancer (12). Enhanced glucose uptake and aerobic glycolysis generates both energy (i.e., ATP) and molecular precursors for the biosynthesis of complex carbohydrates, sugar nucleotides, lipids, proteins, and nucleic acids. However, increased glycolysis alone is insufficient to meet the total metabolic demands of proliferating cancer cells. The Krebs cycle is also a source of energy via the oxidation of pyruvate, fatty acids, and amino acids such as glutamine. Moreover, several Krebs cycle intermediates are essential for anabolic and glutathione metabolism, including citrate, oxaloacetate, and α-ketoglutarate (Figure 1A).

Figure 1. PC is activated in human NSCLC tumors. (A) PC and GLS1 catalyze the major anaplerotic inputs (blue) into the Krebs cycle to support the anabolic demand for biosynthesis (green). Also shown is the fate of 13C from 13C6-glucose through glycolysis and into the Krebs cycle via PC (red).
(B) Representative Western blots of PC and GLS1 protein expression levels in human NC lung (N) and NSCLC (C) tissues. (C) Pairwise PC and GLS1 expression (n = 86) was normalized to α-tubulin and plotted as the log10 ratio of CA/NC tissues. For PC, nearly all log ratios were positive (82 of 86), with a clustering in the 0.5–1 range (i.e., typically 3- to 10-fold higher expression in the tumor tissue; Wilcoxon test, P < 0.0001). In contrast, GLS1 expression was nearly evenly distributed between positive and negative log10 ratios and showed no statistically significant difference between the CA and NC tissues (Wilcoxon test, P = 0.213). Horizontal bar represents the median. (D) In vivo PC activity was enhanced in CA tissue compared with that in paired NC lung tissues (n = 34) resected from the same human patients given 13C6-glucose 2.5–3 hours before tumor resection. PC activity was inferred from the enrichment of 13C3-citrate (Cit+3), 13C5-Cit (Cit+5), 13C3-malate (Mal+3), and 13C3-aspartate (Asp+3) as determined by GC-MS. *P < 0.05 and **P < 0.01 by paired Student t test. Error bars represent the SEM.

Continued functioning of the Krebs cycle requires the replenishment of intermediates that are diverted for anabolic uses or glutathione synthesis. This replenishment process, or anaplerosis, is accomplished via 2 major pathways: glutaminolysis (deamidation of glutamine via glutaminase [GLS] plus transamination of glutamate to α-ketoglutarate) and carboxylation of pyruvate to oxaloacetate via ATP-dependent pyruvate carboxylase (PC) (EC 6.4.1.1) (refs. 3, 20, 21, and Figure 1A). The relative importance of these pathways is likely to depend on the nature of the cancer and its specific metabolic adaptations, including those to the microenvironment (20, 22). For example, glutaminolysis was shown to be activated in the glioma cell line SF188, while PC activity was absent, despite the high PC activity present in normal astrocytes. However, SF188 cells use PC to compensate for GLS1 suppression or glutamine restriction (20), and PC, rather than GLS1, was shown to be the major anaplerotic input to the Krebs cycle in primary glioma xenografts in mice. It is also unclear as to the relative importance of PC and GLS1 in other cancer cell types or, most relevantly, in human tumor tissues in situ. Our preliminary evidence from 5 non–small-cell lung cancer (NSCLC) patients indicated that PC expression and activity are upregulated in cancerous (CA) compared with paired noncancerous (NC) lung tissues (21), although it was unclear whether PC activation applies to a larger NSCLC cohort or whether PC expression was associated with the cancer and/or stromal cells

Here, we have greatly extended our previous findings (21) in a larger cohort (n = 86) by assessing glutaminase 1 (GLS1) status and analyzing in detail the biochemical and phenotypic consequences of PC suppression in NSCLC. We found PC activity and protein expression levels to be, on average, respectively, 100% and 5- to 10-fold higher in cancerous (CA) lung tissues than in paired NC lung tissues resected from NSCLC patients, whereas GLS1 expression showed no significant trend. We have also applied stable isotope–resolved metabolomic (SIRM) analysis to paired freshly resected CA and NC lung tissue slices in culture (analogous to the Warburg slices; ref. 25) using either [U-13C] glucose or [U-13C,15N] glutamine as tracers. This novel method provided information about tumor metabolic pathways and dynamics without the complication of whole-body metabolism in vivo.

PC expression and activity, but not glutaminase expression, are significantly enhanced in early stages of malignant NSCLC tumors. PC protein expression was significantly higher in primary NSCLC tumors than in paired adjacent NC lung tissues (n = 86, P < 0.0001, Wilcoxon test) (Figure 1, B and C). The median PC expression was 7-fold higher in the tumor, and the most probable (modal) overexpression in the tumor was approximately 3-fold higher (see Supple-mental Table 1; supplemental material available online with this article; http://dx.doi.org:/10.1172/JCI72873DS1). We found that PC expression was also higher in the tumor tissue compared with that detected in the NC tissue in 82 of 86 patients. In contrast, GLS1 expression was not significantly different between the tumor and NC tissues (P = 0.213, Wilcoxon test) (Figure 1C and Supplemental Table 1). The 13C3-Asp produced from 13C6-glucose (Figure 1A) infused into NSCLC patients was determined by gas chromatography–mass spectrometry (GC-MS) to estimate in vivo PC activity. A bolus injection of 10 g 13C6-glucose in 50 ml saline led to an average of 44% 13C enrichment in the plasma glucose immediately after infusion (Supplemental Table 2). Because the labeled glucose was absorbed by various tissues over the approximately 2.5 hours between infusion and tumor resection, plasma glucose enrichment dropped to 17% (Supplemental Table 2). The labeled glucose in both CA and NC lung tissues was metabolized to labeled lactate, but this occurred to a much greater extent in the CA tissues (Supplemental Figure 1A), which indicates accelerated glycolysis in these tissues.

Fresh tissue (Warburg) slices confirm enhanced PC and Krebs cycle activity in NSCLC. To further assess PC activity relative to GLS1 activity in human lung tissues, thin (<1 mm thick) slices of paired CA and NC lung tissues freshly resected from 13 human NSCLC patients were cultured in 13C6-glucose or 13C5,15N2-glutamine for 24 hours. These tissues maintain biochemical activity and histological integrity for at least 24 hours under culture conditions (Figure 2A, Supplemental Figure 2, A and B, and ref. 26). When the tissues were incubated with 13C6-glucose, CA slices showed a significantly greater percentage of enrichment in glycolytic 13C3-lactate (3 in Figure 2B) than did the NC slices, indicative of the Warburg effect. In addition, the CA tissues had significantly higher fractions of 13C4-, 13C5-, and 13C6-citrate (4, 5, and 6 of citrate, respectively, in Figure 2B) than did the NC tissues. These isotopologs require the combined action of PDH, PC, and multiple turns of the Krebs cycle (Figure 2C). Consistent with the labeled citrate data, the increase in the percentage of enrichment of 13C3-, 13C4-, and 13C5-glutamate (3, 4, and 5 of glutamate, respectively, in Figure 2B) in the CA tissues indicates enhanced Krebs cycle and PC activity.

Figure 2. Ex vivo CA lung tissue slices have enhanced oxidation of glucose through glycolysis and the Krebs cycle with and without PC input compared with that of paired NC lung slices. Thin slices of CA and NC lung tissues freshly resected from 13 human NSCLC patients were incubated with 13C6-glucose for 24 hours as described in the Methods. The percentage of enrichment of lactate, citrate, glutamate, and aspartate was determined by GC-MS. (A) 1H{13C} HSQC NMR showed an increase in labeled lactate, glutamate, and aspartate. In addition, CA tissues had elevated 13C abundance in the ribose moiety of the adenine-containing nucleotides (1′-AXP), indicating that the tissues were viable and had enhanced capacity for nucleotide synthesis. (B) CA tissue slices (n = 13) showed increased glucose metabolism through glycolysis based on the increased percentage of enrichment of 13C3-lactate (“3”), and through the Krebs cycle based on the increased percentage of enrichment of 13C4–6-citrate (“4–6”) and 13C3–5-glutamate (“3–5”) (see 13C fate tracing in C). *P < 0.05 and **P < 0.01 by paired Student’s t test. Error bars represent the SEM. (C) An atom-resolved map illustrates how PC, PDH, and 2 turns of the Krebs cycle activity produced the 13C isotopologs of citrate and glutamate in B, whose enrichment were significantly enhanced in CA tissue slices.

Figure 4. PC suppression via shRNA inhibits proliferation and tumorigenicity of human NSCLC cell lines in vitro and in vivo. Proliferation and colony-formation assays were initiated 1 week after transduction and selection with puromycin. A549 xenograft in NSG mice was performed 8 days after transduction. *P < 0.01, **P < 0.001, ***P < 0.0001, and ****P < 0.00001 by Student t test, assuming unequal variances. Error bars represent the SEM. (A) NSCLC cells lines were transduced with shPC55 or shEV. Proliferation assays (n = 6) revealed substantial growth inhibition induced by PC knockdown in all 5 cell lines after a relatively long latency period. (B) Colony-formation assays indicated that PC knockdown reduced the capacity of A549 and PC9 cells to form colonies in soft agar (n = 3). (C) Tumor xenografts from shPC55-transduced A549 cells showed a 2-fold slower growth rate than did control shEV tumors (P < 0.001 by the unpaired Welch version of the t test). Tumor size was calculated as πab/4, where a and b are the x,y diameters. Each point represents an average of 6 mice. The solid lines are the nonlinear regression fits to the equation: size = a + bt2, as described in the Methods. (D) The extent of PC knockdown in the mouse xenografts (n = 6) was lesser than that in cell cultures, leading to less attenuation of PC expression (30%–60% of control) and growth inhibition. In addition, PC expression in the excised tumors correlated with the individual growth rates, as determined by Pearson’s correlation coefficient.

Fatty acyl synthesis from 13C5-glutamine (“Even” in Figure 6B) via glutaminolysis and the Krebs cycle was greatly attenuated in PC-suppressed cells. Taken together, these results suggest that PC knockdown severely inhibits lipid production by blocking the biosynthesis of fatty acyl components but not the glucose-derived glycerol backbone. This is consistent with decreased Krebs cycle activity (Figure 5), which in turn curtails citrate export from the mitochondria to supply the fatty acid precursor acetyl CoA in the cytoplasm.

Figure 5. PC knockdown perturbs glucose and glutamine flux through the Krebs cycle. 13C Isotopolog concentrations were determined by GC-MS (n = 3). Values represent the averages of triplicates, with standard errors. *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001 by Student’s t test, assuming unequal variances. The experiments were repeated 3 times. (A) A549 cells were transduced with shPC55 for 10 days before incubation with 13C6-glucose for 24 hours. As expected, the 13C isotopologs of Krebs cycle metabolites produced via PC and Krebs cycle activity were depleted in PC-deficient cells (tracked by blue dots in the atom-resolved map and blue circles in the bar graphs; see also Figure 2C). In addition, 13C6-glucose metabolism via PDH was also perturbed (indicated by red dots and circles). (B) Treatment of PC-knockdown cells with 13C5,15N2-glutamine revealed that anaplerotic input via GLS did not compensate for the loss of PC activity, since GLS activity was attenuated, as inferred from the activity markers (indicated by red dots and circles). Decarboxylation of glutamine-derived malate by malic enzyme (ME) and reentry of glutamine-derived pyruvate into the Krebs cycle via PC or PDH (shown in blue and green, respectively) were also attenuated. Purple diamonds denote 15N; black diamonds denote 14N.

Figure 6. PC suppression hinders Krebs cycle–fueled biosynthesis. (A) 13C atom–resolved pyrimidine biosynthesis from 13C6-glucose and 13C5-glutamine is depicted with a 13C5-ribose moiety (red dots) produced via the pentose phosphate pathway (PPP) and 13C1-3  uracil ring (blue dots) derived from  13C2-4-aspartate produced via the Krebs cycle or the combined action of ME and PC (blue dots). A549 cells transduced with shPC55 or shEV were incubated with 13C6-glucose or 13C5-glutamine for 24 hours. Fractional enrichment of UTP and CTP isotopologs from FT-ICR-MS analysis of polar cell extracts showed reduced enrichment of 13C6-glucose–derived 13C5-ribose (the “5” isotopolog) and 13C6-glucose– or 13C5-glutamine–derived 13C1-3-pyrimidine rings (the “6–8” or “1–3” isotopologs, highlighted by dashed green rectangles; for the “6–8” isotopologs, 5 13Cs arose from ribose and 1–3 13Cs from the ring) (10, 45). These data suggest that PC knockdown inhibits de novo pyrimidine biosynthesis from both glucose and glutamine. (B) Glucose and glutamine carbons enter fatty acids via citrate. FT-ICR-MS analysis of labeled lipids from the nonpolar cell extracts showed that PC knockdown severely inhibited the incorporation of glucose and glutamine carbons into the fatty acyl chains (even) and fatty acyl chains plus glycerol backbone (odd >3) of phosphatidylcholine lipids. However, synthesis of the 13C3-glycerol backbone (the “3” isotopolog) or its precursor 13C3-α-glycerol-3-phosphate (αG3P, m+3) from 13C6-glucose was enhanced rather than inhibited by PC knockdown. These data suggest that PC suppression specifically hinders fatty acid synthesis in A549 cells. Values represent the averages of triplicates (n = 3), with standard errors. *P < 0.05, **P < 0.01,  and ***P < 0.001 by Student’s t test, assuming unequal variances.

De novo glutathione synthesis was analyzed by 1H{13C} HSQC NMR. Glutathione synthesis from both glucose and glutamine was suppressed by PC knockdown (Supplemental Figure 9, A and B). Reduced de novo synthesis led to a large decrease in the total level of reduced glutathione (GSH; Supplemental Figure 12, A and B). At the same time, PC-knockdown cells accumulated slightly more oxidized GSH (GSSG; Supplemental Figure 12, A and B), leading to a significantly reduced GSH/GSSG ratio both in cell culture and in vivo (Supplemental Figure 12C). To determine whether this perturbation of glutathione homeostasis compromises the ability of PC-suppressed cells to handle oxidative stress, we measured ROS production by DCFDA fluorescence. PC-knockdown cells had over 70% more basal ROS than did control cells (0 mM H2O2; Supplemental Figure 12D). When cells were exposed to increasing concentrations of H2O2, the knockdown cells were less able to quench ROS, as they produced up to 300% more ROS than did control cells (Supplemental Figure 12D). However, N-acetylcysteine (NAC) at 10 mM did not rescue the growth of PC-knockdown cells, suggesting that such a growth effect is not simply related to an inability to regenerate GSH from GSSG. Altogether, these results show that PC suppression compromises anaplerotic input into the Krebs cycle, which in turn reduces the activity of the Krebs cycle, while limiting the ability of A549 cells to synthesize nucleotides, lipids, and glutathione. These downstream effects of PC knockdown were also evident when comparing the metabolism of shPC55-transduced A549 cells against that of A549 cells transduced with a scrambled vector (shScr) (Supplemental Figure 13), which suggests that they are on-target effects of PC knockdown.

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    4. Vander Heiden MG, Cantley LC, Thompson CB. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science. 2009;324(5930):1029–1033.
    10. Le A, et al. Glucose-independent glutamine metabolism via TCA cycling for proliferation and survival in B cells. Cell Metab. 2012;15(1):110–121
    20. Cheng T, et al. Pyruvate carboxylase is required for glutamine-independent growth of tumor cells. Proc Natl Acad Sci U S A. 2011;108(21):8674–8679.
    21. Fan TW, et al. Altered regulation of metabolic pathways in human lung cancer discerned by (13) C stable isotope-resolved metabolomics (SIRM). Mol Cancer. 2009;8:41.
    22. Marin-Valencia I, et al. Analysis of tumor metabolism reveals mitochondrial glucose oxidation in genetically diverse human glioblastomas in the mouse brain in vivo. Cell Metab. 2012;15(6):827–837.
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In vivo HIF-mediated reductive carboxylation is regulated by citrate levels and sensitizes VHL-deficient cells to glutamine deprivation.
Gameiro PA, Yang J, Metelo AM,…, Stephanopoulos G, Iliopoulos O.
Cell Metab. 2013 Mar 5; 17(3):372-85.
http://dx.doi.org:/10.1016/j.cmet.2013.02.002

Hypoxic and VHL-deficient cells use glutamine to generate citrate and lipids through reductive carboxylation (RC) of α-ketoglutarate. To gain insights into the role of HIF and the molecular mechanisms underlying RC, we took advantage of a panel of disease-associated VHL mutants and showed that HIF expression is necessary and sufficient for the induction of RC in human renal cell carcinoma (RCC) cells. HIF expression drastically reduced intracellular citrate levels. Feeding VHL-deficient RCC cells with acetate or citrate or knocking down PDK-1 and ACLY restored citrate levels and suppressed RC. These data suggest that HIF-induced low intracellular citrate levels promote the reductive flux by mass action to maintain lipogenesis. Using [(1-13)C]glutamine, we demonstrated in vivo RC activity in VHL-deficient tumors growing as xenografts in mice. Lastly, HIF rendered VHL-deficient cells sensitive to glutamine deprivation in vitro, and systemic administration of glutaminase inhibitors suppressed the growth of RCC cells as mice xenografts.

Cancer cells undergo fundamental changes in their metabolism to support rapid growth, adapt to limited nutrient resources, and compete for these supplies with surrounding normal cells. One of the metabolic hallmarks of cancer is the activation of glycolysis and lactate production even in the presence of adequate oxygen. This is termed the Warburg effect, and efforts in cancer biology have revealed some of the molecular mechanisms responsible for this phenotype (Cairns et al., 2011). More recently, 13C isotopic studies have elucidated the complementary switch of glutamine metabolism that supports efficient carbon utilization for anabolism and growth (DeBerardinis and Cheng, 2010). Acetyl-CoA is a central biosynthetic precursor for lipid synthesis, being generated from glucose-derived citrate in well-oxygenated cells (Hatzivassiliou et al., 2005). Warburg-like cells, and those exposed to hypoxia, divert glucose to lactate, raising the question of how the tricarboxylic acid (TCA) cycle is supplied with acetyl-CoA to support lipogenesis. We and others demonstrated, using 13C isotopic tracers, that cells under hypoxic conditions or defective mitochondria primarily utilize glutamine to generate citrate and lipids through reductive carboxylation (RC) of α-ketoglutarate by isocitrate dehydrogenase 1 (IDH1) or 2 (IDH2) (Filipp et al., 2012; Metallo et al., 2012; Mullen et al., 2012; Wise et al., 2011).

The transcription factors hypoxia inducible factors 1α and 2α (HIF-1α, HIF-2α) have been established as master regulators of the hypoxic program and tumor phenotype (Gordan and Simon, 2007; Semenza, 2010). In addition to tumor-associated hypoxia, HIF can be directly activated by cancer-associated mutations. The von Hippel-Lindau (VHL) tumor suppressor is inactivated in the majority of sporadic clear-cell renal carcinomas (RCC), with VHL-deficient RCC cells exhibiting constitutive HIF-1α and/or HIF-2α activity irrespective of oxygen availability (Kim and Kaelin, 2003). Previously, we showed that VHL-deficient cells also relied on RC for lipid synthesis even under normoxia. Moreover, metabolic profiling of two isogenic clones that differ in pVHL expression (WT8 and PRC3) suggested that reintroduction of wild-type VHL can restore glucose utilization for lipogenesis (Metallo et al., 2012). The VHL tumor suppressor protein (pVHL) has been reported to have several functions other than the well-studied targeting of HIF. Specifically, it has been reported that pVHL regulates the large subunit of RNA polymerase (Pol) II (Mikhaylova et al., 2008), p53 (Roe et al., 2006), and the Wnt signaling regulator Jade-1. VHL has also been implicated in regulation of NF-κB signaling, tubulin polymerization, cilia biogenesis, and proper assembly of extracellular fibronectin (Chitalia et al., 2008; Kim and Kaelin, 2003; Ohh et al., 1998; Thoma et al., 2007; Yang et al., 2007). Hypoxia inactivates the α-ketoglutarate-dependent HIF prolyl hydroxylases, leading to stabilization of HIF. In addition to this well-established function, oxygen tension regulates a larger family of α-ketoglutarate-dependent cellular oxygenases, leading to posttranslational modification of several substrates, among which are chromatin modifiers (Melvin and Rocha, 2012). It is therefore conceivable that the effect of hypoxia on RC that was reported previously may be mediated by signaling mechanisms independent of the disruption of the pVHL-HIF interaction. Here we

  • demonstrate that HIF is necessary and sufficient for RC,
  • provide insights into the molecular mechanisms that link HIF to RC,
  • detected RC activity in vivo in human VHL-deficient RCC cells growing as tumors in nude mice,
  • provide evidence that the reductive phenotype of VHL-deficient cells renders them sensitive to glutamine restriction in vitro, and
  • show that inhibition of glutaminase suppresses growth of VHL-deficient cells in nude mice.

These observations lay the ground for metabolism-based therapeutic strategies for targeting HIF-driven tumors (such as RCC) and possibly the hypoxic compartment of solid tumors in general.

HIF Inactivation Is Necessary for Downregulation of Reductive Carboxylation by pVHL

(A) Expression of HIF-1 α, HIF-2α, and their target protein GLUT1 in UMRC2-derived cell lines, as indicated.

(B) Carbon atom transition map: the fate of [1-13C1] and [5-13C1]glutamine used to trace reductive carboxylation in this work (carbon atoms are represented by circles). The [1-13C1] (green circle) and [5-13C1] (red circle) glutamine-derived isotopic labels are retained during the reductive TCA cycle (bold red pathway). Metabolites containing the acetyl-CoA carbon skeleton are highlighted by dashed circles.

(C) Relative contribution of reductive carboxylation.

(D and E) Relative contribution of glucose oxidation to the carbons of indicated metabolites (D) and citrate (E). Student’s t test compared VHL-reconstituted to vector-only or to VHL mutants (Y98N/Y112N). Error bars represent SEM. Pyr, pyruvate; Lac, lactate; AcCoA, acetyl-CoA, Cit, citrate; IsoCit, isocitrate; Akg, α-ketoglutarate; Suc, succinate; Fum, fumarate; Mal, malate; OAA, oxaloacetate; Asp, aspartate; Glu, glutamate; PDH, pyruvate dehydrogenase; ME, malic enzyme; IDH, isocitrate dehydrogenase enzymes; ACO, aconitase enzymes; ACLY, ATP-citrate lyase; GLS, glutaminase.

To test the effect of HIF activation on the overall glutamine incorporation in the TCA cycle, we labeled an isogenic pair of VHL-deficient and VHL-reconstituted UMRC2 cells with [U-13C5]glutamine, which generates M4 fumarate, M4 malate, M4 aspartate, and M4 citrate isotopomers through glutamine oxidation. As seen in Figure S1B, VHL-deficient/VHL-positive UMRC2 cells exhibit similar enrichment of M4 fumarate, M4 malate, and M4 asparate (but not citrate) showing that VHL-deficient cells upregulate reductive carboxylation without compromising oxidative metabolism from glutamine. Next, we tested whether HIF inactivation by pVHL is necessary to regulate the reductive utilization of glutamine for lipogenesis. To this end, we traced the relative incorporation of [U-13C6]glucose or [5-13C1]glutamine into palmitate. Labeled carbon derived from [5-13C1]glutamine can be incorporated into fatty acids exclusively through RC, and the labeled carbon cannot be transferred to palmitate through the oxidative TCA cycle (Figure 1B, red carbons). Tracer incorporation from [5-13C1]glutamine occurs in the one carbon (C1) of acetyl-CoA, which results in labeling of palmitate at M1, M2, M3, M4, M5, M6, M7, and M8 mass isotopomers. In contrast, lipogenic acetyl-CoA molecules originating from [U-13C6]glucose are fully labeled, and the labeled palmitate is represented by M2, M4, M6, M8, M10, M12, M14, and M16 mass isotopomers. VHL-deficient control cells and cells expressing pVHL type 2B mutants exhibited high palmitate labeling from the [5-13C1]glutamine; conversely, reintroduction of wild-type or type 2C pVHL mutant (L188V) resulted in high labeling from [U-13C6]glucose (Figures 2A and 2B, box inserts highlight the heavier mass isotopomers).

hif-inactivation-is-necessary-for-downregulation-of-reductive-carboxylation-by-pvhl

hif-inactivation-is-necessary-for-downregulation-of-reductive-carboxylation-by-pvhl

Figure 2.  HIF Inactivation Is Necessary for Downregulation of Reductive Lipogenesis by pVHL

Next, to determine the specific contribution from glucose oxidation or glutamine reduction to lipogenic acetyl-CoA, we performed isotopomer spectral analysis (ISA) of palmitate labeling patterns. ISA indicates that wild-type pVHL or pVHL L188V mutant-reconstituted UMRC2 cells relied mainly on glucose oxidation to produce lipogenic acetyl-CoA, while UMRC2 cells reconstituted with a pVHL mutant defective in HIF inactivation (Y112N or Y98N) primarily employed RC. Upon disruption of the pVHL-HIF interaction, glutamine becomes the preferred substrate for lipogenesis, supplying 70%–80% of the lipogenic acetyl-CoA (Figure 2C). This is not a cell-line-specific phenomenon, but it applies to VHL-deficient human RCC cells in general; the same changes are observed in 786-O cells reconstituted with wild-type pVHL or mutant pVHL or infected with vector only as control (Figure S2). Type 2A pVHL mutants (Y112H, which retain partial HIF binding) confer an intermediate reductive phenotype between wild-type VHL (which inactivates HIF) and type 2B pVHL mutants (which are totally defective in HIF regulation) as seen in Figures 1 and ​and 2.2. Taken together, these data demonstrate that the ability of pVHL to regulate reductive carboxylation and lipogenesis from glutamine tracks genetically with its ability to bind and degrade HIF, at least in RCC cells.

HIF Is Sufficient to Induce RC from Glutamine in RCC Cells

To test the hypothesis that HIF-2α is sufficient to promote RC from glutamine, we expressed a pVHL-insensitive HIF-2α mutant (HIF-2α P405A/P531A, marked as HIF-2α P-A) in VHL-reconstituted 786-O cells (Figure 3). HIF-2α P-A is constitutively expressed in this polyclonal cell population, despite the reintroduction of wild-type VHL, reflecting a pseudohypoxia condition (Figure 3A). We confirmed that this mutant is transcriptionally active by assaying for the expression of its targets genes GLUT1, LDHA, HK1, EGLN, HIG2, and VEGF (Figures 3B and S3A). As shown in Figure 3C, reintroduction of wild-type VHLinto 786-O cells suppressed RC, whereas the expression of the constitutively active HIF-2α mutant was sufficient to stimulate this reaction, restoring the M1 enrichment of TCA cycle metabolites observed in VHL-deficient 786-O cells. Expression of HIF-2α P-A also led to a concomitant decrease in glucose oxidation, corroborating the metabolic alterations observed in glutamine metabolism (Figures 3D and 3E). Additional evidence of the HIF2α-regulation on the reductive phenotype was obtained with [U-13C5]glutamine, which generates M5 citrate, M3 fumarate, M3 malate, and M3 aspartate through RC (Figure 3F).

Our current work showed that HIF-2α is sufficient to induce the reductive program in RCC cells that express only the HIF-2α paralog, while mouse NEK cells appeared to use HIF-1α preferentially to promote RC. Together with the evidence that HIF-1α and HIF-2α may have opposite roles in tumor growth, it is possible that the cellular context dictates which paralog activates RC. It is also possible that HIF-2α adopts the RC regulatory function of HIF-1α upon deletion of the latter in RCC cells. Further studies are warranted in understanding the relative role of HIF-α paralogs in regulating RC in different cell types.

Finally, the selective sensitivity to glutaminase inhibitors exhibited by VHL-deficient cells, together with the observed RC activity in vivo, strongly suggests that reductive glutamine metabolism may fuel tumor growth. Investigating whether the reductive flux correlates with tumor hypoxia and/or contributes to the actual cell survival under low oxygen conditions is warranted. Together, our findings underscore the biological significance of reductive carboxylation in VHL-deficient RCC cells. Targeting this metabolic signature of HIF may open viable therapeutic opportunities for the treatment of hypoxic and VHL-deficient tumors.

Elevated levels of 14-3-3 proteins, serotonin, gamma enolase and pyruvate kinase identified in clinical samples from patients diagnosed with colorectal cancer
Dowling P, Hughes DJ, Larkin AM, Meiller J, …, Clynes M
Clin Chim Acta. 2015 Feb 20;441:133-41.
http://dx.doi.org:/10.1016/j.cca.2014.12.005.

Highlights

  • Identification of a number of significant proteins and metabolites in CRC patients
  • 14-3-3 proteins, serotonin, gamma enolase and pyruvate kinase all significant
  • Intense staining for 14-3-3 epsilon in tissue specimens from CRC patients
  • Tissue 14-3-3 epsilon levels concordant with abundance in the circulation
  • Biomolecules provide insight into the biology associated with tumor development

Background: Colorectal cancer (CRC), a heterogeneous disease that is common in both men and women, continues to be one of the predominant cancers worldwide. Lifestyle, diet, environmental factors and gene defects all contribute towards CRC development risk. Therefore, the identification of novel biomarkers to aid in the management of CRC is crucial. The aim of the present study was to identify candidate biomarkers for CRC, and to develop a better understanding of their role in tumorogenesis. Methods: In this study, both plasma and tissue samples from patients diagnosed with CRC, together with non-malignant and normal controls were examined using mass spectrometry based proteomics and metabolomics approaches.
Results: It was established that the level of several biomolecules, including serotonin, gamma enolase, pyruvate kinase and members of the 14-3-3 family of proteins, showed statistically significant changes when comparing malignant versus non-malignant patient samples, with a distinct pattern emerging mirroring cancer cell energy production. Conclusion: The diagnosis and management of CRC could be enhanced by the discovery and validation of new candidate biomarkers, as found in this study, aimed at facilitating early detection and/or patient stratification together with providing information on the complex behavior of cancer cells.

Table 2 – List of proteins found to show statistically significant differences between control (n=10) and CRC (n=16; 8 stage III/8 stage IV) patient plasma samples fractionated using Proteominer beads. Information provided in the table includes accession number, discovery platform used, protein description, the number of unique peptides for quantitation, a mascot score for protein identification (confidence number), ANOVA p-values(≥0.05), fold change in protein abundance (≥2-fold) and highest/lowest mean change.

Table 3 – List of metabolites found to show statistically significant differences between control (n=8) and CRC (n=16; 8 stage III/8 stage IV) patient plasma samples. Included in the table is the Human Metabolome Database (HMDB) entry, platform used to analyse the biochemicals, biochemical name, ANOVA p-values (≥0.05), fold-change and highest/lowest mean change.

Fig.1. Box and whisker plots for: (A) M2-PK, (B) gamma enolase, (C) 14-3-3 (pan) and (D) serotonin. ELISA analysisofM2-PK, gamma enolase, serotonin and 14-3-3 (pan) in plasma samples from control (n = 20), polyps (n = 10), adenoma (n = 10), stage I/II CRC (n= 20) and stage III/IV (n= 20)patients. The figures show statistically significant p-value for various comparisons between the different sample groups. This ELISA measurement for 14-3-3 detects all known isoforms of mammalian 14-3-3 proteins (β/α, γ, ε, η, ζ/δ, θ/τ and σ).

Role of lipid peroxidation derived 4-hydroxynonenal (4-HNE) in cancer- Focusing on mitochondria
Huiqin Zhonga, Huiyong Yin
Redox Biol Apr 2015; 4: 193–199

Oxidative stress-induced lipid peroxidation has been associated with human physiology and diseases including cancer. Overwhelming data suggest that reactive lipid mediators generated from this process, such as 4-hydroxynonenal (4-HNE), are biomarkers for oxidative stress and important players for mediating a number of signaling pathways. The biological effects of 4-HNE are primarily due to covalent modification of important biomolecules including proteins, DNA, and phospholipids containing amino group. In this review, we summarize recent progress on the role of 4-HNE in pathogenesis of cancer and focus on the involvement of mitochondria: generation of 4-HNE from oxidation of mitochondria-specific phospholipid cardiolipin; covalent modification of mitochondrial proteins, lipids, and DNA; potential therapeutic strategies for targeting mitochondrial ROS generation, lipid peroxidation, and 4-HNE.

Reactive oxygen species (ROS), such as superoxide anion, hydrogen peroxide, hydroxyl radicals, singlet oxygen, and lipid peroxyl radicals, are ubiquitous and considered as byproducts of aerobic life [1]. Most of these chemically reactive molecules are short-lived and react with surrounding molecules at the site of formation while some of the more stable molecules diffuse and cause damages far away from their sites of generation. Overproduction of these ROS, termed oxidative stress, may provoke oxidation of polyunsaturated fatty acids (PUFAs) in cellular membranes through free radical chain reactions and form lipid hydroperoxides as primary products [2]; some of these primary oxidation products may decompose and lead to the formation of reactive lipid electrophiles. Among these lipid peroxidation (LPO) products, 4-hydroxy-2-nonenals (4-HNE) represents one of the most bioactive and well-studied lipid alkenals [3]. 4-HNE can modulate a number of signaling processes mainly through forming covalent adducts with nucleophilic functional groups in proteins, nucleic acids, and membrane lipids. These properties have been extensively summarized in some excellent reviews [4], [5], [6], [7], [8], [9] and [10].

Conclusions

Lipid peroxidation-derived 4-HNE is a prototypical reactive lipid electrophile that readily forms covalent adducts with nucleophilic functional groups in macromolecule such as proteins, DNA, and lipids (Fig. 3). A body of work have shown that generation of 4-HNE macromolecule adducts plays important pathological roles in cancer through interactions with mitochondria. First of all, mitochondria are one of the most important cellular sites of 4-HNE production, presumably from oxidation of abundant PUFA-containing lipids, such as L4CL. Emerging evidence suggest that this process play a critical role in apoptosis. Secondly, in response to the toxicity of 4-HNE, mitochondria have developed a number of defense mechanisms to convert 4-HNE to less reactive chemical species and minimize its toxic effects. Thirdly, 4-HNE macromolecule adducts in mitochondria are involved in the cancer initiation and progression by modulating mitochondrial function and metabolic reprogramming. 4-HNE protein adducts have been widely studied but the mtDNA modification by lipid electrophiles has yet to emerge. The biological consequence of PE modification remains to be defined, especially in the context of cancer. Last but not the least, manipulation of mitochondrial ROS generation, lipid peroxidation, and production of lipid electrophiles may be a viable approach for cancer prevention and treatment.

K.J. Davies. Oxidative stress, antioxidant defenses, and damage removal, repair, and replacement systems. IUBMB Life, 50 (4–5) (2000): 279–289. http://dx.doi.org/10.1080/713803728.1132732

Shoeb, N.H. Ansari, S.K. Srivastava, K.V. Ramana. 4-hydroxynonenal in the pathogenesis and progression of human diseases. Current Medicinal Chemistry, 21 (2) (2014):230–237 http://dx.doi.org/10.2174/09298673113209990181 23848536

J.D. West, L.J. Marnett. Endogenous reactive intermediates as modulators of cell signaling and cell death. Chemical Research in Toxicology, 19 (2)(2006): 173–194 http://dx.doi.org/10.1021/tx050321u.16485894

Barrera, S. Pizzimenti,…, A. Lepore, et al. Role of 4-hydroxynonenal-protein adducts in human diseases. Antioxidants & Redox Signaling (2014) http://dx.doi.org/10.1089/ars.2014.6166 25365742

J.R. Roede, D.P. Jones. Reactive species and mitochondrial dysfunction: mechanistic significance of 4-hydroxynonenal. Environmental and Molecular Mutagenesis, 51 (5) (2010):380–390 http://dx.doi.org/10.1002/em.20553 20544880

Guéraud, M. Atalay, N. Bresgen, …, I. Jouanin, W. Siems, K. Uchida. Chemistry and biochemistry of lipid peroxidation products. Free Radical Research, 44 (10) (2010): 1098–1124 http://dx.doi.org/10.3109/10715762.2010.498477.20836659

Z.H. Chen, E. Niki. 4-hydroxynonenal (4-HNE) has been widely accepted as an inducer of oxidative stress. Is this the whole truth about it or can 4-HNE also exert protective effects? IUBMB Life, 58 (5–6) (2006): 372–373. http://dx.doi.org/10.1080/15216540600686896 16754333

Aldini, M. Carini, K.-J. Yeum, G. Vistoli. Novel molecular approaches for improving enzymatic and nonenzymatic detoxification of 4-hydroxynonenal: toward the discovery of a novel class of bioactive compounds. Free Radical Biology and Medicine, 69 (0) (2014): 145–156 http://dx.doi.org/10.1016/j.freeradbiomed.2014.01.017 24456906

Fig. 2.   Catabolism of 4-HNE in mitochondria. ROS induced lipid peroxidation in IMM and OMM (outer membrane of mitochondria) leads to 4-HNE formation. In matrix, 4-HNE conjugation with GSH produces glutathionyl-HNE (GS-HNE); this process occurs spontaneously or can be catalyzed by GSTs. 4-HNE is reduced to 1,4-dihydroxy-2-nonene (DHN) catalyzed ADH or AKRs. ALDH2 catalyzes the oxidation of 4-HNE to form 4-hydroxy-2-nonenoic acid (HNA).

Role of 4-hydroxynonenal in cancer focusing on mitochondria

Role of 4-hydroxynonenal in cancer focusing on mitochondria

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Role of 4-hydroxynonenal in cancer focusing on mitochondria

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Fig. 3. A schematic view of 4-HNE macromolecule adducts in cancer cell. 4-HNE macromolecule adducts are involved in cancer initiation, progression, metabolic reprogramming, and cell death. 4-HNE (depicted as a zigzag line) is produced through ROS-induced lipid peroxidation of mitochondrial and plasma membranes. Biological consequences of 4-HNE adduction:

  1. reducing membrane integrity;
  2. affecting protein function in cytosol;
  3. causing nuclear and mitochondrial DNA damage;
  4. inhibiting ETC activity;
  5. activating UCPs activity;
  6. reducing TCA activity;
  7. inhibiting ALDH2 activity.

DNA methylation paradigm shift: 15-lipoxygenase-1 upregulation in prostatic intraepithelial neoplasia and prostate cancer by atypical promoter hypermethylation.
Kelavkar UP1, Harya NS, … , Chandran U, Dhir R, O’Keefe DS.
Prostaglandins Other Lipid Mediat. 2007 Jan; 82(1-4):185-97

Fifteen (15)-lipoxygenase type 1 (15-LO-1, ALOX15), a highly regulated, tissue- and cell-type-specific lipid-peroxidating enzyme has several functions ranging from physiological membrane remodeling, pathogenesis of atherosclerosis, inflammation and carcinogenesis. Several of our findings support a possible role for 15-LO-1 in prostate cancer (PCa) tumorigenesis. In the present study, we identified a CpG island in the 15-LO-1 promoter and demonstrate that the methylation status of a specific CpG within this island region is associated with transcriptional activation or repression of the 15-LO-1 gene. High levels of 15-LO-1 expression was exclusively correlated with one of the CpG dinucleotides within the 15-LO-1 promoter in all examined PCa cell-lines expressing 15-LO-1 mRNA. We examined the methylation status of this specific CpG in microdissected high grade prostatic intraepithelial neoplasia (HGPIN), PCa, metastatic human prostate tissues, normal prostate cell lines and human donor (normal) prostates. Methylation of this CpG correlated with HGPIN, PCa and metastatic human prostate tissues, while this CpG was unmethylated in all of the normal prostate cell lines and human donor (normal) prostates that either did not display or had minimal basal 15-LO-1 expression. Immunohistochemistry for 15-LO-1 was performed in prostates from PCa patients with Gleason scores 6, 7 [(4+3) and (3+4)], >7 with metastasis, (8-10) and 5 normal (donor) individual males. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was used to detect 15-LO-1 in PrEC, RWPE-1, BPH-1, DU-145, LAPC-4, LNCaP, MDAPCa2b and PC-3 cell lines. The specific methylated CpG dinucleotide within the CpG island of the 15-LO-1 promoter was identified by bisulfite sequencing from these cell lines. The methylation status was determined by COBRA analyses of one specific CpG dinucleotide within the 15-LO-1 promoter in these cell lines and in prostates from patients and normal individuals. Fifteen-LO-1, GSTPi and beta-actin mRNA expression in BPH-1, LNCaP and MDAPCa2b cell lines with or without 5-aza-2′-deoxycytidine (5-aza-dC) and trichostatin-A (TSA) treatment were investigated by qRT-PCR. Complete or partial methylation of 15-LO-1 promoter was observed in all PCa patients but the normal donor prostates showed significantly less or no methylation. Exposure of LNCAP and MDAPCa2b cell lines to 5-aza-dC and TSA resulted in the downregulation of 15-LO-1 gene expression. Our results demonstrate that 15-LO-1 promoter methylation is frequently present in PCa patients and identify a new role for epigenetic phenomenon in PCa wherein hypermethylation of the 15-LO-1 promoter leads to the upregulation of 15-LO-1 expression and enzyme activity contributes to PCa initiation and progression.

Transcriptional regulation of 15-lipoxygenase expression by promoter methylation.
Liu C1, Xu D, Sjöberg J, Forsell P, Björkholm M, Claesson H
Exp Cell Res. 2004 Jul 1; 297(1):61-7.

15-Lipoxygenase type 1 (15-LO), a lipid-peroxidating enzyme implicated in physiological membrane remodeling and the pathogenesis of atherosclerosis, inflammation, and carcinogenesis, is highly regulated and expressed in a tissue- and cell-type-specific fashion. It is known that interleukins (IL) 4 and 13 play important roles in transactivating the 15-LO gene. However, the fact that they only exert such effects on a few types of cells suggests additional mechanism(s) for the profile control of 15-LO expression. In the present study, we demonstrate that hyper- and hypomethylation of CpG islands in the 15-LO promoter region is intimately associated with the transcriptional repression and activation of the 15-LO gene, respectively. The 15-LO promoter was exclusively methylated in all examined cells incapable of expressing 15-LO (certain solid tumor and human lymphoma cell lines and human T lymphocytes) while unmethylated in 15-LO-competent cells (the human airway epithelial cell line A549 and human monocytes) where 15-LO expression is IL4-inducible. Inhibition of DNA methylation in L428 lymphoma cells restores IL4 inducibility to 15-LO expression. Consistent with this, the unmethylated 15-LO promoter reporter construct exhibited threefold higher activity in A549 cells compared to its methylated counterpart. Taken together, demethylation of the 15-LO promoter is a prerequisite for the gene transactivation, which contributes to tissue- and cell-type-specific regulation of 15-LO expression.

mechanism of the lipoxygenase reaction

Radical mechanism of the lipoxygenase reaction pattabhiraman

Radical mechanism of the lipoxygenase reaction pattabhiraman

http://edoc.hu-berlin.de/dissertationen/pattabhiraman-shankaranarayanan-2003-11-03/HTML/pattabhiraman_html_705b7fbd.png

Position determinants of lipoxygenase reaction pattabhiraman

Position determinants of lipoxygenase reaction pattabhiraman

http://edoc.hu-berlin.de/dissertationen/pattabhiraman-shankaranarayanan-2003-11-03/HTML/pattabhiraman_html_m3642741b.jpg

Position determinants of lipoxygenase reaction

This suggests that the space inside the active site cavity plays an important role in the positional specificity (Borngräber et al., 1999). The reverse process on 12-LOX works equally well (Suzuki et al., 1994; Watanabe and Haeggstrom, 1993). However, conversion to 5-LOX by mutagenesis has not been successful. The positional determinant residues on 15-LOX were mutated to those of 5-LOX but the enzyme was inactive (Sloane et al., 1990). 15-LOX possess the ability to oxygenate 15-HpETE to form 5, 15-diHpETE. Methylation of carboxy end of the substrate increased the activity significantly. This phenomenon was hypothesised to be due to an inverse orientation of the substrate at the active site. In this case the caroboxy end may slide into the cavity as suggested by experiments with modified [page 6↓]substrates and site directed mutagenesis (Schwarz et al., 1998; Walther et al., 2001). Thus, the determinant of positional specificity is not only the volume but also the orientation of the substrate in the active site.

The N-terminal domain of the enzyme does not play a major role in the dioxygenation reaction of 12/15 lipoxygenase. N-terminal domain truncations did not impair the lipoxygenase activity. The ability of the enzyme to bind to membranes, however, is impaired in the mutants (point and truncations) of the N-ternimal domain without significant alterations to the catalytic activity (Walther et al., 2002). Mutation to Trp 181, which is localised in the catalytic domain, also impaired membrane binding function. This suggests that the C-terminal domain is responsible for the catalytic activity and a concerted action of N-terminal and C-terminal domain was necessary for effective membrane binding.

Metabolomic studies

New paradigms for metabolic modeling of human cells

Mardinoglu A, Nielsen J
Curr Opin Biotechnol. 2015 Jan 2; 34C:91-97.
http://dx.doi.org:/10.1016/j.copbio.2014

integration of genetic and biochemical knowledge

integration of genetic and biochemical knowledge

http://ars.els-cdn.com/content/image/1-s2.0-S0958166914002286-fx1.jpg

Highlights

  • We presented the timeline of generation and evaluation of global reconstructions of human metabolism.
  • We reviewed the generation of the context specific GEMs through the use of human generic GEMs.
  • We discussed the generation of multi-tissue GEMs in the context of whole-body metabolism.
  • We finally discussed the integration of GEMs with other biological networks.

Abnormalities in cellular functions are associated with the progression of human diseases, often resulting in metabolic reprogramming. GEnome-scale metabolic Models (GEMs) have enabled studying global metabolic reprogramming in connection with disease development in a systematic manner. Here we review recent work on reconstruction of GEMs for human cell/tissue types and cancer, and the use of GEMs for identification of metabolic changes occurring in response to disease development. We further discuss how GEMs can be used for the development of efficient therapeutic strategies. Finally, challenges in integration of cell/tissue models for simulation of whole body functions as well as integration of GEMs with other biological networks for generating complete cell/tissue models are presented.

http://ars.els-cdn.com/content/image/1-s2.0-S0958166914002286-gr2.sml

Inter- and intra-tumor profiling of multi-regional colon cancer and metastasis
Kogita A, Yoshioka Y, …, Nakai T, Okuno K, Nishio K
Biochem Biophys Res Commun. 2015 Feb 27; 458(1):52-6.
http://dx.doi.org:/10.1016/j.bbrc.2015.01.064

Highlights

  • Mutation profiling of tumors of multi-regional colon cancers using targeted sequencing.
  • Formalin-fixed paraffin embedded samples were available for next-generation sequencing.
  • Different clones existed in primary tumors and metastatic tumors.
  • Muti-clonalities between intra- and inter-tumors.

Intra- and inter-tumor heterogeneity may hinder personalized molecular-target treatment that depends on the somatic mutation profiles. We performed mutation profiling of formalin-fixed paraffin embedded tumors of multi-regional colon cancer and characterized the consequences of intra- and inter-tumor heterogeneity and metastasis using targeted re-sequencing. We performed targeted re-sequencing on multiple spatially separated samples obtained from multi-regional primary colon carcinoma and associated metastatic sites in two patients using next-generation sequencing. In Patient 1 with four primary tumors (P1-1, P1-2, P1-3, and P1-4) and one liver metastasis (H1), mutually exclusive pattern of mutations was observed in four primary tumors. Mutations in primary tumors were identified in three regions; KARS (G13D) and APC (R876*) in P1-2, TP53 (A161S) in P1-3, and KRAS (G12D), PIK3CA (Q546R), and ERBB4 (T272A) in P1-4. Similar combinatorial mutations were observed between P1-4 and H1. The ERBB4 (T272A) mutation observed in P1-4, however, disappeared in H1. In Patient 2 with two primary tumors (P2-1 and P2-2) and one liver metastasis (H2), mutually exclusive pattern of mutations were observed in two primary tumors. We identified mutations; KRAS (G12V), SMAD4 (N129K, R445*, and G508D), TP53 (R175H), and FGFR3 (R805W) in P2-1, and NRAS (Q61K) and FBXW7 (R425C) in P2-2. Similar combinatorial mutations were observed between P2-1 and H2. The SMAD4 (N129K and G508D) mutations observed in P2-1, however, were nor detected in H2. These results suggested that different clones existed in primary tumors and metastatic tumor in Patient 1 and 2 likely originated from P1-4 and P2-1, respectively. In conclusion, we detected the muti-clonalities between intra- and inter-tumors based on mutational profiling in multi-regional colon cancer using next-generation sequencing. Primary region from which metastasis originated could be speculated by mutation profile. Characterization of inter- and inter-tumor heterogeneity can lead to underestimation of the tumor genomics landscape and treatment strategy of personal medicine.

Fig.1. Treatment timelines for the two patients. A) Patient 1 (a 55-year-old man) had multifocal sigmoid colon cancers, and all of which were surgically resected in their entirety (P1-1, P1-2, P1-3, and P1-4). The patient received adjuvant chemotherapy (8 courses of XELOX). Eight months later, a single liver metastasis (H1) was detected, and the patients received neoadjuvant treatment of XELOX plus bevacizumab. Thereafter, he received a partial hepatectomy. B) Patient 2 (an 84-year-old woman) had cecal and sigmoid colon cancers (P2-1 and P2-2, respectively) with a single liver metastasis (H2). She received a subtotal colectomy and subsegmental hepatectomy.

Fig. 2. Schematic representation of intra-tumor heterogeneity in two patients. A) In patient 1, primary tumor (P1-4) contains two or more subclones. The clone without the ERBB4 (T272A) mutation created the liver metastasis. B) In patient 2, primary tumor (P2-1) contains two or more subclones. The clone without the SMAD4 (N129K and G508D) mutation created the liver metastasis.

Loss of Raf-1 Kinase Inhibitor Protein Expression Is Associated With Tumor Progression and Metastasis in Colorectal Cancer

Parham MinooInti ZlobecKristi BakerLuigi TornilloLuigi TerraccianoJeremy R. Jass, and Alessandro Lugli
American Journal of Clinical Pathology, 127, 820-827
http://dx.doi.org:/10.1309/5D7MM22DAVGDT1R8(2007)

Raf-1 kinase inhibitor protein (RKIP) is known as a critical down-regulator of the mitogen-activated protein kinase signaling pathway and a potential molecular determinant of malignant metastasis. The aim of this study was to determine the prognostic significance of RKIP expression in colorectal cancer (CRC). Immunohistochemical staining for RKIP was performed on a tissue microarray comprising 1,197 mismatch repair (MMR)-proficient and 141 MMR-deficient CRCs. The association of RKIP with clinicopathologic features was analyzed. Loss of cytoplasmic RKIP was associated with distant metastasis (P = .038), higher N stage (P = .032), vascular invasion (P = .01), and worse survival (P = .001) in the MMR-proficient group. In MMR-deficient CRCs, loss of cytoplasmic RKIP was associated with distant metastasis (P = .043) and independently predicted worse survival (P = .004). Methylation analysis of 28 cases showed that loss of RKIP expression is unlikely to be due to promoter methylation.

Raf-1 kinase inhibitor protein (RKIP) is a ubiquitously expressed and highly conserved protein that belongs to the phosphatidylethanolamine-binding protein family.1,2 RKIP is present in the cytoplasm and at the cell membrane3 and appears to have multiple biologic functions that implicate spermatogenesis, neural development, cardiac function, and membrane biogenesis.4-6 RKIP has also been shown to have a role in the regulation of multiple signaling pathways. Originally, RKIP was identified as a phospholipid-binding protein and, subsequently, as an interacting partner of Raf-1 kinase that blocks mitogen-activated protein kinase (MAPK) initiated by Raf-1.7 Initial studies showed that RKIP achieves this role by competitive interference with the binding of MEK to Raf-1.8 Recently, RKIP was shown to inhibit activation of Raf-1 by blocking phosphorylation of Raf-1 by p21-activated kinase and Src family kinases.9 It has also been suggested that RKIP could be involved in regulation of apoptosis by modulating the NF-κB pathway10 and in regulation of the spindle checkpoint via Aurora B.11 RKIP has also been implicated in tumor biology. In breast and prostate cancers, ectopic expression of RKIP sensitized cells to chemotherapeutic-induced apoptosis, and reduced expression of RKIP led to resistance to chemotherapy.12 A link between RKIP and cancer was first established in prostate cancer, with RKIP showing reduced expression in prostate cancer cells and the lowest expression levels in metastatic cells, suggesting that RKIP expression is inversely associated with the invasiveness of prostate cancer.13 Restoration of RKIP expression in metastatic prostate cancer cells inhibited invasiveness of the cells in vitro and in vivo in spontaneous lung metastasis but not the growth of the primary tumor in a murine model.13

Clinicopathologic Parameters The clinicopathologic data for 1,420 patients included T stage (T1, T2, T3, and T4), N stage (N0, N1, and N2), tumor grade (G1, G2, and G3), vascular invasion (presence or absence), and survival. The distribution of these features has been described previously.18-20 For 478 patients, information on local recurrence and distant metastasis was also available.

Methylation of RKIP Methylation of RKIP promoter was examined by methylation-specific polymerase chain reaction (PCR) using an AmpliTaq Gold kit (Roche, Branchburg, NJ) as described previously.25 The primers for amplification of the unmethylated sequence were 5′-TTTAGTGATATTTTTTGAGATATGA-3′ and 3′-CACTCCCTAACCTCTAATTAACCAA-5′ and for the methylated reaction were 5′-TTTAGCGATATTTTTTGAGATACGA-3′ and 3′-GCTCCCTAACCTCTAATTAACCG- 5′. The conditions for amplification were 10 minutes at 95°C followed by 39 cycles of denaturing at 95°C for 30 seconds, annealing at 52°C for 30 seconds, and 30 seconds of extension at 72°C. The PCR products were subjected to electrophoresis on 8% acrylamide gels and visualized by SYBR gold nucleic acid gel stain (Molecular Probes, Eugene, OR). CpGenome Universal Methylated DNA (Chemicon, Temecula, CA) was used as a positive control sample for methylation. Randomization of MMR-Proficient CRCs The 1,197 MMR-proficient CRCs were randomly assigned into 2 groups consisting of 599 (group 1) and 598 (group 2) cases and matched for sex, tumor location, T stage, N stage, tumor grade, vascular invasion, and survival ❚Table 1❚. Immunohistochemical cutoff scores for RKIP expression were determined for group 1, and the association of RKIP expression and T stage, N stage, tumor grade, vascular invasion, local recurrence, distant metastasis, and 10-year survival were studied in group 2.

❚Table 1❚ Characteristics of the Randomized Mismatch Repair–Proficient Subgroups of Colorectal Cancer Cases*

Variable p
Group Gp 1 (n=599) Gp 2 (n=598) 0.235
Sex M F M F
288 (48.3) 308

(51.7)

287

(48.2)

308

(51.8)

0.82
Tumor location Right-sided 417 (70.6) 417 (71.2) Left-sided 174 (29.4) 169 (28.8)
T1 T2 T3 T4
T stage 25 (4.3) 35 (6.0) 92(15.8) 97(16.7) 375(64.2)
365(62.8)
92(15.8)
84(14.5)
0.514
N stage N0 N1 N2
289(50.7) 154(27.0) 154(26.9) 127(22.3) 120(21.0) 0.847
Tumor grade G1 G2 G3
14 (2.4) 13 (2.2) 503(86.7) 507(86.7) 63 (10.9) 65 (11.1) 0.969
Vascular invasion Presence 412 (70.9) 422 (72.1) Absence 169 (29.1) 163 (27.9) 0.643
Median survival, mo 68.0 (57.0-91.0) 76.0 (62.0-88.0) 0.59

(95% confidence interval) * Data are given as number (percentage) unless otherwise indicated.
Data were not available for all cases; percentages are based on the number of cases available for the variable, not the total number of cases in the group. Cases were assigned into groups matched for all variables listed. †
The χ2 test was used for sex, tumor location, T stage, N stage, tumor grade, and vascular invasion and log-rank test for survival analysis. P > .05 indicates that there is no difference between groups 1 and 2.
Breast and prostate cancer: more similar than different

Gail P. Risbridger1, Ian D. Davis2, Stephen N. Birrell3 & Wayne D. Tilley3
Nature Reviews Cancer 10, 205-212 (March 2010)
http://dx.doi.org:/10.1038/nrc2795

Breast cancer and prostate cancer are the two most common invasive cancers in women and men, respectively. Although these cancers arise in organs that are different in terms of anatomy and physiological function both organs require gonadal steroids for their development, and tumours that arise from them are typically hormone-dependent and have remarkable underlying biological similarities. Many of the recent advances in understanding the pathophysiology of breast and prostate cancers have paved the way for new treatment strategies. In this Opinion article we discuss some key issues common to breast and prostate cancer and how new insights into these cancers could improve patient outcomes.

Emerging field of metabolomics. Big promise for cancer biomarker identification and drug discovery
Patel S, Ahmed S.
J Pharm Biomed Anal. 2015 Mar 25; 107C:63-74.
http://DX.doi.ORG:/10.1016/j.jpba.2014.12.020

Highlights

  • Mass spectrometry, nuclear magnetic resonance and chemometrics have enabled cancer biomarker discovery.
  • Metabolomics can non-invasively identify biomarkers for diagnosis, prognosis and treatment of cancer.
  • All major types of cancers and their biomarkers discovered by metabolomics have been discussed.
  • This review sheds light on the pitfalls and potentials of metabolomics with respect to oncology.

Most cancers are lethal and metabolic alterations are considered a hallmark of this deadly disease. Genomics and proteomics have contributed vastly to understand cancer biology. Still there are missing links as downstream to them molecular divergence occurs. Metabolomics, the omic science that furnishes a dynamic portrait of metabolic profile is expected to bridge these gaps and boost cancer research. Metabolites being the end products are more stable than mRNAs or proteins. Previous studies have shown the efficacy of metabolomics in identifying biomarkers associated with diagnosis, prognosis and treatment of cancer. Metabolites are highly informative about the functional status of the biological system, owing to their proximity to organismal phenotypes. Scores of publications have reported about high-throughput data generation by cutting-edge analytic platforms (mass spectrometry and nuclear magnetic resonance). Further sophisticated statistical softwares (chemometrics) have enabled meaningful information extraction from the metabolomic data. Metabolomics studies have demonstrated the perturbation in glycolysis, tricarboxylic acid cycle, choline and fatty acid metabolism as traits of cancer cells. This review discusses the latest progress in this field, the future trends and the deficiencies to be surmounted for optimally implementation in oncology. The authors scoured through the most recent, high-impact papers archived in Pubmed, ScienceDirect, Wiley and Springer databases to compile this review to pique the interest of researchers towards cancer metabolomics.

Table.  Novel Cancer Markers Identified by Metabolomics

Quantitative analysis of acetyl-CoA production in hypoxic cancer cells reveals substantial contribution from acetate
Jurre J Kamphorst, Michelle K Chung, Jing Fan and Joshua D Rabinowitz
Cancer & Metabolism 2014, 2:23
http://dx.doi.org:/10.1186/2049-3002-2-23

Background: Cell growth requires fatty acids for membrane synthesis. Fatty acids are assembled from 2-carbon units in the form of acetyl-CoA (AcCoA). In nutrient and oxygen replete conditions, acetyl-CoA is predominantly derived from glucose. In hypoxia, however, flux from glucose to acetyl-CoA decreases, and the fractional contribution of glutamine to acetyl-CoA increases. The significance of other acetyl-CoA sources, however, has not been rigorously evaluated. Here we investigate quantitatively, using 13C-tracers and mass spectrometry, the sources of acetyl-CoA in hypoxia. Results: In normoxic conditions, cultured cells produced more than 90% of acetyl-CoA from glucose and glutamine-derived carbon. In hypoxic cells, this contribution dropped, ranging across cell lines from 50% to 80%. Thus, under hypoxia, one or more additional substrates significantly contribute to acetyl-CoA production. 13C-tracer experiments revealed that neither amino acids nor fatty acids are the primary source of this acetyl-CoA. Instead, the main additional source is acetate. A large contribution from acetate occurs despite it being present in the medium at a low concentration (50–500 μM). Conclusions: Acetate is an important source of acetyl-CoA in hypoxia. Inhibition of acetate metabolism may impair tumor growth.

Cancer cells have genetic mutations that drive proliferation. Such proliferation creates a continuous demand for structural components to produce daughter cells [13]. This includes demand for fatty acids for lipid membranes. Cancer cells can obtain fatty acids both through uptake from extracellular sources and through de novo synthesis, with the latter as a major route by which non-essential fatty acids are acquired in many cancer types [4,5].

The first fatty acid to be produced by de novo fatty acid synthesis is palmitate. The enzyme fatty acid synthase (FAS) makes palmitate by catalyzing the ligation and reduction of 8-acetyl (2-carbon) units donated by cytosolic acetyl-CoA. This 16-carbon fatty acid palmitate is then incorporated into structural lipids or subjected to additional elongation (again using acetyl-CoA) and desaturation reactions to produce the diversity of fatty acids required by the cell.

Acetyl-CoA sits at the interface between central carbon and fatty acid metabolism. In well-oxygenated conditions with abundant nutrients, its 2-carbon acetyl unit is largely produced from glucose. First, pyruvate dehydrogenase produces acetyl-CoA from glucose-derived pyruvate in the mitochondrion, followed by ligation of the acetyl group to oxaloacetate to produce citrate. Citrate is then transported into the cytosol and cytosolic acetyl-CoA produced by ATP citrate lyase.

In hypoxia, flux from glucose to acetyl-CoA is impaired. Low oxygen leads to the stabilization of the HIF1 complex, blocking pyruvate dehydrogenase (PDH) activity via activation of HIF1-responsive pyruvate dehydrogenase kinase 1 (PDK1) [6,7]. As a result, the glucose-derived carbon is shunted towards lactate rather than being used for generating acetyl-CoA, affecting carbon availability for fatty acid synthesis.

To understand how proliferating cells rearrange metabolism to maintain fatty acid synthesis under hypoxia, multiple studies focused on the role of glutamine as an alternative carbon donor[810]. The observation that citrate M+5 labeling from U-13C-glutamine increased in hypoxia led to the hypothesis that reductive carboxylation of glutamine-derived α-ketoglutarate enables hypoxic cells to maintain citrate and acetyl-CoA production. As was noted later, though, dropping citrate levels in hypoxic cells make the α-ketoglutarate to citrate conversion more reversible and an alternative explanation of the extensive citrate and fatty acid labeling from glutamine in hypoxia is isotope exchange without a net reductive flux [11]. Instead, we and others found that hypoxic cells can at least in part bypass the need for acetyl-CoA for fatty acid synthesis by scavenging serum fatty acids [12,13].

In addition to increased serum fatty acid scavenging, we observed a large fraction of fatty acid carbon (20%–50% depending on the cell line) in hypoxic cells not coming from either glucose or glutamine. Here, we used 13C-tracers and mass spectrometry to quantify the contribution from various carbon sources to acetyl-CoA and hence identify this unknown source. We found only a minor contribution of non-glutamine amino acids and of fatty acids to acetyl-CoA in hypoxia. Instead, acetate is the major previously unaccounted for carbon donor. Thus, acetate assimilation is a route by which hypoxic cells can maintain lipogenesis and thus proliferation.

Figure 1. Percentage 13C-labeling of cytosolic acetyl-CoA can be quantified from palmitate labeling. (A) Increasing 13C2-acetyl-CoA labeling shifts palmitate labeling pattern to the right. 13C2-acetyl-CoA labeling can be quantified by determining a best fit between observed palmitate labeling and computed binomial distributions (shown on right-hand side) from varying fractions of acetyl-CoA (AcCoA) labeling. (B) Steady-state palmitate labeling from U-13C-glucose and U-13C-glutamine in MDA-MB-468 cells. (C) Percentage acetyl-CoA production from glucose and glutamine. For (B) and (C), data are means ± SD of n = 3.

Fraction palmitate M + x = (16/x)(p)x (1−p)(16−x)

We applied this approach to MDA-MB-468 cells grown in medium containing U-13C-glucose and U-13C-glutamine. The resulting steady-state palmitate labeling patterns showed multiple heavily 13C-labeled forms as well as a remaining unlabeled M0 peak (Figure 1B). The M0-labeled form results from scavenging of unlabeled serum fatty acids and can be disregarded for the purpose of determining AcCoA labeling. From the remaining labeling distribution, we calculated 87% AcCoA labeling from glucose and 6% from glutamine, with 93% collectively accounted for by these two major carbon sources (Additional file 1: Figure S1). Similar results were also obtained for HeLa and A549 cells (Figure 1C)

Figure 2. Acetyl-CoA labeling from 13C-glucose and 13C-glutamine decreases in hypoxia. (A) Steady-state palmitate labeling from U-13C-glucose and U-13C-glutamine in normoxic and hypoxic (1% O2) conditions. (B) Percentage acetyl-CoA production from glucose and glutamine in hypoxia. (C) One or more additional carbon donors contribute substantially to acetyl-CoA production in hypoxia. Abbreviations: Gluc, glucose; Gln, glutamine. Data are means ± SD of n = 3.

Figure 3.  Amino acids (other than glutamine) and fatty acids are not major sources of cytosolic acetyl-CoA in hypoxia. (A) Palmitate labeling in hypoxic (1% O2) MDA-MB-468 cells, grown for 48 h in medium where branched chain amino acids plus lysine and threonine were substituted with their respective U-13C-labeled forms. (B) Same conditions, except that glucose and glutamine only or glucose and all amino acids, were substituted with the U-13C-labeled forms. (C) Palmitate labeling in hypoxic (1% O2) MDA-MB-468 cells, grown in medium supplemented with 20 μM U-13C-palmitate for 48 h. Data are means ± SD of n = 3.

Acetate is the main additional AcCoA carbon source in hypoxia

We next investigated if hypoxic cells could activate acetate to AcCoA. Although we used dialyzed serum in our experiments and acetate is not a component of DMEM, we contemplated the possibility that trace levels could still be present or that acetate is produced as a catabolic intermediate from other sources (for example from protein de-acetylation). We cultured MDA-MB-468 cells in 1% O2 in DMEM containing U-13C-glucose and U-13C-glutamine and added increasing amounts of U-13C-acetate (Figure 4A). AcCoA labeling rose considerably with increasing U-13C-acetate concentrations, from approximately 50% to 86% with 500 μM U-13C-acetate. No significant increase in labeling of AcCoA was observed in normoxic cells following incubation with U-13C-acetate. Thus, acetate selectively contributes to AcCoA in hypoxia.

Figure 4.  The main additional AcCoA source in hypoxia is acetate. (A) Percentage 13C2-acetyl-CoA labeling quantified from palmitate labeling in hypoxic (1% O2) and normoxic MDA-MB-468 cells grown in medium with U-13C-glucose and U-13C-glutamine and additionally supplemented with indicated concentrations of U-13C-acetate. (B) Acetate concentrations in fresh 10% DFBS, DMEM, and DMEM with 10% DFBS. (C) Percentage 13C2-acetyl-CoA labeling for hypoxic (1% O2) HeLa and A549 cells. For (A) and (C), data are means ± SD of n ≥ 2. For (B), data are means ± SEM of n = 3.

Tumors require a constant supply of fatty acids to sustain cellular replication. It is thought that most cancers derive a considerable fraction of the non-essential fatty acids through de novo synthesis. This requires AcCoA with its 2-carbon acetyl group acting as the carbon donor. In nutrient replete and well-oxygenated conditions, AcCoA is predominantly made from glucose. However, tumor cells often experience hypoxia, causing limited entry of glucose-carbon into the TCA cycle. This in turn affects AcCoA production, and it has been proposed that hypoxic cells can compensate by increasing AcCoA production from glutamine-derived carbon in a pathway involving reductive carboxylation of α-ketoglutarate [810].

Irrespective of the precise net contribution of acetate in hypoxia, a remarkable aspect is that a significant contribution occurs based only on contaminating acetate (~300 μM) in the culturing medium. This is considerably less than glucose (25 mM) or glutamine (4 mM). Acetate concentrations in the plasma of human subjects have been reported in the range of 50 to 650 μM [2225], and therefore, significant acetate conversion to AcCoA may occur in human tumors. This is supported by clinical observations that 11C-acetate PET can be used to image tumors, in particular those where conventional FDG-PET typically fails [26]. Our results indicate that 11C-acetate PET could be particularly important in notoriously hypoxic tumors, such as pancreatic cancer. Preliminary results provide evidence in this direction [27].

Finally, as our measurements of fatty acid labeling reflect specifically cytosolic AcCoA, it is likely that the cytosolic acetyl-CoA synthetase ACSS2 plays an important role in the observed acetate assimilation. Accordingly, inhibition of ACSS2 merits investigation as a potential therapeutic approach.

In hypoxic cultured cancer cells, one-quarter to one-half of cytosolic acetyl-CoA is not derived from glucose, glutamine, or other amino acids. A major additional acetyl-CoA source is acetate. Low concentrations of acetate (e.g., 50–650 μM) are found in the human plasma and also occur as contaminants in typical tissue culture media. These amounts are avidly incorporated into cellular acetyl-CoA selectively in hypoxia. Thus, 11C-acetate PET imaging may be useful for probing hypoxic tumors or tumor regions. Moreover, inhibiting acetate assimilation by targeting acetyl-CoA synthetases (e.g., ACSS2) may impair tumor growth.

Differential metabolomic analysis of the potential antiproliferative mechanism of olive leaf extract on the JIMT-1 breast cancer cell line
Barrajón-Catalán E, Taamalli A, Quirantes-Piné R, …, Micol V, Zarrouk M
J Pharm Biomed Anal. 2015 Feb; 105:156-62.
http://dx.doi.org:/10.1016/j.jpba.2014.11.048

A new differential metabolomic approach has been developed to identify the phenolic cellular metabolites derived from breast cancer cells treated with a supercritical fluid extracted (SFE) olive leaf extract. The SFE extract was previously shown to have significant antiproliferative activity relative to several other olive leaf extracts examined in the same model. Upon SFE extract incubation of JIMT-1 human breast cancer cells, major metabolites were identified by using HPLC coupled to electrospray ionization quadrupole-time-of-flight mass spectrometry (ESI-Q-TOF-MS). After treatment, diosmetin was the most abundant intracellular metabolite, and it was accompanied by minor quantities of apigenin and luteolin. To identify the putative antiproliferative mechanism, the major metabolites and the complete extract were assayed for cell cycle, MAPK and PI3K proliferation pathways modulation. Incubation with only luteolin showed a significant effect in cell survival. Luteolin induced apoptosis, whereas the whole olive leaf extract incubation led to a significant cell cycle arrest at the G1 phase. The antiproliferative activity of both pure luteolin and olive leaf extract was mediated by the inactivation of the MAPK-proliferation pathway at the extracellular signal-related kinase (ERK1/2). However, the flavone concentration of the olive leaf extract did not fully explain the strong antiproliferative activity of the extract. Therefore, the effects of other compounds in the extract, probably at the membrane level, must be considered. The potential synergistic effects of the extract also deserve further attention. Our differential metabolomics approach identified the putative intracellular metabolites from a botanical extract that have antiproliferative effects, and this metabolomics approach can be expanded to other herbal extracts or pharmacological complex mixtures.

Pancreatic cancer early detection. Expanding higher-risk group with clinical and metabolomics parameters
Shiro Urayama
World J Gastroenterol. 2015 Feb 14; 21(6): 1707–1717.
http://dx.doi.org:/10.3748/wjg.v21.i6.1707

Pancreatic ductal adenocarcinoma (PDAC) is the fourth and fifth leading cause of cancer death for each gender in developed countries. With lack of effective treatment and screening scheme available for the general population, the mortality rate is expected to increase over the next several decades in contrast to the other major malignancies such as lung, breast, prostate and colorectal cancers. Endoscopic ultrasound, with its highest level of detection capacity of smaller pancreatic lesions, is the commonly employed and preferred clinical imaging-based PDAC detection method. Various molecular biomarkers have been investigated for characterization of the disease, but none are shown to be useful or validated for clinical utilization for early detection. As seen from studies of a small subset of familial or genetically high-risk PDAC groups, the higher yield and utility of imaging-based screening methods are demonstrated for these groups. Multiple recent studies on the unique cancer metabolism including PDAC, demonstrate the potential for utility of the metabolites as the discriminant markers for this disease. In order to generate an early PDAC detection screening strategy available for a wider population, we propose to expand the population of higher risk PDAC group with combination clinical and metabolomics parameters.

Core tip: This is a summary of current pancreatic cancer cohort early detection studies and a potential approach being considered for future application. This is an area that requires heightened efforts as lack of effective treatment and screening scheme for wider population is leading this particular disease to be the second lethal cancer by 2030.

Currently, pancreatic ductal adenocarcinoma (PDAC) is the fourth major cause of cancer mortality in the United States[1]. It is predicted that 46420 new cases and 39590 deaths would result from pancreatic cancer in the United States in 2014[2]. Worldwide, there were 277668 new cases and 266029 deaths from this cancer in 2008[3]. In comparison to other major malignancies such as breast, colon, lung and prostate cancers with their respective 89%, 64%, 16%, 99% 5-year survival rate, PDAC at 6% is conspicuously low[2]. For PDAC, the only curative option is surgical resection, which is applicable in only 10%-15% of patients due to the common discovery of late stage at diagnosis[4]. In fact, PDAC is notorious for late stage discovery as evidenced by the low percentage of localized disease at diagnosis, compared to other malignancies: breast (61%), colon (40%), lung (16%), ovarian (19%), prostate (91%), and pancreatic cancer (7%) [5]. With the existing effective screening methods, the decreasing trends of cancer death rate are seen in major malignancies such as breast, prostate and colorectal cancer. In contrast, it is estimated that PDAC is expected to be surfacing as the second leading cause of cancer death by 2030[6].

With the distinct contribution of late-stage discovery and general lack of effective medical therapy, a critical approach in reversing the poor outcome of pancreatic cancer is to develop an early detection scheme for the tumor. In support of this, we see the trend that despite the poor prognosis of the disease, for those who have undergone curative resection with negative margins, the 5-year survival rate is 22% in contrast to 2% for the advanced-stage with distant metastasis[7,8]. An earlier diagnosis with tumor less than 2 cm (T1) is associated with a better 5-year survival of 58% compared to 17% for stage IIB PDAC[9]. Ariyama et al. [10] reported complete survival of 79 patients with less than 1 cm tumors after surgical resection. Furthermore, as a recent report indicates, the estimated time from the transformation to pre-metastatic growths of pancreatic cancer is approximately 15 years[11]; there is a wide potential window of opportunity to apply developing technologies in early detection of this cancer.

Current screening programs have demonstrated that the EUS evaluation can detect premalignant lesions and early cancers in certain small subset of high-risk groups. However, as the overwhelming majority of PDAC cases involve patients who develop the disease sporadically without a recognized genetic abnormality, the application of this modality for PDAC detection screening is very limited for the general adult population.

Select population based approach

Identification of a higher-PDAC-risk group: As the prevalence of PDAC in the general United States population over the age 55 is approximately 68 per 100000, a candidate discriminant test with a specificity of 98% and a sensitivity of 100% would generate 1999 false-positive test results and 68 true-positives[74]. Thus, relying on a single determinant for distinguishing the PDAC early-stage cases from the general population would necessitate a highly accurate test with a specificity of greater than 99%. More practical approach, then, would be to begin with a subset of population with a higher prevalence, and in conjunction with novel surrogate markers to curtail the at-risk subset, we could begin to identify the group with significantly increased PDAC risk for whom the endoscopic/imaging-based screening strategy could be applied.

An initial approach in selection of the screening population is to utilize selective clinical parameters that could be used to curtail the subset of the general population at increased PDAC risk. For instance, based on the epidemiological evidence, such clinical parameters include hyperglycemia or diabetes, which are noted in 50%-80% of pancreatic cancer patients [7579]. Though not encompassing all PDAC patients, this subset includes a much larger proportion of PDAC patients for whom we may select further for screening. Similarly, patients with a history of chronic pancreatitis or obesity are reported to have increased PDAC risk during their lifetime[8085].

With the recent advancement in the technology and resumed interest in the cancer-associated metabolic abnormality [89,90], application of metabolomics in the cancer field has attracted more attention [91]. Cancer-related metabolic reprogramming, Warburg effect, has been known since nearly a century ago in association with various solid tumors including PDAC [92], as cancer cells undergo energetically inefficient glycolysis even in the presence of oxygen in the environment (aerobic glycolysis)[93]. A number of common cancer mutations including Akt1, HIF (hypoxia-inducible factor), and p53 have been shown to support the Warburg effect through glycolysis and down-regulation of metabolite flux through the Krebs cycle [94101]. In PDAC, increased phosphorylation or activation of Akt1 has also been reported (illuminating on the importance of enzyme functionality)[102] as well as involvement of HIF1 in the tumor growth via effects on glycolytic process [103,104] and membrane-bound glycoprotein (MUC17) regulation [105] – reflective of activation of metabolic pathways. Further evidences of loss-of-function genetic mutations in key mitochondrial metabolic enzymes such as succinate dehydrogenase and fumarate hydratase, isocitrate dehydrogenase, phosphoglycerate dehydrogenase support carcinogenesis and the Warburg effect [106110]. Other important alternative pathways in cancer metabolism such as glutaminolysis and pyruvate kinase isoform suppression have been shown to accumulate respective upstream intermediates and reduction of associated end products such as NADPH, ribose-5-phosphate and nucleic acids [111-116]. As such, various groups have reported metabolomics biomarker applications for different cancers [117,118].

As a major organ involved in metabolic regulation in a healthy individual, pancreatic disorder such as malignancy is anticipated to influence the normal metabolism, presenting further rationale and interest in elucidating the implication of malignant transformation and PDAC development. Proteomic analysis of the pancreatic cancer cells demonstrated alteration in proteins involved in metabolic pathways including increased expression of glycolytic and reduced Krebs cycle enzymes, and accumulation of key proteins involved in glutamine metabolism, in support of Warburg effect. These in turn play significant role in nucleotide and amino acid biosynthesis required for sustaining the proliferating cancer cells[119]. Applications of sensitive mass spectrometric techniques in metabolomics study of PDAC detection biomarkers have led to identification of a set of small molecules or metabolites (or biochemical intermediates) that are potent discriminants of developing PDAC and the controls (See Figure ​1  as an example of metabolomics based analysis, allowing segregation of PDAC from benign cases). Recent reports from our group as well as others have demonstrated that specific candidate metabolites consisting of amino acids, bile acids, and a number of lipids and fatty acids – suspected to be reflective of tumor proliferation as well as many systemic response yet to be determined – were identified as potential discriminant for blood-based PDAC biomarkers[120-123]. As a further supporting data, elucidation of lipids and fatty acids as discriminant factors from PDAC and benign lesions from the cancer tissue and adjacent normal tissue has been reported recently[124].

metabolomics based analysis for PDC WJG-21-1707-g001

metabolomics based analysis for PDC WJG-21-1707-g001

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4323446/bin/WJG-21-1707-g001.gif

Figure 1 Example of metabolomics based analysis, allowing segregation of pancreatic ductal adenocarcinoma from benign cases. Heat map illustration of discriminant capability of a metabolite set derived from gas chromatography and liquid chromatography/mass spectrometry …

By virtue of simultaneously depicting the multiple metabolite levels, metabolomics approach reveals various biochemical pathways that are uniquely involved in malignant conditions and has led to findings such as abnormalities of glycine and its mitochondrial biosynthetic pathway, as a potential therapeutic target in certain cancers[125]. Moreover, in combination with other systems biology approaches such as transcriptomics and proteomics, further refinement in characterization of cancer development and therapeutic targets as well as identification of potential biomarkers could be realized for PDAC. Since many enzymes in a metabolic network determine metabolites’ level and nonlinear quantitative relationship from the genes to the proteome and metabolome levels exist, a metabolome cannot be easily decomposed to a specific single marker, which will designate the cancer state[126]. Thus, in order to delineate a pathological state such as PDAC, multiple metabolomic features might be required for accurate depiction of a developing cancer. Future studies are anticipated to incorporate cancer systems’ biological knowledge, including metabolomics, for optimal designation of PDAC biomarkers, which would be utilized in conjunction with a clinical-parameter-derived population subset for establishing the PDAC screening population. Subsequently, further validation studies for the PDAC biomarkers need to be performed.

Current imaging-based detection and diagnostic methods for PDAC is effectively providing answers to clinical questions raised for patients with signs or symptoms of suspected pancreatic lesions. However, the endoscopic/imaging-based screening schemes are currently limited in applications to early PDAC detection in asymptomatic patients, aside from a small group of known genetically high-risk groups. There is a high demand for developing a method of selecting distinct subsets among the general population for implementing the endoscopic/imaging screening test effectively. Application of combinations of clinical risk parameters/factors with the developing molecular biomarkers from translational science such as metabolomics analysis brings hopes of providing us with early PDAC detection markers, and developing effective early detection screening scheme for the patients in the near future.

Serum metabolomic profiles evaluated after surgery may identify patients with estrogen receptor negative early breast cancer at increased risk of disease recurrence
Tenori L, Oakman C, Morris PG, …, Luchinat C, Di Leo A.
Mol Oncol. 2015 Jan; 9(1):128-39.
http://dx.doi.org:/10.1016/j.molonc.2014.07.012

Purpose: Metabolomics is a global study of metabolites in biological samples. In this study we explored whether serum metabolomic spectra could distinguish between early and metastatic breast cancer patients and predict disease relapse. Methods: Serum samples were analysed from women with metastatic (n = 95) and predominantly oestrogen receptor (ER) negative early stage (n = 80) breast cancer using high resolution nuclear magnetic resonance spectroscopy. Multivariate statistics and a Random Forest classifier were used to create a prognostic model for disease relapse in early patients.
Results: In the early breast cancer training set (n = 40), metabolomics correctly distinguished between early and metastatic disease in 83.7% of cases. A prognostic risk model predicted relapse with 90% sensitivity (95% CI 74.9-94.8%), 67% specificity (95% CI 63.0-73.4%) and 73% predictive accuracy (95% CI 70.6-74.8%). These results were reproduced in an independent early breast cancer set (n = 40), with 82% sensitivity, 72% specificity and 75% predictive accuracy. Disease relapse was associated with significantly lower levels of histidine (p = 0.0003) and higher levels of glucose (p = 0.01), and lipids (p = 0.0003), compared with patients with no relapse.
Conclusions: The performance of a serum metabolomic prognostic model for disease relapse in individuals with ER-negative early stage breast cancer is promising. A confirmation study is ongoing to better define the potential of metabolomics as a host and tumour-derived prognostic tool.

Figure 1 e Clusterization of serum metabolomic profiles. Discrimination between metastatic (green, n [ 95) and early (red, n [ 40) breast cancer patients using the random forest classifier. (a) CPMG; (b) NOESY1D; (c) Diffusion.

Figure 2 e Training set. Comparison between metabolomic classification and actual relapse. The receiver operator curves (ROC) and the area under the curve (AUC) scores are presented for CPMG, NOESY1D and Diffusion.

Figure 3 e Validation set. Comparison between CPMG random forest risk score metabolomic classification and actual relapse The receiver operator curve (ROC) and the area under the curve (AUC) score are presented for the CPMG analysis.

Figure 4 e Discriminant metabolites. Discriminant metabolites (p < 0.05) between profiles from early (green, n [ 80) and metastatic (red, n [ 95) breast cancer patients. Box and whisker plots: horizontal line within the box [ mean; bottom and top lines of the box [ 25th and 75th percentiles, respectively; bottom and top whiskers [ 5th and 95th percentiles, respectively. Median values (arbitrary units) are provided in the associated table, along with raw p values and p values adjusted for multiple testing. pts: patients.

Transparency in metabolic network reconstruction enables scalable biological discovery
Benjamin D Heavner, Nathan D Price
Current Opinion in Biotechnology, Aug 2015; 34: 105–109
Highlights

  • Assembling a network reconstruction can reveal knowledge gaps.
  • Building a functional metabolic model enables testable prediction.
  • Recent work has found that most models contain the same reactions.
  • Reconstruction and functional model building should be explicitly separated.

Reconstructing metabolic pathways has long been a focus of active research. Now, draft models can be generated from genomic annotation and used to simulate metabolic fluxes of mass and energy at the whole-cell scale. This approach has led to an explosion in the number of functional metabolic network models. However, more models have not led to expanded coverage of metabolic reactions known to occur in the biosphere. Thus, there exists opportunity to reconsider the process of reconstruction and model derivation to better support the less-scalable investigative processes of biocuration and experimentation. Realizing this opportunity to improve our knowledge of metabolism requires developing new tools that make reconstructions more useful by highlighting metabolic network knowledge limitations to guide future research.

metabolic network reconstruction

metabolic network reconstruction

http://ars.els-cdn.com/content/image/1-s2.0-S0958166914002250-fx1.jpg

Mapping metabolic pathways has been a focus of significant scientific efforts dating from the emergence of biochemistry as a distinct scientific field in the late 19th century [1]. This endeavor remains an important effort for at least two compelling reasons. First, cataloguing and characterizing the full range of metabolic processes across species (which because of genomics are being discovered at an incredible pace) is a fundamentally important step towards a complete understanding of our ecological environment. Second, mapping metabolic pathways in organisms — many of which can be found with specialized properties shaped by their environment — facilitates metabolic engineering to advance nascent industrial biotechnology efforts ranging from augmenting/replacing petroleum-derived chemical precursors or fuels to biopharmaceutical production [2]. However, despite laudable efforts to enable high-throughput ‘genomic enzymology’ [3•], the traditional biochemical approaches of enzyme expression, purification, and characterization remain time-intensive, capital-intensive, and labor-intensive, and have not expanded in scale like our ability to identify and characterize life genomically. Characterizing new metabolic function is further hampered by the challenge of cultivating environmental isolates in laboratory conditions [4]. Fortunately, recent efforts to leverage genome functional annotation and established knowledge of biochemistry have enabled the computational assembly of ‘draft metabolic reconstructions’ [5], which are parts lists of metabolic network components. In this context, a reconstruction is not just the information embodied in the stoichiometric matrix describing metabolic network structure, but also the associated metadata and annotation that entails an organism-specific knowledge base. Such a reconstruction can serve as the basis for making functional models amenable to mathematical simulation. Thus, a reconstruction is a bottom-up assembly of biochemical information, and a model can serve as a framework for integrating top-down information (for example, model constraints can be generated from statistically inferred gene regulatory networks [6]). Such computational approaches are significantly faster and less expensive than biochemical characterization [7]. They are also providing new resources facilitate cultivation of novel environmental isolates [8], and the scope of draft metabolic network coverage across the biome has increased much faster than wet lab characterization. If the distinction between reconstruction and model formulation can be strengthened and supported through software implementation, there is great opportunity for using both tasks to further advance rapid discovery of biological function.

The iterative process of manual curation of a draft metabolic network reconstruction to assemble a higher confidence compendium of organism-specific metabolism (a process termed ‘biocuration’ [9 and 10]) remains time-intensive and labor-intensive. Biocuration of metabolic reconstructions currently advances on a decadal time scale [11 and 12]. Thus, much research effort has focused instead on developing techniques for rapid development of models that are amenable to simulation [13 and 14]. Thousands of models have been derived from automatically assembled draft reconstructions [15], but most of these models consist of highly conserved portions of metabolism since they are propagated primarily via orthology. Though the number of models is large, they do not reflect the true diversity of cellular metabolic capabilities across different organisms [16•]. Applying the rapid and scalable process of draft network reconstruction to support and accelerate the less-scalable processes of biocuration and in vitro or in vivo experimentation remains an unrealized opportunity. The path forward should focus on increased emphasis on transparently documenting the reconstruction process and developing tools to highlight, rather than obscure, knowledge limitations that ultimately cause limitations to model predictive accuracy.

More explicit annotation of metabolic network reconstruction and model derivation steps can help direct research efforts

Testing implicit hypotheses arising from reconstruction assembly provides one opportunity for guiding experimental efforts. However, the very act of identifying ambiguous information in the literature should also be exploited to contribute to experimental efforts, independent of the choices a researcher makes in assembling a reconstruction. Preliminary steps to facilitate large-scale computational identification of biological uncertainty have been made, such as the development of the Evidence Ontology [18]. However, realizing the potential for using reconstruction assembly to highlight experimental opportunities will require a broader shift to emphasize the limits of our knowledge, rather than only the predictive power of a model that can be derived from a reconstruction. Computational reconstruction of metabolic networks provides two distinct opportunities for guiding experimental efforts even before a mathematically computable model is derived from the assembled knowledge: highlighting areas of uncertainty in the current knowledge of an organism, and introducing hypotheses of metabolic function as choices are made throughout biocuration efforts.

The subsequent process of deriving a mathematically computable model from a reconstruction provides additional opportunities for scalable hypothesis generation that could be exploited to inform experimental efforts. While stoichiometrically constrained models derived from reconstructions are ‘parameter-light’ when compared to dynamic enzyme kinetic models, they are not really ‘parameter free’ [19]. As modelers derive a model from an assembled reconstruction, they must make choices. And, like the ambiguities and choices that are made and should be highlighted in assembling a reconstruction, highlighting the choices made in deriving a model provides further opportunity for scalable hypothesis generation. Examples of choices that often arise in deriving a functional model include adding intracellular transport reactions, filling network gaps, or trimming network dead ends to improve network connectivity [20]. Researchers seeking to conduct Flux Balance Analysis (FBA) [21] or similar approaches must formulate an objective function, can include testable parameters such as ATP maintenance requirements, and can compare model predictions to designated reference phenotype observations. Each of these model-building and tuning activities presents opportunities to rapidly develop and prioritize new hypotheses of metabolic function.

The effort to computationally reconstruct biochemical knowledge to compile organism-specific reconstructions, and to derive computable models from these reconstructions, is a relatively young field of research with abundant opportunity for facilitating biological discovery of metabolic function. Judgment is required in assembling a reconstruction, and there should be careful consideration of the fact that judgment calls represent an implicit hypothesis. Making these hypotheses more explicit would help guide subsequent investigation. Bernhard Palsson and colleagues call for ‘an open discussion to define the minimal quality criteria for a genome scale reconstruction’ [16•] — an effort we fully support. We believe that such a beneficial ‘minimal quality criteria’ should be guided by the goals of reproducibility and transparency, including those aspects that can help to guide discovery of novel gene functions.

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War on Cancer Needs to Refocus to Stay Ahead of Disease Says Cancer Expert


War on Cancer Needs to Refocus to Stay Ahead of Disease Says Cancer Expert

Writer, Curator: Stephen J. Williams, Ph.D.

Is one of the world’s most prominent cancer researchers throwing in the towel on the War On Cancer? Not throwing in the towel, just reminding us that cancer is more complex than just a genetic disease, and in the process, giving kudos to those researchers who focus on non-genetic aspects of the disease (see Dr. Larry Bernstein’s article Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?).

 

National Public Radio (NPR) has been conducting an interview series with MIT cancer biology pioneer, founding member of the Whitehead Institute for Biomedical Research, and National Academy of Science member and National Medal of Science awardee Robert A. Weinberg, Ph.D., who co-discovered one of the first human oncogenes (Ras)[1], isolation of first tumor suppressor (Rb)[2], and first (with Dr. Bill Hahn) proved that cells could become tumorigenic after discrete genetic lesions[3].   In the latest NPR piece, Why The War On Cancer Hasn’t Been Won (seen on NPR’s blog by Richard Harris), Dr. Weinberg discusses a comment in an essay he wrote in the journal Cell[4], basically that, in recent years, cancer research may have focused too much on the genetic basis of cancer at the expense of multifaceted etiology of cancer, including the roles of metabolism, immunity, and physiology. Cancer is the second most cause of medically related deaths in the developed world. However, concerted efforts among most developed nations to eradicate the disease, such as increased government funding for cancer research and a mandated ‘war on cancer’ in the mid 70’s has translated into remarkable improvements in diagnosis, early detection, and cancer survival rates for many individual cancer. For example, survival rate for breast and colon cancer have improved dramatically over the last 40 years. In the UK, overall median survival times have improved from one year in 1972 to 5.8 years for patients diagnosed in 2007. In the US, the overall 5 year survival improved from 50% for all adult cancers and 62% for childhood cancer in 1972 to 68% and childhood cancer rate improved to 82% in 2007. However, for some cancers, including lung, brain, pancreatic and ovarian cancer, there has been little improvement in survival rates since the “war on cancer” has started.

(Other NPR interviews with Dr. Weinberg include How Does Cancer Spread Through The Body?)

As Weinberg said, in the 1950s, medical researchers saw cancer as “an extremely complicated process that needed to be described in hundreds, if not thousands of different ways,”. Then scientists tried to find a unifying principle, first focusing on viruses as the cause of cancer (for example rous sarcoma virus and read Dr. Gallo’s book on his early research on cancer, virology, and HIV in Virus Hunting: AIDS, Cancer & the Human Retrovirus: A Story of Scientific Discovery).

However (as the blog article goes on) “that idea was replaced by the notion that cancer is all about wayward genes.”

“The thought, at least in the early 1980s, was that were a small number of these mutant, cancer-causing oncogenes, and therefore that one could understand a whole disparate group of cancers simply by studying these mutant genes that seemed to be present in many of them,” Weinberg says. “And this gave the notion, the illusion over the ensuing years, that we would be able to understand the laws of cancer formation the way we understand, with some simplicity, the laws of physics, for example.”

According to Weinberg, this gene-directed unifying theory has given way as recent evidences point back once again to a multi-faceted view of cancer etiology.

But this is not a revolutionary or conflicting idea for Dr. Weinberg, being a recipient of the 2007 Otto Warburg Medal and focusing his latest research on complex systems such as angiogenesis, cell migration, and epithelial-stromal interactions.

In fact, it was both Dr. Weinberg and Dr. Bill Hanahan who formulated eight governing principles or Hallmarks of cancer:

  1. Maintaining Proliferative Signals
  2. Avoiding Immune Destruction
  3. Evading Growth Suppressors
  4. Resisting Cell Death
  5. Becoming Immortal
  6. Angiogenesis
  7. Deregulating Cellular Energy
  8. Activating Invasion and Metastasis

Taken together, these hallmarks represent the common features that tumors have, and may involve genetic or non-genetic (epigenetic) lesions … a multi-modal view of cancer that spans over time and across disciplines. As reviewed by both Dr. Larry Bernstein and me in the e-book Volume One: Cancer Biology and Genomics for Disease Diagnosis, each scientific discipline, whether the pharmacologist, toxicologist, virologist, molecular biologist, physiologist, or cell biologist has contributed greatly to our total understanding of this disease, each from their own unique perspective based on their discipline. This leads to a “multi-modal” view on cancer etiology and diagnosis, treatment. Many of the improvements in survival rates are a direct result of the massive increase in the knowledge of tumor biology obtained through ardent basic research. Breakthrough discoveries regarding oncogenes, cancer cell signaling, survival, and regulated death mechanisms, tumor immunology, genetics and molecular biology, biomarker research, and now nanotechnology and imaging, have directly led to the advances we now we in early detection, chemotherapy, personalized medicine, as well as new therapeutic modalities such as cancer vaccines and immunotherapies and combination chemotherapies. Molecular and personalized therapies such as trastuzumab and aromatase inhibitors for breast cancer, imatnib for CML and GIST related tumors, bevacizumab for advanced colorectal cancer have been a direct result of molecular discoveries into the nature of cancer. This then leads to an interesting question (one to be tackled in another post):

Would shifting focus less on cancer genome and back to cancer biology limit the progress we’ve made in personalized medicine?

 

In a 2012 post Genomics And Targets For The Treatment Of Cancer: Is Our New World Turning Into “Pharmageddon” Or Are We On The Threshold Of Great Discoveries? Dr. Leonard Lichtenfield, MD, Deputy Chief Medical Officer for the ACS, comments on issues regarding the changes which genomics and personalized strategy has on oncology drug development. As he notes, in the past, chemotherapy development was sort of ‘hit or miss’ and the dream and promise of genomics suggested an era of targeted therapy, where drug development was more ‘rational’ and targets were easily identifiable.

To quote his post

That was the dream, and there have been some successes–even apparent cures or long term control–with the used of targeted medicines with biologic drugs such as Gleevec®, Herceptin® and Avastin®. But I think it is fair to say that the progress and the impact hasn’t been quite what we thought it would be. Cancer has proven a wily foe, and every time we get answers to questions what we usually get are more questions that need more answers. The complexity of the cancer cell is enormous, and its adaptability and the genetic heterogeneity of even primary cancers (as recently reported in a research paper in the New England Journal of Medicine) has been surprising, if not (realistically) unexpected.

                                                                               ”

Indeed the complexity of a given patient’s cancer (especially solid tumors) with regard to its genetic and mutation landscape (heterogeneity) [please see post with interview with Dr. Swanton on tumor heterogeneity] has been at the forefront of many clinicians minds [see comments within the related post as well as notes from recent personalized medicine conferences which were covered live on this site including the PMWC15 and Harvard Personalized Medicine conference this past fall].

In addition, Dr. Lichtenfeld makes some interesting observations including:

  • A “pharmageddon” where drug development risks/costs exceed the reward so drug developers keep their ‘wallets shut’. For example even for targeted therapies it takes $12 billion US to develop a drug versus $2 billion years ago
  • Drugs are still drugs and failure in clinical trials is still a huge risk
  • “Eroom’s Law” (like “Moore’s Law” but opposite effect) – increasing costs with decreasing success
  • Limited market for drugs targeted to a select mutant; what he called “slice and dice”

The pros and cons of focusing solely on targeted therapeutic drug development versus using a systems biology approach was discussed at the 2013 Institute of Medicine’s national Cancer Policy Summit.

  • Andrea Califano, PhD – Precision Medicine predictions based on statistical associations where systems biology predictions based on a physical regulatory model
  • Spyro Mousses, PhD – open biomedical knowledge and private patient data should be combined to form systems oncology clearinghouse to form evolving network, linking drugs, genomic data, and evolving multiscalar models
  • Razelle Kurzrock, MD – What if every patient with metastatic disease is genomically unique? Problem with model of smaller trials (so-called N=1 studies) of genetically similar disease: drugs may not be easily acquired or re-purposed, and greater regulatory burdens

So, discoveries of oncogenes, tumor suppressors, mutant variants, high-end sequencing, and the genomics and bioinformatic era may have led to advent of targeted chemotherapies with genetically well-defined patient populations, a different focus in chemotherapy development

… but as long as we have the conversation open I have no fear of myopia within the field, and multiple viewpoints on origins and therapeutic strategies will continue to develop for years to come.

References

  1. Parada LF, Tabin CJ, Shih C, Weinberg RA: Human EJ bladder carcinoma oncogene is homologue of Harvey sarcoma virus ras gene. Nature 1982, 297(5866):474-478.
  2. Friend SH, Bernards R, Rogelj S, Weinberg RA, Rapaport JM, Albert DM, Dryja TP: A human DNA segment with properties of the gene that predisposes to retinoblastoma and osteosarcoma. Nature 1986, 323(6089):643-646.
  3. Hahn WC, Counter CM, Lundberg AS, Beijersbergen RL, Brooks MW, Weinberg RA: Creation of human tumour cells with defined genetic elements. Nature 1999, 400(6743):464-468.
  4. Weinberg RA: Coming full circle-from endless complexity to simplicity and back again. Cell 2014, 157(1):267-271.

 

Other posts on this site on The War on Cancer and Origins of Cancer include:

 

2013 Perspective on “War on Cancer” on December 23, 1971

Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?

World facing cancer ‘tidal wave’, warns WHO

2013 American Cancer Research Association Award for Outstanding Achievement in Chemistry in Cancer Research: Professor Alexander Levitzki

Genomics and Metabolomics Advances in Cancer

The Changing Economics of Cancer Medicine: Causes for the Vanishing of Independent Oncology Groups in the US

Cancer Research Pioneer, after 71 years of Immunology Lab Research, Herman Eisen, MD, MIT Professor Emeritus of Biology, dies at 96

My Cancer Genome from Vanderbilt University: Matching Tumor Mutations to Therapies & Clinical Trials

Articles on Cancer-Related Topic in http://pharmaceuticalintelligence.com Scientific Journal

Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing

Issues in Personalized Medicine: Discussions of Intratumor Heterogeneity from the Oncology Pharma forum on LinkedIn

Introduction – The Evolution of Cancer Therapy and Cancer Research: How We Got Here?

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Diet and Cholesterol

Writer and Curator: Larry H. Bernstein, MD, FCAP 

 

Introduction

We are all familiar with the conundrum of diet and cholesterol.  As previously described, cholesterol is made by the liver. It is the backbone for the synthesis of sex hormones, corticosteroids, bile, and vitamin D. It is also under regulatory control, and that is not fully worked out, but it has health consequences. The liver is a synthetic organ that is involved with glycolysis, gluconeogenesis, cholesterol synthesis, and unlike the heart and skeletal muscles – which are energy transducers – the liver is anabolic, largely dependent on NADPH.  The mitochondria, which are associated with aerobic metabolism, respiration, are also rich in the liver.  The other part of this story is the utilization of lipids synthesized by the liver in the vascular endothelium.  The vascular endothelium takes up and utilizes/transforms cholesterol, which is involved in the degenerative development of pathogenic plaque.  Plaque is associated with vascular rigidity, rupture and hemorrhage, essential in myocardial inmfarction. What about steroid hormones?  There is some evidence that sex hormone differences may be a factor in coronary vascular disease and cardiac dysfunction.  The evidence that exercise is beneficial is well established, but acute coronary events can occur during exercise.  WE need food, and food is at the center of the discussion – diet and cholesterol.  The utilization of food varies regionally, and is dependent on habitat.  But it is also strongly influence by culture.  We explore this further in what follows.

A high fat, high cholesterol diet leads to changes in metabolite patterns in pigs – A metabolomic study

Jianghao Sun, Maria Monagas, Saebyeol Jang, Aleksey Molokin, et al.
Food Chemistry 173 (2015) 171–178
http://dx.doi.org/10.1016/j.foodchem.2014.09.161

Non-targeted metabolite profiling can identify biological markers of dietary exposure that lead to a better understanding of interactions between diet and health. In this study, pigs were used as an animal model to discover changes in metabolic profiles between regular basal and high fat/high cholesterol diets. Extracts of plasma, fecal and urine samples from pigs fed high fat or basal regular diets for 11 weeks were analysed using ultra-high performance liquid chromatography with high-resolution mass spectrometry (UHPLC–HRMS) and chemometric analysis. Cloud plots from XCMS online were used for class separation of the most discriminatory metabolites. The major metabolites contributing to the discrimination were identified as bile acids (BAs), lipid metabolites, fatty acids, amino acids and phosphatidic acid (PAs), phosphatidylglycerol (PGs), glycerophospholipids (PI), phosphatidylcholines (PCs) and tripeptides. These results suggest the developed approach can be used to identify biomarkers associated with specific feeding diets and possible metabolic disorders related to diet.

Nutritional metabolomics is a rapidly developing sub-branch of metabolomics, used to profile small-molecules to support integration of diet and nutrition in complex bio-systems research. Recently, the concept of ‘‘food metabolome’’ was introduced and defined as all metabolites derived from food products. Chemical components in foods are absorbed either directly or after digestion, undergo extensive metabolic modification in the gastrointestinal tract and liver and then appear in the urine and feces as final metabolic products. It is well known that diet has a close relationship with the long-term health and well-being of individuals. Hence, investigation of the ‘‘food metabolome’’ in biological samples, after feeding specific diets, has the potential to give objective information about the short- and long-term dietary intake of individuals, and to identify potential biomarkers of certain dietary patterns. Previous studies have identified potential biomarkers after consumption of specific fruits, vegetables, cocoa, and juices. More metabolites were revealed by using metabolomic approaches compared with the detection of pre-defined chemicals found in those foods.

Eating a high-fat and high cholesterol diet is strongly associated with conditions of obesity, diabetes and metabolic syndrome, that are increasingly recognized as worldwide health concerns. For example, a high fat diet is a major risk factor for childhood obesity, cardiovascular diseases and hyperlipidemia. Little is known on the extent to which changes in nutrient content of the human diet elicit changes in metabolic profiles. There are several reports of metabolomic profiling studies on plasma, serum, urine and liver from high fat-diet induced obese mice, rats and humans. Several potential biomarkers of obesity and related diseases, including lysophosphatidylcholines (lysoPCs), fatty acids and branched-amino acids (BCAAs) have been reported.

To model the metabolite response to diet in humans, pigs were fed a high fat diet for 11 weeks and the metabolite profiles in plasma, urine and feces were analyzed. Non-targeted ultra high performance liquid chromatography tandem with high resolution mass spectrometry (UHPLC–MS) was utilized for metabolomics profiling. Bile acids (BAs), lipid metabolites, fatty acids, amino acids and phosphatidic acid (PAs), phosphatidylglycerol (PGs), glycerophospholipids (PI), phosphatidylcholines (PCs), tripeptides and isoflavone conjugates were found to be the final dietary metabolites that differentiated pigs fed a high-fat and high cholesterol diet versus a basal diet. The results of this study illustrate the capacity of this metabolomic profiling approach to identify new metabolites and to recognize different metabolic patterns associated with diet.

Body weight, cholesterol and triglycerides were measured for all the pigs studied. There was no significant body weight gain between pigs fed diet A and diet B after 11 weeks of treatment. The serum cholesterol and triglyceride levels were significantly higher in pigs fed with diet B compared with the control group at the end of experiment.

Plasma, urine and fecal samples were analyzed in both positive and negative ionization mode. To obtain reliable and high-quality metabolomic data, a pooled sample was used as a quality control (QC) sample to monitor the run. The QC sample (a composite of equal volume from 10 real samples) was processed as real samples and placed in the sample queue to monitor the stability of the system. All the samples were submitted in random for analysis. The quantitative variation of the ion features across the QC samples was less than 15%. The ion features from each possible metabolite were annotated by XCMS online to confirm the possible fragment ions, isotopic ions and possible adduct ions. The reproducibility of the chromatography was determined by the retention time variation profiles that were generated by XCMS. The retention time deviation was less than 0.3 min for plasma samples, less than 0.3 min for fecal samples, and less than 0.2 min for urine samples, respectively. On the basis of these results of data quality assessment, the differences between the test samples from different pigs proved more likely to reflect varied metabolite profiles rather than analytical variation. The multivariate analysis results from the QC sample showed the deviation of the analytical system was acceptable.
Good separation can be observed between pigs on the two diets, which is also reflected in the goodness of prediction (Q2), of 0.64 using data from the positive ionization mode. For negative ionization mode data, better separation appears with a Q2of 0.73.

Cloud plot is a new multidimensional data visualization method for global metabolomic data (Patti et al., 2013). Data characteristics, such as the p-value, fold change, retention time, mass-to-charge ratio and signal intensity of features, can be presented simultaneously using the cloud plot. In this study, the cloud plot was used to illustrate the ion features causing the group separation. In Fig. 2 and 82 features with p < 0.05 and fold change >2, including visualisation of the p-value, the directional fold change, the retention time and the mass to charge ratio of features, are shown. Also, the total ion chromato-grams for each sample were shown. The upper panel in (2A) shows the chromatograms of plasma samples from pigs fed the high fat diet, while the lower panel shows the chromatograms of samples from pigs fed the regular diet. Features whose intensity is increased are shown in green, whereas features whose intensity is decreased are shown in pink (2A). The size of each bubble corresponds to the log fold change of the feature: the larger the bubble, the larger the fold changes. The statistical significance of the fold change, as calculated by a Welch t-test with unequal variances, is represented by the intensity of the feature’s color where features with low p-values are brighter compared to features with high p-values. The Y coordinate for each feature corresponds to the mass-to-charge ratio of the compound, as determined by mass spectrometry. Each feature is also color coded, such as features that are shown with a black outline have database hits in METLIN, whereas features shown without a black outline do not have any database hits.

From the cloud plot (Fig. 2A), 82 discriminating ion features from positive data and 48 discriminating ions features from negative data were considered as of great importance for class separation. After filtering out the fragment ions, isotope annotations, and adduct ions, thirty-one metabolites were tentatively assigned using a Metlin library search (Table S4).

Among the assigned metabolites detected, five of the highest abundant metabolites were identified as bile acid and bile acid conjugates (Fig. 2B). This series of compounds shared the following characteristics; the unconjugated bile acids showed [M-H] ion as base peak in the negative mode.

The characteristic consistent with bile acid hyodeoxycholic acid (HDCA) was confirmed with a reference standard. For the conjugated bile acids (usually with glycine and taurine), the [M-H] and [M+H]+ are always observed as the base peaks. For example, the ion feature m/z 448.3065 at 21.18 min was identified as chenodeoxycholic acid glycine conjugate. The neutral loss of 62 amu (H2O + CO2) was considered as a characteristic fragmentation pathway for bile acid glycine conjugates. This above mentioned characteristic can easily identify a series of bile acids compounds. The five metabolite ions detected in plasma were significantly different between pigs fed the high fat diet (Fig. 2B, red bars) and regular diet (Fig. 2B, blue bars) for 11 weeks, and were identified as chenodeoxycholic acid glycine conjugate, tauroursodeoxycholic acid, hyodeoxycholic acid, deoxycholic acid glycine conjugate and glycocholic acid; chenodeoxycholic acid glycine and hyodeoxycholic acid.

Figures 1-4 , not shown.
Fig 1. The PCA score plot of plasma (A) (+)ESI data with all the ion features; (B) (+)ESI data with selected ion features; (C) (-)ESI data with all ion features; (D) (-)ESI data with selected ion features. Samples were taken from pigs fed diet A (BS, blue) and diet B (HF, red). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig 2. Cloud plot showing 82 discriminatory ion features (negative ion data) in plasma, and (B) box-plot of data set of the five most abundant bile acids identified in plasma (negative ion data) samples.

Fig. 3. PCA score plot of fecal samples from pigs fed diet A (BS, blue) and diet B (HF, red) (A) week 0, (B) week 2, (C) week 4 (D) week 6, (E) week 11 for distal samples (F) week 11 for proximal colon samples. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 4. PCA and PLS-DA score plot of urine samples from (+)ESI-data (A and C) and (-)ESI-data (B and D) taken at the end of the study (week 11) from pigs fed diet A (BS, blue) and diet B (HF, red). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Plasma, fecal and urine metabolites from pigs fed either a high fat or regular diet were investigated using a UHPLC–HRMS based metabolomic approach. Their metabolic profiles were compared by multivariate statistical analysis.
Diet is logically believed to have a close relationship with metabolic profiles. Feeding a high fat and high cholesterol diet to pigs for 11 weeks resulted in
an increase in bile acids and their derivatives in plasma, fecal and urine samples, though at this stage, there was no significant weight gain observed.

In a previous study, a significantly higher level of muricholic acid, but not cholic acid, was found in pigs fed a high fat diet. The gut microbiota of these pigs were altered by diet and considered to regulate bile acid metabolism by reducing the levels of tauro-beta-muricholic acid. In our study, the unconjugated bile acids, hyodeoxycholic acid and deoxycholic acid were found to be significantly higher in the fecal samples of pigs fed a high-fat diet.

Chenodeoxycholic acid glycine was 8.6 times higher in pigs fed a high fat and high cholesterol diet compared to those fed a regular diet. These results confirm that feeding a high fat and high cholesterol diet leads to a changing metabolomic pattern over time, represented by excretion of certain bile acids in the feces. We also found that several metabolites associated with lipid metabolism were increased in the feces of pigs fed the high-fat diet. Feeding the high fat diet to pigs for 11 weeks did not induce any overt expression of disease, except for significantly higher levels of circulating cholesterol and triglycerides in the blood. It is likely, however, that longer periods of feeding would increase expression of metabolic syndrome disorders and features of cardiovascular disease in pigs, as have been previously demonstrated. Products of lipid metabolism that changed early in the dietary treatment could be useful as biomarkers. This may be important because the composition of the fats in the diet, used in this study, was complex and from multiple sources including lard, soybean oil and coconut oil.

In summary, a number of metabolite differences were detected in the plasma, urine and feces of pigs fed a high fat and high cholesterol diet versus a regular diet that significantly increased over time. PCA showed a clear separation of metabolites in all biological samples tested from pigs fed the different diets. This methodology could be used to associate metabolic profiles with early markers of disease expression or the responsiveness of metabolic profiles to alterations in the diet. The ability to identify metabolites from bio-fluids, feces, and tissues that change with alterations in the diet has the potential to identify new biomarkers and to better understand mechanisms related to diet and health.

Amino acid, mineral, and polyphenolic profiles of black vinegar, and its lipid lowering and antioxidant effects in vivo

Chung-Hsi Chou, Cheng-Wei Liu, Deng-Jye Yang, Yi-Hsieng S Wuf, Yi-Chen Chen
Food Chemistry 168 (2015) 63–69
http://dx.doi.org/10.1016/j.foodchem.2014.07.035

Black vinegar (BV) contains abundant essential and hydrophobic amino acids, and polyphenolic contents, especially catechin and chlorogenic acid via chemical analyses. K and Mg are the major minerals in BV, and Ca, Fe, Mn, and Se are also measured. After a 9-week experiment, high-fat/cholesterol-diet (HFCD) fed hamsters had higher (p < 0.05) weight gains, relative visceral-fat sizes, serum/liver lipids, and serum cardiac indices than low-fat/cholesterol diet (LFCD) fed ones, but BV supplementation decreased (p < 0.05) them which may resulted from the higher (p < 0.05) fecal TAG and TC contents. Serum ALT value, and hepatic thiobarbituric acid reactive substances (TBARS), and hepatic TNF-α and IL-1β contents in HFCD-fed hamsters were reduced (p < 0.05) by supplementing BV due to increased (p < 0.05) hepatic glutathione (GSH) and trolox equivalent antioxidant capacity (TEAC) levels, and catalase (CAT) and glutathione peroxidase (GPx) activities. Taken together, the component profiles of BV contributed the lipid lowering and antioxidant effects on HFCD fed hamsters.

World Health Organization (WHO) reported that more than 1.4 billion adults were overweight (WHO, 2013). As we know, imbalanced fat or excess energy intake is one of the most important environmental factors resulted in not only increased serum/liver lipids but also oxidative stress, further leading cardiovascular disorders and inflammatory responses. Food scientists strive to improve serum lipid profile and increase serum antioxidant capacity via  medical foods or functional supplementation.

Vinegar is not only used as an acidic seasoning but also is shown to have some beneficial effects, such as digestive, appetite stimulation, antioxidant, exhaustion recovering effects, lipid lowering effects, and regulations of blood pressure. Polyphenols exist in several food categories, such as vegetable, fruits, tea, wine, juice, and vinegar that have effects against lipid peroxidation, hypertension, hyperlipidemia, inflammation, DNA damage, and. Black vinegar (BV) (Kurosu) is produced from unpolished rice with rice germ and bran through a stationary surface fermentation and contains higher amounts of amino acids and organic acids than other vinegars. Black vinegar is also characterised as a health food rather than only an acidic seasoning because it was reported to own a DPPH radical scavenging ability and decrease the adipocyte size in rat models. Moreover, the extract of BV shows the highest radical scavenging activity in a DPPH radical system than rice, grain, apple, and wine vinegars. The extract suppresses increased lipid peroxidation in mouse skin treated with 12-o-tetradecanoylphorbol-13-acetate.

This study focused on the nutritional compositions in BV, and the in-vivo lipid lowering and antioxidant effects. First, the amino acid, mineral, and polyphenolic profile of BV were identified. Hypolipidemic hamsters induced by a high-fat/cholesterol diet (HFCD) were orally administered with different doses of BV. Serum lipid profile and liver damage indices liver and fecal lipid contents, as well as hepatic antioxidant capacities [thiobarbituric acid reactive substances (TBARS), glutathione (GSH), trolox equivalent antioxidant capacity (TEAC), and activities of superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx)] and hepatic cytokine levels were assayed to demonstrated physiological functions of BV.

Higher serum AST, ALT, and free fatty acids, as well as hepatic cholesterol, triacylglycerol, MDA, hydroperoxide, and cytokine (IL-1β and TNF-α) levels were easily observed in a high-fat-consumption rodent. Several reports indicated some amino acids antioxidant activities in vitro and in vivo. Acidic amino acids, such as Asp and Glu and hydrophobic amino acids, such as Ile, Leu, and Val display high antioxidant properties. Recently, an in vivo study indicated that a pepsin hydrolyzation significantly enhanced Asp, Glu, Leu, and Val contents in chicken livers; meanwhile, chicken-liver hydrolysates showed an antioxidant capacity in brain and liver of D-galactose treated mice. In addition, it was also reported that Mg and Se play important roles in SOD and GPx activities, respectively. Uzun and Kalender (2013) used chlorpyrifos, an organophosphorus insecticide, to induce hepatotoxic and hematologic changes in rats, but they observed that catechin can attenuate the chlorpyrifos-induced hepatotoxicity by increasing GPx and glutathione-S-transferase activities and decreasing MDA contents. Meanwhile, chlorogenic acid elevated SOD, CAT, and GPx activities with concomitantly decreased lipid peroxidation of liver and kidney in streptozotocin-nicotinamide induced type-2 diabetic rats. Hence, it is reasonable to assume that increased antioxidant capacities and decreased damage in livers of HFCD fed hamsters supplemented with BV should be highly related to the components, i.e. amino acid profile, mineral profile, and polyphenol contents, as well as the lowered liver lipid accumulations.

In analyses of amino acids, minerals and polyphenols, BV contained abundant essential amino acids and hydrophobic amino acids. Mg, K, Ca, Fe, Mn, and Se were measured in BV where K and Mg were major. Gallic acid, catechin, chlorogenic acid, p-hydroxybezoic acid, p-cumeric acid, ferulic acid, and sinapic acid were also identified in BV where catechin and chlorogenic acid were the majorities. Meanwhile, the lipid-lowering and antioxidant effects of BV were also investigated via a hamster model. BV supplementation apparently decreased weight gain (g and %), relative size of visceral fat, serum/liver TC levels, serum cardiac index, and hepatic TBARS values and damage indices (serum ALT and hepatic TNF-α and IL-1β) but increased fecal lipid contents and hepatic antioxidant capacities (GSH level, TEAC level, CAT activity, and GPx activity) in HFCD fed hamsters. To sum up, those benefits could be attributed to a synergetic effect of compounds in BV.

Analysis of pecan nut (Carya illinoinensis) unsaponifiable fraction – Effect of ripening stage on phytosterols and phytostanols composition

Intidhar Bouali, Hajer Trabelsi, Wahid Herchi, Lucy Martine, et al.
Food Chemistry 164 (2014) 309–316
http://dx.doi.org/10.1016/j.foodchem.2014.05.029

Changes in 4-desmethylsterol, 4-monomethylsterol, 4,4-dimethylsterol and phytostanol composition were quantitatively and qualitatively investigated during the ripening of three varieties of Tunisian grown pecan nuts. These components have many health benefits, especially in lowering LDL-cholesterol and preventing heart disease. The phytosterol composition of whole pecan kernel was quantified by Gas Chromatography–Flame Ionization Detection (GC–FID) and identified by Gas Chromatography–Mass Spectrometry (GC–MS). Fifteen phytosterols and one phytostanol were quantified. The greatest amount of phytosterols (2852.5 mg/100 g of oil) was detected in Mahan variety at 20 weeks after the flowering date (WAFD). Moore had the highest level of phytostanols (7.3 mg/100 g of oil) at 20 WAFD. Phytosterol and phytostanol contents showed a steep decrease during pecan nut development. Results from the quantitative characterization of pecan nut oils revealed that β-sitosterol, D5-avenasterol, and campesterol were the most abundant phytosterol compounds at all ripening stages.

Association between HMW adiponectin, HMW-total adiponectin ratio and early-onset coronary artery disease in Chinese population

Ying Wang, Aihua Zheng, Yunsheng Yan, Fei Song, et al.
Atherosclerosis 235 (2014) 392-397
http://dx.doi.org/10.1016/j.atherosclerosis.2014.05.910

Objective: Adiponectin is an adipose-secreting protein that shows atheroprotective property and has inverse relation with coronary artery disease (CAD). High-molecular weight (HMW) adiponectin is reported as the active form of adiponectin. In the present study, we aimed to investigate the association between total adiponectin, HMW adiponectin, HMW-total adiponectin ratio and the severity of coronary atherosclerosis, and to compare their evaluative power for the risk of CAD. Methods: Serum levels of total and HMW adiponectin were measured in 382 early-onset CAD (EOCAD) patients and 305 matched controls undergoing coronary angiography by enzyme-linked immunosorbent assay (ELISA). Gensini score was used to evaluate the severity of coronary atherosclerosis. Results: CAD onset age was positively correlated with HMW adiponectin (r = 0.383, P < 0.001) and HMW-total adiponectin ratio (r = 0.429, P < 0.001) in EOCAD patients. Total and HMW adiponectin and HMW-total adiponectin ratio were all inversely correlated with Gensini score (r=0.417, r=0.637, r=0.578, respectively; all P < 0.001). Multivariate binary logistic regression analysis demonstrated that HMW adiponectin and HMW-total adiponectin ratio were both inversely correlated with the risk of CAD (P < 0.05). ROC analysis indicated that areas under the ROC curves of HMW adiponectin and HMW-total adiponectin ratio were larger than that of total adiponectin (P < 0.05). Conclusions: Adiponectin is cardioprotective against coronary atherosclerosis onset in EOCAD patients. HMW adiponectin and HMW-total adiponectin ratio show stronger negative associations with the severity of coronary atherosclerosis than total adiponectin does. HMW adiponectin and HMW-total adiponectin ratio are effective biomarkers for the risk of CAD in Chinese population.

Gender and age were well matched between patients and controls. EOCAD patients were tended to have a history of diabetes or hypertension, more current smoking, and more use of lipid lowering drugs. Levels of total cholesterol, LDL-c, FPG, HbA1c and triglycerides were significantly higher in the patients than in controls, while HDL-cholesterol, total adiponectin, HMW adiponectin, and HMW-total adiponectin ratio were significantly lower in the patients. EOCAD patients developed different degrees of coronary atherosclerosis, and had significantly higher levels of high-sensitivity CRP and larger circumferences of waist and hip than controls.

Spearman correlation coefficients between selected cardiovascular risk factors, Gensini score and adiponectin were significant. Total and HMW adiponectin and HMW-total adiponectin ratio were all inversely correlated with Gensini score, BMI and pack years of cigarette smoking. Total and HMW adiponectin were negatively associated with triglycerides and circumference of waist and hip. LDL-cholesterol and high-sensitivity CRP were inversely correlated with HMW adiponectin and HMW-total adiponectin ratio, while HDL-cholesterol and age were positively correlated with them. FPG was only inversely associated with HMW-total adiponectin ratio.

All participants were divided into four groups according to their Gensini score, group A (control, n = 305), group B (<20, n = 154), group C (20-40, n = 121) and group D (>40, n = 105). With the increasing of Gensini score, a stepwise downward trend was observed in levels of total and HMW adiponectin and HMW-total adiponectin ratio (P < 0.001). Specifically, total adiponectin of four groups were 1.58 (0.61-4.36) mg/ml, 1.21 (0.70-2.83) mg/ml, 1.00 (0.73-1.88) mg/ml, and 0.76 (0.37-1.19) mg/ml, respectively. Except group A with B and group B with C, the differences of pairwise comparisons among all the other groups were statistically significant (all P < 0.05). HMW adiponectin of four groups were 0.91 (0.39-3.26) mg/ml, 0.55 (0.32-1.49) mg/ml, 0.46 (0.21-0.876) mg/ml, and 0.23 (0.14-0.39) mg/ml, respectively. The differences of pairwise comparisons among all the other groups were statistically significant (all P < 0.05) except group B with C. HMW-total adiponectin ratio of four groups were 0.58 (0.31-0.81), 0.47 (0.26-0.69), 0.41 (0.24-0.57), and 0.36 (0.21-0.42), respectively. The differences of pairwise comparisons among all the other groups were statistically significant (all P < 0.05) except group B with C. In the model of multivariate binary logistic regression analysis, after adjustment for conventional cardiovascular risk factors, HMW adiponectin (OR = 0.234, P < 0.011) and HMW-total adiponectin ratio (OR = 0.138, P < 0.005) remained inversely correlated with the risk of CAD, while no significant association was observed between total adiponectin and CAD

Areas under the ROC curves were compared pairwise to identify the diagnostic power for CAD among total adiponectin, HMW adiponectin, and HMW-total adiponectin ratio. HMW adiponectin and HMW-total adiponectin ratio showed greater capability for identifying CAD than total adiponectin did (0.797 vs. 0.674, 0.806 vs. 0.674; respectively, all P < 0.05); however, no significant difference was observed between HMW and HMW-total ratio (P > 0.05).

Associations between total adiponectin, HMW adiponectin, HMW-total adiponectin ratio and the severity of coronary atherosclerosis

Associations between total adiponectin, HMW adiponectin, HMW-total adiponectin ratio and the severity of coronary atherosclerosis in EOCAD patients (evaluated by Gensini score). *P < 0.05; **P < 0.001; ***P < 0.005 by Mann-Whitney U test.

Compares diagnostic power

Compares diagnostic power

Fig. Compares diagnostic power among total adiponectin, HMW adiponectin and HMW-total adiponectin ratio for CAD by ROC curves. Diagnostic power for CAD was based on discriminating patients with or without coronary atherosclerosis. The area under the curve for HMW-total adiponectin ratio (dotted black line) was larger than that for total adiponectin (fine black line) (0.806 [95%CI 0.708-0.903] vs. 0.674 [95%CI 0.552-0.797], P < 0.05) and HMW adiponectin (bold black line) (0.806 [95%CI 0.708-0.903] vs. 0.797 [95%CI 0.706-0.888], no statistically difference). Sensitivity, specificity and optimal cut off value for them were total adiponectin (57.38%, 75.86%, 1.11 mg/ml), HMW (55.74%, 93.1%, 0.49 mg/ml) and H/T (78.69%, 75.86%, 0.52), respectively.

There are two strengths in our study. One is the precise Gensini scoring system to carefully evaluate stenosis of coronary artery or branches > 0% diameter as coronary lesion, another is the specific study subjects of EOCAD in a Chinese Han population that is particularly genetically determined and not influenced by racial/ethnic disparities. The limitations of our study lie in the interference of medications such as the effect of lipid lowering drugs on the levels of adiponectin, and cardiovascular risk factors. Smoking is a conventional cardiovascular risk factor, whose interaction with HMW adiponectin level is rarely investigated, but it has been revealed to be associated with HMW adiponectin level in men according to the study from Kawamoto R et al. We did not adjust the result for the pack/year variable in the multivariate logistic regression analysis for the limitation of small sample size of male subjects in our study. The relatively small study sample also restrained our conclusion generalizable to all populations. Future researches in larger study samples and different populations are in need to validate our findings, and to explore the association of smoking with adiponectin in male subgroup analysis, and to investigate the potential mechanisms by which adiponectin affects the progression of coronary atherosclerosis.

In summary, the present study has demonstrated that adiponectin is protective against coronary atherosclerosis onset in EOCAD patients. HMW adiponectin and HMW-total adiponectin ratio show stronger negative associations with the severity of coronary atherosclerosis than total adiponectin does. HMW adiponectin and HMW-total adiponectin ratio are more effective biomarkers for the risk of CAD than total adiponectin.

Berberis aristata combined with Silybum marianum on lipid profile in patients not tolerating statins at high doses

Giuseppe Derosa, Davide Romano, Angela D’Angelo, Pamela Maffioli
Atherosclerosis 239 (2015) 87-92
http://dx.doi.org/10.1016/j.atherosclerosis.2014.12.043

Aim: To evaluate the effects of Berberis aristata combined with Silybum marianum in dyslipidemic patients intolerant to statins at high doses.
Methods: 137 euglycemic, dyslipidemic subjects, with previous adverse events to statins at high doses, were enrolled. Statins were stopped for 1 month (run-in), then they were re-introduced at the half of the previously taken dose. At randomization, patients tolerating the half dose of statin, were assigned to
add placebo or B. aristata/S. marianum 588/105 mg, 1 tablet during the lunch and 1 tablet during the dinner, for six months. We evaluated lipid profile and safety parameters variation at randomization, and after 3, and 6 months.
Results: B. aristata/S. marianum reduced fasting plasma glucose (-9 mg/dl), insulin (-0.7 mU/ml), and HOMA-index (-0.35) levels compared to baseline and also to placebo. Lipid profile did not significantly change after 6 months since the reduction of statin dosage and the introduction of B. aristata/S. marianum, while it worsened in the placebo group both compared to placebo and with active treatment (+23.4 mg/dl for total cholesterol, +19.6 mg/dl for LDL-cholesterol, +23.1 mg/dl for triglycerides with placebo compared to B. aristata/S. marianum). We did not record any variations of safety parameters
in either group. Conclusions: B. aristata/S. marianum can be considered as addition to statins in patients not tolerating high dose of these drugs.

Statins, also known as 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase inhibitors, are effective medications for reducing the risk of death and future cardiovascular disease. In the latest years, however, statin intolerance (including adverse effects related to quality of life, leading to decisions to decrease or stop the use of an otherwise-beneficial drug) has come to the forefront of clinical concern, whereas the safety of statins has come to be regarded as largely favorable. Statin intolerance is defined as any adverse symptoms, signs, or laboratory abnormalities attributed by the patient or physician to the statin and in most cases perceived by the patient to interfere unacceptably with activities of daily living, leading to a decision to stop or reduce statin therapy. The physician might also decide to stop or reduce statin therapy on the basis of clinical/laboratory assessment [abnormal liver function tests, creatine phosphokinase values (CPK)] suggesting undue risk. Adverse events are more common at higher doses of statins, and often contribute to patients low adherence to treatment. For this reason, researchers are testing alternative strategies for lipid treatment when statin intolerance is recognized. One strategy to reduce the risk of statin-induced adverse events includes using a low-dose of statin combined with nonstatin drugs in order to achieve the goals of therapy. Nonstatin drugs include nutraceuticals; in the latest years relatively large number of dietary supplements and nutraceuticals have been studied for their supposed or demonstrated ability to reduce cholesterolemia in humans, in particular Berberis Aristata, has been studied in randomized clinical trials and proved to be effective in improving lipid profile. In particular, B. aristata acts up-regulating LDL-receptor (LDL-R) expression independent of sterol regulatory element binding proteins, but dependent on extracellular signal-regulated kinases (ERK) and c-Jun N-terminal kinase (JNK) activation leading to total cholesterol (TC) and LDL-C reduction of about 30 and 25%, respectively. Hwever, B. aristata is a problem in terms of oral bioavailability, affected by a P-glycoprotein (P-gp) mediated gut extrusion process. P-gp seems to reduce by about 90% the amount of B. aristata able to cross the enterocytes, but the use of a potential P-gp inhibitor could ameliorate its oral poor bioavailability improving its effectiveness. Among the potential Pgp inhibitors, silymarin from S. marianum, an herbal drug used as liver protectant, could be considered a good candidate due to its high safety profile.

Analyzing the results of our study, it can appear, at a first glance, that B. aristata/S. marianum has a neutral effect of lipid profile that did not change during the study after the addition of the nutraceutical combination. This lack of effect, however, is only apparent, because, when we analyzed what happens in placebo group, we observed a worsening of lipid profile after statin dose reduction. In other words, the addition of B. aristata/S. marianum neutralized the worsening of lipid profile observed with placebo after statins dose reduction. These results are in line with what was reported by Kong et al., who evaluated the effects of a combination of berberine and simvastatin in sixty-three outpatients diagnosed with hypercholesterolemia. As compared with monotherapies, the combination showed an improved lipid lowering effect with 31.8% reduction of serum LDL-C, and similar efficacies were observed in the reduction of TC as well as Tg in patients. Considering the results of this study, B. aristata/S. marianum can be considered as addition to statins in patients not tolerating high dose of these drugs.

CETP inhibitors downregulate hepatic LDL receptor and PCSK9 expression in vitro and in vivo through a SREBP2 dependent mechanism

Bin Dong, Amar Bahadur Singh, Chin Fung, Kelvin Kan, Jingwen Liu
Atherosclerosis 235 (2014) 449-462
http://dx.doi.org/10.1016/j.atherosclerosis.2014.05.931

Background: CETP inhibitors block the transfer of cholesteryl ester from HDL-C to VLDL-C and LDL-C, thereby raising HDL-C and lowering LDL-C. In this study, we explored the effect of CETP inhibitors on hepatic LDL receptor (LDLR) and PCSK9 expression and further elucidated the underlying regulatory mechanism. Results: We first examined the effect of anacetrapib (ANA) and dalcetrapib (DAL) on LDLR and PCSK9 expression in hepatic cells in vitro. ANA exhibited a dose-dependent inhibition on both LDLR and PCSK9 expression in CETP-positive HepG2 cells and human primary hepatocytes as well as CETP-negative mouse primary hepatocytes (MPH). Moreover, the induction of LDLR protein expression by rosuvastatin in MPH was blunted by cotreatment with ANA. In both HepG2 and MPH ANA treatment reduced the amount of mature form of SREBP2 (SREBP2-M). In vivo, oral administration of ANA to dyslipidemic C57BL/6J mice at a daily dose of 50 mg/kg for 1 week elevated serum total cholesterol by approximately 24.5% (p < 0.05%) and VLDL-C by 70% (p < 0.05%) with concomitant reductions of serum PCSK9 and liver LDLR/SREBP2-M protein. Finally, we examined the in vitro effect of two other strong CETP inhibitors evacetrapib and torcetrapib on LDLR/PCSK9 expression and observed a similar inhibitory effect as ANA in a concentration range of 1-10 µM. Conclusion: Our study revealed an unexpected off-target effect of CETP inhibitors that reduce the mature form of SREBP2, leading to attenuated transcription of hepatic LDLR and PCSK9. This negative regulation of SREBP pathway by ANA manifested in mice where CETP activity was absent and affected serum cholesterol metabolism.

Effect of Eclipta prostrata on lipid metabolism in hyperlipidemic animals

Yun Zhao, Lu Peng, Wei Lu, Yiqing Wang, Xuefeng Huang, et al.
Experimental Gerontology 62 (2015) 37–44
http://dx.doi.org/10.1016/j.exger.2014.12.017

Eclipta prostrata (Linn.) Linn. is a traditional Chinese medicine and has previously been reported to have hypolipidemic effects. However, its mechanism of action is not well understood. This study was conducted to identify the active fraction of Eclipta, its toxicity, its effect on hyperlipidemia, and its mechanism of action. The ethanol extract (EP) of Eclipta and fractions EPF1–EPF4, obtained by eluting with different concentrations of ethanol from a HPD-450 macroporous resin column chromatography of the EP, were screened in hyperlipidemic mice for lipid lowering activity, and EPF3 was the most active fraction. The LD50 of EPF3 was undetectable because no mice died with administration of EPF3 at 10.4 g/kg. Then, 48 male hamsters were used and randomly assigned to normal chow diet, high-fat diet, high-fat diet with Xuezhikang (positive control) or EPF3 (75, 150 and 250 mg/kg) groups. We evaluated the effects of EPF3 on body weight gain, liver weight gain, serum lipid concentration, antioxidant enzyme activity, and the expression of genes involved in lipid metabolism in hyperlipidemic hamsters. The results showed that EPF3 significantly decreased body-weight gain and liver-weight gain and reduced the serum lipid levels in hyperlipidemic hamsters. EPF3 also increased the activities of antioxidant enzymes; upregulated the mRNA expression of peroxisome proliferator-activated receptor α (PPARα), low density lipoprotein receptor (LDLR), lecithin-cholesterol transferase (LCAT) and scavenger receptor class B type Ι receptor (SR-BI); and down-regulated the mRNA expression of 3-hydroxy-3-methyl-glutaryl-CoA reductase (HMGR) in the liver. These results indicate that EPF3 ameliorates hyperlipidemia, in part, by reducing oxidative stress and modulating the transcription of genes involved in lipid metabolism.

Although Eclipta has long been used as a food additive, no studies or reports have clearly shown any liver or kidney toxicity from its use. Therefore, E. prostrata is safe and beneficial for preventing hyperlipidemia in experimental animals and can be used as an alternative medicine for the regulation of dyslipidemia.

Effect of high fiber products on blood lipids and lipoproteins in hamsters

HE Martinez-Floresa, Y Kil Chang, F Martinez-Bustosc, V Sgarbieri
Nutrition Research 24 (2004) 85–93
http://dx.doi.org:/10.1016/S0271-5317(03)00206-9

Serum and liver lipidemic responses in hamsters fed diets containing 2% cholesterol and different dietary fiber sources were studied. The following diets were made from: a) the control diet made from extruded cassava starch (CSH) contained 9.3% cellulose, b) cassava starch extruded with 9.7% resistant starch (CS-RS), c) cassava starch extruded with 9.9% oat fiber (CS-OF), d) the reference diet contained 9.5% cellulose, and no cholesterol was added. Total cholesterol, LDLVLDL-cholesterol and triglycerides were significantly lower (P < 0.05) in serum of hamsters fed on the CS-RS (17.87%, 62.92% and 9.17%, respectively) and CS-OF (15.12%, 67.41% and 18.35%, respectively) diets, as compared to hamster fed with the CSH diet. Similar results were found in the livers of hamsters fed on the CS-RS and CS-OF diets, as compared to hamsters fed with the CSH diet. The diets containing these fibers could be used as active ingredients in human diets to improve the human health.

A new piece in the puzzling effect of n-3 fatty acids on atherosclerosis?

Wilfried Le Goff
Atherosclerosis 235 (2014) 358-362
http://dx.doi.org/10.1016/j.atherosclerosis.2014.03.038

Omega-3 fatty acids (ω-3) FA are reported to be protective against cardiovascular disease (CVD), notably through their beneficial action on atherosclerosis development. In this context dietary intake of long chain marine eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) is recommended and randomised trials largely support that EPA and DHA intake is associated with a reduction of CVD. However, mechanisms governing the atheroprotective action of ω-3 FA are still unclear and numerous studies using mouse models conducted so far do not allow to reach a precise view of the cellular and molecular effects of ω-3 FA on atherosclerosis. In the current issue of Atherosclerosis, Chang et al. provide important new information on the anti-atherogenic properties of ω-3 FA by analyzing the incremental replacement of saturated FA by pure fish oil as a source of EPA and DHA in Ldlr -/- mice fed a high fat/high cholesterol diet.

Cardiovascular disease (CVD) is the leading causes of death in the world and is frequently associated with atherosclerosis, a pathology characterized by the accumulation of lipids, mainly cholesterol in the arterial wall. Among major risk factors for CVD, circulating levels of lipids and more especially those originating from diets are closely linked to development of atherosclerosis. In this context, not only cholesterol, but also dietary fatty acids (FA) may appear particularly deleterious in regards to atherosclerosis and associated CVD. However, although saturated fats are proatherogenic, omega-3 fatty acids (ω-3 FA), and more especially long-chain marine eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), exert atheroprotective properties through several potential underlying mechanisms. Therefore, the intake of EPA and DHA is recommended around the world and randomised trials with ω-3 FA confirmed that EPA and DHA intake reduced risk for CVD events. However benefits of ω-3 FA intake were challenged by recent clinical trials that failed to replicate protective effects of EPA + DHA on CVD, raising the controversy on the healthy side of marine ω-3 FA.

Animal models are commonly employed in order to decipher mechanisms by which ω-3 FA exert their beneficial actions regarding lipid metabolism and atherosclerosis. Since the last past 20 years, mouse models, and more especially genetically modified mouse models, became the reference model to evaluate the effects of dietary fatty acids, especially ω-3 FA, on atherosclerosis development [7-20]. However, the use of different mouse models of atherosclerosis (Apoe-/-, Ldlr-/-, double Apoe-/- x Ldlr-/- , Ldlr-/- x hApoB mice), as well as diet composition (chow, high cholesterol, high fat, high cholesterol/high fat), source of ω-3 FA supplementation (fish oil, perilla seed oil, flaxseed, pure ALA, EPA or DHA), duration of the diet (from 4 to 32 weeks), size of atherosclerotic lesions in control animals (from 51 to 700.103 mm2) in

those studies led to heterogeneous results and therefore to a partial understanding of the effects of ω-3 FA on atherosclerosis.

Contrary to what observed in Apoe-/- mice, dietary supplementation of Ldlr-/- mice with ω-3 FA led to a reproducible reduction of aortic atherosclerosis, although to various degrees, confirming that Ldlr-/- mice constitute the most appropriate model for studying the atheroprotective effects of ω-3 FA. When evaluated, the decrease of atherosclerosis upon ω-3 FA-rich diet was accompanied by a reduction in the macrophage content as well as inflammation in aortic lesions highlighting the major impact of ω-3 FA on monocyte recruitment and subsequent macrophage accumulation in the arterial wall. However, although supplementation with ω-3 FA allows an efficacious lowering of plasma lipid levels in humans, studies in mouse models suggest that the antiatherogenic action of ω-3 FA is independent of any effects on plasma cholesterol or triglyceride levels. However, that must be asserted with caution as lipid metabolism is quite different in mouse in comparison to humans, highlighting the need to study in the future the effects of ω-3 FA on atherosclerosis in a mouse model exhibiting a more “humanized” lipid metabolism as achieved in hApoB/CETP mice.

In a previous issue of Atherosclerosis, Chang et al. reevaluate the impact of fish oil ω-3 FA on atherosclerosis development by operating an incremental replacement of saturated fats (SAT) by ω-3 FA (pure fish oil, EPA- and DHA-rich) in Ldlr-/- mice fed a high-fat (21%, w/w)/high-cholesterol (0.2%, w/w) diet for a 12-week period. This experimental approach is quite pertinent as dietary fat intake in developed countries, as in United States, derived mostly from saturated FA and is poor in ω-3 FA. Then, using this strategy the authors were able to evaluate the potential beneficial effects of a supplementation with fish oil ω-3 FA in a dietary context for which ω-3 FA intake is relevant.

Here, Chang et al. demonstrated that the progressive increase of dietary intake of fish oil ω-3 FA (EPA and DHA) abrogated the deleterious effects of a SAT diet, thereby suggesting that a dietary ω-3 FA intake on a SAT background is potentially efficient to decrease CVD in humans. Indeed, replacement of SAT by fish oil ω-3 FA markedly reduced plasma cholesterol and triglycerides levels and abolished diet-induced atherosclerosis mediated by SAT in Ldlr-/-mice. To note that in the present study, Ldlr-/- mice only developed small atherosclerosic lesions (~100.103 mm2) after 12 weeks of diet with SAT.

As previously reported, decreased atherosclerotic lesions were accompanied by a reduced content of aortic macrophages and inflammation. Based on their previous works, the authors proposed that the reduction of atherosclerosis upon ω-3 FA resulted from an impairment of cholesterol uptake by arterial macrophages consecutive to the decrease of Lipoprotein Lipase (LPL) expression in those cells. Indeed, beyond its lipolysis action on triglycerides, LPL was reported to promote lipid accumulation, in particular in macrophages, by binding to lipoproteins and cell surface proteoglycans and then acting as a bridging molecule that facilitates cellular lipid uptake. Coherent with this mechanism, macrophage LPL expression was reported to promote foam cell formation and atherosclerosis. In the present study, replacement of SAT by ω-3 FA both decreased expression and altered distribution of arterial LPL. Such a mechanism for ω-3 FA (EPA and DHA) was proposed by this group in earlier studies to favor reduction of arterial LDL-cholesterol. It is noteworthy that lipid rafts alter distribution of LPL at the cell surface and subsequently the LPL dependent accumulation of lipids in macrophages and foam cell formation. As incorporation of ω-3 FA, such as DHA, into cell membrane phospholipids disrupts lipid rafts organization, it cannot be exclude that reduction of lipid accumulation in arterial macrophages upon addition of ω-3 FA results in part from an impairment of the localization and of the anchoring function of LPL at the cell surface of macrophages. Indeed Chang et al. observed that progressive replacement of SAT by ω-3 FA affected aortic FA composition leading to a pronounced increase of arterial EPA and DHA, then suggesting that content of ω-3 FA in macrophage membrane may be equally altered. However, the implication of LPL in the atheroprotective effects of ω-3 FA need to be validated using an appropriate mouse model for which LPL expression may be controlled.

Among the various mechanisms by which ω-3 FA exert anti-inflammatory properties, EPA and DHA repressed inflammation by shutting down NF-kB activation in macrophages. Since expression of TLR-4 and NF-kB target genes, IL-6 and TNFα, in aorta from mice fed diets containing ω-3 FA were decreased when compared to SAT, those results strongly support the contention that ω-3 FA repress inflammation by inhibiting the TLR4/NF-kB signaling cascade likely through the macrophage ω-3 FA receptor GPR120.

Although further studies are needed to explore the complete spectrum of actions of ω-3 FA on atherosclerosis development and CVD, this study provides important information that supports that ω-3 FA intake is a pertinent strategy to reduce risk of CVD.

Effects of dietary hull-less barley β-glucan on the cholesterol metabolism of hypercholesterolemic hamsters

Li-Tao Tong, Kui Zhong, Liya Liu, Xianrong Zhou, Ju Qiu, Sumei Zhou
Food Chemistry 169 (2015) 344–349
http://dx.doi.org/10.1016/j.foodchem.2014.07.157

The aim of the present study is to investigate the hypocholesterolemic effects of dietary hull-less barley β-glucan (HBG) on cholesterol metabolism in hamsters which were fed a hypercholesterolemic diet. The hamsters were divided into 3 groups and fed experimental diets, containing 5‰ HBG or 5‰ oat β-glucan (OG), for 30 days. The HBG, as well as OG, lowered the concentration of plasma LDL-cholesterol significantly. The excretion of total lipids and cholesterol in feces were increased in HBG and OG groups compared with the control group. The activity of 3-hydroxy-3-methyl glutaryl-coenzyme A (HMG-CoA) reductase in liver was reduced significantly in the HBG group compared with the control and OG groups. The activity of cholesterol 7-α hydroxylase (CYP7A1) in the liver, in the HBG and OG groups, was significantly increased compared with the control group. The concentrations of acetate, propionate and total short chain fatty acids (SCFAs) were not significantly different between the HBG and control groups. These results indicate that dietary HBG reduces the concentration of plasma LDL cholesterol by promoting the excretion of fecal lipids, and regulating the activities of HMG-CoA reductase and CYP7A1 in hypercholesterolemic hamsters.

Effects of dietary wheat bran arabinoxylans on cholesterolmetabolism of hypercholesterolemic hamsters

Li-Tao Tong, Kui Zhong, Liya Liu, Ju Qiu, Lina Guo, et al.
Carbohydrate Polymers 112 (2014) 1–5
http://dx.doi.org/10.1016/j.carbpol.2014.05.061

The aim of the present study is to investigate the effects of dietary wheat bran arabinoxylans (AXs) on cholesterol metabolism in hypercholesterolemic hamsters. The hamsters were divided into 3 groups and fed the experimental diets containing AXs or oat β-glucan at a dose of 5 g/kg for 30 days. As the results,the AXs lowered plasma total cholesterol and LDL-cholesterol concentrations, and increased excretions of total lipids, cholesterol and bile acids, as well as oat β-glucan. The AXs reduced the activity of 3-hydroxy-3-methyl glutaryl-coenzyme A (HMG-CoA) reductase, and increased the activity of cholesterol 7-α hydroxylase (CYP7A1) in liver. Moreover, the AXs increased propionate and the total short-chain fatty acids (SCFAs) concentrations. These results indicated that dietary AXs reduced the plasma total cholesterol and LDL-cholesterol concentrations by promoting the excretion of fecal lipids, regulating the activities of HMG-CoA reductase and CYP7A1, and increasing colonic SCFAs in hamsters.

High-fructose feeding promotes accelerated degradation of hepatic LDL receptor and hypercholesterolemia in hamsters via elevated circulating PCSK9 levels

Bin Dong, Amar Bahadur Singh, Salman Azhar, Nabil G. Seidah, Jingwen Liu
Atherosclerosis 239 (2015) 364-374
http://dx.doi.org/10.1016/j.atherosclerosis.2015.01.013

Background: High fructose diet (HFD) induces dyslipidemia and insulin resistance in experimental animals and humans with incomplete mechanistic understanding. By utilizing mice and hamsters as in vivo models, we investigated whether high fructose consumption affects serum PCSK9 and liver LDL receptor (LDLR) protein levels. Results: Feeding mice with an HFD increased serum cholesterol and reduced serum PCSK9 levels as compared with the mice fed a normal chow diet (NCD). In contrast to the inverse relationship in mice, serum PCSK9 and cholesterol levels were co-elevated in HFD-fed hamsters. Liver tissue analysis revealed that PCSK9 mRNA and protein levels were both reduced in mice and hamsters by HFD feeding, however, liver LDLR protein levels were markedly reduced by HFD in hamsters but not in mice. We further showed that circulating PCSK9 clearance rates were significantly lower in hamsters fed an HFD as compared with the hamsters fed NCD, providing additional evidence for the reduced hepatic LDLR function by HFD consumption. The majority of PCSK9 in hamster serum was detected as a 53 kDa N-terminus cleaved protein. By conducting in vitro studies, we demonstrate that this 53 kDa truncated hamster PCSK9 is functionally active in promoting hepatic LDLR degradation. Conclusion: Our studies for the first time demonstrate that high fructose consumption increases serum PCSK9 concentrations and reduces liver LDLR protein levels in hyper-lipidemic hamsters. The positive correlation between circulating cholesterol and PCSK9 and the reduction of liver LDLR protein in HFD-fed hamsters suggest that hamster is a better animal model than mouse to study the modulation of PCSK9/LDLR pathway by atherogenic diets.

High-oleic canola oil consumption enriches LDL particle cholesteryl oleate content and reduces LDL proteoglycan binding in humans

Peter J.H. Jones, Dylan S. MacKay, Vijitha K. Senanayake, Shuaihua Pu, et al.
Atherosclerosis 238 (2015) 231-238
http://dx.doi.org/10.1016/j.atherosclerosis.2014.12.010

Oleic acid consumption is considered cardio-protective according to studies conducted examining effects of the Mediterranean diet. However, animal models have shown that oleic acid consumption increases LDL particle cholesteryl oleate content which is associated with increased LDL-proteoglycan binding and atherosclerosis. The objective was to examine effects of varying oleic, linoleic and docosahexaenoic acid consumption on human LDL-proteoglycan binding in a non-random subset of the Canola Oil Multi-center Intervention Trial (COMIT) participants. COMIT employed a randomized, double-blind, five-period, crossover trial design. Three of the treatment oil diets: 1) a blend of corn/safflower oil (25:75); 2) high oleic canola oil; and 3) DHA-enriched high oleic canola oil were selected for analysis of LDL-proteoglycan binding in 50 participants exhibiting good compliance. LDL particles were isolated from frozen plasma by gel filtration chromatography and LDL cholesteryl esters quantified by mass-spectrometry. LDL-proteoglycan binding was assessed using surface plasmon resonance. LDL particle cholesterol ester fatty acid composition was sensitive to the treatment fatty acid compositions, with the main fatty acids in the treatments increasing in the LDL cholesterol esters. The corn/safflower oil and high-oleic canola oil diets lowered LDL-proteoglycan binding relative to their baseline values (p < 0.0005 and p < 0.0012, respectively). At endpoint, high-oleic canola oil feeding resulted in lower LDL-proteoglycan binding than corn/safflower oil (p < 0.0243) and DHA-enriched high oleic canola oil (p < 0.0249), although high-oleic canola oil had the lowest binding at baseline (p < 0.0344). Our findings suggest that high-oleic canola oil consumption in humans increases cholesteryl oleate percentage in LDL, but in a manner not associated with a rise in LDL-proteoglycan binding.

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