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Archive for the ‘Clinical Diagnostics’ Category

PLD1 tests for Lung Cancer

Larry H. Bernstein, MD, FCAP, Curator

PLBI

 

AACR: Three PD-L1 Biomarker Tests Give Similar Results in Lung Cancer

http://www.oncotherapynetwork.com/lung-cancer-targets/aacr-three-pd-l1-biomarker-tests-give-similar-results-lung-cancer?GUID=08B7ACA4-07B7-4253-8ACC-0C9AAFF0371A&rememberme=1&ts=22042016#sthash.1sWQZ14d.dpuf

 

Three commercial tests that measure levels of PD-L1 on tumors showed similar results on non-small cell lung cancer (NSCLC) tumor samples, according to a study presented at the American Association of Cancer Research (AACR) Annual Meeting, being held April 16-20, 2016, in New Orleans.
Two anti-PD-1 immunotherapy antibodies are now approved for certain patients with metastatic NSCLC (see FDA Approves Keytruda for Metastatic Non-Small Cell Lung Cancer and FDA Approves Nivolumab (Opdivo) for NSCLC).

Several pharmaceutical companies are developing diagnostic tests to measure the levels of PD-L1 protein expression on patients’ tumors. Two clinical trials, with both nivolumab (Opdivo) and pembrolizumab (Keytruda), support the notion that those patients whose tumors have higher levels of PD-L1 on their surface are more likely to respond to anti-PD-1 treatment compared to those patients whose tumors have low or no PD-L1 expression.

“Before our study, we did not know whether the different assays identified the same patients,” said study author Marianne Ratcliffe, PhD, MA, a diagnostic associate director at AstraZeneca, which is developing durvalumab/MEDI4736, an anti-PD-L1 immunotherapy antibody.

The current study compared the Ventana SP263 assay (developed by Ventana) in collaboration with AstraZeneca for use in conjunction with patients treated with durvalumab, the Dako 22C3 assay (by Dako), approved for the US Food and Drug Administration (FDA) as a companion diagnostic to identify patients who are most likely to benefit from pembrolizumab, and the Dako 28-8 assay, approved by the FDA as a complementary diagnostic for nivolumab. All three tests measure the percentage of tumor cells within a sample that stain positive for the surface protein PD-L1. Each test sets a unique cut-off point of test positivity which corresponds to a greater likelihood of response to the immunotherapy among lung cancer patients.

The researchers evaluated the three tests on 500 patient biopsy samples, including both squamous and nonsquamous histologies. The comparison showed that a 25% cutoff using the Ventana SP263 test was similar to the results using the Dako 28-8 test at a 10% cutoff mark. There were similar results between the SP263 and the 22C3 tests at a 50% cut off mark.

The three tests achieved overall percentage agreement of more than 90%, according to Radcliffe. She also noted that this study points to the ability to extrapolate results from one test to another, which in the future, could allow physicians to use the tests interchangeably.

 

Comparison of Three Different PD-L1 Diagnostic Tests Shows a High Degree of Concordance

4/18/2016

NEW ORLEANS — Three commercially available diagnostic tests were similarly effective in measuring  PD-L1 protein expression on non-small cell lung cancer (NSCLC) tumor samples, indicating that health care providers may someday be able to use these tests interchangeably when determining which patients will respond best to anti-PD-L1/PD-1 immunotherapeutic drugs, according to research presented here at the AACR Annual Meeting 2016, April 16-20.Marianne Ratcliffe

“PD-L1-directed antibodies are emerging as effective therapeutics in monotherapy and in combination in multiple oncology settings,” said the study’s lead author, Marianne Ratcliffe, MA, PhD, diagnostic associate director at AstraZeneca. She explained that several different diagnostic tests, or assays, are effective in determining which patients’ tumors express high levels of PD-L1 and might, therefore, respond best to targeted treatment.

“Before our study, we did not know whether the different assays identified the same patients,” Ratcliffe said, adding that the tests were developed on different platforms and use different antibody clones and testing protocols.

“Clearly, for the oncology community, this presents a number of issues, including a lack of confidence in being able to identify appropriate patients for treatment with these targeted therapies,” Ratcliffe said. “Our current study complements the ongoing Blueprint initiative, which is also tackling the issue of PD-L1 assay harmonization.”

This study compared the Ventana SP263 assay, developed by Ventana in collaboration with AstraZeneca for use in evaluating patients for the immunotherapeutic drug durvalumab, an anti-PD-L1; the Dako 22C3 assay, made by Dako and approved for the U.S. Food and Drug Administration (FDA) as a companion diagnostic to identify patients for pembrolizumab (Keytruda), an anti-PD-1; and the Dako 28-8 assay, made by Dako and approved by the FDA as a complementary diagnostic for nivolumab (Opdivo), an anti-PD-1.

All three tests assess the percentage of tumor cells whose membranes stain positive for the PD-L1 protein. A cut-off point is set for each test, and patients whose tumors score above the cut-off point are determined to be more likely to respond to the corresponding therapy, Ratcliffe explained.

In this study, the largest to date, approximately 500 tumor biopsy samples from patients with NSCLC were assessed because all three tests have been previously validated for that disease.
The study determined that the patient population defined by Ventana SP263 at the 25 percent cutoff point is similar to the group identified by the Dako 28-8 at the 10 percent cutoff. The study also showed a high degree of concordance between the SP263 and 22C3 assays if a 50 percent cutoff point was applied in both cases.

Ratcliffe said that the three tests achieved overall percentage agreement of more than 90 percent.  The results also indicate which cutoff points should be used to optimize agreement between a positive or negative PD-L1 result, which will help the medical community to compare results from clinical studies that have used different tests.

Ratcliffe said the study results indicate that it may be possible to extrapolate the results from one test to that of another test, and could someday allow physicians to use the tests interchangeably, though she added that further research is needed to confirm the findings.

 

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New Insights into mtDNA, mitochondrial proteins, aging, and metabolic control

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Newly discovered proteins may protect against age-related illnesses  

The proteins could play a key role in the aging process and the onset of diseases linked to older age

BY Beth Newcomb   APRIL 13, 2016

http://news.usc.edu/98412/newly-discovered-proteins-may-protect-against-agings-illnesses/

http://news.usc.edu/files/2016/04/20160208_PinchasCohen_web-824×549.jpg

Pinchas Cohen led a team that identified tiny proteins that appear to play a role in controlling how the body ages. (Photo/Beth Newcomb)

A group of six newly discovered proteins may help to divulge secrets of how we age, potentially unlocking insights into diabetes, Alzheimer’s, cancer and other aging-related diseases.

The tiny proteins appear to play several big roles in our bodies’ cells, from decreasing the amount of damaging free radicals and controlling the rate at which cells die to boosting metabolism and helping tissues throughout the body respond better to insulin. The naturally occurring amounts of each protein decrease with age, leading researchers to believe that they play an important role in the aging process and the onset of diseases linked to older age.

The research team led by Pinchas Cohen, dean of the USC Davis School of Gerontology, identified the tiny proteins for the first time and observed their surprising origin from organelles in the cell called mitochondria and their game-changing roles in metabolism and cell survival. This latest finding builds upon prior research by Cohen and his team that uncovered two significant proteins, humanin and MOTS-c, hormones that appear to have significant roles in metabolism and diseases of aging.

Unlike most other proteins, humanin and MOTS-c are encoded in mitochondria, the structure within cells that produces energy from food, instead of in the cell’s nucleus where most genes are contained.

Key functions

Mitochondria have their own small collection of genes, which were once thought to play only minor roles within cells but now appear to have important functions throughout the body. Cohen’s team used computer analysis to see if the part of the mitochondrial genome that provides the code for humanin was coding for other proteins as well. The analysis uncovered the genes for six new proteins, which were dubbed small humanin-like peptides, or SHLPs, 1 through 6 (the name of this hardworking group of proteins is appropriately pronounced “schlep”).

After identifying the six SHLPs and successfully developing antibodies to test for several of them, the team examined both mouse tissues and human cells to determine their abundance in different organs as well as their functions. The proteins were distributed quite differently among organs, which suggests that the proteins have varying functions based on where they are in the body.

Of particular interest is SHLP 2, Cohen said. The protein appears to have profound insulin-sensitizing, anti-diabetic effects as well as potent neuro-protective activity that may emerge as a strategy to combat Alzheimer’s disease. He added that SHLP 6 is also intriguing, with a unique ability to promote cancer cell death and thus potentially target malignant diseases.

“Together with the previously identified mitochondrial peptides, the newly recognized SHLP family expands the understanding of the mitochondria as an intracellular signaling organelle that communicates with the rest of the body to regulate metabolism and cell fate,” Cohen said. “The findings are an important advance that will be ripe for rapid translation into drug development for diseases of aging.”

The study first appeared online in the journal Aging on April 10. Cohen’s research team included collaborators from the Albert Einstein College of Medicine; the findings have been licensed to the biotechnology company CohBar for possible drug development.

The research was supported by a Glenn Foundation Award and National Institutes of Health grants to Cohen (1P01AG034906, 1R01AG 034430, 1R01GM 090311, 1R01ES 020812) and an Ellison/AFAR postdoctoral fellowship to Kelvin Yen. Study authors Laura Cobb, Changhan Lee, Nir Barzilai and Pinchas Cohen are consultants and stockholders of CohBar Inc.

Feature: The man who wants to beat back aging

By Stephen S. Hall Sep. 16, 201

Nir Barzilai hopes to persuade FDA to bless the proposed anti-aging trial, which is unconventional in its goals and design.
http://www.sciencemag.org/news/2015/09/feature-man-who-wants-beat-back-aging

On a blazingly hot morning this past June, a half-dozen scientists convened in a hotel conference room in suburban Maryland for the dress rehearsal of what they saw as a landmark event in the history of aging research. In a few hours, the group would meet with officials at the U.S. Food and Drug Administration (FDA), a few kilometers away, to pitch an unprecedented clinical trial—nothing less than the first test of a drug to specifically target the process of human aging.

“We think this is a groundbreaking, perhaps paradigm-shifting trial,” said Steven Austad, chairman of biology at the University of Alabama, Birmingham, and scientific director of the American Federation for Aging Research (AFAR). After Austad’s brief introductory remarks, a scientist named Nir Barzilai tuned up his PowerPoint and launched into a practice run of the main presentation.

Barzilai is a former Israeli army medical officer and head of a well-known study of centenarians based at the Albert Einstein College of Medicine in the Bronx, New York. To anyone who has seen the ebullient scientist in his natural laboratory habitat, often in a short-sleeved shirt and always cracking jokes, he looked uncharacteristically kempt in a blue blazer and dress khakis. But his practice run kept hitting a historical speed bump. He had barely begun to explain the rationale for the trial when he mentioned, in passing, “lots of unproven, untested treatments under the category of anti-aging.” His colleagues pounced.

“Nir,” interrupted S. Jay Olshansky, a biodemographer of aging from the University of Illinois, Chicago. The phrase “anti-aging … has an association that is negative.”

“I wouldn’t dignify them by calling them ‘treatments,’” added Michael Pollak, director of cancer prevention at McGill University in Montreal, Canada. “They’re products.”

Barzilai, a 59-year-old with a boyish mop of gray hair, wore a contrite grin. “We know the FDA is concerned about this,” he conceded, and deleted the offensive phrase.

Then he proceeded to lay out the details of an ambitious clinical trial. The group—academics all—wanted to conduct a double-blind study of roughly 3000 elderly people; half would get a placebo and half would get an old (indeed, ancient) drug for type 2 diabetes called metformin, which has been shown to modify aging in some animal studies. Because there is still no accepted biomarker for aging, the drug’s success would be judged by an unusual standard—whether it could delay the development of several diseases whose incidence increases dramatically with age: cardiovascular disease, cancer, and cognitive decline, along with mortality. When it comes to these diseases, Barzilai is fond of saying, “aging is a bigger risk factor than all of the other factors combined.”

But the phrase “anti-aging” kept creeping into the rehearsal, and critics kept jumping in. “Okay,” Barzilai said with a laugh when it came up again. “Third time, the death penalty.”

The group’s paranoia about the term “anti-aging” captured both the audacity of the proposed trial and the cultural challenge of venturing into medical territory historically associated with charlatans and quacks. The metformin initiative, which Barzilai is generally credited with spearheading, is unusual by almost any standard of drug development. The people pushing for the trial are all academics, none from industry (although Barzilai is co-founder of a biotech company, CohBar Inc., that is working to develop drugs targeting age-related diseases). The trial would be sponsored by the nonprofit AFAR, not a pharmaceutical company. No one stood to make money if the drug worked, the scientists all claimed; indeed, metformin is not only generic, costing just a few cents a dose, but belongs to a class of drugs that has been part of the human apothecary for 500 years. Patient safety was unlikely to be an issue; millions of diabetics have taken metformin since the 1960s, and its generally mild side effects are well-known.

Finally, the metformin group insisted they didn’t need a cent of federal money to proceed (although they do intend to ask for some). Nor did they need formal approval from FDA to proceed. But they very much wanted the agency’s blessing. By recognizing the merit of such a trial, Barzilai believes,  FDA would make aging itself a legitimate target for drug development.

By the time the scientists were done, the rehearsal—which was being filmed for a television documentary—had the feel of a pep rally. They spoke with unguarded optimism. “What we’re talking about here,” Olshansky said, “is a fundamental sea change in how we look at aging and disease.” To Austad, it is “the key, potentially, to saving the health care system.”

As the group piled into a van for the drive to FDA headquarters, there was more talk about setting precedents and opening doors. So it was a little disconcerting when Austad led the delegation up to the main entrance of FDA—and couldn’t get the door open.   ……

Mitochondrial Peptides Found in a Preclinical Study Seen to Control Cell Metabolism

APRIL 13, 2016 BY MAGDALENA KEGEL  IN NEWS.
http://mitochondrialdiseasenews.com/2016/04/13/newly-discovered-mitochondrial-factors-might-give-insights-into-aging/

 

CohBar, a developer of mitochondria-based therapeutics, announced that preclinical research by its academic collaborators has found small humanin-like peptides (SHLPs) that can control metabolism and cell survival. The findings have implications for age-related diseases such as Alzheimer’s and cancer.

The study, “Naturally Occurring Mitochondrial-derived Peptides are Age-dependent Regulators of Apoptosis, Insulin Sensitivity, and Inflammatory Markers,” was the result of a joint effort between researchers at the University of Southern California (USC) and theInstitute for Aging Research at the Albert Einstein College of Medicine of Yeshiva University. The study was published in the journal Aging.

Researchers discovered the SHLPs by examining the genome of mitochondria with the help of a bioinformatics approach, which identified six peptides. The team then verified the presence of the factors and explored their function in laboratory animals.

CohBar, who have the exclusive license to develop SHLPs into therapeutics, works closely with its academic partners to explore the peptides in preclinical models.

While it was previously believed that mitochondria only have 37 genes, research has revealed that the mitochondrial genome is far more versatile, potentially harboring a multitude of new genes, which can encode peptides acting as cellular signaling factors. The peptides, it has turned out, have shown neuroprotective and anti-inflammatory effects, and act to protect cells in disease-modifying ways in preclinical models of aging.

CohBar’s goal is to bring these peptides to the market as therapies for age-related diseases, such as obesity, type 2 diabetes, cancer, atherosclerosis and neurodegenerative disorders.

“Together with the previously described mitochondrial-derived peptides humanin and MOTS-c, the SHLP family expands our understanding of the role that these peptides play in intracellular signaling throughout the body to regulate both metabolism and cell survival,” Pinchas Cohen, dean of the USC Leonard Davis School of Gerontology, founder and director of CohBar, and the study’s senior author, said in a press release. “These findings further illustrate the enormous potential that mitochondria-based therapeutics could have on treating age-associated diseases like Alzheimer’s and cancer.”

“The pre-clinical evidence continues to confirm that these peptides represent a new class of naturally occurring metabolic regulators,” added Simon Allen, CohBar’s CEO. “They form the foundation of our pipeline of first-in-class treatments for age-related diseases, and we are committed to rapidly advancing them through pre-clinical and clinical activities as we move forward.”

Naturally occurring mitochondrial-derived peptides are age-dependent regulators of apoptosis, insulin sensitivity, and inflammatory markers

Laura J. Cobb1,5, Changhan Lee2, Jialin Xiao2, Kelvin Yen2, Richard G. Wong2, Hiromi K. Nakamura1, ….., Derek M. Huffman4, Junxiang Wan2, Radhika Muzumdar3, Nir Barzilai4 , and Pinchas Cohen2
http://www.impactaging.com/papers/v8/n4/full/100943.html

Mitochondria are key players in aging and in the pathogenesis of age-related diseases. Recent mitochondrial transcriptome analyses revealed the existence of multiple small mRNAs transcribed from mitochondrial DNA (mtDNA). Humanin (HN), a peptide encoded in the mtDNA 16S ribosomal RNA region, is a neuroprotective factor. An in silico search revealed six additional peptides in the same region of mtDNA as humanin; we named these peptides small humanin-like peptides (SHLPs). We identified the functional roles for these peptides and the potential mechanisms of action. The SHLPs differed in their ability to regulate cell viability in vitro. We focused on SHLP2 and SHLP3 because they shared similar protective effects with HN. Specifically, they significantly reduced apoptosis and the generation of reactive oxygen species, and improved mitochondrial metabolism in vitro. SHLP2 and SHLP3 also enhanced 3T3-L1 pre-adipocyte differentiation. Systemic hyperinsulinemic-euglycemic clamp studies showed that intracerebrally infused SHLP2 increased glucose uptake and suppressed hepatic glucose production, suggesting that it functions as an insulin sensitizer both peripherally and centrally. Similar to HN, the levels of circulating SHLP2 were found to decrease with age. These results suggest that mitochondria play critical roles in metabolism and survival through the synthesis of mitochondrial peptides, and provide new insights into mitochondrial biology with relevance to aging and human biology.

Human mitochondrial DNA (mtDNA) is a double-stranded, circular molecule of 16,569 bp and contains 37 genes encoding 13 proteins, 22 tRNAs, and 2 rRNAs. Recent mitochondrial transcriptome analyses revealed the existence of small RNAs derived from mtDNA [1]. In 2001, Nishimoto and colleagues identified humanin (HN), a 24-amino-acid peptide encoded from the 16S ribosomal RNA (rRNA) region of mtDNA. HN is a potent neuroprotective factor capable of antagonizing Alzheimer’s disease (AD)-related cellular insults [2]. HN is a component of a novel retrograde signaling pathway from the mitochondria to the nucleus, which is distinct from mitochondrial signaling pathways, such as the SIRT4-AMPK pathway [3]. HN-dependent cellular protection is mediated in part by interacting with and antagonizing pro-apoptotic Bax-related peptides [4] and IGFBP-3 (IGF binding protein 3) [5].

Because of their involvement in energy production and free radical generation, mitochondria likely play a major role in aging and age-related diseases [68]. In fact, improvement of mitochondrial function has been shown to ameliorate age-related memory loss in aged mice [9]. Recent studies have shown that HN levels decrease with age, suggesting that HN could play a role in aging and age-related diseases, such as Alzheimer’s disease (AD), atherosclerosis, and diabetes. Along with lower HN levels in the hypothalamus, skeletal muscle, and cortex of older rodents, the circulating levels of HN were found to decline with age in both humans and mice [10]. Notably, circulating HN levels were found to be (i) significantly higher in long-lived Ames dwarf mice but lower in short-lived growth hormone (GH) transgenic mice, (ii) significantly higher in a GH-deficient cohort of patients with Laron syndrome, and (iii) reduced in mice and humans treated with GH or IGF-1 (insulin-like growth factor 1) [11]. Age-dependent declines in the circulating HN levels may be due to higher levels of reactive oxygen species (ROS) that contribute to atherosclerosis development. Using mouse models of atherosclerosis, it was found that HN-treated mice had a reduced disease burden and significant health improvements [12,13]. In addition, HN improved insulin sensitivity, suggesting clinical potential for mitochondrial peptides in diseases of aging [10]. The discovery of HN represents a unique addition to the spectrum of roles that mitochondria play in the cell [14,15]. A second mitochondrial-derived peptide (MDP), MOTS-c (mitochondrial open reading frame of the 12S rRNA-c), has also been shown to have metabolic effects on muscle and may also play a role in aging [16].

We further investigated mtDNA for the presence of other MDPs. Recent technological advances have led to the identification of small open reading frames (sORFs) in the nuclear genomes ofDrosophila[17,18] and mammals [19,20]. Therefore, we attempted to identify novel sORFs using the following approaches: 1) in silico identification of potential sORFs; 2) determination of mRNA expression levels; 3) development of specific antibodies against these novel peptides to allow for peptide detection in cells, organs, and plasma; 4) elucidating the actions of these peptides by performing cell-based assays for mitochondrial function, signaling, viability, and differentiation; and 5) delivering these peptides in vivo to determine their systemic metabolic effects. Focusing on the 16S rRNA region of the mtDNA where the humanin gene is located, we identified six sORFs and named them small humanin-like peptides (SHLPs) 1-6. While surveying the biological effects of SHLPs, we found that SHLP2 and SHLP3 were cytoprotective; therefore, we investigated their effects on apoptosis and metabolism in greater detail. Further, we showed that circulating SHLP2 levels declined with age, similar to HN, suggesting that SHLP2 is involved in aging and age-related disease progression.

SHLP2 and SHLP3 regulate the expression of metabolic and inflammatory markers

Epidemiological studies have demonstrated that increased levels of mediators of inflammation and acute-phase reactants, such as fibrinogen, C-reactive protein (CRP), and IL-6, correlate with the incidence of type 2 diabetes mellitus (T2DM) [3436]. In humans, anti-inflammatory drugs, such as aspirin and sodium salicylate, reduce fasting plasma glucose levels and ameliorate the symptoms of T2DM. In addition, anti-diabetic drugs, such as fibrates [37] and thiazolidinediones [38], have been found to lower some markers of inflammation. SHLP2 increased the levels of leptin, which is known to improve insulin sensitivity, but had no effect on the levels of the pro-inflammatory cytokines IL-6 and MCP-1. SHLP3 significantly increased the leptin levels, but also elevated IL-6 and MCP-1 levels, which could explain the lack of an in vivo insulin-sensitizing effect of SHLP3. The mechanism by which SHLPs regulate the expression of metabolic and inflammatory markers remains unclear and needs to be further investigated. Furthermore, SHLPs have different effects on inflammatory marker expression, suggesting differential regulation and function of individual SHLPs.

SHLP2 in aging

Mitochondria have been implicated in increased lifespan in several life-extending treatments [39,40]; however, it is not known whether the relationship is correlative or causative [40]. Additionally, it is well known that hormone levels change with aging. For example, levels of aldosterone, calcitonin, growth hormone, and IGF-I decrease with age. Circulating HN levels decline with age in humans and rodents, specifically in the hypothalamus and skeletal muscle of older rats. These changes parallel increases in the incidence of age-associated diseases such as AD and T2DM. The decline in circulating SHLP2 levels with age (Fig. 6), the anti-oxidative stress function of SHLP2 (Fig. 3C), and its neuroprotective effect (Fig. 6B) indicate that SHLP2 has a role in the regulation of aging and age-related diseases.

Conclusion

By analyzing the mitochondrial transcriptome, we found that sORFs from mitochondrial DNA encode functional peptides. We identified many mRNA transcripts within 13 protein-coding mitochondrial genes [1]. Such previously underappreciated sORFs have also been described in the nuclear genome [41]. The MDPs we describe here may represent retrograde communication signals from the mitochondria to the nucleus and may explain important aspects of mitochondrial biology that are implicated in health and longevity.

Larry, John Walker is working on mt proteins dynamics. His rotor – stator mechanism in ATPase synthase, a ‘complex’ that biologist accepted as energy generator is likely wrong. I was suppose to have met him in Germany few years ago. Energy in biological systems has nothing to do with heat. Heat is an outcome of a reaction, meaning that IR spectra accordingly to wave theory is a source of information memorized in water interference with carbon open systems within protein and glyo-proteins complexes as well as genome space-time outcomes. Physically speaking from a pure perspective of science ATP is highly unstable form of phosphate ‘chains’. It cannot hold energy, it is actually in contrary, it is like a resonator, trapping negativity, thus functioning as space propeller by expanding carbon skeleton of protein ‘machines’ Now, we don’t know what is ‘aging’ in a pure physical sense, except that we observe structural changes in what we call complexes. We we know is that proteins are not stationary structures, but highly dynamic forms of matter, seemingly occupying discrete and relative spaces. A piece of mt ATP ase could be discovered in the nucleus as transcription factor. Our notion of operational space in terms of electro dynamics from a motor – stator perspective is now translated toward defining semi conducting and supracoductive strings. The reality of which is so much more fascinating and beautiful as time progresses overally. There are spaces where time does not change, and there are spaces where time walks, and there are spaces, where time flies, and there are spaces where time runs. Amazing, indeed! The story of aging gets a lot deeper that science could even imagine, probably to roots of immortal energy- spaces. We know that matter is transient, that is nearly all living matter, replenishes of about 3 to 7 weeks.

Take a glass full of some kind of liquid, you know the mass of the glass and the mass of the liquid (say wine, beer, water, or milk) You also know to an approximate reality the composition of both. Now lift the glass full of liquid and let it break on a surface of your choice. Depending on the surface pieces of the glass would travel differential from a center projected by the vertical axis of your hand. What technology does today is recollecting those pieces and modelling them to fit in a form again that would resemble a holding device, a glass. The liquid we don’t know exactly how it spilled due the nature of its absorbancy of both surface physics and physical ‘state’ properties. Thus we can say how much approximate energy we have held thinking of m/z as time flight objectives. Each technology can read 1D and approximate the 2D, absolutely lacking computational methodology for 3D dynamic reality. Many scientists confuse space and volume. Volume is a one dimensional characteristic! So is crystalography! BY taking quantum chemical method computing principles following imaginative rules we could approach 2D, however , that is not enough to define 3D. Time we use as a reference frame of clocks we have invented in order to keep track of a sense to observable ‘change’ . But remember, time is absolute and parallel in continuity while energy is discrete , coming in quantum packages, realization of accumulated information. Information is highly redundant we see, so annotating information is an objective to modern days simulations that could predict outcomes of possible parallel realities we call worlds. One could ‘jump’ from one reality to another through guidance of light and water, but what remains unsolved is why people make mistakes, constantly by accusing in name of greed and power , or disobedience of commandments of the Lord!

On Thu, Apr 21, 2016 at 3:41 AM, Leaders in Pharmaceutical Business Intelligence (LPBI) Group wrote:

> larryhbern posted: “New Insights into mtDNA, mitochondrial proteins, > aging, and metabolic control Larry H. Bernstein, MD, FCAP, Curator LPBI > Newly discovered proteins may protect against age-related illnesses The > proteins could play a key role in the ” >

 

Metabolic features of the cell danger response
– Mitochondria in Health and Disease

Mitochondrion  Volume 16, May 2014, Pages 7–17     doi:10.1016/j.mito.2013.08.006

 

Highlights

  •  The Cell Danger Response (CDR) is defined in terms of an ancient metabolic response to threat.
  •  The CDR encompasses inflammation, innate immunity, oxidative stress, and the ER stress response.
  •  The CDR is maintained by extracellular nucleotide (purinergic) signaling.
  •  Abnormal persistence of the CDR lies at the heart of many chronic diseases.
  •  Antipurinergic therapy (APT) has proven effective in many chronic disorders in animal models

The cell danger response (CDR) is the evolutionarily conserved metabolic response that protects cells and hosts from harm. It is triggered by encounters with chemical, physical, or biological threats that exceed the cellular capacity for homeostasis. The resulting metabolic mismatch between available resources and functional capacity produces a cascade of changes in cellular electron flow, oxygen consumption, redox, membrane fluidity, lipid dynamics, bioenergetics, carbon and sulfur resource allocation, protein folding and aggregation, vitamin availability, metal homeostasis, indole, pterin, 1-carbon and polyamine metabolism, and polymer formation. The first wave of danger signals consists of the release of metabolic intermediates like ATP and ADP, Krebs cycle intermediates, oxygen, and reactive oxygen species (ROS), and is sustained by purinergic signaling. After the danger has been eliminated or neutralized, a choreographed sequence of anti-inflammatory and regenerative pathways is activated to reverse the CDR and to heal. When the CDR persists abnormally, whole body metabolism and the gut microbiome are disturbed, the collective performance of multiple organ systems is impaired, behavior is changed, and chronic disease results. Metabolic memory of past stress encounters is stored in the form of altered mitochondrial and cellular macromolecule content, resulting in an increase in functional reserve capacity through a process known as mitocellular hormesis. The systemic form of the CDR, and its magnified form, the purinergic life-threat response (PLTR), are under direct control by ancient pathways in the brain that are ultimately coordinated by centers in the brainstem. Chemosensory integration of whole body metabolism occurs in the brainstem and is a prerequisite for normal brain, motor, vestibular, sensory, social, and speech development. An understanding of the CDR permits us to reframe old concepts of pathogenesis for a broad array of chronic, developmental, autoimmune, and degenerative disorders. These disorders include autism spectrum disorders (ASD), attention deficit hyperactivity disorder (ADHD), asthma, atopy, gluten and many other food and chemical sensitivity syndromes, emphysema, Tourette’s syndrome, bipolar disorder, schizophrenia, post-traumatic stress disorder (PTSD), chronic traumatic encephalopathy (CTE), traumatic brain injury (TBI), epilepsy, suicidal ideation, organ transplant biology, diabetes, kidney, liver, and heart disease, cancer, Alzheimer and Parkinson disease, and autoimmune disorders like lupus, rheumatoid arthritis, multiple sclerosis, and primary sclerosing cholangitis.

The double face of mitochondrial dysfunction

Dmitry Knorre, Anna Zyrina, and Fedor Severin

pp 420-420

Full text | PDF

 

 

Flawed Mitochondrial DNA Could Undermine Stem Cell Therapies

http://www.genengnews.com/gen-news-highlights/flawed-mitochondrial-dna-could-undermine-stem-cell-therapies/81252622/

http://www.genengnews.com/Media/images/GENHighlight/thumb_Apr18_2016_OHSU_ConfocalMicroscopyImage7543773219.jpg

This is a confocal microscopy image of human fibroblasts derived from embryonic stem cells. The nuclei appear in blue, while smaller and more numerous mitochondria appear in red. [Shoukhrat Mitalipov]

Mutations in our mitochondrial DNA tend to be inconspicuous, but they can become more prevalent as we age. They can even vary in frequency from cell to cell. Naturally, some cells will be relatively compromised because they happen to have a higher percentage of mutated mitochondrial DNA. Such cells make a poor basis for stem cell lines. They should be excluded. But how?

To answer this question, a team of scientists scrutinized skin fibroblasts, blood cells, and induced pluripotent stem cells (iPSCs) for mitochondrial genome integrity. When the scientists tested the samples for mitochondrial DNA mutations, the levels of mutations appeared low. But when the scientists sequenced the iPS cell lines, they found higher numbers of mitochondrial DNA mutations, particularly in cells from patients over 60.

The scientists were led by Shoukhrat Mitalipov, Ph.D., director of the Center for Embryonic Cell and Gene Therapy at Oregon Health & Science University, and Taosheng Huang, M.D., a medical geneticist and director of the Mitochondrial Medicine Program at Cincinnati Children’s Hospital. The Mitalipov/Huang-led team also found higher percentages of mitochondria containing mutations within a cell. The higher the load of mutated mitochondrial DNA in a cell, the more compromised the cell’s function.

Since each iPSC line is created from a different cell, each line may contain different types of mitochondrial DNA mutations and mutation loads. To choose the least damaged line, the authors recommend screening multiple lines per patient. “It’s a good idea to check the iPS clones for mitochondrial DNA mutations and make sure you pick a good cell line,” said Dr. Huang.

This recommendation appeared April 14 in the journal Cell Stem Cell, in an article entitled, “Age-Related Accumulation of Somatic Mitochondrial DNA Mutations in Adult-Derived Human iPSCs.” This article holds that mitochondrial genome integrity is a vital readout in assessing the proficiency of patient-derived regenerative products destined for clinical applications.

“We found that pooled skin and blood mtDNA contained low heteroplasmic point mutations, but a panel of ten individual iPSC lines from each tissue or clonally expanded fibroblasts carried an elevated load of heteroplasmic or homoplasmic mutations, suggesting that somatic mutations randomly arise within individual cells but are not detectable in whole tissues,” wrote the article’s authors. “The frequency of mtDNA defects in iPSCs increased with age, and many mutations were nonsynonymous or resided in RNA coding genes and thus can lead to respiratory defects.”

Potential therapies using stem cells hold tremendous promise for treating human disease. However, defects in the mitochondria could undermine the iPS cells’ ability to repair damaged tissue or organs.

“If you want to use iPS cells in a human, you must check for mutations in the mitochondrial genome,” declared Dr. Huang. “Every single cell can be different. Two cells next to each other could have different mutations or different percentages of mutations.”

Prior to the creation of a therapeutic iPS cell line, a collection of cells is taken from the patient. These cells will be tested for mutations. If the tester uses Sanger sequencing, older technology that is not as sensitive as newer next-generation sequencing, any mutation that occurs in less than 20% of the sample will go undetected. But mitochondrial DNA mutations might occur in less than 20% of mitochondria in the pooled cells. As a result, mutation rates have not been well understood. “These mitochondrial mutations are actually hidden,” explained Dr. Mitalipov.

The mitochondrial genome is relatively small, containing just 37 genes, so screening should be feasible using next generation sequencing, Dr. Mitalipov added. “It should be relatively cheap and do-able.”

Dr. Mitalipov also commented on a more general point, the implications of the current study on illuminating the mechanisms of age-related disease: “Pathogenic mutations in our mitochondrial DNA have long been thought to be a driving force in aging and age-onset diseases, though clear evidence was missing. This foundational knowledge of how cells are damaged in the natural process of aging may help to illuminate the role of mutated mitochondria in degenerative disease.”

New Mitalipov paper on stem cell mitochondria: challenge for IPS cell field?

http://www.ipscell.com/tag/age-related-accumulation-of-somatic-mitochondrial-dna-mutations-in-adult-derived-human-ipscs/

A new paper from Shoukhrat Mitalipov’s lab on stem cell mitochondria points to a pattern whereby induced pluripotent stem (IPS) cells tend to have more problems if they are from older patients.

What does this paper mean for the stem cell field and could it impact more specifically the clinical applications of IPS cells?

http://i0.wp.com/www.ipscell.com/wp-content/uploads/2016/04/Mitalipov-mitochondrial-mutations.jpg?resize=300%2C295

Graphical Abstract, Kang, et al. 2016

The new paper Kang, et al is entitled “Age-Related Accumulation of Somatic Mitochondrial DNA Mutations in Adult-Derived Human iPSCs”.

This paper reminds us of the very important realities that mitochondria are key players in stem cell function and that mitochondria have their own genomes that impact that function. A lot of us don’t think about mitochondria and their genome as often as we should.

The paper came to three major scientific conclusions (this from the Highlights section of the paper and also see the graphical abstract for a visual sense of the results overall):

  • Human iPSC clones derived from elderly adults show accumulation of mtDNA mutations
  • Fewer mtDNA mutations are present in ESCs and iPSCs derived from younger adults
  • Accumulated mtDNA mutations can impact metabolic function in iPSCs

Importantly the team looked at IPS cells derived from both blood and skin cells and found that the former were less likely to have mitochondrial mutations.

This study suggests that those teams producing or working with human IPS cells (hIPSCs) should be screening the different lines for mitochondrial mutations. This excellent piece from Sara Reardon on the Mitalipov paper quotes IPS cell expert Jeanne Loring on this very point:

“It’s one of those things most of us don’t think about,” says Jeanne Loring, a stem-cell biologist at the Scripps Research Institute in La Jolla, California. Her lab is working towards using iPS cells to treat Parkinson’s disease, and Loring now plans to go back and examine the mitochondria in her cell lines. She suspects that it will be fairly easy for researchers to screen cells for use in therapies.”

Mitalipov goes further and suggests that his team’s new findings could support the use of human embryonic stem cells (hESC) derived by somatic cell nuclear transfer (SCNT) which would be expected to have mitochondria with fewer mutations. However, as Loring points out in the Reardon article, SCNT is really difficult to successfully perform and only a few labs in the world can do it at present. In that context, working with hIPSC and adding on the additional layer of mitochondrial DNA mutation screening could be more practical.

New York stem cell researcher Dieter Egli, however, is quoted that hIPSC have other differences with hESC as well such as epigenetic differences and he’s quoted in the Reardon piece, “It’s going to be very hard to find a cell line that’s perfect.”

One might reasonably ask both Egli and oneself, “What is a perfect cell line”?

In the end the best approach for use of human pluripotent stem cells of any kind is going to involve a balance between practicality of production and the potentially positive or negative traits of those cells as determined by rigorous validation screening.

With this new paper we’ve just learned more about another layer of screening that is needed. An interesting question is whether adult stem cells such as mesenchymal stromal/stem cells (MSC) also should be screened for mitochondrial mutations. They are often produced from patients who are getting up there in years. I hope that someone will publish on that too.

As to pluripotent cells, I expect that sometimes the best lines, meaning those most perfect for a given clinical application, will be hIPSC (autologous or allogeneic in some instances) and in other cases they may be hESC made from leftover IVF embryos. If SCNT-derived hESC can be more widely produced in an affordable manner and they pass validation as well then those (sometimes called NT-hESC) may also come into play clinically. So far that hasn’t happened for the SCNT cells, but it may over time.   …..

 Age-Related Accumulation of Somatic Mitochondrial DNA Mutations in Adult-Derived Human iPSCs

Eunju Kang, Xinjian Wang, Rebecca Tippner-Hedges, …, Don P. Wolf, Taosheng Huang, Shoukhrat Mitalipov
http://www.cell.com/cell-stem-cell/pdfExtended/S1934-5909(16)00067-9

In Brief Mitalipov, Huang, and colleagues show that human iPSCs derived from older adults carry more mitochondrial DNA mutations than those derived from younger individuals. Defects in metabolic function caused by mtDNA mutations suggest careful screening of hiPSC clones for mutational load before clinical application.

Highlights

  1. Human iPSC clones derived from elderly adults show accumulation of mtDNA mutations
  2. Fewer mtDNA mutations are present in ESCs and iPSCs derived from younger adults
  3. Accumulated mtDNA mutations can impact metabolic function in iPSCs

Kang et al., 2016, Cell Stem Cell 18, 1–12 May 5, 2016 ª2016 Elsevier Inc. http://dx.doi.org/10.1016/j.stem.2016.02.005

The genetic integrity of iPSCs is an important consideration for therapeutic application. In this study, we examine the accumulation of somatic mitochondrial genome (mtDNA) mutations in skin fibroblasts, blood, and iPSCs derived from young and elderly subjects (24–72 years). We found that pooled skin and blood mtDNA contained low heteroplasmic point mutations, but a panel of ten individual iPSC lines from each tissue or clonally expanded fibroblasts carried an elevated load of heteroplasmic or homoplasmic mutations, suggesting that somatic mutations randomly arise within individual cells but are not detectable in whole tissues. The frequency of mtDNA defects in iPSCs increased with age, and many mutations were non-synonymous or resided in RNA coding genes and thus can lead to respiratory defects. Our results highlight a need to monitor mtDNA mutations in iPSCs, especially those generated from older patients, and to examine the metabolic status of iPSCs destined for clinical applications.

Induced pluripotent stem cells (iPSCs) offer an unlimited source for autologous cell replacement therapies to treat age-associated degenerative diseases. Aging is generally characterized by increased DNA damage and genomic instability (Garinis et al., 2008; Lombard et al., 2005); thus, iPSCs derived from elderly subjects may harbor point mutations and larger genomic rearrangements. Indeed, iPSCs display increased chromosome aberrations (Mayshar et al., 2010), subchromosomal copy number variations (CNVs) (Abyzov et al., 2012; Laurent et al., 2011), and exome mutations (Johannesson et al., 2014), compared to natural embryonic stem cell (ESC) counterparts (Ma et al., 2014). The rate of mtDNA mutations is believed to be at least 10- to 20-fold higher than that observed in the nuclear genome (Wallace, 1994), and often both mutated and wild-type mtDNA (heteroplasmy) can coexist in the same cell (Rossignol et al., 2003). Large deletions are most frequently observed mtDNA abnormalities in aged post-mitotic tissues such as brain, heart, and muscle (Bender et al., 2006; Bua et al., 2006; Corral-Debrinski et al., 1992; Cortopassi et al., 1992; Mohamed et al., 2006) and have been implicated in aging and diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), and diabetes (Larsson, 2010; Lin and Beal, 2006; Petersen et al., 2003; Wallace, 2005). In addition, mtDNA point mutations were reported in some tumors and replicating tissues (Chatterjee et al., 2006; Ju et al., 2014; Michikawa et al., 1999; Taylor et al., 2003). However, the extent of mtDNA defects in proliferating peripheral tissues commonly used for iPSC induction, such as skin and blood, is thought to be low and limited to common non-coding variants (Schon et al., 2012; Yao et al., 2015). Accumulation of mtDNA variants in these tissues with age was insignificant (Greaves et al., 2010; Hashizume et al., 2015). Several point mutations were identified in iPSCs generated from the newborn foreskin fibroblasts, although most of these variants were non-coding, common for the general population, and did not affect their metabolic activity (Prigione et al., 2011). Somatic mtDNA mutations may be under-reported secondary to the level of sample interrogation. …..

Figure 2. mtDNA Mutations in Skin Fibroblasts, Blood, and the iPSCs of a 72-YearOld B Subject (A) Sixteen mutations at low heteroplasmy levels were detected in the DNA of PF, while a panel of ten FiPSC lines carried nine mutations, including four that were homoplasmic. Gray rectangles define the mutations shared between PF and FiPSCs. (B) Venn diagram showing only one mutation in FiPSCs shared with PF. (C) All ten FiPSC lines carried between one and five high-heteroplasmy (>15%) mutations. (D) Mutation distribution in whole blood and BiPSCs was similar to that in PF and FiPSCs. Six mutations at low-heteroplasmy levels were observed in blood, while BiPSC lines displayed 21 mutations, including four over the 80% heteroplasmy level. (E) Venn diagram showing four mutations in BiPSCs shared with whole blood and the 17 novel variants. (F) Distribution of mutations in individual BiPSC lines. See also Figures S2 and S3; Table S1; Table S3, sheet 2; and Table S4, sheet 1   ….

Figure 4. Transmission and Distribution of Somatic mtDNA Mutations to iPSCs (A) A total of 112 mtDNA mutations were discovered in parental cells (PF, CF, and blood) from 11 subjects. Of these, 39 variants (35%) were found in corresponding 130 iPSC lines. Among non-transmitted, transmitted, and novel mutations in iPSCs, comparable percentages of variants (68%, 69%, and 79%, respectively) were coding mutations in protein, rRNA, or tRNA genes. This suggests that most pathogenic mutations do not affect iPSC induction. However, certain coding mutations including in ND3, ND4L, and 14 tRNA genes were not detected in iPSCs, suggesting possible pathogenicity. n, the number of mtDNA mutations. Blue font genes were detected in parental cells. (B–D) A total of 80 high heteroplasmic (>15%) variants were detected in the present study in 130 FiPSC or BiPSC lines from 11 subjects. (B) The majority of these variants (76%) were non-synonymous or frame-shift mutations in protein-coding genes or affected rRNA and tRNA genes. (C) More than half of the mutations (56%) were never reported in a database containing whole mtDNA sequences from 26,850 healthy subjects representing the general human population (http://www.mitomap.org/MITOMAP). (D) Most mutations (90%) were never reported in a database containing sequences from healthy subjects with corresponding mtDNA haplotypes. freq., frequent. See also Figure S5 and Tables S3 and S4. ….

sjwilliamspa

Mutations will accumulate over age in mitochondrial DNA, however the current study has the difficulty that the authors could not use patient-age-matched controls, in essence they could only compare induced pluripotent stem cells derived from different patients. This could confound the results but the result with higher frequency of mutation in mtDNA in cells reprogrammed from younger patients is interesting but might limit the ability of autologous regenerative therapy in older patients. However reprogramming, although the method not mentioned here although I am assuming by transfection with lentivirus is a rough procedure, involving multiple dedifferentiation steps. Therefore it is very understandable that cells obtained from elderly patients would respond less favorably to such a rough reprogramming regimen, especially if it produced a higher degree of ROS, which has been shown to alter mtDNA. This is why I feel it is more advantageous to obtain a stem cell population from fat cells and forgo the Oct4, htert, reprogramming with lentiviral vectors.

 

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Imaging of Cancer Cells, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)

Imaging of Cancer Cells

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Microscope uses nanosecond-speed laser and deep learning to detect cancer cells more efficiently

April 13, 2016

Scientists at the California NanoSystems Institute at UCLA have developed a new technique for identifying cancer cells in blood samples faster and more accurately than the current standard methods.

In one common approach to testing for cancer, doctors add biochemicals to blood samples. Those biochemicals attach biological “labels” to the cancer cells, and those labels enable instruments to detect and identify them. However, the biochemicals can damage the cells and render the samples unusable for future analyses. There are other current techniques that don’t use labeling but can be inaccurate because they identify cancer cells based only on one physical characteristic.

Time-stretch quantitative phase imaging (TS-QPI) and analytics system

The new technique images cells without destroying them and can identify 16 physical characteristics — including size, granularity and biomass — instead of just one.

The new technique combines two components that were invented at UCLA:

A “photonic time stretch” microscope, which is capable of quickly imaging cells in blood samples. Invented by Barham Jalali, professor and Northrop-Grumman Optoelectronics Chair in electrical engineering, it works by taking pictures of flowing blood cells using laser bursts (similar to how a camera uses a flash). Each flash only lasts nanoseconds (billionths of a second) to avoid damage to cells, but that normally means the images are both too weak to be detected and too fast to be digitized by normal instrumentation. The new microscope overcomes those challenges by using specially designed optics that amplify and boost the clarity of the images, and simultaneously slow them down enough to be detected and digitized at a rate of 36 million images per second.

A deep learning computer program, which identifies cancer cells with more than 95 percent accuracy. Deep learning is a form of artificial intelligence that uses complex algorithms to extract patterns and knowledge from rich multidimenstional datasets, with the goal of achieving accurate decision making.

The study was published in the open-access journal Nature Scientific Reports. The researchers write in the paper that the system could lead to data-driven diagnoses by cells’ physical characteristics, which could allow quicker and earlier diagnoses of cancer, for example, and better understanding of the tumor-specific gene expression in cells, which could facilitate new treatments for disease.

The research was supported by NantWorks, LLC.

 

Abstract of Deep Learning in Label-free Cell Classification

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.

references:

Claire Lifan Chen, Ata Mahjoubfar, Li-Chia Tai, Ian K. Blaby, Allen Huang, Kayvan Reza Niazi & Bahram Jalali. Deep Learning in Label-free Cell Classification. Scientific Reports 6, Article number: 21471 (2016); doi:10.1038/srep21471 (open access)

Supplementary Information

 

Deep Learning in Label-free Cell Classification

Claire Lifan Chen, Ata Mahjoubfar, Li-Chia Tai, Ian K. Blaby, Allen Huang,Kayvan Reza Niazi & Bahram Jalali

Scientific Reports 6, Article number: 21471 (2016)    http://dx.doi.org:/10.1038/srep21471

Deep learning extracts patterns and knowledge from rich multidimenstional datasets. While it is extensively used for image recognition and speech processing, its application to label-free classification of cells has not been exploited. Flow cytometry is a powerful tool for large-scale cell analysis due to its ability to measure anisotropic elastic light scattering of millions of individual cells as well as emission of fluorescent labels conjugated to cells1,2. However, each cell is represented with single values per detection channels (forward scatter, side scatter, and emission bands) and often requires labeling with specific biomarkers for acceptable classification accuracy1,3. Imaging flow cytometry4,5 on the other hand captures images of cells, revealing significantly more information about the cells. For example, it can distinguish clusters and debris that would otherwise result in false positive identification in a conventional flow cytometer based on light scattering6.

In addition to classification accuracy, the throughput is another critical specification of a flow cytometer. Indeed high throughput, typically 100,000 cells per second, is needed to screen a large enough cell population to find rare abnormal cells that are indicative of early stage diseases. However there is a fundamental trade-off between throughput and accuracy in any measurement system7,8. For example, imaging flow cytometers face a throughput limit imposed by the speed of the CCD or the CMOS cameras, a number that is approximately 2000 cells/s for present systems9. Higher flow rates lead to blurred cell images due to the finite camera shutter speed. Many applications of flow analyzers such as cancer diagnostics, drug discovery, biofuel development, and emulsion characterization require classification of large sample sizes with a high-degree of statistical accuracy10. This has fueled research into alternative optical diagnostic techniques for characterization of cells and particles in flow.

Recently, our group has developed a label-free imaging flow-cytometry technique based on coherent optical implementation of the photonic time stretch concept11. This instrument overcomes the trade-off between sensitivity and speed by using Amplified Time-stretch Dispersive Fourier Transform12,13,14,15. In time stretched imaging16, the object’s spatial information is encoded in the spectrum of laser pulses within a pulse duration of sub-nanoseconds (Fig. 1). Each pulse representing one frame of the camera is then stretched in time so that it can be digitized in real-time by an electronic analog-to-digital converter (ADC). The ultra-fast pulse illumination freezes the motion of high-speed cells or particles in flow to achieve blur-free imaging. Detection sensitivity is challenged by the low number of photons collected during the ultra-short shutter time (optical pulse width) and the drop in the peak optical power resulting from the time stretch. These issues are solved in time stretch imaging by implementing a low noise-figure Raman amplifier within the dispersive device that performs time stretching8,11,16. Moreover, warped stretch transform17,18can be used in time stretch imaging to achieve optical image compression and nonuniform spatial resolution over the field-of-view19. In the coherent version of the instrument, the time stretch imaging is combined with spectral interferometry to measure quantitative phase and intensity images in real-time and at high throughput20. Integrated with a microfluidic channel, coherent time stretch imaging system in this work measures both quantitative optical phase shift and loss of individual cells as a high-speed imaging flow cytometer, capturing 36 million images per second in flow rates as high as 10 meters per second, reaching up to 100,000 cells per second throughput.

Figure 1: Time stretch quantitative phase imaging (TS-QPI) and analytics system; A mode-locked laser followed by a nonlinear fiber, an erbium doped fiber amplifier (EDFA), and a wavelength-division multiplexing (WDM) filter generate and shape a train of broadband optical pulses. http://www.nature.com/article-assets/npg/srep/2016/160315/srep21471/images_hires/m685/srep21471-f1.jpg

 

Box 1: The pulse train is spatially dispersed into a train of rainbow flashes illuminating the target as line scans. The spatial features of the target are encoded into the spectrum of the broadband optical pulses, each representing a one-dimensional frame. The ultra-short optical pulse illumination freezes the motion of cells during high speed flow to achieve blur-free imaging with a throughput of 100,000 cells/s. The phase shift and intensity loss at each location within the field of view are embedded into the spectral interference patterns using a Michelson interferometer. Box 2: The interferogram pulses were then stretched in time so that spatial information could be mapped into time through time-stretch dispersive Fourier transform (TS-DFT), and then captured by a single pixel photodetector and an analog-to-digital converter (ADC). The loss of sensitivity at high shutter speed is compensated by stimulated Raman amplification during time stretch. Box 3: (a) Pulse synchronization; the time-domain signal carrying serially captured rainbow pulses is transformed into a series of one-dimensional spatial maps, which are used for forming line images. (b) The biomass density of a cell leads to a spatially varying optical phase shift. When a rainbow flash passes through the cells, the changes in refractive index at different locations will cause phase walk-off at interrogation wavelengths. Hilbert transformation and phase unwrapping are used to extract the spatial phase shift. (c) Decoding the phase shift in each pulse at each wavelength and remapping it into a pixel reveals the protein concentration distribution within cells. The optical loss induced by the cells, embedded in the pulse intensity variations, is obtained from the amplitude of the slowly varying envelope of the spectral interferograms. Thus, quantitative optical phase shift and intensity loss images are captured simultaneously. Both images are calibrated based on the regions where the cells are absent. Cell features describing morphology, granularity, biomass, etc are extracted from the images. (d) These biophysical features are used in a machine learning algorithm for high-accuracy label-free classification of the cells.

On another note, surface markers used to label cells, such as EpCAM21, are unavailable in some applications; for example, melanoma or pancreatic circulating tumor cells (CTCs) as well as some cancer stem cells are EpCAM-negative and will escape EpCAM-based detection platforms22. Furthermore, large-population cell sorting opens the doors to downstream operations, where the negative impacts of labels on cellular behavior and viability are often unacceptable23. Cell labels may cause activating/inhibitory signal transduction, altering the behavior of the desired cellular subtypes, potentially leading to errors in downstream analysis, such as DNA sequencing and subpopulation regrowth. In this way, quantitative phase imaging (QPI) methods24,25,26,27 that categorize unlabeled living cells with high accuracy are needed. Coherent time stretch imaging is a method that enables quantitative phase imaging at ultrahigh throughput for non-invasive label-free screening of large number of cells.

In this work, the information of quantitative optical loss and phase images are fused into expert designed features, leading to a record label-free classification accuracy when combined with deep learning. Image mining techniques are applied, for the first time, to time stretch quantitative phase imaging to measure biophysical attributes including protein concentration, optical loss, and morphological features of single cells at an ultrahigh flow rate and in a label-free fashion. These attributes differ widely28,29,30,31 among cells and their variations reflect important information of genotypes and physiological stimuli32. The multiplexed biophysical features thus lead to information-rich hyper-dimensional representation of the cells for label-free classification with high statistical precision.

We further improved the accuracy, repeatability, and the balance between sensitivity and specificity of our label-free cell classification by a novel machine learning pipeline, which harnesses the advantages of multivariate supervised learning, as well as unique training by evolutionary global optimization of receiver operating characteristics (ROC). To demonstrate sensitivity, specificity, and accuracy of multi-feature label-free flow cytometry using our technique, we classified (1) OT-IIhybridoma T-lymphocytes and SW-480 colon cancer epithelial cells, and (2) Chlamydomonas reinhardtii algal cells (herein referred to as Chlamydomonas) based on their lipid content, which is related to the yield in biofuel production. Our preliminary results show that compared to classification by individual biophysical parameters, our label-free hyperdimensional technique improves the detection accuracy from 77.8% to 95.5%, or in other words, reduces the classification inaccuracy by about five times.     ……..

 

Feature Extraction

The decomposed components of sequential line scans form pairs of spatial maps, namely, optical phase and loss images as shown in Fig. 2 (see Section Methods: Image Reconstruction). These images are used to obtain biophysical fingerprints of the cells8,36. With domain expertise, raw images are fused and transformed into a suitable set of biophysical features, listed in Table 1, which the deep learning model further converts into learned features for improved classification.

The new technique combines two components that were invented at UCLA:

A “photonic time stretch” microscope, which is capable of quickly imaging cells in blood samples. Invented by Barham Jalali, professor and Northrop-Grumman Optoelectronics Chair in electrical engineering, it works by taking pictures of flowing blood cells using laser bursts (similar to how a camera uses a flash). Each flash only lasts nanoseconds (billionths of a second) to avoid damage to cells, but that normally means the images are both too weak to be detected and too fast to be digitized by normal instrumentation. The new microscope overcomes those challenges by using specially designed optics that amplify and boost the clarity of the images, and simultaneously slow them down enough to be detected and digitized at a rate of 36 million images per second.

A deep learning computer program, which identifies cancer cells with more than 95 percent accuracy. Deep learning is a form of artificial intelligence that uses complex algorithms to extract patterns and knowledge from rich multidimenstional datasets, with the goal of achieving accurate decision making.

The study was published in the open-access journal Nature Scientific Reports. The researchers write in the paper that the system could lead to data-driven diagnoses by cells’ physical characteristics, which could allow quicker and earlier diagnoses of cancer, for example, and better understanding of the tumor-specific gene expression in cells, which could facilitate new treatments for disease.

The research was supported by NantWorks, LLC.

 

http://www.nature.com/article-assets/npg/srep/2016/160315/srep21471/images_hires/m685/srep21471-f2.jpg

The optical loss images of the cells are affected by the attenuation of multiplexed wavelength components passing through the cells. The attenuation itself is governed by the absorption of the light in cells as well as the scattering from the surface of the cells and from the internal cell organelles. The optical loss image is derived from the low frequency component of the pulse interferograms. The optical phase image is extracted from the analytic form of the high frequency component of the pulse interferograms using Hilbert Transformation, followed by a phase unwrapping algorithm. Details of these derivations can be found in Section Methods. Also, supplementary Videos 1 and 2 show measurements of cell-induced optical path length difference by TS-QPI at four different points along the rainbow for OT-II and SW-480, respectively.

Table 1: List of extracted features.

Feature Name    Description         Category

 

Figure 3: Biophysical features formed by image fusion.

(a) Pairwise correlation matrix visualized as a heat map. The map depicts the correlation between all major 16 features extracted from the quantitative images. Diagonal elements of the matrix represent correlation of each parameter with itself, i.e. the autocorrelation. The subsets in box 1, box 2, and box 3 show high correlation because they are mainly related to morphological, optical phase, and optical loss feature categories, respectively. (b) Ranking of biophysical features based on their AUCs in single-feature classification. Blue bars show performance of the morphological parameters, which includes diameter along the interrogation rainbow, diameter along the flow direction, tight cell area, loose cell area, perimeter, circularity, major axis length, orientation, and median radius. As expected, morphology contains most information, but other biophysical features can contribute to improved performance of label-free cell classification. Orange bars show optical phase shift features i.e. optical path length differences and refractive index difference. Green bars show optical loss features representing scattering and absorption by the cell. The best performed feature in these three categories are marked in red.

Figure 4: Machine learning pipeline. Information of quantitative optical phase and loss images are fused to extract multivariate biophysical features of each cell, which are fed into a fully-connected neural network.

The neural network maps input features by a chain of weighted sum and nonlinear activation functions into learned feature space, convenient for classification. This deep neural network is globally trained via area under the curve (AUC) of the receiver operating characteristics (ROC). Each ROC curve corresponds to a set of weights for connections to an output node, generated by scanning the weight of the bias node. The training process maximizes AUC, pushing the ROC curve toward the upper left corner, which means improved sensitivity and specificity in classification.

….   How to cite this article: Chen, C. L. et al. Deep Learning in Label-free Cell Classification.

Sci. Rep. 6, 21471; http://dx.doi.org:/10.1038/srep21471

 

Computer Algorithm Helps Characterize Cancerous Genomic Variations

http://www.genengnews.com/gen-news-highlights/computer-algorithm-helps-characterize-cancerous-genomic-variations/81252626/

To better characterize the functional context of genomic variations in cancer, researchers developed a new computer algorithm called REVEALER. [UC San Diego Health]

Scientists at the University of California San Diego School of Medicine and the Broad Institute say they have developed a new computer algorithm—REVEALER—to better characterize the functional context of genomic variations in cancer. The tool, described in a paper (“Characterizing Genomic Alterations in Cancer by Complementary Functional Associations”) published in Nature Biotechnology, is designed to help researchers identify groups of genetic variations that together associate with a particular way cancer cells get activated, or how they respond to certain treatments.

REVEALER is available for free to the global scientific community via the bioinformatics software portal GenePattern.org.

“This computational analysis method effectively uncovers the functional context of genomic alterations, such as gene mutations, amplifications, or deletions, that drive tumor formation,” said senior author Pablo Tamayo, Ph.D., professor and co-director of the UC San Diego Moores Cancer Center Genomics and Computational Biology Shared Resource.

Dr. Tamayo and team tested REVEALER using The Cancer Genome Atlas (TCGA), the NIH’s database of genomic information from more than 500 human tumors representing many cancer types. REVEALER revealed gene alterations associated with the activation of several cellular processes known to play a role in tumor development and response to certain drugs. Some of these gene mutations were already known, but others were new.

For example, the researchers discovered new activating genomic abnormalities for beta-catenin, a cancer-promoting protein, and for the oxidative stress response that some cancers hijack to increase their viability.

REVEALER requires as input high-quality genomic data and a significant number of cancer samples, which can be a challenge, according to Dr. Tamayo. But REVEALER is more sensitive at detecting similarities between different types of genomic features and less dependent on simplifying statistical assumptions, compared to other methods, he adds.

“This study demonstrates the potential of combining functional profiling of cells with the characterizations of cancer genomes via next-generation sequencing,” said co-senior author Jill P. Mesirov, Ph.D., professor and associate vice chancellor for computational health sciences at UC San Diego School of Medicine.

 

Characterizing genomic alterations in cancer by complementary functional associations

Jong Wook Kim, Olga B Botvinnik, Omar Abudayyeh, Chet Birger, et al.

Nature Biotechnology (2016)              http://dx.doi.org:/10.1038/nbt.3527

Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment. We used REVEALER to uncover complementary genomic alterations associated with the transcriptional activation of β-catenin and NRF2, MEK-inhibitor sensitivity, and KRAS dependency. REVEALER successfully identified both known and new associations, demonstrating the power of combining functional profiles with extensive characterization of genomic alterations in cancer genomes

 

Figure 2: REVEALER results for transcriptional activation of β-catenin in cancer.close

(a) This heatmap illustrates the use of the REVEALER approach to find complementary genomic alterations that match the transcriptional activation of β-catenin in cancer. The target profile is a TCF4 reporter that provides an estimate of…

 

An imaging-based platform for high-content, quantitative evaluation of therapeutic response in 3D tumour models

Jonathan P. Celli, Imran Rizvi, Adam R. Blanden, Iqbal Massodi, Michael D. Glidden, Brian W. Pogue & Tayyaba Hasan

Scientific Reports 4; 3751  (2014)    http://dx.doi.org:/10.1038/srep03751

While it is increasingly recognized that three-dimensional (3D) cell culture models recapitulate drug responses of human cancers with more fidelity than monolayer cultures, a lack of quantitative analysis methods limit their implementation for reliable and routine assessment of emerging therapies. Here, we introduce an approach based on computational analysis of fluorescence image data to provide high-content readouts of dose-dependent cytotoxicity, growth inhibition, treatment-induced architectural changes and size-dependent response in 3D tumour models. We demonstrate this approach in adherent 3D ovarian and pancreatic multiwell extracellular matrix tumour overlays subjected to a panel of clinically relevant cytotoxic modalities and appropriately designed controls for reliable quantification of fluorescence signal. This streamlined methodology reads out the high density of information embedded in 3D culture systems, while maintaining a level of speed and efficiency traditionally achieved with global colorimetric reporters in order to facilitate broader implementation of 3D tumour models in therapeutic screening.

The attrition rates for preclinical development of oncology therapeutics are particularly dismal due to a complex set of factors which includes 1) the failure of pre-clinical models to recapitulate determinants of in vivo treatment response, and 2) the limited ability of available assays to extract treatment-specific data integral to the complexities of therapeutic responses1,2,3. Three-dimensional (3D) tumour models have been shown to restore crucial stromal interactions which are missing in the more commonly used 2D cell culture and that influence tumour organization and architecture4,5,6,7,8, as well as therapeutic response9,10, multicellular resistance (MCR)11,12, drug penetration13,14, hypoxia15,16, and anti-apoptotic signaling17. However, such sophisticated models can only have an impact on therapeutic guidance if they are accompanied by robust quantitative assays, not only for cell viability but also for providing mechanistic insights related to the outcomes. While numerous assays for drug discovery exist18, they are generally not developed for use in 3D systems and are often inherently unsuitable. For example, colorimetric conversion products have been noted to bind to extracellular matrix (ECM)19 and traditional colorimetric cytotoxicity assays reduce treatment response to a single number reflecting a biochemical event that has been equated to cell viability (e.g. tetrazolium salt conversion20). Such approaches fail to provide insight into the spatial patterns of response within colonies, morphological or structural effects of drug response, or how overall culture viability may be obscuring the status of sub-populations that are resistant or partially responsive. Hence, the full benefit of implementing 3D tumour models in therapeutic development has yet to be realized for lack of analytical methods that describe the very aspects of treatment outcome that these systems restore.

Motivated by these factors, we introduce a new platform for quantitative in situ treatment assessment (qVISTA) in 3D tumour models based on computational analysis of information-dense biological image datasets (bioimage-informatics)21,22. This methodology provides software end-users with multiple levels of complexity in output content, from rapidly-interpreted dose response relationships to higher content quantitative insights into treatment-dependent architectural changes, spatial patterns of cytotoxicity within fields of multicellular structures, and statistical analysis of nodule-by-nodule size-dependent viability. The approach introduced here is cognizant of tradeoffs between optical resolution, data sampling (statistics), depth of field, and widespread usability (instrumentation requirement). Specifically, it is optimized for interpretation of fluorescent signals for disease-specific 3D tumour micronodules that are sufficiently small that thousands can be imaged simultaneously with little or no optical bias from widefield integration of signal along the optical axis of each object. At the core of our methodology is the premise that the copious numerical readouts gleaned from segmentation and interpretation of fluorescence signals in these image datasets can be converted into usable information to classify treatment effects comprehensively, without sacrificing the throughput of traditional screening approaches. It is hoped that this comprehensive treatment-assessment methodology will have significant impact in facilitating more sophisticated implementation of 3D cell culture models in preclinical screening by providing a level of content and biological relevance impossible with existing assays in monolayer cell culture in order to focus therapeutic targets and strategies before costly and tedious testing in animal models.

Using two different cell lines and as depicted in Figure 1, we adopt an ECM overlay method pioneered originally for 3D breast cancer models23, and developed in previous studies by us to model micrometastatic ovarian cancer19,24. This system leads to the formation of adherent multicellular 3D acini in approximately the same focal plane atop a laminin-rich ECM bed, implemented here in glass-bottom multiwell imaging plates for automated microscopy. The 3D nodules resultant from restoration of ECM signaling5,8, are heterogeneous in size24, in contrast to other 3D spheroid methods, such as rotary or hanging drop cultures10, in which cells are driven to aggregate into uniformly sized spheroids due to lack of an appropriate substrate to adhere to. Although the latter processes are also biologically relevant, it is the adherent tumour populations characteristic of advanced metastatic disease that are more likely to be managed with medical oncology, which are the focus of therapeutic evaluation herein. The heterogeneity in 3D structures formed via ECM overlay is validated here by endoscopic imaging ofin vivo tumours in orthotopic xenografts derived from the same cells (OVCAR-5).

 

Figure 1: A simplified schematic flow chart of imaging-based quantitative in situ treatment assessment (qVISTA) in 3D cell culture.

(This figure was prepared in Adobe Illustrator® software by MD Glidden, JP Celli and I Rizvi). A detailed breakdown of the image processing (Step 4) is provided in Supplemental Figure 1.

A critical component of the imaging-based strategy introduced here is the rational tradeoff of image-acquisition parameters for field of view, depth of field and optical resolution, and the development of image processing routines for appropriate removal of background, scaling of fluorescence signals from more than one channel and reliable segmentation of nodules. In order to obtain depth-resolved 3D structures for each nodule at sub-micron lateral resolution using a laser-scanning confocal system, it would require ~ 40 hours (at approximately 100 fields for each well with a 20× objective, times 1 minute/field for a coarse z-stack, times 24 wells) to image a single plate with the same coverage achieved in this study. Even if the resources were available to devote to such time-intensive image acquisition, not to mention the processing, the optical properties of the fluorophores would change during the required time frame for image acquisition, even with environmental controls to maintain culture viability during such extended imaging. The approach developed here, with a mind toward adaptation into high throughput screening, provides a rational balance of speed, requiring less than 30 minutes/plate, and statistical rigour, providing images of thousands of nodules in this time, as required for the high-content analysis developed in this study. These parameters can be further optimized for specific scenarios. For example, we obtain the same number of images in a 96 well plate as for a 24 well plate by acquiring only a single field from each well, rather than 4 stitched fields. This quadruples the number conditions assayed in a single run, at the expense of the number of nodules per condition, and therefore the ability to obtain statistical data sets for size-dependent response, Dfrac and other segmentation-dependent numerical readouts.

 

We envision that the system for high-content interrogation of therapeutic response in 3D cell culture could have widespread impact in multiple arenas from basic research to large scale drug development campaigns. As such, the treatment assessment methodology presented here does not require extraordinary optical instrumentation or computational resources, making it widely accessible to any research laboratory with an inverted fluorescence microscope and modestly equipped personal computer. And although we have focused here on cancer models, the methodology is broadly applicable to quantitative evaluation of other tissue models in regenerative medicine and tissue engineering. While this analysis toolbox could have impact in facilitating the implementation of in vitro 3D models in preclinical treatment evaluation in smaller academic laboratories, it could also be adopted as part of the screening pipeline in large pharma settings. With the implementation of appropriate temperature controls to handle basement membranes in current robotic liquid handling systems, our analyses could be used in ultra high-throughput screening. In addition to removing non-efficacious potential candidate drugs earlier in the pipeline, this approach could also yield the additional economic advantage of minimizing the use of costly time-intensive animal models through better estimates of dose range, sequence and schedule for combination regimens.

 

Microscope Uses AI to Find Cancer Cells More Efficiently

Thu, 04/14/2016 – by Shaun Mason

http://www.mdtmag.com/news/2016/04/microscope-uses-ai-find-cancer-cells-more-efficiently

Scientists at the California NanoSystems Institute at UCLA have developed a new technique for identifying cancer cells in blood samples faster and more accurately than the current standard methods.

In one common approach to testing for cancer, doctors add biochemicals to blood samples. Those biochemicals attach biological “labels” to the cancer cells, and those labels enable instruments to detect and identify them. However, the biochemicals can damage the cells and render the samples unusable for future analyses.

There are other current techniques that don’t use labeling but can be inaccurate because they identify cancer cells based only on one physical characteristic.

The new technique images cells without destroying them and can identify 16 physical characteristics — including size, granularity and biomass — instead of just one. It combines two components that were invented at UCLA: a photonic time stretch microscope, which is capable of quickly imaging cells in blood samples, and a deep learning computer program that identifies cancer cells with over 95 percent accuracy.

Deep learning is a form of artificial intelligence that uses complex algorithms to extract meaning from data with the goal of achieving accurate decision making.

The study, which was published in the journal Nature Scientific Reports, was led by Barham Jalali, professor and Northrop-Grumman Optoelectronics Chair in electrical engineering; Claire Lifan Chen, a UCLA doctoral student; and Ata Mahjoubfar, a UCLA postdoctoral fellow.

Photonic time stretch was invented by Jalali, and he holds a patent for the technology. The new microscope is just one of many possible applications; it works by taking pictures of flowing blood cells using laser bursts in the way that a camera uses a flash. This process happens so quickly — in nanoseconds, or billionths of a second — that the images would be too weak to be detected and too fast to be digitized by normal instrumentation.

The new microscope overcomes those challenges using specially designed optics that boost the clarity of the images and simultaneously slow them enough to be detected and digitized at a rate of 36 million images per second. It then uses deep learning to distinguish cancer cells from healthy white blood cells.

“Each frame is slowed down in time and optically amplified so it can be digitized,” Mahjoubfar said. “This lets us perform fast cell imaging that the artificial intelligence component can distinguish.”

Normally, taking pictures in such minuscule periods of time would require intense illumination, which could destroy live cells. The UCLA approach also eliminates that problem.

“The photonic time stretch technique allows us to identify rogue cells in a short time with low-level illumination,” Chen said.

The researchers write in the paper that the system could lead to data-driven diagnoses by cells’ physical characteristics, which could allow quicker and earlier diagnoses of cancer, for example, and better understanding of the tumor-specific gene expression in cells, which could facilitate new treatments for disease.   …..  see also http://www.nature.com/article-assets/npg/srep/2016/160315/srep21471/images_hires/m685/srep21471-f1.jpg

Chen, C. L. et al. Deep Learning in Label-free Cell Classification.    Sci. Rep. 6, 21471;   http://dx.doi.org:/10.1038/srep21471

 

 

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Protein profiling in cancer and metabolic diseases

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Deep Protein Profiling Key

Company has encouraged by two recent reports that emphasise the importance of protein profiling to improve outcomes in cancer treatment.

http://www.technologynetworks.com/Proteomics/news.aspx?ID=190145

Proteome Sciences plc has strongly encouraged by two recent reports that emphasise the importance of protein profiling to improve outcomes in cancer treatment. These highlight the growing need for more detailed, personal assessment of protein profiles to improve the management of cancer treatment.

In the first study two groups from University College London and Cancer Research UK demonstrated that genetic mutations in cancer can lead to changes in the proteins on the cell surface1. These are new sequences which are seen as foreign by the body’s immune system and, with appropriate immunotherapy, the level of response in lung cancer was greatly enhanced.

However many of the patients with these types of mutations unfortunately still did not respond which highlighted the need for deeper analysis of the protein expression in tumours in order to better appreciate the mechanisms that contribute to treatment failure.

The second study, led by Professor Nigel Bundred of Manchester University, reported that use of two drugs that act on the same breast cancer target, an over-expressing protein called Her-2, were able to eradicate detectable tumours in around 10% of those treated in just 11 days, with 87% of those treated having a proteomic change indicating cells had stopped growing and/or cell death had increased2.

Whilst these results appear very promising it is worth noting that the over-expressing Her-2 target is only present in about 20% of breast tumours meaning this combination therapy was successful in clearing tumours in just 2% of the total breast cancer population.

Dr. Ian Pike, Chief Operating Officer of Proteome Sciences commented, “Both these recent studies should rightly be recognised as important steps forward towards better cancer treatment. However, in order to overcome the limitations of current drug therapy programs, a much deeper and more comprehensive analysis of the complex protein networks that regulate tumour growth and survival is required and will be essential to achieve a major advance in the battle to treat cancer.

“Our SysQuant® workflows provide that solution. As an example, in pancreatic cancer3 we have successfully mapped the complex network of regulatory processes and demonstrate the ability to devise personalised treatment combinations on an individual basis for each patient. A retrospective study with SysQuant® to predict response to the targeted drug Sorafenib in liver cancer is in process and we are planning further prospective trials to guide personalised treatment selection in liver cancer.

“We are already delivering systems-wide biology solutions through SysQuant® and TMTcalibrator™ programs to our clients that are generating novel biological data and results using more sensitive profiling that are helping them to better understand their drug development programs and to provide new biomarkers for tracking patient response in clinical trials.

“We are strongly positioned to deliver more comprehensive analysis of proteins and cellular pathways across other areas of disease and in particular to extend the use of SysQuant® with other leading cancer research groups in liver and other cancers.”

Proteome Sciences has also expanded its offering in personalised medicine through the use of its TMTcalibrator™ technology to uniquely identify protein biomarkers that reveal active cancer and other disease processes in body fluid samples. The importance of these ‘mechanistic’ biomarkers is that they are essential to monitor that drugs are being effective and that they can be used as early biomarkers of disease recurrence.

Using SysQuant® and TMTcalibrator™, Proteome Sciences can deliver more comprehensive analysis and provide unparalleled levels of sensitivity and breadth of coverage of the proteome, enabling faster, more efficient drug development and more accurate disease diagnosis.

 

Discovering ‘Outlier’ Enzymes

Researchers at TSRI and Salk Institute have discovered ‘Outlier’ enzymes that could offer new targets to treat type 2 diabetes and inflammatory disorders.

A team led by scientists at The Scripps Research Institute (TSRI) and the Salk Institute for Biological Studies have discovered two enzymes that appear to play a role in metabolism and inflammation—and might someday be targeted with drugs to treat type 2 diabetes and inflammatory disorders. The discovery is unusual because the enzymes do not bear a resemblance—in their structures or amino-acid sequences—to any known class of enzymes.

The team of scientists nevertheless identified them as “outlier” members of the serine/threonine hydrolase class, using newer techniques that detect biochemical activity. “A huge fraction of the human ‘proteome’ remains uncharacterized, and this paper shows how chemical approaches can be used to uncover proteins of a given functionality that have eluded classification based on sequence or predicted structure,” said co-senior author Benjamin F. Cravatt, chair of TSRI’s Department of Chemical Physiology.

“In this study, we found two genes that control levels of lipids with anti-diabetic and anti-inflammatory activity, suggesting exciting targets for diabetes and inflammatory diseases,” said co-senior author Alan Saghatelian, who holds the Dr. Frederik Paulsen Chair at the Salk Institute. The study, which appeared as a Nature Chemical Biology Advance Online Publication on March 28, 2016, began as an effort in the Cravatt laboratory to discover and characterize new serine/threonine hydrolases using fluorophosphonate (FP) probes—molecules that selectively bind and, in effect, label the active sites of these enzymes.

Pulling FP-binding proteins out of the entire proteome of test cells and identifying them using mass spectrometry techniques, the team matched nearly all to known hydrolases. The major outlier was a protein called androgen-induced gene 1 protein (AIG1). The only other one was a distant cousin in terms of sequence, a protein called ADTRP. “Neither of these proteins had been characterized as an enzyme; in fact, there had been little functional characterization of them at all,” said William H. Parsons, a research associate in the Cravatt laboratory who was co-first author of the study.

Experiments on AIG1 and ADTRP revealed that they do their enzymatic work in a unique way. “It looks like they have an active site that is novel—it had never been described in the literature,” said Parsons. Initial tests with panels of different enzyme inhibitors showed that AIG1 and ADTRP are moderately inhibited by inhibitors of lipases—enzymes that break down fats and other lipids. But on what specific lipids do these newly discovered outlier enzymes normally work?

At the Salk Institute, the Saghatelian laboratory was investigating a class of lipids it had discovered in 2014. Known as fatty acid esters of hydroxy fatty acids (FAHFAs), these molecules showed strong therapeutic potential. Saghatelian and his colleagues had found that boosting the levels of one key FAHFA lipid normalizes glucose levels in diabetic mice and also reduces inflammation.

“[Ben Cravatt’s] lab was screening panels of lipids to find the ones that their new enzymes work on,” said Saghatelian, who is a former research associate in the Cravatt laboratory. “We suggested they throw FAHFAs in there—and these turned out to be very good substrates.” The Cravatt laboratory soon developed powerful inhibitors of the newly discovered enzymes, and the two labs began working together, using the inhibitors and genetic techniques to explore the enzymes’ functions in vitro and in cultured cells.

Co-first author Matthew J. Kolar, an MD-PhD student, performed most of the experiments in the Saghatelian lab. The team concluded that AIG1 and ADTRP, at least in the cell types tested, appear to work mainly to break down FAHFAs and not any other major class of lipid. In principle, inhibitors of AIG1 and ADTRP could be developed into FAHFA-boosting therapies.

“Our prediction,” said Saghatelian, “is that if FAHFAs do what we think they’re doing, then using an enzyme inhibitor to block their degradation would make FAHFA levels go up and should thus reduce inflammation as well as improve glucose levels and insulin sensitivity.” The two labs are now collaborating on further studies of the new enzymes—and the potential benefits of inhibiting them—in mouse models of diabetes, inflammation and autoimmune disease.

“One of the neat things this study shows,” said Cravatt, “is that even for enzyme classes as well studied as the hydrolases, there may still be hidden members that, presumably by convergent evolution, arrived at that basic enzyme mechanism despite sharing no sequence or structural homology.”

Other co-authors of the study, “AIG1 and ADTRP are atypical integral membrane hydrolases that degrade bioactive FAHFAs,” were Siddhesh S. Kamat, Armand B. Cognetta III, Jonathan J. Hulce and Enrique Saez, of TSRI; and co-senior author Barbara B. Kahn of Beth Israel Deaconess Medical Center and Harvard Medical School

 

New Weapon Against Breast Cancer

Molecular marker in healthy tissue can predict a woman’s risk of getting the disease, research says.

Harvard Stem Cell Institute (HSCI) researchers at Dana-Farber Cancer Institute (DFCI) and collaborators at Brigham and Women’s Hospital (BWH) have identified a molecular marker in normal breast tissue that can predict a woman’s risk for developing breast cancer, the leading cause of death in women with cancer worldwide.

The work, led by HSCI principal faculty member Kornelia Polyak and Rulla Tamimi of BWH, was published in an early online release and in the April 1 issue of Cancer Research.

The study builds on Polyak’s earlier research finding that women already identified as having a high risk of developing cancer — namely those with a mutation called BRCA1 or BRCA2 — or women who did not give birth before their 30s had a higher number of mammary gland progenitor cells.

In the latest study, Polyak, Tamimi, and their colleagues examined biopsies, some taken as many as four decades ago, from 302 participants in the Nurses’ Health Study and the Nurses’ Health Study II who had been diagnosed with benign breast disease. The researchers compared tissue from the 69 women who later developed cancer to the tissue from the 233 women who did not. They found that women were five times as likely to develop cancer if they had a higher percentage of Ki67, a molecular marker that identifies proliferating cells, in the cells that line the mammary ducts and milk-producing lobules. These cells, called the mammary epithelium, undergo drastic changes throughout a woman’s life, and the majority of breast cancers originate in these tissues.

Doctors already test breast tumors for Ki67 levels, which can inform decisions about treatment, but this is the first time scientists have been able to link Ki67 to precancerous tissue and use it as a predictive tool.

“Instead of only telling women that they don’t have cancer, we could test the biopsies and tell women if they were at high risk or low risk for developing breast cancer in the future,” said Polyak, a breast cancer researcher at Dana-Farber and co-senior author of the paper.

“Currently, we are not able to do a very good job at distinguishing women at high and low risk of breast cancer,” added co-senior author Tamimi, an associate professor at the Harvard T.H. Chan School of Public Health and Harvard Medical School. “By identifying women at high risk of breast cancer, we can better develop individualized screening and also target risk reducing strategies.”

To date, mammograms are the best tool for the early detection, but there are risks associated with screening. False positive and negative results and over-diagnosis could cause psychological distress, delay treatment, or lead to overtreatment, according to the National Cancer Institute (NCI).

Mammography machines also use low doses of radiation. While a single mammogram is unlikely to cause harm, repeated screening can potentially cause cancer, though the NCI writes that the benefits “nearly always outweigh the risks.”

“If we can minimize unnecessary radiation for women at low risk, that would be good,” said Tamimi.

Screening for Ki67 levels would “be easy to apply in the current setting,” said Polyak, though the researchers first want to reproduce the results in an independent cohort of women.

 

AIG1 and ADTRP are atypical integral membrane hydrolases that degrade bioactive FAHFAs

William H ParsonsMatthew J Kolar, …., Barbara B KahnAlan Saghatelian & Benjamin F Cravatt

Nature Chemical Biology 28 March 2016                    http://dx.doi.org:/10.1038/nchembio.2051

Enzyme classes may contain outlier members that share mechanistic, but not sequence or structural, relatedness with more common representatives. The functional annotation of such exceptional proteins can be challenging. Here, we use activity-based profiling to discover that the poorly characterized multipass transmembrane proteins AIG1 and ADTRP are atypical hydrolytic enzymes that depend on conserved threonine and histidine residues for catalysis. Both AIG1 and ADTRP hydrolyze bioactive fatty acid esters of hydroxy fatty acids (FAHFAs) but not other major classes of lipids. We identify multiple cell-active, covalent inhibitors of AIG1 and show that these agents block FAHFA hydrolysis in mammalian cells. These results indicate that AIG1 and ADTRP are founding members of an evolutionarily conserved class of transmembrane threonine hydrolases involved in bioactive lipid metabolism. More generally, our findings demonstrate how chemical proteomics can excavate potential cases of convergent or parallel protein evolution that defy conventional sequence- and structure-based predictions.

Figure 1: Discovery and characterization of AIG1 and ADTRP as FP-reactive proteins in the human proteome.

 

http://www.nature.com/nchembio/journal/vaop/ncurrent/carousel/nchembio.2051-F1.jpg

(a) Competitive ABPP-SILAC analysis to identify FP-alkyne-inhibited proteins, in which protein enrichment and inhibition were measured in proteomic lysates from SKOV3 cells treated with FP-alkyne (20 μM, 1 h) or DMSO using the FP-biotin…

 

  1. Willems, L.I., Overkleeft, H.S. & van Kasteren, S.I. Current developments in activity-based protein profiling. Bioconjug. Chem. 25, 11811191 (2014).
  2. Niphakis, M.J. & Cravatt, B.F. Enzyme inhibitor discovery by activity-based protein profiling.Annu. Rev. Biochem. 83, 341377 (2014).
  3. Berger, A.B., Vitorino, P.M. & Bogyo, M. Activity-based protein profiling: applications to biomarker discovery, in vivo imaging and drug discovery. Am. J. Pharmacogenomics 4,371381 (2004).
  4. Liu, Y., Patricelli, M.P. & Cravatt, B.F. Activity-based protein profiling: the serine hydrolases.Proc. Natl. Acad. Sci. USA 96, 1469414699 (1999).
  5. Simon, G.M. & Cravatt, B.F. Activity-based proteomics of enzyme superfamilies: serine hydrolases as a case study. J. Biol. Chem. 285, 1105111055 (2010).
  6. Bachovchin, D.A. et al. Superfamily-wide portrait of serine hydrolase inhibition achieved by library-versus-library screening. Proc. Natl. Acad. Sci. USA 107, 2094120946 (2010).
  7. Jessani, N. et al. A streamlined platform for high-content functional proteomics of primary human specimens. Nat. Methods 2, 691697 (2005).
  8. Higa, H.H., Diaz, S. & Varki, A. Biochemical and genetic evidence for distinct membrane-bound and cytosolic sialic acid O-acetyl-esterases: serine-active-site enzymes. Biochem. Biophys. Res. Commun. 144, 10991108 (1987).

Academic cross-fertilization by public screening yields a remarkable class of protein phosphatase methylesteras-1 inhibitors

Proc Natl Acad Sci U S A. 2011 Apr 26; 108(17): 6811–6816.    doi:  10.1073/pnas.1015248108
National Institutes of Health (NIH)-sponsored screening centers provide academic researchers with a special opportunity to pursue small-molecule probes for protein targets that are outside the current interest of, or beyond the standard technologies employed by, the pharmaceutical industry. Here, we describe the outcome of an inhibitor screen for one such target, the enzyme protein phosphatase methylesterase-1 (PME-1), which regulates the methylesterification state of protein phosphatase 2A (PP2A) and is implicated in cancer and neurodegeneration. Inhibitors of PME-1 have not yet been described, which we attribute, at least in part, to a dearth of substrate assays compatible with high-throughput screening. We show that PME-1 is assayable by fluorescence polarization-activity-based protein profiling (fluopol-ABPP) and use this platform to screen the 300,000+ member NIH small-molecule library. This screen identified an unusual class of compounds, the aza-β-lactams (ABLs), as potent (IC50 values of approximately 10 nM), covalent PME-1 inhibitors. Interestingly, ABLs did not derive from a commercial vendor but rather an academic contribution to the public library. We show using competitive-ABPP that ABLs are exquisitely selective for PME-1 in living cells and mice, where enzyme inactivation leads to substantial reductions in demethylated PP2A. In summary, we have combined advanced synthetic and chemoproteomic methods to discover a class of ABL inhibitors that can be used to selectively perturb PME-1 activity in diverse biological systems. More generally, these results illustrate how public screening centers can serve as hubs to create spontaneous collaborative opportunities between synthetic chemistry and chemical biology labs interested in creating first-in-class pharmacological probes for challenging protein targets.

Protein phosphorylation is a pervasive and dynamic posttranslational protein modification in eukaryotic cells. In mammals, more than 500 protein kinases catalyze the phosphorylation of serine, threonine, and tyrosine residues on proteins (1). A much more limited number of phosphatases are responsible for reversing these phosphorylation events (2). For instance, protein phosphatase 2A (PP2A) and PP1 are thought to be responsible together for > 90% of the total serine/threonine phosphatase activity in mammalian cells (3). Specificity is imparted on PP2A activity by multiple mechanisms, including dynamic interactions between the catalytic subunit (C) and different protein-binding partners (B subunits), as well as a variety of posttranslational chemical modifications (2, 4). Within the latter category is an unusual methylesterification event found at the C terminus of the catalytic subunit of PP2A that is introduced and removed by a specific methyltransferase (leucine carbxoylmethyltransferase-1 or LCMT1) (5, 6) and methylesterase (protein phosphatase methylesterase-1 or PME-1) (7), respectively (Fig. 1A). PP2A carboxymethylation (hereafter referred to as “methylation”) has been proposed to regulate PP2A activity, at least in part, by modulating the binding interaction of the C subunit with various regulatory B subunits (810). A predicted outcome of these shifts in subunit association is the targeting of PP2A to different protein substrates in cells. PME-1 has also been hypothesized to stabilize inactive forms of nuclear PP2A (11), and recent structural studies have shed light on the physical interactions between PME-1 and the PP2A holoenzyme (12).

There were several keys to the success of our probe development effort. First, screening for inhibitors of PME-1 benefited from the fluopol-ABPP technology, which circumvented the limited throughput of previously described substrate assays for this enzyme. Second, we were fortunate that the NIH compound library contained several members of the ABL class of small molecules. These chiral compounds, which represent an academic contribution to the NIH library, occupy an unusual portion of structural space that is poorly accessed by commercial compound collections. Although at the time of their original synthesis (23) it may not have been possible to predict whether these ABLs would show specific biological activity, their incorporation into the NIH library provided a forum for screening against many proteins and cellular targets, culminating in their identification as PME-1 inhibitors. We then used advanced chemoproteomic assays to confirm the remarkable selectivity displayed by ABLs for PME-1 across (and beyond) the serine hydrolase superfamily. That the mechanism for PME-1 inhibition involves acylation of the enzyme’s conserved serine nucleophile (Fig. 3) suggests that exploration of a more structurally diverse set of ABLs might uncover inhibitors for other serine hydrolases. In this way, the chemical information gained from a single high-throughput screen may be leveraged to initiate probe development programs for additional enzyme targets.

Projecting forward, this research provides an example of how public small-molecule screening centers can serve as a portal for spawning academic collaborations between chemical biology and synthetic chemistry labs. By continuing to develop versatile high-throughput screens and combining them with a small-molecule library of expanding structural diversity conferred by advanced synthetic methodologies, academic biologists and chemists are well-positioned to collaboratively deliver pharmacological probes for a wide range of proteins and pathways in cell biology.

 

New weapon against breast cancer

Molecular marker in healthy tissue can predict a woman’s risk of getting the disease, research says

April 6, 2016 | Popular
BRC_Cancer605

 

New Group of Aging-Related Proteins Discovered

http://www.genengnews.com/gen-news-highlights/new-group-of-aging-related-proteins-discovered/81252599/

Scientists have discovered a group of six proteins that may help to divulge secrets of how we age, potentially unlocking new insights into diabetes, Alzheimer’s, cancer, and other aging-related diseases.

The proteins appear to play several roles in our bodies’ cells, from decreasing the amount of damaging free radicals and controlling the rate at which cells die to boosting metabolism and helping tissues throughout the body respond better to insulin. The naturally occurring amounts of each protein decrease with age, leading investigators to believe that they play an important role in the aging process and the onset of diseases linked to older age.

The research team led by Pinchas Cohen, M.D., dean and professor of the University of Southern California Leonard Davis School of Gerontology, identified the proteins and observed their origin from mitochondria and their game-changing roles in metabolism and cell survival. This latest finding builds upon prior research by Dr. Cohen and his team that uncovered two significant proteins, humanin and MOTS-c, hormones that appear to have significant roles in metabolism and diseases of aging.

Unlike most other proteins, humanin and MOTS-c are encoded in mitochondria. Dr. Cohen’s team used computer analysis to see if the part of the mitochondrial genome that provides the code for humanin was coding for other proteins as well. The analysis uncovered the genes for six new proteins, which were dubbed small humanin-like peptides, or SHLPs, 1 through 6 (pronounced “schlep”).

After identifying the six SHLPs and successfully developing antibodies to test for several of them, the team examined both mouse tissues and human cells to determine their abundance in different organs as well as their functions. The proteins were distributed quite differently among organs, which suggests that the proteins have varying functions based on where they are in the body. Of particular interest is SHLP 2, according to Dr. Cohen.  The protein appears to have insulin-sensitizing, antidiabetic effects as well as neuroprotective activity that may emerge as a strategy to combat Alzheimer’s disease. He added that SHLP 6 is also intriguing, with a unique ability to promote cancer cell death and thus potentially target malignant diseases.

Proteins That May Protect Against Age Related Illnesses Discovered

 

The cell proliferation antigen Ki-67 organises heterochromatin

 Michal Sobecki, 

Antigen Ki-67 is a nuclear protein expressed in proliferating mammalian cells. It is widely used in cancer histopathology but its functions remain unclear. Here, we show that Ki-67 controls heterochromatin organisation. Altering Ki-67 expression levels did not significantly affect cell proliferation in vivo. Ki-67 mutant mice developed normally and cells lacking Ki-67 proliferated efficiently. Conversely, upregulation of Ki-67 expression in differentiated tissues did not prevent cell cycle arrest. Ki-67 interactors included proteins involved in nucleolar processes and chromatin regulators. Ki-67 depletion disrupted nucleologenesis but did not inhibit pre-rRNA processing. In contrast, it altered gene expression. Ki-67 silencing also had wide-ranging effects on chromatin organisation, disrupting heterochromatin compaction and long-range genomic interactions. Trimethylation of histone H3K9 and H4K20 was relocalised within the nucleus. Finally, overexpression of human or Xenopus Ki-67 induced ectopic heterochromatin formation. Altogether, our results suggest that Ki-67 expression in proliferating cells spatially organises heterochromatin, thereby controlling gene expression.

 

A protein called Ki-67 is only produced in actively dividing cells, where it is located in the nucleus – the structure that contains most of the cell’s DNA. Researchers often use Ki-67 as a marker to identify which cells are actively dividing in tissue samples from cancer patients, and previous studies indicated that Ki-67 is needed for cells to divide. However, the exact role of this protein was not clear. Before cells can divide they need to make large amounts of new proteins using molecular machines called ribosomes and it has been suggested that Ki-67 helps to produce ribosomes.

Now, Sobecki et al. used genetic techniques to study the role of Ki-67 in mice. The experiments show that Ki-67 is not required for cells to divide in the laboratory or to make ribosomes. Instead, Ki-67 alters the way that DNA is packaged in the nucleus. Loss of Ki-67 from mice cells resulted in DNA becoming less compact, which in turn altered the activity of genes in those cells.

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3D Imaging of Cancer Cells

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

3D Imaging of Cancer Cells Could Lead to Improved Ability of Pathologists and Radiologists to Plan Cancer Treatments and Monitor Cell Interactions

Dark Daily Apr 8th 2016        Jon Stone

https://www.linkedin.com/pulse/3d-imaging-cancer-cells-could-lead-improved-ability-plan-joseph-colao

 

3D Imaging of Cancer Cells Could Lead to Improved Ability of Pathologists and Radiologists to Plan Cancer Treatments and Monitor Cell Interactions.

New technology from researchers at the University of Texas Southwestern Medical Center enables the ability to study cancer cells in their native microenvironments.

Imaging research is one step closer to giving clinicians a way to do high-resolution scans of malignant cells in order to diagnose cancer and help identify useful therapies. If this technology were to prove successful in clinical studies, it might change how anatomic pathologists and radiologists diagnose and treat cancer.

Researchers at the University of Texas Southwestern Medical Center developed a way to create near-isotropic, high-resolution scans of cells within their microenvironments. The process involves utilizing a combination of two-photonBessel beams and specialized filtering.

New Imaging Approach Could be Useful to Both Pathologists and Radiologists

In a recent press release, senior author Reto Fiolka, PhD, said “there is clear evidence that the environment strongly affects cellular behavior—thus, the value of cell culture experiments on glass must at least be questioned. Our microscope is one tool that may bring us a deeper understanding of the molecular mechanisms that drive cancer cell behavior, since it enables high-resolution imaging in more realistic tumor.”

In a study in Developmental Cell, Erik S. Welf, PhD, et al, described the new microenvironmental selective plane illumination microscopy (meSPIM). When developing the technology, the team outlined three goals:

1. The microscope design must not prohibitively constrain microenvironmental properties.

2. Spatial and temporal resolution must match the cellular features of interest.

3. Spatial resolution must be isotropic to avoid spatial bias in quantitative measurements.

This new technology offers pathologists and medical laboratory scientists a new look at cancer cells and other diseases. The study notes that meSPIM eliminates the influence of stiff barriers, such as glass slide covers, while also allowing a level of control over both mechanical and chemical influences that was previously impossible.

Early meSPIM Research Reveals New Cell Behaviors

Early use of meSPIM in observing melanoma cells is already offering new insights into the relationship between the cell behavior of cellular- and subcellular-scale mechanisms and the microenvironment in which these cells exist. The study notes, “The ability to image fine cellular details in controllable microenvironments revealed morphodynamic features not commonly observed in the narrow range of mechanical environments usually studied in vitro.”

One such difference is the appearance of blebbing. Created by melanoma cells and lines, these small protrusions are thought to aid in cell mobility and survival. Using meSPIM, observers could follow the blebbing process in real-time. Formation of blebs on slides and within an extracellular matrix (ECM) showed significant differences in both formation and manipulation of the surrounding microenvironment.

The team is also using meSPIM to take a look at membrane-associated biosensor and cytosolic biosensor signals in 3D. They hope that investigation of proteins such as phosphatidylinositol 3-kinase (PI3K) and protein kinase C will help to further clarify the roles these signals play in reorientation of fibroblasts.

meSPIM combined with computer vision enables imaging, visualization, and quantification of how cells alter collagen fibers over large distances within an image volume measuring 100 mm on each side. (Photo Copyright: Welf and Driscoll et al.)

The research team believes this opens new possibilities for studying diseases at a subcellular level, saying, “Cell biology is necessarily restricted to studying what we can measure. Accordingly, while the last hundred years have yielded incredible insight into cellular processes, unfortunately most of these studies have involved cells plated onto flat, stiff surfaces that are drastically different from the in vivo microenvironment …

“Here, we introduce an imaging platform that enables detailed subcellular observations without compromising microenvironmental control and thus should open a window for addressing these fundamental questions of cell biology.”

Limitations of meSPIM

One significant issue associated with the use of meSPIM is the need to process the large quantity of data into useful information. Algorithms currently allow for automatic bleb detection. However, manual marking, while time consuming, still provides increased accuracy. Researchers believe the next step in improving the quality of meSPIM scans lie in computer platforms designed to extract and process the scan data.

Until this process is automated, user bias, sample mounting, and data handling will remain risks for introducing errors into the collected data. Yet, even in its early stages, meSPIM offers new options for assessing the state of cancer cells and may eventually provide pathologists and radiologists with additional information when creating treatment plans or assessments.

 

Seeing cancer cells in 3-D (w/ Video)

http://phys.org/news/2016-02-cancer-cells-d-video.html

 

Cancer in 3-D

http://cdn.phys.org/newman/csz/news/800/2016/cancerin3d.png

Extracted surfaces of two cancer cells. (Left) A lung cancer cell colored by actin intensity near the cell surface. Actin is a structural molecule that is integral to cell movement. (Right) A melanoma cell colored by PI3-kinase activity near the cell surface. PI3K is a signaling molecule that is key to many cell processes. Credit: Welf and Driscoll et al.

Cancer cells don’t live on glass slides, yet the vast majority of images related to cancer biology come from the cells being photographed on flat, two-dimensional surfaces—images that are sometimes used to make conclusions about the behaviour of cells that normally reside in a more complex environment. But a new high-resolution microscope, presented February 22 in Developmental Cell, now makes it possible to visualize cancer cells in 3D and record how they are signaling to other parts of their environment, revealing previously unappreciated biology of how cancer cells survive and disperse within living things.

“There is clear evidence that the environment strongly affects cellular behavior—thus, the value of cell culture experiments on glass must at least be questioned,” says senior author Reto Fiolka, an optical scientist at the University of Texas Southwestern Medical Center. “Our is one tool that may bring us a deeper understanding of the molecular mechanisms that drive cancer cell behavior, since it enables high-resolution imaging in more realistic tumor environments.”

Read more at: http://phys.org/news/2016-02-cancer-cells-d-video.html#jCp

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Blood test uses DNA strands of dying cells

Curators:  Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

LPBI

 

Hadassah-Developed Blood Test Detects Multiple Sclerosis, Cancer & Brain Damage

http://www.hadassah.org/news-stories/blood-test-detects-neurodegenerative-disease.html

A new blood test that uses the DNA strands of dying cells to detect diabetes, cancer, traumatic brain injury, and neurodegenerative disease has been developed by researchers at Hadassah Medical Organization (HMO) and The Hebrew University.

In a study involving 320 patients, the researchers were able to infer cell death in specific tissues by looking at the unique chemical modifications (called methylation patterns) of circulating DNA that these dying cells release. Previously, it had not been possible to measure cell death in specific human tissues non-invasively.

The findings are reported in the March 14, 2016 online edition of Proceedings of National Academy of Sciences USA, in an article entitled “Identification of tissue specific cell death using methylation patterns of circulating DNA.”  Prof. Benjamin Glaser, head of Endocrinology at Hadassah, and Dr. Ruth Shemer and Prof. Yuval Dor from The Hebrew University of Jerusalem led an international team in performing the groundbreaking research.

Cell death is a central feature in health and disease. It can signify the early stages of pathology (e.g. a developing tumor or the beginning of an autoimmune or neurodegenerative disease); it can illuminate whether a disease has progressed and whether a particular treatment, such as chemotherapy, is working; and it can alert physicians to unintended toxic effects of treatment or the early rejection of a transplant.

As the researchers relate: “The approach can be adapted to identify cfDNA (cell-free circulating DNA) derived from any cell type in the body, offering a minimally invasive window for diagnosing and monitoring a broad spectrum of human pathologies as well as providing a better understanding of normal tissue dynamics.”

“In the long run,” notes Prof. Glaser, “we envision a new type of blood test aimed at the sensitive detection of tissue damage, even without a-priori suspicion of disease in a specific organ. We believe that such a tool will have broad utility in diagnostic medicine and in the study of human biology.”

The research was performed by Hebrew University students Roni Lehmann-Werman, Daniel Neiman, Hai Zemmour, Joshua Moss and Judith Magenheim, aided by clinicians and scientists from Hadassah Medical Center, Sheba Medical Center, and from institutions in Germany, Sweden, the USA and Canada, who provided precious blood samples from patients.

Scientists have known for decades that dying cells release fragmented DNA into the blood; however, since the DNA sequence of all cells in the body is identical, it had not been possible to determine the tissue of origin of the circulating DNA.  Knowing that the DNA of each cell type carries a unique methylation and that methylation patterns of DNA account for the identity of cells, the researchers were able to use patterns of methylated DNA sequences as biomarkers to detect the origin of the DNA and to identify a specific pathology. For example, they were able to detect evidence of pancreatic beta-cell death in the blood of patients with new-onset type 1 diabetes, oligodendrocyte cell death in patients with relapsing multiple sclerosis, brain cell death in patients after traumatic or ischemic brain damage, and exocrine pancreatic tissue cell death in patients with pancreatic cancer or pancreatitis.

Support for the research came from the Juvenile Diabetes Research Foundation, the Human Islet Research Network of the National Institutes of Health, the Sir Zalman Cowen Universities Fund, the DFG (a Trilateral German-Israel-Palestine program), and the Soyka pancreatic cancer fund.

 Identification of tissue-specific cell death using methylation patterns of circulating DNA.
Minimally invasive detection of cell death could prove an invaluable resource in many physiologic and pathologic situations. Cell-free circulating DNA (cfDNA) released from dying cells is emerging as a diagnostic tool for monitoring cancer dynamics and graft failure. However, existing methods rely on differences in DNA sequences in source tissues, so that cell death cannot be identified in tissues with a normal genome. We developed a method of detecting tissue-specific cell death in humans based on tissue-specific methylation patterns in cfDNA. We interrogated tissue-specific methylome databases to identify cell type-specific DNA methylation signatures and developed a method to detect these signatures in mixed DNA samples. We isolated cfDNA from plasma or serum of donors, treated the cfDNA with bisulfite, PCR-amplified the cfDNA, and sequenced it to quantify cfDNA carrying the methylation markers of the cell type of interest. Pancreatic β-cell DNA was identified in the circulation of patients with recently diagnosed type-1 diabetes and islet-graft recipients; oligodendrocyte DNA was identified in patients with relapsing multiple sclerosis; neuronal/glial DNA was identified in patients after traumatic brain injury or cardiac arrest; and exocrine pancreas DNA was identified in patients with pancreatic cancer or pancreatitis. This proof-of-concept study demonstrates that the tissue origins of cfDNA and thus the rate of death of specific cell types can be determined in humans. The approach can be adapted to identify cfDNA derived from any cell type in the body, offering a minimally invasive window for diagnosing and monitoring a broad spectrum of human pathologies as well as providing a better understanding of normal tissue dynamics.

While impressively organ specific, they did not specifically prove that the DNA was from an actual dying cell. For example, you would need to see if Troponin levels were elevated when assuming the DNA is from injured myocardium. Also, for brain, though impractical , you’d want to see a brain biopsy or imaging for the brain related cases. The experiment of spiking with DNA was clever though. Also, what is the turnaround time for this test in practical use?

Larry HB

Very good comment. I was reluctant to put this up, but it was of interest and published in PNAS.  Perhaps I can find more information.  Troponin levels would be good for 48 hours, longer than CK and comparable to LD.  What about Nat peptides?

Glutamine and cancer: cell biology, physiology, and clinical opportunities

Christopher T. Hensley,1 Ajla T. Wasti,1,2 

J Clin Invest 2013   https://www.jci.org/articles/view/69600

Glutamine is an abundant and versatile nutrient that participates in energy formation, redox homeostasis, macromolecular synthesis, and signaling in cancer cells. These characteristics make glutamine metabolism an appealing target for new clinical strategies to detect, monitor, and treat cancer. Here we review the metabolic functions of glutamine as a super nutrient and the surprising roles of glutamine in supporting the biological hallmarks of malignancy. We also review recent efforts in imaging and therapeutics to exploit tumor cell glutamine dependence, discuss some of the challenges in this arena, and suggest a disease-focused paradigm to deploy these emerging approaches.

It has been nearly a century since the discovery that tumors display metabolic activities that distinguish them from differentiated, non-proliferating tissues and presumably contribute to their supraphysiological survival and growth (1). Interest in cancer metabolism was boosted by discoveries that oncogenes and tumor suppressors could regulate nutrient metabolism, and that mutations in some metabolic enzymes participate in the development of malignancy (2, 3). The persistent appeal of cancer metabolism as a line of investigation lies both in its ability to uncover fundamental aspects of malignancy and in the translational potential of exploiting cancer metabolism to improve the way we diagnose, monitor, and treat cancer. Furthermore, an improved understanding of how altered metabolism contributes to cancer has a high potential for synergy with translational efforts. For example, the demonstration that asparagine is a conditionally essential nutrient in rapidly growing cancer cells paved the way for L-asparaginase therapy in leukemia. Additionally, the avidity of some tumors for glucose uptake led to the development of 18fluoro-2-deoxyglucose imaging by PET; this in turn stimulated hundreds of studies on the biological underpinnings of tumor glucose metabolism.

There continue to be large gaps in understanding which metabolic pathways are altered in cancer, whether these alterations benefit the tumor in a substantive way, and how this information could be used in clinical oncology. In this Review, we consider glutamine, a highly versatile nutrient whose metabolism has implications for tumor cell biology, metabolic imaging, and perhaps novel therapeutics.

Glutamine in intermediary metabolism

Glutamine metabolism has been reviewed extensively and is briefly outlined here (4, 5). The importance of glutamine as a nutrient in cancer derives from its abilities to donate its nitrogen and carbon into an array of growth-promoting pathways (Figure 1). At concentrations of 0.6–0.9 mmol/l, glutamine is the most abundant amino acid in plasma (6). Although most tissues can synthesize glutamine, during periods of rapid growth or other stresses, demand outpaces supply, and glutamine becomes conditionally essential (7). This requirement for glutamine is particularly true in cancer cells, many of which display oncogene-dependent addictions to glutamine in culture (8). Glutamine catabolism begins with its conversion to glutamate in reactions that either donate the amide nitrogen to biosynthetic pathways or release it as ammonia. The latter reactions are catalyzed by the glutaminases (GLSs), of which several isozymes are encoded by human genes GLS and GLS2 (9). Classical studies revealed that GLS isozymes, particularly those encoded by GLS, are expressed in experimental tumors in rats and mice, where their enzyme activity correlates with growth rate and malignancy. Silencing GLS expression or inhibiting GLS activity is sufficient to delay tumor growth in a number of models (1013). The role of GLS2 in cancer appears to be context specific and regulated by factors that are still incompletely characterized. In some tissues, GLS2 is a p53 target gene and seems to function in tumor suppression (14). On the other hand, GLS2 expression is enhanced in some neuroblastomas, where it contributes to cell survival (15). These observations, coupled with the demonstration that c-Myc stimulates GLS expression (12, 16), position at least some of the GLS isozymes as pro-oncogenic.

Glutamine metabolism as a target for diagnostic imaging and therapy in cancFigure 1Glutamine metabolism as a target for diagnostic imaging and therapy in cancer. Glutamine is imported via SLC1A5 and other transporters, then enters a complex metabolic network by which its carbon and nitrogen are supplied to pathways that promote cell survival and growth. Enzymes discussed in the text are shown in green, and inhibitors that target various aspects of glutamine metabolism are shown in red. Green arrows denote reductive carboxylation. 18F-labeled analogs of glutamine are also under development as PET probes for localization of tumor tissue. AcCoA, acetyl-CoA; DON, 6-diazo-5-oxo-L-norleucine; GSH, glutathione; NEAA, nonessential amino acids; ME, malic enzyme; OAA, oxaloacetate; TA, transaminase; 968, compound 968; α-KG, α-ketoglutarate.

Glutamate, the product of the GLS reaction, is a precursor of glutathione, the major cellular antioxidant. It is also the source of amino groups for nonessential amino acids like alanine, aspartate, serine, and glycine, all of which are required for macromolecular synthesis. In glutamine-consuming cells, glutamate is also the major source of α-ketoglutarate, a TCA cycle intermediate and substrate for dioxygenases that modify proteins and DNA. These dioxygenases include prolyl hydroxylases, histone demethylases, and 5-methylcytosine hydroxylases. Their requirement for α-ketoglutarate, although likely accounting for only a small fraction of total α-ketoglutarate utilization, makes this metabolite an essential component of cell signaling and epigenetic networks.

Conversion of glutamate to α-ketoglutarate occurs either through oxidative deamination by glutamate dehydrogenase (GDH) in the mitochondrion or by transamination to produce nonessential amino acids in either the cytosol or the mitochondrion. During avid glucose metabolism, the transamination pathway predominates (17). When glucose is scarce, GDH becomes a major pathway to supply glutamine carbon to the TCA cycle, and is required for cell survival (17, 18). Metabolism of glutamine-derived α-ketoglutarate in the TCA cycle serves several purposes: it generates reducing equivalents for the electron transport chain (ETC) and oxidative phosphorylation, becoming a major source of energy (19); and it is an important anaplerotic nutrient, feeding net production of oxaloacetate to offset export of intermediates from the cycle to supply anabolism (20). Glutamine oxidation also supports redox homeostasis by supplying carbon to malic enzyme, some isoforms of which produce NADPH (Figure 1). In KRAS-driven pancreatic adenocarcinoma cells, a pathway involving glutamine-dependent NADPH production is essential for redox balance and growth (21). In these cells, glutamine is used to produce aspartate in the mitochondria. This aspartate is then trafficked to the cytosol, where it is deaminated to produce oxaloacetate and then malate, the substrate for malic enzyme.

Recent work has uncovered an unexpected role for glutamine in cells with reduced mitochondrial function. Despite glutamine’s conventional role as a respiratory substrate, several studies demonstrated a persistence of glutamine dependence in cells with permanent mitochondrial dysfunction from mutations in the ETC or TCA cycle, or transient impairment secondary to hypoxia (2225). Under these conditions, glutamine-derived α-ketoglutarate is reductively carboxylated by NADPH-dependent isoforms of isocitrate dehydrogenase to produce isocitrate, citrate, and other TCA cycle intermediates (Figure 1). These conditions broaden glutamine’s utility as a carbon source because it becomes not only a major source of oxaloacetate, but also generates acetyl-CoA in what amounts to a striking rewiring of TCA cycle metabolism.

Glutamine promotes hallmarks of malignancy

Deregulated energetics. One hallmark of cancer cells is aberrant bioenergetics (26). Glutamine’s involvement in the pathways outlined above contributes to a phenotype conducive to energy formation, survival, and growth. In addition to its role in mitochondrial metabolism, glutamine also suppresses expression of thioredoxin-interacting protein, a negative regulator of glucose uptake (27). Thus, glutamine contributes to both of the energy-forming pathways in cancer cells: oxidative phosphorylation and glycolysis. Glutamine also modulates hallmarks not traditionally thought to be metabolic, as outlined below. These interactions highlight the complex interplay between glutamine metabolism and many aspects of cell biology.

Sustaining proliferative signaling. Pathological cancer cell growth relies on maintenance of proliferative signaling pathways with increased autonomy relative to non-malignant cells. Several lines of evidence argue that glutamine reinforces activity of these pathways. In some cancer cells, excess glutamine is exported in exchange for leucine and other essential amino acids. This exchange facilitates activation of the serine/threonine kinase mTOR, a major positive regulator of cell growth (28). In addition, glutamine-derived nitrogen is a component of amino sugars, known as hexosamines, that are used to glycosylate growth factor receptors and promote their localization to the cell surface. Disruption of hexosamine synthesis reduces the ability to initiate signaling pathways downstream of growth factors (29).

Enabling replicative immortality. Some aspects of glutamine metabolism oppose senescence and promote replicative immortality in cultured cells. In IMR90 lung fibroblasts, silencing either of two NADPH-generating isoforms of malic enzyme (ME1, ME2) rapidly induced senescence, while malic enzyme overexpression suppressed senescence (30). Both malic enzyme isoforms are repressed at the transcriptional level by p53 and contribute to enhanced levels of glutamine consumption and NADPH production in p53-deficient cells. The ability of p53-replete cells to resist senescence required the expression of ME1 and ME2, and silencing either enzyme reduced the growth of TP53+/+ and, to a lesser degree, TP53–/– tumors (30). These observations position malic enzymes as potential therapeutic targets.

Resisting cell death. Although many cancer cells require glutamine for survival, cells with enhanced expression of Myc oncoproteins are particularly sensitive to glutamine deprivation (8, 12, 16). In these cells, glutamine deprivation induces depletion of TCA cycle intermediates, depression of ATP levels, delayed growth, diminished glutathione pools, and apoptosis. Myc drives glutamine uptake and catabolism by activating the expression of genes involved in glutamine metabolism, including GLSand SLC1A5, which encodes the Na+-dependent amino acid transporter ASCT2 (12, 16). SilencingGLS mimicked some of the effects of glutamine deprivation, including growth suppression in Myc-expressing cells and tumors (10, 12). MYCN amplification occurs in 20%–25% of neuroblastomas and is correlated with poor outcome (31). In cells with high N-Myc levels, glutamine deprivation triggered an ATF4-dependent induction of apoptosis that could be prevented by restoring downstream metabolites oxaloacetate and α-ketoglutarate (15). In this model, pharmacological activation of ATF4, inhibition of glutamine metabolic enzymes, or combinations of these treatments mimicked the effects of glutamine deprivation in cells and suppressed growth of MYCN-amplified subcutaneous and transgenic tumors in mice.

The PKC isoform PKC-ζ also regulates glutamine metabolism. Loss of PKC-ζ enhances glutamine utilization and enables cells to survive glucose deprivation (32). This effect requires flux of carbon and nitrogen from glutamine into serine. PKC-ζ reduces the expression of phosphoglycerate dehydrogenase, an enzyme required for glutamine-dependent serine biosynthesis, and also phosphorylates and inactivates this enzyme. Thus, PKC-ζ loss, which promotes intestinal tumorigenesis in mice, enables cells to alter glutamine metabolism in response to nutrient stress.

Invasion and metastasis. Loss of the epithelial cell-cell adhesion molecule E-cadherin is a component of the epithelial-mesenchymal transition, and is sufficient to induce migration, invasion, and tumor progression (33, 34). Addiction to glutamine may oppose this process because glutamine favors stabilization of tight junctions in some cells (35). Furthermore, the selection of breast cancer cells with the ability to grow without glutamine yielded highly adaptable subpopulations with enhanced mesenchymal marker expression and improved capacity for anchorage-independent growth, therapeutic resistance, and metastasis in vivo (36). It is unknown whether this result reflects a primary role for glutamine in suppressing these markers of aggressiveness in breast cancer, or whether prolonged glutamine deprivation selects for cells with enhanced fitness across a number of phenotypes.

Organ-specific glutamine metabolism in health and disease

As a major player in carbon and nitrogen transport, glutamine metabolism displays complex inter-organ dynamics, with some organs functioning as net producers and others as consumers (Figure 2). Organ-specific glutamine metabolism has frequently been studied in humans and animal models by measuring the arteriovenous difference in plasma glutamine abundance. In healthy subjects, the plasma glutamine pool is largely the result of release from skeletal muscle (3739). In rats, the lungs are comparable to muscle in terms of glutamine production (40, 41), and human lungs also have the capacity for marked glutamine release, although such release is most prominent in times of stress (42, 43). Stress-induced release from the lung is regulated by an induction of glutamine synthase expression as a consequence of glucocorticoid signaling and other mechanisms (44, 45). Although this results in a small arteriovenous difference, the overall release of glutamine is significant because of the large pulmonary perfusion. In rats and humans, adipose tissue is a minor but potentially important source of glutamine (46, 47). The liver has the capacity to synthesize or catabolize glutamine, with these activities subject both to regional heterogeneity among hepatocytes and regulatory effects of systemic acidosis and hyperammonemia. However, the liver does not appear to be a major contributor to the plasma glutamine pool in healthy rats and humans (39, 48, 49).

Model for inter-organ glutamine metabolism in health and cancer.Figure 2Model for inter-organ glutamine metabolism in health and cancer. Organs that release glutamine into the bloodstream are shown in green, and those that consume glutamine are in red; the shade denotes magnitude of consumption/release. For some organs (liver, kidneys), evidence from model systems and/or human studies suggests that there is a change in net glutamine flux during tumorigenesis.

Glutamine consumption occurs largely in the gut and kidney. The organs of the gastrointestinal tract drained by the portal vein, particularly the small intestine, are major consumers of plasma glutamine in both rats and humans (37, 38, 49, 50). Enterocytes oxidize more than half of glutamine carbon to CO2, accounting for a third of the respiration of these cells in fasting animals (51). The kidney consumes net quantities of glutamine to maintain acid-base balance (37, 38, 52, 53). During acidosis, the kidneys substantially increase their uptake of glutamine, cleaving it by GLS to produce ammonia, which is excreted along with organic acids to maintain physiologic pH (52, 54). Glutamine is also a major metabolic substrate in lymphocytes and macrophages, at least during mitogenic stimulation of primary cells in culture (5557).

Importantly, cancer seems to cause major changes in inter-organ glutamine trafficking (Figure 2). Currently, much work in this area is derived from studies in methylcholanthrene-induced fibrosarcoma in the rat, a model of an aggressively growing, glutamine-consuming tumor. In this model, fibrosarcoma induces skeletal muscle expression of glutamine synthetase and greatly increases the release of glutamine into the circulation. As the tumor increases in size, intramuscular glutamine pools are depleted in association with loss of lean muscle mass, mimicking the cachectic phenotype of humans in advanced stages of cancer (52). Simultaneously, both the liver and the kidneys become net glutamine exporters, although the hepatic effect may be diminished as the tumor size becomes very large (48, 49, 52). Glutamine utilization by organs supplied by the portal vein is diminished in cancer (48). In addition to its function as a nutrient for the tumor itself, and possibly for cancer-associated immune cells, glutamine provides additional, indirect metabolic benefits to both the tumor and the host. For example, glutamine was used as a gluconeogenic substrate in cachectic mice with large orthotopic gliomas, providing a significant source of carbon in the plasma glucose pool (58). This glucose was taken up and metabolized by the tumor to produce lactate and to supply the TCA cycle.

It will be valuable to extend work in human inter-organ glutamine trafficking, both in healthy subjects and in cancer patients. Such studies will likely produce a better understanding of the pathophysiology of cancer cachexia, a major source of morbidity and mortality. Research in this area should also aid in the anticipation of organ-specific toxicities of drugs designed to interfere with glutamine metabolism. Alterations of glutamine handling in cancer may induce a different spectrum of toxicities compared with healthy subjects.

Tumors differ according to their need for glutamine

One important consideration is that not all cancer cells need an exogenous supply of glutamine. A panel of lung cancer cell lines displayed significant variability in their response to glutamine deprivation, with some cells possessing almost complete independence (59). Breast cancer cells also demonstrate systematic differences in glutamine dependence, with basal-type cells tending to be glutamine dependent and luminal-type cells tending to be glutamine independent (60). Resistance to glutamine deprivation is associated with the ability to synthesize glutamine de novo and/or to engage alternative pathways of anaplerosis (10, 60).

Tumors also display variable levels of glutamine metabolism in vivo. A study of orthotopic gliomas revealed that genetically diverse, human-derived tumors took up glutamine in the mouse brain but did not catabolize it (58). Rather, the tumors synthesized glutamine de novo and used pyruvate carboxylation for anaplerosis. Cells derived from these tumors did not require glutamine to survive or proliferate when cultured ex vivo. Glutamine synthesis from glucose was also a prominent feature of primary gliomas in human subjects infused with 13C-glucose at the time of surgical resection (61). Furthermore, an analysis of glutamine metabolism in lung and liver tumors revealed that both the tissue of origin and the oncogene influence whether the tumor produces or consumes glutamine (62). MET-induced hepatic tumors produced glutamine, whereas Myc-induced liver tumors catabolized it. In the lung, however, Myc expression was associated with glutamine accumulation.

This variability makes it imperative to develop ways to predict which tumors have the highest likelihood of responding to inhibitors of glutamine metabolism. Methods to image or otherwise quantify glutamine metabolism in vivo would be useful in this regard (63). Infusions of pre-surgical subjects with isotopically labeled glutamine, followed by extraction of metabolites from the tumor and analysis of 13C enrichment, can be used to detect both glutamine uptake and catabolism (58, 62). However, this approach requires a specimen of the tumor to be obtained. Approaches for glutamine-based imaging, which avoid this problem, include a number of glutamine analogs compatible with PET. Although glutamine could in principle be imaged using the radioisotopes 11C, 13N, or 18F, the relatively long half-life of the latter increases its appeal. In mice, 18F-(2S, 4R)4-fluoroglutamine is avidly taken up by tumors derived from highly glutaminolytic cells, and by glutamine-consuming organs including the intestine, kidney, liver, and pancreas (64). Labeled analogs of glutamate are also taken up by some tumors (65, 66). One of these, (4S)-4-(3-[18F] fluoropropyl)-L-glutamate (18F-FSPG, also called BAY 94-9392), was evaluated in small clinical trials involving patients with several types of cancer (65, 67). This analog enters the cell through the cystine/glutamate exchange transporter (xCtransport system), which is linked to glutathione biosynthesis (68). The analog was well tolerated, with high tumor detection rates and good tumor-to-background ratios in hepatocellular carcinoma and lung cancer.

PET approaches detect analog uptake and retention but cannot provide information about downstream metabolism. Analysis of hyperpolarized nuclei can provide a real-time view of enzyme-catalyzed reactions. This technique involves redistribution of the populations of energy levels of a nucleus (e.g., 13C, 15N), resulting in a gain in magnetic resonance signal that can temporarily exceed 10,000-fold (69). This gain in signal enables rapid detection of both the labeled molecule and its downstream metabolites. Glutamine has been hyperpolarized on 15N and 13C (70, 71). In the latter case, the conversion of hyperpolarized glutamine to glutamate could be detected in intact hepatoma cells (70). If these analogs are translated to clinical studies, they might provide a dynamic view of the proximal reactions of glutaminolysis in vivo.

Pharmacological strategies to inhibit glutamine metabolism in cancer

Efforts to inhibit glutamine metabolism using amino acid analogs have an extensive history, including evaluation in clinical trials. Acivicin, 6-diazo-5-oxo-L-norleucine, and azaserine, three of the most widely studied analogs (Figure 1), all demonstrated variable degrees of gastrointestinal toxicity, myelosuppression, and neurotoxicity (72). Because these agents non-selectively target glutamine-consuming processes, recent interest has focused on developing methods directed at specific nodes of glutamine metabolism. First, ASCT2, the Na+-dependent neutral amino acid transporter encoded by SLC1A5, is broadly expressed in lung cancer cell lines and accounts for a majority of glutamine transport in those cells (Figure 1). It has been shown that γ-L-glutamyl-p-nitroanilide (GPNA) inhibits this transporter and limits lung cancer cell growth (73). Additional interest in GPNA lies in its ability to enhance the uptake of drugs imported via the monocarboxylate transporter MCT1. Suppressing glutamine uptake with GPNA enhances MCT1 stability and stimulates uptake of the glycolytic inhibitor 3-bromopyruvate (3-BrPyr) (74, 75). Because enforced MCT1 overexpression is sufficient to sensitize tumor xenografts to 3-BrPyr (76), GPNA may have a place in 3-BrPyr–based therapeutic regimens.

Two inhibitors of GLS isoforms have been characterized in recent years (Figure 1). Compound 968, an inhibitor of the GLS-encoded splice isoform GAC, inhibits the transformation of fibroblasts by oncogenic RhoGTPases and delays the growth of GLS-expressing lymphoma xenografts (13). Bis-2-(5-phenylacetamido-1,2,4-thiadiazol-2-yl)ethyl sulfide (BPTES) also potently inhibits GLS isoforms encoded by GLS (77). BPTES impairs ATP levels and growth rates of P493 lymphoma cells under both normoxic and hypoxic conditions and suppresses the growth of P493-derived xenografts (78).

Evidence also supports a role for targeting the flux from glutamate to α-ketoglutarate, although no potent, specific inhibitors yet exist to inhibit these enzymes in intact cells. Aminooxyacetate (AOA) inhibits aminotransferases non-specifically, but milliomolar doses are typically used to achieve this effect in cultured cells (Figure 1). Nevertheless, AOA has demonstrated efficacy in both breast adenocarcinoma xenografts and autochthonous neuroblastomas in mice (15, 79). Epigallocatechin gallate (EGCG), a green tea polyphenol, has numerous pharmacological effects, one of which is to inhibit GDH (80). The effects of EGCG on GDH have been used to kill glutamine-addicted cancer cells during glucose deprivation or glycolytic inhibition (17, 18) and to suppress growth of neuroblastoma xenografts (15).

A paradigm to exploit glutamine metabolism in cancer

Recent advances in glutamine-based imaging, coupled with the successful application of glutamine metabolic inhibitors in mouse models of cancer, make it possible to conceive of treatment plans that feature consideration of tumor glutamine utilization. A key challenge will be predicting which tumors are most likely to respond to inhibitors of glutamine metabolism. Neuroblastoma is used here as an example of a tumor in which evidence supports the utility of strategies that would involve both glutamine-based imaging and therapy (Figure 3). Neuroblastoma is the second most common extracranial solid malignancy of childhood. High-risk neuroblastoma is defined by age, stage, and biological features of the tumor, including MYCN amplification, which occurs in some 20%–25% of cases (31). Because MYCN-amplified tumor cells require glutamine catabolism for survival and growth (15), glutamine-based PET at the time of standard diagnostic imaging could help predict which tumors would be likely to respond to inhibitors of glutamine metabolism. Infusion of 13C-glutamine coordinated with the diagnostic biopsy could then enable inspection of 13C enrichment in glutamine-derived metabolites from the tumor, confirming the activity of glutamine catabolic pathways. Following on evidence from mouse models of neuroblastoma, treatment could then include agents directed against glutamine catabolism (15). Of note, some tumors were sensitive to the ATF4 agonist fenretinide (FRT), alone or in combination with EGCG. Importantly, FRT has already been the focus of a Phase I clinical trial in children with solid tumors, including neuroblastoma, and was fairly well tolerated (81).

A strategy to integrate glutamine metabolism into the diagnosis, classificaFigure 3A strategy to integrate glutamine metabolism into the diagnosis, classification, treatment, and monitoring of neuroblastoma. Neuroblastoma commonly presents in children as an abdominal mass. A standard evaluation of a child with suspected neuroblastoma includes measurement of urine catecholamines, a bone scan, and full-body imaging with meta-iodobenzylguanidine (MIBG), all of which contribute to diagnosis and disease staging. In animal models, a subset of these tumors requires glutamine metabolism. This finding implies that approaches to image, quantify, or block glutamine metabolism (highlighted in red) in human neuroblastoma could be incorporated into the diagnosis and management of this disease. In particular, glutamine metabolic studies may help predict which tumors would respond to therapies targeting glutamine metabolism. HVA, homovanillic acid; VMA, vanillylmandelic acid.

Conclusions

Glutamine is a versatile nutrient required for the survival and growth of a potentially large subset of tumors. Work over the next several years should produce a more accurate picture of the molecular determinants of glutamine addiction and the identification of death pathways that execute cells when glutamine catabolism is impaired. Advancement of glutamine-based imaging into clinical practice should soon make it possible to differentiate tumors that take up glutamine from those that do not. Finally, the development of safe, high-potency inhibitors of key metabolic nodes should facilitate therapeutic regimens featuring inhibition of glutamine metabolism.

Therapeutic strategies impacting cancer cell glutamine metabolism

The metabolic adaptations that support oncogenic growth can also render cancer cells dependent on certain nutrients. Along with the Warburg effect, increased utilization of glutamine is one of the metabolic hallmarks of the transformed state. Glutamine catabolism is positively regulated by multiple oncogenic signals, including those transmitted by the Rho family of GTPases and by c-Myc. The recent identification of mechanistically distinct inhibitors of glutaminase, which can selectively block cellular transformation, has revived interest in the possibility of targeting glutamine metabolism in cancer therapy. Here, we outline the regulation and roles of glutamine metabolism within cancer cells and discuss possible strategies for, and the consequences of, impacting these processes therapeutically.

Cancer cell metabolism & glutamine addiction

Interest in the metabolic changes characteristic of malignant transformation has undergone a renaissance of sorts in the cancer biology and pharmaceutical communities. However, the recognition that an important connection exists between cellular metabolism and cancer began nearly a century ago with the work of Otto Warburg [13]. Warburg found that rapidly proliferating tumor cells exhibit elevated glucose uptake and glycolytic flux, and furthermore that much of the pyruvate generated by glycolysis is reduced to lactate rather than undergoing mitochondrial oxidation via the tricarboxylic acid (TCA) cycle (Figure 1). This phenomenon persists even under aerobic conditions (‘aerobic glycolysis’), and is known as the Warburg effect [4]. Warburg proposed that aerobic glycolysis was caused by defective mitochondria in cancer cells, but it is now known that mitochondrial dysfunction is relatively rare and that most tumors have an unimpaired capacity for oxidative phosphorylation [5]. In fact, the most important selective advantages provided by the Warburg effect are still debated. Although aerobic glycolysis is an inefficient way to produce ATP (2 ATP/glucose vs ~36 ATP/glucose by complete oxidation), a high glycolytic flux can generate ATP rapidly and furthermore can provide a biosynthetic advantage by supplying precursors and reducing equivalents for the synthesis of macromolecules [4]. The mechanisms underlying the Warburg effect are also not yet fully resolved, although it is increasingly clear that a number of oncogenes and tumor suppressors contribute to the phenomenon. The PI3K/Akt/mTORC1 signaling axis, for example, is a key regulator of aerobic glycolysis and biosynthesis, driving the surface expression of nutrient transporters and the upregulation of glycolytic enzymes [6]. The HIF transcription factor also upregulates expression of glucose transporters and glycolytic enzymes in response to hypoxia and growth factors (or loss of the von Hippel–Landau [VHL] tumor suppressor), and the oncogenic transcription factor c-Myc similarly induces expression of proteins important for glycolysis [6].

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4154374/bin/nihms610340f1.jpg

Cell proliferation requires metabolic reprogramming

A second major change in the metabolic program of many cancer cells, and the primary focus of this review, is the alteration of glutamine metabolism. Glutamine is the major carrier of nitrogen between organs, and the most abundant amino acid in plasma [7]. It is also a key nutrient for numerous intracellular processes including oxidative metabolism and ATP generation, biosynthesis of proteins, lipids and nucleic acids, and also redox homeostasis and the regulation of signal transduction pathways [810]. Although most mammalian cells are capable of synthesizing glutamine, the demand for this amino acid can become so great during rapid proliferation that an additional extracellular supply is required; hence glutamine is considered conditionally essential [11]. Indeed, many cancer cells are ‘glutamine addicted’, and cannot survive in the absence of an exogenous glutamine supply [12,13].

An important step in the elevation of glutamine catabolism is the activation of the mitochondrial enzyme glutaminase, which catalyzes the hydrolysis of glutamine to generate glutamate and ammonium. The subsequent deamination of glutamate releases a second ammonium to yield the TCA cycle intermediate α-ketoglutarate (α-KG), a reaction catalyzed by glutamate dehydrogenase (GLUD1). This series of reactions is particularly important in rapidly proliferating cells, in which a considerable proportion of the TCA cycle metabolite citrate is exported from mitochondria in order to generate cytosolic acetyl-CoA for lipid biosynthesis [14]. Replenishment of TCA cycle intermediates (anaplerosis) is therefore required, and glutamine often serves as the key anaplerotic substrate through its conversion via glutamate to α-KG (Figure 1).

Mammals express two genes for glutaminase enzymes [1517]. The GLS gene encodes a protein initially characterized in kidney and thus called kidney-type glutaminase (KGA), although this enzyme and its shorter splice variant glutaminase C (GAC), collectively referred to as GLS, are now known to be widely distributed [1820]. The KGA and GAC isoforms share identical N-terminal and catalytic domains, encoded by exons 1–14 of the GLS gene, but have distinct C-termini derived from exon 15 in the case of GAC and exons 16–19 in the case of KGA [21]. Upregulation of GLS, in particular the GAC iso-form, is common in cancer cells and the degree of GLS overexpression correlates with both the degree of malignancy and the tumor grade in human breast cancer samples [22,23]. The GLS2 gene encodes a protein originally discovered and characterized in liver, which has thus been referred to as liver-type glutaminase and, more recently, as glutaminase 2 (GLS2) [15].

Both KGA and GAC can be activated by inorganic phosphate (Pi), and this activation correlates closely with a dimer-to-tetramer transition for each enzyme [7, 22]. As the concentration of Pi is raised the apparent catalytic constant, kcatapp, increases and simultaneously the apparent Michaelis constant, Kmapp, decreases; consequently the catalytic efficiency rises dramatically, especially in the case of GAC [22]. x-ray crystal structures of GAC and KGA in different states indicate that the positioning of a key loop within each monomer (Glu312 to Pro329), located between the active site and the dimer–dimer interface, is critical for mediating tetramerization-induced activation [22,24]. Given the ability of Pi to promote tetramerization and activation of GAC and KGA, it has been proposed that the elevated mitochondrial Pi levels found under hypoxic conditions, which are commonly encountered in the tumor microenvironment, could be one trigger for GLS activation [22].

Oncogenic alterations affecting glutamine metabolism

At least two classes of cellular signals regulate glutamine metabolism, influencing both the expression level and the enzymatic activity of GLS. The transcription factor c-Myc can suppress the expression of microRNAs miR-23a and miR-23b and, in doing so, upregulates GLS (specifically GAC) expression [13,25]. Independent of changes in GAC expression, oncogenic diffuse B-cell lymphoma protein (Dbl), a GEF for Rho GTPases and oncogenic variants of downstream Rho GTPases are able to signal to activate GAC in a manner that is dependent on NF-κB [23]. Mitochondria isolated from Dbl- or Rho GTPase-transformed NIH-3T3 fibroblasts demonstrate significantly higher basal glutaminase activity than mitochondria isolated from non-transformed cells [23]. Furthermore, the enzymatic activity of GAC immunoprecipitated from Dbl-transformed cells is elevated relative to GAC from non-transformed cells, indicating the presence of activating post-translational modification(s) [23]. Indeed, when GAC isolated from Dbl-transformed cells is treated with alkaline phosphatase, basal enzymatic activity is dramatically reduced [23]. Collectively, these findings point to phosphorylation events underlying the activation of GAC in transformed cells. Similarly, phosphorylation-dependent regulation of KGA activity downstream of the Raf-Mek-Erk signaling axis occurs in response to EGF stimulation [24].

It is becoming clear that, in addition to c-Myc and Dbl, many other oncogenic signals and environmental conditions can impact cellular glutamine metabolism. Loss of the retinoblastoma tumor suppressor, for example, leads to a marked increase in glutamine uptake and catabolism, and renders mouse embryonic fibroblasts dependent on exogenous glutamine [26]. Cells transformed by KRAS also illustrate increased expression of genes associated with glutamine metabolism and a corresponding increased utilization of glutamine for anabolic synthesis [27]. In fact, KRAS signaling appears to induce glutamine dependence, since the deleterious effects of glutamine withdrawal in KRAS-driven cells can be rescued by expression of a dominant-negative GEF for Ras [28]. Downstream of Ras, the Raf-MEK-ERK signaling pathway has been implicated in the upregulation of glutamine uptake and metabolism [24,29]. A recent study using human pancreatic ductal adenocarcinoma cells identified a novel KRAS-regulated metabolic pathway, through which glutamine supports cell growth [30]. Proliferation of KRAS-mutant pancreatic ductal adenocarcinoma cells depends on GLS-catalyzed production of glutamate, but not on downstream deamination of glutamate to α-KG; instead, transaminase-mediated glutamate metabolism is essential for growth. Glutamine-derived aspartate is subsequently transported into the cytoplasm where it is converted by aspartate transaminase into oxaloacetate, which can be used to generate malate and pyruvate. The series of reactions maintains NADPH levels and thus the cellular redox state [30].

Other recent studies have revealed that another pathway for glutamine metabolism can be essential under hypoxic conditions, and also in cancer cells with mitochondrial defects or loss of the VHL tumor suppressor [3135]. In these situations, glutamine-derived α-KG undergoes reductive carboxylation by IDH1 or IDH2 to generate citrate, which can be exported from mitochondria to support lipogenesis (Figure 1). Activation of HIF is both necessary and sufficient for driving the reductive carboxylation phenotype in renal cell carcinoma, and suppression of HIF activity can induce a switch from glutamine-mediated lipogenesis back to glucose-mediated lipogenesis [32,35]. Furthermore, loss of VHL and consequent downstream activation of HIF renders renal cell carcinoma cells sensitive to inhibitors of GLS [35]. Evidently, the metabolic routes through which glutamine supports cancer cell proliferation vary with genetic background and with microenvironmental conditions. Nevertheless, it is increasingly clear that diverse oncogenic signals promote glutamine utilization and furthermore that hypoxia, a common condition within poorly vascularized tumors, increases glutamine dependence.

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Consistent with the critical role of TCA cycle anaplerosis in cancer cell proliferation, a range of glutamine-dependent cancer cell lines are sensitive to silencing or inhibition of GLS [23,93]. Although loss of GLS suppresses proliferation, in some cases the induction of a compensatory anaplerotic mechanism mediated by pyruvate carboxylase (PC) allows the use of glucose- rather than glutamine-derived carbon for anaplerosis [93]. Low glutamine conditions render glioblastoma cells completely dependent on PC for proliferation; reciprocally, glucose deprivation causes them to become dependent on GLUD1, presumably as a mediator of glutamine-dependent anaplerosis [94]. These studies provide insight into the possibility of inhibiting glutamine-dependent TCA cycle anaplerosis (e.g., with 968 or BPTES) and indicate that high expression of PC could represent a means of resistance to GLS inhibitors.

In c-Myc-induced human Burkitt lymphoma P493 cells, entry of glucose-derived carbon into the TCA cycle is attenuated under hypoxia, whereas glutamine oxidation via the TCA cycle persists [95]. Upon complete withdrawal of glucose, the TCA cycle continues to function and is driven by glutamine. The proportions of viable and proliferating cell populations are almost identical in glucose-replete and -deplete conditions so long as glutamine is present. Inhibition of GLS by BPTES causes a decrease in ATP and glutathione levels, with a simultaneous increase in reactive oxygen species production. Strikingly, whereas BPTES treatment under aerobic conditions suppresses proliferation, under hypoxic conditions it results in cell death, an effect ascribed to glutamine’s critical roles in alleviating oxidative stress in addition to supporting bioenergetics.

In addition to deamidation, glutamine-derived carbon can also reach the TCA cycle through transamination [96], and recent studies indicate that inhibition of this process could be a promising strategy for cancer treatment [30,97,98]. The transaminase inhibitor amino-oxyacetate selectively suppresses proliferation of the aggressive breast cancer cell line MDA-MB-231 relative to normal human mammary epithelial cells, and similar effects were observed with siRNA knockdown of aspartate transaminase [97]. Treatment with amino-oxyacetate killed glutamine-dependent glioblastoma cells, in a manner that could be rescued by α-KG and was dependent on c-Myc expression [13]. Transaminase inhibitors have also been found to suppress both anchorage-dependent and anchorage-independent growth of lung carcinoma cells [98].

Reductive carboxylation

The central metabolic precursor for fatty acid biosynthesis is acetyl-CoA, which can be generated from pyruvate in the mitochondria by pyruvate dehydrogenase. Since acetyl-CoA cannot cross the inner mitochondrial membrane, it is exported to the cytosol via the citrate shuttle following its condensation with oxaloacetate in the TCA cycle (Figure 3). In the cytosol, citrate is converted back to acetyl-CoA and oxaloacetate in a reaction catalyzed by ATP citrate lyase. In addition to its synthesis from glycolytic pyruvate, citrate can also be generated by reductive carboxylation of α-KG [99]. Across a range of cancer cell lines, 10–25% of lipogenic acetyl-CoA is generated from glutamine via this reductive pathway; indeed, reductive metabolism is the primary route for incorporation of glutamine, glutamate and α-KG carbon into lipids [32]. Some of the reductive carboxylation of α-KG is catalyzed by cytosolic IDH1, as well as by mitochondrial IDH2 and/or IDH3.

In A549 lung carcinoma cells, glutamine dependence and reductive carboxylation flux increases under hypoxic conditions [32,34], such that glutamine-derived α-KG accounts for approximately 80% of the carbon used for de novo lipogenesis. Similarly, in melanoma cells, the major source of carbon for acetyl-CoA, citrate and fatty acids switches from glucose under normoxia to glutamine (via reductive carboxylation) under hypoxia [31]. The hypoxic switch to reductive glutamine metabolism is dependent on HIF, and constitutive activation of HIF is sufficient to induce the preferential reductive metabolism of α-KG even under normoxic conditions [32]. Tumor cells with mitochondrial defects, such as electron-transport chain mutations/inhibition, also use glutamine-dependent reductive carboxylation as the major pathway for citrate generation, and loss of electron-transport chain activity is sufficient to induce a switch from glucose to glutamine as the primary source of lipogenic carbon [33].

Together these studies indicate that mitochondrial defects/inhibition, and/or hypoxia, might sensitize cancer cells to inhibition of GLS. The fact that P493 cells are more sensitive to BPTES under hypoxic conditions could in part be explained by an increased reliance on glutamine-dependent reductive carboxylation for lipogenesis [95]. Intriguingly, cancer cells harboring neoenzymatic mutations in IDH1, which results in production of the oncometabolite 2-hydroxyglutarate, are also sensitized to GLS inhibition [100]. 2-hydroxyglutarate is generated primarily from glutamine-derived α-KG [100,101], and therefore tumors expressing mutant IDH might be especially susceptible to alterations in α-KG levels.

……

As with all therapies, the potential side effects of strategies impacting glutamine metabolism must be seriously considered. The widespread use of l-asparaginase to lower plasma asparagine and glutamine concentrations in ALL patients demonstrates the potential for glutamine metabolism to be safely targeted, and also sheds light on potential toxicological consequences. For example, glutamine is known to be essential for the proliferation of lymphocytes, macrophages and neutrophils, and immunosuppression is a known side effect of l-asparaginase treatment, requiring close monitoring [11,105]. Evidence from early trials using glutamine-mimetic anti-metabolites, such as l-DON, indicates that these unselective molecules can cause excessive gastrointestinal toxicity and neurotoxicity. Within the brain, GLS converts glutamine into the neurotransmitter glutamate in neurons; astrocytes then take up synaptically released glutamate and convert it back to glutamine, which is subsequently transported back to neurons [106,107].

……

It has become clear during the past decade that altered metabolism plays a critical, in some cases even causal, role in the development and maintenance of cancers. It is now accepted that virtually all oncogenes and tumor suppressors impact metabolic pathways [5]. Furthermore, mutations in certain metabolic enzymes (e.g., isocitrate dehydrogenase, succinate dehydrogenase and fumarate hydratase) are associated with both familial and sporadic human cancers [113]. With this realization has come a renewed interest in the possibility of selectively targeting the metabolism of cancer cells as a therapeutic strategy. The use of l-asparaginase to treat ALL by depleting plasma asparagine and glutamine levels and the promising outcome of the first use of dichloroacetate (which acts, at least in part, through its inhibition of the metabolic enzyme pyruvate dehydrogenase kinase) in glioblastoma patients [114,115], support the notion that cancer metabolism can be safely and effectively targeted in the clinic. The metabolic adaptations of cancer cells must balance the requirements for modestly increased ATP synthesis, dramatically upregulated macromolecular biosynthesis and maintenance of redox balance. By serving as a carbon source for energy generation, a carbon and nitrogen source for biosynthesis and a precursor of the cellular antioxidant glutathione, glutamine is able to contribute to each of these requirements.

The countless combinations of genetic alterations that are found in human neo-plasias mean that there is not a single rigid metabolic program that is characteristic of all transformed cells. This perhaps explains why some current anti-metabolite chemotherapies (e.g., those targeting nucleotide synthesis) are effective only for certain malignancies. A deeper understanding of the metabolic alterations within specific genetic contexts will allow for better-targeted therapeutic interventions. Furthermore, it seems highly likely that combination therapies based on drug synergisms will be especially important for exploiting therapeutic windows within which cancer cells, but not normal cells, are impacted [37]. Glucose and glutamine metabolic pathways, for example, might be able to compensate for one another under some circumstances. When glucose metabolism is impaired in glioblastoma cells, glutamine catabolism becomes essential for survival [94]; reciprocally, suppression of GLS expression causes cells to become fully dependent on glucose-driven TCA cycle anaplerosis via PC [93]. The implication is that PC inhibition could synergize with GLS inhibition.

A topic warranting further investigation is the role that GLS2 plays in cellular metabolism. GLS, in particular the GAC isoform, is upregulated downstream of oncogenes and downregulated by tumor suppressors, and is essential for growth of many cancer cells. In contrast, GLS2 is activated by the ‘universal’ tumor suppressor p53, and furthermore is significantly downregulated in liver tumors and can block transformed characteristics of some cancer cells when overexpressed [116118]. Emphasizing the importance of genetic context, it was recently reported that GLS2 is significantly upregulated in neuroblastomas overexpressing N-Myc [119]. There are various possible explanations for the apparently different roles of two enzymes that catalyze the same reaction. Because the regulation of GLS and GLS2 is distinct, they will be called up under different conditions. The two enzymes have different kinetic characteristics, and therefore might influence energy metabolism and antioxidant defense in different manners [20]. There is also evidence that GLS2 may act, directly or indirectly, as a transcription factor [118]. Finally, it is possible that the different interactions of GLS and GLS2 with other proteins are responsible for their apparently different roles.

 

Mitochondria as biosynthetic factories for cancer proliferation

Christopher S Ahn and Christian M Metallo

Cancer & Metabolism (2015) 3:1      http://dx.doi.org:/10.1186/s40170-015-0128-2

Unchecked growth and proliferation is a hallmark of cancer, and numerous oncogenic mutations reprogram cellular metabolism to fuel these processes. As a central metabolic organelle, mitochondria execute critical biochemical functions for the synthesis of fundamental cellular components, including fatty acids, amino acids, and nucleotides. Despite the extensive interest in the glycolytic phenotype of many cancer cells, tumors contain fully functional mitochondria that support proliferation and survival. Furthermore, tumor cells commonly increase flux through one or more mitochondrial pathways, and pharmacological inhibition of mitochondrial metabolism is emerging as a potential therapeutic strategy in some cancers. Here, we review the biosynthetic roles of mitochondrial metabolism in tumors and highlight specific cancers where these processes are activated.

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Recent characterizations of metabolic enzymes as tumor suppressors and oncogene-driven metabolic reprogramming have reinvigorated interest in cancer metabolism. Although therapies targeting metabolic processes have long been a staple in cancer treatment (e.g. inhibition of folate metabolism via methotrexate), the focused therapeutic potential surrounding these findings have generated a renewed appreciation for Otto Warburg’s work almost a century ago. Warburg observed that tumor cells ferment much of the glucose taken up during growth to lactate, thus using glycolysis as a major means of adenosine triphosphate (ATP) regeneration [1]. However, the observation of decreased respiration in cancer cells and idea that “the respiration of all cancer cells is damaged” belies the critical role of mitochondria in biosynthesis and cell survival [1]. On the contrary, functional mitochondria are present in all proliferative cells within our body (including all tumors), as they are responsible for converting the diverse nutrients available to cells into the fundamental building blocks required for cell growth. These organelles execute numerous functions in cancer cells to promote tumor growth and survival in response to stress. Here, we outline the critical biosynthetic functions served by mitochondria within tumors (Figure 1). Although many of these functions are similarly important in normal, proliferating cells, we have attempted to highlight potential points where mitochondrial metabolism may be therapeutically targeted to slow cancer growth. This review is organized by specific metabolic pathways or processes (i.e., glucose metabolism and lipogenesis, amino acid metabolism, and nucleotide biosynthesis). Tumors or cancer cell types where enzymes in each pathway have been specifically observed to by dysregulated are described within the text and summarized in Table 1.

https://static-content.springer.com/image/art%3A10.1186%2Fs40170-015-0128-2/MediaObjects/40170_2015_128_Fig1_HTML.gif

Figure 1

Biosynthetic nodes within mitochondria. Metabolic pathways within mitochondria that contribute to biosynthesis in cancer and other proliferating cells. TCA metabolism and FOCM enable cells to convert carbohydrates and amino acids to lipids, non-essential amino acids, nucleotides (including purines used for cofactor synthesis), glutathione, heme, and other cellular components. Critical biosynthetic routes are indicated by yellow arrows. Enzymatic reactions that are dependent on redox-sensitive cofactors are depicted in red.  https://static-content.springer.com/image/art%3A10.1186%2Fs40170-015-0128-2/MediaObjects/40170_2015_128_Fig1_HTML.gif

Table 1

Overview of mitochondrial biosynthetic enzymes important in cancer

TCA cycle, anaplerosis, and AcCoA metabolism

Cancers in which three or more mitochondrial enzymes have been studied and found to be differentially regulated (or mutated, as indicated) in cancers vs. control groups are included. Dysregulation of each enzyme was demonstrated in clinical tumors samples, animal models, or cell lines at the levels of genes, mRNA, protein, metabolites, and/or flux.

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Figure 2

Coordination of carbon and nitrogen metabolism across amino acids. Glutamate and aKG are key substrates in numerous transamination reactions and can also serve as precursors for glutamine, proline, and the TCA cycle. Mitochondrial enzymes catalyzing these reactions are highlighted in blue, and TCA cycle intermediates are highlighted in orange (pyruvate enters the TCA cycle as acetyl-CoA or oxaloacetate).
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Figure 3

Biosynthetic sources for purine and pyrimidine synthesis. Sources and fates of nitrogen, carbon, and oxygen atoms are colored as indicated. Italicized metabolites can be sourced from the mitochondria or cytosol. The double bond formed by the action of DHODH/ubiquinone is also indicated.      https://static-content.springer.com/image/art%3A10.1186%2Fs40170-015-0128-2/MediaObjects/40170_2015_128_Fig3_HTML.gif

Mitochondria operate as both engine and factory in eukaryotes, coordinating cellular energy production and the availability of fundamental building blocks that are required for cell proliferation. Cancer cells must therefore balance their relative bioenergetic and biosynthetic needs to grow, proliferate, and survive within the physical constraints of energy and mass conservation. In contrast to quiescent cells, which predominantly use oxidative mitochondrial metabolism to produce ATP and uptake glucose at much lower rates than proliferating cells, tumor cells exhibit increased glycolytic rates to provide an elevated flux of substrate for biosynthetic pathways, including those executed within mitochondria. Given these higher rates of nutrient utilization, metabolic flux through mitochondrial pathways and the associated ROS production can often be higher in cancer cells. Not surprisingly, activation of cellular antioxidant response pathways is commonly observed in cancer or subpopulations of cells within tumors [46,78]. Cellular compartmentalization affords a degree of protection from such damaging side products of metabolism, and methods which are able to deconvolute the relative contributions of each cellular compartment (e.g. mitochondria, cytosol, peroxisome, etc.) to cancer metabolism will be crucial to more completely understand the metabolism of cancer cells in the future [74,79]. Ultimately, while mitochondrial dysregulation is widely considered to be a hallmark of cancer, numerous mitochondrial functions remain critical for tumor growth and are emerging as clinical targets.

Following this point, it comes as no surprise that mitochondrial metabolism is highly active in virtually all tumors (i.e., cancer cells, stroma, or both), and investigators have begun targeting these pathways to explore potential efficacy. Indeed, some evidence suggests that biguanides such as metformin or phenformin may limit tumor incidence and burden in humans and animals [80,81]. These effects are presumably due, at least in part, to complex I inhibition of the ETC, which significantly perturbs mitochondrial function [82,83]. However, more insights are needed into the mechanisms of these compounds in patients to determine the therapeutic potential of targeting this and other components of mitochondria. In developing new therapies that target cancer metabolism, researchers will face challenges similar to those that are relevant for many established chemotherapies since deleterious effects on normal proliferating cells that also depend on mitochondrial metabolism (and aerobic glycolysis) are likely to arise.

As we acquire a more detailed picture of how specific genetic modifications in a patient’s tumor correlate with its metabolic profile, opportunities for designing targeted or combinatorial therapies will become increasingly apparent. Cancer therapies that address tumor-specific mitochondrial dysregulation and dysfunction may be particularly effective. For example, some cancer cells harbor mutations in TCA enzymes (e.g., FH, SDH, IDH2) or regulatory proteins that control mitophagy (i.e., LKB1) [84]. Such tumors may be compromised with respect to some aspects of mitochondrial biosynthesis and dependent on alternate pathways for growth and/or survival such that synthetically lethal targets emerge. Ultimately, such strategies will require clinicians and researchers to coordinate metabolic, biochemical, and genetic information in the design of therapeutic strategies.

 

David Terrano, M.D., Ph.D. commented on your update
“Not well versed in Nat peptides so I could not say. I also hesitate with any PNAS paper because those in their academy tend to have a fast track to publication. It has been that way since at least early 2000’s wh n I began research. I don’t doubt their goal and approach (this same group leads the way in methylation-based diagnosis of CNS neoplasms, which is apparently highly accurate). But when I see “dying cells” I know what that means biochemically and look for those hallmarks. Organ specific oligonucleosomes would be a nice cell death surrogate. “

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Cellular switch molecule for sperm motility control: a novel target for male contraception and infertility treatments

Reporter and Curator: Sudipta Saha, Ph.D.

 

Researchers have discovered the cellular switch that boosts the activity of sperm cells so that they can travel to the egg.  The finding may lead to new options for male contraception as well as treatments for infertility resulting from problems with sperm mobility.

Inside the male reproductive tract, mature sperm are capable of limited movement. This limited movement, however, is not enough to propel them toward the egg when they enter the female reproductive tract. To begin their journey, they must first be activated by the hormone progesterone, which is released by the egg.

The researchers reported that the molecule to which progesterone must bind is the enzyme alpha/beta hydrolase domain containing protein 2 (ABHD2), found in the sperm cell’s outer membrane. Similarly, strategies to bypass or enhance the enzyme might provide therapies for treating infertility resulting from sperm that lack movement capability.

Before a sperm can transition to the hyper-active phase, calcium must pass through the cell’s outer membrane and enter the flagella, the tail-like appendage the cell uses to propel itself. The sperm protein known as CatSper joins with similar proteins in the flagella to allow the entry of calcium.

When the researchers undertook the current study, it was not known whether progesterone interacted directly with CatSper to trigger the calcium influx, or acted on some other molecule (which, in turn, acted on CatSper). Before treating sperm with progesterone, the researchers exposed them to a chemical that inhibits a particular class of enzymes that they believed could include the candidate molecule that acted on CatSper. The hunch proved correct: the treated cells remained inactive after progesterone exposure, indicating that CatSper was not directly involved.

Working with modified progesterone, the researchers eventually isolated ABHD2 from the sperm tails. When the researchers inactivated ABHD2, exposure to progesterone failed to activate the sperm cells, confirming that ABHD2 is the molecular target for progesterone.

All of the technical terminology aside, this means that the researchers have pinned down the cellular switch that boosts the sperm along to the egg, so by blocking the ABHD2 activity, new male birth control methods could be on the way. Conversely, enhancing the enzyme could lead to new treatments for male infertility.

It will be interesting to see how this discovery impacts future research concerning male birth control and infertility treatments. Perhaps it’s the missing piece of information that will quickly yield an effective new male contraception option.

 

SOURCES

http://www.nih.gov/news-events/news-releases/researchers-identify-molecule-needed-sperm-activation

http://www.ncbi.nlm.nih.gov/pubmed/26989199

http://thescienceexplorer.com/brain-and-body/nih-funded-study-made-breakthrough-discovery-could-lead-new-male-birth-control

http://www.jhunewsletter.com/2016/03/31/researchers-find-a-protein-fertilization-catalyst/

 

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Pull at Cancer’s Levers, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)

Pull at Cancer’s Levers

Curator: Larry H. Bernstein, MD, FCAP

 

Driving Cancer Immunotherapy 

The Stakes in Immuno-Oncology Are Too High for Researchers to Pull at Cancer’s Levers Blindly. Researchers Need a System.

  • Within the past decade or so, a revolutionary idea has emerged in the minds of scientists, physicians, and medical experts. Instead of using man-made chemicals to treat cancer, let us instead unleash the power of our own bodies upon the malignancy.

    This idea is the inspiration behind cancer immunotherapy, which is, according to most experts, a therapeutic approach that involves training the immune system to fight off cancer. In the words of one expert, cancer immunotherapy means “taking the immune system’s inherent properties and turbo charging those to fight cancer.”

    Cancer immunotherapy technologies are being developed to accomplish
    several tasks:

    • Enhance the molecular targeting of cancer cells
    • Report the rate of killing by specific immune agents
    • Direct immune cells toward tumor destruction.

    Since its inception, the field has evolved, and it continues to do so. It began with in vivo investigations of tumor growth and development, and it progressed through laboratory investigations of cellular morphology and survival curves. And now it is adopting pathway analysis to guide therapeutic development and improve patient care.

    To begin to understand cancer immunotherapy, one must understand how the immune system targets tumor cells. One of the prominent adaptive components of the immune system is the T cell, which responds to perceived threats through the massive increase in clonal T cells targeted in some way toward the diseased cell or pathogen.

  • The T-Cell Repertoire

    Adaptive Biotechnologies’ immunoSEQ Assay, a high-throughput research platform for immune system profiling, is designed to generate sequencer-ready libraries using highly optimized primer sets in a multiplex PCR format that targets T- and B-cell receptor genes. This image depicts how the assay’s two-step PCR process can be used to quantify the clonal diversity of immune cells.

    Immunologists call this process VJD rearrangement. It happens during T-lymphocyte development and affects three gene regions, the variable (V), the diversity (D), and the joining (J) regions. This rearrangement of the genetic code allow for the structural diversity in T-cell receptors responsible for antigenic specificity including antigenic targets on tumor cells. In the case of cancer, specificity is complicated because the tumor is actually part of the body itself, one of the reasons cancers naturally evade detection.

    The specificity problem would always hinder attempts to goad the immune system into attacking cancer, scientists realized, unless technologies emerged that could efficiently track the clonal diversity of T cells inside patients. Existing technologies, such as spectratyping, were inadequate.  In 2007, when Dr. Robins and his collaborators began developing the technology, only 10,000 T-cell receptor sequences had been reported in all the literature using older methodologies.

    “The immunology field of the time had no connection with high-throughput sequencing,” notes Dr. Robins, recalling his days as a computational biologist for the Fred Hutchinson Cancer Research Center. “It became clear that instead of using this old technology to look at T-cell receptors, we could just directly sequence them—if we could amplify them correctly.”

    With its first experiment, Dr. Robins’ team ended up with six million T-cell receptor sequences. “Our approach,” Dr. Robins modestly suggests, “kind of changed the scale of what we were able to do.” The team went on to develop advanced multiplex sequencing technology, doing work that essentially started the field of immune sequencing. “Previously,” maintains Dr. Robins, “no one had ever been able to quantitatively do a multiplex PCR.”

    Adaptive Biotechnologies’ product, the ImmunoSEQ® assay, uses several hundred primer pairs to quantify the clonal diversity of T cells. Using this technology, researchers and clinicians can focus on T-cell clones that are expanded specifically in or near a tumor or that are circulating in the blood stream.

    “You obviously can’t get a serial sample of the tumor,” explains Dr. Robins, “but you can get serial samples of blood,” allowing for immune cell repertoire tracking during the progression of a disease. The technology is already being used to assess leukemias in the clinic, directly tracking the leukemia itself based on the massive clonal expansion of a single cancerous B or T cell.

    Eventually, Dr. Robins’ team hopes to monitor serial changes in T cell clones before, during, and after therapeutic intervention. The team has even developed a tumor infiltrating lymphocyte (TIL) assay to examine clones that are attracted to tumors.

     

Circulating Tumor Cells

“Years ago, they were just interested in what was happening in the tumor,” says Daniel Adams, senior research scientist at Creatv MicroTech. “Now people have realized that the immune system is reacting to the tumor.”

Scientists such as Adams have been tracking tumor cells and tumor-modified stromal cells, as well as components of the non-adaptive immune system, directly within the bloodstream to examine changes that occur over time.

“We can’t go back in to re-biopsy the patient every year, or every time there is a recurrence,” says Adams, “It’s just not feasible.”

That is why Creatv MicroTech, with locations in Maryland and New Jersey, has developed the CellSieve, a mechanical cell filter. The CellSieve, which improves on older technology through better polymers and engineering, isolates circulating tumor cells (CTCs) and stromal cells in order to capture them for further clinical analysis.

Isolation, culture and expansion of cells isolated on CellSieve™. (A) MCF-7 cells spiked into vacutainers, isolated by filtration and cultured on CellSieve for 2-3 weeks. A 3 dimensional cluster attributed to this cell line is seen on the filter. (green=anti-cytokeratin, blue=DAPI) (B) PANC-1 cells spiked into vacutainers, isolated by filtration and grown on CellSieve for 2-3 weeks. PANC-1 is seen growing as a monolayer on the filter. (C) SKBR3 cells are spiked into blood, filtered by CellSieve. The CTCs are identified by presence of anti-cytokeratin and anti-EpCAM, and absence of anti-CD45. After CTCs are counted, cells are subtyped by HER2 FISH. (D) SKBR3 cells are spiked into vacutainers, isolated by filtration and grown on CellSieve for 2-3 weeks. Expanded colonies were directly analyzed as a whole colony and as individual cells, molecularly by HER2/CR17 FISH probes. (E) Circulating stromal cell, e.g. a 70 µm giant cancer associated macrophage can be identified for clinical use, myeloid marker in red. (F) A cell of interest can be identified and restained with immunotherapeutic biomarkers, e.g. PD-L1 (green) and PD-1 (purple). (G) After filtration, cells were identified with histopathological stains (e.g. H&E) for cytological analysis. (H) After H&E, external cell structures were analyzed by SEM. [Creatv MicroTech].

 

“As a patient goes through therapy, the patient’s resistance builds, and the cancer recurs in different subpopulations,” states Adams. “And after a few years, the original tumor mass is no longer applicable to what is growing in the patient farther down the road.”

Although CTCs are exceedingly rare in the bloodstream, with just one or two in every 5 to 10 mL of blood, and although these cells have a very low viability, the surviving CTCs have a high prognostic value.

“We looked at 30 to 40 breast cancer patients over two years,” reports Adams. “And we showed that if you have a dividing CTC, you have a 90% chance of dying in two years and a 100% chance of dying within two and half years.”

Furthermore, the immune system response can be tracked, says Adams, by examining stromal cells, which can also be collected with the CellSieve filtration device. That is, these cells can be collected serially. Much recent evidence supports the conclusion that stromal cells in the tumor environment co-evolve with the tumor, suggesting that stromal marker changes reflect tumor changes.

“There is this plethora of stromal cells and tumor cells out there in the circulation for you to look at,” declares Adams. “Once the cells are isolated, you can subject them to pathological approaches, biomarker approaches, or molecular approaches—or all of the above.”

A MicroTech Creatv study published in the Royal Society of Chemistry showed the efficacy of following up CTC isolation with techniques such as fluorescence in situ hybridization (FISH), histopathological analysis, and cell culture.

Cancer-Killing Assays

Diverse mechanisms are at play in cancer biology. Our understanding of these mechanisms contributes to a couple of virtuous cycles. It strengthens and is strengthened by diagnostic approaches, such as immune- and tumor-cell monitoring. The same could be said of therapeutic approaches. Cancer biology will inform and be informed by cancer immunotherapies such as adoptive cell transfer. To maintain the virtuous cycle, however, it will be necessary to conduct in vitro testing.

“There is no doubt that immunotherapy is going to play a major role in the treatment of cancer,” says Brandon Lamarche, Ph.D., technical communicator and scientist at ACEA Biosciences. “Regardless of what the route is, what is going to have to happen in terms of the research area is that you need an effective cell-killing assay.”

ACEA Biosciences, a San Diego-based company, has developed a microtiter plate that is coated with gold electrodes across 75% of the well bottoms. When the microtiter plates are placed in the company’s xCELLigence plate reader, the electrodes enable the detection of changes in cell morphology and viability through electrical impedance.

“The instrument provides a weak electric potential to the electrodes on the plate, so you get electrons flowing between these electrodes,” explains Dr. Lamarche. Researchers can then apply reagents or non-adherent immune cell suspensions to adherent cancer cells and examine the effect.

Dr. Lamarche asserts that the xCELLigence system overcomes problems that bedevil competing cell-killing assays. These problems include leaky and radioactive labels, such as chromium 51, and assays that can only provide users with an endpoint for cell killing. “With xCELLigence,” he insists, “you’re getting the full spectrum of what’s happening, and there’s all kinds of subtleties in the cell-killing curves that are very informative in terms of the biology.”

ACEA would like to see the xCELLigence system become the new standard in cell-killing assays from standard research to clinical testing on patient tumors. Dr. Lamarche envisions a day when patient tumor cells are quickly screened with therapeutic scenarios to determine the most efficacious killing option. “xCELLigence technology,” he suggests, “enables you to quickly sample a broad spectrum of conditions with a very simple workflow.”

Bioinformatics of Immuno-Oncology

From monitoring to treatment modalities, the field of cancer immunotherapy is aided by bioinformatics-minded data-mining experts, such as the analysts at Thompson-Reuters who are compiling data archives and applying advanced analytics to find new targets. “Essentially,” says Richard Harrison Ph.D., the company’s chief scientific officer for the life science division, “for every stage within pharmaceutical drug development, we have a database associated with that.”

The analysts at Thompson-Reuters curate and compile databases such as MedaCore and Cortellis, which they provide to their clients to help them with their research and clinical studies. “We can take customer data, and using our tools and our pathway maps, we can help them understand what their data is telling them,” explains Dr. Harrison.

Matt Wampole, Ph.D., a solutions scientist at Thompson-Reuters, spends his days reaching out and working with customers to help them understand and better use the company’s products. “Bench researchers,” he points out, “don’t necessarily know what is upstream of whatever expression change might be leading to a particular change in regulation.” Dr. Wampole indicates that he is part of a “solution team” that aids clients in determining important signaling cascades, regulators, and so on.

“We have a group of individuals who are very ‘skilling’ experts in the field,” Dr. Wampole continues, “including experts in the areas such as biostatistics, data curation, and data analytics. These experts help clients identify models, stratify patients, understand mechanisms, and look into disease mechanisms.”

Dr. Harrison sums up the Thompson-Reuters approach as follows: “We look for master regulators that can serve as both targets and biomarkers.” By examining the gene signatures from both the patient and from curated datasets, in the case of cancer immunotherapy, they hope to segregate patients according to what drugs will work best for them.

  • “We are working with a number of pharmaceutical companies to put our approach into practice for clinical trials,” informs Dr. Harrison. The approach has already been applied in several studies, including one that used data analysis of cell lines to help predict drug response in patients. Another study helped stratify glioblastoma patients.

  • Tumor-Targeted Delivery Platform

    PsiOxus Therapeutics, which is focused on immune therapeutics in oncology, has developed a patented platform for tumor-targeted delivery based on its oncolytic vaccine, Enadenotucirev (EnAd), which can be delivered systemically via intravenous administration.

    According to company officials, EnAd’s anti-cancer scope can be expanded by adding new genes, thereby enabling the creation of a broad range of unique immuno-oncology therapeutics. In a recent study conducted at the University of Oxford, researchers led by Philip G. Jakeman, Ph.D., sought to improve the models for evaluating cancer therapeutics by introducing ex vivo methodologies for research into colorectal cancer.

    The ex vivo approach utilized was able to exploit a major advantage by preserving the three-dimensional architecture of the tumor and its associated compartments, including immune cells. The study, which was presented at the International Summit on Oncolytic Viral Therapeutics in Quebec, showed the tissue slice model can provide a novel means to assessing an oncolytic vaccine in a system that more accurately recapitulates human tumors, provide a more stringent test for oncolytic viruses, such as EnAd, and allow study of the human immune cells within the tumor 3D context.

    By maintaining the components of the tumor immune microenvironment, this new methodology could become useful in analyzing anti-viral responses within tumors, or even in evaluating therapeutics that target immunosuppressive tumor micro-environments, noted the Oxford team.

     

 

Deciphering the Cancer Transcriptome

A Rogue’s Gallery of Malignant Outliers May Hide in Transcriptome Profiles That Emphasize Averages

http://www.genengnews.com/gen-articles/deciphering-the-cancer-transcriptome/5729/

 

The key link between genomic instability and cancer progression is transcriptome dynamics. The shifts in transcriptome dynamics that contribute to cancer evolution may come down to statistical outliers. [iStock/zmeel]

  • In recent years, scientists have adopted a gene-centric view of cancer, a tendency to see each malignant transformation as the consequence of alterations in a discrete number of genes or pathways. These alterations are, fortunately, absent from healthy cells, but they pervert malignant cells.

    The gene-centric view takes in molecular landscapes illuminated by genomic and transcriptomic technologies. For example, genomes can be cost-effectively sequenced within hours. Such capabilities have made it possible to interrogate associations between genotypes and phenotypes for increasing numbers of conditions, and to collect data from progressively larger patient groups.

    As genomic and transcriptomic technologies rise, they reveal much—but much remains hidden, too. Perhaps these technologies are less like the sun and more like the proverbial streetlight, the one that narrows our searches because we’re inclined to stay in the light, even though what we hope to find may lie in the shadows.

    “Each individual study that looks at the cancer transcriptome is impressive and tells a convincing story, but if we put several high-quality papers together, there are very few genes that overlap,” says Henry H. Heng, Ph.D., professor of molecular medicine, genetics, and pathology at Wayne State University. “This shows that something is wrong.”

  • Distinct Karyotypes

    One of the major observations in Dr. Heng’s lab is that the intra- and intertumor cellular heterogeneity results in nearly every cancer cell having a unique, distinct karyotype, that is, an important but often ignored genotype. “Biological systems need a lot of heterogeneity,” notes Dr. Heng. “People like to think that this is noise, but heterogeneity is a fundamental buffer system for biological function to be achievable. Moreover, it is the key agent for cellular adaptation.”

    To capture the degree of genomic heterogeneity at the genome level and its impact on cancer cell growth, Dr. Heng and colleagues performed serial dilutions to isolate single mouse ovarian surface epithelial cells that had undergone spontaneous transformation. Spectral karyotyping revealed that within a short timeframe each of these unstable cells exhibited a very distinct karyotype. In these unstable cells, cloning at the level of the karyotype was not possible.

    Stable cells exhibited a normal growth distribution, i.e., no subset of stable cells contributed disproportionately to the overall growth of the cell population. In contrast, unstable cell populations showed a non-normal growth distribution, with few cells contributing most to the cell population’s growth. For example, a single unstable colony contributed more than 70% to the cell population’s growth. This finding suggests that although average profiles can be used to describe non-transformed cells, they cannot be taken to represent the biology of malignant cells.

    “Most people who study the transcriptome want to get rid of the noise, but the noise is in fact the strategy that cancer uses to be successful,” explains Dr. Heng. “Each individual cancer cell is very weak but together the entity becomes very robust.”

    In a recent model that Dr. Heng and colleagues proposed, system inheritance visualizes chromosomes not merely as the vehicle for transmitting genetic information, but as the genetic network organizer that shapes the physical interactions between genes in the three-dimensional space. Based on this model, individual genes represent parts of the system. The same genes can be reorganized to form different systems, and chromosomal instability becomes more important than the contribution of individual genes and pathways to cancer biology.

    The vital link between genomic instability and cancer progression is transcriptome dynamics, and the shifts in those dynamics that contribute to cancer evolution may come down to statistical outliers.

    “Transcriptome studies rarely focus on single-cell analyses, which means important outliers are frequently ignored,” declares Dr. Heng. “This preoccupation with uninformative averages explains why we have learned so little despite having examined so many transcriptomes.”

  • Chimeras and Fusion Genes

    “Our focus is on chimeric RNA molecules,” says Laising Yen, Ph.D, assistant professor of pathology at Baylor College of Medicine. “This category of RNAs is very special because their sequences come from different genes.”

    In a study that was designed to capture chimeric RNAs in prostate cancer, Dr. Yen and his colleagues performed high-throughput sequencing of the transcriptomes from human prostate cancer samples. “We found far more chimeric RNAs, in terms of abundance, and a number of species that are not seen in normal tissue,” reports Dr. Yen. This approach identified over 2,300 different chimeric RNA species. Some of these chimeras were present in prostate cancer cell lines, but not in primary human prostate epithelium cells, which points toward their relevance in cancer.

    “Most of these chimeric RNAs do not have a genomic counterpart, which means that they could be produced by trans-splicing,” explains Dr. Yen. During trans-splicing, individual RNAs are generated and trans-spliced together as a single RNA, which provides a mechanism for generating a chimera.

    “The other possibility is that in cancer cells, where gene–gene boundaries are known to become broken, chimeras can be formed by cis-splicing from a very long transcript that encodes several neighboring genes located on the same chromosome,” informs Dr. Yen. Chimeric RNAs formed by either of these two mechanisms can potentially translate into fusion proteins, and these aberrant proteins may have oncogenic consequences.

    Another effort in Dr. Yen’s laboratory focuses on chromosomal aberrations in ovarian cancer. One of the hallmarks of ovarian cancer is the high degree of genomic rearrangement and the increased genomic instability.

    “When we looked at ovarian cancers, we did not find as many chimeric RNAs,” notes Dr. Yen. “But we found many fusion genes.” Gene fusions, similarly to chimeric RNAs, increase the diversity of the cellular proteome, which could be used selectively by cancer cells to increase their rates of proliferation, survival, and migration.

    A recent study in Dr. Yen’s lab identified BCAM-AKT2, a recurrent fusion gene that is specific and unique to high-grade serous ovarian cancer. BCAM-AKT2 is the only fusion gene in this malignancy that was proved to be translated into a fusion kinase in patients, which points toward its functional significance and potential therapeutic value.

    “Recurrent fusion genes, which are repeatedly found in many patients in precisely made forms, indicate that there is a reason that they are present,” concludes Dr. Yen. “This might have important therapeutic implications.”

  • Context-Specific Patterns

    “We contributed to a study of tumor gene expresssion that we are currently revisiting because so much more data has become available,” says Barbara Stranger, Ph.D., assistant professor, Institute for Genomics and Systems Biology, University of Chicago. “The data is being processed in homogenized analytic pipelines, and we can look at many more tumor types across the Cancer Genome Atlas than a few years ago.”

    Previously, Dr. Stranger and colleagues performed expression quantitative trait loci (eQTL) analyses to examine mRNA and miRNA expression in breast, colon, kidney, lung, and prostate cancer samples. This approach identified 149 known cancer risk loci, 42 of which were significantly associated with expression of at least one transcript.

    Causal alleles are being prioritized using a fine-mapping strategy that integrated the eQTL analysis with genome-wide DNAseI hypersensitivity profiles obtained from ENCODE data. These analyses are focusing on capturing differences across tumors and on performing comparisons with normal tissue, and one of the challenges is the lack of normal tissue from the same patients.

    “But still there is a lot of power in these analyses because they are based on large-scale genomic datasets. Also, these tumor datasets can be compared with large-scale normal tissue genomics datasets, such as the NIH’s Genotype-Tissue Expression (GTEx) project,” clarifies Dr. Stranger. “This helps us characterize differences between those tumors and normal tissue in terms of the genetics of gene regulation.”

    An ongoing effort in Dr. Stranger’s laboratory involves elucidating how the effect of genetic polymorphisms is shaped by context. Stimulated cellular states, cell-type differences, cellular senescence, and disease are some of the contexts that are known to impact genetic polymorphisms.

    “We have seen a lot of context specificity,” states Dr. Stranger. “Our observations suggest that a genetic polymorphism can have a specific effect in regulating a particular gene or transcript in one context, and another effect in another context.”

    Another example of cellular context is sex, and an active area of investigation in Dr. Stranger’s lab proposes to dissect the manner in which sex differences shape the regulatory effects of genetic polymorphisms.

    “Thinking about sex-specific differences is not very different from thinking about a different cellular environment,” notes Dr. Stranger.

    The expression of specific transcription factors can be determined by sex; consequently, a polymorphism that interacts with a transcription factor may have functional outcomes that can be seen in only one of the sexes.

    “There are gene-level and gene-splicing differences that we see in normal tissues between males and females, and we want to take the same approach and look at the cancer context to see whether the genetic regulation of gene expression and transcript splicing is different between individuals and whether it has a sex bias,” concludes Dr. Stranger. “Finally, we want to see how that differs in cancer relative to normal tissues.”

    Early Clinical Impact

An increasing number of clinicians are adding the cancer transcriptome to their precision medicine program. They have found that the transcriptome is important in identifying clinically impactful results. [iStock/DeoSum]

“Over the last two years,” says Andrew Kung, M.D., Ph.D., chief of the Division of Pediatric Hematology, Oncology, and Stem Cell Transplantation at Columbia University Medical Center, “we have included the cancer transcriptome as part of our precision medicine program.” Dr. Kung and colleagues developed a clinical genomics test that includes whole-exome sequencing of tumors and normal tissue and RNA-seq of the tumor.

“Our results show that the transcriptome is very important in identifying clinically impactful results,” asserts Dr. Kung. “The technology has really moved from a research tool to real clinical application.” In fact, the test has been approved by New York State for use in cancer patients.

The data from transcriptome profiling has enabled identification of translocations, verification of somatic alterations, and assessment of expression levels of cancer genes.  Dr. Kung and his colleagues are using genomic information for initial diagnosis and prognostic decisions, as well as the investigation of potentially actionable alterations and the monitoring of disease response.

To gain insight into gene-expression changes, transcriptome analysis usually compares two different types of tissues or cells. For example, analyses may attempt to identify differentially expressed genes in cancer cells and normal cells.

“In patients with cancer, we usually do not have access to the normal cell of origin, making it harder to identify the genes that are over- or under-expressed,” explains Dr. Kung. “Fortunately, the vast amounts of existing gene-expression data allow us to identify genes whose expression are most changed relative to models built on the expression data aggregated across large existing datasets.”

These genomic technologies were first used to augment the care of pediatric patients at Columbia. The technologies were so successful that they attracted philanthropic funding, which is being used to expand access to genomic testing to all children with high-risk cancer across New York City.

Other related articles published in this Open Access Online Scientific Journal include the following:

Immunotherapy in Combination, 2016 MassBio Annual Meeting  03/31/2016 8:00 AM – 04/01/2016 3:00 PM Royal Sonesta Hotel, Cambridge, MA

Live Press Coverage: Aviva Lev-Ari, PhD, RN

http://pharmaceuticalintelligence.com/2016/04/01/plenary-session-immunotherapy-in-combination-2016-massbio-annual-meeting-03312016-800-am-04012016-300-pm-royal-sonesta-hotel-cambridge-ma/

 

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Digital PCR

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

GEN Roundup: Digital PCR Advances Partition by Partition  

By Partitioning Samples Digital PCR Is Lowering Detection Limits and Enabling New Applications

GEN  Mar 1, 2016 (Vol. 36, No. 5)       http://www.genengnews.com/gen-articles/gen-roundup-digital-pcr-advances-partition-by-partition/5697

 

  • Digital PCR (dPCR) has generated intense interest because it is showing potential as a clinical diagnostics tool. It has already proven to be a useful technique for any application where extreme sensitivity or precise quantification is essential, such as identifying mutations or copy number variations in tumor cells, or examining gene expression at the single-cell level.

    GEN interviewed several dPCR experts to find out specifically why the technique is increasing in popularity. GEN also asked the experts to envision dPCR’s future capabilities.

  • GEN: What makes dPCR technology such a superior tool for discovery and diagnostic applications?

    Dr. Shelton The high levels of sensitivity, precision, and reproducibility in DNA and quantification are the major strengths of dPCR. The technology is robust where differences in primer efficiency or the presence of sample-specific PCR inhibitors are trivial to the final quantification through an end-point amplification reaction.

    This provides value to discovery as a trusted tool for validating potential biomarkers and hypotheses generated by broad profiling techniques such as microarrays or next-generation sequencing (NGS). In diagnostics applications, the reproducibility and rapid results of dPCR are critical for labs around the world to quickly compare and share data, especially for ultra-low detection of DNA where variability is high.

    Dr. Garner Digital PCR provides a precise direct counting approach for single molecule detection, thereby providing a straightforward process for the absolute quantification of nucleic acids in samples. One of the biggest advantages of using a system such as ours is its ability to do real-time reads on digital samples. When samples go through PCR, their results are recorded after each cycle.

    These results build a curve, and customers can analyze the data if something went wrong. If it isn’t a clean read—from either a contamination issue, primer-dimer issue, or off-target issue—the curve isn’t the classic PCR curve.

    Dr. Menezes Digital PCR allows absolute quantification of target concentration in samples without the need for standard curves. Obtaining consistent, precise, and absolute quantification with regular qPCR is dependent on standard curve generation and amplification efficiency calculations, which can introduce errors.

    Ms. Hibbs At MilliporeSigma Cell Design Studio, the implementation of dPCR has improved and accelerated the custom cell engineering workflow. After the application of zinc finger nuclease or CRISPR/Cas to create precise genetic modifications in mammalian cell lines, dPCR is used to characterize the expected frequency of homologous recombination and develop a screening strategy based on this expected frequency.

    In some cell lines, homologous recombination occurs at a low frequency. In such cases, dPCR is used to screen cell pools and subsequently identify rare clones having the desired mutation. Digital PCR is also used to accurately and expeditiously measure target gene copy number. It is used this way, for example, in polyploid cell lines.

    Dr. Price The ability to partition genomic samples to a level that enables robust detection of single target molecules is what sets dPCR apart as an innovative tool. Each partition (droplet in the case of the RainDrop System) operates as an individual PCR reaction, allowing for sensitive, reproducible, and precise quantification of nucleic acid molecules without the need for reference standards or endogenous controls.

    Partitioning also provides greater tolerance to PCR inhibitors compared to quantitative PCR (qPCR). In doing so, dPCR can remedy many shortcomings of qPCR by transforming the analog, exponential nature of PCR into a digital signal.

    Mr. Wakida Digital PCR is an ideal technology for detecting rare targets at concentrations of 0.1% or lower. By partitioning samples prior to PCR, exceptionally rare targets can be isolated into individual partitions and amplified.

    Digital PCR produces absolute quantitative results, so in some respects, it is easier than qPCR because it doesn’t require a standard curve, with the added advantages of being highly tolerant of inhibitors and being able to detect more minute fold changes. Absolute quantification is useful for generating reference standards, detecting viral load, and preparing NGS libraries.

  • GEN: In what field do you think dPCR will have the greatest impact in the future?

    Dr. Shelton dPCR will have a great impact on precision medicine, especially in liquid biopsy analysis. Cell-free DNA from bodily fluids such as urine or blood plasma can be analyzed quickly and cost-effectively using dPCR. For example, a rapid dPCR test can be performed to determine mutations present in a patient’s tumor and help drive treatment decisions.

    Iterative monitoring of disease states can also be achieved due to the relatively low cost of dPCR, providing faster response times when medications are failing. Gene editing will also be greatly impacted by dPCR. Digital PCR enables refinement and optimization of gene-editing tools and conditions. Digital PCR also serves as quality control of therapeutically modified cells and viral transfer vectors used in gene-therapy efforts.

    Dr. Garner The BioMark™ HD system combines dPCR with simultaneous real-time data for counting and validation. This capability is important for applications such as rare mutation detection, GMO quantitation, and aneuploidy detection—where false positives are intolerable and precision is paramount.

    Any field that requires precision and the ability to detect false positives is a likely target for Fluidigm’s dPCR. Suitable applications include detecting and quantifying cancer-causing genes in patients’ cells, viral RNA that infects bacteria, or fetal DNA in an expectant mother’s plasma.

    Dr. Menezes This technology is particularly useful for samples with low frequency sequences as, for example, those containing rare alleles, low levels of pathogen, or low levels of target gene expression. Teasing out fine differences in copy number variants is another area where this technology delivers more precise data.

    Ms. Hibbs Digital PCR overcomes limitations associated with low-abundance template material and quantification of rare mutations in a high background of wild-type DNA sequence. For this reason, dPCR is poised to have significant impacts in diverse clinical applications such as detection and quantification of rare mutations in liquid biopsies, detection of viral pathogens, and detection of copy number variation and mosaicism.

    Dr. Price Due to its high sensitivity, precision, and absolute quantification, the RainDrop dPCR has the potential to extend the range of nucleic acid analysis beyond the reach of other methods in a number of applications that could lend themselves to diagnostic, prognostic, and predictive applications. The precision of dPCR can be extremely useful in applications that require finer measures of fold change and rare variant detection.

    Digital PCR is suitable for addressing varied research and clinical challenges. These include the early detection of cancer, pathogen/viral detection and quantitation, copy number variation, rare mutation detection, fetal genetic screening, and predicting transplant rejection. Additional applications include gene expression analysis, microRNA analysis, and NGS library quantification.

    Mr. Wakida Digital PCR will have an impact on applications for detecting rare targets by enabling investigators to complement and extend their capabilities beyond traditionally employed methods. One such application is using dPCR to monitor rare targets in peripheral blood, as in liquid biopsies.

    The monitoring of peripheral blood by means of dPCR has been described in several peer-reviewed articles. In one such article, investigators considered the clinical value of Thermo’s QuantStudio™ 3D Digital PCR system for the detection of circulating DNA in metastatic colorectal cancer (Dig Liver Dis. 2015 Oct; 47(10): 884–90).

  • GEN: Is there a new technology on the horizon that will increase the speed and/or efficiency of dPCR?

    Dr. Shelton High-throughput sample analysis can be an issue with some dPCR systems. However, Bio-Rad’s Automated Droplet Generator allows labs to process 96 samples simultaneously, a capability that eliminates user-to-user variability and minimizes hands-on time.

    We also want users to get the most information from one sample. Therefore, we are focused on expanding the multiplexing capabilities of our system. In development at Bio-Rad are new technologies that increase the multiplexing capabilities without loss of specificity or accuracy in the downstream workflow.

    Dr. Garner Much of the industry direction seems to be in offering ever-higher resolution, or the ability to run more samples at the same resolution. Thus far, however, customers haven’t found commercial uses for these tools. Also, with increasing resolution and the search for even rarer mutations, the challenge of detecting false positives becomes an even bigger issue.

    Dr. Menezes Use of ZEN™ Double-Quenched Probes by IDT in digital PCR provides increased sensitivity and a lower limit of detection. Due to the second quencher, ZEN probes provide even lower background than traditional single-quenched probes. And this lower background enables increased sensitivity when analyzing samples with low copy number targets, where every droplet matters.

    Ms. Hibbs Quantification relies upon counting the number of positive partitions at the end point of the reaction. Accordingly, precision and resolution can be increased by increasing the number of partitions. We are now capable of analyzing on the order of millions of partitions per run, further extending the lower limit of detection. Additionally, the workflow is amenable to the integration of automation in order to increase throughput and standardize reaction set up.

    Dr. Price Although dPCR is still an emerging technology, there is tremendous interest in its potential clinical diagnostics applications. Enabling adoption of dPCR in the clinical lab requires addressing current gaps in workflow, cost, throughput, and turnaround time.

    Digital PCR technology has the potential for being improved significantly in two dimensions. First, one can address the problem of serially detecting positive versus negative partitions by leveraging lower-cost imaging detection technologies. Alternatively, one may capitalize on the small partition volumes to dramatically reduce the time to perform PCR. Ideally, the future will bring both capabilities to bear.

    Mr. Wakida Compared to qPCR, dPCR currently requires more hands-on time to set up experiments. We are investigating methods to address this.

 

PCR Shows Off Its Clinical Chops   

Thanks to Advances in Genomics, PCR Is Becoming More Common in Clinical Applications

  • Last May, Roche Molecular Systems announced that its cobas Liat Strep A assay received a CLIA waiver. This clinic-ready assay can detect Streptococcus pyogenes (group A ß-hemolytic streptococcus) DNA in throat swabs by targeting a segment of the S. pyogenes genome.

    Since its invention by Kary B. Mullis in 1985, the polymerase chain reaction (PCR) has become well established, even routine, in research laboratories. And now PCR is becoming more common in clinical applications, thanks to advances in genomics and the evolution of more sensitive quantitative PCR methodologies.

    Examples of clinical applications of PCR include point-of-care (POC) molecular tests for bacterial and viral detection, as well as mutation detection in liquid or tumor biopsies for patient stratification and treatment monitoring.
    Industry leaders recently participated in a CHI conference that was held in San Francisco. This conference—PCR for Molecular Medicine—encompassed research and clinical perspectives and emphasized advanced techniques and tools for effective disease diagnosis.
    To kick off the event, speakers shared their views on POC molecular tests. These tests, the speakers insisted, can provide significant value to healthcare only if they support timely decision making.
    Clinic-ready PCR platforms need to combine speed, ease of use, and accuracy. One such platform, the cobas Liat (“laboratory in a tube”), is manufactured by Roche Molecular Systems. The system employs nucleic acid purification and state-of-art PCR-based assay chemistry to enable POC sites to rapidly provide lab-quality results.
    The cobas Liat Strep A Assay detects Streptococcus pyogenes (group A β-hemolytic streptococcus) DNA by targeting a segment of the S. pyogenes genome. The operator transfers an aliquot of a throat swab sample in Amies medium into a cobas Liat Strep A Assay tube, scans the relevant tube and sample identification barcodes, and then inserts the tube into the analyzer for automated processing and result interpretation. No other operator intervention or interpretation is required. Results are ready in approximately 15 minutes.

    According to Shuqi Chen, Ph.D., vp of Point-of-Care R&D at Roche Molecular Systems, clinical studies of the cobas Liat Strep A Assay demonstrated 97.7% sensitivity when the test was used at CLIA-waived, intended-use sites, such as physicians’ offices. In comparison, rapid antigen tests and diagnostic culture have sensitivities of 70% and 81%, respectively (according to a 2009 study Tanz et al. in Pediatrics).

    The cobas Liat assay preserved the same ease-of-use and rapid turnaround as the rapid antigen tests. It addition, it provided significantly faster turnaround than the lab-based culture test, which can take 24–48 hours.

    A CLIA waiver was announced for the cobas Liat Strep A assay in May 2015. CLIA wavers have been submitted for cobas Liat flu assays, and Roche intends to extend the assay menu.

    POC tests are also moving into field applications. Coyote Bioscience has developed a novel method for one-step gene testing without nucleic acid extraction that can be as fast as 10 minutes from blood sample to result. Their portable devices for molecular diagnostics can be used as genetic biosensors to bring complex clinical testing directly to the patient.

    “Instead of sequential steps, reactions happen in parallel, significantly reducing analysis time. Buffer, enzyme, and temperature profiles are optimized to maximize sensitivity,” explained Sabrina Li, CEO, Coyote Bioscience. “Both RNA and DNA can be analyzed simultaneously from a drop of blood in the same reaction.”

    The first-generation Mini-8 system was used for Ebola detection in Africa where close to 600 samples were tested with 98.8% sensitivity. Recently in China, the Mini-8 system was applied in hospitals and small community clinics for hepatitis B and C and Bunia virus detection. The second-generation InstantGene system is currently being tested internally with clinical samples.

  • Digital PCR

    Conventional real-time PCR technology, while suited to the analysis of high-quality clinical samples, may effectively conceal amplification efficiency changes when sample quality is inconsistent. A more effective alternative, Bio-Rad suggests, is its droplet-digital PCR (ddPCR) technology, which can provide absolute quantification of target DNA or RNA, a critical advantage when samples are limited, degraded, or contain PCR inhibitors. The company says that of the half-dozen clinical trials that are using digital PCR, half rely on the Bio-Rad QX200 ddPCR system.

    Personalized cancer care requires ultra-sensitive detection and monitoring of actionable mutations from patient samples. The high sensitivity and precision of droplet-digital PCR (ddPCR) from Bio-Rad Laboratories offers critical advantages when clinical samples are limited, degraded, or contain PCR inhibitors.

    Typically, formalin-fixed and paraffin-embedded (FFPE) tissue samples are processed. FFPE samples work well for immunohistochemistry and protein analysis; however, the formalin fixation can damage nucleic acids and inhibit the PCR reaction. Samples may yield 100 ng of purified nucleic acid, but the actual amplifiable material is less than 1%, or 1 ng, in most cases.

    “Current qPCR technology depends on real-time fluorescence accumulation as the PCR is occurring, which can be an effective means of detecting and quantifying DNA targets in nondegraded samples,” commented Dawne Shelton, Ph.D., staff scientist, Digital Biology Center, Applications Development Group, Bio-Rad Laboratories. “Amplification efficiency is critical; if that amplification efficiency changes because of sample quality it is hidden in the qPCR methodology.”

    “In ddPCR, that is a big red flag,” Dr. Shelton continued. “It changes the format of how the data look immediately so you know the amount of inhibition and which samples are too inhibited to use.”

    Tissue types vary and contain different degrees of fat or other content that can also act as PCR inhibitors. In blood monitoring, the small circulating fragments of DNA are extremely degraded; in addition, food, supplements, or other compounds ingested by the patient may have an inhibitory effect.

    Clinical labs test for these variabilities and clean the blood, but remnant PCR inhibitors can remain. In ddPCR, a single template is partitioned into a droplet. If the droplet contains a good template, it produces a signal; otherwise, it does not—a simple yes or no answer.

    “Even if there is no PCR inhibition, most clinical samples yield very small amounts of nucleic acid,” Dr. Shelton added. “To make a secure decision using qPCR is difficult because you are in a gray zone at the very end of its linear range. ddPCR operates best with small sample amounts and provides good statistics for confidence in your results.”

    Currently, at least a half dozen clinical trials worldwide are using digital PCR, half of them are using the Bio-Rad QX200 Droplet Digital PCR system. Examples of studies include examining BCR-ABL monitoring in patients with chronic myelogenous leukemia (CML); identifying activating mutations in epidermal growth factor receptor (EGFR) for first-line therapy of new drugs in patients with lung cancer; and the monitoring of resistance mutations such as EFGR T790M in patients with non-small cell lung cancer (NSCLC).

    Clovis Oncology used a technology called BEAMing (Beads, Emulsions, Amplification, and Magnetics), a type of digital PCR for blood-based molecular testing, to perform EGFR testing on almost 250 patients in clinical trials. In BEAMing, individual EGFR gene copies from plasma are separated into individual water droplets in a water-in-oil emulsion. The gene copies are then amplified by PCR on magnetic beads.

    The beads are counted by flow cytometry using fluorescently labeled probes to distinguish mutant beads from wild-type. Because each bead can be traced to an individual EGFR molecule in the patient’s plasma, the method is highly quantitative.

    “BEAMing is particularly well-suited for the detection of known mutations in circulating tumor DNA. In this circumstance, the mutation of interest often occurs at low levels, perhaps only 1–2 copies per milliliter or even less, and in a high background of wild-type DNA that comes from normal tissue. BEAMing can detect one mutant molecule in a background of 5,000 wild-type molecules in clinical samples,” stated Andrew Allen, MRCP, Ph.D., chief medical officer, Clovis Oncology.

    In the studies, the EGFR-resistance mutation T790M could be identified in plasma 81% of the time that it was seen in the matched patient tumor biopsy. Additionally, about 10% of patients in the study had a T790M mutation in plasma that was not identified in tissue, presumably because of tumor heterogeneity. Another 5–10% of the patients did not provide an EGFR result, usually because the tissue biopsy had no tumor cells.

    In aggregate, these results suggest that plasma EGFR testing can be a valuable complement to tumor testing in the clinical management of NSCLC patients, and can provide an alternative when a biopsy is not available. Tumor biopsies may provide only limited tissue, if in fact any tissue is available, for molecular analysis. Also, mutations may be missed due to tumor heterogeneity. These mutations may be captured by sampling the blood, which acts as a reservoir for mutations from all parts of a patient’s tumor burden.

    In the last few years, a panoply of clinically actionable driver mutations have been identified for NSCLC, including mutations in EGFR, BRAF, and HER2, as well as ALK, ROS, and RET rearrangements. These driver mutations will migrate NSCLC molecular diagnostic testing in the next few years toward panel testing of relevant cancer genes using various digital technologies, including next-generation sequencing.

     

PCR Has a History of Amplifying Its Game

A GEN 35th Anniversary Retrospective

PCR Has a History of Amplifying Its Game

PCR is a fast and inexpensive technique used to amplify segments of DNA that continues to adapt and evolve for the demanding needs of molecular biology researchers. This diagram shows the basic principles of PCR amplification. [NHGRI]

  • The influence that the polymerase chain reaction (PCR) has had on modern molecular biology is nothing short of remarkable. This technique, which is akin to molecular photocopying, has been the centerpiece of everything from the OJ Simpson Trial to the completion of the Human Genome Project. Clinical laboratories use this DNA amplification method for infectious disease testing and tissue typing in organ transplantation. Most recently, with the explosion of the molecular diagnostics field and meteoric rise in the use of next-generation sequencing platforms, PCR has enhanced its standing as an essential pillar of genomic science.

    Let’s open the door to the past and take a look back around 35 years ago when GEN started reporting on the relatively new disciplines of genetic engineering and molecular biology. At that time, GEN was among the first to hear the buzz surrounding a new method to synthesize and amplify DNA in the laboratory. In reviewing the fascinating history of PCR, we will see how the molecular diagnostics field took shape and where it could be headed in the future.

  • Some Like It Hot

    The biological sciences rarely advance within a vacuum—rather they rely on previous discoveries to promote directly or indirectly our understanding. The contributions made by scientists in the field of molecular biology that contributed to the functional pieces of PCR were numerous and spread out over more than two decades.

    It began with H. Gobind Khorana’s advances in understanding the genetic code, leading to the use of synthetic DNA oligonucleotides, continued through Kjell Kleepe’s 1971 vision of a two-primer system for replicating DNA segments, to Fredrick Sanger’s method of DNA sequencing—a process that would win him the Nobel prize in 1980—which utilized DNA oligo primers, nucleotide precursors, and a DNA synthesis enzyme.

    All of the previous discoveries were essential to PCR’s birth, yet it would be an egregious mistake to begin a retrospective on PCR and not discuss the enzyme upon which the entire reaction hinges upon—DNA polymerase. In 1956, Nobel laureate Arthur Kornberg and his colleagues discovered DNA polymerase (Pol I), in Escherichia coli. Moreover, the researchers described the fundamental process by which the polymerase enzyme copies the base sequence of a DNA template strand. However, it would take biologists another 20 years to discover a version of DNA polymerase that was stable enough for use for any meaningful laboratory purposes.

    That discovery came in 1976 when a team of researchers from the University of Cincinnati described the activity of a DNA polymerase (Taq) they isolated from the extreme thermophile bacteria, Thermus aquaticus, which lives in hot springs and hydrothermal vents. The fact that this enzyme could withstand typical protein-denaturing temperatures and function optimally around 75–80°C fortuitously set the stage for the development of PCR.

    By 1983, all of the ingredients to bake the molecular cake were sitting in the biological cupboard waiting to be assembled in the proper order. At that time, Nobel laureate Kary Mullis was working as a scientist for the Cetus Corporation trying to perfect oligonucleotide synthesis. Mullis stumbled upon the idea of amplifying segments of DNA using multiple rounds of replication and the two primer system—essentially modifying and expanding upon Sanger’s sequencing reaction. Mullis discovered that the temperatures for each step (melting, annealing, and extension) in the reaction would need to be painstakingly controlled by hand. In addition, he realized that since the reactions were using a non-thermostable DNA polymerase, fresh enzyme would need to be “spiked in” after each successive cycle.

    Mullis’ hard work and persistence paid off as the reaction was successful at amplifying a particular segment of DNA that was flanked by two opposing nucleotide primer molecules. Two years later, the Cetus team presented their work at the annual meeting of the American Society for Human Genetics, and the first mention of the method was published in Science that same year; however, that article did not go into detail about the specifics of the newly developed PCR method—a paper that would be rejected by roughly 15 journals and would not be published until 1987.

    Although scientists were a bit slow on the uptake for the new method, the researchers at Cetus were developing ways to improve upon the original assay. In 1986, the scientists substituted the original heat-liable DNA polymerase for the temperature-resistant Taq polymerase, removing the need to spike in enzyme and dramatically reducing errors while increasing sensitivity. A year later, PerkinElmer launched their creation of a thermal cycler, allowing scientists to regulate the heating and cooling parts of the PCR reaction with greater efficiency.

    Extremely soon after the introduction of Taq and the launch of the thermal cycler, the PCR reaction exploded exponentially among research laboratories and not only vaulted molecular biology to the pinnacle of researcher interests, it also launched a molecular diagnostics revolution that continues today and shows no signs of slowing down.

  • Molecular Workhorse

    In the years since PCR first burst onto the scene, there have been a number of significant advancements to the technique that have widely improved the overall method. For example, in 1991, a new DNA polymerase from the hyperthermophilic bacteria Pyrococcus furiosus, or Pfu, was introduced as a high-fidelity alternative enzyme to Taq. Unlike Taq polymerase, Pfu has built in 3′ to 5′ exonuclease proofreading activity, which allows the enzyme to correct nucleotide incorporation errors on the fly—dramatically increasing base specificity, albeit at a reduced rate of amplification versusTaq.

    In 1995, two advancements were introduced to PCR users. The first, called antibody “hot-start” PCR, utilized an immunoglobulin molecule that is directed to the DNA polymerase and inhibits its activity until the first 95°C melt stage, denaturing the antibody and allowing the polymerase to become active. Although this process was effective in increasing the specificity of the PCR reaction, many researchers found that the technique was time consuming and often caused cross-contamination of samples.

    The second innovation introduced that year began another revolution for molecular biology and the PCR method. Real-time PCR, or quantitative PCR (qPCR), allowed researchers to quantitatively create DNA templates for PCR amplification from RNA transcripts through the use of the reverse-transcriptase enzyme and specifically incorporated fluorescent reporter dyes. The technique is still widely used by researchers to monitor gene expression extremely accurately. Over the past 20 years, many companies have spent many R&D dollars to create more accurate, higher throughput, and simple qPCR machines to meet researcher demands.

    With the advent of next-generation sequencing techniques—and the rise of techniques that started commanding the attention of more and more researchers—PCR machines and methods needed to evolve and modernize to keep pace. PCR remained the lynchpin in almost all the next-generation sequencing reactions that came along, but the traditional technique wasn’t nearly as precise as required.

    Digital PCR (dPCR) was introduced as a refinement of the conventional method, with the first real commercial system emerging around 2006. dPCR can be used to quantify directly and clonally amplify DNA or RNA.

    The apparatus carries out a single reaction within a sample. The sample, however, is separated into a large number of partitions. Moreover, the reaction is performed in each partition individually—allowing a more reliable measurement of nucleic acid content. Researchers often use this method for studying gene-sequence variations, such as copy number variants (CNV), point mutations, rare-sequence detection, and microRNA analysis, as well as for routine amplification of next-generation sequencing samples.

  • Future of PCR: Better, Faster, Stronger!

    It is almost impossible to envision a future laboratory setting that wouldn’t utilize PCR in some fashion, especially due to the heavy reliance of next-generation sequencing techniques for accurate PCR samples and at the very least using the method as a simple amplification tool for creating DNA fragments of interest.

    Yet there is at least one new next-generation sequencing technique that can identify native DNA sequences without an amplification step—nanopore sequencing. Although this technique has performed well in many preliminary trials, it is in its relative infancy. It will probably undergo additional development lasting several years before it approaches large-scale adoption by researchers. Even then, PCR has become so engrained into daily laboratory life that to try to phase out the technique would be like asking molecular biologists to give up their pipettes or restriction enzymes.

    Most PCR equipment manufacturers continue to seek ways to improve the speed and sensitivity of their thermal cyclers, while biologists continue to look toward ways to genetically engineer better DNA polymerase molecules with even greater fidelity than their naturally occurring cousins. Whatever the new advancements are, and wherever they lead the life sciences field, you can count on us at GEN to continue to provide our readers with detailed information for another 35 years … at least!

     

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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

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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

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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

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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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