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Cholesterol-busting gut bacteria affect people’s cardiac health
Reporter: Irina Robu, PhD
Scientists at Broad Institute of MIT and Harvard University, has discovered a group a gut bacterium that can metabolize enough cholesterol to affect metabolism. Their study in Cell Host and Microbe, found that bacteria in the intestines have lower cholesterol levels in their blood. Cholesterol is a key biological molecule that functions as a structural component of all animal cell membranes and is a precursor of steroid hormones, vitamin D, and bile acids. Two main sources of cholesterol are thought to influence concentrations of this metabolite in serum: endogenous cholesterol synthesized in the liver and exogenous cholesterol derived from dietary components of animal origin.
The study shows a roadmap of how enzymes and microbial genes can manipulate metabolism and impact human health. The concept that bacteria can metabolize cholesterol is been known for a long time, but not enough has been known of which species of bacteria was doing this. However, isolating cholesterol metabolizing bacteria and growing them in the lab proved to be difficult.
The idea that bacteria can metabolize cholesterol isn’t a new one; in the early 1900s, scientists reported the existence of bacteria that could chemically transform cholesterol into a compound called coprostanol. Coprostanol-generating bacteria have been found in the guts of rats, baboons, pigs, and even humans, but the biology of these bacteria was poorly understood.
The scientists genetically engineered bacteria in the lab to produce genetically engineered bacteria in the lab to produce four enzymes of interest. Yet, they focused on one gene named Intestinal Stool Metabolism (IsmA) that could metabolize cholesterol. Furthermore, individuals with the IsmA gene had, on average, cholesterol levels in the blood that were 2.7 mg/dL lower than those without any copies of the IsmA genes in their microbiomes. This is a larger average effect on blood cholesterol than human genes such as HMGCR and PCSK9, which are known to alter a person’s risk of high cholesterol levels and are targeted by some FDA-approved cholesterol drugs.
A book has recently been reviewed by Laura Fisher (Feb 19 2016) titled “Junk DNA: a journey through the dark matter of the genome” (Nessa Carey Icon Books 2015 | 352pp ISBN 9781848319158). http://www.rsc.org/chemistryworld/2016/02/junk-dna-genome-nessa-carey-book-review It is important in its focus on, ‘junk DNA’, a term coined in the 1960s that refers to regions of our DNA that don’t code for proteins. It is now known that a large portion of the genome is noncoding. These non-coding areas of our DNA are far from being without function. Whether regulating gene expression and transcription, or providing protein attachment sites, this once-dismissed part of the genome is vital for all life, and this is the focus of Junk DNA. However, in 1869 Friedrich Miescher discovered a new substance (Dahm, 2008) from the cell nuclei that had chemical properties unlike any protein, including a much higher phosphorous content and resistance to proteolysis (protein digestion). He wrote, “It seems probable to me that a whole family of such slightly varying phosphorous-containing substances will appear, as a group of nucleins, equivalent to proteins” (Wolf, 2003). In 1971, Chargaff noted that Miescher’s discovery of nucleic acids was unique among the discoveries of the four major cellular components (i.e., proteins, lipids, polysaccharides, and nucleic acids) in that it could be “dated precisely… [to] one man one place, one date.” We now know that there are two basic categories of nitrogenous bases: the purines (adenine [A] and guanine [G]), each with two fused rings, and the pyrimidines (cytosine [C], thymine [T], and uracil [U]), each with a single ring. Furthermore, it is now widely accepted that RNA contains only A, G, C, and U (no T), whereas DNA contains only A, G, C, and T (no U). Keeping this in mind, the Watson-Crick proposal, as important as it was, was a discovery out of historical proportion, and it set the path of molecular biology for the remainder of the 20th century. A consequence of this seminal event was that the direction of biochemistry and molecular biology became set for several generations into the 21st century, culminating in the Human Genome Project.
As important as this discovery and others related that followed, there were a number of unrelated discoveries that took on huge importance, immediately recognized, but not so soon integrated with the evolving body of knowledge. For example, since the 1920s, the work of Warburg and Meyerhoff, followed by that of Krebs, Kaplan, Chance, and others built a solid foundation in the knowledge of enzymes, coenzymes, adenine and pyridine nucleotides, and metabolic pathways, not to mention the importance of Fe3+, Cu2+, Zn2+, and other metal cofactors. There was also a relevance of the work of Jacob, Monod and Changeux, and the effects of cooperativity in allosteric systems and of repulsion in tertiary structure of proteins related to hydrophobic and hydrophilic interactions. This involves the effect of one ligand on the binding or catalysis of another with no direct interaction between the two ligands. This was demonstrated by the end-product inhibition of the enzyme, L-threonine deaminase (Changeux 1961), L-isoleucine, which differs sterically from the reactant, L-threonine whereby binding at a different, nonoverlapping (regulatory) site, the former could inhibit the enzyme without competing with the latter. Pauling (Pauling 1935) had earlier proposed a model for intramolecular control in hemoglobin to explain the positive cooperativity observed in the binding of oxygen molecules. But Monod, Wyman, and Changeux substantially updated the view of allostery in 1965 with their landmark paper. Present day applications of computational methods to biomolecular systems, combined with structural, thermodynamic, and kinetic studies, make possible an approach to that question, so as to provide a deeper understanding of the requirements for allostery. The current view is that a variety of measurements (e.g., NMR, FRET, and single molecule studies) are providing additional data beyond that available previously from structural, thermodynamic, and kinetic results. These should serve to continue to improve our understanding of the molecular mechanism of allostery, particularly when supplemented by simulations and theoretical analyses. A ‘‘dynamic’’ proposal by Cooper and Dryden (1984) is that the distribution around the average structure changes in allostery; which in turn, affects the subsequent (binding) affinity at a distant site. Such a model focuses on the vibrational contribution to the entropy as the origin of cooperativity, as discussed for the CAPN dimer. Why is this important? It is because it brings a different focus into the conception of how living cells engage with their neighbors and external environment. Moreover, this is not all that has to be considered.
What else do we have to consider? Oxidative stress is essentially an imbalance between the production of free radicals and the ability of the body to counteract or detoxify their harmful effects through neutralization by antioxidants. The measurement of free radicals has increased awareness of radical-induced impairment of the oxidative/antioxidative balance, essential for an understanding of disease progression. Metal-mediated formation of free radicals causes various modifications to DNA bases, enhanced lipid peroxidation, and altered calcium and sulfhydryl homeostasis. Lipid peroxides, formed by the attack of radicals on polyunsaturated fatty acid residues of phospholipids, can further react with redox metals finally producing mutagenic and carcinogenic malondialdehyde, 4-hydroxynonenal and other exocyclic DNA adducts (etheno and/or propano adducts). The unifying factor in determining toxicity and carcinogenicity for all these metals is the generation of reactive oxygen and nitrogen species. Common mechanisms involving the Fenton reaction, generation of the superoxide radical and the hydroxyl radical appear to be involved for iron, copper, chromium, vanadium and cobalt primarily associated with mitochondria, microsomes and peroxisomes. Various studies have confirmed that metals activate signaling pathways and the carcinogenic effect of metals has been related to activation of mainly redox sensitive transcription factors, involving NF-kappaB, AP-1 and p53.
In addition to what I have identified, there is substantial work in the last decade to indicate a more complex model of cellular regulatory processes. On the one hand, there is no uncertainty about the importance of “Junk DNA”. Indeed, not only is “Junk DNA” not junk, but it has either a presence that is an evolutionary remnant, or it has a role in cell regulation, much of which has yet to be understood. Moreover, the relationship between the oligonucleotide sequences to their histones are largely unknown. Beyond the DNA sequences, the language of the gene, we now have a large output of research on noncoding RNA. We now have siRNA, miRNA, and others with roles other than transcription. This is a very active field of investigation that requires major revision of our model of cell regulatory processes. The classic model is solely transcriptional. DNA-> RNA-> Amino Acid in a protein. This would now have to be redrawn because DNA-> RNA-> DNA and DNA->RNA-> protein-> DNA.
I have provided a series of four mechanisms explanatory for transcription and for regulation of the cell. This is not adequate for a more full comprehension because there is a layer beyond the classic model of metabolic pathways associated with the cytoplasm, mitochondria, endoplasmic reticulum, and lysosome, there are critical paths beyond oxidative phosphorylation and glycolysis, such as the cell death pathways, expressed in a homeostasis between apoptosis and repair. Nevertheless, there is still a missing part of this discussion. The missing piece gets at the time and space interactions of the cell, cellular cytoskeleton and extracellular and intracellular substrate interactions in the immediate environment. This can’t be simply accounted for by genetics or epigenetics. There have been papers that call attention to heterogeneity among cancer cells of expected identical type, which would be consistent with differences in phenotypic expression, aligned with epigenetics. There is now the recent publication of the finding that there is heterogeneity in the immediate interstices between cancer cells, which may seem surprising, but it should not be. This refers to the complexity of the cells arranged as tissues and to their immediate environment, which I shall elaborate on. Integration with genome-wide profiling data identified losses of specific genes on 4p14 and 5q13 that were enriched in grade 3 tumors with high microenvironmental diversity that also substratified patients into poor prognostic groups. I did introduce the word gene into this reference, and we are well aware of mutations that occur in cancer progression. In the case of breast cancer, mention is not made of interaction with a hormone, as we refer to in androgen-unresponsive prostate cancer. This is particularly relevant, but incomplete.
The fifth item for discussion is the interaction between enzyme and substrates that may be conditionally unidirectional in defining the activity within the cell. When we speak of the genome, we are dealing with a code defined by an oligonucleotide sequence that has an element of stability, but that can conditionally be altered by a process termed mutagenesis. The altered code can be expected to have a negative, positive, or no effect, depending. In any case, there is a substantial stability inherent in the code that is essential to all living creatures. The activity of the cell is dynamically interacting and at high rates of activity. There are many examples of this. The first example is in a study of energy for reverse pyruvate kinase (PK) reaction. This catalytic activity of the PK reaction was reversed to the thermodynamically unfavorable direction in a muscle preparation by a specific inhibitor. Using the same crude supernatant for the two opposite activities of this enzyme some of the results found in the regulatory assays indicated differences in the active form of pyruvate kinase that were clearly related to the environmental condition – glycolitic or glyconeogenetic – of the assay. The conformational changes indicated by differential regulatory response found in the conditions studied, together with the role of similar factors, for instance, substrates and pH, in the structural states proposed by others, were used together to present a dynamic conformational model functioning at the active site of the enzyme. In the model, the interaction of the enzyme active site with its substrates is described according to its vibrational, translational and rotational components and the activating ions – induced increase in the vibrational energy levels of the active site decreases the energetic barrier for substrate induced changes at the site.
Another example is the pyridine nucleotide-linked dehydrogenases. The lactate dehydrogenase (LD) reaction is ordered so that NADH binds to the enzyme before pyruvate can bind. The H-type isoenzyme, but not the M-type, is characterized by substrate inhibition at high pyruvate concentrations. The inhibition of the H4 lactate dehydrogenase, but not the M4, by high concentrations of pyruvate is caused by the formation of an abortive complex consisting of the enzyme, pyruvate, and NADH. An investigation of the structural properties of the ternary complex revealed that the complex possesses an absorption maximum at 335 nm and that a covalent bond was formed between the nicotinamide ring of the NAD+ and the pyruvate moiety. The same study demonstrated that the enol form of pyruvate is responsible for the complex formation. It was suggested that abortive complex formation is a significant metabolic control mechanism, and the different behavior of the H and M forms has been rationalized in terms of different functional roles for the two isoenzymes. However, similar experiments carried out with the mitochondrial malate dehydrogenase suggested a similar inhibition, but in this case only the mitochondrial malate dehydrogenase is sensitive to inhibition by high concentrations of oxaloacetate. Further studies showed the inhibition is promoted by an abortive binary complex formed by the enzymes and the enol form of oxalacetate. Neither the oxidized coenzyme nor the reduced coenzyme appears to be involved in the formation of this complex. These results suggest that the mechanism of substrate inhibition that occurs with the pig heart malate dehydrogenases is different from that observed with the lactate dehydrogenases.
It was established years later that there is an isoenzyme of isocitrate dehydrogenase that is characteristic for cancer cells. IDH1 and IDH2 mutations occur frequently in some types of World Health Organization grades 2–4 gliomas and in acute myeloid leukemias with normal karyotype. IDH1 and IDH2 mutations are remarkably specific to codons that encode conserved functionally important arginines in the active site of each enzyme. To date, all IDH1 mutations have been identified at the Arg132 codon. Mutations in IDH2 have been identified at the Arg140 codon, as well as at Arg172, which is aligned with IDH1 Arg132. IDH1 and IDH2 mutations are heterozygous in cancer, and they catalyze the production of α-2-hydroxyglutarate. The study found human IDH1 transitions between an inactive open, an inactive semi-open, and a catalytically active closed conformation. In the inactive open conformation, Asp279 occupies the position where the isocitrate substrate normally forms hydrogen bonds with Ser94. This steric hindrance by Asp279 to isocitrate binding is relieved in the active closed conformation.
Finally, what does this have to do with personalized medicine? Personalized medicine has been largely view from a lens of genomics. But genomics is only the reading frame, even taking into consideration the mutations that are found in transition. The living activities of cell processes are dynamic and occur at rapid rates. When we refer to homeostasis and to neoplasia, we have to keep in mind that personalized in reference to genotype is not complete without reconciliation of phenotype, which is the reference to expressed differences in outcomes.
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Current knowledge on the effects of nut consumption on human health has rapidly increased in recent years and it now appears that nuts may play a role in the prevention of chronic age-related diseases. Frequent nut consumption has been associated with better metabolic status, decreased body weight as well as lower body weight gain over time and thus reduce the risk of obesity. The effect of nuts on glucose metabolism, blood lipids, and blood pressure are still controversial. However, significant decreased cardiovascular risk has been reported in a number of observational and clinical intervention studies. Thus, findings from cohort studies show that increased nut consumption is associated with a reduced risk of cardiovascular disease and mortality (especially that due to cardiovascular-related causes). Similarly, nut consumption has been also associated with reduced risk of certain cancers, such as colorectal, endometrial, and pancreatic neoplasms. Evidence regarding nut consumption and neurological or psychiatric disorders is scarce, but a number of studies suggest significant protective effects against depression, mild cognitive disorders and Alzheimer’s disease. The underlying mechanisms appear to include antioxidant and anti-inflammatory actions, particularly related to their mono- and polyunsaturated fatty acids (MUFA and PUFA, as well as vitamin and polyphenol content. MUFA have been demonstrated to improve pancreatic beta-cell function and regulation of postprandial glycemia and insulin sensitivity. PUFA may act on the central nervous system protecting neuronal and cell-signaling function and maintenance. The fiber and mineral content of nuts may also confer health benefits. Nuts therefore show promise as useful adjuvants to prevent, delay or ameliorate a number of chronic conditions in older people. Their association with decreased mortality suggests a potential in reducing disease burden, including cardiovascular disease, cancer, and cognitive impairments.
Global life expectancy has increased from 65 years in 1990 to about 71 years in 2013 [1]. As life expectancy has increased, the number of healthy years lost due to disability has also risen in most countries, consistent with greater morbidity [2]. Reduction of mortality rates in developed countries has been associated with a shift towards more chronic non-communicable diseases [1]. Cardiovascular diseases (CVDs) and related risk factors, such as hypertension, diabetes mellitus, hypercholesterolemia, and obesity are the top causes of death globally, accounting for nearly one-third of all deaths worldwide [3]. Equally, the estimated incidence, mortality, and disability- adjusted life-years (DALYs) for cancer rose to 14.9 million incident cancer cases, 8.2 million deaths, and 196.3 million DALYs, with the highest impact of prostate and breast cancer in men and women, respectively [4]. Depression is a leading cause of disability worldwide (in terms of total years lost due to disability), especially in high-income countries, increasing from 15th to 11th rank (37% increase) and accounting for 18% of total DALYs (almost 100 million DALYs) [5]. Overall, the global rise in chronic non-communicable diseases is congruent with a similar rise in the elderly population. The proportion of people over the age of 60 is growing faster than any other age group and is estimated to double from about 11% to 22% within the next 50 years [6]. Public health efforts are needed to face this epidemiological and demographic transition, both improving the healthcare systems, as well as assuring a better health in older people. Accordingly, a preventive approach is crucial to dealing with an ageing population to reduce the burden of chronic disease.
In this context, lifestyle behaviors have demonstrated the highest impact for older adults in preventing and controlling the morbidity and mortality due to non- communicable diseases [7]. Unhealthy behaviors, such as unbalanced dietary patterns, lack of physical activity and smoking, play a central role in increasing both cardiovascular and cancer risk [7]. Equally, social isolation and depression in later life may boost health decline and significantly contribute to mortality risk [8]. The role of diet in prevention of disability and death is a well-established factor, which has an even more important role in geriatric populations. Research has focused on the effect of both single foods and whole dietary patterns on a number of health outcomes, including mortality, cardiovascular disease (CVD), cancer and mental health disorders (such as cognitive decline and depression) [9-13]. Plantbased dietary patterns demonstrate the most convincing evidence in preventing chronic non-communicable diseases [14-17]. Among the main components (including fruit and vegetables, legumes and cereals), only lately has attention focused on foods such as nuts. Knowledge on the effect of nut consumption on human health has increased rapidly in recent years. The aim of this narrative review is to examine recent evidence regarding the role of nut consumption in preventing chronic disease in older people.
Tree nuts are dry fruits with an edible seed and a hard shell. The most popular tree nuts are almonds (Prunus amigdalis), hazelnuts (Corylus avellana), walnuts (Juglans regia), pistachios (Pistachia vera), cashews (Anacardium occidentale), pecans (Carya illinoiensis), pine nuts (Pinus pinea), macadamias (Macadamia integrifolia), Brazil nuts (Bertholletia excelsa), and chestnuts (Castanea sativa). When considering the “nut” group, researchers also include peanuts (Arachis hypogea), which technically are groundnuts. Nuts are nutrient dense foods, rich in proteins, fats (mainly unsaturated fatty acids), fiber, vitamins, minerals, as well as a number of phytochemicals, such as phytosterols and polyphenols [18]. Proteins account for about 10-25% of energy, including individual aminoacids, such as L-arginine, which is involved in the production of nitric oxide (NO), an endogenous vasodilatator [19].
The fatty acids composition of nuts involves saturated fats for 415% and unsaturated fatty acids for 30-60% of the content. Unsaturated fatty acids are different depending on the nut type, including monounsaturated fatty acids (MUFA, such as oleic acid in most of nuts, whereas polyunsaturated fatty acids (PUFA, such as alpha-linolenic acid) in pine nuts and walnuts [20]. Also fiber content is similar among most nut types (about 10%), although pine nuts and cashews hold the least content. Vitamins contained in nuts are group B vitamins, such as B6 (involved in many aspects of macronutrient metabolism) and folate (necessary for normal cellular function, DNA synthesis and metabolism, and homocysteine detoxification), as well as tocopherols, involved in anti-oxidant mechanisms [21]. Among minerals contained in vegetables, nuts have an optimal content in calcium, magnesium, and potassium, with an extremely low amount of sodium, which is implicated on a number of pathological conditions, such as bone demineralization, hypertension and insulin resistance[22]. Nuts are also rich in phytosterols, non-nutritive components of certain plant-foods that exert both structural (at cellular membrane phospholipids level) and hormonal (estrogen-like) activities [23]. Finally, nuts have been demonstrated to be a rich source of polyphenols, which account for a key role in their antioxidant and anti-inflammatory effects.
Metabolic disorders are mainly characterized by obesity, hypertension, dyslipidemia, and hyperglycemia/ hyperinsulinemia/type-2 diabetes, all of which act synergistically to increase morbidity and mortality of aging population.
Obesity Increasing high carbohydrate and fat food intake in the last decades has contributed significantly to the rise in metabolic disorders. Nuts are energy-dense foods that have been thought to be positively associated with increased body mass index (BMI). As calorie-dense foods, nuts may contain 160–200 calories per ounce. The recommendation from the American Heart Association to consume 5 servings per week (with an average recommended serving size of 28 g) corresponds to a net increase of 800–1000 calories per week, which may cause weight gain. However, an inverse relation between the frequency of nut consumption and BMI has been observed in large cohort studies [24]. Pooling the baseline observations of BMI by category of nut consumption in 5 cohort studies found a significant decreasing trend in BMI values with increasing nut intake [24]. While the evidence regarding nut consumption and obesity is limited, findings so far are encouraging [25, 26]. When the association between nut consumption and body weight has been evaluated longitudinally over time, nut intake was associated with a slightly lower risk of weight gain and obesity [25]. In the Nurses’ Health Study II (NHS II), women who eat nuts ≥2 times per week had slightly less weight gain (5.04 kg) than did women who rarely ate nuts (5.55 kg) and marginally significant 23% lower risk of obesity after 9-year follow-up [25]. Further evaluation of the NHS II data and the Physicians’ Health Study (PHS) comprising a total of 120,877 US women and men and followed up to 20 years revealed that 4-y weight change was inversely associated with a 1-serving increment in the intake of nuts (20.26 kg) [27]. In the “Seguimiento Universidad de Navarra” (SUN) cohort study, a significant decreased weight change has been observed over a period of 6 years [26]. After adjustment for potential confounding factors the analysis was no longer significant, but overall no weight gain associated with >2 servings per week of nuts has been observed. Finally, when considering the role of the whole diet on body weight, a meta-analysis of 31 clinical trials led to the conclusion of a null effect of nut intake on body weight, BMI, and waist circumference [28].
Glucose metabolism and type-2 diabetes The association between nut consumption and risk of type-2 diabetes in prospective cohort studies is controversial [29-32]. A pooled analysis relied on the examination of five large cohorts, including the NHS, the Shanghai Women’s Health Study, the Iowa Women’s Health Study, and the PHS, and two European studies conducted in Spain (the PREDIMED trial) and Finland including a total of more than 230,000 participants and 13,000 cases, respectively. Consumption of 4 servings per week was associated with 13% reduced risk of type-2 diabetes without effect modification by age [29]. In contrast, other pooled analyses showed non-significant reduction of risk for increased intakes of nuts, underlying that the inverse association between the consumption of nuts and diabetes was attenuated after adjustment for confounding factors, including BMI [30]. However, results from experimental studies showed promising results. Thus, nut consumption has been demonstrated to exert beneficial metabolic effects due to their action on post-prandial glycemia an insulin sensitivity. A number of RCTs have demonstrated positive effects of nut consumption on post-prandial glycemia in healthy individuals [33-38]. Moreover, a meta-analysis of RCTs on the effects of nut intake on glycemic control in diabetic individuals including 12 trials and a total of 450 participants showed that diets with an emphasis on nuts (median dose = 56 g/d) significantly lowered HbA1c (Mean Difference [MD] : -0.07%; 95% confidence interval [CI]: -0.10, -0.03%; P = 0.0003) and fasting glucose (MD : -0.15 mmol/L; 95% CI: -0.27, -0.02 mmol/L; P = 0.03) compared with control diets [39]. No significant treatment effects were observed for fasting insulin and homeostatic model assessment (HOMA-IR), despite the direction of effect favoring diet regimens including nuts.
Blood lipids and hypertension Hypertension and dyslipidemia are major risk factors for CVD. Diet alone has a predominant role in blood pressure and plasma lipid homeostasis. One systematic review [40] and 3 pooled quantitative analyses of RCTs [41-43] evaluated the effects of nut consumption on lipid profiles. A general agreement was relevant on certain markers, as daily consumption of nuts (mean = 67 g/d) induced a pooled reduction of total cholesterol concentration (10.9 mg/dL [5.1% change]), low-density lipoprotein cholesterol concentration (LDL-C) (10.2 mg/dL [7.4% change]), ratio of LDL-C to high-density lipoprotein cholesterol concentration (HDL-C) (0.22 [8.3% change]), and ratio of total cholesterol concentration to HDL-C (0.24 [5.6% change]) (P <0.001 for all) [42]. All meta-analyses showed no significant effects of nut (including walnut) consumption on HDL cholesterol or triglyceride concentrations in healthy individuals [41], although reduced plasma triglyceride levels were found in individuals with hypertriglyceridemia [42]. Interestingly, the effects of nut consumption were dose related, and different types of nuts had similar effects on blood lipid concentrations.
There is only limited evidence from observational studies to suggest that nuts have a protective role on blood pressure. A pooled analysis of prospective cohort studies on nut consumption and hypertension reported a decreased risk associated with increased intake of nuts [32]. Specifically, only a limited number of cohort studies have been conducted exploring the association between nut consumption and hypertension (n = 3), but overall reporting an 8% reduced risk of hypertension for individuals consuming >2 servings per week (Risk Ratio [RR] = 0.92, 95% CI: 0.87-0.97) compared with never/rare consumers, whereas consumption of nuts at one serving per week had similar risk estimates (RR = 0.97, 95% CI: 0.83, 1.13) [32]. These findings are consistent with results obtained in a pooled analysis of 21 experimental studies reporting the effect of consuming single or mixed nuts (in doses ranging from 30 to 100 g/d) on systolic (SBP) and diastolic blood pressure (DBP) [44]. A pooled analysis found a significant reduction in SBP in participants without type2 diabetes [MD: -1.29 mmHg; 95% CI: -2.35, -0.22; P = 0.02] and DBP (MD: -1.19; 95% CI: -2.35, -0.03; P = 0.04), whereas subgroup analyses of different nut types showed that pistachios, but not other nuts, significantly reduced SBP (MD: -1.82; 95% CI: -2.97, -0.67; P = 0.002) and SBP (MD: -0.80; 95% CI: -1.43, -0.17; P = 0.01) [44].
Nut consumption and CVD risk Clustering of metabolic risk factors occurs in most obese individuals, greatly increasing risk of CVD. The association between nut consumption and CVD incidence [29-31] and mortality [24] has been explored in several pooled analyses of prospective studies. The overall risk calculated for CVD on a total of 8,862 cases was reduced by 29% for individuals consuming 7 servings per week (RR = 0.71, 95% CI: 0.59, 0.85) [30]. A meta-analysis including 9 studies on coronary artery disease (CAD) including 179,885 individuals and 7,236 cases, reporting that 1-serving/day increment would reduce risk of CAD of about 20% (RR = 0.81, 95% CI: 0.72, 0.91) [31]. Similar risk estimates were calculated for ischemic heart disease (IHD), with a comprehensive reduced risk of about 25-30% associated with a daily intake of nuts [29, 30]. Findings from 4 prospective studies have been pooled to estimate the association between nut consumption and risk of stroke, and a non-significant/borderline reduced risk was found [29-31, 45]. CVD mortality was explored in a recent meta-analysis including a total of 354,933 participants, 44,636 cumulative incident deaths, and 3,746,534 cumulative person-years [24]. One serving of nuts per week and per day resulted in decreased risk of CVD mortality (RR = 0.93, 95% CI: 0.88, 0.99 and RR =0.61, 95% CI: 0.42, 0.91, respectively], primarily driven by decreased coronary artery disease (CAD) deaths rather than stroke deaths [24]. Overall, all pooled analyses demonstrated a significant association between nut consumption and cardiovascular health. However, it has been argued that nut consumption was consistently associated with healthier background characteristics reflecting overall healthier lifestyle choices that eventually lead to decreased CVD mortality risk.
Nut consumption and cancer risk Cancer is one of the leading causes of death in the elderly population. After the evaluation of the impact on cancer burden of food and nutrients, it has been concluded that up to one third of malignancies may be prevented by healthy lifestyle choices. Fruit and vegetable intake has been the focus of major attention, but studies on nut consumption and cancer are scarce. A recent metaanalysis pooled together findings of observational studies on cancer incidence, including a total of 16 cohort and 20 casecontrol studies comprising 30,708 cases, compared the highest category of nut consumption with the lowest category and found a lower risk of any cancer of 25% (RR = 0.85, 95% CI: 0.86, 0.95) [46]. When the analysis was conducted by cancer site, highest consumption of nuts was associated with decreased risk of colorectal (RR = 0.76, 95% CI: 0.61, 0.96), endometrial (RR = 0.58, 95% CI: 0.43, 0.79), and pancreatic cancer (RR = 0.71, 95% CI: 0.51, 0.99), with only one cohort study was conducted on the last [46]. The potential protective effects of nut consumption on cancer outcomes was supported also by pooled analysis of 3 cohort studies [comprising the PREDIMED, the NHS, the HPS, and the Health Professionals Follow-Up Study (HPFS) cohorts] showing a decreased risk of cancer death for individuals consuming 3-5 servings of nuts per week compared with never eaters (RR = 0.86, 95% CI: 0.75, 0.98) [24]. The analysis was recently updated by including results from the Netherlands Cohort Study reaching a total of 14,340 deaths out of 247,030 men and women observed, confirming previous results with no evidence of between-study heterogeneity (RR = 0.85, 95% CI: 0.77, 0.93) [47]. However, a dose- response relation showed the non-linearity of the association, suggesting that only moderate daily consumption up to 5 g reduced risk of cancer mortality, and extra increased intakes were associated with no further decreased risk.
Nut consumption and affective/cognitive disorders Age-related cognitive decline is one of the most detrimental health problems in older people. Cognitive decline is a paraphysiological process of aging, but timing and severity of onset has been demonstrated to be affected by modifiable lifestyle factors, including diet. In fact, the nature of the age- related conditions leading to a mild cognitive impairment (MCI) differs by inflammation-related chronic neurodegenerative diseases, such as dementia, Alzheimer’s disease, Parkinson’s disease and depression. Evidence restricted to nut consumption alone is scarce, but a number of studies have been conducted on dietary patterns including nuts as a major component. A pooled analysis synthesizing findings of studies examining the association between adherence to a traditional Mediterranean diet and risk of depression (n = 9), cognitive decline (n = 8), and Parkinson’s disease (n = 1) showed a reduction of risk of depression (RR = 0.68, 95% CI: 0.54, 0.86) and cognitive impairment (RR = 0.60, 95% CI: 0.43, 0.83) in individuals with increased dietary adherence [10].
The study that first found a decreased risk of Alzheimer’s disease in individuals highly adherent to the Mediterranean diet was conducted in over 2,000 individuals in the Washington/Hamilton Heights-Inwood Columbia Aging Project (WHICAP), a cohort of non-demented elders aged 65 and older living in a multi-ethnic community of Northern Manhattan in the US (Hazard Ratio [HR] = 0.91, 95% CI: 0.83, 0.98) [48]. These results have been replicated in further studies on the Mediterranean diet, however nut consumption was not documented [49, 50]. A number of observational studies also demonstrated a significant association between this dietary pattern and a range of other cognitive outcomes, including slower global cognitive decline [51]. However, evidence from experimental studies is limited to the PREDIMED trial, providing interesting insights on the association between the Mediterranean diet supplemented with mixed nuts and both depression and cognitive outcomes. Regarding depression, the nutritional intervention with a Mediterranean diet supplemented with nuts showed a lower risk of about 40% in participants with type-2 diabetes (RR = 0.59, 95% CI: 0.36, 0.98) compared with the control diet [52]. However the effect was not significant in the whole cohort overall [52]. Regarding cognitive outcomes after a mean follow-up of 4.1 years, findings from the same trial showed significant improvements in memory and global cognition tests for individuals allocated to the Mediterranean diet supplemented with nuts [adjusted differences: -0.09 (95% CI: -0.05, 0.23), P = 0.04 and -0.05 (95% CI: -0.27, 0.18), P = 0.04, respectively], compared to control group, showing that Mediterranean diet plus mixed nuts is associated with improved cognitive function [53].
Potential mechanisms of protection of nut consumption Despite the exact mechanisms by which nuts may ameliorate human health being largely unknown, new evidence has allowed us to start to better understand the protection of some high-fat, vegetable, energy-dense foods such as nuts. Non- communicable disease burden related with nutritional habits is mainly secondary to exaggerated intakes of refined sugars and saturated fats, such as processed and fast- foods. Nuts provide a number of nutrient and non-nutrient compounds and it is only recently that scientists have tried to examine their effects on metabolic pathways.
Metabolic and cardiovascular protection With special regard to body weight and their potential effects in decreasing the risk of obesity (or weight gain, in general), nuts may induce satiation (reduction in the total amount of food eaten in a single meal) and satiety (reduction in the frequency of meals) due to their content in fibers and proteins, which are associated with increased release of glucagon-like protein 1 (GLP-1) and cholecystokinin (CCK), gastrointestinal hormones with satiety effects [54, 55]. The content in fiber of nuts may also increase thermogenesis and resting energy expenditure, and reduce post- prandial changes of glucose, thus ameliorating inflammation and insulin resistance. Moreover, the specific content profile of MUFA and PUFA provides readily oxidized fats than saturated or trans fatty acids, leading to reduced fat accumulation [56, 57]. The beneficial effects of nuts toward glucose metabolism may be provided by their MUFA content that improves the efficiency of pancreatic beta-cell function by enhancing the secretion of GLP1, which in turn helps the regulation of postprandial glycemia and insulin sensitivity [58]. MUFA and PUFA are also able to reduce serum concentrations of the vasoconstrictor thromboxane 2, which might influence blood pressure regulation. Together with polyphenols and anti-oxidant vitamins, nuts may also ameliorate inflammatory status at the vascular level, reducing circulating levels of soluble cellular adhesion molecules, such as intercellular adhesion molecule-1 (ICAM-1), vascular cell adhesion molecule-1 (VCAM-1), and E-selectin, which are released from the activated endothelium and circulating monocytes [59]. Moreover, nuts may improve vascular reactivity due to their content in L-arginine, which is a potent precursor of the endogenous vasodilator nitric oxide. Nuts content in microelements is characterized by a mixture that may exert a direct effect in modulating blood pressure, including low content of sodium and richness in magnesium, potassium and calcium, which may interact to beneficially influence blood pressure
Despite the exact mechanisms by which nuts may ameliorate human health being largely unknown, new evidence has allowed us to start to better understand the protection of some high-fat, vegetable, energy-dense foods such as nuts. Non- communicable disease burden related with nutritional habits is mainly secondary to exaggerated intakes of refined sugars and saturated fats, such as processed and fast- foods. Nuts provide a number of nutrient and non-nutrient compounds and it is only recently that scientists have tried to examine their effects on metabolic pathways.
Cancer protection The potential mechanisms of action of nuts that may intervene in the prevention of cancer have not been totally elucidated. Numerous hypotheses have been proposed on the basis of basic research exploring the antioxidant and anti-inflammatory compounds characterizing nuts [61]. Vitamin E can regulate cell differentiation and proliferation, whereas polyphenols (particularly flavonoids such as quercetin and stilbenes such as resveratrol) have been shown to inhibit chemically-induced carcinogenesis [62]. Polyphenols may regulate the inflammatory response and immunological activity by acting on the formation of the prostaglandins and pro-inflammatory cytokines, which may be an important mechanism involved in a number of cancers, including colorectal, gastric, cervical and pancreatic neoplasms [62]. Among other compounds contained in nuts, dietary fiber may exert protective effects toward certain cancers (including, but not limited to colorectal cancer) by the aforementioned metabolic effects as well as increasing the volume of feces and anaerobic fermentation, and reducing the length of intestinal transit. As a result, the intestinal mucosa is exposed to carcinogens for a reduced time and the carcinogens in the colon are diluted [62]. Finally, there is no specific pathway demonstrating the protective effect of PUFA intake against cancer, but their interference with cytokines and prostaglandin metabolism may inhibit a state of chronic inflammation that may increase cancer risk [63].
Cognitive aging and neuro-protection There is no universal mechanism of action for nuts with regard to age-related conditions. A number of systemic biological conditions, such as oxidative stress, inflammation, and reduced cerebral blood flow have been considered as key factors in the pathogenesis of both normal cognitive ageing and chronic neurodegenerative disease [64]. Nuts, alone or as part of healthy dietary patterns, may exert beneficial effects due to their richness in antioxidants, including vitamins, polyphenols and unsaturated fatty acids, that may be protective against the development of cognitive decline and depression [65, 66]. Both animal studies and experimental clinical trials demonstrated vascular benefits of nuts, including the aforementioned lowering of inflammatory markers and improved endothelial function, which all appear to contribute to improved cognitive function [67]. The antioxidant action may affect the physiology of the ageing brain directly, by protecting neuronal and cell-signaling function and maintenance. Moreover, certain compounds contained in nuts may directly interact with the physiology and functioning of the brain. For instance, walnuts are largely composed of PUFA, especially ALA, which have been suggested to induce structural change in brain areas associated with affective experience [66]. Moreover, PUFA have been associated with improved symptoms in depressed patients, suggesting an active role in the underlying pathophysiological mechanisms [68]. Thus, the mechanisms of action of nut consumption on age-related cognitive and depressive disorders are complex, involving direct effects on brain physiology at the neuronal and cellular level and indirect effects by influencing inflammation.
Summary From an epidemiological point of view, nut eaters have been associated with overall healthier lifestyle habits, such as increased physical activity, lower prevalence of smoking, and increased consumption of fruits and vegetables [24]. These variables represent strong confounding factors in determining the effects of nuts alone on human health and final conclusions cannot be drawn. Nevertheless, results from clinical trials are encouraging. Nuts show promise as useful adjuvants to prevent, delay or ameliorate a number of chronic conditions in older people.
In Dante Alighieri’s Divine Comedy the narrator meets a man named Ciacco who had been sent to Hell for the “Damning sin of Gluttony.” According to Catholic theology, in order to end up in Hell one must willfully commit a serious sin. So Dante believed that fat people chose to be fat. This antiquated view of the cause of obesity is still widespread, even among medical professionals. The consequences of this misconception are significant, because it forms the basis for the discrimination suffered by the obese; for the wasting of scarce resources in attempts to change lifestyle habits by public education; and for the limited availability of subsidized obesity treatments.
While obesity is often labeled a lifestyle disease, poor lifestyle choices alone account for only a 6 to 8 kg weight gain. The body has a powerful negative feedback system to prevent excessive weight gain. The strongest inhibitor of hunger, the hormone leptin, is made by fat cells. A period of increased energy intake will result in fat deposition, which will increase leptin production. Leptin suppresses hunger and increases energy expenditure. This slows down weight gain. To become obese, it may be necessary to harbor a genetic difference that makes the individual resistant to the action of leptin.
Evidence from twin and adoption studies suggests that obesity has a genetic basis, and over the past two decades a number of genes associated with obesity have been described. The most common genetic defect in European populations leading to severe obesity is due to mutations in the gene coding for the melanocortin 4 receptor (MCR4). Still, this defect can explain severe obesity in only approximately 6 percent to 7 percent of cases (J Clin Invest, 106:271-79, 2000). Other genes have been discovered that can cause milder increases in weight; for example, variants of just one gene (FTO) can explain up to 3 kg of weight variation between individuals (Science, 316:889-94, 2007).
Genes do not directly cause weight gain. Rather, genes influence the desire for food and the feeling of satiety. In an environment with either poor access to food or access to only low-calorie food, obesity may not develop even in persons with a genetic predisposition. When there is an abundance of food and a sedentary lifestyle, however, an obesity-prone person will experience greater hunger and reduced satiety, increasing caloric intake and weight gain.
Since the 1980s, there has been a rapid rise in the prevalence of obesity worldwide, a trend that likely results from a variety of complex causes. There is increasing evidence, for example, that the development of obesity on individual or familial levels may be influenced by environmental experiences that occur in early life. For example, if a mother is malnourished during early pregnancy, this results in epigenetic changes to genes involved in the set points for hunger and satiety in the developing child. These changes may then become fixed, resulting in a tendency towards obesity in the offspring.
The biological basis of obesity is further highlighted by the vigorous defense of weight following weight loss. There are at least 10 circulating hormones that modulate hunger. Of these, only one has been confirmed as a hunger-inducing hormone (ghrelin), and it is made and released by the stomach. In contrast, nine hormones suppress hunger, including CCK, PYY, GLP-1, oxyntomodulin, and uroguanylin from the small bowel; leptin from fat cells; and insulin, amylin, and pancreatic polypeptide from the pancreas.
After weight loss, regardless of the diet employed, there are changes in circulating hormones involved in the regulation of body weight. Ghrelin levels tend to increase and levels of multiple appetite-suppressing hormones decrease. There is also a subjective increase in appetite. Researchers have shown that even after three years, these hormonal changes persist (NEJM, 365:1597-604, 2011; Lancet Diabetes and Endocrinology, 2:954-62, 2014). This explains why there is a high rate of weight regain after diet-induced weight loss.
Given that the physiological responses to weight loss predispose people to regain that weight, obesity must be considered a chronic disease. Data show that those who successfully maintain their weight after weight loss do so by remaining vigilant and constantly applying techniques to oppose weight regain. These techniques may involve strict diet and exercise practices and/or pharmacotherapy.
It is imperative for society to move away from a view that obesity is simply a lifestyle issue and to accept that it is a chronic disease. Such a change would not only relieve the stigma of obesity but would also empower politicians, scientists and clinicians to tackle the problem more effectively.
Joseph Proietto was the inaugural Sir Edward Dunlop Medical Research Foundation Professor of Medicine in the Department of Medicine, Austin Health at the University of Melbourne in Australia. He is a researcher and clinician investigating and treating obesity and type 2 diabetes.
A Weighty Anomaly
Why do some obese people actually experience health benefits?
THE ENDOCRINE THEORY: Some researchers have posited that fat cells may secrete molecules that affect glucose homeostasis in muscle or liver tissue.COURTESY OF MITCHELL LAZAR
In the early 19th century, Belgian mathematician Adolphe Quetelet was obsessed with a shape: the bell curve. While helping with a population census, Quetelet proposed that the spread of human traits such as height and weight followed this trend, also known as a Gaussian or normal distribution. On a quest to define a “normal man,” he showed that human height and weight data fell along his beloved bell curves, and in 1823 devised the “Quetelet Index”—more familiar to us today as the BMI, or body mass index, a ratio of weight to height.
Nearly two centuries later, clinicians, researchers, and fitness instructors continue to rely on this metric to pigeonhole people into categories: underweight, healthy, overweight, or obese. But Quetelet never intended the metric to serve as a way to define obesity. And now, a growing body of evidence suggests these categories fail to accurately reflect the health risks—or benefits—of being overweight.
Although there is considerable debate surrounding the prevalence of metabolically healthy obesity, when obesity is defined in terms of BMI (a BMI of 30 or higher), estimates suggest that about 10 percent of adults in the U.S. are obese yet metabolically healthy, while as many as 80 percent of those with a normal BMI may be metabolically unhealthy, with signs of insulin resistance and poor circulating lipid levels, even if they suffer no obvious ill effects. “If all we know about a person is that they have a certain body weight at a certain height, that’s not enough information to know their health risks from obesity,” says health-science researcher Paul McAuley of Winston-Salem State University. “We need better indicators of metabolic health.”
The dangers of being overweight, such as a higher risk of heart disease, type 2 diabetes, and other complications, are well known. But some obese individuals—dubbed the “fat fit”—appear to fare better on many measures of health when they’re heavier. Studies have found lower mortality rates, better response to hemodialysis in chronic kidney disease, and lower incidence of dementia in such people. Mortality, it’s been found, correlates with obesity in a U-shaped curve (J Sports Sci, 29:773-82, 2011). So does extra heft help or hurt?
To answer that question, researchers are trying to elucidate the metabolic reasons for this obesity paradox.
In a recent study, Harvard University epidemiologist Goodarz Danaei and his colleagues analyzed data from nine studies involving a total of more than 58,000 participants to tease apart how obesity and other well-known metabolic risk factors influence the risk of coronary heart disease. Controlling these other risk factors, such as hypertension or high cholesterol, with medication is simpler than curbing obesity itself, Danaei explains. “If you control a person’s obesity you get rid of some health risks, but if you control hypertension or diabetes, that also reduces health risks, and you can do the latter much more easily right now.”
Danaei’s team assessed BMI and metabolic markers such as systolic blood pressure, total serum cholesterol, and fasting blood glucose. The three metabolic markers only explained half of the increased risk of heart disease across all study participants. In obese individuals, the other half appeared to be mediated by fat itself, perhaps via inflammatory markers or other indirect mechanisms (Epidemiology, 26:153-62, 2015). While Danaei’s study was aimed at understanding how obesity hurts health, the results also uncovered unknown mechanisms by which excess adipose tissue might exert its effects. This particular study revealed obesity’s negative effects, but might these unknown mechanisms hold clues that explain the obesity paradox?
Other researchers have suggested additional possibilities—for example, that inflammatory markers such as TNF-α help combat conditions such as chronic kidney disease, or that obesity makes a body more capable of making changes to, and tolerating changes in, blood flow depending on systemic needs (Am J Clin Nutr, 81:543-54, 2005).
According to endocrinologist Mitchell Lazar at the University of Pennsylvania, the key to explaining the obesity paradox may be two nonexclusive ways fat tissue is hypothesized to function. One mechanism, termed the endocrine theory, suggests that fat cells secrete, or don’t secrete enough of, certain molecules that influence glucose homeostasis in other tissues, such as muscle or liver. The first such hormone to be discovered was leptin; later studies reported several other adipocyte-secreted factors, including adiponectin, resistin, and various cytokines.
The other hypothesis, dubbed the spillover theory, suggests that storing lipids in fat cells has some pluses. Adipose tissue might sequester fat-soluble endotoxins, and produce lipoproteins that can bind to and clear harmful lipids from circulation. When fat cells fill up, however, these endotoxins are stashed in the liver, pancreas, or other organs—and that’s when trouble begins. In “fat fit” people, problems typically linked to obesity such as high cholesterol or diabetes may be avoided simply because their adipocytes mop up more endotoxins.
“In this model, one could imagine that if you could store even more fat in fat cells, you could be even more obese, but you might be protected from problems [associated with] obesity because you’re protecting the other tissues from filling up with lipids that cause problems,” says Lazar. “This may be the most popular current model to explain the fat fit.”
Although obesity greatly increases the risk of type 2 diabetes—up to 93-fold in postmenopausal women, for example—not all obese people suffer from the condition. Similarly, a certain subtype of individuals with “normal” BMIs are at greater risk of developing insulin resistance and type 2 diabetes than others with BMIs in the same range. Precisely what distinguishes these two cohorts is still unclear. “Just as important as explaining why some obese people don’t get diabetes is to explain why other subgroups—normal-weight people or those with lipodystrophy—sometimes get it,” Lazar says. “If there are multiple subtypes of obesity and diabetes, can we figure out genetic aspects or biomarkers that cause one of these phenotypes and not the other?”
To Lazar, McAuley, and other researchers, it’s increasingly evident that BMI may not be that metric. Finding better ways to assess a healthy weight, however, has proven challenging. Researchers have tested measures, such as the body shape index (ABSI) or the waist-hip ratio, which attempt to gauge visceral fat—considered to be more metabolically harmful than fat in other body locations. However, these metrics have yet to be implemented widely in clinics, and few are as simple to understand as the BMI (Science, 341:856-58, 2013).
Independent of metrics, however, the health message regarding weight is still unanimous: exercise and healthy dietary choices benefit everyone. “At a certain point, despite all the so-called fit-fat people, the demographics say that there’s a huge risk of diabetes and heart disease at very high BMI,” notes Lazar. “We can’t assume we’ll be one of the lucky ones who will have a BMI in the obese category but will still be protected from heart disease.”
Correction (November 2): The original version of this article misattributed the pull quote above. The attribution for this quote has been corrected, and The Scientist regrets the error.
THE HEALTH RISK OF OBESITY—BETTER METRICS IMPERATIVE
In this review, we examine the original obesity paradox phenomenon (i.e. in cardiovascular disease populations, obese patients survive better), as well as three other related paradoxes (pre-obesity, “fat but fit” theory, and “healthy” obesity). An obesity paradox has been reported in a range of cardiovascular and non-cardiovascular conditions. Pre-obesity (defined as a body mass index of 25.0-29.9 kg · m⁻²) presents another paradox. Whereas “overweight” implies increased risk, it is in fact associated with decreased mortality risk compared with normal weight. Another paradox concerns the observation than when fitness is taken into account, the mortality risk associated with obesity is offset. The final paradox under consideration is the presence of a sizeable subset of obese individuals who are otherwise healthy. Consequently, a large segment of the overweight and obese population is not at increased risk for premature death. It appears therefore that low cardiorespiratory fitness and inactivity are a greater health threat than obesity, suggesting that more emphasis should be placed on increasing leisure time physical activity and cardiorespiratory fitness as the main strategy for reducing mortality risk in the broad population of overweight and obese adults.
Obesity, insulin resistance, and cardiovascular disease.
The ability of insulin to stimulate glucose disposal varies more than six-fold in apparently healthy individuals. The one third of the population that is most insulin resistant is at greatly increased risk to develop cardiovascular disease (CVD), type 2 diabetes, hypertension, stroke, nonalcoholic fatty liver disease, polycystic ovary disease, and certain forms of cancer. Between 25-35% of the variability in insulin action is related to being overweight. The importance of the adverse effects of excess adiposity is apparent in light of the evidence that more than half of the adult population in the United States is classified as being overweight/obese, as defined by a body mass index greater than 25.0 kg/m(2). The current epidemic of overweight/obesity is most-likely related to a combination of increased caloric intake and decreased energy expenditure. In either instance, the fact that CVD risk is increased as individuals gain weight emphasizes the gravity of the health care dilemma posed by the explosive increase in the prevalence of overweight/obesity in the population at large. Given the enormity of the problem, it is necessary to differentiate between the CVD risk related to obesity per se, as distinct from the fact that the prevalence of insulin resistance and compensatory hyperinsulinemia are increased in overweight/obese individuals. Although the majority of individuals in the general population that can be considered insulin resistant are also overweight/obese, not all overweight/obese persons are insulin resistant. Furthermore, the cluster of abnormalities associated with insulin resistance – namely, glucose intolerance, hyperinsulinemia, dyslipidemia, and elevated plasma C-reactive protein concentrations — is limited to the subset of overweight/obese individuals that are also insulin resistant. Of greater clinical relevance is the fact that significant improvement in these metabolic abnormalities following weight loss is seen only in the subset of overweight/obese individuals that are also insulin resistant. In view of the large number of overweight/obese subjects at potential risk to be insulin resistant/hyperinsulinemic (and at increased CVD risk), and the difficulty in achieving weight loss, it seems essential to identify those overweight/obese individuals who are also insulin resistant and will benefit the most from weight loss, then target this population for the most-intensive efforts to bring about weight loss.
Long-Term Persistence of Hormonal Adaptations to Weight Loss
Priya Sumithran, Luke A. Prendergast, Elizabeth Delbridge, Katrina Purcell, Arthur Shulkes, Adamandia Kriketos, and Joseph Proietto
After weight loss, changes in the circulating levels of several peripheral hormones involved in the homeostatic regulation of body weight occur. Whether these changes are transient or persist over time may be important for an understanding of the reasons behind the high rate of weight regain after diet-induced weight loss.
Weight loss (mean [±SE], 13.5±0.5 kg) led to significant reductions in levels of leptin, peptide YY, cholecystokinin, insulin (P<0.001 for all comparisons), and amylin (P=0.002) and to increases in levels of ghrelin (P<0.001), gastric inhibitory polypeptide (P=0.004), and pancreatic polypeptide (P=0.008). There was also a significant increase in subjective appetite (P<0.001). One year after the initial weight loss, there were still significant differences from baseline in the mean levels of leptin (P<0.001), peptide YY (P<0.001), cholecystokinin (P=0.04), insulin (P=0.01), ghrelin (P<0.001), gastric inhibitory polypeptide (P<0.001), and pancreatic polypeptide (P=0.002), as well as hunger (P<0.001).
One year after initial weight reduction, levels of the circulating mediators of appetite that encourage weight regain after diet-induced weight loss do not revert to the levels recorded before weight loss. Long-term strategies to counteract this change may be needed to prevent obesity relapse. (Funded by the National Health and Medical Research Council and others; ClinicalTrials.gov number,NCT00870259.)
Large genome wide association studies have demonstrated that variants in the FTO gene have the strongest association with obesity risk in the general population, but the mechanism of the association has been unclear. However, a nonocoding causal variant in FTO has now been identified that changes the function of adipocytes from energy utilization (beige fat) to energy storage (white fat) with a fivefold decrease in mitochondrial thermogenesis [17]. When the effect of the variant was blocked in genetically engineered mice, thermogenesis increased and weight gain did not occur, despite eating a high-fat diet. Blocking the gene’s effect in human adipocytes also increased energy utilization. This observation has important implications for potential new anti-obesity drugs. (See “Pathogenesis of obesity”, section on ‘FTO variants’.)
Liraglutide for the treatment of obesity (July 2015)
Along with diet, exercise, and behavior modification, drug therapy may be a helpful component of treatment for select patients who are overweight or obese. Liraglutide is a glucagon-like peptide-1 (GLP-1) receptor agonist, used for the treatment of type 2 diabetes, and can promote weight loss in patients with diabetes, as well as those without diabetes.
●In a randomized trial in nondiabetic patients who had a body mass index (BMI) of ≥30 kg/m2 or ≥27 kg/m2 with dyslipidemia and/or hypertension, liraglutide 3 mg once daily, compared with placebo, resulted in greater mean weight loss (-8.0 versus -2.6 kg with placebo) [18]. In addition, cardiometabolic risk factors, glycated hemoglobin (A1C), and quality of life improved modestly. Gastrointestinal side effects transiently affected at least 40 percent of the liraglutide group and were the most common reason for withdrawal (6.4 percent). Liraglutide is an option for select overweight or obese patients, although gastrointestinal side effects (nausea, vomiting) and the need for a daily injection may limit the use of this drug. (See “Obesity in adults: Drug therapy”, section on ‘Liraglutide’.)
●In a trial designed specifically to evaluate the effect of liraglutide on weight loss in overweight or obese patients with type 2 diabetes (mean weight 106 kg), liraglutide, compared with placebo, resulted in greater mean weight loss (-6.4 kg and -5.0 kg for liraglutide 3 mg and 1.8 mg, respectively, versus -2.2 kg for placebo) [19]. Treatment with liraglutide was associated with better glycemic control, a reduction in the use of oral hypoglycemic agents, and a reduction in systolic blood pressure. Although liraglutide is not considered as initial therapy for the majority of patients with type 2 diabetes, it is an option for select overweight or obese patients with type 2 diabetes who fail initial therapy with lifestyle intervention and metformin. (See “Glucagon-like peptide-1 receptor agonists for the treatment of type 2 diabetes mellitus”, section on ‘Weight loss’.)
The Skinny on Fat Cells
Bruce Spiegelman has spent his career at the forefront of adipocyte differentiation and metabolism.
Bruce Spiegelman
Stanley J. Korsmeyer Professor of Cell Biology
and Medicine
Harvard Medical School
Director, Center for Energy Metabolism
and Chronic
Disease, Dana-Farber Cancer Institute, Boston
It’s hard to know whether you have the right stuff to be a scientist, but I had a passion for the research,” says Bruce Spiegelman, professor of cell biology at Harvard Medical School and the Dana-Farber Cancer Institute. After receiving his PhD in biochemistry from Princeton University in 1978, Spiegelman sent an application to do postdoctoral research to just one lab. “I wasn’t thinking I should apply to five different labs. I just marched forward more or less in a straight line,” he says. Spiegelman did know that he had no financial backup and depended on research fellowships throughout the early phase of his science career. “I thought it was fantastic, and still think so, that a PhD in science is supported by the government. I certainly appreciated that, because many of my friends in the humanities had to support themselves by cobbling together fellowships and teaching every semester, whereas we didn’t face similar challenges in the sciences.”
Since his graduate student days, Spiegelman has realized his potential, pioneering the study of adipose tissue biology and metabolism. He was introduced to the field in Howard Green’s laboratory, then at MIT, where Spiegelman began his one and only postdoc in 1978. Green had recently developed a system for culturing adipose cells and asked Spiegelman if he wanted to study fat cell differentiation. “I knew nothing about adipose tissue, but I was really interested in any model of how one cell switches to another. Whether skin or fat didn’t matter too much to me, because I was not coming at this from the perspective of physiology but from the perspective of how do these switches work at a molecular level?”
Spiegelman has stuck with studying the biology and differentiation of fat cells for more than 30 years. While looking for the master transcriptional regulator of fat development—which his laboratory found in 1994—Spiegelman’s group also discovered one of the first examples of a nuclear oncogene that functions as a transcription factor, and, more recently, the team found that brown fat and white fat come from completely different origins and that brown and beige fat are distinct cell types. Spiegelman was also the first to provide evidence for the connection between inflammation, insulin resistance, and fat tissue.
Here, Spiegelman talks about his strong affinity for the East Coast, his laboratory’s search for molecules that can crank up brown fat production and activity, and the culture of his laboratory’s weekly meeting.
Spiegelman Sets Out
First publication. Spiegelman grew up in Massapequa, New York, a town on Long Island. “Birds, insects, fish, and animals were fascinating to me. As a kid, I imagined I would be a wildlife ranger,” he says. Spiegelman and his brother were the first in their family to attend college; Spiegelman entered the College of William and Mary in 1970 thinking he would major in psychology. But before taking his first psychology course, he had to take a biology course, really loved it, and switched his major. For his senior thesis, he chose one of the few labs that did biochemistry-related research. He studied cultures of the filamentous fungus Aspergillus ornatus in which he induced the upregulation of a metabolic enzyme. Spiegelman applied a calculus transformation that related the age of the culture to the age of individual cells, something that had not been previously done. The work earned him his first first-author publication in 1975. “It was not a great breakthrough, but I think it showed that I was maybe applying myself more than the typical undergraduate.”
Full steam ahead. “My interest in laboratory research was intense. Even though it was not particularly inspired work, the first-author publication in a college where not many of the professors published a lot gave me a lot of confidence. It was probably out of proportion to the quality of the actual work.” That confidence and Spiegelman’s interest in the chemistry of living things led him to pursue a PhD in biochemistry at Princeton University. “Very early on, I felt that I couldn’t understand biology if it didn’t go to the molecular level. To me, just describing how an animal lived without understanding how it worked was very unsatisfying. I think it was one of the best decisions that I made in my life, to do a PhD in biochemistry,” he says, “because if you really want to understand living systems, you are very limited in how you can understand them without having a strong background in biochemistry because these are, essentially, chemical systems.”
Embracing molecular biology. Spiegelman initially joined Arthur Pardee’s laboratory, but switched when Pardee left Princeton for Harvard University in 1975. Because he was already collaborating with Marc Kirschner, a cell biologist and biochemist who studies the regulation of the cell cycle and how the cytoskeleton works, it was an easy transition to transfer to the new laboratory. In Kirschner’s group, Spiegelman became the cell biologist among many protein biochemists working on microtubule assembly in vitro. Rather than understanding how the proteins fit together to form the filamentous structures, Spiegelman wanted to understand what controlled their assembly inside cells. Working in mammalian cells, Spiegelman published three consecutive Cell papers on how microtubule assembly occurs in vivo. The firstpaper, from 1977, demonstrated that a nucleotide functions to stabilize the tubulin molecule rather than to regulate tubulin assembly in vivo.
Spiegelman Simmers
A new tool. For his next move, Spiegelman wanted to marry his background in biochemistry and molecular biology with a good cellular model system. He became interested in differentiation at the end of his PhD, while studying how the cytoskeleton is reorganized during neural differentiation, and settled on Green’s MIT laboratory for his postdoc. Green had developed a way to study both skin and fat cell differentiation. Again, Spiegelman was the odd man out, working on the molecular biology of fat cell differentiation while most of the graduate students and postdocs focused on the cellular biology of skin cell differentiation. While there, Spiegelman learned how to clone cDNA—a new method that some researchers thought was just another new fad, he says. “I thought it was pretty obvious that this was a tool that would be a game changer. I could see how I could clone some of the cDNAs and genes that were regulated in the fat cell lineage and then try to understand the regulation of these genes.”
Setting the stage. Spiegelman demonstrated that cAMP regulates the synthesis of certain enzymes in fat cells during differentiation. But while this was the most influential paper from his postdoc, says Spiegelman, it was his demonstration of cloning mRNAs from adipocytes, published in 1983, that set the stage for cloning fat-selective genes. The work, mostly done when Spiegelman was already a new faculty member at the Dana-Farber Cancer Institute, stemmed from his learning molecular cloning in Phillip Sharp’s lab at MIT and Bryan Roberts’s lab at Harvard. “This was the raw material from which we eventually cloned PPARγ and showed it to be the master regulator of fat [cell] development.”
Roots. Spiegelman became an assistant professor at the Harvard Medical School in 1982, when he was not yet 30. Although he had entertained the idea of moving to the West Coast with his wife, whom he had met at Princeton where she obtained a PhD in French literature, Spiegelman says he is really an East Coaster at heart. “My wife and I came to love Boston and were very comfortable there. Our families were both in New York, which was close, but not too close, and we really enjoyed the culture and pace of Boston; it was more ‘us.’ We really liked to visit California but didn’t particularly want to move there. We’re both real Northeastern people.”
Relating to Sisyphus. The transition from doing a postdoc to setting up his own laboratory was “very exciting and terribly stressful,” says Spiegelman. “When I think back, I always tried to be professional with my laboratory, but I was so stressed at suddenly being on my own with no management training.” The people resources he had encountered in his graduate and postdoctoral training labs were also not there yet, and he says his first publication as a principal investigator was like pushing a rock up a hill. But eventually, Spiegelman’s lab built a reputation and reached a critical mass of talented people who advanced the science. Again in 1983, Spiegelman produced a publication showing that morphological manipulation can affect gene expression and adipose differentiation.
A wider scope. In 1993, graduate student Gökhan Hotamisligil found that tumor necrosis factor-alpha(TNF-α), is induced in the fat tissue of rodent models of obesity and diabetes. The paper sparked the formation of the field of immunometabolism and resulted in the expansion of Spiegelman’s lab into the physiology arena, partly thanks to the guidance of C. Ronald Kahn and Jeff Flier, who both study metabolism and diabetes. But the work initially encountered pushback, says Spiegelman, partly because it was the merging of two fields.
A paler shade of brown. More recently, in 2012, Spiegelman’s laboratory showed that within adult white adipose tissue, there are pockets of a yet another type of fat tissue that he called beige fat. “I think the evidence is very good from rodents that if you activate brown and beige fat, you get metabolic benefit both in obesity and diabetes. So the question now is: Can that be done in humans in a way that’s beneficial and not toxic?” The lab is now looking to identify molecules that can either ramp up the activity of brown and beige fat or increase the production of both cell types as possible therapeutics for metabolic disorders or even cancer-associated cachexia. “Anyone who says that either approach will work better is being foolish. We just don’t know enough to go after just one or the other.”
On the irisin controversy. After reporting in 2012 that a muscle-related hormone called irisin could switch white fat to metabolically active brown fat, Spiegelman became embroiled in a media-covered debate about whether the molecule really exists; he was also the victim of a potential fraud plot. Most recently, Spiegelman provided thorough evidence that irisin does in fact exist. On the controversy, he says it’s a fine line between defending his scientific integrity and not adding more fuel to the fire or engaging with his harassers. “We have a long track record of doing credible and reproducible science and it was not that complicated to address the paper that claimed irisin was ‘a myth.’ That study used very outmoded scientific approaches.”
Raw talent. Many of Spiegelman’s trainees have gone on to become very successful scientists, including Tontonoz, Hotamisligil, Evan Rosen, and Randy Johnson. “It’s a quantum change in the experience of doing science when you get people who have their own visions. I would have thought that interacting with smart people would mainly help me get my scientific vision accomplished. And that was partly true, but also it changed my vision. When you have people challenging you on a day-to-day basis, you learn from them through the questions they ask and the way they challenge you in a constructive way. They made me a much better scientist.”
Rigorous mentorship. “I feel very passionately that a major part of my job is to prepare the next generation of scientists. Everyone who comes through my lab will tell you that I take that very seriously. We make sure my students give a lot of talks and get critical assessments of their presentations to our lab group. I am very hands-on both scientifically and in developing the way students project their vision. I had a very good mentor, Marc Kirschner, and I’d like to think that I learned how to be a mentor from him. I want to make sure that when people walk out of my lab they are prepared to run independent research programs.”
Greatest Hits
Identified the master regulator of adipogenesis, the nuclear receptor PPARγ
Was the first to show that a nuclear oncogene, c-fos, codes for a transcription factor that binds to the promoters of genes
Demonstrated that adipose tissue synthesizes tumor necrosis factor-alpha (TNF-α), providing the first direct link between obesity, inflammation, insulin resistance, and fat tissue.
Showed that brown fat cells are not developmentally related to white fat
Identified beige fat as a distinct cell type, different from either white or brown fat
Fanning the Flames
Obesity triggers a fatty acid synthesis pathway, which in turn helps drive T cell differentiation and inflammation.
•A high-fat diet augments Th17 cell development and the expression of Acaca
•ACC1 controls Th17 cell development in vitro and Th17 cell pathogenicity in vivo
•ACC1 modulates RORγt function in developing Th17 cells
•Obesity in humans induces ACACA and IL-17A expression in CD4 T cells
Chronic inflammation due to obesity contributes to the development of metabolic diseases, autoimmune diseases, and cancer. Reciprocal interactions between metabolic systems and immune cells have pivotal roles in the pathogenesis of obesity-associated diseases, although the mechanisms regulating obesity-associated inflammatory diseases are still unclear. In the present study, we performed transcriptional profiling of memory phenotype CD4 T cells in high-fat-fed mice and identified acetyl-CoA carboxylase 1 (ACC1, the gene product of Acaca) as an essential regulator of Th17 cell differentiation in vitro and of the pathogenicity of Th17 cells in vivo. ACC1 modulates the DNA binding of RORγt to target genes in differentiating Th17 cells. In addition, we found a strong correlation between IL-17A-producing CD45RO(+)CD4 T cells and the expression of ACACA in obese subjects. Thus, ACC1 confers the appropriate function of RORγt through fatty acid synthesis and regulates the obesity-related pathology of Th17 cells.
Obesity often comes with a side of chronic inflammation, causing inflammatory chemicals and immune cells to flood adipose tissue, the hypothalamus, the liver, and other areas of the body. Inflammation is a big part of what makes obesity such an unhealthy condition, contributing to Type 2 diabetes, heart disease, cancers, autoimmune disorders, and possibly even neurodegenerative diseases.
To better understand the relationship between obesity and inflammation, Toshinori Nakayama, Yusuke Endo, and their colleagues at Chiba University in Japan started with what often leads to obesity: a high-fat diet. They fed mice rich meals for a couple of months and looked at how gene expression in the animals’ T cells compared to gene expression in the T cells of mice fed a normal diet. Most notably, they found increased expression ofAcaca, a gene that codes for a fatty acid synthesis enzyme called acetyl coA carboxylase 1 (ACC1). They went on to show that the resulting increase in fatty acid levels pushed CD4 T cells to differentiate into inflammatory T helper 17 (Th17) cells.
Th17 cells help fight off invading fungi and some bacteria. But these immune cells can also spin out of control in autoimmune diseases such as multiple sclerosis. Nakayama’s team showed that either blocking ACC1 activity with a drug called TOFA or deleting a key portion of Acaca in mouse CD4 T cells reduced the generation of pathologic Th17 cells. Overexpressing Acaca increased Th17-cell generation.
The researchers also demonstrated that mice fed a high-fat diet had elevated susceptibility to a multiple sclerosis–like disease, and that TOFA reduced the symptoms.
“This is a very intriguing finding, suggesting not only that obesity can directly induce Th17 differentiation but also indicating that pharmacologic targeting of fatty acid synthesis may help to interfere with obesity-associated inflammation,” Tim Sparwasser of the Twincore Center for Experimental and Clinical Infection Research in Hannover, Germany, says in an email. Sparwasser and his colleagues had previously shown that ACC1 is required for the differentiation of Th17 cells in mice and humans.
Nakayama explains that CD4 T cells must undergo profound metabolic changes as they mature and differentiate. “The intracellular metabolites, including fatty acids, are essential for cell proliferation and cell growth,” he says in an email. When fatty acid levels in T cells increase, the cells are activated and begin to proliferate.
“It’s a nice illustration of how, really, immune response is so highly connected to the metabolic state of the cell,” says Gökhan S. Hotamisligil of Harvard University’s T.H. Chan School of Public Health who was not involved in the study. “The immune system launches its responses commensurate with the sources of nutrients and energy from the environment,” he adds in an email.
There are still missing pieces in the path from high-fat diet to increased Acaca expression to ACC1’s influence on T-cell differentiation. It also remains to be seen how this plays out in obese humans, although Nakayama and colleagues did show that inhibiting ACC1 reduced pathologic Th17 generation in human immune cell cultures, and that the T cells of obese humans contain elevated levels of ACC1 and show signs of increased differentiation into Th17 cells.
The prevalence of obesity has been increasing worldwide, and obesity is now a major public health problem in most developed countries (Gregor and Hotamisligil, 2011, Ng et al., 2014). Obesity-induced inflammation contributes to the development of various chronic diseases, such as autoimmune diseases, metabolic diseases, and cancer (Kanneganti and Dixit, 2012, Kim et al., 2014,Osborn and Olefsky, 2012, Winer et al., 2009a). A number of studies have pointed out the importance of reciprocal interactions between metabolic systems and immune cells in the pathogenesis of obesity-associated diseases (Kaminski and Randall, 2010, Kanneganti and Dixit, 2012, Kim et al., 2014, Mauer et al., 2014, Stienstra et al., 2012, Winer et al., 2011).
Elucidating the molecular mechanisms by which naive CD4 T cells differentiate into effector T cells is crucial for understanding helper T (Th) cell-mediated immune pathogenicity. After antigen stimulation, naive CD4 T cells differentiate into at least four distinct Th cell subsets: Th1, Th2, Th17, and inducible regulatory T (iTreg) cells (O’Shea and Paul, 2010, Reiner, 2007). Several specific master transcription factors that regulate Th1/Th2/Th17/iTreg cell differentiation have been identified, including T-bet for Th1 (Szabo et al., 2000), GATA3 (Yamashita et al., 2004, Zheng and Flavell, 1997) for Th2, retinoic-acid-receptor-related orphan receptor γt (RORγt) for Th17 (Ivanov et al., 2006), and forkhead box protein 3 (Foxp3) for iTreg (Sakaguchi et al., 2008). The appropriate expression and function of these transcription factors is essential for proper immune regulation by each Th cell subset.
Among these Th cell subsets, Th17 cells contribute to the host defense against fungi and extracellular bacteria (Milner et al., 2008). However, the pathogenicity of IL-17-producing T cells has been recognized in various autoimmune diseases, including multiple sclerosis, psoriasis, inflammatory bowel diseases, and steroid-resistant asthma (Bettelli et al., 2006, Coccia et al., 2012, Ivanov et al., 2006,Leonardi et al., 2012, McGeachy and Cua, 2008, Nylander and Hafler, 2012,Stockinger et al., 2007, Sundrud et al., 2009).
An HFD Promotes Th17 Cell Differentiation and Affects the Expression of Fatty Acid Enzymes in Memory CD4 T Cells In Vivo
Inhibition of ACC1 Function Results in Decreased Th17 Cell Differentiation and Ameliorates the Development of Autoimmune Disease
ACC1 Controls the Differentiation of Th17 Cells Both In Vitro and In Vivo
ACC1 Controls the Function, but Not Expression, of RORγt in Differentiating Th17 Cells
Extrinsic Fatty Acid Supplementation Restored Acaca−/− Th17 Cell Differentiation through the Functional Improvement of RORγt
Obese Subjects Show Upregulation of ACACA and Increased Th17 Cells in CD45RO+ Memory CD4 T Cells
We herein identified a critical role that ACC1 plays in Th17 cell differentiation and the pathogenicity of Th17 cells through the control of the RORγt function under obese circumstances. High-fat-induced obesity augments Th17 cell differentiation and the expression of enzymes involved in fatty acid metabolism, including ACC1. Pharmacological inhibition or genetic deletion of ACC1 resulted in impaired Th17 cell differentiation in both mice and humans. In contrast, overexpression of Acaca induced Th17 cells in vivo, leaving the expression ofIfng and Il4 largely unchanged. ACC1 modulated the binding of RORγt to theIl17a gene and the subsequent p300 recruitment in differentiating Th17 cells. Memory CD4 T cells from peripheral blood mononuclear cells (PBMCs) of obese subjects showed increased IL-17A production and ACACA expression. Furthermore, a strong correlation was detected between the proportion of IL-17A-producing cells and the expression level of ACACA in memory CD4 T cells in obese subjects. Thus, our findings provide evidence of a mechanism wherein obesity can exacerbate IL-17-mediated pathology via the induction of ACC1.
Using computational modeling, researchers at Carnegie Mellon University, the Colorado School of Mines and the University of California, Davis have come up with a design for a better liposome. Their findings, while theoretical, could provide the basis for efficiently constructing new vehicles for nanodrug delivery.
Liposomes are small containers with shells made of lipids, the same material that makes up the cell membrane. In recent years, liposomes have been used for targeted drug delivery. In this process, the membrane of a drug-containing liposome is engineered to contain proteins that will recognize and interact with complementary proteins on the membrane of a diseased or dysfunctional cell. After the drug-containing liposomes are administered, they travel through the body, ideally connecting with targeted cells where they release the drug.
This packaging technique is often used with highly toxic nanodrugs, like chemotherapy drugs, in an attempt to prevent the free drug from damaging non-cancerous cells. However, studies of this model of delivery have shown that in many cases less than 10 percent of the drugs transported by liposomes end up in tumor cells. Often, the liposome breaks open before it reaches a tumor cell and the drug is absorbed into the body’s organs, including the liver and spleen, resulting in toxic side effects.
“Even with current forms of targeted drug delivery, treatments like chemotherapy are still very brutal. We wanted to see how we could make targeted drug delivery better,” said Markus Deserno, professor of physics at Carnegie Mellon and a member of the university’s Center for Membrane Biology and Biophysics.
Deserno and colleagues propose that targeted drug delivery can be improved by making more stable liposomes. Using three different types of computer modeling, they have shown that liposomes can be made sturdier by incorporating a nanoparticle core made of a material like gold or iron and connecting that core to the liposome’s membrane using polymer tethers. The core and tethers act as a hub-and-spoke-like scaffold and shock-absorber system that help the liposome to weather the stresses and strains it encounters as it travels through the body to its target.
Francesca Stanzione and Amadeu K. Sum of the Colorado School of Mines conducted a fine-grained simulation that looked at how the polymer tethers anchor the liposome’s membrane at an atomistic level. Roland Faller of UC Davis did a meso-scale simulation that looked how a number of tethers held on to a small patch of membrane. Each of these simulations allowed researchers to look at smaller components of the liposome, nanoparticle core and tethers, but not the entire structure.
To see the entire structure, Carnegie Mellon’s Deserno and Mingyang Hu developed a coarse-grained model that represents groupings of components rather than individual atoms. For example, one lipid in the cell membrane might have 100 atoms. In a fine-grain simulation, each atom would be represented. In Deserno’s coarse grain simulation, those atoms might be represented by only three pieces instead of 100.
“Its unfeasible to look at the complete construct at an atomistic level. There are too many atoms to consider, and the timescale is too long. Even with the most advanced supercomputer, we wouldn’t have the power to run an atom-level simulation,” Deserno said. “But the physics that matters isn’t locally specific. It’s more like soft matter physics, which can be described at a much coarser resolution.”
Deserno’s simulation allowed the researchers to see how the entire reinforced liposome construct responded to stress and strain. They proposed that if a liposome was given the right-sized hub and tethers, its membrane would be much more resilient, bending to absorb impact and pressure.
Additionally, they were able to simulate how to best assemble the liposome, hub and tether system. They found that if the hub and tether are attached and placed in a solution of lipids, and solvent conditions are suitably chosen, a correctly sized liposome would self-assemble around the hub and tethers.
The researchers hope that chemists and drug developers will one day be able to use their simulations to determine what size core and polymer tethers they would need to effectively secure a liposome designed to deliver a specific drug or other nanoparticle. Using such simulations could narrow down the design parameters, speed up the development process and reduce costs.
NIHS to use the Lipotype Shotgun Lipidomics Technology for lipid analysis.
Lipotype GmbH and the Nestlé Institute of Health Sciences (NIHS) have collaborated to employ the innovative Lipotype Shotgun Lipidomics Technology to analyze lipids in blood for nutritional research. Recently, Lipotype and NIHS have jointly published results of the robustness of the Lipotype Technology. Lipotype envisions a future use of its technology in clinical diagnostics screens for establishing reliable lipid diagnostic biomarkers.
Innovative Lipotype Technology for lipid analysis
The purpose of this collaboration is to enable NIHS to use the Lipotype Shotgun Lipidomics Technology for lipid analysis. The mass spectrometry-based Lipotype technology covers a broad spectrum of lipid molecules and delivers quantitative results in high-throughput. The Nestlé Institute of Health Sciences uses this technology platform for nutritional research. NIHS is a specialized biomedical research institute and is part of Nestlé’s global Research & Development network.
Joint research project reveals robustness of Lipotype Technology
During the collaboration, Lipotype and NIHS conducted a joint research project and demonstrated that the Lipotype technology was robust enough to deliver data with high precision and negligible technical variation between different sites. In addition, important features are the high coverage and throughput, which were confirmed when applying the Lipotype technology.
Lipotype envisions these as important features, required for future use in clinical diagnostics screens, in order to establish and validate reliable lipid diagnostic biomarkers. The results have been published in October 2015, in the European Journal of Lipid Science and Technology (Surma et al. “An Automated Shotgun Lipidomics Platform for High Throughput, Comprehensive, and Quantitative Analysis of Blood Plasma Intact Lipids.”).
Lipids play an important role for health and disease
Lipotype is a spin-off company of the Max-Planck-Institute of Molecular Cell Biology and Genetics in Dresden, Germany. Prof. Kai Simons, CEO of Lipotype explains: “We developed a novel Shotgun-Lipidomics technology to analyze lipids in blood and other biological samples. Our analysis is quick and covers hundreds of lipid molecules at the same time. Our technology can be used to identify disease related lipid signatures.”
Researchers at the University of Liverpool, working with a global healthcare company, have helped develop a new treatment for obesity.
The treatment, which is a once-daily injectable derivative of a metabolic hormone called GLP-1 conventionally used in the treatment of type 2 diabetes, has proved successful in helping non-diabetic obese patients lose weight.
Professor John Wilding, who leads Obesity and Endocrinology research in the Institute of Ageing and Chronic Disease, investigates the pathophysiology and treatment of both obesity and type 2 diabetes and is applying his expertise in this area to work with, and often act as a consultant for, a number of large pharmaceutical companies looking to develop new treatments for obesity and diabetes.
Exciting development
Professor Wilding, said: “The biology of GLP-1 has been a focus of my research for 20 years; in particular when I was working at Hammersmith Hospital in London, I was part of the team that demonstrated that it was involved in appetite regulation; work on GLP-1 has continued during my time in Liverpool. Being involved in the development of a treatment, from the basic research right through to clinical trials in patients is very exciting”.
“It is likely that the treatment will be used initially in very specific situations, such as helping patients who are severely obese. It differs from current treatments used for diabetes, as it has stronger appetite regulating effects but no greater effect on glucose control.”
In 2014 more than 1.9 billion adults worldwide were classed as obese by the World Health Organisation; in the UK numbers have more than tripled since 1980. This Obesity can lead to other serious health-related illnesses including type 2 diabetes, hypertension and obstructive sleep apnoea as well as increasing the risk for many common cancers.
The drug has been approved in the European Union, but has not yet launched in the UK.
Professor Wilding added: “Consultancy like this can help relationship and reputation building and informs my research keeping it at the forefront of developments. It also brings many other benefits such as publications and income generation, which can help support other research, for example by such as funding for pilot projects that can lead to grant applications and investigator-initiated trials funded by the company”.
Researchers in the West Midlands have made a breakthrough in explaining how an incurable type of blood cancer develops from an often symptomless prior blood disorder.
The findings could lead to more effective treatments and ways to identify those most at risk of developing the cancer.
All patients diagnosed with myeloma, a cancer of the blood-producing bone marrow, first develop a relatively benign condition called ‘monoclonal gammopathy of undetermined significance’ or ‘MGUS’.
MGUS is fairly common in the older population and only progresses to cancer in approximately one in 100 cases. However, currently there is no way of accurately predicting which patients with MGUS are likely to go on to get myeloma.
Myeloma is diagnosed in around 4,000 people each year in the UK. It specifically affects antibody-producing white blood cells found in the bone marrow, called plasma cells. The researcher team from the University of Birmingham, New Cross and Heartlands Hospitals compared the cellular chemistry of bone marrow and blood samples taken from patients with myeloma, patients with MGUS and healthy volunteers.
Surprisingly, the researchers found that the metabolic activity of the bone marrow of patients with MGUS was significantly different to plasma from healthy volunteers, but there were very few differences at all between the MGUS and myeloma samples. The research was funded by the blood cancer charity Bloodwise, which changed its name from Leukaemia & Lymphoma in September.
The findings suggest that the biggest metabolic changes occur with the development of the symptomless condition MGUS and not with the later progression to myeloma.
Dr Daniel Tennant, who led the research at the University of Birmingham, said, “Our findings show that very few changes are required for a MGUS patient to progress to myeloma as we now know virtually all patients with myeloma evolve from MGUS. A drug that interferes with these specific initial metabolic changes could make a very effective treatment for myeloma, so this is a very exciting discovery.”
The research team found over 200 products of metabolism differed between the healthy volunteers and patients with MGUS or myeloma, compared to just 26 differences between MGUS patients and myeloma patients. The researchers believe that these small changes could drive the key shifts in the bone marrow required to support myeloma growth.
Biochemical Insights of Dr. Jose Eduardo de Salles Roselino
How is it that developments late in the 20th century diverted the attention of
biological processes from a dynamic construct involving interacting chemical
reactions under rapidly changing external conditions effecting tissues and cell
function to a rigid construct that is determined unilaterally by the genome
construct, diverting attention from mechanisms essential for seeing the complete
cellular construct?
Larry, I assume that in case you read the article titled Neo – Darwinism, The
Modern Synthesis and Selfish Genes that bares no relationship with Physiology
with Molecular Biology J. Physiol 2011; 589(5): 1007-11 by Denis Noble, you might
find that it was the key factor required in order to understand the dislodgment
of physiology as a foundation of medical reasoning. In the near unilateral emphasis
of genomic activity as a determinant of cellular activity all of the required general
support for the understanding of my reasoning. The DNA to protein link goes
from triplet sequence to amino acid sequence. That is the realm of genetics.
Further, protein conformation, activity and function requires that environmental
and micro-environmental factors should be considered (Biochemistry). If that
were not the case, we have no way to bridge the gap between the genetic
code and the evolution of cells, tissues, organs, and organisms.
Consider this example of hormonal function. I would like to stress in
the cAMP dependent hormonal response, the transfer of information
that occurs through conformation changes after protein interactions.
This mechanism therefore, requires that proteins must not have their
conformation determined by sequence alone.
Regulatory protein conformation is determined by its sequence plus
the interaction it has in its micro-environment. For instance, if your
scheme takes into account what happens inside the membrane and
that occurs before cAMP, then production is increased by hormone
action. A dynamic scheme will show an effect initially, over hormone
receptor (hormone binding causing change in its conformation) followed
by GTPase change in conformation caused by receptor interaction and
finally, Adenylate cyclase change in conformation and in activity after
GTPase protein binding in a complex system that is dependent on self-
assembly and also, on changes in their conformation in response to
hormonal signals (see R. A Kahn and A. G Gilman 1984 J. Biol. Chem.
v. 259,n 10 pp6235-6240. In this case, trimeric or dimeric G does not
matter). Furthermore, after the step of cAMP increased production we
also can see changes in protein conformation. The effect of increased
cAMP levels over (inhibitor protein and protein kinase protein complex)
also is an effect upon protein conformation. Increased cAMP levels led
to the separation of inhibitor protein (R ) from cAMP dependent protein
kinase (C ) causing removal of the inhibitor R and the increase in C activity.
R stands for regulatory subunit and C for catalytic subunit of the protein
complex.
This cAMP effect over the quaternary structure of the enzyme complex
(C protein kinase + R the inhibitor) may be better understood as an
environmental information producing an effect in opposition to
what may be considered as a tendency towards a conformation
“determined” by the genetic code. This “ideal” conformation
“determined” by the genome would be only seen in crystalline
protein. In carbohydrate metabolism in the liver the hormonal signal
causes a biochemical regulatory response that preserves homeostatic
levels of glucose (one function) and in the muscle, it is a biochemical
regulatory response that preserves intracellular levels of ATP (another
function).
Therefore, sequence alone does not explain conformation, activity
and function of regulatory proteins. If this important regulatory
mechanism was not ignored, the work of S. Prusiner (Prion diseases
and the BSE crisis Stanley B. Prusiner 1997 Science; 278: 245 – 251,
10 October) would be easily understood. We would be accustomed
to reason about changes in protein conformation caused by protein
interaction with other proteins, lipids, small molecules and even ions.
In case this wrong biochemical reasoning is used in microorganisms.
Still it is wrong but, it will cause a minor error most of the time, since
we may reduce almost all activity of microorganism´s proteins to a
single function – The production of another microorganism. However,
even microorganisms respond differently to their micro-environment
despite a single genome (See M. Rouxii dimorphic fungus works,
later). The reason for the reasoning error is, proteins are proteins
and DNA are DNA quite different in chemical terms. Proteins must
change their conformation to allow for fast regulatory responses and
DNA must preserve its sequence to allow for genetic inheritance.
Introduction to Impairments in Pathological States: Endocrine Disorders, Stress Hypermetabolism and Cancer
Author and Curator: Larry H. Bernstein, MD, FCAP
This leads into a series of presentations and the metabolic imbalance central to findings of endocrine, metabolic, inflammatory, immune diseases and cancer. All of this has been a result of discoveries based on the methods of study of genomiocs, proteomics, transcriptomics, and metabolomics that have preceded this. In some cases there has been the use of knockout methods. The completion of the human genomic and other catalogues have been instrumental in the past few years. In all cases there has been a thorough guidance by a biological concept of mechanism based on gene expression, metabolic disturbance, signaling pathways, and up- or down- regulation of metabolic circuits. It is interesting to recall that a concept of metabolic circuits was not yet formulated at the time of the mid 20th century physiology, except perhaps with respect to the coagulation pathways, and to some extent, glycolysis, gluconeogenesis, the hexose monophosphate shunt, and mitochondrial respiration, which were linear strings of enzyme substrate reactions that intersected and that had flow restraints not then understood as to the complexity we now appreciate. We did know the importance of cytochrome c, the adenine and pyridine nucleotides, and the energy balance. Electron microscopy had opened the door to understanding the mechanism of contraction of skeletal muscle and myocardium, but it also opened the door to understanding kidney structure and function, explaining the “mesangium”. The first cardiac maker was discovered by Arthur Karmen in the serum alanine and aspartate aminotransferases, with a consequent differentiation between hepatic and myocardial damage. This was followed by lactic dehydrogenase and the H- and M-type isoenzymes in the 1960s, and in the next decade, by the MB-isoenzyme of creatine kinase. Troponins T and then I would not be introduced until the mid 1980s, and they have become a gold standard for the diagnosis of myocardial infarction.
In the 1980s we also saw the development of antiplatelet therapy that rapidly advanced interventional cardiology. But advances in surgical as well as medical intervention also proceeded as the understanding of the lipid metabolism was opened by the work of Brown and Goldstein, and UTSW Medical Campus, and major advances in treatment came at Baylor and UT Medical Center in Houston, and at the Cleveland Clinic. The next important advance came with the discovery of nitric oxide synthase role in endothelium and oxidative stress. The field of endocrinology saw advances as well for a solid period of 30 years in a comparable period for the adrenals, thyroid, and pituitary glands, and for the understanding of the male and female sex hormones, and discoveries in breast, ovarian, and prostate cancer. There were cancer markers, such as, CA125 and CA15-3, and PSA. This had more of an impact on timely surgical intervention, and if not that, post surgical followup. Despite a long time into the war on cancer, introduced by President Lynden Johnson, the fundamental knowledge needed was not sufficient. In the meantime, there were advances in the treatment of diabetes, with eventual introduction of the insulin pump for type I diabetes. The problem of Type 2 DM increased in prevalence, reaching into the childhood age group, with ascendent obesity. An epidemiological pattern of disease comorbidities was emergent. Our population has aged out, and with it we are seeing an increase in dementias, especially Alzheimer’s disease. But the knowledge of the brain has lagged far behind.
What follows is a series of chapters that address what has currently been advanced with repect to the alignment of our knowledge of the last decade and pharmacetical discovery. Pharmaceuticals were suitable for bacterial infections until the 1990s, when we saw the rise of resistance to penicillins and Vancomycin, and we had issues with gram negative enterobacter, salmonella, and E. coli strains. That has been and is a significant challenge. The elucidation of the gut microbiome in recent years will help to relieve this problem. The problem of the variety and different aggressive types of cancer has been another challenge. The door has been opened to better diagnostic tools with respsct to imaging and targeted biomarkers for localization. I am not dealing with imaging, which is not the subject here.
HLA targeting efficiency correlates with human T-cell response magnitude and with mortality from influenza A infection
the carriage frequencies of the alleles with the lowest targeting efficiencies, A*24,
were associated with pH1N1 mortality (r = 0.37, P = 0.031) and
are common in certain indigenous populations in which
increased pH1N1 morbidity has been reported.
HLA efficiency scores and HLA use are associated with
CD8 T-cell magnitude in humans after influenza infection.
The computational tools used in this study may be useful predictors of
potential morbidity and identify immunologic differences of new variant influenza strains
more accurately than evolutionary sequence comparisons.
Population-based studies of the relative frequency of these alleles
in severe vs. mild influenza cases might advance clinical practices
for severe H1N1 infections among genetically susceptible populations.
A deeper look into cholesterol synthesis
By Swathi Parasuraman
The human body needs cholesterol to maintain membrane fluidity, and
it acts as a precursor molecule for several important biochemical pathways.
Its regulation requires strict control, as it can cause problems if it’s produced in excess. In 1964, Konrad Bloch received a Nobel Prize for his work elucidating the mechanisms of cholesterol synthesis. His work
eventually contributed to the discovery of statins, drugs used today to lower blood cholesterol levels.
The biosynthesis of cholesterol is a complex process with more than 20 steps. One of the first enzymes is
3-hydroxy-3-methylglutaryl-CoA reductase, also known as HMGCR, the main target of statins.
As links between intermediates in cholesterol synthesis and various diseases are being discovered continually, more information about the regulatory role of the post-HMGCR pathway is needed.
In a recent minireview in The Journal of Biological Chemistry, Laura Sharpe and Andrew Brown of the University of New South Wales describe
multiple ways various enzymes other than HMGCR
are implicated in the modulation of cholesterol synthesis.
One such enzyme is squalene monooxygenase, which, like HMGCR, can be destroyed
by the proteasome when cholesterol levels are high.
The minireview also explains how pathway intermediates
can have functions distinct from those of cholesterol.
For example, intermediate 7-dehydrocholesterol usually is converted to cholesterol by the enzyme DHCR7
but is also a vitamin D precursor.
To synthesize the enzymes necessary to make cholesterol,
SREBPs, short for sterol regulatory element binding proteins, have special functions.
Along with transcriptional cofactors, they activate gene expression
in response to low sterol levels and, conversely,
are suppressed when there is enough cholesterol around.
Additionally, SREBPs control production of
nicotinamide adenine dinucleotide phosphate, or NADPH,
which is the reducing agent required to carry out the different steps in the pathway.
Lipid carrier proteins also can facilitate cholesterol synthesis. One example is SPF, or supernatant protein factor,
which transfers substrate from an inactive to an active pool or
from one enzyme site to another.
Furthermore, translocation of several cholesterogenic enzymes
from the endoplasmic reticulum to other cell compartments can occur under various conditions,
thereby regulating levels and sites of intracellular cholesterol accumulation.
Immunology in the gut mucosa:
20 Feb 2013 by Kausik Datta, posted in Immunology, Science (Nature)
The human gut can be the scene for devastating conditions such as inflammatory bowel disease,
which arises through an improperly controlled immune response.
The gut is often the body’s first point of contact with microbes; every mouthful of food is accompanied by a cargo of micro-organisms that go on to encounter the mucosa, the innermost layer of the gut. Most microbes are destroyed by the harsh acidic environment in the stomach, but a hardy few make it through to the intestines.
The intestinal surface is covered with finger-like protrusions called villi,
whose primary function is the absorption of nutrients.
These structures and the underlying tissues
host the body’s largest population of immune cells.
Scattered along the intestinal mucosa are
dome-like structures called Peyer’s Patches.
These are enriched in lymphoid tissue, making them key sites for
coordinating immune responses to pathogens,
whilst promoting tolerance to harmless microbes and food.
The villi contain a network of blood vessels to transport nutrients from food to the rest of the body. Lymphatics
from both the Peyer’s Patches and the villi
drain into the mesenteric lymph nodes.
Within the villi is a network of loose connective tissue called the lamina propria, and
at the base of the villi are the crypts which host the stem cells that replenish the epithelium.
The epithelium together with its overlying mucus forms
a barrier against microbial invasion.
A mix of immune cells including T- and B-lymphocytes, macrophages, and dendritic cells are
embedded within the matrix of the Peyer’s Patches, .
A key function of the Peyer’s Patch is the sampling of antigens present in the gut. The Peyer’s Patch has a thin mucous layer and specialized phagocytic cells, called M-cells, which
transport material across the epithelial barrier via a process called transcytosis.
Dendritic cells extend dendrites between epithelial cells to sample antigens that are then
broken down and used for presenting to lymphocytes.
Sampling antigens in this way typically results in so-called tolerogenic activation, where
the immune system initiates an anti-inflammatory response.
With their cargo of antigens, these Dendritic Cells then
traffic to the T-cell zones of the Peyer’s Patch.
Upon encounter with specific T-cells, the Dendritic Cells
convert them into an immunomodulatory cell called regulatory T-cell or T-reg.
Defects in the function of these cells are associated with
inflammatory bowel disease in both animals and humans.
These T-regs migrate to lamina propria of the villi via the lymphatics. Here, the T-regs
secrete a molecule called Interleukin (IL)-10,
which exerts a suppressive action on immune cells within the lamina propria
and upon the epithelial layer itself.
IL10 is, therefore, critical in maintaining immune quiescence
and preventing unnecessary inflammation.
However, a breakdown in this process of immune homeostasis results in gut pathology and
when this occurs over a prolonged period and in an uncontrolled manner,
it can lead to inflammatory bowel disease.
Chemical, mechanical or pathogen-triggered barrier disruption
coupled with particular genetic susceptibilities may all combine to set off inflammation.
Epithelium coming into contact with bacteria
is activated, leading to bacterial influx.
Alarm molecules released by the epithelium
activates immune cells, and T-regs in the vicinity
scale down their IL10 secretion to enable an immune response to proceed.
Dendritic cells are also activated by this environment, and
start to release key inflammatory molecules,
such as IL6, IL12, and IL23.
Effector T-cells also appear on the scene and
these coordinate an escalation of the immune response
by secreting their own inflammatory molecules,
Tumor Necrosis Factor (TNF)-α, Interferon (IFN)-γ and IL17.
Soon after the effector T-cells are arrived, a voracious phagocyte called a neutrophil is recruited. Neutrophils are critical for the clearance of the bacteria. One weapon in the neutrophil armory is
the ability to undergo self-destruction.
This leaves behind a jumble of DNA saturated with enzymes, called the Neutrophil Extracellular Trap.
Although this can effectively destroy the bacterial invaders
and plug any breaches in the epithelial wall,
it also causes collateral damage to tissues.
Slowly the tide begins to turn and the bacterial invasion is repulsed. Any remaining neutrophils die off,
and are cleared by macrophages.
Epithelial integrity is restored by replacement of damaged cells with new ones from the intestinal crypts. Finally T-regs are recruited once again to calm the immune response.
Targeting the molecules involved in gut pathology is leading to
effective therapies for inflammatory bowel disease.
Notes:
T- and B-lymphocytes, Macrophages, and Dendritic Cells: These are all important immune effector cells. Macrophages and Dendritic cells are primary defence cells that can eat up (‘phagocytosis’) microbes and destroy them; they also can present parts of these microbes to lymphocytes. T-lymphocytes or T-cells help B-lymphocytes or B-cells recognize the antigen and form antibodies against it. Other types of T-cells can themselves kill microbes. All these cells also secrete various chemical substances, called cytokines and chemokines, which act as molecular messengers in recruiting various immune cells, coordinating and fine-tuning the immune response. Some of these cytokines are called Interleukins, shortened to IL.
Anti-inflammatory response: A type of immune response in which molecular messengers are used to scale down heavy-handed immune cell activity and switch off processes that recruit immune cells. This helps the body recognize and selectively tolerate beneficial substances such as commensalic microbes that live in the gut.
Neutrophils: These are highly versatile immune effector cells. Usually, they are one of the first cells recruited to the site of infection or tissue damage via message spread by molecular messengers. Neutrophils can themselves elaborate cytokines and chemokines, and have the ability to directly kill microbes.
Oxazoloisoindolinones with in vitro antitumor activity selectively activate a p53-pathway through potential inhibition of the p53-MDM2 interaction.
The metabolic network of a cell represents the catabolic and anabolic reactions that interconvert small molecules (metabolites) through the activity of enzymes, transporters and non-catalyzed chemical reactions. Our understanding of individual metabolic networks is increasing as we learn more about the enzymes that are active in particular cells under particular conditions and as technologies advance to allow detailed measurements of the cellular metabolome.
Metabolic network databases are important in allowing us to
contextualise data sets emerging from transcriptomic, proteomic and metabolomic experiments.
This concludes the series on metabolomics, a rapidly developing science that is interconnected with a group termed – OMICS: proteomics, transcriptomics, genomics, and metabolomics. This chapter is most representative of the many important studies being done in the field, which ranges most widely because it has opened doors into nutrition and nutritional supplements, plant biochemistry, agricultural crops and breeding, animal breeding, worldwide malnutrition, diabetes, cancer, neurosciences, circulatory, respiratory, and musculosletal disorders, infectious diseases and immune system disorders. Obviously, it is not possible to cover the full range of activity, but metabolomics is most comprehensive in exploring the full range of metabolic changes that occur in health during the full age range from development to the geriatric years. It can be integrated well with gene expression, proteomics studies, and epidemiological investigations.
The subchapters are given here:
7.1 Extracellular evaluation of intracellular flux in yeast cells
This chapter will be followed by an exploration of disease and pharmaceutical directed studies using these methods 8.Impairments in pathological states: endocrine disorders; stress
hypermetabolism; cancer.
Networking metabolites and diseases
P Braun, E Rietman, and M Vidal
Center for Cancer Systems Biology and Department of Cancer Biology, Dana-Farber Cancer Institute, and Department of Genetics, Harvard Medical School, Boston, MA; and Physical Sciences Inc., Andover, MA 01810
PNAS July 22, 2008; 105(29): 9849–9850. http://pnas.org/cgi/doi/10.1073/pnas.0805644105
Biological systems are increasingly viewed and analyzed as
highly complex networks of interlinked macromolecules and metabolites.
Network analysis has been applied to
interactome maps of protein–protein, protein–DNA, and protein–RNA interactions
as well as transcriptional, metabolic, and genetic data.
Such network views of biological systems should facilitate the detection of
nonlinear long-range effects of perturbations, for example, by mutations, and
help identification of unanticipated indirect causal connections.
Diseasome and Drug-Target Network
Recently, Goh et al. (1) constructed a ‘‘diseasome’’ network in which
two diseases are linked to each other if
they share at least one gene, in which mutations are associated with both diseases.
In the resulting network, related disease families cluster tightly together, thus
phenotypically defining functional modules.
Importantly, for the first time this study applied concepts from network biology to human diseases,
thus opening the door for discovering causal relationships between
disregulated networks and resulting ailments.
Subsequently Yilderim et al. (2) linked drugs to protein targets in a drug–target network,
which could then be overlaid with the diseasome network.
One notable finding was the recent trend toward the development of
new compounds directly targeted at disease gene products, whereas previous drugs,
often found by trial and error, appear to target proteins only indirectly related to
the actual disease molecular mechanisms.
An important question that remains in this emerging field of network analysis consists of
investigating the extent to which directly targeting the product of mutated genes is an efficient approach or
whether targeting network properties instead, and
thereby accounting for indirect nonlinear effects of system perturbations by drugs, may prove more fruitful.
However, to answer such questions it is important to have a good understanding of the various influences that can lead to diseases.
Metabolic Connections
One group of diseases that was very poorly connected in the original diseasome network was the family of metabolic diseases.
In this issue of PNAS, Lee et al. (3) hypothesize that metabolic diseases may instead be connected
via metabolites and common reactions.
To investigate this hypothesis Lee et al. first constructed a metabolic network from data available in
two manually curated databases detailing well known
a metabolic disease network (MDN) was constructed by connecting
two diseases if their associated genes are linked in the metabolic network
by a common metabolite or metabolites used in a common reaction.
Metabolites are not only linked by common reactions, but
on a larger scale by coupled fluxes within a metabolic network,
which may also influence disease phenotypes.
An increase in the concentration of one metabolite may increase several fluxes
across reaction pathways that use this compound, which
may lead to diverse phenotypes and distinct diseases.
The fluxes within the metabolic network are calculated by using
the Flux Coupling Finder method described by Nikolaev et al. (4) and Burgard et al. (5),
which is based on the assumption that pools of metabolites are conserved.
To functionally validate the network, coexpression correlations are measured for genes
linked by adjacent reactions and those linked by fluxes.
Interestingly, the average coexpression correlation for flux-coupled genes (0.31)
is higher than that for genes simply catalyzing adjacent reactions (0.24)
(compared with 0.10 for all gene pairs in the network).
If the links between diseases identified in the MDN are functionally and causally relevant
it should be expected that linked diseases occur more frequently in the same individual.
To test this hypothesis, Lee et al. (3) measured the co-occurrence of diseases in patients by using detailed Medicare information
of 13 million patients and 32 million hospital visits within a 3-year period.
A comorbidity index was computed to measure the degree to which one disease
will increase the likelihood of a second disease in the same patient.
The average comorbidity for all genes is 0.0008 (Pearson correlation coefficient),
which increases 3-fold to 0.0027 when disease pairs that are metabolically linked are analyzed,
which is highly statistically significant (P 108).
When diseases are analyzed that are directionally coupled by a flux (see ref. 3 for details),
the correlation increases to 0.0062.
Thus, whereas 17% of all diseases in the network show significant comorbidity, this fraction
nearly doubles to 31% for metabolically linked diseases.
Further analysis reveals that comorbidity effects can be detected up to three links (metabolites, reactions)
apart from each other with statistical significance, but not farther away.
In the MDN, several highly connected hubs, e.g., hypertension and hemolytic anemia, are
linked to many different co-occurring diseases not unexpected for such complex diseases
that can result from many different genetic alterations or variants.
Importantly, though, most of the connections to the different linked diseases
are mediated by diverse connections in the metabolic network.
Thus, in the future such insights may be helpful for finer classification of the complex hub disease.
Furthermore, depending on the onset of the complex (hub) disease in relation to the associated diseases,
such relationships may potentially be used to systematically
stratify patients and develop targeted treatments acting on
the underlying metabolic links.
Returning to the starting point of their study, Lee et al. (3) next investigated
whether metabolic diseases are better linked through the metabolic network
than they are in the previously described gene–disease network.
When purely metabolic diseases are considered, the comorbidity is, in fact,
best predicted by metabolic links.
Interestingly, when all diseases linked to metabolic enzymes are considered,
which involves many diseases that are merely related to metabolic diseases through multifunctional enzymes,
the gene and metabolic networks are nearly equally predictive of comorbidity,
indicating that as a general approach information from
many different biological dimensions should be integrated to identify the most relevant connections.
Together, all these findings support the initial hypothesis that metabolic diseases are linked by metabolic networks.
Practically, alteration of one metabolite or one reaction can have numerous repercussions in the network,
each of which can manifest as different diseases that frequently occur together in affected patients.
Radoslav Bozov
Glycine, as the only amino acid having no isomer driven central carbon allowing for hing occupancy of ‘free’
motifs, where quark (proton) ‘fluxes’ play at, is a one – step away observable (1) from synthesis of pyrimidines
to glyoxylate mitochondrial ‘shunt’ entangling at least two differential compartments longly objected by
Japanese metabolomics study groups.
One carbon systems emerge out of a glycoprotein ‘complex’, pyrimidine synthase pathway, that possesses
significant similarity to BRCA2 and most other transcription factors suggesting that protein allocation is
coorchestrated by modifications and spatially transforming construes as an outcome of energy processing.
Directly deduced by TCS, life cannot exist without mutations, as mutations and chromatin states appear to
be a sort of energy hold and release ‘gates’.
Phosphorylations and small molecules as such as cGMP, cAMP play a role of decompression machinery
for amplifying bio signal processing C-S, C-N, C-O, interference open systems. By decompression
of one relative discrete space, another one becomes compressed, which gets uncertainty of absolute energy
processing within space scalar wise into vector objected space represented by chromatin remodeling processes,
possibly seen as network identities information.
Unifying network and quantum theory possess implications to relativity concepts and energy relevant computational methodology.
translational medicine
Shifts in steady-state profiles caused by kinetic perturbations
mapping metabolomic data using three different approaches
network genetics metabotypes – integrated metabolome and interactome mapping (iMIM)
We have completed a series of discussions on proteomics, a scientific endeavor that is essentially 15 years old. It is quite remarkable what has been accomplished in that time. The interest is abetted by the understanding of the limitations of the genomic venture that has preceded it. The thorough, yet incomplete knowledge of the genome, has led to the clarification of its limits. It is the coding for all that lives, but all that lives has evolved to meet a demanding and changing environment with respect to
availability of nutrients
salinity
temperature
radiation exposure
toxicities in the air, water, and food
stresses – both internal and external
We have seen how both transcription and translation of the code results in a protein, lipoprotein, or other complex than the initial transcript that was modeled from tRNA. What you see in the DNA is not what you get in the functioning cell, organ, or organism. There are comparabilities as well as significant differences between plants, prokaryotes, and eukaryotes. There is extensive variation. The variation goes beyond genomic expression, and includes the functioning cell, organ type, and species.
Here, I return to the introductory discussion. Proteomics is a goal directed, sophisticated science that uses a combination of methods to find the answers to biological questions. Graves PR and Haystead TAJ. Molecular Biologist’s Guide to Proteomics. Microbiol Mol Biol Rev. Mar 2002; 66(1): 39–63. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC120780/
Peptide mass tag searching
Peptide mass tag searching. Shown is a schematic of how information from an unknown peptide (top) is matched to a peptide sequence in a database (bottom) for protein identification. The partial amino acid sequence or “tag” obtained by MS/MS is combined with the peptide mass (parent mass), the mass of the peptide at the start of the sequence (mass tag 1), and the mass of the peptide at the end of the sequence (mass tag 2). The specificity of the protease used (trypsin is shown) can also be included in the search.
ICAT method for measuring differential protein expression
The ICAT method for measuring differential protein expression. (A) Structure of the ICAT reagent. ICAT consists of a biotin affinity group, a linker region that can incorporate heavy (deuterium) or light (hydrogen) atoms, and a thiol-reactive end group for linkage to cysteines. (B) ICAT strategy. Proteins are harvested from two different cell states and labeled on cysteine residues with either the light or heavy form of the ICAT reagent. Following labeling, the two protein samples are mixed and digested with a protease such as trypsin. Peptides labeled with the ICAT reagent can be purified by virtue of the biotin tag by using avidin chromatography. Following purification, ICAT-labeled peptides can be analyzed by MS to quantitate the peak ratios and proteins can be identified by sequencing the peptides with MS/MS.
Strategies for determination of phosphorylation sites in proteins
Strategies for determination of phosphorylation sites in proteins. Proteins phosphorylated in vitro or in vivo can be isolated by protein electrophoresis and analyzed by MS. (A) Identification of phosphopeptides by peptide mass fingerprinting. In this method, phosphopeptides are identified by comparing the mass spectrum of an untreated sample to that of a sample treated with phosphatase. In the phosphatase-treated sample, potential phosphopeptides are identified by a decrease in mass due to loss of a phosphate group (80 Da). (B) Phosphorylation sites can be identified by peptide sequencing using MS/MS. (C) Edman degradation can be used to monitor the release of inorganic 32P to provide information about phosphorylation sites in peptides.
protein mining strategy
Proteome-mining strategy. Proteins are isolated on affinity column arrays from a cell line, organ, or animal source and purified to remove nonspecific adherents. Then, compound libraries are passed over the array and the proteins eluted are analyzed by protein electrophoresis. Protein information obtained by MS or Edman degradation is then used to search DNA and protein databases. If a relevant target is identified, a sublibrary of compounds can be evaluated to refine the lead. From this method a protein target and a drug lead can be simultaneously identified.
Although the technology for the analysis of proteins is rapidly progressing, it is still not feasible to study proteins on a scale equivalent to that of the nucleic acids. Most of proteomics relies on methods, such as protein purification or PAGE, that are not high-throughput methods. Even performing MS can require considerable time in either data acquisition or analysis. Although hundreds of proteins can be analyzed quickly and in an automated fashion by a MALDI-TOF mass spectrometer, the quality of data is sacrificed and many proteins cannot be identified. Much higher quality data can be obtained for protein identification by MS/MS, but this method requires considerable time in data interpretation. In our opinion, new computer algorithms are needed to allow more accurate interpretation of mass spectra without operator intervention. In addition, to access unannotated DNA databases across species, these algorithms should be error tolerant to allow for sequencing errors, polymorphisms, and conservative substitutions. New technologies will have to emerge before protein analysis on a large-scale (such as mapping the human proteome) becomes a reality.
Another major challenge for proteomics is the study of low-abundance proteins. In some eukaryotic cells, the amounts of the most abundant proteins can be 106-fold greater than those of the low-abundance proteins. Many important classes of proteins (that may be important drug targets) such as transcription factors, protein kinases, and regulatory proteins are low-copy proteins. These low-copy proteins will not be observed in the analysis of crude cell lysates without some purification. Therefore, new methods must be devised for subproteome isolation.
Tissue Proteomics for the Next Decade? Towards a Molecular Dimension in Histology
R Longuespe´e, M Fle´ron, C Pottier, F Quesada-Calvo, Marie-Alice Meuwis, et al.
OMICS A Journal of Integrative Biology 2014; 18: 9. http://dx.doi.org:/10.1089/omi.2014.0033
The concept of tissues appeared more than 200 years ago, since textures and attendant differences were described within the whole organism components. Instrumental developments in optics and biochemistry subsequently paved the way to transition from classical to molecular histology in order to decipher the molecular contexts associated with physiological or pathological development or function of a tissue. In 1941, Coons and colleagues performed the first systematic integrated examination of classical histology and biochemistry when his team localized pneumonia antigens in infected tissue sections. Most recently, in the early 21st century, mass spectrometry (MS) has progressively become one of the most valuable tools to analyze biomolecular compounds. Currently, sampling methods, biochemical procedures, and MS instrumentations
allow scientists to perform ‘‘in depth’’ analysis of the protein content of any type of tissue of interest. This article reviews the salient issues in proteomics analysis of tissues. We first outline technical and analytical considerations for sampling and biochemical processing of tissues and subsequently the instrumental possibilities for proteomics analysis such as shotgun proteomics in an anatomical context. Specific attention concerns formalin fixed and paraffin embedded (FFPE) tissues that are potential ‘‘gold mines’’ for histopathological investigations. In all, the matrix assisted laser desorption/ionization (MALDI) MS imaging, which allows for differential mapping of hundreds of compounds on a tissue section, is currently the most striking evidence of linkage and transition between ‘‘classical’’ and ‘‘molecular’’ histology. Tissue proteomics represents a veritable field of research and investment activity for modern biomarker discovery and development for the next decade.
Progressively, tissue analyses evolved towards the description of the whole molecular content of a given sample. Currently, mass spectrometry (MS) is the most versatile
analytical tool for protein identification and has proven its great potential for biological and clinical applications. ‘‘Omics’’ fields, and especially proteomics, are of particular
interest since they allow the analysis of a biomolecular picture associated with a given physiological or pathological state. Biochemical techniques were then adapted for an optimal extraction of several biocompounds classes from tissues of different natures.
Laser capture microdissection (LCM) is used to select and isolate tissue areas of interest for further analysis. The developments of MS instrumentations have then definitively transformed the scientific scene, pushing back more and more detection and identification limits. Since a few decades, new approaches of analyses appeared, involving the use of tissue sections dropped on glass slides as starting material. Two types of analyses can then be applied on tissue sections: shotgun proteomics and the very promising MS imaging (MSI) using Matrix Assisted Laser Desorption/Ionization (MALDI) sources. Also known as ‘‘molecular histology,’’ MSI is the most striking hyphen between histology and molecular analysis. In practice, this method allows visualization of the spatial distribution of proteins, peptides, drugs, or others analytes directly on tissue sections. This technique paved new ways of research, especially in the field of histopathology, since this approach appeared to be complementary to conventional histology.
Tissue processing workflows for molecular analyses
Tissue processing workflows for molecular analyses. Tissues can either be processed in solution or directly on tissue sections. In solution, processing involves protein
extraction from tissue pieces in order to perform 2D gel separation and identification of proteins, shotgun proteomics, or MALDI analyses. Extracts can also be obtained from
tissues area selection and protein extraction after laser micro dissection or on-tissue processing. Imaging techniques are dedicated to the morphological characterization or molecular mapping of tissue sections. Histology can either be conducted by hematoxylin/eosin staining or by molecular mapping using antibodies with IHC. Finally, mass spectrometry imaging allows the cartography of numerous compounds in a single analysis. This approach is a modern form of ‘‘molecular histology’’ as it grafts, with the use of mathematical calculations, a molecular dimension to classical histology. (AR, antigen retrieval; FFPE, formalin fixed and paraffin embedded; fr/fr, fresh frozen; IHC, immunohistochemistry; LCM, laser capture microdissection; MALDI, matrix assisted laser desorption/ionization; MSI, mass spectrometry imaging; PTM, post translational modification.)
Analysis of tissue proteomes has greatly evolved with separation methods and mass spectrometry instrumentation. The choice of the workflow strongly depends on whether a bottom-up or a top-down analysis has to be performed downstream. In-gel or off-gel proteomics principally differentiates proteomic workflows. The almost simultaneous discoveries of the MS ionization sources (Nobel Prize awarded) MALDI (Hillenkamp and Karas, 1990; Tanaka et al., 1988) and electrospray ionization (ESI) (Fenn et al., 1989) have paved the way for analysis of intact proteins and peptides. Separation methods such as two-dimension electrophoresis (2DE) (Fey and Larsen, 2001) and nanoscale reverse phase liquid chromatography (nanoRP-LC) (Deterding et al., 1991) lead to efficient preparation of proteins for respectively topdown and bottom-up strategies. A huge panel of developments was then achieved mostly for LC-MS based proteomics in order to improve ion fragmentation approaches and peptide
identification throughput relying on database interrogation. Moreover, approaches were developed to analyze post translational modifications (PTM) such as phosphorylations (Ficarro et al., 2002; Oda et al., 2001; Zhou et al., 2001) or glycosylations (Zhang et al., 2003), proposing as well different quantification procedures. Regarding instrumentation, the most cutting edge improvements are the gain of mass accuracy for an optimal detection of the eluted peptides during LC-MS runs (Mann and Kelleher, 2008; Michalski et al., 2011) and the increase in scanning speed, for example with the use of Orbitrap analyzers (Hardman and Makarov, 2003; Makarov et al., 2006; Makarov et al., 2009; Olsen et al., 2009). Ion transfer efficiency was also drastically improved with the conception of ion funnels that homogenize the ion transmission
capacities through m/z ranges (Kelly et al., 2010; Kim et al., 2000; Page et al., 2006; Shaffer et al., 1998) or by performing electrospray ionization within low vacuum (Marginean et al., 2010; Page et al., 2008; Tang et al., 2011). Beside collision induced dissociation (CID) that is proposed for many applications (Li et al., 2009; Wells and McLuckey, 2005), new fragmentation methods were investigated, such as higher-energy collisional dissociation (HCD) especially for phosphoproteomic
applications (Nagaraj et al., 2010), and electron transfer dissociation (ETD) and electron capture dissociation (ECD) that are suited for phospho- and glycoproteomics (An
et al., 2009; Boersema et al., 2009; Wiesner et al., 2008). Methods for data-independent MS2 analysis based on peptide fragmentation in given m/z windows without precursor selection neither information knowledge, also improves identification throughput (Panchaud et al., 2009; Venable et al., 2004), especially with the use of MS instruments with high resolution and high mass accuracy specifications (Panchaud et al., 2011). Gas fractionation methods such as ion mobility (IM) can also be used as a supplementary separation dimension which enable more efficient peptide identifications (Masselon et al., 2000; Shvartsburg et al., 2013; Shvartsburg et al., 2011).
Microdissection relies on a laser ablation principle. The tissue section is dropped on a plastic membrane covering a glass slide. The preparation is then placed into a microscope
equipped with a laser. A highly focused beam will then be guided by the user at the external limit of the area of interest. This area composed by the plastic membrane, and the tissue section will then be ejected from the glass slide and collected into a tube cap for further processing. This mode of microdissection is the most widely used due to its ease of handling and the large panels of devices proposed by constructors. Indeed, Leica microsystem proposed the Leica LMD system (Kolble, 2000), Molecular Machine and Industries, the MMI laser microdissection system Microcut, which was used in combination with IHC (Buckanovich et al., 2006), Applied Biosystems developed the Arcturus
microdissection System, and Carl Zeiss patented P.A.L.M. MicroBeam technology (Braakman et al., 2011; Espina et al., 2006a; Espina et al., 2006b; Liu et al., 2012; Micke
et al., 2005). LCM represents a very adequate link between classical histology and sampling methods for molecular analyses as it is a simple customized microscope. Indeed,
optical lenses of different magnification can be used and the method is compatible with classical IHC (Buckanovich et al., 2006). Only the laser and the tube holder need to be
added to the instrumentation.
After microdissection, the tissue pieces can be processed for analyses using different available MS devices and strategies. The simplest one consists in the direct analysis of the
protein profiles by MALDI-TOF-MS (MALDI-time of flight-MS). The microdissected tissues are dropped on a MALDI target and directly covered by the MALDI matrix (Palmer-Toy et al., 2000; Xu et al., 2002). This approach was already used in order to classify breast cancer tumor types (Sanders et al., 2008), identify intestinal neoplasia protein biomarkers (Xu et al., 2009), and to determine differential profiles in glomerulosclerosis (Xu et al., 2005).
Currently the most common proteomic approach for LCM tissue analysis is LC-MS/MS. Label free LC-MS approaches have been used to study several cancers like head and neck squamous cell carcinomas (Baker et al., 2005), esophageal cancer (Hatakeyama et al., 2006), dysplasic cervical cells (Gu et al., 2007), breast carcinoma tumors (Hill et al., 2011; Johann et al., 2009), tamoxifen-resistant breast cancer cells (Umar et al., 2009), ER + / – breast cancer cells (Rezaul et al., 2010), Barretts esophagus (Stingl et al., 2011), and ovarian endometrioid cancer (Alkhas et al., 2011). Different isotope labeling methods have been used in order to compare proteins expression. ICAT was first used to investigate proteomes of hepatocellular carcinoma (Li et al., 2004; 2008). The O16/O18 isotopic labeling was then used for proteomic analysis of ductal carcinoma of the breast (Zang et al., 2004).
Currently, the lowest amount of collected cells for a relevant single analysis using fr/fr breast cancer tissues was 3000–4000 (Braakman et al., 2012; Liu et al., 2012; Umar et al., 2007). With a Q-Exactive (Thermo, Waltham) mass spectrometer coupled to LC, Braakman was able to identify up to 1800 proteins from 4000 cells. Processing
of FFPE microdissected tissues of limited sizes still remains an issue which is being addressed by our team.
Among direct tissue analyses modes, two categories of investigations can be done. MALDI profiling consists in the study of molecular localization of compounds and can be
combined with parallel shotgun proteomic methods. Imaging methods give less detailed molecular information, but is more focused on the accurate mapping of the detected compounds through tissue area. In 2007, a concept of direct tissue proteomics (DTP) was proposed for high-throughput examination of tissue microarray samples. However, contrary to the classical workflow, tissue section chemical treatment involved a first step of scrapping each FFPE tissue spot with a razor blade from the glass slide. The tissues were then transferred into a tube and processed with RIPA buffer and finally submitted to boiling as an AR step (Hwang et al., 2007). Afterward, several teams proved that it was possible to perform the AR directly on tissue sections. These applications were mainly dedicated to MALDI imaging analyses (Bonnel et al., 2011; Casadonte and Caprioli, 2011; Gustafsson et al., 2010). However, more recently, Longuespe´e used citric acid antigen retrieval (CAAR) before shotgun proteomics associated to global profiling proteomics (Longuespee et al., 2013).
MALDI imaging workflow
MALDI imaging workflow. For MALDI imaging experiments, tissue sections are dropped on conductive glass slides. Sample preparations are then adapted depending on the nature of the tissue sample (FFPE or fr/fr). Then, matrix is uniformly deposited on the tissue section using dedicated devices. A laser beam subsequently irradiates the preparation following a given step length and a MALDI spectrum is acquired for each position. Using adapted software, the different detected ions are then mapped through the tissue section, in function of their differential intensities. The ‘‘molecular maps’’ are called images. (FFPE, formalin fixed and paraffin embedded; fr/fr, fresh frozen; MALDI, matrix assisted laser desorption ionization.)
Proteomics instrumentations, specific biochemical preparations, and sampling methods such as LCM altogether allow for the deep exploration and comparison of different proteomes between regions of interest in tissues with up to 104 detected proteins. MALDI MS imaging that allows for differential mapping of hundreds of compounds on a tissue section is currently the most striking illustration of association between ‘‘classical’’ and ‘‘molecular’’ histology.
Novel serum protein biomarker panel revealed by mass spectrometry and its prognostic value in breast cancer
Introduction: Serum profiling using proteomic techniques has great potential to detect biomarkers that might improve diagnosis and predict outcome for breast cancer patients (BC). This study used surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry (MS) to identify differentially expressed proteins in sera from BC and healthy volunteers (HV), with the goal of developing a new prognostic biomarker panel.
Methods: Training set serum samples from 99 BC and 51 HV subjects were applied to four adsorptive chip surfaces (anion-exchange, cation-exchange, hydrophobic, and metal affinity) and analyzed by time-of-flight MS. For validation, 100 independent BC serum samples and 70 HV samples were analyzed similarly. Cluster analysis of protein spectra was performed to identify protein patterns related to BC and HV groups. Univariate and multivariate statistical analyses were used to develop a protein panel to distinguish breast cancer sera from healthy sera, and its prognostic potential was evaluated.
Results: From 51 protein peaks that were significantly up- or downregulated in BC patients by univariate analysis, binary logistic regression yielded five protein peaks that together classified BC and HV with a receiver operating characteristic (ROC) area-under-the-curve value of 0.961. Validation on an independent patient cohort confirmed
the five-protein parameter (ROC value 0.939). The five-protein parameter showed positive association with large tumor size (P = 0.018) and lymph node involvement (P = 0.016). By matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS, immunoprecipitation and western blotting the proteins were identified as a fragment
of apolipoprotein H (ApoH), ApoCI, complement C3a, transthyretin, and ApoAI. Kaplan-Meier analysis on 181 subjects after median follow-up of >5 years demonstrated that the panel significantly predicted disease-free survival (P = 0.005), its efficacy apparently greater in women with estrogen receptor (ER)-negative tumors (n = 50, P = 0.003) compared to ER-positive (n = 131, P = 0.161), although the influence of ER status needs to be confirmed after longer follow-up.
Conclusions: Protein mass profiling by MS has revealed five serum proteins which, in combination, can distinguish between serum from women with breast cancer and healthy control subjects with high sensitivity and specificity. The five-protein panel significantly predicts recurrence-free survival in women with ER-negative tumors and may have value in the management of these patients.
Cellular prion protein is required for neuritogenesis: fine-tuning of multiple signaling pathways involved in focal adhesions and actin cytoskeleton dynamics
Aurélie Alleaume-Butaux, et al. Cell Health and Cytoskeleton 2013:5 1–12
Neuritogenesis is a dynamic phenomenon associated with neuronal differentiation that allows a rather spherical neuronal stem cell to develop dendrites and axon, a prerequisite for the integration and transmission of signals. The acquisition of neuronal polarity occurs in three steps:
(1) neurite sprouting, which consists of the formation of buds emerging from the postmitotic neuronal soma;
(2) neurite outgrowth, which represents the conversion of buds into neurites, their elongation and evolution into axon or dendrites; and
(3) the stability and plasticity of neuronal polarity.
In neuronal stem cells, remodeling and activation of focal adhesions (FAs)
associated with deep modifications of the actin cytoskeleton is
a prerequisite for neurite sprouting and subsequent neurite outgrowth.
A multiple set of growth factors and interactors located in
the extracellular matrix and the plasma membrane orchestrate neuritogenesis
by acting on intracellular signaling effectors, notably small G proteins such as RhoA, Rac, and Cdc42,
which are involved in actin turnover and the dynamics of FAs.
The cellular prion protein (PrPC), a glycosylphosphatidylinositol (GPI)-anchored membrane protein
mainly known for its role in a group of fatal neurodegenerative diseases,
has emerged as a central player in neuritogenesis.
Here, we review the contribution of PrPC to neuronal polarization and
detail the current knowledge on the signaling pathways fine-tuned
by PrPC to promote neurite sprouting, outgrowth, and maintenance.
We emphasize that PrPC-dependent neurite sprouting is a process in which
PrPC governs the dynamics of FAs and the actin cytoskeleton via β1 integrin signaling.
The presence of PrPC is necessary to render neuronal stem cells
competent to respond to neuronal inducers and to develop neurites.
In differentiating neurons, PrPC exerts a facilitator role towards neurite elongation.
This function relies on the interaction of PrPC with a set of diverse partners such as
elements of the extracellular matrix,
plasma membrane receptors,
adhesion molecules, and
soluble factors that control actin cytoskeleton turnover
through Rho-GTPase signaling.
Once neurons have reached their terminal stage of differentiation and
acquired their polarized morphology,
PrPC also takes part in the maintenance of neurites.
By acting on tissue nonspecific alkaline phosphatase, or matrix metalloproteinase type 9,
PrPC stabilizes interactions between neurites and the extracellular matrix.
Fusion-pore expansion during syncytium formation is restricted by an actin network
Background: Role of uncoupling proteins (UCP) in the brain is unclear.
Results: UCP, present in astrocytes, mediate the intra-mitochondrial acidification leading to a decrease in mitochondrial ATP production.
Conclusion: Astrocyte pH regulation promotes ATP synthesis by glycolysis whose final product, lactate, increases neuronal survival.
Significance: We describe a new role for a brain uncoupling protein.
Brain activity is energetically costly and requires a steady and
highly regulated flow of energy equivalents between neural cells.
It is believed that a substantial share of cerebral glucose, the major source of energy of the brain,
will preferentially be metabolized in astrocytes via aerobic glycolysis.
The aim of this study was to evaluate whether uncoupling proteins (UCPs),
located in the inner membrane of mitochondria,
play a role in setting up the metabolic response pattern of astrocytes.
UCPs are believed to mediate the transmembrane transfer of protons
resulting in the uncoupling of oxidative phosphorylation from ATP production.
UCPs are therefore potentially important regulators of energy fluxes. The main UCP isoforms
expressed in the brain are UCP2, UCP4, and UCP5.
We examined in particular the role of UCP4 in neuron-astrocyte metabolic coupling
and measured a range of functional metabolic parameters
including mitochondrial electrical potential and pH,
reactive oxygen species production,
NAD/NADH ratio,
ATP/ADP ratio,
CO2 and lactate production, and
oxygen consumption rate (OCR).
In brief, we found that UCP4 regulates the intra-mitochondrial pH of astrocytes
which acidifies as a consequence of glutamate uptake,
with the main consequence of reducing efficiency of mitochondrial ATP production.
the diminished ATP production is effectively compensated by enhancement of glycolysis.
this non-oxidative production of energy is not associated with deleterious H2O2 production.
We show that astrocytes expressing more UCP4 produced more lactate,
used as energy source by neurons, and had the ability to enhance neuronal survival.
Jose Eduardo des Salles Roselino
The problem with genomics was it was set as explanation for everything. In fact, when something is genetic in nature the genomic reasoning works fine. However, this means whenever an inborn error is found and only in this case the genomic knowledge afterwards may indicate what is wrong and not the completely way to put biology upside down by reading everything in the DNA genetic as well as non-genetic problems.
The following chapter of the metabolism/transcriptomics/proteomics/metabolomics series deals with the subcellular structure of the cell. This would have to include the cytoskeleton, which has a key role in substrate and ion efflux and influx, and in cell movement mediated by tubulins. It has been extensively covered already. Much of the contributions here are concerned with the mitochondrion, which is also covered in metabolic pathways. The ribosome is the organelle that we have discussed with respect to the transcription and translation of the genetic code through mRNA and tRNA, and the therapeutic implications of SiRNA as well as the chromatin regulation of lncRNA.
We have also encountered the mitochondrion and the lysosome in the discussion of apoptosis and autophagy, maintaining the balance between cell regeneration and cell death.
Found within the cytoplasm of both plant and animal cells, the Golgi is composed of stacks of membrane-bound structures known as cisternae (singular: cisterna). An individual stack is sometimes called a dictyosome (from Greek dictyon: net + soma: body), especially in plant cells. A mammalian cell typically contains 40 to 100 stacks. Between four and eight cisternae are usually present in a stack; however, in some protists as many as sixty have been observed. Each cisterna comprises a flat, membrane-enclosed disc that includes special Golgi enzymes which modify or help to modify cargo proteins that travel through it.
The cisternae stack has four functional regions: the cis-Golgi network, medial-Golgi, endo-Golgi, and trans-Golgi network. Vesicles from the endoplasmic reticulum (via the vesicular-tubular clusters) fuse with the network and subsequently progress through the stack to the trans-Golgi network, where they are packaged and sent to their destination.
The Golgi apparatus is integral in modifying, sorting, and packaging these macromolecules for cell secretion (exocytosis) or use within the cell. It primarily modifies proteins delivered from the rough endoplasmic reticulum, but is also involved in the transport of lipids around the cell, and the creation of lysosomes. Enzymes within the cisternae are able to modify the proteins by addition of carbohydrates (glycosylation) and phosphates (phosphorylation). In order to do so, the Golgi imports substances such as nucleotide sugars from the cytosol. These modifications may also form a signal sequence which determines the final destination of the protein. For example, the Golgi apparatus adds a mannose-6-phosphate label to proteins destined for lysosomes.
The Golgi plays an important role in the synthesis of proteoglycans, which are molecules present in the extracellular matrix of animals. It is also a major site of carbohydrate synthesis. This includes the production of glycosaminoglycans (GAGs), long unbranched polysaccharides which the Golgi then attaches to a protein synthesised in the endoplasmic reticulum to form proteoglycans. Enzymes in the Golgi polymerize several of these GAGs via a xylose link onto the core protein. Another task of the Golgi involves the sulfation of certain molecules passing through its lumen via sulfotranferases that gain their sulfur molecule from a donor called PAPS. This process occurs on the GAGs of proteoglycans as well as on the core protein. Sulfation is generally performed in the trans-Golgi network. The level of sulfation is very important to the proteoglycans’ signalling abilities, as well as giving the proteoglycan its overall negative charge.
The phosphorylation of molecules requires that ATP is imported into the lumen of the Golgi and utilised by resident kinases such as casein kinase 1 and casein kinase 2. One molecule that is phosphorylated in the Golgi is apolipoprotein, which forms a molecule known as VLDL that is found in plasma. It is thought that the phosphorylation of these molecules labels them for secretion into the blood.
The Golgi has a putative role in apoptosis, with several Bcl-2 family members localised there, as well as to the mitochondria. A newly characterized protein, GAAP (Golgi anti-apoptotic protein), almost exclusively resides in the Golgi and protects cells from apoptosis by an as-yet undefined mechanism.
The vesicles that leave the rough endoplasmic reticulum are transported to the cis face of the Golgi apparatus, where they fuse with the Golgi membrane and empty their contents into the lumen. Once inside the lumen, the molecules are modified, then sorted for transport to their next destinations. The Golgi apparatus tends to be larger and more numerous in cells that synthesize and secrete large amounts of substances; for example, the plasma B cells and the antibody-secreting cells of the immune system have prominent Golgi complexes.
Those proteins destined for areas of the cell other than either the endoplasmic reticulum or Golgi apparatus are moved towards the trans face, to a complex network of membranes and associated vesicles known as the trans-Golgi network (TGN). This area of the Golgi is the point at which proteins are sorted and shipped to their intended destinations by their placement into one of at least three different types of vesicles, depending upon the molecular marker they carry.
Nucleus_ER_golgi
Diagram of secretory process from endoplasmic reticulum (orange) to Golgi apparatus (pink). 1. Nuclear membrane; 2. Nuclear pore; 3. Rough endoplasmic reticulum (RER); 4. Smooth endoplasmic reticulum (SER); 5. Ribosome attached to RER; 6. Macromolecules; 7. Transport vesicles; 8. Golgi apparatus; 9. Cis face of Golgi apparatus; 10. Trans face of Golgi apparatus; 11. Cisternae of the Golgi Apparatus
Exocytotic vesicles
After packaging, the vesicles bud off and immediately move towards the plasma membrane, where they fuse and release the contents into the extracellular space in a process known as constitutive secretion. (Antibody release by activated plasma B cells)
Secretory vesicles
After packaging, the vesicles bud off and are stored in the cell until a signal is given for their release. When the appropriate signal is received they move towards the membrane and fuse to release their contents. This process is known as regulated secretion. (Neurotransmitter release from neurons)
Lysosomal vesicles
Vesicle contains proteins and ribosomes destined for the lysosome, an organelle of degradation containing many acid hydrolases, or to lysosome-like storage organelles. These proteins include both digestive enzymes and membrane proteins. The vesicle first fuses with the late endosome, and the contents are then transferred to the lysosome via unknown mechanisms.
Enzymes of the lysosomes are synthesised in the rough endoplasmic reticulum. The enzymes are released from Golgi apparatus in small vesicles which ultimately fuse with acidic vesicles called endosomes, thus becoming full lysosomes. In the process the enzymes are specifically tagged with mannose 6-phosphate to differentiate them from other enzymes. Lysosomes are interlinked with three intracellular processes namely phagocytosis, endocytosis and autophagy. Extracellular materials such as microorganisms taken up by phagocytosis, macromolecules by endocytosis, and unwanted cell organelles are fused with lysosomes in which they are broken down to their basic molecules. Thus lysosomes are the recycling units of a cell.
The endoplasmic reticulum (ER) is a type of organelle in the cells of eukaryotic organisms that forms an interconnected network of flattened, membrane-enclosed sacs or tubes known as cisternae. The membranes of the ER are continuous with the outer membrane of the nuclear envelope. Endoplasmic reticulum occurs in most types of eukaryotic cells, including the most primitive Giardia, but is absent from red blood cells and spermatozoa. There are two types of endoplasmic reticulum, rough endoplasmic reticulum (RER) and smooth endoplasmic reticulum (SER). The outer (cytosolic) face of the rough endoplasmic reticulum is studded with ribosomes that are the sites of protein synthesis. The rough endoplasmic reticulum is especially prominent in cells such as hepatocytes where active smooth endoplasmic reticulum lacks ribosomes and functions in lipid metabolism, carbohydrate metabolism, and detoxificationand is especially abundant in mammalian liver and gonad cells. The lacey membranes of the endoplasmic reticulum were first seen in 1945 by Keith R. Porter, Albert Claude, Brody Meskers and Ernest F. Fullam, using electron microscopy.
The Effects of Actomyosin Tension on Nuclear Pore Transport
Rachel Sammons
Undergraduate Honors Thesis
Spring 2011
The cytoskeleton maintains cellular structure and tension through a force balance with the nucleus, where actomyosin is anchored to the nuclear envelope by nesprin integral proteins. It is hypothesized that the presence or absence of this tension alters the transport of molecules through the nuclear pore complex. We tested the effects of cytoskeletal tension on nuclear transport in human umbilical vein endothelial cells (HUVECs) by performing fluorescence recovery after photo-bleaching (FRAP) experiments on the nuclei to monitor the passive transport of the molecules through nuclear pores.
Using myosin inhibitors, as well as siRNA transfections to reduce the expression of nesprin-1, we altered the nucleo-cytoskeletal force balance and monitored the effect of each on the nuclear pore. FRAP data was fit to a diffusion model by assuming pseudo-steady state inside the nuclear pore, perfect mixing within both the cytoplasm and the nucleus, and no intracellular binding of the fluorescent probes. From these results and a model from the current literature relating diffusion rate constants to nuclear pore radii, we were able to determine that changing cytoskeletal tension alters nuclear pore size and passive transport.
nuclear pores in nuclear envelope
image of nuclear pores on the external surface of the nuclear envelope
nuclear envelope and FG filaments
nuclear envelope and FG filaments
Figure 1: The structure and location of the nuclear pore, shown by (a) AFM image of nuclear pores on the external surface of the nuclear envelope[5] and (b) computer model cross-section. The nuclear envelope is shown in cyan, and FG filaments in blue can be seen throughout the channel. The nuclear basket extends into the nucleoplasm.
Fusion-pore expansion during syncytium formation is restricted by an actin network
A Chen, E Leikina, K Melikov, B Podbilewicz, MM. Kozlov and LV. Chernomordik,*
J Cell Sci 1 Nov 2008;121: 3619-3628. http://dx.doi.org:/10.1242/jcs.032169
Effects of actin-modifying agents indicate that the actin cortex slows down pore expansion. We propose that the growth of the strongly bent fusion-pore rim is restricted by a dynamic resistance of the actin network and driven by membrane-bending proteins that are involved in the generation of highly curved intracellular membrane compartments.
2012pharmaceutical
Amandeep Kaur
Aashir Awan, Phd
Abhisar Anand
Adina Hazan
Alan F. Kaul, PharmD., MS, MBA, FCCP
alexcrystal6
anamikasarkar
apreconasia
aviralvatsa
David Orchard-Webb, PhD
danutdaagmailcom
Demet Sag, Ph.D., CRA, GCP
Dror Nir
dsmolyar
Ethan Coomber
evelinacohn
Gail S Thornton
Irina Robu
jayzmit48
jdpmd
jshertok
kellyperlman
Ed Kislauskis
larryhbern
Madison Davis
marzankhan
megbaker58
ofermar2020
Dr. Pati
pkandala
ritusaxena
Rick Mandahl
sjwilliamspa
Srinivas Sriram
stuartlpbi
Dr. Sudipta Saha
tildabarliya
vaishnavee24
zraviv06
zs22
