Editorial FocusThe proof of the pudding is in the eating: Editorial Focus on “Hyperphagia, not hypometabolism, causes early onset obesity in melanocortin-4-receptor knockout mice”D. Euan MacIntyre, and Susan B. GlueckD. Euan MacIntyreDepartment of Pharmacology, Merck Research Laboratories, and Susan B. GlueckDeputy Editor, Physiological GenomicsPublished Online:18 Mar 2003https://doi.org/10.1152/physiolgenomics.00018.2003MoreSectionsPDF (54 KB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations ShareShare onFacebookTwitterLinkedInWeChat for higher organisms to survive, they must efficiently procure, utilize, and conserve energy. Accordingly, mammalian species have developed complex mechanisms to ensure a constant supply of energy for cellular functions during fluctuations in their environment. Despite imbalances between day-to-day food intake and energy expenditure, adiposity (body fat content) remains remarkably constant over time in normal adult individuals. Such energy homeostasis requires the coordinated regulation of appetite and adiposity and involves a complex neuroendocrine system in which circulating hormones and neural signals convey information about energy balance to brain pathways that control eating and energy expenditure. Our understanding of the genetic and molecular basis of energy balance regulation has increased markedly over the last few years, driven initially by positional cloning and characterization of the molecular defects underlying certain mouse obesity mutations, then galvanized by the emergence of obesity as a preeminent public health problem (11). Indeed, the increasing prevalence of obesity suggests that the systems controlling energy homeostasis defend more effectively against weight loss than weight gain (12). It also follows that a detailed understanding of the mediators and mechanisms involved in energy homeostasis should assist in the identification of suitable and/or novel molecular targets for the pharmacotherapy of obesity.Short-term aspects of feeding such as taste perception, meal size, and satiety are regulated by nutrient, neural, and peptide signals (“satiety signals”) originating from the gut, whereas neural signals from the liver report meal composition. Longer term regulation of body weight and adiposity is mediated by “adiposity signals” in the form of hormones that circulate at concentrations proportional to body fat content, such as leptin, secreted by adipocytes, or insulin, secreted by pancreatic β-cells. Leptin defieciency produces behavioral and neuroendocrine profiles analogous to those evoked by chronic starvation. Reception and integration of the adiposity and satiety signals occurs within various brain regions: the brain stem for satiety signals, and the hypothalamus for adiposity signals. Diverse neuronal circuits then coordinate the resultant neuroendocrine, autonomic, and/or behavioral responses, which directly or indirectly influence food intake or energy expenditure (1, 10).A variety of studies have identified multiple hypothalamic neurotransmitters and peptides implicated in the modulation of food intake and energy expenditure and their functional interrelationships. Neuropeptide Y (NPY) and agouti-related protein (AgRP) are coexpressed within a subset of neurons in the hypothalamic arcuate nucleus (ARC), and an adjacent subset of ARC neurons coexpress pro-opiomelanocortin (POMC) and the cocaine- and amphetamine-related transcript (CART). Neurons expressing POMC can synthesize the POMC product α-melanocyte stimulating hormone (α-MSH). These NPY/AgRP and POMC/CART neurons project to the lateral hypothalamic area where there exist distinct neuronal subsets expressing melanin-concentrating hormone or orexins, and to the paraventricular nucleus where different neurons express thyrotropin-releasing hormone or corticotropin-releasing hormone.Based upon the pharmacological effects of their peptide constituents, and the neuronal response to the adiposity signals from leptin and insulin, the ARC neuronal pathways can be characterized as anabolic or catabolic (11). ARC NPY/AgRP neurons are considered to be anabolic, i.e., they are inhibited by leptin or insulin, but when activated they stimulate food intake, inhibit energy expenditure, and promote weight gain. Interestingly, genetic knockout of NPY does not alter feeding responses in (otherwise) normal mice, but reduces the degree of hyperphagia and obesity in ob/ob mice. Npy−/− mice display enhanced responsiveness to the anorectic effects of leptin. AgRP is an inverse agonist at neuronal melanocortin receptors, MC3R and MC4R, and when injected intracerebroventicularly in rodents produces marked stimulation of food intake and weight gain. Agrp−/− mice are phenotypically normal and respond normally to leptin.By contrast, POMC/CART neurons are considered to be catabolic, i.e., they are stimulated by leptin or insulin, and, when activated, serve to inhibit food intake, enhance energy expenditure, and promote fat loss. α-MSH is an agonist at both MC4R and MC3R. Blockade of MC4R (e.g., in mice ectopically expressing agouti protein or overexpressing AgRP) results in obesity. Moreover, Pomc1−/− mice and Mc4r−/− mice are obese, and humans with MC4R or POMC mutations are insulin resistant and predispose to obesity that may be of early onset (15). Activation of MC4R by agonists evokes inhibition of food intake and stimulation of thermogenesis. Current research suggests that MC4R-expressing neurons are downstream targets for some, but not all, of the effects of leptin. For example, MC4R antagonism blocks leptin-induced sympatho-excitation, and Mc4r−/− mice are resistant to leptin-induced thermogenesis in brown adipose tissue, as well as to the anorectic effects of leptin. MC4Rs are widely expressed in the central nervous system and are present in the key feeding, endocrine, and autonomic control sites within the hypothalamus and brain stem. In addition, MC4R are present in sympathetic and parasympathetic preganglionic neurons, consistent with their potential involvement in autonomic regulation of energy expenditure and pancreatic β-cell function (7). Thus the central melanocortin system, and in particular the MC4R, plays a pivotal role in mammalian energy homeostasis (3, 8).In this release of Physiological Genomics, Weide et al. (Ref. 16; see page 47 in this release), explore the early phenotypic manifestations of mice either heterozygous or homozygous for the MC4R knockout, which was originally generated by homologous recombination. They sought to determine whether the onset of obesity in Mc4r−/− mice, which has been previously characterized in adults (6), was due to lower energy expenditure or to hyperphagia. In addition, they wished to identify the time point when the affected individuals first evinced a higher body fat content than their wild-type littermates. Their survey consisted of a battery of tests of physiological function, body composition, and genetic expression upon sets of littermates fed an identical diet and ranging in age from 10–56 days: either homozygous null vs. heterozygotes, or homozygous wild-type vs. heterozygotes.Mice were euthanized at 36 and 56 days. The body composition (percent water, fat, and fat-free dry mass) was determined, in addition to the plasma leptin concentration and mRNA levels of the orexigenic peptide NPY and the anorexigenic molecule POMC in the ARC. In addition, the levels of oxygen consumption and food intake were measured, either directly or indirectly via estimates, to determine any differences in energy intake between genotypes.Weide et al. employed a variety of careful statistical analyses, taking into account the effects of genotype, litter, and sex, to evaluate differences in body composition; plasma leptin concentration; and energy balance. Their main findings were that both food intake and energy expenditure, measured over postnatal days 21 to 35, were higher in the homozygous null mice than in the other genotypes. Consequently, the excessive body fat in homozygous null mice, which first became significant at 35 days, could not be attributed to hypometabolism, but to a higher net energy intake. In other words, the Mc4r−/− mice were eating more and consuming more oxygen than either their heterozygous or wild-type littermates. Relative to their wild-type littermates, the heterozygotes also showed increased food intake and energy expenditure with higher net energy intake, leading to a higher body fat content.That hyperphagia is the mechanism that initiates fat deposition in weanling Mc4r−/− mice contrasts with published observations in adult (10–12 wk old) mice, where pair-feeding studies clearly indicate that hypometabolism is the primary cause of obesity (13). Moreover, that the onset of fat deposition occurs under conditions of hyperphagia and modest hypermetabolism is an unusual finding that potentially differentiates Mc4r−/− mice from the vast majority of other knockout models of obesity where hyperphagia and/or hypometabolism are listed as causative (2). These differences could be attributed to the chronology of the measurements relative to obesity development in this vs. other studies and/or to the precision and sensitivity of the measurements of energy balance used in the present studies. However, as there is controversy regarding the quantification of energy expenditure in animals differing markedly in body mass and composition, the observed modest hypermetabolism may merely reflect methodological differences among laboratories. Indeed, previous reports indicate increases in metabolic rate (i.e., hypermetabolism) of Mc4r−/− mice relative to wild-type littermates when the data are expressed on a per animal basis or in terms of lean body mass, whereas modest hypometabolism is evident if the data are expressed in terms of total body mass. Furthermore, by measuring both oxygen consumption and carbon dioxide production, one can estimate respiratory exchange ratio (RER; Vco2/Vo2), which reports indices of energy substrate utilization. The RER of Mc4r−/− mice reportedly is higher than that of wild-type littermates, indicative of reduced fat metabolism in the former (4). It would be of interest to assess the chronology and genotype-dependence of changes in RER in Mc4r−/− mice.As in wild-type animals, the plasma concentration of leptin was found to be strongly correlated with body fat content, regardless of genotype. However, mRNA levels of POMC and NPY in the brains of Mc4r knockout mice varied depending upon genotype. There was a nonstatistically significant tendency for a lower expression level of NPY in the ARC of Mc4r−/− individuals vs. either +/+ or +/− mice, and this level decreased with age. The opposite was observed for POMC levels, which increased significantly both with age and with more copies of the defective gene. The directional changes in NPY- and POMC-mRNA accord with the known effects of adiposity on expression of these neuropeptides and are consistent with the concept that coordinate regulation of NPY/AgRP and POMC/CART neurons defends body weight and maintains energy homeostasis.Identifying cause and effect relationships between genotype and changes in neuropeptide expression is complicated by accompanying changes in adiposity and adiposity signals. Weide et al. believe that this variability of expression levels is influenced more strongly by body fat composition and by plasma leptin concentrations than by genotype. Interestingly, when the impact of change in body fat content was taken into consideration, analysis of covariance revealed that both NPY- and POMC-mRNA were increased in Mc4r−/− individuals compared with +/+ mice. The observed upregulation of POMC contrasts with previous studies using adult Mc4r−/− mice which revealed no changes in ARC expression of POMC but is consistent with reports that chronic MC4R blockade in rats upregulates hypothalamic POMC mRNA. As Weide et al. suggest, upregulation of agonist (POMC) expression may be an adaptive response to the absence of the MC4R. As Mc4r−/− mice respond normally to the hyperphagic effects of NPY, suggesting that NPY acts independently or downstream of MC4R (9), upregulation of NPY mRNA in Mc4r−/− mice seems paradoxical: why would an anabolic/orexigenic pathway be activated under conditions of hyperphagia and fat deposition?The interplay between NPY and POMC pathways is complex and only now is beginning to be elucidated (5). It is known that a subset of NPY neurons, which also contain γ-aminobutyric acid (GABA), innervate POMC neurons and serve to inhibit neuronal activation. Leptin activation of POMC neurons reportedly is mediated directly by depolarization of POMC neurons and indirectly by hyperpolarizing NPY/GABA neurons. This inhibits the release of GABA from presynaptic nerve terminals onto POMC neurons. POMC products activate MC3R, and MC3R agonists are known to modulate NPY/AgRP and NPY/GABA neurons. Whether MC3R modulation of NPY/AgRP neuronal activity and NPY gene expression is altered in Mc4r−/− mice remains to be determined.Although the studies of Weide et al. document the time course of obesity development associated with MC4R deficiency in weanling mice and identify the greater importance of perturbation of appetitive vs. thermogenic mechanisms in the ensuing fat deposition, the relevance of these observations to the human condition remain to be determined. Of course, the fidelity with which experimental findings in animals accurately reflect the pathoetiology of human disease frequently is problematic, and the authors acknowledge the limitations of their experimental approaches in this regard, with temperature and diet being major considerations. As reported by the extent of fat deposition relative to lean littermates, obesity in ob/ob and db/db mice is accentuated at temperatures below thermoneutrality, and the increased metabolic efficiency is attributed to reduced energy expenditure on thermoregulatory thermogenesis (14). Such observations led to the concept that development of obesity in leptin-resistant mice stems from hyperphagia and hypometabolism. As Weide et al. imply, it would be of interest to evaluate the kinetics and the mechanisms underlying the development of obesity in Mc4r−/− mice under thermoneutral conditions and in animals exposed to a moderately high-fat diet. In addition, studies where Mc4r−/− mice are pair fed to age-matched wild-type or heterozygous littermates would be useful to substantiate the conclusions regarding the relative importance of hyperphagia vs. hypometabolism in the genesis of the obese state in these animals. As this study identifies in Mc4r−/− mice potential differences between the mechanisms underlying the development of obesity and those maintaining the obese state in adulthood, careful evaluation of the chronology and etiology of obesity development in other knockout models would be insightful.Overall, the data from Weide et al. are consistent with the concept that MC4R and the melanocortin pathway are important determinants of obesity. That this pathway can be modulated for therapeutic benefit by using MC4R agonists to treat obesity in humans is attractive, but remains to be proven, and testing of this hypothesis awaits the development of potent, selective human MC4R agonists (8).References1 Barsh G and Schwartz MW. Genetic approaches to studying energy balance perception and integration. Nat Genet 3: 589–600, 2002.Crossref | ISI | Google Scholar2 Butler AA and Cone RD. Knockout models resulting in the development of obesity. Trends Genet 17: S51–S54, 2001.Google Scholar3 Butler AA and Cone RD. The melanocortin receptors: lessons from knockout models. Neuropeptides 36: 77–84, 2002.Crossref | PubMed | ISI | Google Scholar4 Chen AS, Metzger JM, Trumbauer ME, Guan X, Yu H, Frazier EG, Marsh DJ, Forrest MJ, Gopal-Truter S, Fisher J, Camacho R, Strack AM, Mellin TN, MacIntyre DE, Chen HY, and Van der Ploeg LHT. Role of the melanocortin-4 receptor in metabolic rate and food intake in mice. Transgenic Res 9: 145–154, 2000.Crossref | PubMed | ISI | Google Scholar5 Cowley MA, Smart JL, Rubinstein M, Cerdan MG, Diano S, Horvath TL, Cone RD, and Low MJ. Leptin activates anorexigenic POMC neurons through a neural network in the arcuate nucleus. Nature 411: 480–484, 2001.Crossref | PubMed | ISI | Google Scholar6 Huszar D, Lynch CA, Fairchild-Huntress V, Dunmore JH, Fang Q, Berkmeier LR, Gu W, Kesterson RA, Boston BA, Cone RD, Smith FJ, Campfield LA, Burn P, and Lee F. Targeted disruption of the melanocortin 4 receptor results in obesity in mice. Cell 88: 131–141, 1997.Crossref | PubMed | ISI | Google Scholar7 Kishi T, Aschkenasi CJ, Lee CE, Mountjoy KG, Saper CB, and Elmquist JK. Expression of melanocortin 4 receptor mRNA in the central nervous system of the rat. J Comp Neurol 457: 213–235, 2003.Crossref | PubMed | ISI | Google Scholar8 MacNeil D, Howard AH, Guan X, Fong TM, Nargund RP, Bednarek MA, Goulet MT, Weinberg DH, Strack AM, Marsh DJ, Chen HY, Shen C, Chen AS, Rosenblum CI, MacNeil T, Tota M, MacIntyre DE, and Van der Ploeg LHT. The role of melanocortins in body weight regulation: opportunities for the treatment of obesity. Eur J Pharmacol 450: 93–109, 2002.Crossref | PubMed | ISI | Google Scholar9 Marsh DJ, Hollopeter G, Huszar D, Laufer R, Yagaloff KA, Fisher SL, Burn P, and Palmiter RD. Response of melanocortin-4-deficient mice to anorectic and orexigenic peptides. Nat Genet 21: 119–122, 1999.Crossref | PubMed | ISI | Google Scholar10 Saper CB, Chou TC, and Elmquist JK. The need to feed: homeostatic and hedonic control of eating. Neuron 36: 199–211, 2002.Crossref | PubMed | ISI | Google Scholar11 Schwartz MW, Woods SC, Porte D, Seeley RJ, and Baskin DG. Central nervous system control of food intake. Nature 404: 661–671, 2000.Crossref | PubMed | ISI | Google Scholar12 Schwartz MW, Woods SC, Seeley RJ, Barsh GS, Baskin DG, and Leibel RL. Is the enegy homeostasis system inherently biased towards weight gain. Diabetes 52: 232–238, 2003.Crossref | PubMed | ISI | Google Scholar13 Ste Marie L, Miura GI, Marsh DJ, Yagaloff K, and Palmiter RD. A metabolic defect promotes obesity in mice lacking melanocortin-4 receptors. Proc Natl Acad Sci USA 97: 12339–12344, 2000.Crossref | PubMed | ISI | Google Scholar14 Trayhurn P and Fuller L. The development of obesity in genetically dibaetic-obese (db/db) mice pair-fed with lean siblings. The importance of thermoregulatory thermogenesis. Diabetologia 19: 148–153, 1980.Crossref | PubMed | ISI | Google Scholar15 Vaisse C, Clement K, Durand E, Hercberg S, Guy-Grandm B, and Frogel P. Melanocortin-4 receptor mutations are a frequent and heterogeneous cause of morbid obesity. J Clin Invest 106: 253–262, 2000.Crossref | PubMed | ISI | Google Scholar16 Weide K, Christ N, Moar KM, Arens J, Hinney A, Mercer JG, Eiden S, and Schmidt I. Hyperphagia, not hypometabolism, causes early onset obesity in melanocortin-4-receptor knockout mice. Physiol Genomics 13: 47–56, 2003. First published January 14, 2003; 10.1152/physiolgenomics.00129. 2002.Link | ISI | Google Scholar Download PDF Previous Back to Top Next FiguresReferencesRelatedInformationCited ByLateral hypothalamic ‘command neurons’ with axonal projections to regions involved in both feeding and thermogenesis16 April 2007 | European Journal of Neuroscience, Vol. 25, No. 8 More from this issue > Volume 13Issue 1March 2003Pages 11-14 Copyright & PermissionsCopyright © 2003 the American Physiological Societyhttps://doi.org/10.1152/physiolgenomics.00018.2003PubMed12644629History Published online 18 March 2003 Published in print 18 March 2003 Metrics

EditorialNote from the Deputy Editor: reviews in mouse CV phenotypingSusan Glueck, Susan GlueckPhysiological Genomics, Deputy EditorPublished Online:13 May 2003https://doi.org/10.1152/physiolgenomics.00046.2003MoreSectionsPDF (26 KB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations A number of papers submitted in conjunction with the October 2002 NHLBI symposium on mouse cardiovascular phenotyping1 The “NHLBI Symposium on Phenotyping: Mouse Cardiovascular Function and Development” was held at the Natcher Conference Center, NIH, Bethesda, MD, on October 10–11, 2002. appear in this issue of Physiological Genomics. Researchers at the symposium discussed current technologies, such as advances in imaging and measurements of electrophysiological phenotypes, used to assess mouse models of human cardiovascular disease. Meeting organizer Cecilia Lo has contributed a meeting report (5) detailing highlighted topics.In a review article, Collins et al. (3) describe the use of Doppler echocardiography to evaluate cardiac phenotypes in transgenic mice. With ultrasound, dimensions and performance of the mouse heart can readily be obtained. Two-dimensional Doppler echocardiography enables scientists to determine cardiac output and to assess cardiac performance of animals with valvular disease. Klaas Kramer and Lewis Kinter (4) describe the use of implanted radiotelemetry devices to record rodent cardiovascular measurements free from the stress-related perturbations that generally accompany human handling of animals. Such implants can be used to record a number of signals, including blood oxygen content, heart rate, and blood pressure. Radio implants also provide researchers with the opportunity to reduce the number of mice or rats used in experiments, since instrumented animals may be “reused” in different studies. Daniel Bernstein (1) reviews exercise testing in mouse models of cardiovascular disease, noting that many transgenic models only evince an altered cardiac phenotype under the stress of exercise. For example, β1-adrenergic receptor knockout mice have exercise-induced increases in V̇o2 that are identical to wild-type animals, but their heart rate does not increase as much during exercise. For these transgenic mice to maintain a normal V̇o2, they must have increased left ventricular stroke volume.Charles Berul (2) describes mouse models of human cardiac arrhythmias and their electrophysiological assessment. Animals may be studied using an ex vivo method with perfused hearts or by using an in vivo method with implantation of subcutaneous electrodes to record electrocardiograms (ECG). Mice may be followed long term via implanted telemetry devices that transmit ECG data at desired intervals. Soufan et al. (6) detail the three-dimensional (3D) reconstruction of gene expression patterns in mouse hearts during cardiac development, a method in which hearts are sectioned, hybridized in situ to a suite of myocardium-specific genes, digitally photographed, and then “reconstructed” using imaging software. By staining sections with probes for other heart-specific mRNAs, it is possible to generate a 3D record of gene expression at a certain time point in development.These review articles demonstrate the depth of current research into mouse models of cardiovascular disease, which, coupled with the availability of the mouse genome, holds the promise of significant future advances in translational research.REFERENCES1 Bernstein D. Exercise assessment of transgenic models of human cardiovascular disease. Physiol Genomics 13: 217–226, 2003; 10.1152/physiolgenomics.00188.2002.Link | ISI | Google Scholar2 Berul CI. Electrophysiological phenotyping in genetically engineered mice. Physiol Genomics 13: 207–216, 2003; 10.1152/physiolgenomics.00183.2002.Link | ISI | Google Scholar3 Collins KA, Korcarz CE, and Lang RM. Use of echocardiography for the phenotypic assessment of genetically altered mice. Physiol Genomics 13: 227–239, 2003; 10.1152/physiolgenomics.00005.2003.Link | ISI | Google Scholar4 Kramer K and Kinter LB. Evaluation and applications of radiotelemetry in small laboratory animals. Physiol Genomics 13: 197–205, 2003; 10.1152/physiolgenomics.00164.2002.Link | ISI | Google Scholar5 Lo C, Nabel E, and Balaban R. Meeting report: NHLBI Symposium on Phenotyping: Mouse Cardiovascular Function and Development. Physiol Genomics 13: 185–186, 2003; 10.1152/physiolgenomics.00047.2003.Link | Google Scholar6 Soufan AT, Ruijter JM, van den Hoff MJB, de Boer PAJ, Hagoort J, and Moorman AFM. Three-dimensional reconstruction of gene expression patterns during cardiac development. Physiol Genomics 13: 187–195, 2003; 10.1152/physiolgenomics.00182.2002.Link | ISI | Google Scholar Download PDF Back to Top Next FiguresReferencesRelatedInformation More from this issue > Volume 13Issue 3May 2003Pages 183-183 Copyright & PermissionsCopyright © 2003 the American Physiological Societyhttps://doi.org/10.1152/physiolgenomics.00046.2003History Published online 13 May 2003 Published in print 13 May 2003 Metrics

life developed in a stressful environment. Stressors at the cellular level include heat, hypoxia, oxidative or reductive substances, mechanical or osmotic pressure, and toxic compounds like heavy metals. Various molecular pathways, more or less specific for the different stressors, developed during

EditorialThe future of Physiological GenomicsVictor J. Dzau, and Susan B. GlueckVictor J. DzauFounding Editor-in-Chief, and Susan B. GlueckOutgoing Deputy EditorPublished Online:15 Aug 2003https://doi.org/10.1152/physiolgenomics.00114.2003MoreSectionsPDF (38 KB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations ShareShare onFacebookTwitterLinkedInEmailWeChat In an introductory editorial published in 1999 (4), we envisioned Physiological Genomics as “a ‘one-stop shop’ while serving as a bridge linking genome sequencing and mapping to integrative physiology and clinical medicine.” On the cusp of the so-called postgenomic era, the goal was to found a journal pioneering an emerging multidisciplinary field that encompassed genomics, proteomics, classical genetics, clinical research, and the broad spectrum of physiology research. One of the most frequently encountered questions we have been asked over the last four years has been for a specific definition of physiological genomics. Indeed, over the course of managing this journal, we learned that because physiological genomics is a work in progress, the essential key to self-definition has been to find the least common denominator in the research we publish. At its heart, the field of physiological genomics involves the study of the mechanisms at the gene and molecular levels which mediate an organism’s response to disease, the environment, and its own inheritance. Understandably, this is a tall order, and our greatest challenge has been to set limits around this definition in order to best define work suitable for the Journal.We have not sought to publish detailed explorations of the structure of genes and chromosomes, which differentiates us from other “genomics” journals. At the same time, we have insisted of our authors that their research investigate the underlying heritable component of observed physiological response. While this practice is rapidly becoming commonplace and expected in every physiology journal, Physiological Genomics has attempted to position itself as a leader in the field. Not only, then, is this journal intended to be a repository of the newest research in genetic physiology, it is also meant to be the place for the best research.One measurement of our success to date is the makeup of our readership. Physiological Genomics is read with interest by researchers in both academic medicine and the biopharmaceutical industry. Because of the breadth of papers that we publish, we are relevant to researchers in a number of fields, particularly those whose research is multidisciplinary by definition. Many universities and research institutions have founded multidisciplinary institutes and centers recently to bridge genetics and medicine, and the joint professorships, research agendas, and training programs created as a result are an ideal platform for the Journal. For example, recently Harvard University Medical School, in conjunction with Partners Healthcare, created the Harvard-Partners Center for Genetics and Genomics ( http://www.hpcgg.org/), and recently Harvard, the Massachusetts Institute of Technology, and the Whitehead Institute announced a new collaborative research institute, the Broad Institute, dedicated to the study of postgenomic human genetics and medicine ( http://www.wi.mit.edu/nap/features/nap_feature_broadinstitute.html). In addition, our journal’s strength in a variety of fields has served to attract new readership from diverse disciplines.Throughout the first four years of this journal’s existence, we have striven to keep the quality of manuscripts high. One benchmark of this success, though by no means the only account, is our continued improvement in the ISI Impact Factor for the last three years in a row (1.353 in 2000; 3.352 in 2001; 4.667 in 2002). Another gauge of success is the publication of important papers in a variety of fields. We have published a number of “firsts,” especially pioneering the use of microarray expression profiling to identify a particular physiological state. These papers have been extensively referenced and cited by the scientific community. For example, in work by Carmel et al. (3), researchers employed expression profiling to describe those RNAs abundant in the spinal cord of rats following acute injury. The resulting picture, which helps to delineate the inflammatory response, may be useful in designing therapy tailored to specific gene targets.Other papers have provided technological advances to the field. One such example is the work of Yang et al. (8), who described a statistical normalization method that highlights weak signals from particular spots on microarrays. Butz and Davisson (2) developed an implantable telemetric device for recording heart rate and blood pressure suitable even for use in pregnant mice since it does not interfere with infrarenal blood flow.We have also published insightful review articles in important research areas. An article by Turchin and Kohane (7), for example, details online gene homology databases and summarizes their utility to the scientist. A review by Barr (1) describes the characteristics that make a particular animal a good “model organism,” and it provides useful summaries of the genome size, available online resources, and groundbreaking research carried out in a number of organisms including Caenorhabditis elegans and Saccharomyces cerevisiae.Physiological Genomics has also been a journal of record for a number of research conferences, including a meeting on the physiological genomics of cardiovascular disease (5) and another on mouse cardiovascular phenotyping (6). We intend to continue this practice of association with scientific meetings of interest to our readership, including an October 2003 APS-sponsored meeting on cardiorenal physiology.As the field of physiological genomics research matures, so will the Journal. The best way that we can accomplish this is to remain flexible by publishing research from a variety of disciplines as they relate to the genome, while maintaining the high standards made possible by our rapid online peer review process and skilled editorial board. There are a number of nascent fields which have received funding through the National Heart, Lung, and Blood Institute’s Programs for Genomic Applications ( http://www.nhlbi.nih.gov/resources/pga/index.htm) and which we expect to have a great impact on the study of genomics, physiology, and medicine in the future. These will include systems biology, which will emphasize an integrated approach, informed by digital technology, to the spectrum of physiology and genetics related fields. Advances in imaging from the molecular to the physiological level will generate useful data but also will create challenges in data storage and management. High-throughput research methodologies that can be used from gene expression profiling to model organism mutagenesis to pharmacogenomics will become more affordable and accessible and will transform basic research. Lastly, the important connecting path between genetics and physiology will extend into translational physiology; in keeping with the other journals of the American Physiological Society, Physiological Genomics will remain committed to publishing papers in this area.REFERENCES1 Barr MM. Super models. Physiol Genomics 13: 15–24, 2003; 10.1152/physiolgenomics.00075.2002.Link | ISI | Google Scholar2 Butz GM and Davisson RL. Long-term telemetric measurement of cardiovascular parameters in awake mice: a physiological genomics tool. Physiol Genomics 5: 89–97, 2001.Link | ISI | Google Scholar3 Carmel JB, Galante A, Soteropoulos P, Tolias P, Recce M, Young W, and Hart RP. Gene expression profiling of acute spinal cord injury reveals spreading inflammatory signals and neuron loss. Physiol Genomics 7: 201–213, 2001. First published November 15, 2001; 10.1152/physiolgenomics.00074.2001.Link | ISI | Google Scholar4 Dzau VJ, Austin MJ, Brown P, Cowley A, Housman D, Mulligan R, and Rosenberg R. Revolution and renaissance. Physiol Genomics 1: 1–2, 1999.Link | ISI | Google Scholar5 Glueck SB and Sigmund CD. Meeting report: Physiological Genomics of Cardiovascular Disease: from Technology to Physiology. Physiol Genomics 9: 135–136, 2002; 10.1152/physiolgenomics.00053.2002.Link | Google Scholar6 Lo C, Nabel E, and Balaban R. Meeting report: NHLBI Symposium on Phenotyping: Mouse Cardiovascular Function and Development. Physiol Genomics 13: 185–186, 2003; 10.1152/physiolgenomics.00047.2003.Link | Google Scholar7 Turchin A and Kohane IS. Gene homology resources on the World Wide Web. Physiol Genomics 11: 165–177, 2002; 10.1152/physiolgenomics.00112.2002.Link | ISI | Google Scholar8 Yang MC, Ruan QG, Yang JJ, Eckenrode S, Wu S, McIndoe RA, and She JX. A statistical method for flagging weak spots improves normalization and ratio estimates in microarrays. Physiol Genomics 7: 45–53, 2001. First published August 8, 2001; 10.1152/physiolgenomics.00020.2001.Link | ISI | Google Scholar Download PDF Back to Top Next FiguresReferencesRelatedInformationCited ByPhysiological genomics - what is in a name?Bina Joe1 July 2015 | Physiological Genomics, Vol. 47, No. 7Physiological Genomics: the next three yearsAllen W. Cowley15 August 2003 | Physiological Genomics, Vol. 14, No. 3 More from this issue > Volume 14Issue 3August 2003Pages 167-168 Copyright & PermissionsCopyright © 2003 the American Physiological Societyhttps://doi.org/10.1152/physiolgenomics.00114.2003PubMed12923298History Published online 15 August 2003 Published in print 15 August 2003 Metrics

EditorialOur new requirement for MIAME standardsSusan B. Glueck, , and Victor J. Dzau, Susan B. Glueck, Deputy Editor, and Victor J. Dzau, Editor-in-ChiefPublished Online:18 Mar 2003https://doi.org/10.1152/physiolgenomics.00019.2003MoreFiguresReferencesRelatedInformationSectionsHistory.Rationale.Guidelines.PDF (55 KB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations ShareShare onFacebookTwitterLinkedInWeChat Physiological Genomics, along with the other APS journals, now requires authors of manuscripts containing microarray data to submit complete information about their data sets and the data gathering process, in keeping with the “minimum information about a microarray experiment” (MIAME) standards instituted by the Microarray Gene Expression Data society (MGED) working group ( http://www.mged.org/Workgroups/MIAME/miame.html). This editorial outlines the history of this initiative, details the utility of publication of such information, and provides authors with links and descriptions to assist them with manuscript preparation.History.In 2001, members of the MGED ( http://www.mged.org) recommended formalized standards for the publication of microarray data (1). They noted that an important principle in peer-reviewed biomedical literature is the requirement for provision of materials associated with an experiment in order to encourage assessments of reproducibility. Late last year, a working group of the MGED contacted editors of several genomics journals, including Physiological Genomics, to encourage them to adopt their recommended guidelines in the instructions to authors of each journal. In response, Nature announced that, effective December 1, 2002, authors of manuscripts containing new microarray data must submit complete supplemental information to the editor and at an online data repository (2).Rationale.Within a short time span, microarrays have become an important, commonly used tool in molecular genetics and physiology research. However, there is not yet widespread standardization. Variations between probe arrays, array reader equipment, software, annotation, and laboratories introduce noise into experimental results and make it more difficult for authors to reach similar conclusions independently. For microarray analysis of gene expression to have any long-term impact, it is crucial that the issue of reproducibility be adequately addressed.One way to do that is to require authors to provide readership with sufficient information either to build upon or reproduce published research. In addition, since microarray analytic standards are certain to change, it is crucial that authors identify the nature of the experimental conditions prevalent at the time of their research. Genomics is a rapidly evolving field, and microarray technology is continually improving to yield more accurate results. Nonetheless, if today’s research is to be relevant tomorrow, then the core elements that are impervious to obsolescence must be made clear.Physiological Genomics has adopted the MIAME standards to ensure that what is cutting-edge today is not out-of-date 5 years hence. We and other genomics journals have previously published papers describing expression analysis that do not provide complete microarray information. These papers have a shorter citation half-life than ones that make supplemental data readily available to the public. For Physiological Genomics to build upon its reputation and enhance the value of microarray studies, then, taking the step of requiring more from our authors will help both them and us in the long run.Guidelines.Guidelines are provided in the Information for Authors at the American Physiological Society’s Publications web page( http://www.the-aps.org/publications/i4a/prep_manuscript.htm#miame_standard). We have attempted to make this a streamlined process so that authors can readily upload data files that they will have already generated in the process of carrying out their research. We have included a link to the MGED society’s MIAME web pages, where this information has already been summarized in useable form. We inform authors that supplemental data should be in the form of tab-delimited tables or Excel spreadsheets. On our site, the summarized published guidelines to the format indicate that the first table or spreadsheet “could contain the ‘raw’ output of the image analysis software (spot quantitation matrix), the second could contain the ’processed’ data following normalization and transformation (gene expression data matrix), and if one is produced, the final table could contain ‘summary’ data that was ultimately used in the analysis, such as the subset of differentially expressed genes identified or gene clusters.”We request that authors provide the supplemental data at the time of manuscript submission, just as they would submit new nucleic acid sequences to GenBank. In addition, we require that data be deposited at the Gene Expression Omnibus (GEO) web site of the National Center for Biotechnology Information (NCBI) ( http://www.ncbi.nlm.nih.gov/geo/). This public data repository has the same high standards, ease of user interface, and attention to detail as GenBank, is readily searchable, and will be long-lived. While authors are free to maintain their data sets on their personal or research institution web pages as well, these pages are likely to come and go in the course of an academic career. Last, authors should provide the URL for their GEO-deposited data in the “Materials and Methods” section of their manuscript, so that referees may access it during the peer review process.We would like to thank authors of future submissions for complying with the MIAME standards because in addition to augmenting the microarray database, they will enhance the value of their publications and of Physiological Genomics overall. We encourageresearchers to deposit data from previously published papers to ensure the scientific longevity of their efforts and to otherwise support this initiative of the functional genomics research community. Download PDF Back to Top Next FiguresReferencesRelatedInformationREFERENCES1 Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, Gaasterland T, Glenisson P, Holstege FC, Kim IF, Markowitz V, Matese JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J, and Vingron M. Minimum information about a microarray experiment (MIAME): toward standards for microarray data. Nat Genet 29: 365–371, 2001.Crossref | PubMed | ISI | Google Scholar2 Editorial. Microarray standards at last. Nature 419: 323, 2002.Google Scholar Cited ByToward Supportive Data Collection Tools for Plant Metabolomics11 May 2005 | Plant Physiology, Vol. 138, No. 1A proposed framework for the description of plant metabolomics experiments and their results6 December 2004 | Nature Biotechnology, Vol. 22, No. 12 More from this issue > Volume 13Issue 1March 2003Pages 1-2 Copyright & PermissionsCopyright © 2003 the American Physiological Societyhttps://doi.org/10.1152/physiolgenomics.00019.2003History Published online 18 March 2003 Published in print 18 March 2003 Metrics Downloaded 116 times 2 CITATIONS 2 Total citations 0 Recent citations 0.15 Field Citation Ratio n/a Relative Citation Ratio publications2supporting0mentioning1contrasting0Smart Citations2010Citing PublicationsSupportingMentioningContrastingView CitationsSee how this article has been cited at scite.aiscite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

EditorialNote from the Deputy EditorSusan Glueck, Susan GlueckPhysiological GenomicsDeputy EditorPublished Online:03 Dec 2002https://doi.org/10.1152/physiolgenomics.00145.2002MoreSectionsPDF (25 KB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations ShareShare onFacebookTwitterLinkedInWeChat In this issue of Physiological Genomics appear the first articles associated with our journal’s Special Call for Papers in large-scale ethylnitrosourea (ENU) mouse mutagenesis. This important methodology enables researchers to explore the genetics and physiology of the mouse in the same manner already successfully utilized by Drosophila, zebrafish, and C. elegans researchers. The fruits of such studies will include the identification of novel genes and pathways responsible for aberrant physiology, as well as further clues into the workings of already characterized pathways.An editorial by David Beier (1) describes the successes and challenges of ENU mutagenesis detailed in a recent workshop on the subject. In a research article, Peters et al. (3) describe their method for high-throughput screening of hematologic and coagulation phenotypes in mice. By modifying equipment and protocols originally intended for human blood analysis, the authors have developed a tool for large-scale characterization of a variety of blood factors, including prothrombin, fibrinogen, and antithrombin III. This methodology would be useful for either inbred or ENU-mutagenized strains of mice. Two review articles, one by Bockamp et al. (2) and one by van der Weyden et al. (4), evaluate methodologies including ENU and beyond which can be used to engineer the mouse genome. As evinced by these reviews, ENU mutagenesis is one weapon in an armamentarium of methods for producing mutations with specific characteristics. Bockamp et al. focus on conditional mouse knockout technologies, especially those reliant upon tetracycline-inducible vectors. Such constructs allow researchers to study the effects of particular gene products in particular tissues or at precise points in animal development. The authors provide a comprehensive list of currently available mouse strains engineered with particular “tet on/tet off” effectors and reporters. Van der Weyden et al. provide an exhaustive overview of the world of mouse genomic tools currently available, covering not only mutagenesis, but also site-specific recombination, homologous recombination, and gene trapping. These reviews are certain to be helpful for researchers planning their own mouse research programs or searching for specificity in their research strategy. In addition, they provide a snapshot of the current state of the art in the field. In the future, it is to be hoped that articles detailing ground-breaking research using these technologies will find their place in the pages of this journal.REFERENCES1 Beier, DR. ENU mutagenesis: a work in progress. Physiol Genomics 11: 111–113, 2002; 10.1152/physiolgenomics.00140.2002.Link | ISI | Google Scholar2 Bockamp E, Maringer M, Spangenberg C, Fees S, Fraser S, Eshkind L, Oesch F, and Zabel B. Of mice and models: improved animal models for biomedical research. Physiol Genomics 11: 115–132, 2002; 10.1152/physiolgenomics.00067.2002.Link | ISI | Google Scholar3 Peters LL, Cheever EM, Ellis HR, Magnani PA, Svenson KL, Von Smith R, and Bogue MA. Large-scale, high-throughput screening for coagulation and hematologic phenotypes in mice. Physiol Genomics 11: 185–193, 2002. First published November 5, 2002; 10.1152/physiolgenomics.00077.2002.Link | ISI | Google Scholar4 van der Weyden L, Adams DJ, and Bradley A. Tools for targeted manipulation of the mouse genome. Physiol Genomics 11: 133–164, 2002; 10.1152/physiolgenomics.00074.2002.Link | ISI | Google Scholar Download PDF Back to Top Next FiguresReferencesRelatedInformation More from this issue > Volume 11Issue 3December 2002Pages 109-109 Copyright & PermissionsCopyright © 2002 the American Physiological Societyhttps://doi.org/10.1152/physiolgenomics.00145.2002History Published online 3 December 2002 Published in print 3 December 2002 Metrics

hibernating mammals utilize efficient physiological mechanisms to minimize energy consumption during the winter. The process involves not only a decrease in the overall metabolic rate, but also a metabolic shift from preferential use of carbohydrates to triacylglycerols. In this online release of

Editorial FocusSeparating the wheat from the chaff: Focus on “In silico data filtering to identify new angiogenesis targets from a large in vitro gene profile data set”William C. Aird, Susan B. Glueck, Victor J. Dzau, and Richard E. PrattWilliam C. AirdBeth Israel Deaconess Medical Center, Harvard Medical School, Boston 02115, Susan B. GlueckDeputy Editor, Physiological Genomics, Victor J. DzauEditor-in-Chief, Physiological GenomicsDepartment of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, and Richard E. PrattDepartment of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MassachusettsPublished Online:12 Jul 2002https://doi.org/10.1152/physiolgenomics.00071.2002MoreSectionsPDF (50 KB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations ShareShare onFacebookTwitterLinkedInWeChat the development and use of microarray technology in cell biology and physiology has tremendous potential in the elucidation of pathways involved in normal and disease states. However, the very magnitude of these studies means that an investigator may find an overwhelming number of genes that appear to be differentially regulated between two or more states. Therefore, an important starting point in designing and interpreting array experiments is to minimize the frequency of false-positive results. In general, this is accomplished through the tight control of culture conditions and/or animal subjects, the use of biological and/or technical replicates, or the use of various filtering strategies. Other strategies include tightening the level of significance, imposing fold-change criteria, demanding 100% “present” calls and discarding genes where replicates exhibit less than 100% concordance. While decreasing the number of hits and eliminating false-positive results, these latter criteria also increase the likelihood of false-negative results. In short, an investigator must choose to either analyze a vast pool of candidate genes or risk discarding potentially useful, biologically relevant hits.A report by Gerritsen et al. (6) in this online release of Physiological Genomics (Ref. 6; see page 13 in this release) illustrates the magnitude of this problem and offers a potential solution that may prove applicable to a wide number of studies. This manuscript involves the identification of genes expressed in endothelial cells that are involved in the induction of angiogenesis.Endothelial cells display remarkable heterogeneity in health and disease (5, 7, 10, 13). Such heterogeneity provides unique opportunities for developing site-directed therapies. As a gatekeeper to the underlying tissue, the endothelium is a highly accessible and attractive therapeutic target. The presence of phenotypic differences, for example, at the level of cell surface receptors, may be exploited to deliver drugs or genes to defined vascular beds. Therefore, an important goal is to map endothelial cell phenotypes under normal and pathophysiological conditions.Endothelial cell phenotypes are governed largely by signals residing in the extracellular environment (15). When endothelial cells are harvested and grown in tissue culture, they are uncoupled from these critical extracellular cues and undergo phenotypic drift. The net result is a loss of complexity and site-specific signatures.Given that the cultured endothelial cells are phenotypically modulated with culture, how does one identify genes relevant to an in vivo phenotype? There are several approaches to this problem. One is to utilize proteomics to identify novel cell surface receptors within intact endothelium. For example, antibody and subfractionated strategies have been employed to generate monoclonal antibodies that specifically target the caveolae in one vascular bed or another (9). Others have used phage display peptide libraries to select for peptides that home to specific vascular beds in vivo (3, 11). These latter studies have uncovered a vascular “address system” that allows for site-specific targeting of biologically active compounds, for example, to the endothelial lining of tumor blood vessels (2, 4). These proteomic approaches are valuable in that they are conducted in the native environment of the endothelial cell and they select for cell surface molecules that may be amenable to therapeutic targeting.In general, proteomic strategies do not yet have the power or breadth of genomic screens. How then can one apply recent advances in genomics to an understanding of cell type-specific function, when that function is so tightly coupled in both time and space to the environment in vivo? This question has received increasing attention in the angiogenesis field. One approach has been to compare gene expression profiles in endothelial cells from normal tissue and tumor tissue (14). This strategy involves the isolation of relatively pure populations of endothelial cells but is limited by the potential for contamination with nonendothelial cells and by the inability to control for altered gene expression in the ex vivo setting. Another approach has been to screen for genes that are expressed in endothelial cells undergoing tube formation in vitro, a model that mimics certain steps in angiogenesis, including endothelial cell migration, proliferation, and extracellular matrix interactions. Two independent groups have successfully employed a three-dimensional collagen gel system to identify genes that are upregulated during the process of in vitro angiogenesis (1, 8). Interestingly, the transcriptional profiles in these latter two studies differed to some extent, suggesting that subtle changes in the conditions, such as the absence or presence of phorbol ester or perhaps the source of endothelial cells, may influence gene expression.Gerritsen and colleagues (6) have extended these studies to identify genes that are differentially expressed in tumor endothelium. The authors employed three different models of human umbilical vein endothelial cell (HUVEC) tube formation. The assays differed in their matrix (collagen vs. fibrin) and growth factor composition [vascular endothelial growth factor (VEGF) + basic fibroblast growth factor + phorbol ester vs. VEGF + hepatocyte growth factor]. Affymetrix microarrays were used to compare mRNA transcripts differentially expressed in the three models to reference expression profiles generated from the RNA of endothelial cells in monolayer. The experiments were carried out in triplicate at five distinct time points and repeated in HUVEC derived from three independent donors. Cognizant of the importance of the microenvironment in modulating cell phenotype, the investigators took care to employ identical lot numbers of growth factors, plastic ware, and matrices.Several years ago, we hypothesized that the use of different but related models would provide a convenient filter for large databases (12). By demanding concordance across multiple models, model-specific genes within the gene set would be predicted to drop out, as might many of the associated, noncausally related genes. Using a similar approach, Gerritsen et al. (6) began by identifying those genes that were upregulated in all three in vitro models of tube formation by at least a factor of 2. Despite the improved level of stringency, the studies initially yielded an overwhelming list of 1,038 genes, pointing to the need for additional filters.To further refine the list of regulated genes, the authors followed several critical steps. First, bioinformatics programs were utilized to narrow the list to those genes with the predicted structure of either transmembrane or secreted proteins (n = 397). To identify genes that may be involved in tumor angiogenesis, total RNA was obtained from six different colon adenocarcinoma samples for microarray analysis compared with normal colon mucosa. By overlapping the resulting list of colon tumor-specific genes with the list of (predicted) transmembrane and secreted proteins, Gerritsen et al. (6) generated a panel of 128 transcripts. The last step was to subtract all those transcripts expressed in the colon epithelial tumor cell lines to enrich for endothelial and stromal cell-specific genes, resulting in a final list of 24 tumor angiogenesis-associated genes.As a means to validate the patterns of expression, Gerritsen et al. (6) examined one of the upregulated genes, STC-1, which is homologous with a fish protein involved in calcium and phosphate regulation, to determine its profile in angiogenesis. Pellets containing VEGF, which stimulates angiogenesis, were implanted in rat corneas. After 6 days, the mRNA levels of STC-1 were compared with control corneas, revealing significantly higher levels in the corneas undergoing angiogenesis. Furthermore, in situ hybridization with antibodies against STC-1 revealed that the protein is highly expressed in the blood vessels of colon adenocarcinomas.This study demonstrates the rational use of data filters and higher order screening to provide a bridge between the ex vivo biology of cultured endothelial monolayers and the complexity and heterogeneity of the intact endothelium. As a starting point, the authors focused on a model of in vitro tube formation as a crude approximation of angiogenesis. The identification of genes that are common to three models of tube formation provides a high degree of stringency and thereby reduces the likelihood of false-positive markers. The selection of genes that encode for cell surface receptors or secreted proteins is not only helpful in narrowing down the number of candidate genes, but, as with the proteomic approaches, facilitates the identification of markers with therapeutic or diagnostic relevance. Finally, by overlapping the genes with those from colon adenocarcinoma and subtracting out the transcripts from the tumor cells themselves, the investigators theoretically enriched for genes that are specific for tumor-associated angiogenesis. Continued efforts to validate these putative angiogenesis markers may lead to new diagnostic and/or therapeutic avenues while at same time providing mechanistic insight into neovessel formation.Several important questions remain to be answered. For example, it will be interesting to compare the level of expression of these 24 markers in blood vessels of tumors, healing wounds, corpora lutea, and embryos. In view of the importance of the microenvironment in dictating endothelial cell phenotypes, it is also reasonable to predict that the repertoire of genes expressed in the endothelium of colon tumors will differ from that of other tumors. Indeed, the approach described by Gerritsen et al. (6) should prove useful in interrogating transcriptional profiles of other tumor beds.In addition, it will be very important to determine which of these 24 genes may encode proteins that are playing a causal role in angiogenesis. Gain-of-function or loss-of-function experiments both in cell culture and in vivo will be necessary to address this issue.REFERENCES1 Aitkenhead M, Wang SJ, Nakatsu MN, Mestas J, Heard C, and Hughes CC. Identification of endothelial cell genes expressed in an in vitro model of angiogenesis: induction of ESM-1, betaig-h3, and NrCAM. Microvasc Res63 :159 –171,2002 .Crossref | PubMed | ISI | Google Scholar2 Arap W, Haedicke W, Bernasconi M, Kain R, Rajotte D, Krajewski S, Ellerby HM, Bredesen DE, Pasqualini R, and Ruoslahti E. Targeting the prostate for destruction through a vascular address. Proc Natl Acad Sci USA99 :1527 –1531,2002 .Crossref | PubMed | ISI | Google Scholar3 Arap W, Kolonin MG, Trepel M, Lahdenranta J, Cardo-Vila M, Giordano RJ, Mintz PJ, Ardelt PU, Yao VJ, Vidal CI, Chen L, Flamm A, Valtanen H, Weavind LM, Hicks ME, Pollock RE, Botz GH, Bucana CD, Koivunen E, Cahill D, Troncoso P, Baggerly KA, Pentz RD, Do KA, Logothetis CJ, and Pasqualini R. Steps toward mapping the human vasculature by phage display. Nat Med8 :121 –127,2002 .Crossref | PubMed | ISI | Google Scholar4 Arap W, Pasqualini R, and Ruoslahti E. Cancer treatment by targeted drug delivery to tumor vasculature in a mouse model. Science279 :377 –380,1998 .Crossref | PubMed | ISI | Google Scholar5 Garlanda C and Dejana E. Heterogeneity of endothelial cells. Specific markers. Arterioscler Thromb Vasc Biol17 :1193 –1202,1997 .Crossref | PubMed | ISI | Google Scholar6 Gerritsen ME, Soriano R, Yang S, Ingle G, Zlot C, Toy K, Winer J, Draksharapu A, Peale F, Wu TD, and Williams PM. In silico data filtering to identify new angiogenesis targets from a large in vitro gene profiling data set. Physiol Genomics10 :13 –20,2002 . First published May 15, 2002; 10.1152/physiolgenomics.00035.2002.Link | ISI | Google Scholar7 Gerritsen ME. Functional heterogeneity of vascular endothelial cells. Biochem Pharmacol36 :2701 –2711,1987 .Crossref | PubMed | ISI | Google Scholar8 Kahn J, Mehraban F, Ingle G, Xin X, Bryant JE, Vehar G, Schoenfeld J, Grimaldi CJ, Peale F, Draksharapu A, Lewin DA, and Gerritsen ME. Gene expression profiling in an in vitro model of angiogenesis. Am J Pathol156 :1887 –1900,2000 .Crossref | PubMed | ISI | Google Scholar9 McIntosh DP, Tan XY, Oh P, and Schnitzer JE. Targeting endothelium and its dynamic caveolae for tissue-specific transcytosis in vivo: a pathway to overcome cell barriers to drug and gene delivery. Proc Natl Acad Sci USA99 :1996 –2001,2002 .Crossref | PubMed | ISI | Google Scholar10 Page C, Rose M, Yacoub M, and Pigott R. Antigenic heterogeneity of vascular endothelium. Am J Pathol141 :673 –683,1992 .PubMed | ISI | Google Scholar11 Pasqualini R and Ruoslahti E. Organ targeting in vivo using phage display peptide libraries. Nature380 :364 –366,1996 .Crossref | PubMed | ISI | Google Scholar12 Pratt RE and Dzau VJ. Genomics and hypertension: concepts, potentials, and opportunities. Hypertension33 :238 –247,1999 .Crossref | PubMed | ISI | Google Scholar13 Risau W. Differentiation of endothelium. FASEB J9 :926 –933,1995 .Crossref | PubMed | ISI | Google Scholar14 St Croix B, Rago C, Velculescu V, Traverso G, Romans KE, Montgomery E, Lal A, Riggins GJ, Lengauer C, Vogelstein B, and Kinzler KW. Genes expressed in human tumor endothelium. Science289 :1197 –1202,2000 .Crossref | PubMed | ISI | Google Scholar15 Stevens T, Rosenberg R, Aird W, Quertermous T, Johnson FL, Garcia JG, Hebbel RP, Tuder RM, and Garfinkel S. NHLBI workshop report: endothelial cell phenotypes in heart, lung, and blood diseases. Am J Physiol Cell Physiol281 :C1422 –C1433,2001 .PubMed | ISI | Google Scholar Download PDF Back to Top Next FiguresReferencesRelatedInformationCited ByIn silico analysis of angiogenesis associated gene expression identifies angiogenic stage related profilesBiochimica et Biophysica Acta (BBA) - Reviews on Cancer, Vol. 1755, No. 2Endothelial cell heterogeneityCritical Care Medicine, Vol. 31, No. Supplement More from this issue > Volume 10Issue 1July 2002Pages 1-3 Copyright & PermissionsCopyright © 2002 the American Physiological Societyhttps://doi.org/10.1152/physiolgenomics.00071.2002PubMed12118099History Published online 12 July 2002 Published in print 12 July 2002 Metrics

Editorial FocusContaining multitudes: Focus on “Novel and nondetected human signaling protein polymorphisms”Dietrich A. Stephan, and Susan B. GlueckDietrich A. StephanResearch Center for Genetic Medicine, Children’s National Medical Center, Washington, District of Columbia 20010, and Susan B. GlueckDeputy Editor, Physiological GenomicsPublished Online:03 Sep 2002https://doi.org/10.1152/physiolgenomics.00103.2002MoreSectionsPDF (50 KB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations ShareShare onFacebookTwitterLinkedInWeChat single nucleotide polymorphisms (SNPs) can contribute directly to disease predisposition by modifying a gene’s function, or they can be used as genetic markers to detect nearby disease-causing mutations through association or linkage studies. There are three general classes of single nucleotide variants: those with strong functional significance which dramatically alter a gene’s behavior (classic mutations); those with more subtle functional effects that predispose to disease in concert with an individual’s genetic background or environment (functional SNPs); and those which are completely silent with respect to function (nonfunctional SNPs). Single nucleotide mutations cause a detectable phenotype on their own and can result in the familiar Mendelian inheritance diseases. Functional SNPs are by far the most interesting class of variants since they are thought to occur at high frequencies within the general population and, when present in disadvantageous combinations, can result in disease. Examples of such common multigenic diseases are thought to include diabetes, cancer progression, and heart disease (the subject of this editorial). The distinction between a mutation and a functional SNP is often vague and can be boiled down to the level of penetrance of the nucleotide variant. For example, single nucleotide mutations have very high penetrance and cause disease on their own in most cases, whereas functional SNPs have lowered penetrance and enhance an individual’s risk for disease by a small amount. This risk can be raised when combined with another functional SNP in the genome. Finally, SNPs can be completely silent functionally. This class of single nucleotide variant is by far the most common, with a polymorphic base estimated to occur approximately once every 1,300 nucleotides throughout the genome (1). These types of SNPs are useful as genetic markers to localize nearby disease-causing events through association analyses, linkage disequilibrium studies in founder populations, and classic linkage analyses.It is extremely difficult to assign a pathogenic role to a common functional SNP. Almost exclusively, nonsynonymous (amino acid changing) SNPs have been assumed to be the sole type of functional SNPs that predispose to multigenic disease. This is in part because it is immediately obvious that a protein alteration exists which could change function, especially in the case where the nucleotide change resides in an important peptide motif (9). Even so, one is ultimately forced to validate any presumed causative nonsynonymous SNP finding in vitro or in vivo, a substantial task. This undertaking is further complicated if there are multiple variants which are presumed to work in concert to produce an altered phenotype, and which must be validated together. Functional SNPs may also reside within regulatory elements, for example, which may be within introns, promoters, or distant enhancer or repressor elements. These functional SNPs, in most cases, will not result in an amino acid change (synonymous SNPs), but could alter splicing, regulation, transcript stability, etc. Examples of these are rare, simply because it is much more difficult to assess the mechanism of action of these events. Thus synonymous SNPs are largely ignored with respect to disease causality.The great, yet unrealized, promise of nonfunctional SNP markers lies in being able to perform whole-genome high-density SNP typing to identify blocks of haplotypes (as a first step to identifying the functional changes) that come together in affected individuals to contribute to common multigenic diseases. The technology for this type of whole-genome SNP typing analysis in large numbers of individuals is still several years away, even though significant effort is being invested in identifying a minimum number of informative SNPs which would ascertain all human haplotypes (3, 5). In the interim, we are forced to take a candidate gene approach to multigenic disease. This entails preselecting genes based on function and then typing SNPs in these genes in affected individuals and controls to identify those SNPs which have skewed frequencies in affected individuals. Presumably, if the control population is matched correctly on ethnicity and geography, this would indicate that a certain “flavor” of gene has a pathogenic role.To facilitate SNP identification for use as a tool for disease gene identification, the Human Genome Project (HGP) and the Celera genome sequencing endeavor have established large databases of SNPs which have been identified primarily through expressed sequence tag (EST) sequencing projects (HGP) or redundant genome sequencing from 10 alleles (Celera) (6, 13). There are acknowledged problems with these data repositories. The SNPs identified by the HGP are largely biased toward the last exons of genes and probably have many false-positives due to the single-read nature of EST sequencing data used to annotate these SNPs (10, 11). The Celera SNP database has a more even distribution of SNPs across the genome, but is derived from a small number of chromosomes and thus probably does not capture the rarer SNPs in the representative populations sequenced. It is clear that there are both sequencing errors and undetected SNPs in the SNP repositories. There are many ongoing efforts to remedy inherent errors in the repositories so that they are accurate as well as comprehensive. The only true way to do this is to sequence the genomes of many individuals from all ethnicities and geographies, and of course this is not yet feasible.There are a number of functional SNPs in a variety of genes (such as angiotensinogen, for example) that have been found to be associated with heart failure. In this release of Physiological Genomics, Lynch et al. (Ref. 7; see page 159 in this release) seek to identify additional functional SNPs that may contribute to congestive heart failure (CHF) acting either independently or in concert. The targets of investigation in this study are the small G proteins (7) and their downstream signal transducers. This set of candidate genes was chosen based on previous reports that perturbation of this signal transduction pathway can contribute to heart failure (reviewed in Ref. 8).Lynch and colleagues (7) used both denaturing high-performance liquid chromatography (dHPLC) and double-stranded sequencing to screen for functional SNPs in a number of genes in a cohort of 144 white and African-American heart failure patients. The sensitivity of the dHPLC screening method for a subset of exons was first verified by direct sequencing. By sequencing exons from the heterotrimeric G proteins Gαq, Gα11, and Gαs; the Rab small G proteins; the signaling factors Ras and Rad; and the MAP kinase, Erk2, the authors identified a number of novel SNPs in addition to those previously characterized by the Celera and HGP SNP databases. For example, in the Rad gene, Lynch et al. found a novel SNP resulting in an amino acid substitution in exon 2. The functional role of this nonsynonymous SNP and of the many synonymous SNPs that were identified was not interrogated. In this sense, the study is strictly a mutational analysis of several candidate genes for CHF with a negative outcome.As a byproduct of the mutation screening, the authors were able to draw some conclusions regarding the accuracy of the SNP databases, at least with respect to the several genes that they screened. Interestingly, 69% of the SNPs found in the heart failure patients in this study were not annotated in either the Celera or National Center for Biotechnology Information (NCBI) databases. It is not surprising that Lynch et al. identified novel SNPs in their cohort of whites and African Americans by thorough sequencing of representative alleles for these genes. This has been found to be the case in numerous SNP identification studies and relates to the strategies employed by the large consortia building the repositories as described above (2, 5, 12). In addition, population-specific SNPs were never the stated focus of the consortia. What is a bit more disconcerting for the end users of the repositories is the high false-positive rate reported by Lynch and colleagues; 56% of SNPs in the Celera and HGP databases were not found in the cohort of patients screened. As an example, previously reported database SNPs for a total of six exons of two of the heterotrimeric G proteins, Gαq and Gαs, were not detected. The ramifications of this high false-positive rate on the selection of SNPs from the databases for genotyping efforts are obvious. However, drawing conclusions regarding the integrity of the SNP data across the entire genome from a small study of ethnically homogeneous individuals is probably premature (4). What will be required before the SNP data repositories become user friendly is annotation of the SNP frequencies across all human populations. However, this would be a very difficult task in the absence of large-scale sequencing in a large cohort of diverse individuals at many loci.There may be significant information embedded in the study that is still untapped. Polymorphisms that were identified were not utilized in a case-control type fashion to determine whether any of the SNP alleles were over- or underrepresented in the CHF patients. This would entail enlisting a similarly sized cohort of matched controls and genotyping them for the SNPs that showed variation. It may be the case that a synonymous SNP is overwhelmingly present in the cases and not present in controls, which would lead to an assumption of causality (or a variant nearby causing disease). Conversely, a functional SNP may be absent in the cohort of patients studied and thereby be causative. Clearly, we are still at the beginning of our journey toward an accurate catalog of all human variation. Similarly, we do not yet appreciate the multitude of ways that synonymous SNPs can affect the function of a gene to contribute to disease risk. Finally, we still have no way to cost-effectively type SNPs across the whole genome or analyze multiple locus interactions to come up with truly comprehensive diagnostics for multigenic disease caused by interacting functional SNPs. When bolstered by careful and thorough explorations of genetic variation, studies such the one presented here by Lynch and colleagues may pave the way to understanding devastating common diseases such as congestive heart failure.REFERENCES1 Cargill M, Altshuler D, Ireland J, Sklar P, Ardlie K, Patil N, Shaw N, Lane CR, Lim EP, Kalyanaraman N, Nemesh J, Ziaugra L, Friedland L, Rolfe A, Warrington J, Lipshutz R, Daley GQ, and Lander ES. Characterization of single-nucleotide polymorphisms in coding regions of human genes. Nat Genet 22: 231–238, 1999.Crossref | PubMed | ISI | Google Scholar2 Douabin-Gicquel V, Soriano N, Ferran H, Wojcik F, Palierne E, Tamim S, Jovelin T, McKie AT, Le Gall JY, David V, and Mosser J. Identification of 96 single nucleotide polymorphisms in eight genes involved in iron metabolism: efficiency of bioinformatic extraction compared with a systematic sequencing approach. Hum Genet 109: 393–401, 2001.Crossref | PubMed | ISI | Google Scholar3 Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J, Blumenstiel B, Higgins J, DeFelice M, Lochner A, Faggart M, Liu-Cordero SN, Rotimi C, Adeyemo A, Cooper R, Ward R, Lander ES, Daly MJ, and Altshuler D. The structure of haplotype blocks in the human genome. Science 296: 2225–2229, 2002.Crossref | PubMed | ISI | Google Scholar4 Iwasaki H, Shinohara Y, Ezura Y, Ishida R, Kodaira M, Kajita M, Nakajima T, Shiba T, and Emi M. Thirteen single-nucleotide polymorphisms in the human osteopontin gene identified by sequencing of the entire gene in Japanese individuals. J Hum Genet 46: 544–546, 2001.Crossref | PubMed | ISI | Google Scholar5 Johnson GC, Esposito L, Barratt BJ, Smith AN, Heward J, Di Genova G, Ueda H, Cordell HJ, Eaves IA, Dudbridge F, Twells RC, Payne F, Hughes W, Nutland S, Stevens H, Carr P, Tuomilehto-Wolf E, Tuomilehto J, Gough SC, Clayton DG, and Todd JA. Haplotype tagging for the identification of common disease genes. Nat Genet 29: 233–237, 2001.Crossref | PubMed | ISI | Google Scholar6 Lander ES et al. (International Human Genome Sequencing Consortium). Nature 409: 860–921, 2001.Crossref | PubMed | ISI | Google Scholar7 Lynch RA, Wagoner L, Shunan L, Sparks L, Molkentin J, and Dorn GW II. Novel and nondetected human signaling protein polymorphisms. Physiol Genomics 10: 159–168, 2002. First published July 9, 2002; 10.1152/physiolgenomics.00030.2002.Link | ISI | Google Scholar8 Molkentin JD and Dorn GW II. Cytoplasmic signaling pathways that regulate cardiac hypertrophy. Annu Rev Physiol 63: 391–426, 2001.Crossref | PubMed | ISI | Google Scholar9 Ng PC and Henikoff S. Accounting for human polymorphisms predicted to affect protein function. Genome Res 12: 436–446, 2002.Crossref | PubMed | ISI | Google Scholar10 Sherry ST, Ward M, and Sirotkin K. Use of molecular variation in the NCBI dbSNP database. Hum Mutat 15: 68–75, 2000.Crossref | PubMed | ISI | Google Scholar11 Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, and Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res 29: 308–311, 2001.Crossref | PubMed | ISI | Google Scholar12 Small KM, Seman CA, Castator A, Brown KM, and Liggett SB. False positive non-synonymous polymorphisms of G-protein coupled receptor genes. FEBS Lett 516: 253–256, 2002.Crossref | PubMed | ISI | Google Scholar13 Venter JC et al. (Celera Genomics). The sequence of the human genome. Science 291: 1304–1351, 2001.Crossref | PubMed | ISI | Google Scholar Download PDF Back to Top Next FiguresReferencesRelatedInformationCited ByGenetic Factors in the Etiology of Preeclampsia/Eclampsia More from this issue > Volume 10Issue 3September 2002Pages 127-129 Copyright & PermissionsCopyright © 2002 the American Physiological Societyhttps://doi.org/10.1152/physiolgenomics.00103.2002PubMed12209015History Published online 3 September 2002 Published in print 3 September 2002 Metrics

SINGLE NUCLEOTIDE POLYMORPHISMS (SNPs) can contribute directly to disease predisposition by modifying a gene's function, or they can be used as genetic markers to detect nearby disease-causing mutations through asso- ciation or linkage studies. There are three general classes of single nucleotide variants: those with strong functional significance which dramatically alter a gene's behavior (classic mutations); those with more subtle functional effects that predispose to disease in concert with an individual's genetic background or environment (functional SNPs); and those which are completely silent with respect to function (nonfunc- tional SNPs). Single nucleotide mutations cause a de- tectable phenotype on their own and can result in the familiar Mendelian inheritance diseases. Functional SNPs are by far the most interesting class of variants since they are thought to occur at high frequencies within the general population and, when present in disadvantageous combinations, can result in disease. Examples of such common multigenic diseases are thought to include diabetes, cancer progression, and heart disease (the subject of this editorial). The distinc- tion between a mutation and a functional SNP is often vague and can be boiled down to the level of penetrance of the nucleotide variant. For example, single nucleo- tide mutations have very high penetrance and cause disease on their own in most cases, whereas functional SNPs have lowered penetrance and enhance an indi- vidual's risk for disease by a small amount. This risk can be raised when combined with another functional SNP in the genome. Finally, SNPs can be completely silent functionally. This class of single nucleotide vari- ant is by far the most common, with a polymorphic base estimated to occur approximately once every 1,300 nucleotides throughout the genome (1). These types of SNPs are useful as genetic markers to localize nearby disease-causing events through association analyses, linkage disequilibrium studies in founder

Editorial FocusCalcium, contractions, and tropomyosin Focus on “Divergent abnormal muscle relaxation by hypertrophic cardiomyopathy and nemaline myopathy mutant tropomyosins”Steven B. Marston, Joanne S. Ingwall, and Susan B. GlueckSteven B. MarstonImperial College School of Medicine at National Heart and Lung Institute, London SW3 6LY, United Kingdom, Joanne S. IngwallBrigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, and Susan B. GlueckDeputy Editor, Physiological GenomicsPublished Online:10 May 2002https://doi.org/10.1152/physiolgenomics.00038.2002MoreSectionsPDF (40 KB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations ShareShare onFacebookTwitterLinkedInWeChat in skeletal and cardiac muscle, tropomyosin in association with troponin plays an essential role in Ca2+ regulation of the thin filament interaction with myosin that is responsible for contraction. Tropomyosin is an elongated coiled-coil α-helical dimer with 284 amino acids per peptide chain (9). Three genes code for tropomyosin in striated muscle: TPM1 for α-tropomyosin; TPM2 for β-tropomyosin; and TPM3 for the α-tropomyosin of slow skeletal muscle. Although tropomyosin is a highly conserved molecule, tissue-specific variants are expressed both at the gene level and by alternative splicing of the transcript. Most fast striated muscle contains αβ-tropomyosin heterodimers, whereas the TPM3 α-tropomyosin replaces TPM1 tropomyosin in slow striated muscle fibers. By contrast, human heart muscle is composed almost exclusively of the αα -homodimer. Additional complexity arises from alternative splicing of exons 2, 6, and 9 of α-tropomyosin and exons 6 and 9 of β-tropomyosin to form variants specific to striated muscle, smooth muscle, fibroblasts, or brain.The tropomyosin sequence is fine-tuned to the physiological duties of a particular type of muscle; it is therefore not surprising that some mutations in tropomyosin are associated with muscle diseases. The mutation M9R in TPM3 is associated with autosomal dominant nemaline myopathy (2); four mutations in the human TPM1 gene (D175N, E180G, K70T, A63T) have been associated with hypertrophic cardiomyopathy (HCM) (6), and two additional mutations in TPM1 (E40K, E54K) have been associated with dilated cardiomyopathy (8).In this online release of Physiological Genomics, Michele et al. (Ref. 6; see page 103 in this release) describe the changes in contractility of adult rat cardiac myocytes expressing either an HCM mutation (A63V) or the nemaline myopathy mutation M9R. To do this, they treated the myocytes with recombinant adenoviral vectors containing full-length human α-tropomyosin cDNA incorporating the mutations. In these treated myocytes about half of the endogenous tropomyosin was replaced by human α-tropomyosin. The authors speculate that this level of expression corresponds to the expected level of the mutant protein in an affected individual who would be heterozygous. In an important study of the D175N α-tropomyosin mutation by Bottinelli et al. (1) skeletal muscle biopsy samples revealed that this is indeed the case.Based on their previous studies (4, 5) of steady-state force production in cells transfected with mutant tropomyosin, Michele et al. (6) hypothesized that the mutations would have different effects upon the dynamics of muscle contractions. The time course of single twitches of unloaded rat cardiac cells was studied. Compared with cells containing wild-type human α -tropomyosin, expression of HCM mutant tropomyosin caused a considerable slowing down of the relaxation phase. The nemaline myopathy mutation produced a different result. At 37°C, the wild-type and mutant were indistinguishable, but at 30°C, the nemaline myopathy mutant cells relaxed more quickly than the wild-type. Michele et al. (6) note that 30°C is within the physiological temperature range for limb muscles and would most likely be found in the extremities where the characteristic muscle weakness of nemaline myopathy patients is most pronounced.Importantly, during the 5-day time period of these experiments, there were no apparent changes in myocyte sarcomere ultrastructure at the electron-microscopic level. The implication drawn from these findings is that the myocyte disarray characteristic of HCM and the nemaline bodies (actually paracrystalline aggregates of tropomyosin) that are diagnostic of nemaline myopathy may be secondary consequences of acute contractile dysfunction. However, it might equally be argued that the chronic changes in cell structure in nemaline myopathy are primary and that the acute changes in contractility are not relevant to the development of the disease.The observation that different mutations in the same sarcomeric protein, or even in the same structural domain within a protein, produce different phenotypes is not new. Notable examples include the thick filament protein β-myosin heavy chain (11) and the thin filament protein troponin T (13). How do mutations in closely related genes, or even the same gene, produce divergent effects in striated muscle (3)? The answer must lie in the structure of tropomyosin and its functional interactions with the rest of the thin filament. Tropomyosin lies in the grooves of the actin double helix and forms a single strand the length of the thin filament. The key interactions are end-end interactions between adjacent tropomyosin molecules (this would involve methionine-9), multiple interactions with actin all along the tropomyosin sequence (these could involve alanine-63), and a specific interaction (primarily located in the exon 5 sequence) with troponin T (TnT) which links the troponin complex to tropomyosin.The main function of tropomyosin is to confer cooperativity upon the troponin complex so that Ca2+ switching of one troponin can control the activity of many actins. In vitro, tropomyosin alone confers a cooperative unit size of about 5 actins, rising to 10–12 when troponin is bound to the thin filament. The most important determinant for cooperative unit size seems to be the rigidity of the tropomyosin strand. Alteration of this, either by isoform switching or by mutations, can alter cooperativity and provides a mechanism by which mutations remote from the TnT binding site may nevertheless affect Ca2+ switching. A recent study comparing two tropomyosin isoforms differing only in exon 2 sequence illustrates this point (10). If the M9R mutation reduces cooperativity, it could induce the generally hypocontractile phenotype exhibited in the transfected myocytes and in the muscles of patients with nemaline myopathy; conversely, enhanced cooperativity could be responsible for the enhanced contractility (and diminished relaxation) of the A63V transfected cells and the generally hypercontractile phenotype that seems to be characteristic of hypertrophic cardiomyopathy.The energy cost for force development can differ in intact hearts bearing specific mutations in thick (12) and thin (7) filament proteins, adding another layer of complexity to the regulation of contraction. It has been suggested that reduced energy economy provides the link between these molecular changes and the development of hypertrophy. This may be important for the studies presented by Michele et al., since the energy costs of actomyosin ATPase and the SR Ca2+ pump are high in cardiac muscle.REFERENCES1 Bottinelli R, Coviello DA, Redwood CS, Pellegrino MA, Maron BJ, Spirito P, Watkins H, and Reggiani C. A mutant tropomyosin that causes hypertrophic cardiomyopathy is expressed in vivo and associated with an increased calcium sensitivity. Circ Res 82: 106–115, 1998.Crossref | PubMed | ISI | Google Scholar2 Laing NG, Wilton SD, Akkari PA, Dorosz S, Boundy K, Kneebone C, Blumbergs P, White S, Watkins H, and Love DR. A mutation in the alpha tropomyosin gene TPM3 associated with autosomal dominant nemaline myopathy. Nat Genet 9: 75–79, 1995. (Erratum appears in Nat Genet 10: 249, 1995.)Crossref | PubMed | ISI | Google Scholar3 Marston SB and Hodgkinson JL. Cardiac and skeletal myopathies: can genotype explain phenotype? J Muscle Res Cell Motil 22: 1–4, 2001.Crossref | PubMed | ISI | Google Scholar4 Michele DE, Albayya FP, and Metzger JM. Direct, convergent hypersensitivity of calcium-activated force generation produced by hypertrophic cardiomyopathy mutant alpha- tropomyosins in adult cardiac myocytes. Nat Med 5: 1413–1417, 1999.Crossref | PubMed | ISI | Google Scholar5 Michele DE, Albayya FP, and Metzger JM. A nemaline myopathy mutation in alpha-tropomyosin causes defective regulation of striated muscle force production. J Clin Invest 104: 1575–1581, 1999.Crossref | PubMed | ISI | Google Scholar6 Michele DE, Coutu P, and Metzger JM. Divergent abnormal muscle relaxation by hypertrophic cardiomyopathy and nemaline myopathy mutant tropomyosins. Physiol Genomics 9: 103–111, 2002. First published March 26, 2002; 10.1152/physiolgenomics.00099.2001.Link | ISI | Google Scholar7 Montgomery DE, Tardiff JC, and Chandra M. Cardiac troponin T mutations: correlation between the type of mutation and the nature of myofilament dysfunction in transgenic mice. J Physiol 536: 583–592, 2001Crossref | PubMed | ISI | Google Scholar8 Olson TM, Kishimoto NY, Whitby FG, and Michels VV. Mutations that alter the surface charge of alpha-tropomyosin are associated with dilated cardiomyopathy. J Mol Cell Cardiol 33: 723–732, 2001.Crossref | PubMed | ISI | Google Scholar9 Perry SV. Vertebrate tropomyosin: distribution, properties and function. J Muscle Res Cell Motil 22: 5–49, 2001.Crossref | PubMed | ISI | Google Scholar10 Sano KI, Maeda K, Taniguchi H, and Maeda Y. Amino-acid replacements in an internal region of tropomyosin alter the properties of the entire molecule. Eur J Biochem 267: 4870–4877, 2000.Crossref | PubMed | Google Scholar11 Seidman JG and Seidman C. The genetic basis for cardiomyopathy: from mutation identification to mechanistic paradigms. Cell 104: 557–567, 2001.Crossref | PubMed | ISI | Google Scholar12 Spindler M, Saupe KW, Christe ME, Sweeney HL, Seidman CE, Seidman JG, and Ingwall JS. Diastolic dysfunction and altered energetics in the alphaMHC403/+ mouse model of familial hypertrophic cardiomyopathy. J Clin Invest 101: 1775–1783, 1998.Crossref | PubMed | ISI | Google Scholar13 Tardiff JC, Hewett TE, Palmer BM, Olsson C, Factor SM, Moore RL, Robbins J, and Leinwand LA. Cardiac troponin T mutations result in allele-specific phenotypes in a mouse model for hypertrophic cardiomyopathy. J Clin Invest 104: 469–481, 1999.Crossref | PubMed | ISI | Google Scholar Download PDF Back to Top Next FiguresReferencesRelatedInformationCited ByPotential causes of sudden cardiac death in nemaline myopathy29 September 2015 | Italian Journal of Pediatrics, Vol. 41, No. 1The flexibility of two tropomyosin mutants, D175N and E180G, that cause hypertrophic cardiomyopathyBiochemical and Biophysical Research Communications, Vol. 424, No. 3Down-regulation of glutaminase C in human hepatocarcinoma cell by diphenylarsinic acid, a degradation product of chemical warfare agentsToxicology and Applied Pharmacology, Vol. 220, No. 3Congenital myopathies: diseases of the actin cytoskeleton19 October 2004 | The Journal of Pathology, Vol. 204, No. 4 More from this issue > Volume 9Issue 2May 2002Pages 57-58 Copyright & PermissionsCopyright © 2002 the American Physiological Societyhttps://doi.org/10.1152/physiolgenomics.00038.2002PubMed12006671History Published online 10 May 2002 Published in print 10 May 2002 Metrics

Editorial FocusExercise, genetics, and blood pressure: Focus on “Physical exercise and blood pressure with reference to the angiotensinogen M235T polymorphism” and on “Angiotensinogen M235T polymorphism associates with exercise hemodynamics in postmenopausal women”Larry A. Sonna, Susan B. Glueck, and Xavier JeunemaîtreLarry A. SonnaUnited States Army Research Institute of Environmental Medicine, Natick, MassachusettsBrigham and Women’s Hospital, Boston, Massachusetts, Susan B. GlueckDeputy Editor, Physiological Genomics, Hôpital Europeen Georges Pompidou AP-HP 75015, France, and Xavier JeunemaîtreDepartement de Genetique, Hôpital Europeen Georges Pompidou AP-HP 75015, FranceCollege de France-Institut National de la Santé et de la Recherche Médicale, Paris 75005, FrancePublished Online:14 Aug 2002https://doi.org/10.1152/physiolgenomics.00081.2002MoreSectionsPDF (48 KB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations ShareShare onFacebookTwitterLinkedInEmailWeChat blood pressure is a complex trait that is influenced by a number of hereditary, environmental, and culturally transmissible factors (3). Genetic factors are estimated to account for ∼30% of the variance in blood pressure in adult populations, and it appears likely that this hereditary contribution is polygenic in nature (3, 6, 18). Furthermore, there is evidence that the genetic factors contributing to resting blood pressure also affect blood pressure during exercise (19).Angiotensinogen (AGT) has attracted attention as a candidate gene contributing to blood pressure. AGT is a pro-hormone produced by the liver (and other tissues) that is converted to angiotensin I by renin. In turn, angiotensin I is converted to its active form, angiotensin II, by angiotensin converting enzyme (ACE). Angiotensin II raises systemic blood pressure by serving both as a vasoconstrictor and a stimulus (both direct and by way of aldosterone) for sodium retention by the kidney. Several studies have implicated angiotensinogen polymorphisms in the genetics of hypertension in adults and include reports of linkage (1, 4, 5, 10) and association (5, 10). By contrast, with the exception of the angiotensin II type 1 receptor gene, for which both association with (2) and linkage to (14) blood pressure have been reported, there is little evidence to suggest an independent role for other components of the renin-angiotensin system in the genetics of human blood pressure (3, 8, 9).Two papers in this release of Physiological Genomics (Refs. 12 and17; see pages 63 and 71, respectively, in this release) examine the effect of a common polymorphism in the angiotensinogen gene on blood pressure phenotypes. The polymorphism in question (M235T) involves a T-for-C substitution at nucleotide 704, which leads to a methionine-for-threonine substitution at codon 235. This polymorphism is an attractive candidate for study because of its high prevalence [with allele frequencies in the population of 0.4 and 0.6, respectively, for the T and M variants in whites (6)] and because of evidence implicating angiotensinogen in the genetics of hypertension (as noted). Furthermore, it has been reported that presence of the T allele correlates with slightly increased plasma angiotensinogen levels (reviewed in Ref. 6). However, the phenotypic effects of the M235T polymorphism may not be due to the amino acid substitution itself, but rather to a functionally significant A−6G polymorphism in the AGT gene promoter that is in linkage disequilibrium with the M235T polymorphism (reviewed in Ref. 6).The first study, by McCole and colleagues (12), examines the role of the AGT M235T polymorphism in the cardiovascular responses to exercise, in a cohort of postmenopausal women. Because of the known effect of training on blood pressure, the authors were careful to stratify their analysis by level of habitual activity in this cross-sectional study. Among their findings was a significant effect of AGT genotype on blood pressure during exercise that was dependent on level of habitual activity. In the sedentary women, but not in other subgroups, there was a statistically significant effect of AGT genotype on maximal systolic blood pressure during exercise, with subjects homozygous for the M allele (who would be expected to have the lowest levels of plasma angiotensinogen at baseline) exhibiting significantly lower maximal systolic blood pressures during exercise than subjects who were homozygous for the T allele or who were heterozygous. Importantly, the genotype-specific differences in the blood pressure during exercise in the susceptible group were quite large, with the maximum systolic pressure achieved averaging between 195 and 200 mmHg in the two sedentary women homozygous for the T allele and about 150 mmHg in the six sedentary women homozygous for the M allele. In middle-aged men, cardiovascular mortality risk has been reported to increase with rising systolic blood pressure during exercise (7, 13), even after controlling for habitual activity (7). If a similar epidemiological association exists in postmenopausal women, then differences in exercise systolic pressure of the magnitude reported by McCole et al., if confirmed, may be of clinical importance. However, the results of this study must be interpreted cautiously in light of the relatively small number of subjects (as the authors point out) and the well-known hazards of subgroup analysis. A larger, confirmatory, and longitudinal follow-up study is warranted.The second study, by Rauramaa et al. (17), examines the effect of exercise and AGT genotype on age-related gains in resting blood pressure in a cohort of middle-aged men in Finland. Subjects were randomized to no intervention or an exercise program of moderate intensity (resulting in an estimated energy expenditure of 1,300 kcal/wk, the equivalent of a 70-kg man walking a total of 3.7 h/wk at a 15 min/mile pace). The authors achieved excellent compliance with the study program and were able to obtain follow-up data on 120 of their 140 randomized subjects over a 6-yr period. Their results revealed a significant interaction between physical activity and AGT genotype. Specifically, subjects homozygous for the M allele who engaged in exercise did not demonstrate the age-related gains in resting systolic blood pressure that occurred in individuals of other AGT genotypes (heterozygotes and individuals homozygous for the T allele) who exercised and in individuals of the same AGT genotype (homozygous for the M allele) who belonged to the control (no intervention) group. Furthermore, subjects homozygous for the M allele who exercised experienced a decrease in resting diastolic blood pressure over time, whereas subjects homozygous for this allele in the control group exhibited a gain in resting diastolic blood pressure (a difference that was statistically significant). By contrast, among heterozygous subjects and those homozygous for the T allele, there were no significant differences in the changes in resting diastolic blood pressure over time between individuals who exercised and those in the control group. The observation that AGT genotype influences the effect of aerobic training on longitudinal changes in resting blood pressure over the course of several years is novel and may potentially be of clinical value, especially if confirmed in larger cohorts and other ethnic groups.The results of these and other studies suggest that the angiotensinogen M235T polymorphism influences blood pressure but that the effect is highly sensitive to environmental context (Table 1). In four of these five studies, being homozygous for the M allele correlated with lower blood pressure (systolic, diastolic, or both) in at least one subgroup, although the specific subgroup and conditions in which the effect was observed did vary somewhat from study to study (Table 1). Importantly, the effect of AGT genotype on blood pressure appears to be affected by physical activity, but here again, the precise nature of the interaction appears to be complex. Two studies (12, 15) found that physical training or habitual activity can mask the effects of AGT genotype on blood pressure during maximal exercise. However, the beneficial effect of training on resting (17) and submaximal exercise (15) diastolic blood pressure in males has also been found to be greatest in [and in one study (17), limited to] subjects homozygous for the M allele. Such apparent discrepancies are not necessarily surprising. As shown in Table 1, there were substantial methodological differences in the studies that have examined the relationship between AGT genotype, exercise, and blood pressure. As noted, blood pressure is a complex trait that is influenced by a number of genetic and nongenetic factors. Thus the effects of unmeasured genotypes and other confounding factors (such as salt intake, alcohol use, etc.) might significantly influence the effect of AGT in any given study. For example, AGT has been found to interact significantly with the angiotensin converting enzyme insertion/deletion (ACE I/D) polymorphism (15) in the response of submaximal exercise diastolic blood pressure to training in men and with body fat mass in women with respect to both resting and submaximal exercise diastolic blood pressure (16).Much remains to be learned about the role that this and other candidate genes play in blood pressure in adults. The studies presented here illustrate how genetic polymorphisms that individually account for a small part of the variance in blood pressure in the population at large might nevertheless produce potentially important effects in select subgroups. Further understanding of these genetic effects, and how they interact with environmental factors, may some day enable physicians to use genetic information to tailor preventive and therapeutic interventions to individual characteristics. Table 1. Association studies of M235T AGT genotype, blood pressure, and exerciseSubjectsMean Age, yrNStudy DesignBlood Pressure PhenotypesFindingsInteraction with Training or Habitual Activity?Ref.Adult white males22 and 45 (bimodal)25Cross-sectional; untrained subjectsResting and submaximal exercise BPLower rise in DBP with exercise in TT malesNA11Adult white males and females36229 males, 247 femalesLongitudinal (before and after a 20-wk training program)Resting, submaximal, and maximal exercise BPGreater training-related decrease in submaximal DBP in MM and MT males; lower maximal exercise DBP in MM and MT males before training but not after trainingYes15Adult white males and females (parents and offspring)53 and 25 (bimodal)257 males, 265 femalesCross-sectional; untrained subjectsResting and submaximal exercise BPLower resting DBP, resting SBP, and exercise DBP in MM females with fat mass ≥24 kg but not in leaner womenNA16Postmenopausal females6461Cross-sectional; stratified by habitual level of activityResting, submaximal, and maximal exercise BPLower submaximal and maximal SBP in sedentary MM females but not in active women and athletesYes12Adult white males57120Longitudinal (6 yr); randomized to aerobic exercise vs. habitual activityResting BP over a 6-yr periodSmaller age-related gains in SBP in MM males who exercised; decreases in DBP in MM males who exercisedYes17AGT, angiotensinogen; DBP, diastolic blood pressure; SBP, systolic blood pressure. 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