Harvard Biomarkers of Sleepiness Conference Abstracts

Several abstracts were presented at the 2010 Biomarkers of Sleepiness Conference, discussing the possible biological indicators of sleepiness.

For text of abstracts from the 2010 Biomarkers of Sleepiness Conference, click the links below:
Evaluation of Biomarkers in Chronic Disease Risk
The Next Step: Behavioral Biomarkers of Sleepiness
Exhaled Breath Analysis and Sleep
New technologies for integrating  Genomic, Enviromental and Trait Data
Biomarkers and Cancer: Lessons for Sleep Research
Behavioral and Genetic Markers of Sleepiness
Traffic and Work Safety: Nonlinear Transient Analysis to Enhance a Posturographic Sleepiness Tester
Electrophysiologic  Markers of Sleepiness
Biochemical Regulation of Sleep and Implications for a Sleep Biomarker
Association of Inflammatory Markers and Sleepiness
Potential of Proteomics as a Bioanalytic Technique for Quantifying Sleepiness
Changes in Gene Expression with Sleep

Evaluation of Biomarkers in Chronic Disease Risk

Michelle A. Albert, M.D.
Assistant Professor of Medicine, Harvard Medical School, Boston, MA

This presentation will outline the Institute of Medicine’s new framework for the evaluation of biomarkers in chronic disease risk.  The potential pitfalls of the use of biomarkers in disease risk prediction will be discussed. Beyond the applicability common biomarkers (e.g LDL-cholesterol, blood pressure and high sensitivity C-reactive protein) to cardiovascular disease research and treatment, the role of biomarkers in health claims will also be briefly discussed.
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The Next Step: Behavioral Biomarkers of Sleepiness

Thomas Balkin, Ph.D.
Chief, Department of Behavioral Biology, Walter Reed Army Institute of Research, Silver Spring, MD

For those endeavoring to develop better methods of measuring/quantifying sleepiness, the “Holy Grail” is a measure that is maximally objective, completely unobtrusive, exquisitely sensitive, and absolutely specific ( i.e., varies only as a function of sleepiness).   By these criteria, physiological measures (e.g., based on brain activity such as EEG, fMRI, near-infrared spectroscopy, etc.) would appear to hold the most promise.  However, from an operational standpoint, the utility of a sleepiness measure is derived not from its ability to sensitively reflect the brain’s extant level of sleepiness per se, but from the implications that this level of sleepiness has for the individual’s current and near-term ability to safely and efficiently perform operationally-relevant tasks.   Thus, an ideal operationally-relevant sleepiness measure is one that is unobtrusively embedded in the actual operational task, and allows sleepiness-related performance deficits to be distinguished from performance deficits due to other causes.  Toward this end, we have developed a PVT-derived metric that incorporates the entire distribution of responses within a PVT session, and reflects changes in the pattern of performance that can be used to identify and quantify ‘state instability’ – the putative physiological state that specifically underlies sleepiness-induced performance deficits.
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Exhaled Breath Analysis and Sleep

Giovanna Elisiana Carpagnano, M.D., Ph.D.
Assistant Professor of Medicine at the Department of Medical and Occupational Sciences, University of Foggia, Italy

It is currently estimated that the economic burden for obstructive sleep apnea syndrome (OSAS) cases not coming to medical attention is steadily increasing, thus making OSAS a major public health concern. For its increasing incidence amongst the common population, the interest of researchers and clinicians has been recently directed to the study of pathological mechanisms underlying sleep disorders. The current opinion is that airway inflammation and oxidative stress play a crucial role in the pathophysiology of OSAS. These key events seem to be the consequence  of the local, repeated mechanical trauma related to the intermittent airway occlusion typical of the disease. Airway inflammation is certainly a central process in obstructive sleep apnea, but monitoring inflammation is not included in the current management of this disease.
       The direct sampling of airway cells and mediators can be achieved by quite invasive techniques, such as bronchoscopy with broncho lavage and biopsy. However, these collection methods are not always well accepted by patients, as well as being not repeatable, and are therefore not suitable for clinical monitoring. Recently there has been increasing interest in the investigation of lungs by non-invasive means measuring the inflammatory cells in the induced sputum, the exhaled breath volatile mediators, such as nitric oxide (NO), carbon monoxide (CO), ethane and pentane and finally the non-volatile substances in the liquid phase of exhalate, termed breath condensate.
The non-invasiveness of these techniques for the study of airways affected by different respiratory disorders and among those, the OSAS, makes these ideally suited for the evaluation and serial monitoring of patients. Notwithstanding the increasing number of scientific contributions on the use of the exhaled markers in sleep disorders at the moment, their use is not completely suitable for clinical application. An important contribution to the increase of our knowledge on exhaled markers and for their  possible concrete application in clinical practice may come from future prospectives such as proteomics, genomics and metabolomics.
We think that our knowledge on airways inflammation and oxidative stress in OSAS is still relatively scanty, in particular on the physiopathology mechanism, as well as on the links with systemic inflammation and regarding its role in the development of the comorbidity associated with OSAS in particular cardiovascular diseases. At any rate, exhaled breath analysis do allow us to better understand these phenomena, and so it is necessary to encourage research in these directions.
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New technologies for integrating  Genomic, Enviromental and Trait Data

George Church, Ph.D.
Professor of Genetics, Harvard Medical School, Boston, MA
 
Rare diseases, which (by definition) occur at a frequency less than 1/2000 per allele -- individually rare, yet common collectively (10% affected and 50% carrier rates).  There are 1800 genes which have tests considered highly predictive and actionable.  Human genes with known variants causing  insomnia, narcolepsy, and circadian variation include PRNP, HCRT, DQB1, and PER2.  Other genes impact drug effects. We have developed human genome sequencing technology that lowered costs a million-fold over the past 6 years.   This has increasingly enabled the use of the causative alleles above, which are far more valuable than merely correlated or common variants.  To expand this further we have established community resources for open-access collection, integration and interpretation of diverse personal genomic, environmental and trait data (evidence.personalgenomes.org)
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Biomarkers and Cancer: Lessons for Sleep Research

Robin Farias-Eisner, M.D.
Professor of Obstetrics and Gynecology, University of California, Los Angeles

Ovarian cancer has the highest mortality rate amongst all gynecologic malignancies (Edwards BK, et al. Cancer 2010). At the time of diagnosis, over 85% of patients with ovarian cancer present with advanced stage III or IV disease characterized by intraperitoneal, lymphatic, and/or distant spread of disease; the poor prognosis associated with ovarian cancer is attributed to a lack of symptoms at early stages of the disease as well as a lack of biomarkers for the detection of early stage disease. Moreover, despite appropriate surgery and receiving highly effective first-line chemotherapy approximately 20-30% of patients with advanced stage disease continue to have evidence of residual disease during treatment and never have a complete clinical response (Nosov et al, AJOG 2008). Thus, there is an immediate need for both biomarkers and therapeutic targets for treating ovarian cancer.
We utilized Surface Enhanced Laser Desorption and Ionization Time-Of-Flight Mass Spectrometry (SELDI-TOF-MS) and identified 14 protein biomarkers, which comprised three separate protein panels that reliably identified early stage malignant ovarian neoplasia with high sensitivity and specificity (Kozak et al. PNAS 2003). Kozak et al. demonstrated that 3 of the 14 differentially expressed proteins are lower in the serum of patients with early stage ovarian neoplasia compared to normal individuals (Kozak et al. Proteomics 2005). The three ovarian cancer biomarkers, apolipoprotein A-I (apoA-I), transthyretin (TTR) and transferin (TF) (Kozak et al. Proteomics 2005; Su et al, Biomarker Insights, 2007), when used as a panel were better predictors of early stage ovarian cancer, compared to serum CA125 levels (Kozak et al. Proteomics 2005; Su et al, Biomarker Insights, 2007; Nosov et al, AJOG, 2008; Nosov et al, AJOG, 2009). More recently (09/12/2009), the U.S. Food and Drug Administration (FDA) cleared the first laboratory test that can indicate the likelihood of ovarian cancer, OVA1TM Test (www.medicalnewstoday.com/articles/163761.php), which utilizes apoA-I, TTR, TF, CA 125, and Beta2-Microglobulin (Beta2M).
We have recently demonstrated that apoA-I and L-4F, D-4F, or L-5F (apoA-I mimetic peptides) inhibit tumor growth and improve survival in a mouse model of ovarian cancer (Su et al., PNAS, in review 2010). These data showed that reduced levels of apoA-I in ovarian cancer patients are causal in ovarian cancer development in a mouse model. Mice expressing a human apoA-I transgene had i) increased survival (p< 0.0001), and ii) decreased tumor development (p<0.01), when compared to C57BL/6J littermates following injection both subcutaneously and intraperitoneally of mouse ovarian epithelial papillary serous adenocarcinoma cells (ID-8 cells). ApoA-I mimetic peptides inhibited viability and proliferation of ID8 cells and cis-platinum resistant human ovarian cancer cells, and decreased ID-8 cell-mediated tumor burden in C57BL/6J mice. Serum levels of lysophosphatidic acid (LPA), a well-characterized modulator of tumor cell proliferation, were significantly reduced (>50% compared to control mice, p<0.05) in mice that received apoA-I mimetic peptides suggesting that binding and removal of LPA may be a potential mechanism for the inhibition of tumor development by apoA-I mimetic peptides, which may serve as a new class of anti-cancer agents.

References
1.    Edwards BK, et al. (2010) Annual report to the nation on the status of cancer, 1975-2006, featuring colorectal cancer trends and impact of interventions (risk factors, screening, and treatment) to reduce future rates. Cancer  116:544-73.
2.    Nossov V, et al. (2008) The early detection of ovarian cancer: from traditional methods to proteomics. Can we really do better than serum CA-125? Am J Obstet Gynecol  199:215-23.
3.    Kozak KR, et al. (2003) Identification of biomarkers for ovarian cancer using strong anion-exchange ProteinChips: potential use in diagnosis and prognosis. Proc Natl Acad Sci USA     100:12343-8.
4.    Kozak KR, et al. (2005) Characterization of serum biomarkers for detection of early stage ovarian cancer. Proteomics  5:4589-96.
5.    Su F, et al. (2007) Validation of candidate serum ovarian cancer biomarkers for early detection. Biomark Insights  2:369-75.
6.    Nosov V, et al. (2009) Validation of serum biomarkers for detection of early-stage ovarian cancer. Am J Obstet Gynecol  200:639.e1-5.
7.    Su F,  et al. (2010) ApoA-I and apoA-I Mimetic Peptides Inhibit Tumor Development in a Mouse Model of Ovarian Cancer. PNAS in review
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Behavioral and Genetic Markers of Sleepiness

Namni Goel, PhD
Research Assistant Professor, Division of Sleep and Chronobiology, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA

Neurobehavioral responses to acute total and chronic partial sleep deprivation (PSD) occur in healthy adults and are particularly evident in vigilant attention performance. There are large inter-individual differences in the degree of cognitive deficits—these are manifested in proportionality between the mean and variance as sleep loss progresses. It has recently been demonstrated experimentally that differential neurobehavioral vulnerability to sleep deprivation is not random—but rather is stable and trait-like—strongly suggesting an underlying genetic component. These experiments also showed that vulnerability was not explained by subjects’ baseline functioning or a number of other potential predictors. Intraclass correlation coefficients (ICCs) revealed a substantial magnitude of interindividual variability accounting for 60-90% of the variance, even in the presence of a potent state variable (prior sleep history), in three domains of neurobehavioral outcomes: (1) cognitive processing task performance capability; (2) behavioral alertness/attention as measured by the Psychomotor Vigilance Test (PVT); and (3) self-evaluation measures (subjective outcomes of sleepiness, fatigue and mood). This differential vulnerability also has been shown to extend to chronic PSD. One potential biomarker for such differential vulnerability is the human leukocyte antigen (HLA) DQB1*0602, which has been demonstrated to predict interindividual differences in sleepiness, physiological sleep, and fatigue to chronic PSD.

Funding: National Space Biomedical Research Institute through NASA NCC 9-58, NIH NR004281, CTRC UL1RR024134 and the Institute for Translational Medicine and Therapeutics’ (ITMAT) Transdisciplinary Program in Translational Medicine and Therapeutics.
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Traffic and Work Safety: Nonlinear Transient Analysis to Enhance a Posturographic Sleepiness Tester

Edward Haeggstrom, Ph.D., M.B.A.
Professor of Electronics and Measurements
University of Helsinki, Helsinki, Finland

Briefly: We try to develop a portable sleepiness tester. We present efforts towards this goal detailing instrumentation, simulations, data analysis as well as validation efforts in the lab and at the roadside.

Deliverable: Improved work and traffic safety with a posturographic sleepiness tester.
Problem: No ‘sleepiness breathalyzer exists. A sleepiness tester could improve work and traffic safety; it can determine whether a person is alert enough to work and drive safely.
Solution: A tester that quantifies sleepiness by implementing a state-of-the-art signal processing linear&nonlinear –transient analysis. This instrument is tested in traffic surveillance.
Why do it: This research addresses a current issue –sleepiness– that has global and costly impact. It is beyond its initial state, aiming now to a field-usable device.  

Motivation: Sleepiness is the largest identifiable and preventable cause of accidents in commercial transport corresponding to 10-12% of all accidents. The cost of sleepiness-related accidents is $80 billion worldwide. At-risk professions, such as car- and truck drivers, pilots and medical doctors, could benefit from a fast, simple, and reliable sleepiness tester.

Method: Posturography evaluates postural steadiness (balance) by recording a subject’s center-of-pressure (COP) trace while he/she stands erect on a force plate. Balance deteriorates during sustained wakefulness exhibiting a sinusoidal component due to the circadian rhythm and a monotonous homeostatic component due to increased time awake (TA). To quantify sleepiness, our tester estimates TA rather than the more ambiguous sleepiness.
Transient detection and characterization: Balance is classically quantified with continuous sway measures related to: -amplitudes, -distances and -frequencies. Since COP traces are non-stationary, these measures discard local (in time) information. Transients are unexpected, short-duration events in a signal, manifested as a brief change in phase, frequency, amplitude or internal fractality. We implement transient analysis (wavelets, fractal dimension, and multiscale fuzzy sample entropy) for quantitative sleepiness estimation. The TA estimation accuracy in laboratory tests was ±2.8 h. The system was also simulated based with an inverted pendulum model.

Validation of a posturographic approach to monitor sleepiness: We evaluated the accuracy (86%) and precision (97%) of repeated estimates of TA: during 36 hours of sustained wakefulness, then once a day over one week, and finally once a week over one month.
Field use: We used the Nintendo Wii Fit and a clinical platform in the field (n=76).
Outlook: to establish whether post-anesthesia posturography can infer states of extreme sleepiness (equivalent to 100+ hours of acute sleep deprivation).
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Electrophysiologic  Markers of Sleepiness

James M. Krueger, Ph.D.
Regents Professor of Veterinary and Comparative Anatomy, Pharmacology & Physiology Washington State University, Pullman, WA

The first quantification of EEG delta power after sleep loss was done in John Pappenheimer’s Harvard laboratory; EEG delta waves during NREMS after sleep deprivation are enhanced (J. Neurophy. 38:1299, 1975).  That observation eventually led to the use of EEG delta power as a parameter to model process S in the two-process model of sleep.  It works remarkably well as a model parameter because it often co-varies with sleep duration and intensity.  Nevertheless there is a large literature indicating that EEG delta power is regulated independently of sleep duration.  For example, high amplitude EEG delta waves occur in wakefulness after systemic atropine administration or after hyperventilation in children.  Human neonates have periods of sleep with an almost flat EEG.  Similarly, elderly people have reduced EEG delta power, yet retain substantial NREMS.  Rats provided with a cafeteria diet have excess duration of NREMS but simultaneously decreased EEG delta power for days.  Mice challenged with influenza virus have excessive EEG delta power and NREMS.  In contrast if mice lacking TNF receptors are infected, they still sleep more but have reduced EEG delta power.  Sleep regulatory substances, e.g. IL1, TNF, and GHRH, directly injected unilaterally onto the cortex induce state-dependent ipsilateral enhancement of EEG delta power without changing duration of organism sleep.  IL1 given systemically enhances duration of NREMS but reduces EEG delta power in mice.  Benzodiazepines enhance NREMS but inhibit EEG delta power.  If duration of NREMS is an indicator of prior sleepiness then simultaneous EEG delta power may or may not be a useful index of sleepiness.  Finally, most sleep regulatory substances are cerebral vasodilators and blood flow affects EEG delta power.  In conclusion, it seems unlikely that a single EEG measure will be reliable as a marker of sleepiness for all conditions.
Supported by NIH (USA) NS25378 and NS31453
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Biochemical Regulation of Sleep and Implications for a Sleep Biomarker

James M. Krueger, Ph.D.
Regents Professor of Veterinary and Comparative Anatomy, Pharmacology & Physiology Washington State University, Pullman, WA

Symptoms commonly associated with sleep loss and chronic inflammation include sleepiness, fatigue, poor cognition, enhanced sensitivity to pain and kindling stimuli, excess sleep and increases in circulating levels of tumor necrosis factor alpha (TNF) in humans and brain levels of interleukin-1 beta (IL1) and TNF in animals.  Cytokines including IL1 and TNF partake in non-rapid eye movement sleep (NREMS) regulation under physiological and inflammatory conditions.  Administration of exogenous IL1 or TNF mimics the accumulation of these cytokines occurring during sleep loss to the extent that it induces the aforementioned symptoms.  Extracellular ATP associated with neuro- and glio-transmission, acting via purine type 2 receptors, e.g. the P2X7 receptor, has a role in glia release of IL1 and TNF.  These substances in turn act on neurons to change their intrinsic membrane properties and sensitivities to neurotransmitters and neuromodulators such as adenosine, glutamate and GABA.  These actions change the network input-output properties, i.e. a state shift for the network.  State oscillations occur locally within cortical columns and are defined using evoked response potentials.  One such state, so defined, shares properties with whole animal sleep in that it is dependent on prior cellular activity—it shows homeostasis. The cortical column sleep-like state is induced by TNF and is associated with experimental performance detriments.  ATP released extracellularly as a consequence of cellular activity is posited to initiate a mechanism by which the brain tracks its prior sleep-state history to induce/prohibit sleep.  Thus, sleep is an emergent property of populations of local neural networks undergoing state transitions.  Specific neuronal groups participating in sleep depend upon prior network use driving local network state changes via the ATP-cytokine-adenosine mechanism.  Such considerations add complexity to finding biochemical markers for sleepiness.
Supported by NIH (USA) NS25378 and NS31453
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Association of Inflammatory Markers and Sleepiness

Michelle Miller, Ph.D.
Associate Professor (Reader) of Biochemical Medicine
University of Warwick Medical School, Coventry, UK

This talk will consider inflammatory markers as biomarkers of sleepiness by reference to the use of biomarkers in cardiovascular disease. It will briefly examine the role of inflammation in the development of atherosclerosis.  It will examine the utility of inflammatory markers and, in particular, adhesion molecules as a biomarker for cardiovascular risk and the factors that affect their level in the circulation. It will also examine the relationship between sleep and markers of inflammation and the role of sleep in immune function. It will briefly address the biochemical pathways and signalling systems involved. The emerging evidence which suggests that disturbances in sleep and sleep disorders play a role in the morbidity of chronic conditions including obesity and hypertension as well as in the development of type 2 diabetes, will be discussed.
The hypothesis that a lack of sleep (both short term and chronic) may adversely affect inflammatory processes which lead to an increase in cardiovascular disease (CVD) will be explored along with evidence to suggest that sleep may have an effect on thrombotic factors important in atherosclerotic development. The relationships between known sleep disorders and cardiovascular risk, along with the role of inflammation in the development of obstructive sleep apnoea (OSA) will also be briefly discussed. The question as to whether the association between sleep and inflammation merely reflects cardiovascular disease progression or whether it may be in some way causally related will be considered. Possible effects of age, gender, ethnicity and genetic make up on these relationships will be examined. The utility of inflammatory markers as a biomarker for sleepiness will thus be presented.
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Potential of Proteomics as a Bioanalytic Technique for Quantifying Sleepiness

Nirinjini Naidoo, Ph.D.
Research Assistant Professor, Division of Sleep Medicine
University of Pennsylvania, Philadelphia, PA

Sleep loss is common in the American population (IOM report on Sleep Disorders and Sleep Deprivation: An Unmet Public Health Problem, 2006). Sleep deprivation can result from a period of acute sleep loss or from insufficient sleep day after day. Sleep loss has a number of consequences. It leads to what has been termed wake-state instability. This results in lapses in performance and also compromises other aspects of cognitive function including executive attention and working memory. Sleep loss also has important metabolic and cardiovascular consequences. Epidemiological studies indicate an association between sleep loss and increased rates of obesity, type-2 diabetes and an increased risk of cardiovascular disease.   
    Currently, however, we do not have simple ways to assess the degree of sleep loss in individual subjects. We do know that there is a large difference between individuals in how affected they are by sleep loss; some individuals are relatively resistant while others are markedly affected. In a 36 hour sleep deprivation and performance study carried out in monozygotic and dyzygotic twins we determined that the behavioral response to sleep loss has high heritability, i.e., 0.80. Subsequently, we obtained blood samples every 4 hours during baseline (normal sleep/wake), during sleep deprivation and then recovery sleep from 10 individuals (only one member of any twin pair) who had the lowest behavioral response to sleep deprivation, i.e., few lapses, and 10 individuals (only one member of any twin pair) who had the highest behavioral response to sleep deprivation (high responder) for microarray and proteomic studies. We are currently using a broad state of the art plasma proteomic strategy to assess proteins changing expression over 36 hours of sleep deprivation in both groups. The study to be presented will include a discovery strategy using pooled samples at 12 hr, 24 hr and 36 hr of sleep deprivation and a validation protocol based on individual subjects. It is anticipated that proteins related to sleep drive should change expression progressively (either up or down) in relationship to increasing sleep drive with sleep loss.
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Changes in Gene Expression with Sleep

Matthew S. Thimgan, Ph.D., Stephen P. Duntley, M.D., and Paul J. Shaw
Department of Anatomy and Neurobiology, Washington University, St. Louis. MO

The sleep community has reached consensus that an important step for minimizing the negative impact of sleep loss on public health and safety is to identify simple and quantifiable biomarkers of sleepiness. Given the complexity of this problem, we have developed a strategy for identifying biomarkers of sleepiness using genetic and pharmacological tools that dissociate sleep drive from wake time in the model organism Drosophila melanogaster. With these protocols it is possible to quickly and efficiently determine whether an analyte behaves as a biomarker of sleepiness or whether it is simply and non-specifically activated during waking. Candidate analytes that behave as biomarkers in flies can then be evaluated in human subjects to determine if the response to sleep loss is evolutionarily conserved. Using this approach we have identified a biomarker in flies, Amylase, and subsequently demonstrated that Amylase mRNA is upregulated in saliva of healthy adults following 28 h of waking. We have now extended these results to show that Amylase mRNA is substantially elevated in patients with obstructive sleep apnea. Thus, the fly can be used as a discovery tool to identify biomarkers of sleepiness. Indeed, we have begun to identify several biomarkers in flies. Unfortunately, many of these products are not found in human saliva. Thus we initiated a discovery protocol to identify candidate analytes in saliva from sleep deprived humans using TaqMan® Low Density Arrays. Although these experiments have successfully identified several candidate genes that are differentially regulated during sleep loss in humans, it is not possible to establish these candidates as biomarkers based upon a single study. Thus, we examined transcript levels in flies using the genetic and pharmacological protocols mentioned above. Interestingly, we found that a candidate, integrin alpha 5 (ITGA5), was upregulated in humans following sleep deprivation and behaved as a biomarker of sleepiness in flies across a variety of genetic and pharmacological conditions. Together these data demonstrate that the fly can be used to identify biomarkers of sleepiness in humans, and that candidate genes identified in humans can, in turn, be validated in the fly.
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