Publications by authors named "Seth A Ament"

24 Publications

  • Page 1 of 1

Genetic versus stress and mood determinants of sleep in the Amish.

Am J Med Genet B Neuropsychiatr Genet 2021 03 1;186(2):113-121. Epub 2021 Mar 1.

Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA.

Sleep is essential to the human brain and is regulated by genetics with many features conserved across species. Sleep is also influenced by health and environmental factors; identifying replicable genetic variants contributing to sleep may require accounting for these factors. We examined how stress and mood disorder contribute to sleep and impact its heritability. Our sample included 326 Amish/Mennonite individuals with a lifestyle with limited technological interferences with sleep. Sleep measures included Pittsburgh Sleep Quality Index (PSQI), bedtime, wake time, and time to sleep onset. Current stress level, cumulative life stressors, and mood disorder were also evaluated. We estimated the heritability of sleep features and examined the impact of current stress, lifetime stress, mood diagnosis on sleep quality. The results showed current stress, lifetime stress, and mood disorder were independently associated with PSQI score (p < .05). Heritability of PSQI was low (0-0.23) before and after accounting for stress and mood. Bedtime, wake time, and minutes to sleep time did show significant heritability at 0.44, 0.42, and 0.29. However, after adjusting for shared environment, only heritability of wake time remained significant. Sleep is affected by environmental stress and mental health factors even in a society with limited technological interference with sleep. Wake time may be a more biological marker of sleep as compared to the evening measures which are more influenced by other household members. Accounting for nongenetic and partially genetic determinants of sleep particularly stress and mood disorder is likely important for improving the precision of genetic studies of sleep.
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http://dx.doi.org/10.1002/ajmg.b.32840DOI Listing
March 2021

Repeated sampling facilitates within- and between-subject modeling of the human sperm transcriptome to identify dynamic and stress-responsive sncRNAs.

Sci Rep 2020 10 15;10(1):17498. Epub 2020 Oct 15.

Department of Pharmacology and Center for Epigenetic Research in Child Health and Brain Development, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.

Epidemiological studies from the last century have drawn strong associations between paternal life experiences and offspring health and disease outcomes. Recent studies have demonstrated sperm small non-coding RNA (sncRNA) populations vary in response to diverse paternal insults. However, for studies in retrospective or prospective human cohorts to identify changes in paternal germ cell epigenetics in association with offspring disease risk, a framework must first be built with insight into the expected biological variation inherent in human populations. In other words, how will we know what to look for if we don't first know what is stable and what is dynamic, and what is consistent within and between men over time? From sperm samples from a 'normative' cohort of healthy human subjects collected repeatedly from each subject over 6 months, 17 healthy male participants met inclusion criteria and completed donations and psychological evaluations of perceived stress monthly. sncRNAs (including miRNA, piRNA, and tRNA) isolated from mature sperm from these samples were subjected to Illumina small RNA sequencing, aligned to subtype-specific reference transcriptomes, and quantified. The repeated measures design allowed us to define both within- and between-subject variation in the expression of 254 miRNA, 194 tRNA, and 937 piRNA in sperm over time. We developed screening criteria to identify a subset of potential environmentally responsive 'dynamic' sperm sncRNA. Implementing complex modeling of the relationships between individual dynamic sncRNA and perceived stress states in these data, we identified 5 miRNA (including let-7f-5p and miR-181a-5p) and 4 tRNA that are responsive to the dynamics of prior stress experience and fit our established mouse model. In the current study, we aligned repeated sampling of human sperm sncRNA expression data with concurrent measures of perceived stress as a novel framework that can now be applied across a range of studies focused on diverse environmental factors able to influence germ cell programming and potentially impact offspring development.
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http://dx.doi.org/10.1038/s41598-020-73867-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7562703PMC
October 2020

Biological insights from multi-omic analysis of 31 genomic risk loci for adult hearing difficulty.

PLoS Genet 2020 09 28;16(9):e1009025. Epub 2020 Sep 28.

Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, United States of America.

Age-related hearing impairment (ARHI), one of the most common medical conditions, is strongly heritable, yet its genetic causes remain largely unknown. We conducted a meta-analysis of GWAS summary statistics from multiple hearing-related traits in the UK Biobank (n = up to 330,759) and identified 31 genome-wide significant risk loci for self-reported hearing difficulty (p < 5x10-8), of which eight have not been reported previously in the peer-reviewed literature. We investigated the regulatory and cell specific expression for these loci by generating mRNA-seq, ATAC-seq, and single-cell RNA-seq from cells in the mouse cochlea. Risk-associated genes were most strongly enriched for expression in cochlear epithelial cells, as well as for genes related to sensory perception and known Mendelian deafness genes, supporting their relevance to auditory function. Regions of the human genome homologous to open chromatin in epithelial cells from the mouse were strongly enriched for heritable risk for hearing difficulty, even after adjusting for baseline effects of evolutionary conservation and cell-type non-specific regulatory regions. Epigenomic and statistical fine-mapping most strongly supported 50 putative risk genes. Of these, 39 were expressed robustly in mouse cochlea and 16 were enriched specifically in sensory hair cells. These results reveal new risk loci and risk genes for hearing difficulty and suggest an important role for altered gene regulation in the cochlear sensory epithelium.
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http://dx.doi.org/10.1371/journal.pgen.1009025DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7544108PMC
September 2020

Atlas of Transcription Factor Binding Sites from ENCODE DNase Hypersensitivity Data across 27 Tissue Types.

Cell Rep 2020 08;32(7):108029

Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA; Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD 21201, USA. Electronic address:

Characterizing the tissue-specific binding sites of transcription factors (TFs) is essential to reconstruct gene regulatory networks and predict functions for non-coding genetic variation. DNase-seq footprinting enables the prediction of genome-wide binding sites for hundreds of TFs simultaneously. Despite the public availability of high-quality DNase-seq data from hundreds of samples, a comprehensive, up-to-date resource for the locations of genomic footprints is lacking. Here, we develop a scalable footprinting workflow using two state-of-the-art algorithms: Wellington and HINT. We apply our workflow to detect footprints in 192 ENCODE DNase-seq experiments and predict the genomic occupancy of 1,515 human TFs in 27 human tissues. We validate that these footprints overlap true-positive TF binding sites from ChIP-seq. We demonstrate that the locations, depth, and tissue specificity of footprints predict effects of genetic variants on gene expression and capture a substantial proportion of genetic risk for complex traits.
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http://dx.doi.org/10.1016/j.celrep.2020.108029DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7462736PMC
August 2020

Clinical and genetic validity of quantitative bipolarity.

Transl Psychiatry 2019 09 16;9(1):228. Epub 2019 Sep 16.

Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, 21228, USA.

Research has yet to provide a comprehensive understanding of the genetic basis of bipolar disorder (BP). In genetic studies, defining the phenotype by diagnosis may miss risk-allele carriers without BP. The authors aimed to test whether quantitatively detected subclinical symptoms of bipolarity identifies a heritable trait that infers risk for BP. The Quantitative Bipolarity Scale (QBS) was administered to 310 Old Order Amish or Mennonite individuals from multigenerational pedigrees; 110 individuals had psychiatric diagnoses (20 BP, 61 major depressive disorders (MDD), 3 psychotic disorders, 26 other psychiatric disorders). Familial aggregation of QBS was calculated using the variance components method to derive heritability and shared household effects. The QBS score was significantly higher in BP subjects (31.5 ± 3.6) compared to MDD (16.7 ± 2.0), other psychiatric diagnoses (7.0 ± 1.9), and no psychiatric diagnosis (6.0 ± 0.65) (all p < 0.001). QBS in the whole sample was significantly heritable (h = 0.46 ± 0.15, p < 0.001) while the variance attributed to the shared household effect was not significant (p = 0.073). When subjects with psychiatric illness were removed, the QBS heritability was similar (h = 0.59 ± 0.18, p < 0.001). These findings suggest that quantitative bipolarity as measured by QBS can separate BP from other psychiatric illnesses yet is significantly heritable with and without BP included in the pedigrees suggesting that the quantitative bipolarity describes a continuous heritable trait that is not driven by a discrete psychiatric diagnosis. Bipolarity trait assessment may be used to supplement the diagnosis of BP in future genetic studies and could be especially useful for capturing subclinical genetic contributions to a BP phenotype.
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http://dx.doi.org/10.1038/s41398-019-0561-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6746871PMC
September 2019

Genome-Scale Transcriptional Regulatory Network Models of Psychiatric and Neurodegenerative Disorders.

Cell Syst 2019 02 13;8(2):122-135.e7. Epub 2019 Feb 13.

Institute for Systems Biology, Seattle, WA, USA; Institute of Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA. Electronic address:

Transcriptional regulatory changes in the developing and adult brain are prominent features of brain diseases, but the involvement of specific transcription factors (TFs) remains poorly understood. We integrated brain-specific DNase footprinting and TF-gene co-expression to reconstruct a transcriptional regulatory network (TRN) model for the human brain. We identified key regulator TFs whose predicted target genes were enriched for differentially expressed genes in the prefrontal cortex of individuals with psychiatric and neurodegenerative diseases. Many of these TFs were further implicated in the same diseases through disruption of their binding sites by disease-associated SNPs and associations of TF loci with disease risk. Using primary human neural stem cells, we validated network predictions that link the TF POU3F2 to schizophrenia and bipolar disorder via both cis- and trans-acting mechanisms. Our models of brain-specific TF binding sites and target genes provide a resource for network analysis of brain diseases.
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http://dx.doi.org/10.1016/j.cels.2019.01.002DOI Listing
February 2019

Rediscovering the value of families for psychiatric genetics research.

Mol Psychiatry 2019 04 28;24(4):523-535. Epub 2018 Jun 28.

South Texas Diabetes and Obesity Institute, Department of Human Genetics, School of Medicine, University of Texas of the Rio Grande Valley, Brownsville, TX, USA.

As it is likely that both common and rare genetic variation are important for complex disease risk, studies that examine the full range of the allelic frequency distribution should be utilized to dissect the genetic influences on mental illness. The rate limiting factor for inferring an association between a variant and a phenotype is inevitably the total number of copies of the minor allele captured in the studied sample. For rare variation, with minor allele frequencies of 0.5% or less, very large samples of unrelated individuals are necessary to unambiguously associate a locus with an illness. Unfortunately, such large samples are often cost prohibitive. However, by using alternative analytic strategies and studying related individuals, particularly those from large multiplex families, it is possible to reduce the required sample size while maintaining statistical power. We contend that using whole genome sequence (WGS) in extended pedigrees provides a cost-effective strategy for psychiatric gene mapping that complements common variant approaches and WGS in unrelated individuals. This was our impetus for forming the "Pedigree-Based Whole Genome Sequencing of Affective and Psychotic Disorders" consortium. In this review, we provide a rationale for the use of WGS with pedigrees in modern psychiatric genetics research. We begin with a focused review of the current literature, followed by a short history of family-based research in psychiatry. Next, we describe several advantages of pedigrees for WGS research, including power estimates, methods for studying the environment, and endophenotypes. We conclude with a brief description of our consortium and its goals.
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http://dx.doi.org/10.1038/s41380-018-0073-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7028329PMC
April 2019

Transcriptional regulatory networks underlying gene expression changes in Huntington's disease.

Mol Syst Biol 2018 03 26;14(3):e7435. Epub 2018 Mar 26.

Institute for Systems Biology, Seattle, WA, USA

Transcriptional changes occur presymptomatically and throughout Huntington's disease (HD), motivating the study of transcriptional regulatory networks (TRNs) in HD We reconstructed a genome-scale model for the target genes of 718 transcription factors (TFs) in the mouse striatum by integrating a model of genomic binding sites with transcriptome profiling of striatal tissue from HD mouse models. We identified 48 differentially expressed TF-target gene modules associated with age- and CAG repeat length-dependent gene expression changes in CAG knock-in mouse striatum and replicated many of these associations in independent transcriptomic and proteomic datasets. Thirteen of 48 of these predicted TF-target gene modules were also differentially expressed in striatal tissue from human disease. We experimentally validated a specific model prediction that SMAD3 regulates HD-related gene expression changes using chromatin immunoprecipitation and deep sequencing (ChIP-seq) of mouse striatum. We found CAG repeat length-dependent changes in the genomic occupancy of SMAD3 and confirmed our model's prediction that many SMAD3 target genes are downregulated early in HD.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5868199PMC
http://dx.doi.org/10.15252/msb.20167435DOI Listing
March 2018

Peripheral huntingtin silencing does not ameliorate central signs of disease in the B6.HttQ111/+ mouse model of Huntington's disease.

PLoS One 2017 28;12(4):e0175968. Epub 2017 Apr 28.

Behavioral Neuroscience Program, Psychology Department, Western Washington University, Bellingham, WA, United States of America.

Huntington's disease (HD) is an autosomal dominant neurodegenerative disease whose predominant neuropathological signature is the selective loss of medium spiny neurons in the striatum. Despite this selective neuropathology, the mutant protein (huntingtin) is found in virtually every cell so far studied, and, consequently, phenotypes are observed in a wide range of organ systems both inside and outside the central nervous system. We, and others, have suggested that peripheral dysfunction could contribute to the rate of progression of striatal phenotypes of HD. To test this hypothesis, we lowered levels of huntingtin by treating mice with antisense oligonucleotides (ASOs) targeting the murine Huntingtin gene. To study the relationship between peripheral huntingtin levels and striatal HD phenotypes, we utilized a knock-in model of the human HD mutation (the B6.HttQ111/+ mouse). We treated mice with ASOs from 2-10 months of age, a time period over which significant HD-relevant signs progressively develop in the brains of HttQ111/+ mice. Peripheral treatment with ASOs led to persistent reduction of huntingtin protein in peripheral organs, including liver (64% knockdown), brown adipose (66% knockdown), and white adipose tissues (71% knockdown). This reduction was not associated with alterations in the severity of HD-relevant signs in the striatum of HttQ111/+ mice at the end of the study, including transcriptional dysregulation, the accumulation of neuronal intranuclear inclusions, and behavioral changes such as subtle hypoactivity and reduced exploratory drive. These results suggest that the amount of peripheral reduction achieved in the current study does not significantly impact the progression of HD-relevant signs in the central nervous system.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0175968PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5409169PMC
September 2017

High resolution time-course mapping of early transcriptomic, molecular and cellular phenotypes in Huntington's disease CAG knock-in mice across multiple genetic backgrounds.

Hum Mol Genet 2017 03;26(5):913-922

Institute for Systems Biology, Seattle, WA, USA.

Huntington's disease is a dominantly inherited neurodegenerative disease caused by the expansion of a CAG repeat in the HTT gene. In addition to the length of the CAG expansion, factors such as genetic background have been shown to contribute to the age at onset of neurological symptoms. A central challenge in understanding the disease progression that leads from the HD mutation to massive cell death in the striatum is the ability to characterize the subtle and early functional consequences of the CAG expansion longitudinally. We used dense time course sampling between 4 and 20 postnatal weeks to characterize early transcriptomic, molecular and cellular phenotypes in the striatum of six distinct knock-in mouse models of the HD mutation. We studied the effects of the HttQ111 allele on the C57BL/6J, CD-1, FVB/NCr1, and 129S2/SvPasCrl genetic backgrounds, and of two additional alleles, HttQ92 and HttQ50, on the C57BL/6J background. We describe the emergence of a transcriptomic signature in HttQ111/+  mice involving hundreds of differentially expressed genes and changes in diverse molecular pathways. We also show that this time course spanned the onset of mutant huntingtin nuclear localization phenotypes and somatic CAG-length instability in the striatum. Genetic background strongly influenced the magnitude and age at onset of these effects. This work provides a foundation for understanding the earliest transcriptional and molecular changes contributing to HD pathogenesis.
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http://dx.doi.org/10.1093/hmg/ddx006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6075528PMC
March 2017

Motivational, proteostatic and transcriptional deficits precede synapse loss, gliosis and neurodegeneration in the B6.Htt model of Huntington's disease.

Sci Rep 2017 02 8;7:41570. Epub 2017 Feb 8.

Behavioral Neuroscience Program, Department of Psychology, Western Washington University, Bellingham, WA, USA.

We investigated the appearance and progression of disease-relevant signs in the B6.Htt mouse, a genetically precise model of the mutation that causes Huntington's disease (HD). We find that B6.Htt mice are healthy, show no overt signs of central or peripheral inflammation, and no gross motor impairment as late as 12 months of age. Behaviorally, we find that 4-9 month old B6.Htt mice have normal activity levels and show no clear signs of anxiety or depression, but do show clear signs of reduced motivation. The neuronal density, neuronal size, synaptic density and number of glia is normal in B6.Htt striatum, the most vulnerable brain region in HD, up to 12 months of age. Despite this preservation of the synaptic and cellular composition of the striatum, we observe clear progressive, striatal-specific transcriptional dysregulation and accumulation of neuronal intranuclear inclusions (NIIs). Simulation studies suggest these molecular endpoints are sufficiently robust for future preclinical studies, and that B6.Htt mice are a useful tool for modeling disease-modifying or neuroprotective strategies for disease processes before the onset of overt phenotypes.
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http://dx.doi.org/10.1038/srep41570DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5296868PMC
February 2017

Diet and endocrine effects on behavioral maturation-related gene expression in the pars intercerebralis of the honey bee brain.

J Exp Biol 2015 Dec 13;218(Pt 24):4005-14. Epub 2015 Nov 13.

Department of Entomology, UIUC, Urbana, IL 61801, USA Institute for Genomic Biology, UIUC, Urbana, IL 61801, USA

Nervous and neuroendocrine systems mediate environmental conditions to control a variety of life history traits. Our goal was to provide mechanistic insights as to how neurosecretory signals mediate division of labor in the honey bee (Apis mellifera). Worker division of labor is based on a process of behavioral maturation by individual bees, which involves performing in-hive tasks early in adulthood, then transitioning to foraging for food outside the hive. Social and nutritional cues converge on endocrine factors to regulate behavioral maturation, but whether neurosecretory systems are central to this process is not known. To explore this, we performed transcriptomic profiling of a neurosecretory region of the brain, the pars intercerebralis (PI). We first compared PI transcriptional profiles for bees performing in-hive tasks and bees engaged in foraging. Using these results as a baseline, we then performed manipulative experiments to test whether the PI is responsive to dietary changes and/or changes in juvenile hormone (JH) levels. Results reveal a robust molecular signature of behavioral maturation in the PI, with a subset of gene expression changes consistent with changes elicited by JH treatment. In contrast, dietary changes did not induce transcriptomic changes in the PI consistent with behavioral maturation or JH treatment. Based on these results, we propose a new verbal model of the regulation of division of labor in honey bees in which the relationship between diet and nutritional physiology is attenuated, and in its place is a relationship between social signals and nutritional physiology that is mediated by JH.
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http://dx.doi.org/10.1242/jeb.119420DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6514457PMC
December 2015

Identification of copy number variants in whole-genome data using Reference Coverage Profiles.

Front Genet 2015 17;6:45. Epub 2015 Feb 17.

Institute for Systems Biology Seattle, WA, USA.

The identification of DNA copy numbers from short-read sequencing data remains a challenge for both technical and algorithmic reasons. The raw data for these analyses are measured in tens to hundreds of gigabytes per genome; transmitting, storing, and analyzing such large files is cumbersome, particularly for methods that analyze several samples simultaneously. We developed a very efficient representation of depth of coverage (150-1000× compression) that enables such analyses. Current methods for analyzing variants in whole-genome sequencing (WGS) data frequently miss copy number variants (CNVs), particularly hemizygous deletions in the 1-100 kb range. To fill this gap, we developed a method to identify CNVs in individual genomes, based on comparison to joint profiles pre-computed from a large set of genomes. We analyzed depth of coverage in over 6000 high quality (>40×) genomes. The depth of coverage has strong sequence-specific fluctuations only partially explained by global parameters like %GC. To account for these fluctuations, we constructed multi-genome profiles representing the observed or inferred diploid depth of coverage at each position along the genome. These Reference Coverage Profiles (RCPs) take into account the diverse technologies and pipeline versions used. Normalization of the scaled coverage to the RCP followed by hidden Markov model (HMM) segmentation enables efficient detection of CNVs and large deletions in individual genomes. Use of pre-computed multi-genome coverage profiles improves our ability to analyze each individual genome. We make available RCPs and tools for performing these analyses on personal genomes. We expect the increased sensitivity and specificity for individual genome analysis to be critical for achieving clinical-grade genome interpretation.
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http://dx.doi.org/10.3389/fgene.2015.00045DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4330915PMC
March 2015

Rare variants in neuronal excitability genes influence risk for bipolar disorder.

Proc Natl Acad Sci U S A 2015 Mar 17;112(11):3576-81. Epub 2015 Feb 17.

Institute for Systems Biology, Seattle, WA 98109;

We sequenced the genomes of 200 individuals from 41 families multiply affected with bipolar disorder (BD) to identify contributions of rare variants to genetic risk. We initially focused on 3,087 candidate genes with known synaptic functions or prior evidence from genome-wide association studies. BD pedigrees had an increased burden of rare variants in genes encoding neuronal ion channels, including subunits of GABAA receptors and voltage-gated calcium channels. Four uncommon coding and regulatory variants also showed significant association, including a missense variant in GABRA6. Targeted sequencing of 26 of these candidate genes in an additional 3,014 cases and 1,717 controls confirmed rare variant associations in ANK3, CACNA1B, CACNA1C, CACNA1D, CACNG2, CAMK2A, and NGF. Variants in promoters and 5' and 3' UTRs contributed more strongly than coding variants to risk for BD, both in pedigrees and in the case-control cohort. The genes and pathways identified in this study regulate diverse aspects of neuronal excitability. We conclude that rare variants in neuronal excitability genes contribute to risk for BD.
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http://dx.doi.org/10.1073/pnas.1424958112DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4371952PMC
March 2015

An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge.

Genome Biol 2014 Mar 25;15(3):R53. Epub 2014 Mar 25.

Background: There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance.

Results: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization.

Conclusions: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups.
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http://dx.doi.org/10.1186/gb-2014-15-3-r53DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4073084PMC
March 2014

Cell type-specific genes show striking and distinct patterns of spatial expression in the mouse brain.

Proc Natl Acad Sci U S A 2013 Feb 5;110(8):3095-100. Epub 2013 Feb 5.

Institute for Genomic Biology and Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

To characterize gene expression patterns in the regional subdivisions of the mammalian brain, we integrated spatial gene expression patterns from the Allen Brain Atlas for the adult mouse with panels of cell type-specific genes for neurons, astrocytes, and oligodendrocytes from previously published transcriptome profiling experiments. We found that the combined spatial expression patterns of 170 neuron-specific transcripts revealed strikingly clear and symmetrical signatures for most of the brain's major subdivisions. Moreover, the brain expression spatial signatures correspond to anatomical structures and may even reflect developmental ontogeny. Spatial expression profiles of astrocyte- and oligodendrocyte-specific genes also revealed regional differences; these defined fewer regions and were less distinct but still symmetrical in the coronal plane. Follow-up analysis suggested that region-based clustering of neuron-specific genes was related to (i) a combination of individual genes with restricted expression patterns, (ii) region-specific differences in the relative expression of functional groups of genes, and (iii) regional differences in neuronal density. Products from some of these neuron-specific genes are present in peripheral blood, raising the possibility that they could reflect the activities of disease- or injury-perturbed networks and collectively function as biomarkers for clinical disease diagnostics.
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http://dx.doi.org/10.1073/pnas.1222897110DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3581870PMC
February 2013

New meta-analysis tools reveal common transcriptional regulatory basis for multiple determinants of behavior.

Proc Natl Acad Sci U S A 2012 Jun 12;109(26):E1801-10. Epub 2012 Jun 12.

Neuroscience Program, Department of Computer Science, and Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

A fundamental problem in meta-analysis is how to systematically combine information from multiple statistical tests to rigorously evaluate a single overarching hypothesis. This problem occurs in systems biology when attempting to map genomic attributes to complex phenotypes such as behavior. Behavior and other complex phenotypes are influenced by intrinsic and environmental determinants that act on the transcriptome, but little is known about how these determinants interact at the molecular level. We developed an informatic technique that identifies statistically significant meta-associations between gene expression patterns and transcription factor combinations. Deploying this technique for brain transcriptome profiles from ca. 400 individual bees, we show that diverse determinants of behavior rely on shared combinations of transcription factors. These relationships were revealed only when we considered complex and variable regulatory rules, suggesting that these shared transcription factors are used in distinct ways by different determinants. This regulatory code would have been missed by traditional gene coexpression or cis-regulatory analytic methods. We expect that our meta-analysis tools will be useful for a broad array of problems in systems biology and other fields.
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http://dx.doi.org/10.1073/pnas.1205283109DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3387076PMC
June 2012

The transcription factor ultraspiracle influences honey bee social behavior and behavior-related gene expression.

PLoS Genet 2012 29;8(3):e1002596. Epub 2012 Mar 29.

Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.

Behavior is among the most dynamic animal phenotypes, modulated by a variety of internal and external stimuli. Behavioral differences are associated with large-scale changes in gene expression, but little is known about how these changes are regulated. Here we show how a transcription factor (TF), ultraspiracle (usp; the insect homolog of the Retinoid X Receptor), working in complex transcriptional networks, can regulate behavioral plasticity and associated changes in gene expression. We first show that RNAi knockdown of USP in honey bee abdominal fat bodies delayed the transition from working in the hive (primarily "nursing" brood) to foraging outside. We then demonstrate through transcriptomics experiments that USP induced many maturation-related transcriptional changes in the fat bodies by mediating transcriptional responses to juvenile hormone. These maturation-related transcriptional responses to USP occurred without changes in USP's genomic binding sites, as revealed by ChIP-chip. Instead, behaviorally related gene expression is likely determined by combinatorial interactions between USP and other TFs whose cis-regulatory motifs were enriched at USP's binding sites. Many modules of JH- and maturation-related genes were co-regulated in both the fat body and brain, predicting that usp and cofactors influence shared transcriptional networks in both of these maturation-related tissues. Our findings demonstrate how "single gene effects" on behavioral plasticity can involve complex transcriptional networks, in both brain and peripheral tissues.
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http://dx.doi.org/10.1371/journal.pgen.1002596DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3315457PMC
September 2012

Mechanisms of stable lipid loss in a social insect.

J Exp Biol 2011 Nov;214(Pt 22):3808-21

Neuroscience Program, University of Illinois, Urbana, IL 61801, USA.

Worker honey bees undergo a socially regulated, highly stable lipid loss as part of their behavioral maturation. We used large-scale transcriptomic and proteomic experiments, physiological experiments and RNA interference to explore the mechanistic basis for this lipid loss. Lipid loss was associated with thousands of gene expression changes in abdominal fat bodies. Many of these genes were also regulated in young bees by nutrition during an initial period of lipid gain. Surprisingly, in older bees, which is when maximum lipid loss occurs, diet played less of a role in regulating fat body gene expression for components of evolutionarily conserved nutrition-related endocrine systems involving insulin and juvenile hormone signaling. By contrast, fat body gene expression in older bees was regulated more strongly by evolutionarily novel regulatory factors, queen mandibular pheromone (a honey bee-specific social signal) and vitellogenin (a conserved yolk protein that has evolved novel, maturation-related functions in the bee), independent of nutrition. These results demonstrate that conserved molecular pathways can be manipulated to achieve stable lipid loss through evolutionarily novel regulatory processes.
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http://dx.doi.org/10.1242/jeb.060244DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3202514PMC
November 2011

Behavior-specific changes in transcriptional modules lead to distinct and predictable neurogenomic states.

Proc Natl Acad Sci U S A 2011 Nov 29;108(44):18020-5. Epub 2011 Sep 29.

Center for Biophysics and Computational Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

Using brain transcriptomic profiles from 853 individual honey bees exhibiting 48 distinct behavioral phenotypes in naturalistic contexts, we report that behavior-specific neurogenomic states can be inferred from the coordinated action of transcription factors (TFs) and their predicted target genes. Unsupervised hierarchical clustering of these transcriptomic profiles showed three clusters that correspond to three ecologically important behavioral categories: aggression, maturation, and foraging. To explore the genetic influences potentially regulating these behavior-specific neurogenomic states, we reconstructed a brain transcriptional regulatory network (TRN) model. This brain TRN quantitatively predicts with high accuracy gene expression changes of more than 2,000 genes involved in behavior, even for behavioral phenotypes on which it was not trained, suggesting that there is a core set of TFs that regulates behavior-specific gene expression in the bee brain, and other TFs more specific to particular categories. TFs playing key roles in the TRN include well-known regulators of neural and behavioral plasticity, e.g., Creb, as well as TFs better known in other biological contexts, e.g., NF-κB (immunity). Our results reveal three insights concerning the relationship between genes and behavior. First, distinct behaviors are subserved by distinct neurogenomic states in the brain. Second, the neurogenomic states underlying different behaviors rely upon both shared and distinct transcriptional modules. Third, despite the complexity of the brain, simple linear relationships between TFs and their putative target genes are a surprisingly prominent feature of the networks underlying behavior.
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http://dx.doi.org/10.1073/pnas.1114093108DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3207651PMC
November 2011

Nutritional regulation of division of labor in honey bees: toward a systems biology perspective.

Wiley Interdiscip Rev Syst Biol Med 2010 Sep-Oct;2(5):566-576

Neuroscience Program, University of Illinois, Urbana, IL 61801, USA.

Organisms adapt their behavior and physiology to environmental conditions through processes of phenotypic plasticity. In one well-studied example, the division of labor among worker honey bees involves a stereotyped yet plastic pattern of behavioral and physiological maturation. Early in life, workers perform brood care and other in-hive tasks and have large internal nutrient stores; later in life, they forage for nectar and pollen outside the hive and have small nutrient stores. The pace of maturation depends on colony conditions, and the environmental, physiological, and genomic mechanisms by which this occurs are being actively investigated. Here we review current knowledge of the mechanisms by which a key environmental variable, nutritional status, influences worker honey bee division of labor. These studies demonstrate that changes in individual nutritional status and conserved food-related molecular and hormonal pathways regulate the age at which individual bees begin to forage. We then outline ways in which systems biology approaches, enabled by the sequencing of the honey bee genome, will allow researchers to gain deeper insight into nutritional regulation of honey bee behavior, and phenotypic plasticity in general.
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http://dx.doi.org/10.1002/wsbm.73DOI Listing
January 2011

Quantitative peptidomics reveal brain peptide signatures of behavior.

Proc Natl Acad Sci U S A 2009 Feb 28;106(7):2383-8. Epub 2009 Jan 28.

Department of Entomology, Neuroscience Program, Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

The honey bee genome predicts approximately 100 peptides from 36 prohormones, but the functions of many of these peptides are unknown. We used differential isotope labeling combined with mass spectrometric analysis to quantify approximately 50% of known bee brain peptides in the context of foraging, with 8 showing robust and dynamic regulation. Some showed differences in brain abundance as a function of experience; specifically, nectar and pollen collection led to quick changes in abundance. These changes were related to the act of food collection, not ingestion, because foragers bring food back to the hive for storage rather than eating it themselves. Other peptide differences in brain abundance were seen in bees that either flew to a nectar feeder or a pollen feeder, but did not yet collect any food. These differences likely reflect well-known predispositions of some bees to collect either nectar or pollen, but not both. Tachykinin, PBAN, and sNPF were among the peptides with the strongest changes in association with nectar and pollen foraging. These peptides are known to be involved in regulating food intake in solitary insects, suggesting an evolutionary connection between that behavior and social foraging. These results demonstrate that it is now possible to use quantitative peptidomics to help determine which brain peptides are bioactive and to elucidate their function in the regulation of behavior.
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http://dx.doi.org/10.1073/pnas.0813021106DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2632711PMC
February 2009

Insulin signaling is involved in the regulation of worker division of labor in honey bee colonies.

Proc Natl Acad Sci U S A 2008 Mar 12;105(11):4226-31. Epub 2008 Mar 12.

Neuroscience Program, Department of Entomology, and Institute for Genomic Biology, University of Illinois at Urbana-Champaign, 320 Morrill Hall, 505 South Goodwin Avenue, Urbana, IL 61801, USA.

It has been proposed that one route of behavioral evolution involves novel regulation of conserved genes. Age-related division of labor in honey bee colonies, a highly derived behavioral system, involves the performance of different feeding-related tasks by different groups of individuals. Older bees acquire the colony's food by foraging for nectar and pollen, and the younger "nurse" bees feed larvae processed foods. The transition from hive work to foraging has been shown to be socially regulated and associated both with decreases in abdominal lipid stores and with increases in brain expression of genes implicated in feeding behavior in Drosophila melanogaster. Here we show that division of labor is influenced by a canonical regulator of food intake and energy balance in solitary species, the insulin/insulin-like growth factor signaling (IIS) pathway. Foragers had higher levels of IIS gene expression in the brain and abdomen than did nurses, despite their low lipid stores. These differences are likely nutritionally mediated because manipulations that induced low lipid stores in young bees also up-regulated these genes. Changes in IIS also causally influenced the timing of behavioral maturation: inhibition of the insulin-related target of rapamycin pathway delayed the onset of foraging in a seasonally dependent manner. In addition, pathway analyses of microarray data revealed that nurses and foragers differ in brain energy metabolism gene expression, but the differences are opposite predictions based on their insulin-signaling status. These results suggest that changes in the regulation of the IIS pathway are associated with social behavior.
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http://dx.doi.org/10.1073/pnas.0800630105DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2393790PMC
March 2008