Reduced Brd1 expression leads to reversible depression-like behaviors and gene-expression changes in female mice.

The schizophrenia-associated gene, BRD1, encodes an epigenetic regulator in which chromatin interactome is enriched with genes implicated in mental health. Alterations in histone modifications and epigenetic regulation contribute to brain transcriptomic changes in affective disorders and preclinical data supports a role for BRD1 in psychopathology. However, the implication of BRD1 on affective pathology remains poorly understood. In this study, we assess affective behaviors and associated neurobiology in Brd1+/- mice along with their responses to Fluoxetine and Imipramine. This involves behavioral, neurostructural, and neurochemical characterizations along with regional cerebral gene expression profiling combined with integrative functional genomic analyses. We report behavioral changes in female Brd1+/- mice with translational value to depressive symptomatology that can be alleviated by the administration of antidepressant medications. Behavioral changes are accompanied by altered brain morphometry and imbalances in monoaminergic systems. In accordance, gene expression changes across brain tissues reveal altered neurotransmitter signaling and cluster in functional pathways associated with depression including 'Adrenergic-, GPCR-, cAMP-, and CREB/CREM-signaling'. Integrative gene expression analysis specifically links changes in amygdaloid intracellular signaling activity to the behavioral treatment response in Brd1+/- mice. Collectively, our study highlights the importance of BRD1 as a modulator of affective pathology and adds to our understanding of the molecular mechanisms underlying affective disorders and their treatment response.

An integrative systems genetic analysis of mammalian lipid metabolism.

Dysregulation of lipid homeostasis is a precipitating event in the pathogenesis and progression of hepatosteatosis and metabolic syndrome. These conditions are highly prevalent in developed societies and currently have limited options for diagnostic and therapeutic intervention. Here, using a proteomic and lipidomic-wide systems genetic approach, we interrogated lipid regulatory networks in 107 genetically distinct mouse strains to reveal key insights into the control and network structure of mammalian lipid metabolism. These include the identification of plasma lipid signatures that predict pathological lipid abundance in the liver of mice and humans, defining subcellular localization and functionality of lipid-related proteins, and revealing functional protein and genetic variants that are predicted to modulate lipid abundance. Trans-omic analyses using these datasets facilitated the identification and validation of PSMD9 as a previously unknown lipid regulatory protein. Collectively, our study serves as a rich resource for probing mammalian lipid metabolism and provides opportunities for the discovery of therapeutic agents and biomarkers in the setting of hepatic lipotoxicity.

Experimental validation of methods for differential gene expression analysis and sample pooling in RNA-seq.

BACKGROUND: Massively parallel cDNA sequencing (RNA-seq) experiments are gradually superseding microarrays in quantitative gene expression profiling. However, many biologists are uncertain about the choice of differentially expressed gene (DEG) analysis methods and the validity of cost-saving sample pooling strategies for their RNA-seq experiments. Hence, we performed experimental validation of DEGs identified by Cuffdiff2, edgeR, DESeq2 and Two-stage Poisson Model (TSPM) in a RNA-seq experiment involving mice amygdalae micro-punches, using high-throughput qPCR on independent biological replicate samples. Moreover, we sequenced RNA-pools and compared their results with sequencing corresponding individual RNA samples. RESULTS: False-positivity rate of Cuffdiff2 and false-negativity rates of DESeq2 and TSPM were high. Among the four investigated DEG analysis methods, sensitivity and specificity of edgeR was relatively high. We documented the pooling bias and that the DEGs identified in pooled samples suffered low positive predictive values. CONCLUSIONS: Our results highlighted the need for combined use of more sensitive DEG analysis methods and high-throughput validation of identified DEGs in future RNA-seq experiments. They indicated limited utility of sample pooling strategies for RNA-seq in similar setups and supported increasing the number of biological replicate samples.

RNA sequencing supports distinct reactive oxygen species-mediated pathways of apoptosis by high and low size mass fractions of Bay leaf (Lauris nobilis) in HT-29 cells.

Anti-proliferative and pro-apoptotic effects of Bay leaf (Laurus nobilis) in mammalian cancer and HT-29 adenocarcinoma cells have been previously attributed to effects of polyphenolic and essential oil chemical species. Recently, we demonstrated differentiated growth-regulating effects of high (HFBL) versus low molecular mass (LFBL) aqueous fractions of bay leaf and now confirm by comparative effects on gene expression, that HFBL and LFBL suppress HT-29 growth by distinct mechanisms. Induction of intra-cellular lesions including DNA strand breakage by extra-cellular HFBL, invoked the hypothesis that iron-mediated reactive oxygen species with capacity to penetrate cell membrane, were responsible for HFBL-mediated effects, supported by equivalent effects of HFBL in combination with γ radiation. Activities of HFBL and LFBL were interpreted to reflect differentiated responses to iron-mediated reactive oxygen species (ROS), occurring either outside or inside cells. In the presence of LFBL, apoptotic death was relatively delayed compared with HFBL. ROS production by LFBL mediated p53-dependent apoptosis and recovery was suppressed by promoting G1/S phase arrest and failure of cellular tight junctions. In comparison, intra-cellular anti-oxidant protection exerted by LFBL was absent for extra-cellular HFBL (likely polysaccharide-rich), which potentiated more rapid apoptosis by producing DNA double strand breaks. Differentiated effects on expression of genes regulating ROS defense and chromatic condensation by LFBL versus HFBL, were observed. The results support ferrous iron in cell culture systems and potentially in vivo, can invoke different extra-cellular versus intra-cellular ROS-mediated chemistries, that may be regulated by exogenous, including dietary species.

Development of novel activin-targeted therapeutics.

Soluble activin type II receptors (ActRIIA/ActRIIB), via binding to diverse TGF-ß proteins, can increase muscle and bone mass, correct anemia or protect against diet-induced obesity. While exciting, these multiple actions of soluble ActRIIA/IIB limit their therapeutic potential and highlight the need for new reagents that target specific ActRIIA/IIB ligands. Here, we modified the activin A and activin B prodomains, regions required for mature growth factor synthesis, to generate specific activin antagonists. Initially, the prodomains were fused to the Fc region of mouse IgG2A antibody and, subsequently, "fastener" residues (Lys(45), Tyr(96), His(97), and Ala(98); activin A numbering) that confer latency to other TGF-ß proteins were incorporated. For the activin A prodomain, these modifications generated a reagent that potently (IC(50) 5 nmol/l) and specifically inhibited activin A signaling in vitro, and activin A-induced muscle wasting in vivo. Interestingly, the modified activin B prodomain inhibited both activin A and B signaling in vitro (IC(50) ~2 nmol/l) and in vivo, suggesting it could serve as a general activin antagonist. Importantly, unlike soluble ActRIIA/IIB, the modified prodomains did not inhibit myostatin or GDF-11 activity. To underscore the therapeutic utility of specifically antagonising activin signaling, we demonstrate that the modified activin prodomains promote significant increases in muscle mass.

Non-referenced genome assembly from epigenomic short-read data.

Current computational methods used to analyze changes in DNA methylation and chromatin modification rely on sequenced genomes. Here we describe a pipeline for the detection of these changes from short-read sequence data that does not require a reference genome. Open source software packages were used for sequence assembly, alignment, and measurement of differential enrichment. The method was evaluated by comparing results with reference-based results showing a strong correlation between chromatin modification and gene expression. We then used our de novo sequence assembly to build the DNA methylation profile for the non-referenced Psammomys obesus genome. The pipeline described uses open source software for fast annotation and visualization of unreferenced genomic regions from short-read data.

Integrated genome-wide association, coexpression network, and expression single nucleotide polymorphism analysis identifies novel pathway in allergic rhinitis.

BACKGROUND: Allergic rhinitis is a common disease whose genetic basis is incompletely explained. We report an integrated genomic analysis of allergic rhinitis. METHODS: We performed genome wide association studies (GWAS) of allergic rhinitis in 5633 ethnically diverse North American subjects. Next, we profiled gene expression in disease-relevant tissue (peripheral blood CD4+ lymphocytes) collected from subjects who had been genotyped. We then integrated the GWAS and gene expression data using expression single nucleotide (eSNP), coexpression network, and pathway approaches to identify the biologic relevance of our GWAS. RESULTS: GWAS revealed ethnicity-specific findings, with 4 genome-wide significant loci among Latinos and 1 genome-wide significant locus in the GWAS meta-analysis across ethnic groups. To identify biologic context for these results, we constructed a coexpression network to define modules of genes with similar patterns of CD4+ gene expression (coexpression modules) that could serve as constructs of broader gene expression. 6 of the 22 GWAS loci with P-value ≤ 1x10-6 tagged one particular coexpression module (4.0-fold enrichment, P-value 0.0029), and this module also had the greatest enrichment (3.4-fold enrichment, P-value 2.6 × 10-24) for allergic rhinitis-associated eSNPs (genetic variants associated with both gene expression and allergic rhinitis). The integrated GWAS, coexpression network, and eSNP results therefore supported this coexpression module as an allergic rhinitis module. Pathway analysis revealed that the module was enriched for mitochondrial pathways (8.6-fold enrichment, P-value 4.5 × 10-72). CONCLUSIONS: Our results highlight mitochondrial pathways as a target for further investigation of allergic rhinitis mechanism and treatment. Our integrated approach can be applied to provide biologic context for GWAS of other diseases.

Maternal overnutrition programs changes in the expression of skeletal muscle genes that are associated with insulin resistance and defects of oxidative phosphorylation in adult male rat offspring.

Children of obese mothers have increased risk of metabolic syndrome as adults. Here we report the effects of a high-fat diet in the absence of maternal obesity at conception on skeletal muscle metabolic and transcriptional profiles of adult male offspring. Female Sprague Dawley rats were fed a diet rich in saturated fat and sucrose [high-fat diet (HFD): 23.5% total fat, 9.83% saturated fat, 20% sucrose wt:wt] or a normal control diet [(CD) 7% total fat, 0.5% saturated fat, 10% sucrose wt:wt] for the 3 wk prior to mating and throughout pregnancy and lactation. Maternal weights were not different at conception; however, HFD-fed dams were 22% heavier than controls during pregnancy. On a normal diet, the male offspring of HFD-fed dams were not heavier than controls but demonstrated features of insulin resistance, including elevated plasma insulin concentration [40.1 ± 2.5 (CD) vs 56.2 ± 6.1 (HFD) mU/L; P = 0.023]. Next-generation mRNA sequencing was used to identify differentially expressed genes in the offspring soleus muscle, and gene set enrichment analysis (GSEA) was used to detect coordinated changes that are characteristic of a biological function. GSEA identified 15 upregulated pathways, including cytokine signaling (P < 0.005), starch and sucrose metabolism (P < 0.017), inflammatory response (P < 0.024), and cytokine-cytokine receptor interaction (P < 0.037). A further 8 pathways were downregulated, including oxidative phosphorylation (P < 0.004), mitochondrial matrix (P < 0.006), and electron transport/uncoupling (P < 0.022). Phosphorylation of the insulin signaling protein kinase B was reduced [2.86 ± 0.63 (CD) vs 1.02 ± 0.27 (HFD); P = 0.027] and mitochondrial complexes I, II, and V protein were downregulated by 50-68% (P < 0.005). On a normal diet, the male offspring of HFD-fed dams did not become obese adults but developed insulin resistance, with transcriptional evidence of muscle cytokine activation, inflammation, and mitochondrial dysfunction. These data indicate that maternal overnutrition, even in the absence of prepregnancy obesity, can promote metabolic dysregulation and predispose offspring to type 2 diabetes.

Automated influenza-like illness reporting--an efficient adjunct to traditional sentinel surveillance.

OBJECTIVES: We compared an electronic health record-based influenza-like illness (ILI) surveillance system with manual sentinel surveillance and virologic data to evaluate the utility of the automated system for routine ILI surveillance. METHODS: We obtained weekly aggregate ILI reports from the Electronic medical record Support for Public Health (ESP) disease-detection and reporting system, which used an automated algorithm to identify ILI visits among a patient population of about 700,000 in Eastern Massachusetts. The percentage of total visits for ILI ("percent ILI") in ESP, percent ILI in the Massachusetts Department of Public Health's sentinel surveillance system, and percentage of laboratory specimens submitted to participating Massachusetts laboratories that tested positive for influenza were compared for the period October 2007-September 2011. We calculated Spearman's correlation coefficients and compared ESP and sentinel surveillance systems qualitatively, in terms of simplicity, flexibility, data quality, acceptability, timeliness, and usefulness. RESULTS: ESP and sentinel surveillance percent ILI always peaked within one week of each other. There was 80% correlation between the two and 71%-73% correlation with laboratory data. Sentinel surveillance percent ILI was higher than ESP percent ILI during influenza seasons. The amplitude of variation in ESP percent ILI was greatest for 5- to 49-year-olds and typically peaked for the 5- to 24-year-old age group before the others. CONCLUSIONS: The ESP system produces percent ILI data of similar quality to sentinel surveillance and offers the advantages of shifting disease reporting burden from clinicians to information systems, allowing tracking of disease by age group, facilitating efficient surveillance for very large populations, and producing consistent and timely reports.

Time course-dependent changes in the transcriptome of human skeletal muscle during recovery from endurance exercise: from inflammation to adaptive remodeling.

Reprogramming of gene expression is fundamental for skeletal muscle adaptations in response to endurance exercise. This study investigated the time course-dependent changes in the muscular transcriptome after an endurance exercise trial consisting of 1 h of intense cycling immediately followed by 1 h of intense running. Skeletal muscle samples were taken at baseline, 3 h, 48 h, and 96 h postexercise from eight healthy, endurance-trained men. RNA was extracted from muscle. Differential gene expression was evaluated using Illumina microarrays and validated with qPCR. Gene set enrichment analysis identified enriched molecular signatures chosen from the Molecular Signatures Database. Three hours postexercise, 102 gene sets were upregulated [family wise error rate (FWER), P < 0.05], including groups of genes related with leukocyte migration, immune and chaperone activation, and cyclic AMP responsive element binding protein (CREB) 1 signaling. Forty-eight hours postexercise, among 19 enriched gene sets (FWER, P < 0.05), two gene sets related to actin cytoskeleton remodeling were upregulated. Ninety-six hours postexercise, 83 gene sets were enriched (FWER, P < 0.05), 80 of which were upregulated, including gene groups related to chemokine signaling, cell stress management, and extracellular matrix remodeling. These data provide comprehensive insights into the molecular pathways involved in acute stress, recovery, and adaptive muscular responses to endurance exercise. The novel 96 h postexercise transcriptome indicates substantial transcriptional activity potentially associated with the prolonged presence of leukocytes in the muscles. This suggests that muscular recovery, from a transcriptional perspective, is incomplete 96 h after endurance exercise involving muscle damage.

Transcriptome analysis of neutrophils after endurance exercise reveals novel signaling mechanisms in the immune response to physiological stress.

Neutrophils serve as an intriguing model for the study of innate immune cellular activity induced by physiological stress. We measured changes in the transcriptome of circulating neutrophils following an experimental exercise trial (EXTRI) consisting of 1 h of intense cycling immediately followed by 1 h of intense running. Blood samples were taken at baseline, 3 h, 48 h, and 96 h post-EXTRI from eight healthy, endurance-trained, male subjects. RNA was extracted from isolated neutrophils. Differential gene expression was evaluated using Illumina microarrays and validated with quantitative PCR. Gene set enrichment analysis identified enriched molecular signatures chosen from the Molecular Signatures Database. Blood concentrations of muscle damage indexes, neutrophils, interleukin (IL)-6 and IL-10 were increased (P < 0.05) 3 h post-EXTRI. Upregulated groups of functionally related genes 3 h post-EXTRI included gene sets associated with the recognition of tissue damage, the IL-1 receptor, and Toll-like receptor (TLR) pathways (familywise error rate, P value < 0.05). The core enrichment for these pathways included TLRs, low-affinity immunoglobulin receptors, S100 calcium binding protein A12, and negative regulators of innate immunity, e.g., IL-1 receptor antagonist, and IL-1 receptor associated kinase-3. Plasma myoglobin changes correlated with neutrophil TLR4 gene expression (r = 0.74; P < 0.05). Neutrophils had returned to their nonactivated state 48 h post-EXTRI, indicating that their initial proinflammatory response was transient and rapidly counterregulated. This study provides novel insight into the signaling mechanisms underlying the neutrophil responses to endurance exercise, suggesting that their transcriptional activity was particularly induced by damage-associated molecule patterns, hypothetically originating from the leakage of muscle components into the circulation.

Motif analysis in DNAse hypersensitivity regions uncovers distal cis elements associated with gene expression.

UNLABELLED: Reliable identification of cis regulatory elements influencing transcription remains a challenging problem in molecular bioinformatics. This is especially true for enhancer elements which are often located hundreds of kilobases from the gene promoter. High resolution DNase hypersensitivity and connectivity profiling by the ENCODE consortium provides evidence of millions of interacting cis-acting elements in the human genome. This prior knowledge can be incorporated into genome-wide expression analyses, in the form of gene sets sharing regulatory sequence motifs in known DNase hypersensitivity peak regions. High proportions of enrichment among the most extreme differentially transcribed genes from controlled biological experiments may suggest novel hypotheses about signalling pathways. The utility of this approach is demonstrated with the reanalysis of a microarray-derived gene expression data set through the Gene Set Enrichment Analysis pipeline, uncovering new putative distal cis elements in the context of innate immunity. The DNase Hypersensitivity Connectivity informed Motif Enrichment in Gene Expression (DHC-MEGE) method described here has the advantage of identifying distal elements such as enhancers, which are often overlooked with standard promoter motif analysis. AVAILABILITY: The DHC-MEGE shell script can be obtained from Sourceforge https://sourceforge.net/projects/dhcmege/ and the generated GMT file is attached as supplementary data.

Automated detection and classification of type 1 versus type 2 diabetes using electronic health record data.

OBJECTIVE: To create surveillance algorithms to detect diabetes and classify type 1 versus type 2 diabetes using structured electronic health record (EHR) data. RESEARCH DESIGN AND METHODS: We extracted 4 years of data from the EHR of a large, multisite, multispecialty ambulatory practice serving ∼700,000 patients. We flagged possible cases of diabetes using laboratory test results, diagnosis codes, and prescriptions. We assessed the sensitivity and positive predictive value of novel combinations of these data to classify type 1 versus type 2 diabetes among 210 individuals. We applied an optimized algorithm to a live, prospective, EHR-based surveillance system and reviewed 100 additional cases for validation. RESULTS: The diabetes algorithm flagged 43,177 patients. All criteria contributed unique cases: 78% had diabetes diagnosis codes, 66% fulfilled laboratory criteria, and 46% had suggestive prescriptions. The sensitivity and positive predictive value of ICD-9 codes for type 1 diabetes were 26% (95% CI 12-49) and 94% (83-100) for type 1 codes alone; 90% (81-95) and 57% (33-86) for two or more type 1 codes plus any number of type 2 codes. An optimized algorithm incorporating the ratio of type 1 versus type 2 codes, plasma C-peptide and autoantibody levels, and suggestive prescriptions flagged 66 of 66 (100% [96-100]) patients with type 1 diabetes. On validation, the optimized algorithm correctly classified 35 of 36 patients with type 1 diabetes (raw sensitivity, 97% [87-100], population-weighted sensitivity, 65% [36-100], and positive predictive value, 88% [78-98]). CONCLUSIONS: Algorithms applied to EHR data detect more cases of diabetes than claims codes and reasonably discriminate between type 1 and type 2 diabetes.

Creating reusable tools from scripts: the Galaxy Tool Factory.

MOTIVATION: Galaxy is a software application supporting high-throughput biology analyses and work flows, available as a free on-line service or as source code for local deployment. New tools can be written to extend Galaxy, and these can be shared using public Galaxy Tool Shed (GTS) repositories, but converting even simple scripts into tools requires effort from a skilled developer. RESULTS: The Tool Factory is a novel Galaxy tool that automates the generation of all code needed to execute user-supplied scripts, and wraps them into new Galaxy tools for upload to a GTS, ready for review and installation through the Galaxy administrative interface. AVAILABILITY AND IMPLEMENTATION: The Galaxy administrative interface supports automated installation from the main GTS. Source code and support are available at the project website, https://bitbucket.org/fubar/galaxytoolfactory. The Tool Factory is implemented as an installable Galaxy tool. CONTACT: ross.lazarus@channing.harvard.edu.

Integrating clinical practice and public health surveillance using electronic medical record systems.

Electronic medical record (EMR) systems have rich potential to improve integration between primary care and the public health system at the point of care. EMRs make it possible for clinicians to contribute timely, clinically detailed surveillance data to public health practitioners without changing their existing workflows or incurring extra work. New surveillance systems can extract raw data from providers' EMRs, analyze them for conditions of public health interest, and automatically communicate results to health departments. We describe a model EMR-based public health surveillance platform called Electronic Medical Record Support for Public Health (ESP). The ESP platform provides live, automated surveillance for notifiable diseases, influenza-like illness, and diabetes prevalence, care, and complications. Results are automatically transmitted to state health departments.

Integrating clinical practice and public health surveillance using electronic medical record systems.

Electronic medical record (EMR) systems have rich potential to improve integration between primary care and the public health system at the point of care. EMRs make it possible for clinicians to contribute timely, clinically detailed surveillance data to public health practitioners without changing their existing workflows or incurring extra work. New surveillance systems can extract raw data from providers' EMRs, analyze them for conditions of public health interest, and automatically communicate results to health departments. The current paper describes a model EMR-based public health surveillance platform called Electronic Medical Record Support for Public Health (ESP). The ESP platform provides live, automated surveillance for notifiable diseases, influenza-like illness, and diabetes prevalence, care, and complications. Results are automatically transmitted to state health departments.

A pipeline for the identification and characterization of chromatin modifications derived from ChIP-Seq datasets.

The advent of massive parallel sequencing of immunopurified chromatin and its determinants has provided new avenues for researchers to map epigenome-wide changes and there is tremendous interest to uncover regulatory signatures to understand fundamental questions associated with chromatin structure and function. Indeed, the rapid development of large genome annotation projects has seen a resurgence in chromatin immunoprecipitation (ChIP) based protocols which are used to distinguish protein interactions coupled with large scale sequencing (Seq) to precisely map epigenome-wide interactions. Despite some of the great advances in our understanding of chromatin modifying complexes and their determinants, the development of ChIP-Seq technologies also pose specific demands on the integration of data for visualization, manipulation and analysis. In this article we discuss some of the considerations for experimental design planning, quality control, and bioinformatic analysis. The key aspects of post sequencing analysis are the identification of regions of interest, differentiation between biological conditions and the characterization of sequence differences for chromatin modifications. We provide an overview of best-practise approaches with background information and considerations of integrative analysis from ChIP-Seq experiments.

Genomewide association between GLCCI1 and response to glucocorticoid therapy in asthma.

BACKGROUND: The response to treatment for asthma is characterized by wide interindividual variability, with a significant number of patients who have no response. We hypothesized that a genomewide association study would reveal novel pharmacogenetic determinants of the response to inhaled glucocorticoids. METHODS: We analyzed a small number of statistically powerful variants selected on the basis of a family-based screening algorithm from among 534,290 single-nucleotide polymorphisms (SNPs) to determine changes in lung function in response to inhaled glucocorticoids. A significant, replicated association was found, and we characterized its functional effects. RESULTS: We identified a significant pharmacogenetic association at SNP rs37972, replicated in four independent populations totaling 935 persons (P=0.0007), which maps to the glucocorticoid-induced transcript 1 gene (GLCCI1) and is in complete linkage disequilibrium (i.e., perfectly correlated) with rs37973. Both rs37972 and rs37973 are associated with decrements in GLCCI1 expression. In isolated cell systems, the rs37973 variant is associated with significantly decreased luciferase reporter activity. Pooled data from treatment trials indicate reduced lung function in response to inhaled glucocorticoids in subjects with the variant allele (P=0.0007 for pooled data). Overall, the mean (±SE) increase in forced expiratory volume in 1 second in the treated subjects who were homozygous for the mutant rs37973 allele was only about one third of that seen in similarly treated subjects who were homozygous for the wild-type allele (3.2±1.6% vs. 9.4±1.1%), and their risk of a poor response was significantly higher (odds ratio, 2.36; 95% confidence interval, 1.27 to 4.41), with genotype accounting for about 6.6% of overall inhaled glucocorticoid response variability. CONCLUSIONS: A functional GLCCI1 variant is associated with substantial decrements in the response to inhaled glucocorticoids in patients with asthma.

Genetic adaptation of the antibacterial human innate immunity network.

BACKGROUND: Pathogens have represented an important selective force during the adaptation of modern human populations to changing social and other environmental conditions. The evolution of the immune system has therefore been influenced by these pressures. Genomic scans have revealed that immune system is one of the functions enriched with genes under adaptive selection. RESULTS: Here, we describe how the innate immune system has responded to these challenges, through the analysis of resequencing data for 132 innate immunity genes in two human populations. Results are interpreted in the context of the functional and interaction networks defined by these genes. Nucleotide diversity is lower in the adaptors and modulators functional classes, and is negatively correlated with the centrality of the proteins within the interaction network. We also produced a list of candidate genes under positive or balancing selection in each population detected by neutrality tests and showed that some functional classes are preferential targets for selection. CONCLUSIONS: We found evidence that the role of each gene in the network conditions the capacity to evolve or their evolvability: genes at the core of the network are more constrained, while adaptation mostly occurred at particular positions at the network edges. Interestingly, the functional classes containing most of the genes with signatures of balancing selection are involved in autoinflammatory and autoimmune diseases, suggesting a counterbalance between the beneficial and deleterious effects of the immune response.

Quantifying differential gene connectivity between disease states for objective identification of disease-relevant genes.

BACKGROUND: Network modeling of whole transcriptome expression data enables characterization of complex epistatic (gene-gene) interactions that underlie cellular functions. Though numerous methods have been proposed and successfully implemented to develop these networks, there are no formal methods for comparing differences in network connectivity patterns as a function of phenotypic trait. RESULTS: Here we describe a novel approach for quantifying the differences in gene-gene connectivity patterns across disease states based on Graphical Gaussian Models (GGMs). We compare the posterior probabilities of connectivity for each gene pair across two disease states, expressed as a posterior odds-ratio (postOR) for each pair, which can be used to identify network components most relevant to disease status. The method can also be generalized to model differential gene connectivity patterns within previously defined gene sets, gene networks and pathways. We demonstrate that the GGM method reliably detects differences in network connectivity patterns in datasets of varying sample size. Applying this method to two independent breast cancer expression data sets, we identified numerous reproducible differences in network connectivity across histological grades of breast cancer, including several published gene sets and pathways. Most notably, our model identified two gene hubs (MMP12 and CXCL13) that each exhibited differential connectivity to more than 30 transcripts in both datasets. Both genes have been previously implicated in breast cancer pathobiology, but themselves are not differentially expressed by histologic grade in either dataset, and would thus have not been identified using traditional differential gene expression testing approaches. In addition, 16 curated gene sets demonstrated significant differential connectivity in both data sets, including the matrix metalloproteinases, PPAR alpha sequence targets, and the PUFA synthesis pathway. CONCLUSIONS: Our results suggest that GGM can be used to formally evaluate differences in global interactome connectivity across disease states, and can serve as a powerful tool for exploring the molecular events that contribute to disease at a systems level.