A Scalable Permutation Approach Reveals Replication and Preservation Patterns of Network Modules in Large Datasets

Systematical Comparison Reveals Distinct Brain Transcriptomic Characteristics in Depression Models Induced by Gut Microbiota Dysbiosis and Chronic Stress

Major depressive disorder (MDD) is a devastating psychiatric illness with various etiologies. Both chronic stress and gut microbiome dysbiosis are implicated in the pathogenesis of MDD. However, limited research has been conducted to delineate the distinct effects of these two pathogenic factors on the brain transcriptome. We generated and compared transcriptomic features of the anterior cingulate cortex (ACC) from depressive-like mice induced by gut microbiome dysbiosis and canonical chronic stress paradigms, focusing on gene expression patterns and network characteristics. Data derived from MDD patients served as a reference standard to filter the molecular alterations associated with the disorder. Chronic stress induced a plethora of altered genes and biological functions associated with depression, prominently involving mitochondrial dysfunction. However, gut microbiota dysbiosis specifically regulated narrower range of genes and biological mechanisms, targeting aberrations in vesicular transport systems and perturbations of autophagy pathways. Network analysis revealed that hierarchical gene co-expression was specifically affected by gut microbiota dysbiosis rather than chronic stress. Further functional clustering analysis, along with the central distribution of inflammation-related differentially expressed genes, suggested an intricate interplay between disrupted autophagy processes, microglia-mediated inflammation, and synaptic dysfunctions in the network influenced by gut microbiota dysbiosis. Our findings reveal the distinctive transcriptomic alterations of brain shaped by gut microbiota and chronic stress in the development of MDD, contributing to a deeper understanding the heterogeneity of depression. Additionally, we provide a valuable data resource and bioinformatic analysis template for future studies.

Macrophage HM13/SPP Enhances Foamy Macrophage Formation and Atherogenesis

Aryl Hydrocarbon Receptor-Interacting Protein (AIP) reduces macrophage cholesterol-ester accumulation and may prevent atherogenic foamy macrophage formation. Analyzing AIP-associated regulatory gene networks can aid in identifying key regulatory mechanism(s) underlying foamy macrophage formation. A weighted gene co-expression network analysis on the Stockholm Atherosclerosis Gene Expression (STAGE) patient cohort identifies AIP as a negative correlate of Histocompatibility Minor 13 (HM13), which encodes the ER-associated degradation (ERAD) protein Signal Peptide Peptidase (HM13/SPP). The negative correlation between AIP and HM13/SPP on mRNA and protein levels is validated in oxLDL-stimulated macrophages and human plaque foamy macrophages. Mechanistically, AIP, via its chaperone interaction with Aryl Hydrocarbon Receptor (AHR), inhibits p38-c-JUN-mediated HM13 transactivation, thereby suppressing macrophage lipid accumulation. Myeloid HM13/SPP overexpression enhances oxLDL-induced foamy macrophage formation in vitro as well as atherogenesis and plaque foamy macrophage load in vivo, while myeloid HM13/SPP knockout produces the opposite effects. Mechanistically, myeloid HM13/SPP enhances oxLDL-induced foamy macrophage formation in vitro as well as atherogenesis and plaque foamy macrophage load in vivo via promoting ERAD-mediated proteasomal degradation of the metabolic regulator Heme Oxygenase-1 (HO-1). In conclusion, AIP downregulates macrophage HM13/SPP, a driver of oxLDL-induced lipid loading, foamy macrophage generation, and atherogenesis.

Single-cell RNA sequencing reveals sex differences in the subcellular composition and associated gene-regulatory network activity of human carotid plaques

Carotid stenosis causes ischemic stroke in both sexes, but the clinical presentation and plaque characteristics differ. Here we run deep single-cell sequencing of 7,690 human carotid plaque cells from male and female patients. While we found no sex differences in major cell types, we identified a predominance of the osteogenic phenotype in smooth muscle cells, immunomodulating macrophages (MPs) and endothelial cells (ECs) undergoing endothelial-to-mesenchymal transition in females. In males, we found smooth muscle cells with the chondrocytic phenotype, MPs involved in tissue remodeling and ECs with angiogenic activity. Sex-biased subcellular clusters were integrated with tissue-specific gene-regulatory networks (GRNs) from the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task study. We identified GRN195 involved in angiogenesis and T cell-mediated cytotoxicity in male ECs, while in females, we found GRN33 and GRN122 related to TREM2-/TREM1+ MPs and endothelial-to-mesenchymal transition. The impact of GRN195 on EC proliferation in males was functionally validated, providing evidence for potential therapy targets for atherosclerosis that are sex specific.

Transcriptional signature of a hypersensitive glucocorticoid receptor variant in the neuroendocrine system suggests enhanced vulnerability to brain disorders

The natural substitution Ala610Val in the porcine glucocorticoid receptor (GRAla610Val) leads to a profound compensatory downregulation of the hypothalamic-pituitary-adrenal (HPA) axis in early ontogeny. In this study, we leveraged this unique animal model to explore mechanisms of HPA axis regulation and consequences of its genetically-based persistent hypoactivity. To this end, we examined transcriptional signature of GRAla610Val in the hypothalamus, hippocampus, amygdala and adrenal gland in resting conditions (i.e. baseline glucocorticoid level) using mRNA sequencing. In addition, we studied transcriptome responses to two different doses of dexamethasone in the hypothalamus and hippocampus, depending on GRAla610Val. Across tissues, GRAla610Val consistently influenced the expression of several clustered protocadherins, particularly PCDHB7. Clustered protocadherins play an important role in neuronal connectivity and are implicated in different neurobiological disorders. Moreover, in line with our previous findings in blood immune cells, we found higher expression of pro-inflammatory genes, including canonical members of the TLR4 signaling pathway, in the brain of Val carriers. While the pro-inflammatory priming occurs already at resting conditions in the amygdala, in hypothalamus and hippocampus this seems to be associated with a stronger downregulation of several marker genes of homeostatic microglia, such as SALL1, by dexamethasone in Val carriers. Regarding the regulation of the HPA axis, GRAla610Val showed a dose-dependent effect on the central regulator of the axis, CRH, suggesting a dynamic adaptation to the glucocorticoid hypersensitivity of the Val variant. In the adrenal gland, GRAla610Val appears to downregulate cortisol production by impairing mitochondrial function. Overall, the transcriptional signature of GRAla610Val provides strong evidence that GR hypersensitivity leads to increased susceptibility to brain disorders.

Airborne fine particulate matter exposure induces transcriptomic alterations resembling asthmatic signatures: insights from integrated omics analysis

Fine particulate matter (PM2.5), an atmospheric pollutant that settles deep in the respiratory tract, is highly harmful to human health. Despite its well-known impact on lung function and its ability to exacerbate asthma, the molecular basis of this effect is not fully understood. This integrated transcriptomic and epigenomic data analysis from publicly available datasets aimed to determine the impact of PM2.5 exposure and its association with asthma in human airway epithelial cells. Differential gene expression and binding analyses identified 349 common differentially expressed genes and genes associated with differentially enriched H3K27ac regions in both conditions. Co-expression network analysis revealed three preserved modules (Protein Folding, Cell Migration, and Hypoxia Response) significantly correlated with PM2.5 exposure and preserved in asthma networks. Pathways dysregulated in both conditions included epithelial function, hypoxia response, interleukin-17 and TNF signaling, and immune/inflammatory processes. Hub genes like TGFB2, EFNA5, and PFKFB3 were implicated in airway remodeling, cell migration, and hypoxia-induced glycolysis. These findings elucidate common altered expression patterns and processes between PM2.5 exposure and asthma, helping to understand their molecular connection. This provides guidance for future research to utilize them as potential biomarkers or therapeutic targets and generates evidence supporting the need for implementing effective air quality management strategies.

Asymmetric genome merging leads to gene expression novelty through nucleo-cytoplasmic disruptions and transcriptomic shock in Chlamydomonas triploids

Genome merging is a common phenomenon causing a wide range of consequences on phenotype, adaptation, and gene expression, yet its broader implications are not well-understood. Two consequences of genome merging on gene expression remain particularly poorly understood: dosage effects and evolution of expression. We employed Chlamydomonas reinhardtii as a model to investigate the effects of asymmetric genome merging by crossing a diploid with a haploid strain to create a novel triploid line. Five independent clonal lineages derived from this triploid line were evolved for 425 asexual generations in a laboratory natural selection experiment. Utilizing fitness assays, flow cytometry, and RNA-Seq, we assessed the immediate consequences of genome merging and subsequent evolution. Our findings reveal substantial alterations in genome size, gene expression, protein homeostasis, and cytonuclear stoichiometry. Gene expression exhibited expression-level dominance and transgressivity (i.e. expression level higher or lower than either parent). Ongoing expression-level dominance and a pattern of 'functional dominance' from the haploid parent was observed. Despite major genomic and nucleo-cytoplasmic disruptions, enhanced fitness was detected in the triploid strain. By comparing gene expression across generations, our results indicate that proteostasis restoration is a critical component of rapid adaptation following genome merging in Chlamydomonas reinhardtii and possibly other systems.

A Selective Review of Network Analysis Methods for Gene Expression Data

With the development of high-throughput profiling techniques, gene expressions have drawn significant attention due to their important biological implications, widespread data availability, and promising biological findings. The complex interactions and regulations among genes naturally lead to a network structure, which can provide a global view of molecular mechanisms and biological processes. This chapter provides a selective overview of constructing gene expression networks and utilizing them in downstream analysis. It also includes a demonstrating example.

Backdoor attacks on unsupervised graph representation learning

Unsupervised graph learning techniques have garnered increasing interest among researchers. These methods employ the technique of maximizing mutual information to generate representations of nodes and graphs. We show that these methods are susceptible to backdoor attacks, wherein the adversary can poison a small portion of unlabeled graph data (e.g., node features and graph structure) by introducing triggers into the graph. This tampering disrupts the representations and increases the risk to various downstream applications. Previous backdoor attacks in supervised learning primarily operate directly on the label space and may not be suitable for unlabeled graph data. To tackle this challenge, we introduce GRBA,1 a gradient-based first-order backdoor attack method. To the best of our knowledge, this constitutes a pioneering endeavor in investigating backdoor attacks within the domain of unsupervised graph learning. The initiation of this method does not necessitate prior knowledge of downstream tasks, as it directly focuses on representations. Furthermore, it is versatile and can be applied to various downstream tasks, including node classification, node clustering and graph classification. We evaluate GRBA on state-of-the-art unsupervised learning models, and the experimental results substantiate the effectiveness and evasiveness of GRBA in both node-level and graph-level tasks.

Impact of air pollution exposure on cytokines and histone modification profiles at single-cell levels during pregnancy

Fine particulate matter (PM2.5) exposure can induce immune system pathology via epigenetic modification, affecting pregnancy outcomes. Our study investigated the association between PM2.5 exposure and immune response, as well as epigenetic changes using high-dimensional epigenetic landscape profiling using cytometry by time-of-flight (EpiTOF) at the single cell. We found statistically significant associations between PM2.5 exposure and levels of certain cytokines [interleukin-1RA (IL-1RA), IL-8/CXCL8, IL-18, and IL-27)] and histone posttranslational modifications (HPTMs) in immune cells (HPTMs: H3K9ac, H3K23ac, H3K27ac, H2BK120ub, H4K20me1/3, and H3K9me1/2) among pregnant and nonpregnant women. The cord blood of neonates with high maternal PM2.5 exposure showed lower IL-27 than those with low exposure. Furthermore, PM2.5 exposure affects the co-modification profiles of cytokines between pregnant women and their neonates, along with HPTMs in each immune cell type between pregnant and nonpregnant women. These modifications in specific histones and cytokines could indicate the toxicological mechanism of PM2.5 exposure in inflammation, inflammasome pathway, and pregnancy complications.

Shared patterns of glial transcriptional dysregulation link Huntington's disease and schizophrenia

Huntington's disease and juvenile-onset schizophrenia have long been regarded as distinct disorders. However, both manifest cell-intrinsic abnormalities in glial differentiation, with resultant astrocytic dysfunction and hypomyelination. To assess whether a common mechanism might underlie the similar glial pathology of these otherwise disparate conditions, we used comparative correlation network approaches to analyse RNA-sequencing data from human glial progenitor cells (hGPCs) produced from disease-derived pluripotent stem cells. We identified gene sets preserved between Huntington's disease and schizophrenia hGPCs yet distinct from normal controls that included 174 highly connected genes in the shared disease-associated network, focusing on genes involved in synaptic signalling. These synaptic genes were largely suppressed in both schizophrenia and Huntington's disease hGPCs, and gene regulatory network analysis identified a core set of upstream regulators of this network, of which OLIG2 and TCF7L2 were prominent. Among their downstream targets, ADGRL3, a modulator of glutamatergic synapses, was notably suppressed in both schizophrenia and Huntington's disease hGPCs. Chromatin immunoprecipitation sequencing confirmed that OLIG2 and TCF7L2 each bound to the regulatory region of ADGRL3, whose expression was then rescued by lentiviral overexpression of these transcription factors. These data suggest that the disease-associated suppression of OLIG2 and TCF7L2-dependent transcription of glutamate signalling regulators may impair glial receptivity to neuronal glutamate. The consequent loss of activity-dependent mobilization of hGPCs may yield deficient oligodendrocyte production, and hence the hypomyelination noted in these disorders, as well as the disrupted astrocytic differentiation and attendant synaptic dysfunction associated with each. Together, these data highlight the importance of convergent glial molecular pathology in both the pathogenesis and phenotypic similarities of two otherwise unrelated disorders, Huntington's disease and schizophrenia.

The Human Developing Cerebral Cortex Is Characterized by an Elevated De Novo Expression of Long Noncoding RNAs in Excitatory Neurons

The outstanding human cognitive capacities are computed in the cerebral cortex, a mammalian-specific brain region and the place of massive biological innovation. Long noncoding RNAs have emerged as gene regulatory elements with higher evolutionary turnover than mRNAs. The many long noncoding RNAs identified in neural tissues make them candidates for molecular sources of cerebral cortex evolution and disease. Here, we characterized the genomic and cellular shifts that occurred during the evolution of the long noncoding RNA repertoire expressed in the developing cerebral cortex and explored putative roles for these long noncoding RNAs in the evolution of the human brain. Using transcriptomics and comparative genomics, we comprehensively annotated the cortical transcriptomes of humans, rhesus macaques, mice, and chickens and classified human cortical long noncoding RNAs into evolutionary groups as a function of their predicted minimal ages. Long noncoding RNA evolutionary groups showed differences in expression levels, splicing efficiencies, transposable element contents, genomic distributions, and transcription factor binding to their promoters. Furthermore, older long noncoding RNAs showed preferential expression in germinative zones, outer radial glial cells, and cortical inhibitory (GABAergic) neurons. In comparison, younger long noncoding RNAs showed preferential expression in cortical excitatory (glutamatergic) neurons, were enriched in primate and human-specific gene co-expression modules, and were dysregulated in neurodevelopmental disorders. These results suggest different evolutionary routes for older and younger cortical long noncoding RNAs, highlighting old long noncoding RNAs as a possible source of molecular evolution of conserved developmental programs; conversely, we propose that the de novo expression of primate- and human-specific young long noncoding RNAs is a putative source of molecular evolution and dysfunction of cortical excitatory neurons, warranting further investigation.

Single-Cell Gene-Regulatory Networks of Advanced Symptomatic Atherosclerosis

BACKGROUND

While our understanding of the single-cell gene expression patterns underlying the transformation of vascular cell types during the progression of atherosclerosis is rapidly improving, the clinical and pathophysiological relevance of these changes remains poorly understood.

METHODS

Single-cell RNA sequencing data generated with SmartSeq2 (≈8000 genes/cell) in 16 588 single cells isolated during atherosclerosis progression in Ldlr-/-Apob100/100 mice with human-like plasma lipoproteins and from humans with asymptomatic and symptomatic carotid plaques was clustered into multiple subtypes. For clinical and pathophysiological context, the advanced-stage and symptomatic subtype clusters were integrated with 135 tissue-specific (atherosclerotic aortic wall, mammary artery, liver, skeletal muscle, and visceral and subcutaneous, fat) gene-regulatory networks (GRNs) inferred from 600 coronary artery disease patients in the STARNET (Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task) study.

RESULTS

Advanced stages of atherosclerosis progression and symptomatic carotid plaques were largely characterized by 3 smooth muscle cells (SMCs), and 3 macrophage subtype clusters with extracellular matrix organization/osteogenic (SMC), and M1-type proinflammatory/Trem2-high lipid-associated (macrophage) phenotypes. Integrative analysis of these 6 clusters with STARNET revealed significant enrichments of 3 arterial wall GRNs: GRN33 (macrophage), GRN39 (SMC), and GRN122 (macrophage) with major contributions to coronary artery disease heritability and strong associations with clinical scores of coronary atherosclerosis severity. The presence and pathophysiological relevance of GRN39 were verified in 5 independent RNAseq data sets obtained from the human coronary and aortic artery, and primary SMCs and by targeting its top-key drivers, FRZB and ALCAM in cultured human coronary artery SMCs.

CONCLUSIONS

By identifying and integrating the most gene-rich single-cell subclusters of atherosclerosis to date with a coronary artery disease framework of GRNs, GRN39 was identified and independently validated as being critical for the transformation of contractile SMCs into an osteogenic phenotype promoting advanced, symptomatic atherosclerosis.

Transcriptomic meta-analysis reveals ERRα-mediated oxidative phosphorylation is downregulated in Fuchs' endothelial corneal dystrophy

BACKGROUND

Late-onset Fuchs' endothelial corneal dystrophy (FECD) is a degenerative disease of cornea and the leading indication for corneal transplantation. Genetically, FECD patients can be categorized as with (RE+) or without (RE-) the CTG trinucleotide repeat expansion in the transcription factor 4 gene. The molecular mechanisms underlying FECD remain unclear, though there are plausible pathogenic models proposed for RE+ FECD.

METHOD

In this study, we performed a meta-analysis on RNA sequencing datasets of FECD corneal endothelium including 3 RE+ datasets and 2 RE- datasets, aiming to compare the transcriptomic profiles of RE+ and RE- FECD. Gene differential expression analysis, co-expression networks analysis, and pathway analysis were conducted.

RESULTS

There was a striking similarity between RE+ and RE- transcriptomes. There were 1,184 genes significantly upregulated and 1,018 genes significantly downregulated in both RE+ and RE- cases. Pathway analysis identified multiple biological processes significantly enriched in both-mitochondrial functions, energy-related processes, ER-nucleus signaling pathway, demethylation, and RNA splicing were negatively enriched, whereas small GTPase mediated signaling, actin-filament processes, extracellular matrix organization, stem cell differentiation, and neutrophil mediated immunity were positively enriched. The translational initiation process was downregulated in the RE+ transcriptomes. Gene co-expression analysis identified modules with relatively distinct biological processes enriched including downregulation of mitochondrial respiratory chain complex assembly. The majority of oxidative phosphorylation (OXPHOS) subunit genes, as well as their upstream regulator gene estrogen-related receptor alpha (ESRRA), encoding ERRα, were downregulated in both RE+ and RE- cases, and the expression level of ESRRA was correlated with that of OXPHOS subunit genes.

CONCLUSION

Meta-analysis increased the power of detecting differentially expressed genes. Integrating differential expression analysis with co-expression analysis helped understand the underlying molecular mechanisms. FECD RE+ and RE- transcriptomic profiles are much alike with the hallmark of downregulation of genes in pathways related to ERRα-mediated OXPHOS.

Gene communities in co-expression networks across different tissues

With the recent availability of tissue-specific gene expression data, e.g., provided by the GTEx Consortium, there is interest in comparing gene co-expression patterns across tissues. One promising approach to this problem is to use a multilayer network analysis framework and perform multilayer community detection. Communities in gene co-expression networks reveal groups of genes similarly expressed across individuals, potentially involved in related biological processes responding to specific environmental stimuli or sharing common regulatory variations. We construct a multilayer network in which each of the four layers is an exocrine gland tissue-specific gene co-expression network. We develop methods for multilayer community detection with correlation matrix input and an appropriate null model. Our correlation matrix input method identifies five groups of genes that are similarly co-expressed in multiple tissues (a community that spans multiple layers, which we call a generalist community) and two groups of genes that are co-expressed in just one tissue (a community that lies primarily within just one layer, which we call a specialist community). We further found gene co-expression communities where the genes physically cluster across the genome significantly more than expected by chance (on chromosomes 1 and 11). This clustering hints at underlying regulatory elements determining similar expression patterns across individuals and cell types. We suggest that KRTAP3-1, KRTAP3-3, and KRTAP3-5 share regulatory elements in skin and pancreas. Furthermore, we find that CELA3A and CELA3B share associated expression quantitative trait loci in the pancreas. The results indicate that our multilayer community detection method for correlation matrix input extracts biologically interesting communities of genes.

Network-based quantitative trait linkage analysis of microbiome composition in inflammatory bowel disease families

Introduction: Inflammatory bowel disease (IBD) is characterized by a dysbiosis of the gut microbiome that results from the interaction of the constituting taxa with one another, and with the host. At the same time, host genetic variation is associated with both IBD risk and microbiome composition. Methods: In the present study, we defined quantitative traits (QTs) from modules identified in microbial co-occurrence networks to measure the inter-individual consistency of microbial abundance and subjected these QTs to a genome-wide quantitative trait locus (QTL) linkage analysis. Results: Four microbial network modules were consistently identified in two cohorts of healthy individuals, but three of the corresponding QTs differed significantly between IBD patients and unaffected individuals. The QTL linkage analysis was performed in a sub-sample of the Kiel IBD family cohort (IBD-KC), an ongoing study of 256 German families comprising 455 IBD patients and 575 first- and second-degree, non-affected relatives. The analysis revealed five chromosomal regions linked to one of three microbial module QTs, namely on chromosomes 3 (spanning 10.79 cM) and 11 (6.69 cM) for the first module, chr9 (0.13 cM) and chr16 (1.20 cM) for the second module, and chr13 (19.98 cM) for the third module. None of these loci have been implicated in a microbial phenotype before. Discussion: Our study illustrates the benefit of combining network and family-based linkage analysis to identify novel genetic drivers of microbiome composition in a specific disease context.

Patterns of pan-genome occupancy and gene coexpression under water-deficit in Brachypodium distachyon

Natural populations are characterized by abundant genetic diversity driven by a range of different types of mutation. The tractability of sequencing complete genomes has allowed new insights into the variable composition of genomes, summarized as a species pan-genome. These analyses demonstrate that many genes are absent from the first reference genomes, whose analysis dominated the initial years of the genomic era. Our field now turns towards understanding the functional consequence of these highly variable genomes. Here, we analysed weighted gene coexpression networks from leaf transcriptome data for drought response in the purple false brome Brachypodium distachyon and the differential expression of genes putatively involved in adaptation to this stressor. We specifically asked whether genes with variable "occupancy" in the pan-genome - genes which are either present in all studied genotypes or missing in some genotypes - show different distributions among coexpression modules. Coexpression analysis united genes expressed in drought-stressed plants into nine modules covering 72 hub genes (87 hub isoforms), and genes expressed under controlled water conditions into 13 modules, covering 190 hub genes (251 hub isoforms). We find that low occupancy pan-genes are under-represented among several modules, while other modules are over-enriched for low-occupancy pan-genes. We also provide new insight into the regulation of drought response in B. distachyon, specifically identifying one module with an apparent role in primary metabolism that is strongly responsive to drought. Our work shows the power of integrating pan-genomic analysis with transcriptomic data using factorial experiments to understand the functional genomics of environmental response.

A framework for non-preserved consensus gene module detection in Johne's disease

Johne's disease caused by Mycobacterium avium subsp. paratuberculosis (MAP) is a major concern in dairy industry. Since, the pathogenesis of the disease is not clearly known, it is necessary to develop an approach to discover molecular mechanisms behind this disease with high confidence. Biological studies often suffer from issues with reproducibility. Lack of a method to find stable modules in co-expression networks from different datasets related to Johne's disease motivated us to present a computational pipeline to identify non-preserved consensus modules. Two RNA-Seq datasets related to MAP infection were analyzed, and consensus modules were detected and were subjected to the preservation analysis. The non-preserved consensus modules in both datasets were determined as they are modules whose connectivity and density are affected by the disease. Long non-coding RNAs (lncRNAs) and TF genes in the non-preserved consensus modules were identified to construct integrated networks of lncRNA-mRNA-TF. These networks were confirmed by protein-protein interactions (PPIs) networks. Also, the overlapped hub genes between two datasets were considered hub genes of the consensus modules. Out of 66 consensus modules, 21 modules were non-preserved consensus modules, which were common in both datasets and 619 hub genes were members of these modules. Moreover, 34 lncRNA and 152 TF genes were identified in 12 and 19 non-preserved consensus modules, respectively. The predicted PPIs in 17 non-preserved consensus modules were significant, and 283 hub genes were commonly identified in both co-expression and PPIs networks. Functional enrichment analysis revealed that eight out of 21 modules were significantly enriched for biological processes associated with Johne's disease including “inflammatory response,” “interleukin-1-mediated signaling pathway”, “type I interferon signaling pathway,” “cytokine-mediated signaling pathway,” “regulation of interferon-beta production,” and “response to interferon-gamma.” Moreover, some genes (hub mRNA, TF, and lncRNA) were introduced as potential candidates for Johne's disease pathogenesis such as TLR2, NFKB1, IRF1, ATF3, TREM1, CDH26, HMGB1, STAT1, ISG15, CASP3. This study expanded our knowledge of molecular mechanisms involved in Johne's disease, and the presented pipeline enabled us to achieve more valid results.

Identification of Novel Prognostic Biomarkers Relevant to Immune Infiltration in Lung Adenocarcinoma

Background: Programmed death ligand-1 (PD-L1) is a biomarker for assessing the immune microenvironment, prognosis, and response to immune checkpoint inhibitors in the clinical treatment of lung adenocarcinoma (LUAD), but it does not work for all patients. This study aims to discover alternative biomarkers.

Methods: Public data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Weighted gene co-expression network analysis (WGCNA) and gene ontology (GO) were used to determine the gene modules relevant to tumor immunity. Protein–protein interaction (PPI) network and GO semantic similarity analyses were applied to identify the module hub genes with functional similarities to PD-L1, and we assessed their correlations with immune infiltration, patient prognosis, and immunotherapy response. Immunohistochemistry (IHC) and hematoxylin and eosin (H&E) staining were used to validate the outcome at the protein level.

Results: We identified an immune response–related module, and two hub genes (PSTPIP1 and PILRA) were selected as potential biomarkers with functional similarities to PD-L1. High expression levels of PSTPIP1 and PILRA were associated with longer overall survival and rich immune infiltration in LUAD patients, and both were significantly high in patients who responded to anti–PD-L1 treatment. Compared to PD-L1–negative LUAD tissues, the protein levels of PSTPIP1 and PILRA were relatively increased in the PD-L1–positive tissues, and the expression of PSTPIP1 and PILRA positively correlated with the tumor-infiltrating lymphocytes.

Conclusion: We identified PSTPIP1 and PILRA as prognostic biomarkers relevant to immune infiltration in LUAD, and both are associated with the response to anti–PD-L1 treatment.

A mechanistic framework for cardiometabolic and coronary artery diseases

Coronary atherosclerosis results from the delicate interplay of genetic and exogenous risk factors, principally taking place in metabolic organs and the arterial wall. Here we show that 224 gene-regulatory coexpression networks (GRNs) identified by integrating genetic and clinical data from patients with (n = 600) and without (n = 250) coronary artery disease (CAD) with RNA-seq data from seven disease-relevant tissues in the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) study largely capture this delicate interplay, explaining >54% of CAD heritability. Within 89 cross-tissue GRNs associated with clinical severity of CAD, 374 endocrine factors facilitated inter-organ interactions, primarily along an axis from adipose tissue to the liver (n = 152). This axis was independently replicated in genetically diverse mouse strains and by injection of recombinant forms of adipose endocrine factors (EPDR1, FCN2, FSTL3 and LBP) that markedly altered blood lipid and glucose levels in mice. Altogether, the STARNET database and the associated GRN browser (http://starnet.mssm.edu) provide a multiorgan framework for exploration of the molecular interplay between cardiometabolic disorders and CAD.

Development of a 15-Gene Signature Model as a Prognostic Tool in Sex Hormone-Dependent Cancers

Sex hormone dependence is associated with tumor progression and prognosis. Here, we explored the molecular basis of luminal A-like phenotype in sex hormone-dependent cancers. RNA-sequencing data from 8 cancer types were obtained from The Cancer Genome Atlas (TCGA). We investigated the enrichment function of differentially expressed genes (DEGs) in luminal A breast cancer (BRCA). Weighted coexpression network analysis (WGCNA) was used to identify gene modules associated with the luminal A-like phenotype, and we calculated the module's preservation in 8 cancer types. Module hub genes screened using least absolute shrinkage and selection operator (LASSO) were used to construct a gene signature model for the luminal A-like phenotype, and we assessed the model's relationship with prognosis, enriched pathways, and immune infiltration using bioinformatics approaches. Compared to other BRCA subtypes, the enrichment functions of upregulated genes in luminal A BRCA were related to hormone biological processes and receptor activity, and the downregulated genes were associated with the cell cycle and nuclear division. A gene module significantly associated with luminal A BRCA was shared by uterine corpus endometrial carcinoma (UCEC), leading to a similar phenotype. Fifteen hub genes were used to construct a gene signature model for the assessment of the luminal A-like phenotype, and the corrected C-statistics and Brier scores were 0.986 and 0.023, respectively. Calibration plots showed good performance, and decision curve analysis indicated a high net benefit of the model. The 15-gene signature model was associated with better overall survival in BRCA and UCEC and was characterized by downregulation of DNA replication, cell cycle and activated CD4 T cells. In conclusion, our study elucidated that BRCA and UCEC share a similar sex hormone-dependent phenotype and constructed a 15-gene signature model for use as a prognostic tool to quantify the probability of the phenotype.

BioNERO: an all-in-one R/Bioconductor package for comprehensive and easy biological network reconstruction

Currently, standard network analysis workflows rely on many different packages, often requiring users to have a solid statistics and programming background. Here, we present BioNERO, an R package that aims to integrate all aspects of network analysis workflows, including expression data preprocessing, gene coexpression and regulatory network inference, functional analyses, and intraspecies and interspecies network comparisons. The state-of-the-art methods implemented in BioNERO ensure that users can perform all analyses with a single package in a simple pipeline, without needing to learn a myriad of package-specific syntaxes. BioNERO offers a user-friendly framework that can be easily incorporated in systems biology pipelines.

Multi-faceted semantic clustering with text-derived phenotypes

Identification of ontology concepts in clinical narrative text enables the creation of phenotype profiles that can be associated with clinical entities, such as patients or drugs. Constructing patient phenotype profiles using formal ontologies enables their analysis via semantic similarity, in turn enabling the use of background knowledge in clustering or classification analyses. However, traditional semantic similarity approaches collapse complex relationships between patient phenotypes into a unitary similarity scores for each pair of patients. Moreover, single scores may be based only on matching terms with the greatest information content (IC), ignoring other dimensions of patient similarity. This process necessarily leads to a loss of information in the resulting representation of patient similarity, and is especially apparent when using very large text-derived and highly multi-morbid phenotype profiles. Moreover, it renders finding a biological explanation for similarity very difficult; the black box problem. In this article, we explore the generation of multiple semantic similarity scores for patients based on different facets of their phenotypic manifestation, which we define through different sub-graphs in the Human Phenotype Ontology. We further present a new methodology for deriving sets of qualitative class descriptions for groups of entities described by ontology terms. Leveraging this strategy to obtain meaningful explanations for our semantic clusters alongside other evaluation techniques, we show that semantic clustering with ontology-derived facets enables the representation, and thus identification of, clinically relevant phenotype relationships not easily recoverable using overall clustering alone. In this way, we demonstrate the potential of faceted semantic clustering for gaining a deeper and more nuanced understanding of text-derived patient phenotypes.

Modularity in Biological Networks

Network modeling, from the ecological to the molecular scale has become an essential tool for studying the structure, dynamics and complex behavior of living systems. Graph representations of the relationships between biological components open up a wide variety of methods for discovering the mechanistic and functional properties of biological systems. Many biological networks are organized into a modular structure, so methods to discover such modules are essential if we are to understand the biological system as a whole. However, most of the methods used in biology to this end, have a limited applicability, as they are very specific to the system they were developed for. Conversely, from the statistical physics and network science perspective, graph modularity has been theoretically studied and several methods of a very general nature have been developed. It is our perspective that in particular for the modularity detection problem, biology and theoretical physics/network science are less connected than they should. The central goal of this review is to provide the necessary background and present the most applicable and pertinent methods for community detection in a way that motivates their further usage in biological research.

Comparative analysis of hippocampal transcriptional features between major depressive disorder patients and animal models

BACKGROUND

Major depressive disorder (MDD) is a psychiatric disorder caused by various etiologies. Chronic stress models are used to simulate the heterogeneous pathogenic processes of depression. However, few studies have compared transcriptional features between stress models and MDD patients.

METHODS

We generated hippocampal transcriptional profiles of the chronic social defeat model by RNA sequencing and downloaded raw data of the same brain region from public databases of the chronic unpredictable mild stress model, the learned helplessness model, and MDD patients. Differential expression and gene co-expression analyses were integrated to compare transcriptional features between stress models and MDD patients.

RESULTS

Each stress model shared 11.4% to 16.3% of differentially expressed genes with MDD patients. Functional analysis at the gene expression level identified altered ensheathment of neurons in both stress models and MDD patients. At the gene network level, each stress model shared 20.9% to 41.6% of co-expressed genes with MDD patients. Functional analysis based on these genes found that axon guidance signaling is the most significantly enriched pathway that was shared by all stress models and MDD patients.

LIMITATIONS

This study was limited by considering only a single brain region and a single sex of stress model animals.

CONCLUSIONS

Our results show that hippocampal transcriptional features of stress models partially overlap with those of MDD patients. The canonical pathways of MDD patients, including ensheathment of neurons, PTEN signaling, and axonal guidance signaling, were shared with all stress models. Our findings provide further clues to understand the molecular mechanisms of depression.

Preservation Analysis on Spatiotemporal Specific Co-expression Networks Suggests the Immunopathogenesis of Alzheimer's Disease

The occurrence and development of Alzheimer’s disease (AD) is a continuous clinical and pathophysiological process, molecular biological, and brain functional change often appear before clinical symptoms, but the detailed underlying mechanism is still unclear. The expression profiling of postmortem brain tissue from AD patients and controls provides evidence about AD etiopathogenesis. In the current study, we used published AD expression profiling data to construct spatiotemporal specific coexpression networks in AD and analyzed the network preservation features of each brain region in different disease stages to identify the most dramatically changed coexpression modules and obtained AD-related biological pathways, brain regions and circuits, cell types and key genes based on these modules. As result, we constructed 57 spatiotemporal specific networks (19 brain regions by three disease stages) in AD and observed universal expression changes in all 19 brain regions. The eight most dramatically changed coexpression modules were identified in seven brain regions. Genes in these modules are mostly involved in immune response-related pathways and non-neuron cells, and this supports the immune pathology of AD and suggests the role of blood brain barrier (BBB) injuries. Differentially expressed genes (DEGs) meta-analysis and protein–protein interaction (PPI) network analysis suggested potential key genes involved in AD development that might be therapeutic targets. In conclusion, our systematical network analysis on published AD expression profiling data suggests the immunopathogenesis of AD and identifies key brain regions and genes.

GWENA: gene co-expression networks analysis and extended modules characterization in a single Bioconductor package

BACKGROUND

Network-based analysis of gene expression through co-expression networks can be used to investigate modular relationships occurring between genes performing different biological functions. An extended description of each of the network modules is therefore a critical step to understand the underlying processes contributing to a disease or a phenotype. Biological integration, topology study and conditions comparison (e.g. wild vs mutant) are the main methods to do so, but to date no tool combines them all into a single pipeline.

RESULTS

Here we present GWENA, a new R package that integrates gene co-expression network construction and whole characterization of the detected modules through gene set enrichment, phenotypic association, hub genes detection, topological metric computation, and differential co-expression. To demonstrate its performance, we applied GWENA on two skeletal muscle datasets from young and old patients of GTEx study. Remarkably, we prioritized a gene whose involvement was unknown in the muscle development and growth. Moreover, new insights on the variations in patterns of co-expression were identified. The known phenomena of connectivity loss associated with aging was found coupled to a global reorganization of the relationships leading to expression of known aging related functions.

CONCLUSION

GWENA is an R package available through Bioconductor ( https://bioconductor.org/packages/release/bioc/html/GWENA.html ) that has been developed to perform extended analysis of gene co-expression networks. Thanks to biological and topological information as well as differential co-expression, the package helps to dissect the role of genes relationships in diseases conditions or targeted phenotypes. GWENA goes beyond existing packages that perform co-expression analysis by including new tools to fully characterize modules, such as differential co-expression, additional enrichment databases, and network visualization.

Identification of 15 lncRNAs Signature for Predicting Survival Benefit of Advanced Melanoma Patients Treated with Anti-PD-1 Monotherapy

The blockade of programmed cell death protein 1 (PD-1) as monotherapy has been widely used in melanoma, but to identify melanoma patients with survival benefit from anti-PD-1 monotherapy is still a big challenge. There is an urgent need for prognostic signatures improving the prediction of immunotherapy responses of these patients. We analyzed transcriptomic data of pre-treatment tumor biopsies and clinical profiles in advanced melanoma patients receiving only anti-PD-1 monotherapy (nivolumab or pembrolizumab) from the PRJNA356761 and PRJEB23709 data sets as the training and validation cohort, respectively. Weighted gene co-expression network analysis was used to identify the key module, then least absolute shrinkage and selection operator was conducted to determine prognostic-related long noncoding RNAs (lncRNAs). Subsequently, the differentially expressed genes between different clusters were identified, and their function and pathway annotation were performed. In this investigation, 92 melanoma patients with complete survival information (51 from training cohort and 41 from validation cohort) were included in our analyses. We initiallyidentified the key module (skyblue) by weighted gene co-expression network analysis, and then identified a 15 predictive lncRNAs (AC010904.2, LINC01126, AC012360.1, AC024933.1, AL442128.2, AC022211.4, AC022211.2, AC127496.5, NARF-AS1, AP000919.3, AP005329.2, AC023983.1, AC023983.2, AC139100.1, and AC012615.4) signature in melanoma patients treated with anti-PD-1 monotherapy by least absolute shrinkage and selection operator in the training cohort. These results were then validated in the validation cohort. Finally, enrichment analysis showed that the functions of differentially expressed genes between two consensus clusters were mainly related to the immune process and treatment. In summary, the 15 lncRNAs signature is a novel effective predictor for prognosis in advanced melanoma patients treated with anti-PD-1 monotherapy.

Disrupted Gene Networks in Subfertile Hybrid House Mice

The Dobzhansky–Muller (DM) model provides a widely accepted mechanism for the evolution of reproductive isolation: incompatible substitutions disrupt interactions between genes. To date, few candidate incompatibility genes have been identified, leaving the genes driving speciation mostly uncharacterized. The importance of interactions in the DM model suggests that gene coexpression networks provide a powerful framework to understand disrupted pathways associated with postzygotic isolation. Here, we perform weighted gene coexpression network analysis to infer gene interactions in hybrids of two recently diverged European house mouse subspecies, Mus mus domesticus and M. m. musculus, which commonly show hybrid male sterility or subfertility. We use genome-wide testis expression data from 467 hybrid mice from two mapping populations: F₂s from a laboratory cross between wild-derived pure subspecies strains and offspring of natural hybrids captured in the Central Europe hybrid zone. This large data set enabled us to build a robust consensus network using hybrid males with fertile phenotypes. We identify several expression modules, or groups of coexpressed genes, that are disrupted in subfertile hybrids, including modules functionally enriched for spermatogenesis, cilium and sperm flagellum organization, chromosome organization, and DNA repair, and including genes expressed in spermatogonia, spermatocytes, and spermatids. Our network-based approach enabled us to hone in on specific hub genes likely to be influencing module-wide gene expression and hence potentially driving large-effect DM incompatibilities. A disproportionate number of hub genes lie within sterility loci identified previously in the hybrid zone mapping population and represent promising candidate barrier genes and targets for future functional analysis.

Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data

Dynamic studies in time course experimental designs and clinical approaches have been widely used by the biomedical community. These applications are particularly relevant in stimuli-response models under environmental conditions, characterization of gradient biological processes in developmental biology, identification of therapeutic effects in clinical trials, disease progressive models, cell-cycle, and circadian periodicity. Despite their feasibility and popularity, sophisticated dynamic methods that are well validated in large-scale comparative studies, in terms of statistical and computational rigor, are less benchmarked, comparing to their static counterparts. To date, a number of novel methods in bulk RNA-Seq data have been developed for the various time-dependent stimuli, circadian rhythms, cell-lineage in differentiation, and disease progression. Here, we comprehensively review a key set of representative dynamic strategies and discuss current issues associated with the detection of dynamically changing genes. We also provide recommendations for future directions for studying non-periodical, periodical time course data, and meta-dynamic datasets.

Genotype by environment interaction for gene expression in Drosophila melanogaster

The genetics of phenotypic responses to changing environments remains elusive. Using whole-genome quantitative gene expression as a model, here we study how the genetic architecture of regulatory variation in gene expression changed in a population of fully sequenced inbred Drosophila melanogaster strains when flies developed in different environments (25 °C and 18 °C). We find a substantial fraction of the transcriptome exhibited genotype by environment interaction, implicating environmentally plastic genetic architecture of gene expression. Genetic variance in expression increases at 18 °C relative to 25 °C for most genes that have a change in genetic variance. Although the majority of expression quantitative trait loci (eQTLs) for the gene expression traits in the two environments are shared and have similar effects, analysis of the environment-specific eQTLs reveals enrichment of binding sites for two transcription factors. Finally, although genotype by environment interaction in gene expression could potentially disrupt genetic networks, the co-expression networks are highly conserved across environments. Genes with higher network connectivity are under stronger stabilizing selection, suggesting that stabilizing selection on expression plays an important role in promoting network robustness.

Genetic networks in Parkinson's and Alzheimer's disease

Parkinson’s disease (PD) and Alzheimer’s disease (AD) are the most common neurodegenerative diseases and there is increasing evidence that they share common physiological and pathological links. Here we have conducted the largest network analysis of PD and AD based on their gene expressions in blood to date. We identified modules that were not preserved between disease and healthy control (HC) networks, and important hub genes and transcription factors (TFs) in these modules. We highlighted that the PD module not preserved in HCs was associated with insulin resistance, and HDAC6 was identified as a hub gene in this module which may have the role of influencing tau phosphorylation and autophagic flux in neurodegenerative disease. The AD module associated with regulation of lipolysis in adipocytes and neuroactive ligand-receptor interaction was not preserved in healthy and mild cognitive impairment networks and the key hubs TRPC5 and BRAP identified as potential targets for therapeutic treatments of AD. Our study demonstrated that PD and AD share common disrupted genetics and identified novel pathways, hub genes and TFs that may be new areas for mechanistic study and important targets in both diseases.

Genetic Signatures of Acute Asthma Exacerbation Related With Ineffective Response to Corticosteroid

Purpose

Acute exacerbation (AE) is an important domain of asthma management and may be related with ineffective response to corticosteroid. This study aimed to find mechanisms of AE using genome-wide gene expression profiles of blood cells from asthmatics and its perturbation by in vitro dexamethasone (Dex)-treatment.

Methods

We utilized lymphoblastoid B cells from 107 childhood asthmatics and peripheral blood mononuclear cells from 29 adult asthmatics who were treated with inhaled corticosteroids. We searched for a preserved co-expression gene module significantly associated with the AE rate in both cohorts and measured expression changes of genes belong to this module after Dex-treatment.

Results

We identified a preserved module composed of 77 genes. Among them, expressions of 2 genes (EIF2AK2 and NOL11) decreased significantly after Dex-treatment in both cohorts. EIF2AK2, a key gene acting antiviral defense mechanism, showed significantly higher expressions in asthmatics with AE. The protein repair pathway was enriched significantly in 64 genes which belong to the preserved module but showed no expression differences after Dex-treatment in both cohorts. Among them, MSRA and MSRB2 may play key roles by controlling oxidative stress.

Conclusions

Many genes belong to the AE rate-associated and preserved module identified in blood cells from childhood and adults asthmatics showed no expression changes after in vitro Dex-treatment. These findings suggest that we may need alternative treatment options to corticosteroids to prevent AE. EIF2AK2, MSRA and MSRB2 expressions on blood cells may help us select AE-susceptible asthmatics and adjust treatments to prevent AE.

Identification of a key gene module associated with glucocorticoid- induced derangement in bone mineral density in patients with asthma

Derangement in bone mineral density (BMD) caused by glucocorticoid is well-known. The present study aimed to find key biological pathways associated with low BMD after glucocorticoid treatment in asthmatics using gene expression profiles of peripheral blood cells. We utilized immortalized B cells (IBCs) from 32 childhood asthmatics after multiple oral glucocorticoid bursts and peripheral blood mononuclear cells (PBMCs) from 17 adult asthmatics after a long-term use of oral glucocorticoid. We searched co-expressed gene modules significantly related with the BMD Z score in childhood asthmatics and tested if these gene modules were preserved and significantly associated with the BMD Z score in adult asthmatics as well. We identified a gene module composed of 199 genes significantly associated with low BMD in both childhood and adult asthmatics. The structure of this module was preserved across gene expression profiles. We found that the cellular metabolic pathway was significantly enriched in this module. Among 18 hub genes in this module, we postulated that 2 genes, CREBBP and EP300, contributed to low BMD following a literature review. A novel biologic pathway identified in this study highlighted a gene module and several genes as playing possible roles in the pathogenesis of glucocorticoid- induced derangement in BMD.

Elevated serum alpha-1 antitrypsin is a major component of GlycA-associated risk for future morbidity and mortality

Background

GlycA is a nuclear magnetic resonance (NMR) spectroscopy biomarker that predicts risk of disease from myriad causes. It is heterogeneous; arising from five circulating glycoproteins with dynamic concentrations: alpha-1 antitrypsin (AAT), alpha-1-acid glycoprotein (AGP), haptoglobin (HP), transferrin (TF), and alpha-1-antichymotrypsin (AACT). The contributions of each glycoprotein to the disease and mortality risks predicted by GlycA remain unknown.

Methods

We trained imputation models for AAT, AGP, HP, and TF from NMR metabolite measurements in 626 adults from a population cohort with matched NMR and immunoassay data. Levels of AAT, AGP, and HP were estimated in 11,861 adults from two population cohorts with eight years of follow-up, then each biomarker was tested for association with all common endpoints. Whole blood gene expression data was used to identify cellular processes associated with elevated AAT.

Results

Accurate imputation models were obtained for AAT, AGP, and HP but not for TF. While AGP had the strongest correlation with GlycA, our analysis revealed variation in imputed AAT levels was the most predictive of morbidity and mortality for the widest range of diseases over the eight year follow-up period, including heart failure (meta-analysis hazard ratio = 1.60 per standard deviation increase of AAT, P-value = 1×10⁻¹⁰), influenza and pneumonia (HR = 1.37, P = 6×10⁻¹⁰), and liver diseases (HR = 1.81, P = 1×10⁻⁶). Transcriptional analyses revealed association of elevated AAT with diverse inflammatory immune pathways.

Conclusions

This study clarifies the molecular underpinnings of the GlycA biomarker’s associated disease risk, and indicates a previously unrecognised association between elevated AAT and severe disease onset and mortality.

Systematic genetic analyses of GWAS data reveal an association between the immune system and insomnia

BACKGROUND

Previous studies have inferred a strong genetic component for insomnia. However, the etiology of insomnia is still unclear. The aim of the current study was to explore potential biological pathways, gene networks, and brain regions associated with insomnia.

METHODS

Using pathways (gene sets) from Reactome, we carried out a two-stage gene set enrichment analysis strategy. From a large genome-wide association studies (GWASs) of insomnia symptoms (32,155 cases/26,973 controls), significant gene sets were tested for replication in other large GWASs of insomnia complaints (32,384 cases/80,622 controls). After the network analysis of unique genes within the replicated pathways, a gene set analysis for genes in each cluster/module of the enhancing neuroimaging genetics through meta-analysis GWAS data was performed for the volumes of the intracranial and seven subcortical regions.

RESULTS

A total of 31 of 1,816 Reactome pathways were identified and showed associations with insomnia risk. In addition, seven functionally and topologically interconnected clusters (clusters 0-6) and six gene modules (named Yellow, Blue, Brown, Green, Red, and Turquoise) were associated with insomnia. Moreover, significant associations were detected between common variants of the genes in Cluster 2 with hippocampal volume (p = 0.035; family wise error [FWE] correction) and the red module with intracranial volume (p = 0.047; FWE correction). Functional enrichment for genes in the Cluster 2 and the Red module revealed the involvement of immune responses, nervous system development, NIK/NF-kappaB signaling, and I-kappaB kinase/NF-kappaB signaling. Core genes (UBC, UBB, and UBA52) in the interconnected functional network were found to be involved in regulating brain development.

CONCLUSIONS

The current study demonstrates that the immune system and the hippocampus may play central roles in neurodevelopment and insomnia risk.

Gene Coexpression Networks in Whole Blood Implicate Multiple Interrelated Molecular Pathways in Obesity in People with Asthma

OBJECTIVE

Asthmatic children who develop obesity through adolescence have poorer disease outcomes compared with those who do not. This study aimed to characterize the biology of childhood asthma complicated by adult obesity.

METHODS

Gene expression networks are powerful statistical tools for characterizing human disease that leverage the putative coregulatory relationships of genes to infer relevant biological pathways. Weighted gene coexpression network analysis of gene expression data was performed in whole blood from 514 adult asthmatic subjects. Then, module preservation and association replication analyses were performed in 418 subjects from two independent asthma cohorts (one pediatric and one adult).

RESULTS

A multivariate model was identified in which three gene coexpression network modules were associated with incident obesity in the discovery cohort (each P < 0.05). Two module memberships were enriched for genes in pathways related to platelets, integrins, extracellular matrix, smooth muscle, NF-κB signaling, and Hedgehog signaling. The network structures of each of the obesity modules were significantly preserved in both replication cohorts (permutation P = 9.999E-05). The corresponding module gene sets were significantly enriched for differential expression in subjects with obesity in both replication cohorts (each P < 0.05).

CONCLUSIONS

The gene coexpression network profiles thus implicate multiple interrelated pathways in the biology of an important endotype of asthma with obesity.

On the Evaluation of Module Preservation

The ability to compare the modular structure of gene expression networks requires fast and accurate statistical tools. NetRep software provides a permutation approach which validates seven measures used to contrast the preservation of co-expression across diverse datasets.

Evidence for widespread dysregulation of circadian clock progression in human cancer

The ubiquitous daily rhythms in mammalian physiology are guided by progression of the circadian clock. In mice, systemic disruption of the clock can promote tumor growth. In vitro, multiple oncogenes can disrupt the clock. However, due to the difficulties of studying circadian rhythms in solid tissues in humans, whether the clock is disrupted within human tumors has remained unknown. We sought to determine the state of the circadian clock in human cancer using publicly available transcriptome data. We developed a method, called the clock correlation distance (CCD), to infer circadian clock progression in a group of samples based on the co-expression of 12 clock genes. Our method can be applied to modestly sized datasets in which samples are not labeled with time of day and coverage of the circadian cycle is incomplete. We used the method to define a signature of clock gene co-expression in healthy mouse organs, then validated the signature in healthy human tissues. By then comparing human tumor and non-tumor samples from twenty datasets of a range of cancer types, we discovered that clock gene co-expression in tumors is consistently perturbed. Subsequent analysis of data from clock gene knockouts in mice suggested that perturbed clock gene co-expression in human cancer is not caused solely by the inactivation of clock genes. Furthermore, focusing on lung cancer, we found that human lung tumors showed systematic changes in expression in a large set of genes previously inferred to be rhythmic in healthy lung. Our findings suggest that clock progression is dysregulated in many solid human cancers and that this dysregulation could have broad effects on circadian physiology within tumors. In addition, our approach opens the door to using publicly available data to infer circadian clock progression in a multitude of human phenotypes.

Network Analysis of Gene Expression

Studies have pointed out that the expression of genes are highly regulated, which result in a cascade of distinct patterns of coexpression forming a network. Identifying and understanding such patterns is crucial in deciphering molecular mechanisms that underlie the pathophysiology of diseases. With the advance of high throughput assay of messenger RNA (mRNA) and high performance computing, reconstructing such network from molecular data such as gene expression is now possible. This chapter discusses an overview of methods of constructing such networks, practical considerations, and an example.

An interaction map of circulating metabolites, immune gene networks, and their genetic regulation

BACKGROUND

Immunometabolism plays a central role in many cardiometabolic diseases. However, a robust map of immune-related gene networks in circulating human cells, their interactions with metabolites, and their genetic control is still lacking. Here, we integrate blood transcriptomic, metabolomic, and genomic profiles from two population-based cohorts (total N = 2168), including a subset of individuals with matched multi-omic data at 7-year follow-up.

RESULTS

We identify topologically replicable gene networks enriched for diverse immune functions including cytotoxicity, viral response, B cell, platelet, neutrophil, and mast cell/basophil activity. These immune gene modules show complex patterns of association with 158 circulating metabolites, including lipoprotein subclasses, lipids, fatty acids, amino acids, small molecules, and CRP. Genome-wide scans for module expression quantitative trait loci (mQTLs) reveal five modules with mQTLs that have both cis and trans effects. The strongest mQTL is in ARHGEF3 (rs1354034) and affects a module enriched for platelet function, independent of platelet counts. Modules of mast cell/basophil and neutrophil function show temporally stable metabolite associations over 7-year follow-up, providing evidence that these modules and their constituent gene products may play central roles in metabolic inflammation. Furthermore, the strongest mQTL in ARHGEF3 also displays clear temporal stability, supporting widespread trans effects at this locus.

CONCLUSIONS

This study provides a detailed map of natural variation at the blood immunometabolic interface and its genetic basis, and may facilitate subsequent studies to explain inter-individual variation in cardiometabolic disease.

Experimental and Human Evidence for Lipocalin-2 (Neutrophil Gelatinase-Associated Lipocalin [NGAL]) in the Development of Cardiac Hypertrophy and heart failure

BACKGROUND

Cardiac hypertrophy increases the risk of developing heart failure and cardiovascular death. The neutrophil inflammatory protein, lipocalin-2 (LCN2/NGAL), is elevated in certain forms of cardiac hypertrophy and acute heart failure. However, a specific role for LCN2 in predisposition and etiology of hypertrophy and the relevant genetic determinants are unclear. Here, we defined the role of LCN2 in concentric cardiac hypertrophy in terms of pathophysiology, inflammatory expression networks, and genomic determinants.

METHODS AND RESULTS

We used 3 experimental models: a polygenic model of cardiac hypertrophy and heart failure, a model of intrauterine growth restriction and Lcn2-knockout mouse; cultured cardiomyocytes; and 2 human cohorts: 114 type 2 diabetes mellitus patients and 2064 healthy subjects of the YFS (Young Finns Study). In hypertrophic heart rats, cardiac and circulating Lcn2 was significantly overexpressed before, during, and after development of cardiac hypertrophy and heart failure. Lcn2 expression was increased in hypertrophic hearts in a model of intrauterine growth restriction, whereas Lcn2-knockout mice had smaller hearts. In cultured cardiomyocytes, Lcn2 activated molecular hypertrophic pathways and increased cell size, but reduced proliferation and cell numbers. Increased LCN2 was associated with cardiac hypertrophy and diastolic dysfunction in diabetes mellitus. In the YFS, LCN2 expression was associated with body mass index and cardiac mass and with levels of inflammatory markers. The single-nucleotide polymorphism, rs13297295, located near LCN2 defined a significant cis-eQTL for LCN2 expression.

CONCLUSIONS

Direct effects of LCN2 on cardiomyocyte size and number and the consistent associations in experimental and human analyses reveal a central role for LCN2 in the ontogeny of cardiac hypertrophy and heart failure.