Genome-Wide Analysis of the DNA Methylation Profile Identifies the Fragile Histidine Triad (FHIT) Gene as a New Promising Biomarker of Crohn's Disease

Inflammatory bowel disease is known to be associated with a genetic predisposition involving multiple genes; however, there is growing evidence that abnormal interactions with environmental factors, particularly epigenetic factors, can also significantly contribute to the development of inflammatory bowel disease (IBD). Although many genome-wide association studies have been performed to identify the genetic changes underlying the pathogenesis of Crohn’s disease, the role of epigenetic alterations based on molecular complications arising from Crohn’s disease (CD) is poorly understood. We employed an unbiased approach to define DNA methylation alterations in colonoscopy samples from patients with CD using the HumanMethylation450K BeadChip platform. Technical and functional validation was performed by methylation-specific PCR (MSP) and bisulfite sequencing of a validation set of 207 patients with CD samples. Immunohistochemistry (IHC) analysis was performed in the representative sample sets. DNA methylation profile in CD revealed that 135 probes (24 hypermethylated and 111 hypomethylated probes) were differentially methylated. We validated the methylation levels of 19 genes that showed hypermethylation in patients with CD compared with normal controls. We uniquely identified that the fragile histidine triad (FHIT) gene was hypermethylated in a disease-specific manner and its protein level was downregulated in patients with CD. Pathway analysis of the hypermethylated candidates further suggested putative molecular interactions relevant to IBD pathology. Our data provide information on the biological and clinical implications of DNA hypermethylated genes in CD, identifying FHIT methylation as a promising new biomarker for CD. Further study of the role of FHIT in IBD pathogenesis may lead to the development of new therapeutic targets.

Constructing gene network for type 1 narcolepsy based on genome-wide association study and differential gene expression analysis (STROBE)

Although many genes that affect narcolepsy risk have been identified, the interactions among these genes are still unclear. Moreover, there is a lack of research on the construction of the genetic network of narcolepsy. To screen candidate genes related to the onset of narcolepsy type 1, the function and distribution of important genes related to narcolepsy type 1 were studied and a gene network was constructed to study the pathogenesis of narcolepsy type 1.A case-control study (observational study) of 1075 Chinese narcoleptic patients and 1997 controls was conducted. The gene-sequencing data was analyzed using genome-wide association analysis. The candidate genes related to narcolepsy were identified by differential gene expression analysis and literature research. Then, the 28 candidate genes were input into the KEGG database and 32 pathway data related to candidate genes were obtained. A gene network, with the pathways as links and the genes as nodes, was constructed. According to our results, TNF, MHC II, NFATC2, and CXCL8 were the top genes in the gene network.TNF, MHC II, NFATC2, and CXCL8 are closely related to narcolepsy type I and require further study. By analyzing the pathways of disease-related genes and the network of gene interaction, we can provide an outlinefor the study of specific mechanisms of and treatments for narcolepsy.

Genetic Dissection of Hypertrophic Cardiomyopathy with Myocardial RNA-Seq

Hypertrophic cardiomyopathy (HCM) is an inherited disorder of the myocardium, and pathogenic mutations in the sarcomere genes myosin heavy chain 7 (MYH7) and myosin-binding protein C (MYBPC3) explain 60%–70% of observed clinical cases. The heterogeneity of phenotypes observed in HCM patients, however, suggests that novel causative genes or genetic modifiers likely exist. Here, we systemically evaluated RNA-seq data from 28 HCM patients and 9 healthy controls with pathogenic variant identification, differential expression analysis, and gene co-expression and protein–protein interaction network analyses. We identified 43 potential pathogenic variants in 19 genes in 24 HCM patients. Genes with more than one variant included the following: MYBPC3, TTN, MYH7, PSEN2, and LDB3. A total of 2538 protein-coding genes, six microRNAs (miRNAs), and 1617 long noncoding RNAs (lncRNAs) were identified differentially expressed between the groups, including several well-characterized cardiomyopathy-related genes (ANKRD1, FHL2, TGFB3, miR-30d, and miR-154). Gene enrichment analysis revealed that those genes are significantly involved in heart development and physiology. Furthermore, we highlighted four subnetworks: mtDNA-subnetwork, DSP-subnetwork, MYH7-subnetwork, and MYBPC3-subnetwork, which could play significant roles in the progression of HCM. Our findings further illustrate that HCM is a complex disease, which results from mutations in multiple protein-coding genes, modulation by non-coding RNAs and perturbations in gene networks.

Mining Drug-Target Associations in Cancer: Analysis of Gene Expression and Drug Activity Correlations

Cancer is a complex disease affecting millions of people worldwide, with over a hundred clinically approved drugs available. In order to improve therapy, treatment, and response, it is essential to draw better maps of the targets of cancer drugs and possible side interactors. This study presents a large-scale screening method to find associations of cancer drugs with human genes. The analysis is focused on the current collection of Food and Drug Administration (FDA)-approved drugs (which includes about one hundred chemicals). The approach integrates global gene-expression transcriptomic profiles with drug-activity profiles of a set of 60 human cell lines obtained for a collection of chemical compounds (small bioactive molecules). Using a standardized expression for each gene versus standardized activity for each drug, Pearson and Spearman correlations were calculated for all possible pairwise gene-drug combinations. These correlations were used to build a global bipartite network that includes 1007 gene-drug significant associations. The data are integrated into an open web-tool called GEDA (Gene Expression and Drug Activity) which includes a relational view of cancer drugs and genes, disclosing the putative indirect interactions found for FDA-approved drugs as well as the known targets of these drugs. The results also provide insight into the complex action of pharmaceuticals, presenting an alternative view to address predicted pleiotropic effects of the drugs.

Defects in mTORC1 Network and mTORC1-STAT3 Pathway Crosstalk Contributes to Non-inflammatory Hepatocellular Carcinoma

Background and Aims

Mammalian target of rapamycin complex 1 (mTORC1) is frequently hyperactivated in hepatocellular carcinoma (HCC). Cases of HCC without inflammation and cirrhosis are not rarely seen in clinics. However, the molecular basis of non-inflammatory HCC remains unclear.

Methods

Spontaneous non-inflammatory HCC in mice was triggered by constitutive elevation of mTORC1 by liver-specific TSC1 knockout (LTsc1KO). A multi-omics approach was utilized on tumor tissues to better understand the molecular basis for the development of HCC in the LTsc1KO model.

Results

We showed that LTsc1KO in mice triggered spontaneous non-inflammatory HCC, with molecular characteristics similar to those of diethylnitrosamine-mediated non-cirrhotic HCC. Mitochondrial and autophagy defects, as well as hepatic metabolic disorder were manifested in HCC development by LTsc1KO. mTORC1 activation on its own regulated an oncogenic network (DNA-damage-inducible transcript 4, nuclear protein 1, and fibroblast growth factor 21), and mTORC1-signal transducer and activator of transcription pathway crosstalk that altered specific metabolic pathways contributed to the development of non-inflammatory HCC.

Conclusion

Our findings reveal the mechanisms of mTORC1-driven non-inflammatory HCC and provide insight into further development of a protective strategy against non-inflammatory HCC.

Interaction Between Neurogenic Stimuli and the Gene Network Controlling the Activation of Stem Cells of the Adult Neurogenic Niches, in Physiological and Pathological Conditions

In the adult mammalian brain new neurons are continuously generated throughout life in two niches, the dentate gyrus of the hippocampus and the subventricular zone. This process, called adult neurogenesis, starts from stem cells, which are activated and enter the cell cycle. The proliferative capability of stem cells progressively decreases during aging. The population of stem cells is generally quiescent, and it is not clear whether the potential for stem cells to expand is limited, or whether they can expand and then return to quiescence, remaining available for further activation. Certain conditions may deregulate stem cells quiescence and self-renewal. In fact we discuss the possibility of activation of stem cells by neurogenic stimuli as a function of the intensity of the stimulus (i.e., whether this is physiological or pathological), and of the deregulation of the system (i.e., whether the model is aged or carrying genetic mutations in the gene network controlling quiescence). It appears that when the system is aged and/or carrying mutations of quiescence-maintaining genes, preservation of the quiescent state of stem cells is more critical and stem cells can be activated by a neurogenic stimulus which is ineffective in normal conditions. Moreover, when a neurogenic stimulus is in itself a cause of brain damage (e.g., kainic acid treatment) the activation of stem cells occurs bypassing any inhibitory control. Plausibly, with strong neurogenic stimuli, such as kainic acid injected into the dentate gyrus, the self-renewal capacity of stem cells may undergo rapid exhaustion. However, the self-renewal capability of stem cells persists when normal stimuli are elicited in the presence of a mutation of one of the quiescence-maintaining genes, such as p16Ink4a, p21Cip1 or Btg1. In this case, stem cells become promptly activated by a neurogenic stimulus even during aging. This indicates that stem cells retain a high proliferative capability and plasticity, and suggests that stem cells are protected against the response to stimulus and are resilient to exhaustion. It will be interesting to assess at which functional degree of deregulation of the quiescence-maintaining system, stem cells will remain responsive to repeated neurogenic stimuli without undergoing exhaustion of their pool.

In Silico Discovery of Candidate Drugs against Covid-19

Previous studies reported that Angiotensin converting enzyme 2 (ACE2) is the main cell receptor of SARS-CoV and SARS-CoV-2. It plays a key role in the access of the virus into the cell to produce the final infection. In the present study we investigated in silico the basic mechanism of ACE2 in the lung and provided evidences for new potentially effective drugs for Covid-19. Specifically, we used the gene expression profiles from public datasets including The Cancer Genome Atlas, Gene Expression Omnibus and Genotype-Tissue Expression, Gene Ontology and pathway enrichment analysis to investigate the main functions of ACE2-correlated genes. We constructed a protein-protein interaction network containing the genes co-expressed with ACE2. Finally, we focused on the genes in the network that are already associated with known drugs and evaluated their role for a potential treatment of Covid-19. Our results demonstrate that the genes correlated with ACE2 are mainly enriched in the sterol biosynthetic process, Aryldialkylphosphatase activity, adenosylhomocysteinase activity, trialkylsulfonium hydrolase activity, acetate-CoA and CoA ligase activity. We identified a network of 193 genes, 222 interactions and 36 potential drugs that could have a crucial role. Among possible interesting drugs for Covid-19 treatment, we found Nimesulide, Fluticasone Propionate, Thiabendazole, Photofrin, Didanosine and Flutamide.

Cell Type-Specific Gene Network-Based Analysis Depicts the Heterogeneity of Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a complex neuropsychiatric disorder characterized by substantial heterogeneity. To identify the convergence of disease pathology on common pathways, it is essential to understand the correlations among ASD candidate genes and study shared molecular pathways between them. Investigating functional interactions between ASD candidate genes in different cell types of normal human brains may shed new light on the genetic heterogeneity of ASD. Here we apply cell type-specific gene network-based analysis to analyze human brain nucleus gene expression data and identify cell type-specific ASD-associated gene modules. ASD-associated modules specific to different cell types are relevant to different gene functions, for instance, the astrocytes-specific module is involved in functions of axon and neuron projection guidance, GABAergic interneuron-specific modules are involved in functions of postsynaptic membrane, extracellular matrix structural constituent, and ion transmembrane transporter activity. Our findings can promote the study of cell type heterogeneity of ASD, providing new insights into the pathogenesis of ASD. Our method has been shown to be effective in discovering cell type-specific disease-associated gene expression patterns and can be applied to other complex diseases.

Bayesian Network Marker Selection via the Thresholded Graph Laplacian Gaussian Prior

Selecting informative nodes over large-scale networks becomes increasingly important in many research areas. Most existing methods focus on the local network structure and incur heavy computational costs for the large-scale problem. In this work, we propose a novel prior model for Bayesian network marker selection in the generalized linear model (GLM) framework: the Thresholded Graph Laplacian Gaussian (TGLG) prior, which adopts the graph Laplacian matrix to characterize the conditional dependence between neighboring markers accounting for the global network structure. Under mild conditions, we show the proposed model enjoys the posterior consistency with a diverging number of edges and nodes in the network. We also develop a Metropolis-adjusted Langevin algorithm (MALA) for efficient posterior computation, which is scalable to large-scale networks. We illustrate the superiorities of the proposed method compared with existing alternatives via extensive simulation studies and an analysis of the breast cancer gene expression dataset in the Cancer Genome Atlas (TCGA).

BarleyNet: A Network-Based Functional Omics Analysis Server for Cultivated Barley, Hordeum vulgare L

Cultivated barley (Hordeum vulgare L.) is one of the most produced cereal crops worldwide after maize, bread wheat, and rice. Barley is an important crop species not only as a food source, but also in plant genetics because it harbors numerous stress response alleles in its genome that can be exploited for crop engineering. However, the functional annotation of its genome is relatively poor compared with other major crops. Moreover, bioinformatics tools for system-wide analyses of omics data from barley are not yet available. We have thus developed BarleyNet, a co-functional network of 26,145 barley genes, along with a web server for network-based predictions (http://www.inetbio.org/barleynet). We demonstrated that BarleyNet's prediction of biological processes is more accurate than that of an existing barley gene network. We implemented three complementary network-based algorithms for prioritizing genes or functional concepts to study genetic components of complex traits such as environmental stress responses: (i) a pathway-centric search for candidate genes of pathways or complex traits; (ii) a gene-centric search to infer novel functional concepts for genes; and (iii) a context-centric search for novel genes associated with stress response. We demonstrated the usefulness of these network analysis tools in the study of stress response using proteomics and transcriptomics data from barley leaves and roots upon drought or heat stresses. These results suggest that BarleyNet will facilitate our understanding of the underlying genetic components of complex traits in barley.

System-Based Differential Gene Network Analysis for Characterizing a Sample-Specific Subnetwork

Gene network estimation is a method key to understanding a fundamental cellular system from high throughput omics data. However, the existing gene network analysis relies on having a sufficient number of samples and is required to handle a huge number of nodes and estimated edges, which remain difficult to interpret, especially in discovering the clinically relevant portions of the network. Here, we propose a novel method to extract a biomedically significant subnetwork using a Bayesian network, a type of unsupervised machine learning method that can be used as an explainable and interpretable artificial intelligence algorithm. Our method quantifies sample specific networks using our proposed Edge Contribution value (ECv) based on the estimated system, which realizes condition-specific subnetwork extraction using a limited number of samples. We applied this method to the Epithelial-Mesenchymal Transition (EMT) data set that is related to the process of metastasis and thus prognosis in cancer biology. We established our method-driven EMT network representing putative gene interactions. Furthermore, we found that the sample-specific ECv patterns of this EMT network can characterize the survival of lung cancer patients. These results show that our method unveils the explainable network differences in biological and clinical features through artificial intelligence technology.

Rapid Evolution of Gained Essential Developmental Functions of a Young Gene via Interactions with Other Essential Genes

New genes are of recent origin and only present in a subset of species in a phylogeny. Accumulated evidence suggests that new genes, like old genes that are conserved across species, can also take on important functions and be essential for the survival and reproductive success of organisms. Although there are detailed analyses of the mechanisms underlying new genes' gaining fertility functions, how new genes rapidly become essential for viability remains unclear. We focused on a young retro-duplicated gene (CG7804, which we named Cocoon) in Drosophila that originated between 4 and 10 Ma. We found that, unlike its evolutionarily conserved parental gene, Cocoon has evolved under positive selection and accumulated many amino acid differences at functional sites from the parental gene. Despite its young age, Cocoon is essential for the survival of Drosophila melanogaster at multiple developmental stages, including the critical embryonic stage, and its expression is essential in different tissues from those of its parental gene. Functional genomic analyses found that Cocoon acquired unique DNA-binding sites and has a contrasting effect on gene expression to that of its parental gene. Importantly, Cocoon binding predominantly locates at genes that have other essential functions and/or have multiple gene-gene interactions, suggesting that Cocoon acquired novel essential function to survival through forming interactions that have large impacts on the gene interaction network. Our study is an important step toward deciphering the evolutionary trajectory by which new genes functionally diverge from parental genes and become essential.

Identification of a genetic network for an ecologically relevant behavioural phenotype in Drosophila melanogaster

Pupation site choice of Drosophila third-instar larvae is critical for the survival of individuals, as pupae are exposed to various biotic and abiotic dangers while immobilized during the 3-4 days of metamorphosis. This singular behavioural choice is sensitive to both environmental and genetic factors. Here, we developed a high-throughput phenotyping approach to assay the variation in pupation height in Drosophila melanogaster, while controlling for possibly confounding factors. We find substantial variation of mean pupation height among sampled natural stocks and we show that the Drosophila Genetic Reference Panel (DGRP) reflects this variation. Using the DGRP stocks for genome-wide association (GWA) mapping, 16 loci involved in determining pupation height could be resolved. The candidate genes in these loci are enriched for high expression in the larval central nervous system. A genetic network could be constructed from the candidate loci, which places scribble (scrib) at the centre, plus other genes known to be involved in nervous system development, such as Epidermal growth factor receptor (Egfr) and p53. Using gene disruption lines, we could functionally validate several of the initially identified loci, as well as additional loci predicted from network analysis. Our study shows that the combination of high-throughput phenotyping with a genetic analysis of variation captured from the wild can be used to approach the genetic dissection of an environmentally relevant behavioural phenotype.

Long non-coding RNA HCP5 serves as a ceRNA sponging miR-17-5p and miR-27a/b to regulate the pathogenesis of childhood obesity via the MAPK signaling pathway

Background This study aimed to investigate the completing endogenous RNA (ceRNA) network involved in childhood obesity. Methods The microarray dataset GSE9624 was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed long non-coding RNAs (lncRNAs) (DELs) and messenger RNAs (DEMs) were isolated between the childhood obesity and non-obesity tissue samples. Then, Gene Ontology (GO) functional and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of isolated DEMs were performed. DELs and DEMs targeted miRNAs were predicted to construct a ceRNA regulatory network. Finally, critical lncRNAs were validated in another dataset. Results A total of 1257 differentially expressed RNAs were screened, including 28 lncRNAs and 1229 mRNAs. In addition, these RNAs were mainly involved in defense response, cell cycle, mitogen-activated protein kinase (MAPK) signaling pathway, apoptosis, etc. Three lncRNAs (human leukocyte antigen complex 5 [HCP5], long intergenic non-protein coding RNA 839 [LINC00839] and receptor activity modifying protein 2 [RAMP2-AS1]) and two related miRNAs (hsa-miR-17-5p and hsa-miR-27a/b-3p) were identified as key RNAs in childhood obesity. Specifically, lncRNA HCP5 interacted with miR-17-5p and miR-27a/b to regulate nemo-like kinase (NLK) and Ras-related protein 2 (RRAS2) via the MAPK signaling pathway. Finally, four genes (RRAS2, NLK, bcl2/adenovirus E1B protein-interacting protein 3 [BNIP3] and phorbol-12-myristate-13-acetate-induced protein 1 [PMAIP1]) targeted by miRNAs were predicted as critical genes and might be novel diagnostic biomarkers of childhood obesity. Conclusions lncRNA HCP5 could serve as a ceRNA sponging miR-17-5p and miR-27a/b to regulate the pathogenesis of childhood obesity via NLK and RRAS2 in the MAPK signaling pathway.

Phylostratigraphic Analysis Shows the Earliest Origination of the Abiotic Stress Associated Genes in A. thaliana

Plants constantly fight with stressful factors as high or low temperature, drought, soil salinity and flooding. Plants have evolved a set of stress response mechanisms, which involve physiological and biochemical changes that result in adaptive or morphological changes. At a molecular level, stress response in plants is performed by genetic networks, which also undergo changes in the process of evolution. The study of the network structure and evolution may highlight mechanisms of plants adaptation to adverse conditions, as well as their response to stresses and help in discovery and functional characterization of the stress-related genes. We performed an analysis of Arabidopsis thaliana genes associated with several types of abiotic stresses (heat, cold, water-related, light, osmotic, salt, and oxidative) at the network level using a phylostratigraphic approach. Our results show that a substantial fraction of genes associated with various types of abiotic stress is of ancient origin and evolves under strong purifying selection. The interaction networks of genes associated with stress response have a modular structure with a regulatory component being one of the largest for five of seven stress types. We demonstrated a positive relationship between the number of interactions of gene in the stress gene network and its age. Moreover, genes of the same age tend to be connected in stress gene networks. We also demonstrated that old stress-related genes usually participate in the response for various types of stress and are involved in numerous biological processes unrelated to stress. Our results demonstrate that the stress response genes represent the ancient and one of the fundamental molecular systems in plants.

Measurement of Conditional Relatedness Between Genes Using Fully Convolutional Neural Network

Measuring conditional relatedness, the degree of relation between a pair of genes in a certain condition, is a basic but difficult task in bioinformatics, as traditional co-expression analysis methods rely on co-expression similarities, well known with high false positive rate. Complement with prior-knowledge similarities is a feasible way to tackle the problem. However, classical combination machine learning algorithms fail in detection and application of the complex mapping relations between similarities and conditional relatedness, so a powerful predictive model will have enormous benefit for measuring this kind of complex mapping relations. To this need, we propose a novel deep learning model of convolutional neural network with a fully connected first layer, named fully convolutional neural network (FCNN), to measure conditional relatedness between genes using both co-expression and prior-knowledge similarities. The results on validation and test datasets show FCNN model yields an average 3.0% and 2.7% higher accuracy values for identifying gene–gene interactions collected from the COXPRESdb, KEGG, and TRRUST databases, and a benchmark dataset of Xiao-Yong et al. research, by grid-search 10-fold cross validation, respectively. In order to estimate the FCNN model, we conduct a further verification on the GeneFriends and DIP datasets, and the FCNN model obtains an average of 1.8% and 7.6% higher accuracy, respectively. Then the FCNN model is applied to construct cancer gene networks, and also calls more practical results than other compared models and methods. A website of the FCNN model and relevant datasets can be accessed from https://bmbl.bmi.osumc.edu/FCNN.

Integrated Network Analysis Reveals FOXM1 and MYBL2 as Key Regulators of Cell Proliferation in Non-small Cell Lung Cancer

Background: Loss of control on cell division is an important factor for the development of non-small cell lung cancer (NSCLC), however, its molecular mechanism and gene regulatory network are not clearly understood. This study utilized the systems bioinformatics approach to reveal the “driver-network” involve in tumorigenic processes in NSCLC.

Methods: A meta-analysis of gene expression data of NSCLC was integrated with protein-protein interaction (PPI) data to construct an NSCLC network. MCODE and iRegulone were used to identify the local clusters and its upstream transcription regulators involve in NSCLC. Pair-wise gene expression correlation was performed using GEPIA. The survival analysis was performed by the Kaplan-Meier plot.

Results: This study identified a local “driver-network” with highest MCODE score having 26 up-regulated genes involved in the process of cell proliferation in NSCLC. Interestingly, the “driver-network” is under the regulation of TFs FOXM1 and MYBL2 as well as miRNAs. Furthermore, the overexpression of member genes in “driver-network” and the TFs are associated with poor overall survival (OS) in NSCLC patients.

Conclusion: This study identified a local “driver-network” and its upstream regulators responsible for the cell proliferation in NSCLC, which could be promising biomarkers and therapeutic targets for NSCLC treatment.

MTGO-SC, A Tool to Explore Gene Modules in Single-Cell RNA Sequencing Data

The identification of functional modules in gene interaction networks is a key step in understanding biological processes. Network interpretation is essential for unveiling biological mechanisms, candidate biomarkers, or potential targets for drug discovery/repositioning. Plenty of biological module identification algorithms are available, although none is explicitly designed to perform the task on single-cell RNA sequencing (scRNA-seq) data. Here, we introduce MTGO-SC, an adaptation for scRNA-seq of our biological network module detection algorithm MTGO. MTGO-SC isolates gene functional modules by leveraging on both the network topological structure and the annotations characterizing the nodes (genes). These annotations are provided by an external source, such as databases and literature repositories (e.g., the Gene Ontology, Reactome). Thanks to the depth of single-cell data, it is possible to define one network for each cell cluster (typically, cell type or state) composing each sample, as opposed to traditional bulk RNA-seq, where the emerging gene network is averaged over the whole sample. MTGO-SC provides two complexity levels for interpretation: the gene-gene interaction and the intermodule interaction networks. MTGO-SC is versatile in letting the users define the rules to extract the gene network and integrated with the Seurat scRNA-seq analysis pipeline. MTGO-SC is available at https://github.com/ne1s0n/MTGOsc.

Patterns and Constraints in the Evolution of Sperm Individualization Genes in Insects, with an Emphasis on Beetles

Gene expression profiles can change dramatically between sexes and sex bias may contribute specific macroevolutionary dynamics for sex-biased genes. However, these dynamics are poorly understood at large evolutionary scales due to the paucity of studies that have assessed orthology and functional homology for sex-biased genes and the pleiotropic effects possibly constraining their evolutionary potential. Here, we explore the correlation of sex-biased expression with macroevolutionary processes that are associated with sex-biased genes, including duplications and accelerated evolutionary rates. Specifically, we examined these traits in a group of 44 genes that orchestrate sperm individualization during spermatogenesis, with both unbiased and sex-biased expression. We studied these genes in the broad evolutionary framework of the Insecta, with a particular focus on beetles (order Coleoptera). We studied data mined from 119 insect genomes, including 6 beetle models, and from 19 additional beetle transcriptomes. For the subset of physically and/or genetically interacting proteins, we also analyzed how their network structure may condition the mode of gene evolution. The collection of genes was highly heterogeneous in duplication status, evolutionary rates, and rate stability, but there was statistical evidence for sex bias correlated with faster evolutionary rates, consistent with theoretical predictions. Faster rates were also correlated with clocklike (insect amino acids) and non-clocklike (beetle nucleotides) substitution patterns in these genes. Statistical associations (higher rates for central nodes) or lack thereof (centrality of duplicated genes) were in contrast to some current evolutionary hypotheses, highlighting the need for more research on these topics.

Reconstruction and Analysis of Gene Networks of Human Neurotransmitter Systems Reveal Genes with Contentious Manifestation for Anxiety, Depression, and Intellectual Disabilities

Background: The study of the biological basis of anxiety, depression, and intellectual disabilities in humans is one of the most actual problems of modern neurophysiology. Of particular interest is the study of complex interactions between molecular genetic factors, electrophysiological properties of the nervous system, and the behavioral characteristics of people. The neurobiological understanding of neuropsychiatric disorders requires not only the identification of genes that play a role in the molecular mechanisms of the occurrence and course of diseases, but also the understanding of complex interactions that occur between these genes. A systematic study of such interactions obviously contributes to the development of new methods of diagnosis, prevention, and treatment of disorders, as the orientation to allele variants of individual loci is not reliable enough, because the literature describes a number of genes, the same alleles of which can be associated with different, sometimes extremely different variants of phenotypic traits, depending on the genetic background, of their carriers, habitat, and other factors. Results: In our study, we have reconstructed a series of gene networks (in the form of protein–protein interactions networks, as well as networks of transcription regulation) to build a model of the influence of complex interactions of environmental factors and genetic risk factors for intellectual disability, depression, and other disorders in human behavior. Conclusion: A list of candidate genes whose expression is presumably associated with environmental factors and has potentially contentious manifestation for behavioral and neurological traits is identified for further experimental verification.

Regulation of Early Host Immune Responses Shapes the Pathogenicity of Avian Influenza A Virus

Avian influenza A viruses (IAV) can cross the species barrier and cause disease in humans. Understanding the pathogenesis of avian IAV remains a challenge. Interferon-mediated antiviral responses and multiple cytokines production are important host cellular antiviral immunity against IAV infection. To elucidate the pathogenicity of avian IAV, a system approach was adopted to investigate dysregulation of the two host cellular antiviral immune responses in contrast with human IAV. As a result, we revealed that avian IAV not only disrupted normal early host cellular interferon-mediated antiviral responses, but also caused abnormal cytokines production through different pathways. For avian IAV infection, dysregulation of STAT2 was mainly responsible for abnormal cellular interferon-mediated antiviral responses, and IRF5 and NFKB1 played crucial roles in unusual cytokines production. In contrast, for human IAV infection, IRF1, IRF7, and STAT1 contributed to cellular cytokines production. Furthermore, differential activation of pattern recognition receptors (PRRs) likely led to avian IAV-related abnormal early host cellular antiviral immunity, where TLR7 and RIG-I were activated by avian and human IAV, respectively. Finally, a pathogenesis model was proposed that combined of early host cellular interferon-mediated antiviral responses with cytokines production could partly explain the pathogenicity of avian IAV. In conclusion, our study provides a new perspective of the pathogenesis of avian IAV, which will be helpful in preventing their infections in the future.

Co-expression networks for plant biology: why and how

Co-expression network analysis is one of the most powerful approaches for interpretation of large transcriptomic datasets. It enables characterization of modules of co-expressed genes that may share biological functional linkages. Such networks provide an initial way to explore functional associations from gene expression profiling and can be applied to various aspects of plant biology. This review presents the applications of co-expression network analysis in plant biology and addresses optimized strategies from the recent literature for performing co-expression analysis on plant biological systems. Additionally, we describe the combined interpretation of co-expression analysis with other genomic data to enhance the generation of biologically relevant information.

Global Transcriptome and Co-Expression Network Analysis Reveal Contrasting Response of Japonica and Indica Rice Cultivar to γ Radiation

Japonica and indica are two important subspecies in cultivated Asian rice. Irradiation is a classical approach to induce mutations and create novel germplasm. However, little is known about the differential response between japonica and indica rice after γ radiation. Here, we utilized the RNA sequencing and Weighted Gene Co-expression Network Analysis (WGCNA) to compare the transcriptome differences between japonica Nipponbare (NPB) and indica Yangdao6 (YD6) in response to irradiation. Japonica subspecies are more sensitive to irradiation than the indica subspecies. Indica showed a higher seedling survival rate than japonica. Irradiation caused more extensive DNA damage in shoots than in roots, and the severity was higher in NPB than in YD6. GO and KEGG pathway analyses indicate that the core genes related to DNA repair and replication and cell proliferation are similarly regulated between the varieties, however the universal stress responsive genes show contrasting differential response patterns in japonica and indica. WGCNA identifies 37 co-expressing gene modules and ten candidate hub genes for each module. This provides novel evidence indicating that certain peripheral pathways may dominate the molecular networks in irradiation survival and suggests more potential target genes in breeding for universal stress tolerance in rice.

NGSEA: Network-Based Gene Set Enrichment Analysis for Interpreting Gene Expression Phenotypes with Functional Gene Sets

Gene set enrichment analysis (GSEA) is a popular tool to identify underlying biological processes in clinical samples using their gene expression phenotypes. GSEA measures the enrichment of annotated gene sets that represent biological processes for differentially expressed genes (DEGs) in clinical samples. GSEA may be suboptimal for functional gene sets; however, because DEGs from the expression dataset may not be functional genes per se but dysregulated genes perturbed by bona fide functional genes. To overcome this shortcoming, we developed network-based GSEA (NGSEA), which measures the enrichment score of functional gene sets using the expression difference of not only individual genes but also their neighbors in the functional network. We found that NGSEA outperformed GSEA in identifying pathway gene sets for matched gene expression phenotypes. We also observed that NGSEA substantially improved the ability to retrieve known anti-cancer drugs from patient-derived gene expression data using drug-target gene sets compared with another method, Connectivity Map. We also repurposed FDA-approved drugs using NGSEA and experimentally validated budesonide as a chemical with anti-cancer effects for colorectal cancer. We, therefore, expect that NGSEA will facilitate both pathway interpretation of gene expression phenotypes and anti-cancer drug repositioning. NGSEA is freely available at www.inetbio.org/ngsea.