Differential Network Analysis Research Articles

PrInCE: an R/Bioconductor package for protein-protein interaction network inference from co-fractionation mass spectrometry data.

We present PrInCE, an R/Bioconductor package that employs a machine-learning approach to infer protein-protein interaction networks from co-fractionation mass spectrometry (CF-MS) data. Previously distributed as a collection of Matlab scripts, our ground-up rewrite of this software package in an open-source language dramatically improves runtime and memory requirements. We describe several new features in the R implementation, including a test for the detection of co-eluting protein complexes and a method for differential network analysis. PrInCE is extensively documented and fully compatible with Bioconductor classes, ensuring it can fit seamlessly into existing proteomics workflows. PrInCE is available from Bioconductor (https://www.bioconductor.org/packages/devel/bioc/html/PrInCE.html). Source code is freely available from GitHub under the MIT license (https://github.com/fosterlab/PrInCE). Support is provided via the GitHub issues tracker (https://github.com/fosterlab/PrInCE/issues). Supplementary data are available at Bioinformatics online.

Differential Network Analysis Reveals Regulatory Patterns in Neural Stem Cell Fate Decision.

Deciphering regulatory patterns of neural stem cell (NSC) differentiation with multiple stages is essential to understand NSC differentiation mechanisms. Recent single-cell transcriptome datasets became available at individual differentiation. However, a systematic and integrative analysis of multiple datasets at multiple temporal stages of NSC differentiation is lacking. In this study, we propose a new method integrating prior information to construct three gene regulatory networks at pair-wise stages of transcriptome and apply this method to investigate five NSC differentiation paths on four different single-cell transcriptome datasets. By constructing gene regulatory networks for each path, we delineate their regulatory patterns via differential topology and network diffusion analyses. We find 12 common differentially expressed genes among the five NSC differentiation paths, with one common regulatory pattern (Gsk3b_App_Cdk5) shared by all paths. The identified regulatory pattern, partly supported by previous experimental evidence, is essential to all differentiation paths, but it plays a different role in each path when regulating other genes. Together, our integrative analysis provides both common and specific regulatory mechanisms for each of the five NSC differentiation paths.

Identification of plasma proteins relating to brain neurodegeneration and vascular pathology in cognitively normal individuals.

IntroductionThis study aims to first discover plasma proteomic biomarkers relating to neurodegeneration (N) and vascular (V) damage in cognitively normal individuals and second to discover proteins mediating sex‐related difference in N and V pathology.MethodsFive thousand and thirty‐two plasma proteins were measured in 1061 cognitively normal individuals (628 females and 433 males), nearly 90% of whom had magnetic resonance imaging measures of hippocampal volume (as N) and white matter hyperintensities (as V).ResultsDifferential protein expression analysis and co‐expression network analysis revealed different proteins and modules associated with N and V, respectively. Furthermore, causal mediation analysis revealed four proteins mediated sex‐related difference in N and one protein mediated such difference in V damage.DiscussionOnce validated, the identified proteins could help to select cognitively normal individuals with N and V pathology for Alzheimer's disease clinical trials and provide targets for further mechanistic studies on brain sex differences, leading to sex‐specific therapeutic strategies.

Transcriptomic organization of the human brain in post-traumatic stress disorder.

Despite extensive study of the neurobiological correlates of post-traumatic stress disorder (PTSD), little is known about its molecular determinants. Here, differential gene expression and network analyses of four prefrontal cortex subregions from postmortem tissue of people with PTSD demonstrate extensive remodeling of the transcriptomic landscape. A highly connected downregulated set of interneuron transcripts is present in the most significant gene network associated with PTSD. Integration of this dataset with genotype data from the largest PTSD genome-wide association study identified the interneuron synaptic gene ELFN1 as conferring significant genetic liability for PTSD. We also identified marked transcriptomic sexual dimorphism that could contribute to higher rates of PTSD in women. Comparison with a matched major depressive disorder cohort revealed significant divergence between the molecular profiles of individuals with PTSD and major depressive disorder despite their high comorbidity. Our analysis provides convergent systems-level evidence of genomic networks within the prefrontal cortex that contribute to the pathophysiology of PTSD in humans.

Association of Serum Bile Acids Profile and Pathway Dysregulation With the Risk of Developing Diabetes Among Normoglycemic Chinese Adults: Findings From the 4C Study

Comprehensive assessment of serum bile acids (BAs) aberrations before diabetes onset remains inconclusive. We examined the association of serum BA profile and coregulation with the risk of developing type 2 diabetes mellitus (T2DM) among normoglycemic Chinese adults. We tested 23 serum BA species in subjects with incident diabetes (n = 1,707) and control subjects (n = 1,707) matched by propensity score (including age, sex, BMI, and fasting glucose) from the China Cardiometabolic Disease and Cancer Cohort (4C) Study, which was composed of 54,807 normoglycemic Chinese adults with a median follow-up of 3.03 years. Multivariable-adjusted odds ratios (ORs) for associations of BAs with T2DM were estimated using conditional logistic regression. In multivariable-adjusted logistic regression analysis, per SD increment of unconjugated primary and secondary BAs were inversely associated with incident diabetes, with an OR (95% CI) of 0.89 (0.83-0.96) for cholic acid, 0.90 (0.84-0.97) for chenodeoxycholic acid, and 0.90 (0.83-0.96) for deoxycholic acid (P < 0.05 and false discovery rate <0.05). On the other hand, conjugated primary BAs (glycocholic acid, taurocholic acid, glycochenodeoxycholic acid, taurochenodeoxycholic acid, and sulfated glycochenodeoxycholic acid) and secondary BA (tauroursodeoxycholic acid) were positively related with incident diabetes, with ORs ranging from 1.11 to 1.19 (95% CIs ranging between 1.05 and 1.28). In a fully adjusted model additionally adjusted for liver enzymes, HDL cholesterol, diet, 2-h postload glucose, HOMA-insulin resistance, and waist circumference, the risk estimates were similar. Differential correlation network analysis revealed that perturbations in intraclass (i.e., primary and secondary) and interclass (i.e., unconjugated and conjugated) BA coregulation preexisted before diabetes onset. These findings reveal novel changes in BAs exist before incident T2DM and support a potential role of BA metabolism in the pathogenesis of diabetes.

Navigating differential structures in complex networks.

Structural changes in a network representation of a system, due to different experimental conditions, different connectivity across layers, or to its time evolution, can provide insight on its organization, function, and on how it responds to external perturbations. The deeper understanding of how gene networks cope with diseases and treatments is maybe the most incisive demonstration of the gains obtained through this differential network analysis point of view, which led to an explosion of new numeric techniques in the last decade. However, where to focus one's attention, or how to navigate through the differential structures in the context of large networks, can be overwhelming even for a few experimental conditions. In this paper, we propose a theory and a methodological implementation for the characterization of shared "structural roles" of nodes simultaneously within and between networks. Inspired by recent methodological advances in chaotic phase synchronization analysis, we show how the information about the shared structures of a set of networks can be split and organized in an automatic fashion, in scenarios with very different (i) community sizes, (ii) total number of communities, and (iii) even for a large number of 100 networks compared using numerical benchmarks generated by a stochastic block model. Then, we investigate how the network size, number of networks, and mean size of communities influence the method performance in a series of Monte Carlo experiments. To illustrate its potential use in a more challenging scenario with real-world data, we show evidence that the method can still split and organize the structural information of a set of four gene coexpression networks obtained from two cell types × two treatments (interferon-β stimulated or control). Aside from its potential use as for automatic feature extraction and preprocessing tool, we discuss that another strength of the method is its "story-telling"-like characterization of the information encoded in a set of networks, which can be used to pinpoint unexpected shared structure, leading to further investigations and providing new insights. Finally, the method is flexible to address different research-field-specific questions, by not restricting what scientific-meaningful characteristic (or relevant feature) of a node shall be used.

Differential Network Analysis Reveals Molecular Determinants Associated with Blood Pressure and Heart Rate in Healthy Subjects.

There is mounting evidence that subclinical nonpathological high blood pressure and heart rate during youth and adulthood steadily increase the risk of developing a cardiovascular disease at a later stage. For this reason, it is important to understand the mechanisms underlying the subclinical elevation of blood pressure and heart rate in healthy, relatively young individuals. In the present study, we present a network-based metabolomic study of blood plasma metabolites and lipids measured using nuclear magnetic resonance spectroscopy on 841 adult healthy blood donor volunteers, which were stratified for subclinical low and high blood pressure (systolic and diastolic) and heart rate. Our results indicate a rewiring of metabolic pathways active in high and low groups, indicating that the subjects with subclinical high blood pressure and heart rate could present latent cardiometabolic dysregulations.

Whole transcriptomic network analysis using Co-expression Differential Network Analysis (CoDiNA).

Biological and medical sciences are increasingly acknowledging the significance of gene co-expression-networks for investigating complex-systems, phenotypes or diseases. Typically, complex phenotypes are investigated under varying conditions. While approaches for comparing nodes and links in two networks exist, almost no methods for the comparison of multiple networks are available and—to best of our knowledge—no comparative method allows for whole transcriptomic network analysis. However, it is the aim of many studies to compare networks of different conditions, for example, tissues, diseases, treatments, time points, or species. Here we present a method for the systematic comparison of an unlimited number of networks, with unlimited number of transcripts: Co-expression Differential Network Analysis (CoDiNA). In particular, CoDiNA detects links and nodes that are common, specific or different among the networks. We developed a statistical framework to normalize between these different categories of common or changed network links and nodes, resulting in a comprehensive network analysis method, more sophisticated than simply comparing the presence or absence of network nodes. Applying CoDiNA to a neurogenesis study we identified candidate genes involved in neuronal differentiation. We experimentally validated one candidate, demonstrating that its overexpression resulted in a significant disturbance in the underlying gene regulatory network of neurogenesis. Using clinical studies, we compared whole transcriptome co-expression networks from individuals with or without HIV and active tuberculosis (TB) and detected signature genes specific to HIV. Furthermore, analyzing multiple cancer transcription factor (TF) networks, we identified common and distinct features for particular cancer types. These CoDiNA applications demonstrate the successful detection of genes associated with specific phenotypes. Moreover, CoDiNA can also be used for comparing other types of undirected networks, for example, metabolic, protein-protein interaction, ecological and psychometric networks. CoDiNA is publicly available as an R package in CRAN (https://CRAN.R-project.org/package=CoDiNA).

Genome-wide translational profiling of amygdala Crh-expressing neurons reveals role for CREB in fear extinction learning

Fear and extinction learning are adaptive processes caused by molecular changes in specific neural circuits. Neurons expressing the corticotropin-releasing hormone gene (Crh) in central amygdala (CeA) are implicated in threat regulation, yet little is known of cell type-specific gene pathways mediating adaptive learning. We translationally profiled the transcriptome of CeA Crh-expressing cells (Crh neurons) after fear conditioning or extinction in mice using translating ribosome affinity purification (TRAP) and RNAseq. Differential gene expression and co-expression network analyses identified diverse networks activated or inhibited by fear vs extinction. Upstream regulator analysis demonstrated that extinction associates with reduced CREB expression, and viral vector-induced increased CREB expression in Crh neurons increased fear expression and inhibited extinction. These findings suggest that CREB, within CeA Crh neurons, may function as a molecular switch that regulates expression of fear and its extinction. Cell-type specific translational analyses may suggest targets useful for understanding and treating stress-related psychiatric illness.

Parallel gene expression evolution in natural and laboratory evolved populations.

Ecological adaptation is frequently inferred by the comparison of natural populations from different environments. Nevertheless, inference of the selective forces suffers the challenge that many environmental factors covary. With well‐controlled environmental conditions, experimental evolution provides a powerful approach to complement the analysis of natural populations. On the other hand, it is apparent that laboratory conditions differ in many ways from natural environments, which raises the question as to what extent selection responses in experimental evolution studies can inform us about adaptation processes in the wild. In this study, we compared the expression profiles of replicated Drosophila melanogaster populations which have been exposed to two distinct temperature regimes (18/28 and 10/20°C) in the laboratory for more than 80 generations. Using gene‐wise differential expression analysis and co‐expression network analysis, we identified 541 genes and three coregulated gene modules that evolved in the same direction in both temperature regimes, and most of these changes probably reflect an adaptation to the space constraint or diurnal temperature fluctuation that is common in both selection regimes. In total, 203 genes and seven modules evolved temperature‐specific expression changes. Remarkably, we detected a significant overlap of these temperature‐adaptive genes/modules from experimental evolution with temperature‐adaptive genes inferred from natural Drosophila populations covering two different temperature clines. We conclude that well‐designed experimental evolution studies are a powerful tool to dissect evolutionary responses.

Differential network analysis of bovine muscle reveals changes in gene coexpression patterns in response to changes in maternal nutrition

BackgroundCoexpression network analysis is a powerful tool to reveal transcriptional regulatory mechanisms, identify transcription factors, and discover gene functions. It can also be used to investigate changes in coexpression patterns in response to environmental insults or changes in experimental conditions. Maternal nutrition is considered a major intrauterine regulator of fetal developmental programming. The objective of this study was to investigate structural changes in gene coexpression networks in the muscle of bull beef calves gestated under diets with or without methionine supplementation. Both muscle transcriptome and methylome were evaluated using next generation sequencing.ResultsMaternal methionine supplementation significantly perturbed coexpression patterns in the offspring’s muscle. Indeed, we found that neither the connection strength nor the connectivity pattern of six modules (subnetworks) detected in the control diet were preserved in the methionine-rich diet. Functional characterization revealed that some of the unpreserved modules are implicated in myogenesis, adipogenesis, fibrogenesis, canonical Wnt/β-catenin pathway, ribosome structure, rRNA binding and processing, mitochondrial activities, ATP synthesis and NAD(P) H oxidoreductases, among other functions. The bisulfite sequencing analysis showed that nearly 2% of all evaluated cytosines were differentially methylated between maternal diets. Interestingly, there were significant differences in the levels of gene body DNA methylation between preserved and unpreserved modules.ConclusionsOverall, our findings provide evidence that maternal nutrition can significantly alter gene coexpression patterns in the offspring, and some of these perturbations are mediated by changes in DNA methylation.

Transcriptome analysis of peripheral whole blood identifies crucial lncRNAs implicated in childhood asthma

BackgroundAsthma is a chronic disorder of both adults and children affecting more than 300 million people heath worldwide. Diagnose and treatment for asthma, particularly in childhood asthma have always remained a great challenge because of its complex pathogenesis and multiple triggers, such as allergen, viral infection, tobacco smoke, dust, etc. It is thereby great significant to deeply investigate the transcriptome changes in asthmatic children before and after desensitization treatment, in order that we could identify potential and key mRNAs and lncRNAs which might be considered as useful RNA molecules for observing and supervising desensitization therapy for asthma, which might guide the diagnose and therapy in childhood asthma.MethodsIn the present study, we performed a systematic transcriptome analysis based on the deep RNA sequencing of ten asthmatic children before and after desensitization treatment, including identification of lncRNAs using a stringent filtering pipeline, differential expression analysis and network analysis, etc.ResultsFirst, a large number of lncRNAs were identified and characterized. Then differential expression analysis revealed 39 mRNAs and 15 lncRNAs significantly differentially expressed which involved in two biological processes and pathways. A co-expressed network analysis figured out a desensitization-treatment-related module which contains 27 mRNAs and 21 lncRNAs using WGCNA R package. Module analysis disclosed 17 genes associated to asthma at distinct level. Subsequent network analysis based on PCC figured out several key lncRNAs probably interacted to those key asthma-related genes, i.e., LINC02145, GUSBP2. Our functional investigation indicated that their functions might involve in immune, inflammatory response and apoptosis process.ConclusionsOur study successfully discovered many key noncoding RNA molecules related to pathogenesis of asthma and relevant treatment, which may provide some clues for asthmatic diagnose and therapy in future.

Brain connectivity alteration detection via matrix-variate differential network model.

Brain functional connectivity reveals the synchronization of brain systems through correlations in neurophysiological measures of brain activities. Growing evidence now suggests that the brain connectivity network experiences alterations with the presence of numerous neurological disorders, thus differential brain network analysis may provide new insights into disease pathologies. The data from neurophysiological measurement are often multidimensional and in a matrix form, posing a challenge in brain connectivity analysis. Existing graphical model estimation methods either assume a vector normal distribution that in essence requires the columns of the matrix data to be independent or fail to address the estimation of differential networks across different populations. To tackle these issues, we propose an innovative matrix-variate differential network (MVDN) model. We exploit the D-trace loss function and a Lasso-type penalty to directly estimate the spatial differential partial correlation matrix and use an alternating direction method of multipliers algorithm for the optimization problem. Theoretical and simulation studies demonstrate that MVDN significantly outperforms other state-of-the-art methods in dynamic differential network analysis. We illustrate with a functional connectivity analysis of an attention deficit hyperactivity disorder dataset. The hub nodes and differential interaction patterns identified are consistent with existing experimentalstudies.

Transcriptomic determinants of the response of ST-111 Pseudomonas aeruginosa AG1 to ciprofloxacin identified by a top-down systems biology approach

Pseudomonas aeruginosa is an opportunistic pathogen that thrives in diverse environments and causes a variety of human infections. Pseudomonas aeruginosa AG1 (PaeAG1) is a high-risk sequence type 111 (ST-111) strain isolated from a Costa Rican hospital in 2010. PaeAG1 has both blaVIM-2 and blaIMP-18 genes encoding for metallo-β-lactamases, and it is resistant to β-lactams (including carbapenems), aminoglycosides, and fluoroquinolones. Ciprofloxacin (CIP) is an antibiotic commonly used to treat P. aeruginosa infections, and it is known to produce DNA damage, triggering a complex molecular response. In order to evaluate the effects of a sub-inhibitory CIP concentration on PaeAG1, growth curves using increasing CIP concentrations were compared. We then measured gene expression using RNA-Seq at three time points (0, 2.5 and 5 h) after CIP exposure to identify the transcriptomic determinants of the response (i.e. hub genes, gene clusters and enriched pathways). Changes in expression were determined using differential expression analysis and network analysis using a top–down systems biology approach. A hybrid model using database-based and co-expression analysis approaches was implemented to predict gene–gene interactions. We observed a reduction of the growth curve rate as the sub-inhibitory CIP concentrations were increased. In the transcriptomic analysis, we detected that over time CIP treatment resulted in the differential expression of 518 genes, showing a complex impact at the molecular level. The transcriptomic determinants were 14 hub genes, multiple gene clusters at different levels (associated to hub genes or as co-expression modules) and 15 enriched pathways. Down-regulation of genes implicated in several metabolism pathways, virulence elements and ribosomal activity was observed. In contrast, amino acid catabolism, RpoS factor, proteases, and phenazines genes were up-regulated. Remarkably, > 80 resident-phage genes were up-regulated after CIP treatment, which was validated at phenomic level using a phage plaque assay. Thus, reduction of the growth curve rate and increasing phage induction was evidenced as the CIP concentrations were increased. In summary, transcriptomic and network analyses, as well as the growth curves and phage plaque assays provide evidence that PaeAG1 presents a complex, concentration-dependent response to sub-inhibitory CIP exposure, showing pleiotropic effects at the systems level. Manipulation of these determinants, such as phage genes, could be used to gain more insights about the regulation of responses in PaeAG1 as well as the identification of possible therapeutic targets. To our knowledge, this is the first report of the transcriptomic analysis of CIP response in a ST-111 high-risk P. aeruginosa strain, in particular using a top-down systems biology approach.

Abstract 3604: Identification of genes involved in pathogenesis of esophageal adenocarcinoma using RNA sequencing from laser capture micro-dissected formalin-fixed paraffin-embedded tissue specimens

Abstract Esophageal adenocarcinoma (EAC) is one of the most aggressive human cancers and the only major cancer in the United States with increasing incidence. The overall 5-year survival rate of EAC is below 20 percent and treatment remains largely ineffective due to the high resistance to chemotherapy, despite recent advances in systemic treatment options as well as surgical techniques. Gastroesophageal reflux disease (GERD) causes Barrett's esophagus (BE), a columnar metaplasia containing goblet cells of the esophagus strongly associated with the development of EAC. However, the mechanisms driving EAC development remains poorly understood. In this study, transcriptomic profiling was performed using RNA-sequencing (RNA-seq) on normal, premalignant Barrett's and malignant adenocarcinoma esophageal tissues for better understanding the pathogenesis of EAC. Pure population of cells were collected by laser capture microdissection (LCM) of histologically confirmed formalin-fixed paraffin-embedded (FFPE) esophageal tissues. Total RNA was extracted from micro-dissected sections using the Ambion RecoverAll kit followed by RNA quality assessment using Bioanalyzer to determine the RNA integrity number (RIN) and DV200 metric. 15 RNA samples with sufficient purity and appropriate concentrations were sent for library preparations and sequenced on an Illumina NextSeq500. Differential gene expression and network analysis methods were then applied to discover novel driver genes and pathways for EAC development. Clustering for RNA-seq data shows nice clustering of normal esophagus (NE), Barrett's esophagus (BE) and esophageal adenocarcinoma (EAC) samples in relation to gene expression patters. Comparing differentially expressed genes using FDR&amp;lt;0.001 we found many genes differentially expressed, especially between each of the BE and EAC when compared to the NE. Interestingly, there are much less differentially expressed genes when comparing each of the BE and EAD to each other. This shows that BE and EAC are closer to each other than to the normal esophagus, but there are still key differences between them. Using pathway analysis and functional annotation we have identified differentially expressed candidate genes and pathways in BE and EAC involving quite a few pathways including PI3 kinase, focal adhesion kinase and metabolic pathways. In summary, this transcriptomic profiling will improve our understanding of EAC pathogenesis and these candidate genes could be diagnostic and therapeutic targets for EAC. Citation Format: Matthew Guggenbiller, Md Sazzad Hassan, Min Yan, Jun Li, Victoria Makuru, Urs von Holzen. Identification of genes involved in pathogenesis of esophageal adenocarcinoma using RNA sequencing from laser capture micro-dissected formalin-fixed paraffin-embedded tissue specimens [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3604.

Atopic dermatitis displays stable and dynamic skin transcriptome signatures

Skin transcriptome studies in atopic dermatitis (AD) showed broad dysregulation as well as "improvement" under therapy. These observations were mainly made in trials and based on microarray data. Our aim was to explore the skin transcriptome and the impact of systemic treatment in patients of the TREATgermany registry. Biopsy specimens from 59 patients with moderate-to-severe AD before and 30 patients 12 weeks after start of systemic treatment (dupilumab [n= 22] or cyclosporine [n= 8]) and from 31 healthy controls were subjected to mRNA sequencing. Differential expression, pathway enrichment, correlation, and coexpression network analysis were conducted. Both lesional and nonlesional skin showed a stable "core" signature characterized by disturbed epidermal differentiation and activation of IL-31/IL-1 signaling. Asecond dynamic signature showed progressive enrichment for type 2 inflammation, TH17 signaling, and natural killer cell function. Markers correlated with disease activity have functions in epidermal barrier properties and immune modulation. IL4RA was among the top 3 central dysregulated genes. Cyclosporine led to a more pronounced global transcriptome reversion and normalized TH17 cell/IL23 signaling, whereas dupilumab led to a stronger increase in level of epidermal differentiation markers. Both treatments strongly decreased levels of type 2 markers, but overall the residual profile was still profoundly different from that of healthy skin. Lower levels of IL4RA and IL13 and high IL36A expression were related to a stronger clinical response to dupilumab. The AD core signature is characterized by dysregulation of genes related to keratinocyte differentiation and itch signaling. Adynamic signature reflects progressive immune responses dominated by type 2 cytokines with an additional role of TH17 and natural killer cell signaling.

Omics-Driven Systems Interrogation of Metabolic Dysregulation in COVID-19 Pathogenesis

SummaryThe coronavirus disease 2019 (COVID-19) pandemic presents an unprecedented threat to global public health. Herein, we utilized a combination of targeted and untargeted tandem mass spectrometry to analyze the plasma lipidome and metabolome in mild, moderate, and severe COVID-19 patients and healthy controls. A panel of 10 plasma metabolites effectively distinguished COVID-19 patients from healthy controls (AUC = 0.975). Plasma lipidome of COVID-19 resembled that of monosialodihexosyl ganglioside (GM3)-enriched exosomes, with enhanced levels of sphingomyelins (SMs) and GM3s, and reduced diacylglycerols (DAGs). Systems evaluation of metabolic dysregulation in COVID-19 was performed using multiscale embedded differential correlation network analyses. Using exosomes isolated from the same cohort, we demonstrated that exosomes of COVID-19 patients with elevating disease severity were increasingly enriched in GM3s. Our work suggests that GM3-enriched exosomes may partake in pathological processes related to COVID-19 pathogenesis and presents the largest repository on the plasma lipidome and metabolome distinct to COVID-19.

Genome Regulation and Gene Interaction Networks Inferred From Muscle Transcriptome Underlying Feed Efficiency in Pigs

Improvement of feed efficiency (FE) is key for Sustainability and cost reduction in pig production. Our aim was to characterize the muscle transcriptomic profiles in Danbred Duroc (Duroc; n = 13) and Danbred Landrace (Landrace; n = 28), in relation to FE for identifying potential biomarkers. RNA-seq data on the 41 pigs was analyzed employing differential gene expression methods, gene-gene interaction and network analysis, including pathway and functional analysis. We also compared the results with genome regulation in human exercise data, hypothesizing that increased FE mimics processes triggered in exercised muscle. In the differential expression analysis, 13 genes were differentially expressed, including: MRPS11, MTRF1, TRIM63, MGAT4A, KLH30. Based on a novel gene selection method, the divergent count, we performed pathway enrichment analysis. We found five significantly enriched pathways related to feed conversion ratio (FCR). These pathways were mainly related to mitochondria, and summarized in the mitochondrial translation elongation (MTR) pathway. In the gene interaction analysis, the most interesting genes included the mitochondrial genes: PPIF, MRPL35, NDUFS4 and the fat metabolism and obesity genes: AACS, SMPDL3B, CTNNBL1, NDUFS4, and LIMD2. In the network analysis, we identified two modules significantly correlated with FCR. Pathway enrichment of module genes identified MTR, electron transport chain and DNA repair as enriched pathways. The network analysis revealed the mitochondrial gene group NDUF as key network hub genes, showing their potential as biomarkers. Results show that genes related to human exercise were enriched in identified FCR related genes. We conclude that mitochondrial activity is a key driver for FCR in muscle tissue, and mitochondrial genes could be potential biomarkers for FCR in pigs.

Identifying Gene Network Rewiring Based on Partial Correlation.

It is an important task to learn how gene regulatory networks change under different conditions. Several Gaussian graphical model-based methods have been proposed to deal with this task by inferring differential networks from gene expression data. However, most existing methods define the differential networks as the difference of precision matrices, which may include false differential edges caused by the change of conditional variances. In addition, prior information about the condition-specific networks and the differential networks can be obtained from other domains. It is useful to incorporate prior information into differential network analysis. In this study, we propose a new differential network analysis method to address the above challenges. Instead of using the precision matrices, we define the differential networks as the difference of partial correlations, which can exclude the spurious differential edges due to the variants of conditional variances. Furthermore, prior information from multiple hypothesis testing is incorporated using a weighted fused penalty. Simulation studies show that our method outperforms the competing methods. We also apply our method to identify the differential network between luminal A and basal-like subtypes of breast cancers and the differential network between acute myeloid leukemia tumors and normal samples. The hub genes in the differential networks identified by our method carry out important biological functions.

Dynamic rewiring of the human interactome by interferon signaling

BackgroundThe type I interferon (IFN) response is an ancient pathway that protects cells against viral pathogens by inducing the transcription of hundreds of IFN-stimulated genes. Comprehensive catalogs of IFN-stimulated genes have been established across species and cell types by transcriptomic and biochemical approaches, but their antiviral mechanisms remain incompletely characterized. Here, we apply a combination of quantitative proteomic approaches to describe the effects of IFN signaling on the human proteome, and apply protein correlation profiling to map IFN-induced rearrangements in the human protein-protein interaction network.ResultsWe identify > 26,000 protein interactions in IFN-stimulated and unstimulated cells, many of which involve proteins associated with human disease and are observed exclusively within the IFN-stimulated network. Differential network analysis reveals interaction rewiring across a surprisingly broad spectrum of cellular pathways in the antiviral response. We identify IFN-dependent protein-protein interactions mediating novel regulatory mechanisms at the transcriptional and translational levels, with one such interaction modulating the transcriptional activity of STAT1. Moreover, we reveal IFN-dependent changes in ribosomal composition that act to buffer IFN-stimulated gene protein synthesis.ConclusionsOur map of the IFN interactome provides a global view of the complex cellular networks activated during the antiviral response, placing IFN-stimulated genes in a functional context, and serves as a framework to understand how these networks are dysregulated in autoimmune or inflammatory disease.