From 'differential expression' to 'differential networking' - identification of dysfunctional regulatory networks in diseases

A dynamic co-expression approach reveals Gins2 as a potential upstream modulator of HNSCC metastasis

Head and neck squamous cell carcinoma (HNSCC) is an aggressive cancer that is notably associated with a high risk of lymph node metastasis, a major cause of cancer mortality. Current therapeutic options remain limited to surgery supplemented by radio- or chemotherapy; however, these interventions often result in high-grade toxicities. Distant metastasis significantly contributed to the poor prognosis and decreased survival rates. However, the underlying molecular mechanisms remain poorly understood. Disease-related "omics" data provide a comprehensive overview of gene relationships, helping to decode the complex molecular mechanisms involved. Interactions between biological molecules are complex and highly dynamic across various cellular conditions, making traditional co-expression methods inadequate for understanding these intricate relationships. In the present study, a novel three-way interaction approach was employed to uncover dynamic co-expression relationships underlying the metastatic nature of HNSCC. Subsequently, the biologically relevant triples from statistically significant ones were defined through gene set enrichment analysis and reconstruction of the gene regulatory network. Finally, the validity of biologically relevant triplets was assessed at the protein level. The results highlighted the "PI3K/AKT/mTOR (PAM) signaling pathway" as a disrupted pathway involved in the metastatic nature of HNSCC. Notably, Gins2, identified as a switch gene, along with the gene pair {Akt2, Anxa2}, formed a statistically significant and biologically relevant triplet. It suggests that Gins2 could serve as a potential upstream modulator in the PAM signaling pathway, playing a crucial role in the distant metastasis of HNSCC. In addition, survival analysis of significant switch genes indicated that two genes, C19orf33 and Usp13, may be especially important for prognostic purposes in HNSCC.

Non parametric differential network analysis: a tool for unveiling specific molecular signatures

BACKGROUND

The rewiring of molecular interactions in various conditions leads to distinct phenotypic outcomes. Differential network analysis (DINA) is dedicated to exploring these rewirings within gene and protein networks. Leveraging statistical learning and graph theory, DINA algorithms scrutinize alterations in interaction patterns derived from experimental data.

RESULTS

Introducing a novel approach to differential network analysis, we incorporate differential gene expression based on sex and gender attributes. We hypothesize that gene expression can be accurately represented through non-Gaussian processes. Our methodology involves quantifying changes in non-parametric correlations among gene pairs and expression levels of individual genes.

CONCLUSIONS

Applying our method to public expression datasets concerning diabetes mellitus and atherosclerosis in liver tissue, we identify gender-specific differential networks. Results underscore the biological relevance of our approach in uncovering meaningful molecular distinctions.

Partial correlation network analysis identifies coordinated gene expression within a regional cluster of COPD genome-wide association signals

Chronic obstructive pulmonary disease (COPD) is a complex disease influenced by well-established environmental exposures (most notably, cigarette smoking) and incompletely defined genetic factors. The chromosome 4q region harbors multiple genetic risk loci for COPD, including signals near HHIP, FAM13A, GSTCD, TET2, and BTC. Leveraging RNA-Seq data from lung tissue in COPD cases and controls, we estimated the co-expression network for genes in the 4q region bounded by HHIP and BTC (~70MB), through partial correlations informed by protein-protein interactions. We identified several co-expressed gene pairs based on partial correlations, including NPNT-HHIP, BTC-NPNT and FAM13A-TET2, which were replicated in independent lung tissue cohorts. Upon clustering the co-expression network, we observed that four genes previously associated to COPD: BTC, HHIP, NPNT and PPM1K appeared in the same network community. Finally, we discovered a sub-network of genes differentially co-expressed between COPD vs controls (including FAM13A, PPA2, PPM1K and TET2). Many of these genes were previously implicated in cell-based knock-out experiments, including the knocking out of SPP1 which belongs to the same genomic region and could be a potential local key regulatory gene. These analyses identify chromosome 4q as a region enriched for COPD genetic susceptibility and differential co-expression.

Gene regulatory networks in disease and ageing

The precise control of gene expression is required for the maintenance of cellular homeostasis and proper cellular function, and the declining control of gene expression with age is considered a major contributor to age-associated changes in cellular physiology and disease. The coordination of gene expression can be represented through models of the molecular interactions that govern gene expression levels, so-called gene regulatory networks. Gene regulatory networks can represent interactions that occur through signal transduction, those that involve regulatory transcription factors, or statistical models of gene-gene relationships based on the premise that certain sets of genes tend to be coexpressed across a range of conditions and cell types. Advances in experimental and computational technologies have enabled the inference of these networks on an unprecedented scale and at unprecedented precision. Here, we delineate different types of gene regulatory networks and their cell-biological interpretation. We describe methods for inferring such networks from large-scale, multi-omics datasets and present applications that have aided our understanding of cellular ageing and disease mechanisms.

Systematic Analysis of Biological Processes Reveals Gene Co-expression Modules Driving Pathway Dysregulation in Alzheimer's Disease

Alzheimer's disease (AD) manifests as a complex systems pathology with intricate interplay among various genes and biological processes. Traditional differential gene expression (DEG) analysis, while commonly employed to characterize AD-driven perturbations, does not sufficiently capture the full spectrum of underlying biological processes. Utilizing single-nucleus RNA-sequencing data from postmortem brain samples across key regions-middle temporal gyrus, superior frontal gyrus, and entorhinal cortex-we provide a comprehensive systematic analysis of disrupted processes in AD. We go beyond the DEG-centric analysis by integrating pathway activity analysis with weighted gene co-expression patterns to comprehensively map gene interconnectivity, identifying region- and cell-type-specific drivers of biological processes associated with AD. Our analysis reveals profound modular heterogeneity in neurons and glia as well as extensive AD-related functional disruptions. Co-expression networks highlighted the extended involvement of astrocytes and microglia in biological processes beyond neuroinflammation, such as calcium homeostasis, glutamate regulation, lipid metabolism, vesicle-mediated transport, and TOR signaling. We find limited representation of DEGs within dysregulated pathways across neurons and glial cells, suggesting that differential gene expression alone may not adequately represent the disease complexity. Further dissection of inferred gene modules revealed distinct dynamics of hub DEGs in neurons versus glia, suggesting that DEGs exert more impact on neurons compared to glial cells in driving modular dysregulations underlying perturbed biological processes. Interestingly, we observe an overall downregulation of astrocyte and microglia modules across all brain regions in AD, indicating a prevailing trend of functional repression in glial cells across these regions. Notable genes from the CALM and HSP90 families emerged as hub genes across neuronal modules in all brain regions, suggesting conserved roles as drivers of synaptic dysfunction in AD. Our findings demonstrate the importance of an integrated, systems-oriented approach combining pathway and network analysis to comprehensively understand the cell-type-specific roles of genes in AD-related biological processes.

Dysregulation of long non-coding RNA gene expression pathways in monocytes of type 2 diabetes patients with cardiovascular disease

BACKGROUND

Monocytes play a central role in the pathophysiology of cardiovascular complications in type 2 diabetes (T2D) patients through different mechanisms. We investigated diabetes-induced changes in lncRNA genes from T2D patients with cardiovascular disease (CVD), long-duration diabetes, and poor glycemic control.

METHODS

We performed paired-end RNA sequencing of monocytes from 37 non-diabetes controls and 120 patients with T2D, of whom 86 had either macro or microvascular disease or both. Monocytes were sorted from peripheral blood using flow cytometry; their RNA was purified and sequenced. Alignments and gene counts were obtained with STAR to reference GRCh38 using Gencode (v41) annotations followed by batch correction with CombatSeq. Differential expression analysis was performed with EdgeR and pathway analysis with IPA software focusing on differentially expressed genes (DEGs) with a p-value < 0.05. Additionally, differential co-expression analysis was done with csdR to identify lncRNAs highly associated with diabetes-related expression networks with network centrality scores computed with Igraph and network visualization with Cytoscape.

RESULTS

Comparing T2D vs. non-T2D, we found two significantly upregulated lncRNAs (ENSG00000287255, FDR = 0.017 and ENSG00000289424, FDR = 0.048) and one significantly downregulated lncRNA (ENSG00000276603, FDR = 0.017). Pathway analysis on DEGs revealed networks affecting cellular movement, growth, and development. Co-expression analysis revealed ENSG00000225822 (UBXN7-AS1) as the highest-scoring diabetes network-associated lncRNA. Analysis within T2D patients and CVD revealed one lncRNA upregulated in monocytes from patients with microvascular disease without clinically documented macrovascular disease. (ENSG00000261654, FDR = 0.046). Pathway analysis revealed DEGs involved in networks affecting metabolic and cardiovascular pathologies. Co-expression analysis identified lncRNAs strongly associated with diabetes networks, including ENSG0000028654, ENSG00000261326 (LINC01355), ENSG00000260135 (MMP2-AS1), ENSG00000262097, and ENSG00000241560 (ZBTB20-AS1) when we combined the results from all patients with CVD. Similarly, we identified from co-expression analysis of diabetes patients with a duration ≥ 10 years vs. <10 years two lncRNAs: ENSG00000269019 (HOMER3-AS10) and ENSG00000212719 (LINC02693). The comparison of patients with good vs. poor glycemic control also identified two lncRNAs: ENSG00000245164 (LINC00861) and ENSG00000286313.

CONCLUSION

We identified dysregulated diabetes-related genes and pathways in monocytes of diabetes patients with cardiovascular complications, including lncRNA genes of unknown function strongly associated with networks of known diabetes genes.

A method for mining condition-specific co-expressed genes in Camellia sinensis based on k-means clustering

BACKGROUND

As one of the world's most important beverage crops, tea plants (Camellia sinensis) are renowned for their unique flavors and numerous beneficial secondary metabolites, attracting researchers to investigate the formation of tea quality. With the increasing availability of transcriptome data on tea plants in public databases, conducting large-scale co-expression analyses has become feasible to meet the demand for functional characterization of tea plant genes. However, as the multidimensional noise increases, larger-scale co-expression analyses are not always effective. Analyzing a subset of samples generated by effectively downsampling and reorganizing the global sample set often leads to more accurate results in co-expression analysis. Meanwhile, global-based co-expression analyses are more likely to overlook condition-specific gene interactions, which may be more important and worthy of exploration and research.

RESULTS

Here, we employed the k-means clustering method to organize and classify the global samples of tea plants, resulting in clustered samples. Metadata annotations were then performed on these clustered samples to determine the "conditions" represented by each cluster. Subsequently, we conducted gene co-expression network analysis (WGCNA) separately on the global samples and the clustered samples, resulting in global modules and cluster-specific modules. Comparative analyses of global modules and cluster-specific modules have demonstrated that cluster-specific modules exhibit higher accuracy in co-expression analysis. To measure the degree of condition specificity of genes within condition-specific clusters, we introduced the correlation difference value (CDV). By incorporating the CDV into co-expression analyses, we can assess the condition specificity of genes. This approach proved instrumental in identifying a series of high CDV transcription factor encoding genes upregulated during sustained cold treatment in Camellia sinensis leaves and buds, and pinpointing a pair of genes that participate in the antioxidant defense system of tea plants under sustained cold stress.

CONCLUSIONS

To summarize, downsampling and reorganizing the sample set improved the accuracy of co-expression analysis. Cluster-specific modules were more accurate in capturing condition-specific gene interactions. The introduction of CDV allowed for the assessment of condition specificity in gene co-expression analyses. Using this approach, we identified a series of high CDV transcription factor encoding genes related to sustained cold stress in Camellia sinensis. This study highlights the importance of considering condition specificity in co-expression analysis and provides insights into the regulation of the cold stress in Camellia sinensis.

Quantifying transcriptome diversity: a review

Following the central dogma of molecular biology, gene expression heterogeneity can aid in predicting and explaining the wide variety of protein products, functions and, ultimately, heterogeneity in phenotypes. There is currently overlapping terminology used to describe the types of diversity in gene expression profiles, and overlooking these nuances can misrepresent important biological information. Here, we describe transcriptome diversity as a measure of the heterogeneity in (1) the expression of all genes within a sample or a single gene across samples in a population (gene-level diversity) or (2) the isoform-specific expression of a given gene (isoform-level diversity). We first overview modulators and quantification of transcriptome diversity at the gene level. Then, we discuss the role alternative splicing plays in driving transcript isoform-level diversity and how it can be quantified. Additionally, we overview computational resources for calculating gene-level and isoform-level diversity for high-throughput sequencing data. Finally, we discuss future applications of transcriptome diversity. This review provides a comprehensive overview of how gene expression diversity arises, and how measuring it determines a more complete picture of heterogeneity across proteins, cells, tissues, organisms and species.

Systematic Analysis of Biological Processes Reveals Gene Co-expression Modules Driving Pathway Dysregulation in Alzheimer's Disease

Alzheimer's disease (AD) manifests as a complex systems pathology with intricate interplay among various genes and biological processes. Traditional differential gene expression (DEG) analysis, while commonly employed to characterize AD-driven perturbations, does not sufficiently capture the full spectrum of underlying biological processes. Utilizing single-nucleus RNA-sequencing data from postmortem brain samples across key regions-middle temporal gyrus, superior frontal gyrus, and entorhinal cortex-we provide a comprehensive systematic analysis of disrupted processes in AD. We go beyond the DEG-centric analysis by integrating pathway activity analysis with weighted gene co-expression patterns to comprehensively map gene interconnectivity, identifying region- and cell-type-specific drivers of biological processes associated with AD. Our analysis reveals profound modular heterogeneity in neurons and glia as well as extensive AD-related functional disruptions. Co-expression networks highlighted the extended involvement of astrocytes and microglia in biological processes beyond neuroinflammation, such as calcium homeostasis, glutamate regulation, lipid metabolism, vesicle-mediated transport, and TOR signaling. We find limited representation of DEGs within dysregulated pathways across neurons and glial cells, indicating that differential gene expression alone may not adequately represent the disease complexity. Further dissection of inferred gene modules revealed distinct dynamics of hub DEGs in neurons versus glia, highlighting the differential impact of DEGs on neurons compared to glial cells in driving modular dysregulations underlying perturbed biological processes. Interestingly, we note an overall downregulation of both astrocyte and microglia modules in AD across all brain regions, suggesting a prevailing trend of functional repression in glial cells across these regions. Notable genes, including those of the CALM and HSP90 family genes emerged as hub genes across neuronal modules in all brain regions, indicating conserved roles as drivers of synaptic dysfunction in AD. Our findings demonstrate the importance of an integrated, systems-oriented approach combining pathway and network analysis for a comprehensive understanding of the cell-type-specific roles of genes in AD-related biological processes.

Inference of differential gene regulatory networks using boosted differential trees

Summary

Diseases can be caused by molecular perturbations that induce specific changes in regulatory interactions and their coordinated expression, also referred to as network rewiring. However, the detection of complex changes in regulatory connections remains a challenging task and would benefit from the development of novel nonparametric approaches. We develop a new ensemble method called BoostDiff (boosted differential regression trees) to infer a differential network discriminating between two conditions. BoostDiff builds an adaptively boosted (AdaBoost) ensemble of differential trees with respect to a target condition. To build the differential trees, we propose differential variance improvement as a novel splitting criterion. Variable importance measures derived from the resulting models are used to reflect changes in gene expression predictability and to build the output differential networks. BoostDiff outperforms existing differential network methods on simulated data evaluated in four different complexity settings. We then demonstrate the power of our approach when applied to real transcriptomics data in COVID-19, Crohn's disease, breast cancer, prostate adenocarcinoma, and stress response in Bacillus subtilis. BoostDiff identifies context-specific networks that are enriched with genes of known disease-relevant pathways and complements standard differential expression analyses.

Availability and implementation

BoostDiff is available at https://github.com/scibiome/boostdiff_inference.

Virus-host protein co-expression networks reveal temporal organization and strategies of viral infection

Viral replication is a complex dynamical process involving the global remodeling of the host cellular machinery across several stages. In this study, we provide a unified view of the virus-host interaction at the proteome level reconstructing protein co-expression networks from quantitative temporal data of four large DNA viruses. We take advantage of a formal framework, the theory of competing networks, to describe the viral infection as a dynamical system taking place on a network of networks where perturbations induced by viral proteins spread to hijack the host proteome for the virus benefit. Our methodology demonstrates how the viral replication cycle can be effectively examined as a complex interaction between protein networks, providing useful insights into the viral and host's temporal organization and strategies, key protein nodes targeted by the virus and dynamical bottlenecks during the course of the infection.

PRANA: an R package for differential co-expression network analysis with the presence of additional covariates

BACKGROUND

Advances in sequencing technology and cost reduction have enabled an emergence of various statistical methods used in RNA-sequencing data, including the differential co-expression network analysis (or differential network analysis). A key benefit of this method is that it takes into consideration the interactions between or among genes and do not require an established knowledge in biological pathways. As of now, none of existing softwares can incorporate covariates that should be adjusted if they are confounding factors while performing the differential network analysis.

RESULTS

We develop an R package PRANA which a user can easily include multiple covariates. The main R function in this package leverages a novel pseudo-value regression approach for a differential network analysis in RNA-sequencing data. This software is also enclosed with complementary R functions for extracting adjusted p-values and coefficient estimates of all or specific variable for each gene, as well as for identifying the names of genes that are differentially connected (DC, hereafter) between subjects under biologically different conditions from the output.

CONCLUSION

Herewith, we demonstrate the application of this package in a real data on chronic obstructive pulmonary disease. PRANA is available through the CRAN repositories under the GPL-3 license: https://cran.r-project.org/web/packages/PRANA/index.html .

PLEX.I: a tool to discover features in multiplex networks that reflect clinical variation

Molecular profiling technologies, such as RNA sequencing, offer new opportunities to better discover and understand the molecular networks involved in complex biological processes. Clinically important variations of diseases, or responses to treatment, are often reflected, or even caused, by the dysregulation of molecular interaction networks specific to particular network regions. In this work, we propose the R package PLEX.I, that allows quantifying and testing variation in the direct neighborhood of a given node between networks corresponding to different conditions or states. We illustrate PLEX.I in two applications in which we discover variation that is associated with different responses to tamoxifen treatment and to sex-specific responses to bacterial stimuli. In the first case, PLEX.I analysis identifies two known pathways i) that have already been implicated in the same context as the tamoxifen mechanism of action, and ii) that would have not have been identified using classical differential gene expression analysis.

Gene network based analysis identifies a coexpression module involved in regulating plasma lipids with high-fat diet response

Plasma lipids are modulated by gene variants and many environmental factors, including diet-associated weight gain. However, understanding how these factors jointly interact to influence molecular networks that regulate plasma lipid levels is limited. Here, we took advantage of the BXD recombinant inbred family of mice to query weight gain as an environmental stressor on plasma lipids. Coexpression networks were examined in both nonobese and obese livers, and a network was identified that specifically responded to the obesogenic diet. This obesity-associated module was significantly associated with plasma lipid levels and enriched with genes known to have functions related to inflammation and lipid homeostasis. We identified key drivers of the module, including Cidec, Cidea, Pparg, Cd36, and Apoa4. The Pparg emerged as a potential master regulator of the module as it can directly target 19 of the top 30 hub genes. Importantly, activation of this module is causally linked to lipid metabolism in humans, as illustrated by correlation analysis and inverse-variance weighed Mendelian randomization. Our findings provide novel insights into gene-by-environment interactions for plasma lipid metabolism that may ultimately contribute to new biomarkers, better diagnostics, and improved approaches to prevent or treat dyslipidemia in patients.

Long non-coding RNA-mediated competing endogenous RNA regulatory network during flower development and color formation in Melastoma candidum

M. candidum, an evergreen shrubby flower known for its superior adaptation ability in South China, has gained increased attention in garden applications. However, scant attention has been paid to its flower development and color formation process at the non-coding RNA level. To fill this gap, we conducted a comprehensive analysis based on long non-coding RNA sequencing (lncRNA-seq), RNA-seq, small RNA sequencing (sRNA-seq), and widely targeted metabolome detection of three different flower developmental stages of M. candidum. After differentially expressed lncRNAs (DElncRNAs), differentially expressed mRNAs (DEmRNAs), differentially expressed microRNAs (DEmiRNAs), and differentially synthesized metabolites (DSmets) analyses between the different flower developmental stages, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted to identify some key genes and metabolites in flavonoid, flavone, anthocyanin, carotenoid, and alkaloid-related GO terms and biosynthetic pathways. Three direct-acting models, including antisense-acting, cis-acting, and trans-acting between lncRNAs and mRNAs, were detected to illustrate the direct function of lncRNAs on target genes during flower development and color formation. Based on the competitive endogenous RNA (ceRNA) regulatory theory, we constructed a lncRNA-mediated regulatory network composed of DElncRNAs, DEmiRNAs, DEmRNAs, and DSmets to elucidate the indirect role of lncRNAs in the flower development and color formation of M. candidum. By utilizing correlation analyses between DERNAs and DSmets within the ceRNA regulatory network, alongside verification trials of the ceRNA regulatory mechanism, the study successfully illustrated the significance of lncRNAs in flower development and color formation process. This research provides a foundation for improving and regulating flower color at the lncRNA level in M. candidum, and sheds light on the potential applications of non-coding RNA in studies of flower development.

A robust and fast two-sample test of equal correlations with an application to differential co-expression

A robust and fast two-sample test for equal Pearson correlation coefficients (PCCs) is important in solving many biological problems, including, for example, analysis of differential co-expression. However, few existing methods for this test can achieve robustness against deviation from normal distributions, accuracy under small sample sizes, and computational efficiency simultaneously. Here, we propose a new method for testing differential correlation using a saddlepoint approximation of the residual bootstrap (DICOSAR). To achieve robustness, accuracy, and efficiency, DICOSAR combines the ideas underlying the pooled residual bootstrap, the signed root of a likelihood ratio statistic, and a multivariate saddlepoint approximation. Through a comprehensive simulation study and a real data analysis of gene co-expression, we demonstrate that DICOSAR is accurate and robust in controlling the type I error rate for detecting differential correlation and provides a faster alternative to the bootstrap and permutation methods. We further show that DICOSAR can also be used for testing differential correlation matrices. These results suggest that DICOSAR provides an analytical approach to facilitate rapid testing for the equality of PCCs in large-scale analysis.

Human myeloid progenitor glucocorticoid receptor activation causes genomic instability, type 1 IFN- response pathway activation and senescence in differentiated microglia; an early life stress model

One form of early life stress, prenatal exposure to glucocorticoids (GCs), confers a higher risk of psychiatric and neurodevelopmental disorders in later life. Increasingly, the importance of microglia in these disorders is recognized. Studies on GCs exposure during microglial development have been limited, and there are few, if any, human studies. We established an in vitro model of ELS by continuous pre-exposure of human iPS-microglia to GCs during primitive hematopoiesis (the critical stage of iPS-microglial differentiation) and then examined how this exposure affected the microglial phenotype as they differentiated and matured to microglia, using RNA-seq analyses and functional assays. The iPS-microglia predominantly expressed glucocorticoid receptors over mineralocorticoid receptors, and in particular, the GR-α splice variant. Chronic GCs exposure during primitive hematopoiesis was able to recapitulate in vivo ELS effects. Thus, pre-exposure to prolonged GCs resulted in increased type I interferon signaling, the presence of Cyclic GMP-AMP synthase-positive (cGAS) micronuclei, cellular senescence and reduced proliferation in the matured iPS-microglia. The findings from this in vitro ELS model have ramifications for the responses of microglia in the pathogenesis of GC- mediated ELS-associated disorders such as schizophrenia, attention-deficit hyperactivity disorder and autism spectrum disorder.

Gender-specific dysregulations of nondifferentially expressed biomarkers of metastatic colon cancer

Colon cancer is a common cancer type in both sexes and its mortality rate increases at the metastatic stage. Most studies exclude nondifferentially expressed genes from biomarker analysis of metastatic colon cancers. The motivation of this study is to find the latent associations of the nondifferentially expressed genes with metastatic colon cancers and to evaluate the gender specificity of such associations. This study formulates the expression level prediction of a gene as a regression model trained for primary colon cancers. The difference between a gene's predicted and original expression levels in a testing sample is defined as its mqTrans value (model-based quantitative measure of transcription regulation), which quantitatively measures the change of the gene's transcription regulation in this testing sample. We use the mqTrans analysis to detect the messenger RNA (mRNA) genes with nondifferential expression on their original expression levels but differentially expressed mqTrans values between primary and metastatic colon cancers. These genes are referred to as dark biomarkers of metastatic colon cancer. All dark biomarker genes were verified by two transcriptome profiling technologies, RNA-seq and microarray. The mqTrans analysis of a mixed cohort of both sexes could not recover gender-specific dark biomarkers. Most dark biomarkers overlap with long non-coding RNAs (lncRNAs), and these lncRNAs might have contributed their transcripts to calculating the dark biomarkers' expression levels. Therefore, mqTrans analysis serves as a complementary approach to identify dark biomarkers generally ignored by conventional studies, and it is essential to separate the female and male samples into two analysis experiments. The dataset and mqTrans analysis code are available at https://figshare.com/articles/dataset/22250536.

Expression profiles of meiotic genes in male vs. female gonads and gametes: Insights into fertility issues

Gametes are specialized cells that, at fertilization, give rise to a totipotent zygote capable of generating an entire organism. Female and male germ cells undergo meiosis to produce mature gametes; however, sex-specific events of oogenesis and spermatogenesis contribute to specific roles of gametes in reproductive issues. We investigate the differential gene expression (DGE) of meiosis-related genes in human female and male gonads and gametes in normal and pathological conditions. The transcriptome data for the DGE analysis was obtained through the Gene Expression Omnibus repository, comprising human ovary and testicle samples of the prenatal period and adulthood, additionally to male (non-obstructive azoospermia (NOA) and teratozoospermia), and female (polycystic ovary syndrome (PCOS) and advanced maternal age) reproductive conditions. Gene ontology terms related to meiosis were associated with 678 genes, of which 17 genes in common were differentially expressed between the testicle and ovary during the prenatal period and adulthood. Except for SERPINA5 and SOX9, the 17 meiosis-related genes were downregulated in the testicle during the prenatal period and upregulated in adulthood compared to the ovary. No differences were observed in the oocytes of PCOS patients; however, meiosis-related genes were differentially expressed according to the patient’s age and maturity of the oocyte. In NOA and teratozoospermia, 145 meiosis-related genes were differentially expressed in comparison to the control, including OOEP; despite no recognized role in male reproduction, OOEP was co-expressed with genes related to male fertility. Taking together, these results shed light on potential genes that might be relevant to comprehend human fertility disorders.

mRNA Signatures in Peripheral White Blood Cells Predict Reproductive Potential in Beef Heifers at Weaning

Reproductive failure is a major contributor to inefficiency within the cow-calf industry. Particularly problematic is the inability to diagnose heifer reproductive issues prior to pregnancy diagnosis following their first breeding season. Therefore, we hypothesized that gene expression from the peripheral white blood cells at weaning could predict the future reproductive potential of beef heifers. To investigate this, the gene expression was measured using RNA-Seq in Angus-Simmental crossbred heifers sampled at weaning and retrospectively classified as fertile (FH, n = 8) or subfertile (SFH, n = 7) after pregnancy diagnosis. We identified 92 differentially expressed genes between the groups. Network co-expression analysis identified 14 and 52 hub targets. ENSBTAG00000052659, OLR1, TFF2, and NAIP were exclusive hubs to the FH group, while 42 hubs were exclusive to the SFH group. The differential connectivity between the networks of each group revealed a gain in connectivity due to the rewiring of major regulators in the SFH group. The exclusive hub targets from FH were over-represented for the CXCR chemokine receptor pathway and inflammasome complex, while for the SFH, they were over-represented for immune response and cytokine production pathways. These multiple interactions revealed novel targets and pathways predicting reproductive potential at an early stage of heifer development.

WMDS.net: a network control framework for identifying key players in transcriptome programs

MOTIVATION

Mammalian cells can be transcriptionally reprogramed to other cellular phenotypes. Controllability of such complex transitions in transcriptional networks underlying cellular phenotypes is an inherent biological characteristic. This network controllability can be interpreted by operating a few key regulators to guide the transcriptional program from one state to another. Finding the key regulators in the transcriptional program can provide key insights into the network state transition underlying cellular phenotypes.

RESULTS

To address this challenge, here, we proposed to identify the key regulators in the transcriptional co-expression network as a minimum dominating set (MDS) of driver nodes that can fully control the network state transition. Based on the theory of structural controllability, we developed a weighted MDS network model (WMDS.net) to find the driver nodes of differential gene co-expression networks. The weight of WMDS.net integrates the degree of nodes in the network and the significance of gene co-expression difference between two physiological states into the measurement of node controllability of the transcriptional network. To confirm its validity, we applied WMDS.net to the discovery of cancer driver genes in RNA-seq datasets from The Cancer Genome Atlas. WMDS.net is powerful among various cancer datasets and outperformed the other top-tier tools with a better balance between precision and recall.

AVAILABILITY AND IMPLEMENTATION

https://github.com/chaofen123/WMDS.net.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Using single cell atlas data to reconstruct regulatory networks

Inference of global gene regulatory networks from omics data is a long-term goal of systems biology. Most methods developed for inferring transcription factor (TF)-gene interactions either relied on a small dataset or used snapshot data which is not suitable for inferring a process that is inherently temporal. Here, we developed a new computational method that combines neural networks and multi-task learning to predict RNA velocity rather than gene expression values. This allows our method to overcome many of the problems faced by prior methods leading to more accurate and more comprehensive set of identified regulatory interactions. Application of our method to atlas scale single cell data from 6 HuBMAP tissues led to several validated and novel predictions and greatly improved on prior methods proposed for this task.

A pseudo-value regression approach for differential network analysis of co-expression data

BACKGROUND

The differential network (DN) analysis identifies changes in measures of association among genes under two or more experimental conditions. In this article, we introduce a pseudo-value regression approach for network analysis (PRANA). This is a novel method of differential network analysis that also adjusts for additional clinical covariates. We start from mutual information criteria, followed by pseudo-value calculations, which are then entered into a robust regression model.

RESULTS

This article assesses the model performances of PRANA in a multivariable setting, followed by a comparison to dnapath and DINGO in both univariable and multivariable settings through variety of simulations. Performance in terms of precision, recall, and F1 score of differentially connected (DC) genes is assessed. By and large, PRANA outperformed dnapath and DINGO, neither of which is equipped to adjust for available covariates such as patient-age. Lastly, we employ PRANA in a real data application from the Gene Expression Omnibus database to identify DC genes that are associated with chronic obstructive pulmonary disease to demonstrate its utility.

CONCLUSION

To the best of our knowledge, this is the first attempt of utilizing a regression modeling for DN analysis by collective gene expression levels between two or more groups with the inclusion of additional clinical covariates. By and large, adjusting for available covariates improves accuracy of a DN analysis.

Elucidating genes and gene networks linked to individual susceptibility to milk fat depression in dairy goats

Dietary supplementation with marine lipids modulates ruminant milk composition toward a healthier fatty acid profile for consumers, but it also causes milk fat depression (MFD). Because the dairy goat industry is mainly oriented toward cheese manufacturing, MFD can elicit economic losses. There is large individual variation in animal susceptibility with goats more (RESPO+) or less (RESPO-) responsive to diet-induced MFD. Thus, we used RNA-Seq to examine gene expression profiles in mammary cells to elucidate mechanisms underlying MFD in goats and individual variation in the extent of diet-induced MFD. Differentially expression analyses (DEA) and weighted gene co-expression network analysis (WGCNA) of RNA-Seq data were used to study milk somatic cell transcriptome changes in goats consuming a diet supplemented with marine lipids. There were 45 differentially expressed genes (DEGs) between control (no-MFD, before diet-induced MFD) and MFD, and 18 between RESPO+ and RESPO-. Biological processes and pathways such as "RNA transcription" and "Chromatin modifying enzymes" were downregulated in MFD compared with controls. Regarding susceptibility to diet-induced MFD, we identified the "Triglyceride Biosynthesis" pathway upregulated in RESPO- goats. The WGCNA approach identified 9 significant functional modules related to milk fat production and one module to the fat yield decrease in diet-induced MFD. The onset of MFD in dairy goats is influenced by the downregulation of SREBF1, other transcription factors and chromatin-modifying enzymes. A list of DEGs between RESPO+ and RESPO- goats (e.g., DBI and GPD1), and a co-related gene network linked to the decrease in milk fat (ABCD3, FABP3, and PLIN2) was uncovered. Results suggest that alterations in fatty acid transport may play an important role in determining individual variation. These candidate genes should be further investigated.

Defining the extent of gene function using ROC curvature

MOTIVATION

Interactions between proteins help us understand how genes are functionally related and how they contribute to phenotypes. Experiments provide imperfect 'ground truth' information about a small subset of potential interactions in a specific biological context, which can then be extended to the whole genome across different contexts, such as conditions, tissues or species, through machine learning methods. However, evaluating the performance of these methods remains a critical challenge. Here, we propose to evaluate the generalizability of gene characterizations through the shape of performance curves.

RESULTS

We identify Functional Equivalence Classes (FECs), subsets of annotated and unannotated genes that jointly drive performance, by assessing the presence of straight lines in ROC curves built from gene-centric prediction tasks, such as function or interaction predictions. FECs are widespread across data types and methods, they can be used to evaluate the extent and context-specificity of functional annotations in a data-driven manner. For example, FECs suggest that B cell markers can be decomposed into shared primary markers (10-50 genes), and tissue-specific secondary markers (100-500 genes). In addition, FECs suggest the existence of functional modules that span a wide range of the genome, with marker sets spanning at most 5% of the genome and data-driven extensions of Gene Ontology sets spanning up to 40% of the genome. Simple to assess visually and statistically, the identification of FECs in performance curves paves the way for novel functional characterization and increased robustness in the definition of functional gene sets.

AVAILABILITY AND IMPLEMENTATION

Code for analyses and figures is available at https://github.com/yexilein/pyroc.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DiffBrainNet: Differential analyses add new insights into the response to glucocorticoids at the level of genes, networks and brain regions

Genome-wide gene expression analyses are invaluable tools for studying biological and disease processes, allowing a hypothesis-free comparison of expression profiles. Traditionally, transcriptomic analysis has focused on gene-level effects found by differential expression. In recent years, network analysis has emerged as an important additional level of investigation, providing information on molecular connectivity, especially for diseases associated with a large number of linked effects of smaller magnitude, like neuropsychiatric disorders. Here, we describe how combined differential expression and prior-knowledge-based differential network analysis can be used to explore complex datasets. As an example, we analyze the transcriptional responses following administration of the glucocorticoid/stress receptor agonist dexamethasone in 8 mouse brain regions important for stress processing. By applying a combination of differential network- and expression-analyses, we find that these explain distinct but complementary biological mechanisms of the glucocorticoid responses. Additionally, network analysis identifies new differentially connected partners of risk genes and can be used to generate hypotheses on molecular pathways affected. With DiffBrainNet (http://diffbrainnet.psych.mpg.de), we provide an analysis framework and a publicly available resource for the study of the transcriptional landscape of the mouse brain which can identify molecular pathways important for basic functioning and response to glucocorticoids in a brain-region specific manner.

Differential gene co-expression network analyses reveal novel molecules associated with transcriptional dysregulation of key biological processes in osteoarthritis knee cartilage

OBJECTIVES

To compare co-expression networks of normal and osteoarthritis knee cartilage to uncover molecules associated with the transcriptional misregulation compromising biological processes (BPs) critical for cartilage homeostasis.

DESIGN

Normal and osteoarthritis human knee cartilage RNA-seq GSE114007 dataset was obtained from the Gene Expression Omnibus database. Partial Correlation and Information Theory (PCIT) algorithm was used to build co-expression networks containing all nodes connecting to at least one differentially expressed gene (DEG) in normal and osteoarthritis networks. Hub and hub centrality genes were used to perform functional enrichment analysis. Enriched BPs known to be associated with both healthy and diseased cartilage were compared in depth.

RESULTS

Differential co-expression network analyses allowed the identification of DDX43 and USP42 as exclusively co-expressed with DEGs in normal and osteoarthritis networks, respectively. The top hub and hub centrality genes of these networks were HIST1H3A and SNHG12 (normal) and TAF9B and OTUD1 (osteoarthritis). Enrichment analysis revealed several shared BPs between the contrasting groups, which are well-known in osteoarthritis pathogenesis. Protein-protein interaction network analysis for these BPs showed a global down-regulation of transcription factors in osteoarthritis. Specific transcription factors were identified as pleiotropic mediators in articular cartilage maintenance since they take part in several BPs. In addition, chromatin organisation and modification proteins were found relevant for osteoarthritis development.

CONCLUSION

Differential gene co-expression analysis allowed the identification of novel and high priority therapeutic candidate genes that may drive modifications in the transcriptional "status" of cartilage in osteoarthritis.

Interaction-based transcriptome analysis via differential network inference

Gene-based transcriptome analysis, such as differential expression analysis, can identify the key factors causing disease production, cell differentiation and other biological processes. However, this is not enough because basic life activities are mainly driven by the interactions between genes. Although there have been already many differential network inference methods for identifying the differential gene interactions, currently, most studies still only use the information of nodes in the network for downstream analyses. To investigate the insight into differential gene interactions, we should perform interaction-based transcriptome analysis (IBTA) instead of gene-based analysis after obtaining the differential networks. In this paper, we illustrated a workflow of IBTA by developing a Co-hub Differential Network inference (CDN) algorithm, and a novel interaction-based metric, pivot APC2. We confirmed the superior performance of CDN through simulation experiments compared with other popular differential network inference algorithms. Furthermore, three case studies are given using colorectal cancer, COVID-19 and triple-negative breast cancer datasets to demonstrate the ability of our interaction-based analytical process to uncover causative mechanisms.

Applying differential network analysis to longitudinal gene expression in response to perturbations

Differential Network (DN) analysis is a method that has long been used to interpret changes in gene expression data and provide biological insights. The method identifies the rewiring of gene networks in response to external perturbations. Our study applies the DN method to the analysis of RNA-sequencing (RNA-seq) time series datasets. We focus on expression changes: (i) in saliva of a human subject after pneumococcal vaccination (PPSV23) and (ii) in primary B cells treated ex vivo with a monoclonal antibody drug (Rituximab). The DN method enabled us to identify the activation of biological pathways consistent with the mechanisms of action of the PPSV23 vaccine and target pathways of Rituximab. The community detection algorithm on the DN revealed clusters of genes characterized by collective temporal behavior. All saliva and some B cell DN communities showed characteristic time signatures, outlining a chronological order in pathway activation in response to the perturbation. Moreover, we identified early and delayed responses within network modules in the saliva dataset and three temporal patterns in the B cell data.

Toxicity by descent: A comparative approach for chemical hazard assessment

Toxicology is traditionally divided between human and eco-toxicology. In the shared pursuit of environmental health, this separation does not account for discoveries made in the comparative studies of animal genomes. Here, we provide evidence on the feasibility of understanding the health impact of chemicals on all animals, including ecological keystone species and humans, based on a significant number of conserved genes and their functional associations to health-related outcomes across much of animal diversity. We test four conditions to understand the value of comparative genomics data to inform mechanism-based human and environmental hazard assessment: (1) genes that are most fundamental for health evolved early during animal evolution; (2) the molecular functions of pathways are better conserved among distantly related species than the individual genes that are members of these pathways; (3) the most conserved pathways among animals are those that cause adverse health outcomes when disrupted; (4) gene sets that serve as molecular signatures of biological processes or disease-states are largely enriched by evolutionarily conserved genes across the animal phylogeny. The concept of homology is applied in a comparative analysis of gene families and pathways among invertebrate and vertebrate species compared with humans. Results show that over 70% of gene families associated with disease are shared among the greatest variety of animal species through evolution. Pathway conservation between invertebrates and humans is based on the degree of conservation within vertebrates and the number of interacting genes within the human network. Human gene sets that already serve as biomarkers are enriched by evolutionarily conserved genes across the animal phylogeny. By implementing a comparative method for chemical hazard assessment, human and eco-toxicology converge towards a more holistic and mechanistic understanding of toxicity disrupting biological processes that are important for health and shared among animals (including humans).

Bayesian dynamic network modelling: an application to metabolic associations in cardiovascular diseases

We propose a novel approach to the estimation of multiple Graphical Models to analyse temporal patterns of association among a set of metabolites over different groups of patients. Our motivating application is the Southall And Brent REvisited (SABRE) study, a tri-ethnic cohort study conducted in the UK. We are interested in identifying potential ethnic differences in metabolite levels and associations as well as their evolution over time, with the aim of gaining a better understanding of different risk of cardio-metabolic disorders across ethnicities. Within a Bayesian framework, we employ a nodewise regression approach to infer the structure of the graphs, borrowing information across time as well as across ethnicities. The response variables of interest are metabolite levels measured at two time points and for two ethnic groups, Europeans and South-Asians. We use nodewise regression to estimate the high-dimensional precision matrices of the metabolites, imposing sparsity on the regression coefficients through the dynamic horseshoe prior, thus favouring sparser graphs. We provide the code to fit the proposed model using the software Stan, which performs posterior inference using Hamiltonian Monte Carlo sampling, as well as a detailed description of a block Gibbs sampling scheme.

Approaches in Gene Coexpression Analysis in Eukaryotes

Simple Summary

Genes whose expression levels rise and fall similarly in a large set of samples, may be considered coexpressed. Gene coexpression analysis refers to the en masse discovery of coexpressed genes from a large variety of transcriptomic experiments. The type of biological networks that studies gene coexpression, known as Gene Coexpression Networks, consist of an undirected graph depicting genes and their coexpression relationships. Coexpressed genes are clustered in smaller subnetworks, the predominant biological roles of which can be determined through enrichment analysis. By studying well-annotated gene partners, the attribution of new roles to genes of unknown function or assumption for participation in common metabolic pathways can be achieved, through a guilt-by-association approach. In this review, we present key issues in gene coexpression analysis, as well as the most popular tools that perform it.

Abstract

Gene coexpression analysis constitutes a widely used practice for gene partner identification and gene function prediction, consisting of many intricate procedures. The analysis begins with the collection of primary transcriptomic data and their preprocessing, continues with the calculation of the similarity between genes based on their expression values in the selected sample dataset and results in the construction and visualisation of a gene coexpression network (GCN) and its evaluation using biological term enrichment analysis. As gene coexpression analysis has been studied extensively, we present most parts of the methodology in a clear manner and the reasoning behind the selection of some of the techniques. In this review, we offer a comprehensive and comprehensible account of the steps required for performing a complete gene coexpression analysis in eukaryotic organisms. We comment on the use of RNA-Seq vs. microarrays, as well as the best practices for GCN construction. Furthermore, we recount the most popular webtools and standalone applications performing gene coexpression analysis, with details on their methods, features and outputs.

ATTED-II v11: A Plant Gene Coexpression Database Using a Sample Balancing Technique by Subagging of Principal Components

ATTED-II (https://atted.jp) is a gene coexpression database for nine plant species based on publicly available RNAseq and microarray data. One of the challenges in constructing condition-independent coexpression data based on publicly available gene expression data is managing the inherent sampling bias. Here, we report ATTED-II version 11, wherein we adopted a coexpression calculation methodology to balance the samples using principal component analysis and ensemble calculation. This approach has two advantages. First, omitting principal components with low contribution rates reduces the main contributors of noise. Second, balancing large differences in contribution rates enables considering various sample conditions entirely. In addition, based on RNAseq- and microarray-based coexpression data, we provide species-representative, integrated coexpression information to enhance the efficiency of interspecies comparison of the coexpression data. These coexpression data are provided as a standardized z-score to facilitate integrated analysis with different data sources. We believe that with these improvements, ATTED-II is more valuable and powerful for supporting interspecies comparative studies and integrated analyses using heterogeneous data.

Identification of Transcription Factors Regulating SARS-CoV-2 Tropism Factor Expression by Inferring Cell-Type-Specific Transcriptional Regulatory Networks in Human Lungs

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus that caused the coronavirus disease 2019 (COVID-19) pandemic. Though previous studies have suggested that SARS-CoV-2 cellular tropism depends on the host-cell-expressed proteins, whether transcriptional regulation controls SARS-CoV-2 tropism factors in human lung cells remains unclear. In this study, we used computational approaches to identify transcription factors (TFs) regulating SARS-CoV-2 tropism for different types of lung cells. We constructed transcriptional regulatory networks (TRNs) controlling SARS-CoV-2 tropism factors for healthy donors and COVID-19 patients using lung single-cell RNA-sequencing (scRNA-seq) data. Through differential network analysis, we found that the altered regulatory role of TFs in the same cell types of healthy and SARS-CoV-2-infected networks may be partially responsible for differential tropism factor expression. In addition, we identified the TFs with high centralities from each cell type and proposed currently available drugs that target these TFs as potential candidates for the treatment of SARS-CoV-2 infection. Altogether, our work provides valuable cell-type-specific TRN models for understanding the transcriptional regulation and gene expression of SARS-CoV-2 tropism factors.

The dynamic alteration of transcriptional regulation by crucial TFs during tumorigenesis of gastric cancer

Background

The mechanisms of Gastric cancer (GC) initiation and progression are complicated, at least partly owing to the dynamic changes of gene regulation during carcinogenesis. Thus, investigations on the changes in regulatory networks can improve the understanding of cancer development and provide novel insights into the molecular mechanisms of cancer.

Methods

Differential co-expression analysis (DCEA), differential gene regulation network (GRN) modeling and differential regulation analysis (DRA) were integrated to detect differential transcriptional regulation events between gastric normal mucosa and cancer samples based on GSE54129 dataset. Cytological experiments and IHC staining assays were used to validate the dynamic changes of CREB1 regulated targets in different stages.

Results

A total of 1955 differentially regulated genes (DRGs) were identified and prioritized in a quantitative way. Among the top 1% DRGs, 14 out of 19 genes have been reported to be GC relevant. The four transcription factors (TFs) among the top 1% DRGs, including CREB1, BPTF, GATA6 and CEBPA, were regarded as crucial TFs relevant to GC progression. The differentially regulated links (DRLs) around the four crucial TFs were then prioritized to generate testable hypotheses on the differential regulation mechanisms of gastric carcinogenesis. To validate the dynamic alterations of gene regulation patterns of crucial TFs during GC progression, we took CREB1 as an example to screen its differentially regulated targets by using cytological and IHC staining assays. Eventually, TCEAL2 and MBNL1 were proved to be differentially regulated by CREB1 during tumorigenesis of gastric cancer.

Conclusions

By combining differential networking information and molecular cell experiments verification, testable hypotheses on the regulation mechanisms of GC around the core TFs and their top ranked DRLs were generated. Since TCEAL2 and MBNL1 have been reported to be potential therapeutic targets in SCLC and breast cancer respectively, their translation values in GC are worthy of further investigation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10020-022-00468-7.

Co-Expression Networks Unveiled Long Non-Coding RNAs as Molecular Targets of Drugs Used to Treat Bipolar Disorder

Long non-coding RNAs (lncRNAs) may play a role in psychiatric diseases including bipolar disorder (BD). We investigated mRNA-lncRNA co-expression patterns in neuronal-like cells treated with widely prescribed BD medications. The aim was to unveil insights into the complex mechanisms of BD medications and highlight potential targets for new drug development. Human neuronal-like (NT2-N) cells were treated with either lamotrigine, lithium, quetiapine, valproate or vehicle for 24 h. Genome-wide mRNA expression was quantified for weighted gene co-expression network analysis (WGCNA) to correlate the expression levels of mRNAs with lncRNAs. Functional enrichment analysis and hub lncRNA identification was conducted on key co-expressed modules associated with the drug response. We constructed lncRNA-mRNA co-expression networks and identified key modules underlying these treatments, as well as their enriched biological functions. Processes enriched in key modules included synaptic vesicle cycle, endoplasmic reticulum-related functions and neurodevelopment. Several lncRNAs such as GAS6-AS1 and MIR100HG were highlighted as driver genes of key modules. Our study demonstrates the key role of lncRNAs in the mechanism(s) of action of BD drugs. Several lncRNAs have been suggested as major regulators of medication effects and are worthy of further investigation as novel drug targets to treat BD.

Kinase-dead mutation: A novel strategy for improving soybean resistance to soybean cyst nematode Heterodera glycines

Protein kinases phosphorylate proteins for functional changes and are involved in nearly all cellular processes, thereby regulating almost all aspects of plant growth and development, and responses to biotic and abiotic stresses. We generated two independent co-expression networks of soybean genes using control and stress response gene expression data and identified 392 differentially highly interconnected kinase hub genes among the two networks. Of these 392 kinases, 90 genes were identified as "syncytium highly connected hubs", potentially essential for activating kinase signalling pathways in the nematode feeding site. Overexpression of wild-type coding sequences of five syncytium highly connected kinase hub genes using transgenic soybean hairy roots enhanced plant susceptibility to soybean cyst nematode (SCN; Heterodera glycines) Hg Type 0 (race 3). In contrast, overexpression of kinase-dead variants of these five syncytium kinase hub genes significantly enhanced soybean resistance to SCN. Additionally, three of the five tested kinase hub genes enhanced soybean resistance to SCN Hg Type 1.2.5.7 (race 2), highlighting the potential of the kinase-dead approach to generate effective and durable resistance against a wide range of SCN Hg types. Subcellular localization analysis revealed that kinase-dead mutations do not alter protein cellular localization, confirming the structure-function of the kinase-inactive variants in producing loss-of-function phenotypes causing significant decrease in nematode susceptibility. Because many protein kinases are highly conserved and are involved in plant responses to various biotic and abiotic stresses, our approach of identifying kinase hub genes and their inactivation using kinase-dead mutation could be translated for biotic and abiotic stress tolerance.

csdR, an R package for differential co-expression analysis

Background

Differential co-expression network analysis has become an important tool to gain understanding of biological phenotypes and diseases. The CSD algorithm is a method to generate differential co-expression networks by comparing gene co-expressions from two different conditions. Each of the gene pairs is assigned conserved (C), specific (S) and differentiated (D) scores based on the co-expression of the gene pair between the two conditions. The result of the procedure is a network where the nodes are genes and the links are the gene pairs with the highest C-, S-, and D-scores. However, the existing CSD-implementations suffer from poor computational performance, difficult user procedures and lack of documentation.

Results

We created the R-package aimed at reaching good performance together with ease of use, sufficient documentation, and with the ability to play well with other tools for data analysis. was benchmarked on a realistic dataset with 20,645 genes. After verifying that the chosen number of iterations gave sufficient robustness, we tested the performance against the two existing CSD implementations. was superior in performance to one of the implementations, whereas the other did not run. Our implementation can utilize multiple processing cores. However, we were unable to achieve more than $$\sim$$ ∼ 2.7 parallel speedup with saturation reached at about 10 cores.

Conclusion

The results suggest that is a useful tool for differential co-expression analysis and is able to generate robust results within a workday on datasets of realistic sizes when run on a workstation or compute server.

Protection against severe infant lower respiratory tract infections by immune training: Mechanistic studies

BACKGROUND

Results from recent clinical studies suggest potential efficacy of immune training (IT)-based approaches for protection against severe lower respiratory tract infections in infants, but underlying mechanisms are unclear.

OBJECTIVE

We used systems-level analyses to elucidate IT mechanisms in infants in a clinical trial setting.

METHODS

Pre- and posttreatment peripheral blood mononuclear cells from a placebo-controlled trial in which winter treatment with the IT agent OM85 reduced infant respiratory infection frequency and/or duration were stimulated for 24 hours with the virus/bacteria mimics polyinosinic:polycytidylic acid/lipopolysaccharide. Transcriptomic profiling via RNA sequencing, pathway and upstream regulator analyses, and systems-level gene coexpression network analyses were used sequentially to elucidate and compare responses in treatment and placebo groups.

RESULTS

In contrast to subtle changes in antivirus-associated polyinosinic:polycytidylic acid response profiles, the bacterial lipopolysaccharide-triggered gene coexpression network responses exhibited OM85 treatment-associated upregulation of IFN signaling. This was accompanied by network rewiring resulting in increased coordination of TLR4 expression with IFN pathway-associated genes (especially master regulator IRF7); segregation of TNF and IFN-γ (which potentially synergize to exaggerate inflammatory sequelae) into separate expression modules; and reduced size/complexity of the main proinflammatory network module (containing, eg, IL-1,IL-6, and CCL3). Finally, we observed a reduced capacity for lipopolysaccharide-induced inflammatory cytokine (eg, IL-6 and TNF) production in the OM85 group.

CONCLUSION

These changes are consistent with treatment-induced enhancement of bacterial pathogen detection/clearance capabilities concomitant with enhanced capacity to regulate ensuing inflammatory response intensity and duration. We posit that IT agents exemplified by OM85 potentially protect against severe lower respiratory tract infections in infants principally by effects on innate immune responses targeting the bacterial components of the mixed respiratory viral/bacterial infections that are characteristic of this age group.

Identification and Validation of Key Genes of Differential Correlations in Gastric Cancer

Background: Gastric cancer (GC) is aggressive cancer with a poor prognosis. Previously bulk transcriptome analysis was utilized to identify key genes correlated with the development, progression and prognosis of GC. However, due to the complexity of the genetic mutations, there is still an urgent need to recognize core genes in the regulatory network of GC.

Methods: Gene expression profiles (GSE66229) were retrieved from the GEO database. Weighted correlation network analysis (WGCNA) was employed to identify gene modules mostly correlated with GC carcinogenesis. R package ‘DiffCorr’ was applied to identify differentially correlated gene pairs in tumor and normal tissues. Cytoscape was adopted to construct and visualize the gene regulatory network.

Results: A total of 15 modules were detected in WGCNA analysis, among which three modules were significantly correlated with GC. Then genes in these modules were analyzed separately by “DiffCorr”. Multiple differentially correlated gene pairs were recognized and the network was visualized by the software Cytoscape. Moreover, GEMIN5 and PFDN2, which were rarely discussed in GC, were identified as key genes in the regulatory network and the differential expression was validated by real-time qPCR, WB and IHC in cell lines and GC patient tissues.

Conclusions: Our research has shed light on the carcinogenesis mechanism by revealing differentially correlated gene pairs during transition from normal to tumor. We believe the application of this network-based algorithm holds great potential in inferring relationships and detecting candidate biomarkers.

Re-wiring and gene expression changes of AC025034.1 and ATP2B1 play complex roles in early-to-late breast cancer progression

Background

Elucidating the dynamic topological changes across different stages of breast cancer, called stage re-wiring, could lead to identifying key latent regulatory signatures involved in cancer progression. Such dynamic regulators and their functions are mostly unknown. Here, we reconstructed differential co-expression networks for four stages of breast cancer to assess the dynamic patterns of cancer progression. A new computational approach was applied to identify stage-specific subnetworks for each stage. Next, prognostic traits of genes and the efficiency of stage-related groups were evaluated and validated, using the Log-Rank test, SVM classifier, and sample clustering. Furthermore, by conducting the stepwise VIF-feature selection method, a Cox-PH model was developed to predict patients’ risk. Finally, the re-wiring network for prognostic signatures was reconstructed and assessed across stages to detect gain/loss, positive/negative interactions as well as rewired-hub nodes contributing to dynamic cancer progression.

Results

After having implemented our new approach, we could identify four stage-specific core biological pathways. We could also detect an essential non-coding RNA, AC025034.1, which is not the only antisense to ATP2B1 (cell proliferation regulator), but also revealed a statistically significant stage-descending pattern; Moreover, AC025034.1 revealed both a dynamic topological pattern across stages and prognostic trait. We also identified a high-performance Overall-Survival-Risk model, including 12 re-wired genes to predict patients’ risk (c-index = 0.89). Finally, breast cancer-specific prognostic biomarkers of LINC01612, AC092142.1, and AC008969.1 were identified.

Conclusions

In summary new scoring method highlighted stage-specific core pathways for early-to-late progressions. Moreover, detecting the significant re-wired hub nodes indicated stage-associated traits, which reflects the importance of such regulators from different perspectives.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-021-01015-9.

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.

Personal Network Inference Unveils Heterogeneous Immune Response Patterns to Viral Infection in Children with Acute Wheezing

Human rhinovirus (RV)-induced exacerbations of asthma and wheeze are a major cause of emergency room presentations and hospital admissions among children. Previous studies have shown that immune response patterns during these exacerbations are heterogeneous and are characterized by the presence or absence of robust interferon responses. Molecular phenotypes of asthma are usually identified by cluster analysis of gene expression levels. This approach however is limited, since genes do not exist in isolation, but rather work together in networks. Here, we employed personal network inference to characterize exacerbation response patterns and unveil molecular phenotypes based on variations in network structure. We found that personal gene network patterns were dominated by two major network structures, consisting of interferon-response versus FCER1G-associated networks. Cluster analysis of these structures divided children into subgroups, differing in the prevalence of atopy but not RV species. These network structures were also observed in an independent cohort of children with virus-induced asthma exacerbations sampled over a time course, where we showed that the FCER1G-associated networks were mainly observed at late time points (days four-six) during the acute illness. The ratio of interferon- and FCER1G-associated gene network responses was able to predict recurrence, with low interferon being associated with increased risk of readmission. These findings demonstrate the applicability of personal network inference for biomarker discovery and therapeutic target identification in the context of acute asthma which focuses on variations in network structure.

Conditional transcriptional relationships may serve as cancer prognostic markers

BACKGROUND

While most differential coexpression (DC) methods are bound to quantify a single correlation value for a gene pair across multiple samples, a newly devised approach under the name Correlation by Individual Level Product (CILP) revolutionarily projects the summary correlation value to individual product correlation values for separate samples. CILP greatly widened DC analysis opportunities by allowing integration of non-compromised statistical methods.

METHODS

Here, we performed a study to verify our hypothesis that conditional relationships, i.e., gene pairs of remarkable differential coexpression, may be sought as quantitative prognostic markers for human cancers. Alongside the seeking of prognostic gene links in a pan-cancer setting, we also examined whether a trend of global expression correlation loss appeared in a wide panel of cancer types and revisited the controversial subject of mutual relationship between the DE approach and the DC approach.

RESULTS

By integrating CILP with classical univariate survival analysis, we identified up to 244 conditional gene links as potential prognostic markers in five cancer types. In particular, five prognostic gene links for kidney renal papillary cell carcinoma tended to condense around cancer gene ESPL1, and the transcriptional synchrony between ESPL1 and PTTG1 tended to be elevated in patients of adverse prognosis. In addition, we extended the observation of global trend of correlation loss in more than ten cancer types and empirically proved DC analysis results were independent of gene differential expression in five cancer types.

CONCLUSIONS

Combining the power of CILP and the classical survival analysis, we successfully fetched conditional transcriptional relationships that conferred prognosis power for five cancer types. Despite a general trend of global correlation loss in tumor transcriptomes, most of these prognosis conditional links demonstrated stronger expression correlation in tumors, and their stronger coexpression was associated with poor survival.

Deep Learning Analyses to Delineate the Molecular Remodeling Process after Myocardial Infarction

Specific proteins and processes have been identified in post-myocardial infarction (MI) pathological remodeling, but a comprehensive understanding of the complete molecular evolution is lacking. We generated microarray data from swine heart biopsies at baseline and 6, 30, and 45 days after infarction to feed machine-learning algorithms. We cross-validated the results using available clinical and experimental information. MI progression was accompanied by the regulation of adipogenesis, fatty acid metabolism, and epithelial-mesenchymal transition. The infarct core region was enriched in processes related to muscle contraction and membrane depolarization. Angiogenesis was among the first morphogenic responses detected as being sustained over time, but other processes suggesting post-ischemic recapitulation of embryogenic processes were also observed. Finally, protein-triggering analysis established the key genes mediating each process at each time point, as well as the complete adverse remodeling response. We modeled the behaviors of these genes, generating a description of the integrative mechanism of action for MI progression. This mechanistic analysis overlapped at different time points; the common pathways between the source proteins and cardiac remodeling involved IGF1R, RAF1, KPCA, JUN, and PTN11 as modulators. Thus, our data delineate a structured and comprehensive picture of the molecular remodeling process, identify new potential biomarkers or therapeutic targets, and establish therapeutic windows during disease progression.