Differential network biology

Biological network comparison identifies a novel synergistic mechanism of Ginseng Radix-Astragali Radix herb pair in cancer-related fatigue

ETHNOPHARMACOLOGICAL RELEVANCE

Ginseng Radix and Astragali Radix are commonly combined to tonify Qi and alleviate fatigue. Previous studies have employed biological networks to investigate the mechanisms of herb pairs in treating different diseases. However, these studies have only elucidated a single network for each herb pair, without emphasizing the superiority of the herb combination over individual herbs.

AIM OF THE STUDY

This study proposes an approach of comparing biological networks to highlight the synergistic effect of the pair in treating cancer-related fatigue (CRF).

METHODS

The compounds and targets of Ginseng Radix, Astragali Radix, and CRF diseases were collected and predicted using different databases. Subsequently, the overlapping targets between herbs and disease were imported into the STRING and DAVID tools to build protein-protein interaction (PPI) networks and analyze enriched KEGG pathways. The biological networks of Ginseng Radix and Astragali Radix were compared separately or together using the DyNet application. Molecular docking was used to verify the predicted results. Further, in vitro experiments were conducted to validate the synergistic pathways identified in in silico studies.

RESULTS

In the PPI network comparison, the combination created 89 new interactions and an increased average degree (11.260) when compared to single herbs (10.296 and 9.394). The new interactions concentrated on HRAS, STAT3, JUN, and IL6. The topological analysis identified 20 core targets of the combination, including three Ginseng Radix-specific targets, three Astragali Radix-specific targets, and 14 shared targets. In KEGG enrichment analysis, the combination regulated additional signaling pathways (152) more than Ginseng Radix (146) and Astragali Radix (134) alone. The targets of the herb pair synergistically regulated cancer pathways, specifically hypoxia-inducible factor 1 (HIF-1) signaling pathway. In vitro experiments including enzyme-linked immunosorbent assay and Western blot demonstrated that two herbs combination could up-regulate HIF-1α signaling pathway at different combined concentrations compared to either single herb alone.

CONCLUSION

The herb pair increased protein interactions and adjusted metabolic pathways more than single herbs. This study provides insights into the combination of Ginseng Radix and Astragali Radix in clinical practice.

Transcriptomic Module Discovery of Diarrhea-Predominant Irritable Bowel Syndrome: A Causal Network Inference Approach

Irritable bowel syndrome with diarrhea (IBS-D) is the most prevalent subtype of IBS, characterized by chronic gastrointestinal symptoms in the absence of identifiable pathological findings. This study aims to investigate the molecular mechanisms underlying IBS-D using transcriptomic data. By employing causal network inference methods, we identify key transcriptomic modules associated with IBS-D. Utilizing data from public databases and applying advanced computational techniques, we uncover potential biomarkers and therapeutic targets. Our analysis reveals significant molecular alterations that affect cellular functions, offering new insights into the complex pathophysiology of IBS-D. These findings enhance our understanding of the disease and may foster the development of more effective treatments.

ScreenDMT reveals DiHOMEs are replicably inversely associated with BMI and stimulate adipocyte calcium influx

Activating brown adipose tissue (BAT) improves systemic metabolism, making it a promising target for metabolic syndrome. BAT is activated by 12,13-dihydroxy-9Z-octadecenoic acid (12,13-diHOME), which we previously identified to be inversely associated with BMI and which directly improves metabolism in multiple tissues. Here we profile plasma lipidomics from 83 people and test which lipids' association with BMI replicates in a concordant direction using our novel tool ScreenDMT, whose power and validity we demonstrate via mathematical proofs and simulations. We find that the linoleic acid diols 12,13-diHOME and 9,10-diHOME are both replicably inversely associated with BMI and mechanistically activate calcium influx in mouse brown and white adipocytes in vitro, which implicates this signaling pathway and 9,10-diHOME as candidate therapeutic targets. ScreenDMT can be applied to test directional mediation, directional replication, and qualitative interactions, such as identifying biomarkers whose association is shared (replication) or opposite (qualitative interaction) across diverse populations.

Detecting responsible nodes in differential Bayesian networks

To study the roles that different nodes play in differentiating Bayesian networks under two states, such as control versus disease, we formulate two node-specific scores to facilitate such assessment. The first score is motivated by the prediction invariance property of a causal model. The second score results from modifying an existing score constructed for differential analysis of undirected networks. We develop strategies based on these scores to identify nodes responsible for topological differences between two Bayesian networks. Synthetic data and real-life data from designed experiments are used to demonstrate the efficacy of the proposed methods in detecting responsible nodes.

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.

Global Interactome Mapping Reveals Pro-tumorigenic Interactions of NF-κB in Breast Cancer

NF-κB pathway is involved in inflammation; however, recent data shows its role also in cancer development and progression, including metastasis. To understand the role of NF-κB interactome dynamics in cancer, we study the complexity of breast cancer interactome in luminal A breast cancer model and its rearrangement associated with NF-κB modulation. Liquid chromatography-mass spectrometry measurement of 160 size-exclusion chromatography fractions identifies 5460 protein groups. Seven thousand five hundred sixty eight interactions among these proteins have been reconstructed by PrInCE algorithm, of which 2564 have been validated in independent datasets. NF-κB modulation leads to rearrangement of protein complexes involved in NF-κB signaling and immune response, cell cycle regulation, and DNA replication. Central NF-κB transcription regulator RELA co-elutes with interactors of NF-κB activator PRMT5, and these complexes are confirmed by AlphaPulldown prediction. A complementary immunoprecipitation experiment recapitulates RELA interactions with other NF-κB factors, associating NF-κB inhibition with lower binding of NF-κB activators to RELA. This study describes a network of pro-tumorigenic protein interactions and their rearrangement upon NF-κB inhibition with potential therapeutic implications in tumors with high NF-κB activity.

Statistical inference on qualitative differences in the magnitude of an effect

Qualitative interactions occur when a treatment effect or measure of association varies in sign by sub-population. Of particular interest in many biomedical settings are absence/presence qualitative interactions, which occur when an effect is present in one sub-population but absent in another. Absence/presence interactions arise in emerging applications in precision medicine, where the objective is to identify a set of predictive biomarkers that have prognostic value for clinical outcomes in some sub-population but not others. They also arise naturally in gene regulatory network inference, where the goal is to identify differences in networks corresponding to diseased and healthy individuals, or to different subtypes of disease; such differences lead to identification of network-based biomarkers for diseases. In this paper, we argue that while the absence/presence hypothesis is important, developing a statistical test for this hypothesis is an intractable problem. To overcome this challenge, we approximate the problem in a novel inference framework. In particular, we propose to make inferences about absence/presence interactions by quantifying the relative difference in effect size, reasoning that when the relative difference is large, an absence/presence interaction occurs. The proposed methodology is illustrated through a simulation study as well as an analysis of breast cancer data from the Cancer Genome Atlas.

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.

Bile acid fitness determinants of a Bacteroides fragilis isolate from a human pouchitis patient

IMPORTANCE

The Gram-negative bacterium Bacteroides fragilis is a common member of the human gut microbiota that colonizes multiple host niches and can influence human physiology through a variety of mechanisms. Identification of genes that enable B. fragilis to grow across a range of host environments has been impeded in part by the relatively limited genetic tractability of this species. We have developed a high-throughput genetic resource for a B. fragilis strain isolated from a UC pouchitis patient. Bile acids limit microbial growth and are altered in abundance in UC pouches, where B. fragilis often blooms. Using this resource, we uncovered pathways and processes that impact B. fragilis fitness in bile and that may contribute to population expansions during bouts of gut inflammation.

EGFR-T790M Mutation-Derived Interactome Rerouted EGFR Translocation Contributing to Gefitinib Resistance in Non-Small Cell Lung Cancer

Secondary mutation, T790M, conferring tyrosine kinase inhibitors (TKIs) resistance beyond oncogenic epidermal growth factor receptor (EGFR) mutations presents a challenging unmet need. Although TKI-resistant mechanisms are intensively investigated, the underlying responses of cancer cells adapting drug perturbation are largely unknown. To illuminate the molecular basis linking acquired mutation to TKI resistance, affinity purification coupled mass spectrometry was adopted to dissect EGFR interactome in TKI-sensitive and TKI-resistant non-small cell lung cancer cells. The analysis revealed TKI-resistant EGFR-mutant interactome allocated in diverse subcellular distribution and enriched in endocytic trafficking, in which gefitinib intervention activated autophagy-mediated EGFR degradation and thus autophagy inhibition elevated gefitinib susceptibility. Alternatively, gefitinib prompted TKI-sensitive EGFR translocating toward cell periphery through Rab7 ubiquitination which may favor efficacy to TKIs suppression. This study revealed that T790M mutation rewired EGFR interactome that guided EGFR to autophagy-mediated degradation to escape treatment, suggesting that combination therapy with TKI and autophagy inhibitor may overcome acquired resistance in non-small cell lung cancer.

ScreenDMT reveals linoleic acid diols replicably associate with BMI and stimulate adipocyte calcium fluxes

Activating brown adipose tissue (BAT) improves systemic metabolism, making it a promising target for metabolic syndrome. BAT is activated by 12, 13-dihydroxy-9Z-octadecenoic acid (12, 13-diHOME), which we previously identified to be inversely associated with BMI and which directly improves metabolism in multiple tissues. Here we profile plasma lipidomics from a cohort of 83 people and test which lipids' association with BMI replicates in a concordant direction using our novel tool ScreenDMT, whose power and validity we demonstrate via mathematical proofs and simulations. We find that the linoleic acid diols 12, 13-diHOME and 9, 10-diHOME both replicably inversely associate with BMI and mechanistically activate calcium fluxes in mouse brown and white adipocytes in vitro, which implicates this pathway and 9, 10-diHOME as candidate therapeutic targets. ScreenDMT can be applied to test directional mediation, directional replication, and qualitative interactions, such as identifying biomarkers whose association is shared (replication) or opposite (qualitative interaction) across diverse populations.

MOBILE pipeline enables identification of context-specific networks and regulatory mechanisms

Robust identification of context-specific network features that control cellular phenotypes remains a challenge. We here introduce MOBILE (Multi-Omics Binary Integration via Lasso Ensembles) to nominate molecular features associated with cellular phenotypes and pathways. First, we use MOBILE to nominate mechanisms of interferon-γ (IFNγ) regulated PD-L1 expression. Our analyses suggest that IFNγ-controlled PD-L1 expression involves BST2, CLIC2, FAM83D, ACSL5, and HIST2H2AA3 genes, which were supported by prior literature. We also compare networks activated by related family members transforming growth factor-beta 1 (TGFβ1) and bone morphogenetic protein 2 (BMP2) and find that differences in ligand-induced changes in cell size and clustering properties are related to differences in laminin/collagen pathway activity. Finally, we demonstrate the broad applicability and adaptability of MOBILE by analyzing publicly available molecular datasets to investigate breast cancer subtype specific networks. Given the ever-growing availability of multi-omics datasets, we envision that MOBILE will be broadly useful for identification of context-specific molecular features and pathways.

A multi-scale map of protein assemblies in the DNA damage response

The DNA damage response (DDR) ensures error-free DNA replication and transcription and is disrupted in numerous diseases. An ongoing challenge is to determine the proteins orchestrating DDR and their organization into complexes, including constitutive interactions and those responding to genomic insult. Here, we use multi-conditional network analysis to systematically map DDR assemblies at multiple scales. Affinity purifications of 21 DDR proteins, with/without genotoxin exposure, are combined with multi-omics data to reveal a hierarchical organization of 605 proteins into 109 assemblies. The map captures canonical repair mechanisms and proposes new DDR-associated proteins extending to stress, transport, and chromatin functions. We find that protein assemblies closely align with genetic dependencies in processing specific genotoxins and that proteins in multiple assemblies typically act in multiple genotoxin responses. Follow-up by DDR functional readouts newly implicates 12 assembly members in double-strand-break repair. The DNA damage response assemblies map is available for interactive visualization and query (ccmi.org/ddram/).

Genetic epidemiology of resistance to M. tuberculosis Infection: importance of study design and recent findings

Resistance to M. tuberculosis, often referred to as "RSTR" in the literature, is being increasingly studied because of its potential relevance as a clinical outcome in vaccine studies. This review starts by addressing the importance of epidemiological characterization of this phenotype, and ongoing challenges in that characterization. Then, this review summarizes the extant genetic and genomic studies of this phenotype, including heritability studies, candidate gene studies, and genome-wide association studies, as well as whole transcriptome studies. Findings from recent studies that used longitudinal characterization of the RSTR phenotype are compared to those using a cross-sectional definition, and the challenges of using tuberculin skin test and interferon-gamma release assay are discussed. Finally, future directions are proposed. Since this is a rapidly evolving area of public health significance, this review will help frame future research questions and study designs.

Rewiring of miRNA-mRNA bipartite co-expression network as a novel way to understand the prostate cancer related players

The differential expression and direct targeting of mRNA by miRNA are two main logics of the traditional approach to constructing the miRNA-mRNA network. This approach, could be led to the loss of considerable information and some challenges of direct targeting. To avoid these problems, we analyzed the rewiring network and constructed two miRNA-mRNA expression bipartite networks for both normal and primary prostate cancer tissue obtained from PRAD-TCGA. We then calculated beta-coefficient of the regression-model when miR was dependent and mRNA independent for each miR and mRNA and separately in both networks. We defined the rewired edges as a significant change in the regression coefficient between normal and cancer states. The rewired nodes through multinomial distribution were defined and network from rewired edges and nodes was analyzed and enriched. Of the 306 rewired edges, 112(37%) were new, 123(40%) were lost, 44(14%) were strengthened, and 27(9%) weakened connections were discovered. The highest centrality of 106 rewired mRNAs belonged to PGM5, BOD1L1, C1S, SEPG, TMEFF2, and CSNK2A1. The highest centrality of 68 rewired miRs belonged to miR-181d, miR-4677, miR-4662a, miR-9.3, and miR-1301. SMAD and beta-catenin binding were enriched as molecular functions. The regulation was a frequently repeated concept in the biological process. Our rewiring analysis highlighted the impact of β-catenin and SMAD signaling as also some transcript factors like TGFB1I1 in prostate cancer progression. Altogether, we developed a miRNA-mRNA co-expression bipartite network to identify the hidden aspects of the prostate cancer mechanism, which traditional analysis -like differential expression- was not detect it.

Network modeling helps to tackle the complexity of drug-disease systems

From the (patho)physiological point of view, diseases can be considered as emergent properties of living systems stemming from the complexity of these systems. Complex systems display some typical features, including the presence of emergent behavior and the organization in successive hierarchic levels. Drug treatments increase this complexity scenario, and from some years the use of network models has been introduced to describe drug-disease systems and to make predictions about them with regard to several aspects related to drug discovery. Here, we review some recent examples thereof with the aim to illustrate how network science tools can be very effective in addressing both tasks. We will examine the use of bipartite networks that lead to the important concept of "disease module", as well as the introduction of more articulated models, like multi-scale and multiplex networks, able to describe disease systems at increasing levels of organization. Examples of predictive models will then be discussed, considering both those that exploit approaches purely based on graph theory and those that integrate machine learning methods. A short account of both kinds of methodological applications will be provided. Finally, the point will be made on the present situation of modeling complex drug-disease systems highlighting some open issues. This article is categorized under: Neurological Diseases > Computational Models Infectious Diseases > Computational Models Cardiovascular Diseases > Computational Models.

PLA-GNN: Computational inference of protein subcellular location alterations under drug treatments with deep graph neural networks

The aberrant protein sorting has been observed in many conditions, including complex diseases, drug treatments, and environmental stresses. It is important to systematically identify protein mis-localization events in a given condition. Experimental methods for finding mis-localized proteins are always costly and time consuming. Predicting protein subcellular localizations has been studied for many years. However, only a handful of existing works considered protein subcellular location alterations. We proposed a computational method for identifying alterations of protein subcellular locations under drug treatments. We took three drugs, including TSA (trichostain A), bortezomib and tacrolimus, as instances for this study. By introducing dynamic protein-protein interaction networks, graph neural network algorithms were applied to aggregate topological information under different conditions. We systematically reported potential protein mis-localization events under drug treatments. As far as we know, this is the first attempt to find protein mis-localization events computationally in drug treatment conditions. Literatures validated that a number of proteins, which are highly related to pharmacological mechanisms of these drugs, may undergo protein localization alterations. We name our method as PLA-GNN (Protein Localization Alteration by Graph Neural Networks). It can be extended to other drugs and other conditions. All datasets and codes of this study has been deposited in a GitHub repository (https://github.com/quinlanW/PLA-GNN).

The coral microbiome: towards an understanding of the molecular mechanisms of coral-microbiota interactions

Corals live in a complex, multipartite symbiosis with diverse microbes across kingdoms, some of which are implicated in vital functions, such as those related to resilience against climate change. However, knowledge gaps and technical challenges limit our understanding of the nature and functional significance of complex symbiotic relationships within corals. Here, we provide an overview of the complexity of the coral microbiome focusing on taxonomic diversity and functions of well-studied and cryptic microbes. Mining the coral literature indicate that while corals collectively harbour a third of all marine bacterial phyla, known bacterial symbionts and antagonists of corals represent a minute fraction of this diversity and that these taxa cluster into select genera, suggesting selective evolutionary mechanisms enabled these bacteria to gain a niche within the holobiont. Recent advances in coral microbiome research aimed at leveraging microbiome manipulation to increase coral's fitness to help mitigate heat stress-related mortality are discussed. Then, insights into the potential mechanisms through which microbiota can communicate with and modify host responses are examined by describing known recognition patterns, potential microbially derived coral epigenome effector proteins and coral gene regulation. Finally, the power of omics tools used to study corals are highlighted with emphasis on an integrated host-microbiota multiomics framework to understand the underlying mechanisms during symbiosis and climate change-driven dysbiosis.

Identification of novel miRNAs potentially involved in the pathogenesis of adult T-cell leukemia/lymphoma using WGCNA followed by RT-qPCR test of hub genes

BACKGROUND

Adult T-cell Lymphoma/Leukemia (ATLL) is characterized by the malignant proliferation of T-cells in Human T-Lymphotropic Virus Type 1 and a high mortality rate. Considering the emerging roles of microRNAs (miRNAs) in various malignancies, the analysis of high-throughput miRNA data employing computational algorithms helps to identify potential biomarkers.

METHODS

Weighted gene co-expression network analysis was utilized to analyze miRNA microarray data from ATLL and healthy uninfected samples. To identify miRNAs involved in the progression of ATLL, module preservation analysis was used. Subsequently, based on the target genes of the identified miRNAs, the STRING database was employed to construct protein-protein interaction networks (PPIN). Real-time quantitative PCR was also performed to validate the expression of identified hub genes in the PPIN network.

RESULTS

After constructing co-expression modules and then performing module preservation analysis, four out of 15 modules were determined as ATLL-specific modules. Next, the hub miRNA including hsa-miR-18a-3p, has-miR-187-5p, hsa-miR-196a-3p, and hsa-miR-346 were found as hub miRNAs. The protein-protein interaction networks were constructed for the target genes of each hub miRNA and hub genes were identified. Among them, UBB, RPS15A, and KMT2D were validated by Reverse-transcriptase PCR in ATLL patients.

CONCLUSION

The results of the network analysis of miRNAs and their target genes revealed the major players in the pathogenesis of ATLL. Further studies are required to confirm the role of these molecular factors and to discover their potential benefits as treatment targets and diagnostic biomarkers.

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.

Causal Structural Learning via Local Graphs

We consider the problem of learning causal structures in sparse high-dimensional settings that may be subject to the presence of (potentially many) unmeasured confounders, as well as selection bias. Based on structure found in common families of large random networks, we propose a new local notion of sparsity for structure learning in the presence of latent and selection variables, and develop a new version of the Fast Causal Inference (FCI) algorithm, which we refer to as local FCI (lFCI). Under the new sparsity condition and an additional assumption that ensures that conditional dependencies can be determined locally, lFCI is consistent and offers reduced computational and sample complexity when compared to standard FCI algorithms. The new notion of sparsity allows the presence of highly connected hub nodes, which are common in real-world networks, but problematic for existing methods. Our numerical experiments indicate that the lFCI algorithm achieves state-of-the-art performance across many classes of large random networks, and its performance is superior to that of existing methods for networks containing hub nodes.

Network-based approaches for modeling disease regulation and progression

Molecular interaction networks lay the foundation for studying how biological functions are controlled by the complex interplay of genes and proteins. Investigating perturbed processes using biological networks has been instrumental in uncovering mechanisms that underlie complex disease phenotypes. Rapid advances in omics technologies have prompted the generation of high-throughput datasets, enabling large-scale, network-based analyses. Consequently, various modeling techniques, including network enrichment, differential network extraction, and network inference, have proven to be useful for gaining new mechanistic insights. We provide an overview of recent network-based methods and their core ideas to facilitate the discovery of disease modules or candidate mechanisms. Knowledge generated from these computational efforts will benefit biomedical research, especially drug development and precision medicine. We further discuss current challenges and provide perspectives in the field, highlighting the need for more integrative and dynamic network approaches to model disease development and progression.

NETWORK DIFFERENTIAL CONNECTIVITY ANALYSIS

Identifying differences in networks has become a canonical problem in many biological applications. Existing methods try to accomplish this goal by either directly comparing the estimated structures of two networks, or testing the null hypothesis that the covariance or inverse covariance matrices in two populations are identical. However, estimation approaches do not provide measures of uncertainty, e.g., p-values, whereas existing testing approaches could lead to misleading results, as we illustrate in this paper. To address these shortcomings, we propose a qualitative hypothesis testing framework, which tests whether the connectivity structures in the two networks are the same. our framework is especially appropriate if the goal is to identify nodes or edges that are differentially connected. No existing approach could test such hypotheses and provide corresponding measures of uncertainty. Theoretically, we show that under appropriate conditions, our proposal correctly controls the type-I error rate in testing the qualitative hypothesis. Empirically, we demonstrate the performance of our proposal using simulation studies and applications in cancer genomics.

NetTDP: permutation-based true discovery proportions for differential co-expression network analysis

Existing methods for differential network analysis could only infer whether two networks of interest have differences between two groups of samples, but could not quantify and localize network differences. In this work, a novel method, permutation-based Network True Discovery Proportions (NetTDP), is proposed to quantify the number of edges (correlations) or nodes (genes) for which the co-expression networks are different. In the NetTDP method, we propose an edge-level statistic and a node-level statistic, and detect true discoveries of edges and nodes in the sense of differential co-expression network, respectively, by the permutation-based sumSome method. Furthermore, the NetTDP method could further localize the differences by inferring the TDPs for edge or gene subsets of interest, which can be selected post hoc. Our NetTDP method allows inference on data-driven modules or biology-driven gene sets, and remains valid even when these sub-networks are optimized using the same data. Experimental results on both simulation data sets and five real data sets show the effectiveness of the proposed method in inferring the quantification and localization of differential co-expression networks. The R code is available at https://github.com/LiminLi-xjtu/NetTDP.

The GR2D2 estimator for the precision matrices

Biological networks are important for the analysis of human diseases, which summarize the regulatory interactions and other relationships between different molecules. Understanding and constructing networks for molecules, such as DNA, RNA and proteins, can help elucidate the mechanisms of complex biological systems. The Gaussian Graphical Models (GGMs) are popular tools for the estimation of biological networks. Nonetheless, reconstructing GGMs from high-dimensional datasets is still challenging. The current methods cannot handle the sparsity and high-dimensionality issues arising from datasets very well. Here, we developed a new GGM, called the GR2D2 (Graphical $R^2$-induced Dirichlet Decomposition) model, based on the R2D2 priors for linear models. Besides, we provided a data-augmented block Gibbs sampler algorithm. The R code is available at https://github.com/RavenGan/GR2D2. The GR2D2 estimator shows superior performance in estimating the precision matrices compared with the existing techniques in various simulation settings. When the true precision matrix is sparse and of high dimension, the GR2D2 provides the estimates with smallest information divergence from the underlying truth. We also compare the GR2D2 estimator with the graphical horseshoe estimator in five cancer RNA-seq gene expression datasets grouped by three cancer types. Our results show that GR2D2 successfully identifies common cancer pathways and cancer-specific pathways for each dataset.

SpaceX: gene co-expression network estimation for spatial transcriptomics

MOTIVATION

The analysis of spatially resolved transcriptome enables the understanding of the spatial interactions between the cellular environment and transcriptional regulation. In particular, the characterization of the gene-gene co-expression at distinct spatial locations or cell types in the tissue enables delineation of spatial co-regulatory patterns as opposed to standard differential single gene analyses. To enhance the ability and potential of spatial transcriptomics technologies to drive biological discovery, we develop a statistical framework to detect gene co-expression patterns in a spatially structured tissue consisting of different clusters in the form of cell classes or tissue domains.

RESULTS

We develop SpaceX (spatially dependent gene co-expression network), a Bayesian methodology to identify both shared and cluster-specific co-expression network across genes. SpaceX uses an over-dispersed spatial Poisson model coupled with a high-dimensional factor model which is based on a dimension reduction technique for computational efficiency. We show via simulations, accuracy gains in co-expression network estimation and structure by accounting for (increasing) spatial correlation and appropriate noise distributions. In-depth analysis of two spatial transcriptomics datasets in mouse hypothalamus and human breast cancer using SpaceX, detected multiple hub genes which are related to cognitive abilities for the hypothalamus data and multiple cancer genes (e.g. collagen family) from the tumor region for the breast cancer data.

AVAILABILITY AND IMPLEMENTATION

The SpaceX R-package is available at github.com/bayesrx/SpaceX.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

DrDimont: explainable drug response prediction from differential analysis of multi-omics networks

MOTIVATION

While it has been well established that drugs affect and help patients differently, personalized drug response predictions remain challenging. Solutions based on single omics measurements have been proposed, and networks provide means to incorporate molecular interactions into reasoning. However, how to integrate the wealth of information contained in multiple omics layers still poses a complex problem.

RESULTS

We present DrDimont, Drug response prediction from Differential analysis of multi-omics networks. It allows for comparative conclusions between two conditions and translates them into differential drug response predictions. DrDimont focuses on molecular interactions. It establishes condition-specific networks from correlation within an omics layer that are then reduced and combined into heterogeneous, multi-omics molecular networks. A novel semi-local, path-based integration step ensures integrative conclusions. Differential predictions are derived from comparing the condition-specific integrated networks. DrDimont's predictions are explainable, i.e. molecular differences that are the source of high differential drug scores can be retrieved. We predict differential drug response in breast cancer using transcriptomics, proteomics, phosphosite and metabolomics measurements and contrast estrogen receptor positive and receptor negative patients. DrDimont performs better than drug prediction based on differential protein expression or PageRank when evaluating it on ground truth data from cancer cell lines. We find proteomic and phosphosite layers to carry most information for distinguishing drug response.

AVAILABILITY AND IMPLEMENTATION

DrDimont is available on CRAN: https://cran.r-project.org/package=DrDimont.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A Network Approach to Genetic Circuit Designs

As genetic circuits become more sophisticated, the size and complexity of data about their designs increase. The data captured goes beyond genetic sequences alone; information about circuit modularity and functional details improves comprehension, performance analysis, and design automation techniques. However, new data types expose new challenges around the accessibility, visualization, and usability of design data (and metadata). Here, we present a method to transform circuit designs into networks and showcase its potential to enhance the utility of design data. Since networks are dynamic structures, initial graphs can be interactively shaped into subnetworks of relevant information based on requirements such as the hierarchy of biological parts or interactions between entities. A significant advantage of a network approach is the ability to scale abstraction, providing an automatic sliding level of detail that further tailors the visualization to a given situation. Additionally, several visual changes can be applied, such as coloring or clustering nodes based on types (e.g., genes or promoters), resulting in easier comprehension from a user perspective. This approach allows circuit designs to be coupled to other networks, such as metabolic pathways or implementation protocols captured in graph-like formats. We advocate using networks to structure, access, and improve synthetic biology information.

ceRNAR: An R package for identification and analysis of ceRNA-miRNA triplets

Competitive endogenous RNA (ceRNA) represents a novel mechanism of gene regulation that controls several biological and pathological processes. Recently, an increasing number of in silico methods have been developed to accelerate the identification of such regulatory events. However, there is still a need for a tool supporting the hypothesis that ceRNA regulatory events only occur at specific miRNA expression levels. To this end, we present an R package, ceRNAR, which allows identification and analysis of ceRNA-miRNA triplets via integration of miRNA and RNA expression data. The ceRNAR package integrates three main steps: (i) identification of ceRNA pairs based on a rank-based correlation between pairs that considers the impact of miRNA and a running sum correlation statistic, (ii) sample clustering based on gene-gene correlation by circular binary segmentation, and (iii) peak merging to identify the most relevant sample patterns. In addition, ceRNAR also provides downstream analyses of identified ceRNA-miRNA triplets, including network analysis, functional annotation, survival analysis, external validation, and integration of different tools. The performance of our proposed approach was validated through simulation studies of different scenarios. Compared with several published tools, ceRNAR was able to identify true ceRNA triplets with high sensitivity, low false-positive rates, and acceptable running time. In real data applications, the ceRNAs common to two lung cancer datasets were identified in both datasets. The bridging miRNA for one of these, the ceRNA for MAP4K3, was identified by ceRNAR as hsa-let-7c-5p. Since similar cancer subtypes do share some biological patterns, these results demonstrated that our proposed algorithm was able to identify potential ceRNA targets in real patients. In summary, ceRNAR offers a novel algorithm and a comprehensive pipeline to identify and analyze ceRNA regulation. The package is implemented in R and is available on GitHub (https://github.com/ywhsiao/ceRNAR).

Polypeptide N-acetylgalactosamine transferase 3: a post-translational writer on human health

Polypeptide N-acetylgalactosamine transferase 3 (ppGalNAc-T3) is an enzyme involved in the initiation of O-GalNAc glycan biosynthesis. Acting as a writer of frequent post-translational modification (PTM) on human proteins, ppGalNAc-T3 has key functions in the homeostasis of human cells and tissues. We review the relevant roles of this molecule in the biosynthesis of O-GalNAc glycans, as well as in biological functions related to human physiological and pathological conditions. With main emphasis in ppGalNAc-T3, we draw attention to the different ways involved in the modulation of ppGalNAc-Ts enzymatic activity. In addition, we take notice on recent reports of ppGalNAc-T3 having different subcellular localizations, highlight critical intrinsic and extrinsic functions in cellular physiology that are exerted by ppGalNAc-T3-synthesized PTMs, and provide an update on several human pathologies associated with dysfunctional ppGalNAc-T3. Finally, we propose biotechnological tools as new therapeutic options for the treatment of pathologies related to altered ppGalNAc-T3. KEY MESSAGES: ppGalNAc-T3 is a key enzyme in the human O-GalNAc glycans biosynthesis. enzyme activity is regulated by PTMs, lectin domain and protein-protein interactions. ppGalNAc-T3 is located in human Golgi apparatus and cell nucleus. ppGalNAc-T3 has a central role in cell physiology as well as in several pathologies. Biotechnological tools for pathological management are proposed.

Identification and Validation of TRIM25 as a Glucose Metabolism Regulator in Prostate Cancer

Prostate cancer (PCa) malignant progression is accompanied with the reprogramming of glucose metabolism. However, the genes involved in the regulation of glucose metabolism in PCa are not fully understood. Here, we propose a new method, DMRG, which constructs a weighted differential network (W-K-DN) to define the important metabolism-related genes. Based on biological knowledge and prostate cancer transcriptome data, a tripartite motif-containing 25 (TRIM25) was defined using DMRG; TRIM25 was involved in the regulation of glucose metabolism, which was verified by overexpressing or knocking down TRIM25 in PCa cell lines. Differential expression analysis of TCA cycle enzymes revealed that TRIM25 regulated isocitrate dehydrogenase 1 (IDH1) and fumarate hydratase (FH) expression. Moreover, a protein–RNA interaction network of TRIM25 revealed that TRIM25 interacted with RNA-binding proteins, including DExH-box helicase 9 and DEAD-box helicase 5, to play a role in regulating the RNA processing of metabolic enzymes, including IDH1 and FH. Furthermore, TRIM25 expression level was found to be positively correlated with Gleason scores in PCa patient tissues. In conclusion, this study provides a new method to define genes influencing tumor progression, and sheds light on the role of the defined TRIM25 in regulating glucose metabolism and promoting PCa malignancy.

Connecting omics signatures and revealing biological mechanisms with iLINCS

There are only a few platforms that integrate multiple omics data types, bioinformatics tools, and interfaces for integrative analyses and visualization that do not require programming skills. Here we present iLINCS ( http://ilincs.org ), an integrative web-based platform for analysis of omics data and signatures of cellular perturbations. The platform facilitates mining and re-analysis of the large collection of omics datasets (>34,000), pre-computed signatures (>200,000), and their connections, as well as the analysis of user-submitted omics signatures of diseases and cellular perturbations. iLINCS analysis workflows integrate vast omics data resources and a range of analytics and interactive visualization tools into a comprehensive platform for analysis of omics signatures. iLINCS user-friendly interfaces enable execution of sophisticated analyses of omics signatures, mechanism of action analysis, and signature-driven drug repositioning. We illustrate the utility of iLINCS with three use cases involving analysis of cancer proteogenomic signatures, COVID 19 transcriptomic signatures and mTOR signaling.

Adrenergic signaling controls early transcriptional programs during CD8+ T cell responses to viral infection

Norepinephrine is a key sympathetic neurotransmitter, which acts to suppress CD8 + T cell cytokine secretion and lytic activity by signaling through the β2-adrenergic receptor (ADRB2). Although ADRB2 signaling is considered generally immunosuppressive, its role in regulating the differentiation of effector T cells in response to infection has not been investigated. Using an adoptive transfer approach, we compared the expansion and differentiation of wild type (WT) to Adrb2-/- CD8 + T cells throughout the primary response to vesicular stomatitis virus (VSV) infection in vivo. We measured the dynamic changes in transcriptome profiles of antigen-specific CD8 + T cells as they responded to VSV. Within the first 7 days of infection, WT cells out-paced the expansion of Adrb2-/- cells, which correlated with reduced expression of IL-2 and the IL-2Rα in the absence of ADRB2. RNASeq analysis identified over 300 differentially expressed genes that were both temporally regulated following infection and selectively regulated in WT vs Adrb2-/- cells. These genes contributed to major transcriptional pathways including cytokine receptor activation, signaling in cancer, immune deficiency, and neurotransmitter pathways. By parsing genes within groups that were either induced or repressed over time in response to infection, we identified three main branches of genes that were differentially regulated by the ADRB2. These gene sets were predicted to be regulated by specific transcription factors involved in effector T cell development, such as Tbx21 and Eomes. Collectively, these data demonstrate a significant role for ADRB2 signaling in regulating key transcriptional pathways during CD8 + T cells responses to infection that may dramatically impact their functional capabilities and downstream memory cell development.

SANA: cross-species prediction of Gene Ontology GO annotations via topological network alignment

Topological network alignment aims to align two networks node-wise in order to maximize the observed common connection (edge) topology between them. The topological alignment of two protein-protein interaction (PPI) networks should thus expose protein pairs with similar interaction partners allowing, for example, the prediction of common Gene Ontology (GO) terms. Unfortunately, no network alignment algorithm based on topology alone has been able to achieve this aim, though those that include sequence similarity have seen some success. We argue that this failure of topology alone is due to the sparsity and incompleteness of the PPI network data of almost all species, which provides the network topology with a small signal-to-noise ratio that is effectively swamped when sequence information is added to the mix. Here we show that the weak signal can be detected using multiple stochastic samples of "good" topological network alignments, which allows us to observe regions of the two networks that are robustly aligned across multiple samples. The resulting network alignment frequency (NAF) strongly correlates with GO-based Resnik semantic similarity and enables the first successful cross-species predictions of GO terms based on topology-only network alignments. Our best predictions have an AUPR of about 0.4, which is competitive with state-of-the-art algorithms, even when there is no observable sequence similarity and no known homology relationship. While our results provide only a "proof of concept" on existing network data, we hypothesize that predicting GO terms from topology-only network alignments will become increasingly practical as the volume and quality of PPI network data increase.

On the current failure-but bright future-of topology-driven biological network alignment

Since the function of a protein is defined by its interaction partners, and since we expect similar interaction patterns across species, the alignment of protein-protein interaction (PPI) networks between species, based on network topology alone, should uncover functionally related proteins across species. Surprisingly, despite the publication of more than fifty algorithms aimed at performing PPI network alignment, few have demonstrated a statistically significant link between network topology and functional similarity, and none have demonstrated that orthologs can be recovered using network topology alone. We find that the major contributing factors to this surprising failure are: (i) edge densities in most currently available experimental PPI networks are demonstrably too low to expect topological network alignment to succeed; (ii) in the few cases where the edge densities are high enough, some measures of topological similarity easily uncover functionally similar proteins while others do not; and (iii) most network alignment algorithms to date perform poorly at optimizing even their own topological objective functions, hampering their ability to use topology effectively. We demonstrate that SANA-the Simulated Annealing Network Aligner-significantly outperforms existing aligners at optimizing their own objective functions, even achieving near-optimal solutions when the optimal solution is known. We offer the first demonstration of global network alignments based on topology alone that align functionally similar proteins with p-values in some cases below 10^-300. We predict that topological network alignment has a bright future as edge densities increase toward the value where good alignments become possible. We demonstrate that when enough common topology is present at high enough edge densities-for example in the recent, partly synthetic networks of the Integrated Interaction Database-topological network alignment easily recovers most orthologs, paving the way toward high-throughput functional prediction based on topology-driven network alignment.

Dynamic rewiring of biological activity across genotype and lineage revealed by context-dependent functional interactions

BACKGROUND

Coessentiality networks derived from CRISPR screens in cell lines provide a powerful framework for identifying functional modules in the cell and for inferring the roles of uncharacterized genes. However, these networks integrate signal across all underlying data and can mask strong interactions that occur in only a subset of the cell lines analyzed.

RESULTS

Here, we decipher dynamic functional interactions by identifying significant cellular contexts, primarily by oncogenic mutation, lineage, and tumor type, and discovering coessentiality relationships that depend on these contexts. We recapitulate well-known gene-context interactions such as oncogene-mutation, paralog buffering, and tissue-specific essential genes, show how mutation rewires known signal transduction pathways, including RAS/RAF and IGF1R-PIK3CA, and illustrate the implications for drug targeting. We further demonstrate how context-dependent functional interactions can elucidate lineage-specific gene function, as illustrated by the maturation of proreceptors IGF1R and MET by proteases FURIN and CPD.

CONCLUSIONS

This approach advances our understanding of context-dependent interactions and how they can be gleaned from these data. We provide an online resource to explore these context-dependent interactions at diffnet.hart-lab.org.

Integrated Multi-Omics Analysis Model to Identify Biomarkers Associated With Prognosis of Breast Cancer

Background

With the rapid development and wide application of high-throughput sequencing technology, biomedical research has entered the era of large-scale omics data. We aim to identify genes associated with breast cancer prognosis by integrating multi-omics data.

Method

Gene-gene interactions were taken into account, and we applied two differential network methods JDINAC and LGCDG to identify differential genes. The patients were divided into case and control groups according to their survival time. The TCGA and METABRIC database were used as the training and validation set respectively.

Result

In the TCGA dataset, C11orf1, OLA1, RPL31, SPDL1 and IL33 were identified to be associated with prognosis of breast cancer. In the METABRIC database, ZNF273, ZBTB37, TRIM52, TSGA10, ZNF727, TRAF2, TSPAN17, USP28 and ZNF519 were identified as hub genes. In addition, RPL31, TMEM163 and ZNF273 were screened out in both datasets. GO enrichment analysis shows that most of these hub genes were involved in zinc ion binding.

Conclusion

In this study, a total of 15 hub genes associated with long-term survival of breast cancer were identified, which can promote understanding of the molecular mechanism of breast cancer and provide new insight into clinical research and treatment.

Discovery of pan-cancer related genes via integrative network analysis

Identification of cancer-related genes is helpful for understanding the pathogenesis of cancer, developing targeted drugs and creating new diagnostic and therapeutic methods. Considering the complexity of the biological laboratory methods, many network-based methods have been proposed to identify cancer-related genes at the global perspective with the increasing availability of high-throughput data. Some studies have focused on the tissue-specific cancer networks. However, cancers from different tissues may share common features, and those methods may ignore the differences and similarities across cancers during the establishment of modeling. In this work, in order to make full use of global information of the network, we first establish the pan-cancer network via differential network algorithm, which not only contains heterogeneous data across multiple cancer types but also contains heterogeneous data between tumor samples and normal samples. Second, the node representation vectors are learned by network embedding. In contrast to ranking analysis-based methods, with the help of integrative network analysis, we transform the cancer-related gene identification problem into a binary classification problem. The final results are obtained via ensemble classification. We further applied these methods to the most commonly used gene expression data involving six tissue-specific cancer types. As a result, an integrative pan-cancer network and several biologically meaningful results were obtained. As examples, nine genes were ultimately identified as potential pan-cancer-related genes. Most of these genes have been reported in published studies, thus showing our method's potential for application in identifying driver gene candidates for further biological experimental verification.

Identifying network biomarkers of cancer by sample-specific differential network

Abundant datasets generated from various big science projects on diseases have presented great challenges and opportunities, which contributed to unfolding the complexity of diseases. The discovery of disease-associated molecular networks for each individual plays an important role in personalized therapy and precision treatment of cancer-based on the reference networks. However, there are no effective ways to distinguish the consistency of different reference networks. In this study, we developed a statistical method, i.e. a sample-specific differential network (SSDN), to construct and analyze such networks based on gene expression of a single sample against a reference dataset. We proved that the SSDN is structurally consistent even with different reference datasets if the reference dataset can follow certain conditions. The SSDN also can be used to identify patient-specific disease modules or network biomarkers as well as predict the potential driver genes of a tumor sample.

tensorGSEA: Detecting Differential Pathways in Type 2 Diabetes via Tensor-Based Data Reconstruction

Detecting significant signaling pathways in disease progression highlights the dysfunctions and pathogenic mechanisms of complex disease development. Since tensor decomposition has been proven effective for multi-dimensional data representation and reconstruction, differences between original and tensor-processed data are expected to extract crucial information and differential indication. This paper provides a tensor-based gene set enrichment analysis, called tensorGSEA, based on a data reconstruction method to identify relevant significant pathways during disease development. As a proof-of-concept study, we identify the differential pathways of diabetes in rats. Specifically, we first arrange gene expression profiles of each documented pathway as tensors with three dimensions: genes, samples, and periods. Then we compress tensors into core tensors with lower ranks. The pathways with lower reconstruction rates are obtained after reconstructing gene expression profiles in another state via these cores. Thus, differences underlying pathways are extracted by cross-state data reconstruction between controls and diseases. The experiments reveal several critical pathways with diabetes-specific functions which otherwise cannot be identified by alternative methods. Our proposed tensorGSEA is efficient in evaluating pathways by achieving their empirical statistical significance, respectively. The classification experiments demonstrate that the selected pathways can be implemented as biomarkers to identify the diabetic state. The code of tensorGSEA is available at https://github.com/zhxr37/tensorGSEA .

Molecular Subtyping of Cancer Based on Distinguishing Co-Expression Modules and Machine Learning

Molecular subtyping of cancer is recognized as a critical and challenging step towards individualized therapy. Most existing computational methods solve this problem via multi-classification of gene-expressions of cancer samples. Although these methods, especially deep learning, perform well in data classification, they usually require large amounts of data for model training and have limitations in interpretability. Besides, as cancer is a complex systemic disease, the phenotypic difference between cancer samples can hardly be fully understood by only analyzing single molecules, and differential expression-based molecular subtyping methods are reportedly not conserved. To address the above issues, we present here a new framework for molecular subtyping of cancer through identifying a robust specific co-expression module for each subtype of cancer, generating network features for each sample by perturbing correlation levels of specific edges, and then training a deep neural network for multi-class classification. When applied to breast cancer (BRCA) and stomach adenocarcinoma (STAD) molecular subtyping, it has superior classification performance over existing methods. In addition to improving classification performance, we consider the specific co-expressed modules selected for subtyping to be biologically meaningful, which potentially offers new insight for diagnostic biomarker design, mechanistic studies of cancer, and individualized treatment plan selection.

Protein–Protein Interaction (PPI) Network of Zebrafish Oestrogen Receptors: A Bioinformatics Workflow

Protein–protein interaction (PPI) is involved in every biological process that occurs within an organism. The understanding of PPI is essential for deciphering the cellular behaviours in a particular organism. The experimental data from PPI methods have been used in constructing the PPI network. PPI network has been widely applied in biomedical research to understand the pathobiology of human diseases. It has also been used to understand the plant physiology that relates to crop improvement. However, the application of the PPI network in aquaculture is limited as compared to humans and plants. This review aims to demonstrate the workflow and step-by-step instructions for constructing a PPI network using bioinformatics tools and PPI databases that can help to predict potential interaction between proteins. We used zebrafish proteins, the oestrogen receptors (ERs) to build and analyse the PPI network. Thus, serving as a guide for future steps in exploring potential mechanisms on the organismal physiology of interest that ultimately benefit aquaculture research.

Combination of Enrichment Using Gene Ontology and Transcriptomic Analysis Revealed Contribution of Interferon Signaling to Severity of COVID-19

Introduction

The severity of coronavirus disease 2019 (COVID-19) was known to be affected by hyperinflammation. Identification of important proteins associated with hyperinflammation is critical. These proteins can be a potential target either as biomarkers or targets in drug discovery. Therefore, we combined enrichment analysis of these proteins to identify biological knowledge related to hyperinflammation. Moreover, we conducted transcriptomic data analysis to reveal genes contributing to disease severity.

Methods

We performed large-scale gene function analyses using gene ontology to identify significantly enriched biological processes, molecular functions, and cellular components associated with our proteins. One of the appropriate methods to functionally group large-scale protein-protein interaction (PPI) data into small-scale clusters is fuzzy K-partite clustering. We collected the transcriptomics data from GEO Database (GSE 164805 and GPL26963 platform). Moreover, we created a data set and analyzed gene expression using Orange Data-mining version 3.30. PPI analysis was performed using the STRING database with a confidence score >0.9.

Results

This study indicated that four proteins were associated with 25 molecular functions, three were associated with 22 cellular components, and one was associated with ten biological processes. All GOs of molecular function, cellular components, and 9 of 14 biological processes were associated with important cytokines related to the COVID-19 cytokine storm present in the resulting cluster. The expression analysis showed the interferon-related genes IFNAR1, IFI6, IFIT1, and IFIT3 were significant genes, whereas PPIs showed their interactions were closely related.

Conclusion

A combination of enrichment using GOs and transcriptomic analysis showed that hyperinflammation and severity of COVID-19 may be caused by interferon signaling.

Identification of Hub Genes With Differential Correlations in Sepsis

As a multifaceted syndrome, sepsis leads to high risk of death worldwide. It is difficult to be intervened due to insufficient biomarkers and potential targets. The reason is that regulatory mechanisms during sepsis are poorly understood. In this study, expression profiles of sepsis from GSE134347 were integrated to construct gene interaction network through weighted gene co-expression network analysis (WGCNA). R package DiffCorr was utilized to evaluate differential correlations and identify significant differences between sepsis and healthy tissues. As a result, twenty-six modules were detected in the network, among which blue and darkred modules exhibited the most significant associations with sepsis. Finally, we identified some novel genes with opposite correlations including ZNF366, ZMYND11, SVIP and UBE2H. Further biological analysis revealed their promising roles in sepsis management. Hence, differential correlations-based algorithm was firstly established for the discovery of appealing regulators in sepsis.

Forecasting Gastric Cancer Diagnosis, Prognosis, and Drug Repurposing with Novel Gene Expression Signatures

Gastric cancer (GC) is a prevalent disease worldwide with high mortality and poor treatment success. Early diagnosis of GC and forecasting of its prognosis with the use of biomarkers are directly relevant to achieve both personalized/precision medicine and innovation in cancer therapeutics. Gene expression signatures offer one of the promising avenues of research in this regard, as well as guiding drug repurposing analyses in cancers. Using publicly accessible gene expression datasets from the Gene Expression Omnibus and The Cancer Genome Atlas (TCGA), we report here original findings on co-expressed gene modules that are differentially expressed between 133 GC samples and 46 normal tissues, and thus hold potential for novel diagnostic candidates for GC. Furthermore, we found two co-expressed gene modules were significantly associated with poor survival outcomes revealed by survival analysis of the RNA-Seq TCGA datasets. We identified STAT6 (signal transducer and activator of transcription 6) as a key regulator of the identified gene modules. Finally, potential therapeutic drugs that may target and reverse the expression of the identified altered gene modules examined for drug repurposing analyses and the unraveled compounds were further investigated in the literature by the text mining method. Accordingly, we found several repurposed drug candidates, including Trichostatin A, Vorinostat, Parthenolide, Panobinostat, Brefeldin A, Belinostat, and Danusertib. Through text mining analysis and literature search validation, Belinostat and Danusertib were suggested as possible novel drug candidates for GC treatment. These findings collectively inform multiple aspects of GC medical management, including its precision diagnosis, forecasting of possible outcomes, and drug repurposing for innovation in GC medicines in the future.

Relating Translation Efficiency to Protein Networks Provides Evolutionary Insights in Shewanella and Its Implications for Extracellular Electron Transfer

Shewanella species are well-known for their extracellular electron transfer (EET) capacity, by which these microorganisms can transfer the electrons from intracellular environment to extracellular space for the reduction of the extracellular insoluble electron acceptors. Using a time-stamped data for the paired protein-mRNA, we investigate the impact of differential translation on the EET process of Shewanella oneidensis MR-1. Firstly, differentially translated proteins when O₂ levels are switched from high-O₂ to low-O₂ are identified by using a soft clustering method, 629 up-regulated translated proteins and 767 down-regulated translated proteins are considered to reflect the changes from inactivated to activated EET process. Then, we showed that the degrees of connectivity of differentially translated proteins were significantly larger than those of non-differentially translated proteins, and thereby these differentially translated proteins will be more important in the protein networks. After that, we networked these differentially translated proteins to construct the differentially translated sub-networks, and discussed the most important proteins that are involved in the EET process with the help of centralization analysis of these differentially translated networks. Furthermore, we also studied the differentially translated operonic genes. Taking together, this work searches the key proteins that potentially activated the EET process from a translational efficiency viewpoint.

Data analysis methods for defining biomarkers from omics data

Omics mainly includes genomics, epigenomics, transcriptomics, proteomics and metabolomics. The rapid development of omics technology has opened up new ways to study disease diagnosis and prognosis and to define prospective information of complex diseases. Since omics data are usually large and complex, the method used to analyze the data and to define important information is crucial in omics study. In this review, we focus on advances in biomarker discovery methods based on omics data in the last decade, and categorize them as individual feature analysis, combinatorial feature analysis and network analysis. We also discuss the challenges and perspectives in this field.