Gene co-expression analysis for functional classification and gene-disease predictions

Multi-layer Bundling as a New Approach for Determining Multi-scale Correlations Within a High-Dimensional Dataset

The growing complexity of biological data has spurred the development of innovative computational techniques to extract meaningful information and uncover hidden patterns within vast datasets. Biological networks, such as gene regulatory networks and protein-protein interaction networks, hold critical insights into biological features' connections and functions. Integrating and analyzing high-dimensional data, particularly in gene expression studies, stands prominent among the challenges in deciphering these networks. Clustering methods play a crucial role in addressing these challenges, with spectral clustering emerging as a potent unsupervised technique considering intrinsic geometric structures. However, spectral clustering's user-defined cluster number can lead to inconsistent and sometimes orthogonal clustering regimes. We propose the Multi-layer Bundling (MLB) method to address this limitation, combining multiple prominent clustering regimes to offer a comprehensive data view. We call the outcome clusters "bundles". This approach refines clustering outcomes, unravels hierarchical organization, and identifies bridge elements mediating communication between network components. By layering clustering results, MLB provides a global-to-local view of biological feature clusters enabling insights into intricate biological systems. Furthermore, the method enhances bundle network predictions by integrating the bundle co-cluster matrix with the affinity matrix. The versatility of MLB extends beyond biological networks, making it applicable to various domains where understanding complex relationships and patterns is needed.

The functional evolution of collembolan Ubx on the regulation of abdominal appendage formation

Folsomia candida is a tiny soil-living arthropod belonging to the Collembola, which is an outgroup to Insecta. It resembles insects as having a pair of antennae and three pairs of thorax legs, while it also possesses three abdominal appendages: a ventral tube located in the first abdominal segment (A1), a retinaculum in A3, and a furca in A4. Collembolan Ubx and AbdA specify abdominal appendages, but they are unable to repress appendage marker gene Dll. The genetic basis of collembolan appendage formation and the mechanisms by which Ubx and AbdA regulate Dll transcription and appendage development remains unknown. In this study, we analysed the developmental transcriptomes of F. candida and identified candidate appendage formation genes, including Ubx (FcUbx). The expression data revealed the dominance of Dll over Ubx during the embryonic 3.5 and 4.5 days, suggesting that Ubx is deficient in suppressing Dll at early appendage formation stages. Furthermore, via electrophoretic mobility shift assays and dual luciferase assays, we found that the binding and repression capacity of FcUbx on Drosophila Dll resembles those of the longest isoform of Drosophila Ubx (DmUbx_Ib), while the regulatory mechanism of the C-terminus of FcUbx on Dll repression is similar to that of the crustacean Artemia franciscana Ubx (AfUbx), demonstrating that the function of collembolan Ubx is intermediate between that of Insecta and Crustacea. In summary, our study provides novel insights into collembolan appendage formation and sheds light on the functional evolution of Ubx. Additionally, we propose a model that collembolan Ubx regulates abdominal segments in a context-specific manner.

Transcriptomics : Approaches to Quantifying Gene Expression and Their Application to Studying the Human Brain

To date, the field of transcriptomics has been characterized by rapid methods development and technological advancement, with new technologies continuously rendering older ones obsolete.This chapter traces the evolution of approaches to quantifying gene expression and provides an overall view of the current state of the field of transcriptomics, its applications to the study of the human brain, and its place in the broader emerging multiomics landscape.

Massively integrated coexpression analysis reveals transcriptional regulation, evolution and cellular implications of the yeast noncanonical translatome

BACKGROUND

Recent studies uncovered pervasive transcription and translation of thousands of noncanonical open reading frames (nORFs) outside of annotated genes. The contribution of nORFs to cellular phenotypes is difficult to infer using conventional approaches because nORFs tend to be short, of recent de novo origins, and lowly expressed. Here we develop a dedicated coexpression analysis framework that accounts for low expression to investigate the transcriptional regulation, evolution, and potential cellular roles of nORFs in Saccharomyces cerevisiae.

RESULTS

Our results reveal that nORFs tend to be preferentially coexpressed with genes involved in cellular transport or homeostasis but rarely with genes involved in RNA processing. Mechanistically, we discover that young de novo nORFs located downstream of conserved genes tend to leverage their neighbors' promoters through transcription readthrough, resulting in high coexpression and high expression levels. Transcriptional piggybacking also influences the coexpression profiles of young de novo nORFs located upstream of genes, but to a lesser extent and without detectable impact on expression levels. Transcriptional piggybacking influences, but does not determine, the transcription profiles of de novo nORFs emerging nearby genes. About 40% of nORFs are not strongly coexpressed with any gene but are transcriptionally regulated nonetheless and tend to form entirely new transcription modules. We offer a web browser interface ( https://carvunislab.csb.pitt.edu/shiny/coexpression/ ) to efficiently query, visualize, and download our coexpression inferences.

CONCLUSIONS

Our results suggest that nORF transcription is highly regulated. Our coexpression dataset serves as an unprecedented resource for unraveling how nORFs integrate into cellular networks, contribute to cellular phenotypes, and evolve.

Bayesian estimation of gene constraint from an evolutionary model with gene features

Measures of selective constraint on genes have been used for many applications, including clinical interpretation of rare coding variants, disease gene discovery and studies of genome evolution. However, widely used metrics are severely underpowered at detecting constraints for the shortest ~25% of genes, potentially causing important pathogenic mutations to be overlooked. Here we developed a framework combining a population genetics model with machine learning on gene features to enable accurate inference of an interpretable constraint metric, shet. Our estimates outperform existing metrics for prioritizing genes important for cell essentiality, human disease and other phenotypes, especially for short genes. Our estimates of selective constraint should have wide utility for characterizing genes relevant to human disease. Finally, our inference framework, GeneBayes, provides a flexible platform that can improve the estimation of many gene-level properties, such as rare variant burden or gene expression differences.

SGCP: a spectral self-learning method for clustering genes in co-expression networks

BACKGROUND

A widely used approach for extracting information from gene expression data employs the construction of a gene co-expression network and the subsequent computational detection of gene clusters, called modules. WGCNA and related methods are the de facto standard for module detection. The purpose of this work is to investigate the applicability of more sophisticated algorithms toward the design of an alternative method with enhanced potential for extracting biologically meaningful modules.

RESULTS

We present self-learning gene clustering pipeline (SGCP), a spectral method for detecting modules in gene co-expression networks. SGCP incorporates multiple features that differentiate it from previous work, including a novel step that leverages gene ontology (GO) information in a self-leaning step. Compared with widely used existing frameworks on 12 real gene expression datasets, we show that SGCP yields modules with higher GO enrichment. Moreover, SGCP assigns highest statistical importance to GO terms that are mostly different from those reported by the baselines.

CONCLUSION

Existing frameworks for discovering clusters of genes in gene co-expression networks are based on relatively simple algorithmic components. SGCP relies on newer algorithmic techniques that enable the computation of highly enriched modules with distinctive characteristics, thus contributing a novel alternative tool for gene co-expression analysis.

Systems genetics analysis of human body fat distribution genes identifies adipocyte processes

Excess abdominal fat is a sexually dimorphic risk factor for cardio-metabolic disease and is approximated by the waist-to-hip ratio adjusted for body mass index (WHRadjBMI). Whereas this trait is highly heritable, few causal genes are known. We aimed to identify novel drivers of WHRadjBMI using systems genetics. We used two independent cohorts of adipose tissue gene expression and constructed sex- and depot-specific Bayesian networks to model gene-gene interactions from 8,492 genes. Using key driver analysis, we identified genes that, in silico and putatively in vitro, regulate many others. 51-119 key drivers in each network were replicated in both cohorts. In other cell types, 23 of these genes are found in crucial adipocyte pathways: Wnt signaling or mitochondrial function. We overexpressed or down-regulated seven key driver genes in human subcutaneous pre-adipocytes. Key driver genes ANAPC2 and RSPO1 inhibited adipogenesis, whereas PSME3 increased adipogenesis. RSPO1 increased Wnt signaling activity. In differentiated adipocytes, MIGA1 and UBR1 down-regulation led to mitochondrial dysfunction. These five genes regulate adipocyte function, and we hypothesize that they regulate fat distribution.

LSTrAP-denovo: Automated Generation of Transcriptome Atlases for Eukaryotic Species Without Genomes

Despite the abundance of species with transcriptomic data, a significant number of species still lack sequenced genomes, making it difficult to study gene function and expression in these organisms. While de novo transcriptome assembly can be used to assemble protein-coding transcripts from RNA-sequencing (RNA-seq) data, the datasets used often only feature samples of arbitrarily selected or similar experimental conditions, which might fail to capture condition-specific transcripts. We developed the Large-Scale Transcriptome Assembly Pipeline for de novo assembled transcripts (LSTrAP-denovo) to automatically generate transcriptome atlases of eukaryotic species. Specifically, given an NCBI TaxID, LSTrAP-denovo can (1) filter undesirable RNA-seq accessions based on read data, (2) select RNA-seq accessions via unsupervised machine learning to construct a sample-balanced dataset for download, (3) assemble transcripts via over-assembly, (4) functionally annotate coding sequences (CDS) from assembled transcripts and (5) generate transcriptome atlases in the form of expression matrices for downstream transcriptomic analyses. LSTrAP-denovo is easy to implement, written in Python, and is freely available at https://github.com/pengkenlim/LSTrAP-denovo/.

Systems Genetics Analyses Reveals Key Genes Related to Behavioral Traits in the Striatum of CFW Mice

The striatum plays a central role in directing many complex behaviors ranging from motor control to action choice and reward learning. In our study, we used 55 male CFW mice with rapid decay linkage disequilibrium to systematically mine the striatum-related behavioral functional genes by analyzing their striatal transcriptomes and 79 measured behavioral phenotypic data. By constructing a gene coexpression network, we clustered the genes into 13 modules, with most of them being positively correlated with motor traits. Based on functional annotations as well as Fisher's exact and hypergeometric distribution tests, brown and magenta modules were identified as core modules. They were significantly enriched for striatal-related functional genes. Subsequent Mendelian randomization analysis verified the causal relationship between the core modules and dyskinesia. Through the intramodular gene connectivity analysis, Adcy5 and Kcnma1 were identified as brown and magenta module hub genes, respectively. Knock outs of both Adcy5 and Kcnma1 lead to motor dysfunction in mice, and KCNMA1 acts as a risk gene for schizophrenia and smoking addiction in humans. We also evaluated the cellular composition of each module and identified oligodendrocytes in the striatum to have a positive role in motor regulation.

Water deficit response in nodulated soybean roots: a comprehensive transcriptome and translatome network analysis

BACKGROUND

Soybean establishes a mutualistic interaction with nitrogen-fixing rhizobacteria, acquiring most of its nitrogen requirements through symbiotic nitrogen fixation. This crop is susceptible to water deficit; evidence suggests that its nodulation status-whether it is nodulated or not-can influence how it responds to water deficit. The translational control step of gene expression has proven relevant in plants subjected to water deficit.

RESULTS

Here, we analyzed soybean roots' differential responses to water deficit at transcriptional, translational, and mixed (transcriptional + translational) levels. Thus, the transcriptome and translatome of four combined-treated soybean roots were analyzed. We found hormone metabolism-related genes among the differentially expressed genes (DEGs) at the translatome level in nodulated and water-restricted plants. Also, weighted gene co-expression network analysis followed by differential expression analysis identified gene modules associated with nodulation and water deficit conditions. Protein-protein interaction network analysis was performed for subsets of mixed DEGs of the modules associated with the plant responses to nodulation, water deficit, or their combination.

CONCLUSIONS

Our research reveals that the stand-out processes and pathways in the before-mentioned plant responses partially differ; terms related to glutathione metabolism and hormone signal transduction (2 C protein phosphatases) were associated with the response to water deficit, terms related to transmembrane transport, response to abscisic acid, pigment metabolic process were associated with the response to nodulation plus water deficit. Still, two processes were common: galactose metabolism and branched-chain amino acid catabolism. A comprehensive analysis of these processes could lead to identifying new sources of tolerance to drought in soybean.

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.

De novo assembly of transcriptomes and differential gene expression analysis using short-read data from emerging model organisms - a brief guide

Many questions in biology benefit greatly from the use of a variety of model systems. High-throughput sequencing methods have been a triumph in the democratization of diverse model systems. They allow for the economical sequencing of an entire genome or transcriptome of interest, and with technical variations can even provide insight into genome organization and the expression and regulation of genes. The analysis and biological interpretation of such large datasets can present significant challenges that depend on the 'scientific status' of the model system. While high-quality genome and transcriptome references are readily available for well-established model systems, the establishment of such references for an emerging model system often requires extensive resources such as finances, expertise and computation capabilities. The de novo assembly of a transcriptome represents an excellent entry point for genetic and molecular studies in emerging model systems as it can efficiently assess gene content while also serving as a reference for differential gene expression studies. However, the process of de novo transcriptome assembly is non-trivial, and as a rule must be empirically optimized for every dataset. For the researcher working with an emerging model system, and with little to no experience with assembling and quantifying short-read data from the Illumina platform, these processes can be daunting. In this guide we outline the major challenges faced when establishing a reference transcriptome de novo and we provide advice on how to approach such an endeavor. We describe the major experimental and bioinformatic steps, provide some broad recommendations and cautions for the newcomer to de novo transcriptome assembly and differential gene expression analyses. Moreover, we provide an initial selection of tools that can assist in the journey from raw short-read data to assembled transcriptome and lists of differentially expressed genes.

Transcriptional response to an alternative diet on liver, muscle, and rumen of beef cattle

Feed cost represents a major economic determinant within cattle production, amounting to an estimated 75% of the total variable costs. Consequently, comprehensive approaches such as optimizing feed utilization through alternative feed sources, alongside the selection of feed-efficient animals, are of great significance. Here, we investigate the effect of two diets, traditional corn-grain fed and alternative by-product based, on 14 phenotypes related to feed, methane emission and production efficiency and on multi-tissue transcriptomics data from liver, muscle, and rumen wall, derived from 52 Nellore bulls, 26 on each diet. To this end, diets were contrasted at the level of phenotype, gene expression, and gene-phenotype network connectivity. As regards the phenotypic level, at a P value < 0.05, significant differences were found in favour of the alternative diet for average daily weight gain at finishing, dry matter intake at finishing, methane emission, carcass yield and subcutaneous fat thickness at the rib-eye muscle area. In terms of the transcriptional level of the 14,776 genes expressed across the examined tissues, we found 487, 484, and 499 genes differentially expressed due to diet in liver, muscle, and rumen, respectively (P value < 0.01). To explore differentially connected phenotypes across both diet-based networks, we focused on the phenotypes with the largest change in average number of connections within diets and tissues, namely methane emission and carcass yield, highlighting, in particular, gene expression changes involving SREBF2, and revealing the largest differential connectivity in rumen and muscle, respectively. Similarly, from examination of differentially connected genes across diets, the top-ranked most differentially connected regulators within each tissue were MEOX1, PTTG1, and BASP1 in liver, muscle, and rumen, respectively. Changes in gene co-expression patterns suggest activation or suppression of specific biological processes and pathways in response to dietary interventions, consequently impacting the phenotype. The identification of genes that respond differently to diets and their associated phenotypic effects serves as a crucial stepping stone for further investigations, aiming to build upon our discoveries. Ultimately, such advancements hold the promise of improving animal welfare, productivity, and sustainability in livestock farming.

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.

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.

Co-expression analysis reveals distinct alliances around two carbon fixation pathways in hydrothermal vent symbionts

Most autotrophic organisms possess a single carbon fixation pathway. The chemoautotrophic symbionts of the hydrothermal vent tubeworm Riftia pachyptila, however, possess two functional pathways: the Calvin-Benson-Bassham (CBB) and the reductive tricarboxylic acid (rTCA) cycles. How these two pathways are coordinated is unknown. Here we measured net carbon fixation rates, transcriptional/metabolic responses and transcriptional co-expression patterns of Riftia pachyptila endosymbionts by incubating tubeworms collected from the East Pacific Rise at environmental pressures, temperature and geochemistry. Results showed that rTCA and CBB transcriptional patterns varied in response to different geochemical regimes and that each pathway is allied to specific metabolic processes; the rTCA is allied to hydrogenases and dissimilatory nitrate reduction, whereas the CBB is allied to sulfide oxidation and assimilatory nitrate reduction, suggesting distinctive yet complementary roles in metabolic function. Furthermore, our network analysis implicates the rTCA and a group 1e hydrogenase as key players in the physiological response to limitation of sulfide and oxygen. Net carbon fixation rates were also exemplary, and accordingly, we propose that co-activity of CBB and rTCA may be an adaptation for maintaining high carbon fixation rates, conferring a fitness advantage in dynamic vent environments.

Co-expression network analysis and identification of core genes in the interaction between wheat and Puccinia striiformis f. sp. tritici

The epidemic of stripe rust, caused by the pathogen Puccinia striiformis f. sp. tritici (Pst), would reduce wheat (Triticum aestivum) yields seriously. Traditional experimental methods are difficult to discover the interaction between wheat and Pst. Multi-omics data analysis provides a new idea for efficiently mining the interactions between host and pathogen. We used 140 wheat-Pst RNA-Seq data to screen for differentially expressed genes (DEGs) between low susceptibility and high susceptibility samples, and carried out Gene Ontology (GO) enrichment analysis. Based on this, we constructed a gene co-expression network, identified the core genes and interacted gene pairs from the conservative modules. Finally, we checked the distribution of Nucleotide-binding and leucine-rich repeat (NLR) genes in the co-expression network and drew the wheat NLR gene co-expression network. In order to provide accessible information for related researchers, we built a web-based visualization platform to display the data. Based on the analysis, we found that resistance-related genes such as TaPR1, TaWRKY18 and HSP70 were highly expressed in the network. They were likely to be involved in the biological processes of Pst infecting wheat. This study can assist scholars in conducting studies on the pathogenesis and help to advance the investigation of wheat-Pst interaction patterns.

Genome-wide association studies reveal differences in genetic susceptibility between single events vs. recurrent events of atrial fibrillation and myocardial infarction: the HUNT study

Genetic research into atrial fibrillation (AF) and myocardial infarction (MI) has predominantly focused on comparing afflicted individuals with their healthy counterparts. However, this approach lacks granularity, thus overlooking subtleties within patient populations. In this study, we explore the distinction between AF and MI patients who experience only a single disease event and those experiencing recurrent events. Integrating hospital records, questionnaire data, clinical measurements, and genetic data from more than 500,000 HUNT and United Kingdom Biobank participants, we compare both clinical and genetic characteristics between the two groups using genome-wide association studies (GWAS) meta-analyses, phenome-wide association studies (PheWAS) analyses, and gene co-expression networks. We found that the two groups of patients differ in both clinical characteristics and genetic risks. More specifically, recurrent AF patients are significantly younger and have better baseline health, in terms of reduced cholesterol and blood pressure, than single AF patients. Also, the results of the GWAS meta-analysis indicate that recurrent AF patients seem to be at greater genetic risk for recurrent events. The PheWAS and gene co-expression network analyses highlight differences in the functions associated with the sets of single nucleotide polymorphisms (SNPs) and genes for the two groups. However, for MI patients, we found that those experiencing single events are significantly younger and have better baseline health than those with recurrent MI, yet they exhibit higher genetic risk. The GWAS meta-analysis mostly identifies genetic regions uniquely associated with single MI, and the PheWAS analysis and gene co-expression networks support the genetic differences between the single MI and recurrent MI groups. In conclusion, this work has identified novel genetic regions uniquely associated with single MI and related PheWAS analyses, as well as gene co-expression networks that support the genetic differences between the patient subgroups of single and recurrent occurrence for both MI and AF.

Bayesian estimation of gene constraint from an evolutionary model with gene features

Measures of selective constraint on genes have been used for many applications including clinical interpretation of rare coding variants, disease gene discovery, and studies of genome evolution. However, widely-used metrics are severely underpowered at detecting constraint for the shortest ∼25% of genes, potentially causing important pathogenic mutations to be overlooked. We developed a framework combining a population genetics model with machine learning on gene features to enable accurate inference of an interpretable constraint metric, shet. Our estimates outperform existing metrics for prioritizing genes important for cell essentiality, human disease, and other phenotypes, especially for short genes. Our new estimates of selective constraint should have wide utility for characterizing genes relevant to human disease. Finally, our inference framework, GeneBayes, provides a flexible platform that can improve estimation of many gene-level properties, such as rare variant burden or gene expression differences.

Reassessing the modularity of gene co-expression networks using the Stochastic Block Model

Finding communities in gene co-expression networks is a common first step toward extracting biological insight from these complex datasets. Most community detection algorithms expect genes to be organized into assortative modules, that is, groups of genes that are more associated with each other than with genes in other groups. While it is reasonable to expect that these modules exist, using methods that assume they exist a priori is risky, as it guarantees that alternative organizations of gene interactions will be ignored. Here, we ask: can we find meaningful communities without imposing a modular organization on gene co-expression networks, and how modular are these communities? For this, we use a recently developed community detection method, the weighted degree corrected stochastic block model (SBM), that does not assume that assortative modules exist. Instead, the SBM attempts to efficiently use all information contained in the co-expression network to separate the genes into hierarchically organized blocks of genes. Using RNA-seq gene expression data measured in two tissues derived from an outbred population of Drosophila melanogaster, we show that (a) the SBM is able to find ten times as many groups as competing methods, that (b) several of those gene groups are not modular, and that (c) the functional enrichment for non-modular groups is as strong as for modular communities. These results show that the transcriptome is structured in more complex ways than traditionally thought and that we should revisit the long-standing assumption that modularity is the main driver of the structuring of gene co-expression networks.

Transcriptome organization of white blood cells through gene co-expression network analysis in a large RNA-seq dataset

Gene co-expression network analysis enables identification of biologically meaningful clusters of co-regulated genes (modules) in an unsupervised manner. We present here the largest study conducted thus far of co-expression networks in white blood cells (WBC) based on RNA-seq data from 624 individuals. We identify 41 modules, 13 of them related to specific immune-related functions and cell types (e.g. neutrophils, B and T cells, NK cells, and plasmacytoid dendritic cells); we highlight biologically relevant lncRNAs for each annotated module of co-expressed genes. We further characterize with unprecedented resolution the modules in T cell sub-types, through the availability of 95 immune phenotypes obtained by flow cytometry in the same individuals. This study provides novel insights into the transcriptional architecture of human leukocytes, showing how network analysis can advance our understanding of coding and non-coding gene interactions in immune system cells.

Integrated annotation prioritizes metabolites with bioactivity in inflammatory bowel disease

Microbial biochemistry is central to the pathophysiology of inflammatory bowel diseases (IBD). Improved knowledge of microbial metabolites and their immunomodulatory roles is thus necessary for diagnosis and management. Here, we systematically analyzed the chemical, ecological, and epidemiological properties of ~82k metabolic features in 546 Integrative Human Microbiome Project (iHMP/HMP2) metabolomes, using a newly developed methodology for bioactive compound prioritization from microbial communities. This suggested >1000 metabolic features as potentially bioactive in IBD and associated ~43% of prevalent, unannotated features with at least one well-characterized metabolite, thereby providing initial information for further characterization of a significant portion of the fecal metabolome. Prioritized features included known IBD-linked chemical families such as bile acids and short-chain fatty acids, and less-explored bilirubin, polyamine, and vitamin derivatives, and other microbial products. One of these, nicotinamide riboside, reduced colitis scores in DSS-treated mice. The method, MACARRoN, is generalizable with the potential to improve microbial community characterization and provide therapeutic candidates.

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.

Identification of pyroptosis-related gene signature in nonalcoholic steatohepatitis

Non-alcoholic fatty liver disease (NAFLD) has emerged as one of the major causes of liver-related morbidity and mortality globally. It ranges from simple steatosis to non-alcoholic steatohepatitis (NASH) characterized by ballooning and hepatic inflammation. In the past few years, pyroptosis has been shown as a type of programmed cell death that triggers inflammation and plays a role in the development of NASH. However, the roles of pyroptosis-related genes (PRGs) in NASH remained unclear. In this study, we studied the expression level of pyroptosis-related genes (PRGs) in NASH and healthy controls, developed a diagnostic model of NASH based on PRGs and explored the pathological mechanisms associated with pyroptosis. We further compared immune status between NASH and healthy controls, analyzed immune status in different subtypes of NASH. We identified altogether twenty PRGs that were differentially expressed between NASH and normal liver tissues. Then, a novel diagnostic model consisting of seven PRGs including CASP3, ELANE, GZMA, CASP4, CASP9, IL6 and TP63 for NASH was constructed with an area under the ROC curve (AUC) of 0.978 (CI 0.965-0.99). Obvious variations in immune status between healthy controls and NASH cases were detected. Subsequently, the consensus clustering method based on differentially expressed PRGs was constructed to divide all NASH cases into two distinct pyroptosis subtypes with different immune and biological characteristics. Pyroptosis-related genes may play an important role in NASH and can provide new insights into the diagnosis and underlying mechanisms of NASH.

Alternative polyadenylation and dynamic 3' UTR length is associated with polysome recruitment throughout the cardiomyogenic differentiation of hESCs

Alternative polyadenylation (APA) increases transcript diversity through the generation of isoforms with varying 3' untranslated region (3' UTR) lengths. As the 3' UTR harbors regulatory element target sites, such as miRNAs or RNA-binding proteins, changes in this region can impact post-transcriptional regulation and translation. Moreover, the APA landscape can change based on the cell type, cell state, or condition. Given that APA events can impact protein expression, investigating translational control is crucial for comprehending the overall cellular regulation process. Revisiting data from polysome profiling followed by RNA sequencing, we investigated the cardiomyogenic differentiation of pluripotent stem cells by identifying the transcripts that show dynamic 3' UTR lengthening or shortening, which are being actively recruited to ribosome complexes. Our findings indicate that dynamic 3' UTR lengthening is not exclusively associated with differential expression during cardiomyogenesis but rather with recruitment to polysomes. We confirm that the differentiated state of cardiomyocytes shows a preference for shorter 3' UTR in comparison to the pluripotent stage although preferences vary during the days of the differentiation process. The most distinct regulatory changes are seen in day 4 of differentiation, which is the mesoderm commitment time point of cardiomyogenesis. After identifying the miRNAs that would target specifically the alternative 3' UTR region of the isoforms, we constructed a gene regulatory network for the cardiomyogenesis process, in which genes related to the cell cycle were identified. Altogether, our work sheds light on the regulation and dynamic 3' UTR changes of polysome-recruited transcripts that take place during the cardiomyogenic differentiation of pluripotent stem cells.

FAVA: high-quality functional association networks inferred from scRNA-seq and proteomics data

MOTIVATION

Protein networks are commonly used for understanding how proteins interact. However, they are typically biased by data availability, favoring well-studied proteins with more interactions. To uncover functions of understudied proteins, we must use data that are not affected by this literature bias, such as single-cell RNA-seq and proteomics. Due to data sparseness and redundancy, functional association analysis becomes complex.

RESULTS

To address this, we have developed FAVA (Functional Associations using Variational Autoencoders), which compresses high-dimensional data into a low-dimensional space. FAVA infers networks from high-dimensional omics data with much higher accuracy than existing methods, across a diverse collection of real as well as simulated datasets. FAVA can process large datasets with over 0.5 million conditions and has predicted 4210 interactions between 1039 understudied proteins. Our findings showcase FAVA's capability to offer novel perspectives on protein interactions. FAVA functions within the scverse ecosystem, employing AnnData as its input source.

AVAILABILITY AND IMPLEMENTATION

Source code, documentation, and tutorials for FAVA are accessible on GitHub at https://github.com/mikelkou/fava. FAVA can also be installed and used via pip/PyPI as well as via the scverse ecosystem https://github.com/scverse/ecosystem-packages/tree/main/packages/favapy.

Patient-specific analysis of co-expression to measure biological network rewiring in individuals

To effectively understand the underlying mechanisms of disease and inform the development of personalized therapies, it is critical to harness the power of differential co-expression (DCE) network analysis. Despite the promise of DCE network analysis in precision medicine, current approaches have a major limitation: they measure an average differential network across multiple samples, which means the specific etiology of individual patients is often overlooked. To address this, we present Cosinet, a DCE-based single-sample network rewiring degree quantification tool. By analyzing two breast cancer datasets, we demonstrate that Cosinet can identify important differences in gene co-expression patterns between individual patients and generate scores for each individual that are significantly associated with overall survival, recurrence-free interval, and other clinical outcomes, even after adjusting for risk factors such as age, tumor size, HER2 status, and PAM50 subtypes. Cosinet represents a remarkable development toward unlocking the potential of DCE analysis in the context of precision medicine.

Integrative analysis identifies gene signatures mediating the effect of DNA methylation on asthma severity and lung function

DNA methylation (DNAm) changes play a key role in regulating gene expression in asthma. To investigate the role of epigenetics and transcriptomics change in asthma, we used publicly available DNAm (asthmatics, n = 96 and controls, n = 46) and gene expression (asthmatics, n = 79 and controls, n = 39) data derived from bronchial epithelial cells (BECs). We performed differential methylation/expression and weighted co-methylation/co-expression network analyses to identify co-methylated and co-expressed modules associated with asthma severity and lung function. For subjects with both DNAm and gene expression data (asthmatics, n = 79 and controls, n = 39), machine-learning technique was used to prioritize CpGs and differentially expressed genes (DEGs) for asthma risk prediction, and mediation analysis was used to uncover DEGs that mediate the effect of DNAm on asthma severity and lung function in BECs. Finally, we validated CpGs and their associated DEGs and the asthma risk prediction model in airway epithelial cells (AECs) dataset. The asthma risk prediction model based on 18 CpGs and 28 DEGs showed high accuracy in both the discovery BEC dataset with area under the receiver operating characteristic curve (AUC) = 0.99 and the validation AEC dataset (AUC = 0.82). Genes in the three co-methylated and six co-expressed modules were enriched in multiple pathways including WNT/beta-catenin signaling and notch signaling. Moreover, we identified 35 CpGs correlated with DEGs in BECs, of which 17 CpGs including cg01975495 (SERPINE1), cg10528482 (SLC9A3), cg25477769 (HNF1A) and cg26639146 (CD9), cg17945560 (TINAGL1) and cg10290200 (FLNC) were replicated in AECs. These DEGs mediate the association between DNAm and asthma severity and lung function. Overall, our study investigated the role of DNAm and gene expression change in asthma and provided an insight into the mechanisms underlying the effects of DNA methylation on asthma, asthma severity and lung function.

Leveraging technology-driven strategies to untangle omics big data: circumventing roadblocks in clinical facets of oral cancer

Oral cancer is one of the 19most rapidly progressing cancers associated with significant mortality, owing to its extreme degree of invasiveness and aggressive inclination. The early occurrences of this cancer can be clinically deceiving leading to a poor overall survival rate. The primary concerns from a clinical perspective include delayed diagnosis, rapid disease progression, resistance to various chemotherapeutic regimens, and aggressive metastasis, which collectively pose a substantial threat to prognosis. Conventional clinical practices observed since antiquity no longer offer the best possible options to circumvent these roadblocks. The world of current cancer research has been revolutionized with the advent of state-of-the-art technology-driven strategies that offer a ray of hope in confronting said challenges by highlighting the crucial underlying molecular mechanisms and drivers. In recent years, bioinformatics and Machine Learning (ML) techniques have enhanced the possibility of early detection, evaluation of prognosis, and individualization of therapy. This review elaborates on the application of the aforesaid techniques in unraveling potential hints from omics big data to address the complexities existing in various clinical facets of oral cancer. The first section demonstrates the utilization of omics data and ML to disentangle the impediments related to diagnosis. This includes the application of technology-based strategies to optimize early detection, classification, and staging via uncovering biomarkers and molecular signatures. Furthermore, breakthrough concepts such as salivaomics-driven non-invasive biomarker discovery and omics-complemented surgical interventions are articulated in detail. In the following part, the identification of novel disease-specific targets alongside potential therapeutic agents to confront oral cancer via omics-based methodologies is presented. Additionally, a special emphasis is placed on drug resistance, precision medicine, and drug repurposing. In the final section, we discuss the research approaches oriented toward unveiling the prognostic biomarkers and constructing prediction models to capture the metastatic potential of the tumors. Overall, we intend to provide a bird's eye view of the various omics, bioinformatics, and ML approaches currently being used in oral cancer research through relevant case studies.

Differential expression of the genes encoding immune system components in response to Pseudomonas syringae and Pseudomonas aeruginosa in Arabidopsis thaliana

In innate immunity, the first layer of defense against any microbial infection is triggered by the perception of pathogen-associated molecular patterns by highly specific pattern recognition receptors. The Pseudomonas syringae pv. tomato and Pseudomonas aeruginosa are plant-pathogenic bacterial species that include pathogenic strains in a wide range of different plant species. In the current study, extensive analysis including gene expression of 12 hub genes, gene ontology, protein-protein interaction, and cis-element prediction to dissect the Arabidopsis response to above-mentioned bacteria were performed. Further, we evaluated weighted co-expression network analysis (WGCNA) in the wild-type plants and coi-1 mutant line and determined changes in responsive genes at two time-points (4 and 8 h) of post-treatment with P. syringae and P. aeruginosa. Compared to the wild-type plants, coi-1 mutant showed significant expression in most of the genes involved, indicating that their protein products have important role in innate immunity and RNA silencing pathways. Our findings showed that 12 hub genes were co-expressed in response to P. syringae and P. aeruginosa infections. Based on the network analysis, transcription factors, receptors, protein kinase, and pathogenesis-related protein (PR1) were involved in the immunity system. Gene ontology related to each module was involved in defense response, protein serine kinase activity, and primary miRNA processing. Based on the cis-elements prediction, MYB, MYC, WRE3, W-box, STRE, and ARE contained the most number of cis-elements in co-expressed network genes. Also, in coi-1 mutant, most responsive genes against theses pathogens were up-regulated. The knowledge gained in the gene expression analysis in response to P. syringae and P. aeruginosa in the model plant, i.e., Arabidopsis, is essential to allow us to gain more insight about the innate immunity in other crops.

LUM as a novel prognostic marker and its correlation with immune infiltration in gastric cancer: a study based on immunohistochemical analysis and bioinformatics

BACKGROUND

Gastric cancer (GC) is considered the sixth highly prevailing malignant neoplasm and is ranked third in terms of cancer mortality rates. To enable an early and efficient diagnosis of GC, it is important to detect the fundamental processes involved in the oncogenesis and progression of gastric malignancy. The understanding of molecular signaling pathways can facilitate the development of more effective therapeutic strategies for GC patients.

METHODS

The screening of genes that exhibited differential expression in early and advanced GC was performed utilizing the Gene Expression Omnibus databases (GSE3438). Based on this, the protein and protein interaction network was constructed to screen for hub genes. The resulting list of hub genes was evaluated with bioinformatic analysis and selected genes were validated the protein expression by immunohistochemistry (IHC). Finally, a competing endogenous RNA network of GC was constructed.

RESULTS

The three genes (ITGB1, LUM, and COL5A2) overexpressed in both early and advanced GC were identified for the first time. Their upregulation has been linked with worse overall survival (OS) time in patients with GC. Only LUM was identified as an independent risk factor for OS among GC patients by means of additional analysis. IHC results demonstrated that the expression of LUM protein was increased in GC tissue, and was positively associated with the pathological T stage. LUM expression can effectively differentiate tumorous tissue from normal tissue (area under the curve = 0.743). The area under 1-, 3-, and 5-year survival relative operating characteristics were greater than 0.6. Biological function enrichment analyses suggested that the genes related to LUM expression were involved in extracellular matrix development-related pathways and enriched in several cancer-related pathways. LUM affects the infiltration degree of cells linked to the immune system in the tumor microenvironment. In GC progression, the AC117386.2/hsa-miR-378c/LUM regulatory axis was also identified.

CONCLUSION

Collectively, a thorough bioinformatics analysis was carried out and an AC117386.2/hsa-miR-378c/LUM regulatory axis in the stomach adenocarcinoma dataset was detected. These findings should serve as a guide for future experimental investigations and warrant confirmation from larger studies.

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

BACKGROUND

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

METHOD

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

RESULTS

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

CONCLUSION

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

Gene communities in co-expression networks across different tissues

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

Hub genes and pathways related to caries-free dental biofilm: clinical metatranscriptomic study

OBJECTIVE

This study aimed to evaluate the microbial functional profile of biofilms related to caries-free (CF, n = 6) and caries-arrested (CI, n = 3) compared to caries-active (CA, n = 5) individuals.

MATERIALS AND METHODS

A metatranscriptomic was performed in supragingival biofilm from different clinical conditions related to caries or health. Total RNA was extracted and cDNAs were obtained and sequenced (Illumina HiSeq3000). Trimmed data (SortMeRNA) were submitted to the SqueezeMeta pipeline in the co-assembly mode for functional analysis and further differential gene expression analysis (DESeq2) and weighted gene co-expression network analysis (WCGNA) to explore and identify gene modules related to these clinical conditions.

RESULTS

A total of 5303 genes were found in the metatranscriptomic analysis. A co-expression network identified the most relevant modules strongly related to specific caries status. Correlation coefficients were calculated between the eigengene modules and the clinical conditions (CA, CI, and CF) discriminating multiple modules. CA and CI showed weak correlation coefficient strength across the modules, while the CF condition presented a very strong positive correlation coefficient (r = 0.9, p value = 4 × 10-9). Pearson's test was applied to further analyze the module membership and gene significance in CF conditions, and the most relevant were HSPA1s-K03283, Epr- K13277, and SLC1A-K05613. Gene Ontology (GO) shows important bioprocesses, such as two-component system, fructose and mannose metabolism, pentose and glucuronate interconversions, and flagellar assembly (p-adjust < 0.05). The ability to use different carbohydrates, integrate multiple signals, swarm, and bacteriocin production are significant metabolic advantages in the oral environment related to CF.

CONCLUSIONS

A distinct functional health profile could be found in CF, where co-occurring genes can act in different pathways at the same time. Genes HSPA1s, Epr, and SLC1A may be appointed as potential biomarkers for caries-free biofilms.

CLINICAL RELEVANCE

Potential biomarkers for caries-free biofilms could contribute to the knowledge of caries prevention and control.

Impact of sarcosine on diabetic retinopathy: Findings based on weighted gene co-expression network analysis and machine learning techniques

AIM

To quantify the association between serum sarcosine and diabetic retinopathy (DR) using weighted gene co-expression network analysis (WGCNA).

METHODS

We measured serum metabolites in 69 pairs of type 2 diabetes (T2D) patients with and without DR matched by age, gender, body mass index（BMI and HbA1c, using a propensity score matching-based approach. To identify modules and metabolites linked to DR, pathway analysis was performed using WGCNA, the Kyoto Encyclopedia of Genes and Genomes and Small-Molecule Pathway Database. The association of sarcosine with DR was estimated by restricted cubic spline and conditional logistic regression models. Its joint effects with covariates on DR were also extensively examined.

RESULTS

With per interquartile range elevation of sarcosine, the adjusted odds ratio (AOR) of DR significantly decreased by 67% (AOR: 0.33, 95% confidence interval [CI]: 0.19-0.58). Similar results were also found in the tertile analysis. Compared with those in the first tertile of sarcosine, the AOR significantly decreased by 54% (AOR: 0.46, 95% CI: 0.18-1.17) and 78% (AOR: 0.22, 95% CI: 0.08-0.59) for subjects in the second and third tertiles, respectively. Compared with subjects with lower sarcosine and lower HDL-C levels, those with higher sarcosine and lower HDL-C levels had the lowest odds of DR (OR: 0.13, 95% CI: 0.04, 0.43).

CONCLUSIONS

Serum sarcosine was inversely related to DR, especially in T2D patients with insufficient HDL-C. This study provides insights on a possible novel target for DR precision prevention and control, as well as a better understanding of the DR mechanism.

Identification of age-specific biomarkers of spinal cord injury: A bioinformatics analysis of young and aged mice models

BACKGROUND

Spinal cord injury (SCI) is a debilitating event that often results in long-term physical damage, disability, and a significant impact on a patient's quality of life. Past research has noted an age-dependent decline in regeneration post-SCI. This study seeks to identify potential biomarkers and enriched pathways in young and aged SCI mouse models.

METHODS

We retrieved the microarray data of spinal cord samples from SCI mice and control mice from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the R software and the Linear Models for Microarray Data (limma) package. The Gene Set Enrichment Analysis (GSEA) determined enrichment differences among gene sets. The Weighted Gene Co-expression Network Analysis (WGCNA) pinpointed clinically significant modules and hub genes in SCI. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was employed to uncover significantly related pathways of crucial genes in SCI.

RESULTS

We found 2560 DEGs in the young SCI group, comprised of 1733 upregulated RNAs and 827 downregulated RNAs. In the aged SCI group, 3048 DEGs were revealed including 1856 upregulated and 1192 downregulated genes. The GSEA revealed 12 enriched signaling pathways in the young SCI group, such as IL6/JAK/STAT3 signaling, interferon alpha response, and interferon gamma response, and 21 signaling pathways in the aged SCI group such as IL6/JAK/STAT3 signaling, E2F targets, and angiogenesis-related pathways. The WGCNA identified the turquoise module as significantly associated with the clinical traits of both young and aged SCI, and revealed 3181 hub genes. Ultimately, 1858 significant genes in SCI were found, with associated signaling pathways including epithelial-mesenchymal transition (EMT), interferon gamma response, and KARS signaling.

CONCLUSION

Our study explored the RNA expression patterns and enriched signaling pathways in young and aged SCI mice. These findings may provide potential biomarkers for targeted SCI therapy.

The construction of a testis transcriptional cell atlas from embryo to adult reveals various somatic cells and their molecular roles

BACKGROUND

The testis is a complex organ that undergoes extensive developmental changes from the embryonic stage to adulthood. The development of germ cells, which give rise to spermatozoa, is tightly regulated by the surrounding somatic cells.

METHODS

To better understand the dynamics of these changes, we constructed a transcriptional cell atlas of the testis, integrating single-cell RNA sequencing data from over 26,000 cells across five developmental stages: fetal germ cells, infants, childhood, peri-puberty, and adults. We employed various analytical techniques, including clustering, cell type assignments, identification of differentially expressed genes, pseudotime analysis, weighted gene co-expression network analysis, and evaluation of paracrine cell-cell communication, to comprehensively analyze this transcriptional cell atlas of the testis.

RESULTS

Our analysis revealed remarkable heterogeneity in both somatic and germ cell populations, with the highest diversity observed in Sertoli and Myoid somatic cells, as well as in spermatogonia, spermatocyte, and spermatid germ cells. We also identified key somatic cell genes, including RPL39, RPL10, RPL13A, FTH1, RPS2, and RPL18A, which were highly influential in the weighted gene co-expression network of the testis transcriptional cell atlas and have been previously implicated in male infertility. Additionally, our analysis of paracrine cell-cell communication supported specific ligand-receptor interactions involved in neuroactive, cAMP, and estrogen signaling pathways, which support the crucial role of somatic cells in regulating germ cell development.

CONCLUSIONS

Overall, our transcriptional atlas provides a comprehensive view of the cell-to-cell heterogeneity in the testis and identifies key somatic cell genes and pathways that play a central role in male fertility across developmental stages.

Altered Expression of Two Small Secreted Proteins (ssp4 and ssp6) Affects the Degradation of a Natural Lignocellulosic Substrate by Pleurotus ostreatus

Pleurotus ostreatus is a white-rot fungus that can degrade lignin in a preferential manner using a variety of extracellular enzymes, including manganese and versatile peroxidases (encoded by the vp1-3 and mnp1-6 genes, respectively). This fungus also secretes a family of structurally related small secreted proteins (SSPs) encoded by the ssp1-6 genes. Using RNA sequencing (RNA-seq), we determined that ssp4 and ssp6 are the predominant members of this gene family that were expressed by P. ostreatus during the first three weeks of growth on wheat straw. Downregulation of ssp4 in a strain harboring an ssp RNAi construct (KDssp1) was then confirmed, which, along with an increase in ssp6 transcript levels, coincided with reduced lignin degradation and the downregulation of vp2 and mnp1. In contrast, we observed an increase in the expression of genes related to pectin and side-chain hemicellulose degradation, which was accompanied by an increase in extracellular pectin-degrading capacity. Genome-wide comparisons between the KDssp1 and the wild-type strains demonstrated that ssp silencing conferred accumulated changes in gene expression at the advanced cultivation stages in an adaptive rather than an inductive mode of transcriptional response. Based on co-expression networking, crucial gene modules were identified and linked to the ssp knockdown genotype at different cultivation times. Based on these data, as well as previous studies, we propose that P. ostreatus SSPs have potential roles in modulating the lignocellulolytic and pectinolytic systems, as well as a variety of fundamental biological processes related to fungal growth and development.

Comprehensive Transcriptomic Investigation of Rett Syndrome Reveals Increasing Complexity Trends from Induced Pluripotent Stem Cells to Neurons with Implications for Enriched Pathways

Rett syndrome (RTT) is a rare genetic neurodevelopmental disorder that has no cure apart from symptomatic treatments. While intense research efforts are required to fulfill this unmet need, the fundamental challenge is to obtain sufficient patient data. In this study, we used human transcriptomic data of four different sample types from RTT patients including induced pluripotent stem cells, differentiated neural progenitor cells, differentiated neurons, and postmortem brain tissues with an increasing in vivo-like complexity to unveil specific trends in gene expressions across the samples. Based on DEG analysis, we identified F8A3, CNTN6, RPE65, and COL19A1 to have differential expression levels in three sample types and also observed previously reported genes such as MECP2, FOXG1, CACNA1G, SATB2, GABBR2, MEF2C, KCNJ10, and CUX2 in our study. Considering the significantly enriched pathways for each sample type, we observed a consistent increase in numbers from iPSCs to NEUs where MECP2 displayed profound effects. We also validated our GSEA results by using single-cell RNA-seq data. In WGCNA, we elicited a connection among MECP2, TNRC6A, and HOXA5. Our findings highlight the utility of transcriptomic analyses to determine genes that might lead to therapeutic strategies.

Integrative systems biology framework discovers common gene regulatory signatures in multiple mechanistically distinct inflammatory skin diseases

More than 20% of the population across the world is affected by non-communicable inflammatory skin diseases including psoriasis, atopic dermatitis, hidradenitis suppurativa, rosacea, etc. Many of these chronic diseases are painful and debilitating with limited effective therapeutic interventions. However, recent advances in psoriasis treatment have improved the effectiveness and provide better management of the disease. This study aims to identify common regulatory pathways and master regulators that regulate molecular pathogenesis. We designed an integrative systems biology framework to identify the significant regulators across several inflammatory skin diseases. With conventional transcriptome analysis, we identified 55 shared genes, which are enriched in several immune-associated pathways in eight inflammatory skin diseases. Next, we exploited the gene co-expression-, and protein-protein interaction-based networks to identify shared genes and protein components in different diseases with relevant functional implications. Additionally, the network analytics unravels 55 high-value proteins as significant regulators in molecular pathogenesis. We believe that these significant regulators should be explored with critical experimental approaches to identify the putative drug targets for more effective treatments. As an example, we identified IKZF1 as a shared significant master regulator in three inflammatory skin diseases, which can serve as a putative drug target with known disease-derived molecules for developing efficacious combinatorial treatments for hidradenitis suppurativa, atopic dermatitis, and rosacea. The proposed framework is very modular, which can indicate a significant path of molecular mechanism-based drug development from complex transcriptomics data and other multi-omics data.

The network structure of hematopoietic cancers

Hematopoietic cancers (HCs) are a heterogeneous group of malignancies that affect blood, bone marrow and lymphatic system. Here, by analyzing 1960 RNA-Seq samples from three independent datasets, we explored the co-expression landscape in HCs, by inferring gene co-expression networks (GCNs) with four cancer phenotypes (B and T-cell acute leukemia -BALL, TALL-, acute myeloid leukemia -AML-, and multiple myeloma -MM-) as well as non-cancer bone marrow. We characterized their structure (topological features) and function (enrichment analyses). We found that, as in other types of cancer, the highest co-expression interactions are intra-chromosomal, which is not the case for control GCNs. We also detected a highly co-expressed group of overexpressed pseudogenes in HC networks. The four GCNs present only a small fraction of common interactions, related to canonical functions, like immune response or erythrocyte differentiation. With this approach, we were able to reveal cancer-specific features useful for detection of disease manifestations.

KidneyNetwork: using kidney-derived gene expression data to predict and prioritize novel genes involved in kidney disease

Genetic testing in patients with suspected hereditary kidney disease may not reveal the genetic cause for the disorder as potentially pathogenic variants can reside in genes that are not yet known to be involved in kidney disease. We have developed KidneyNetwork, that utilizes tissue-specific expression to inform candidate gene prioritization specifically for kidney diseases. KidneyNetwork is a novel method constructed by integrating a kidney RNA-sequencing co-expression network of 878 samples with a multi-tissue network of 31,499 samples. It uses expression patterns and established gene-phenotype associations to predict which genes could be related to what (disease) phenotypes in an unbiased manner. We applied KidneyNetwork to rare variants in exome sequencing data from 13 kidney disease patients without a genetic diagnosis to prioritize candidate genes. KidneyNetwork can accurately predict kidney-specific gene functions and (kidney disease) phenotypes for disease-associated genes. The intersection of prioritized genes with genes carrying rare variants in a patient with kidney and liver cysts identified ALG6 as plausible candidate gene. We strengthen this plausibility by identifying ALG6 variants in several cystic kidney and liver disease cases without alternative genetic explanation. We present KidneyNetwork, a publicly available kidney-specific co-expression network with optimized gene-phenotype predictions for kidney disease phenotypes. We designed an easy-to-use online interface that allows clinicians and researchers to use gene expression and co-regulation data and gene-phenotype connections to accelerate advances in hereditary kidney disease diagnosis and research. TRANSLATIONAL STATEMENT: Genetic testing in patients with suspected hereditary kidney disease may not reveal the genetic cause for the patient's disorder. Potentially pathogenic variants can reside in genes not yet known to be involved in kidney disease, making it difficult to interpret the relevance of these variants. This reveals a clear need for methods to predict the phenotypic consequences of genetic variation in an unbiased manner. Here we describe KidneyNetwork, a tool that utilizes tissue-specific expression to predict kidney-specific gene functions. Applying KidneyNetwork to a group of undiagnosed cases identified ALG6 as a candidate gene in cystic kidney and liver disease. In summary, KidneyNetwork can aid the interpretation of genetic variants and can therefore be of value in translational nephrogenetics and help improve the diagnostic yield in kidney disease patients.

Unannotated Open Reading Frame in Saccharomyces cerevisiae Encodes Protein Localizing to the Endoplasmic Reticulum

There are thousands of unannotated translated open reading frames (ORFs) in the Saccharomyces cerevisiae genome. Previous investigation into one such unannotated ORF, which was systemically labeled YGR016C-A based on its genomic coordinates, showed that replacing the ORF's ATG start codon with AAG led to a change in cellular fitness under different stress conditions (Wacholder et al., 2023). This suggested translation of YGR016C-A plays a role in cellular fitness. Here, we investigate Ygr016c-a's subcellular localization to gain insight into its cellular function. Computational prediction tools, co-expression analysis and fluorescence microscopy suggest that the Ygr016c-a protein localizes to the endoplasmic reticulum.

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.

Global co-expression network for key factor selection on environmental stress RNA-seq dataset in Capsicum annuum

Environmental stresses significantly affect plant growth, development, and productivity. Therefore, a deeper understanding of the underlying stress responses at the molecular level is needed. In this study, to identify critical genetic factors associated with environmental stress responses, the entire 737.3 Gb clean RNA-seq dataset across abiotic, biotic stress, and phytohormone conditions in Capsicum annuum was used to perform individual differentially expressed gene analysis and to construct gene co-expression networks for each stress condition. Subsequently, gene networks were reconstructed around transcription factors to identify critical factors involved in the stress responses, including the NLR gene family, previously implicated in resistance. The abiotic and biotic stress networks comprise 233 and 597 hubs respectively, with 10 and 89 NLRs. Each gene within the NLR groups in the network exhibited substantial expression to particular stresses. The integrated analysis strategy of the transcriptome network revealed potential key genes for complex environmental conditions. Together, this could provide important clues to uncover novel key factors using high-throughput transcriptome data in other species as well as plants.

Identification of the potential regulatory interactions in rheumatoid arthritis through a comprehensive analysis of lncRNA-related ceRNA networks

OBJECTIVE

This study aimed at constructing a network of competing endogenous RNA (ceRNA) in the synovial tissues of rheumatoid arthritis (RA). It seeks to discern potential biomarkers and explore the long non-coding RNA (lncRNA)-microRNA (miRNA)-messenger RNA (mRNA) axes that are intricately linked to the pathophysiological mechanisms underpinning RA, and providing a scientific basis for the pathogenesis and treatment of RA.

METHODS

Microarray data pertaining to RA synovial tissue, GSE103578, GSE128813, and GSE83147, were acquired from the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo ). Conducted to discern both differentially expressed lncRNAs (DELncRNAs) and differentially expressed genes (DEGs). A ceRNA network was obtained through key lncRNAs, key miRNAs, and key genes. Further investigations involved co-expression analyses to uncover the lncRNA-miRNA-mRNA axes contributing to the pathogenesis of RA. To delineate the immune-relevant facets of this axis, we conducted an assessment of key genes, emphasizing those with the most substantial immunological correlations, employing the GeneCards database. Finally, gene set enrichment analysis (GSEA) was executed on the identified key lncRNAs to elucidate their functional implications in RA.

RESULTS

The 2 key lncRNAs, 7 key miRNAs and 6 key genes related to the pathogenesis of RA were obtained, as well as 2 key lncRNA-miRNA-mRNA axes (KRTAP5-AS1-hsa-miR-30b-5p-PNN, XIST-hsa-miR-511-3p/hsa-miR-1277-5p-F2RL1). GSEA of two key lncRNAs obtained biological processes and signaling pathways related to RA synovial lesions.

CONCLUSION

The findings of this investigation hold promise in furnishing a foundational framework and guiding future research endeavors aimed at comprehending the etiology and therapeutic interventions for RA.

Pre-pregnancy gene expression signatures are associated with subsequent improvement/worsening of rheumatoid arthritis during pregnancy

BACKGROUND

While many women with rheumatoid arthritis (RA) improve during pregnancy and others worsen, there are no biomarkers to predict this improvement or worsening. In our unique RA pregnancy cohort that includes a pre-pregnancy baseline, we have examined pre-pregnancy gene co-expression networks to identify differences between women with RA who subsequently improve during pregnancy and those who worsen.

METHODS

Blood samples were collected before pregnancy (T0) from 19 women with RA and 13 healthy women enrolled in our prospective pregnancy cohort. RA improvement/worsening between T0 and 3rd trimester was assessed by changes in the Clinical Disease Activity Index (CDAI). Pre-pregnancy expression profiles were examined by RNA sequencing and differential gene expression analysis. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules correlated with the improvement/worsening of RA during pregnancy and to assess their functional relevance.

RESULTS

Of the 19 women with RA, 14 improved during pregnancy (RAimproved) while 5 worsened (RAworsened). At the T0 baseline, however, the mean CDAI was similar between the two groups. WGCNA identified one co-expression module related to B cell function that was significantly correlated with the worsening of RA during pregnancy and was significantly enriched in genes differentially expressed between the RAimproved and RAworsened groups. A neutrophil-related expression signature was also identified in the RAimproved group at the T0 baseline.

CONCLUSION

The pre-pregnancy gene expression signatures identified represent potential biomarkers to predict the subsequent improvement/worsening of RA during pregnancy, which has important implications for the personalized treatment of RA during pregnancy.

From imaging to clinical outcome: dual-region CT radiomics predicting FOXM1 expression and prognosis in hepatocellular carcinoma

Background

Liver cancer, especially hepatocellular carcinoma (HCC), remains a significant global health challenge. Traditional prognostic indicators for HCC often fall short in providing comprehensive insights for individualized treatment. The integration of genomics and radiomics offers a promising avenue for enhancing the precision of HCC diagnosis and prognosis.

Methods

From the Cancer Genome Atlas (TCGA) database, we categorized mRNA of HCC patients by Forkhead Box M1 (FOXM1) expression and performed univariate and multivariate studies to pinpoint autonomous HCC risk factors. We deployed subgroup, correlation, and interaction analyses to probe FOXM1's link with clinicopathological elements. The connection between FOXM1 and immune cells was evaluated using the CIBERSORTx database. The functions of FOXM1 were investigated through analyses of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). After filtering through TCGA and the Cancer Imaging Archive (TCIA) database, we employed dual-region computed tomography (CT) radiomics technology to noninvasively predict the mRNA expression of FOXM1 in HCC tissues. Radiomic features were extracted from both tumoral and peritumoral regions, and a radiomics score (RS) was derived. The performance and robustness of the constructed models were evaluated using 10-fold cross-validation. A radiomics nomogram was developed by incorporating RS and clinical variables from the TCGA database. The models' discriminative abilities were assessed using metrics such as the area under the curve (AUC) of the receiver operating characteristic curves (ROC) and precision-recall (PR) curves.

Results

Our findings emphasized the overexpression of FOXM1 as a determinant of poor prognosis in HCC and illustrated its impact on immune cell infiltration. After selecting arterial phase CT, we chose 7 whole-tumor features and 3 features covering both the tumor and its surroundings to create WT and WP models for FOXM1 prediction. The WT model showed strong predictive capabilities for FOXM1 expression by PR curve. Conversely, the WP model did not demonstrate the good predictive ability. In our study, the radiomics score (RS) was derived from whole-tumor regions on CT images. The RS was significantly associated with FOXM1 expression, with an AUC of 0.918 in the training cohort and 0.837 in the validation cohort. Furthermore, the RS was correlated with oxidative stress genes and was integrated with clinical variables to develop a nomogram, which demonstrated good calibration and discrimination in predicting 12-, 36-, and 60-month survival probabilities. Additionally, bioinformatics analysis revealed FOXM1's potential role in shaping the immune microenvironment, with its expression linked to immune cell infiltration.

Conclusion

This study highlights the potential of integrating FOXM1 expression and radiomics in understanding HCC's complexity. Our approach offers a new perspective in utilizing radiomics for non-invasive tumor characterization and suggests its potential in providing insights into molecular profiles. Further research is needed to validate these findings and explore their clinical implications in HCC management.

Systems genetics analysis of human body fat distribution genes identifies Wnt signaling and mitochondrial activity in adipocytes

BACKGROUND

Excess fat in the abdomen is a sexually dimorphic risk factor for cardio-metabolic disease. The relative storage between abdominal and lower-body subcutaneous adipose tissue depots is approximated by the waist-to-hip ratio adjusted for body mass index (WHRadjBMI). Genome-wide association studies (GWAS) identified 346 loci near 495 genes associated with WHRadjBMI. Most of these genes have unknown roles in fat distribution, but many are expressed and putatively act in adipose tissue. We aimed to identify novel sex- and depot-specific drivers of WHRadjBMI using a systems genetics approach.

METHODS

We used two independent cohorts of adipose tissue gene expression with 362 - 444 males and 147 - 219 females, primarily of European ancestry. We constructed sex- and depot- specific Bayesian networks to model the gene-gene interactions from 8,492 adipose tissue genes. Key driver analysis identified genes that, in silico and putatively in vitro, regulate many others, including the 495 WHRadjBMI GWAS genes. Key driver gene function was determined by perturbing their expression in human subcutaneous pre-adipocytes using lenti-virus or siRNA.

RESULTS

51 - 119 key drivers in each network were replicated in both cohorts. We used single-cell expression data to select replicated key drivers expressed in adipocyte precursors and mature adipocytes, prioritized genes which have not been previously studied in adipose tissue, and used public human and mouse data to nominate 53 novel key driver genes (10 - 21 from each network) that may regulate fat distribution by altering adipocyte function. In other cell types, 23 of these genes are found in crucial adipocyte pathways: Wnt signaling or mitochondrial function. We selected seven genes whose expression is highly correlated with WHRadjBMI to further study their effects on adipogenesis/Wnt signaling (ANAPC2, PSME3, RSPO1, TYRO3) or mitochondrial function (C1QTNF3, MIGA1, PSME3, UBR1).Adipogenesis was inhibited in cells overexpressing ANAPC2 and RSPO1 compared to controls. RSPO1 results are consistent with a positive correlation between gene expression in the subcutaneous depot and WHRadjBMI, therefore lower relative storage in the subcutaneous depot. RSPO1 inhibited adipogenesis by increasing β-catenin activation and Wnt-related transcription, thus repressing PPARG and CEBPA. PSME3 overexpression led to more adipogenesis than controls. In differentiated adipocytes, MIGA1 and UBR1 downregulation led to mitochondrial dysfunction, with lower oxygen consumption than controls; MIGA1 knockdown also lowered UCP1 expression.

SUMMARY

ANAPC2, MIGA1, PSME3, RSPO1, and UBR1 affect adipocyte function and may drive body fat distribution.

Predictive overfitting in immunological applications: Pitfalls and solutions

Overfitting describes the phenomenon where a highly predictive model on the training data generalizes poorly to future observations. It is a common concern when applying machine learning techniques to contemporary medical applications, such as predicting vaccination response and disease status in infectious disease or cancer studies. This review examines the causes of overfitting and offers strategies to counteract it, focusing on model complexity reduction, reliable model evaluation, and harnessing data diversity. Through discussion of the underlying mathematical models and illustrative examples using both synthetic data and published real datasets, our objective is to equip analysts and bioinformaticians with the knowledge and tools necessary to detect and mitigate overfitting in their research.