Network neighborhood analysis with the multi-node topological overlap measure

Identification and experimental verification of a biomarker by combining the unfolded protein response with the immune cells in colon cancer

BACKGROUND

The unfolded protein response (UPR) is associated with immune cells that regulate the biological behavior of tumors. This article aims to combine UPR-associated genes with immune cells to find a prognostic marker and to verify its connection to the UPR.

METHODS

Univariate cox analysis was used to screen prognostically relevant UPRs and further screened for key UPRs among them by machine learning. ssGSEA was used to calculate immune cell abundance. Univariate cox analysis was used to screen for prognostically relevant immune cells. Multivariate cox analysis was used to calculate UPR_score and Tumor Immune Microenvironment score (TIME_score). WGCNA was used to screen UPR-Immune-related (UI-related) genes. Consensus clustering analysis was used to classify patients into molecular subtype. Based on the UI-related genes, we classified colon adenocarcinoma (COAD) samples by cluster analysis. Single-cell analysis was used to analyze the role of UI-related genes. We detected the function of TIMP1 by cell counting and transwell. Immunoblotting was used to detect whether TIMP1 was regulated by key UPR genes.

RESULTS

Combined UPR-related genes and immune cells can determine the prognosis of COAD patients. Cluster analysis showed that UI-related genes were associated with clinical features of COAD. Single-cell analysis revealed that UI-related genes may act through stromal cells. We defined three key UI-related genes by machine learning algorithms. Finally, we found that TIMP1, regulated by key genes of UPR, promoted colon cancer proliferation and metastasis.

CONCLUSIONS

We found that TIMP1 was a prognostic marker and experimentally confirmed that TIMP1 was regulated by key genes of UPR.

Molecular Targets and Mechanisms of Hedyotis diffusa Willd. for Esophageal Adenocarcinoma Treatment Based on Network Pharmacology and Weighted Gene Co-expression Network Analysis

BACKGROUND

Hedyotis diffusa Willd. (HDW) is a common anticancer herbal medicine in China, and its therapeutic effectiveness has been demonstrated in a range of cancer patients. There is no consensus about the therapeutic targets and molecular mechanisms of HDW, which contains many active ingredients.

AIM

To clarify the mechanism of HDW for esophageal adenocarcinoma (EAC), we utilized network pharmacology and weighted gene co-expression network analysis methods (WGCNA).

METHODS

The gene modules that were linked with the clinical features of EAC were obtained through the WGCNA method. Then, the potential target genes were retrieved through the network pharmacology method in order to determine the targets of the active components. After enrichment analysis, a variety of signaling pathways with significant ratios of target genes were found, including regulation of trans-synaptic signaling, neuroactive ligand-receptor interaction and modulation of chemical synaptic transmission. By means of protein-protein interaction (PPI) network analysis, we have successfully identified the hub genes, which were AR, CNR1, GRIK1, MAPK10, MAPT, PGR and PIK3R1.

RESULT

Our study employed molecular docking simulations to evaluate the binding affinity of the active components with the hub gene. The identified active anticancer constituents in HDW are scopoletol, quercetin, ferulic acid, coumarin, and trans-4-methoxycinnamyl alcohol.

CONCLUSION

Our findings shed light on the molecular underpinnings of HDW in the treatment of EAC and hold great promise for the identification of potential HDW compounds and biomarkers for EAC therapy.

ICON-GEMs: integration of co-expression network in genome-scale metabolic models, shedding light through systems biology

BACKGROUND

Flux Balance Analysis (FBA) is a key metabolic modeling method used to simulate cellular metabolism under steady-state conditions. Its simplicity and versatility have led to various strategies incorporating transcriptomic and proteomic data into FBA, successfully predicting flux distribution and phenotypic results. However, despite these advances, the untapped potential lies in leveraging gene-related connections like co-expression patterns for valuable insights.

RESULTS

To fill this gap, we introduce ICON-GEMs, an innovative constraint-based model to incorporate gene co-expression network into the FBA model, facilitating more precise determination of flux distributions and functional pathways. In this study, transcriptomic data from both Escherichia coli and Saccharomyces cerevisiae were integrated into their respective genome-scale metabolic models. A comprehensive gene co-expression network was constructed as a global view of metabolic mechanism of the cell. By leveraging quadratic programming, we maximized the alignment between pairs of reaction fluxes and the correlation of their corresponding genes in the co-expression network. The outcomes notably demonstrated that ICON-GEMs outperformed existing methodologies in predictive accuracy. Flux variabilities over subsystems and functional modules also demonstrate promising results. Furthermore, a comparison involving different types of biological networks, including protein-protein interactions and random networks, reveals insights into the utilization of the co-expression network in genome-scale metabolic engineering.

CONCLUSION

ICON-GEMs introduce an innovative constrained model capable of simultaneous integration of gene co-expression networks, ready for board application across diverse transcriptomic data sets and multiple organisms. It is freely available as open-source at https://github.com/ThummaratPaklao/ICOM-GEMs.git .

Identification of transcriptome characteristics of granulosa cells and the possible role of UBE2C in the pathogenesis of premature ovarian insufficiency

BACKGROUND

Premature ovarian insufficiency (POI) is an important cause of infertility characterized by the functional decline of the ovary. Granulosa cells (GCs) around oocytes are critical for folliculogenesis, and GC dysfunction is one of the important etiologies of POI. The aim of this study was to explore the potential biomarkers of POI by identifying hub genes and analyze the correlation of biomarkers with immune infiltration in POI using RNA profiling and bioinformatics analysis.

METHODS

RNA sequencing was performed on GCs from biochemical POI (bPOI) patients and controls. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were used to explore the candidate genes. qRT‒PCR was performed to verify the expression of hub genes. Western blot, Cell Counting Kit-8, 5-ethynyl-2'-deoxyuridine (EdU) assays, TUNEL (TdT-mediated dUTP Nick-End Labeling) and flow cytometry analysis were used to validate the possible role of ubiquitin-conjugating enzyme 2C (UBE2C) in POI. CIBERSORT was adopted to explore immune cell infiltration and the correlation between UBE2C and immune cells in bPOI.

RESULTS

Through analysis of differentially expressed genes (DEGs) and WGCNA, we obtained 143 candidate genes. After construction of the protein‒protein interaction (PPI) network and analysis with Cytoscape, 10 hub genes, including UBE2C, PBK, BUB1, CDC20, NUSAP1, CENPA, CCNB2, TOP2A, AURKB, and FOXM1, were identified and verified by qRT‒PCR. Subsequently, UBE2C was chosen as a possible biomarker of POI because knockdown of UBE2C could inhibit the proliferation and promote the apoptosis of GCs. Immune infiltration analysis indicated that monocytes and M1 macrophages may be associated with the pathogenesis of POI. In addition, UBE2C was negatively correlated with monocytes and M1 macrophages in POI.

CONCLUSIONS

This study identified a hub gene in GCs that might be important in the pathogenesis of POI and revealed the key role of UBE2C in driving POI. Immune infiltration may be highly related with the onset and etiology of POI.

Exploring the role of detoxification genes in the resistance of Bursaphelenchus xylophilus to different exogenous nematicidal substances using transcriptomic analyses

Bursaphelenchus xylophilus (Pine wood nematode, PWN) has become a worldwide forest disease due to its rapid infection ability, high lethality and difficulty in control. The main means of countering B. xylophilus is currently chemical control, but nematicides can present problems such as environmental pollution and drug resistance. The development of novel environmentally-friendly nematicides has thus become a focus of recent research. In this study, BxUGT3 and BxUGT34, which might be related to detoxification, were investigated by comparing transcriptomic and WGCNA approaches. Three other genes with a similar expression pattern, BxUGT13, BxUGT14, and BxUGT16, were found by gene family analysis. Further bioassays and qPCR assays confirmed that these five genes showed significant changes in transcript levels upon exposure to α-pinene and carvone, demonstrating that they respond to exogenous nematicidal substances. Finally, RNAi and bioassays showed that B. xylophilus with silenced BxUGT16 had increased mortality in the face of α-pinene and carvone stress, suggesting that BxUGT16 plays an important role in detoxification. Taken together, this study used novel molecular research methods, explored the detoxification mechanism of B. xylophilus at a transcriptomic level, and revealed a molecular target for the development of novel biopesticides.

Cross-Platform Transcriptomic Data Integration, Profiling, and Mining in Vibrio cholerae

A large number of transcriptome studies generate important data and information for the study of pathogenic mechanisms of pathogens, including Vibrio cholerae. V. cholerae transcriptome data include RNA-seq and microarray: microarray data mainly include clinical human and environmental samples, and RNA-seq data mainly focus on laboratory processing conditions, including different stresses and experimental animals in vivo. In this study, we integrated the data sets of both platforms using Rank-in and the Limma R package normalized Between Arrays function, achieving the first cross-platform transcriptome data integration of V. cholerae. By integrating the entire transcriptome data, we obtained the profiles of the most active or silent genes. By transferring the integrated expression profiles into the weighted correlation network analysis (WGCNA) pipeline, we identified the important functional modules of V. cholerae in vitro stress treatment, gene manipulation, and in vitro culture as DNA transposon, chemotaxis and signaling, signal transduction, and secondary metabolic pathways, respectively. The analysis of functional module hub genes revealed the uniqueness of clinical human samples; however, under specific expression patterning, the Δhns, ΔoxyR1 strains, and tobramycin treatment group showed high expression profile similarity with human samples. By constructing a protein-protein interaction (PPI) interaction network, we discovered several unreported novel protein interactions within transposon functional modules. IMPORTANCE We used two techniques to integrate RNA-seq data for laboratory studies with clinical microarray data for the first time. The interactions between V. cholerae genes were obtained from a global perspective, as well as comparing the similarity between clinical human samples and the current experimental conditions, and uncovering the functional modules that play a major role under different conditions. We believe that this data integration can provide us with some insight and basis for elucidating the pathogenesis and clinical control of V. cholerae.

Magnesium promotes tea plant growth via enhanced glutamine synthetase-mediated nitrogen assimilation

Acidic tea (Camellia sinensis) plantation soil usually suffers from magnesium (Mg) deficiency, and as such, application of fertilizer containing Mg can substantially increase tea quality by enhancing the accumulation of nitrogen (N)-containing chemicals such as amino acids in young tea shoots. However, the molecular mechanisms underlying the promoting effects of Mg on N assimilation in tea plants remain unclear. Here, both hydroponic and field experiments were conducted to analyze N, Mg, metabolite contents, and gene expression patterns in tea plants. We found that N and amino acids accumulated in tea plant roots under Mg deficiency, while metabolism of N was enhanced by Mg supplementation, especially under a low N fertilizer regime. 15N tracing experiments demonstrated that assimilation of N was induced in tea roots following Mg application. Furthermore, weighted gene correlation network analysis (WGCNA) analysis of RNA-seq data suggested that genes encoding glutamine synthetase isozymes (CsGSs), key enzymes regulating N assimilation, were markedly regulated by Mg treatment. Overexpression of CsGS1.1 in Arabidopsis (Arabidopsis thaliana) resulted in a more tolerant phenotype under Mg deficiency and increased N assimilation. These results validate our suggestion that Mg transcriptionally regulates CsGS1.1 during the enhanced assimilation of N in tea plant. Moreover, results of a field experiment demonstrated that high Mg and low N had positive effects on tea quality. This study deepens our understanding of the molecular mechanisms underlying the interactive effects of Mg and N in tea plants while also providing both genetic and agronomic tools for future improvement of tea production.

Diffusion Alignment Coefficient (DAC): A Novel Similarity Metric for Protein-Protein Interaction Network

Interaction networks can be used to predict the functions of unknown proteins using known interactions and proteins with known functions. Many graph theory or diffusion-based methods have been proposed, using the assumption that the topological properties of a protein in a network are related to its biological function. Here we seek to improve function prediction by finding more similar neighbors with a new diffusion-based alignment technique to overcome the topological information loss of the node. In this study, we introduce the Diffusion Alignment Coefficient (DAC) algorithm, which combines diffusion, longest common subsequence, and longest common substring techniques to measure the similarity of two nodes in protein interaction networks. As a proof of concept, our experiments, conducted on a real PPI networks S.cerevisiae and Homo Sapiens, demonstrated that our method obtained better results than competitors for MIPS and MSigDB Collections hallmark gene set functional categories. This is the first study to develop a measure of node function similarity using alignment to consider the positions of nodes in protein-protein interaction networks. According to the experimental results, the use of spatial information belonging to the nodes in the network has a positive effect on the detection of more functionally similar neighboring nodes.

Identification of Lipocalin 2 as a Ferroptosis-Related Key Gene Associated with Hypoxic-Ischemic Brain Damage via STAT3/NF-κB Signaling Pathway

Hypoxic-ischemic brain damage (HIBD) is a common cause of death or mental retardation in newborns. Ferroptosis is a novel form of iron-dependent cell death driven by lipid peroxidation, and recent studies have confirmed that ferroptosis plays an important role in the development of HIBD. However, HIBD ferroptosis-related biomarkers remain to be discovered. An artificial neural network (ANN) was established base on differentially expressed genes (DEGs) related to HIBD and ferroptosis and validated by external dataset. The protein-protein interaction (PPI) network, support vector machine-recursive feature elimination (SVM-RFE) algorithms, and random forest (RF) algorithm were utilized to identify core genes of HIBD. An in vitro model of glutamate-stimulated HT22 cell HIBD was constructed, and glutamate-induced ferroptosis and mitochondrial structure and function in HT22 cells were examined by propidium iodide (PI) staining, flow cytometry, Fe2+ assay, Western blot, JC-1 kit, and transmission electron microscopy (TEM). In addition, Western blot and immunofluorescence assays were used to detect the NF-κB/STAT3 pathway. An HIBD classification model was constructed and presented excellent performance. The PPI network and two machine learning algorithms indicated two hub genes in HIBD. Lipocalin 2 (LCN2) was the core gene correlated with the risk of HIBD according to the results of differential expression analysis and logistic regression diagnostics. Subsequently, we verified in an in vitro model that LCN2 is highly expressed in glutamate-induced ferroptosis in HT22 cells. More importantly, LCN2 silencing significantly inhibited glutamate-stimulated ferroptosis in HT22 cells. We also found that glutamate-stimulated HT22 cells produced mitochondrial dysfunction. Furthermore, in vitro experiments confirmed that NF-κB and STAT3 were activated and that silencing LCN2 could have the effect of inhibiting their activation. In short, our findings reveal a molecular mechanism by which LCN2 may promote ferroptosis in HIBD through activation of the NF-κB/STAT3 pathway, providing new and unique insights into LCN2 as a biomarker for HIBD and suggesting new preventive and therapeutic strategies for HIBD.

Transcriptome analysis of two pepper genotypes infected with pepper mild mottle virus

Pepper mild mottle virus (PMMoV) poses a significant threat to pepper production because it is highly contagious and extremely persistent in soil. Despite this threat, little is known about the molecular processes that underlie plant responses to pepper mild mottle virus. Here, we performed RNA sequencing of tolerant ("17-p63") and susceptible ("16-217") pepper genotypes after pepper mild mottle virus or mock inoculation. Viral accumulation in systemic leaves was lower in the pepper mild mottle virus-resistant 17-p63 genotype than in the pepper mild mottle virus-sensitive 16-217 genotype, and infection symptoms were more apparent in systemic leaves of 16-217 than in those of 17-p63 at the same timepoints during the infection process. We identified 2,959 and 2,159 differentially expressed genes (DEGs) in systemic leaves of infected 16-217 and 17-p63, respectively. Through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of differentially expressed genes from both genotypes revealed significant enrichment of the MAPK signaling pathway, plant-pathogen interaction, and flavonoid biosynthesis. A number of differentially expressed genes showed opposite trends in relation to stress resistance and disease defense in the two genotypes. We also performed weighted gene co-expression network analysis (WGCNA) of all samples and identified modules associated with resistance to pepper mild mottle virus, as well as seven hub genes. These results identify candidate virus resistance genes and provide insight into pepper defense mechanisms against pepper mild mottle virus.

Identification of long non-coding RNAs involved in floral scent of Rosa hybrida

Long non-coding RNAs (lncRNAs) were found to play important roles in transcriptional, post-transcriptional, and epigenetic gene regulation in various biological processes. However, lncRNAs and their regulatory roles remain poorly studied in horticultural plants. Rose is economically important not only for their wide use as garden and cut flowers but also as important sources of natural fragrance for perfume and cosmetics industry, but presently little was known about the regulatory mechanism of the floral scent production. In this paper, a RNA-Seq analysis with strand-specific libraries, was performed to rose flowers in different flowering stages. The scented variety 'Tianmidemeng' (Rosa hybrida) was used as plant material. A total of 13,957 lncRNAs were identified by mining the RNA-Seq data, including 10,887 annotated lncRNAs and 3070 novel lncRNAs. Among them, 10,075 lncRNAs were predicted to possess a total of 29,622 target genes, including 54 synthase genes and 24 transcription factors related to floral scent synthesis. 425 lncRNAs were differentially expressed during the flowering process, among which 19 were differentially expressed among all the three flowering stages. Using weighted correlation network analysis (WGCNA), we correlate the differentially-expressed lncRNAs to synthesis of individual floral scent compounds. Furthermore, regulatory function of one of candidate lncRNAs for floral scent synthesis was verified using VIGS method in the rose. In this study, we were able to show that lncRNAs may play important roles in floral scent production in the rose. This study also improves our understanding of how plants regulate their secondary metabolism by lncRNAs.

Integrative System Biology Analysis of Transcriptomic Responses to Drought Stress in Soybean (Glycine max L.)

Drought is a major abiotic stressor that causes yield losses and limits the growing area for most crops. Soybeans are an important legume crop that is sensitive to water-deficit conditions and suffers heavy yield losses from drought stress. To improve drought-tolerant soybean cultivars through breeding, it is necessary to understand the mechanisms of drought tolerance in soybeans. In this study, we applied several transcriptome datasets obtained from soybean plants under drought stress in comparison to those grown under normal conditions to identify novel drought-responsive genes and their underlying molecular mechanisms. We found 2168 significant up/downregulated differentially expressed genes (DEGs) and 8 core modules using gene co-expression analysis to predict their biological roles in drought tolerance. Gene Ontology and KEGG analyses revealed key biological processes and metabolic pathways involved in drought tolerance, such as photosynthesis, glyceraldehyde-3-phosphate dehydrogenase and cytokinin dehydrogenase activity, and regulation of systemic acquired resistance. Genome-wide analysis of plants' cis-acting regulatory elements (CREs) and transcription factors (TFs) was performed for all of the identified DEG promoters in soybeans. Furthermore, the PPI network analysis revealed significant hub genes and the main transcription factors regulating the expression of drought-responsive genes in each module. Among the four modules associated with responses to drought stress, the results indicated that GLYMA_04G209700, GLYMA_02G204700, GLYMA_06G030500, GLYMA_01G215400, and GLYMA_09G225400 have high degrees of interconnection and, thus, could be considered as potential candidates for improving drought tolerance in soybeans. Taken together, these findings could lead to a better understanding of the mechanisms underlying drought responses in soybeans, which may useful for engineering drought tolerance in plants.

Analysis of the different growth years accumulation of flavonoids in Dendrobium moniliforme (L.) Sw. by the integration of metabolomic and transcriptomic approaches

Dendrobium moniliforme (L.) Sw. is a valuable herbal crop, and flavonoids are primarily distributed as active ingredients in the stem, but the composition and synthesis mechanisms of flavonoids in different growth years are not clear. The accumulation of flavonoids in D. moniliforme from four different years was investigated, using a combined metabolomics and transcriptomics approach in this study. The phenylpropanoid and flavonoid biosynthetic pathways were significantly enriched in the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs). The widely targeted metabolomics technique revealed a total of 173 kinds of flavonoid metabolites. The metabolomics data confirmed the trend of total flavonoids (TF) content in stems of D. moniliforme, with chalcone, naringenin, eriodictyol, dihydroquercetin, and other flavonoids considerably up-accumulating in the third year. Twenty DEGs were detected that regulate flavonoid synthesis and the expression of these genes in different growth years was verified using real-time quantitative PCR (qRT-PCR). Furthermore, a comprehensive regulatory network was built for flavonoid biosynthesis and it was discovered that there is one FLS gene, one CCR gene and two MYB transcription factors (TFs) with a high connection with flavonoid biosynthesis by weighted gene co-expression network analysis (WGCNA). In this study, the correlation between genes involved in flavonoid biosynthesis and metabolites was revealed, and a new regulatory mechanism related to flavonoid biosynthesis in D. moniliforme was proposed. These results provide an important reference for the farmers involved in the cultivation of D. moniliforme.

Integration of Metabolomics and Transcriptomics Reveal the Mechanism Underlying Accumulation of Flavonols in Albino Tea Leaves

Albino tea plants (Camellia sinensis) have been reported to possess highly inhibited metabolism of flavonoids compared to regular green tea leaves, which improves the quality of the tea made from these leaves. However, the mechanisms underlying the metabolism of catechins and flavonols in albino tea leaves have not been well elucidated. In this study, we analyzed a time series of leaf samples in the greening process from albino to green in a thermosensitive leaf-color tea mutant using metabolomics and transcriptomics. The total content of polyphenols dramatically decreased, while flavonols (such as rutin) were highly accumulated in albino leaves compared to in green leaves. After treatment with increasing environment temperature, total polyphenols and catechins were increased in albino mutant tea leaves; however, flavonols (especially ortho-dihydroxylated B-rings such as rutin) were decreased. Meanwhile, weighted gene co-expression network analysis of RNA-seq data suggested that the accumulation of flavonols was highly correlated with genes related to reactive oxygen species scavenging. Histochemical localization further demonstrated that this specific accumulation of flavonols might be related to their biological functions in stress tolerance. These findings suggest that the temperature-stimulated accumulation of total polyphenols and catechins in albino mutant tea leaves was highly induced by enhanced photosynthesis and accumulation of its products, while the initial accumulation and temperature inhibition of flavonols in albino mutant tea leaves were associated with metabolism related to oxidative stress. In conclusion, our results indicate that the biosynthesis of flavonoids could be driven by many different factors, including antioxidation and carbon skeleton storage, under favorable and unfavorable circumstances, respectively. This work provides new insights into the drivers of flavonoid biosynthesis in albino tea leaves, which will further help to increase tea quality by improving cultivation measures.

Revealing the Key MSCs Niches and Pathogenic Genes in Influencing CEP Homeostasis: A Conjoint Analysis of Single-Cell and WGCNA

Degenerative disc disease (DDD), a major contributor to discogenic pain, which is mainly resulted from the dysfunction of nucleus pulposus (NP), annulus fibrosis (AF) and cartilage endplate (CEP) cells. Genetic and cellular components alterations in CEP may influence disc homeostasis, while few single-cell RNA sequencing (scRNA-seq) report in CEP makes it a challenge to evaluate cellular heterogeneity in CEP. Here, this study conducted a first conjoint analysis of weighted gene co-expression network analysis (WGCNA) and scRNA-seq in CEP, systematically analyzed the interested module, immune infiltration situation, and cell niches in CEP. WGCNA and protein-protein interaction (PPI) network determined a group of gene signatures responsible for degenerative CEP, including BRD4, RAF1, ANGPT1, CHD7 and NOP56; differentially immune analysis elucidated that CD4⁺ T cells, NK cells and dendritic cells were highly activated in degenerative CEP; then single-cell resolution transcriptomic landscape further identified several mesenchymal stem cells and other cellular components focused on human CEP, which illuminated niche atlas of different cell subpopulations: 8 populations were identified by distinct molecular signatures. Among which, NP progenitor/mesenchymal stem cells (NPMSC), also served as multipotent stem cells in CEP, exhibited regenerative and therapeutic potentials in promoting bone repair and maintaining bone homeostasis through SPP1, NRP1-related cascade reactions; regulatory and effector mesenchymal chondrocytes could be further classified into 2 different subtypes, and each subtype behaved potential opposite effects in maintaining cartilage homeostasis; next, the potential functional differences of each mesenchymal stem cell populations and the possible interactions with different cell types analysis revealed that JAG1, SPP1, MIF and PDGF etc. generated by different cells could regulate the CEP homeostasis by bone formation or angiogenesis, which could be served as novel therapeutic targets for degenerative CEP. In brief, this study mainly revealed the mesenchymal stem cells populations complexity and phenotypic characteristics in CEP. In brief, this study filled the gap in the knowledge of CEP components, further enhanced researchers’ understanding of CEP and their cell niches constitution.

Use of a graph neural network to the weighted gene co-expression network analysis of Korean native cattle

In the general framework of the weighted gene co-expression network analysis (WGCNA), a hierarchical clustering algorithm is commonly used to module definition. However, hierarchical clustering depends strongly on the topological overlap measure. In other words, this algorithm may assign two genes with low topological overlap to different modules even though their expression patterns are similar. Here, a novel gene module clustering algorithm for WGCNA is proposed. We develop a gene module clustering network (gmcNet), which simultaneously addresses single-level expression and topological overlap measure. The proposed gmcNet includes a “co-expression pattern recognizer” (CEPR) and “module classifier”. The CEPR incorporates expression features of single genes into the topological features of co-expressed ones. Given this CEPR-embedded feature, the module classifier computes module assignment probabilities. We validated gmcNet performance using 4,976 genes from 20 native Korean cattle. We observed that the CEPR generates more robust features than single-level expression or topological overlap measure. Given the CEPR-embedded feature, gmcNet achieved the best performance in terms of modularity (0.261) and the differentially expressed signal (27.739) compared with other clustering methods tested. Furthermore, gmcNet detected some interesting biological functionalities for carcass weight, backfat thickness, intramuscular fat, and beef tenderness of Korean native cattle. Therefore, gmcNet is a useful framework for WGCNA module clustering.

T-cell activation is associated with high-grade serous ovarian cancer survival

AIM

High-grade serous ovarian cancer (HGSOC) is an aggressive disease that is largely resistant to today's immunotherapies. Here, we aimed to investigate the prognostic significance of CTLA4, PD-1, and T-cell activation status in HGSOC.

METHODS

Using a publicly accessed microarray dataset including 260 HGSOC samples, we calculated Kaplan-Meier survival curves for overall survival (OS), evaluated associations with multivariate Cox regression models to evaluate the associations, and summarized using a hazard ratio (HR). The correlations between PD-1 gene expression and that of other genes were calculated by Pearson correlation.

RESULTS

Multivariate survival analyses showed that high PD-1 expression but not CTLA4 was associated with longer OS (HR = 0.69; 95% confidence interval [CI] = 0.52-0.91; p = 0.01), and that higher T-cell activation score was associated with better outcome (HR = 0.74; 95% confidence interval [CI] = 0.58-0.95; p = 0.02). The top three PD-1 highly correlated genes were SIRPG (r = 0.90, p < 2E-16), FASL (r = 0.89, p < 2E-16), and CD8a (r = 0.87, p < 2E-16). HGSOC patients' OS is positively associated T-cell activation score and PD-1 expression but not CTLA4.

CONCLUSION

T cell activation score may serve as a candidate for personalized immunotherapy in HGSOC. The application of anti-PD-1 therapy to HGSOC should be cautious.

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.

Cerebral Intraparenchymal Hemorrhage Changes Patients’ Gut Bacteria Composition and Function

Gut bacteria consists of 150 times more genes than humans that are vital for health. Several studies revealed that gut bacteria are associated with disease status and influence human behavior and mentality. Whether human brain injury alters the gut bacteria is yet unclear, we tested 20 fecal samples from patients with cerebral intraparenchymal hemorrhage and corresponding healthy controls through metagenomic shotgun sequencing. The composition of patients’ gut bacteria changed significantly at the phylum level; Verrucomicrobiota was the specific phylum colonized in the patients’ gut. The functional alteration was observed in the patients’ gut bacteria, including high metabolic activity for nutrients or neuroactive compounds, strong antibiotic resistance, and less virulence factor diversity. The changes in the transcription and metabolism of differential species were more evident than those of the non-differential species between groups, which is the primary factor contributing to the functional alteration of patients with cerebral intraparenchymal hemorrhage.

DecOT: Bulk Deconvolution With Optimal Transport Loss Using a Single-Cell Reference

Tissues are constituted of heterogeneous cell types. Although single-cell RNA sequencing has paved the way to a deeper understanding of organismal cellular composition, the high cost and technical noise have prevented its wide application. As an alternative, computational deconvolution of bulk tissues can be a cost-effective solution. In this study, we propose DecOT, a deconvolution method that uses the Wasserstein distance as a loss and applies scRNA-seq data as references to characterize the cell type composition from bulk tissue RNA-seq data. The Wasserstein loss in DecOT is able to utilize additional information from gene space. DecOT also applies an ensemble framework to integrate deconvolution results from multiple individuals’ references to mitigate the individual/batch effect. By benchmarking DecOT with four recently proposed square loss-based methods on pseudo-bulk data from four different single-cell data sets and real pancreatic islet bulk samples, we show that DecOT outperforms other methods and the ensemble framework is robust to the choice of references.

Revealing oxidative stress-related genes in osteoporosis and advanced structural biological study for novel natural material discovery regarding MAPKAPK2

OBJECTIVES

This study aimed to find novel oxidative stress (OS)-related biomarkers of osteoporosis (OP), together with targeting the macromolecule Mitogen-activated protein kinase-activated protein kinase 2 (MAPKAPK2) protein to further discover potential novel materials based on an advanced structural biology approach.

METHODS

Gene expression profiles of GSE35958 were obtained from the Gene Expression Omnibus (GEO) database, which were included for weighted gene co-expression network analysis (WGCNA) and differential analysis to identify the most correlated module, to identify OS-related hub genes in the progression of OP. Functional annotations were also analyzed on the interested module to get a comprehensive understanding of these genes. Then, a series of advanced structural biology methods, including high-throughput screening, pharmacological characteristic prediction, precise molecular docking, molecular dynamics simulation, etc., was implemented to discover novel natural inhibitor materials against the MAPKAPK2 protein.

RESULTS

The brown module containing 720 genes was identified as the interested module, and a group set of genes was determined as the hub OS-related genes, including PPP1R15A, CYB5R3, BCL2L1, ABCD1, MAPKAPK2, HSP90AB1, CSF1, RELA, P4HB, AKT1, HSP90B1, and CTNNB1. Functional analysis demonstrated that these genes were primarily enriched in response to chemical stress and several OS-related functions. Then, Novel Materials Discovery demonstrated that two compounds, ZINC000014951634 and ZINC000040976869, were found binding to MAPKAPK2 with a favorable interaction energy together with a high binding affinity, relatively low hepatoxicity and carcinogenicity, high aqueous solubility and intestinal absorption levels, etc., indicating that the two compounds were ideal potential inhibitor materials targeting MAPKAPK2.

CONCLUSION

This study found a group set of OS-related biomarkers of OP, providing further insights for OS functions in the development of OP. This study then focused on one of the macromolecules, MAPKAPK2, to further discover potential novel materials, which was of great significance in guiding the screening of MAPKAPK2 potential materials.

Dynamically Accumulating Homologous Recombination Deficiency Score Served as an Important Prognosis Factor in High-Grade Serous Ovarian Cancer

Background: The homologous recombination (HR) pathway defects in cancers induced abrogation of cell cycle checkpoints, resulting in the accumulation of DNA damage, mitotic catastrophe, and cell death. Cancers with BRCA1/2 loss and other accumulation of similar genomic scars resulting in HRD displayed increased sensitivity to chemotherapy. Our study aimed to explore HRD score genetic mechanisms and subsequent clinical outcomes in human cancers, especially ovarian cancer.

Methods: We analyzed TCGA data of HRD score in 33 cancer types and evaluated HRD score distribution and difference among tumor stages and between primary and recurrent tumor tissues. A weighted gene co-expression network analysis (WGCNA) was performed to identify highly correlated genes representing essential modules contributing to the HRD score and distinguish the hub genes and significant pathways. We verified HRD status predicting roles in patients’ overall survival (OS) with univariate and multivariate Cox regression analyses and built the predicting model for patient survival.

Results: We found that the HRD score increased with the rise in tumor stage, except for stage IV. The HRD score tended to grow up higher in recurrent tumor tissue than in their primary counterparts (p = 0.083). We constructed 15 co-expression modules with WGCNA, identified co-expressed genes and pathways impacting the HRD score, and concluded that the HRD score was tightly associated with tumor cells replication and proliferation. A combined HRD score ≥42 was associated with shorter OS in 33 cancer types (HR = 1.010, 95% CI: 1.008–1.011, p < 0.001). However, in ovarian cancer, which ranked the highest HRD score among other cancers, HRD ≥42 cohort was significantly associated with longer OS (HR = 0.99, 95% CI: 0.98–0.99, p < 0.0001). We also built a predicting model for 3 and 5 years survival in HGSC patients.

Conclusion: A quantitative HRD score representing the accumulated genomic scars was dynamically increasing in proliferating tumor cells since the HRD score was tightly correlated to tumor cell division and replication. We highlighted HRD score biomarker role in prognosis prediction of ovarian cancer.

Identified Three Interferon Induced Proteins as Novel Biomarkers of Human Ischemic Cardiomyopathy

Ischemic cardiomyopathy is the most frequent type of heart disease, and it is a major cause of myocardial infarction (MI) and heart failure (HF), both of which require expensive medical treatment. Precise biomarkers and therapy targets must be developed to enhance improve diagnosis and treatment. In this study, the transcriptional profiles of 313 patients' left ventricle biopsies were obtained from the PubMed database, and functional genes that were significantly related to ischemic cardiomyopathy were screened using the Weighted Gene Co-Expression Network Analysis and protein-protein interaction (PPI) networks enrichment analysis. The rat myocardial infarction model was developed to validate these findings. Finally, the putative signature genes were blasted through the common Cardiovascular Disease Knowledge Portal to explore if they were associated with cardiovascular disorder. Three interferon stimulated genes (IFIT2, IFIT3 and IFI44L), as well as key pathways, have been identified as potential biomarkers and therapeutic targets for ischemic cardiomyopathy, and their alternations or mutations have been proven to be strongly linked to cardiac disorders. These novel signature genes could be utilized as bio-markers or potential therapeutic objectives in precise clinical diagnosis and treatment of ischemic cardiomyopathy.

Integrated Network Analysis to Identify Key Modules and Potential Hub Genes Involved in Bovine Respiratory Disease: A Systems Biology Approach

Background: Bovine respiratory disease (BRD) is the most common disease in the beef and dairy cattle industry. BRD is a multifactorial disease resulting from the interaction between environmental stressors and infectious agents. However, the molecular mechanisms underlying BRD are not fully understood yet. Therefore, this study aimed to use a systems biology approach to systematically evaluate this disorder to better understand the molecular mechanisms responsible for BRD. Methods: Previously published RNA-seq data from whole blood of 18 healthy and 25 BRD samples were downloaded from the Gene Expression Omnibus (GEO) and then analyzed. Next, two distinct methods of weighted gene coexpression network analysis (WGCNA), i.e., module-trait relationships (MTRs) and module preservation (MP) analysis were used to identify significant highly correlated modules with clinical traits of BRD and non-preserved modules between healthy and BRD samples, respectively. After identifying respective modules by the two mentioned methods of WGCNA, functional enrichment analysis was performed to extract the modules that are biologically related to BRD. Gene coexpression networks based on the hub genes from the candidate modules were then integrated with protein-protein interaction (PPI) networks to identify hub-hub genes and potential transcription factors (TFs). Results: Four significant highly correlated modules with clinical traits of BRD as well as 29 non-preserved modules were identified by MTRs and MP methods, respectively. Among them, two significant highly correlated modules (identified by MTRs) and six nonpreserved modules (identified by MP) were biologically associated with immune response, pulmonary inflammation, and pathogenesis of BRD. After aggregation of gene coexpression networks based on the hub genes with PPI networks, a total of 307 hub-hub genes were identified in the eight candidate modules. Interestingly, most of these hub-hub genes were reported to play an important role in the immune response and BRD pathogenesis. Among the eight candidate modules, the turquoise (identified by MTRs) and purple (identified by MP) modules were highly biologically enriched in BRD. Moreover, STAT1, STAT2, STAT3, IRF7, and IRF9 TFs were suggested to play an important role in the immune system during BRD by regulating the coexpressed genes of these modules. Additionally, a gene set containing several hub-hub genes was identified in the eight candidate modules, such as TLR2, TLR4, IL10, SOCS3, GZMB, ANXA1, ANXA5, PTEN, SGK1, IFI6, ISG15, MX1, MX2, OAS2, IFIH1, DDX58, DHX58, RSAD2, IFI44, IFI44L, EIF2AK2, ISG20, IFIT5, IFITM3, OAS1Y, HERC5, and PRF1, which are potentially critical during infection with agents of bovine respiratory disease complex (BRDC). Conclusion: This study not only helps us to better understand the molecular mechanisms responsible for BRD but also suggested eight candidate modules along with several promising hub-hub genes as diagnosis biomarkers and therapeutic targets for BRD.

An Advanced Systems Pharmacology Strategy Reveals AKR1B1, MMP2, PTGER3 as Key Genes in the Competing Endogenous RNA Network of Compound Kushen Injection Treating Gastric Carcinoma by Integrated Bioinformatics and Experimental Verification

Gastric carcinoma (GC) is a severe tumor of the digestive tract with high morbidity and mortality and poor prognosis, for which novel treatment options are urgently needed. Compound Kushen injection (CKI), a classical injection of Chinese medicine, has been widely used to treat various tumors in clinical practice for decades. In recent years, a growing number of studies have confirmed that CKI has a beneficial therapeutic effect on GC, However, there are few reports on the potential molecular mechanism of action. Here, using systems pharmacology combined with proteomics analysis as a core concept, we identified the ceRNA network, key targets and signaling pathways regulated by CKI in the treatment of GC. To further explore the role of these key targets in the development of GC, we performed a meta-analysis to compare the expression differences between GC and normal gastric mucosa tissues. Functional enrichment analysis was further used to understand the biological pathways significantly regulated by the key genes. In addition, we determined the significance of the key genes in the prognosis of GC by survival analysis and immune infiltration analysis. Finally, molecular docking simulation was performed to verify the combination of CKI components and key targets. The anti-gastric cancer effect of CKI and its key targets was verified by in vivo and in vitro experiments. The analysis of ceRNA network of CKI on GC revealed that the potential molecular mechanism of CKI can regulate PI3K/AKT and Toll-like receptor signaling pathways by interfering with hub genes such as AKR1B1, MMP2 and PTGERR3. In conclusion, this study not only partially highlighted the molecular mechanism of CKI in GC therapy but also provided a novel and advanced systems pharmacology strategy to explore the mechanisms of traditional Chinese medicine formulations.

Transcriptome research identifies four hub genes related to primary myelofibrosis: a holistic research by weighted gene co-expression network analysis

Objectives: This study aimed to identify specific diagnostic as well as predictive targets of primary myelofibrosis (PMF).

Methods: The gene expression profiles of GSE26049 were obtained from Gene Expression Omnibus (GEO) dataset, WGCNA was constructed to identify the most related module of PMF. Subsequently, Gene Ontology (GO), Kyoto Encyclopedia Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA) and Protein-Protein interaction (PPI) network were conducted to fully understand the detailed information of the interested green module. Machine learning, Principal component analysis (PCA), and expression pattern analysis including immunohistochemistry and immunofluorescence of genes and proteins were performed to validate the reliability of these hub genes.

Results: Green module was strongly correlated with PMF disease after WGCNA analysis. 20 genes in green module were identified as hub genes responsible for the progression of PMF. GO, KEGG revealed that these hub genes were primarily enriched in erythrocyte differentiation, transcription factor binding, hemoglobin complex, transcription factor complex and cell cycle, etc. Among them, EPB42, CALR, SLC4A1 and MPL had the most correlations with PMF. Machine learning, Principal component analysis (PCA), and expression pattern analysis proved the results in this study.

Conclusions: EPB42, CALR, SLC4A1 and MPL were significantly highly expressed in PMF samples. These four genes may be considered as candidate prognostic biomarkers and potential therapeutic targets for early stage of PMF. The effects are worth expected whether in the diagnosis at early stage or as therapeutic target.

Construction of a ceRNA network of hub genes affecting immune infiltration in ovarian cancer identified by WGCNA

Background

Ovarian cancer is the leading cause of death among gynecological malignancies. Immunotherapy has demonstrated potential effects in ovarian cancer. However, few studies on immune-related prognostic signatures in ovarian cancer have been reported. This study aimed to identify hub genes associated with immune infiltrates to provide insight into the immune regulatory mechanisms in ovarian cancer.

Methods

Raw data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and University of California, Santa Cruz (UCSC) Xena websites. Single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) were used to identify hub genes. Kaplan-Meier analysis and differential expression analysis were applied to explore the real hub genes.

Results

Through ssGSEA and WGCNA, 7 hub genes (LY9, CD5, CXCL9, IL2RG, SLAMF1, SLAMF6, and SLAMF7) were identified. Finally, LY9 and SLAMF1 were recognized as the real hub genes in immune infiltrates of ovarian cancer. LY9 and SLAMF1 are classified as SLAM family receptors involved in the activation of hematopoietic cells and the pathogenesis of multiple malignancies. Furthermore, 12 lncRNAs and 43 miRNAs significantly related to the 2 hub genes were applied to construct a lncRNA-miRNA-mRNA ceRNA network. The lncRNA-miRNA-mRNA ceRNA network shows upstream regulatory sites of the 2 hub genes.

Conclusions

These findings improve our understanding of the regulatory mechanism of and reveal potential immune checkpoints for immunotherapy for ovarian cancer.

Identification of Key Genes and Pathways associated with Endometriosis by Weighted Gene Co-expression Network Analysis

Background: Endometriosis is a common gynecological disorder with high rates of infertility and pelvic pain. However, its pathogenesis and diagnostic biomarkers remain unclear. This study aimed to elucidate potential hub genes and key pathways associated with endometriosis in ectopic endometrium (EC) and eutopic endometrium (EU). Material and Method: EC and EU-associated microarray datasets were obtained from the gene expression omnibus (GEO) database. Gene set enrichment analysis was performed to obtain further biological insight into the EU and EC-associated genes. Weighted gene co-expression network analysis (WGCNA) was performed to find clinically significant modules of highly-correlated genes. The hub genes that belong to both the weighted gene co-expression network and protein-protein interaction (PPI) network were identified using a Venn diagram. Results: We obtained EC and EU-associated microarray datasets GSE7305 and GSE120103. Genes in the EC were mainly enriched in the immune response and immune cell trafficking, and genes in the EU were mainly enriched in stress response and steroid hormone biosynthesis. PPI networks and weighted gene co-expression networks were constructed. An EC-associated blue module and an EU-associated magenta module were identified, and their function annotations revealed that hormone receptor signaling or inflammatory microenvironments may promote EU passing through the oviducts and migrating to the ovarian surfaces, and adhesion and immune correlated genes may induce the successful ectopic implantation of the endometrium (EC). Twelve hub genes in the EC and sixteen hub genes in the EU were recognized and further validated in independent datasets. Conclusion: Our study identified, for the first time, the hub genes and enrichment pathways in the EC and EU using WGCNA, which may provide a comprehensive understanding of the pathogenesis of endometriosis and have important clinical implications for the treatment and diagnosis of endometriosis.

Genome-wide DNA methylation and gene expression analyses in monozygotic twins identify potential biomarkers of depression

Depression is currently the leading cause of disability around the world. We conducted an epigenome-wide association study (EWAS) in a sample of 58 depression score-discordant monozygotic twin pairs, aiming to detect specific epigenetic variants potentially related to depression and further integrate with gene expression profile data. Association between the methylation level of each CpG site and depression score was tested by applying a linear mixed effect model. Weighted gene co-expression network analysis (WGCNA) was performed for gene expression data. The association of DNA methylation levels of 66 CpG sites with depression score reached the level of P < 1 × 10^-4. These top CpG sites were located at 34 genes, especially PTPRN2, HES5, GATA2, PRDM7, and KCNIP1. Many ontology enrichments were highlighted, including Notch signaling pathway, Huntington disease, p53 pathway by glucose deprivation, hedgehog signaling pathway, DNA binding, and nucleic acid metabolic process. We detected 19 differentially methylated regions (DMRs), some of which were located at GRIK2, DGKA, and NIPA2. While integrating with gene expression data, HELZ2, PTPRN2, GATA2, and ZNF624 were differentially expressed. In WGCNA, one specific module was positively correlated with depression score (r = 0.62, P = 0.002). Some common genes (including BMP2, PRDM7, KCNIP1, and GRIK2) and enrichment terms (including complement and coagulation cascades pathway, DNA binding, neuron fate specification, glial cell differentiation, and thyroid gland development) were both identified in methylation analysis and WGCNA. Our study identifies specific epigenetic variations which are significantly involved in regions, functional genes, biological function, and pathways that mediate depression disorder.

Key Candidate Genes - VSIG2 of Colon Cancer Identified by Weighted Gene Co-Expression Network Analysis

Background

Colon adenocarcinoma (COAD) is one of the most common malignancies. To identify candidate genes that may be involved in colon adenocarcinoma development and progression, weighted gene co-expression network analysis (WGCNA) was used to construct gene co-expression networks to explore associations between gene sets and clinical features and to identify candidate biomarkers. Moreover, we intend to make a preliminary exploration on it.

Methods

Gene expression profiles and clinical information were collected from The Cancer Genome Atlas COAD database for analysis. The gene expression profiles of GSE106582 and GSE110224 were screened from the Gene Expression Omnibus database for verification. WGCNA analysis, functional pathway enrichment analysis, and prognosis analysis were performed on three databases. Target genes were selected from the key genes for experimental verification and research.

Results

Key genes obtained by WGCNA analysis were mainly enriched in key functions and pathways such as drug metabolism, steroid hormones, and retinol metabolism. A total of four prognostic genes were screened out: SELENBP1, NAT2, VSIG2, and CES2. VSIG2 was selected as the target gene for experimental verification, and its encoded protein was found to be mainly expressed in immune cells. Its expression was positively correlated with immune infiltration.

Conclusions

VSIG2 was shown to be associated with immune invasion and antigen presentation in COAD, suggesting it plays an important role in COAD development and progression. It could be used as a potential biomarker or therapeutic target for COAD.

Identification of key immune regulatory genes in HIV-1 progression

Human immunodeficiency virus (HIV) infection causes acquired immunodeficiency syndrome (AIDS), one of the most devastating diseases affecting humankind. Here, we have proposed a framework to examine the differences among microarray gene expression data of uninfected and three different HIV-1 infection stages using module preservation statistics. We leverage the advantage of gene co-expression networks (GCN) constructed for each infection stages to detect the topological and structural changes of a group of differentially expressed genes. We examine the relationship among a set of co-expression modules by constructing a module eigengene network considering the overall similarity/dissimilarity among the genes within the modules. We have utilized different module preservation statistics with two composite statistics: "Z_summary" and "MedianRank" to examine the changes in co-expression patterns between modules. We have found several interesting results on the preservation characteristics of gene modules across different stages. Some genes are identified to be preserved in a pair of stages while altering their characteristics across other stages. We further validated the obtained results using permutation test and classification techniques. The biological significances of the obtained modules have also been examined using gene ontology and pathway-based analysis. Additionally, we have identified a set of key immune regulatory hub genes in the associated protein-protein interaction networks (PPINs) of the differentially expressed (DE) genes, which interacts with HIV-1 proteins and are likely to act as potential biomarkers in HIV-1 progression.

Cytoplasmic FUS triggers early behavioral alterations linked to cortical neuronal hyperactivity and inhibitory synaptic defects

Gene mutations causing cytoplasmic mislocalization of the RNA-binding protein FUS lead to severe forms of amyotrophic lateral sclerosis (ALS). Cytoplasmic accumulation of FUS is also observed in other diseases, with unknown consequences. Here, we show that cytoplasmic mislocalization of FUS drives behavioral abnormalities in knock-in mice, including locomotor hyperactivity and alterations in social interactions, in the absence of widespread neuronal loss. Mechanistically, we identified a progressive increase in neuronal activity in the frontal cortex of Fus knock-in mice in vivo, associated with altered synaptic gene expression. Synaptic ultrastructural and morphological defects were more pronounced in inhibitory than excitatory synapses and associated with increased synaptosomal levels of FUS and its RNA targets. Thus, cytoplasmic FUS triggers synaptic deficits, which is leading to increased neuronal activity in frontal cortex and causing related behavioral phenotypes. These results indicate that FUS mislocalization may trigger deleterious phenotypes beyond motor neuron impairment in ALS, likely relevant also for other neurodegenerative diseases characterized by FUS mislocalization.

Early stem cell aging in the mature brain

Stem cell dysfunction drives many age-related disorders. Identifying mechanisms that initially compromise stem cell behavior represent early targets to promote tissue function later in life. Here, we pinpoint multiple factors that disrupt neural stem cell (NSC) behavior in the adult hippocampus. Clonal tracing showed that NSCs exhibit asynchronous depletion by identifying short-term NSCs (ST-NSCs) and long-term NSCs (LT-NSCs). ST-NSCs divide rapidly to generate neurons and deplete in the young brain. Meanwhile, multipotent LT-NSCs are maintained for months but are pushed out of homeostasis by lengthening quiescence. Single-cell transcriptome analysis of deep NSC quiescence revealed several hallmarks of molecular aging in the mature brain and identified tyrosine-protein kinase Abl1 as an NSC aging factor. Treatment with the Abl inhibitor imatinib increased NSC activation without impairing NSC maintenance in the middle-aged brain. Our study indicates that hippocampal NSCs are particularly vulnerable and adaptable to cellular aging.

An In Silico Method for Predicting Drug Synergy Based on Multitask Learning

To make better use of all kinds of knowledge to predict drug synergy, it is crucial to successfully establish a drug synergy prediction model and leverage the reconstruction of sparse known drug targets. Therefore, we present an in silico method that predicts the synergy scores of drug pairs based on multitask learning (DSML) that could fuse drug targets, protein-protein interactions, anatomical therapeutic chemical codes, a priori knowledge of drug combinations. To simultaneously reconstruct drug-target protein interactions and synergistic drug combinations, DSML benefits indirectly from the associations with relation through proteins. In cross-validation experiments, DSML improved the ability to predict drug synergy. Moreover, the reconstruction of drug-target interactions and the incorporation of multisource knowledge significantly improved drug combination predictions by a large margin. The potential drug combinations predicted by DSML demonstrate its ability to predict drug synergy.

Impact of storage conditions on peripheral leukocytes transcriptome

Leukocytes reflect the physiological and pathological states of each individual, and transcriptomic data of leukocytes have been used to reflect health conditions. Since the overall impact of ex vivo conditions on the leukocyte transcriptome before RNA stabilization remains unclear, we evaluated the influence of temporary storage conditions on the leukocyte transcriptome through RNA sequencing. We collected peripheral blood with EDTA tubes, which were processed immediately or stored either at 4 °C or room temperature (RT, 18-22 °C) for 2 h, 6 h and 24 h. Total cellular RNA was extracted from 42 leukocyte samples after red blood cells lysis for subsequent RNA sequencing. We applied weighted gene co-expression network analysis to construct co-expression networks of mRNA and lncRNA among the samples, and then performed gene ontology (GO) term enrichment to explore possible biological processes affected by storage conditions. Storage conditions change the gene expression of peripheral leukocytes. Comparing with fresh leukocytes, storage for 24 h at 4 °C and RT affected 1515 (1.51%) and 10,823 (10.82%) genes, respectively. Pathway enrichment analysis identified nucleosome assembly enriched in up-regulated genes at both conditions. When blood was stored at RT for 24 h, genes involved in apoptotic signaling pathway, negative regulation of cell cycle and lymphocyte activation were upregulated, while the relative proportion of neutrophils was significantly decreased. Temporary storage conditions profoundly affect the gene expression profiles of leukocytes and might further change cell viability and state. Storage of blood samples at 4 °C within 6 h largely maintains their original transcriptome.

Identification of Infertility-Associated Topologically Important Genes Using Weighted Co-expression Network Analysis

Endometriosis has been associated with a high risk of infertility. However, the underlying molecular mechanism of infertility in endometriosis remains poorly understood. In our study, we aimed to discover topologically important genes related to infertility in endometriosis, based on the structure network mining. We used microarray data from the Gene Expression Omnibus (GEO) database to construct a weighted gene co-expression network for fertile and infertile women with endometriosis and to identify gene modules highly correlated with clinical features of infertility in endometriosis. Additionally, the protein–protein interaction network analysis was used to identify the potential 20 hub messenger RNAs (mRNAs) while the network topological analysis was used to identify nine candidate long non-coding RNAs (lncRNAs). Functional annotations of clinically significant modules and lncRNAs revealed that hub genes might be involved in infertility in endometriosis by regulating G protein-coupled receptor signaling (GPCR) activity. Gene Set Enrichment Analysis showed that the phospholipase C-activating GPCR signaling pathway is correlated with infertility in patients with endometriosis. Taken together, our analysis has identified 29 hub genes which might lead to infertility in endometriosis through the regulation of the GPCR network.

Integrated Bioinformatics Analysis of Hub Genes and Pathways Associated with a Compression Model of Spinal Cord Injury in Rats

Background

Spinal cord injury (SCI) is a serious nervous system condition that can cause lifelong disability. The aim of this study was to identify potential molecular mechanisms and therapeutic targets for SCI.

Material/Methods

We constructed a weighted gene coexpression network and predicted which hub genes are involved in SCI. A compression model of SCI was established in 45 Sprague-Dawley rats, which were divided into 5 groups (n=9 per group): a sham operation group, and 1, 3, 5, and 7 days post-SCI groups. The spinal cord tissue on the injured site was harvested on 1, 3, 5, and 7 days after SCI and 3 days after surgery in the sham operation group. High-throughput sequencing was applied to investigate the expression profile of the mRNA in all samples. Differentially expressed genes were screened and included in weighted gene coexpression network analysis (WGCNA). Co-expressed modules and hub genes were identified by WGCNA. The biological functions of each module were investigated using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases.

Results

According to the RNA-seq data, a total of 1965 differentially expressed genes were screened, and WGCNA identified 10 coexpression modules and 5 hub genes. Module function analysis revealed that SCI was associated with immune response, cell division, neuron projection development, and collagen fibril organization.

Conclusions

Our study revealed dynamic changes in a variety of biological processes following SCI and identified 5 hub genes via WGCNA. These results provide insights into the molecular mechanisms and therapeutic targets of SCI.

Tau Pathology Drives Dementia Risk-Associated Gene Networks toward Chronic Inflammatory States and Immunosuppression

To understand how neural-immune-associated genes and pathways contribute to neurodegenerative disease pathophysiology, we performed a systematic functional genomic analysis in purified microglia and bulk tissue from mouse and human AD, FTD, and PSP. We uncover a complex temporal trajectory of microglial-immune pathways involving the type 1 interferon response associated with tau pathology in the early stages, followed by later signatures of partial immune suppression and, subsequently, the type 2 interferon response. We find that genetic risk for dementias shows disease-specific patterns of pathway enrichment. We identify drivers of two gene co-expression modules conserved from mouse to human, representing competing arms of microglial-immune activation (NAct) and suppression (NSupp) in neurodegeneration. We validate our findings by using chemogenetics, experimental perturbation data, and single-cell sequencing in post-mortem brains. Our results refine the understanding of stage- and disease-specific microglial responses, implicate microglial viral defense pathways in dementia pathophysiology, and highlight therapeutic windows.

Rexach et al. use transcriptional network analysis to define dynamic microglial transitions across neurodegeneration, discovering that three dementias with tau pathology involve dysregulated microglial viral and antiviral pathways. Bio-informatics coupled with experimental validation identifies regulatory drivers, implicating double-stranded RNA and interferon-response genes as drivers of early immune suppression in disease.

Integrated bioinformatics analysis to decipher molecular mechanism of compound Kushen injection for esophageal cancer by combining WGCNA with network pharmacology

Compound Kushen injection (CKI), a medicine in widespread clinical use in China, has proven therapeutic effects on cancer. However, few molecular mechanism analyses have been carried out. To address this problem, bioinformatics approaches combining weighted gene co-expression network analysis with network pharmacology methods were undertaken to elucidate the underlying molecular mechanisms of CKI in the treatment of esophageal cancer (ESCA). First, the key gene modules related to the clinical traits of ESCA were analysed by WCGNA. Based on the results, the hub genes related to CKI treatment for ESCA were explored through network pharmacology. Molecular docking simulation was performed to recognize the binding activity of hub genes with CKI compounds. The results showed that the potential hub targets, including EGFR, ErbB2, CCND1 and IGF1R, are therapeutic targets of CKI for the treatment of ESCA. Moreover, these targets were significantly enriched in many pathways related to cancer and signalling pathways, such as the PI3K-Akt signalling pathway and ErbB signalling pathway. In conclusion, this research partially highlighted the molecular mechanism of CKI in the treatment of ESCA, offering great potential in the identification of the effective compounds in CKI and biomarkers for ESCA treatment.

A Comprehensive Coexpression Network Analysis in Vibrio cholerae

Cholera is a devastating illness that kills tens of thousands of people annually. Vibrio cholerae, the causative agent of cholera, is an important model organism to investigate both bacterial pathogenesis and the impact of horizontal gene transfer on the emergence and dissemination of new virulent strains. Despite the importance of this pathogen, roughly one-third of V. cholerae genes are functionally unannotated, leaving large gaps in our understanding of this microbe. Through coexpression network analysis of existing RNA sequencing data, this work develops an approach to uncover novel gene-gene relationships and contextualize genes with no known function, which will advance our understanding of V. cholerae virulence and evolution.

Research into the evolution and pathogenesis of Vibrio cholerae has benefited greatly from the generation of high-throughput sequencing data to drive molecular analyses. The steady accumulation of these data sets now provides a unique opportunity for in silico hypothesis generation via coexpression analysis. Here, we leverage all published V. cholerae RNA sequencing data, in combination with select data from other platforms, to generate a gene coexpression network that validates known gene interactions and identifies novel genetic partners across the entire V. cholerae genome. This network provides direct insights into genes influencing pathogenicity, metabolism, and transcriptional regulation, further clarifies results from previous sequencing experiments in V. cholerae (e.g., transposon insertion sequencing [Tn-seq] and chromatin immunoprecipitation sequencing [ChIP-seq]), and expands upon microarray-based findings in related Gram-negative bacteria.

IMPORTANCE Cholera is a devastating illness that kills tens of thousands of people annually. Vibrio cholerae, the causative agent of cholera, is an important model organism to investigate both bacterial pathogenesis and the impact of horizontal gene transfer on the emergence and dissemination of new virulent strains. Despite the importance of this pathogen, roughly one-third of V. cholerae genes are functionally unannotated, leaving large gaps in our understanding of this microbe. Through coexpression network analysis of existing RNA sequencing data, this work develops an approach to uncover novel gene-gene relationships and contextualize genes with no known function, which will advance our understanding of V. cholerae virulence and evolution.

Stability Analysis of Biological Networks' Diffusion State

Computational knowledge acquired from noisy networks is not reliable and the network topology determines the reliability. Protein-protein interaction networks have uncertain topologies and noise that contain false positive and false negative edges at high rates. In this study, we analyze effects of the existing mutations in a network topology to the diffusion state of that network. To evaluate the sensitivity of the diffusion state, we derive the fitness measures based on the mathematically defined stability of a network. Searching for an influential set of edges in a network is a difficult problem. We handle the computational challenge by developing a novel metaheuristic optimization method and we find influential mutations time-efficiently. Our experiments, conducted on both synthetic and real networks from public databases, demonstrated that our method obtained better results than competitors for all types of network topologies. This is the first-time that the diffusion has been evaluated under topological mutations. Our analysis identifies significant biological results about the stability of biological - synthetic networks and diffusion state. In this manner, mutations in protein-protein interaction network topologies have a significant influence on the diffusion state of the network. Network stability is more affected by the network model than the network size.

Weighted gene correlation network analysis reveals novel regulatory modules associated with recurrent early pregnancy loss

At present, the etiology and pathogenesis of recurrent early pregnancy loss (REPL) are not completely clear. Therefore, identifying the underlying diagnostic and prognostic biomarkers of REPL can provide new ideas for the diagnosis and treatment of REPL. The chip data of REPL (GSE63901) were downloaded from the NCBI Gene Expression Omnibus (GEO) database. Weighted Gene Co-Expression Network Analysis (WGCNA) was used to construct a co-expression module for studying the relationship between gene modules and clinical features. In addition, functional analysis of hub genes in modules of interest was performed. A total of 23 co-expression modules were identified, two of which were most significantly associated with three clinical features. The MEbrown module was positively correlated with cyclin E level and the out-of-phase trait while the MEred module was positively correlated with the effect of progesterone. We identified 17 hub genes in the MEred module. The functional enrichment analysis indicated that such hub genes were mainly involved in pathways related to cellular defense response and natural killer (NK) cell-mediated cytotoxicity. In the MEbrown module, we identified 19 hub genes, which were mainly enriched in cell adhesion molecule production, regulation of cellular response to growth factor stimulus, epithelial cell proliferation, and transforming growth factor-β (TGF-β) signaling pathway. In addition, the hub genes were validated by using other datasets and three true hub genes were finally obtained, namely DOCK2 for the MEred module, and TRMT44 and ERVMER34-1 for the MEbrown module. In conclusion, our results screened potential biomarkers that might contribute to the diagnosis and treatment of REPL.

Applications of weighted association networks applied to compositional data in biology

Next-generation sequencing technologies have generated, and continue to produce, an increasingly large corpus of biological data. The data generated are inherently compositional as they convey only relative information dependent upon the capacity of the instrument, experimental design and technical bias. There is considerable information to be gained through network analysis by studying the interactions between components within a system. Network theory methods using compositional data are powerful approaches for quantifying relationships between biological components and their relevance to phenotype, environmental conditions or other external variables. However, many of the statistical assumptions used for network analysis are not designed for compositional data and can bias downstream results. In this mini-review, we illustrate the utility of network theory in biological systems and investigate modern techniques while introducing researchers to frameworks for implementation. We overview (1) compositional data analysis, (2) data transformations and (3) network theory along with insight on a battery of network types including static-, temporal-, sample-specific- and differential-networks. The intention of this mini-review is not to provide a comprehensive overview of network methods, rather to introduce microbiology researchers to (semi)-unsupervised data-driven approaches for inferring latent structures that may give insight into biological phenomena or abstract mechanics of complex systems.

Construction, comparison and evolution of networks in life sciences and other disciplines

Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.

Identification of potential novel biomarkers to differentiate malignant thyroid nodules with cytological indeterminate

Background

The fine-needle aspiration (FNA) biopsy was broadly applied to clinical diagnostics evaluation for thyroid carcinomas nodule, while companioning with higher uncertainty rate (15~30%) to identify malignancy for cytological indeterminate cases. It is requirement to discover novel molecular biomarkers to differentiate malignant thyroid nodule more precise.

Methods

We employed weighted gene co-expression network analysis (WGCNA) to discover genes significantly associated with malignant histopathology for cytological indeterminate nodules. In addition, identified significantly genes were validated through another independently investigations of thyroid carcinomas patient’s samples via cBioportal and Geipa. The key function pathways of significant genes involving were blast through GenClip.

Results

Twenty-four signature genes were identified significantly related to thyroid nodules malignancy. Furthermore, five novel genes with missense mutation, FN1 (R534P), PROS1((K200I), (Q571K)), SCEL (T320S), SLC34A2(T688M) and TENM1 (S1131F), were highlighted as potential biomarkers to rule out nodules malignancy. It was identified that the key functional pathways involving in thyroid carcinomas.

Conclusion

These results will be helpful to better understand the mechanism of thyroid nodules malignant transformation and characterize the potentially biomarkers for thyroid carcinomas early diagnostics.

MicroRNAs organize intrinsic variation into stem cell states

Understanding how mammalian organisms achieve the full diversity of cell types in the adult organism is a central goal of developmental cell biology. Recent work has shown that some embryonic precursor cells can self-organize into developmental structures but the mechanisms of gene regulation that contribute to this process remain unknown. Here we show embryonic stem cells self-organize into distinct gene expression states that resemble developmental gene programs. We find that microRNAs, small noncoding regulators of gene expression, play a critical role in organizing fluctuations across gene networks to help achieve this organization into distinct expression states.

Pluripotent embryonic stem cells (ESCs) contain the potential to form a diverse array of cells with distinct gene expression states, namely the cells of the adult vertebrate. Classically, diversity has been attributed to cells sensing their position with respect to external morphogen gradients. However, an alternative is that diversity arises in part from cooption of fluctuations in the gene regulatory network. Here we find ESCs exhibit intrinsic heterogeneity in the absence of external gradients by forming interconverting cell states. States vary in developmental gene expression programs and display distinct activity of microRNAs (miRNAs). Notably, miRNAs act on neighborhoods of pluripotency genes to increase variation of target genes and cell states. Loss of miRNAs that vary across states reduces target variation and delays state transitions, suggesting variable miRNAs organize and propagate variation to promote state transitions. Together these findings provide insight into how a gene regulatory network can coopt variation intrinsic to cell systems to form robust gene expression states. Interactions between intrinsic heterogeneity and environmental signals may help achieve developmental outcomes.

Revealing Dynamic Mechanisms of Cell Fate Decisions From Single-Cell Transcriptomic Data

Cell fate decisions play a pivotal role in development, but technologies for dissecting them are limited. We developed a multifunction new method, Topographer, to construct a "quantitative" Waddington's landscape of single-cell transcriptomic data. This method is able to identify complex cell-state transition trajectories and to estimate complex cell-type dynamics characterized by fate and transition probabilities. It also infers both marker gene networks and their dynamic changes as well as dynamic characteristics of transcriptional bursting along the cell-state transition trajectories. Applying this method to single-cell RNA-seq data on the differentiation of primary human myoblasts, we not only identified three known cell types, but also estimated both their fate probabilities and transition probabilities among them. We found that the percent of genes expressed in a bursty manner is significantly higher at (or near) the branch point (~97%) than before or after branch (below 80%), and that both gene-gene and cell-cell correlation degrees are apparently lower near the branch point than away from the branching. Topographer allows revealing of cell fate mechanisms in a coherent way at three scales: cell lineage (macroscopic), gene network (mesoscopic), and gene expression (microscopic).

Alcohol shifts gut microbial networks and ameliorates a murine model of neuroinflammation in a sex-specific pattern

Alcohol is a widely consumed dietary component by patients with autoimmune neuroinflammatory diseases, but current evidence on the effects of alcohol in these conditions is confounding. Epidemiological studies suggest moderate consumption of alcohol may be protective in some autoimmune diseases; however, this correlation has not been directly investigated. Here, we characterize the effects of moderate-dose alcohol in a model system of autoimmune neuroinflammation, experimental autoimmune encephalomyelitis (EAE). Male and female C57BL/6J mice were fed a 2.6% alcohol or isocaloric diet for 3 wk prior to MOG_35-55 EAE induction. Surprisingly, alcohol-fed males experienced significantly greater disease remission compared to alcohol-fed females and control-fed counterparts. We observed a male-specific decrease in microglial density in alcohol-consuming animals in cervical and thoracic spinal cord in late-stage disease. In the gut, alcohol diet resulted in several sex-specific alterations in key microbiota known for their regulatory immune roles, including Turicibacter, Akkermansia, Prevotella, and Clostridium Using a correlation network modeling approach, we identified unique bacterial modules that are significantly enriched in response to treatment and sex, composed of Clostridial taxa and several Firmicutes known to be protective in EAE. Together, these data demonstrate the potential of alcohol to significantly alter the course of autoimmunity differentially in males and females via effects on gut bacterial networks and support further need to evaluate dose and sex-specific alcohol effects in multiple sclerosis (MS) and other autoimmune neuroinflammatory conditions.

The relevance between the immune response-related gene module and clinical traits in head and neck squamous cell carcinoma

Purpose

Head and neck squamous cell carcinoma (HNSCC) is the sixth most prevalent cancer in the world, accounting for more than 90% of head and neck malignant tumors. However, its molecular mechanism is largely unknown. To help elucidate the potential mechanism of HNSCC tumorigenesis, we investigated the gene interaction patterns associated with tumorigenesis.

Methods

Weighted gene co-expression network analysis (WGCNA) can help us to predict the intrinsic relationship or correlation between gene expression. Additionally, we further explored the combination of clinical information and module construction.

Results

Sixteen modules were constructed, among which the key module most closely associated with clinical information was identified. By analyzing the genes in this module, we found that the latter may be related to the immune response, inflammatory response and formation of the tumor microenvironment. Sixteen hub genes were identified-ARHGAP9, SASH3, CORO1A, ITGAL, PPP1R16B, TBC1D10C, IL10RA, ITK, AKNA, PRKCB, TRAF3IP3, GIMAP4, CCR7, P2RY8, GIMAP7, and SP140. We further validated these genes at the transcriptional and translation levels.

Conclusion

The innovative use of a weighted network to analyze HNSCC samples provides new insights into the molecular mechanism and prognosis of HNSCC. Additionally, the hub genes we identified can be used as biomarkers and therapeutic targets of HNSCC, laying the foundation for the accurate diagnosis and treatment of HNSCC in clinical and research in the future.

Modulation of chromatin remodeling proteins SMYD1 and SMARCD1 promotes contractile function of human pluripotent stem cell-derived ventricular cardiomyocyte in 3D-engineered cardiac tissues

Human embryonic stem cells (hESCs) and induced pluripotent stem cells (iPSCs) have the ability of differentiating into functional cardiomyocytes (CMs) for cell replacement therapy, tissue engineering, drug discovery and toxicity screening. From a scale-free, co-expression network analysis of transcriptomic data that distinguished gene expression profiles of undifferentiated hESC, hESC-, fetal- and adult-ventricular(V) CM, two candidate chromatin remodeling proteins, SMYD1 and SMARCD1 were found to be differentially expressed. Using lentiviral transduction, SMYD1 and SMARCD1 were over-expressed and suppressed, respectively, in single hESC-VCMs as well as the 3D constructs Cardiac Micro Tissues (CMT) and Tissue Strips (CTS) to mirror the endogenous patterns, followed by dissection of their roles in controlling cardiac gene expression, contractility, Ca²⁺-handling, electrophysiological functions and in vitro maturation. Interestingly, compared to independent single transductions, simultaneous SMYD1 overexpression and SMARCD1 suppression in hESC-VCMs synergistically interacted to increase the contractile forces of CMTs and CTSs with up-regulated transcripts for cardiac contractile, Ca²⁺-handing, and ion channel proteins. Certain effects that were not detected at the single-cell level could be unleashed under 3D environments. The two chromatin remodelers SMYD1 and SMARCD1 play distinct roles in cardiac development and maturation, consistent with the notion that epigenetic priming requires triggering signals such as 3D environmental cues for pro-maturation effects.