Weighted Gene Co-Expression Network Analysis Identifies Key Modules and Hub Genes Associated with Mycobacterial Infection of Human Macrophages

Identification and validation of core genes associated with polycystic ovary syndrome and metabolic syndrome

Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic disorder affecting women of reproductive age, affecting reproductive health, and increasing the incidence of diabetes mellitus and hypertension. Metabolic syndrome (MetS) is the most common metabolic disorder. Although clinical studies have shown a close association between PCOS and MetS, the molecular mechanisms are unknown. In this study, datasets of PCOS and MetS were obtained from the Gene Expression Omnibus database; differential expression analysis and weighted gene coexpression network analysis (WGCNA) were performed; and gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses also performed of differentially expressed genes (DEGs). The PCOS- and MetS-coexpressed DEGs were subsequently intersected with the coexpressed genes in the WGCNA module to obtain the core genes. By constructing receiver operating characteristic curves, we verified the predictive effects of the core genes. We also validated the expression of the core genes in the datasets. Finally, we verified the expression of the core genes by quantitative polymerase chain reaction in human follicular fluid granulosa cells. In addition, we used Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts to analyze the immune infiltration of immune cells in PCOS and MetS. Finally, we obtained 52 coexpressed DEGs of PCOS and MetS and 3 coexpressed genes in the WGCNA module. By taking the intersection of coexpressed DEGs and coexpressed genes of the WGCNA module, we get ELOVL fatty acid elongase 7 (ELOVL7) as the core gene. Receiver operating characteristic curve analysis showed that ELOVL7 is a reliable biological marker for PCOS and MetS. The expression level of ELOVL7 in human follicular fluid granulosa cells from PCOS patients was significantly higher than that of controls, as verified by quantitative polymerase chain reaction. This study provides the first evidence of the role of ELOVL7 in developing PCOS and MetS. This gene may serve as a potential diagnostic marker and therapeutic target for both conditions.

Differential gene expression analysis combined with molecular dynamics simulation study to elucidate the novel potential biomarker involved in pulmonary TB

Tuberculosis (TB) is a lethal multisystem disease that attacks the lungs' first line of defense. A substantial threat to public health and a primary cause of death is pulmonary TB. This study aimed to identify and investigate the probable differentially expressed genes (DEGs) primarily involved in Pulmonary TB. Accordingly, three independent gene expression data sets, numbered GSE139825, GSE139871, and GSE54992, were utilized for this purpose. The identified DEGs were used for bioinformatics-based analysis, including physical gene interaction, Gene Ontology (GO), network analysis and pathway studies using the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG). The computational analysis predicted that TNFAIP6 is the significant DEG in the gene expression profiling of TB datasets. According to gene ontology analysis, TNFAIP6 is also essential in injury and inflammation. Further, TNFA1P6 is strongly linked to arsenic poisoning, evident from the results of NetworkAnalyst, a comprehensive and interactive platform for gene expression profiling via network visual analytics. As a result, the TNFAIP6 gene was ultimately chosen as a candidate DEG and subsequently employed for in silico structural characterization studies. The tertiary structure of TNFAIP6 was modelled using the ROBETTA server, followed by validation with SAVES and ProSA webserver. Additionally, structural dynamic studies, including molecular dynamics simulation (MDS) and essential dynamics analysis, including principal component (PC) based free energy landscape (FEL) analysis, was used for checking the stability of TNFAIP6 models. The dynamics result established the structural rigidity of modelled TNFAIP6 through RMSD, RMSF and RoG results. The FEL analysis revealed the restricted conformational flexibility of TNFAIP6 by displaying a single minimum energy basin in the contour plot. The comprehensive computational analysis established that TNFAIP6 could serve as a viable biomarker to assess the severity of pulmonary TB.

Increased expression of ELOVL7 contributes to production of inflammatory cytokines in THP-1 cell-derived M1-like macrophages

The elevation of intracellular very long-chain fatty acids (VLCFAs) augments pro-inflammatory activity of macrophages. VLCFAs are considered to function as regulators in macrophage inflammatory responses; however, the precise mechanism of regulating the production of VLCFAs is unclear. In this study, we focused on elongation of the very‑long‑chain fatty acid protein (ELOVL) family, rate-determining enzymes for VLCFA synthesis, in macrophages. ELOVL7 mRNA was upregulated in human monocytic THP-1 cell-derived M1-like macrophages. Metascape analysis using the RNA-seq data set showed the involvement of NF-κB and STAT1 in transcriptional regulation of ELOVL7 highly correlated genes. Gene ontology (GO) enrichment analysis suggested that ELOVL7 highly correlated genes were closely associated with multiple pro-inflammatory responses, including response to virus and positive regulation of NF-κB signaling. Consistent with RNA-seq analysis, the NF-κB inhibitor BAY11-7082, but not the STAT1 inhibitor fludarabine, canceled ELOVL7 upregulation in M1-like macrophages. ELOVL7 knockdown decreased interleukin (IL)-6 and IL-12/IL-23 p40 production. Moreover, RNA-seq analysis of plasmacytoid dendritic cells (pDCs) revealed that ELOVL7 was upregulated in pDCs treated with TLR7 and TLR9 agonists. In conclusion, we propose that ELOVL7 is a novel pro-inflammatory gene that is upregulated by inflammatory stimuli, and regulates M1-like macrophage and pDC functions.

Advances in computational frameworks in the fight against TB: The way forward

Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its "End TB" strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for-early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.

Using a whole genome co-expression network to inform the functional characterisation of predicted genomic elements from Mycobacterium tuberculosis transcriptomic data

A whole genome co-expression network was created using Mycobacterium tuberculosis transcriptomic data from publicly available RNA-sequencing experiments covering a wide variety of experimental conditions. The network includes expressed regions with no formal annotation, including putative short RNAs and untranslated regions of expressed transcripts, along with the protein-coding genes. These unannotated expressed transcripts were among the best-connected members of the module sub-networks, making up more than half of the 'hub' elements in modules that include protein-coding genes known to be part of regulatory systems involved in stress response and host adaptation. This data set provides a valuable resource for investigating the role of non-coding RNA, and conserved hypothetical proteins, in transcriptomic remodelling. Based on their connections to genes with known functional groupings and correlations with replicated host conditions, predicted expressed transcripts can be screened as suitable candidates for further experimental validation.

In-depth systems biological evaluation of bovine alveolar macrophages suggests novel insights into molecular mechanisms underlying Mycobacterium bovis infection

Objective

Bovine tuberculosis (bTB) is a chronic respiratory infectious disease of domestic livestock caused by intracellular Mycobacterium bovis infection, which causes ~$3 billion in annual losses to global agriculture. Providing novel tools for bTB managements requires a comprehensive understanding of the molecular regulatory mechanisms underlying the M. bovis infection. Nevertheless, a combination of different bioinformatics and systems biology methods was used in this study in order to clearly understand the molecular regulatory mechanisms of bTB, especially the immunomodulatory mechanisms of M. bovis infection.

Methods

RNA-seq data were retrieved and processed from 78 (39 non-infected control vs. 39 M. bovis-infected samples) bovine alveolar macrophages (bAMs). Next, weighted gene co-expression network analysis (WGCNA) was performed to identify the co-expression modules in non-infected control bAMs as reference set. The WGCNA module preservation approach was then used to identify non-preserved modules between non-infected controls and M. bovis-infected samples (test set). Additionally, functional enrichment analysis was used to investigate the biological behavior of the non-preserved modules and to identify bTB-specific non-preserved modules. Co-expressed hub genes were identified based on module membership (MM) criteria of WGCNA in the non-preserved modules and then integrated with protein-protein interaction (PPI) networks to identify co-expressed hub genes/transcription factors (TFs) with the highest maximal clique centrality (MCC) score (hub-central genes).

Results

As result, WGCNA analysis led to the identification of 21 modules in the non-infected control bAMs (reference set), among which the topological properties of 14 modules were altered in the M. bovis-infected bAMs (test set). Interestingly, 7 of the 14 non-preserved modules were directly related to the molecular mechanisms underlying the host immune response, immunosuppressive mechanisms of M. bovis, and bTB development. Moreover, among the co-expressed hub genes and TFs of the bTB-specific non-preserved modules, 260 genes/TFs had double centrality in both co-expression and PPI networks and played a crucial role in bAMs-M. bovis interactions. Some of these hub-central genes/TFs, including PSMC4, SRC, BCL2L1, VPS11, MDM2, IRF1, CDKN1A, NLRP3, TLR2, MMP9, ZAP70, LCK, TNF, CCL4, MMP1, CTLA4, ITK, IL6, IL1A, IL1B, CCL20, CD3E, NFKB1, EDN1, STAT1, TIMP1, PTGS2, TNFAIP3, BIRC3, MAPK8, VEGFA, VPS18, ICAM1, TBK1, CTSS, IL10, ACAA1, VPS33B, and HIF1A, had potential targets for inducing immunomodulatory mechanisms by M. bovis to evade the host defense response.

Conclusion

The present study provides an in-depth insight into the molecular regulatory mechanisms behind M. bovis infection through biological investigation of the candidate non-preserved modules directly related to bTB development. Furthermore, several hub-central genes/TFs were identified that were significant in determining the fate of M. bovis infection and could be promising targets for developing novel anti-bTB therapies and diagnosis strategies.