Co-Expression Networks for Causal Gene Identification Based on RNA-Seq Data of Corynebacterium pseudotuberculosis

Franco, Edian F.; Rana, Pratip; Cavalcante, Ana Lidia Queiroz; Silva, Artur da Costa da Luiz; Gomide, Anne Cybelle Pinto; Folador, Adriana Ribeiro Carneiro; Azevedo, Vasco; Ghosh, Preetam; Ramos, Rommel Thiago Jucá

doi:10.3390/genes11070794

Cited by 4 publications

(6 citation statements)

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“…To verify the presence of the putative CoRP transcript in Corynebacteriales , we explored publicly available datasets for C. glutamicum ( 75–78 ), C. pseudotuberculosis ( 79 , 80 ) and C. diphtheriae ( 81 ). We found the transcript in several C. glutamicum datasets ( Supplementary Figure S9B ), in C. pseudotuberculosis ( Supplementary Figure S9C ) and C. diphtheriae ( Supplementary Figure S9D ).…”

Section: Resultsmentioning

confidence: 99%

RIP-seq reveals RNAs that interact with RNA polymerase and primary sigma factors in bacteria

Vaňková Hausnerová,

Shoman,

Kumar

et al. 2024

Nucleic Acids Research

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Bacteria have evolved structured RNAs that can associate with RNA polymerase (RNAP). Two of them have been known so far—6S RNA and Ms1 RNA but it is unclear if any other types of RNAs binding to RNAP exist in bacteria. To identify all RNAs interacting with RNAP and the primary σ factors, we have established and performed native RIP-seq in Bacillus subtilis, Corynebacterium glutamicum, Streptomyces coelicolor, Mycobacterium smegmatis and the pathogenic Mycobacterium tuberculosis. Besides known 6S RNAs in B. subtilis and Ms1 in M. smegmatis, we detected MTS2823, a homologue of Ms1, on RNAP in M. tuberculosis. In C. glutamicum, we discovered novel types of structured RNAs that associate with RNAP. Furthermore, we identified other species-specific RNAs including full-length mRNAs, revealing a previously unknown landscape of RNAs interacting with the bacterial transcription machinery.

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Section: Resultsmentioning

confidence: 99%

RIP-seq reveals RNAs that interact with RNA polymerase and primary sigma factors in bacteria

Vaňková Hausnerová,

Shoman,

Kumar

et al. 2024

Nucleic Acids Research

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“…Such networks have been used to model how regulatory processes work inside the cell, including amino acid synthesis and virulence mechanisms [ 50 , 51 , 52 , 53 ]. Franco et al [ 54 ] and Parise et al [ 22 ] performed GCN analysis and TRN transfer, respectively, in C. pseudotuberculosis .…”

Section: Gene Co-expression Network and Transcriptional Regulatormentioning

confidence: 99%

“…Franco et al inferred the GCNs of four C. pseudotuberculosis strains (258, T1, Cp13 and 1002) using RNA-Seq datasets [ 40 , 41 , 42 , 43 ]. The authors applied the following bioinformatic tools: (i) miRsig [ 55 ] to infer the GCNs of all genes and differentially expressed genes (DEGs), (ii) miRinfluence [ 56 ] to identify the predicted networks’ influential and causal genes and (iii) Online GEne Essentiality (OGEE) database v2 [ 57 ] to classify the causal genes as essential, nonessential or conditionally essential [ 54 ]. Essential, nonessential and conditionally essential genes demonstrate the consensus of the level of essentiality of a certain gene for bacterial survival, for more details see [ 57 ].…”

Section: Gene Co-expression Network and Transcriptional Regulatormentioning

confidence: 99%

“…In this system the HrrS is the sensor kinase and the HrrA is the response regulator. Furthermore, Franco et al [ 54 ] identified three TCS genes, namely, tcsS4 , mprA_2 and tcsR3, as influential genes. However, they remain to be studied; there is no regulatory information in CoryneRegNet 7 for the HrrSA TCS and the three genes found by Franco et al…”

Section: Regulators Of Gene Expressionmentioning

confidence: 99%

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The Transcriptional Regulatory Network of Corynebacterium pseudotuberculosis

Parise

Gomide

et al. 2021

Microorganisms

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Corynebacterium pseudotuberculosis is a Gram-positive, facultative intracellular, pathogenic bacterium that infects several different hosts, yielding serious economic losses in livestock farming. It causes several diseases including oedematous skin disease (OSD) in buffaloes, ulcerative lymphangitis (UL) in horses, and caseous lymphadenitis (CLA) in sheep, goats and humans. Despite its economic and medical-veterinary importance, our understanding concerning this organism’s transcriptional regulatory mechanisms is still limited. Here, we review the state of the art knowledge on transcriptional regulatory mechanisms of this pathogenic species, covering regulatory interactions mediated by two-component systems, transcription factors and sigma factors. Key transcriptional regulatory players involved in virulence and pathogenicity of C. pseudotuberculosis, such as the PhoPR system and DtxR, are in the focus of this review, as these regulators are promising targets for future vaccine design and drug development. We conclude that more experimental studies are needed to further understand the regulatory repertoire of this important zoonotic pathogen, and that regulators are promising targets for future vaccine design and drug development.

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“…One of the influential network theories to infer system-level gene–disease associations from genome-wide gene expression is the gene co-expression network approach ( van Dam et al, 2017 ), which is based on correlation patterns among the expression of genes ( Li J. et al, 2018 ). Gene co-expression network-based methods have been widely used to process gene expression data obtained from microarray ( Wei et al, 2015 ; Han, 2019 ; Jaime-Lara et al, 2020 ) and RNA-seq ( Wan et al, 2018 ; Franco et al, 2020 ; Kong et al, 2020 ) techniques in various animal and human diseases. In this regard, a well-known and helpful co-expression network-based method is weighted gene co-expression network analysis (WGCNA; Langfelder and Horvath, 2008 ).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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ObjectiveBovine 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.MethodsRNA-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).ResultsAs 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.ConclusionThe 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.

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Co-Expression Networks for Causal Gene Identification Based on RNA-Seq Data of Corynebacterium pseudotuberculosis

Cited by 4 publications

References 59 publications

RIP-seq reveals RNAs that interact with RNA polymerase and primary sigma factors in bacteria

RIP-seq reveals RNAs that interact with RNA polymerase and primary sigma factors in bacteria

The Transcriptional Regulatory Network of Corynebacterium pseudotuberculosis

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

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