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 Table of Contents  
ORIGINAL ARTICLE
Year : 2019  |  Volume : 8  |  Issue : 1  |  Page : 70-74

Upregulated bovine tuberculosis microRNAs Trigger oncogenic pathways: An In silico perception


1 Department of Animal Production and Fisheries, Laboratory of Animal Physiology and Health, Institute of Agricultural Research for Development, Bambui, Cameroon
2 Department of Computational Biology and Bioinformatics, University of Kerala, Thiruvananthapuram, Kerala, India
3 Division of Immunology and South Africa Medical Research Council, Immunology of Infectious Disease, Faculty of Health Sciences, University of Cape Town, Institute of Infectious Diseases and Molecular Medicine, South Africa; Department of Biochemistry, University of Douala, Cameroon
4 System Biology Laboratory, Chantal Biya International Reference Center, Yaounde; Department of Biology, Higher Teachers' Training College, University of Yaounde I, Cameroon

Date of Web Publication12-Mar-2019

Correspondence Address:
Elvis Ndukong Ndzi
Laboratory of Animal Physiology and Health, Institute of Agricultural Research for Development, P. O. Box 51, Irad Bambui
Cameroon
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijmy.ijmy_9_19

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  Abstract 


Background/Objective: Although microRNA (miRNA)-directed regulation of bovine tuberculosis (bTB) has already been reported, very little is known about the incited pathways and genes. We profiled bTB-upregulated miRNAs through an in silico methodology. Methods: The data of upregulated miRNAs in bTB versus healthy controls were collected and clustered into three groups by their tissue specificity as follows: G1 (mammary gland-specific): bta-miR-146a; G2 (peripheral blood mononuclear cell-specific): bta-miR-155; and G3 (alveolar macrophage-specific): bta-miR-146a, bta-miR-155, bta-miR-142-5p, bta-miR-423-3p, bta-miR-21-5p, bta-miR-27a-3p, bta-miR-99b, bta-miR-147, bta-miR-223, and bta-let-7i. The miRNA–mRNA interaction network was defined by TargetScan. The gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways of these transcripts were examined. Results: The results illustrate the induction of pathways in cancer, highly enriched, and unanimous to all three gene sets (G1, G2, and G3). Mitogen-activated protein kinase and PI3K-Akt signaling were specific to G2 and G3 with fibroblast growth factors formed the key factors. Conclusion: The inferred cancer cascades denote a probable modulation of innate immune response in an infectious state. These baseline pictures could lay the ground for further substantive studies.

Keywords: Bovine tuberculosis, computational, microRNA, pathway prediction, target gene


How to cite this article:
Ndzi EN, Indu Viswanath AN, Adzemye NG, Tamgue O, Nsongka MV, Nair AS, Nkenfou CN. Upregulated bovine tuberculosis microRNAs Trigger oncogenic pathways: An In silico perception. Int J Mycobacteriol 2019;8:70-4

How to cite this URL:
Ndzi EN, Indu Viswanath AN, Adzemye NG, Tamgue O, Nsongka MV, Nair AS, Nkenfou CN. Upregulated bovine tuberculosis microRNAs Trigger oncogenic pathways: An In silico perception. Int J Mycobacteriol [serial online] 2019 [cited 2019 Jul 21];8:70-4. Available from: http://www.ijmyco.org/text.asp?2019/8/1/70/253967




  Introduction Top


Human (Homo sapiens) and cattle (Bos taurus) microRNA (miRNAs) have been shown to be associated with tuberculosis (TB).[1],[2],[3],[4],[5],[6],[7],[8],[9],[10],[11] However, little is known about the role played or pathways influenced by these miRNAs in the pathogenicity of TB.

The present study performed the functional profiling of miRNAs relatively upregulated in bovine TB compared to healthy controls using a computational approach. We compared the effector genes and functional pathways of miRNAs specific to the mammary gland, peripheral blood mononuclear cells (PBMCs), and alveolar macrophages induced at higher levels in the infectious state.


  Methods Top


Data acquisition

A systematic biomedical literature hunt in PubMed was made with the keywords “miRNA and bovine TB (bTB).” A parallel search was also conducted in Google with the same queries to calibrate the data mining. In total, 15 articles were tabulated; the retrieved substances were collated and sorted to retain miRNAs overexpressed in bTB as compared to non-TB infected (healthy controls) bovine. The miRNA sequences were downloaded from miRBase release 22 (www.mirbase.org),[12] and alterations in the nomenclature were corrected by miRBase tracker (www.mirbasetracker.org).[13]

Target microRNA prediction

The miRNA-targeting mRNAs were predicted by TargetScanHuman v7.2 (http://www.targetscan.org/vert_72/).[14] This algorithm predicts biological targets of miRNAs by searching for the presence of conserved 8 mer, 7 mer, and 6 mer sites that match the seed region of each miRNA.[15] It also identifies mismatches in the seed region that is compensated by conserved 3' pairing [16] and centered sites. The species was selected as “cow,” and the miRNA full name as found in miRBase was given as the input. Predictions were ranked based on the predicted efficacy of targeting as calculated using cumulative weighted context++ scores of the sites.[14]

Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses

To unify the molecular, biological, cellular, and pathway level functionalities of tissue-specific bTB miRNAs, the predicted genes were submitted to Database for Annotation, Visualization, and Integrated Discovery Bioinformatics Resources 6.8 (http://david.abcc.ncifcrf.gov/home.jsp), a freely available integrated biological knowledge base and analytical tool.[17] The gene list with the identifier “OFFICIAL_GENE_SYMBOL” was uploaded by selecting B. taurus as the background. The three gene ontology (GO) classifiers, namely, biological process (BP), molecular function (MF), and cellular components (CC) were applied. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the genes were also carried out. The statistical significance of each term/pathway was evaluated by setting the EASE score (modified Fisher's exact P value) to ≤ 0.05 and the respective functional class of the three most enriched pathways was recorded.


  Results Top


Clustering of tissue-specific microRNAs

An extensive literature surfing was conducted for the 2010–2018 period to yield ten miRNAs reportedly upregulated in bTB with respect to healthy controls [Table 1]. These miRNAs were clustered by their tissue-specific induction into three groups – G1 (mammary gland-specific, one miRNA): bta-miR-146a; G2 (PBMC-specific, one miRNA): bta-miR-155; and G3 (alveolar macrophage-specific, ten miRNAs): bta-miR-146a, bta-miR-155, bta-miR-142-5p, bta-miR-423-3p, bta-miR-21-5p, bta-miR-27a-3p, bta-miR-99b, bta-miR-147, bta-miR-223, and bta-let-7i. All ten miRNAs were validated to be upregulated in the bovine alveolar macrophage (G3).
Table 1: Retrieved microRNAs upregulated in bovine tuberculosis as compared to healthy controls

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MicroRNA-predicted target genes

The predicted gene counts for G1, G2, and G3 were 246, 498, and 3379, respectively. G3 had the highest number of gene records due to the participation of relatively higher number of miRNAs in the group.

Kyoto Encyclopedia of Genes and Genomes pathway enrichment

For G1, G2, and G3 genes, 57, 232, and 692 chart records (pathways) were characterized. The topmost enriched three channels by G1 were pathways in cancer, human T-cell leukemia virus type 1 infection, and T-cell receptor signaling [Table 2] while the G2-G3 gene sets augmented pathways in cancer, MAPK, and PI3K-Akt signaling [Table 3] and [Table 4]. Pathways in cancer were common to all three groups (G1, G2, and G3), while MAPK and PI3K-Akt signaling pathways were pertinent to G2 and G3 members. The fibroblast growth factors (FGFs) were identified as the core genes nourishing the prime signaling cascades in G2 and G3, while for G1, the key players were clusters of differentiation (CD) molecules.
Table 2: Top 3 Kyoto Encyclopedia of Genes and Genomes functional pathways enriched by target genes of G1 microRNAs

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Table 3: Top 3 Kyoto Encyclopedia of Genes and Genomes functional pathways enriched by target genes of G2 microRNAs

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Table 4: Top 3 Kyoto Encyclopedia of Genes and Genomes functional pathways enriched by target genes of G3 microRNAs

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Gene ontology annotation

The GO terms for the gene groups were detailed. The BP of G1, G2, and G3 is marked as the regulation of transcription from RNA polymerase II. They bind to metal – zinc ions, adenosine triphosphate (ATP), and DNA (MF). They mostly found in the nucleus and cytoplasm (CC) [Table 5], [Table 6], [Table 7] for respective GO terms of G1, G2, and G3].
Table 5: Top 3 gene ontology biological process, molecular function, and cellular components terms enriched by target genes of G1 microRNAs

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Table 6: Top 3 gene ontology biological process, molecular function, and cellular components terms enriched by target genes of G2 microRNAs

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Table 7: Top 3 gene ontology biological process, molecular function, and cellular components terms enriched by target genes of G3 microRNAs

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  Discussion Top


This work is designed to disclose the pathogenic cascades and regulatory units (genes) enriched by ten upregulated bTB miRNAs. Our roadmap included grouping of miRNAs in a tissue-specific manner ensured by modeling the interaction network between referred miRNAs and their target genes and elucidation of signal transduction pathways.

Selection of a reliable target gene prediction tool is the first step in the workflow. Although a number of gene prediction algorithms (TargetScan, Diana Tools, TarBase, mirPath, miExTra, Miranda, PicTar, PITA, rna22, miRBridge, and miRSystem) are available, only TargetScan has the provision to predict miRNA target genes in B. taurus (bovine or cow). Most prediction algorithms follow the basic attributes of complementarity between seed region (miRNA) and target sites (mRNA), seed conservation, and miRNA/mRNA duplex energy.

The KEGG annotation accentuated that pathways in cancer were well-furnished by bTB miRNA target genes of all three tissue locations. These results were similar to a previous study in humans where tissue-specific miRNAs overexpressed in TB were involved in the regulation of multiple cancer pathways.[18] Barh et al.[19] recently disclosed the coexistence of TB and lung cancer through noncoding RNAs.

We identified MAPK and PI3K-Akt signaling as importantly enriched pathways common to bTB miRNAs of PBMC and alveoli macrophages with FGFs being the major enrichment factor. MAPK and PI3K-Akt pathways are usually triggered by the activation of Ras, a small GTPase, that plays a key role in fine-tuning the innate immune response.[20] The MAPK pathway is involved in boosting the proliferation and survival of immune cells while PI3K works toward the activation and functioning of immune cells.[20] Accidental activation of these pathways in a bacterial infection may, therefore, influence the state of innate immune system. Vegh et al.[4] already corroborated the involvement of bovine alveolar macrophage upregulated miRNAs in innate immunity. All these expositions indicate that bTB contagion is established through a complex network of bTB miRNA-driven immune system alteration through different genes.

The GO structuring of the transcriptomic data revealed that upregulated bTB miRNAs mostly target transcription regulators from RNA polymerase II promoter with their prime MF being binding to metal ions, zinc ion, ATP, and DNA. They inferred to occupy the nucleus and cytoplasm.

MiRNAs regulate major signaling pathways and hence crucial to maintain the homeostasis. Aberrant expression of these molecules is indicative of pathological states of many infectious diseases. The potential application of miRNAs as clinical, diagnostic, prognostic, and therapeutic follow-up biomarkers for TB has extensively been explored in humans.[5],[6],[7],[8],[9] However, very little is known about the role of miRNAs in bovine TB pathophysiology. Application of in vitro-in vivo methods to discern this enigma is cumbersome where in silico (computational) approaches form a good alternative stance. In silico platform generates large data sets which constitute an effective baseline for planned experiments. The present work intends to give a comprehensive understanding on the role of tissue-specific bTB transcripts in modulating the pathogenicity by inciting multiple signaling pathways. Our preliminary data need to be ascertained through realistic biological models.

The major limitation to present study is the use of a single miRNA target gene prediction algorithm (TargetScan). The practice of gene finding by two or more sophisticated tools could increase the accuracy and specificity of the results. Since TargetScan is the only tool with a provision for direct miRNA–mRNA interaction prediction in the species B. taurus, we pursued with this one.


  Conclusion Top


This study demonstrated a direct link between bTB miRNAs and oncogenic cascades. Pathways in cancer were common to G1, G2, and G3 classes, whereas MAPK and PI3K-Akt signaling were specific to PBMC (G2) and alveolar macrophage (G3) miRNAs. FGFs were the key determinants in G2–G3 while CD molecules regulate the G1 functions. These genes could be novel therapeutic targets for anti-TB treatments. The GO classifiers disclosed that bTB miRNAs modulate the transcription from RNA polymerase II promoter (BP), target genes functionally involved in the binding interactions with ions and organic compounds (MF), and are confined to nucleus and cytoplasm (CC). The upregulated bTB transcripts presumed to complete the pathogenic circuit by governing specific molecular pathways. The oncogenic MAPK and PI3K-Akt signaling also connected to innate immune response. An addendum to this work with consolidated experimental proof could provide vital clues on the mechanistic and epigenetic roles of bTB miRNAs.

Acknowledgments

The authors would like to thank the Institute of Agricultural Research for Development (IRAD), Bambui, Cameroon, for providing the platform for the realization of this work.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7]



 

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