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Research Article
Olanrewaju Morenikeji
University of Pittsburgh at Bradford
Corresponding Author
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Pre- and post-transcriptional modifications of gene expression are emerging as foci of disease studies, with some studies revealing the importance of non-coding transcripts, like long non-coding RNAs (lncRNAs) and microRNAs (miRNAs). We hypothesize that TFs, lncRNAs and miRNAs can modulate the immune response in bovine mastitis and potentially serve as disease biomarkers and/or drug targets. With computational analyses, we identified candidate genes potentially regulated by miRNAs and lncRNAs base pair complementation and thermodynamic stability of binding regions. Remarkably, we found six miRNAs, two being bta-miR-223 and bta-miR-24-3p, to bind to several targets. NONBTAT027932.1 and XR_003029725.1, were identified to target several genes. Functional and pathway analyses revealed lipopolysaccharide-mediated signaling pathway, regulation of chemokine (C-X-C motif) ligand 2 production and regulation of IL-23 production among others. The overarching interactome deserves further in vitro/in vivo explication for specific molecular regulatory mechanisms during bovine mastitis immune response and could lay the foundation for development of disease markers and therapeutic intervention.
Keywords
lncRNA, miRNA, transcription factor, prediction, markers, regulation, bovine mastitis
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No competing interests reported.
This is a list of supplementary files associated with this preprint. Click to download.
- SupplFigure1.pdf
Figure S1. Venn diagram of bovine mastitis immune genes harvested from meta-analysis and Genomatix. In total was 919 genes identified from the meta-analysis (purple) and 20 from Genomatix software (pink). The overlap region represents the 16 target bovine mastitis genes used for further analysis in this study.
- SupplFigure2.pdf
Figure S2. Venn diagram involving the three miRNAs prediction software; miRWalk, miRNet, and TargetScan. The blue circle represents the number of miRNA predicted by miRWalk (693), pink miRNet (47), and green Target Scan (146). The center region represents the six miRNAs used for further analysis in this study.
- SupplFigure3.pdf
Figure S3. Multiple sequence alignment of the six predicted bovine miRNAs and corresponding genes in 15 other species using MultiAlin; bta-miR-24-3p, bta-miR-149-5p, bta-miR-185, bta-miR-223, bta-miR-328, and bta-miR-874. The highlighted yellow region within the gene sequences represent the miRNA sequence. Red indicates regions of high consensus and blue low consensus.
- SupplFigure4a.pdf
Figure S4a-c. Evolutionary analysis of the six miRNAs generated from the multiple sequence alignment using MEGA-X and iTOL(A-C); phylogenetic trees of bta-miR-24-3p and bta-miR-149-5p and their corresponding gene in the 15 other species (A); phylogenetic trees of bta-miR-185 and bta-miR-223 (B); phylogenetic trees of bta-miR-328 and bta-miR-874 (C).
- SupplFigure4b.pdf
Figure S4a-c. Evolutionary analysis of the six miRNAs generated from the multiple sequence alignment using MEGA-X and iTOL(A-C); phylogenetic trees of bta-miR-24-3p and bta-miR-149-5p and their corresponding gene in the 15 other species (A); phylogenetic trees of bta-miR-185 and bta-miR-223 (B); phylogenetic trees of bta-miR-328 and bta-miR-874 (C).
- SupplFigure4c.pdf
Figure S4a-c. Evolutionary analysis of the six miRNAs generated from the multiple sequence alignment using MEGA-X and iTOL(A-C); phylogenetic trees of bta-miR-24-3p and bta-miR-149-5p and their corresponding gene in the 15 other species (A); phylogenetic trees of bta-miR-185 and bta-miR-223 (B); phylogenetic trees of bta-miR-328 and bta-miR-874 (C).
- SupplFigure5a.pdf
Figure S5a-d. Evolutionary analysis of the eight lncRNA generated from multiple sequence alignment using MEGA-X and iTOL (A-D); phylogenetic trees of NONBTAT001181.2 and NONBTAT007847.2 (A); phylogenetic trees of NONBTAT011890.2 and NONBTAT010129.2 (B); phylogenetic trees of NONBTAT013032.2 and NONBTAT017501.2 (C); and phylogenetic trees for NONBTAT021220.2 and NONBTAT027932.1.
- SupplFigure5b.pdf
Figure S5a-d. Evolutionary analysis of the eight lncRNA generated from multiple sequence alignment using MEGA-X and iTOL (A-D); phylogenetic trees of NONBTAT001181.2 and NONBTAT007847.2 (A); phylogenetic trees of NONBTAT011890.2 and NONBTAT010129.2 (B); phylogenetic trees of NONBTAT013032.2 and NONBTAT017501.2 (C); and phylogenetic trees for NONBTAT021220.2 and NONBTAT027932.1.
- SupplFigure5c.png
Figure S5a-d. Evolutionary analysis of the eight lncRNA generated from multiple sequence alignment using MEGA-X and iTOL (A-D); phylogenetic trees of NONBTAT001181.2 and NONBTAT007847.2 (A); phylogenetic trees of NONBTAT011890.2 and NONBTAT010129.2 (B); phylogenetic trees of NONBTAT013032.2 and NONBTAT017501.2 (C); and phylogenetic trees for NONBTAT021220.2 and NONBTAT027932.1.
- SupplFigure5d.pdf
Figure S5a-d. Evolutionary analysis of the eight lncRNA generated from multiple sequence alignment using MEGA-X and iTOL (A-D); phylogenetic trees of NONBTAT001181.2 and NONBTAT007847.2 (A); phylogenetic trees of NONBTAT011890.2 and NONBTAT010129.2 (B); phylogenetic trees of NONBTAT013032.2 and NONBTAT017501.2 (C); and phylogenetic trees for NONBTAT021220.2 and NONBTAT027932.1.
- SupplementaryTable1.docx
Table S1. Characteristics of species sequences used to create phylogenetic trees for candidate lncRNA and miRNA.
- SupplementaryTable2.docx
Table S2. miRNA and their target gene as predicted by miRWalk.
- SupplementaryTable3.docx
Table S3. List of miRNAs from all three software and their target candidate gene (three or more).
- SupplementaryTable4.docx
Table S4. List of Bovine lncRNA mined from NONCODE and their target candidate genes, genomic location, strand type, and length. If available, the accession number for NCBI is listed.
- SupplementaryTable5.docx
Table S5. GO Pathway Analysis for candidate bovine mastitis genes.
- SupplementaryTable6.docx
Table S6. All transcription factors identified for each of the target bovine mastitis genes.
- SupplementaryTable7.docx
Table S7. lncRNA-miRNA predicted binding data.
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A new preprint design is coming!
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This preprint is under consideration at Scientific Reports. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Olanrewaju Morenikeji
University of Pittsburgh at Bradford
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 14 Jun, 2021
No community comments so far
Editorial decision: Major revision
On 17 Sep, 2021
Reviews received
Received 15 Sep, 2021
Reviews received
Received 19 Jul, 2021
Reviews received
Received 20 Jun, 2021
Reviewers agreed
On 14 Jun, 2021
Reviewers agreed
On 10 Jun, 2021
Reviewers invited
Invitations sent on 10 Jun, 2021
Editor assigned
On 10 Jun, 2021
Editor invited
On 10 Jun, 2021
Submission checks complete
On 09 Jun, 2021
First submitted
On 03 Jun, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Biomedical Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Pre- and post-transcriptional modifications of gene expression are emerging as foci of disease studies, with some studies revealing the importance of non-coding transcripts, like long non-coding RNAs (lncRNAs) and microRNAs (miRNAs). We hypothesize that TFs, lncRNAs and miRNAs can modulate the immune response in bovine mastitis and potentially serve as disease biomarkers and/or drug targets. With computational analyses, we identified candidate genes potentially regulated by miRNAs and lncRNAs base pair complementation and thermodynamic stability of binding regions. Remarkably, we found six miRNAs, two being bta-miR-223 and bta-miR-24-3p, to bind to several targets. NONBTAT027932.1 and XR_003029725.1, were identified to target several genes. Functional and pathway analyses revealed lipopolysaccharide-mediated signaling pathway, regulation of chemokine (C-X-C motif) ligand 2 production and regulation of IL-23 production among others. The overarching interactome deserves further in vitro/in vivo explication for specific molecular regulatory mechanisms during bovine mastitis immune response and could lay the foundation for development of disease markers and therapeutic intervention.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
No competing interests reported.
This is a list of supplementary files associated with this preprint. Click to download.
- SupplFigure1.pdf
Figure S1. Venn diagram of bovine mastitis immune genes harvested from meta-analysis and Genomatix. In total was 919 genes identified from the meta-analysis (purple) and 20 from Genomatix software (pink). The overlap region represents the 16 target bovine mastitis genes used for further analysis in this study.
- SupplFigure2.pdf
Figure S2. Venn diagram involving the three miRNAs prediction software; miRWalk, miRNet, and TargetScan. The blue circle represents the number of miRNA predicted by miRWalk (693), pink miRNet (47), and green Target Scan (146). The center region represents the six miRNAs used for further analysis in this study.
- SupplFigure3.pdf
Figure S3. Multiple sequence alignment of the six predicted bovine miRNAs and corresponding genes in 15 other species using MultiAlin; bta-miR-24-3p, bta-miR-149-5p, bta-miR-185, bta-miR-223, bta-miR-328, and bta-miR-874. The highlighted yellow region within the gene sequences represent the miRNA sequence. Red indicates regions of high consensus and blue low consensus.
- SupplFigure4a.pdf
Figure S4a-c. Evolutionary analysis of the six miRNAs generated from the multiple sequence alignment using MEGA-X and iTOL(A-C); phylogenetic trees of bta-miR-24-3p and bta-miR-149-5p and their corresponding gene in the 15 other species (A); phylogenetic trees of bta-miR-185 and bta-miR-223 (B); phylogenetic trees of bta-miR-328 and bta-miR-874 (C).
- SupplFigure4b.pdf
Figure S4a-c. Evolutionary analysis of the six miRNAs generated from the multiple sequence alignment using MEGA-X and iTOL(A-C); phylogenetic trees of bta-miR-24-3p and bta-miR-149-5p and their corresponding gene in the 15 other species (A); phylogenetic trees of bta-miR-185 and bta-miR-223 (B); phylogenetic trees of bta-miR-328 and bta-miR-874 (C).
- SupplFigure4c.pdf
Figure S4a-c. Evolutionary analysis of the six miRNAs generated from the multiple sequence alignment using MEGA-X and iTOL(A-C); phylogenetic trees of bta-miR-24-3p and bta-miR-149-5p and their corresponding gene in the 15 other species (A); phylogenetic trees of bta-miR-185 and bta-miR-223 (B); phylogenetic trees of bta-miR-328 and bta-miR-874 (C).
- SupplFigure5a.pdf
Figure S5a-d. Evolutionary analysis of the eight lncRNA generated from multiple sequence alignment using MEGA-X and iTOL (A-D); phylogenetic trees of NONBTAT001181.2 and NONBTAT007847.2 (A); phylogenetic trees of NONBTAT011890.2 and NONBTAT010129.2 (B); phylogenetic trees of NONBTAT013032.2 and NONBTAT017501.2 (C); and phylogenetic trees for NONBTAT021220.2 and NONBTAT027932.1.
- SupplFigure5b.pdf
Figure S5a-d. Evolutionary analysis of the eight lncRNA generated from multiple sequence alignment using MEGA-X and iTOL (A-D); phylogenetic trees of NONBTAT001181.2 and NONBTAT007847.2 (A); phylogenetic trees of NONBTAT011890.2 and NONBTAT010129.2 (B); phylogenetic trees of NONBTAT013032.2 and NONBTAT017501.2 (C); and phylogenetic trees for NONBTAT021220.2 and NONBTAT027932.1.
- SupplFigure5c.png
Figure S5a-d. Evolutionary analysis of the eight lncRNA generated from multiple sequence alignment using MEGA-X and iTOL (A-D); phylogenetic trees of NONBTAT001181.2 and NONBTAT007847.2 (A); phylogenetic trees of NONBTAT011890.2 and NONBTAT010129.2 (B); phylogenetic trees of NONBTAT013032.2 and NONBTAT017501.2 (C); and phylogenetic trees for NONBTAT021220.2 and NONBTAT027932.1.
- SupplFigure5d.pdf
Figure S5a-d. Evolutionary analysis of the eight lncRNA generated from multiple sequence alignment using MEGA-X and iTOL (A-D); phylogenetic trees of NONBTAT001181.2 and NONBTAT007847.2 (A); phylogenetic trees of NONBTAT011890.2 and NONBTAT010129.2 (B); phylogenetic trees of NONBTAT013032.2 and NONBTAT017501.2 (C); and phylogenetic trees for NONBTAT021220.2 and NONBTAT027932.1.
- SupplementaryTable1.docx
Table S1. Characteristics of species sequences used to create phylogenetic trees for candidate lncRNA and miRNA.
- SupplementaryTable2.docx
Table S2. miRNA and their target gene as predicted by miRWalk.
- SupplementaryTable3.docx
Table S3. List of miRNAs from all three software and their target candidate gene (three or more).
- SupplementaryTable4.docx
Table S4. List of Bovine lncRNA mined from NONCODE and their target candidate genes, genomic location, strand type, and length. If available, the accession number for NCBI is listed.
- SupplementaryTable5.docx
Table S5. GO Pathway Analysis for candidate bovine mastitis genes.
- SupplementaryTable6.docx
Table S6. All transcription factors identified for each of the target bovine mastitis genes.
- SupplementaryTable7.docx
Table S7. lncRNA-miRNA predicted binding data.
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