Data Mining Mycobacterium tuberculosis Pathogenic Gene Transcription Factors and Their Regulatory Network Nodes
Tuberculosis (TB) is one of the deadliest infectious diseases worldwide. In Mycobacterium tuberculosis, changes in gene expression are highly variable and involve many genes, so traditional single-gene screening of M. tuberculosis targets has been unable to meet the needs of clinical diagnosis. In t...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | International Journal of Genomics |
| Online Access: | http://dx.doi.org/10.1155/2018/3079730 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832551479598120960 |
|---|---|
| author | Guangxin Yuan Yu Bai Yuhang Zhang Guangyu Xu |
| author_facet | Guangxin Yuan Yu Bai Yuhang Zhang Guangyu Xu |
| author_sort | Guangxin Yuan |
| collection | DOAJ |
| description | Tuberculosis (TB) is one of the deadliest infectious diseases worldwide. In Mycobacterium tuberculosis, changes in gene expression are highly variable and involve many genes, so traditional single-gene screening of M. tuberculosis targets has been unable to meet the needs of clinical diagnosis. In this study, using the National Center for Biotechnology Information (NCBI) GEO Datasets, whole blood gene expression profile data were obtained in patients with active pulmonary tuberculosis. Linear model-experience Bayesian statistics using the Limma package in R combined with t-tests were applied for nonspecific filtration of the expression profile data, and the differentially expressed human genes were determined. Using DAVID and KEGG, the functional analysis of differentially expressed genes (GO analysis) and the analysis of signaling pathways were performed. Based on the differentially expressed gene, the transcriptional regulatory element databases (TRED) were integrated to construct the M. tuberculosis pathogenic gene regulatory network, and the correlation of the network genes with disease was analyzed with the DAVID online annotation tool. It was predicted that IL-6, JUN, and TP53, along with transcription factors SRC, TNF, and MAPK14, could regulate the immune response, with their function being extracellular region activity and protein binding during infection with M. tuberculosis. |
| format | Article |
| id | doaj-art-a699f2553f654b45a61feb0c4802ce2a |
| institution | Kabale University |
| issn | 2314-436X 2314-4378 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Genomics |
| spelling | doaj-art-a699f2553f654b45a61feb0c4802ce2a2025-02-03T06:01:26ZengWileyInternational Journal of Genomics2314-436X2314-43782018-01-01201810.1155/2018/30797303079730Data Mining Mycobacterium tuberculosis Pathogenic Gene Transcription Factors and Their Regulatory Network NodesGuangxin Yuan0Yu Bai1Yuhang Zhang2Guangyu Xu3College of Pharmacy, Beihua University, Jilin, Jilin 132013, ChinaPharmaceutical College, Jilin Medical University, Jilin, Jilin 132011, ChinaCollege of Pharmacy, Beihua University, Jilin, Jilin 132013, ChinaCollege of Pharmacy, Beihua University, Jilin, Jilin 132013, ChinaTuberculosis (TB) is one of the deadliest infectious diseases worldwide. In Mycobacterium tuberculosis, changes in gene expression are highly variable and involve many genes, so traditional single-gene screening of M. tuberculosis targets has been unable to meet the needs of clinical diagnosis. In this study, using the National Center for Biotechnology Information (NCBI) GEO Datasets, whole blood gene expression profile data were obtained in patients with active pulmonary tuberculosis. Linear model-experience Bayesian statistics using the Limma package in R combined with t-tests were applied for nonspecific filtration of the expression profile data, and the differentially expressed human genes were determined. Using DAVID and KEGG, the functional analysis of differentially expressed genes (GO analysis) and the analysis of signaling pathways were performed. Based on the differentially expressed gene, the transcriptional regulatory element databases (TRED) were integrated to construct the M. tuberculosis pathogenic gene regulatory network, and the correlation of the network genes with disease was analyzed with the DAVID online annotation tool. It was predicted that IL-6, JUN, and TP53, along with transcription factors SRC, TNF, and MAPK14, could regulate the immune response, with their function being extracellular region activity and protein binding during infection with M. tuberculosis.http://dx.doi.org/10.1155/2018/3079730 |
| spellingShingle | Guangxin Yuan Yu Bai Yuhang Zhang Guangyu Xu Data Mining Mycobacterium tuberculosis Pathogenic Gene Transcription Factors and Their Regulatory Network Nodes International Journal of Genomics |
| title | Data Mining Mycobacterium tuberculosis Pathogenic Gene Transcription Factors and Their Regulatory Network Nodes |
| title_full | Data Mining Mycobacterium tuberculosis Pathogenic Gene Transcription Factors and Their Regulatory Network Nodes |
| title_fullStr | Data Mining Mycobacterium tuberculosis Pathogenic Gene Transcription Factors and Their Regulatory Network Nodes |
| title_full_unstemmed | Data Mining Mycobacterium tuberculosis Pathogenic Gene Transcription Factors and Their Regulatory Network Nodes |
| title_short | Data Mining Mycobacterium tuberculosis Pathogenic Gene Transcription Factors and Their Regulatory Network Nodes |
| title_sort | data mining mycobacterium tuberculosis pathogenic gene transcription factors and their regulatory network nodes |
| url | http://dx.doi.org/10.1155/2018/3079730 |
| work_keys_str_mv | AT guangxinyuan dataminingmycobacteriumtuberculosispathogenicgenetranscriptionfactorsandtheirregulatorynetworknodes AT yubai dataminingmycobacteriumtuberculosispathogenicgenetranscriptionfactorsandtheirregulatorynetworknodes AT yuhangzhang dataminingmycobacteriumtuberculosispathogenicgenetranscriptionfactorsandtheirregulatorynetworknodes AT guangyuxu dataminingmycobacteriumtuberculosispathogenicgenetranscriptionfactorsandtheirregulatorynetworknodes |