Transcriptional signatures of <i>Mycobacterium tuberculosis</i> in mouse model of intraocular tuberculosis is a research paper published in Pathogens and Disease (2019). On theSindex it has a DataRank of 0.417. It has been cited 9 times, with 5 citing works in its 1-hop citation network.
ABSTRACT Background Studies on human intraocular tuberculosis (IOTB) are extremely challenging. For understanding the pathogenesis of IOTB, it is important to investigate the mycobacterial transcriptional changes in ocular environment. Methods Mice were challenged intravenously with Mycobacterium tuberculosis H37Rv and at 45 days post-infection, experimental IOTB was confirmed based on bacteriological and molecular assays. M. tuberculosis transcriptome was analyzed in the infected eyes using microarray technology. The identified M. tuberculosis signature genes were further validated and investigated in human IOTB samples using real-time polymerase chain reaction. Results Following intravenous challenge with M. tuberculosis, 45% (5/12) mice showed bacilli in the eyes with positivity for M. tuberculosis ribonucleic acid in 100% (12/12), thus confirming the paucibacillary nature of IOTB similar to human IOTB. M. tuberculosis transcriptome in these infected eyes showed significant upregulation of 12 M. tuberculosis genes and five of these transcripts (Rv0962c, Rv0984, Rv2612c, Rv0974c and Rv0971c) were also identified in human clinically confirmed cases of IOTB. Conclusions Differentially expressed mycobacterial genes identified in an intravenously challenged paucibacillary mouse IOTB model and presence of these transcripts in human IOTB samples highlight the possible role of these genes for survival of M. tuberculosis in the ocular environment, thus contributing to pathogenesis of IOTB.
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Base Score Contribution
0.345
From this paper's citation signal
Citation Network Contribution
0.0721
From 3 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 83% comes from its base citations and 17% from the citation network (3 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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