This month’s featured article is from a research group led by Julia Tzu-Ya Weng at Yuan Ze University in Taoyuan, Taiwan. The article was published on 11 January 2016 in the journal BMC Bioinformatics, based on findings first reported at the 14th Asia Pacific Bioinformatics Conference 2016. This study investigates gene expression profiling underlying latent and active tuberculosis (TB). The authors compared gene expression profiles of peripheral blood mononuclear cells taken from healthy patients and those that had active TB or latent TB infection. For these purposes, they used Phalanx Biotech’s Human OneArray Whole Genome Microarray.
First, the authors identified differentially expressed genes between the 3 experimental conditions, and subjected those genes to gene set enrichment analyses. Consistent with expectations, genes differentially expressed during active TB relative to both healthy controls and latent TB were enriched for categories related to immune responses: lymphocyte differentiation and activation, and immune signaling pathways. Interestingly, genes differentially expressed during latent TB relative to healthy controls were involved in very different biological processes. Rather than immune related processes, they mainly included categories involved in metabolism, cell death, and transcriptional regulation.
Next, the authors sought to identify biomarkers for latent and active TB by validating some of the differentially expressed genes using qPCR. They focused on genes expressed in the lung that were not involved in other respiratory conditions. The targets were NEMF, ASUN, DHX29, and PTPRC. The results indicated that PTPRC can differentiate active TB from healthy and latent TB; and ASUN, NEMF, and DHX29 can differentiate latent TB from healthy and active TB.
The results of this study are very exciting; however, the authors note that further validation of these candidate biomarkers is required. This includes testing on a larger sample size and functional investigations of how these biomarkers are involved in TB infection.
Lee SW et al. Gene expression profiling identifies candidate biomarkers for active and latent tuberculosis (2016). BMC Bioinformatics 17(Suppl 1): 3.