Plasma levels of C-reactive protein, matrix metalloproteinase-7 and lipopolysaccharide-binding protein distinguish active pulmonary or extrapulmonary tuberculosis from uninfected controls in children.

The immune profile associated with distinct clinical forms of tuberculosis (TB) has been extensively described for adult populations. Nevertheless, studies describing immune determinants of pulmonary or extrapulmonary TB (PTB or EPTB, respectively) in children are scarce. Here, we retrospectively assessed plasma levels of several mediators of inflammation in age and sex-matched children from South India presenting with PTB (n = 14) or EPTB (n = 22) as well as uninfected healthy controls (n = 19) to identify biomarkers that could accurately distinguish different TB clinical forms. Furthermore, we performed exploratory analyses testing the influence of sex on the systemic inflammatory profile. The analyses identified a biosignature of 10 biomarkers capable of distinguishing the three clinical groups simultaneously. Machine-learning decision trees indicated that C-reactive protein (CRP), matrix metalloproteinase (MMP)-7 and lipopolysaccharide-binding protein (LBP) were the markers that, when combined, displayed the highest accuracy in identifying the clinical groups. Additional exploratory analyses suggested that the disease signatures were highly influenced by sex. Therefore, sex differentially impacted status of systemic inflammation, immune activation and tissue remodeling in children with distinct clinical forms of TB. Regardless of such nuances related to biological sex, MMP-7, CRP and LBP were strong discriminators of active TB and thus could be considered as biomarkers useful in discrimination different TB clinical forms. These observations have implications on our understanding of the immunopathology of both clinical forms of TB in pediatric patients. If validated by other studies in the future, the combination of identified biomarkers may help development of point-of-care diagnostic or prognostic tools.

In silico transcriptional analysis of mRNA and miRNA reveals unique biosignatures that characterizes different types of diabetes.

Diabetes (DM) has a significant impact on public health. We performed an in silico study of paired datasets of messenger RNA (mRNA) micro-RNA (miRNA) transcripts to delineate potential biosignatures that could distinguish prediabetes (pre-DM), type-1DM (T1DM) and type-2DM (T2DM). Two publicly available datasets containing expression values of mRNA and miRNA obtained from individuals diagnosed with pre-DM, T1DM or T2DM, and normoglycemic controls (NC), were analyzed using systems biology approaches to define combined signatures to distinguish different clinical groups. The mRNA profile of both pre-DM and T2DM was hallmarked by several differentially expressed genes (DEGs) compared to NC. Nevertheless, T1DM was characterized by an overall low number of DEGs. The miRNA signature profiles were composed of a substantially lower number of differentially expressed targets. Gene enrichment analysis revealed several inflammatory pathways in T2DM and fewer in pre-DM, but with shared findings such as Tuberculosis. The integration of mRNA and miRNA datasets improved the identification and discriminated the group composed by pre-DM and T2DM patients from that constituted by normoglycemic and T1DM individuals. The integrated transcriptomic analysis of mRNA and miRNA expression revealed a unique biosignature able to characterize different types of DM.