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

Systems biology analysis of publicly available transcriptomic data reveals a critical link between AKR1B10 gene expression, smoking and occurrence of lung cancer.

BACKGROUND:Cigarette smoking is associated with an increased risk of developing respiratory diseases and various types of cancer. Early identification of such unfavorable outcomes in patients who smoke is critical for optimizing personalized medical care. METHODS:Here, we perform a comprehensive analysis using Systems Biology tools of publicly available data from a total of 6 transcriptomic studies, which examined different specimens of lung tissue and/or cells of smokers and nonsmokers to identify potential markers associated with lung cancer. RESULTS:Expression level of 22 genes was capable of classifying smokers from non-smokers. A machine learning algorithm revealed that AKR1B10 was the most informative gene among the 22 differentially expressed genes (DEGs) accounting for the classification of the clinical groups. AKR1B10 expression was higher in smokers compared to non-smokers in datasets examining small and large airway epithelia, but not in the data from a study of sorted alveolar macrophages. Moreover, AKR1B10 expression was relatively higher in lung cancer specimens compared to matched healthy tissue obtained from nonsmoking individuals. Although the overall accuracy of AKR1B10 expression level in distinction between cancer and healthy lung tissue was 76%, with a specificity of 98%, our results indicated that such marker exhibited low sensitivity, hampering its use for cancer screening such specific setting. CONCLUSION:The systematic analysis of transcriptomic studies performed here revealed a potential critical link between AKR1B10 expression, smoking and occurrence of lung cancer

A multi-center, prospective cohort study of whole blood gene expression in the tuberculosis-diabetes interaction

Abstract Diabetes mellitus (DM) increases tuberculosis (TB) severity. We compared blood gene expression in adults with pulmonary TB, with or without diabetes mellitus (DM) from sites in Brazil and India. RNA sequencing (RNAseq) performed at baseline and during TB treatment. Publicly available baseline RNAseq data from South Africa and Romania reported by the TANDEM Consortium were also analyzed. Across the sites, differentially expressed genes varied for each condition (DM, TB, and TBDM) and no pattern classified any one group across all sites. A concise signature of TB disease was identified but this was expressed equally in TB and TBDM. Pathway enrichment analysis failed to distinguish TB from TBDM, although there was a trend for greater neutrophil and innate immune pathway activation in TBDM participants. Pathways associated with insulin resistance, metabolic dysfunction, diabetic complications, and chromosomal instability were positively correlated with glycohemoglobin. The immune response to pulmonary TB as reflected by whole blood gene expression is substantially similar with or without comorbid DM. Gene expression pathways associated with the microvascular and macrovascular complications of DM are upregulated during TB, supporting a syndemic interaction between these coprevalent diseases

An integrative multi-omics approach to characterize interactions between tuberculosis and diabetes mellitus

Summary: Tuberculosis-diabetes mellitus (TB-DM) is linked to a distinct inflammatory profile, which can be assessed using multi-omics analyses. Here, a machine learning algorithm was applied to multi-platform data, including cytokines and gene expression in peripheral blood and eicosanoids in urine, in a Brazilian multi-center TB cohort. There were four clinical groups: TB-DM(n = 24), TB only(n = 28), DM(HbA1c ≥ 6.5%) only(n = 11), and a control group of close TB contacts who did not have TB or DM(n = 13). After cross-validation, baseline expression or abundance of MMP-28, LTE-4, 11-dTxB2, PGDM, FBXO6, SECTM1, and LINCO2009 differentiated the four patient groups. A distinct multi-omic-derived, dimensionally reduced, signature was associated with TB, regardless of glycemic status. SECTM1 and FBXO6 mRNA levels were positively correlated with sputum acid-fast bacilli grade in TB-DM. Values of the biomarkers decreased during the course of anti-TB therapy. Our study identified several markers associated with the pathophysiology of TB-DM that could be evaluated in future mechanistic investigations

Mycobacterium tuberculosis Induction of Heme Oxygenase-1 Expression Is Dependent on Oxidative Stress and Reflects Treatment Outcomes

The antioxidant enzyme heme oxygenase-1 (HO-1) is implicated in the pathogenesis of tuberculosis (TB) and has been proposed as a biomarker of active disease. Nevertheless, the mechanisms by which Mycobacterium tuberculosis (Mtb) induces HO-1 as well as how its expression is affected by HIV-1 coinfection and successful antitubercular therapy (ATT) are poorly understood. We found that HO-1 expression is markedly increased in rabbits, mice, and non-human primates during experimental Mtb infection and gradually decreased during ATT. In addition, we examined circulating concentrations of HO-1 in a cohort of 130 HIV-1 coinfected and uninfected pulmonary TB patients undergoing ATT to investigate changes in expression of this biomarker in relation to HIV-1 status, radiological disease severity, and treatment outcome. We found that plasma levels of HO-1 were elevated in untreated HIV-1 coinfected TB patients and correlated positively with HIV-1 viral load and negatively with CD4+ T cell count. In both HIV-1 coinfected and Mtb monoinfected patients, HO-1 levels were substantially reduced during successful TB treatment but not in those who experienced treatment failure or subsequently relapsed. To further delineate the molecular mechanisms involved in induction of HO-1 by Mtb, we performed a series of in vitro experiments using mouse and human macrophages. We found that Mtb-induced HO-1 expression requires NADPH oxidase-dependent reactive oxygen species production induced by the early-secreted antigen ESAT-6, which in turn triggers nuclear translocation of the transcription factor NRF-2. These observations provide further insight into the utility of HO-1 as a biomarker of both disease and successful therapy in TB monoinfected and HIV-TB coinfected patients and reveal a previously undocumented pathway linking expression of the enzyme with oxidative stress