Proteomic-based identification of APCS as candidate protein for diagnosis of patients exhibiting anti-tubercular drug induced liver injury.

Traditional markers evaluate anti-tubercular drug-induced liver injury (AT-DILI). However, these markers have certain limitations and studies are in progress to characterize AT-DILI at an early stage. In the present study, 40 patients were categorized and equally distributed into healthy controls, newly diagnosed tuberculosis (TB), TB without hepatotoxicity and TB with hepatotoxicity groups based on their conventional liver function tests. Relative protein quantification was performed on depleted pooled serum samples of each representative group by LC-MS/MS, and validation of shortlisted protein was done by ELISA. Levels of all analysed biochemical parameters showed a statistical increment in the hepatotoxicity group compared to the other three groups, representing AT-DILI. Comparative proteomic analysis between TB with hepatotoxicity versus TB without hepatotoxicity groups highlighted 24 significant differentially expressed proteins, including PROS1, KNG1, CFH, LCAT, APCS and ADIPOQ. Identified proteins were involved in complement activation, triglyceride-rich lipoprotein particle remodelling and pathways comprising complement, coagulation cascades and cholesterol metabolism. Based on functional relevance, the serum amyloid P component (APCS) was shortlisted for validation, and it showed a similar trend as observed in the discovery phase with 100% sensitivity and 87% specificity; however, findings need exploration in larger cohorts.

Molecular and cellular remodeling of HepG2 cells upon treatment with antitubercular drugs.

Drug-induced liver injury (DILI) is an adverse outcome of the currently used tuberculosis treatment regimen, which results in patient noncompliance, poor treatment outcomes, and the emergence of drug-resistant tuberculosis. DILI is primarily caused by the toxicity of the drugs and their metabolites, which affect liver cells, biliary epithelial cells, and liver vasculature. However, the precise mechanism behind the cellular damage attributable to first-line antitubercular drugs (ATDs), as well as the effect of toxicity on the cell survival strategies, is yet to be elucidated. In the current study, HepG2 cells upon treatment with a high concentration of ATDs showed increased perforation within the cell, cuboidal shape, and membrane blebbing as compared with control/untreated cells. It was observed that ATD-induced toxicity in HepG2 cells leads to altered mitochondrial membrane permeability, which was depicted by the decreased fluorescence intensity of the MitoRed tracker dye at higher drug concentrations. In addition, high doses of ATDs caused cell damage through an increase in reactive oxygen species production in HepG2 cells and a simultaneous reduction in glutathione levels. Further, high dose of isoniazid (50-200 mM), pyrazinamide (50-200 mM), and rifampicin (20-100 µM) causes cell apoptosis and affects cell survival during toxic conditions by decreasing the expression of potent autophagy markers Atg5, Atg7, and LC3B. Thus, ATD-mediated toxicity contributes to the reduced ability of hepatocytes to tolerate cellular damage caused by altered mitochondrial membrane permeability, increased apoptosis, and decreased autophagy. These findings further emphasize the need to develop adjuvant therapies that can mitigate ATD-induced toxicity for the effective treatment of tuberculosis.

Proteomic profiling of peripheral blood mononuclear cells isolated from patients with tuberculosis and diabetes copathogenesis - A pilot study.

BACKGROUND: Diabetes is an important risk factor for developing tuberculosis. This association leads to exacerbation of tuberculosis symptoms and delayed treatment of both the diseases. Molecular mechanism and biomarkers/drug targets related to copathogenesis of tuberculosis and diabetes are still poorly understood. In this study, proteomics based 2D-MALDI/MS approach was employed to identify host signature proteins which are altered during copathogenesis of tuberculosis and diabetes. METHODS: Comparative proteome of human peripheral blood mononuclear cells (PBMCs) from healthy controls, tuberculosis and diabetes patients in comparison to comorbid diabetes and tuberculosis patients was analyzed. Gel based proteomics approach followed by in gel trypsin digestion and peptide identification by mass spectrometry was used for signature protein identification. RESULTS: Total of 18 protein spots with differential expression in tuberculosis and diabetes copathogenesis (TBDM) patients in comparison to other groups were identified. These proteins belonged to four functional categories i.e. structural, cell cycle/growth regulation, signaling and intermediary metabolism. These include Vimentin, tubulin beta chain protein, Actin related protein 2/3 complex subunit 2, coffilin 1 (Structural), PDZ LIM domain protein, Rho-GDP dissociation inhibitor, Ras related protein Rab (signaling), superoxide dismutase, dCTPpyrophosphatase 1, Transcription initiation factor TFIID subunit 12, three isoforms of Peptidylprolylcis-trans isomerase A, SH3 domain containing protein (metabolism), three isoforms of Protein S100A9 and S100A8 (cell cycle progression/growth regulation). CONCLUSION: Proteins identified to be differentially expressed in TBDM patient can act as potent biomarkers and as predictors for copathogenesis of tuberculosis and diabetes.

In-silico approach for drug induced liver injury prediction: Recent advances.

Drug induced liver injury (DILI) is the prime cause of liver disfunction which may lead to mild non-specific symptoms to more severe signs like hepatitis, cholestasis, cirrhosis and jaundice. Not only the prescription medications, but the consumption of herbs and health supplements have also been reported to cause these adverse reactions resulting into high mortality rates and post marketing withdrawal of drugs. Due to the continuously increasing DILI incidences in recent years, robust prediction methods with high accuracy, specificity and sensitivity are of priority. Bioinformatics is the emerging field of science that has been used in the past few years to explore the mechanisms of DILI. The major emphasis of this review is the recent advances of in silico tools for the diagnostic and therapeutic interventions of DILI. These tools have been developed and widely used in the past few years for the prediction of pathways induced from both hepatotoxic as well as hepatoprotective Chinese drugs and for the identification of DILI specific biomarkers for prognostic purpose. In addition to this, advanced machine learning models have been developed for the classification of drugs into DILI causing and non-DILI causing. Moreover, development of 3 class models over 2 class offers better understanding of multi-class DILI risks and at the same time providing authentic prediction of toxicity during drug designing before clinical trials.