Severe acute respiratory syndrome coronavirus (SARS-CoV)-2 infection in the Golden Syrian hamster causes lung pathology that resembles human coronavirus disease (COVID-19). However, extra-pulmonary pathologies associated with SARS-CoV-2 infection and post COVID sequelae remain to be understood. Here we show, using a hamster model, that the early phase of SARS-CoV-2 infection leads to an acute inflammatory response and lung pathologies, while the late phase of infection causes cardiovascular complications (CVC) characterized by ventricular wall thickening associated with increased ventricular mass/ body mass ratio and interstitial coronary fibrosis. Molecular profiling further substantiated our findings of CVC, as SARS-CoV-2-infected hamsters showed elevated levels of serum cardiac Troponin-I (cTnI), cholesterol, low-density lipoprotein and long-chain fatty acid triglycerides. Serum metabolomics profiling of SARS-CoV-2-infected hamsters identified N-acetylneuraminate, a functional metabolite found to be associated with CVC, as a metabolic marker was found to be common between SARS-CoV-2-infected hamsters and COVID-19 patients. Together, we propose hamsters as a suitable animal model to study post-COVID sequelae associated with CVC which could be extended to therapeutic interventions.
Building molecular correlates of drug resistance in cancer and exploiting them for therapeutic intervention remains a pressing clinical need. To identify factors that impact drug resistance herein we built a model that couples inherent cell-based response toward drugs with transcriptomes of resistant/sensitive cells. To test this model, we focused on a group of genes called metastasis suppressor genes (MSGs) that influence aggressiveness and metastatic potential of cancers. Interestingly, modeling of 84 000 drug response transcriptome combinations predicted multiple MSGs to be associated with resistance of different cell types and drugs. As a case study, on inducing MSG levels in a drug resistant breast cancer line resistance to anticancer drugs caerulomycin, camptothecin and topotecan decreased by more than 50–60%, in both culture conditions and also in tumors generated in mice, in contrast to control un-induced cells. To our knowledge, this is the first demonstration of engineered reversal of drug resistance in cancer cells based on a model that exploits inherent cellular response profiles.
BackgroundCoronary artery disease (CAD) is the leading cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM). The purpose of the present study was to discriminate the Indian CAD patients with or without T2DM by using multiple pathophysiological biomarkers.MethodsUsing sensitive multiplex protein assays, we assessed 46 protein markers including cytokines/chemokines, metabolic hormones, adipokines and apolipoproteins for evaluating different pathophysiological conditions of control, T2DM, CAD and T2DM with CAD patients (T2DM_CAD). Network analysis was performed to create protein-protein interaction networks by using significantly (p < 0.05) altered protein markers in each disease using STRING 10.5 database. We used two supervised analysis methods i.e., between class analysis (BCA) and principal component analysis (PCA) to reveals distinct biomarkers profiles. Further, random forest classification (RF) was used to classify the diseases by the panel of markers.ResultsOur two supervised analysis methods BCA and PCA revealed a distinct biomarker profiles and high degree of variability in the marker profiles for T2DM_CAD and CAD. Thereafter, the present study identified multiple potential biomarkers to differentiate T2DM, CAD, and T2DM_CAD patients based on their relative abundance in serum. RF classified T2DM based on the abundance patterns of nine markers i.e., IL-1β, GM-CSF, glucagon, PAI-I, rantes, IP-10, resistin, GIP and Apo-B; CAD by 14 markers i.e., resistin, PDGF-BB, PAI-1, lipocalin-2, leptin, IL-13, eotaxin, GM-CSF, Apo-E, ghrelin, adipsin, GIP, Apo-CII and IP-10; and T2DM _CAD by 12 markers i.e., insulin, resistin, PAI-1, adiponectin, lipocalin-2, GM-CSF, adipsin, leptin, Apo-AII, rantes, IL-6 and ghrelin with respect to the control subjects. Using network analysis, we have identified several cellular network proteins like PTPN1, AKT1, INSR, LEPR, IRS1, IRS2, IL1R2, IL6R, PCSK9 and MYD88, which are responsible for regulating inflammation, insulin resistance, and atherosclerosis.ConclusionWe have identified three distinct sets of serum markers for diabetes, CAD and diabetes associated with CAD in Indian patients using nonparametric-based machine learning approach. These multiple marker classifiers may be useful for monitoring progression from a healthy person to T2DM and T2DM to T2DM_CAD. However, these findings need to be further confirmed in the future studies with large number of samples.Electronic supplementary materialThe online version of this article (10.1186/s12967-018-1755-5) contains supplementary material, which is available to authorized users.
With the advancement in proteomics separation techniques and improvements in mass analyzers, the data generated in a mass-spectrometry based proteomics experiment is rising exponentially. Such voluminous datasets necessitate automated computational tools for high-throughput data analysis and appropriate statistical control. The data is searched using one or more of the several popular database search algorithms. The matches assigned by these tools can have false positives and statistical validation of these false matches is necessary before making any biological interpretations. Without such procedures, the biological inferences do not hold true and may be outright misleading. There is a considerable overlap between true and false positives. To control the false positives amongst a set of accepted matches, there is a need for some statistical estimate that can reflect the amount of false positives present in the data processed. False discovery rate (FDR) is the metric for global confidence assessment of a large-scale proteomics dataset. This chapter covers the basics of FDR, its application in proteomics, and methods to estimate FDR.
Trypanosomiasis infects more than 21 million people and claims approximately 2 million lives annually. Due to the development of resistance against currently available anti-trypanosomal drugs, there is a growing need for specific inhibitors and novel drug targets. Of late, the proteins from the Ubiquitin Proteasome Pathway (UPP): ubiquitin ligases and deubiquitinase have received attention as potential drug targets in other parasites from the apicomplexan family. The completion of Trypanosoma cruzi (Tc) genome sequencing in 2005 and subsequent availability of database resources like TriTrypDB has provided a platform for the systematic study of the proteome of this parasite. Here, we present the first comprehensive survey of the UPP enzymes, their homologs and other associated proteins in trypanosomes and the UPPs from T. cruzi were explored in detail. After extensive computational analyses using various bioinformatics tools, we have identified 269 putative UPP proteins in the T. cruzi proteome along with their homologs in other Trypanosoma species. Characterization of T. cruzi proteome was done based on their predicted subcellular localization, domain architecture and overall expression profiles. Specifically, unique domain architectures of the enzymes and the UPP players expressed exclusively in the amastigote stage provide a rationale for designing inhibitors against parasite UPP proteins.
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