Dataset Information

A Quantitative Model to Estimate Drug Resistance in Pathogens.

ABSTRACT: Pneumocystis pneumonia (PCP) is an opportunistic infection that occurs in humans and other mammals with debilitated immune systems. These infections are caused by fungi in the genus Pneumocystis, which are not susceptible to standard antifungal agents. Despite decades of research and drug development, the primary treatment and prophylaxis for PCP remains a combination of trimethoprim (TMP) and sulfamethoxazole (SMX) that targets two enzymes in folic acid biosynthesis, dihydrofolate reductase (DHFR) and dihydropteroate synthase (DHPS), respectively. There is growing evidence of emerging resistance by Pneumocystis jirovecii (the species that infects humans) to TMP-SMX associated with mutations in the targeted enzymes. In the present study, we report the development of an accurate quantitative model to predict changes in the binding affinity of inhibitors (K_i, IC₅₀) to the mutated proteins. The model is based on evolutionary information and amino acid covariance analysis. Predicted changes in binding affinity upon mutations highly correlate with the experimentally measured data. While trained on Pneumocystis jirovecii DHFR/TMP data, the model shows similar or better performance when evaluated on the resistance data for a different inhibitor of PjDFHR, another drug/target pair (PjDHPS/SMX) and another organism (Staphylococcus aureus DHFR/TMP). Therefore, we anticipate that the developed prediction model will be useful in the evaluation of possible resistance of the newly sequenced variants of the pathogen and can be extended to other drug targets and organisms.

SUBMITTER: Baker FN

PROVIDER: S-EPMC5179226 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background Development of drug resistance is often dynamic and requires time series experiments to understand these processes. The most abundant source of information regarding such progressive activity is tracking single cell gene expression data. Our understanding of the imminent role of phenotype switching in modifying therapeutic response has considerably increased.The analysis of single cell transcriptomic data along with machine learning models offers a constructive tool to describe dynamic cellular processes to elucidate cell-to-cell variability within the population after drug exposure. Here we studied how genomic and non-genomic basis for transcriptome differences can influence the developing drug resistance and clonal fitness. Results: This study describes the scRNA-seq analysis of untreated, treated and drug holiday PDX transplants of TNBC exposed to platinum. This builds from the materials and findings of our paper by Salehi, Kabeer et al, Nature 2021 (PMID: 3416307), in which we established the measurement of fitness in this time series. In the new work, we have mapped single cell transcription in conjunction, allowing a dissection of copy number driven (in-cis) clonal transcription and non-clonal, ie non-genomic transcription. We developed an experimental and computational platform consisting of four major steps: (1) timeseries sampling of scRNA-seq 126,556 TNBC PDX cells >2.5 years; (2) clustering the tumor cells into groups reflecting their drug sensitivity, resistance and other characteristics; (3) performing trajectory inference to identify lineages of transitional processes along with drug and tumor progression; (4) estimating the longitudinal gene expression of each lineage; and identifying significant genes between-lineage differential expression modules. Interestingly, we identified a small group of genes (18.9%) that display a new cell state that is not towards the untreated (Un-Rx) nor towards treated Rx which are distant from untreated cell states. The pathway results are shared between two different quantitative measurements of clone-aware differential expression and pseudotime analysis.

Project description:Acquired drug resistance impacts the majority of patients being treated with tyrosine kinase inhibitors (TKIs) and remains a key challenge in modern anti-cancer therapy. The lack of clinically effective therapies to overcome resistance represents an unmet need. Understanding the signalling that drives drug resistance will facilitate the development of new salvage therapies to treat patients with secondary TKI resistance. In this study, we utilise mass spectrometry to characterise the global phosphoproteomic alterations that accompany the acquisition of resistance to two FDA-approved TKIs, pazopanib and dasatinib, in the A204 rhabdoid tumour cell line. Our analysis finds that only 6% and 9.7% of the quantified phosphoproteome is altered upon the acquisition of pazopanib and dasatinib resistance, respectively. Pazopanib resistant cells display elevated phosphorylation in cytoskeletal regulatory pathways while dasatinib resistant cells show an upregulation of the insulin receptor/IGF-1R signalling pathway. Drug response profiling rediscovers several previously reported vulnerabilities associated with pazopanib and dasatinib resistance and identifies a new dependency to the second generation HSP90 inhibitor NVP-AUY-922. This study provides a useful resource detailing the candidate signalling determinants of acquired TKI resistance; and reveals a therapeutic approach of inhibiting HSP90 function as a means of salvage therapy to overcome pazopanib and dasatinib resistance. SIGNIFICANCE:Pazopanib and dasatinib are tyrosine kinase inhibitors (TKIs) approved for the treatment of multiple cancer types. Patients who are treated with these drugs are prone to the development of drug resistance and consequently tumour relapse. Here we use quantitative phosphoproteomics to characterise the signalling pathways which are enriched in cells that have acquired resistance to these two drugs. Furthermore, targeted drug screens were used to identify salvage therapies capable of overcoming pazopanib and dasatinib resistance. This data advances our understanding of the mechanisms of TKI resistance and highlights candidate targets for cancer therapy.

Dataset Information

A Quantitative Model to Estimate Drug Resistance in Pathogens.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets