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1. Biochemical characterization of Mycobacterial RNA polymerases

   https://doi.org/10.1128/jb.00256-24  

   Cooper, Stephanie L; Requijo, Ryan M; Lucius, Aaron L; Schneider, David A (October 2024, Journal of Bacteriology)

   Champion, Patricia A (Ed.)

   ABSTRACT Tuberculosis is caused by the bacteriumMycobacterium tuberculosis(Mtb). While eukaryotic species employ several specialized RNA polymerases (Pols) to fulfill the RNA synthesis requirements of the cell, bacterial species use a single RNA polymerase (RNAP). To contribute to the foundational understanding of how Mtb and the related non-pathogenic mycobacterial species,Mycobacterium smegmatis(Msm), perform the essential function of RNA synthesis, we performed a series ofin vitrotranscription experiments to define the unique enzymatic properties of Mtb and Msm RNAPs. In this study, we characterize the mechanism of nucleotide addition used by these bacterial RNAPs with comparisons to previously characterized eukaryotic Pols I, II, and III. We show that Mtb RNAP and Msm RNAP demonstrate similar enzymatic properties and nucleotide addition kinetics to each other but diverge significantly from eukaryotic Pols. We also show that Mtb RNAP and Msm RNAP uniquely bind a nucleotide analog with significantly higher affinity than canonical nucleotides, in contrast to eukaryotic RNA polymerase II. This affinity for analogs may reveal a vulnerability for selective inhibition of the pathogenic bacterial enzyme.IMPORTANCETuberculosis, caused by the bacteriumMycobacterium tuberculosis(Mtb), remains a severe global health threat. The World Health Organization (WHO) has reported that tuberculosis is second only to COVID-19 as the most lethal infection worldwide, with more annual deaths than HIV and AIDS (WHO.int). The first-line treatment for tuberculosis, Rifampin (or Rifampicin), specifically targets the Mtb RNA polymerase. This drug has been used for decades, leading to increased numbers of multi-drug-resistant infections (Stephanie,et al). To effectively treat tuberculosis, there is an urgent need for new therapeutics that selectively target vulnerabilities of the bacteria and not the host. Characterization of the differences between Mtb enzymes and host enzymes is critical to inform these ongoing drug design efforts. 

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2. The flowering time regulator FLK controls pathogen defense in Arabidopsis thaliana

   https://doi.org/10.1093/plphys/kiad021  

   Fabian, Matthew; Gao, Min; Zhang, Xiao-Ning; Shi, Jiangli; Vrydagh, Leah; Kim, Sung-Ha; Patel, Priyank; Hu, Anna R; Lu, Hua (January 2023, Plant Physiology)

   Abstract Plant disease resistance is a complex process that is maintained in an intricate balance with development. Increasing evidence indicates the importance of posttranscriptional regulation of plant defense by RNA binding proteins. In a genetic screen for suppressors of Arabidopsis (Arabidopsis thaliana) accelerated cell death 6-1 (acd6-1), a small constitutive defense mutant whose defense level is grossly in a reverse proportion to plant size, we identified an allele of the canonical flowering regulatory gene FLOWERING LOCUS K HOMOLOGY DOMAIN (FLK) encoding a putative protein with triple K homology (KH) repeats. The KH repeat is an ancient RNA binding motif found in proteins from diverse organisms. The relevance of KH-domain proteins in pathogen resistance is largely unexplored. In addition to late flowering, the flk mutants exhibited decreased resistance to the bacterial pathogen Pseudomonas syringae and increased resistance to the necrotrophic fungal pathogen Botrytis cinerea. We further found that the flk mutations compromised basal defense and defense signaling mediated by salicylic acid (SA). Mutant analysis revealed complex genetic interactions between FLK and several major SA pathway genes. RNA-seq data showed that FLK regulates expression abundance of some major defense- and development-related genes as well as alternative splicing of a number of genes. Among the genes affected by FLK is ACD6, whose transcripts had increased intron retentions influenced by the flk mutations. Thus, this study provides mechanistic support for flk suppression of acd6-1 and establishes that FLK is a multifunctional gene involved in regulating pathogen defense and development of plants. 

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3. Identifying gene expression patterns associated with drug-specific survival in cancer patients

   https://doi.org/10.1038/s41598-021-84211-y  

   Neary, Bridget; Zhou, Jie; Qiu, Peng (December 2021, Scientific Reports)

   null (Ed.)

   Abstract The ability to predict the efficacy of cancer treatments is a longstanding goal of precision medicine that requires improved understanding of molecular interactions with drugs and the discovery of biomarkers of drug response. Identifying genes whose expression influences drug sensitivity can help address both of these needs, elucidating the molecular pathways involved in drug efficacy and providing potential ways to predict new patients’ response to available therapies. In this study, we integrated cancer type, drug treatment, and survival data with RNA-seq gene expression data from The Cancer Genome Atlas to identify genes and gene sets whose expression levels in patient tumor biopsies are associated with drug-specific patient survival using a log-rank test comparing survival of patients with low vs. high expression for each gene. This analysis was successful in identifying thousands of such gene–drug relationships across 20 drugs in 14 cancers, several of which have been previously implicated in the respective drug’s efficacy. We then clustered significant genes based on their expression patterns across patients and defined gene sets that are more robust predictors of patient outcome, many of which were significantly enriched for target genes of one or more transcription factors, indicating several upstream regulatory mechanisms that may be involved in drug efficacy. We identified a large number of genes and gene sets that were potentially useful as transcript-level biomarkers for predicting drug-specific patient survival outcome. Our gene sets were robust predictors of drug-specific survival and our results included both novel and previously reported findings, suggesting that the drug-specific survival marker genes reported herein warrant further investigation for insights into drug mechanisms and for validation as biomarkers to aid cancer therapy decisions. 

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4. Dynamical model of antibiotic responses linking expression of resistance genes to metabolism explains emergence of heterogeneity during drug exposures

   https://doi.org/10.1088/1478-3975/ad2d64  

   Stevanovic, Mirjana; Teuber_Carvalho, João_Pedro; Bittihn, Philip; Schultz, Daniel (April 2024, Physical Biology)

   Antibiotic responses in bacteria are highly dynamic and heterogeneous, with sudden exposure of bacterial colonies to high drug doses resulting in the coexistence of recovered and arrested cells. The dynamics of the response is determined by regulatory circuits controlling the expression of resistance genes, which are in turn modulated by the drug’s action on cell growth and metabolism. Despite advances in understanding gene regulation at the molecular level, we still lack a framework to describe how feedback mechanisms resulting from the interdependence between expression of resistance and cell metabolism can amplify naturally occurring noise and create heterogeneity at the population level. To understand how this interplay affects cell survival upon exposure, we constructed a mathematical model of the dynamics of antibiotic responses that links metabolism and regulation of gene expression, based on the tetracycline resistancetetoperon inE. coli. We use this model to interpret measurements of growth and expression of resistance in microfluidic experiments, both in single cells and in biofilms. We also implemented a stochastic model of the drug response, to show that exposure to high drug levels results in large variations of recovery times and heterogeneity at the population level. We show that stochasticity is important to determine how nutrient quality affects cell survival during exposure to high drug concentrations. A quantitative description of how microbes respond to antibiotics in dynamical environments is crucial to understand population-level behaviors such as biofilms and pathogenesis. 

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5. Deep transfer learning of cancer drug responses by integrating bulk and single-cell RNA-seq data

   https://doi.org/10.1038/s41467-022-34277-7  

   Chen, Junyi; Wang, Xiaoying; Ma, Anjun; Wang, Qi-En; Liu, Bingqiang; Li, Lang; Xu, Dong; Ma, Qin (October 2022, Nature Communications)

   Abstract Drug screening data from massive bulk gene expression databases can be analyzed to determine the optimal clinical application of cancer drugs. The growing amount of single-cell RNA sequencing (scRNA-seq) data also provides insights into improving therapeutic effectiveness by helping to study the heterogeneity of drug responses for cancer cell subpopulations. Developing computational approaches to predict and interpret cancer drug response in single-cell data collected from clinical samples can be very useful. We propose scDEAL, a deep transfer learning framework for cancer drug response prediction at the single-cell level by integrating large-scale bulk cell-line data. The highlight in scDEAL involves harmonizing drug-related bulk RNA-seq data with scRNA-seq data and transferring the model trained on bulk RNA-seq data to predict drug responses in scRNA-seq. Another feature of scDEAL is the integrated gradient feature interpretation to infer the signature genes of drug resistance mechanisms. We benchmark scDEAL on six scRNA-seq datasets and demonstrate its model interpretability via three case studies focusing on drug response label prediction, gene signature identification, and pseudotime analysis. We believe that scDEAL could help study cell reprogramming, drug selection, and repurposing for improving therapeutic efficacy. 

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