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PubMed2026

Ecr positively regulates activity of the PhoQ/PhoP signalling system in Klebsiella pneumoniae.

BACKGROUND: The rising prevalence of polymyxin resistance in multidrug-resistant Klebsiella pneumoniae presents a critical situation with limited therapeutic options. METHODS: Methods Genomic sequencing of 15 clinical polymyxin-resistant K. pneumoniae strains with multidrug resistance revealed that MgrB inactivation, predominantly disrupted by insertion sequences (ISs) in the IS1, IS4, and IS5 families, was the leading cause of polymyxin resistance. Comparative transcriptomics of wild-type, ΔmgrB, and ΔmgrBΔphoP were performed to elucidate the MgrB-PhoPQ regulatory network. RESULTS: This study conducted a system-wide analysis of the regulatory network and identified a species-specific PhoPQ regulon in K. pneumoniae.Beyond the classical MgrB-PhoPQ-ArnBCADTEF pathway, we identified a previously unannotated PhoPQ-regulated gene, 144 bp LN739_RS09850, encoding an Ecr homologue from Enterobacter cloacae. This protein has been reported to confer colistin heteroresistance, with the underlying mechanism not yet functionally validated. This study revealed that overexpression of Ecr homologues decreased colistin susceptibility in both K. pneumoniae and E. cloacae, but this phenotype was abolished upon phoP deletion, confirming PhoP's essential role. Consistent with this dependency, comparative transcriptomics of Ecr-overexpressing K. pneumoniae vs. control revealed significant upregulation of mgrB, phoPQ, arnBCADTE, and pmrD. Two-hybrid bacterial assays further demonstrated direct Ecr-PhoQ interaction. Electrophoretic mobility shift assay confirmed that PhoP directly binds to the ecr promoter in vitro, and a β-galactosidase reporter assay demonstrated that PhoP enhanced ecr promoter activity, indicating that PhoP regulates ecr expression by directly controlling its transcription. CONCLUSION: Collectively, these findings suggest that PhoP may directly activate the transcription of Ecr, with Ecr feedback activating the PhoPQ system via interaction with PhoQ, leading to induction of the arn operon and consequent polymyxin resistance.

Klebsiella pneumoniae↗
PubMed2026

A contextual activity score (CAS) for inferring ADAR-associated transcriptional activity across RNA-seq, single-cell, and spatial transcriptomics.

BACKGROUND AND OBJECTIVE: Adenosine-to-inosine RNA editing, catalyzed by Adenosine Deaminases Acting on RNA (ADARs), is a widespread modification involved in neural function, immune regulation, and cancer. The Alu Editing Index (AEI) is the standard metric to estimate ADAR activity but requires raw sequencing reads and is poorly suited for single-cell and spatial transcriptomic data. This study aimed to develop an alternative framework for inferring ADAR-associated transcriptional activity from gene expression data across diverse transcriptomic technologies. METHODS: We developed the Contextual Activity Score (CAS), a framework based on transcriptional signatures from ADAR perturbation experiments. Context-specific signatures were generated for human neurons, mouse neurons, and cancer models to infer ADAR1 and ADAR2 activity. CAS was computed from normalized gene expression matrices using regulon-based enrichment analysis. Performance was evaluated by comparing with the Alu Editing Index across bulk RNA sequencing datasets, simulated sequencing depths, and library preparation protocols. RESULTS: CAS showed strong concordance with the Alu Editing Index across multiple datasets, while remaining robust to reduced sequencing depth and different library protocols. Unlike the Alu Editing Index, CAS can be applied to single-cell and spatial transcriptomic data and enables the independent assessment of ADAR2 activity. In cancer and neuronal contexts, CAS captured biologically meaningful variations in ADAR-associated transcriptional activity at sample, cell-type, and spatial levels. CONCLUSION: CAS provides a scalable approach applicable across multiple RNA-seq protocols for estimating ADAR-associated transcriptional activity using gene expression data. This method, implemented in an open-source R package for broad adoption, expands the ability to study ADAR-associated transcriptional activity across transcriptomic modalities where direct editing quantification is challenging, such as single-cell and spatial transcriptomics.

Adenosine Deaminase↗