Search PubMedSearch

PubMed · 39601576

Antibiotic resistance genotype, phenotype, and clinical outcomes in patients with Gram-negative infections at Rabin Medical Center in Israel.

Abstract

UNLABELLED: Antibiotic resistance is a major cause of morbidity and mortality. However, a better understanding of the relationship between bacterial genetic markers, phenotypic resistance, and clinical outcomes is needed. We performed whole-genome sequencing on five medically important pathogens (Acinetobacter baumannii, Enterobacter cloacae, Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa) to investigate how resistance genes impact patient outcomes. A total of 168 isolates from 162 patients with Gram-negative infections admitted to Beilinson Hospital at Rabin Medical Center in Israel were included for final analysis. Genomes were analyzed for resistance determinants and correlated with microbiologic and clinical data. Thirty-day mortality from time of culture was 26.5% (43/162). Twenty-nine patients had carbapenem-resistant isolates (29/168, 17.2%), while 63 patients had multidrug-resistant isolates (63/168, 37.5%). Albumin levels were inversely associated with mortality and length of stay, while arrival from a healthcare facility and cancer chemotherapy predicted having a multidrug-resistant isolate. Sequencing revealed possible patient-to-patient transmission events. blaCTX-M-15 was associated with multidrug-resistance in E. coli (OR = 3.888, P = 0.023) on multivariate analysis. Increased blaOXA-72 copy number was associated with carbapenem-resistance in A. baumannii (P = 0.003) and meropenem minimum inhibitory concentration (P = 0.005), yet carbapenem-resistant isolates retained sensitivity to cefiderocol and sulbactam-durlobactam. RJX84154 was associated with multidrug-resistance across all pathogens (P = 0.0018) and in E. coli (P = 0.0024). Low albumin levels were associated with mortality and length of stay in this sample population. blaCTX-M-15 was correlated with multidrug-resistance in E. coli, and blaOXA-72 depth predicted meropenem minimum inhibitory concentration in A. baumannii. RJX84154 may play a role in multidrug-resistance. IMPORTANCE: While there have been several studies that attempt to find clinical predictors of outcomes in patients hospitalized with bacterial infections, less has been done to combine clinical data with genomic mechanisms of antibiotic resistance. This study focused on a hospitalized patient population in Israel with infections due to medically important bacterial pathogens as a way to build a framework that would unite clinical data with both bacterial antibiotic susceptibility and genomic data. Merging both clinical and genomic data allowed us to find both bacterial and clinical factors that impact certain clinical outcomes. As genome sequencing of bacteria becomes both rapid and commonplace, near real-time monitoring of resistance determinants could help to optimize clinical care and potentially improve outcomes in these patients.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Rachelle E Koch, Jackson Barth, Andrew E Clark, Dhara Desai, Jiwoong Kim, Christine A Pybus, Xiaowei Zhan, Leonard Leibovici, Dafna Yahav, David E Greenberg. 2024-11-27. Antibiotic resistance genotype, phenotype, and clinical outcomes in patients with Gram-negative infections at Rabin Medical Center in Israel.. https://doi.org/10.1128/spectrum.00383-24

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans

Identification of Genome-Wide Chromatin Structural Aberration in Cancer by Hi-C Analysis.

Aberrant three-dimensional genome organization is a hallmark of cancer, often driving oncogene activation through mechanisms such as enhancer hijacking. High-throughput chromosome conformation capture (Hi-C) maps these interactions on a genome-wide scale. Unlike earlier dilution-based methods, in situ Hi-C performs proximity ligation within intact nuclei, minimizing random ligation noise and enabling fine-scale structure detection. This chapter describes an optimized in situ Hi-C protocol tailored for cancer cell lines using MboI digestion and biotin-mediated pull-down to generate high-complexity libraries. We further outline a computational workflow that extends beyond standard topological mapping of compartments and topologically associating domains to identify cancer-specific aberrations. Specifically, we focus on detecting chromosomal rearrangements (structural variants) and characterizing the distinct circular topology of extrachromosomal DNA. This integrated experimental and analytical framework provides the necessary tools to dissect the spatial dysregulation underlying tumor evolution.

Humans