Dataset Information

Gene expression in Klebsiella pneumoniae

ABSTRACT: Elucidating the RamA Regulon in Klebsiella pneumoniae and the transcriptome profiles of multidrug resistant Klebsiella pneumoniae Overall design: various strains of Klebsiella pneumoniae

INSTRUMENT(S): NimbleGen Klebsiella pneumoniae 385k version 1

SUBMITTER: Alasdair Ivens

PROVIDER: GSE24688 | GEO | 2017-12-25

SECONDARY ACCESSION(S): PRJNA132259

REPOSITORIES: GEO

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Project description:Liao2011 - Genome-scale metabolic reconstruction of Klebsiella pneumoniae (iYL1228) This model is described in the article: An experimentally validated genome-scale metabolic reconstruction of Klebsiella pneumoniae MGH 78578, iYL1228. Liao YC, Huang TW, Chen FC, Charusanti P, Hong JS, Chang HY, Tsai SF, Palsson BO, Hsiung CA. J. Bacteriol. 2011 Apr; 193(7): 1710-1717 Abstract: Klebsiella pneumoniae is a Gram-negative bacterium of the family Enterobacteriaceae that possesses diverse metabolic capabilities: many strains are leading causes of hospital-acquired infections that are often refractory to multiple antibiotics, yet other strains are metabolically engineered and used for production of commercially valuable chemicals. To study its metabolism, we constructed a genome-scale metabolic model (iYL1228) for strain MGH 78578, experimentally determined its biomass composition, experimentally determined its ability to grow on a broad range of carbon, nitrogen, phosphorus and sulfur sources, and assessed the ability of the model to accurately simulate growth versus no growth on these substrates. The model contains 1,228 genes encoding 1,188 enzymes that catalyze 1,970 reactions and accurately simulates growth on 84% of the substrates tested. Furthermore, quantitative comparison of growth rates between the model and experimental data for nine of the substrates also showed good agreement. The genome-scale metabolic reconstruction for K. pneumoniae presented here thus provides an experimentally validated in silico platform for further studies of this important industrial and biomedical organism. This model is hosted on BioModels Database and identified by: MODEL1507180054. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:With global increase in use of carbapenems, several gram-negative bacteria have acquired carbapenem resistance, thereby limiting treatment options. Klebsiella pneumoniae is one of such notorious pathogen that is being widely studied to find novel resistance mechanisms and drug targets. These antibiotic-resistant clinical isolates generally harbor many genetic alterations, and identification of causal mutations will provide insights into the molecular mechanisms of antibiotic resistance. We propose a method to prioritize mutated genes responsible for antibiotic resistance, in which mutated genes that also show significant expression changes among their functionally coupled genes become more likely candidates. For network-based analyses, we developed a genome-scale co-functional network of K. pneumoniae genes, KlebNet (www.inetbio.org/klebnet). Using KlebNet, we could reconstruct functional modules for antibiotic-resistance, and virulence, and retrieved functional association between them. With complementation assays with top candidate genes, we could validated two genes for negative regulation of meropenem resistance and four genes for positive regulation of virulence in Galleria mellonella larvae. Therefore, our study demonstrated feasibility of network-based identification of genes required for antimicrobial resistance and virulence of human pathogenic bacteria with genomic and transcriptomic profiles from antibiotic-resistant clinical isolates. Overall design: We used two strains of K. pneumoniae clinical isolates from a single patient who had undergone meropenem treatment: K26 and K56 that were isolated before and after the meropenem administration, respectively. A significant increase in meropenem minimum inhibitory concentration (MIC) was observed in K56 compared with K26 from 0.5 µg/ml to ≥16 µg/ml. We also generated a mutant strain that acquired meropenem resistance during in vitro culture condition. The meropenem-susceptible K. pneumoniae strain K26 was used as the parent strain to obtain in vitro meropenem-resistant mutants. The resistant strains were isolated at wide range of meropenem concentrations, a strain K26M used in this study was obtained at the concentration of 16 µg/ml of meropenem.

Project description:Purpose: The goal of this study was to use RNA-seq to define the Klebsiella pneumoniae transcriptome recorded under 5 different experimental conditions, and to identify signature genes of each condition by comparing global transcriptional profiles. Methods: mRNA profiles were generated for Klebsiella pneumoniae CH1034 clinical isolate, in triplicate, by deep sequencing. Total RNAs were harvested from bacteria cultured at 37°C in M63B1 minimal media under different conditions: (i) planktonic aerobic condition at OD 620nm=0.250 (exponential growth-phase), (ii) overnight planktonic aerobic condition (stationnary growth-phase), (iii) biofilm in a flow-cell chamber after 7 hours of incubation (7-hours old biofilm), (iv) biofilm in a flow-cell chamber after 13 hours of incubation (13-hours old biofilm), (v) bacteria self-dispersed from biofilm recovered in the flow-cell effluent (biofilm-dispersed bacteria). Ribosomal RNAs were removed using the Bacteria Ribo-Zero Magnetic kit (Epicentre Biotechnologies). Libraries were prepared using the TruSeq Stranded mRNA Sample Preparation kit (Illumina), and 50bp single-reads were obtained by HiSeq 2000 (Illumina).The sequence reads that passed FastQC quality filters were mapped to the CH1034 genome using Burrows–Wheeler Aligner (BWA) (0.7.12-r1039 version). The transcript levels were determined using HTSeq-count (0.6.1p1 version) with union mode followed by DESeq (1.16.0 version) analysis. qRT–PCR validation was performed using SYBR Green assays. Results: We found that each condition has a specific transcriptional profile, and we identify 4 robust signature genes for each. Conclusion: Our study represents the first detailed analysis of K. pneumoniae transcriptomes under different experimental conditions generated by RNA-seq technology. The data reported here should permit the dissection of complex biologic functions involved in the transition between the sessile and planktonic modes of growth. Overall design: Determination of the transcriptional profiling of Klebsiella pneumoniae under 5 different experimental conditions. mRNA profiles were generated for bacteria under exponential planktonic growth-phase, stationary planktonic growth-phase, 7 hours-old biofilm, 13 hours-old biofilm and biofilm-dispersed modes, each in three biological replicates, by deep sequencing using Illumina HiSeq