Dataset Information

Identification of Candidate Adherent-Invasive E. coli Signature Transcripts by Genomic/Transcriptomic Analysis.

ABSTRACT: Adherent-invasive Escherichia coli (AIEC) strains are detected more frequently within mucosal lesions of patients with Crohn's disease (CD). The AIEC phenotype consists of adherence and invasion of intestinal epithelial cells and survival within macrophages of these bacteria in vitro. Our aim was to identify candidate transcripts that distinguish AIEC from non-invasive E. coli (NIEC) strains and might be useful for rapid and accurate identification of AIEC by culture-independent technology. We performed comparative RNA-Sequence (RNASeq) analysis using AIEC strain LF82 and NIEC strain HS during exponential and stationary growth. Differential expression analysis of coding sequences (CDS) homologous to both strains demonstrated 224 and 241 genes with increased and decreased expression, respectively, in LF82 relative to HS. Transition metal transport and siderophore metabolism related pathway genes were up-regulated, while glycogen metabolic and oxidation-reduction related pathway genes were down-regulated, in LF82. Chemotaxis related transcripts were up-regulated in LF82 during the exponential phase, but flagellum-dependent motility pathway genes were down-regulated in LF82 during the stationary phase. CDS that mapped only to the LF82 genome accounted for 747 genes. We applied an in silico subtractive genomics approach to identify CDS specific to AIEC by incorporating the genomes of 10 other previously phenotyped NIEC. From this analysis, 166 CDS mapped to the LF82 genome and lacked homology to any of the 11 human NIEC strains. We compared these CDS across 13 AIEC, but none were homologous in each. Four LF82 gene loci belonging to clustered regularly interspaced short palindromic repeats region (CRISPR)--CRISPR-associated (Cas) genes were identified in 4 to 6 AIEC and absent from all non-pathogenic bacteria. As previously reported, AIEC strains were enriched for pdu operon genes. One CDS, encoding an excisionase, was shared by 9 AIEC strains. Reverse transcription quantitative polymerase chain reaction assays for 6 genes were conducted on fecal and ileal RNA samples from 22 inflammatory bowel disease (IBD), and 32 patients without IBD (non-IBD). The expression of Cas loci was detected in a higher proportion of CD than non-IBD fecal and ileal RNA samples (p <0.05). These results support a comparative genomic/transcriptomic approach towards identifying candidate AIEC signature transcripts.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC4509574 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of Candidate Adherent-Invasive E. coli Signature Transcripts by Genomic/Transcriptomic Analysis.

Zhang Yuanhao Y Rowehl Leahana L Krumsiek Julia M JM Orner Erika P EP Shaikh Nurmohammad N Tarr Phillip I PI Sodergren Erica E Weinstock George M GM Boedeker Edgar C EC Xiong Xuejian X Parkinson John J Frank Daniel N DN Li Ellen E Gathungu Grace G

PloS one 20150630 6

Adherent-invasive Escherichia coli (AIEC) strains are detected more frequently within mucosal lesions of patients with Crohn's disease (CD). The AIEC phenotype consists of adherence and invasion of intestinal epithelial cells and survival within macrophages of these bacteria in vitro. Our aim was to identify candidate transcripts that distinguish AIEC from non-invasive E. coli (NIEC) strains and might be useful for rapid and accurate identification of AIEC by culture-independent technology. We p ...[more]

PMID: 26125937

Similar Datasets

Project description:BackgroundLung cancer is the leading cause of the largest number of deaths worldwide and lung adenocarcinoma is the most common form of lung cancer. In order to understand the molecular basis of lung adenocarcinoma, integrative analysis have been performed by using genomics, transcriptomics, epigenomics, proteomics and clinical data. Besides, molecular prognostic signatures have been generated for lung adenocarcinoma by using gene expression levels in tumor samples. However, we need signatures including different types of molecular data, even cohort or patient-based biomarkers which are the candidates of molecular targeting.ResultsWe built an R pipeline to carry out an integrated meta-analysis of the genomic alterations including single-nucleotide variations and the copy number variations, transcriptomics variations through RNA-seq and clinical data of patients with lung adenocarcinoma in The Cancer Genome Atlas project. We integrated significant genes including single-nucleotide variations or the copy number variations, differentially expressed genes and those in active subnetworks to construct a prognosis signature. Cox proportional hazards model with Lasso penalty and LOOCV was used to identify best gene signature among different gene categories. We determined a 12-gene signature (BCHE, CCNA1, CYP24A1, DEPTOR, MASP2, MGLL, MYO1A, PODXL2, RAPGEF3, SGK2, TNNI2, ZBTB16) for prognostic risk prediction based on overall survival time of the patients with lung adenocarcinoma. The patients in both training and test data were clustered into high-risk and low-risk groups by using risk scores of the patients calculated based on selected gene signature. The overall survival probability of these risk groups was highly significantly different for both training and test datasets.ConclusionsThis 12-gene signature could predict the prognostic risk of the patients with lung adenocarcinoma in TCGA and they are potential predictors for the survival-based risk clustering of the patients with lung adenocarcinoma. These genes can be used to cluster patients based on molecular nature and the best candidates of drugs for the patient clusters can be proposed. These genes also have a high potential for targeted cancer therapy of patients with lung adenocarcinoma.

Project description:The adherent invasive Escherichia coli (AIEC) pathotype has been associated with the aetiology of Crohn's disease (CD). Scarce reports have shown the antimicrobial resistance (AMR) profiles of AIEC. Despite antibiotics not being recommended to treat CD, antimicrobial therapy could be useful in stratified patients, such as AIEC carriers. We examined the antimicrobial resistance profiles of AIEC strains to identify which therapies could be effective or confer a risk for such patients. Phenotypic resistance to 30 antimicrobials was tested according to CLSI standards. AIEC (n = 22) and non-pathogenic E. coli (non-AIEC) strains (n = 37) isolated from the gut mucosa of 31 CD patients and 18 controls were studied. De novo genome sequencing was carried out for 39 of the 59 strains, and AMR genes were searched using the DeepARG database in these genomes and 33 additional AIEC publicly available genomes. The strains isolated from CD and controls showed similar phenotypic AMR profiles. The genomic analysis did not reveal an increased prevalence of AMR genes. However, AIEC strains were more frequently resistant to ?-lactams than non-AIEC strains (11 AIEC (50%) and 5 non-AIEC (22%) strains were resistant to at least one ?-lactam; p < 0.042). Two AIEC strains were resistant to expanded-spectrum cephalosporins. One strain carried a plasmid-mediated AmpC ?-lactamase (CMY-69), and the other presented mutations in the promotor of the intrinsic chromosomal AmpC related to the hyperproduction of this enzyme. The rest of the strains were resistant to ?-lactams not including expanded-spectrum cephalosporins. The majority carried TEM-related ?-lactamases. Genomic analysis including external AIEC revealed that the gene sul1 encoding for sulphonamide resistance was more frequent in AIEC strains than non-AIEC strains (34.6% vs. 9.5%, p = 0.030). AMR in AIEC is a matter of concern regarding the putative implication of the pathotype in CD. The high proportion of AIEC resistant to ?-lactams warrants caution about the risk there may be in the use of these antimicrobials in AIEC-colonized CD patients.

Dataset Information

Identification of Candidate Adherent-Invasive E. coli Signature Transcripts by Genomic/Transcriptomic Analysis.

Publications

Identification of Candidate Adherent-Invasive E. coli Signature Transcripts by Genomic/Transcriptomic Analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets