Ontology highlight
ABSTRACT:
PROVIDER: PRJNA788733 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR17301900_1.fastq.gz | Fastqsanger.gz | |||
| SRR17301900_2.fastq.gz | Fastqsanger.gz | |||
| SRR17302000_1.fastq.gz | Fastqsanger.gz | |||
| SRR17302000_2.fastq.gz | Fastqsanger.gz | |||
| SRR17302100_1.fastq.gz | Fastqsanger.gz |
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Project description:<p>The study of antimicrobial resistance (AMR) in infectious diarrhea has generally been limited to cultivation, antimicrobial susceptibility testing and targeted PCR assays. When individual strains of significance are identified, whole genome shotgun (WGS) sequencing of important clones and clades is performed. Genes that encode resistance to antibiotics have been detected in environmental, insect, human and animal metagenomes and are known as "resistomes". While metagenomic datasets have been mined to characterize the healthy human gut resistome in the Human Microbiome Project and MetaHIT and in a Yanomani Amerindian cohort, directed metagenomic sequencing has not been used to examine the epidemiology of AMR. Especially in developing countries where sanitation is poor, diarrhea and enteric pathogens likely serve to disseminate antibiotic resistance elements of clinical significance. Unregulated use of antibiotics further exacerbates the problem by selection for acquisition of resistance. This is exemplified by recent reports of multiple antibiotic resistance in Shigella strains in India, in Escherichia coli in India and Pakistan, and in nontyphoidal Salmonella (NTS) in South-East Asia. We propose to use deep metagenomic sequencing and genome level assembly to study the epidemiology of AMR in stools of children suffering from diarrhea. Here the epidemiology component will be surveillance and analysis of the microbial composition (to the bacterial species/strain level where possible) and its constituent antimicrobial resistance genetic elements (such as plasmids, integrons, transposons and other mobile genetic elements, or MGEs) in samples from a cohort where diarrhea is prevalent and antibiotic exposure is endemic. The goal will be to assess whether consortia of specific mobile antimicrobial resistance elements associate with species/strains and whether their presence is enhanced or amplified in diarrheal microbiomes and in the presence of antibiotic exposure. This work could potentially identify clonal complexes of organisms and MGEs with enhanced resistance and the potential to transfer this resistance to other enteric pathogens.</p> <p>We have performed WGS, metagenomic assembly and gene/protein mapping to examine and characterize the types of AMR genes and transfer elements (transposons, integrons, bacteriophage, plasmids) and their distribution in bacterial species and strains assembled from DNA isolated from diarrheal and non-diarrheal stools. The samples were acquired from a cohort of pediatric patients and controls from Colombia, South America where antibiotic use is prevalent. As a control, the distribution and abundance of AMR genes can be compared to published studies where resistome gene lists from healthy cohort sequences were compiled. Our approach is more epidemiologic in nature, as we plan to identify and catalogue antimicrobial elements on MGEs capable of spread through a local population and further we will, where possible, link mobile antimicrobial resistance elements with specific strains within the population.</p>
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Project description:Antimicrobial resistance (AMR) is an increasing challenge for therapy and management of bacterial infections. Currently, antimicrobial resistance detection relies on phenotypic assays, which are performed independently of species identification. On the contrary, phenotypic prediction from molecular data using genomics is gaining interest in clinical microbiology and might become a serious alternative in the future. Although, in general protein analysis should be superior to genomics for phenotypic prediction, no untargeted proteomics workflow specifically related to AMR detection has been proposed so far. In this study, we present a universal proteomics workflow to detect the bacterial species and antimicrobial resistance related proteins in the absence of secondary antibiotic cultivation in less than 4 h from a primary culture. The method was validated using a sample cohort of 7 bacterial species and 11 AMR determinants represented by 13 protein isoforms which resulted in a sensitivity of 92 % (100 % with vancomycin inference) and a specificity of 100 % with respect to AMR determinants. This proof-of concept study demonstrates the high potential of untargeted proteomics for clinical microbiology.
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