Detection of antibiotic resistance plasmids in environmental metagenomes.
Stage · Stage M2 · 6 mois Bac+5 / Master LBBE – Laboratoire de Biométrie et Biologie Evolutive, UMR5558, Team BPGE · Villeurbanne (France) ~500 euros par mois
Date de prise de poste : 3 janvier 2025
Mots-Clés
Antibiotic resistance Bacterial evolution Plasmids Comparative genomics Metagenomics.
Description
Description du projet / Project description (subject and technics):
Antimicrobial resistance (AMR) has emerged as one of the leading public health threats
around the world. The spread of antimicrobial resistance can be largely attributed to the
dissemination of antibiotic resistance genes (ARGs) through horizontal gene transfer,
primarily mediated by plasmids. However, ARG-plasmids are not exclusive to pathogenic
species. Non-pathogenic microbial communities serve as significant reservoirs for
plasmids carrying ARGs, where they can persist even without antibiotic pressure [1]. In
fact, many plasmid-mediated resistance genes in pathogens may originate from
environmental, animal, or non-clinical human sources [2]. Therefore, the AMR crisis can
only be tackled with a One Health approach [3].
Yet, despite evidence of plasmids mobilizing ARGs between these habitats, our
understanding of the evolutionary mechanisms driving this process remains limited.
Previous studies on the spread of ARG-plasmids have focused mainly on clinically
relevant pathogenic bacteria in single isolate cultures [4]. However, tackling antibiotic
resistance demands a global perspective. Therefore, it is essential to investigate the
presence and evolution of these plasmids within metagenomes of various habitats.
Recent advancements in metagenomic approaches offer solutions to this challenge by
providing a substantial number of metagenomes from various environments. However,
existing methods still struggle to detect novel plasmids in metagenomes as they rely
heavily on reference genome sequences. Put simply, the absence of universal genetic
markers for plasmids limits current approaches from a complete understanding of
plasmid diversity across different habitats.
The internship project will investigate the presence and distribution of antibiotic
resistance-associated plasmids across different habitats. To achieve this, we will
develop a computational pipeline tailored for the detection and analysis of plasmids
within metagenomic datasets. It will rely on the plasmid marker database we have
recently established in the laboratory [5]. This pipeline will then be applied to screen
metagenomic samples from various environments, including soil, water, and human-
associated microbiomes. The findings from this study will offer valuable insights into
how different environments contribute to the spread of antibiotic resistance genes.
Ultimately, the project can be continued as a PhD project and focus on the investigation
of the role of different habitats as potential reservoirs for antibiotic resistance plasmids,
contributing to our understanding of environmental AMR dynamics.
Specific tasks
•Develop a Comprehensive Metagenome Database: Download, organize, and
index metagenomic sequences, categorizing them based on their environmental
habitats to create a robust and accessible database.
•Establish Detection Thresholds for Plasmids in Metagenomes: Determine and
validate optimal thresholds for the accurate identification of plasmids within
metagenomic datasets.
•Identify Antibiotic Resistance Plasmids Across Diverse Habitats: Detect and
analyze plasmids linked to antibiotic resistance within metagenomes from
various environments, utilizing the database.
As part of this project, the student will develop valuable skills in processing high-
throughput genomic data and in designing bioinformatics workflows. They will become
proficient in handling common genomic data formats and gain hands-on experience with
a wide range of clustering and comparative genomics techniques. Additionally, the
student will deepen their understanding of microbiology and antibiotic resistance.
Expected profile and skills of the candidate
The ideal candidate should have a strong interest in microbiology and be willing to
acquire extensive training in bioinformatics and big data analysis. They should possess
strong communication skills in English and have a genuine enthusiasm for science.
Candidature
Procédure : Envoyez un CV et une lettre de motivation à l'adresse e-mail indiquée. Idéalement, joignez un ou plusieurs contacts de référence (par exemple, un ancien responsable de stage).
Date limite : 15 novembre 2024
Contacts
Charles Coluzzi
chNOSPAMarles.coluzzi@univ-lyon1.fr
Offre publiée le 17 septembre 2024, affichage jusqu'au 15 novembre 2024