Dataset Information

Rapid MinION profiling of preterm microbiota and antimicrobial-resistant pathogens.

ABSTRACT: The MinION sequencing platform offers near real-time analysis of DNA sequence; this makes the tool attractive for deployment in fieldwork or clinical settings. We used the MinION platform coupled to the NanoOK RT software package to perform shotgun metagenomic sequencing and profile mock communities and faecal samples from healthy and ill preterm infants. Using Nanopore data, we reliably classified a 20-species mock community and captured the diversity of the immature gut microbiota over time and in response to interventions such as probiotic supplementation, antibiotic treatment or episodes of suspected sepsis. We also performed rapid real-time runs to assess gut-associated microbial communities in critically ill and healthy infants, facilitated by NanoOK RT software package, which analysed sequences as they were generated. Our pipeline reliably identified pathogenic bacteria (that is, Klebsiella pneumoniae and Enterobacter cloacae) and their corresponding antimicrobial resistance gene profiles within as little as 1?h of sequencing. Results were confirmed using pathogen isolation, whole-genome sequencing and antibiotic susceptibility testing, as well as mock communities and clinical samples with known antimicrobial resistance genes. Our results demonstrate that MinION (including cost-effective Flongle flow cells) with NanoOK RT can process metagenomic samples to a rich dataset in < 5?h, which creates a platform for future studies aimed at developing these tools and approaches in clinical settings with a focus on providing tailored patient antimicrobial treatment options.

SUBMITTER: Leggett RM

PROVIDER: S-EPMC7044117 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Rapid MinION profiling of preterm microbiota and antimicrobial-resistant pathogens.

Leggett Richard M RM Alcon-Giner Cristina C Heavens Darren D Caim Shabhonam S Brook Thomas C TC Kujawska Magdalena M Martin Samuel S Peel Ned N Acford-Palmer Holly H Hoyles Lesley L Clarke Paul P Hall Lindsay J LJ Clark Matthew D MD

Nature microbiology 20191216 3

The MinION sequencing platform offers near real-time analysis of DNA sequence; this makes the tool attractive for deployment in fieldwork or clinical settings. We used the MinION platform coupled to the NanoOK RT software package to perform shotgun metagenomic sequencing and profile mock communities and faecal samples from healthy and ill preterm infants. Using Nanopore data, we reliably classified a 20-species mock community and captured the diversity of the immature gut microbiota over time an ...[more]

PMID: 31844297

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Project description:Antimicrobial peptides (AMPs) are part of the inherent immune system. In fact, they occur in almost all organisms including, e.g., plants, animals, and humans. Remarkably, they show effectivity also against multi-resistant pathogens with a high selectivity. This is especially crucial in times, where society is faced with the major threat of an ever-increasing amount of antibiotic resistant microbes. In addition, AMPs can also exhibit antitumor and antiviral effects, thus a variety of scientific studies dealt with the prediction of active peptides in recent years. Due to their potential, even the pharmaceutical industry is keen on discovering and developing novel AMPs. However, AMPs are difficult to verify in vitro, hence researchers conduct sequence similarity experiments against known, active peptides. Unfortunately, this approach is very time-consuming and limits potential candidates to sequences with a high similarity to known AMPs. Machine learning methods offer the opportunity to explore the huge space of sequence variations in a timely manner. These algorithms have, in principal, paved the way for an automated discovery of AMPs. However, machine learning models require a numerical input, thus an informative encoding is very important. Unfortunately, developing an appropriate encoding is a major challenge, which has not been entirely solved so far. For this reason, the development of novel amino acid encodings is established as a stand-alone research branch. The present review introduces state-of-the-art encodings of amino acids as well as their properties in sequence and structure based aggregation. Moreover, albeit a well-chosen encoding is essential, performant classifiers are required, which is reflected by a tendency towards specifically designed models in the literature. Furthermore, we introduce these models with a particular focus on encodings derived from support vector machines and deep learning approaches. Albeit a strong focus has been set on AMP predictions, not all of the mentioned encodings have been elaborated as part of antimicrobial research studies, but rather as general protein or peptide representations.

Dataset Information

Rapid MinION profiling of preterm microbiota and antimicrobial-resistant pathogens.

Publications

Rapid MinION profiling of preterm microbiota and antimicrobial-resistant pathogens.

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