Identification and reconstruction of novel antibiotic resistance genes from metagenomes
Artikel i vetenskaplig tidskrift, 2019

BackgroundEnvironmental and commensal bacteria maintain a diverse and largely unknown collection of antibiotic resistance genes (ARGs) that, over time, may be mobilized and transferred to pathogens. Metagenomics enables cultivation-independent characterization of bacterial communities but the resulting data is noisy and highly fragmented, severely hampering the identification of previously undescribed ARGs. We have therefore developed fARGene, a method for identification and reconstruction of ARGs directly from shotgun metagenomic data.ResultsfARGene uses optimized gene models and can therefore with high accuracy identify previously uncharacterized resistance genes, even if their sequence similarity to known ARGs is low. By performing the analysis directly on the metagenomic fragments, fARGene also circumvents the need for a high-quality assembly. To demonstrate the applicability of fARGene, we reconstructed -lactamases from five billion metagenomic reads, resulting in 221 ARGs, of which 58 were previously not reported. Based on 38 ARGs reconstructed by fARGene, experimental verification showed that 81% provided a resistance phenotype in Escherichia coli. Compared to other methods for detecting ARGs in metagenomic data, fARGene has superior sensitivity and the ability to reconstruct previously unknown genes directly from the sequence reads.ConclusionsWe conclude that fARGene provides an efficient and reliable way to explore the unknown resistome in bacterial communities. The method is applicable to any type of ARGs and is freely available via GitHub under the MIT license.

Gene assembly

Environmental sequencing

Resistome

Antibiotic resistance

Beta-lactamases

Microbiome

Författare

Fanny Berglund

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Göteborgs universitet

Tobias Österlund

Göteborgs universitet

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Fredrik Boulund

Karolinska Institutet

Nachiket P. Marathe

Göteborgs universitet

D. G. Joakim Larsson

Göteborgs universitet

Erik Kristiansson

Göteborgs universitet

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Microbiome

2049-2618 (eISSN)

Vol. 7 52

Ämneskategorier

Mikrobiologi

Bioinformatik (beräkningsbiologi)

Bioinformatik och systembiologi

DOI

10.1186/s40168-019-0670-1

PubMed

30935407

Mer information

Senast uppdaterat

2019-06-17