Microbial abundance, activity and population genomic profiling with mOTUs2

Milanese, Alessio; Mende, Daniel R.; Paoli, Lucas; Salazar, Guillem; Ruscheweyh, Hans‐Joachim; Cuenca, Miguelangel; Hingamp, Pascal; Alves, Renato; Costea, Paul Igor; Coelho, Luís Pedro; Schmidt, Thomas Sebastian Benedikt; Almeida, Alexandre; Mitchell, Alex L.; Finn, Robert D.; Huerta-Cepas, Jaime; Bork, Peer; Zeller, Georg; Sunagawa, Shinichi

doi:10.1038/s41467-019-08844-4

Cited by 324 publications

(337 citation statements)

References 57 publications

(116 reference statements)

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“…One such dataset consists of 11 bacterial species and is available from a metagenomics benchmarking study performed by Peabody et al 15 . We evaluated Metalign against MetaPhlAn2 22 and two new versions of popular methods from the CAMI 7 study: Kraken2 23 , with its abundance estimation companion method Bracken 24 , and mOTUs2 25 . For all methods, we used their full standard databases and default settings.…”

Section: Metalign Outperforms Other Methods Across Cutoff Settings Onmentioning

confidence: 99%

Metalign: Efficient alignment-based metagenomic profiling via containment min hash

LaPierre

Alser

Eskin³

et al. 2020

Preprint

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Whole-genome shotgun sequencing enables the analysis of microbial communities in unprecedented detail, with major implications in medicine and ecology. Predicting the presence and relative abundances of microbes in a sample, known as "metagenomic profiling", is a critical first step in microbiome analysis. Existing profiling methods have been shown to suffer from poor false positive or false negative rates, while alignment-based approaches are often considered accurate but computationally infeasible. Here we present a novel method, Metalign, that addresses these concerns by performing efficient alignment-based metagenomic profiling.We use a containment min hash approach to reduce the reference database size dramatically before alignment and a method to estimate organism relative abundances in the sample by resolving reads aligned to multiple genomes. We show that Metalign achieves significantly improved results over existing methods on simulated datasets from a large benchmarking study, CAMI, and performs well on in vitro mock community data and environmental data from the Tara Oceans project. Metalign is freely available at https://github.com/nlapier2/Metalign , along with the results and plots used in this paper, and a docker image is also available at https://hub.docker.com/repository/docker/nlapier2/metalign .

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Section: Metalign Outperforms Other Methods Across Cutoff Settings Onmentioning

confidence: 99%

Metalign: Efficient alignment-based metagenomic profiling via containment min hash

LaPierre

Alser

Eskin³

et al. 2020

Preprint

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“…Estimates on the abundance and prevalence of species in the healthy human gut microbiome were calculated using mOTUs v2 48 as follows: Relative species abundances were determined in 727 shotgun metagenomic samples from donors in the control groups of multiple studies from various countries and continents 49-53 . Prior to taxonomic profiling, metagenomes were quality controlled using the MOCAT2 -rtf procedure 54 , which removed reads with ≥95% sequence identity and an alignment length of ≥45bp to the human genome hg19.…”

Section: Methodsmentioning

confidence: 99%

Dissecting the collateral damage of antibiotics on gut microbes

Maier

Goemans

Pruteanu

et al. 2020

Preprint

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24Antibiotics are used for fighting pathogens, but also target our commensal bacteria as a side 25 effect, disturbing the gut microbiota composition and causing dysbiosis and disease 1-3 . 26Despite this well-known collateral damage, the activity spectrum of the different antibiotic 27 classes on gut bacteria remains poorly characterized. Having monitored the activities of 28 >1,000 marketed drugs on 38 representative species of the healthy human gut microbiome 4 , 29 we here characterize further the 144 antibiotics therein, representing all major classes. We 30 determined >800 Minimal Inhibitory Concentrations (MICs) and extended the antibiotic 31 profiling to 10 additional species to validate these results and link to available data on 32 antibiotic breakpoints for gut microbes. Antibiotic classes exhibited distinct inhibition spectra, 33including generation-dependent effects by quinolones and phylogeny-independence by β-34 lactams. Macrolides and tetracyclines, two prototypic classes of bacteriostatic protein 35 synthesis inhibitors, inhibited almost all commensals tested. We established that both kill 36 different subsets of prevalent commensal bacteria, and cause cell lysis in specific cases. 37This species-specific activity challenges the long-standing divide of antibiotics into 38 bactericidal and bacteriostatic, and provides a possible explanation for the strong impact of 39 macrolides on the gut microbiota composition in animals 5-8 and humans 9-11 . To mitigate the 40 collateral damage of macrolides and tetracyclines on gut commensals, we exploited the fact 41 that drug combinations have species-specific outcomes in bacteria 12 and sought marketed 42 drugs, which could antagonize the activity of these antibiotics in abundant gut commensal 43 species. By screening >1,000 drugs, we identified several such antidotes capable of 44 protecting gut species from these antibiotics without compromising their activity against 45 relevant pathogens. Altogether, this study broadens our understanding of antibiotic action on 46 gut commensals, uncovers a previously unappreciated and broad bactericidal effect of 47 prototypical bacteriostatic antibiotics on gut bacteria, and opens avenues for preventing the48 collateral damage caused by antibiotics on human gut commensals.49 3 MAIN TEXT 50 Medication is emerging as major contributor for changes in the composition of the human gut 51 microbiota 4,13-15 . Such severe and long-lasting changes are associated, and in some cases 52 causatively linked, to dysbiosis and a wide range of diseases 16 . Although several non-53 antibiotic drugs may also have a previously unappreciated impact on the gut microbiome 54 composition 4,16,17 , antibiotics, developed to have broad spectra and thereby target very 55 diverse pathogens, are long known to take a heavy toll on our gut flora, causing a variety of 56 gastrointestinal side-effects 18 , including Clostridioides (former Clostridium) difficile infections. 57Recently more attention has been given to this collateral damage of antibiot...

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“…Beyond these tools, many other metagenomic methods have been designed for finding the taxonomic composition of a mixed sample (e.g., Liu et al, 2010;Milanese et al, 2019;Nguyen et al, 2014;Segata et al, 2012). However, these tools do not seek to classify every read from anywhere in the genome; they are either marker-based or use composition data.…”

Section: Filtering Methodsmentioning

confidence: 99%

On the impact of contaminants on the accuracy of genome skimming and the effectiveness of exclusion read filters

Rachtman

Balaban

Bafna

et al. 2019

Preprint

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The ability to detect the identity of a sample obtained from its environment is a cornerstone of molecular ecological research. Thanks to the falling price of shotgun sequencing, genome skimming, the acquisition of short reads spread across the genome at low coverage, is emerging as an alternative to traditional barcoding. By obtaining far more data across the whole genome, skimming has the promise to increase the precision of sample identification beyond traditional barcoding while keeping the costs manageable.While methods for assembly-free sample identification based on genome skims are now available, little is known about how these methods react to the presence of DNA from organisms other than the target species. In this paper, we show that the accuracy of distances computed between a pair of genome skims based on k-mer similarity can degrade dramatically if the skims include contaminant reads; i.e., any reads originating from other organisms. We establish a theoretical model of the impact of contamination. We then suggest and evaluate a solution to the contamination problem: Query reads in a genome skim against an extensive database of possible contaminants (e.g., all microbial organisms) and filter out any read that matches. We evaluate the effectiveness of this strategy when implemented using Kraken-II, in detailed analyses. Our results show substantial improvements in accuracy as a result of filtering but also point to limitations, including a need for relatively close matches in the contaminant database.

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Microbial abundance, activity and population genomic profiling with mOTUs2

Cited by 324 publications

References 57 publications

Metalign: Efficient alignment-based metagenomic profiling via containment min hash

Metalign: Efficient alignment-based metagenomic profiling via containment min hash

Dissecting the collateral damage of antibiotics on gut microbes

On the impact of contaminants on the accuracy of genome skimming and the effectiveness of exclusion read filters

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