Shallow Shotgun Metagenomics as a cost-effective and accurate alternative to WGS for taxonomic profiling and clinical diagnosis

International audienceShallow shotgun metagenomics has been recently suggested as a promising strategy to study human microbiota, providing nearly identical taxonomic profiles than deep shotgun metagenomics with a sequencing cost similar to metabarcoding. With shallow sequencing approach (typically <1M reads/samples), taxonomic profiles are directly built by mapping reads on a catalog of reference genomes, without assembly step. In the present study, we first used simulated data set to design a dedicated workflow in order to obtain reliable taxonomic profiles from shallow sequencing reads. We propose a novel data-driven filtering method based on machine learning techniques that largely outperformed basic filtering methods. We then used this approach on 3 real data sets, covering patients from several continents and clinical conditions. Even if one looses some information like rare taxa, our results clearly show that shallow shotgun metagenomics is able to correctly retrieve structures like differences between groups of patients and diagnosis-like classification

Characterizing the limits of shallow shotgun metagenomics for taxonomic profiling of human gut microbiota in clinical studies

Background Shallow shotgun metagenomics (SSM) has been recently suggested as a promising strategy to study human microbiota, providing nearly identical taxonomic profiles to deep shotgun metagenomics but at a sequencing cost as low as that of metabarcoding. To help clinical researchers determine whether shallow sequencing is appropriate for their projects, it is crucial to ascertain the accuracy of the information it provides, compared to deep sequencing. Here, we design a mapping-based workflow to build taxonomic profiles from SSM data and assess its accuracy at varying sequencing depths at both sample and cohort levels using extensive simulations and several public data sets. Results To identify genuinely present species and spuriously identified ones, we propose a novel data-driven filtering method based on machine learning techniques that largely outperforms basic filtering strategies based on pre defined thresholds, resulting in reliable taxonomic profiles at different sequencing depths, ranging from 50K to 10M reads/samples. Up to 90% of species with relative abundances higher than 4.10 −4 were recovered correctly at 500K reads/sample showing that only information about rare taxa is lost at shallow depths. Furthermore, our results clearly show that SSM is able to correctly recover relevant biological signal from the confidently identified taxa, such as differences between groups of patients and diagnosis-like classification. Conclusions This study confirms that SSM is suitable for clinical research on human gut microbiota. We recommend that researchers should consider moving from 16S to SSM to limit biases in taxonomic profiles, or moving from deep to shallow sequencing, when functional analyses are not the main focus, to reduce costs and be able to include more patients in research projects

Shallow Shotgun Metagenomics as a cost-effective and accurate alternative to WGS for taxonomic profiling and clinical diagnosis

International audienceShallow shotgun metagenomics has been recently suggested as a promising strategy to study human microbiota, providing nearly identical taxonomic profiles than deep shotgun metagenomics with a sequencing cost similar to metabarcoding. With shallow sequencing approach (typically <1M reads/samples), taxonomic profiles are directly built by mapping reads on a catalog of reference genomes, without assembly step. In the present study, we first used simulated data set to design a dedicated workflow in order to obtain reliable taxonomic profiles from shallow sequencing reads. We propose a novel data-driven filtering method based on machine learning techniques that largely outperformed basic filtering methods. We then used this approach on 3 real data sets, covering patients from several continents and clinical conditions. Even if one looses some information like rare taxa, our results clearly show that shallow shotgun metagenomics is able to correctly retrieve structures like differences between groups of patients and diagnosis-like classification

Shallow Shotgun Metagenomics as a cost-effective and accurate alternative to WGS for taxonomic profiling and clinical diagnosis

International audienceShallow shotgun metagenomics has been recently suggested as a promising strategy to study human microbiota, providing nearly identical taxonomic profiles than deep shotgun metagenomics with a sequencing cost similar to metabarcoding. With shallow sequencing approach (typically <1M reads/samples), taxonomic profiles are directly built by mapping reads on a catalog of reference genomes, without assembly step. In the present study, we first used simulated data set to design a dedicated workflow in order to obtain reliable taxonomic profiles from shallow sequencing reads. We propose a novel data-driven filtering method based on machine learning techniques that largely outperformed basic filtering methods. We then used this approach on 3 real data sets, covering patients from several continents and clinical conditions. Even if one looses some information like rare taxa, our results clearly show that shallow shotgun metagenomics is able to correctly retrieve structures like differences between groups of patients and diagnosis-like classification

Shallow sequencing: a cost-effective and accurate alternative to WGS for taxonomic profiling ?

International audienc

Gut microbiota in systemic lupus erythematosus patients and lupus mouse model: a cross species comparative analysis for biomarker discovery

International audienceAn increasing number of studies have provided strong evidence that gut microbiota interact with the immune system and stimulate various mechanisms involved in the pathogenesis of auto-immune diseases such as Systemic Lupus Erythematosus (SLE). Indeed, gut microbiota could be a source of diagnostic and prognostic biomarkers but also hold the promise to discover novel therapeutic strategies. Thus far, specific SLE microbial signatures have not yet been clearly identified with alteration patterns that may vary between human and animal studies. In this study, a comparative analysis of a clinically well-characterized cohort of adult patients with SLE showed reduced biodiversity, a lower Firmicutes/Bacteroidetes ( F/B ) ratio, and six differentially abundant taxa compared with healthy controls. An unsupervised clustering of patients with SLE patients identified a subgroup of patients with a stronger alteration of their gut microbiota. Interestingly, this clustering was strongly correlated with the disease activity assessed with the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) score ( p = 0.03 , odd ratio = 15) and the identification of specific alterations involving the F/B ratio and some different taxa. Then, the gut microbiota of pristane-induced lupus and control mice were analyzed for comparison with our human data. Among the six differentially abundant taxa of the human disease signature, five were common with our murine model. Finally, an exhaustive cross-species comparison between our data and previous human and murine SLE studies revealed a core-set of gut microbiome species that might constitute biomarker panels relevant for future validation studies

Shallow sequencing: a cost-effective and accurate alternative to WGS for taxonomic profiling ?

International audienc

Gut microbiota in systemic lupus erythematosus patients and lupus mouse model: a cross species comparative analysis for biomarker discovery

International audienceAn increasing number of studies have provided strong evidence that gut microbiota interact with the immune system and stimulate various mechanisms involved in the pathogenesis of auto-immune diseases such as Systemic Lupus Erythematosus (SLE). Indeed, gut microbiota could be a source of diagnostic and prognostic biomarkers but also hold the promise to discover novel therapeutic strategies. Thus far, specific SLE microbial signatures have not yet been clearly identified with alteration patterns that may vary between human and animal studies. In this study, a comparative analysis of a clinically well-characterized cohort of adult patients with SLE showed reduced biodiversity, a lower Firmicutes/Bacteroidetes ( F/B ) ratio, and six differentially abundant taxa compared with healthy controls. An unsupervised clustering of patients with SLE patients identified a subgroup of patients with a stronger alteration of their gut microbiota. Interestingly, this clustering was strongly correlated with the disease activity assessed with the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) score ( p = 0.03 , odd ratio = 15) and the identification of specific alterations involving the F/B ratio and some different taxa. Then, the gut microbiota of pristane-induced lupus and control mice were analyzed for comparison with our human data. Among the six differentially abundant taxa of the human disease signature, five were common with our murine model. Finally, an exhaustive cross-species comparison between our data and previous human and murine SLE studies revealed a core-set of gut microbiome species that might constitute biomarker panels relevant for future validation studies

Human Stool Preservation Impacts Taxonomic Profiles in 16S Metagenomics Studies

International audienceMicrobiotas play critical roles in human health, yet in most cases scientists lack standardized and reproducible methods from collection and preservation of samples, as well as the choice of omic analysis, up to the data processing. To date, stool sample preservation remains a source of technological bias in metagenomic sequencing, despite newly developed storage solutions. Here, we conducted a comparative study of 10 storage methods for human stool over a 14-day period of storage at fluctuating temperatures. We first compared the performance of each stabilizer with observed bacterial composition variation within the same specimen. Then, we identified the nature of the observed variations to determine which bacterial populations were more impacted by the stabilizer. We found that DNA stabilizers display various stabilizing efficacies and affect the recovered bacterial profiles thus highlighting that some solutions are more performant in preserving the true gut microbial community. Furthermore, our results showed that the bias associated with the stabilizers can be linked to the phenotypical traits of the bacterial populations present in the studied samples. Although newly developed storage solutions have improved our capacity to stabilize stool microbial content over time, they are nevertheless not devoid of biases hence requiring the implantation of standard operating procedures. Acknowledging the biases and limitations of the implemented method is key to better interpret and support true associated microbiome patterns that will then lead us towards personalized medicine, in which the microbiota profile could constitute a reliable tool for clinical practice