inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains

Olm, Matthew R.; Crits-Christoph, Alexander; Bouma‐Gregson, Keith; Firek, Brian; Morowitz, Michael J.; Banfield, Jillian F.

doi:10.1038/s41587-020-00797-0

Cited by 268 publications

(410 citation statements)

References 68 publications

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“…To measure putative transmission between human microbiomes with high levels of strain diversity 29 we used the program inStrain 66 and the strain diversity-aware metric population ANI (popANI).…”

Section: Discussionmentioning

confidence: 99%

“…MAGs often fail to include plasmids, which play important roles in bacterial virulence and antibiotic resistance. To conduct a more sensitive analysis, we used the strain diversity-aware, SNP-based method inStrain 66 . InStrain compares alignments of short reads from multiple samples to the same reference genome and reports two metrics: Consensus ANI (conANI) and Population ANI (popANI).…”

mentioning

confidence: 99%

“…Due to expected noise and errors in sequencing data, we set the lower bound for transmission in our HCT cohort at 99.999% popANI, equivalent to 30 population SNPs in a 3 megabase (Mb) genome. The same threshold was used to classify two strains as the same by the authors of inStrain 66 .…”

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confidence: 99%

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Rare transmission of commensal and pathogenic bacteria in the gut microbiome of hospitalized adults

Siranosian

Brooks

Andermann

et al. 2021

Preprint

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Bacterial bloodstream infections are a major cause of morbidity and mortality among patients undergoing hematopoietic cell transplantation (HCT). Although previous research has demonstrated that pathogenic organisms may translocate from the gut microbiome into the bloodstream to cause infections, the mechanisms by which HCT patients acquire pathogens in their microbiome have not yet been described. We hypothesized that patient-patient transmission may be responsible for pathogens colonizing the microbiome of HCT patients, and that patients who share time and space in the hospital are more likely to share bacterial strains. Here, we used linked-read and short-read metagenomic sequencing to analyze 401 stool samples collected from 149 adults undergoing HCT and hospitalized in the same unit over five years. We used metagenomic assembly and strain-specific comparison methods to investigate transmission of gut microbiota between individuals. While transmission of pathogens was found to be rare, we did observe four pairs of patients who harbor identical or nearly identical E. faecium strains in their microbiome. These strains may be the result of transmission between patients who shared a room and bathroom, acquisition from a common source in the hospital or transmission from an unsampled source. We also observed identical Akkermansia muciniphila and Hungatella hathewayi strains in two pairs of patients. In both cases, the patients were roommates for at least one day, the strain was absent in the putative recipient's microbiome prior to the period of roommate overlap and the putative recipient had a microbiome perturbed by antibiotic treatment for a bloodstream infection. Finally, we identified multiple patients who acquired identical strains of several species commonly found in commercial probiotics and dairy products, including Lactobacillus rhamnosus, Lactobacillus gasseri and Streptococcus thermophilus. Overall, the limited amount of putative transmission observed indicates that current infection control and contact precautions are successful in preventing interpersonal exchange of microbes. However, the potential transmission of commensal microbes with immunomodulatory properties raises questions about the recovery of microbiome diversity after HCT, and indicates that patients in this setting may acquire new microbes by sharing space with others.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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Rare transmission of commensal and pathogenic bacteria in the gut microbiome of hospitalized adults

Siranosian

Brooks

Andermann

et al. 2021

Preprint

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“…InStrain ( Olm et al, 2021 ) was devised to detect SNVs and profile intra-population genetic diversity based on metagenomic short-read alignment. InStrain first performs read filtering to remove read pairs that do not meet the quality criteria.…”

Section: Methodsmentioning

confidence: 99%

“…At present, there are several tools that have been developed specifically for metagenomic variant calling, such as MetaSNV ( Costea et al, 2017 ) and InStrain ( Olm et al, 2021 ). However, other variant callers have also been designed to be ploidy naïve or to address complications like an unknown number of pooled samples, including VarScan2 ( Koboldt et al, 2012 ), freebayes ( Garrison and Marth, 2012 ), and GATK’s Mutect2 ( DePristo et al, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

A Benchmark of Genetic Variant Calling Pipelines Using Metagenomic Short-Read Sequencing

et al. 2021

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Microbes live in complex communities that are of major importance for environmental ecology, public health, and animal physiology and pathology. Short-read metagenomic shotgun sequencing is currently the state-of-the-art technique for exploring these communities. With the aid of metagenomics, our understanding of the microbiome is moving from composition toward functionality, even down to the genetic variant level. While the exploration of single-nucleotide variation in a genome is a standard procedure in genomics, and many sophisticated tools exist to perform this task, identification of genetic variation in metagenomes remains challenging. Major factors that hamper the widespread application of variant-calling analysis include low-depth sequencing of individual genomes (which is especially significant for the microorganisms present in low abundance), the existence of large genomic variation even within the same species, the absence of comprehensive reference genomes, and the noise introduced by next-generation sequencing errors. Some bioinformatics tools, such as metaSNV or InStrain, have been created to identify genetic variants in metagenomes, but the performance of these tools has not been systematically assessed or compared with the variant callers commonly used on single or pooled genomes. In this study, we benchmark seven bioinformatic tools for genetic variant calling in metagenomics data and assess their performance. To do so, we simulated metagenomic reads to mimic human microbial composition, sequencing errors, and genetic variability. We also simulated different conditions, including low and high depth of coverage and unique or multiple strains per species. Our analysis of the simulated data shows that probabilistic method-based tools such as HaplotypeCaller and Mutect2 from the GATK toolset show the best performance. By applying these tools to longitudinal gut microbiome data from the Human Microbiome Project, we show that the genetic similarity between longitudinal samples from the same individuals is significantly greater than the similarity between samples from different individuals. Our benchmark shows that probabilistic tools can be used to call metagenomes, and we recommend the use of GATK’s tools as reliable variant callers for metagenomic samples.

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Integrated Human Skin Bacteria Genome Catalog Reveals Extensive Unexplored Habitat‐Specific Microbiome Diversity and Function

Li,

Ju,

Xia

et al. 2023

Advanced Science

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The skin is the largest organ in the human body. Various skin environments on its surface constitutes a complex ecosystem. One of the characteristics of the skin micro‐ecosystem is low biomass, which greatly limits a comprehensive identification of the microbial species through sequencing. In this study, deep‐shotgun sequencing (average 21.5 Gigabyte (Gb)) from 450 facial samples and publicly available skin metagenomic datasets of 2069 samples to assemble a Unified Human Skin Genome (UHSG) catalog is integrated. The UHSG encompasses 813 prokaryotic species derived from 5779 metagenome‐assembled genomes, among which 470 are novel species covering 20 phyla with 1385 novel assembled genomes. Based on the UHSG, the core functions of the skin microbiome are described and the differences in amino acid metabolism, carbohydrate metabolism, and drug resistance functions among different phyla are identified. Furthermore, analysis of secondary metabolites of the near‐complete genomes further find 1220 putative novel secondary metabolites, several of which are found in previously unknown genomes. Single nucleotide variant (SNV) reveals a possible skin protection mechanism: the negative selection process of the skin environment to conditional pathogens. UHSG offers a convenient reference database that will facilitate a more in‐depth understanding of the role of skin microorganisms in the skin.

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inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains

Cited by 268 publications

References 68 publications

Rare transmission of commensal and pathogenic bacteria in the gut microbiome of hospitalized adults

Rare transmission of commensal and pathogenic bacteria in the gut microbiome of hospitalized adults

A Benchmark of Genetic Variant Calling Pipelines Using Metagenomic Short-Read Sequencing

Integrated Human Skin Bacteria Genome Catalog Reveals Extensive Unexplored Habitat‐Specific Microbiome Diversity and Function

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