scholarly journals MiCoP: Microbial Community Profiling method for detecting viral and fungal organisms in metagenomic samples

2018 ◽  
Author(s):  
Nathan LaPierre ◽  
Serghei Mangul ◽  
Mohammed Alser ◽  
Igor Mandric ◽  
Nicholas C. Wu ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Human Microbiome ◽  
Real Data ◽  
Human Microbiome Project ◽  
Direct Analysis ◽  
Fungal Species ◽  
Reference Database ◽  
Marker Genes ◽  
Full Diversity ◽  
Community Profiling

AbstractBackgroundHigh throughput sequencing has spurred the development of metagenomics, which involves the direct analysis of microbial communities in various environments such as soil, ocean water, and the human body. Many existing methods based on marker genes or k-mers have limited sensitivity or are too computationally demanding for many users. Additionally, most work in metagenomics has focused on bacteria and archaea, neglecting to study other key microbes such as viruses and eukaryotes.ResultsHere we present a method, MiCoP (Microbiome Community Profiling), that uses fast-mapping of reads to build a comprehensive reference database of full genomes from viruses and eukaryotes to achieve maximum read usage and enable the analysis of the virome and eukaryome in each sample. We demonstrate that mapping of metagenomic reads is feasible for the smaller viral and eukaryotic reference databases. We show that our method is accurate on simulated and mock community data and identifies many more viral and fungal species than previously-reported results on real data from the Human Microbiome Project.ConclusionsMiCoP is a mapping-based method that proves more effective than existing methods at abundance profiling of viruses and eukaryotes in metagenomic samples. MiCoP can be used to detect the full diversity of these communities. The code, data, and documentation is publicly available on GitHub at: https://github.com/smangul1/MiCoP

scholarly journals Misclassification of a whole genome sequence reference defined by the Human Microbiome Project: a detrimental carryover effect to microbiome studies

2019 ◽  
Author(s):  
DJ Darwin R. Bandoy ◽  
B Carol Huang ◽  
Bart C. Weimer
Keyword(s):  
Human Microbiome ◽  
Human Microbiome Project ◽  
Outbreak Detection ◽  
Whole Genome Sequence ◽  
Reference Database ◽  
Whole Genome ◽  
Reference Species ◽  
Genome Sequences ◽  
Genome Homology ◽  
Microbiome Data

AbstractTaxonomic classification is an essential step in the analysis of microbiome data that depends on a reference database of whole genome sequences. Taxonomic classifiers are built on established reference species, such as the Human Microbiome Project database, that is growing rapidly. While constructing a population wide pangenome of the bacterium Hungatella, we discovered that the Human Microbiome Project reference species Hungatella hathewayi (WAL 18680) was significantly different to other members of this genus. Specifically, the reference lacked the core genome as compared to the other members. Further analysis, using average nucleotide identity (ANI) and 16s rRNA comparisons, indicated that WAL18680 was misclassified as Hungatella. The error in classification is being amplified in the taxonomic classifiers and will have a compounding effect as microbiome analyses are done, resulting in inaccurate assignment of community members and will lead to fallacious conclusions and possibly treatment. As automated genome homology assessment expands for microbiome analysis, outbreak detection, and public health reliance on whole genomes increases this issue will likely occur at an increasing rate. These observations highlight the need for developing reference free methods for epidemiological investigation using whole genome sequences and the criticality of accurate reference databases.

scholarly journals Identifying personal microbiomes using metagenomic codes

2015 ◽  
Vol 112 (22) ◽  
pp. E2930-E2938 ◽  
Author(s):  
Eric A. Franzosa ◽  
Katherine Huang ◽  
James F. Meadow ◽  
Dirk Gevers ◽  
Katherine P. Lemon ◽  
...  
Keyword(s):  
Human Microbiome ◽  
Human Microbiome Project ◽  
Sampling Interval ◽  
Marker Genes ◽  
Specific Marker ◽  
Body Site ◽  
Ethical Implications ◽  
Large Populations ◽  
Initial Sampling ◽  
Time Point

Community composition within the human microbiome varies across individuals, but it remains unknown if this variation is sufficient to uniquely identify individuals within large populations or stable enough to identify them over time. We investigated this by developing a hitting set-based coding algorithm and applying it to the Human Microbiome Project population. Our approach defined body site-specific metagenomic codes: sets of microbial taxa or genes prioritized to uniquely and stably identify individuals. Codes capturing strain variation in clade-specific marker genes were able to distinguish among 100s of individuals at an initial sampling time point. In comparisons with follow-up samples collected 30–300 d later, ∼30% of individuals could still be uniquely pinpointed using metagenomic codes from a typical body site; coincidental (false positive) matches were rare. Codes based on the gut microbiome were exceptionally stable and pinpointed >80% of individuals. The failure of a code to match its owner at a later time point was largely explained by the loss of specific microbial strains (at current limits of detection) and was only weakly associated with the length of the sampling interval. In addition to highlighting patterns of temporal variation in the ecology of the human microbiome, this work demonstrates the feasibility of microbiome-based identifiability—a result with important ethical implications for microbiome study design. The datasets and code used in this work are available for download from huttenhower.sph.harvard.edu/idability.

scholarly journals Searching more genomic sequence with less memory for fast and accurate metagenomic profiling

2016 ◽  
Author(s):  
Shea N Gardner ◽  
Sasha K Ames ◽  
Maya B Gokhale ◽  
Tom R Slezak ◽  
Jonathan Allen
Keyword(s):  
Large Scale ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Low Cost ◽  
False Negative ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Metagenomic Data ◽  
Reference Database ◽  
Metagenomic Sequence

Software for rapid, accurate, and comprehensive microbial profiling of metagenomic sequence data on a desktop will play an important role in large scale clinical use of metagenomic data. Here we describe LMAT-ML (Livermore Metagenomics Analysis Toolkit-Marker Library) which can be run with 24 GB of DRAM memory, an amount available on many clusters, or with 16 GB DRAM plus a 24 GB low cost commodity flash drive (NVRAM), a cost effective alternative for desktop or laptop users. We compared results from LMAT with five other rapid, low-memory tools for metagenome analysis for 131 Human Microbiome Project samples, and assessed discordant calls with BLAST. All the tools except LMAT-ML reported overly specific or incorrect species and strain resolution of reads that were in fact much more widely conserved across species, genera, and even families. Several of the tools misclassified reads from synthetic or vector sequence as microbial or human reads as viral. We attribute the high numbers of false positive and false negative calls to a limited reference database with inadequate representation of known diversity. Our comparisons with real world samples show that LMAT-ML is the only tool tested that classifies the majority of reads, and does so with high accuracy.

scholarly journals Comparison of qPCR and Metabarcoding Methods as Tools for the Detection of Airborne Inoculum of Forest Fungal Pathogens

2021 ◽  
Vol 111 (3) ◽  
pp. 570-581
Author(s):  
Anne Chandelier ◽  
Julie Hulin ◽  
Gilles San Martin ◽  
Frédéric Debode ◽  
Sébastien Massart
Keyword(s):  
Fungal Pathogens ◽  
High Throughput Sequencing ◽  
Disease Outbreaks ◽  
Rapid Identification ◽  
Fungal Species ◽  
Reference Database ◽  
Airborne Inoculum ◽  
Important Pathway ◽  
Spore Trapping ◽  
The Impact

Forest diseases caused by invasive fungal pathogens are becoming more common, sometimes with dramatic consequences to forest ecosystems. The development of early detection systems is necessary for efficient surveillance and to mitigate the impact of invasive pathogens. Windborne spores are an important pathway for introduction of fungal pathogens into new areas; the design of spore trapping devices adapted to forests, capable of collecting different types of spores, and aligned with development of efficient molecular methods for detection of the pathogen, should help forest managers anticipate new disease outbreaks. Two types of Rotorod samplers were evaluated for the collection of airborne inoculum of forest fungal pathogens with a range of spore sizes in five forest types. Detection was by specific quantitative PCR (qPCR) and by high-throughput sequencing (HTS) of amplified internal transcribed spacer sequences using a new bioinformatic pipeline, FungiSearch, developed for diagnostic purposes. Validation of the pipeline was conducted on mock communities of 10 fungal species belonging to different taxa. Although the sensitivity of the new HTS pipeline was lower than the specific qPCR, it was able to detect a wide variety of fungal pathogens. FungiSearch is easy to use, and the reference database is updatable, making the tool suitable for rapid identification of new pathogens. This new approach combining spore trapping and HTS detection is promising as a diagnostic tool for invasive fungal pathogens.

scholarly journals A broken promise: microbiome differential abundance methods do not control the false discovery rate

2017 ◽  
Vol 20 (1) ◽  
pp. 210-221 ◽  
Author(s):  
Stijn Hawinkel ◽  
Federico Mattiello ◽  
Luc Bijnens ◽  
Olivier Thas
Keyword(s):  
Statistical Methods ◽  
High Throughput Sequencing ◽  
Bacterial Species ◽  
Human Microbiome ◽  
Real Data ◽  
Differential Abundance ◽  
Sequencing Technologies ◽  
Antibiotic Drugs ◽  
Count Distribution ◽  
Microbiome Data

Abstract High-throughput sequencing technologies allow easy characterization of the human microbiome, but the statistical methods to analyze microbiome data are still in their infancy. Differential abundance methods aim at detecting associations between the abundances of bacterial species and subject grouping factors. The results of such methods are important to identify the microbiome as a prognostic or diagnostic biomarker or to demonstrate efficacy of prodrug or antibiotic drugs. Because of a lack of benchmarking studies in the microbiome field, no consensus exists on the performance of the statistical methods. We have compared a large number of popular methods through extensive parametric and nonparametric simulation as well as real data shuffling algorithms. The results are consistent over the different approaches and all point to an alarming excess of false discoveries. This raises great doubts about the reliability of discoveries in past studies and imperils reproducibility of microbiome experiments. To further improve method benchmarking, we introduce a new simulation tool that allows to generate correlated count data following any univariate count distribution; the correlation structure may be inferred from real data. Most simulation studies discard the correlation between species, but our results indicate that this correlation can negatively affect the performance of statistical methods.

scholarly journals Sequence Comparison of Vaginolysin from Different Gardnerella Species

Pathogens ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 86
Author(s):  
Erin M. Garcia ◽  
Myrna G. Serrano ◽  
Laahirie Edupuganti ◽  
David J. Edwards ◽  
Gregory A. Buck ◽  
...  
Keyword(s):  
Amino Acid ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Distinct Species ◽  
Vaginal Swab ◽  
Metagenomic Data ◽  
Gardnerella Vaginalis ◽  
Vaginal Epithelial Cells ◽  
Species Specific

Gardnerella vaginalis has recently been split into 13 distinct species. In this study, we tested the hypotheses that species-specific variations in the vaginolysin (VLY) amino acid sequence could influence the interaction between the toxin and vaginal epithelial cells and that VLY variation may be one factor that distinguishes less virulent or commensal strains from more virulent strains. This was assessed by bioinformatic analyses of publicly available Gardnerella spp. sequences and quantification of cytotoxicity and cytokine production from purified, recombinantly produced versions of VLY. After identifying conserved differences that could distinguish distinct VLY types, we analyzed metagenomic data from a cohort of female subjects from the Vaginal Human Microbiome Project to investigate whether these different VLY types exhibited any significant associations with symptoms or Gardnerella spp.-relative abundance in vaginal swab samples. While Type 1 VLY was most prevalent among the subjects and may be associated with increased reports of symptoms, subjects with Type 2 VLY dominant profiles exhibited increased relative Gardnerella spp. abundance. Our findings suggest that amino acid differences alter the interaction of VLY with vaginal keratinocytes, which may potentiate differences in bacterial vaginosis (BV) immunopathology in vivo.

scholarly journals scSorter: assigning cells to known cell types according to marker genes

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Hongyu Guo ◽  
Jun Li
Keyword(s):  
Real Data ◽  
Cell Types ◽  
Exact Expression ◽  
Marker Genes ◽  
Specific Marker ◽  
Sequencing Data ◽  
Reference Dataset ◽  
Over Expression ◽  
Higher Power ◽  
Cell Type Specific

AbstractOn single-cell RNA-sequencing data, we consider the problem of assigning cells to known cell types, assuming that the identities of cell-type-specific marker genes are given but their exact expression levels are unavailable, that is, without using a reference dataset. Based on an observation that the expected over-expression of marker genes is often absent in a nonnegligible proportion of cells, we develop a method called scSorter. scSorter allows marker genes to express at a low level and borrows information from the expression of non-marker genes. On both simulated and real data, scSorter shows much higher power compared to existing methods.

scholarly journals Validation and standardization of DNA extraction and library construction methods for metagenomics-based human fecal microbiome measurements

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Dieter M. Tourlousse ◽  
Koji Narita ◽  
Takamasa Miura ◽  
Mitsuo Sakamoto ◽  
Akiko Ohashi ◽  
...  
Keyword(s):  
Dna Extraction ◽  
High Throughput Sequencing ◽  
Performance Metrics ◽  
Collaborative Study ◽  
Human Microbiome ◽  
Second Phase ◽  
Intermediate Precision ◽  
Fecal Microbiome ◽  
Library Construction ◽  
Wide Range

Abstract Background Validation and standardization of methodologies for microbial community measurements by high-throughput sequencing are needed to support human microbiome research and its industrialization. This study set out to establish standards-based solutions to improve the accuracy and reproducibility of metagenomics-based microbiome profiling of human fecal samples. Results In the first phase, we performed a head-to-head comparison of a wide range of protocols for DNA extraction and sequencing library construction using defined mock communities, to identify performant protocols and pinpoint sources of inaccuracy in quantification. In the second phase, we validated performant protocols with respect to their variability of measurement results within a single laboratory (that is, intermediate precision) as well as interlaboratory transferability and reproducibility through an industry-based collaborative study. We further ascertained the performance of our recommended protocols in the context of a community-wide interlaboratory study (that is, the MOSAIC Standards Challenge). Finally, we defined performance metrics to provide best practice guidance for improving measurement consistency across methods and laboratories. Conclusions The validated protocols and methodological guidance for DNA extraction and library construction provided in this study expand current best practices for metagenomic analyses of human fecal microbiota. Uptake of our protocols and guidelines will improve the accuracy and comparability of metagenomics-based studies of the human microbiome, thereby facilitating development and commercialization of human microbiome-based products.

scholarly journals Competition among Nasal Bacteria Suggests a Role for Siderophore-Mediated Interactions in Shaping the Human Nasal Microbiota

2018 ◽  
Vol 85 (10) ◽  
Author(s):  
Reed M. Stubbendieck ◽  
Daniel S. May ◽  
Marc G. Chevrette ◽  
Mia I. Temkin ◽  
Evelyn Wendt-Pienkowski ◽  
...  
Keyword(s):  
Nasal Cavity ◽  
Iron Acquisition ◽  
Interference Competition ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Siderophore Production ◽  
Coagulase Negative Staphylococci ◽  
Exploitation Competition ◽  
Content Type

ABSTRACTResources available in the human nasal cavity are limited. Therefore, to successfully colonize the nasal cavity, bacteria must compete for scarce nutrients. Competition may occur directly through interference (e.g., antibiotics) or indirectly by nutrient sequestration. To investigate the nature of nasal bacterial competition, we performed coculture inhibition assays between nasalActinobacteriaandStaphylococcusspp. We found that isolates of coagulase-negative staphylococci (CoNS) were sensitive to growth inhibition byActinobacteriabut thatStaphylococcus aureusisolates were resistant to inhibition. AmongActinobacteria, we observed thatCorynebacteriumspp. were variable in their ability to inhibit CoNS. We sequenced the genomes of 10Corynebacteriumspecies isolates, including 3Corynebacterium propinquumisolates that strongly inhibited CoNS and 7 otherCorynebacteriumspecies isolates that only weakly inhibited CoNS. Using a comparative genomics approach, we found that theC. propinquumgenomes were enriched in genes for iron acquisition and harbored a biosynthetic gene cluster (BGC) for siderophore production, absent in the noninhibitoryCorynebacteriumspecies genomes. Using a chrome azurol S assay, we confirmed thatC. propinquumproduced siderophores. We demonstrated that iron supplementation rescued CoNS from inhibition byC. propinquum, suggesting that inhibition was due to iron restriction through siderophore production. Through comparative metabolomics and molecular networking, we identified the siderophore produced byC. propinquumas dehydroxynocardamine. Finally, we confirmed that the dehydroxynocardamine BGC is expressedin vivoby analyzing human nasal metatranscriptomes from the NIH Human Microbiome Project. Together, our results suggest that bacteria produce siderophores to compete for limited available iron in the nasal cavity and improve their fitness.IMPORTANCEWithin the nasal cavity, interference competition through antimicrobial production is prevalent. For instance, nasalStaphylococcusspecies strains can inhibit the growth of other bacteria through the production of nonribosomal peptides and ribosomally synthesized and posttranslationally modified peptides. In contrast, bacteria engaging in exploitation competition modify the external environment to prevent competitors from growing, usually by hindering access to or depleting essential nutrients. As the nasal cavity is a nutrient-limited environment, we hypothesized that exploitation competition occurs in this system. We determined thatCorynebacterium propinquumproduces an iron-chelating siderophore, and this iron-sequestering molecule correlates with the ability to inhibit the growth of coagulase-negative staphylococci. Furthermore, we found that the genes required for siderophore production are expressedin vivo. Thus, although siderophore production by bacteria is often considered a virulence trait, our work indicates that bacteria may produce siderophores to compete for limited iron in the human nasal cavity.

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