Computational Methods for Strain-Level Microbial Detection in Colony and Metagenome Sequencing Data

An integrated strain-level analytic pipeline utilizing longitudinal metagenomic data

With the development of sequencing technology and analytic tools, studying within-species variations enhances the understanding of microbial biological processes. Nevertheless, most existing methods designed for strain-level analysis lack the capability to concurrently assess both strain proportions and genome-wide single nucleotide variants (SNVs) across longitudinal metagenomic samples. In this study, we introduce LongStrain, an integrated pipeline for the analysis of large-scale metagenomic data from individuals with longitudinal or repeated samples. In LongStrain, we first utilize two efficient tools, Kraken2 and Bowtie2, for the taxonomic classification and alignment of sequencing reads, respectively. Subsequently, we propose to jointly model strain proportions and shared haplotypes across samples within individuals. This approach specifically targets tracking a primary strain and a secondary strain for each subject, providing their respective proportions and SNVs as output. With extensive simulation studies of a microbial community and single species, our results demonstrate that LongStrain is superior to two genotyping methods and two deconvolution methods across a majority of scenarios. Furthermore, we illustrate the potential applications of LongStrain in the real data analysis of The Environmental Determinants of Diabetes in the Young study and a gastric intestinal metaplasia microbiome study. In summary, the proposed analytic pipeline demonstrates marked statistical efficiency over the same type of methods and has great potential in understanding the genomic variants and dynamic changes at strain level. LongStrain and its tutorial are freely available online at https://github.com/BoyanZhou/LongStrain.

IMPORTANCE

The advancement in DNA-sequencing technology has enabled the high-resolution identification of microorganisms in microbial communities. Since different microbial strains within species may contain extreme phenotypic variability (e.g., nutrition metabolism, antibiotic resistance, and pathogen virulence), investigating within-species variations holds great scientific promise in understanding the underlying mechanism of microbial biological processes. To fully utilize the shared genomic variants across longitudinal metagenomics samples collected in microbiome studies, we develop an integrated analytic pipeline (LongStrain) for longitudinal metagenomics data. It concurrently leverages the information on proportions of mapped reads for individual strains and genome-wide SNVs to enhance the efficiency and accuracy of strain identification. Our method helps to understand strains' dynamic changes and their association with genome-wide variants. Given the fast-growing longitudinal studies of microbial communities, LongStrain which streamlines analyses of large-scale raw sequencing data should be of great value in microbiome research communities.

Genomic insights into fish pathogenic bacteria: A systems biology perspective for sustainable aquaculture

Fish diseases significantly challenge global aquaculture, causing substantial financial losses and impacting sustainability, trade, and socioeconomic conditions. Understanding microbial pathogenesis and virulence at the molecular level is crucial for disease prevention in commercial fish. This review provides genomic insights into fish pathogenic bacteria from a systems biology perspective, aiming to promote sustainable aquaculture. It covers the genomic characteristics of various fish pathogens and their industry impact. The review also explores the systems biology of zebrafish, fish bacterial pathogens, and probiotic bacteria, offering insights into fish production, potential vaccines, and therapeutic drugs. Genome-scale metabolic models aid in studying pathogenic bacteria, contributing to disease management and antimicrobial development. Researchers have also investigated probiotic strains to improve aquaculture health. Additionally, the review highlights bioinformatics resources for fish and fish pathogens, which are essential for researchers. Systems biology approaches enhance understanding of bacterial fish pathogens by revealing virulence factors and host interactions. Despite challenges from the adaptability and pathogenicity of bacterial infections, sustainable alternatives are necessary to meet seafood demand. This review underscores the potential of systems biology in understanding fish pathogen biology, improving production, and promoting sustainable aquaculture.

Simulation of 69 microbial communities indicates sequencing depth and false positives are major drivers of bias in prokaryotic metagenome-assembled genome recovery

We hypothesize that sample species abundance, sequencing depth, and taxonomic relatedness influence the recovery of metagenome-assembled genomes (MAGs). To test this hypothesis, we assessed MAG recovery in three in silico microbial communities composed of 42 species with the same richness but different sample species abundance, sequencing depth, and taxonomic distribution profiles using three different pipelines for MAG recovery. The pipeline developed by Parks and colleagues (8K) generated the highest number of MAGs and the lowest number of true positives per community profile. The pipeline by Karst and colleagues (DT) showed the most accurate results (~ 92%), outperforming the 8K and Multi-Metagenome pipeline (MM) developed by Albertsen and collaborators. Sequencing depth influenced the accurate recovery of genomes when using the 8K and MM, even with contrasting patterns: the MM pipeline recovered more MAGs found in the original communities when employing sequencing depths up to 60 million reads, while the 8K recovered more true positives in communities sequenced above 60 million reads. DT showed the best species recovery from the same genus, even though close-related species have a low recovery rate in all pipelines. Our results highlight that more bins do not translate to the actual community composition and that sequencing depth plays a role in MAG recovery and increased community resolution. Even low MAG recovery error rates can significantly impact biological inferences. Our data indicates that the scientific community should curate their findings from MAG recovery, especially when asserting novel species or metabolic traits.

RAPiD: a rapid and accurate plant pathogen identification pipeline for on-site nanopore sequencing

Nanopore sequencing technology has enabled the rapid, on-site taxonomic identification of samples from anything and anywhere. However, sequencing errors, inadequate databases, as well as the need for bioinformatic expertise and powerful computing resources, have hampered the widespread use of the technology for pathogen identification in the agricultural sector. Here we present RAPiD, a lightweight and accurate real-time taxonomic profiling pipeline. Compared to other metagenomic profilers, RAPiD had a higher classification precision achieved through the use of a curated, non-redundant database of common agricultural pathogens and extensive quality filtering of alignments. On a fungal, bacterial and mixed mock community RAPiD was the only pipeline to detect all members of the communities. We also present a protocol for in-field sample processing enabling pathogen identification from plant sample to sequence within 3 h using low-cost equipment. With sequencing costs continuing to decrease and more high-quality reference genomes becoming available, nanopore sequencing provides a viable method for rapid and accurate pathogen identification in the field. A web implementation of the RAPiD pipeline for real-time analysis is available at https://agrifuture.senckenberg.de.

Foodborne bacterial pathogens: genome-based approaches for enduring and emerging threats in a complex and changing world

Foodborne illnesses pose a substantial health and economic burden, presenting challenges in prevention due to the diverse microbial hazards that can enter and spread within food systems. Various factors, including natural, political and commercial drivers, influence food production and distribution. The risks of foodborne illness will continue to evolve in step with these drivers and with changes to food systems. For example, climate impacts on water availability for agriculture, changes in food sustainability targets and evolving customer preferences can all have an impact on the ecology of foodborne pathogens and the agrifood niches that can carry microorganisms. Whole-genome and metagenome sequencing, combined with microbial surveillance schemes and insights from the food system, can provide authorities and businesses with transformative information to address risks and implement new food safety interventions across the food chain. In this Review, we describe how genome-based approaches have advanced our understanding of the evolution and spread of enduring bacterial foodborne hazards as well as their role in identifying emerging foodborne hazards. Furthermore, foodborne hazards exist in complex microbial communities across the entire food chain, and consideration of these co-existing organisms is essential to understanding the entire ecology supporting pathogen persistence and transmission in an evolving food system.

CAIM: coverage-based analysis for identification of microbiome

Accurate taxonomic profiling of microbial taxa in a metagenomic sample is vital to gain insights into microbial ecology. Recent advancements in sequencing technologies have contributed tremendously toward understanding these microbes at species resolution through a whole shotgun metagenomic approach. In this study, we developed a new bioinformatics tool, coverage-based analysis for identification of microbiome (CAIM), for accurate taxonomic classification and quantification within both long- and short-read metagenomic samples using an alignment-based method. CAIM depends on two different containment techniques to identify species in metagenomic samples using their genome coverage information to filter out false positives rather than the traditional approach of relative abundance. In addition, we propose a nucleotide-count-based abundance estimation, which yield lesser root mean square error than the traditional read-count approach. We evaluated the performance of CAIM on 28 metagenomic mock communities and 2 synthetic datasets by comparing it with other top-performing tools. CAIM maintained a consistently good performance across datasets in identifying microbial taxa and in estimating relative abundances than other tools. CAIM was then applied to a real dataset sequenced on both Nanopore (with and without amplification) and Illumina sequencing platforms and found high similarity of taxonomic profiles between the sequencing platforms. Lastly, CAIM was applied to fecal shotgun metagenomic datasets of 232 colorectal cancer patients and 229 controls obtained from 4 different countries and 44 primary liver cancer patients and 76 controls. The predictive performance of models using the genome-coverage cutoff was better than those using the relative-abundance cutoffs in discriminating colorectal cancer and primary liver cancer patients from healthy controls with a highly confident species markers.

Sequencing-based analysis of microbiomes

Microbiomes occupy a range of niches and, in addition to having diverse compositions, they have varied functional roles that have an impact on agriculture, environmental sciences, and human health and disease. The study of microbiomes has been facilitated by recent technological and analytical advances, such as cheaper and higher-throughput DNA and RNA sequencing, improved long-read sequencing and innovative computational analysis methods. These advances are providing a deeper understanding of microbiomes at the genomic, transcriptional and translational level, generating insights into their function and composition at resolutions beyond the species level.

Strain tracking in complex microbiomes using synteny analysis reveals per-species modes of evolution

Microbial species diversify into strains through single-nucleotide mutations and structural changes, such as recombination, insertions and deletions. Most strain-comparison methods quantify differences in single-nucleotide polymorphisms (SNPs) and are insensitive to structural changes. However, recombination is an important driver of phenotypic diversification in many species, including human pathogens. We introduce SynTracker, a tool that compares microbial strains using genome synteny-the order of sequence blocks in homologous genomic regions-in pairs of metagenomic assemblies or genomes. Genome synteny is a rich source of genomic information untapped by current strain-comparison tools. SynTracker has low sensitivity to SNPs, has no database requirement and is robust to sequencing errors. It outperforms existing tools when tracking strains in metagenomic data and is particularly suited for phages, plasmids and other low-data contexts. Applied to single-species datasets and human gut metagenomes, SynTracker, combined with an SNP-based tool, detects strains enriched in either point mutations or structural changes, providing insights into microbial evolution in situ.

SNP-slice resolves mixed infections: simultaneously unveiling strain haplotypes and linking them to hosts

MOTIVATION

Multi-strain infection is a common yet under-investigated phenomenon of many pathogens. Currently, biologists analyzing SNP information sometimes have to discard mixed infection samples as many downstream analyses require monogenomic inputs. Such a protocol impedes our understanding of the underlying genetic diversity, co-infection patterns, and genomic relatedness of pathogens. A scalable tool to learn and resolve the SNP-haplotypes from polygenomic data is an urgent need in molecular epidemiology.

RESULTS

We develop a slice sampling Markov Chain Monte Carlo algorithm, named SNP-Slice, to learn not only the SNP-haplotypes of all strains in the populations but also which strains infect which hosts. Our method reconstructs SNP-haplotypes and individual heterozygosities accurately without reference panels and outperforms the state-of-the-art methods at estimating the multiplicity of infections and allele frequencies. Thus, SNP-Slice introduces a novel approach to address polygenomic data and opens a new avenue for resolving complex infection patterns in molecular surveillance. We illustrate the performance of SNP-Slice on empirical malaria and HIV datasets and provide recommendations for using our method on empirical datasets.

AVAILABILITY AND IMPLEMENTATION

The implementation of the SNP-Slice algorithm, as well as scripts to analyze SNP-Slice outputs, are available at https://github.com/nianqiaoju/snp-slice.

Easing genomic surveillance: A comprehensive performance evaluation of long-read assemblers across multi-strain mixture data of HIV-1 and Other pathogenic viruses for constructing a user-friendly bioinformatic pipeline

Background

Determining the appropriate computational requirements and software performance is essential for efficient genomic surveillance. The lack of standardized benchmarking complicates software selection, especially with limited resources.

Methods

We developed a containerized benchmarking pipeline to evaluate seven long-read assemblers-Canu, GoldRush, MetaFlye, Strainline, HaploDMF, iGDA, and RVHaplo-for viral haplotype reconstruction, using both simulated and experimental Oxford Nanopore sequencing data of HIV-1 and other viruses. Benchmarking was conducted on three computational systems to assess each assembler's performance, utilizing QUAST and BLASTN for quality assessment.

Results

Our findings show that assembler choice significantly impacts assembly time, with CPU and memory usage having minimal effect. Assembler selection also influences the size of the contigs, with a minimum read length of 2,000 nucleotides required for quality assembly. A 4,000-nucleotide read length improves quality further. Canu was efficient among de novo assemblers but not suitable for multi-strain mixtures, while GoldRush produced only consensus assemblies. Strainline and MetaFlye were suitable for metagenomic sequencing data, with Strainline requiring high memory and MetaFlye operable on low-specification machines. Among reference-based assemblers, iGDA had high error rates, RVHaplo showed the best runtime and accuracy but became ineffective with similar sequences, and HaploDMF, utilizing machine learning, had fewer errors with a slightly longer runtime.

Conclusions

The HIV-64148 pipeline, containerized using Docker, facilitates easy deployment and offers flexibility to select from a range of assemblers to match computational systems or study requirements. This tool aids in genome assembly and provides valuable information on HIV-1 sequences, enhancing viral evolution monitoring and understanding.

Readsynth: short-read simulation for consideration of composition-biases in reduced metagenome sequencing approaches

BACKGROUND

The application of reduced metagenomic sequencing approaches holds promise as a middle ground between targeted amplicon sequencing and whole metagenome sequencing approaches but has not been widely adopted as a technique. A major barrier to adoption is the lack of read simulation software built to handle characteristic features of these novel approaches. Reduced metagenomic sequencing (RMS) produces unique patterns of fragmentation per genome that are sensitive to restriction enzyme choice, and the non-uniform size selection of these fragments may introduce novel challenges to taxonomic assignment as well as relative abundance estimates.

RESULTS

Through the development and application of simulation software, readsynth, we compare simulated metagenomic sequencing libraries with existing RMS data to assess the influence of multiple library preparation and sequencing steps on downstream analytical results. Based on read depth per position, readsynth achieved 0.79 Pearson's correlation and 0.94 Spearman's correlation to these benchmarks. Application of a novel estimation approach, fixed length taxonomic ratios, improved quantification accuracy of simulated human gut microbial communities when compared to estimates of mean or median coverage.

CONCLUSIONS

We investigate the possible strengths and weaknesses of applying the RMS technique to profiling microbial communities via simulations with readsynth. The choice of restriction enzymes and size selection steps in library prep are non-trivial decisions that bias downstream profiling and quantification. The simulations investigated in this study illustrate the possible limits of preparing metagenomic libraries with a reduced representation sequencing approach, but also allow for the development of strategies for producing and handling the sequence data produced by this promising application.

CAIM: Coverage-based Analysis for Identification of Microbiome

Accurate taxonomic profiling of microbial taxa in a metagenomic sample is vital to gain insights into microbial ecology. Recent advancements in sequencing technologies have contributed tremendously toward understanding these microbes at species resolution through a whole shotgun metagenomic (WMS) approach. In this study, we developed a new bioinformatics tool, CAIM, for accurate taxonomic classification and quantification within both long- and short-read metagenomic samples using an alignment-based method. CAIM depends on two different containment techniques to identify species in metagenomic samples using their genome coverage information to filter out false positives rather than the traditional approach of relative abundance. In addition, we propose a nucleotide-count based abundance estimation, which yield lesser root mean square error than the traditional read-count approach. We evaluated the performance of CAIM on 28 metagenomic mock communities and 2 synthetic datasets by comparing it with other top-performing tools. CAIM maintained a consitently good performance across datasets in identifying microbial taxa and in estimating relative abundances than other tools. CAIM was then applied to a real dataset sequenced on both Nanopore (with and without amplification) and Illumina sequencing platforms and found high similality of taxonomic profiles between the sequencing platforms. Lastly, CAIM was applied to fecal shotgun metagenomic datasets of 232 colorectal cancer patients and 229 controls obtained from 4 different countries and primary 44 liver cancer patients and 76 controls. The predictive performance of models using the genome-coverage cutoff was better than those using the relative-abundance cutoffs in discriminating colorectal cancer and primary liver cancer patients from healthy controls with a highly confident species markers.

Defining Porphyromonas gingivalis strains associated with periodontal disease

Porphyromonas gingivalis, a Gram-negative anaerobic bacterium commonly found in human subgingival plaque, is a major etiologic agent for periodontitis and has been associated with multiple systemic pathologies. Many P. gingivalis strains have been identified and different strains possess different virulence factors. Current oral microbiome approaches (16S or shotgun) have been unable to differentiate P. gingivalis strains. This study presents a new approach that aims to improve the accuracy of strain identification, using a detection method based on sequencing of the intergenic spacer region (ISR) which is variable between P. gingivalis strains. Our approach uses two-step PCR to amplify only the P. gingivalis ISR region. Samples are then sequenced with an Illumina sequencer and mapped to specific strains. Our approach was validated by examining subgingival plaque from 153 participants with and without periodontal disease. We identified the avirulent strain ATCC33277/381 as the most abundant strain across all sample types. The W83/W50 strain was significantly enriched in periodontitis, with 13% of participants harboring that strain. Overall, this approach can have significant implications not only for the diagnosis and treatment of periodontal disease but also for other diseases where P. gingivalis or its toxins have been implicated, such as Alzheimer's disease.

Consideration of within-patient diversity highlights transmission pathways and antimicrobial resistance gene variability in vancomycin-resistant Enterococcus faecium

BACKGROUND

WGS is increasingly being applied to healthcare-associated vancomycin-resistant Enterococcus faecium (VREfm) outbreaks. Within-patient diversity could complicate transmission resolution if single colonies are sequenced from identified cases.

OBJECTIVES

Determine the impact of within-patient diversity on transmission resolution of VREfm.

MATERIALS AND METHODS

Fourteen colonies were collected from VREfm positive rectal screens, single colonies were collected from clinical samples and Illumina WGS was performed. Two isolates were selected for Oxford Nanopore sequencing and hybrid genome assembly to generate lineage-specific reference genomes. Mapping to closely related references was used to identify genetic variations and closely related genomes. A transmission network was inferred for the entire genome set using Phyloscanner.

RESULTS AND DISCUSSION

In total, 229 isolates from 11 patients were sequenced. Carriage of two or three sequence types was detected in 27% of patients. Presence of antimicrobial resistance genes and plasmids was variable within genomes from the same patient and sequence type. We identified two dominant sequence types (ST80 and ST1424), with two putative transmission clusters of two patients within ST80, and a single cluster of six patients within ST1424. We found transmission resolution was impaired using fewer than 14 colonies.

CONCLUSIONS

Patients can carry multiple sequence types of VREfm, and even within related lineages the presence of mobile genetic elements and antimicrobial resistance genes can vary. VREfm within-patient diversity could be considered in future to aid accurate resolution of transmission networks.

Host DNA depletion on frozen human respiratory samples enables successful metagenomic sequencing for microbiome studies

Background

Most respiratory microbiome studies have focused on amplicon rather than metagenomics sequencing due to high host DNA content. We evaluated efficacy of five host DNA depletion methods on previously frozen human bronchoalveolar lavage (BAL), nasal swabs, and sputum prior to metagenomic sequencing.

Results

Median sequencing depth was 76.4 million reads per sample. Untreated nasal, sputum and BAL samples had 94.1%, 99.2%, and 99.7% host-reads. The effect of host depletion differed by sample type. Most treatment methods increased microbial reads, species richness and predicted functional richness; the increase in species and predicted functional richness was mediated by higher effective sequencing depth. For BAL and nasal samples, most methods did not change Morisita-Horn dissimilarity suggesting limited bias introduced by host depletion.

Conclusions

Metagenomics sequencing without host depletion will underestimate microbial diversity of most respiratory samples due to shallow effective sequencing depth and is not recommended. Optimal host depletion methods vary by sample type.

Strain-level structure of gut microbiome showed potential association with cognitive function in major depressive disorder: A pilot study

BACKGROUND

Although the association between gut microbiota and the pathogenesis of major depressive disorder (MDD) has been well studied, it is unclear whether gut microbiota affects cognitive function in patients with MDD. In this study, we explored the association between gut microbiota and cognitive function in MDD and its possible mechanisms.

METHODS

We enrolled 57 patients with MDD and 30 healthy controls (HCs) and used 16S rRNA gene sequencing analysis and shotgun metagenomic sequencing analysis to determine gut microbial composition.

RESULTS

The richness and diversity of gut microbiota in patients with MDD were the same as those in HCs, but there were differences in the abundance of Bifidobacterium and Blautia. Compared with HCs, two strains (bin_32 and bin_55) were significantly increased, and one strain (bin_31) was significantly decreased in patients with MDD based on the strain-level meta-analysis. Time to complete the Stroop-C had significant negative correlations with bin_31 and bin_32. Bin_55 had significant negative correlations with time to complete the Stroop-C, time to complete the Stroop-CW, and repeated animal words in 60 s but significant positive correlations with correct answers in 120 s on the Stroop-CW.

LIMITATIONS

This study only tested the cognitive function of MDD in a small sample, which may have caused some bias.

CONCLUSIONS

Based on our strain-level analysis, we found that gut microbiota may be associated with the pathogenesis of MDD and may have potential effects on cognitive function.

Are the predicted known bacterial strains in a sample really present? A case study

With mutations constantly accumulating in bacterial genomes, it is unclear whether the previously identified bacterial strains are really present in an extant sample. To address this question, we did a case study on the known strains of the bacterial species S. aureus and S. epidermis in 68 atopic dermatitis shotgun metagenomic samples. We evaluated the likelihood of the presence of all sixteen known strains predicted in the original study and by two popular tools in this study. We found that even with the same tool, only two known strains were predicted by the original study and this study. Moreover, none of the sixteen known strains was likely present in these 68 samples. Our study thus indicates the limitation of the known-strain-based studies, especially those on rapidly evolving bacterial species. It implies the unlikely presence of the previously identified known strains in a current environmental sample. It also called for de novo bacterial strain identification directly from shotgun metagenomic reads.

mStrain: strain-level identification of Yersinia pestis using metagenomic data

Motivation

High-resolution target pathogen detection using metagenomic sequencing data represents a major challenge due to the low concentration of target pathogens in samples. We introduced mStrain, a novel Yesinia pestis strain/lineage-level identification tool that utilizes metagenomic data. mStrain successfully identified Y. pestis at the strain/lineage level by extracting sufficient information regarding single-nucleotide polymorphisms (SNPs), which can therefore be an effective tool for identification and source tracking of Y. pestis based on metagenomic data during a plague outbreak.

Definition

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Strain-level identification

Assigning the reads in the metagenomic sequencing data to an exactly known or most closely representative Y. pestis strain.

Lineage-level identification

Assigning the reads in the metagenomic sequencing data to a specific lineage on the phylogenetic tree.

canoSNPs

The unique and typical SNPs present in all representative strains.

Ancestor/derived state

An SNP is defined as the ancestor state when consistent with the allele of Yersinia pseudotuberculosis strain IP32953; otherwise, the SNP is defined as the derived state.

Availability and implementation

The code for running mStrain, the test dataset, and instructions for running the code can be found at the following GitHub repository: https://github.com/xwqian1123/mStrain.

SNP-Slice Resolves Mixed Infections: Simultaneously Unveiling Strain Haplotypes and Linking Them to Hosts

Multi-strain infection is a common yet under-investigated phenomenon of many pathogens. Currently, biologists analyzing SNP information have to discard mixed infection samples, because existing downstream analyses require monogenomic inputs. Such a protocol impedes our understanding of the underlying genetic diversity, co-infection patterns, and genomic relatedness of pathogens. A reliable tool to learn and resolve the SNP haplotypes from polygenomic data is an urgent need in molecular epidemiology. In this work, we develop a slice sampling Markov Chain Monte Carlo algorithm, named SNP-Slice, to learn not only the SNP haplotypes of all strains in the populations but also which strains infect which hosts. Our method reconstructs SNP haplotypes and individual heterozygosities accurately without reference panels and outperforms the state of art methods at estimating the multiplicity of infections and allele frequencies. Thus, SNP-Slice introduces a novel approach to address polygenomic data and opens a new avenue for resolving complex infection patterns in molecular surveillance. We illustrate the performance of SNP-Slice on empirical malaria and HIV datasets and provide recommendations for the practical use of the method.

High-resolution strain-level microbiome composition analysis from short reads

BACKGROUND

Bacterial strains under the same species can exhibit different biological properties, making strain-level composition analysis an important step in understanding the dynamics of microbial communities. Metagenomic sequencing has become the major means for probing the microbial composition in host-associated or environmental samples. Although there are a plethora of composition analysis tools, they are not optimized to address the challenges in strain-level analysis: highly similar strain genomes and the presence of multiple strains under one species in a sample. Thus, this work aims to provide a high-resolution and more accurate strain-level analysis tool for short reads.

RESULTS

In this work, we present a new strain-level composition analysis tool named StrainScan that employs a novel tree-based k-mers indexing structure to strike a balance between the strain identification accuracy and the computational complexity. We tested StrainScan extensively on a large number of simulated and real sequencing data and benchmarked StrainScan with popular strain-level analysis tools including Krakenuniq, StrainSeeker, Pathoscope2, Sigma, StrainGE, and StrainEst. The results show that StrainScan has higher accuracy and resolution than the state-of-the-art tools on strain-level composition analysis. It improves the F1 score by 20% in identifying multiple strains at the strain level.

CONCLUSIONS

By using a novel k-mer indexing structure, StrainScan is able to provide strain-level analysis with higher resolution than existing tools, enabling it to return more informative strain composition analysis in one sample or across multiple samples. StrainScan takes short reads and a set of reference strains as input and its source codes are freely available at https://github.com/liaoherui/StrainScan . Video Abstract.

Whole-Genome Sequencing Exhibits Better Diagnostic Performance than Variable-Number Tandem Repeats for Identifying Mixed Infections of Mycobacterium tuberculosis

Mixed infections of Mycobacterium tuberculosis, defined as the coexistence of multiple genetically distinct strains within a single host, have been associated with unfavorable treatment outcomes. Different methods have been used to detect mixed infections, but their performances have not been carefully evaluated. To compare the sensitivity of whole-genome sequencing (WGS) and variable-number tandem repeats (VNTR) typing to detect mixed infections, we prepared 10 artificial samples composed of DNA mixtures from two strains in different proportions and retrospectively collected 1,084 clinical isolates. The limit of detection (LOD) for the presence of a minor strain was 5% for both WGS and VNTR typing. The overall clinical detection rate of mixed infections was 3.7% (40/1,084) for the two methods combined, WGS identified 37/1,084 (3.4%), and VNTR typing identified 14/1,084 (1.3%), including 11 also identified by WGS. Multivariate analysis demonstrated that retreatment patients had a 2.7 times (95% confidence interval [CI], 1.2 to 6.0) higher risk of mixed infections than new cases. Collectively, WGS is a more reliable tool to identify mixed infections than VNTR typing, and mixed infections are more common in retreated patients. IMPORTANCE Mixed infections of M. tuberculosis have the potential to render treatment regimens ineffective and affect the transmission dynamics of the disease. VNTR typing, currently the most widely used method for the detection of mixed infections, detects mixed infections only by interrogating a small fraction of the M. tuberculosis genome, which necessarily limits sensitivity. With the introduction of WGS, it became possible to study the entire genome, but no quantitative comparison has yet been undertaken. Our systematic comparison of the ability of WGS and VNTR typing to detect mixed infections, using both artificial samples and clinical isolates, revealed the superior performance of WGS at a high sequencing depth (~100×) and found that mixed infections are more common in patients being retreated for tuberculosis (TB) in the populations studied. This provides valuable information for the application of WGS in the detection of mixed infections and the implications of mixed infections for tuberculosis control.

STEMSIM: a simulator of within-strain short-term evolutionary mutations for longitudinal metagenomic data

MOTIVATION

As the resolution of metagenomic analysis increases, the evolution of microbial genomes in longitudinal metagenomic data has become a research focus. Some software has been developed for the simulation of complex microbial communities at the strain level. However, the tool for simulating within-strain evolutionary signals in longitudinal samples is still lacking.

RESULTS

In this study, we introduce STEMSIM, a user-friendly command-line simulator of short-term evolutionary mutations for longitudinal metagenomic data. The input is simulated longitudinal raw sequencing reads of microbial communities or single species. The output is the modified reads with within-strain evolutionary mutations and the relevant information of these mutations. STEMSIM will be of great use for the evaluation of analytic tools that detect short-term evolutionary mutations in metagenomic data.

AVAILABILITY AND IMPLEMENTATION

STEMSIM and its tutorial are freely available online at https://github.com/BoyanZhou/STEMSim.

Genomic characterization of coexisting anatoxin-producing and non-toxigenic Microcoleus subspecies in benthic mats from the Wolastoq, New Brunswick, Canada

The presence of toxigenic benthic cyanobacteria in riverine ecosystems is an increasing concern around the world. In 2018, the death of three dogs along the Wolastoq (also known as the Saint John River) in New Brunswick, Canada, was attributed to anatoxin exposure after they ingested benthic microbial mats found along the shore. Here, we shotgun sequenced the DNA of 15 non-axenic cyanobacterial isolates derived from four anatoxin-containing benthic mat samples associated with the dog deaths. Anatoxins were produced by some of the isolates, but not all. We retrieved near-complete Microcoleus metagenome-assembled genomes (MAGs) from the isolates that are closely related to anatoxin-producing Microcoleus from the Cardrona River (New Zealand), although the Microcoleus MAGs from the Wolastoq varied in the presence/absence of the anatoxin-a biosynthesis cluster. Sequence similarity at the genomic level suggests that toxigenic and non-toxigenic Microcoleus MAGs from the Wolastoq belong to the same species but are separate subspecies. The toxigenic and nontoxic Wolastoq Microcoleus subspecies coexisted in the mat samples in similar relative abundance. Overall genomic comparisons revealed that toxigenic Microcoleus MAGs are longer and code for more accessory genes than their non-toxigenic relatives, suggesting a differential responsiveness to changing environments, stress conditions and nutrient availability.

Comparison of long- and short-read metagenomic assembly for low-abundance species and resistance genes

Recent technological and computational advances have made metagenomic assembly a viable approach to achieving high-resolution views of complex microbial communities. In previous benchmarking, short-read (SR) metagenomic assemblers had the highest accuracy, long-read (LR) assemblers generated the most contiguous sequences and hybrid (HY) assemblers balanced length and accuracy. However, no assessments have specifically compared the performance of these assemblers on low-abundance species, which include clinically relevant organisms in the gut. We generated semi-synthetic LR and SR datasets by spiking small and increasing amounts of Escherichia coli isolate reads into fecal metagenomes and, using different assemblers, examined E. coli contigs and the presence of antibiotic resistance genes (ARGs). For ARG assembly, although SR assemblers recovered more ARGs with high accuracy, even at low coverages, LR assemblies allowed for the placement of ARGs within longer, E. coli-specific contigs, thus pinpointing their taxonomic origin. HY assemblies identified resistance genes with high accuracy and had lower contiguity than LR assemblies. Each assembler type's strengths were maintained even when our isolate was spiked in with a competing strain, which fragmented and reduced the accuracy of all assemblies. For strain characterization and determining gene context, LR assembly is optimal, while for base-accurate gene identification, SR assemblers outperform other options. HY assembly offers contiguity and base accuracy, but requires generating data on multiple platforms, and may suffer high misassembly rates when strain diversity exists. Our results highlight the trade-offs associated with each approach for recovering low-abundance taxa, and that the optimal approach is goal-dependent.

Pitfalls of genotyping microbial communities with rapidly growing genome collections

Detecting genetic variants in metagenomic data is a priority for understanding the evolution, ecology, and functional characteristics of microbial communities. Many tools that perform this metagenotyping rely on aligning reads of unknown origin to a database of sequences from many species before calling variants. In this synthesis, we investigate how databases of increasingly diverse and closely related species have pushed the limits of current alignment algorithms, thereby degrading the performance of metagenotyping tools. We identify multi-mapping reads as a prevalent source of errors and illustrate a trade-off between retaining correct alignments versus limiting incorrect alignments, many of which map reads to the wrong species. Then we evaluate several actionable mitigation strategies and review emerging methods showing promise to further improve metagenotyping in response to the rapid growth in genome collections. Our results have implications beyond metagenotyping to the many tools in microbial genomics that depend upon accurate read mapping.

Patient-Reported Outcomes Correlate With Microbial Community Composition Independent of Mucosal Inflammation in Pediatric Inflammatory Bowel Disease

BACKGROUND

Inflammatory bowel diseases (IBDs) involve an aberrant host response to intestinal microbiota causing mucosal inflammation and gastrointestinal symptoms. Patient-reported outcomes (PROs) are increasingly important in clinical care and research. Our aim was to examine associations between PROs and fecal microbiota in patients 0 to 22 years of age with IBD.

METHODS

A longitudinal, prospective, single-center study tested for associations between microbial community composition via shotgun metagenomics and PROs including stool frequency and rectal bleeding in ulcerative colitis (UC) and abdominal pain and stool frequency in Crohn's disease (CD). Mucosal inflammation was assessed with fecal calprotectin. A negative binomial mixed-effects model including clinical characteristics and fecal calprotectin tested for differentially abundant species and metabolic pathways by PROs.

RESULTS

In 70 CD patients with 244 stool samples, abdominal pain correlated with increased relative abundance of Haemophilus and reduced Clostridium spp. There were no differences relative to calprotectin level. In 23 UC patients with 76 samples, both rectal bleeding and increased stool frequency correlated with increased Klebsiella and reduced Bacteroides spp. Conversely, UC patients with lower calprotectin had reduced Klebsiella. Both UC and CD patients with active symptoms exhibited less longitudinal microbial community stability. No differences in metabolic pathways were observed in CD. Increased sulfoglycolysis and ornithine biosynthesis correlated with symptomatic UC.

CONCLUSIONS

Microbial community composition correlated with PROs in both CD and UC. Metabolic pathways differed relative to PROs in UC, but not CD. Data suggest that microbiota may contribute to patient symptoms in IBD, in addition to effects of mucosal inflammation.

The microbiota as a modulator of mucosal inflammation and HIV/HPV pathogenesis: From association to causation

Although the microbiota has largely been associated with the pathogenesis of viral infections, most studies using omics techniques are correlational and hypothesis-generating. The mechanisms affecting the immune responses to viral infections are still being fully understood. Here we focus on the two most important sexually transmitted persistent viruses, HPV and HIV. Sophisticated omics techniques are boosting our ability to understand microbiota-pathogen-host interactions from a functional perspective by surveying the host and bacterial protein and metabolite production using systems biology approaches. However, while these strategies have allowed describing interaction networks to identify potential novel microbiota-associated biomarkers or therapeutic targets to prevent or treat infectious diseases, the analyses are typically based on highly dimensional datasets -thousands of features in small cohorts of patients-. As a result, we are far from getting to their clinical use. Here we provide a broad overview of how the microbiota influences the immune responses to HIV and HPV disease. Furthermore, we highlight experimental approaches to understand better the microbiota-host-virus interactions that might increase our potential to identify biomarkers and therapeutic agents with clinical applications.

Clasnip: a web-based intraspecies classifier and multi-locus sequence typing for pathogenic microorganisms using fragmented sequences

Bioinformatic approaches for the identification of microorganisms have evolved rapidly, but existing methods are time-consuming, complicated or expensive for massive screening of pathogens and their non-pathogenic relatives. Also, bioinformatic classifiers usually lack automatically generated performance statistics for specific databases. To address this problem, we developed Clasnip (www.clasnip.com), an easy-to-use web-based platform for the classification and similarity evaluation of closely related microorganisms at interspecies and intraspecies levels. Clasnip mainly consists of two modules: database building and sample classification. In database building, labeled nucleotide sequences are mapped to a reference sequence, and then single nucleotide polymorphisms (SNPs) statistics are generated. A probability model of SNPs and classification groups is built using Hidden Markov Models and solved using the maximum likelihood method. Database performance is estimated using three replicates of two-fold cross-validation. Sensitivity (recall), specificity (selectivity), precision, accuracy and other metrics are computed for all samples, training sets, and test sets. In sample classification, Clasnip accepts inputs of genes, short fragments, contigs and even whole genomes. It can report classification probability and a multi-locus sequence typing table for SNPs. The classification performance was tested using short sequences of 16S, 16-23S and 50S rRNA regions for 12 haplotypes of Candidatus Liberibacter solanacearum (CLso), a regulated plant pathogen associated with severe disease in economically important Apiaceous and Solanaceous crops. The program was able to classify CLso samples with even only 1-2 SNPs available, and achieved 97.2%, 98.8% and 100.0% accuracy based on 16S, 16-23S, and 50S rRNA sequences, respectively. In comparison with all existing 12 haplotypes, we proposed that to be classified as a new haplotype, given samples have at least 2 SNPs in the combined region of 16S rRNA (OA2/Lsc2) and 16-23S IGS (Lp Frag 4-1611F/Lp Frag 4-480R) regions, and 2 SNPs in the 50S rplJ/rplL (CL514F/CL514R) regions. Besides, we have included the databases for differentiating Dickeya spp., Pectobacterium spp. and Clavibacter spp. In addition to bacteria, we also tested Clasnip performance on potato virus Y (PVY). 251 PVY genomes were 100% correctly classified into seven groups (PVY^C, PVY^N, PVY^O, PVY^NTN, PVY^N:O, Poha, and Chile3). In conclusion, Clasnip is a statistically sound and user-friendly bioinformatic application for microorganism classification at the intraspecies level. Clasnip service is freely available at www.clasnip.com.

Statistical and Computational Methods for Microbial Strain Analysis

Microbial strains are interpreted as a lineage derived from a recent ancestor that have not experienced "too many" recombination events and can be successfully retrieved with culture-independent techniques using metagenomic sequencing. Such a strain variability has been increasingly shown to display additional phenotypic heterogeneities that affect host health, such as virulence, transmissibility, and antibiotics resistance. New statistical and computational methods have recently been developed to track the strains in samples based on shotgun metagenomics data either based on reference genome sequences or Metagenome-assembled genomes (MAGs). In this paper, we review some recent statistical methods for strain identifications based on frequency counts at a set of single nucleotide variants (SNVs) within a set of single-copy marker genes. These methods differ in terms of whether reference genome sequences are needed, how SNVs are called, what methods of deconvolution are used and whether the methods can be applied to multiple samples. We conclude our review with areas that require further research.

Cross-generational bacterial strain transfer to an infant after fecal microbiota transplantation to a pregnant patient: a case report

BACKGROUND

Fecal microbiota transplantation (FMT) effectively prevents the recurrence of Clostridioides difficile infection (CDI). Long-term engraftment of donor-specific microbial consortia may occur in the recipient, but potential further transfer to other sites, including the vertical transmission of donor-specific strains to future generations, has not been investigated. Here, we report, for the first time, the cross-generational transmission of specific bacterial strains from an FMT donor to a pregnant patient with CDI and further to her child, born at term, 26 weeks after the FMT treatment.

METHODS

A pregnant woman (gestation week 12 + 5) with CDI was treated with FMT via colonoscopy. She gave vaginal birth at term to a healthy baby. Fecal samples were collected from the feces donor, the mother (before FMT, and 1, 8, 15, 22, 26, and 50 weeks after FMT), and the infant (meconium at birth and 3 and 6 months after birth). Fecal samples were profiled by deep metagenomic sequencing for strain-level analysis. The microbial transfer was monitored using single nucleotide variants in metagenomes and further compared to a collection of metagenomic samples from 651 healthy infants and 58 healthy adults.

RESULTS

The single FMT procedure led to an uneventful and sustained clinical resolution in the patient, who experienced no further CDI-related symptoms up to 50 weeks after treatment. The gut microbiota of the patient with CDI differed considerably from the healthy donor and was characterized as low in alpha diversity and enriched for several potential pathogens. The FMT successfully normalized the patient's gut microbiota, likely by donor microbiota transfer and engraftment. Importantly, our analysis revealed that some specific strains were transferred from the donor to the patient and then further to the infant, thus demonstrating cross-generational microbial transfer.

CONCLUSIONS

The evidence for cross-generational strain transfer following FMT provides novel insights into the dynamics and engraftment of bacterial strains from healthy donors. The data suggests FMT treatment of pregnant women as a potential strategy to introduce beneficial strains or even bacterial consortia to infants, i.e., neonatal seeding. Video Abstract.

Strain level microbial detection and quantification with applications to single cell metagenomics

Computational identification and quantification of distinct microbes from high throughput sequencing data is crucial for our understanding of human health. Existing methods either use accurate but computationally expensive alignment-based approaches or less accurate but computationally fast alignment-free approaches, which often fail to correctly assign reads to genomes. Here we introduce CAMMiQ, a combinatorial optimization framework to identify and quantify distinct genomes (specified by a database) in a metagenomic dataset. As a key methodological innovation, CAMMiQ uses substrings of variable length and those that appear in two genomes in the database, as opposed to the commonly used fixed-length, unique substrings. These substrings allow to accurately decouple mixtures of highly similar genomes resulting in higher accuracy than the leading alternatives, without requiring additional computational resources, as demonstrated on commonly used benchmarking datasets. Importantly, we show that CAMMiQ can distinguish closely related bacterial strains in simulated metagenomic and real single-cell metatranscriptomic data.

Strain-level resolution and pneumococcal carriage dynamics by single-molecule real-time (SMRT) sequencing of the plyNCR marker: a longitudinal study in Swiss infants

BACKGROUND

Pneumococcal carriage has often been studied from a serotype perspective; however, little is known about the strain-specific carriage and inter-strain interactions. Here, we examined the strain-level carriage and co-colonization dynamics of Streptococcus pneumoniae in a Swiss birth cohort by PacBio single-molecule real-time (SMRT) sequencing of the plyNCR marker.

METHODS

A total of 872 nasal swab (NS) samples were included from 47 healthy infants during the first year of life. Pneumococcal carriage was determined based on the quantitative real-time polymerase chain reaction (qPCR) targeting the lytA gene. The plyNCR marker was amplified from 214 samples having lytA-based carriage for pneumococcal strain resolution. Amplicons were sequenced using SMRT technology, and sequences were analyzed with the DADA2 pipeline. In addition, pneumococcal serotypes were determined using conventional, multiplex PCR (cPCR).

RESULTS

PCR-based plyNCR amplification demonstrated a 94.2% sensitivity and 100% specificity for Streptococcus pneumoniae if compared to lytA qPCR. The overall carriage prevalence was 63.8%, and pneumococcal co-colonization (≥ 2 plyNCR amplicon sequence variants (ASVs)) was detected in 38/213 (17.8%) sequenced samples with the relative proportion of the least abundant strain(s) ranging from 1.1 to 48.8% (median, 17.2%; IQR, 5.8-33.4%). The median age to first acquisition was 147 days, and having ≥ 2 siblings increased the risk of acquisition.

CONCLUSION

The plyNCR amplicon sequencing is species-specific and enables pneumococcal strain resolution. We therefore recommend its application for longitudinal strain-level carriage studies of Streptococcus pneumoniae. Video Abstract.

StrainPanDA: Linked reconstruction of strain composition and gene content profiles via pangenome-based decomposition of metagenomic data

Microbial strains of variable functional capacities coexist in microbiomes. Current bioinformatics methods of strain analysis cannot provide the direct linkage between strain composition and their gene contents from metagenomic data. Here we present Strain-level Pangenome Decomposition Analysis (StrainPanDA), a novel method that uses the pangenome coverage profile of multiple metagenomic samples to simultaneously reconstruct the composition and gene content variation of coexisting strains in microbial communities. We systematically validate the accuracy and robustness of StrainPanDA using synthetic data sets. To demonstrate the power of gene-centric strain profiling, we then apply StrainPanDA to analyze the gut microbiome samples of infants, as well as patients treated with fecal microbiota transplantation. We show that the linked reconstruction of strain composition and gene content profiles is critical for understanding the relationship between microbial adaptation and strain-specific functions (e.g., nutrient utilization and pathogenicity). Finally, StrainPanDA has minimal requirements for computing resources and can be scaled to process multiple species in a community in parallel. In short, StrainPanDA can be applied to metagenomic data sets to detect the association between molecular functions and microbial/host phenotypes to formulate testable hypotheses and gain novel biological insights at the strain or subspecies level.

Combination of Whole Genome Sequencing and Metagenomics for Microbiological Diagnostics

Whole genome sequencing (WGS) provides the highest resolution for genome-based species identification and can provide insight into the antimicrobial resistance and virulence potential of a single microbiological isolate during the diagnostic process. In contrast, metagenomic sequencing allows the analysis of DNA segments from multiple microorganisms within a community, either using an amplicon- or shotgun-based approach. However, WGS and shotgun metagenomic data are rarely combined, although such an approach may generate additive or synergistic information, critical for, e.g., patient management, infection control, and pathogen surveillance. To produce a combined workflow with actionable outputs, we need to understand the pre-to-post analytical process of both technologies. This will require specific databases storing interlinked sequencing and metadata, and also involves customized bioinformatic analytical pipelines. This review article will provide an overview of the critical steps and potential clinical application of combining WGS and metagenomics together for microbiological diagnosis.

Islet autoantibody seroconversion in type-1 diabetes is associated with metagenome-assembled genomes in infant gut microbiomes

The immune system of some genetically susceptible children can be triggered by certain environmental factors to produce islet autoantibodies (IA) against pancreatic β cells, which greatly increases their risk for Type-1 diabetes. An environmental factor under active investigation is the gut microbiome due to its important role in immune system education. Here, we study gut metagenomes that are de-novo-assembled in 887 at-risk children in the Environmental Determinants of Diabetes in the Young (TEDDY) project. Our results reveal a small set of core protein families, present in >50% of the subjects, which account for 64% of the sequencing reads. Time-series binning generates 21,536 high-quality metagenome-assembled genomes (MAGs) from 883 species, including 176 species that hitherto have no MAG representation in previous comprehensive human microbiome surveys. IA seroconversion is positively associated with 2373 MAGs and negatively with 1549 MAGs. Comparative genomics analysis identifies lipopolysaccharides biosynthesis in Bacteroides MAGs and sulfate reduction in Anaerostipes MAGs as functional signatures of MAGs with positive IA-association. The functional signatures in the MAGs with negative IA-association include carbohydrate degradation in lactic acid bacteria MAGs and nitrate reduction in Escherichia MAGs. Overall, our results show a distinct set of gut microorganisms associated with IA seroconversion and uncovered the functional genomics signatures of these IA-associated microorganisms.

Towards Strain-Level Complexity: Sequencing Depth Required for Comprehensive Single-Nucleotide Polymorphism Analysis of the Human Gut Microbiome

Intestinal bacteria strains play crucial roles in maintaining host health. Researchers have increasingly recognized the importance of strain-level analysis in metagenomic studies. Many analysis tools and several cutting-edge sequencing techniques like single cell sequencing have been proposed to decipher strains in metagenomes. However, strain-level complexity is far from being well characterized up to date. As the indicator of strain-level complexity, metagenomic single-nucleotide polymorphisms (SNPs) have been utilized to disentangle conspecific strains. Lots of SNP-based tools have been developed to identify strains in metagenomes. However, the sufficient sequencing depth for SNP and strain-level analysis remains unclear. We conducted ultra-deep sequencing of the human gut microbiome and constructed an unbiased framework to perform reliable SNP analysis. SNP profiles of the human gut metagenome by ultra-deep sequencing were obtained. SNPs identified from conventional and ultra-deep sequencing data were thoroughly compared and the relationship between SNP identification and sequencing depth were investigated. The results show that the commonly used shallow-depth sequencing is incapable to support a systematic metagenomic SNP discovery. In contrast, ultra-deep sequencing could detect more functionally important SNPs, which leads to reliable downstream analyses and novel discoveries. We also constructed a machine learning model to provide guidance for researchers to determine the optimal sequencing depth for their projects (SNPsnp, https://github.com/labomics/SNPsnp). To conclude, the SNP profiles based on ultra-deep sequencing data extend current knowledge on metagenomics and highlights the importance of evaluating sequencing depth before starting SNP analysis. This study provides new ideas and references for future strain-level investigations.

The evolution of bacterial genome assemblies - where do we need to go next?

Genome sequencing has fundamentally changed our ability to decipher and understand the genetic blueprint of life and how it changes over time in response to environmental and evolutionary pressures. The pace of sequencing is still increasing in response to advances in technologies, paving the way from sequenced genes to genomes to metagenomes to metagenome-assembled genomes (MAGs). Our ability to interrogate increasingly complex microbial communities through metagenomes and MAGs is opening up a tantalizing future where we may be able to delve deeper into the mechanisms and genetic responses emerging over time. In the near future, we will be able to detect MAG assembly variations within strains originating from diverging sub-populations, and one of the emerging challenges will be to capture these variations in a biologically relevant way. Here, we present a brief overview of sequencing technologies and the current state of metagenome assemblies to suggest the need to develop new data formats that can capture the genetic variations within strains and communities, which previously remained invisible due to sequencing technology limitations.

Strain-resolved analysis in a randomized trial of antibiotic pretreatment and maintenance dose delivery mode with fecal microbiota transplant for ulcerative colitis

Fecal microbiota transplant is a promising therapy for ulcerative colitis. Parameters maximizing effectiveness and tolerability are not yet clear, and it is not known how import the transmission of donor microbes to patients is. Here (clinicaltrails.gov: NCT03006809) we have tested the effects of antibiotic pretreatment and compared two modes of maintenance dose delivery, capsules versus enema, in a randomized, pilot, open-label, 2 × 2 factorial design with 22 patients analyzed with mild to moderate UC. Clinically, the treatment was well-tolerated with favorable safety profile. Of patients who received antibiotic pretreatment, 6 of 11 experienced remission after 6 weeks of treatment, versus 2 of 11 non-pretreated patients (log odds ratio: 1.69, 95% confidence interval: −0.25 to 3.62). No significant differences were found between maintenance dosing via capsules versus enema. In exploratory analyses, microbiome turnover at both the species and strain levels was extensive and significantly more pronounced in the pretreated patients. Associations were also revealed between taxonomic turnover and changes in the composition of primary and secondary bile acids. Together these findings suggest that antibiotic pretreatment contributes to microbiome engraftment and possibly clinical effectiveness, and validate longitudinal strain tracking as a powerful way to monitor the dynamics and impact of microbiota transfer.

StrainGE: a toolkit to track and characterize low-abundance strains in complex microbial communities

Human-associated microbial communities comprise not only complex mixtures of bacterial species, but also mixtures of conspecific strains, the implications of which are mostly unknown since strain level dynamics are underexplored due to the difficulties of studying them. We introduce the Strain Genome Explorer (StrainGE) toolkit, which deconvolves strain mixtures and characterizes component strains at the nucleotide level from short-read metagenomic sequencing with higher sensitivity and resolution than other tools. StrainGE is able to identify strains at 0.1x coverage and detect variants for multiple conspecific strains within a sample from coverages as low as 0.5x.

Towards Improved Detection and Identification of Rust Fungal Pathogens in Environmental Samples Using a Metabarcoding Approach

The dispersion of fungal inocula such as the airborne spores of rust fungi (Pucciniales) can be monitored through metabarcoding of the internal transcribed spacer 2 (ITS2) of the rRNA gene in environmental DNAs. This method is largely dependent on a high-quality reference database (refDB) and primers with proper taxonomic coverage and specificity. For this study, a curated ITS2 reference database (named CR-ITS2-refDB) comprising representatives of the major cereal rust fungi and phylogenetically related species was compiled. Interspecific and intraspecific variation analyses suggested that the ITS2 region had reasonable discriminating power for the majority of the Puccinia species or species complexes in the database. In silico evaluation of nine forward and seven reverse ITS2 primers, including three newly designed, revealed marked variation in DNA amplification efficiency for the rusts. We validated the theoretical assessment of rust-enhanced (Rust2inv/ITS4var_H) and universal fungal (ITS9F/ITS4) ITS2 primer pairs by profiling the airborne rust fungal communities from environmental samples via a metabarcoding approach. Species- or subspecies-level identification of the rusts was improved by use of CR-ITS2-refDB and the Automated Oligonucleotide Design Pipeline (AODP), which identified all mutations distinguishing highly conserved DNA markers between close relatives. A generic bioinformatics pipeline was developed, including all steps used in this study from in silico evaluation of primers to accurate identification of short metabarcodes at the level of interest for defining phytopathogens. The results highlight the importance of primer selection, refDBs that are resolved to reflect phylogenetic relationships, and the use of AODP for improving the reliability of metabarcoding in phytopathogen biosurveillance.

Metagenomics Approaches for Improving Food Safety: A Review

ABSTRACT

Advancements in next-generation sequencing technology have dramatically reduced the cost and increased the ease of microbial whole genome sequencing. This approach is revolutionizing the identification and analysis of foodborne microbial pathogens, facilitating expedited detection and mitigation of foodborne outbreaks, improving public health outcomes, and limiting costly recalls. However, next-generation sequencing is still anchored in the traditional laboratory practice of the selection and culture of a single isolate. Metagenomic-based approaches, including metabarcoding and shotgun and long-read metagenomics, are part of the next disruptive revolution in food safety diagnostics and offer the potential to directly identify entire microbial communities in a single food, ingredient, or environmental sample. In this review, metagenomic-based approaches are introduced and placed within the context of conventional detection and diagnostic techniques, and essential considerations for undertaking metagenomic assays and data analysis are described. Recent applications of the use of metagenomics for food safety are discussed alongside current limitations and knowledge gaps and new opportunities arising from the use of this technology.

HIGHLIGHTS

Computational analyses of bacterial strains from shotgun reads

Shotgun sequencing is routinely employed to study bacteria in microbial communities. With the vast amount of shotgun sequencing reads generated in a metagenomic project, it is crucial to determine the microbial composition at the strain level. This study investigated 20 computational tools that attempt to infer bacterial strain genomes from shotgun reads. For the first time, we discussed the methodology behind these tools. We also systematically evaluated six novel-strain-targeting tools on the same datasets and found that BHap, mixtureS and StrainFinder performed better than other tools. Because the performance of the best tools is still suboptimal, we discussed future directions that may address the limitations.

Toward accurate diagnosis and surveillance of bacterial infections using enhanced strain-level metagenomic next-generation sequencing of infected body fluids

Metagenomic next-generation sequencing (mNGS) enables comprehensive pathogen detection and has become increasingly popular in clinical diagnosis. The distinct pathogenic traits between strains require mNGS to achieve a strain-level resolution, but an equivocal concept of 'strain' as well as the low pathogen loads in most clinical specimens hinders such strain awareness. Here we introduce a metagenomic intra-species typing (MIST) tool (https://github.com/pandafengye/MIST), which hierarchically organizes reference genomes based on average nucleotide identity (ANI) and performs maximum likelihood estimation to infer the strain-level compositional abundance. In silico analysis using synthetic datasets showed that MIST accurately predicted the strain composition at a 99.9% average nucleotide identity (ANI) resolution with a merely 0.001× sequencing depth. When applying MIST on 359 culture-positive and 359 culture-negative real-world specimens of infected body fluids, we found the presence of multiple-strain reached considerable frequencies (30.39%-93.22%), which were otherwise underestimated by current diagnostic techniques due to their limited resolution. Several high-risk clones were identified to be prevalent across samples, including Acinetobacter baumannii sequence type (ST)208/ST195, Staphylococcus aureus ST22/ST398 and Klebsiella pneumoniae ST11/ST15, indicating potential outbreak events occurring in the clinical settings. Interestingly, contaminations caused by the engineered Escherichia coli strain K-12 and BL21 throughout the mNGS datasets were also identified by MIST instead of the statistical decontamination approach. Our study systemically characterized the infected body fluids at the strain level for the first time. Extension of mNGS testing to the strain level can greatly benefit clinical diagnosis of bacterial infections, including the identification of multi-strain infection, decontamination and infection control surveillance.

Gut bless you: The microbiota-gut-brain axis in irritable bowel syndrome

Irritable bowel syndrome (IBS) is a common clinical label for medically unexplained gastrointestinal symptoms, recently described as a disturbance of the microbiota-gut-brain axis. Despite decades of research, the pathophysiology of this highly heterogeneous disorder remains elusive. However, a dramatic change in the understanding of the underlying pathophysiological mechanisms surfaced when the importance of gut microbiota protruded the scientific picture. Are we getting any closer to understanding IBS' etiology, or are we drowning in unspecific, conflicting data because we possess limited tools to unravel the cluster of secrets our gut microbiota is concealing? In this comprehensive review we are discussing some of the major important features of IBS and their interaction with gut microbiota, clinical microbiota-altering treatment such as the low FODMAP diet and fecal microbiota transplantation, neuroimaging and methods in microbiota analyses, and current and future challenges with big data analysis in IBS.

Advances in Metagenomics and Its Application in Environmental Microorganisms

Metagenomics is a new approach to study microorganisms obtained from a specific environment by functional gene screening or sequencing analysis. Metagenomics studies focus on microbial diversity, community constitute, genetic and evolutionary relationships, functional activities, and interactions and relationships with the environment. Sequencing technologies have evolved from shotgun sequencing to high-throughput, next-generation sequencing (NGS), and third-generation sequencing (TGS). NGS and TGS have shown the advantage of rapid detection of pathogenic microorganisms. With the help of new algorithms, we can better perform the taxonomic profiling and gene prediction of microbial species. Functional metagenomics is helpful to screen new bioactive substances and new functional genes from microorganisms and microbial metabolites. In this article, basic steps, classification, and applications of metagenomics are reviewed.

Single-cell genomics for resolution of conserved bacterial genes and mobile genetic elements of the human intestinal microbiota using flow cytometry

As our understanding of the importance of the human microbiota in health and disease grows, so does our need to carefully resolve and delineate its genomic content. 16S rRNA gene-based analyses yield important insights into taxonomic composition, and metagenomics-based approaches reveal the functional potential of microbial communities. However, these methods generally fail to directly link genetic features, including bacterial genes and mobile genetic elements, to each other and to their source bacterial genomes. Further, they are inadequate to capture the microdiversity present within a genus, species, or strain of bacteria within these complex communities. Here, we present a method utilizing fluorescence-activated cell sorting for isolation of single bacterial cells, amplifying their genomes, screening them by 16S rRNA gene analysis, and selecting cells for genomic sequencing. We apply this method to both a cultured laboratory strain of Escherichia coli and human stool samples. Our analyses reveal the capacity of this method to provide nearly complete coverage of bacterial genomes when applied to isolates and partial genomes of bacterial species recovered from complex communities. Additionally, this method permits exploration and comparison of conserved and variable genomic features between individual cells. We generate assemblies of novel genomes within the Ruminococcaceae family and the Holdemanella genus by combining several 16S rRNA gene-matched single cells, and report novel prophages and conjugative transposons for both Bifidobacterium and Ruminococcaceae. Thus, we demonstrate an approach for flow cytometric separation and sequencing of single bacterial cells from the human microbiota, which yields a variety of critical insights into both the functional potential of individual microbes and the variation among those microbes. This method definitively links a variety of conserved and mobile genomic features, and can be extended to further resolve diverse elements present in the human microbiota.

Identification of isolated or mixed strains from long reads: a challenge met on Streptococcus thermophilus using a MinION sequencer

This study aimed to provide efficient recognition of bacterial strains on personal computers from MinION (Nanopore) long read data. Thanks to the fall in sequencing costs, the identification of bacteria can now proceed by whole genome sequencing. MinION is a fast, but highly error-prone sequencing device and it is a challenge to successfully identify the strain content of unknown simple or complex microbial samples. It is heavily constrained by memory management and fast access to the read and genome fragments. Our strategy involves three steps: indexing of known genomic sequences for a given or several bacterial species; a request process to assign a read to a strain by matching it to the closest reference genomes; and a final step looking for a minimum set of strains that best explains the observed reads. We have applied our method, called ORI, on 77 strains of Streptococcus thermophilus. We worked on several genomic distances and obtained a detailed classification of the strains, together with a criterion that allows merging of what we termed 'sibling' strains, only separated by a few mutations. Overall, isolated strains can be safely recognized from MinION data. For mixtures of several non-sibling strains, results depend on strain abundance.