Ultra-deep sequencing of Hadza hunter-gatherers recovers vanishing gut microbes.

The gut microbiome modulates immune and metabolic health. Human microbiome data are biased toward industrialized populations, limiting our understanding of non-industrialized microbiomes. Here, we performed ultra-deep metagenomic sequencing on 351 fecal samples from the Hadza hunter-gatherers of Tanzania and comparative populations in Nepal and California. We recovered 91,662 genomes of bacteria, archaea, bacteriophages, and eukaryotes, 44% of which are absent from existing unified datasets. We identified 124 gut-resident species vanishing in industrialized populations and highlighted distinct aspects of the Hadza gut microbiome related to in situ replication rates, signatures of selection, and strain sharing. Industrialized gut microbes were found to be enriched in genes associated with oxidative stress, possibly a result of microbiome adaptation to inflammatory processes. This unparalleled view of the Hadza gut microbiome provides a valuable resource, expands our understanding of microbes capable of colonizing the human gut, and clarifies the extensive perturbation induced by the industrialized lifestyle.

Gut microbiome of multiple sclerosis patients and paired household healthy controls reveal associations with disease risk and course.

Changes in gut microbiota have been associated with several diseases. Here, the International Multiple Sclerosis Microbiome Study (iMSMS) studied the gut microbiome of 576 MS patients (36% untreated) and genetically unrelated household healthy controls (1,152 total subjects). We observed a significantly increased proportion of Akkermansia muciniphila, Ruthenibacterium lactatiformans, Hungatella hathewayi, and Eisenbergiella tayi and decreased Faecalibacterium prausnitzii and Blautia species. The phytate degradation pathway was over-represented in untreated MS, while pyruvate-producing carbohydrate metabolism pathways were significantly reduced. Microbiome composition, function, and derived metabolites also differed in response to disease-modifying treatments. The therapeutic activity of interferon-ß may in part be associated with upregulation of short-chain fatty acid transporters. Distinct microbial networks were observed in untreated MS and healthy controls. These results strongly support specific gut microbiome associations with MS risk, course and progression, and functional changes in response to treatment.

Extensive Unexplored Human Microbiome Diversity Revealed by Over 150,000 Genomes from Metagenomes Spanning Age, Geography, and Lifestyle.

The body-wide human microbiome plays a role in health, but its full diversity remains uncharacterized, particularly outside of the gut and in international populations. We leveraged 9,428 metagenomes to reconstruct 154,723 microbial genomes (45% of high quality) spanning body sites, ages, countries, and lifestyles. We recapitulated 4,930 species-level genome bins (SGBs), 77% without genomes in public repositories (unknown SGBs [uSGBs]). uSGBs are prevalent (in 93% of well-assembled samples), expand underrepresented phyla, and are enriched in non-Westernized populations (40% of the total SGBs). We annotated 2.85 M genes in SGBs, many associated with conditions including infant development (94,000) or Westernization (106,000). SGBs and uSGBs permit deeper microbiome analyses and increase the average mappability of metagenomic reads from 67.76% to 87.51% in the gut (median 94.26%) and 65.14% to 82.34% in the mouth. We thus identify thousands of microbial genomes from yet-to-be-named species, expand the pangenomes of human-associated microbes, and allow better exploitation of metagenomic technologies.

Utilizing profile hidden Markov model databases for discovering viruses from metagenomic data: a comprehensive review.

Profile hidden Markov models (pHMMs) are able to achieve high sensitivity in remote homology search, making them popular choices for detecting novel or highly diverged viruses in metagenomic data. However, many existing pHMM databases have different design focuses, making it difficult for users to decide the proper one to use. In this review, we provide a thorough evaluation and comparison for multiple commonly used profile HMM databases for viral sequence discovery in metagenomic data. We characterized the databases by comparing their sizes, their taxonomic coverage, and the properties of their models using quantitative metrics. Subsequently, we assessed their performance in virus identification across multiple application scenarios, utilizing both simulated and real metagenomic data. We aim to offer researchers a thorough and critical assessment of the strengths and limitations of different databases. Furthermore, based on the experimental results obtained from the simulated and real metagenomic data, we provided practical suggestions for users to optimize their use of pHMM databases, thus enhancing the quality and reliability of their findings in the field of viral metagenomics.

Influence of dengue virus serotypes on the abundance of Aedes aegypti insect-specific viruses (ISVs).

A comprehensive understanding of the virome in mosquito vectors is crucial for assessing the potential transmission of viral agents, designing effective vector control strategies, and advancing our knowledge of insect-specific viruses (ISVs). In this study, we utilized Oxford Nanopore Technologies metagenomics to characterize the virome of Aedes aegypti mosquitoes collected in various regions of Colombia, a country hyperendemic for dengue virus (DENV). Analyses were conducted on groups of insects with previous natural DENV infection (DENV-1 and DENV-2 serotypes), as well as mosquito samples that tested negative for virus infection (DENV-negative). Our findings indicate that the Ae. aegypti virome exhibits a similar viral composition at the ISV family and species levels in both DENV-positive and DENV-negative samples across all study sites. However, differences were observed in the relative abundance of viral families such as Phenuiviridae, Partitiviridae, Flaviviridae, Rhabdoviridae, Picornaviridae, Bromoviridae, and Virgaviridae, depending on the serotype of DENV-1 and DENV-2. In addition, ISVs are frequently found in the core virome of Ae. aegypti, such as Phasi Charoen-like phasivirus (PCLV), which was the most prevalent and showed variable abundance in relation to the presence of specific DENV serotypes. Phylogenetic analyses of the L, M, and S segments of the PCLV genome are associated with sequences from different regions of the world but show close clustering with sequences from Brazil and Guadeloupe, indicating a shared evolutionary relationship. The profiling of the Ae. aegypti virome in Colombia presented here improves our understanding of viral diversity within mosquito vectors and provides information that opens the way to possible connections between ISVs and arboviruses. Future studies aimed at deepening our understanding of the mechanisms underlying the interactions between ISVs and DENV serotypes in Ae. aegypti could provide valuable information for the design of effective vector-borne viral disease control and prevention strategies.IMPORTANCEIn this study, we employed a metagenomic approach to characterize the virome of Aedes aegypti mosquitoes, with and without natural DENV infection, in several regions of Colombia. Our findings indicate that the mosquito virome is predominantly composed of insect-specific viruses (ISVs) and that infection with different DENV serotypes (DENV-1 and DENV-2) could lead to alterations in the relative abundance of viral families and species constituting the core virome in Aedes spp. The study also sheds light on the identification of the genome and evolutionary relationships of the Phasi Charoen-like phasivirus in Ae. aegypti in Colombia, a widespread ISV in areas with high DENV incidence.

VIGA: a one-stop tool for eukaryotic virus identification and genome assembly from next-generation-sequencing data.

Identification of viruses and further assembly of viral genomes from the next-generation-sequencing data are essential steps in virome studies. This study presented a one-stop tool named VIGA (available at https://github.com/viralInformatics/VIGA) for eukaryotic virus identification and genome assembly from NGS data. It was composed of four modules, namely, identification, taxonomic annotation, assembly and novel virus discovery, which integrated several third-party tools such as BLAST, Trinity, MetaCompass and RagTag. Evaluation on multiple simulated and real virome datasets showed that VIGA assembled more complete virus genomes than its competitors on both the metatranscriptomic and metagenomic data and performed well in assembling virus genomes at the strain level. Finally, VIGA was used to investigate the virome in metatranscriptomic data from the Human Microbiome Project and revealed different composition and positive rate of viromes in diseases of prediabetes, Crohn's disease and ulcerative colitis. Overall, VIGA would help much in identification and characterization of viromes, especially the known viruses, in future studies.

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.

A survey on computational strategies for genome-resolved gut metagenomics.

Recovering high-quality metagenome-assembled genomes (HQ-MAGs) is critical for exploring microbial compositions and microbe-phenotype associations. However, multiple sequencing platforms and computational tools for this purpose may confuse researchers and thus call for extensive evaluation. Here, we systematically evaluated a total of 40 combinations of popular computational tools and sequencing platforms (i.e. strategies), involving eight assemblers, eight metagenomic binners and four sequencing technologies, including short-, long-read and metaHiC sequencing. We identified the best tools for the individual tasks (e.g. the assembly and binning) and combinations (e.g. generating more HQ-MAGs) depending on the availability of the sequencing data. We found that the combination of the hybrid assemblies and metaHiC-based binning performed best, followed by the hybrid and long-read assemblies. More importantly, both long-read and metaHiC sequencings link more mobile elements and antibiotic resistance genes to bacterial hosts and improve the quality of public human gut reference genomes with 32% (34/105) HQ-MAGs that were either of better quality than those in the Unified Human Gastrointestinal Genome catalog version 2 or novel.

NanoDeep: a deep learning framework for nanopore adaptive sampling on microbial sequencing.

Nanopore sequencers can enrich or deplete the targeted DNA molecules in a library by reversing the voltage across individual nanopores. However, it requires substantial computational resources to achieve rapid operations in parallel at read-time sequencing. We present a deep learning framework, NanoDeep, to overcome these limitations by incorporating convolutional neural network and squeeze and excitation. We first showed that the raw squiggle derived from native DNA sequences determines the origin of microbial and human genomes. Then, we demonstrated that NanoDeep successfully classified bacterial reads from the pooled library with human sequence and showed enrichment for bacterial sequence compared with routine nanopore sequencing setting. Further, we showed that NanoDeep improves the sequencing efficiency and preserves the fidelity of bacterial genomes in the mock sample. In addition, NanoDeep performs well in the enrichment of metagenome sequences of gut samples, showing its potential applications in the enrichment of unknown microbiota. Our toolkit is available at https://github.com/lysovosyl/NanoDeep.

To assemble or not to assemble: metagenomic profiling of microbially mediated biogeochemical pathways in complex communities.

High-throughput profiling of microbial functional traits involved in various biogeochemical cycling pathways using shotgun metagenomic sequencing has been routinely applied in microbial ecology and environmental science. Multiple bioinformatics data processing approaches are available, including assembly-based (single-sample assembly and multi-sample assembly) and read-based (merged reads and raw data). However, it remains not clear how these different approaches may differ in data analyses and affect result interpretation. In this study, using two typical shotgun metagenome datasets recovered from geographically distant coastal sediments, the performance of different data processing approaches was comparatively investigated from both technical and biological/ecological perspectives. Microbially mediated biogeochemical cycling pathways, including nitrogen cycling, sulfur cycling and B12 biosynthesis, were analyzed. As a result, multi-sample assembly provided the most amount of usable information for targeted functional traits, at a high cost of computational resources and running time. Single-sample assembly and read-based analysis were comparable in obtaining usable information, but the former was much more time- and resource-consuming. Critically, different approaches introduced much stronger variations in microbial profiles than biological differences. However, community-level differences between the two sampling sites could be consistently observed despite the approaches being used. In choosing an appropriate approach, researchers shall balance the trade-offs between multiple factors, including the scientific question, the amount of usable information, computational resources and time cost. This study is expected to provide valuable technical insights and guidelines for the various approaches used for metagenomic data analysis.

Benchmarking genome assembly methods on metagenomic sequencing data.

Metagenome assembly is an efficient approach to reconstruct microbial genomes from metagenomic sequencing data. Although short-read sequencing has been widely used for metagenome assembly, linked- and long-read sequencing have shown their advancements in assembly by providing long-range DNA connectedness. Many metagenome assembly tools were developed to simplify the assembly graphs and resolve the repeats in microbial genomes. However, there remains no comprehensive evaluation of metagenomic sequencing technologies, and there is a lack of practical guidance on selecting the appropriate metagenome assembly tools. This paper presents a comprehensive benchmark of 19 commonly used assembly tools applied to metagenomic sequencing datasets obtained from simulation, mock communities or human gut microbiomes. These datasets were generated using mainstream sequencing platforms, such as Illumina and BGISEQ short-read sequencing, 10x Genomics linked-read sequencing, and PacBio and Oxford Nanopore long-read sequencing. The assembly tools were extensively evaluated against many criteria, which revealed that long-read assemblers generated high contig contiguity but failed to reveal some medium- and high-quality metagenome-assembled genomes (MAGs). Linked-read assemblers obtained the highest number of overall near-complete MAGs from the human gut microbiomes. Hybrid assemblers using both short- and long-read sequencing were promising methods to improve both total assembly length and the number of near-complete MAGs. This paper also discussed the running time and peak memory consumption of these assembly tools and provided practical guidance on selecting them.

Metagenomic next-generation sequencing of cerebrospinal fluid reveals etiological and microbiological features in patients with various central nervous system infections.

The application of metagenomic next-generation sequencing (mNGS) in pathogens detection of cerebrospinal fluid (CSF) is limited because clinical, microbiological, and biological information are not well connected. We analyzed the 428 enrolled patients' clinical features, pathogens diagnostic efficiency of mNGS in CSF, microbial community structure and composition in CSF, and correlation of microbial and clinical biomarkers in CSF. General characteristics were unspecific but helpful in formulating a differential diagnosis. CSF mNGS has a higher detection rate (34.6%) compared to traditional methods (5.4%). mNGS detection rate was higher when the time from onset to CSF collection was ≤20 days, the CSF leukocytes count was >200 × 106/L, the CSF protein concentration was >1.3 g/L, or CSF glucose concentration was ≤2.5 mmol/L in non-postoperative bacterial CNS infections (CNSi). CSF was not strictly a sterile environment, and the potential pathogens may contribute to the dysbiosis of CSF microbiome. Furthermore, clinical biomarkers were significantly relevant to CNS pathogens. Clinical data are helpful in choosing a proper opportunity to obtain an accurate result of mNGS, and can speculate whether the mNGS results are correct or not. Our study is a pioneering study exploring the CSF microbiome in different CNSIs.

Serious neurological adverse events in immunocompetent children and adolescents caused by viral reactivation in the years following varicella vaccination.

Serious adverse events following vaccination include medical complications that require hospitalisation. The live varicella vaccine that was approved by the Food and Drug Administration in the United States in 1995 has an excellent safety record. Since the vaccine is a live virus, adverse events are more common in immunocompromised children who are vaccinated inadvertently. This review includes only serious adverse events in children considered to be immunocompetent. The serious adverse event called varicella vaccine meningitis was first reported in a hospitalised immunocompetent child in 2008. When we carried out a literature search, we found 15 cases of immunocompetent children and adolescents with varicella vaccine meningitis; the median age was 11 years. Eight of the children had received two varicella vaccinations. Most of the children also had a concomitant herpes zoster rash, although three did not. The children lived in the United States, Greece, Germany, Switzerland, and Japan. During our literature search, we found five additional cases of serious neurological events in immunocompetent children; these included 4 cases of progressive herpes zoster and one case of acute retinitis. Pulses of enteral corticosteroids as well as a lack of herpes simplex virus antibody may be risk factors for reactivation in immunocompetent children. All 20 children with adverse events were treated with acyclovir and recovered; 19 were hospitalised and one child was managed as an outpatient. Even though the number of neurological adverse events remains exceedingly low following varicella vaccination, we recommend documentation of those caused by the vaccine virus.

Agnostic Sequencing for Detection of Viral Pathogens.

The advent of next-generation sequencing (NGS) technologies has expanded our ability to detect and analyze microbial genomes and has yielded novel molecular approaches for infectious disease diagnostics. While several targeted multiplex PCR and NGS-based assays have been widely used in public health settings in recent years, these targeted approaches are limited in that they still rely on a priori knowledge of a pathogen's genome, and an untargeted or unknown pathogen will not be detected. Recent public health crises have emphasized the need to prepare for a wide and rapid deployment of an agnostic diagnostic assay at the start of an outbreak to ensure an effective response to emerging viral pathogens. Metagenomic techniques can nonspecifically sequence all detectable nucleic acids in a sample and therefore do not rely on prior knowledge of a pathogen's genome. While this technology has been reviewed for bacterial diagnostics and adopted in research settings for the detection and characterization of viruses, viral metagenomics has yet to be widely deployed as a diagnostic tool in clinical laboratories. In this review, we highlight recent improvements to the performance of metagenomic viral sequencing, the current applications of metagenomic sequencing in clinical laboratories, as well as the challenges that impede the widespread adoption of this technology.

Clinical Impact of Plasma Metagenomic Next-Generation Sequencing on Infection Diagnosis and Antimicrobial Therapy in Immunocompromised Patients.

BACKGROUND: Clinical impact of plasma metagenomic next-generation sequencing (mNGS) on infection diagnosis and antimicrobial therapy in immunocompromised patients with suspected infection remains unclear. METHODS: Between March and December 2022, 424 cases with fever, infection history, mechanical ventilation, or imaging abnormalities underwent plasma mNGS testing at a single center. Eleven patients have received solid organ transplantation, and the remaining patients were categorised into febrile neutropenia (FN), non-neutropenia (NN), and non-haematologic disease (NTHD) groups based on immunosuppression severity. The diagnostic rate of infection and the utilisation of antimicrobial agents based on mNGS were assessed. RESULTS: The use of mNGS significantly improved the diagnostic rates for fungi in the FN (56.1%, P = 0.003) and NN (58.8%, P = 0.008) groups versus the NHD group (33.3%). Positive impacts associated with therapy were significantly greater than negative impacts across all three groups (all P < 0.001), and the utilisation of escalation therapy was significantly more frequent in the FN group than in the NN groups (P = 0.006). Over 70% of cases with negative mNGS results across the three groups underwent de-escalation therapy, with >1/3 being discontinued, preventing antimicrobial overuse. CONCLUSIONS: Plasma mNGS has a clinically confirmed positive impact in immunocompromised patients with neutropenia, improving the diagnosis of fungal infections and antimicrobial therapy.

Validation of an automated, end-to-end metagenomic sequencing assay for agnostic detection of respiratory viruses.

BACKGROUND: Current molecular diagnostics are limited in the number and type of detectable pathogens. Metagenomic next generation sequencing (mNGS) is an emerging, and increasingly feasible, pathogen-agnostic diagnostic approach. Translational barriers prohibit the widespread adoption of this technology in clinical laboratories. We validate an end-to-end mNGS assay for detection of respiratory viruses. Our assay is optimized to reduce turnaround time, lower cost-per-sample, increase throughput, and deploy secure and actionable bioinformatic results. METHODS: We validated our assay using residual nasopharyngeal swab specimens from Vancouver General Hospital (n = 359), RT-PCR-positive, or negative for Influenza, SARS-CoV-2, and RSV. We quantified sample stability, assay precision, the effect of background nucleic acid levels, and analytical limits of detection. Diagnostic performance metrics were estimated. RESULTS: We report that our mNGS assay is highly precise, semi-quantitative, with analytical limits of detection ranging from 103-104 copies/mL. Our assay is highly specific (100%) and sensitive (61.9% Overall: 86.8%; RT-PCR Ct < 30). Multiplexing capabilities enable processing of up to 55-specimens simultaneously on an Oxford Nanopore GridION device, with results reported within 12-hours. CONCLUSIONS: This study outlines the diagnostic performance and feasibility of mNGS for respiratory viral diagnostics, infection control, and public health surveillance. We addressed translational barriers to widespread mNGS adoption.

Detection of Mpox Virus Using Microbial Cell-Free DNA: The Potential of Pathogen-Agnostic Sequencing for Rapid Identification of Emerging Pathogens.

BACKGROUND: The 2022 global outbreak of Monkeypox virus (MPXV) highlighted challenges with polymerase chain reaction detection as divergent strains emerged and atypical presentations limited the applicability of swab sampling. Recommended testing in the United States requires a swab of lesions, which arise late in infection and may be unrecognized. We present MPXV detections using plasma microbial cell-free DNA (mcfDNA) sequencing. METHODS: Fifteen plasma samples from 12 case-patients were characterized through mcfDNA sequencing. Assay performance was confirmed through in silico inclusivity and exclusivity assessments. MPXV isolates were genotyped using mcfDNA, and phylodynamic information was imputed using publicly available sequences. RESULTS: MPXV mcfDNA was detected in 12 case-patients. Mpox was not suspected in 5, with 1 having documented resolution of mpox >6 months previously. Six had moderate to severe mpox, supported by high MPXV mcfDNA concentrations; 4 died. In 7 case-patients, mcfDNA sequencing detected coinfections. Genotyping by mcfDNA sequencing identified 22 MPXV mutations at 10 genomic loci in 9 case-patients. Consistent with variation observed in the 2022 outbreak, 21 of 22 variants were G > A/C > T. Phylogenetic analyses imputed isolates to sublineages arising at different time points and from different geographic locations. CONCLUSIONS: We demonstrate the potential of plasma mcfDNA sequencing to detect, quantify, and, for acute infections with high sequencing coverage, subtype MPXV using a single noninvasive test. Sequencing plasma mcfDNA may augment existing mpox testing in vulnerable patient populations or in patients with atypical symptoms or unrecognized mpox. Strain type information may supplement disease surveillance and facilitate tracking emerging pathogens.

Impact of Metagenomic Next-Generation Sequencing of Bronchoalveolar Lavage Fluid on Antimicrobial Stewardship in Patients With Lower Respiratory Tract Infections: A Retrospective Cohort Study.

BACKGROUND: The impact of metagenomic next-generation sequencing (mNGS) on antimicrobial stewardship in patients with lower respiratory tract infections (LRTIs) is still unknown. METHODS: This retrospective cohort study included patients who had LRTIs diagnosed and underwent bronchoalveolar lavage between September 2019 and December 2020. Patients who underwent both mNGS and conventional microbiologic tests were classified as the mNGS group, while those with conventional tests only were included as a control group. A 1:1 propensity score match for baseline variables was conducted, after which changes in antimicrobial stewardship between the 2 groups were assessed. RESULTS: A total of 681 patients who had an initial diagnosis of LRTIs and underwent bronchoalveolar lavage were evaluated; 306 patients were finally included, with 153 in each group. mNGS was associated with lower rates of antibiotic escalation than in the control group (adjusted odds ratio, 0.466 [95% confidence interval, .237-.919]; P = .02), but there was no association with antibiotic de-escalation. Compared with the control group, more patients discontinued the use of antivirals in the mNGS group. CONCLUSIONS: The use of mNGS was associated with lower rates of antibiotic escalation and may facilitate the cessation of antivirals, but not contribute to antibiotic de-escalation in patients with LRTIs.

Rapid turnover and short-term blooms of Escherichia coli in the human gut.

Escherichia coli (E. coli) is a common microorganism that is widely present in the environment and closely related to human health. The extent of E. coli presence in the human gut has been a subject of ongoing debate. Through whole-genome shotgun metagenomic sequencing, our study revealed that E. coli exists in the human body at a low abundance (average abundance 1.21%), with occasional short-term bursts leading to temporary increases in abundance, with the highest recorded at 50.91%. Further investigations into the factors contributing to these short-term blooms of E. coli showed significant variations in strain types and genomes within fecal samples collected from the same individuals at different time points. Evolutionary tree analysis indicated that samples from different individuals crossed, suggesting a change in the dominant E. coli strains within the human gut. Therefore, it can be inferred that E. coli in the human body are more likely to be transient bacteria rather than permanent residents in the gut. The rapid rate of turnover among months (87.5% within a month) and short-term blooms of E. coli in the human body can establish "latent infections" of nonpathogenic strains in healthy individuals while also posing a potential risk of introducing pathogenic strains, thereby impacting human health. In summary, our study revealed the variation in E. coli abundance and strains within the human gut, influenced by geographic area and temporal factors. These findings contribute to a better understanding of the relationship between E. coli, the gut microbiota, and human health. IMPORTANCE Escherichia coli (E. coli) is a microorganism closely linked to human health, and its presence in the human gut has been a topic of debate. Our study, using whole-genome shotgun metagenomic sequencing, revealed that E. coli exists at a low abundance in the human body, with occasional short-term bursts leading to temporary increases. Strain and genome variations were observed within fecal samples from the same individuals at different time points, suggesting transient rather than permanent residence of E. coli in the gut. The rapid turnover rate and short-term blooms of E. coli can establish latent infections while also posing a risk of introducing pathogenic strains. These findings enhance our understanding of the relationship between E. coli, the gut microbiota, and human health.

AFITbin: a metagenomic contig binning method using aggregate l-mer frequency based on initial and terminal nucleotides.

BACKGROUND: Using next-generation sequencing technologies, scientists can sequence complex microbial communities directly from the environment. Significant insights into the structure, diversity, and ecology of microbial communities have resulted from the study of metagenomics. The assembly of reads into longer contigs, which are then binned into groups of contigs that correspond to different species in the metagenomic sample, is a crucial step in the analysis of metagenomics. It is necessary to organize these contigs into operational taxonomic units (OTUs) for further taxonomic profiling and functional analysis. For binning, which is synonymous with the clustering of OTUs, the tetra-nucleotide frequency (TNF) is typically utilized as a compositional feature for each OTU. RESULTS: In this paper, we present AFIT, a new l-mer statistic vector for each contig, and AFITBin, a novel method for metagenomic binning based on AFIT and a matrix factorization method. To evaluate the performance of the AFIT vector, the t-SNE algorithm is used to compare species clustering based on AFIT and TNF information. In addition, the efficacy of AFITBin is demonstrated on both simulated and real datasets in comparison to state-of-the-art binning methods such as MetaBAT 2, MaxBin 2.0, CONCOT, MetaCon, SolidBin, BusyBee Web, and MetaBinner. To further analyze the performance of the purposed AFIT vector, we compare the barcodes of the AFIT vector and the TNF vector. CONCLUSION: The results demonstrate that AFITBin shows superior performance in taxonomic identification compared to existing methods, leveraging the AFIT vector for improved results in metagenomic binning. This approach holds promise for advancing the analysis of metagenomic data, providing more reliable insights into microbial community composition and function. AVAILABILITY: A python package is available at: https://github.com/SayehSobhani/AFITBin .