Time series community genomics analysis reveals rapid shifts in bacterial species, strains, and phage during infant gut colonization

Constructing phylogenetic trees for microbiome data analysis: A mini-review

As next-generation sequencing technologies advance rapidly and the cost of metagenomic sequencing continues to decrease, researchers now face an unprecedented volume of microbiome data. This surge has stimulated the development of scalable microbiome data analysis methods and necessitated the incorporation of phylogenetic information into microbiome analysis for improved accuracy. Tools for constructing phylogenetic trees from 16S rRNA sequencing data are well-established, as the highly conserved regions of the 16S gene are limited, simplifying the identification of marker genes. In contrast, metagenomic and whole genome shotgun (WGS) sequencing involve sequencing from random fragments of the entire gene, making identification of consistent marker genes challenging owing to the vast diversity of genomic regions, resulting in a scarcity of robust tools for constructing phylogenetic trees. Although bacterial sequence tree construction tools exist for upstream bioinformatics, many downstream researchers-those integrating these trees into statistical models or machine learning-are either unaware of these tools or find them difficult to use due to the steep learning curve of processing raw sequences. This is compounded by the fact that public datasets often lack phylogenetic trees, providing only abundance tables and taxonomic classifications. To address this, we present a comprehensive review of phylogenetic tree construction techniques for microbiome data (16S rRNA or whole-genome shotgun sequencing). We outline the strengths and limitations of current methods, offering expert insights and step-by-step guidance to make these tools more accessible and widely applicable in quantitative microbiome data analysis.

4CAC: 4-class classifier of metagenome contigs using machine learning and assembly graphs

Microbial communities usually harbor a mix of bacteria, archaea, plasmids, viruses and microeukaryotes. Within these communities, viruses, plasmids, and microeukaryotes coexist in relatively low abundance, yet they engage in intricate interactions with bacteria. Moreover, viruses and plasmids, as mobile genetic elements, play important roles in horizontal gene transfer and the development of antibiotic resistance within microbial populations. However, due to the difficulty of identifying viruses, plasmids, and microeukaryotes in microbial communities, our understanding of these minor classes lags behind that of bacteria and archaea. Recently, several classifiers have been developed to separate one or more minor classes from bacteria and archaea in metagenome assemblies. However, these classifiers often overlook the issue of class imbalance, leading to low precision in identifying the minor classes. Here, we developed a classifier called 4CAC that is able to identify viruses, plasmids, microeukaryotes, and prokaryotes simultaneously from metagenome assemblies. 4CAC generates an initial four-way classification using several sequence length-adjusted XGBoost models and further improves the classification using the assembly graph. Evaluation on simulated and real metagenome datasets demonstrates that 4CAC substantially outperforms existing classifiers and combinations thereof on short reads. On long reads, it also shows an advantage unless the abundance of the minor classes is very low. 4CAC runs 1-2 orders of magnitude faster than the other classifiers. The 4CAC software is available at https://github.com/Shamir-Lab/4CAC.

Analysis of novel bacterial metagenome-assembled genomes from lignin-degrading microbial consortia

Despite recent progress, bacterial degradation of lignin is not completely understood. To address the mechanisms that bacteria from unknown taxonomic groups use to perform lignin-monomer degradation, functional analysis of bacterial metagenome-assembled genomes from soil-derived consortia enriched for microorganisms capable of degrading lignin was performed. A total of 232 metagenome-assembled genomes were recovered. After applying quality criteria of at least 70 % genome completeness and contamination less than or equal to 10 %, 39 genomes were obtained. From these, a total of 14 genomes from bacteria of unknown classification at lower taxonomic levels (i.e., only classified to the order level or higher) were chosen for further functional analysis. A global analysis of the potential ecological functions of these bacteria was performed, followed by a detailed analysis of monolignol degradation pathways. The phylum with the highest number of genomes was Proteobacteria. The genomes presented functions consistent with soil-derived bacteria, like denitrification, with different metabolic capacities related to the sulfur, chlorine, arsenic and carbon cycles, in addition to the degradation of plant cell wall components like cellulose, hemicellulose, and lignin. The Sphingomonadales_OP 08 genome showed the greatest potential to degrade cellulose and hemicellulose, although it does not appear to be able to degrade lignin. The Actinobacteria_BY 70 genome presented the highest number of enzymes and pathways related to the degradation of monolignols; furthermore, it showed the greatest potential for aromatic ring breakage by different fission pathways. The genomes of the two Actinobacteria showed the caffeic acid pathway, an important phenolic compound presenting several biological properties, such as antimicrobial and antioxidant. To our knowledge, this is the first time this pathway has been reported in this class of bacteria.

Metaproteogenomics resolution of a high-CO2 aquifer community reveals a complex cellular adaptation of groundwater Gracilibacteria to a host-dependent lifestyle

BACKGROUND

Bacteria of the candidate phyla radiation (CPR), constituting about 25% of the bacterial biodiversity, are characterized by small cell size and patchy genomes without complete key metabolic pathways, suggesting a symbiotic lifestyle. Gracilibacteria (BD1-5), which are part of the CPR branch, possess alternate coded genomes and have not yet been cultivated. The lifestyle of Gracilibacteria, their temporal dynamics, and activity in natural ecosystems, particularly in groundwater, has remained largely unexplored. Here, we aimed to investigate Gracilibacteria activity in situ and to discern their lifestyle based on expressed genes, using the metaproteogenome of Gracilibacteria as a function of time in the cold-water geyser Wallender Born in the Volcanic Eifel region in Germany.

RESULTS

We coupled genome-resolved metagenomics and metaproteomics to investigate a cold-water geyser microbial community enriched in Gracilibacteria across a 12-day time-series. Groundwater was collected and sequentially filtered to fraction CPR and other bacteria. Based on 725 Gbps of metagenomic data, 1129 different ribosomal protein S3 marker genes, and 751 high-quality genomes (123 population genomes after dereplication), we identified dominant bacteria belonging to Gallionellales and Gracilibacteria along with keystone microbes, which were low in genomic abundance but substantially contributing to proteomic abundance. Seven high-quality Gracilibacteria genomes showed typical limitations, such as limited amino acid or nucleotide synthesis, in their central metabolism but no co-occurrence with potential hosts. The genomes of these Gracilibacteria were encoded for a high number of proteins involved in cell to cell interaction, supporting the previously surmised host-dependent lifestyle, e.g., type IV and type II secretion system subunits, transporters, and features related to cell motility, which were also detected on protein level.

CONCLUSIONS

We here identified microbial keystone taxa in a high-CO2 aquifer, and revealed microbial dynamics of Gracilibacteria. Although Gracilibacteria in this ecosystem did not appear to target specific organisms in this ecosystem due to lack of co-occurrence despite enrichment on 0.2-µm filter fraction, we provide proteomic evidence for the complex machinery behind the host-dependent lifestyle of groundwater Gracilibacteria. Video Abstract.

A Proteogenomic Approach to Unveiling the Complex Biology of the Microbiome

The complex biology of the microbiome was elucidated once the genomics era began. The proteogenomic approach analyzes and integrates genetic makeup (genomics) and microbial communities' expressed proteins (proteomics). Therefore, researchers gained insights into gene expression, protein functions, and metabolic pathways, understanding microbial dynamics and behavior, interactions with host cells, and responses to environmental stimuli. In this context, our work aims to bring together data regarding the application of genomics, proteomics, and bioinformatics in microbiome research and to provide new perspectives for applying microbiota modulation in clinical practice with maximum efficiency. This review also synthesizes data from the literature, shedding light on the potential biomarkers and therapeutic targets for various diseases influenced by the microbiome.

PUPpy: a primer design pipeline for substrain-level microbial detection and absolute quantification

Characterizing microbial communities at high resolution and with absolute quantification is crucial to unravel the complexity and diversity of microbial ecosystems. This can be achieved with PCR assays, which enable highly selective detection and absolute quantification of microbial DNA. However, a major challenge that has hindered PCR applications in microbiome research is the design of highly specific primer sets that exclusively amplify intended targets. Here, we introduce Phylogenetically Unique Primers in python (PUPpy), a fully automated pipeline to design microbe- and group-specific primers within a given microbial community. PUPpy can be executed from a user-friendly graphical user interface, or two simple terminal commands, and it only requires coding sequence files of the community members as input. PUPpy-designed primers enable the detection of individual microbes and quantification of absolute microbial abundance in defined communities below the strain level. We experimentally evaluated the performance of PUPpy-designed primers using two bacterial communities as benchmarks. Each community comprises 10 members, exhibiting a range of genetic similarities that spanned from different phyla to substrains. PUPpy-designed primers also enable the detection of groups of bacteria in an undefined community, such as the detection of a gut bacterial family in a complex stool microbiota sample. Taxon-specific primers designed with PUPpy showed 100% specificity to their intended targets, without unintended amplification, in each community tested. Lastly, we show the absolute quantification of microbial abundance using PUPpy-designed primers in droplet digital PCR, benchmarked against 16S rRNA and shotgun sequencing. Our data shows that PUPpy-designed microbe-specific primers can be used to quantify substrain-level absolute counts, providing more resolved and accurate quantification in defined communities than short-read 16S rRNA and shotgun sequencing.

IMPORTANCE

Profiling microbial communities at high resolution and with absolute quantification is essential to uncover hidden ecological interactions within microbial ecosystems. Nevertheless, achieving resolved and quantitative investigations has been elusive due to methodological limitations in distinguishing and quantifying highly related microbes. Here, we describe Phylogenetically Unique Primers in python (PUPpy), an automated computational pipeline to design taxon-specific primers within defined microbial communities. Taxon-specific primers can be used to selectively detect and quantify individual microbes and larger taxa within a microbial community. PUPpy achieves substrain-level specificity without the need for computationally intensive databases and prioritizes user-friendliness by enabling both terminal and graphical user interface applications. Altogether, PUPpy enables fast, inexpensive, and highly accurate perspectives into microbial ecosystems, supporting the characterization of bacterial communities in both in vitro and complex microbiota settings.

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 .

Dietary supplementation with Lacticaseibacillus rhamnosus IDCC3201 alleviates sarcopenia by modulating the gut microbiota and metabolites in dexamethasone-induced models

Probiotics can exert direct or indirect influences on various aspects of health claims by altering the composition of the gut microbiome and producing bioactive metabolites. The aim of this study was to examine the effect of Lacticaseibacillus rhamnosus IDCC3201 on skeletal muscle atrophy in dexamethasone-induced C2C12 cells and a mouse animal model. Dexamethasone treatment significantly reduced C2C12 muscle cell viability, myotube diameter, and levels of muscle atrophic markers (Atrogin-1 and MuRF-1). These effects were alleviated by conditioned media (CM) and cell extract (EX) derived from L. rhamnosus IDCC3201. In addition, we assessed the in vivo therapeutic effect of L. rhamnosus IDCC3201 in a mouse model of dexamethasone (DEX)-induced muscle atrophy. Supplementation with IDCC3201 resulted in significant enhancements in body composition, particularly in lean mass, muscle strength, and myofibril size, in DEX-induced muscle atrophy mice. In comparison to the DEX-treatment group, the normal and DEX + L. rhamnosus IDCC3201 groups showed a higher transcriptional level of myosin heavy chain family genes (MHC1, MHC1b, MHC2A, 2bB, and 2X) and a reduction in atrophic muscle makers. These analyses revealed that L. rhamnosus IDCC3201 supplementation led to increased production of branched-chain amino acids (BCAAs) and improved the Allobaculum genus within the gut microbiota of muscle atrophy-induced groups. Taken together, our findings suggest that L. rhamnosus IDCC3201 represents a promising dietary supplement with the potential to alleviate sarcopenia by modulating the gut microbiome and metabolites.

MuDoGeR: Multi-Domain Genome recovery from metagenomes made easy

Several computational frameworks and workflows that recover genomes from prokaryotes, eukaryotes and viruses from metagenomes exist. Yet, it is difficult for scientists with little bioinformatics experience to evaluate quality, annotate genes, dereplicate, assign taxonomy and calculate relative abundance and coverage of genomes belonging to different domains. MuDoGeR is a user-friendly tool tailored for those familiar with Unix command-line environment that makes it easy to recover genomes of prokaryotes, eukaryotes and viruses from metagenomes, either alone or in combination. We tested MuDoGeR using 24 individual-isolated genomes and 574 metagenomes, demonstrating the applicability for a few samples and high throughput. While MuDoGeR can recover eukaryotic viral sequences, its characterization is predominantly skewed towards bacterial and archaeal viruses, reflecting the field's current state. However, acting as a dynamic wrapper, the MuDoGeR is designed to constantly incorporate updates and integrate new tools, ensuring its ongoing relevance in the rapidly evolving field. MuDoGeR is open-source software available at https://github.com/mdsufz/MuDoGeR. Additionally, MuDoGeR is also available as a Singularity container.

VirGrapher: a graph-based viral identifier for long sequences from metagenomes

Viruses are the most abundant biological entities on earth and are important components of microbial communities. A metagenome contains all microorganisms from an environmental sample. Correctly identifying viruses from these mixed sequences is critical in viral analyses. It is common to identify long viral sequences, which has already been passed thought pipelines of assembly and binning. Existing deep learning-based methods divide these long sequences into short subsequences and identify them separately. This makes the relationships between them be omitted, leading to poor performance on identifying long viral sequences. In this paper, VirGrapher is proposed to improve the identification performance of long viral sequences by constructing relationships among short subsequences from long ones. VirGrapher see a long sequence as a graph and uses a Graph Convolutional Network (GCN) model to learn multilayer connections between nodes from sequences after a GCN-based node embedding model. VirGrapher achieves a better AUC value and accuracy on validation set, which is better than three benchmark methods.

Infant gut DNA bacteriophage strain persistence during the first 3 years of life

Bacteriophages are key components of gut microbiomes, yet the phage colonization process in the infant gut remains uncertain. Here, we establish a large phage sequence database and use strain-resolved analyses to investigate DNA phage succession in infants throughout the first 3 years of life. Analysis of 819 fecal metagenomes collected from 28 full-term and 24 preterm infants and their mothers revealed that early-life phageome richness increases over time and reaches adult-like complexity by age 3. Approximately 9% of early phage colonizers, which are mostly maternally transmitted and infect Bacteroides, persist for 3 years and are more prevalent in full-term than in preterm infants. Although rare, phages with stop codon reassignment are more likely to persist than non-recoded phages and generally display an increase in in-frame reassigned stop codons over 3 years. Overall, maternal seeding, stop codon reassignment, host CRISPR-Cas locus prevalence, and diverse phage populations contribute to stable viral colonization.

High niche specificity and host genetic diversity of groundwater viruses

Viruses are key members of microbial communities that exert control over host abundance and metabolism, thereby influencing ecosystem processes and biogeochemical cycles. Aquifers are known to host taxonomically diverse microbial life, yet little is known about viruses infecting groundwater microbial communities. Here, we analysed 16 metagenomes from a broad range of groundwater physicochemistries. We recovered 1571 viral genomes that clustered into 468 high-quality viral operational taxonomic units. At least 15% were observed to be transcriptionally active, although lysis was likely constrained by the resource-limited groundwater environment. Most were unclassified (95%), and the remaining 5% were Caudoviricetes. Comparisons with viruses inhabiting other aquifers revealed no shared species, indicating substantial unexplored viral diversity. In silico predictions linked 22.4% of the viruses to microbial host populations, including to ultra-small prokaryotes, such as Patescibacteria and Nanoarchaeota. Many predicted hosts were associated with the biogeochemical cycling of carbon, nitrogen, and sulfur. Metabolic predictions revealed the presence of 205 putative auxiliary metabolic genes, involved in diverse processes associated with the utilization of the host's intracellular resources for biosynthesis and transformation reactions, including those involved in nucleotide sugar, glycan, cofactor, and vitamin metabolism. Viruses, prokaryotes overall, and predicted prokaryotic hosts exhibited narrow spatial distributions, and relative abundance correlations with the same groundwater parameters (e.g. dissolved oxygen, nitrate, and iron), consistent with host control over viral distributions. Results provide insights into underexplored groundwater viruses, and indicate the large extent to which viruses may manipulate microbial communities and biogeochemistry in the terrestrial subsurface.

The Diversity of Bacteriophages in the Human Gut

Bacteriophages, commonly referred to as phages, are viruses that infect bacteria and are among the most numerous microorganisms on the planet. They occur throughout nature occupying every habitat where their bacterial hosts can be found. Within these communities, phages are responsible for shaping the bacterial community structure and function through their interactions. Phages shape the community structure and function within the human gut but are also able to influence the human host. As such, there is increased interest in understanding the composition and activity of the gastrointestinal phages, although these studies have been hindered by the difficulties accompanying the study of the human gut. Here, we summarize the methods and findings pertaining to the diversity of the human gastrointestinal phages.

Dysbiosis of lower respiratory tract microbiome are associated with proinflammatory states in non-small cell lung cancer patients

BACKGROUND

The lung has a sophisticated microbiome, and respiratory illnesses are greatly influenced by the lung microbiota. Despite the fact that numerous studies have shown that lung cancer patients have a dysbiosis as compared to healthy people, more research is needed to explore the association between the microbiota dysbiosis and immune profile within the tumor microenvironment (TME).

METHODS

In this study, we performed metagenomic sequencing of tumor and normal tissues from 61 non-small cell lung cancer (NSCLC) patients and six patients with other lung diseases. In order to characterize the impact of the microbes in TME, the cytokine concentrations of 24 lung tumor and normal tissues were detected using a multiple cytokine panel.

RESULTS

Our results showed that tumors had lower microbiota diversity than the paired normal tissues, and the microbiota of NSCLC was enriched in Proteobacteria, Firmicutes, and Actinobacteria. In addition, proinflammatory cytokines such as IL-8, MIF, TNF- α, and so on, were significantly upregulated in tumor tissues.

CONCLUSION

We discovered a subset of bacteria linked to host inflammatory signaling pathways and, more precisely, to particular immune cells. We determined that lower airway microbiome dysbiosis may be linked to the disruption of the equilibrium of the immune system causing lung inflammation. The spread of lung cancer may be linked to specific bacteria.

Role of bacteriophages in shaping gut microbial community

Phages are the most diversified and dominant members of the gut virobiota. They play a crucial role in shaping the structure and function of the gut microbial community and consequently the health of humans and animals. Phages are found mainly in the mucus, from where they can translocate to the intestinal organs and act as a modulator of gut microbiota. Understanding the vital role of phages in regulating the composition of intestinal microbiota and influencing human and animal health is an emerging area of research. The relevance of phages in the gut ecosystem is supported by substantial evidence, but the importance of phages in shaping the gut microbiota remains unclear. Although information regarding general phage ecology and development has accumulated, detailed knowledge on phage-gut microbe and phage-human interactions is lacking, and the information on the effects of phage therapy in humans remains ambiguous. In this review, we systematically assess the existing data on the structure and ecology of phages in the human and animal gut environments, their development, possible interaction, and subsequent impact on the gut ecosystem dynamics. We discuss the potential mechanisms of prophage activation and the subsequent modulation of gut bacteria. We also review the link between phages and the immune system to collect evidence on the effect of phages on shaping the gut microbial composition. Our review will improve understanding on the influence of phages in regulating the gut microbiota and the immune system and facilitate the development of phage-based therapies for maintaining a healthy and balanced gut microbiota.

MRGM: an enhanced catalog of mouse gut microbial genomes substantially broadening taxonomic and functional landscapes

Mouse gut microbiome research is pivotal for understanding the human gut microbiome, providing insights into disease modeling, host-microbe interactions, and the dietary influence on the gut microbiome. To enhance the translational value of mouse gut microbiome studies, we need detailed and high-quality catalogs of mouse gut microbial genomes. We introduce the Mouse Reference Gut Microbiome (MRGM), a comprehensive catalog with 42,245 non-redundant mouse gut bacterial genomes across 1,524 species. MRGM marks a 40% increase in the known taxonomic diversity of mouse gut microbes, capturing previously underrepresented lineages through refined genome quality assessment techniques. MRGM not only broadens the taxonomic landscape but also enriches the functional landscape of the mouse gut microbiome. Using deep learning, we have elevated the Gene Ontology annotation rate for mouse gut microbial proteins from 3.2% with orthology to 60%, marking an over 18-fold increase. MRGM supports both DNA- and marker-based taxonomic profiling by providing custom databases, surpassing previous catalogs in performance. Finally, taxonomic and functional comparisons between human and mouse gut microbiota reveal diet-driven divergences in their taxonomic composition and functional enrichment. Overall, our study highlights the value of high-quality microbial genome catalogs in advancing our understanding of the co-evolution between gut microbes and their host.

Limitation of amino acid availability by bacterial populations during enhanced colitis in IBD mouse model

IMPORTANCE

Inflammatory bowel disease is associated with an increase in Enterobacteriaceae and Enterococcus species; however, the specific mechanisms are unclear. Previous research has reported the associations between microbiota and inflammation, here we investigate potential pathways that specific bacteria populations use to drive gut inflammation. Richie et al. show that these bacterial populations utilize an alternate sulfur metabolism and are tolerant of host-derived immune-response products. These metabolic pathways drive host gut inflammation and fuel over colonization of these pathobionts in the dysbiotic colon. Cultured isolates from dysbiotic mice indicated faster growth supplemented with L-cysteine, showing these microbes can utilize essential host nutrients.

Orange juice containing Pediococcus acidilactici CE51 modulates the intestinal microbiota and reduces induced inflammation in a murine model of colitis

The management of inflammatory bowel diseases has been widely investigated, especially ulcerative colitis. Thus, studies with the application of new probiotic products are needed in the prevention/treatment of these clinical conditions. The objective of this work was to evaluate the effects of probiotic orange juice containing Pediococcus acidilactici CE51 in a murine model of colitis. 45 male Swiss lineage mice were used, divided into five groups (n = 9): control, colitis, colitis + probiotic (probiotic orange juice containing CE51), colitis + placebo (orange juice) and colitis + sulfasalazine (10 mg/kg/Weight). The induction of colitis was performed with dextran sodium sulfate (3%). The treatment time was 5 and 15 days after induction. Histopathological analysis, serum measurements of TNF-α and C-reactive protein and metagenomic analysis of feces were performed after euthanasia. Probiotic treatment reduced inflammation in the small intestine, large intestine and spleen. The probiotic did not alter the serum dosages of TNF-α and C-reactive protein. Their use maintained the quantitative ratio of the phylum Firmicutes/Bacteroidetes and increased Lactobacillus helveticus with 15 days of treatment (p < 0.05). The probiotic orange juice containing P. acidilactici CE51 positively modulated the gut microbiota composition and attenuated the inflammation induced in colitis.

Composition and functional profiles of gut microbiota reflect the treatment stage, severity, and etiology of acute pancreatitis

Acute pancreatitis (AP) is a type of digestive system disease with high mortality. Previous studies have shown that gut microbiota can participate in developing and treating acute pancreatitis by affecting the host's metabolism. In this study, we followed 20 AP patients to generate longitudinal gut microbiota profiles and activity during disease (before treatment, on the third day of treatment, and 1 month after discharge). We analyzed species composition and metabolic pathways' changes across the treatment phase, severity, and etiology. The diversity of the gut microbiome of patients with AP did not show much variation with treatment. In contrast, the metabolic functions of the gut microbiota, such as the essential chemical reactions that produce energy and maintain life, were partially reinstated after treatment. The severe AP (SAP) patients contained less beneficial bacteria (i.e., Bacteroides xylanisolvens, Clostridium lavalense, and Roseburia inulinivorans) and weaker sugar degradation function than mild AP patients before treatment. Moreover, etiology was one of the drivers of gut microbiome composition and explained the 3.54% variation in species' relative abundance. The relative abundance of pathways related to lipid synthesis was higher in the gut of hyperlipidemia AP patients than in biliary AP patients. The composition and functional profiles of the gut microbiota reflect the severity and etiology of AP. Otherwise, we also identified bacterial species associated with SAP, i.e., Oscillibacter sp. 57_20, Parabacteroides johnsonii, Bacteroides stercoris, Methanobrevibacter smithii, Ruminococcus lactaris, Coprococcus comes, and Dorea formicigenerans, which have the potential to identify the SAP at an early stage. IMPORTANCE Acute pancreatitis (AP) is a type of digestive system disease with high mortality. Previous studies have shown that gut microbiota can participate in the development and treatment of acute pancreatitis by affecting the host's metabolism. However, fewer studies acquired metagenomic sequencing data to associate species to functions intuitively and performed longitudinal analysis to explore how gut microbiota influences the development of AP. We followed 20 AP patients to generate longitudinal gut microbiota profiles and activity during disease and studied the differences in intestinal flora under different severities and etiologies. We have two findings. First, the gut microbiota profile has the potential to identify the severity and etiology of AP at an early stage. Second, gut microbiota likely acts synergistically in the development of AP. This study provides a reference for characterizing the driver flora of severe AP to identify the severity of acute pancreatitis at an early stage.

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.

DETIRE: a hybrid deep learning model for identifying viral sequences from metagenomes

A metagenome contains all DNA sequences from an environmental sample, including viruses, bacteria, archaea, and eukaryotes. Since viruses are of huge abundance and have caused vast mortality and morbidity to human society in history as a type of major pathogens, detecting viruses from metagenomes plays a crucial role in analyzing the viral component of samples and is the very first step for clinical diagnosis. However, detecting viral fragments directly from the metagenomes is still a tough issue because of the existence of a huge number of short sequences. In this study a hybrid Deep lEarning model for idenTifying vIral sequences fRom mEtagenomes (DETIRE) is proposed to solve the problem. First, the graph-based nucleotide sequence embedding strategy is utilized to enrich the expression of DNA sequences by training an embedding matrix. Then, the spatial and sequential features are extracted by trained CNN and BiLSTM networks, respectively, to enrich the features of short sequences. Finally, the two sets of features are weighted combined for the final decision. Trained by 220,000 sequences of 500 bp subsampled from the Virus and Host RefSeq genomes, DETIRE identifies more short viral sequences (<1,000 bp) than the three latest methods, such as DeepVirFinder, PPR-Meta, and CHEER. DETIRE is freely available at Github (https://github.com/crazyinter/DETIRE).

Metabolic independence drives gut microbial colonization and resilience in health and disease

BACKGROUND

Changes in microbial community composition as a function of human health and disease states have sparked remarkable interest in the human gut microbiome. However, establishing reproducible insights into the determinants of microbial succession in disease has been a formidable challenge.

RESULTS

Here we use fecal microbiota transplantation (FMT) as an in natura experimental model to investigate the association between metabolic independence and resilience in stressed gut environments. Our genome-resolved metagenomics survey suggests that FMT serves as an environmental filter that favors populations with higher metabolic independence, the genomes of which encode complete metabolic modules to synthesize critical metabolites, including amino acids, nucleotides, and vitamins. Interestingly, we observe higher completion of the same biosynthetic pathways in microbes enriched in IBD patients.

CONCLUSIONS

These observations suggest a general mechanism that underlies changes in diversity in perturbed gut environments and reveal taxon-independent markers of "dysbiosis" that may explain why widespread yet typically low-abundance members of healthy gut microbiomes can dominate under inflammatory conditions without any causal association with disease.

Virsearcher: Identifying Bacteriophages from Metagenomes by Combining Convolutional Neural Network and Gene Information

Metagenome sequencing provides an unprecedented opportunity for the discovery of unknown microbes and viruses. A large number of phages and prokaryotes are mixed together in metagenomes. To study the influence of phages on human bodies and environments, it is of great significance to isolate phages from metagenomes. However, it is difficult to identify novel phages because of the diversity of their sequences and the frequent presence of short contigs in metagenomes. Here, virSearcher is developed to identify phages from metagenomes by combining the convolutional neural network (CNN) and the gene information of input sequences. Firstly, an input sequence is encoded in accordance with the different functions of its coding and the non-coding regions and then is converted into word embedding code through a word embedding layer before a convolutional layer. Meanwhile, the hit ratio of the virus genes is combined with the output of the CNN to further improve the performance of the network. The genes used by virSearcher consist of complete and incomplete genes. Experiments on several metagenomes have showed that, compared with others, virSearcher can significantly improve the performance for the identification of short sequences, while maintaining the performance for long ones. The source code of virSearcher is freely available from http://github.com/DrJackson18/virSearcher.

Genome-Centric Dynamics Shape the Diversity of Oral Bacterial Populations

Two major viewpoints have been put forward for how microbial populations change, differing in whether adaptation is driven principally by gene-centric or genome-centric processes. Longitudinal sampling at microbially relevant timescales, i.e., days to weeks, is critical for distinguishing these mechanisms. Because of its significance for both microbial ecology and human health and its accessibility and high level of curation, we used the oral microbiota to study bacterial intrapopulation genome dynamics. Metagenomes were generated by shotgun sequencing of total community DNA from the healthy tongues of 17 volunteers at four to seven time points obtained over intervals of days to weeks. We obtained 390 high-quality metagenome-assembled genomes (MAGs) defining population genomes from 55 genera. The vast majority of genes in each MAG were tightly linked over the 2-week sampling window, indicating that the majority of the population's genomes were temporally stable at the MAG level. MAG-defined populations were composed of up to 5 strains, as determined by single-nucleotide-variant frequencies. Although most were stable over time, individual strains carrying over 100 distinct genes that rose from low abundance to dominance in a population over a period of days were detected. These results indicate a genome-wide as opposed to a gene-level process of population change. We infer that genome-wide selection of ecotypes is the dominant mode of adaptation in the oral populations over short timescales. IMPORTANCE The oral microbiome represents a microbial community of critical relevance to human health. Recent studies have documented the diversity and dynamics of different bacteria to reveal a rich, stable ecosystem characterized by strain-level dynamics. However, bacterial populations and their genomes are neither monolithic nor static; their genomes are constantly evolving to lose, gain, or alter their functional potential. To better understand how microbial genomes change in complex communities, we used culture-independent approaches to reconstruct the genomes (MAGs) for bacterial populations that approximated different species, in 17 healthy donors' mouths over a 2-week window. Our results underscored the importance of strain-level dynamics, which agrees with and expands on the conclusions of previous research. Altogether, these observations reveal patterns of genomic dynamics among strains of oral bacteria occurring over a matter of days.

Binning Metagenomic Contigs Using Unsupervised Clustering and Reference Databases

Metagenomics can directly extract the genetic material of all microorganisms from the environment, and obtain metagenomic samples with a large number of unknown DNA sequences. Binning of metagenomic contigs is a hot topic in metagenomics research. There are two key challenges for the current unsupervised metagenomic clustering algorithms. First, unsupervised metagenomic clustering methods rarely use reference databases, causing a certain waste of resources. Second, unsupervised metagenomic clustering methods are restricted by the characteristics of the sequences and the clustering algorithms, and the binning effect is limited. Therefore, a new binning method for metagenomic contigs using unsupervised clustering methods and reference databases is proposed to address these challenges, to make full use of the advantages of unsupervised clustering methods and reference databases constructed by scientists to improve the overall binning effect. This method uses the integrated SVM classification model to further bin the unsupervised clustering parts that do not perform well. Our proposed method was tested on simulated datasets and a real dataset and compared with other state-of-the-art metagenomic clustering methods including CONCOCT, Metabin2.0, Autometa, and MetaBAT. The results show that our method can achieve higher precision rate and improve the binning effect.

A Deep Clustering-based Novel Approach for Binning of Metagenomics Data

Background

One major challenge in binning Metagenomics data is the limited availability of reference datasets, as only 1% of the total microbial population is yet cultured. This has given rise to the efficacy of unsupervised methods for binning in the absence of any reference datasets.

Objective

To develop a deep clustering-based binning approach for Metagenomics data and to evaluate results with suitable measures.

Methods

In this study, a deep learning-based approach has been taken for binning the Metagenomics data. The results are validated on different datasets by considering features such as Tetra-nucleotide frequency (TNF), Hexa-nucleotide frequency (HNF) and GC-Content. Convolutional Autoencoder is used for feature extraction and for binning; the K-means clustering method is used.

Results

In most cases, it has been found that evaluation parameters such as the Silhouette index and Rand index are more than 0.5 and 0.8, respectively, which indicates that the proposed approach is giving satisfactory results. The performance of the developed approach is compared with current methods and tools using benchmarked low complexity simulated and real metagenomic datasets. It is found better for unsupervised and at par with semi-supervised methods.

Conclusion

An unsupervised advanced learning-based approach for binning has been proposed, and the developed method shows promising results for various datasets. This is a novel approach for solving the lack of reference data problem of binning in metagenomics.

The synergistic interaction of systemic inflammation, dysbiosis and antimicrobial resistance promotes growth restriction in children with acute severe malnutrition: An emphasis on Escherichia coli

A healthy development is denied to millions of children worldwide as harsh life conditions manifest themselves in an altered inflammation-prone microbiome crosstalk environment. Keynote of this tragedy is that insufficient nutritious amino acid blocks lipids-intake to sustain diverse microbiota, and promotes the generalist strategy followed by Escherichia coli -besides other proteobacteria- of shifting gut metabolism, subverting the site specificity of first immune reaction. Furthermore, it could be hypothesized that selective success lies in their ability to induce inflammation, since this phenomenon also fuels horizontal gene transfer (HGT). In this review, we dilucidate how immune mechanisms of environmental enteric dysfunction affect overgrowth restriction, infectious morbidity rate, and acquired lifelong risks among severe acute malnourished children. Also, despite acknowledging complexities of antimicrobial resistant enrichment, we explore and speculate over the links between virulence regulation and HGT as an indissociable part in the quest for new inflammatory niches by open genome bacteria, particularly when both collide in the most vulnerable.

Maternal effects on early-life gut microbiota maturation in a wild nonhuman primate

Early-life microbial colonization is an important process shaping host physiology,^1-3 immunity,^4-6 and long-term health outcomes^7-10 in humans. However, our understanding of this dynamic process remains poorly investigated in wild animals,^11-13 where developmental mechanisms can be better understood within ecological and evolutionarily relevant contexts.^11,12 Using one of the largest developmental datasets on a wild primate-the gelada (Theropithecus gelada)-we used 16S rRNA amplicon sequencing to characterize gut microbiota maturation during the first 3 years of life and assessed the role of maternal effects in shaping offspring microbiota assembly. In contrast to recent data on chimpanzees, postnatal microbial colonization in geladas was highly similar to humans:¹⁴ microbial alpha diversity increased rapidly following birth, followed by gradual changes in composition until weaning. Dietary changes associated with weaning (from milk- to plant-based diet) were the main drivers of shifts in taxonomic composition and microbial predicted functional pathways. Maternal effects were also an important factor influencing the offspring gut microbiota. During nursing (<12 months), offspring of experienced (multi-time) mothers exhibited faster functional microbial maturation, likely reflecting the general faster developmental pace of infants born to these mothers. Following weaning (>18 months), the composition of the juvenile microbiota tended to be more similar to the maternal microbiota than to the microbiota of other adult females, highlighting that maternal effects may persist even after nursing cessation.^15,16 Together, our findings highlight the dynamic nature of early-life gut colonization and the role of maternal effects in shaping this trajectory in a wild primate.

BinSPreader: Refine binning results for fuller MAG reconstruction

Despite the recent advances in high-throughput sequencing, metagenome analysis of microbial populations still remains a challenge. In particular, the metagenome-assembled genomes (MAGs) are often fragmented due to interspecies repeats, uneven coverage, and varying strain abundance. MAGs are constructed via a binning process that uses features of input data in order to cluster long contigs presumably belonging to the same species. In this work, we present BinSPreader-a binning refiner tool that exploits the assembly graph topology and other connectivity information to refine binning, correct binning errors, and propagate binning to shorter contigs. We show that BinSPreader could increase the completeness of the bins without sacrificing the purity and could predict contigs belonging to several MAGs. BinSPreader is effective in binning shorter contigs that often contain important conservative sequences that might be of great interest to researchers.

Metagenomic binning with assembly graph embeddings

MOTIVATION

Despite recent advancements in sequencing technologies and assembly methods, obtaining high-quality microbial genomes from metagenomic samples is still not a trivial task. Current metagenomic binners do not take full advantage of assembly graphs and are not optimized for long-read assemblies. Deep graph learning algorithms have been proposed in other fields to deal with complex graph data structures. The graph structure generated during the assembly process could be integrated with contig features to obtain better bins with deep learning.

RESULTS

We propose GraphMB, which uses graph neural networks to incorporate the assembly graph into the binning process. We test GraphMB on long-read datasets of different complexities, and compare the performance with other binners in terms of the number of High Quality (HQ) genome bins obtained. With our approach, we were able to obtain unique bins on all real datasets, and obtain more bins on most datasets. In particular, we obtained on average 17.5% more HQ bins when compared with state-of-the-art binners and 13.7% when aggregating the results of our binner with the others. These results indicate that a deep learning model can integrate contig-specific and graph-structure information to improve metagenomic binning.

AVAILABILITY AND IMPLEMENTATION

GraphMB is available from https://github.com/MicrobialDarkMatter/GraphMB.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Cronos: A Machine Learning Pipeline for Description and Predictive Modeling of Microbial Communities Over Time

Microbial time-series analysis, typically, examines the abundances of individual taxa over time and attempts to assign etiology to observed patterns. This approach assumes homogeneous groups in terms of profiles and response to external effectors. These assumptions are not always fulfilled, especially in complex natural systems, like the microbiome of the human gut. It is actually established that humans with otherwise the same demographic or dietary backgrounds can have distinct microbial profiles. We suggest an alternative approach to the analysis of microbial time-series, based on the following premises: 1) microbial communities are organized in distinct clusters of similar composition at any time point, 2) these intrinsic subsets of communities could have different responses to the same external effects, and 3) the fate of the communities is largely deterministic given the same external conditions. Therefore, tracking the transition of communities, rather than individual taxa, across these states, can enhance our understanding of the ecological processes and allow the prediction of future states, by incorporating applied effects. We implement these ideas into Cronos, an analytical pipeline written in R. Cronos’ inputs are a microbial composition table (e.g., OTU table), their phylogenetic relations as a tree, and the associated metadata. Cronos detects the intrinsic microbial profile clusters on all time points, describes them in terms of composition, and records the transitions between them. Cluster assignments, combined with the provided metadata, are used to model the transitions and predict samples’ fate under various effects. We applied Cronos to available data from growing infants’ gut microbiomes, and we observe two distinct trajectories corresponding to breastfed and formula-fed infants that eventually converge to profiles resembling those of mature individuals. Cronos is freely available at https://github.com/Lagkouvardos/Cronos.

Oxford Nanopore R10.4 long-read sequencing enables the generation of near-finished bacterial genomes from pure cultures and metagenomes without short-read or reference polishing

Long-read Oxford Nanopore sequencing has democratized microbial genome sequencing and enables the recovery of highly contiguous microbial genomes from isolates or metagenomes. However, to obtain near-finished genomes it has been necessary to include short-read polishing to correct insertions and deletions derived from homopolymer regions. Here, we show that Oxford Nanopore R10.4 can be used to generate near-finished microbial genomes from isolates or metagenomes without short-read or reference polishing.

The role of virome in the gastrointestinal tract and beyond

The human gut virome is comprised of diverse commensal and pathogenic viruses. The colonization by these viruses begins right after birth through vaginal delivery, then continues through breastfeeding, and broader environmental exposure. Their constant interaction with their bacterial hosts in the body shapes not only our microbiomes but us. In addition, these viruses interact with the immune cells, trigger a broad range of immune responses, and influence different metabolic pathways. Besides its key role in regulating the human gut homeostasis, the intestinal virome contributes to disease development in distant organs, both directly and indirectly. In this review, we will describe the changes in the gut virome through life, health, and disease, followed by discussing the interactions between the virome, the microbiome, and the human host as well as providing an overview of their contribution to gut disease and disease of distant organs.

MetaConClust - Unsupervised Binning of Metagenomics Data using Consensus Clustering

Background: Binning of metagenomic reads is an active area of research, and many unsupervised machine learning-based techniques have been used for taxonomic independent binning of metagenomic reads. Objective: It is important to find the optimum number of the cluster as well as develop an efficient pipeline for deciphering the complexity of the microbial genome. Methods: Applying unsupervised clustering techniques for binning requires finding the optimal number of clusters beforehand and is observed to be a difficult task. This paper describes a novel method, MetaConClust, using coverage information for grouping of contigs and automatically finding the optimal number of clusters for binning of metagenomics data using a consensus-based clustering approach. The coverage of contigs in a metagenomics sample has been observed to be directly proportional to the abundance of species in the sample and is used for grouping of data in the first phase by MetaConClust. The Partitioning Around Medoid (PAM) method is used for clustering in the second phase for generating bins with the initial number of clusters determined automatically through a consensus-based method. Results: Finally, the quality of the obtained bins is tested using silhouette index, rand Index, recall, precision, and accuracy. Performance of MetaConClust is compared with recent methods and tools using benchmarked low complexity simulated and real metagenomic datasets and is found better for unsupervised and comparable for hybrid methods. Conclusion: This is suggestive of the proposition that the consensus-based clustering approach is a promising method for automatically finding the number of bins for metagenomics data.

RNN-VirSeeker: A Deep Learning Method for Identification of Short Viral Sequences From Metagenomes

Viruses are the most abundant biological entities on earth, and play vital roles in many aspects of microbial communities. As major human pathogens, viruses have caused huge mortality and morbidity to human society in history. Metagenomic sequencing methods could capture all microorganisms from microbiota, with sequences of viruses mixed with these of other species. Therefore, it is necessary to identify viral sequences from metagenomes. However, existing methods perform poorly on identifying short viral sequences. To solve this problem, a deep learning based method, RNN-VirSeeker, is proposed in this paper. RNN-VirSeeker was trained by sequences of 500bp sampled from known Virus and Host RefSeq genomes. Experimental results on the testing set have shown that RNN-VirSeeker exhibited AUROC of 0.9175, recall of 0.8640 and precision of 0.9211 for sequences of 500bp, and outperformed three widely used methods, VirSorter, VirFinder, and DeepVirFinder, on identifying short viral sequences. RNN-VirSeeker was also used to identify viral sequences from a CAMI dataset and a human gut metagenome. Compared with DeepVirFinder, RNN-VirSeeker identified more viral sequences from these metagenomes and achieved greater values of AUPRC and AUROC. RNN-VirSeeker is freely available at https://github.com/crazyinter/RNN-VirSeeker.

Early indicators of microbial strain dysbiosis in the human gastrointestinal microbial community of certain healthy humans and hospitalized COVID-19 patients

Dysbiosis in the human gastrointestinal microbial community could functionally impact microbial metabolism and colonization resistance to pathogens. To further elucidate the indicators of microbial strain dysbiosis, we have developed an analytic method that detects patterns of presence/absence of selected KEGG metabolic pathways for a selected strain (PKS). Using a metagenomic data set consisting of multiple high-density fecal samples from six normal individuals, we found three had unique PKS for important gut commensal microbes, Bacteroides vulgatus and Bacteroides uniformis, at all sample times examined. Two individuals had multiple shared PKS clusters of B. vulgatus or B. uniformis over time. Analysis of a data set of high-density fecal samples from eight COVID-19 hospitalized patients taken over a short period revealed that two patients had shared PKS clusters for B. vulgatus and one shared cluster for B. uniformis. Our analysis demonstrates that while the majority of normal individuals with no B. vulgatus or B. uniformis strain change over time have unique PKS, in some healthy humans and patients hospitalized with COVID-19, we detected shared PKS clusters at the different times suggesting a slowing down of the intrinsic rates of strain variation that could eventually lead to a dysbiosis in the microbial strain community.

Microbiota dynamics in a randomized trial of gut decontamination during allogeneic hematopoietic cell transplantation

BACKGROUNDGut decontamination (GD) can decrease the incidence and severity of acute graft-versus-host disease (aGVHD) in murine models of allogeneic hematopoietic cell transplantation (HCT). In this pilot study, we examined the impact of GD on gut microbiome composition and the incidence of aGVHD in HCT patients.METHODSWe randomized 20 patients undergoing allogeneic HCT to receive (GD) or not receive (no-GD) oral vancomycin-polymyxin B from day -5 through neutrophil engraftment. We evaluated shotgun metagenomic sequencing of serial stool samples to compare the composition and diversity of the gut microbiome between study arms. We assessed clinical outcomes in the 2 arms and performed strain-specific analyses of pathogens that caused bloodstream infections (BSI).RESULTSThe 2 arms did not differ in the predefined primary outcome of Shannon diversity of the gut microbiome at 2 weeks post-HCT (genus, P = 0.8; species, P = 0.44) or aGVHD incidence (P = 0.58). Immune reconstitution of T cell and B cell subsets was similar between groups. Five patients in the no-GD arm had 8 BSI episodes versus 1 episode in the GD arm (P = 0.09). The BSI-causing pathogens were traceable to the gut in 7 of 8 BSI episodes in the no-GD arm, including Staphylococcus species.CONCLUSIONWhile GD did not differentially affect Shannon diversity or clinical outcomes, our findings suggest that GD may protect against gut-derived BSI in HCT patients by decreasing the prevalence or abundance of gut pathogens.TRIAL REGISTRATIONClinicalTrials.gov NCT02641236.FUNDINGNIH, Damon Runyon Cancer Research Foundation, V Foundation, Sloan Foundation, Emerson Collective, and Stanford Maternal & Child Health Research Institute.

Disentangle genus microdiversity within a complex microbial community by using a multi-distance long-read binning method: example of Candidatus Accumulibacter

Complete genomes can be recovered from metagenomes by assembling and binning DNA sequences into metagenome assembled genomes (MAGs). Yet, the presence of microdiversity can hamper the assembly and binning processes, possibly yielding chimeric, highly fragmented and incomplete genomes. Here, the metagenomes of four samples of aerobic granular sludge bioreactors containing Candidatus (Ca.) Accumulibacter, a phosphate-accumulating organism of interest for wastewater treatment, were sequenced with both PacBio and Illumina. Different strategies of genome assembly and binning were investigated, including published protocols and a binning procedure adapted to the binning of long contigs (MuLoBiSC). Multiple criteria were considered to select the best strategy for Ca. Accumulibacter, whose multiple strains in every sample represent a challenging microdiversity. In this case, the best strategy relies on long-read only assembly and a custom binning procedure including MuLoBiSC in metaWRAP. Several high-quality Ca. Accumulibacter MAGs, including a novel species, were obtained independently from different samples. Comparative genomic analysis showed that MAGs retrieved in different samples harbour genomic rearrangements in addition to accumulation of point mutations. The microdiversity of Ca. Accumulibacter, likely driven by mobile genetic elements, causes major difficulties in recovering MAGs, but it is also a hallmark of the panmictic lifestyle of these bacteria.

Gut Virome: Role and Distribution in Health and Gastrointestinal Diseases

The study of the intestinal microbiome is an evolving field of research that includes comprehensive analysis of the vast array of microbes – bacterial, archaeal, fungal, and viral. Various gastrointestinal (GI) diseases, such as Crohn’s disease and ulcerative colitis, have been associated with instability of the gut microbiota. Many studies have focused on importance of bacterial communities with relation to health and disease in humans. The role of viruses, specifically bacteriophages, have recently begin to emerge and have profound impact on the host. Here, we comprehensively review the importance of viruses in GI diseases and summarize their influence in the complex intestinal environment, including their biochemical and genetic activities. We also discuss the distribution of the gut virome as it relates with treatment and immunological advantages. In conclusion, we suggest the need for further studies on this critical component of the intestinal microbiome to decipher the role of the gut virome in human health and disease.

Ecological Processes Shaping Microbiomes of Extremely Low Birthweight Infants

The human microbiome has been implicated in affecting health outcomes in premature infants, but the ecological processes governing early life microbiome assembly remain poorly understood. Here, we investigated microbial community assembly and dynamics in extremely low birth weight infants (ELBWI) over the first 2 weeks of life. We profiled the gut, oral cavity and skin microbiomes over time using 16S rRNA gene amplicon sequencing and evaluated the ecological forces shaping these microbiomes. Though microbiomes at all three body sites were characterized by compositional instability over time and had low body-site specificity (PERMANOVA, r 2 = 0.09, p = 0.001), they could nonetheless be clustered into four discrete community states. Despite the volatility of these communities, deterministic assembly processes were detectable in this period of initial microbial colonization. To further explore these deterministic dynamics, we developed a probabilistic approach in which we modeled microbiome state transitions in each ELBWI as a Markov process, or a "memoryless" shift, from one community state to another. This analysis revealed that microbiomes from different body sites had distinctive dynamics as well as characteristic equilibrium frequencies. Time-resolved microbiome sampling of premature infants may help to refine and inform clinical practices. Additionally, this work provides an analysis framework for microbial community dynamics based on Markov modeling that can facilitate new insights, not only into neonatal microbiomes but also other human-associated or environmental microbiomes.

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.

Virtifier: a deep learning-based identifier for viral sequences from metagenomes

MOTIVATION

Viruses, the most abundant biological entities on earth, are important components of microbial communities, and as major human pathogens, they are responsible for human mortality and morbidity. The identification of viral sequences from metagenomes is critical for viral analysis. As massive quantities of short sequences are generated by next-generation sequencing, most methods utilize discrete and sparse one-hot vectors to encode nucleotide sequences, which are usually ineffective in viral identification.

RESULTS

In this article, Virtifier, a deep learning-based viral identifier for sequences from metagenomic data is proposed. It includes a meaningful nucleotide sequence encoding method named Seq2Vec and a variant viral sequence predictor with an attention-based long short-term memory (LSTM) network. By utilizing a fully trained embedding matrix to encode codons, Seq2Vec can efficiently extract the relationships among those codons in a nucleotide sequence. Combined with an attention layer, the LSTM neural network can further analyze the codon relationships and sift the parts that contribute to the final features. Experimental results of three datasets have shown that Virtifier can accurately identify short viral sequences (<500 bp) from metagenomes, surpassing three widely used methods, VirFinder, DeepVirFinder and PPR-Meta. Meanwhile, a comparable performance was achieved by Virtifier at longer lengths (>5000 bp).

AVAILABILITY AND IMPLEMENTATION

A Python implementation of Virtifier and the Python code developed for this study have been provided on Github https://github.com/crazyinter/Seq2Vec. The RefSeq genomes in this article are available in VirFinder at https://dx.doi.org/10.1186/s40168-017-0283-5. The CAMI Challenge Dataset 3 CAMI_high dataset in this article is available in CAMI at https://data.cami-challenge.org/participate. The real human gut metagenomes in this article are available at https://dx.doi.org/10.1101/gr.142315.112.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MetaCRS: unsupervised clustering of contigs with the recursive strategy of reducing metagenomic dataset's complexity

Background

Metagenomics technology can directly extract microbial genetic material from the environmental samples to obtain their sequencing reads, which can be further assembled into contigs through assembly tools. Clustering methods of contigs are subsequently applied to recover complete genomes from environmental samples. The main problems with current clustering methods are that they cannot recover more high-quality genes from complex environments. Firstly, there are multiple strains under the same species, resulting in assembly of chimeras. Secondly, different strains under the same species are difficult to be classified. Thirdly, it is difficult to determine the number of strains during the clustering process.

Results

In view of the shortcomings of current clustering methods, we propose an unsupervised clustering method which can improve the ability to recover genes from complex environments and a new method for selecting the number of sample’s strains in clustering process. The sequence composition characteristics (tetranucleotide frequency) and co-abundance are combined to train the probability model for clustering. A new recursive method that can continuously reduce the complexity of the samples is proposed to improve the ability to recover genes from complex environments. The new clustering method was tested on both simulated and real metagenomic datasets, and compared with five state-of-the-art methods including CONCOCT, Maxbin2.0, MetaBAT, MyCC and COCACOLA. In terms of the number and quality of recovered genes from metagenomic datasets, the results show that our proposed method is more effective.

Conclusions

A new contigs clustering method is proposed, which can recover more high-quality genes from complex environmental samples.

Look Who's Talking: Host and Pathogen Drivers of Staphylococcus epidermidis Virulence in Neonatal Sepsis

Preterm infants are at increased risk for invasive neonatal bacterial infections. S. epidermidis, a ubiquitous skin commensal, is a major cause of late-onset neonatal sepsis, particularly in high-resource settings. The vulnerability of preterm infants to serious bacterial infections is commonly attributed to their distinct and developing immune system. While developmentally immature immune defences play a large role in facilitating bacterial invasion, this fails to explain why only a subset of infants develop infections with low-virulence organisms when exposed to similar risk factors in the neonatal ICU. Experimental research has explored potential virulence mechanisms contributing to the pathogenic shift of commensal S. epidermidis strains. Furthermore, comparative genomics studies have yielded insights into the emergence and spread of nosocomial S. epidermidis strains, and their genetic and functional characteristics implicated in invasive disease in neonates. These studies have highlighted the multifactorial nature of S. epidermidis traits relating to pathogenicity and commensalism. In this review, we discuss the known host and pathogen drivers of S. epidermidis virulence in neonatal sepsis and provide future perspectives to close the gap in our understanding of S. epidermidis as a cause of neonatal morbidity and mortality.