Metagenomic Sequence Data Research Articles

Genomic Features and Comparative Genomic Analysis of Streptococcus sp. v1. nov., Isolated from an Endophthalmitis Patient.

Endophthalmitis is an acute inflammatory intraocular condition that can cause permanent vision loss. The treatment strategy and visual outcome partly depend on the identification of the agents of pathogens. In this study, metagenomic sequencing was conducted to investigate the microbial and antibiotic resistance genes (ARGs) composition in the vitreous (intraocular body fluid) of an endophthalmitis patient, who progressed rapidly and accompanied by severe pain. Metagenomic sequencing data revealed that the vitreous sample was predominated by Streptococcus, with a low-diversity microbiome in the vitreous. This strain harbor's the ARGs mainly against beta-lactam, macrolide-lincosamide-streptogramin, and multidrug. Additionally, metagenome-assembled genome sequence of Streptococcus sp. v1. nov. was identified. The Tetra Correlation Search (TCS) analysis uncovered that the closest relative of the Streptococcus sp. v1. nov. was Streptococcus mitis SK321. Pan/core genome analysis for Streptococcus sp. v1. nov. and TCS top 25 hits strains revealed that most unique genes of Streptococcus sp. v1. nov. were linked to ATP-binding cassette transport system, which could indicate unique virulence and pathogenic potentials of Streptococcus sp. v1. nov. In addition, a total of 7 virulence factors were identified, and the overwhelming of them were classified into "offensive virulence factors". The high pathogenicity of Streptococcus sp. v1. nov. could be a reason for the patient's rapid disease progression. Our study was first isolated an ocular pathogen with highly virulent based on metagenomic sequencing and bioinformatics analysis, which has important reference value for revealing the composition and genome characteristics of pathogens in endophthalmitis patient in the future.

MGS2AMR: a gene-centric mining of metagenomic sequencing data for pathogens and their antimicrobial resistance profile

BackgroundIdentification of pathogenic bacteria from clinical specimens and evaluating their antimicrobial resistance (AMR) are laborious tasks that involve in vitro cultivation, isolation, and susceptibility testing. Recently, a number of methods have been developed that use machine learning algorithms applied to the whole-genome sequencing data of isolates to approach this problem. However, making AMR assessments from more easily available metagenomic sequencing data remains a big challenge.ResultsWe present the Metagenomic Sequencing to Antimicrobial Resistance (MGS2AMR) pipeline, which detects antibiotic resistance genes (ARG) and their possible organism of origin within a sequenced metagenomics sample. This in silico method allows for the evaluation of bacterial AMR directly from clinical specimens, such as stool samples. We have developed two new algorithms to optimize and annotate the genomic assembly paths within the raw Graphical Fragment Assembly (GFA): the GFA Linear Optimal Path through seed segments (GLOPS) algorithm and the Adapted Dijkstra Algorithm for GFA (ADAG). These novel algorithms improve the sensitivity of ARG detection and aid in species annotation. Tests based on 1200 microbiome samples show a high ARG recall rate and correct assignment of the ARG origin. The MGS2AMR output can further be used in many downstream applications, such as evaluating AMR to specific antibiotics in samples from emerging intestinal infections. We demonstrate that the MGS2AMR-derived data is as informative for the entailing prediction models as the whole-genome sequencing (WGS) data. The performance of these models is on par with our previously published method (WGS2AMR), which is based on the sequencing data of bacterial isolates.ConclusionsMGS2AMR can provide researchers with valuable insights into the AMR content of microbiome environments and may potentially improve patient care by providing faster quantification of resistance against specific antibiotics, thereby reducing the use of broad-spectrum antibiotics. The presented pipeline also has potential applications in other metagenome analyses focused on the defined sets of genes.6MP9Kjm5NAthB1ioNbPxBHVideo

CgMSI: pathogen detection within species from nanopore metagenomic sequencing data

BackgroundMetagenomic sequencing is an unbiased approach that can potentially detect all the known and unidentified strains in pathogen detection. Recently, nanopore sequencing has been emerging as a highly potential tool for rapid pathogen detection due to its fast turnaround time. However, identifying pathogen within species is nontrivial for nanopore sequencing data due to the high sequencing error rate.ResultsWe developed the core gene alleles metagenome strain identification (cgMSI) tool, which uses a two-stage maximum a posteriori probability estimation method to detect pathogens at strain level from nanopore metagenomic sequencing data at low computational cost. The cgMSI tool can accurately identify strains and estimate relative abundance at 1× coverage.ConclusionsWe developed cgMSI for nanopore metagenomic pathogen detection within species. cgMSI is available at https://github.com/ZHU-XU-xmu/cgMSI.

Application of quasimetagenomics methods to define microbial diversity and subtype Listeria monocytogenes in dairy and seafood production facilities.

Microorganisms frequently colonize surfaces within food production facilities. Detection of Listeria monocytogenes in this setting relies on culture-dependent methods, but the complex dynamics of bacterial interactions within these environments and their impact on pathogen detection remain largely unexplored. To address this challenge, we applied both 16S rRNA and shotgun quasimetagenomic (enriched microbiome) sequencing of swab culture enrichments from five seafood and seven dairy production environments. Utilizing 16S rRNA amplicon sequencing, we observed variability between 355 samples taken from these 12 production facilities and a distinctive microbiome for each environment. With shotgun quasimetagenomic sequencing, we were able to assemble L. monocytogenes metagenome-assembled genomes (MAGs) from 28 of the 32 culture-positive samples. We compared these MAGs to their corresponding whole-genome sequencing assemblies, which resulted in two polyphyletic clades consisting of L. monocytogenes lineages I and II with 13,195 and 25,556 single-nucleotide polymorphism sites, respectively. The remaining four MAGs did not produce sufficient genome coverage. To understand and establish limits for pathogen detection and subtyping using shotgun quasimetagenomics, these same data sets were downsampled in slilico to produce a titration series of abundances of L. monocytogenes and analyzed. Pathogen detection was achieved for all downsampled data sets, even those with only 3× genome coverage. This study contributes to the understanding of microbial diversity within food production environments and presents insights into the level of genome coverage needed in a metagenome sequencing data set to detect, subtype, and source track a foodborne pathogenIMPORTANCEIn developed countries, the human diet is predominated by food commodities, which have been manufactured, processed, and stored in a food production facility. Little is known about the application of metagenomic sequencing approaches for detecting foodborne pathogens, such as L. monocytogenes, and characterizing microbial diversity in food production ecosystems. In this work, we investigated the utility of 16S rRNA amplicon and quasimetagenomic sequencing for the taxonomic and phylogenetic classification of Listeria culture enrichments of environmental swabs collected from dairy and seafood production facilities. We demonstrated that single-nucleotide polymorphism (SNP) analyses of L. monocytogenes metagenome-assembled genomes (MAGs) from quasimetagenomic data sets can achieve similar resolution as culture isolate whole-genome sequencing. To further understand the impact of genome coverage on MAG SNP cluster resolution, an in silico downsampling approach was employed to reduce the percentage of target pathogen sequence reads, providing an initial estimate of required MAG coverage for subtyping resolution of L. monocytogenes.

Colorectal cancer–associated T cell receptor repertoire abnormalities are linked to gut microbiome shifts and somatic cell mutations

ABSTRACT As with many diseases, tumor formation in colorectal cancer (CRC) is multifactorial and involves immune, environmental factors and various genetics that contribute to disease development. Accumulating evidence suggests that the gut microbiome is linked to the occurrence and development of CRC, and these microorganisms are important for immune maturation. However, a systematic perspective integrating microbial profiling, T cell receptor (TCR) and somatic mutations in humans with CRC is lacking. Here, we report distinct features of the expressed TCRβ repertoires in the peripheral blood of and CRC patients (n = 107) and healthy donors (n = 30). CRC patients have elevated numbers of large TCRβ clones and they have very low TCR diversity. The metagenomic sequencing data showed that the relative abundance of Fusobacterium nucleatum (F. nucleatum), Escherichia coli and Dasheen mosaic virus were elevated consistently in CRC patients (n = 97) compared to HC individuals (n = 30). The abundance of Faecalibacterium prausnitzii and Roseburia intestinalis was reduced in CRC (n = 97) compared to HC (n = 30). The correlation between somatic mutations of target genes (16 genes, n = 79) and TCR clonality and microbial biomarkers in CRC had been investigated. Importantly, we constructed a random forest classifier (contains 15 features) based on microbiome and TCR repertoires, which can be used as a clinical detection method to screen patients for CRC. We also analysis of F. nucleatum-specific TCR repertoire characteristics. Collectively, our large-cohort multi-omics data aimed to identify novel biomarkers to inform clinical decision-making in the detection and diagnosis of CRC, which is of possible etiological and diagnostic significance.

HoCoRT: host contamination removal tool

BackgroundShotgun metagenome sequencing data obtained from a host environment will usually be contaminated with sequences from the host organism. Host sequences should be removed before further analysis to avoid biases, reduce downstream computational load, or ensure privacy in the case of a human host. The tools that we identified, as designed specifically to perform host contamination sequence removal, were either outdated, not maintained, or complicated to use. Consequently, we have developed HoCoRT, a fast and user-friendly tool that implements several methods for optimised host sequence removal. We have evaluated the speed and accuracy of these methods.ResultsHoCoRT is an open-source command-line tool for host contamination removal. It is designed to be easy to install and use, offering a one-step option for genome indexing. HoCoRT employs a variety of well-known mapping, classification, and alignment methods to classify reads. The user can select the underlying classification method and its parameters, allowing adaptation to different scenarios. Based on our investigation of various methods and parameters using synthetic human gut and oral microbiomes, and on assessment of publicly available data, we provide recommendations for typical datasets with short and long reads.ConclusionsTo decontaminate a human gut microbiome with short reads using HoCoRT, we found the optimal combination of speed and accuracy with BioBloom, Bowtie2 in end-to-end mode, and HISAT2. Kraken2 consistently demonstrated the highest speed, albeit with a trade-off in accuracy. The same applies to an oral microbiome, but here Bowtie2 was notably slower than the other tools. For long reads, the detection of human host reads is more difficult. In this case, a combination of Kraken2 and Minimap2 achieved the highest accuracy and detected 59% of human reads. In comparison to the dedicated DeconSeq tool, HoCoRT using Bowtie2 in end-to-end mode proved considerably faster and slightly more accurate. HoCoRT is available as a Bioconda package, and the source code can be accessed at https://github.com/ignasrum/hocort along with the documentation. It is released under the MIT licence and is compatible with Linux and macOS (except for the BioBloom module).

Identification and functional analysis of novel protein-encoding sequences related to stress-resistance.

Currently, industrial bioproducts are less competitive than chemically produced goods due to the shortcomings of conventional microbial hosts. Thus, is essential developing robust bacteria for improved cell tolerance to process-specific parameters. In this context, metagenomic approaches from extreme environments can provide useful biological parts to improve bacterial robustness. Here, in order to build genetic constructs that increase bacterial resistance to diverse stress conditions, we recovered novel protein-encoding sequences related to stress-resistance from metagenomic databases using an in silico approach based on Hidden-Markov-Model profiles. For this purpose, we used metagenomic shotgun sequencing data from microbial communities of extreme environments to identify genes encoding chaperones and other proteins that confer resistance to stress conditions. We identified and characterized 10 novel protein-encoding sequences related to the DNA-binding protein HU, the ATP-dependent protease ClpP, and the chaperone protein DnaJ. By expressing these genes in Escherichia coli under several stress conditions (including high temperature, acidity, oxidative and osmotic stress, and UV radiation), we identified five genes conferring resistance to at least two stress conditions when expressed in E. coli. Moreover, one of the identified HU coding-genes which was retrieved from an acidic soil metagenome increased E. coli tolerance to four different stress conditions, implying its suitability for the construction of a synthetic circuit directed to expand broad bacterial resistance.

A MERS-CoV antibody neutralizes a pre-emerging group 2c bat coronavirus.

The repeated emergence of zoonotic human betacoronaviruses (β-CoVs) dictates the need for broad therapeutics and conserved epitope targets for countermeasure design. Middle East respiratory syndrome (MERS)-related coronaviruses (CoVs) remain a pressing concern for global health preparedness. Using metagenomic sequence data and CoV reverse genetics, we recovered a full-length wild-type MERS-like BtCoV/li/GD/2014-422 (BtCoV-422) recombinant virus, as well as two reporter viruses, and evaluated their human emergence potential and susceptibility to currently available countermeasures. Similar to MERS-CoV, BtCoV-422 efficiently used human and other mammalian dipeptidyl peptidase protein 4 (DPP4) proteins as entry receptors and an alternative DPP4-independent infection route in the presence of exogenous proteases. BtCoV-422 also replicated efficiently in primary human airway, lung endothelial, and fibroblast cells, although less efficiently than MERS-CoV. However, BtCoV-422 shows minor signs of infection in 288/330 human DPP4 transgenic mice. Several broad CoV antivirals, including nucleoside analogs and 3C-like/Mpro protease inhibitors, demonstrated potent inhibition against BtCoV-422 in vitro. Serum from mice that received a MERS-CoV mRNA vaccine showed reduced neutralizing activity against BtCoV-422. Although most MERS-CoV-neutralizing monoclonal antibodies (mAbs) had limited activity, one anti-MERS receptor binding domain mAb, JC57-11, neutralized BtCoV-422 potently. A cryo-electron microscopy structure of JC57-11 in complex with BtCoV-422 spike protein revealed the mechanism of cross-neutralization involving occlusion of the DPP4 binding site, highlighting its potential as a broadly neutralizing mAb for group 2c CoVs that use DPP4 as a receptor. These studies provide critical insights into MERS-like CoVs and provide candidates for countermeasure development.

Microbiome Metabolome Integration Platform (MMIP): a web-based platform for microbiome and metabolome data integration and feature identification.

A microbial community maintains its ecological dynamics via metabolite crosstalk. Hence, knowledge of the metabolome, alongside its populace, would help us understand the functionality of a community and also predict how it will change in atypical conditions. Methods that employ low-cost metagenomic sequencing data can predict the metabolic potential of a community, that is, its ability to produce or utilize specific metabolites. These, in turn, can potentially serve as markers of biochemical pathways that are associated with different communities. We developed MMIP (Microbiome Metabolome Integration Platform), a web-based analytical and predictive tool that can be used to compare the taxonomic content, diversity variation and the metabolic potential between two sets of microbial communities from targeted amplicon sequencing data. MMIP is capable of highlighting statistically significant taxonomic, enzymatic and metabolic attributes as well as learning-based features associated with one group in comparison with another. Furthermore, MMIP can predict linkages among species or groups of microbes in the community, specific enzyme profiles, compounds or metabolites associated with such a group of organisms. With MMIP, we aim to provide a user-friendly, online web server for performing key microbiome-associated analyses of targeted amplicon sequencing data, predicting metabolite signature, and using learning-based linkage analysis, without the need for initial metabolomic analysis, and thereby helping in hypothesis generation.

Oral probiotics increased the proportion of Treg, Tfr, and Breg cells to inhibit the inflammatory response and impede gestational diabetes mellitus

BackgroundChildren of mothers with gestational diabetes mellitus (GDM) are more prone to acquire type 2 diabetes and obesity as adults. Due to this link, early intervention strategies that alter the gut microbiome may benefit the mother and kid long-term. This work uses metagenomic and transcriptome sequencing to investigate how probiotics affect gut microbiota dysbiosis and inflammation in GDM.MethodsGDM and control metagenomic sequencing data were obtained from the SRA database. This metagenomic data helped us understand gut microbiota abundance and function. KEGG detected and extracted functional pathway genes. Transcriptome sequencing data evaluated GDM-related gene expression. Finally, GDM animal models were given probiotics orally to evaluate inflammatory response, regulatory immune cell fractions, and leptin protein levels.ResultsGDM patients had more Fusobacteria and Firmicutes, while healthy people had more Bacteroidetes. Gut microbiota composition may affect GDM by altering the L-aspartate and L-asparagine super pathways. Mannan degradation and the super pathway of L-aspartate and L-asparagine synthesis enhanced in GDM mice with leptin protein overexpression. Oral probiotics prevent GDM by lowering leptin. Oral probiotics increased Treg, Tfr, and Breg cells, which decreased TNF-α and IL-6 and increased TGF-β and IL-10, preventing inflammation and preserving mouse pregnancy.ConclusionDysbiosis of the gut microbiota may increase leptin expression and cause GDM. Oral probiotics enhance Treg, Tfr, and Breg cells, which limit the inflammatory response and assist mice in sustaining normal pregnancy. Thus, oral probiotics may prevent GDM, enabling targeted gut microbiota modulation and maternal and fetal health.

Removal of false positives in metagenomics-based taxonomy profiling via targeting Type IIB restriction sites

Accurate species identification and abundance estimation are critical for the interpretation of whole metagenome sequencing (WMS) data. Yet, existing metagenomic profilers suffer from false-positive identifications, which can account for more than 90% of total identified species. Here, by leveraging species-specific Type IIB restriction endonuclease digestion sites as reference instead of universal markers or whole microbial genomes, we present a metagenomic profiler, MAP2B (MetAgenomic Profiler based on type IIB restriction sites), to resolve those issues. We first illustrate the pitfalls of using relative abundance as the only feature in determining false positives. We then propose a feature set to distinguish false positives from true positives, and using simulated metagenomes from CAMI2, we establish a false-positive recognition model. By benchmarking the performance in metagenomic profiling using a simulation dataset with varying sequencing depth and species richness, we illustrate the superior performance of MAP2B over existing metagenomic profilers in species identification. We further test the performance of MAP2B using real WMS data from an ATCC mock community, confirming its superior precision against sequencing depth. Finally, by leveraging WMS data from an IBD cohort, we demonstrate the taxonomic features generated by MAP2B can better discriminate IBD and predict metabolomic profiles.

Identification of donor Bacteroides vulgatus genes encoding proteins that correlate with early colonization following fecal transplant of patients with recurrent Clostridium difficile

Due to suppressive antibiotics, patients with recurrent Clostridium difficile have gut microbial communities that are devoid of most commensal microbes. Studies have shown that most of the failures using fecal microbe transplantation (FMT) for recurrent C. difficile occur during the first 4 weeks following transplantation. To identify features of donor Bacteroides vulgatus that lead to early colonization, we used two data sets that collected fecal samples from recipients at early times points post FMT. The first analysis used the shotgun metagenomic DNA sequencing data set from Aggarwala et al. consisting of 7 FMT donors and 13 patients with recurrent C. difficile with fecal samples taken as early as 24 h post FMT. We identified 2 FMT donors in which colonization of recipients by donor B. vulgatus was detected as early as 24 h post FMT. We examined a second data set from Hourigan et al. that collected fecal samples from C. difficile infected children and identified 1 of 3 FMT that also had early colonization of the donor B. vulgatus. We found 19 genes out of 4911 encoding proteins were unique to the 3 donors that had early colonization. A gene encoding a putative chitobiase was identified that was in a gene complex that had been previously identified to enhance colonization in mice. A gene encoding a unique fimbrillin (i.e., pili) family protein and 17 genes encoding hypothetical proteins were also specific for early colonizing donors. Most of the genes encoding hypothetical proteins had neighboring genes that encoded proteins involved in mobilization or transposition. Finally, analysis of 42 paired fecal samples from the human microbiome project (HMP) found no individuals had all 19 genes while 2 individuals had none of the 19 genes. Based on the results from our study, consideration should be given to the screening of FMT donors for these B. vulgatus genes found to enhance early colonization that would be of benefit to promote colonization following FMT.

Multimodal metagenomic analysis reveals microbial single nucleotide variants as superior biomarkers for early detection of colorectal cancer

ABSTRACT Microbial signatures show remarkable potentials in predicting colorectal cancer (CRC). This study aimed to evaluate the diagnostic powers of multimodal microbial signatures, multi-kingdom species, genes, and single-nucleotide variants (SNVs) for detecting precancerous adenomas. We performed cross-cohort analyses on whole metagenome sequencing data of 750 samples via xMarkerFinder to identify adenoma-associated microbial multimodal signatures. Our data revealed that fungal species outperformed species from other kingdoms with an area under the ROC curve (AUC) of 0.71 in distinguishing adenomas from controls. The microbial SNVs, including dark SNVs with synonymous mutations, displayed the strongest diagnostic capability with an AUC value of 0.89, sensitivity of 0.79, specificity of 0.85, and Matthews correlation coefficient (MCC) of 0.74. SNV biomarkers also exhibited outstanding performances in three independent validation cohorts (AUCs = 0.83, 0.82, 0.76; sensitivity = 1.0, 0.72, 0.93; specificity = 0.67, 0.81, 0.67, MCCs = 0.69, 0.83, 0.72) with high disease specificity for adenoma. In further support of the above results, functional analyses revealed more frequent inter-kingdom associations between bacteria and fungi, and abnormalities in quorum sensing, purine and butanoate metabolism in adenoma, which were validated in a newly recruited cohort via qRT-PCR. Therefore, these data extend our understanding of adenoma-associated multimodal alterations in the gut microbiome and provide a rationale of microbial SNVs for the early detection of CRC.

StrainIQ: A Novel n-Gram-Based Method for Taxonomic Profiling of Human Microbiota at the Strain Level.

The emergence of next-generation sequencing (NGS) technology has greatly influenced microbiome research and led to the development of novel bioinformatics tools to deeply analyze metagenomics datasets. Identifying strain-level variations in microbial communities is important to understanding the onset and progression of diseases, host-pathogen interrelationships, and drug resistance, in addition to designing new therapeutic regimens. In this study, we developed a novel tool called StrainIQ (strain identification and quantification) based on a new n-gram-based (series of n number of adjacent nucleotides in the DNA sequence) algorithm for predicting and quantifying strain-level taxa from whole-genome metagenomic sequencing data. We thoroughly evaluated our method using simulated and mock metagenomic datasets and compared its performance with existing methods. On average, it showed 85.8% sensitivity and 78.2% specificity on simulated datasets. It also showed higher specificity and sensitivity using n-gram models built from reduced reference genomes and on models with lower coverage sequencing data. It outperforms alternative approaches in genus- and strain-level prediction and strain abundance estimation. Overall, the results show that StrainIQ achieves high accuracy by implementing customized model-building and is an efficient tool for site-specific microbial community profiling.

Microbial electron flow promotes naphthalene degradation in anaerobic digestion in the presence of nitrate electron acceptor: Focus on electron flow regulation and microbial interaction succession

Microbial electron flow (MEF) is produced from microbial degradation of organic compounds. Regulating MEF to promote organic pollutants biodegradation such as naphthalene (Nap) is a potential way but remains a lack of theoretical basis. Here, we regulated MEF by adding electron acceptor NO3- to achieve 2.6 times increase of Nap biodegradation with cyclodextrin as co-metabolism carbon source. With the NO3- addition, the genes inhibited by Nap of electron generation significantly up-regulated. Especially, key genes ubiD and nahD for anaerobic Nap degradation significantly up-regulated respectively 3.7 times and 6.7 times. Moreover, the ability of electron transfer in MEF was also improved consistent with 7.2 times increase of electron transfer system (ETS) activity. Furthermore, total 60 metagenome-assembled genomes (MAGs) were reconstructed through the metagenomic sequencing data with assembly and binning strategies. Interestingly, it was also first found that the Klebsiella MAG. SDU (Shandong University) 14 had the ability of simultaneous Nap biodegradation and denitrification. Our results firstly offered an effective method of regulating MEF to promote polycyclic aromatic hydrocarbons (PAHs) degradation and simultaneous methanogenesis.

New Phage-Derived Antibacterial Enzyme PolaR Targeting Rothia spp.

Rothia is an opportunistic pathogen, particularly life-threatening for the immunocompromised. It is associated with pneumonia, endocarditis, peritonitis and many other serious infections, including septicemia. Of note, Rothia mucilaginousa produces metabolites that support and increase overgrowth of Pseudomonas aeruginosa, one of the ESKAPE bacteria. Endolysins are considered as antibacterial enzymes derived from bacteriophages that selectively and efficiently kill susceptible bacteria without harming human cells or the normal microbiome. Here, we applied a computational analysis of metagenomic sequencing data of the gastric mucosa phageome extracted from human patients' stomach biopsies. A selected candidate anti-Rothia sequence was produced in an expression system, purified and confirmed as a Rothia mucilaginosa- and Rothia dentocariosa-specific endolysin PolaR, able to destroy bacterial cells even when aggregated, as in a biofilm. PolaR had no cytotoxic or antiproliferative effects on mammalian cells. PolaR is the first described endolysin selectively targeting Rothia species, with a high potential to combat infections caused by Rothia mucilaginosa and Rothia dentocariosa, and possibly other bacterial groups. PolaR is the first antibacterial enzyme selected from the gastric mucosa phageome, which underlines the biological complexity and probably underestimated biological role of the phageome in the human gastric mucosa.

Risk assessment and dissemination mechanism of antibiotic resistance genes in compost

In recent years, the excessive of antibiotics in livestock and poultry husbandry, stemming from extensive industry experience, has resulted in the accumulation of residual antibiotics and antibiotic resistance genes (ARGs) in livestock manure. Composting, as a crucial approach for the utilization of manure resources, has the potential to reduce the levels of antibiotics and ARGs in manure, although complete elimination is challenging. Previous studies have primarily focused on the diversity and abundance of ARGs in compost or have solely examined the correlation between ARGs and their carriers, potentially leading to a misjudgment of the actual risk associated with ARGs in compost. To address this gap, this study investigated the transfer potential of ARGs in compost and their co-occurrence with opportunistic pathogenic bacteria by extensively analyzing metagenomic sequencing data of compost worldwide. The results demonstrated that the potential risk of ARGs in compost was significantly lower than in manure, suggesting that composting effectively reduces the risk of ARGs. Further analysis showed that the microbes shifted their life history strategy in manure and compost due to antibiotic pressure and formed metabolic interactions dominated by antibiotic-resistant microbes, increasing ARG dissemination frequency. Therefore, husbandry practice without antibiotic addition was recommended to control ARG evolution, dissemination, and abatement both at the source and throughout processing.

Connect the dots: sketching out microbiome interactions through networking approaches.

Microbiome networking analysis has emerged as a powerful tool for studying the complex interactions among microorganisms in various ecological niches, including the human body and several environments. This analysis has been used extensively in both human and environmental studies, revealing key taxa and functional units peculiar to the ecosystem considered. In particular, it has been mainly used to investigate the effects of environmental stressors, such as pollution, climate change or therapies, on host-associated microbial communities and ecosystem function. In this review, we discuss the latest advances in microbiome networking analysis, including methods for constructing and analyzing microbiome networks, and provide a case study on how to use these tools. These analyses typically involve constructing a network that represents interactions among microbial taxa or functional units, such as genes or metabolic pathways. Such networks can be based on a variety of data sources, including 16S rRNA sequencing, metagenomic sequencing, and metabolomics data. Once constructed, these networks can be analyzed to identify key nodes or modules important for the stability and function of the microbiome. By providing insights into essential ecological features of microbial communities, microbiome networking analysis has the potential to transform our understanding of the microbial world and its impact on human health and the environment.

Genetic strategies for sex-biased persistence of gut microbes across human life

Although compositional variation in the gut microbiome during human development has been extensively investigated, strain-resolved dynamic changes remain to be fully uncovered. In the current study, shotgun metagenomic sequencing data of 12,415 fecal microbiomes from healthy individuals are employed for strain-level tracking of gut microbiota members to elucidate its evolving biodiversity across the human life span. This detailed longitudinal meta-analysis reveals host sex-related persistence of strains belonging to common, maternally-inherited species, such as Bifidobacterium bifidum and Bifidobacterium longum subsp. longum. Comparative genome analyses, coupled with experiments including intimate interaction between microbes and human intestinal cells, show that specific bacterial glycosyl hydrolases related to host-glycan metabolism may contribute to more efficient colonization in females compared to males. These findings point to an intriguing ancient sex-specific host-microbe coevolution driving the selective persistence in women of key microbial taxa that may be vertically passed on to the next generation.

MetaTransformer: deep metagenomic sequencing read classification using self-attention models.

Deep learning has emerged as a paradigm that revolutionizes numerous domains of scientific research. Transformers have been utilized in language modeling outperforming previous approaches. Therefore, the utilization of deep learning as a tool for analyzing the genomic sequences is promising, yielding convincing results in fields such as motif identification and variant calling. DeepMicrobes, a machine learning-based classifier, has recently been introduced for taxonomic prediction at species and genus level. However, it relies on complex models based on bidirectional long short-term memory cells resulting in slow runtimes and excessive memory requirements, hampering its effective usability. We present MetaTransformer, a self-attention-based deep learning metagenomic analysis tool. Our transformer-encoder-based models enable efficient parallelization while outperforming DeepMicrobes in terms of species and genus classification abilities. Furthermore, we investigate approaches to reduce memory consumption and boost performance using different embedding schemes. As a result, we are able to achieve 2× to 5× speedup for inference compared to DeepMicrobes while keeping a significantly smaller memory footprint. MetaTransformer can be trained in 9hours for genus and 16hours for species prediction. Our results demonstrate performance improvements due to self-attention models and the impact of embedding schemes in deep learning on metagenomic sequencing data.