Microbial Sequence Data Research Articles

DNASimCLR: a contrastive learning-based deep learning approach for gene sequence data classification

BackgroundThe rapid advancements in deep neural network models have significantly enhanced the ability to extract features from microbial sequence data, which is critical for addressing biological challenges. However, the scarcity and complexity of labeled microbial data pose substantial difficulties for supervised learning approaches. To address these issues, we propose DNASimCLR, an unsupervised framework designed for efficient gene sequence data feature extraction.ResultsDNASimCLR leverages convolutional neural networks and the SimCLR framework, based on contrastive learning, to extract intricate features from diverse microbial gene sequences. Pre-training was conducted on two classic large scale unlabelled datasets encompassing metagenomes and viral gene sequences. Subsequent classification tasks were performed by fine-tuning the pretrained model using the previously acquired model. Our experiments demonstrate that DNASimCLR is at least comparable to state-of-the-art techniques for gene sequence classification. For convolutional neural network-based approaches, DNASimCLR surpasses the latest existing methods, clearly establishing its superiority over the state-of-the-art CNN-based feature extraction techniques. Furthermore, the model exhibits superior performance across diverse tasks in analyzing biological sequence data, showcasing its robust adaptability.ConclusionsDNASimCLR represents a robust and database-agnostic solution for gene sequence classification. Its versatility allows it to perform well in scenarios involving novel or previously unseen gene sequences, making it a valuable tool for diverse applications in genomics.

Using Machine Learning and Natural Language Processing for Unveiling Similarities between Microbial Data

Microbiota analysis can provide valuable insights in various fields, including diet and nutrition, understanding health and disease, and in environmental contexts, such as understanding the role of microorganisms in different ecosystems. Based on the results, we can provide targeted therapies, personalized medicine, or detect environmental contaminants. In our research, we examined the gut microbiota of 16 animal taxa, including humans, as well as the microbiota of cattle and pig manure, where we focused on 16S rRNA V3-V4 hypervariable regions. Analyzing these regions is common in microbiome studies but can be challenging since the results are high-dimensional. Thus, we utilized machine learning techniques and demonstrated their applicability in processing microbial sequence data. Moreover, we showed that techniques commonly employed in natural language processing can be adapted for analyzing microbial text vectors. We obtained the latter through frequency analyses and utilized the proposed hierarchical clustering method over them. All steps in this study were gathered in a proposed microbial sequence data processing pipeline. The results demonstrate that we not only found similarities between samples but also sorted groups’ samples into semantically related clusters. We also tested our method against other known algorithms like the Kmeans and Spectral Clustering algorithms using clustering evaluation metrics. The results demonstrate the superiority of the proposed method over them. Moreover, the proposed microbial sequence data pipeline can be utilized for different types of microbiota, such as oral, gut, and skin, demonstrating its reusability and robustness.

AMRomics: a scalable workflow to analyze large microbial genome collections

Whole genome analysis for microbial genomics is critical to studying and monitoring antimicrobial resistance strains. The exponential growth of microbial sequencing data necessitates a fast and scalable computational pipeline to generate the desired outputs in a timely and cost-effective manner. Recent methods have been implemented to integrate individual genomes into large collections of specific bacterial populations and are widely employed for systematic genomic surveillance. However, they do not scale well when the population expands and turnaround time remains the main issue for this type of analysis. Here, we introduce AMRomics, an optimized microbial genomics pipeline that can work efficiently with big datasets. We use different bacterial data collections to compare AMRomics against competitive tools and show that our pipeline can generate similar results of interest but with better performance. The software is open source and is publicly available at https://github.com/amromics/amromics under an MIT license.

Identification and characterization of a skin microbiome on Caenorhabditis elegans suggests environmental microbes confer cuticle protection.

The genetic model organism C. elegans has recently emerged as a tool for understanding host-microbiome interactions. Nearly all of these studies either focus on pathogenic or gut-resident microbes. Little is known about the existence of native, nonpathogenic skin microbes or their function. We demonstrate that members of a modified C. elegans model microbiome, mCeMbio, can adhere to the animal's cuticle and confer protection from noxious environments. We combine a novel micro-swab tool, the first 16S microbial sequencing data from relatively unperturbed C. elegans, and physiological assays to demonstrate microbially mediated protection of the skin. This work serves as a foundation to explore wild C. elegans skin microbiomes and use C. elegans as a model for skin research.

Graphite: painting genomes using a colored de Bruijn graph.

The recent growth of microbial sequence data allows comparisons at unprecedented scales, enabling the tracking of strains, mobile genetic elements, or genes. Querying a genome against a large reference database can easily yield thousands of matches that are tedious to interpret and pose computational challenges. We developed Graphite that uses a colored de Bruijn graph (cDBG) to paint query genomes, selecting the local best matches along the full query length. By focusing on the best genomic match of each query region, Graphite reduces the number of matches while providing the most promising leads for sequence tracking or genomic forensics. When applied to hundreds of Campylobacter genomes we found extensive gene sharing, including a previously undetected C. coli plasmid that matched a C. jejuni chromosome. Together, genome painting using cDBGs as enabled by Graphite, can reveal new biological phenomena by mitigating computational hurdles.

Hierarchical determinants in cytotoxic necrotizing factor (CNF) toxins driving Rho G-protein deamidation versus transglutamination.

The cytotoxic necrotizing factor (CNF) family of AB-type bacterial protein toxins catalyze two types of modification on their Rho GTPase substrates: deamidation and transglutamination. It has been established that E. coli CNF1 and its close homolog proteins catalyze primarily deamidation and Bordetella dermonecrotic toxin (DNT) catalyzes primarily transglutamination. The rapidly expanding microbial genome sequencing data have revealed that there are at least 13 full-length variants of CNF1 homologs. CNFx from E. coli strain GN02091 is the most distant from all other members of the CNF family with 50%-55% sequence identity at the protein level and 0.45-0.52 nucleotide substitutions per site at the DNA level. CNFx modifies RhoA, Rac1, and Cdc42, and like CNF1, activates downstream SRE-dependent mitogenic signaling pathways in human HEK293T cells, but at a 1,000-fold higher EC50 value. Unlike other previously characterized CNF toxins, CNFx modifies Rho proteins primarily through transglutamination, as evidenced by gel-shift assay and confirmed by MALDI mass spectral analysis, when coexpressed with Rho-protein substrates in E. coli BL21 cells or through direct treatment of HEK293T cells. A comparison of CNF1 and CNFx sequences identified two critical active-site residues corresponding to positions 832 and 862 in CNF1. Reciprocal site-specific mutations at these residues in each toxin revealed hierarchical rules that define the preference for deamidase versus a transglutaminase activity in CNFs. An additional unique Cys residue at the C-terminus of CNFx was also discovered to be critical for retarding cargo delivery.IMPORTANCECytotoxic necrotizing factor (CNF) toxins not only play important virulence roles in pathogenic E. coli and other bacterial pathogens, but CNF-like genes have also been found in an expanding number of genomes from clinical isolates. Harnessing the power of evolutionary relationships among the CNF toxins enabled the deciphering of the hierarchical active-site determinants that define whether they modify their Rho GTPase substrates through deamidation or transglutamination. With our finding that a distant CNF variant (CNFx) unlike other known CNFs predominantly transglutaminates its Rho GTPase substrates, the paradigm of "CNFs deamidate and DNTs transglutaminate" could finally be attributed to two critical amino acid residues within the active site other than the previously identified catalytic Cys-His dyad residues. The significance of our approach and research findings is that they can be applied to deciphering enzyme reaction determinants and substrate specificities for other bacterial proteins in the development of precision therapeutic strategies.

SWGTS-a platform for stream-based host DNA depletion.

Microbial sequencing data from clinical samples is often contaminated with human sequences, which have to be removed prior to sharing. Existing methods for human read removal, however, are applicable only after the target dataset has been retrieved in its entirety, putting the recipient at least temporarily in control of a potentially identifiable genetic dataset with potential implications under regulatory frameworks such as the GDPR. In some instances, the ability to carry out stream-based host depletion as part of the data transfer process may be preferable. We present SWGTS, a client-server application for the transfer and stream-based host depletion of sequencing reads. SWGTS enforces a robust upper bound on the maximum amount of human genetic data from any one client held in memory at any point in time by storing all incoming sequencing data in a limited-size, client-specific intermediate processing buffer, and by throttling the rate of incoming data if it exceeds the speed of host depletion carried out on the SWGTS server in the background. SWGTS exposes a HTTP-REST interface, is implemented using docker-compose, Redis and traefik, and requires less than 8 Gb of RAM for deployment. We demonstrate high filtering accuracy of SWGTS; incoming data transfer rates of up to 1.65 megabases per second in a conservative configuration; and mitigation of re-identification risks by the ability to limit the number of SNPs present on a popular population-scale genotyping array covered by reads in the SWGTS buffer to a low user-defined number, such as 10 or 100. SWGTS is available on GitHub: https://github.com/AlBi-HHU/swgts (https://doi.org/10.5281/zenodo.10891052). The repository also contains a jupyter notebook that can be used to reproduce all the benchmarks used in this article. All datasets used for benchmarking are publicly available.

Gut Microbiome and Metabolic and Immune Indices in Males with or without Evidence of Metabolic Dysregulation.

The contributions of the gut microbiota to obesity and metabolic disease represent a potentially modifiable factor that may explain variation in risk between individuals. This study aimed to explore relationships among microbial composition and imputed functional attributes, a range of soluble metabolic and immune indices, and gene expression markers in males with or without evidence of metabolic dysregulation (MetDys). This case-control study included healthy males (n=15; 41.9±11.7 years; body mass index [BMI], 22.9±1.2 kg/m2) and males with evidence of MetDys (n=14; 46.6±10.0 years; BMI, 35.1±3.3 kg/m2) who provided blood and faecal samples for assessment of a range of metabolic and immune markers and microbial composition using 16S rRNA gene sequencing. Metagenomic functions were imputed from microbial sequence data for analysis. In addition to elevated values in a range of traditional metabolic, adipokine and inflammatory indices in the MetDys group, 23 immunomodulatory genes were significantly altered in the MetDys group. Overall microbial diversity did not differ between groups; however, a trend for a higher relative abundance of the Bacteroidetes (P=0.06) and a lower relative abundance of the Verrucomicrobia (P=0.09) phyla was noted in the MetDys group. Using both family- and genera-level classifications, a partial least square discriminant analysis revealed unique microbial signatures between the groups. These findings confirm the need for ongoing investigations in human clinical cohorts to further resolve the relationships between the gut microbiota and metabolic and immune markers and risk for metabolic disease.

Meta-analysis reveals obesity associated gut microbial alteration patterns and reproducible contributors of functional shift

ABSTRACT The majority of cohort-specific studies associating gut microbiota with obesity are often contradictory; thus, the replicability of the signature remains questionable. Moreover, the species that drive obesity-associated functional shifts and their replicability remain unexplored. Thus, we aimed to address these questions by analyzing gut microbial metagenome sequencing data to develop an in-depth understanding of obese host-gut microbiota interactions using 3329 samples (Obese, n = 1494; Control, n = 1835) from 17 different countries, including both 16S rRNA gene and metagenomic sequence data. Fecal metagenomic data from diverse geographical locations were curated, profiled, and pooled using a machine learning-based approach to identify robust global signatures of obesity. Furthermore, gut microbial species and pathways were systematically integrated through the genomic content of the species to identify contributors to obesity-associated functional shifts. The community structure of the obese gut microbiome was evaluated, and a reproducible depletion of diversity was observed in the obese compared to the lean gut. From this, we infer that the loss of diversity in the obese gut is responsible for perturbations in the healthy microbial functional repertoire. We identified 25 highly predictive species and 37 pathway associations as signatures of obesity, which were validated with remarkably high accuracy (AUC, Species: 0.85, and pathway: 0.80) with an independent validation dataset. We observed a reduction in short-chain fatty acid (SCFA) producers (several Alistipes species, Odoribacter splanchnicus, etc.) and depletion of promoters of gut barrier integrity (Akkermansia muciniphila and Bifidobacterium longum) in obese guts. Our analysis underlines SCFAs and purine/pyrimidine biosynthesis, carbohydrate metabolism pathways in control individuals, and amino acid, enzyme cofactor, and peptidoglycan biosynthesis pathway enrichment in obese individuals. We also mapped the contributors to important obesity-associated functional shifts and observed that these are both dataset-specific and shared across the datasets. In summary, a comprehensive analysis of diverse datasets unveils species specifically contributing to functional shifts and consistent gut microbial patterns associated to obesity.

Integrated Analysis of the Effects of Cecal Microbiota and Serum Metabolome on Market Weights of Chinese Native Chickens.

The gut microbiota plays an important role in the physiological activities of the host and affects the formation of important economic traits in livestock farming. The effects of cecal microbiota on chicken weights were investigated using the Guizhou yellow chicken as a model. Experimental cohorts from chickens with high- (HC, n = 16) and low-market-weights (LC, n = 16) were collected. Microbial 16S rRNA gene sequencing and non-targeted serum metabolome data were integrated to explore the effect and metabolic mechanism of cecal microbiota on market weight. The genera Lachnoclostridium, Alistipes, Negativibacillus, Sellimonas, and Ruminococcus torques were enriched in the HC group, while Phascolarctobacterium was enriched in the LC group (p < 0.05). Metabolomic analysis determined that pantothenic acid (vitamin B5), luvangetin (2H-1-benzopyran-6-acrylic acid), and menadione (vitamin K3) were significantly higher in HC serum, while beclomethasone dipropionate (a glucocorticoid) and chlorophene (2-benzyl-4-chlorophenol) were present at higher levels in the LC group. The microbes enriched in HC were significantly positively correlated with metabolites, including pantothenic acid and menadione, and negatively correlated with beclomethasone dipropionate and chlorophene. These results indicated that specific cecal bacteria in Guizhou yellow chickens alter the host metabolism and growth performance. This study provides a reference for revealing the mechanism of cecal microbe actions that affect chicken body weight.

A streamlined pipeline based on HmmUFOtu for microbial community profiling using 16S rRNA amplicon sequencing.

Microbial community profiling using 16S rRNA amplicon sequencing allows for taxonomic characterization of diverse microorganisms. While amplicon sequence variant (ASV) methods are increasingly favored for their fine-grained resolution of sequence variants, they often discard substantial portions of sequencing reads during quality control, particularly in datasets with large number samples. We present a streamlined pipeline that integrates FastP for read trimming, HmmUFOtu for operational taxonomic units (OTU) clustering, Vsearch for chimera checking, and Kraken2 for taxonomic assignment. To assess the pipeline's performance, we reprocessed two published stool datasets of normal Korean populations: one with 890 and the other with 1,462 independent samples. In the first dataset, HmmUFOtu retained 93.2% of over 104 million read pairs after quality trimming, discarding chimeric or unclassifiable reads, while DADA2, a commonly used ASV method, retained only 44.6% of the reads. Nonetheless, both methods yielded qualitatively similar β-diversity plots. For the second dataset, HmmUFOtu retained 89.2% of read pairs, while DADA2 retained a mere 18.4% of the reads. HmmUFOtu, being a closed-reference clustering method, facilitates merging separately processed datasets, with shared OTUs between the two datasets exhibiting a correlation coefficient of 0.92 in total abundance (log scale). While the first two dimensions of the β-diversity plot exhibited a cohesive mixture of the two datasets, the third dimension revealed the presence of a batch effect. Our comparative evaluation of ASV and OTU methods within this streamlined pipeline provides valuable insights into their performance when processing large-scale microbial 16S rRNA amplicon sequencing data. The strengths of HmmUFOtu and its potential for dataset merging are highlighted.

A DNA-barcode biodiversity standard analysis method (DNA-BSAM) reveals a large variance in the effect of a range of biological, chemical and physical soil management interventions at different sites, but location is one of the most important aspects determining the nature of agricultural soil microbiology

There are significant gaps in our understanding of how to sustainably manage agricultural soils to preserve soil biodiversity. Here we evaluate and quantify the effects of agricultural management and location on soil microbiology using nine field trials that have consistently applied different soil management practices in the United Kingdom using DNA barcode sequence data. We tested the basic hypothesis that various agricultural management interventions have a significant and greater effect on soil bacterial and fungal diversity than geographic location. The analyses of soil microbial DNA sequence data to date has lacked standardisation which prevents meaningful comparisons across sites and studies. Therefore, to analyse these data and crucially compare and quantify the size of any effects on soil bacterial and fungal biodiversity between sites, we developed and employed a post-sequencing DNA-barcode biodiversity standard analysis method (DNA-BSAM). The DNA-BSAM comprises a series of standardised bioinformatic steps for processing sequences but more importantly defines a standardised set of ecological indices and statistical tests. Use of the DNA-BSAM reveals the hypothesis was not strongly supported, and this was primarily because: 1) there was a large variance in the effects of various management interventions at different sites, and 2) location had an equivalent or greater effect size than most management interventions for most metrics. Some dispersed sites imposed the same organic amendments interventions but showed different responses, and this combined with observations of strong differences in soil microbiomes by location tentatively suggests that any effect of management may be contingent on location. This means it could be unreliable to more widely extrapolate the findings from trials performed only at one location. The widespread use of a standard approach will allow meaningful cross-comparisons between soil microbiome studies and thus a substantial evidence-base of the effects of land-use on soil microbiology to accumulate and inform soil management decisions.

Magnetic-activated cell sorting identifies a unique lung microbiome community

BackgroundThe advent of culture-independent, next-generation DNA sequencing has led to the discovery of distinct lung bacterial communities. Studies of lung microbiome taxonomy often reveal only subtle differences between health and disease, but host recognition and response may distinguish the members of similar bacterial communities in different populations. Magnetic-activated cell sorting has been applied to the gut microbiome to identify the numbers and types of bacteria eliciting a humoral response. We adapted this technique to examine the populations of immunoglobulin-bound bacteria in the lung.MethodsSixty-four individuals underwent bronchoalveolar lavage (BAL). We separated immunoglobulin G-bound bacteria using magnetic-activated cell sorting and sequenced the 16S rRNA gene on the Illumina MiSeq platform. We compared microbial sequencing data in IgG-bound bacterial communities compared to raw BAL then examined the differences in individuals with and without HIV as a representative disease state.ResultsImmunoglobulin G-bound bacteria were identified in all individuals. The community structure differed when compared to raw BAL, and there was a greater abundance of Pseudomonas and fewer oral bacteria in IgG-bound BAL. Examination of IgG-bound communities in individuals with HIV demonstrated the differences in Ig-bound bacteria by HIV status that were not seen in a comparison of raw BAL, and greater numbers of immunoglobulin-bound bacteria were associated with higher pulmonary cytokine levels.ConclusionsWe report a novel application of magnetic-activated cell sorting to identify immunoglobulin G-bound bacteria in the lung. This technique identified distinct bacterial communities which differed in composition from raw bronchoalveolar lavage, revealing the differences not detected by traditional analyses. Cytokine response was also associated with differential immunoglobulin binding of lung bacteria, suggesting the functional importance of these communities.6btME6LTfMPeXYkHvqtMADVideo

Microbial signature inferred from genomic breeding selection on milk urea concentration and its relation to proxies of nitrogen-utilization efficiency in Holsteins

Increasing the nitrogen-utilization efficiency (NUE) of dairy cows by breeding selection would offer advantages from nutritional, environmental, and economic perspectives. Because data collection of NUE phenotypes is not feasible in large cow cohorts, the cow individual milk urea concentration (MU) has been suggested as an indicator trait. Considering the symbiotic interplay between dairy cows and their rumen microbiome, individual MU was thought to be influenced by host genetics and by the rumen microbiome, the latter in turn being partly attributed to host genetics. To enhance our knowledge of MU as an indicator trait for NUE, we aimed to identify differential abundant rumen microbial genera between Holstein cows with divergent genomic breeding values for MU (GBVMU; GBVHMU vs. GBVLMU, where H and L indicate high and low MU phenotypes, respectively). The microbial genera identified were further investigated for their correlations with MU and 7 additional NUE-associated traits in urine, milk, and feces in 358 lactating Holsteins. Statistical analysis of microbial 16S rRNA amplicon sequencing data revealed significantly higher abundances of the ureolytic genus Succinivibrionaceae UCG-002 in GBVLMU cows, whereas GBVHMU animals hosted higher abundances of Clostridia unclassified and Desulfovibrio. The entire discriminating ruminal signature of 24 microbial taxa included a further 3 genera of the Lachnospiraceae family that revealed significant correlations to MU values and were therefore proposed as considerable players in the GBVMU-microbiome-MU axis. The significant correlations of Prevotellaceae UCG-003, Anaerovibrio, Blautia, and Butyrivibrio abundances with MU measurements, milk nitrogen, and N content in feces suggested their contribution to genetically determined N-utilization in Holstein cows. The microbial genera identified might be considered for future breeding programs to enhance NUE in dairy herds.

Pairwise ratio-based differential abundance analysis of infant microbiome 16S sequencing data.

Differential abundance analysis of infant 16S microbial sequencing data is complicated by challenging data properties, including high sparsity, extreme dispersion and the relative nature of the information contained within the data. In this study, we propose a pairwise ratio analysis that uses the compositional data analysis principle of subcompositional coherence and merges it with a beta-binomial regression model. The resulting method provides a flexible and easily interpretable approach to infant 16S sequencing data differential abundance analysis that does not require zero imputation. We evaluate the proposed method using infant 16S data from clinical trials and demonstrate that the proposed method has the power to detect differences, and demonstrate how its results can be used to gain insights. We further evaluate the method using data-inspired simulations and compare its power against related methods. Our results indicate that power is high for pairwise differential abundance analysis of taxon pairs that have a large abundance. In contrast, results for sparse taxon pairs show a decrease in power and substantial variability in method performance. While our method shows promising performance on well-measured subcompositions, we advise strong filtering steps in order to avoid excessive numbers of underpowered comparisons in practical applications.

The case for including microbial sequences in the electronic health record.

Integrating microbial sequencing data into electronic health records, while presenting privacy concerns, will improve patient care and population health and will expand the secondary uses of such data.

C3NA: correlation and consensus-based cross-taxonomy network analysis for compositional microbial data

BackgroundStudying the co-occurrence network structure of microbial samples is one of the critical approaches to understanding the perplexing and delicate relationship between the microbe, host, and diseases. It is also critical to develop a tool for investigating co-occurrence networks and differential abundance analyses to reveal the disease-related taxa–taxa relationship. In addition, it is also necessary to tighten the co-occurrence network into smaller modules to increase the ability for functional annotation and interpretability of these taxa-taxa relationships. Also, it is critical to retain the phylogenetic relationship among the taxa to identify differential abundance patterns, which can be used to resolve contradicting functions reported by different studies.ResultsIn this article, we present Correlation and Consensus-based Cross-taxonomy Network Analysis (C3NA), a user-friendly R package for investigating compositional microbial sequencing data to identify and compare co-occurrence patterns across different taxonomic levels. C3NA contains two interactive graphic user interfaces (Shiny applications), one of them dedicated to the comparison between two diagnoses, e.g., disease versus control. We used C3NA to analyze two well-studied diseases, colorectal cancer, and Crohn’s disease. We discovered clusters of study and disease-dependent taxa that overlap with known functional taxa studied by other discovery studies and differential abundance analyses.ConclusionC3NA offers a new microbial data analyses pipeline for refined and enriched taxa–taxa co-occurrence network analyses, and the usability was further expanded via the built-in Shiny applications for interactive investigation.

Equine sinusitis aetiology is linked to sinus microbiome by amplicon sequencing.

Information regarding the microbiome in sinusitis using genetic sequencing is lacking and more-in-depth understanding of the microbiome could improve antimicrobial selection and treatment outcomes for cases of primary sinusitis. To describe sinus microbiota in samples from horses with sinusitis and compare microbiota and the presence of antimicrobial resistance genes between primary, dental-related and other secondary causes of sinusitis. Retrospective case series. Records of equine sinusitis from 2017 to 2021 were reviewed and historical microbial amplicon sequence data were obtained from clinical diagnostic testing of sinus secretions. Following bioinformatic processing of bacterial and fungal sequence data, the sinus microbiota and importance of sinusitis aetiology among other factors were investigated from the perspectives of alpha diversity (e.g., number of operational taxonomic units [OTUs], Hill1 Diversity), beta diversity, and differentially abundant taxa. Quantitative PCR allowed for comparisons of estimated bacterial abundance and detection rate of common antibiotic resistance-associated genes. In a smaller subset, longitudinal analysis was performed to evaluate similarity in samples over time. Of 81 samples analysed from 70 horses, the bacterial microbiome was characterised in 66, and fungal in five. Only sinusitis aetiology was shown to significantly influence microbiome diversity and composition (p< 0.05). Dental-related sinusitis (n= 44) was associated with a significantly higher proportion of obligate anaerobic bacteria, whereas primary sinusitis (n= 12) and other (n= 10) groups were associated with fewer bacteria and higher proportions of facultative anaerobic and aerobic genera. Antimicrobial resistance genes and fungal components were exclusively identified in dental-related sinusitis. Retrospective nature, incomplete prior antimicrobial administration data. Molecular characterisation in sinusitis identifies microbial species which may be difficult to isolate via culture, and microbiome profiling can differentiate sinusitis aetiology, which may inform further treatment, including antimicrobial therapy.

Systematic discovery of recombinases for efficient integration of large DNA sequences into the human genome

Large serine recombinases (LSRs) are DNA integrases that facilitate the site-specific integration of mobile genetic elements into bacterial genomes. Only a few LSRs, such as Bxb1 and PhiC31, have been characterized to date, with limited efficiency as tools for DNA integration in human cells. In this study, we developed a computational approach to identify thousands of LSRs and their DNA attachment sites, expanding known LSR diversity by >100-fold and enabling the prediction of their insertion site specificities. We tested their recombination activity in human cells, classifying them as landing pad, genome-targeting or multi-targeting LSRs. Overall, we achieved up to seven-fold higher recombination than Bxb1 and genome integration efficiencies of 40–75% with cargo sizes over 7 kb. We also demonstrate virus-free, direct integration of plasmid or amplicon libraries for improved functional genomics applications. This systematic discovery of recombinases directly from microbial sequencing data provides a resource of over 60 LSRs experimentally characterized in human cells for large-payload genome insertion without exposed DNA double-stranded breaks.

GutMDisorder v2.0: a comprehensive database for dysbiosis of gut microbiota in phenotypes and interventions.

Gut microbiota plays a significant role in maintaining host health, and conversely, disorders potentially lead to dysbiosis, an imbalance in the composition of the gut microbial community. Intervention approaches, such as medications, diets, and several others, also alter the gut microbiota in either a beneficial or harmful direction. In 2020, the gutMDisorder was developed to facilitate researchers in the investigation of dysbiosis of gut microbes as occurs in various disorders as well as with therapeutic interventions. The database has been updated this year, following revision of previous publications and newly published reports to manually integrate confirmed associations under multitudinous conditions. Additionally, the microbial contents of downloaded gut microbial raw sequencing data were annotated, the metadata of the corresponding hosts were manually curated, and the interactive charts were developed to enhance visualization. The improvements have assembled into gutMDisorder v2.0, a more advanced search engine and an upgraded web interface, which can be freely accessed via http://bio-annotation.cn/gutMDisorder/.