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PubMed2026

MHASS: Microbiome HiFi Amplicon Sequencing Simulator.

SUMMARY: Microbiome HiFi Amplicon Sequence Simulator (MHASS) creates realistic synthetic PacBio HiFi amplicon sequencing datasets for microbiome studies, by integrating genome-aware abundance modeling, realistic dual-barcoding strategies, and empirically derived pass-number distributions from actual sequencing runs. MHASS generates datasets tailored for rigorous benchmarking and validation of long-read microbiome analysis workflows, including ASV clustering and taxonomic assignment. AVAILABILITY AND IMPLEMENTATION: Implemented in Python with automated dependency management, the source code for MHASS is freely available at https://github.com/rhowardstone/MHASS along with installation instructions. Our code is also published on Zenodo at https://doi.org/10.5281/zenodo.17486364. The data underlying this article are available on GitHub at https://github.com/rhowardstone/MHASS_evaluation/.

Software↗

PubMed2025

Study research protocol for Phenome India-CSIR Health Cohort Knowledgebase: A prospective multi-modal follow-up study on a nationwide employee cohort.

Predicting individual health trajectories based on risk scores can help formulate effective preventive strategies for diseases and their complications. Currently, most risk prediction algorithms rely on epidemiological data from the Caucasian population, which often do not translate well to the Indian population due to ethnic diversity, differing dietary and lifestyle habits, and unique risk profiles. In this multi-center prospective longitudinal study conducted across India, we aim to address these challenges by developing clinically relevant risk prediction scores for cardio-metabolic diseases specifically tailored to the Indian population. India, which accounts for nearly 18% of the global population, also has a significant diaspora worldwide. This program targets longitudinal collection and bio-banking of samples from over 10 000 employees both working and retirees of the Council of Scientific and Industrial Research and their spouses, with baseline sample collection already completed. During the baseline collection, we gathered multi-parametric data including clinical questionnaires, lifestyle and dietary habits, anthropometric parameters, lung function assessments, liver elastography by Fibroscan, electrocardiogram readings, biochemical data, and molecular assays, including but not limited to genomics, plasma proteomics, metabolomics, and fecal microbiome analysis. In addition to exploring associations between these parameters and their cardio-metabolic outcomes, we plan to employ artificial intelligence algorithms to develop predictive models for phenotypic conditions. This study could pave the way for precision medicine tailored to the Indian population, particularly for the middle-income strata, and help refine the normative values for health and disease indicators in India.

cardio-metabolic↗

PubMed2026

Characterization of the oral microbiota and antimicrobial resistance genes in shelter dogs in Japan.

Companion animals can serve as reservoirs of antimicrobial resistance genes and zoonotic microorganisms, yet information on shelter dogs remains limited. This study characterized the oral microbiota and screened for antimicrobial resistance genes in shelter dogs in Japan. Oral swabs were collected from 81 dogs, microbial genomic DNA was extracted, bacterial communities were profiled by 16S rRNA gene amplicon sequencing, and antimicrobial resistance genes were screened by PCR. We detected genes conferring resistance to several antimicrobial classes, including β-lactams, tetracyclines, macrolide-lincosamide-streptogramin B, phenicols, and sulfonamides. cfxA was detected in all 81 samples, followed by sul1 (66/81), tet(M) and sul2 (65/81), floR (39/81), mecA (17/81), and erm(B) (15/81). We identified potentially pathogenic genera including Capnocytophaga, Pasteurella, Fusobacterium, Campylobacter and Corynebacterium. Microbiome analysis revealed that at the phylum level, Pseudomonadota and Bacteroidota were the most dominant, while Porphyromonas, Frederiksenia and Moraxella were the most prevalent genera. Our findings highlight that (i) the oral microbiota of shelter dogs broadly resembles that reported in companion dogs and (ii) shelter dogs represent an overlooked reservoir of clinically relevant antimicrobial resistance genes and potentially zoonotic bacteria. Therefore, it is necessary to include shelter animals in antimicrobial resistance surveillance programs to capture any potential gaps in the antimicrobial resistance prevalence in companion animals and prevent dissemination of resistant bacteria to humans following adoption of shelter dogs and cats.

antimicrobial resistance gene↗

PubMed2024

Shotgun metagenomic analysis of saliva microbiome suggests Mogibacterium as a factor associated with chronic bacterial osteomyelitis.

Osteomyelitis of the jaw is a severe inflammatory disorder that affects bones, and it is categorized into two main types: chronic bacterial and nonbacterial osteomyelitis. Although previous studies have investigated the association between these diseases and the oral microbiome, the specific taxa associated with each disease remain unknown. In this study, we conducted shotgun metagenome sequencing (≥10 Gb from ≥66,395,670 reads per sample) of bulk DNA extracted from saliva obtained from patients with chronic bacterial osteomyelitis (N = 5) and chronic nonbacterial osteomyelitis (N = 10). We then compared the taxonomic composition of the metagenome in terms of both taxonomic and sequence abundances with that of healthy controls (N = 5). Taxonomic profiling revealed a statistically significant increase in both the taxonomic and sequence abundance of Mogibacterium in cases of chronic bacterial osteomyelitis; however, such enrichment was not observed in chronic nonbacterial osteomyelitis. We also compared a previously reported core saliva microbiome (59 genera) with our data and found that out of the 74 genera detected in this study, 47 (including Mogibacterium) were not included in the previous meta-analysis. Additionally, we analyzed a core-genome tree of Mogibacterium from chronic bacterial osteomyelitis and healthy control samples along with a reference complete genome and found that Mogibacterium from both groups was indistinguishable at the core-genome and pan-genome levels. Although limited by the small sample size, our study provides novel evidence of a significant increase in Mogibacterium abundance in the chronic bacterial osteomyelitis group. Moreover, our study presents a comparative analysis of the taxonomic and sequence abundances of all genera detected using deep salivary shotgun metagenome data. The distinct enrichment of Mogibacterium suggests its potential as a marker to distinguish between patients with chronic nonbacterial osteomyelitis and chronic bacterial osteomyelitis, particularly at the early stages when differences are unclear.

Humans↗

PubMed2025

Dual-approach analysis of gut microbiome in patients with type 1 diabetes and diabetic kidney disease.

BACKGROUND: Type 1 diabetes (T1D) is a multifactorial autoimmune disease mediated by genetic, epigenetic, and environmental factors. Diabetic kidney disease (DKD) is a major complication of diabetes mellitus which affects 30-40% of T1D patients. Increasing evidence suggests the significant role of the microbiome in the progression of both T1D and DKD. MATERIALS AND METHODS: Here we recruited 76 T1D patients and 22 healthy controls and combined data from sigmoid colon biopsy samples analysed with V3-V4 region amplification of 16S rRNA gene and shotgun metagenomics data obtained from faecal samples. Additionally, we compared T1D patients with and without progression of DKD. RESULTS: We observed significant differences within both sample types at various taxonomic and functional levels. T1D patient microbiota detected using biopsy samples had a lower abundance of the Bacteroides genus when compared to healthy controls. Significantly, despite only a few taxonomic differences patients with and without DKD progression were vastly different at the functional pathway level within the faecal samples - we observed 2 and 61 enriched pathways in these groups. respectively, with several of these pathways linked to the mediation of renal function. CONCLUSION: Altogether, we present novel data about microbial signatures relevant to T1D and DKD progression, which partly supports previous data and also presents possible tissue type or population-specific elements. DKD progression is characterized with significant differences within the functional level of the gut microbiome.

Humans↗

PubMed2024

Metagenomic Analysis of Gut Microbiome of Persistent Pulmonary Hypertension of the Newborn.

Persistent pulmonary hypertension of the newborn (PPHN) is one of the most common diseases in the neonatal intensive care unit which severely affects neonatal survival. Gut microbes play an increasingly important role in human health, but there are rarely reported how gut microbiota contribute to PPHN. In our study, the metagenomic sequencing of feces from 12 PPHN's neonates and 8 controls were performed to expose the relation between neonatal gut microbes and PPHN disease. Firstly, we found that the abundance of Actinobacteria, Proteobacteria, Bacteroidetes were significantly increased in PPHN compared with controls, but the Firmicutes components was reduced. And some pathogenic strains (like Vibrio metschnikovii) were significantly enriched in the PPHN compared with controls. Secondly, functional annotation of genes found that PPHN up-regulated transmembrane transport, but down-regulated ribosome and ATP binding. Lastly, microbial metabolic pathway enrichment analysis indicated that some metabolic pathway in PPHN were conflicting and contradictory, showed that an abnormally increased metabolism, disturbed protein synthesis and genomic instability in the PPHN neonate. Our results contribute to understanding the changes in the species and function of gut microbiota in PPHN, thus providing a theoretical basis for the explanation and treatment of PPHN.

Gastrointestinal Microbiome↗

PubMed2025

Uncovering potential biomarkers and metabolic pathways in systemic lupus erythematosus and lupus nephritis through integrated microbiome and metabolome analysis.

OBJECTIVE: This study aims to explore the relationship between gut microbiota and fecal metabolomic profiles in patients with systemic lupus erythematosus (SLE), with and without lupus nephritis (LN), in order to identify potentially relevant biomarkers and better understand their association with disease progression. METHODS: Fecal samples from 15 healthy controls (HC) and 36 SLE patients (18 SLE-nonLN and 18 SLE-LN) were analyzed using 16S rRNA gene sequencing and untargeted metabolomics. Differential microbial taxa and metabolites were identified using Linear Discriminant Analysis Effect Size (LEfSe) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Receiver Operating Characteristic (ROC) curve analyses were used to assess the potential clinical relevance of selected metabolites. RESULTS: Beta diversity analysis demonstrated distinct microbial clustering between groups (p < 0.05). SLE-LN samples showed an increased relative abundance of Proteobacteria and decreased Firmicutes compared to SLE-nonLN. Metabolomic profiling identified multiple differentially abundant metabolites, with notable enrichment in primary bile acid biosynthesis pathways (e.g., Glycocholic acid, AUC = 0.951). In the SLE-nonLN group, increased Glycoursodeoxycholic acid levels (AUC = 0.922) were observed in pathways related to taurine and hypotaurine metabolism. Correlation analysis indicated a negative association between Escherichia-Shigella and bile acid levels (p < 0.01). CONCLUSION: This integrative analysis suggests that patients with SLE and LN harbor distinct gut microbiota and metabolomic profiles. The identified microbial taxa and metabolites may have potential as non-invasive biomarkers and could contribute to a better understanding of SLE pathogenesis and progression.

Humans↗

PubMed2025

Analysis of Blood Microbiome From People Living With HIV and Donors by 16S rRNA Metagenomic Sequencing.

Utilize 16S rRNA sequencing technology to characterize bacterial species susceptible to people living with HIV (PLWH) across different stages. This mapping aims to establish a foundational framework for preventing secondary HIV infections, prolonging patient survival, enhancing quality of life, and advancing the diagnosis, treatment, and research of bacterial co-infections. In this study, we classified the participants into three groups: The blood of donors living with HIV (DI group), AIDS patients who have received ART treatment (PI group), and healthy blood donors as the control group (DH group). Each group was divided into three parallel subgroups, with 30 samples pooled from each parallel group for plasma extraction. As initial processing steps, the nine parallel subgroups were subjected to nucleic acid extraction and PCR amplification targeting the 16SV34 region. The resulting amplified products were subsequently forwarded to a sequencing company. It can be seen from the Venn diagram that the DI groups showed significantly higher bacterial diversity than the PI group and the DH group. The PI group had lower bacterial relative abundance and diversity compared to the DI group, with a community structure more similar to the control group. The DI group is particularly susceptible to several significant pathogens, including Ralstonia, Pseudomonas, Acinetobacter, Methyloversatilis, and Vibrio. The study revealed a greater quantity and diversity of bacteria in the DI blood compared to the PI and DH groups. This observation may be attributed to PI group patients in this study being hospitalized and receiving treatment.

Humans↗

PubMed2026

Metagenomic analysis of the midgut microbiome in Dermacentor abaensis ticks at different feeding states.

Ticks are blood-sucking ectoparasites of humans and animals, ranking second only to mosquitoes as vectors of diseases. Dermacentor abaensis is distributed in Sichuan, Qinghai, and Gansu, China. Because D. abaensis harbors several pathogens, it poses a threat to public health and livestock production. However, the midgut microbiota of D. abaensis at distinct feeding states remains poorly characterized. Adult D. abaensis ticks at various feeding states were collected from yaks in Gansu Province, China. Genomic DNA was extracted from midguts and midgut contents of unfed, partially fed, and fully engorged female D. abaensis. A metagenomic sequencing approach was employed to profile the midgut microflora among three groups. A total of 83 phyla, 908 genera, and 1857 species were annotated across the three groups. At the phylum level, Pseudomonadota, Mucoromycota, and Ascomycota were the most abundant. At the species level, common bacterial species such as Klebsiella pneumoniae and Anaplasma phagocytophilum, alongside viruses and eukaryotes, were detected in all three groups. Unique microorganisms were also observed in each group: unfed (n = 305), partially fed (n = 59), and fully engorged (n = 20). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis suggested that the D. abaensis microbiome contains a relatively high abundance of functional genes involved in lipid and amino acid metabolism across the three different feeding states. These findings indicate that while core microbial taxa are shared in the midgut of female D. abaensis, observable trends suggest variations in microbial diversity and composition as blood-feeding progresses. The present study provides a descriptive baseline of the midgut microbial composition of D. abaensis, which may inform future studies on tick biology and the ecology of tick-borne pathogens.

Animals↗

PubMed2026

A streamlined workflow for high throughput metaproteomic analysis of the rumen microbiome.

Metaproteomics can provide direct functional insights into complex microbial communities, yet its application in rumen research remains limited due to labor-intensive and low-throughput sample preparation workflows before the MS analysis. This work aimed to develop and characterize a streamlined, high throughput metaproteomic workflow optimized for rumen samples. Key steps, including microbial cell extraction, cell lysis, protein digestion, and LC-MS/MS acquisition, were systematically assessed and optimized to reduce hands-on time while maintaining deep proteome coverage. The optimized workflow integrates a minimized cell extraction protocol using 0.5 g starting material and in-solution tryptic digestion. Application of the final workflow to 72 samples from in vitro fermentation revealed that biological variability between inocula dominated technical variability, which remained moderate (median CV of 21-24% across batches). Overall, the optimized workflow supports robust taxonomic and functional characterization of the rumen microbiome with improved scalability. These advances provide a foundation for applying metaproteomics to larger experimental designs, including nutritional trials and cohort studies, thereby enabling broader functional interrogation of rumen microbial ecosystems. SIGNIFICANCE: This study addresses current limitations in the application of metaproteomics to rumen microbiome research by developing a streamlined and scalable sample preparation workflow. By optimizing key steps and reducing sample input while maintaining reproducibility and proteome coverage, this work enables more efficient processing of larger sample sets. These advances support the broader use of metaproteomics in rumen studies and facilitate functional investigations relevant to animal nutrition and sustainable livestock production.

Animals↗

PubMed2026

Dual-transcriptomic analysis of human nasal transcriptome and microbiome reveals host-bacteria associations in symptomatic respiratory infection.

BACKGROUND: The human nasopharynx is colonized by a diverse community of commensal microbiota linked to many respiratory diseases, yet their associations with the host remain unclear. RESULTS: In this study, we introduced a dual-transcriptomics analysis strategy, which can characterize the host transcriptome and microbiome from nasal samples simultaneously. We applied this workflow to a local SARS-CoV-2 cohort with 76 asymptomatic infected patients, among whom 52 (68.42%) developed symptomatic infection during a 1-week follow-up period. Nasal swabs were collected from all 76 patients at enrollment and from 73 patients at one-week later follow-up. We detected a median of 8.94% reads that did not map to the human genome across all 149 samples, among which around half (median 49.68%) were successfully mapped to microbiome genome. Meta-transcriptomic analysis detected significantly higher SARS-related coronavirus loads in samples from the symptomatic group at enrollment (P = 0.004), and both groups showed decreased loads one week later (symptomatic, P = 0.001; asymptomatic, P = 0.035). Compared with benchmarking 16 S rRNA sequencing on 53 samples, our computational strategy showed high correlation of relative abundance in all top 20 genera (median Rho = 0.90, Pmax < 0.001). A total of 670 bacteria species were identified to show a relative abundance ≥ 0.01% in at least 10% samples. Differential abundance analysis identified 76 species (DASs) from six phyla with significantly decreased abundance in samples from the symptomatic group (log2(fold change or FC) < -1 and adjusted P < 0.05) compared to the asymptomatic group at enrollment. Integrating these symptom-associated DASs with host's gene expression using an expression quantitative trait bacteria (eQTB) model, we found 45 symptom-associated DASs identified at enrollment were significantly associated with one to 14 genes (adjusted P < 0.05). GSEA showed a series of symptom-associated DASs were significantly correlated with pathways related to olfactory function, keratinocyte differentiation, and DNA methylation. CONCLUSIONS: In summary, our dual-transcriptomic analysis strategy effectively characterized host-microbiome associations, offering insights into microbial contributions to respiratory diseases.

Humans↗

PubMed2026

Comprehensive evaluation of new sequencer T20 and well-established T7 with 507 human samples.

The DNBSEQ-T20×2 (T20) sequencer, developed by MGI Tech, enables cost-effective human whole-genome sequencing (WGS) at 30× coverage for less than $100 per genome. Here, we evaluate the sequencing performance and data quality of the T20 platform by benchmarking it against the established DNBSEQ-T7 (T7) sequencer using 507 samples derived from blood (N = 75), stool (N = 242), and saliva (N = 190). The T20 exhibited lower sequencing quality metrics compared with the T7, with Q20 scores of 95.76%-95.83% and Q30 scores of 87.25%-87.40%, compared with 97.81%-97.93% and 93.26%-93.60%, respectively, for T7 data. Quality differences were more evident toward the end of reads, and PCR-free libraries sequenced on the T20 showed similar reductions in quality scores. The median empirical base error rate estimated from 102 ZymoBIOMICS samples was 0.33%. The T20 demonstrated comparable coverage uniformity to the T7 and showed high concordance in microbiome composition analysis, with a median Bray-Curtis dissimilarity of 0.02. Variant calling performance was highly consistent between the two platforms. Among variants with non-missing genotype calls on both platforms, 94.92% of SNPs and 87.20% of InDels showed concordant genotypes between T20 and T7. Overall, the T20 delivers reliable sequencing accuracy and reproducibility for large-scale genomic and microbiome studies, providing a cost-effective alternative for high-throughput sequencing applications.

Metagenomics↗

PubMed2026

Deciphering the Microbiome-Gut-Eye Axis: A Mendelian Randomization Analysis of the Causal Influence of Gut Microbiota on Myopia.

INTRODUCTION: The intricate relationship between the gut microbiome and myopia is increasingly recognized, underscoring the need to explore its causal dynamics. Despite emerging evidence, the influence of Gut Microbiota (GM) on ocular development remains underexplored. METHODS: This study utilized Mendelian Randomization (MR) to investigate the causal impact of GM on the development of myopia. Instrumental variables (IVs) were identified from Genome-Wide Association Studies (GWAS), focusing on genetic variants significantly associated with microbiome composition. A comprehensive array of MR techniques was applied to ensure a robust estimation of causal effects and to adjust for potential confounders and pleiotropy. RESULTS: The Inverse-Variance Weighted (IVW) method was used to identify significant associations between GM and myopia. Increased risk of myopia was linked to the class Betaproteobacteria (OR=1.01, 95% CI 1.004-1.017, P=0.003), the order Burkholderiales (OR=1.009, 95% CI 1.001-1.016, P=0.02), the family Oxalobacteraceae (OR=1.005, 95% CI 1.001-1.01, P=0.023), and several genera including Eubacterium xylanophilum group (OR=1.007, 95% CI 1.001-1.013, P=0.033), and Bifidobacterium (OR=1.005, 95% CI 1-1.01, P=0.038). Protective effects were noted for the order Mollicutes RF9 (OR=0.994, 95% CI 0.99-0.999, P=0.014), the genus Allisonella (OR=0.996, 95% CI 0.993-0.999, P=0.019), the genus Lachnospiraceae UCG001 (OR=0.994, 95% CI 0.989-1, P=0.045), and the family Enterobacteraceae (OR=0.991, 95% CI 0.982-1, P=0.047) and order Enterobacteriales (OR=0.991, 95% CI 0.982-1, P=0.047). Sensitivity analyses further confirmed the robustness of these findings. DISCUSSION: This study provides causal evidence for the "Microbiome-Gut-Eye Axis" in myopia development, identifying specific gut microbiota that influence myopia risk. These findings suggest potential for microbiota-targeted interventions, warranting further research in diverse populations. CONCLUSIONS: The findings support the "Microbiome-Gut-Eye Axis" as a potential factor in myopia pathogenesis and highlight microbiota-targeted interventions as novel therapeutic strategies for managing myopia. This study lays the groundwork for further research on how modifying GM can influence eye health and offers new perspectives on preventive health strategies.

Humans↗

PubMed2006

Metagenomic analysis of the human distal gut microbiome.

The human intestinal microbiota is composed of 10(13) to 10(14) microorganisms whose collective genome ("microbiome") contains at least 100 times as many genes as our own genome. We analyzed approximately 78 million base pairs of unique DNA sequence and 2062 polymerase chain reaction-amplified 16S ribosomal DNA sequences obtained from the fecal DNAs of two healthy adults. Using metabolic function analyses of identified genes, we compared our human genome with the average content of previously sequenced microbial genomes. Our microbiome has significantly enriched metabolism of glycans, amino acids, and xenobiotics; methanogenesis; and 2-methyl-d-erythritol 4-phosphate pathway-mediated biosynthesis of vitamins and isoprenoids. Thus, humans are superorganisms whose metabolism represents an amalgamation of microbial and human attributes.

Adult↗

PubMed2026

Spatial scaling of metagenomic diversity reveals ecological disruption in the gut microbiome of gout patients.

Gout, a painful inflammatory arthritis, is characterized by hyperuricemia and monosodium urate crystal deposition, with growing evidence linking its pathogenesis to gut microbiome dysbiosis. However, traditional diversity metrics fail to capture the complex spatial organization of microbial communities. This study addresses this gap by applying the novel metagenomic Diversity-Area Relationship (m-DAR) model to investigate scaling laws in the gout microbiome-quantifying how metagenomic diversity changes with the number of individuals sampled. Our analysis of gut microbiomes from gout patients and healthy controls revealed fundamental ecological disruptions. We found that gout microbiomes exhibited significantly altered scaling patterns: they showed greater inter-individual dissimilarity (higher z-values) at the level of rare genes (q = 0), but weaker scaling of dominant genes (q = 1-3) compared to healthy controls. Crucially, the maximal accrual diversity (MAD) was substantially lower in gout patients, indicating a severely constrained potential for total microbial gene diversity. Furthermore, profiling of metagenomic functional gene clusters (MFGCs) uncovered widespread functional perturbations, including increased diversity scaling for carbohydrate-active enzymes (CAZy) but decreased scaling in essential metabolic pathways (KEGG, KO). These results demonstrate that the gout gut microbiome is defined by a loss of ecological structure, featuring reduced homogeneity in dominant taxa, expanded rare biosphere variation, and an overall collapsed diversity capacity. This work introduces an ecological framework for characterizing dysbiosis in gout that complements traditional diversity metrics and may inform the development of microbiome-based therapeutic strategies. Further research is needed to translate these ecological patterns into clinical applications.

Humans↗

PubMed2026

Intra-individual genetic diversity of vaginal Lactobacillus crispatus revealed through citizen science-driven isolation and pangenome analysis.

AIMS: A vaginal microbiome dominated by Lactobacillus crispatus is associated with positive reproductive and sexual health outcomes, yet intra-individual genetic diversity within this species remains largely unexplored. This study characterized inter- and intra-individual genomic variation in L. crispatus strains isolated through a citizen science initiative and assessed implications for multi-strain probiotic development. METHODS AND RESULTS: Fifty-three women participated in this citizen science project. Self-sampling resulted in 48 shotgun metagenomes. Twenty-two participants isolated their own L. crispatus strains using selective enrichment and LAMP-based species confirmation, resulting in 53 whole-genome-sequenced isolates. Lactobacillus crispatus dominated 20 of 48 metagenomes (50.1%-99.6% relative abundance). Pangenome analysis revealed 3456 gene families, of which 43.7% were core and 56.3% accessory. A 14-kb plasmid harbouring a Fic-domain toxin-antitoxin protein, but devoid of antimicrobial resistance genes, was present in 44 of 53 strains. Strains from the same individual clustered closely together yet harboured 1-123 gene differences. Intra-individual variation was observed in the pullulanase type I gene required for glycogen degradation: 40 strains were predicted to grow on glycogen, six showed genetic disruptions with unknown consequences, and seven were predicted to lack this ability entirely. Variation within individuals was also found for bacteriocin classes and CRISPR-Cas genes. CONCLUSIONS: Substantial functional diversity exists within L. crispatus, even among strains from the same individual, supporting the rationale for multi-strain vaginal probiotics. This citizen science approach enabled discovery of host-specific adaptations while ensuring participant ownership of their strains.

Female↗

PubMed2024

Metagenomic analysis demonstrates distinct changes in the gut microbiome of Kawasaki diseases children.

BACKGROUND: Kawasaki disease (KD) has been considered as the most common required pediatric cardiovascular diseases among the world. However, the molecular mechanisms of KD were not fully underlined, leading to a confused situation in disease management and providing precious prognosis prediction. The disorders of gut microbiome had been identified among several cardiovascular diseases and inflammation conditions. Therefore, it is urgent to elucidate the characteristics of gut microbiome in KD and demonstrate its potential role in regulating intravenous immunoglobulin (IVIG) resistance and coronary artery injuries. METHODS: A total of 96 KD children and 62 controls were enrolled in the study. One hundred forty fecal samples had been harvested from KD patients, including individuals before or after IVIG treatment, with or without early coronary artery lesions and IVIG resistance. Fecal samples had been collected before and after IVIG administration and stored at -80°C. Then, metagenomic analysis had been done using Illumina NovaSeq 6000 platform. After that, the different strains and functional differences among comparisons were identified. RESULTS: First, significant changes had been observed between KD and their controls. We found that the decrease of Akkermansia muciniphila, Faecalibacterium prausnitzii, Bacteroides uniformis, and Bacteroides ovatus and the increase of pathogenic bacteria Finegoldia magna, Abiotrophia defectiva, and Anaerococcus prevotii perhaps closely related to the incidence of KD. Then, metagenomic and responding functional analysis demonstrated that short-chain fatty acid pathways and related strains were associated with different outcomes of therapeutic efficacies. Among them, the reduction of Bacteroides thetaiotaomicron, the enrichment of Enterococcus faecalis and antibiotic resistance genes had been found to be involved in IVIG resistance of KD. Moreover, our data also revealed several potential pathogenetic microbiome of that KD patients with coronary artery lesions. CONCLUSION: These results strongly proved that distinct changes in the gut microbiome of KD and the dysfunction of gut microbiomes should be responsible for the pathogenesis of KD and significantly impact the prognosis of KD.

Humans↗

PubMed2025

Establishing the ELIXIR Microbiome Community.

Microbiome research has grown substantially over the past decade in terms of the range of biomes sampled, identified taxa, and the volume of data derived from the samples. In particular, experimental approaches such as metagenomics, metabarcoding, metatranscriptomics and metaproteomics have provided profound insights into the vast, hitherto unknown, microbial biodiversity. The ELIXIR Marine Metagenomics Community, initiated amongst researchers focusing on marine microbiomes, has concentrated on promoting standards around microbiome-derived sequence analysis, as well as understanding the gaps in methods and reference databases, and identifying solutions to the computational overheads of performing such analyses. Nevertheless, the methods used and the challenges faced are not confined to marine microbiome studies, but are broadly applicable to other biomes. Thus, expanding this Marine Metagenomics Community to a more inclusive ELIXIR Microbiome Community will enable it to encompass a broader range of biomes and link expertise across 'omics technologies. Furthermore, engaging with a large number of researchers will improve the efficiency and sustainability of bioinformatics infrastructure and resources for microbiome research (standards, data, tools, workflows, training), which will enable a deeper understanding of the function and taxonomic composition of the different microbial communities.

Computational Biology↗