SpLitteR: diploid genome assembly using TELL-Seq linked-reads and assembly graphs.

Background: Recent advances in long-read sequencing technologies enabled accurate and contiguous de novo assemblies of large genomes and metagenomes. However, even long and accurate high-fidelity (HiFi) reads do not resolve repeats that are longer than the read lengths. This limitation negatively affects the contiguity of diploid genome assemblies since two haplomes share many long identical regions. To generate the telomere-to-telomere assemblies of diploid genomes, biologists now construct their HiFi-based phased assemblies and use additional experimental technologies to transform them into more contiguous diploid assemblies. The barcoded linked-reads, generated using an inexpensive TELL-Seq technology, provide an attractive way to bridge unresolved repeats in phased assemblies of diploid genomes. Results: We developed the SpLitteR tool for diploid genome assembly using linked-reads and assembly graphs and benchmarked it against state-of-the-art linked-read scaffolders ARKS and SLR-superscaffolder using human HG002 genome and sheep gut microbiome datasets. The benchmark showed that SpLitteR scaffolding results in 1.5-fold increase in NGA50 compared to the baseline LJA assembly and other scaffolders while introducing no additional misassemblies on the human dataset. Conclusion: We developed the SpLitteR tool for assembly graph phasing and scaffolding using barcoded linked-reads. We benchmarked SpLitteR on assembly graphs produced by various long-read assemblers and have demonstrated that TELL-Seq reads facilitate phasing and scaffolding in these graphs. This benchmarking demonstrates that SpLitteR improves upon the state-of-the-art linked-read scaffolders in the accuracy and contiguity metrics. SpLitteR is implemented in C++ as a part of the freely available SPAdes package and is available at https://github.com/ablab/spades/releases/tag/splitter-preprint.

Intratumoral and fecal microbiota reveals microbial markers associated with gastric carcinogenesis.

Background: The relationship between dysbiosis of the gastrointestinal microbiota and gastric cancer (GC) has been extensively studied. However, microbiota alterations in GC patients vary widely across studies, and reproducible diagnostic biomarkers for early GC are still lacking in multiple populations. Thus, this study aimed to characterize the gastrointestinal microbial communities involved in gastric carcinogenesis through a meta-analysis of multiple published and open datasets. Methods: We analyzed 16S rRNA sequencing data from 1,642 gastric biopsy samples and 394 stool samples across 11 independent studies. VSEARCH, QIIME and R packages such as vegan, phyloseq, cooccur, and random forest were used for data processing and analysis. PICRUSt software was employed to predict functions. Results: The α-diversity results indicated significant differences in the intratumoral microbiota of cancer patients compared to non-cancer patients, while no significant differences were observed in the fecal microbiota. Network analysis showed that the positive correlation with GC-enriched bacteria increased, and the positive correlation with GC-depleted bacteria decreased compared to healthy individuals. Functional analyses indicated that pathways related to carbohydrate metabolism were significantly enriched in GC, while biosynthesis of unsaturated fatty acids was diminished. Additionally, we investigated non-Helicobacter pylori (HP) commensals, which are crucial in both HP-negative and HP-positive GC. Random forest models, constructed using specific taxa associated with GC identified from the LEfSe analysis, revealed that the combination of Lactobacillus and Streptococcus included alone could effectively discriminate between GC patients and healthy individuals in fecal samples (area under the curve (AUC) = 0.7949). This finding was also validated in an independent cohort (AUC = 0.7712). Conclusions: This study examined the intratumoral and fecal microbiota of GC patients from a dual microecological perspective and identified Lactobacillus, Streptococcus, Roseburia, Faecalibacterium and Phascolarctobacterium as intratumoral and intestinal-specific co-differential bacteria. Furthermore, it confirmed the validity of the combination of Lactobacillus and Streptococcus as GC-specific microbial markers across multiple populations, which may aid in the early non-invasive diagnosis of GC.

Fecal shedding of SARS-CoV-2 in infants born to SARS-CoV-2 positive mothers: a pilot study.

Background: Fecal shedding of SARS-CoV-2 occurs during infection, particularly in pediatric populations. The gut microbiota are associated with resistance to enteric pathogens. COVID-19 is associated with alterations to the gut microbiome. We hypothesized that the gut microbiome of infants born to SARS-CoV-2+ mothers differs between infants with and without fecal shedding of the virus. Methods: We enrolled 10 infants born to SARS-CoV-2+ mothers. We used qPCR on fecal RNA to test for SARS-CoV-2 and 16S rRNA gene sequencing of the V4 region to assess the gut microbiome. Infant SARS-CoV-2 status from nasal swabs was abstracted from medical records. Results: Of the 10 included infants, nine were tested for SARS-CoV-2 by nasal swab with 1 testing positive. Four infants, including the nasal swab positive infant, had at least one sample with detectable levels of SARS-CoV-2 fecal shedding. Detection of both SARS-CoV-2 genes in feces was associated with increased gut alpha diversity compared to no detection by a linear mixed effects model (p < 0.001). Detection of both SARS-CoV-2 genes was associated with increased levels Erysipelotrichaceae, Lactobacillaceae, and Ruminococceae by MaAsLin2. Conclusion: Fecal shedding of SARS-CoV-2 occurs in infants who test negative on nasal swabs and is associated with differences in the gut microbiome.

Causal relationships between gut microbiota and depression/anxiety disorders: A 2-sample Mendelian randomization study.

Evidence shows that the composition of the gut microbiota (GM) is associated with depression and anxiety disorders. However, the causal relationship between them remains controversial. To investigate the potential causal relationship between the GM and depression/anxiety disorders and to identify specific bacterial taxa, we conducted a 2-sample Mendelian randomization (MR) analysis on the gut microbiome implicated in depression and anxiety disorders. We incorporated summary data from genome-wide association studies (GWAS) of the microbiome derived from 7738 individuals in the Dutch Microbiome Project and 18,340 individuals in the MiBioGen consortium as our exposure variable. Concurrently, the GWAS of depression and anxiety disorders was employed as our outcome variable. The principal estimates were procured using the inverse-variance weighted test complemented by 4 robust methods: MR Egger, weighted median, simple mode, and weighted mode. In addition, we performed comprehensive sensitivity and directionality analyses. The results showed that 5 bacterial taxa were positively correlated with depression, 6 were negatively correlated; 5 were positively correlated with anxiety disorders, and 11 were negatively correlated. This study provides new insights into the connection between the GM and the pathogenesis of depression and anxiety disorders and offers new perspectives for the diagnosis and treatment of these disorders.

Evaluation of diagnostic value and Mendelian randomization study of appendicitis hub genes obtained by WGCNA analysis.

The timely and precise diagnosis of appendicitis was deemed essential. This study sought to examine the diagnostic significance of hub genes linked to appendicitis and to delve deeper into the pathophysiology of the condition. Differential gene expression analysis revealed distinct genes in the appendicitis group compared to other abdominal pain group, while weighted gene co-expression network analysis identified appendicitis-associated modules. Further analysis of common genes was conducted using Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analysis. The diagnostic efficiency of hub genes was explored through the use of nomograms and receiver operator characteristic curves. Additionally, immunoinfiltration analysis was performed to investigate the immune cell infiltration in both groups. The causal relationship between hub genes and appendicitis, as well as gut microbiota and appendicitis, was ultimately examined through Mendelian randomization. By conducting differential expression analysis and weighted gene co-expression network analysis, a total of 757 common genes were identified. Subsequent Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analyses revealed that these common genes were primarily associated with positive regulation of cell adhesion, focal adhesion, protein serine kinase activity, and amyotrophic lateral sclerosis. Utilizing Cytoscape software, the top 10 genes with the highest degree of interaction were identified as RPS3A, RPSA, RPL5, RPL37A, RPS27L, FLT3LG, ARL6IP1, RPL32, MRPL3, and GSPT1. Evaluation using nomograms and receiver operator characteristic curves demonstrated the diagnostic value of these hub genes. Ultimately, a causal relationship between hub genes and appendicitis was not identified in our study. Nevertheless, our findings indicate that appendicitis is correlated with 9 gut microbiota. This study identified 5 hub genes, specifically HSP90AA1, RPL5, MYC, CD44, and RPS3A, which exhibit diagnostic significance of appendicitis. Furthermore, the elucidation of these hub genes aids in enhancing our comprehension of the molecular pathways implicated in the development of appendicitis.

Gut metagenomes of Asian octogenarians reveal metabolic potential expansion and distinct microbial species associated with aging phenotypes.

While rapid demographic changes in Asia are driving the incidence of chronic aging-related diseases, the limited availability of high-quality in vivo data hampers our ability to understand complex multi-factorial contributions, including gut microbial, to healthy aging. Leveraging a well-phenotyped cohort of community-living octogenarians in Singapore, we used deep shotgun-metagenomic sequencing for high-resolution taxonomic and functional characterization of their gut microbiomes (n = 234). Joint species-level analysis with other Asian cohorts identified distinct age-associated shifts characterized by reduction in microbial richness, and specific Alistipes and Bacteroides species enrichment (e.g., Alistipes shahii and Bacteroides xylanisolvens). Functional analysis confirmed these changes correspond to metabolic potential expansion in aging towards alternate pathways synthesizing and utilizing amino-acid precursors, vis-à-vis dominant microbial guilds producing butyrate in gut from pyruvate (e.g., Faecalibacterium prausnitzii, Roseburia inulinivorans). Extending these observations to key clinical markers helped identify >10 robust microbial associations to inflammation, cardiometabolic and liver health, including potential probiotic species (e.g., Parabacteroides goldsteinii) and pathobionts (e.g., Klebsiella pneumoniae), highlighting the microbiome's role as biomarkers and potential targets for promoting healthy aging.

Temporal stability of the rumen microbiome and its longitudinal associations with performance traits in beef cattle.

The rumen microbiome is the focus of a growing body of research, mostly based on investigation of rumen fluid samples collected once from each animal. Exploring the temporal stability of rumen microbiome profiles is imperative, as it enables evaluating the reliability of findings obtained through single-timepoint sampling. We explored the temporal stability of rumen microbiomes considering taxonomic and functional aspects across the 7-month growing-finishing phase spanning 6 timepoints. We identified a temporally stable core microbiome, encompassing 515 microbial genera (e.g., Methanobacterium) and 417 microbial KEGG genes (e.g., K00856-adenosine kinase). The temporally stable core microbiome profiles collected from all timepoints were strongly associated with production traits with substantial economic and environmental impact (e.g., average daily gain, daily feed intake, and methane emissions); 515 microbial genera explained 45-83%, and 417 microbial genes explained 44-83% of their phenotypic variation. Microbiome profiles influenced by the bovine genome explained 54-87% of the genetic variation of bovine traits. Overall, our results provide evidence that the temporally stable core microbiome identified can accurately predict host performance traits at phenotypic and genetic level based on a single timepoint sample taken as early as 7 months prior to slaughter.

Metagenomic analysis reveals high diversity of gut viromes in yaks (Bos grunniens) from the Qinghai-Tibet Plateau.

The Qinghai-Tibet Plateau (QTP), renowned for its exceptional biological diversity, is home to numerous endemic species. However, research on the virology of vulnerable vertebrates like yaks remains limited. In this study, our objective was to use metagenomics to provide a comprehensive understanding of the diversity and evolution of the gut virome in yak populations across different regions of the QTP. Our findings revealed a remarkably diverse array of viruses in the gut of yaks, including those associated with vertebrates and bacteriophages. Notably, some vertebrate-associated viruses, such as astrovirus and picornavirus, showed significant sequence identity across diverse yak populations. Additionally, we observed differences in the functional profiles of genes carried by the yak gut virome across different regions. Moreover, the virus-bacterium symbiotic network that we discovered holds potential significance in maintaining the health of yaks. Overall, this research expands our understanding of the viral communities in the gut of yaks and highlights the importance of further investigating the interactions between viruses and their hosts. These data will be beneficial for revealing the crucial role that viruses play in the yak gut ecology in future studies.

Global transcriptomic network analysis of the crosstalk between microbiota and cancer-related cells in the oral-gut-lung axis.

Background: The diagnosis and treatment of lung, colon, and gastric cancer through the histologic characteristics and genomic biomarkers have not had a strong impact on the mortality rates of the top three global causes of death by cancer. Methods: Twenty-five transcriptomic analyses (10 lung cancer, 10 gastric cancer, and 5 colon cancer datasets) followed our own bioinformatic pipeline based on the utilization of specialized libraries from the R language and DAVID´s gene enrichment analyses to identify a regulatory metafirm network of transcription factors and target genes common in every type of cancer, with experimental evidence that supports its relationship with the unlocking of cell phenotypic plasticity for the acquisition of the hallmarks of cancer during the tumoral process. The network's regulatory functional and signaling pathways might depend on the constant crosstalk with the microbiome network established in the oral-gut-lung axis. Results: The global transcriptomic network analysis highlighted the impact of transcription factors (SOX4, TCF3, TEAD4, ETV4, and FOXM1) that might be related to stem cell programming and cancer progression through the regulation of the expression of genes, such as cancer-cell membrane receptors, that interact with several microorganisms, including human T-cell leukemia virus 1 (HTLV-1), the human papilloma virus (HPV), the Epstein-Barr virus (EBV), and SARS-CoV-2. These interactions can trigger the MAPK, non-canonical WNT, and IFN signaling pathways, which regulate key transcription factor overexpression during the establishment and progression of lung, colon, and gastric cancer, respectively, along with the formation of the microbiome network. Conclusion: The global transcriptomic network analysis highlights the important interaction between key transcription factors in lung, colon, and gastric cancer, which regulates the expression of cancer-cell membrane receptors for the interaction with the microbiome network during the tumorigenic process.

Causation between the gut microbiota and inguinal hernia: a two-sample double-sided Mendelian randomization study.

Inguinal hernias are the most common type of enterocele and are frequently caused by defects in the abdominal wall muscles in the groin area. Numerous animal models and human studies have shown that the gut microbiota is associated with skeletal muscle aging and loss. However, the causation between the gut microbiota and inguinal hernia remains unclear. To reveal the causal association between the gut microbiota and inguinal hernia, we conducted a two-sample double-sided Mendelian randomization analysis. We used genome-wide association analysis (GWAS) summary statistics of the gut microbiota from the MiBioGen consortium and GWAS statistics of inguinal hernia from the FinnGen R10 database. The causation between the gut microbiota and inguinal hernia was explored through the inverse variance weighted (IVW) method, MR Egger regression method, weighted median method, weighted model method, and simple model method. Sensitivity analysis was used to test whether the Mendelian randomization analysis results were reliable. Reverse Mendelian randomization was used to conduct effect analysis and sensitivity analysis using the entire gut microbiota as the outcome. The IVW results indicated that Verrucomicrobia, Lactobacilliales, Clostridiaceae1, Butyricococcus, Categorybacter, Hungatella, Odoribacter, and Olsenella had a direct negative causation with the gut microbiota. The reverse Mendelian Randomization results showed that Eubacterium brachygroup, Eubacterium eligensgroup, Eubacterium xylanophilumgroup, Coprococcus3, Ruminococcus1, and Senegalimassilia were directly related to inguinal hernia. The bilateral sensitivity analysis revealed no heterogeneity or horizontal pleiotropy. The results confirmed that 8 bacterial traits had a negative causation with inguinal hernia. Reverse MR analysis revealed a positive correlation between inguinal hernia and 6 bacterial traits. Modulating the diversity and components of the gut microbiota is envisaged to contribute to improving the incidence and prognosis of inguinal hernia.

Exploring the effects of short-course antibiotics on children's gut microbiota by using 16S rRNA gene sequencing: a case-control study.

BACKGROUND: With the widespread use of antibiotics, more attention has been paid to their side effects. We paid extra attention to the impact of antibiotics on children's bodies. Therefore, we analyzed the characteristic changes in the gut microbiota of children after antibiotic treatment to explore the pathogenesis of antibiotic-associated diseases in more depth and to provide a basis for diagnosis and treatment. METHODS: We recruited 28 children with bronchopneumonia in the western district of Zhuhai, China, and divided them into three treatment groups based on antibiotic type. We took stool samples from children before and 3-5 days after antibiotic treatment. 16S rRNA gene sequencing was used to analyze the effects of antibiotic therapy on the gut microbiota of children. Continuous nonparametric data are represented as median values and analyzed using the Wilcoxon rank-sum test. RESULTS: While alpha diversity analysis found no significant changes in the mean abundance of the gut microbiota of children after a short course of antibiotic treatment, beta diversity analysis demonstrated significant changes in the composition and diversity of the gut microbiota of children even after a short course of antibiotic therapy. We also found that meloxicillin sulbactam can inhibit the growth of Proteobacteria, Bacteroidetes, and Verrucomicrobia, ceftriaxone inhibits Verrucomicrobia and Bacteroides, and azithromycin inhibits Fusobacteria, Actinobacteria, Proteobacteria, and Verrucomicrobia. We further performed a comparative analysis at the genus level and found significantly different clusters in each group. Finally, we found that azithromycin had the greatest effect on the metabolic function of intestinal microbiota, followed by ceftriaxone, and no significant change in the metabolic process of intestinal microbiota after meloxicillin sulbactam treatment. CONCLUSIONS: Antibiotic treatment significantly affects the diversity of intestinal microbiota in children, even after a short course of antibiotic treatment. Different classes of antibiotics affect diverse microbiota primarily, leading to varying alterations in metabolic function. Meanwhile, we identified a series of intestinal microbiota that differed significantly after antibiotic treatment. These groups of microbiota could be used as biomarkers to provide an additional basis for diagnosing and treating antibiotic-associated diseases.

The Response of Murine Gut Microbiome in the Presence of Altered rpoS Gene of Klebsiella pneumoniae.

The murine model is invaluable for studying intricate interactions among gut microbes; hosts; and diseases. However; the impact of genetic variations in the murine microbiome; especially in disease contexts such as Klebsiella pneumoniae (Kp) infection; still needs to be explored. Kp; an opportunistic global pathogen; is becoming increasingly prevalent in regions like Asia; especially China. This study explored the role of the gut microbiota during Kp infection using mouse model; including wild-type and rpoS mutants of Kp138; KpC4; and KpE4 from human; maize; and ditch water; respectively. Under stress conditions; RpoS reconfigures global gene expression in bacteria; shifting the cells from active growth to survival mode. Our study examined notable differences in microbiome composition; finding that Lactobacillus and Klebsiella (particularly in WKp138) were the most abundant genera in mice guts at the genus level in all wild-type treated mice. In contrast; Firmicutes were predominant in the healthy control mice. Furthermore; Clostridium was the dominant genus in all mutants; mainly in ∆KpC4; and was absent in wild-type treated mice. Differential abundance analysis identified that these candidate taxa potentially influence disease progression and pathogen virulence. Functional prediction analysis showed that most bacterial groups were functionally involved in biosynthesis; precursor metabolites; degradation; energy generation; and metabolic cluster formation. These findings challenge the conventional understanding and highlight the need for nuanced interpretations in murine studies. Additionally; this study sheds light on microbiome-immune interactions in K. pneumoniae infection and proposes new potential therapeutic strategies.

Deciphering DNA Methylation in Gestational Diabetes Mellitus: Epigenetic Regulation and Potential Clinical Applications.

Gestational diabetes mellitus (GDM) represents a prevalent complication during pregnancy, exerting both short-term and long-term impacts on maternal and offspring health. This review offers a comprehensive outline of DNA methylation modifications observed in various maternal and offspring tissues affected by GDM, emphasizing the intricate interplay between DNA methylation dynamics, gene expression, and the pathogenesis of GDM. Furthermore, it explores the influence of environmental pollutants, maternal nutritional supplementation, and prenatal gut microbiota on GDM development through alterations in DNA methylation profiles. Additionally, this review summarizes recent advancements in DNA methylation-based diagnostics and predictive models in early GDM detection and risk assessment for subsequent type 2 diabetes. These insights contribute significantly to our understanding of the epigenetic mechanisms underlying GDM development, thereby enhancing maternal and fetal health outcomes and advocating further efforts in this field.

Baseline gut microbiota diversity and composition and albendazole efficacy in hookworm-infected individuals.

Soil-transmitted helminth (STH) infections account for a significant global health burden, necessitating mass drug administration with benzimidazole-class anthelmintics, such as albendazole (ALB), for morbidity control. However, ALB efficacy shows substantial variability, presenting challenges for achieving consistent treatment outcomes. We have explored the potential impact of the baseline gut microbiota on ALB efficacy in hookworm-infected individuals through microbiota profiling and machine learning (ML) techniques. Our investigation included 89 stool samples collected from hookworm-infected individuals that were analyzed by microscopy and quantitative PCR (qPCR). Of these, 44 were negative by microscopy for STH infection using the Kato-Katz method and qPCR 21 days after treatment, which entails a cure rate of 49.4%. Microbiota characterization was based on amplicon sequencing of the V3-V4 16S ribosomal RNA gene region. Alpha and beta diversity analyses revealed no significant differences between participants who were cured and those who were not cured, suggesting that baseline microbiota diversity does not influence ALB treatment outcomes. Furthermore, differential abundance analysis at the phylum, family and genus levels yielded no statistically significant associations between bacterial communities and ALB efficacy. Utilizing supervised ML models failed to predict treatment response accurately. Our investigation did not provide conclusive insights into the relationship between gut microbiota and ALB efficacy. However, the results highlight the need for future research to incorporate longitudinal studies that monitor changes in the gut microbiota related to the infection and the cure with ALB, as well as functional metagenomics to better understand the interaction of the microbiome with the drug, and its role, if there is any, in modulating anthelmintic treatment outcomes in STH infections. Interdisciplinary approaches integrating microbiology, pharmacology, genetics and data science will be pivotal in advancing our understanding of STH infections and optimizing treatment strategies globally.

Metagenomic profiling of gut microbiota in Fall Armyworm (Spodoptera frugiperda) larvae fed on different host plants.

BACKGROUND: The fall armyworm (FAW, Spodoptera frugiperda) is a polyphagous pest known for causing significant crop damage. The gut microbiota plays a pivotal role in influencing the biology, physiology and adaptation of the host. However, understanding of the taxonomic composition and functional characteristics of the gut microbiota in FAW larvae fed on different host plants remains limited. METHODS: This study utilized metagenomic sequencing to explore the structure, function and antibiotic resistance genes (ARGs) of the gut microbiota in FAW larvae transferred from an artificial diet to four distinct host plants: maize, sorghum, tomato and pepper. RESULTS: The results demonstrated significant variations in gut microbiota structure among FAW larvae fed on different host plants. Firmicutes emerged as the dominant phylum, with Enterococcaceae as the dominant family and Enterococcus as the prominent genus. Notably, Enterococcus casseliflavus was frequently observed in the gut microbiota of FAW larvae across host plants. Metabolism pathways, particularly those related to carbohydrate and amino acid metabolism, played a crucial role in the adaptation of the FAW gut microbiota to different host plants. KEGG orthologs associated with the regulation of the peptide/nickel transport system permease protein in sorghum-fed larvae and the 6-phospho-ß-glucosidase gene linked to glycolysis/gluconeogenesis as well as starch and sucrose metabolism in pepper-fed larvae were identified. Moreover, the study identified the top 20 ARGs in the gut microbiota of FAW larvae fed on different host plants, with the maize-fed group exhibiting the highest abundance of vanRC. CONCLUSIONS: Our metagenomic sequencing study reveals significant variations in the gut microbiota composition and function of FAW larvae across diverse host plants. These findings underscore the intricate co-evolutionary relationship between hosts and their gut microbiota, suggesting that host transfer profoundly influences the gut microbiota and, consequently, the adaptability and pest management strategies for FAW.

Study on the relationship between adolescent myopia and gut microbiota via 16S rRNA sequencing.

Myopia has become a global public health problem, with a high incidence among adolescents. In recent years, the correlation between gut microbiota and various diseases has become a research hotspot. This paper analyzes the relationship between myopia and gut microbiota in adolescents based on 16S rRNA sequencing, opening up a new avenue for the prevention and control of myopia. 80 adolescents aged 6-15 years were included; fecal samples were collected to compare their diversity and species differences. There was no significant difference in α diversity when considering richness and evenness at the same time (P > 0.05). While the group difference in ß diversity reached a significant level (R2 = 0.022, P < 0.05). The absolute quantification and relative abundance of phylum level Firmicutes and Actinobacteriota are different; among the top 30 genera, myopic group only one genus decreased in absolute quantification, while 13 genera decreased in relative quantification; so LEfSe analysis was performed, and the result showed that microbial community composition changed under Linear discriminant analysis (LDA) score, the top ten changes are shown in the figure; the Wilcoxon Rank sum test also found some significant changes in the absolute abundance of differential microbiota among different groups, at the phylum level, one bacterial phylum decreased and three bacterial phyla increased; at the genus level, 2 bacteria genera decreased and 29 bacteria genera increased. Functional pathways prediction found many myopic-related pathways were functionally enhanced in myopic patients (P < 0.05). Multivariate logistic regression analysis results showed that the area under the curve (AUC) of myopic patients predicted was close to or equal to 1. In conclusion, adolescent myopia is closely related to the gut microbiota, and the characteristic gut microbiota can distinguish myopia from healthy controls to a large extent. Therefore, it can be considered to regulate these characteristic gut microbiota to prevent and control myopia.

Highly accurate and sensitive absolute quantification of bacterial strains in human fecal samples.

BACKGROUND: Next-generation sequencing (NGS) approaches have revolutionized gut microbiome research and can provide strain-level resolution, but these techniques have limitations in that they are only semi-quantitative, suffer from high detection limits, and generate data that is compositional. The present study aimed to systematically compare quantitative PCR (qPCR) and droplet digital PCR (ddPCR) for the absolute quantification of Limosilactobacillus reuteri strains in human fecal samples and to develop an optimized protocol for the absolute quantification of bacterial strains in fecal samples. RESULTS: Using strain-specific PCR primers for L. reuteri 17938, ddPCR showed slightly better reproducibility, but qPCR was almost as reproducible and showed comparable sensitivity (limit of detection [LOD] around 104 cells/g feces) and linearity (R2 > 0.98) when kit-based DNA isolation methods were used. qPCR further had a wider dynamic range and is cheaper and faster. Based on these findings, we conclude that qPCR has advantages over ddPCR for the absolute quantification of bacterial strains in fecal samples. We provide an optimized and easy-to-follow step-by-step protocol for the design of strain-specific qPCR assays, starting from primer design from genome sequences to the calibration of the PCR system. Validation of this protocol to design PCR assays for two L. reuteri strains, PB-W1 and DSM 20016 T, resulted in a highly accurate qPCR with a detection limit in spiked fecal samples of around 103 cells/g feces. Applying our strain-specific qPCR assays to fecal samples collected from human subjects who received live L. reuteri PB-W1 or DSM 20016 T during a human trial demonstrated a highly accurate quantification and sensitive detection of these two strains, with a much lower LOD and a broader dynamic range compared to NGS approaches (16S rRNA gene sequencing and whole metagenome sequencing). CONCLUSIONS: Based on our analyses, we consider qPCR with kit-based DNA extraction approaches the best approach to accurately quantify gut bacteria at the strain level in fecal samples. The provided step-by-step protocol will allow scientists to design highly sensitive strain-specific PCR systems for the accurate quantification of bacterial strains of not only L. reuteri but also other bacterial taxa in a broad range of applications and sample types. Video Abstract.

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

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

A streamlined culturomics case study for the human gut microbiota research.

Bacterial culturomics is a set of techniques to isolate and identify live bacteria from complex microbial ecosystems. Despite its potential to revolutionize microbiome research, bacterial culturomics has significant challenges when applied to human gut microbiome studies due to its labor-intensive nature. Therefore, we established a streamlined culturomics approach with minimal culture conditions for stool sample preincubation. We evaluated the suitability of non-selective medium candidates for maintaining microbial diversity during a 30-day incubation period based on 16S rRNA gene amplicon analysis. Subsequently, we applied four culture conditions (two preincubation media under an aerobic/anaerobic atmosphere) to isolate gut bacteria on a large scale from eight stool samples of healthy humans. We identified 8141 isolates, classified into 263 bacterial species, including 12 novel species candidates. Our analysis of cultivation efficiency revealed that seven days of aerobic and ten days of anaerobic incubation captured approximately 91% and 95% of the identified species within each condition, respectively, with a synergistic effect confirmed when selected preincubation media were combined. Moreover, our culturomics findings expanded the coverage of gut microbial diversity compared to 16S rRNA gene amplicon sequencing results. In conclusion, this study demonstrated the potential of a streamlined culturomics approach for the efficient isolation of gut bacteria from human stool samples. This approach might pave the way for the broader adoption of culturomics in human gut microbiome studies, ultimately leading to a more comprehensive understanding of this complex microbial ecosystem.

Causal associations between gut microbiota and premature rupture of membranes: a two-sample Mendelian randomization study.

Background: Previous study has indicated a potential link between gut microbiota and maternal pregnancy outcomes. However, the causal relationship between gut microbiota and premature rupture of membranes (PROM) remains a topic of ongoing debate. Methods: A two-sample Mendelian Randomization (MR) study was used to investigate the relationship between gut microbiota and PROM. Genetic data on gut microbiota was obtained from the MiBioGen consortium's largest genome-wide association study (GWAS) (n=14,306). Genetic data on PROM (3011 cases and 104247 controls) were sourced from publicly available GWAS data from the Finnish National Biobank FinnGen consortium. Various methods including Inverse variance weighted (IVW), MR-Egger, simple mode, weighted median, and weighted mode were utilized to assess the causal relationship by calculating the odd ratio (OR) value and confidence interval (CI). Sensitivity analyses for quality control were performed using MR-Egger intercept tests, Cochran's Q tests, and leave-one-out analyses. Results: The IVW method revealed that class Mollicutes (IVW, OR=0.773, 95%CI: 0.61-0.981, pval = 0.034), genus Marvinbryantia (IVW, OR=00.736, 95%CI: 0.555-0.977, pval = 0.034), genus Ruminooccaceae UCG003 (IVW, OR=0.734, 95%CI: 0.568-0.947, pval = 0.017) and phylum Tenericutes (IVW, OR=0.773, 95%CI: 0.566-1.067, pval = 0.034) were associated with a reduced risk of PROM, while genus Collinsella (IVW, OR=1.444, 95%CI: 1.028-2.026, pval = 0.034), genus Intestinibacter (IVW, OR=1.304, 95%CI: 1.047-1.623, pval = 0.018) and genus Turicibacter (IVW, OR=1.282, 95%CI: 1.02-1.611, pval = 0.033) increased the risk of PROM. Based on the other four supplementary methods, six gut microbiota may have a potential effect on PROM. Due to the presence of pleiotropy (pval=0.045), genus Lachnoclostridium should be ruled out. No evidence of horizontal pleiotropy or heterogeneity was found in other microbiota (pval >0.05). Conclusions: In this study, we have discovered a causal relationship between the presence of specific probiotics and pathogens in the host and the risk of PROM. The identification of specific gut microbiota associated with PROM through MR studies offers a novel approach to diagnosing and treating this condition, thereby providing a new strategy for clinically preventing PROM.