EzBioCloud: a genome-driven database and platform for microbiome identification and discovery.

With the continued evolution of DNA sequencing technologies, the role of genome sequence data has become more integral in the classification and identification of Bacteria and Archaea. Six years after introducing EzBioCloud, an integrated platform representing the taxonomic hierarchy of Bacteria and Archaea through quality-controlled 16S rRNA gene and genome sequences, we present an updated version, that further refines and expands its capabilities. The current update recognizes the growing need for accurate taxonomic information as defining a species increasingly relies on genome sequence comparisons. We also incorporated an advanced strategy for addressing underrepresented or less studied lineages, bolstering the comprehensiveness and accuracy of our database. Our rigorous quality control protocols remain, where whole-genome assemblies from the NCBI Assembly Database undergo stringent screening to remove low-quality sequence data. These are then passed through our enhanced identification bioinformatics pipeline which initiates a 16S rRNA gene similarity search and then calculates the average nucleotide identity (ANI). For genome sequences lacking a 16S rRNA sequence and without a closely related genomic representative for ANI calculation, we apply a different ANI approach using bacterial core genes for improved taxonomic placement (core gene ANI, cgANI). Because of the increase in genome sequences available in NCBI and our newly introduced cgANI method, EzBioCloud now encompasses a total of 109 835 species, of which 21 964 have validly published names. 47 896 are candidate species identified either through 16S rRNA sequence similarity (phylotypes) or through whole genome ANI (genomospecies), and the remaining 39 975 were positioned in the taxonomic tree by cgANI (species clusters). Our EzBioCloud database is accessible at www.ezbiocloud.net/db.

Gut microbial and clinical characteristics of individuals with autism spectrum disorder differ depending on the ecological structure of the gut microbiome.

Understanding the relationship between the gut microbiome and autism spectrum disorder (ASD) is challenging due to the heterogeneous nature of ASD. Here, we analyzed the microbial and clinical characteristics of individuals with ASD using enterotypes. A total of 456 individuals participated in the study, including 249 participants with ASD, 106 typically developing siblings, and 101 controls. The alpha and beta diversities of the ASD, sibling, and control groups did not show significant differences. Analysis revealed a negative association between the Bifidobacterium longum group and the Childhood Autism Rating Scale, as well as a negative association between the Streptococcus salivarus group and the Social Responsiveness Scale (SRS) within the ASD group. When clustered based on microbial composition, participants with ASD exhibited two distinct enterotypes, E1 and E2. In the E2 group, the SRS score was significantly higher, and the Vineland Adaptive Behavior Scale score was significantly lower compared to the E1 group. Machine learning results indicated that the microbial species predicting SRS scores were distinct between the two enterotypes. Our study suggests that the microbial composition in individuals with ASD exhibits considerable variability, and the patterns of associations between the gut microbiome and clinical symptoms may vary depending on the enterotype.

Longitudinal associations between gut microbiome diversity and emotional well-being.

OBJECTIVE: While a significant link between emotional well-being (EWB) and the gut microbiome has been reported recently, their temporal relationships remain elusive. This study aims to fill this gap by examining the longitudinal associations between EWB and the Shannon Index (SI), an indicator of gut microbiome diversity. METHOD: The analysis focused on a dataset that collected participants' current EWB and fecal samples in both 2019 and 2022 (N = 57, 56.1% female, Mage = 52.47 years, SD = 12.65). Gut microbiome profiles were generated by sequencing the 16S rRNA gene, from which SI was subsequently calculated. RESULTS: The cross-lagged panel analysis revealed significant positive cross-sectional associations between EWB and SI in both 2019 (ß = .296, SE = 0.121, p = .014) and 2022 (ß = .324, SE = 0.119, p = .006). However, no significant longitudinal associations were found between 2019 EWB and 2022 SI (ß = .068, SE = 0.138, p = .623), nor between 2019 SI and 2022 EWB (ß = -.016, SE = 0.13, p = .899). CONCLUSIONS: Our findings indicate that emotional happiness may be associated with gut microbiome profiles at a particular time point, but they may not serve as predictive factors for each other over time. Future research is needed to establish causal relationships between them. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

UACG: Up-to-Date Archaeal Core Genes and Software for Phylogenomic Tree Reconstruction.

In the post-genomic era, phylogenomics is a powerful and routinely-used tool to discover evolutionary relationships between microorganisms. Inferring phylogenomic trees by concatenating core gene sequences into a supermatrix is the standard method. The previously released up-to-date bacterial core gene (UBCG) tool provides a pipeline to infer phylogenomic trees using single-copy core genes for the Bacteria domain. In this study, we established up-to-date archaeal core gene (UACG), comprising 128 genes suitable for inferring archaeal phylogenomic trees. To test the gene set, we selected the Haloarcula genus and scrutinized its phylogeny. The phylogeny inferred using the UACG tool was consistent with the orthoANIu dendrogram, whereas the 16S rRNA gene phylogeny showed high intragenomic heterogeneity resulting in phylogenetic discrepancies. The software tool using the UACG set is available at https://www.ezbiocloud.net/tools/uacg .

UFCG: database of universal fungal core genes and pipeline for genome-wide phylogenetic analysis of fungi.

In phylogenomics the evolutionary relationship of organisms is studied by their genomic information. A common approach to phylogenomics is to extract related genes from each organism, build a multiple sequence alignment and then reconstruct evolution relations through a phylogenetic tree. Often a set of highly conserved genes occurring in single-copy, called core genes, are used for this analysis, as they allow efficient automation within a taxonomic clade. Here we introduce the Universal Fungal Core Genes (UFCG) database and pipeline for genome-wide phylogenetic analysis of fungi. The UFCG database consists of 61 curated fungal marker genes, including a novel set of 41 computationally derived core genes and 20 canonical genes derived from literature, as well as marker gene sequences extracted from publicly available fungal genomes. Furthermore, we provide an easy-to-use, fully automated and open-source pipeline for marker gene extraction, training and phylogenetic tree reconstruction. The UFCG pipeline can identify marker genes from genomic, proteomic and transcriptomic data, while producing phylogenies consistent with those previously reported, and is publicly available together with the UFCG database at https://ufcg.steineggerlab.com.

Compositional Changes in the Gut Microbiota of Responders and Non-responders to Probiotic Treatment Among Patients With Diarrhea-predominant Irritable Bowel Syndrome: A Post Hoc Analysis of a Randomized Clinical Trial.

Background/Aims: We aim to evaluate the differences in the microbiome of responders and non-responders, as well as predict the response to probiotic therapy, based on fecal microbiome data in patients with diarrhea-predominant irritable bowel syndrome (IBS-D). Methods: A multi-strain probiotics that contains Lactobacillus acidophilus (KCTC 11906BP), Lactobacillus plantarum (KCTC11867BP), Lactobacillus rhamnosus (KCTC 11868BP), Bifidobacterium breve (KCTC 11858BP), Bifidobacterium lactis (KCTC 11903BP), Bifidobacterium longum (KCTC 11860BP), and Streptococcus thermophilus (KCTC 11870BP) were used. Patients were categorized into probiotic and placebo groups, and fecal samples were collected from all patients before and at the end of 8 weeks of treatment. The probiotic group was further divided into responders and non-responders. Responders were defined as patients who experienced adequate relief of overall irritable bowel syndrome symptoms after probiotic therapy. Fecal microbiota were investigated using Illumina MiSeq and analyzed using the EzBioCloud 16S database and microbiome pipeline (https://www.EZbiocloud.net). Results: There was no significant difference in the alpha and beta diversity between the responder and non-responder groups. The abundances of the phylum Proteobacteria and genus Bacteroides significantly decreased after probiotic treatment. Bifidobacterium bifidum, Pediococcus acidilactici, and Enterococcus faecium showed a significantly higher abundance in the probiotic group after treatment compared to the placebo group. Enterococcus faecalis and Lactococcus lactis were identified as biomarkers of non-response to probiotics. The abundance of Fusicatenibacter saccharivorans significantly increased in the responders after treatment. Conclusions: Probiotic treatment changes some composition of fecal bacteria in patients with IBS-D. E. faecalis and L. lactis may be prediction biomarkers for non-response to probiotics. Increased abundance of F. sccharivorans is correlated to symptom improvement by probiotics in patients with IBS-D.

Genomic and evolutionary study from SARS-CoV-2 virus isolates from Bangladesh during the early stage of pandemic strongly correlate with European origin and not with China.

The goal of this study was to identify the genomic variants and determine molecular epidemiology of SARS-CoV-2 virus during the early pandemic stage in Bangladesh. Viral RNA was extracted, converted to cDNA, and amplified using Ion AmpliSeq™ SARS-CoV-2 Research Panel. 413 unique mutants from 151 viral isolates were identified. 80% of cases belongs to 8 mutants: 241C toT, 1163A toT, 3037C toT, 14408C toT, 23403A toG, 28881G toA, 28,882 G toA, and 28883G toC. Observed dominance of GR clade variants that have strong presence in Europe, suggesting European channel a possible entry route. Among 37 genomic mutants significantly associated with clinical symptoms, 3916CtoT (associated with sore-throat), 14408C to T (associated with cough-protection), 28881G to A, 28882G to A, and 28883G to C (associated with chest pain) were notable. These findings may inform future research platforms for disease management and epidemiological study.

Proposal of a health gut microbiome index based on a meta-analysis of Korean and global population datasets.

The disruption of the human gut microbiota has been linked to host health conditions, including various diseases. However, no reliable index for measuring and predicting a healthy microbiome is currently available. Here, the sequencing data of 1,663 Koreans were obtained from three independent studies. Furthermore, we pooled 3,490 samples from public databases and analyzed a total of 5,153 fecal samples. First, we analyzed Korean gut microbiome covariates to determine the influence of lifestyle on variation in the gut microbiota. Next, patterns of microbiota variations across geographical locations and disease statuses were confirmed using a global cohort and di-sease data. Based on comprehensive comparative analysis, we were able to define three enterotypes among Korean cohorts, namely, Prevotella type, Bacteroides type, and outlier type. By a thorough categorization of dysbiosis and the evaluation of microbial characteristics using multiple datasets, we identified a wide spectrum of accuracy levels in classifying health and disease states. Using the observed microbiome patterns, we devised an index named the gut microbiome index (GMI) that could consistently predict health conditions from human gut microbiome data. Compared to ecological metrics, the microbial marker index, and machine learning approaches, GMI distinguished between healthy and non-healthy individuals with a higher accuracy across various datasets. Thus, this study proposes a potential index to measure health status of gut microbiome that is verified from multiethnic data of various diseases, and we expect this model to facilitate further clinical application of gut microbiota data in future.

Genome insights into the pharmaceutical and plant growth promoting features of the novel species Nocardia alni sp. nov.

BACKGROUND: Recent studies highlighted the biosynthetic potential of nocardiae to produce diverse novel natural products comparable to that of Streptomyces, thereby making them an attractive source of new drug leads. Many of the 119 Nocardia validly named species were isolated from natural habitats but little is known about the diversity and the potential of the endophytic nocardiae of root nodule of actinorhizal plants. RESULTS: The taxonomic status of an actinobacterium strain, designated ncl2T, was established in a genome-based polyphasic study. The strain was Gram-stain-positive, produced substrate and aerial hyphae that fragmented into coccoid and rod-like elements and showed chemotaxonomic properties that were also typical of the genus Nocardia. It formed a distinct branch in the Nocardia 16S rRNA gene tree and was most closely related to the type strains of Nocardia nova (98.6%), Nocardia jiangxiensis (98.4%), Nocardia miyuensis (97.8%) and Nocardia vaccinii (97.7%). A comparison of the draft genome sequence generated for the isolate with the whole genome sequences of its closest phylogenetic neighbours showed that it was most closely related to the N. jiangxiensis, N. miyuensis and N. vaccinii strains, a result underpinned by average nucleotide identity and digital DNA-DNA hybridization data. Corresponding taxogenomic data, including those from a pan-genome sequence analysis showed that strain ncl2T was most closely related to N. vaccinii DSM 43285T. A combination of genomic, genotypic and phenotypic data distinguished these strains from one another. Consequently, it is proposed that strain ncl2T (= DSM 110931T = CECT 30122T) represents a new species within the genus Nocardia, namely Nocardia alni sp. nov. The genomes of the N. alni and N. vaccinii strains contained 36 and 29 natural product-biosynthetic gene clusters, respectively, many of which were predicted to encode for a broad range of novel specialised products, notably antibiotics. Genome mining of the N. alni strain and the type strains of its closest phylogenetic neighbours revealed the presence of genes associated with direct and indirect mechanisms that promote plant growth. The core genomes of these strains mainly consisted of genes involved in amino acid transport and metabolism, energy production and conversion and transcription. CONCLUSIONS: Our genome-based taxonomic study showed that isolate ncl2T formed a new centre of evolutionary variation within the genus Nocardia. This novel endophytic strain contained natural product biosynthetic gene clusters predicted to synthesize novel specialised products, notably antibiotics and genes associated with the expression of plant growth promoting compounds.

AMDB: a database of animal gut microbial communities with manually curated metadata.

Variations in gut microbiota can be explained by animal host characteristics, including host phylogeny and diet. However, there are currently no databases that allow for easy exploration of the relationship between gut microbiota and diverse animal hosts. The Animal Microbiome Database (AMDB) is the first database to provide taxonomic profiles of the gut microbiota in various animal species. AMDB contains 2530 amplicon data from 34 projects with manually curated metadata. The total data represent 467 animal species and contain 10 478 bacterial taxa. This novel database provides information regarding gut microbiota structures and the distribution of gut bacteria in animals, with an easy-to-use interface. Interactive visualizations are also available, enabling effective investigation of the relationship between the gut microbiota and animal hosts. AMDB will contribute to a better understanding of the gut microbiota of animals. AMDB is publicly available without login requirements at http://leb.snu.ac.kr/amdb.

VicPred: A Vibrio cholerae Genotype Prediction Tool.

Genomic information can be used to predict major pathogenic traits of pathogens without the need for laboratory experimentation. However, no Vibrio cholerae genome-based trait identification tools currently exist. The aim of this study was to develop a web-based prediction tool to identify Vibrio pathogenic traits using publicly available 796 whole-genome sequences of V. cholerae. Using this application, 68 structural O-antigen gene clusters belonging to 49 serogroups of V. cholerae were classified, and the composition of the genes within the O-antigen cluster of each serogroup was identified. The arrangement and location of the CTX prophage and related elements of the seventh cholera pandemic strains were also revealed. With the versatile tool, named VicPred, we analyzed the assemblage of various SXTs (sulfamethoxazole/trimethoprim resistance element) and major genomic islands (GIs) of V. cholerae, and the increasing trend in drug-resistance revealing high resistance of the V. cholerae strains to certain antibiotics. The pathogenic traits of newly sequenced V. cholerae strains could be analyzed based on these characteristics. The accumulation of further genome data will expedite the establishment of a more precise genome-based pathogenic traits analysis tool.

UBCG2: Up-to-date bacterial core genes and pipeline for phylogenomic analysis.

Phylogenomic tree reconstruction has recently become a routine and critical task to elucidate the evolutionary relationships among bacterial species. The most widely used method utilizes the concatenated core genes, universally present in a single-copy throughout the bacterial domain. In our previous study, a bioinformatics pipeline termed Up-to-date Bacterial Core Genes (UBCG) was developed with a set of bacterial core genes selected from 1,429 species covering 28 phyla. In this study, we revised a new bacterial core gene set, named UBCG2, that was selected from the more extensive genome sequence set belonging to 3,508 species spanning 43 phyla. UBCG2 comprises 81 genes with nine Clusters of Orthologous Groups of proteins (COGs) functional categories. The new gene set and complete pipeline are available at http://leb.snu.ac.kr/ubcg2 .

Characterization of Gut Microbiome in Korean Patients with Metabolic Associated Fatty Liver Disease.

Metabolic associated fatty liver disease (MAFLD) is a new concept where the presence of both fatty liver and metabolic abnormality are necessary for diagnosis. Several studies have reported that altered gut microbiome is closely associated with metabolic diseases and non-alcoholic fatty liver disease. However, the studies on MAFLD population are scarce. This prospective study aimed to identify differences in gut microbiome between patients with MAFLD and healthy controls in Korean population. In this study, patients with MAFLD and age, sex-matched healthy controls were included, and their stool samples were collected. Taxonomic composition of gut microbiota was analyzed using 16S ribosomal ribonucleic acid pyrosequencing. Twenty-two MAFLD patients and 44 healthy controls were included. Taxonomic diversity was lower in patients with MAFLD in the aspect of alpha and beta diversity. The differences were also found at phylum, class, family, and genus levels between the two groups. Phylum Proteobacteria, family Enterobactereriaceae, genus Citrobacter abundance was significantly increased and genus Faecalibacterium was significantly decreased in patients with MAFLD. In addition, butyrate-producing bacteria were decreased and ethanol-producing bacteria were increased in patients with MAFLD. The composition of gut microbiome was different between MAFLD and healthy controls in Korean population. This could offer potential targets for therapeutic intervention in MAFLD.

Introducing EzAAI: a pipeline for high throughput calculations of prokaryotic average amino acid identity.

The average amino acid identity (AAI) is an index of pairwise genomic relatedness, and multiple studies have proposed its application in prokaryotic taxonomy and related disciplines. AAI demonstrates better resolution in elucidating taxonomic structure beyond the species rank when compared with average nucleotide identity (ANI), which is a standard criterion in species delineation. However, an efficient and easy-to-use computational tool for AAI calculation in large-scale taxonomic studies is not yet available. Here, we introduce a bioinformatic pipeline, named EzAAI, which allows for rapid and accurate AAI calculation in prokaryote sequences. The EzAAI tool is based on the MMSeqs2 program and computes AAI values almost identical to those generated by the standard BLAST algorithm with significant improvements in the speed of these evaluations. Our pipeline also provides a function for hierarchical clustering to create dendrograms, which is an essential part of any taxonomic study. EzAAI is available for download as a standalone JAVA program at http://leb.snu.ac.kr/ezaai .

Taxonomic composition and variation in the gut microbiota of laboratory mice.

The gut microbiota can affect host health, including humans. Mouse models have been used extensively to study the relationships between the host and the gut microbiota. With the development of cost-effective high-throughput DNA sequencing, several methods have been used to identify members of the gut microbiota of laboratory mice. In recent years, the amount of research and knowledge about the mouse gut microbiota has exploded, leading to significant breakthroughs in understanding of the taxonomic composition of and variation in this community. In addition, the rapidly increasing volume of data has allowed the development of public resources for exploring the mouse gut microbiota. In this review, we describe the concepts and pros and cons of basic methodologies that can be used to determine the gut bacterial profile in laboratory mice. We also present the key bacterial components of the mouse gut microbiota from the phylum to the species level and then compare them with those identified in other references. Additionally, we discuss variations in the mouse gut microbiota and their association with experiments using mice. Finally, we summarize the properties and functions of currently available public resources for exploring the mouse gut microbiota.

Epidemiologic Linkage of COVID-19 Outbreaks at Two University-affiliated Hospitals in the Seoul Metropolitan Area in March 2020.

BACKGROUND: Coronavirus disease 2019 (COVID-19) outbreaks emerged at two university-affiliated hospitals in Seoul (hospital A) and Uijeongbu City (hospital S) in the metropolitan Seoul area in March 2020. The aim of this study was to investigate epidemiological links between the outbreaks using whole genome sequencing (WGS) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS: Fifteen patients were enrolled in the study, including four non-outbreak (A1-A4) and three outbreak cases (A5-A7) in hospital A and eight cases (S1-S8) in hospital S. Patients' hospital stays, COVID-19 symptoms, and transfer history were reviewed. RNA samples were submitted for WGS and genome-wide single nucleotide variants and phylogenetic relationships were analyzed. RESULTS: The index patient (A5) in hospital A was transferred from hospital S on 26 March. Patients A6 and A7 were the family caregiver and sister, respectively, of the patient who shared a room with A5 for 4 days. Prior to transfer, A5 was at the next bed to S8 in the emergency room on 25 March. Patient S6, a professional caregiver, took care of the patient in the room next to S8's room for 5 days until 22 March and then S5 for another 3 days. WGS revealed that SARS-CoV-2 in A2, A3, and A4 belong to clades V/B.2, S/A, and G/B.1, respectively, whereas that of A5-A7 and S1-S5 are of the V/B.2.1 clade and closely clustered. In particular, SARS-CoV-2 in patients A5 and S5 showed perfect identity. CONCLUSION: WGS is a useful tool to understand epidemiology of SARS-CoV-2. It is the first study to elucidate the role of patient transfer and caregivers as links of nosocomial outbreaks of COVID-19 in multiple hospitals.

Metagenomic Association Analysis of Gut Symbiont Limosilactobacillus reuteri Without Host-Specific Genome Isolation.

Limosilactobacillus reuteri is a model symbiont that colonizes the guts of vertebrates in studies on host adaptation of the gut symbiont. Previous studies have investigated host-specific phylogenetic and functional properties by isolating the genomic sequence. This dependency on genome isolation is a significant bottleneck. Here, we propose a method to study the association between L. reuteri and its hosts directly from metagenomic reads without strain isolation using pan-genomes. We characterized the host-specificity of L. reuteri in metagenomic samples, not only in previously studied organisms (mice and pigs) but also in dogs. For each sample, two types of profiles were generated: (1) genome-based strain type abundance profiles and (2) gene composition profiles. Our profiles showed host-association of L. reuteri in both phylogenetic and functional aspects without depending on host-specific genome isolation. We observed not only the presence of host-specific lineages, but also the dominant lineages associated with the different hosts. Furthermore, we showed that metagenome-assembled genomes provide detailed insights into the host-specificity of L. reuteri. We inferred evolutionary trajectories of host-associative L. reuteri strains in the metagenomic samples by placing the metagenome-assembled genomes into a phylogenetic tree and identified novel host-specific genes that were unannotated in existing pan-genome databases. Our pan-genomic approach reduces the need for time-consuming and expensive host-specific genome isolation, while producing consistent results with previous host-association findings in mice and pigs. Additionally, we predicted associations that have not yet been studied in dogs.

Emotional well-being and gut microbiome profiles by enterotype.

With increasing attention being paid to improving emotional well-being, recent evidence points to gut microbiota as a key player in regulating mental and physical health via bidirectional communication between the brain and gut. Here, we examine the association between emotional well-being and gut microbiome profiles (i.e., gut microbiome composition, diversity, and the moderating role of the enterotypes) among healthy Korean adults (n = 83, mean age = 48.9, SD = 13.2). The research was performed using high-throughput 16S rRNA gene sequencing to obtain gut microbiome profiles, as well as a self-report survey that included the Positive Affect Negative Affect Schedule (PANAS). The cluster-based analysis identified two enterotypes dominated by the genera Bacteroides (n = 49) and Prevotella (n = 34). Generalized linear regression analysis reveals significant associations between positive emotion and gut microbiome diversity (Shannon Index) among participants in the Prevotella dominant group, whereas no such relationship emerged among participants in the Bacteroides group. Moreover, a novel genus from the family Lachnospiraceae is associated with emotional well-being scores, both positive and negative. Together, the current findings highlight the enterotype-specific links between the gut microbiota community and emotion in healthy adults and suggest the possible roles of the gut microbiome in promoting mental health.