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.

Variability and bias in microbiome metagenomic sequencing: an interlaboratory study comparing experimental protocols.

Several studies have documented the significant impact of methodological choices in microbiome analyses. The myriad of methodological options available complicate the replication of results and generally limit the comparability of findings between independent studies that use differing techniques and measurement pipelines. Here we describe the Mosaic Standards Challenge (MSC), an international interlaboratory study designed to assess the impact of methodological variables on the results. The MSC did not prescribe methods but rather asked participating labs to analyze 7 shared reference samples (5 × human stool samples and 2 × mock communities) using their standard laboratory methods. To capture the array of methodological variables, each participating lab completed a metadata reporting sheet that included 100 different questions regarding the details of their protocol. The goal of this study was to survey the methodological landscape for microbiome metagenomic sequencing (MGS) analyses and the impact of methodological decisions on metagenomic sequencing results. A total of 44 labs participated in the MSC by submitting results (16S or WGS) along with accompanying metadata; thirty 16S rRNA gene amplicon datasets and 14 WGS datasets were collected. The inclusion of two types of reference materials (human stool and mock communities) enabled analysis of both MGS measurement variability between different protocols using the biologically-relevant stool samples, and MGS bias with respect to ground truth values using the DNA mixtures. Owing to the compositional nature of MGS measurements, analyses were conducted on the ratio of Firmicutes: Bacteroidetes allowing us to directly apply common statistical methods. The resulting analysis demonstrated that protocol choices have significant effects, including both bias of the MGS measurement associated with a particular methodological choices, as well as effects on measurement robustness as observed through the spread of results between labs making similar methodological choices. In the analysis of the DNA mock communities, MGS measurement bias was observed even when there was general consensus among the participating laboratories. This study was the result of a collaborative effort that included academic, commercial, and government labs. In addition to highlighting the impact of different methodological decisions on MGS result comparability, this work also provides insights for consideration in future microbiome measurement study design.

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 .

The influence of microbial dysbiosis on immunochemotherapy-related efficacy and safety in diffuse large B-cell lymphoma.

The gut microbiome influences cancer development and the efficacy and safety of chemotherapy but little is known about its effects on lymphoma. We obtained stool samples from treatment-naive, newly diagnosed patients with diffuse large B-cell lymphoma (DLBCL) (n = 189). We first performed 16S ribosomal RNA gene sequencing (n = 158) and then conducted whole-genome shotgun sequencing on additional samples (n = 106). We compared the microbiome data from these patients with data from healthy controls and assessed whether microbiome characteristics were associated with treatment outcomes. The alpha diversity was significantly lower in patients with DLBCL than in healthy controls (P < .001), and the microbial composition differed significantly between the groups (P < .001). The abundance of the Enterobacteriaceae family belonging to the Proteobacteria phylum was markedly higher in patients than in healthy controls. Functional analysis of the microbiome revealed an association with opportunistic pathogenesis through type 1 pili, biofilm formation, and antibiotics resistance. Enterobacteriaceae members were significantly enriched in patients who experienced febrile neutropenia and in those who experienced relapse or progression (P < .001). Interestingly, greater abundance of Enterobacteriaceae correlated with shorter progression-free survival (P = .007). The cytokine profiles of patients whose microbiome was enriched with Enterobacteriaceae were significantly associated with interleukin 6 (P = .035) and interferon gamma (P = .045) levels. In summary, patients with DLBCL exhibited gut microbial dysbiosis. The abundance of Enterobacteriaceae correlated with treatment outcomes and febrile neutropenia. Further study is required to elucidate the origin and role of gut dysbiosis in DLBCL.

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.

Oral and gut dysbiosis leads to functional alterations in Parkinson's disease.

Although several studies have identified a distinct gut microbial composition in Parkinson's disease (PD), few studies have investigated the oral microbiome or functional alteration of the microbiome in PD. We aimed to investigate the connection between the oral and gut microbiome and the functional changes in the PD-specific gut microbiome using shotgun metagenomic sequencing. The taxonomic composition of the oral and gut microbiome was significantly different between PD patients and healthy controls (P = 0.003 and 0.001, respectively). Oral Lactobacillus was more abundant in PD patients and was associated with opportunistic pathogens in the gut (FDR-adjusted P < 0.038). Functional analysis revealed that microbial gene markers for glutamate and arginine biosynthesis were downregulated, while antimicrobial resistance gene markers were upregulated in PD patients than healthy controls (all P < 0.001). We identified a connection between the oral and gut microbiota in PD, which might lead to functional alteration of the microbiome in PD.

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.

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.

Improved Metagenomic Taxonomic Profiling Using a Curated Core Gene-Based Bacterial Database Reveals Unrecognized Species in the Genus Streptococcus.

Shotgun metagenomics is of great importance in order to understand the composition of the microbial community associated with a sample and the potential impact it may exert on its host. For clinical metagenomics, one of the initial challenges is the accurate identification of a pathogen of interest and ability to single out that pathogen within a complex community of microorganisms. However, in absence of an accurate identification of those microorganisms, any kind of conclusion or diagnosis based on misidentification may lead to erroneous conclusions, especially when comparing distinct groups of individuals. When comparing a shotgun metagenomic sample against a reference genome sequence database, the classification itself is dependent on the contents of the database. Focusing on the genus Streptococcus, we built four synthetic metagenomic samples and demonstrated that shotgun taxonomic profiling using the bacterial core genes as the reference database performed better in both taxonomic profiling and relative abundance prediction than that based on the marker gene reference database included in MetaPhlAn2. Additionally, by classifying sputum samples of patients suffering from chronic obstructive pulmonary disease, we showed that adding genomes of genomospecies to a reference database offers higher taxonomic resolution for taxonomic profiling. Finally, we show how our genomospecies database is able to identify correctly a clinical stool sample from a patient with a streptococcal infection, proving that genomospecies provide better taxonomic coverage for metagenomic analyses.

VCGIDB: A Database and Web Resource for the Genomic Islands from Vibrio Cholerae.

Vibrio cholerae is the causative agent of cholera, which is a severe, life-threatening diarrheal disease. The current seventh pandemic has not been eradicated and the outbreak is still ongoing around the world. The evolution of the pandemic-causing strain has been greatly influenced by lateral gene transfer, and the mechanisms of acquisition of pathogenicity in V. cholerae are mainly involved with genomic islands (GIs). Thus, detecting GIs and their comprehensive information is necessary to understand the continuing resurgence and newly emerging pathogenic V. cholerae strains. In this study, 798 V. cholerae strains were tested using the GI-Scanner algorithm, which was developed to detect candidate GIs and identify them in a comparative genomics approach. The algorithm predicted 435 highly possible genomic islands, and we built a database, called Vibrio cholerae Genomic Island Database (VCGIDB). This database shows advanced results that were acquired from a large genome set using phylogeny-based predictions. Moreover, VCGIDB is a highly expendable database that does not require intensive computation, which enables us to update it with a greater number of genomes using a novel genomic island prediction method. The VCGIDB website allows the user to browse the data and presents the results in a visual manner.

Comparative Genomic Analysis of the 2016 Vibrio cholerae Outbreak in South Korea.

In August 2016, South Korea experienced a cholera outbreak that caused acute watery diarrhea in three patients. This outbreak was the first time in 15 years that an outbreak was not linked to an overseas source. To identify the cause and to study the epidemiological implications of this outbreak, we sequenced the whole genome of Vibrio cholerae isolates; three from each patient and one from a seawater sample. Herein we present comparative genomic data which reveals that the genome sequences of these four isolates are very similar. Interestingly, these isolates form a monophyletic clade with V. cholerae strains that caused an outbreak in the Philippines in 2011. The V. cholerae strains responsible for the Korean and Philippines outbreaks have almost identical genomes in which two unique genomic islands are shared, and they both lack SXT elements. Furthermore, we confirm that seawater is the likely source of this outbreak, which suggests the necessity for future routine surveillance of South Korea's seashore.

ContEst16S: an algorithm that identifies contaminated prokaryotic genomes using 16S RNA gene sequences.

Thanks to the recent advancement of DNA sequencing technology, the cost and time of prokaryotic genome sequencing have been dramatically decreased. It has repeatedly been reported that genome sequencing using high-throughput next-generation sequencing is prone to contaminations due to its high depth of sequencing coverage. Although a few bioinformatics tools are available to detect potential contaminations, these have inherited limitations as they only use protein-coding genes. Here we introduce a new algorithm, called ContEst16S, to detect potential contaminations using 16S rRNA genes from genome assemblies. We screened 69 745 prokaryotic genomes from the NCBI Assembly Database using ContEst16S and found that 594 were contaminated by bacteria, human and plants. Of the predicted contaminated genomes, 8 % were not predicted by the existing protein-coding gene-based tool, implying that both methods can be complementary in the detection of contaminations. A web-based service of the algorithm is available at www.ezbiocloud.net/tools/contest16s.