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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-naïve, 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 (n = 106) with additional samples. We compared the microbiome data from these patients with those of 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 < 0.001), and the microbial composition differed significantly between the groups (P < 0.001). The abundance of 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 < 0.001). Interestingly, greater abundance of Enterobacteriaceae correlated with shorter progression-free survival (P = 0.007). The cytokine profiles of patients whose microbiome was enriched with Enterobacteriaceae were significantly associated with interleukin 6 (P = 0.035) and interferon-g (P = 0.045) 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.

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.

RationaleThe global public health is in serious crisis due to emergence of SARS-CoV-2 virus. Studies are ongoing to reveal the genomic variants of the virus circulating in various parts of the world. However, data generated from low- and middle-income countries are scarce due to resource limitation. This study was focused to perform whole genome sequencing of 151 SARS-CoV-2 isolates from COVID-19 positive Bangladeshi patients. The goal of this study was to identify the genomic variants among the SARS-CoV-2 virus isolates in Bangladesh, to determine the molecular epidemiology and to develop a relationship between host clinical trait with the virus genomic variants.MethodSuspected patients were tested for COVID-19 using one step commercial qPCR kit for SARS-CoV-2 Virus. Viral RNA was extracted from positive patients, converted to cDNA which was amplified using Ion AmpliSeq™ SARS-CoV-2 Research Panel. Massive parallel sequencing was carried out using Ion AmpliSeq™ Library Kit Plus. Assembly of raw data is done by aligning the reads to a pre-defined reference genome (NC_045512.2) while retaining the unique variations of the input raw data by creating a consensus genome. A random forest-based association analysis was carried out to correlate the viral genomic variants with the clinical traits present in the host.ResultAmong the 151 viral isolates, we observed the 413 unique variants. Among these 8 variants occurred in more than 80 % of cases which include 241C to T, 1163A to T, 3037C to T,14408C to T, 23403A to G, 28881G to A, 28882 G to A, and finally the 28883G to C. Phylogenetic analysis revealed a predominance of variants belonging to GR clade, which have a strong geographical presence in Europe, indicating possible introduction of the SARS-CoV-2 virus into Bangladesh through a European channel. However, other possibilities like a route of entry from China cannot be ruled out as viral isolate belonging to L clade with a close relationship to Wuhan reference genome was also detected. We observed a total of 37 genomic variants to be strongly associated with clinical symptoms such as fever, sore throat, overall symptomatic status, etc. (Fisher’s Exact Test p-valueConclusionTo our knowledge, this study is the first large scale phylogenomic studies of SARS-CoV-2 virus circulating in Bangladesh. The observed epidemiological and genomic features may inform future research platform for disease management, vaccine development and epidemiological study.

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., Graphical Abstract

Background: Diffuse large B cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma (NHL), and overall, 60% of patients have achieved complete remission (CR) with standard chemotherapy with rituximab. However, we did not find the exact answers to which molecular mechanisms influence the DLBCL development and why over 40% of patients did not achieve a CR. Recently, it has been reported that the gut microbiome is deeply related to immune system modulation, as well as, these associations can influence cancer development or the quality of chemotherapy efficacy. Thus, we performed a stool analysis to assess whether differences in gut microbial composition between healthy groups and DLBCL patients are likely to influence the DLBCL development. Furthermore, we discussed whether microbial dysbiosis or specific microbes might affect treatment efficacy and safety. Methods: Since March 2019, we have collected microbial information from 93 feces samples taken from the treatment naïve DLBCL patients. When we compared gut microbes between the healthy group and the DLBCL patients, we used the gut microbiome data from the healthy group, which had already been analyzed. All fecal samples were collected with a stool sampling kit (Chunlab Inc, Seoul, Korea) for microbial community analysis. The species richness was assessed using the Chao1, and evenness was calculated using the Shannon index. The beta-diversity was calculated with the Bray-Curtis metric and visualized with principal coordinate analysis (PCoA). The linear discriminant analysis effect size (LEfSe) was employed to determine the taxa and inferred pathways associated with one of the two groups to identify the potential biomarkers. The variation in taxonomic profiles and diversity indices between patients and healthy controls were analyzed using the Mann-Whitney U test and permutational multivariate analysis of variance (PERMANOVA). We used a stringent logarithmic, linear discriminant analysis (LDA) score threshold of 3.0 for taxonomic and functional markers for the LEfse outputs. Results: Among the 93 patients, 41 patients (44.1%) had stage III or IV disease. According to the alpha diversity analysis, there were no differences in richness (Chao1) and evenness (Shannon) between DLBCL patients and the healthy control group (Fig 1A). However, we recognized that the microbial composition between these two groups was significantly different (PERMANOVA, P-value=0.001, Fig 1B). We found increased pathogenic Enterobacteriaceae genera, such as Enterococcus, Citrobacter, Escherichia, Enterobacter, and Sutterella, were markers of gut dysbiosis in DLBCL patients (Fig 1C). Furthermore, ABC subtype of DLBCL showed higher proteobacteria at the phylum level and Citrobacter at the genus level than GCB subtype (Fig 2A,B). Besides, genus Escherichia, Klebsiella, Lactobacillus, and Weissella showed a positive correlation with the indicators predicted to be related to the disease burden (number of extranodal sites, Ann Arbor stage, international prognostic index) and the side effects (admission or visit in the emergency room [ER] due to any chemotherapy-related toxicities) estimated by Spearman correlation and canonical correspondence analysis (Fig 3A, B). Additionally, there was a significant difference in intestinal microbes composition between patients who experienced the side effects or not (PERMANOVA, P-value=0.003, Fig 4A). Furthermore, facultative anaerobe and opportunistic pathogens, such as genus Escherichia, Klebsiella, Lactobacillus, and Weissella, were abundant in patients who experienced the side effects. These results suggested that patients with severe dysbiosis are more susceptible to the side effects of chemotherapy (Fig 4B, C). Conclusion: Through these results, it was possible to know the specific intestinal microbiome distribution of DLBCL. Also, we found an abundance of opportunistic pathogens (Escherichia, Klebsiella, Lactobacillus, and Weissella) in DLBCL patients with a high disease burden or some susceptible features about chemotherapy. However, we presented the intestinal microbes configuration and did not prove the mechanism by which the gut microbes affect the tumor environment related to lymphoma evolution. Therefore, we expect that the currently planned functional study of specific DLBCL-related microbes and diversity will answer this question. Figure 1 Figure 1. Disclosures Kim: Roche: Research Funding; Sanofi: Research Funding; IGM Biosciences: Research Funding; Celltrion: Research Funding; Dong-A Pharmaceutical: Research Funding; Eisai: Research Funding; Johnson & Johnson: Research Funding; Kyowa Kirin: Research Funding.

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.

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.