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.

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.

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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 &lt; 0.001), and the microbial composition differed significantly between the groups (P &lt; 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 &lt; 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.

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.