Phocaeicola oris sp. nov., an anaerobic bacterium isolated from the saliva of a patient with oral squamous cell carcinoma.

A Gram-stain-negative or -positive, strictly anaerobic, non-spore-forming and pleomorphic bacterium (designated 14-104T) was isolated from the saliva sample of a patient with oral squamous cell carcinoma. It was an acid-tolerant neutralophilic mesophile, growing at between 20 and 40 °C (with optimum growth at 30 °C) and pH between pH 3.0 and 7.0 (with optimum growth at pH 6.0-7.0). It contained anteiso-C15 : 0 and C15 : 0 as the major fatty acids. The genome size of strain 14-104T was 2.98 Mbp, and the G+C content was 39.6 mol%. It shared <87 % 16S rRNA sequence similarity, <71 % orthologous average nucleotide identity, <76 % average amino acid identity and <68 %% of conserved proteins with its closest relative, Phocaeicola abscessus CCUG 55929T. Reconstruction of phylogenetic and phylogenomic trees revealed that strain 14-104T and P. abscessus CCUG 55929T were clustered as a distinct clade without any other terminal node. The phylogenetic and phylogenomic analyses along with physiological and chemotaxonomic data indicated that strain 14-104T represents a novel species in the genus Phocaeicola, for which the name Phocaeicola oris sp. nov. is proposed. The type strain is 14-104T (=BCRC 81305T= NBRC 115041T).

The hitchhikers' guide to RNA sequencing and functional analysis.

DNA and RNA sequencing technologies have revolutionized biology and biomedical sciences, sequencing full genomes and transcriptomes at very high speeds and reasonably low costs. RNA sequencing (RNA-Seq) enables transcript identification and quantification, but once sequencing has concluded researchers can be easily overwhelmed with questions such as how to go from raw data to differential expression (DE), pathway analysis and interpretation. Several pipelines and procedures have been developed to this effect. Even though there is no unique way to perform RNA-Seq analysis, it usually follows these steps: 1) raw reads quality check, 2) alignment of reads to a reference genome, 3) aligned reads' summarization according to an annotation file, 4) DE analysis and 5) gene set analysis and/or functional enrichment analysis. Each step requires researchers to make decisions, and the wide variety of options and resulting large volumes of data often lead to interpretation challenges. There also seems to be insufficient guidance on how best to obtain relevant information and derive actionable knowledge from transcription experiments. In this paper, we explain RNA-Seq steps in detail and outline differences and similarities of different popular options, as well as advantages and disadvantages. We also discuss non-coding RNA analysis, multi-omics, meta-transcriptomics and the use of artificial intelligence methods complementing the arsenal of tools available to researchers. Lastly, we perform a complete analysis from raw reads to DE and functional enrichment analysis, visually illustrating how results are not absolute truths and how algorithmic decisions can greatly impact results and interpretation.

Microbiome dysbiosis inhibits carcinogen-induced murine oral tumorigenesis.

Oral cancer is one of the most common cancers worldwide and ranks fourth for the mortality rate of cancers in males in Taiwan. The oral microbiota is the microbial community in the oral cavity, which is essential for maintaining oral health, but the relationship between oral tumorigenesis and the oral microbiota remains to be clarified. This study evaluated the effect of microbiome dysbiosis on oral carcinogenesis in mice, and the impact of the microbiome and its metabolic pathways on regulating oral carcinogenesis. We found that antibiotics treatment decreases carcinogen-induced oral epithelial malignant transformation. Microbiome analysis based on 16S rRNA gene sequencing revealed that the species richness of fecal specimens was significantly reduced in antibiotic-treated mice, while that in the salivary specimens was not decreased accordingly. Differences in bacterial composition, including Lactobacillus animalis abundance, in the salivary samples of cancer-bearing mice was dramatically decreased. L. animalis was the bacterial species that increased the most in the saliva of antibiotic-treated mice, suggesting that L. animalis may be negatively associated with oral carcinogenesis. In functional analysis, the microbiome in the saliva of the tumor-bearing group showed greater potential for polyamine biosynthesis. Immunochemical staining proved that spermine oxidase, an effective polyamine oxidase, was upregulated in mouse oral cancer lesions. In conclusion, oral microbiome dysbiosis may alter polyamine metabolic pathways and reduce carcinogen-induced malignant transformation of the oral epithelium.

Taxonomic and Functional Dysregulation in Salivary Microbiomes During Oral Carcinogenesis.

Exploring microbial community compositions in humans with healthy versus diseased states is crucial to understand the microbe-host interplay associated with the disease progression. Although the relationship between oral cancer and microbiome was previously established, it remained controversial, and yet the ecological characteristics and their responses to oral carcinogenesis have not been well studied. Here, using the bacterial 16S rRNA gene amplicon sequencing along with the in silico function analysis by PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2), we systematically characterized the compositions and the ecological drivers of saliva microbiome in the cohorts of orally healthy, non-recurrent oral verrucous hyperplasia (a pre-cancer lesion), and oral verrucous hyperplasia-associated oral cancer at taxonomic and function levels, and compared them with the re-analysis of publicly available datasets. Diversity analyses showed that microbiome dysbiosis in saliva was significantly linked to oral health status. As oral health deteriorated, the number of core species declined, and metabolic pathways predicted by PICRUSt2 were dysregulated. Partitioned beta-diversity revealed an extremely high species turnover but low function turnover. Functional beta-diversity in saliva microbiome shifted from turnover to nestedness during oral carcinogenesis, which was not observed at taxonomic levels. Correspondingly, the quantitative analysis of stochasticity ratios showed that drivers of microbial composition and functional gene content of saliva microbiomes were primarily governed by the stochastic processes, yet the driver of functional gene content shifted toward deterministic processes as oral cancer developed. Re-analysis of publicly accessible datasets supported not only the distinctive family taxa of Veillonellaceae and Actinomycetaceae present in normal cohorts but also that Flavobacteriaceae and Peptostreptococcaceae as well as the dysregulated metabolic pathways of nucleotides, amino acids, fatty acids, and cell structure were related to oral cancer. Using predicted functional profiles to elucidate the correlations to the oral health status shows superior performance than using taxonomic data among different studies. These findings advance our understanding of the oral ecosystem in relation to oral carcinogenesis and provide a new direction to the development of microbiome-based tools to study the interplay of the oral microbiome, metabolites, and host health.

Leveraging knowledge engineering and machine learning for microbial bio-manufacturing.

Genome scale modeling (GSM) predicts the performance of microbial workhorses and helps identify beneficial gene targets. GSM integrated with intracellular flux dynamics, omics, and thermodynamics have shown remarkable progress in both elucidating complex cellular phenomena and computational strain design (CSD). Nonetheless, these models still show high uncertainty due to a poor understanding of innate pathway regulations, metabolic burdens, and other factors (such as stress tolerance and metabolite channeling). Besides, the engineered hosts may have genetic mutations or non-genetic variations in bioreactor conditions and thus CSD rarely foresees fermentation rate and titer. Metabolic models play important role in design-build-test-learn cycles for strain improvement, and machine learning (ML) may provide a viable complementary approach for driving strain design and deciphering cellular processes. In order to develop quality ML models, knowledge engineering leverages and standardizes the wealth of information in literature (e.g., genomic/phenomic data, synthetic biology strategies, and bioprocess variables). Data driven frameworks can offer new constraints for mechanistic models to describe cellular regulations, to design pathways, to search gene targets, and to estimate fermentation titer/rate/yield under specified growth conditions (e.g., mixing, nutrients, and O2). This review highlights the scope of information collections, database constructions, and machine learning techniques (such as deep learning and transfer learning), which may facilitate "Learn and Design" for strain development.

Microbiome Dynamics in a Shrimp Grow-out Pond with Possible Outbreak of Acute Hepatopancreatic Necrosis Disease.

Acute hepatopancreatic necrosis disease (AHPND) (formerly, early mortality syndrome) is a high-mortality-rate shrimp disease prevalent in shrimp farming areas. Although AHPND is known to be caused by pathogenic Vibrio parahaemolyticus hosting the plasmid-related PirABvp toxin gene, the effects of disturbances in microbiome have not yet been studied. We took 62 samples from a grow-out pond during an AHPND developing period from Days 23 to 37 after stocking white postlarvae shrimp and sequenced the 16S rRNA genes with Illumina sequencing technology. The microbiomes of pond seawater and shrimp stomachs underwent varied dynamic succession during the period. Despite copies of PirABvp, principal co-ordinates analysis revealed two distinctive stages of change in stomach microbiomes associated with AHPND. AHPND markedly changed the bacterial diversity in the stomachs; it decreased the Shannon index by 53.6% within approximately 7 days, shifted the microbiome with Vibrio and Candidatus Bacilloplasma as predominant populations, and altered the species-to-species connectivity and complexity of the interaction network. The AHPND-causing Vibrio species were predicted to develop a co-occurrence pattern with several resident and transit members within Candidatus Bacilloplasma and Cyanobacteria. This study's insights into microbiome dynamics during AHPND infection can be valuable for minimising this disease in shrimp farming ponds.