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 PirAB^vp 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 PirAB^vp, 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.

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.

Most oral squamous cell carcinoma (OSCC) tumors arise from oral premalignant lesions. Oral submucous fibrosis (OSF), usually occurring in male chewers of betel quid, is a premalignant stromal disease characterized by a high malignant transformation rate and high prevalence. Although a relationship between the inhabited microbiome and carcinogenesis has been proposed, no detailed information regarding the oral microbiome of patients with OSF exists; the changes of the salivary microbiome during cancer formation remain unclear. This study compared the salivary microbiomes of male patients with OSCC and a predisposing OSF background (OSCC-OSF group) and those with OSF only (OSF group). The results of high-throughput sequencing of the bacterial 16S rRNA gene indicated that OSF-related carcinogenesis and smoking status significantly contributed to phylogenetic composition variations in the salivary microbiome, leading to considerable reductions in species richness and phylogenetic diversity. The microbiome profile of OSF-related malignancy was associated with increased microbial stochastic fluctuation, which dominated the salivary microbiome assembly and caused species co-occurrence network collapse. Artificial intelligence selection algorithms consistently identified 5 key species in the OSCC-OSF group: Porphyromonas catoniae, Prevotella multisaccharivorax, Prevotella sp. HMT-300, Mitsuokella sp. HMT-131, and Treponema sp. HMT-927. Robust accuracy in predicting oral carcinogenesis was obtained with our exploratory and validation data sets. In functional analysis, the microbiome of the OSCC-OSF group had greater potential for S-adenosyl-l-methionine and norspermidine synthesis but lower potential for l-ornithine and pyrimidine deoxyribonucleotide synthesis and formaldehyde metabolism. These findings indicated that the salivary microbiome plays important roles in modulating microbial metabolites during oral carcinogenesis. In conclusion, our results provided new insights into salivary microbiome alterations during the malignant transformation of OSF.

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.