Propionate-producing Veillonella parvula regulates the malignant properties of tumor cells of OSCC

Detecting and quantifying Veillonella by real-time quantitative PCR and droplet digital PCR

Veillonella spp. are Gram-negative opportunistic pathogens present in the respiratory, digestive, and reproductive tracts of mammals. An abnormal increase in Veillonella relative abundance in the body is closely associated with periodontitis, inflammatory bowel disease, urinary tract infections, and many other diseases. We designed a pair of primers and a probe based on the 16S rRNA gene sequences of Veillonella and conducted real-time quantitative PCR (qPCR) and droplet digital PCR (ddPCR) to quantify the abundance of Veillonella in fecal samples. These two methods were tested for specificity and sensitivity using simulated clinical samples. The sensitivity of qPCR was 100 copies/μL, allowing for the accurate detection of a wide range of Veillonella concentrations from 103 to 108 CFU/mL. The sensitivity of ddPCR was 11.3 copies/μL, only allowing for the accurate detection of Veillonella concentrations from 101 to 104 CFU/mL because of the limited number of droplets generated by ddPCR. ddPCR is therefore more suitable for the detection of low-abundance Veillonella samples. To characterize the validity of the assay system, clinical samples from children with inflammatory bowel disease were collected and analyzed, and the results were verified using isolation methods. We conclude that molecular assays targeting the 16S rRNA gene provides an important tool for the rapid diagnosis of chronic and infectious diseases caused by Veillonella and also supports the isolation and identification of Veillonella for research purposes. KEY POINTS: • With suitable primer sets, the qPCR has a wider detection range than ddPCR. • ddPCR is suitable for the detection of low-abundance samples. • Methods successfully guided the isolation of Veillonella in clinical sample.

Modulation of Gut Microbial Biomarkers and Metabolites in Cancer Management by Tea Compounds

Cancers are causing millions of deaths and leaving a huge clinical and economic burden. High costs of cancer drugs are limiting their access to the growing number of cancer cases. The development of more affordable alternative therapy could reach more patients. As gut microbiota plays a significant role in the development and treatment of cancer, microbiome-targeted therapy has gained more attention in recent years. Dietary and natural compounds can modulate gut microbiota composition while providing broader and more accessible access to medicine. Tea compounds have been shown to have anti-cancer properties as well as modulate the gut microbiota and their related metabolites. However, there is no comprehensive review that focuses on the gut modulatory effects of tea compounds and their impact on reshaping the metabolic profiles, particularly in cancer models. In this review, the effects of different tea compounds on gut microbiota in cancer settings are discussed. Furthermore, the relationship between these modulated bacteria and their related metabolites, along with the mechanisms of how these changes led to cancer intervention are summarized.

gNOMO2: a comprehensive and modular pipeline for integrated multi-omics analyses of microbiomes

BACKGROUND

In recent years, omics technologies have offered an exceptional chance to gain a deeper insight into the structural and functional characteristics of microbial communities. As a result, there is a growing demand for user-friendly, reproducible, and versatile bioinformatic tools that can effectively harness multi-omics data to provide a holistic understanding of microbiomes. Previously, we introduced gNOMO, a bioinformatic pipeline tailored to analyze microbiome multi-omics data in an integrative manner. In response to the evolving demands within the microbiome field and the growing necessity for integrated multi-omics data analysis, we have implemented substantial enhancements to the gNOMO pipeline.

RESULTS

Here, we present gNOMO2, a comprehensive and modular pipeline that can seamlessly manage various omics combinations, ranging from 2 to 4 distinct omics data types, including 16S ribosomal RNA (rRNA) gene amplicon sequencing, metagenomics, metatranscriptomics, and metaproteomics. Furthermore, gNOMO2 features a specialized module for processing 16S rRNA gene amplicon sequencing data to create a protein database suitable for metaproteomics investigations. Moreover, it incorporates new differential abundance, integration, and visualization approaches, enhancing the toolkit for a more insightful analysis of microbiomes. The functionality of these new features is showcased through the use of 4 microbiome multi-omics datasets encompassing various ecosystems and omics combinations. gNOMO2 not only replicated most of the primary findings from these studies but also offered further valuable perspectives.

CONCLUSIONS

gNOMO2 enables the thorough integration of taxonomic and functional analyses in microbiome multi-omics data, offering novel insights in both host-associated and free-living microbiome research. gNOMO2 is available freely at https://github.com/muzafferarikan/gNOMO2.