Fecal Microbiota and Associated Volatile Organic Compounds Distinguishing No-Adenoma from High-Risk Colon Adenoma Adults

Abstract

Microbiota and the metabolites they produce within the large intestine interact with the host epithelia under the influence of a range of host-derived metabolic, immune, and homeostatic factors. This complex host–microbe interaction affects intestinal tumorigenesis, but established microbial or metabolite profiles predicting colorectal cancer (CRC) risk are missing. Here, we aimed to identify fecal bacteria, volatile organic compounds (VOC), and their associations that distinguish healthy (non-adenoma, NA) from CRC prone (high-risk adenoma, HRA) individuals. Analyzing fecal samples obtained from 117 participants ≥15 days past routine colonoscopy, we highlight the higher abundance of Proteobacteria and Parabacteroides distasonis, and the lower abundance of Lachnospiraceae species, Roseburia faecis, Blautia luti, Fusicatenibacter saccharivorans, Eubacterium rectale, and Phascolarctobacterium faecium in the samples of HRA individuals. Volatolomic analysis of samples from 28 participants revealed a higher concentration of five compounds in the feces of HRA individuals, isobutyric acid, methyl butyrate, methyl propionate, 2-hexanone, and 2-pentanone. We used binomial logistic regression modeling, revealing 68 and 96 fecal bacteria-VOC associations at the family and genus level, respectively, that distinguish NA from HRA endpoints. For example, isobutyric acid associations with Lachnospiraceae incertae sedis and Bacteroides genera exhibit positive and negative regression lines for NA and HRA endpoints, respectively. However, the same chemical associates with Coprococcus and Colinsella genera exhibit the reverse regression line trends. Thus, fecal microbiota and VOC profiles and their associations in NA versus HRA individuals indicate the significance of multiple levels of analysis towards the identification of testable CRC risk biomarkers.

A Pilot Interaction Analysis of Gut Microbiota and Peripheral Markers of Aging in Severe Psychiatric Disorders: A Role for Lachnoclostridium?

“…The methodology for interaction analyses between continuous variables, 16S metagenomics, and disease status is described in detail in Ref. [30]. The Mann-Whitney U test and chi-square test were used to compare clinical, socio-demographic, and biological variables among the independent groups "HCs", "MDD" and "SCZ" as appropriate.…”

“…However, we found several statistically significant interactions with TL and bacterial organisms' relative abundance across taxa in fecal samples. The methodology for interaction analyses between continuous variables, 16S metagenomics, and disease status has been recently published [30], but, to our knowledge, this is the first application of interaction testing between 16S metagenomics and blood biomarkers in psychiatric disorders. Considering the pilot nature of our investigation, the findings were not corrected for multiple comparisons.…”