Fecal Metabolites as Biomarkers for Predicting Food Intake by Healthy Adults

Fecal metabolite profiling identifies liver transplant recipients at risk for postoperative infection

Metabolites produced by the intestinal microbiome modulate mucosal immune defenses and optimize epithelial barrier function. Intestinal dysbiosis, including loss of intestinal microbiome diversity and expansion of antibiotic-resistant pathobionts, is accompanied by changes in fecal metabolite concentrations and increased incidence of systemic infection. Laboratory tests that quantify intestinal dysbiosis, however, have yet to be incorporated into clinical practice. We quantified fecal metabolites in 107 patients undergoing liver transplantation (LT) and correlated these with fecal microbiome compositions, pathobiont expansion, and postoperative infections. Consistent with experimental studies implicating microbiome-derived metabolites with host-mediated antimicrobial defenses, reduced fecal concentrations of short- and branched-chain fatty acids, secondary bile acids, and tryptophan metabolites correlate with compositional microbiome dysbiosis in LT patients and the relative risk of postoperative infection. Our findings demonstrate that fecal metabolite profiling can identify LT patients at increased risk of postoperative infection and may provide guideposts for microbiome-targeted therapies.

Fecal Metagenomics to Identify Biomarkers of Food Intake in Healthy Adults: Findings from Randomized, Controlled, Nutrition Trials

BACKGROUND

Undigested components of the human diet affect the composition and function of the microorganisms present in the gastrointestinal tract. Techniques like metagenomic analyses allow researchers to study functional capacity, thus revealing the potential of using metagenomic data for developing objective biomarkers of food intake.

OBJECTIVES

As a continuation of our previous work using 16S and metabolomic datasets, we aimed to utilize a computationally intensive, multivariate, machine-learning approach to identify fecal KEGG (Kyoto encyclopedia of genes and genomes) Orthology (KO) categories as biomarkers that accurately classify food intake.

METHODS

Data were aggregated from 5 controlled feeding studies that studied the individual impact of almonds, avocados, broccoli, walnuts, barley, and oats on the adult gastrointestinal microbiota. Deoxyribonucleic acid from preintervention and postintervention fecal samples underwent shotgun genomic sequencing. After preprocessing, sequences were aligned and functionally annotated with Double Index AlignMent Of Next-generation sequencing Data v2.0.11.149 and MEtaGenome ANalyzer v6.12.2, respectively. After the count normalization, the log of the fold change ratio for resulting KOs between pre- and postintervention of the treatment group against its corresponding control was utilized to conduct differential abundance analysis. Differentially abundant KOs were used to train machine-learning models examining potential biomarkers in both single-food and multi-food models.

RESULTS

We identified differentially abundant KOs in the almond (n = 54), broccoli (n = 2474), and walnut (n = 732) groups (q < 0.20), which demonstrated classification accuracies of 80%, 87%, and 86% for the almond, broccoli, and walnut groups using a random forest model to classify food intake into each food group's respective treatment and control arms, respectively. The mixed-food random forest achieved 81% accuracy.

CONCLUSIONS

Our findings reveal promise in utilizing fecal metagenomics to objectively complement self-reported measures of food intake. Future research on various foods and dietary patterns will expand these exploratory analyses for eventual use in feeding study compliance and clinical settings.

Revealing the Potential Impacts of Nutraceuticals Formulated with Freeze-Dried Jabuticaba Peel and Limosilactobacillus fermentum Strains Candidates for Probiotic Use on Human Intestinal Microbiota

This study evaluated the impacts of novel nutraceuticals formulated with freeze-dried jabuticaba peel (FJP) and three potentially probiotic Limosilactobacillus fermentum strains on the abundance of bacterial groups forming the human intestinal microbiota, metabolite production, and antioxidant capacity during in vitro colonic fermentation. The nutraceuticals had high viable counts of L. fermentum after freeze-drying (≥ 9.57 ± 0.09 log CFU/g). The nutraceuticals increased the abundance of Lactobacillus ssp./Enterococcus spp. (2.46-3.94%), Bifidobacterium spp. (2.28-3.02%), and Ruminococcus albus/R. flavefaciens (0.63-4.03%), while decreasing the abundance of Bacteroides spp./Prevotella spp. (3.91-2.02%), Clostridium histolyticum (1.69-0.40%), and Eubacterium rectale/C. coccoides (3.32-1.08%), which were linked to positive prebiotic indices (> 1.75). The nutraceuticals reduced the pH and increased the sugar consumption, short-chain fatty acid production, phenolic acid content, and antioxidant capacity, besides altering the metabolic profile during colonic fermentation. The combination of FJP and probiotic L. fermentum is a promising strategy to produce nutraceuticals targeting intestinal microbiota.

Predicting metabolic response to dietary intervention using deep learning

Due to highly personalized biological and lifestyle characteristics, different individuals may have different metabolic responses to specific foods and nutrients. In particular, the gut microbiota, a collection of trillions of microorganisms living in our gastrointestinal tract, is highly personalized and plays a key role in our metabolic responses to foods and nutrients. Accurately predicting metabolic responses to dietary interventions based on individuals' gut microbial compositions holds great promise for precision nutrition. Existing prediction methods are typically limited to traditional machine learning models. Deep learning methods dedicated to such tasks are still lacking. Here we develop a new method McMLP (Metabolic response predictor using coupled Multilayer Perceptrons) to fill in this gap. We provide clear evidence that McMLP outperforms existing methods on both synthetic data generated by the microbial consumer-resource model and real data obtained from six dietary intervention studies. Furthermore, we perform sensitivity analysis of McMLP to infer the tripartite food-microbe-metabolite interactions, which are then validated using the ground-truth (or literature evidence) for synthetic (or real) data, respectively. The presented tool has the potential to inform the design of microbiota-based personalized dietary strategies to achieve precision nutrition.