Inside the microbial black box: a redox-centric framework for deciphering microbial metabolism.

Microbial metabolism influences the global climate and human health and is governed by the balance between NADH and NAD+ through redox reactions. Historically, oxidative (i.e., catabolism) and reductive (i.e., fermentation) pathways have been studied in isolation, obscuring the complete metabolic picture. However, new omics technologies and biotechnological tools now allow an integrated system-level understanding of the drivers of microbial metabolism through observation and manipulation of redox reactions. Here we present perspectives on the importance of viewing microbial metabolism as the dynamic interplay between oxidative and reductive processes and apply this framework to diverse microbial systems. Additionally, we highlight novel biotechnologies to monitor and manipulate microbial redox status to control metabolism in unprecedented ways. This redox-focused systems biology framework enables a more mechanistic understanding of microbial metabolism.

Sulforaphane and Sulforaphane-Nitrile Metabolism in Humans Following Broccoli Sprout Consumption: Inter-individual Variation, Association with Gut Microbiome Composition, and Differential Bioactivity.

SCOPE: The glucosinolate glucoraphanin from broccoli is converted to sulforaphane (SFN) or sulforaphane-nitrile (SFN-NIT) by plant enzymes or the gut microbiome. Human feeding studies typically observe high inter-individual variation in absorption and excretion of SFN, however, the source of this variation is not fully known. To address this, a human feeding trial to comprehensively evaluate inter-individual variation in the absorption and excretion of all known SFN metabolites in urine, plasma, and stool, and tested the hypothesis that gut microbiome composition influences inter-individual variation in total SFN excretion has been conducted. METHODS AND RESULTS: Participants (n = 55) consumed a single serving of broccoli or alfalfa sprouts and plasma, stool, and total urine are collected over 72 h for quantification of SFN metabolites and gut microbiome profiling using 16S gene sequencing. SFN-NIT excretion is markedly slower than SFN excretion (72 h vs 24 h). Members of genus Bifidobacterium, Dorea, and Ruminococcus torques are positively associated with SFN metabolite excretion while members of genus Alistipes and Blautia has a negative association. CONCLUSION: This is the first report of SFN-NIT metabolite levels in human plasma, urine, and stool following consumption of broccoli sprouts. The results help explain factors driving inter-individual variation in SFN metabolism and are relevant for precision nutrition.

Identification of biological signatures of cruciferous vegetable consumption utilizing machine learning-based global untargeted stable isotope traced metabolomics.

In recent years there has been increased interest in identifying biological signatures of food consumption for use as biomarkers. Traditional metabolomics-based biomarker discovery approaches rely on multivariate statistics which cannot differentiate between host- and food-derived compounds, thus novel approaches to biomarker discovery are required to advance the field. To this aim, we have developed a new method that combines global untargeted stable isotope traced metabolomics and a machine learning approach to identify biological signatures of cruciferous vegetable consumption. Participants consumed a single serving of broccoli (n = 16), alfalfa sprouts (n = 16) or collard greens (n = 26) which contained either control unlabeled metabolites, or that were grown in the presence of deuterium-labeled water to intrinsically label metabolites. Mass spectrometry analysis indicated 133 metabolites in broccoli sprouts and 139 metabolites in the alfalfa sprouts were labeled with deuterium isotopes. Urine and plasma were collected and analyzed using untargeted metabolomics on an AB SCIEX TripleTOF 5,600 mass spectrometer. Global untargeted stable isotope tracing was completed using openly available software and a novel random forest machine learning based classifier. Among participants who consumed labeled broccoli sprouts or collard greens, 13 deuterium-incorporated metabolomic features were detected in urine representing 8 urine metabolites. Plasma was analyzed among collard green consumers and 11 labeled features were detected representing 5 plasma metabolites. These deuterium-labeled metabolites represent potential biological signatures of cruciferous vegetables consumption. Isoleucine, indole-3-acetic acid-N-O-glucuronide, dihydrosinapic acid were annotated as labeled compounds but other labeled metabolites could not be annotated. This work presents a novel framework for identifying biological signatures of food consumption for biomarker discovery. Additionally, this work presents novel applications of metabolomics and machine learning in the life sciences.

Gut enterotype-dependent modulation of gut microbiota and their metabolism in response to xanthohumol supplementation in healthy adults.

Xanthohumol (XN), a polyphenol found in the hop plant (Humulus lupulus), has antioxidant, anti-inflammatory, prebiotic, and anti-hyperlipidemic activity. Preclinical evidence suggests the gut microbiome is essential in mediating these bioactivities; however, relatively little is known about XN's impact on human gut microbiota in vivo. We conducted a randomized, triple-blinded, placebo-controlled clinical trial (ClinicalTrials.gov NCT03735420) to determine safety and tolerability of XN in healthy adults. Thirty healthy participants were randomized to 24 mg/day XN or placebo for 8 weeks. As secondary outcomes, quantification of bacterial metabolites and 16S rRNA gene sequencing were utilized to explore the relationships between XN supplementation, gut microbiota, and biomarkers of gut health. Although XN did not significantly change gut microbiota composition, it did re-shape individual taxa in an enterotype-dependent manner. High levels of inter-individual variation in metabolic profiles and bioavailability of XN metabolites were observed. Moreover, reductions in microbiota-derived bile acid metabolism were observed, which were specific to Prevotella and Ruminococcus enterotypes. These results suggest interactions between XN and gut microbiota in healthy adults are highly inter-individualized and potentially indicate that XN elicits effects on gut health in an enterotype-dependent manner.

Microbial polyphenol metabolism is part of the thawing permafrost carbon cycle.

With rising global temperatures, permafrost carbon stores are vulnerable to microbial degradation. The enzyme latch theory states that polyphenols should accumulate in saturated peatlands due to diminished phenol oxidase activity, inhibiting resident microbes and promoting carbon stabilization. Pairing microbiome and geochemical measurements along a permafrost thaw-induced saturation gradient in Stordalen Mire, a model Arctic peatland, we confirmed a negative relationship between phenol oxidase expression and saturation but failed to support other trends predicted by the enzyme latch. To inventory alternative polyphenol removal strategies, we built CAMPER, a gene annotation tool leveraging polyphenol enzyme knowledge gleaned across microbial ecosystems. Applying CAMPER to genome-resolved metatranscriptomes, we identified genes for diverse polyphenol-active enzymes expressed by various microbial lineages under a range of redox conditions. This shifts the paradigm that polyphenols stabilize carbon in saturated soils and highlights the need to consider both oxic and anoxic polyphenol metabolisms to understand carbon cycling in changing ecosystems.