ENZYMOLOGY AND MOLECULAR BIOLOGY OF BILE ACID 7alpha- AND 7beta- DEHYDROXYLATION BY THE INTESTINAL BACTERIA CLOSTRIDIUM SCINDENS AND CLOSTRIDIUM HYLEMONAE

The collective microbial genomes within our gut(microbiome) represent a powerful metabolic force, leading many authors to call our GI flora an organ within an organ , and the metagenomic sequencing of our microbiome, the second human genome project . Bile acids, endogenously produced by the host liver, represent both a strong selective pressure for potential colonizers, aswell as substrates for microbial metabolism. Indeed, microbes have evolved enzymes to deconjugate bile salts, epimerize bile acid hydroxyl groups, and 7alpha-dehydroxylateprimary bile acids. The products of microbial 7alpha-dehydroxylation, secondary bile acids, are suggested by numerous lines of evidence to be involved in promoting colon carcinogenesis. 7alpha-dehydroxylating activity is a multi-step pathway, genes of which have only been identified in a small number of organisms within the genusClostridium. The biochemistry of this pathway has been largely worked out. The third step in the pathway is introduction of a delta-4-double bond; however, the gene product(s) responsible have not been identified. The baiCD and baiH genes were cloned, expressed and shown to have NAD-dependent 3-oxo-delta-4-steroid oxidoreductase activity showing stereospecificity for 7alpha-hydroxy and 7beta-hydroxy bile acid, respectively.In addition, bai genes were isolated from C.hylemonae TN271 by bidirectional genome-walking by PCR. This represents the first report of bai genes from a low activity 7alpha-dehydroxylating bacterium. The gene organization and sequence of the baiBCDEFGHI operon was highly conserved between C. hylemonae TN271 and the high activity 7alpha-dehydroxylating bacterium C. scindens VPI12708. The baiA gene was located by PCR using degenerate oligonucleotides. Bi-directional genome-walking revealed what appears to be several novel genes involved in bile acid metabolism which were also located in C. scindens VPI 12708. Expression of a 62 kDa flavoprotein and reactionwith [24-14C] 3-oxo-DCA and NADP resulted in a product of greater hydrophilicity than deoxycholic acid. The identity of this product was not determined. A second gene appears to share a common evolutionary origin with the baiF gene. A hypothesis is offered regarding the function of these homologues as Type III CoA transferasesrecognizing 5alpha-bile acids, or 5beta-bile acids (allo-bile acids). A third gene encodes a putative short chain reductase, similar in size and predicted function to the baiA gene, which may be involved in the final reductive step in the pathway. These novel genes also contained a conserved upstream regulatory region with the baioxidative genes. Finally, two genes were identified which may serve as potential drug targets to inhibit bile acid 7alpha-dehydroxylation. The first is an ABC transporter which may be co-transcribed with the other novel bile acid metabolizing genes, and what appears to be a bile acid sensor/regulator similar to the Tryptophan-rich sensory protein (TspO)/mitochondrial peripheral benzodiazepinereceptor (MBR) family of proteins

Modulation of the Metabiome by Rifaximin in Patients with Cirrhosis and Minimal Hepatic Encephalopathy

Hepatic encephalopathy (HE) represents a dysfunctional gut-liver-brain axis in cirrhosis which can negatively impact outcomes. This altered gut-brain relationship has been treated using gut-selective antibiotics such as rifaximin, that improve cognitive function in HE, especially its subclinical form, minimal HE (MHE). However, the precise mechanism of the action of rifaximin in MHE is unclear. We hypothesized that modulation of gut microbiota and their end-products by rifaximin would affect the gut-brain axis and improve cognitive performance in cirrhosis. Aim To perform a systems biology analysis of the microbiome, metabolome and cognitive change after rifaximin in MHE. Methods Twenty cirrhotics with MHE underwent cognitive testing, endotoxin analysis, urine/serum metabolomics (GC and LC-MS) and fecal microbiome assessment (multi-tagged pyrosequencing) at baseline and 8 weeks post-rifaximin 550 mg BID. Changes in cognition, endotoxin, serum/urine metabolites (and microbiome were analyzed using recommended systems biology techniques. Specifically, correlation networks between microbiota and metabolome were analyzed before and after rifaximin. Results There was a significant improvement in cognition(six of seven tests improved,pVeillonellaceaeand increase inEubacteriaceae was observed. Rifaximin resulted in a significant reduction in network connectivity and clustering on the correlation networks. The networks centered onEnterobacteriaceae, Porphyromonadaceae and Bacteroidaceae indicated a shift from pathogenic to beneficial metabolite linkages and better cognition while those centered on autochthonous taxa remained similar. Conclusions Rifaximin is associated with improved cognitive function and endotoxemia in MHE, which is accompanied by alteration of gut bacterial linkages with metabolites without significant change in microbial abundance. Trial Registration ClinicalTrials.gov NCT0106913

Distinct microbes, metabolites, and ecologies define the microbiome in deficient and proficient mismatch repair colorectal cancers

Abstract Background Links between colorectal cancer (CRC) and the gut microbiome have been established, but the specific microbial species and their role in carcinogenesis remain an active area of inquiry. Our understanding would be enhanced by better accounting for tumor subtype, microbial community interactions, metabolism, and ecology. Methods We collected paired colon tumor and normal-adjacent tissue and mucosa samples from 83 individuals who underwent partial or total colectomies for CRC. Mismatch repair (MMR) status was determined in each tumor sample and classified as either deficient MMR (dMMR) or proficient MMR (pMMR) tumor subtypes. Samples underwent 16S rRNA gene sequencing and a subset of samples from 50 individuals were submitted for targeted metabolomic analysis to quantify amino acids and short-chain fatty acids. A PERMANOVA was used to identify the biological variables that explained variance within the microbial communities. dMMR and pMMR microbial communities were then analyzed separately using a generalized linear mixed effects model that accounted for MMR status, sample location, intra-subject variability, and read depth. Genome-scale metabolic models were then used to generate microbial interaction networks for dMMR and pMMR microbial communities. We assessed global network properties as well as the metabolic influence of each microbe within the dMMR and pMMR networks. Results We demonstrate distinct roles for microbes in dMMR and pMMR CRC. Bacteroides fragilis and sulfidogenic Fusobacterium nucleatum were significantly enriched in dMMR CRC, but not pMMR CRC. These findings were further supported by metabolic modeling and metabolomics indicating suppression of B. fragilis in pMMR CRC and increased production of amino acid proxies for hydrogen sulfide in dMMR CRC. Conclusions Integrating tumor biology and microbial ecology highlighted distinct microbial, metabolic, and ecological properties unique to dMMR and pMMR CRC. This approach could critically improve our ability to define, predict, prevent, and treat colorectal cancers

Univariate serum metabolomic analysis.

<p>There was a significant increase in fatty acids and intermediates of carbohydrate metabolism after rifaximin therapy in the serum.</p

Subset of correlation differences before and after rifaximin.

<p>This figure is limited to the metabolomics and clinical/cognitive features that changed with rifaximin and their interaction with the bacterial taxa. The linkages that significantly changed in nature (positive to negative or vice-versa) or intensity (less to more or vice-versa while remaining positive or negative) with p<0.05 are shown. Nodes: Blue: bacterial taxa, green: serum metabolites, Yellow: cognitive or clinical data. Linkages were dark blue if correlations were positive before and changed significantly to negative, light blue if they changed significantly but remained positive throughout, red if correlations were negative at baseline but changed to positive after therapy and green is negative relationship throughout but a significant change.</p

Consort Flowchart of the Open-label trial.

<p>Consort Flowchart of the Open-label trial.</p

A: Principal Component Analysis of Microbiota.

<p>There was no significant change in the PCO of microbiota before and after rifaximin therapy (yellow dots are before and red dots are after rifaximin) B and C: Composition of microbiota families before (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060042#pone-0060042-g002" target="_blank">figure 2B</a>) and after (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060042#pone-0060042-g002" target="_blank">figure 2C</a>) rifaximin. There was a significant decrease in <i>Veillonellaceae</i> and increase in <i>Eubacteriaceae</i> abundance after rifaximin therapy (marked in red).</p

Correlation networks before and after rifaximin.

<p><u>Legend common for </u><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060042#pone-0060042-g004" target="_blank"><u>figures 4A, 4B and 4C</u>:</a> The complex correlation network represented parameters that were linked with a correlation coefficient >0.6 (negative or positive) and with a p value <0.05. Red nodes represent bacterial taxa, green ones the serum metabolites, yellow nodes indicate urinary metabolites while blue ones indicate clinical parameters. Red edges represent negative correlation between connected nodes and blue edges indicate positive correlations. <u>A</u>: Correlation network before rifaximin (BCN) with r>0.6 or <−0.6 and p<0.001. <u>B</u>: Correlation network after rifaximin (ACN) with r>0.6 or <−0.6 and p<0.001. <u>C</u>: is the intersection of 5A and B. It demonstrates those nodes and correlations that remain exactly same before and after rifaximin. <u>D:</u> Cumulative Degree Function curve. This graph plots the cumulative degree function of the node frequency distributions before and after rifaximin. It shows that after rifaximin therapy there was a significant reduction in network complexity (p<0.0001). Blue line: before and red line: after rifaximin. <u>E</u>: Correlation difference before and after rifaximin. This figure shows the correlations that significantly changed between the before and after rifaximin state; i.e. if two nodes were connected positively in the before rifaximin network but aftr rifaximin changed to negative, they are represented here. While the color coding of the nodes is similar, red edges demonstrate linkages that were positive in the BCN but became negative in ACN, while blue edges represent correlations that changed from negative to positive after the use of rifaximin.</p