Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer.

Multi-omic data and genome-scale microbial metabolic models have allowed us to examine microbial communities, community function, and interactions in ways that were not available to us historically. Now, one of our biggest challenges is determining how to integrate data and maximize data potential. Our study demonstrates one way in which to test a hypothesis by combining multi-omic data and community metabolic models. Specifically, we assess hydrogen sulfide production in colorectal cancer based on stool, mucosa, and tissue samples collected on and off the tumor site within the same individuals. 16S rRNA microbial community and abundance data were used to select and inform the metabolic models. We then used MICOM, an open source platform, to track the metabolic flux of hydrogen sulfide through a defined microbial community that either represented on-tumor or off-tumor sample communities. We also performed targeted and untargeted metabolomics, and used the former to quantitatively evaluate our model predictions. A deeper look at the models identified several unexpected but feasible reactions, microbes, and microbial interactions involved in hydrogen sulfide production for which our 16S and metabolomic data could not account. These results will guide future in vitro, in vivo, and in silico tests to establish why hydrogen sulfide production is increased in tumor tissue.

Impact of insulin deprivation and treatment on sphingolipid distribution in different muscle subcellular compartments of streptozotocin-diabetic C57Bl/6 mice.

Insulin deprivation in type 1 diabetes (T1D) individuals increases lipolysis and plasma free fatty acids (FFA) concentration, which can stimulate synthesis of intramyocellular bioactive lipids such as ceramides (Cer) and long-chain fatty acid-CoAs (LCFa-CoAs). Ceramide was shown to decrease muscle insulin sensitivity, and at mitochondrial levels it stimulates reactive oxygen species production. Here, we show that insulin deprivation in streptozotocin diabetic C57BL/6 mice increases quadriceps muscle Cer content, which was correlated with a concomitant decrease in the body fat and increased plasma FFA, glycosylated hemoglobin level (%Hb A1c), and muscular LCFa-CoA content. The alternations were accompanied by an increase in protein expression in LCFa-CoA and Cer synthesis (FATP1/ACSVL5, CerS1, CerS5), a decrease in the expression of genes implicated in muscle insulin sensitivity (GLUT4, GYS1), and inhibition of insulin signaling cascade by Aktα and GYS3ß phosphorylation under acute insulin stimulation. Both the content and composition of sarcoplasmic fraction sphingolipids were most affected by insulin deprivation, whereas mitochondrial fraction sphingolipids remained stable. The observed effects of insulin deprivation were reversed, except for content and composition of LCFa-CoA, CerS protein expression, GYS1 gene expression, and phosphorylation status of Akt and GYS3ß when exogenous insulin was provided by subcutaneous insulin implants. Principal component analysis and Pearson's correlation analysis revealed close relationships between the features of the diabetic phenotype, the content of LCFa-CoAs and Cers containing C18-fatty acids in sarcoplasm, but not in mitochondria. Insulin replacement did not completely rescue the phenotype, especially regarding the content of LCFa-CoA, or proteins implicated in Cer synthesis and muscle insulin sensitivity. These persistent changes might contribute to muscle insulin resistance observed in T1D individuals.

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

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.