Identification and Visualization of CD8+ T Cell Mediated IFN-γ Signaling in Target Cells during an Antiviral Immune Response in the Brain

CD8+ T cells infiltrate the brain during an anti-viral immune response. Within the brain CD8+ T cells recognize cells expressing target antigens, become activated, and secrete IFNγ. However, there are no methods to recognize individual cells that respond to IFNγ. Using a model that studies the effects of the systemic anti-adenoviral immune response upon brain cells infected with an adenoviral vector in mice, we describe a method that identifies individual cells that respond to IFNγ. To identify individual mouse brain cells that respond to IFNγ we constructed a series of adenoviral vectors that contain a transcriptional response element that is selectively activated by IFNγ signaling, the gamma-activated site (GAS) promoter element; the GAS element drives expression of a transgene, Cre recombinase (Ad-GAS-Cre). Upon binding of IFNγ to its receptor, the intracellular signaling cascade activates the GAS promoter, which drives expression of the transgene Cre recombinase. We demonstrate that upon activation of a systemic immune response against adenovirus, CD8+ T cells infiltrate the brain, interact with target cells, and cause an increase in the number of cells expressing Cre recombinase. This method can be used to identify, study, and eventually determine the long term fate of infected brain cells that are specifically targeted by IFNγ. The significance of this method is that it will allow to characterize the networks in the brain that respond to the specific secretion of IFNγ by anti-viral CD8+ T cells that infiltrate the brain. This will allow novel insights into the cellular and molecular responses underlying brain immune responses

Unusual Aerobic Stabilization of Cob(I)alamin by a B₁₂-Trafficking Protein Allows Chemoenzymatic Synthesis of Organocobalamins

CblC, a B₁₂ trafficking protein, exhibits glutathione transferase and reductive decyanase activities for processing alkylcobalamins and cyanocobalamin, respectively, to a common intermediate that is subsequently converted to the biologically active forms of the cofactor. We recently discovered that the Caenorhabditis elegans CblC catalyzes thiol-dependent decyanation of CNCbl and reduction of OH₂Cbl and stabilizes the paramagnetic cob­(II)­alamin product under aerobic conditions. In this study, we report the striking ability of the worm CblC to stabilize the highly reactive cob­(I)­alamin product of the glutathione transferase reaction. The unprecedented stabilization of the supernucleophilic cob­(I)­alamin species under aerobic conditions by the intrinsic thiol oxidase activity of CblC, was exploited for the chemoenzymatic synthesis of organocobalamin derivatives under mild conditions

Fecal Short-Chain Fatty Acids Are Not Predictive of Colonic Tumor Status and Cannot Be Predicted Based on Bacterial Community Structure

Considering that colorectal cancer is the third leading cancer-related cause of death within the United States, it is important to detect colorectal tumors early and to prevent the formation of tumors. Short-chain fatty acids (SCFAs) are often used as a surrogate for measuring gut health and for being anticarcinogenic because of their anti-inflammatory properties. We evaluated the fecal SCFA concentrations of a cohort of individuals with different colonic tumor burdens who were previously analyzed to identify microbiome-based biomarkers of tumors. We were unable to find an association between SCFA concentration and tumor burden or use SCFAs to improve our microbiome-based models of classifying people based on their tumor status. Furthermore, we were unable to find an association between the fecal community structure and SCFA concentrations. Our results indicate that the association between fecal SCFAs, the gut microbiome, and tumor burden is weak.Colonic bacterial populations are thought to have a role in the development of colorectal cancer with some protecting against inflammation and others exacerbating inflammation. Short-chain fatty acids (SCFAs) have been shown to have anti-inflammatory properties and are produced in large quantities by colonic bacteria that produce SCFAs by fermenting fiber. We assessed whether there was an association between fecal SCFA concentrations and the presence of colonic adenomas or carcinomas in a cohort of individuals using 16S rRNA gene and metagenomic shotgun sequence data. We measured the fecal concentrations of acetate, propionate, and butyrate within the cohort and found that there were no significant associations between SCFA concentration and tumor status. When we incorporated these concentrations into random forest classification models trained to differentiate between people with healthy colons and those with adenomas or carcinomas, we found that they did not significantly improve the ability of 16S rRNA gene or metagenomic gene sequence-based models to classify individuals. Finally, we generated random forest regression models trained to predict the concentration of each SCFA based on 16S rRNA gene or metagenomic gene sequence data from the same samples. These models performed poorly and were able to explain at most 14% of the observed variation in the SCFA concentrations. These results support the broader epidemiological data that questions the value of fiber consumption for reducing the risks of colorectal cancer. Although other bacterial metabolites may serve as biomarkers to detect adenomas or carcinomas, fecal SCFA concentrations have limited predictive power

A Framework for Effective Application of Machine Learning to Microbiome-Based Classification Problems

Diagnosing diseases using machine learning (ML) is rapidly being adopted in microbiome studies. However, the estimated performance associated with these models is likely overoptimistic. Moreover, there is a trend toward using black box models without a discussion of the difficulty of interpreting such models when trying to identify microbial biomarkers of disease. This work represents a step toward developing more-reproducible ML practices in applying ML to microbiome research. We implement a rigorous pipeline and emphasize the importance of selecting ML models that reflect the goal of the study. These concepts are not particular to the study of human health but can also be applied to environmental microbiology studies.Machine learning (ML) modeling of the human microbiome has the potential to identify microbial biomarkers and aid in the diagnosis of many diseases such as inflammatory bowel disease, diabetes, and colorectal cancer. Progress has been made toward developing ML models that predict health outcomes using bacterial abundances, but inconsistent adoption of training and evaluation methods call the validity of these models into question. Furthermore, there appears to be a preference by many researchers to favor increased model complexity over interpretability. To overcome these challenges, we trained seven models that used fecal 16S rRNA sequence data to predict the presence of colonic screen relevant neoplasias (SRNs) (n = 490 patients, 261 controls and 229 cases). We developed a reusable open-source pipeline to train, validate, and interpret ML models. To show the effect of model selection, we assessed the predictive performance, interpretability, and training time of L2-regularized logistic regression, L1- and L2-regularized support vector machines (SVM) with linear and radial basis function kernels, a decision tree, random forest, and gradient boosted trees (XGBoost). The random forest model performed best at detecting SRNs with an area under the receiver operating characteristic curve (AUROC) of 0.695 (interquartile range [IQR], 0.651 to 0.739) but was slow to train (83.2 h) and not inherently interpretable. Despite its simplicity, L2-regularized logistic regression followed random forest in predictive performance with an AUROC of 0.680 (IQR, 0.625 to 0.735), trained faster (12 min), and was inherently interpretable. Our analysis highlights the importance of choosing an ML approach based on the goal of the study, as the choice will inform expectations of performance and interpretability

The Glucoamylase Inhibitor Acarbose Has a Diet-Dependent and Reversible Effect on the Murine Gut Microbiome

The gut microbial community has a profound influence on host physiology in both health and disease. In diabetic individuals, the gut microbiota can affect the course of disease, and some medications for diabetes, including metformin, seem to elicit some of their benefits via an interaction with the microbiota. Here, we report that acarbose, a glucoamylase inhibitor for type 2 diabetes, changes the murine gut bacterial community structure in a reversible and diet-dependent manner. In both high-starch and high-fiber diet backgrounds, acarbose treatment results in increased short-chain fatty acids, particularly butyrate, as measured in stool samples. As we learn more about how human disease is affected by the intestinal bacterial community, the interplay between medications such as acarbose and the diet will become increasingly important to evaluate.Acarbose is a safe and effective medication for type 2 diabetes that inhibits host glucoamylases to prevent starch digestion in the small intestines and thus decrease postprandial blood glucose levels. This results in an increase in dietary starch in the distal intestine, where it becomes food for the gut bacterial community. Here, we examined the effect of acarbose therapy on the gut community structure in mice fed either a high-starch (HS) or high-fiber diet rich in plant polysaccharides (PP). The fecal microbiota of animals consuming a low dose of acarbose (25 ppm) was not significantly different from that of control animals that did not receive acarbose. However, a high dose of acarbose (400 ppm) with the HS diet resulted in a substantial change to the microbiota structure. Most notably, the HS diet with a high dose of acarbose lead to an expansion of the Bacteroidaceae and Bifidobacteriaceae and a decrease in the Verrucomicrobiaceae (such as Akkermansia muciniphila) and the Bacteroidales S24-7. Once acarbose treatment ceased, the community composition quickly reverted to mirror that of the control group, suggesting that acarbose does not irreversibly alter the gut community. The high dose of acarbose in the PP diet resulted in a distinct community structure with increased representation of Bifidobacteriaceae and Lachnospiraceae. Short-chain fatty acids (SCFAs) measured from stool samples were increased, especially butyrate, as a result of acarbose treatment in both diets. These data demonstrate the potential of acarbose to change the gut community structure and increase beneficial SCFA output in a diet-dependent manner