SUMMARY The microbiome is being characterized by large-scale sequencing efforts, yet it is not known whether it regulates host metabolism in a general versus tissue-specific manner or which bacterial metabolites are important. Here, we demonstrate that microbiota have a strong effect on energy homeostasis in the colon compared to other tissues. This tissue specificity is due to colonocytes utilizing bacterially-produced butyrate as their primary energy source. Colonocytes from germfree mice are in an energy-deprived state and exhibit decreased expression of enzymes that catalyze key steps in intermediary metabolism including the TCA cycle. Consequently, there is a marked decrease in NADH/NAD+, oxidative phosphorylation, and ATP levels, which results in AMPK activation, p27kip1 phosphorylation, and autophagy. When butyrate is added to germfree colonocytes, it rescues their deficit in mitochondrial respiration and prevents them from undergoing autophagy. The mechanism is due to butyrate acting as an energy source rather than as an HDAC inhibitor.

BackgroundRecursive partitioning is a non-parametric modeling technique, widely used in regression and classification problems. Model-based recursive partitioning is used to identify groups of observations with similar values of parameters of the model of interest. The mob() function in the party package in R implements model-based recursive partitioning method. This method produces predictions based on single tree models. Predictions obtained through single tree models are very sensitive to small changes to the learning sample. We extend the model-based recursive partition method to produce predictions based on multiple tree models constructed on random samples achieved either through bootstrapping (random sampling with replacement) or subsampling (random sampling without replacement) on learning data.ResultsHere we present an R package called “mobForest” that implements bagging and random forests methodology for model-based recursive partitioning. The mobForest package constructs large number of model-based trees and the predictions are aggregated across these trees resulting in more stable predictions. The package also includes functions for computing predictive accuracy estimates and plots, residuals plot, and variable importance plot.ConclusionThe mobForest package implements a random forest type approach for model-based recursive partitioning. The R package along with it source code is available at http://CRAN.R-project.org/package=mobForest.

Homeostasis in the immune system is maintained by specialized regulatory CD4+ T cells (Treg) expressing transcription factor Foxp3. According to the current paradigm, high-affinity interactions between TCRs and class II MHC-peptide complexes in thymus “instruct” developing thymocytes to up-regulate Foxp3 and become Treg cells. However, the loss or down-regulation of Foxp3 does not disrupt the development of Treg cells but abrogates their suppressor function. In this study, we show that Foxp3-deficient Treg cells in scurfy mice harboring a null mutation of the Foxp3 gene retained cellular features of Treg cells including in vitro anergy, impaired production of inflammatory cytokines, and dependence on exogenous IL-2 for proliferation and homeostatic expansion. Foxp3-deficient Treg cells expressed a low level of activation markers, did not expand relative to other CD4+ T cells, and produced IL-4 and immunomodulatory cytokines IL-10 and TGF-β when stimulated. Global gene expression profiling revealed significant similarities between Treg cells expressing and lacking Foxp3. These results argue that Foxp3 deficiency alone does not convert Treg cells into conventional effector CD4+ T cells but rather these cells constitute a distinct cell subset with unique features.

Dendritic cells uniquely orchestrate the delicate balance between T cell immunity and regulation and an imbalance favoring immunogenic rather than tolerogenic DC is believed to contribute to the development of autoimmune diseases such as type 1 diabetes (T1D). In this study, we determined the frequencies of three blood DC subsets (pDC, mDC1 and mDC2) in 72 T1D patients and 75 normal controls using the Miltenyi blood DC enumeration kit. The frequency of blood pDC was found to be negatively correlated with subject age in both normal controls and T1D patients (p = 0.0007), while the frequency of mDC1 and mDC2 do not change significantly with subject age. More importantly, the mean frequency of pDC in blood was, after adjusting for age, significantly lower in T1D (mean = 0.127%) than controls (mean = 0.188%) (p < 6.0×10 -5 ), whereas no difference was observed for mDC1 and mDC2 between T1D and controls. Furthermore, T1D patients have lower proportion of pDC and higher proportion of mDC1 among the total blood DC population than normal controls. These results indicate that the frequency of blood pDC and the pDC/mDC1 ratio are negatively associated with T1D.

Population-based variability in protein expression patterns, especially in humans, is often observed but poorly understood. Moreover, very little is known about how interindividual genetic variation contributes to protein expression patterns. To begin to address this, we describe elements of technical and biological variations contributing to expression of 544 proteins in a population of 24 individual human lymphoblastoid cell lines that have been extensively genotyped as part of the International HapMap Project. We determined that expression levels of 10% of the proteins were tightly correlated to cell doubling rates. Using the publicly available genotypes for these lymphoblastoid cell lines, we applied a genetic association approach to identify quantitative trait loci associated with protein expression variation. Results identified 24 protein forms corresponding to 15 proteins for which genetic elements were responsible for >50% of the expression variation. The genetic variation associated with protein expression levels were located in cis with the gene coding for the transcript of the protein for 19 of these protein forms. Four of the genetic elements identified were coding non-synonymous single nucleotide polymorphisms that resulted in migration pattern changes in the two-dimensional gel. This is the first description of large scale proteomics analysis demonstrating the direct relationship between genome and proteome variations in human cells. Molecular & Cellular Proteomics 9: 1383-1399, 2010.