Murine T cells exposed to rapamycin maintain flexibility towards Th1/Tc1 differentiation, thereby indicating that rapamycin promotion of regulatory T cells (Tregs) is conditional. The degree to which rapamycin might inhibit human Th1/Tc1 differentiation has not been evaluated. In the presence of rapamycin, T cell costimulation and polarization with IL-12 or IFNα permitted human CD4+ and CD8+ T cell differentiation towards a Th1/Tc1 phenotype; activation of STAT1 and STAT4 pathways essential for Th1/Tc1 polarity was preserved during mTOR blockade but instead abrogated by PI3 kinase inhibition. Such rapamycin-resistant human Th1/Tc1 cells: (1) were generated through autophagy (increased LC3BII expression; phenotype reversion by autophagy inhibition via 3-MA or siRNA for Beclin 1); (2) expressed anti-apoptotic bcl-2 family members (reduced Bax, Bak; increased phospho-Bad); (3) maintained mitochondrial membrane potentials; and (4) displayed reduced apoptosis. In vivo, type I polarized and rapamycin-resistant human T cells caused increased xenogeneic graft-versus-host disease (x-GVHD). Murine recipients of rapamycin-resistant human Th1/Tc1 cells had: (1) persistent T cell engraftment; (2) increased T cell cytokine and cytolytic effector function; and (3) T cell infiltration of skin, gut and liver. Rapamycin therefore does not impair human T cell capacity for type I differentiation. Rather, rapamycin yields an anti-apoptotic Th1/Tc1 effector phenotype by promoting autophagy.
The structure and function of proteins underlie most aspects of biology and their mutational perturbations often cause disease. To identify the molecular determinants of function as well as targets for drugs, it is central to characterize the important residues and how they cluster to form functional sites. The Evolutionary Trace (ET) achieves this by ranking the functional and structural importance of the protein sequence positions. ET uses evolutionary distances to estimate functional distances and correlates genotype variations with those in the fitness phenotype. Thus, ET ranks are worse for sequence positions that vary among evolutionarily closer homologs but better for positions that vary mostly among distant homologs. This approach identifies functional determinants, predicts function, guides the mutational redesign of functional and allosteric specificity, and interprets the action of coding sequence variations in proteins, people and populations. Now, the UET database offers pre-computed ET analyses for the protein structure databank, and on-the-fly analysis of any protein sequence. A web interface retrieves ET rankings of sequence positions and maps results to a structure to identify functionally important regions. This UET database integrates several ways of viewing the results on the protein sequence or structure and can be found at http://mammoth.bcm.tmc.edu/uet/.
The strongest genetic risk factor for idiopathic late‐onset Alzheimer's disease (LOAD) is apolipoprotein E (APOE) ɛ4, while the APOE ɛ2 allele is protective. However, there are paradoxical APOE ɛ4 carriers who remain disease‐free and APOE ɛ2 carriers with LOAD. We compared exomes of healthy APOE ɛ4 carriers and APOE ɛ2 Alzheimer's disease (AD) patients, prioritizing coding variants based on their predicted functional impact, and identified 216 genes with differential mutational load between these two populations. These candidate genes were significantly dysregulated in LOAD brains, and many modulated tau‐ or β42‐induced neurodegeneration in Drosophila. Variants in these genes were associated with AD risk, even in APOE ɛ3 homozygotes, showing robust predictive power for risk stratification. Network analyses revealed involvement of candidate genes in brain cell type‐specific pathways including synaptic biology, dendritic spine pruning and inflammation. These potential modifiers of LOAD may constitute novel biomarkers, provide potential therapeutic intervention avenues, and support applying this approach as larger whole exome sequencing cohorts become available.
Motivation: Precision medicine is an emerging field with hopes to improve patient treatment and reduce morbidity and mortality. To these ends, computational approaches have predicted associations among genes, chemicals and diseases. Such efforts, however, were often limited to using just some available association types. This lowers prediction coverage and, since prior evidence shows that integrating heterogeneous data is likely beneficial, it may limit accuracy. Therefore, we systematically tested whether using more association types improves prediction. Results: We study multimodal networks linking diseases, genes and chemicals (drugs) by applying three diffusion algorithms and varying information content. Tenfold cross-validation shows that these networks are internally consistent, both within and across association types. Also, diffusion methods recovered missing edges, even if all the edges from an entire mode of association were removed. This suggests that information is transferable between these association types. As a realistic validation, time-stamped experiments simulated the predictions of future associations based solely on information known prior to a given date. The results show that many future published results are predictable from current associations. Moreover, in most cases, using more association types increases prediction coverage without significantly decreasing sensitivity and specificity. In case studies, literature-supported validation shows that these predictions mimic humanformulated hypotheses. Overall, this study suggests that diffusion over a more comprehensive multimodal network will generate more useful hypotheses of associations among diseases, genes and chemicals, which may guide the development of precision therapies.
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