Structure based inhibitor design targeting glycogen phosphorylase b. Virtual screening, synthesis, biochemical and biological assessment of novel N-acyl-β-d-glucopyranosylamines

Glycogen phosphorylase (GP) is a validated target for the development of new type 2 diabetes treatments. Exploiting the Zinc docking database, we report the in silico screening of 1888 β- D-glucopyranose-NH-CO-R putative GP inhibitors differing only in their R groups. CombiGlide and GOLD docking programs with different scoring functions were employed with the best performing methods combined in a “consensus scoring” approach to ranking of ligand binding affinities for the active site. Six selected candidates from the screening were then synthesized and their inhibitory potency was assessed both in vitro and ex vivo. Their inhibition constants’ values, in vitro, ranged from 5 to 377 µM while two of them were effective at causing inactivation of GP in rat hepatocytes at low µM concentrations. The crystal structures of GP in complex with the inhibitors were defined and provided the structural basis for their inhibitory potency and data for further structure based design of more potent inhibitors

The Importance of Context in Understanding Homelessness and Mental Illness: Lessons Learned From a Research Demonstration Project

Research reports on the housing outcomes for persons who are homeless and mentally ill have focused on client characteristics, program type, and services as independent variables, with mixed results. From social work practice, evaluation theory, and public policy perspectives, context is an important variable. Yet, it has received scant research attention in studies of the outcomes of persons who are mentally ill and homeless. This article summarizes research results from a demonstration project providing outreach or linkage services to this target population, illustrating the significant impact of context variables (site and recruitment source) on client characteristics, implementation, qualitative and quantitative service assessments, and housing outcomes. The discussion suggests how these contextual factors may operate, and it goes on to make recommendations to improve social work research and practice concerning the important dimensions of context that should be assessed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/69136/2/10.1177_104973159800800203.pd

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Peptide and nucleic acid-directed self-assembly of cationic nanovehicles through giant unilamellar vesicle modification: targetable nanocomplexes for in vivo nucleic acid delivery

One of the greatest challenges for the development of genetic therapies is the efficient targeted delivery of therapeutic nucleic acids. Towards this goal, we have introduced a new engineering initiative in self-assembly of biologically safe and stable nanovesicle complexes (∼90-140 nm) derived from giant unilamellar vesicle (GUV) precursors and comprising plasmid DNA or siRNA and targeting peptide ligands. The biological performance of the engineered nanovesicle complexes were studied both in vitro and in vivo and compared with cationic liposome-based lipopolyplexes. Compared with cationic lipopolyplexes, nanovesicle complexes did not show advantages in transfection and cell uptake. However, nanovesicle complexes neither displayed significant cytotoxicity nor activated the complement system, which are advantageous for intravenous injection and tumour therapy. On intravenous administration into a neuroblastoma xenograft mouse model, nanovesicle complexes were found to distribute throughout the tumour interstitium, thus providing an alternative safer approach for future development of tumour-specific therapeutic nucleic acid interventions. On oropharyngeal instillation, nanovesicle complexes displayed better transfection efficiency than cationic lipopolyplexes. The technological advantages of nanovesicle complexes, originating from GUVs, over traditional cationic liposome-based lipopolyplexes are discussed. STATEMENT OF SIGNIFICANCE: The efficient targeted delivery of nucleic acids in vivo provides some of the greatest challenges to the development of genetic therapies. Giant unilamellar lipid vesicles (GUVs) have been used mainly as cell and tissue mimics and are instrumental in studying lipid bilayers and interactions. Here, the GUVs have been modified into smaller nanovesicles. We have then developed novel nanovesicle complexes comprising self-assembling mixtures of the nanovesicles, plasmid DNA or siRNA, and targeting peptide ligands. Their biophysical properties were studied and their transfection efficiency was investigated. They transfected cells efficiently without any associated cytotoxicity and with targeting specificity, and in vivo they resulted in very high and tumour-specific uptake and in addition, efficiently transfected the lung. The peptide-targeted nanovesicle complexes allow for the specific targeted enhancement of nucleic acid delivery with improved biosafety over liposomal formulations and represent a promising tool to improve our arsenal of safe, non-viral vectors to deliver therapeutic cargos in a variety of disorders

Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy

Neuroimaging-based classification of PTSD using data-driven computational approaches: a multisite big data study from the ENIGMA-PGC PTSD consortium

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for D-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.Stress-related psychiatric disorders across the life spa

Altered white matter microstructural organization in posttraumatic stress disorder across 3047 adults: results from the PGC-ENIGMA PTSD consortium

A growing number of studies have examined alterations in white matter organization in people with posttraumatic stress disorder (PTSD) using diffusion MRI (dMRI), but the results have been mixed which may be partially due to relatively small sample sizes among studies. Altered structural connectivity may be both a neurobiological vulnerability for, and a result of, PTSD. In an effort to find reliable effects, we present a multi-cohort analysis of dMRI metrics across 3047 individuals from 28 cohorts currently participating in the PGC-ENIGMA PTSD working group (a joint partnership between the Psychiatric Genomics Consortium and the Enhancing NeuroImaging Genetics through Meta-Analysis consortium). Comparing regional white matter metrics across the full brain in 1426 individuals with PTSD and 1621 controls (2174 males/873 females) between ages 18-83, 92% of whom were trauma-exposed, we report associations between PTSD and disrupted white matter organization measured by lower fractional anisotropy (FA) in the tapetum region of the corpus callosum (Cohen's d = -0.11, p = 0.0055). The tapetum connects the left and right hippocampus, for which structure and function have been consistently implicated in PTSD. Results were consistent even after accounting for the effects of multiple potentially confounding variables: childhood trauma exposure, comorbid depression, history of traumatic brain injury, current alcohol abuse or dependence, and current use of psychotropic medications. Our results show that PTSD may be associated with alterations in the broader hippocampal network.New methods for child psychiatric diagnosis and treatment outcome evaluatio

The Immune Landscape of Cancer

We performed an extensive immunogenomic anal-ysis of more than 10,000 tumors comprising 33diverse cancer types by utilizing data compiled byTCGA. Across cancer types, we identified six im-mune subtypes\u2014wound healing, IFN-gdominant,inflammatory, lymphocyte depleted, immunologi-cally quiet, and TGF-bdominant\u2014characterized bydifferences in macrophage or lymphocyte signa-tures, Th1:Th2 cell ratio, extent of intratumoral het-erogeneity, aneuploidy, extent of neoantigen load,overall cell proliferation, expression of immunomod-ulatory genes, and prognosis. Specific drivermutations correlated with lower (CTNNB1,NRAS,orIDH1) or higher (BRAF,TP53,orCASP8) leukocytelevels across all cancers. Multiple control modalitiesof the intracellular and extracellular networks (tran-scription, microRNAs, copy number, and epigeneticprocesses) were involved in tumor-immune cell inter-actions, both across and within immune subtypes.Our immunogenomics pipeline to characterize theseheterogeneous tumors and the resulting data areintended to serve as a resource for future targetedstudies to further advance the field