Brain function is governed by precise regulation of gene expression across its anatomically distinct structures; however, the expression patterns of genes across hundreds of brain structures are not clearly understood. Here, we describe a gene expression model, which is representative of the healthy human brain transcriptome by using data from the Allen Brain Atlas. Our in-depth gene expression profiling revealed that 84% of genes are expressed in at least one of the 190 brain structures studied. Hierarchical clustering based on gene expression profiles delineated brain regions into structurally tiered spatial groups and we observed striking enrichment for region-specific processes. Further, weighted coexpression network analysis identified 19 robust modules of highly correlated genes enriched with functional associations for neurogenesis, dopamine signaling, immune regulation and behavior. Also, structural distribution maps of major neurotransmission systems in the brain were generated. Finally, we developed a supervised classification model, which achieved 84% and 81% accuracies for predicting autism-and Parkinson's-implicated genes, respectively, using our expression model as a baseline. This study represents the first use of global gene expression profiling from healthy human brain to develop a disease gene prediction model and this generic methodology can be applied to study any neurological disorder.Power of the brain arises from its hundreds of distinct structures and the orchestrated regulation of genes across them 1, 2 . It has been known that the expression profiles of genes in the brain are reasonably stereotyped between individuals 2, 3 . The recent availability of comprehensive expression data at high neuroanatomical resolution from sources like Allen Brain Atlas (ABA) 4 has now made it possible to discover intricate expression patterns. Such data can be used to generate a profile of gene expression patterns that are consistent across healthy human brains in different individuals. We can then extend the application of these homogenous expression patterns as a baseline to predict new genes that may be implicated in neurological disorders by employing machine learning algorithms.A number of studies have examined the global gene expression profiles in human central nervous system (CNS), but these comparisons were either between CNS and non-CNS tissues 5 or between different species like humans and mice 6, 7 or humans and non-human primates 8 . However, the anatomical structural differences and a large difference in size between the human and mouse brains limits the use of mice for understanding the human brain 6,9,10 . Also, the transcriptome profile of human brain differs significantly from that of other primates [11][12][13][14] . As for a handful of high-throughput transcriptome studies that use the human brain samples, they were conducted in pre-set anatomical areas of interest 1,15 , which restrict the broader interpretation of global gene expression patterns. Additionally, meta-analysis of transc...

LocSigDB (http://genome.unmc.edu/LocSigDB/) is a manually curated database of experimental protein localization signals for eight distinct subcellular locations; primarily in a eukaryotic cell with brief coverage of bacterial proteins. Proteins must be localized at their appropriate subcellular compartment to perform their desired function. Mislocalization of proteins to unintended locations is a causative factor for many human diseases; therefore, collection of known sorting signals will help support many important areas of biomedical research. By performing an extensive literature study, we compiled a collection of 533 experimentally determined localization signals, along with the proteins that harbor such signals. Each signal in the LocSigDB is annotated with its localization, source, PubMed references and is linked to the proteins in UniProt database along with the organism information that contain the same amino acid pattern as the given signal. From LocSigDB webserver, users can download the whole database or browse/search for data using an intuitive query interface. To date, LocSigDB is the most comprehensive compendium of protein localization signals for eight distinct subcellular locations.Database URL: http://genome.unmc.edu/LocSigDB/

Epidermal growth factor receptor (EGFR) is a prototype receptor tyrosine kinase and an oncoprotein in many solid tumors. Cell surface display of EGFR is essential for cellular responses to its ligands. While postactivation endocytic trafficking of EGFR has been well elucidated, little is known about mechanisms of basal/preactivation surface display of EGFR. Here, we identify a novel role of the endocytic regulator EHD1 and a potential EHD1 partner, RUSC2, in cell surface display of EGFR. EHD1 and RUSC2 colocalize with EGFR in vesicular/tubular structures and at the Golgi compartment. Inducible EHD1 knockdown reduced the cell surface EGFR expression with accumulation at the Golgi compartment, a phenotype rescued by exogenous EHD1. RUSC2 knockdown phenocopied the EHD1 depletion effects. EHD1 or RUSC2 depletion impaired the EGF-induced cell proliferation, demonstrating that the novel, EHD1- and RUSC2-dependent transport of unstimulated EGFR from the Golgi compartment to the cell surface that we describe is functionally important, with implications for physiologic and oncogenic roles of EGFR and targeted cancer therapies.

Natural products provide new opportunities for anticancer chemotherapeutics. We examined the associations of molecular features in the NCI‐60 cancer cell lines with response to 1302 plant, marine, and microbial compounds. Expression or mutations in multiple genes were associated with treatment responses, suggesting potential mechanisms of action of natural compounds. This information will assist in future design of new chemotherapy agents.

BackgroundObesity is now a worldwide epidemic disease and poses a major risk for diet related diseases like type 2 diabetes, cardiovascular disease, stroke and fatty liver among others. In the present study we employed the murine model of diet-induced obesity to determine the early, tissue-specific, gene expression signatures that characterized progression to obesity and type 2 diabetes.ResultsWe used the C57BL/6 J mouse which is known as a counterpart for diet-induced human diabetes and obesity model. Our initial experiments involved two groups of mice, one on normal diet (ND) and the other on high-fat and high-sucrose (HFHSD). The later were then further separated into subgroups that either received no additional treatment, or were treated with different doses of the Ayurvedic formulation KAL-1. At different time points (week3, week6, week9, week12, week15 and week18) eight different tissues were isolated from mice being fed on different diet compositions. These tissues were used to extract gene-expression data through microarray experiment. Simultaneously, we also measured different body parameters like body weight, blood Glucose level and cytokines profile (anti-inflammatory & pro-inflammatory) at each time point for all the groups.Using partial least square discriminant analysis (PLS-DA) method we identified gene-expression signatures that predict physiological parameters like blood glucose levels, body weight and the balance of pro- versus anti-inflammatory cytokines. The resulting models successfully predicted diet-induced changes in body weight and blood glucose levels, although the predictive power for cytokines profiles was relatively poor. In the former two instances, however, we could exploit the models to further extract the early gene-expression signatures that accurately predict the onset of diabetes and obesity. These extracted genes allowed definition of the regulatory network involved in progression of disease.ConclusionWe identified the early gene-expression signature for the onset of obesity and type 2 diabetes. Further analysis of this data suggests that some of these genes could be used as potential biomarkers for these two disease-states.