SUMMARY The postsynaptic density (PSD) contains a collection of scaffold proteins used for the assembly of synaptic signaling complexes. However, it is not known how the core-scaffold machinery associates in protein-interaction networks and how proteins coding for genes involved in complex brain disorders are distributed through spatio-temporal protein complexes. Here, using immunopurification, proteomics, and bioinformatics, we isolated 2876 proteins across 41 in-vivo interactomes and determined their protein domain composition, correlation to gene expression levels, and developmental integration to the PSD. We defined clusters for enrichment of schizophrenia (SCZ), autism spectrum disorders (ASD), developmental delay (DD), and intellectual disability (ID) risk factors at embryonic day 14 and the adult PSD. Mutations in highly-connected nodes alter protein-protein interactions modulating macromolecular complexes enriched in disease risk candidates. These results were integrated into a software platform: Synaptic Protein/Pathways Resource (SyPPRes), enabling the prioritization of disease risk factors and their placement within synaptic protein interaction networks.

Motivation: The explosion of next-generation sequencing data has spawned the design of new algorithms and software tools to provide efficient mapping for different read lengths and sequencing technologies. In particular, ABI's sequencer (SOLiD system) poses a big computational challenge with its capacity to produce very large amounts of data, and its unique strategy of encoding sequence data into color signals.Results: We present the mapping software, named PerM (Periodic Seed Mapping) that uses periodic spaced seeds to significantly improve mapping efficiency for large reference genomes when compared with state-of-the-art programs. The data structure in PerM requires only 4.5 bytes per base to index the human genome, allowing entire genomes to be loaded to memory, while multiple processors simultaneously map reads to the reference. Weight maximized periodic seeds offer full sensitivity for up to three mismatches and high sensitivity for four and five mismatches while minimizing the number random hits per query, significantly speeding up the running time. Such sensitivity makes PerM a valuable mapping tool for SOLiD and Solexa reads.Availability: http://code.google.com/p/perm/Contact: tingchen@usc.eduSupplementary information: Supplementary data are available at Bioinformatics online.

The prevailing demographic model for Drosophila melanogaster suggests that the colonization of North America occurred very recently from a subset of European flies that rapidly expanded across the continent. This model implies a sudden population growth and range expansion consistent with very low or no population subdivision. As flies adapt to new environments, local adaptation events may be expected. In order to describe demographic and selective events during North American colonization, we have generated a dataset of 35 individual whole genome sequences from inbred lines of D. melanogaster from a west coast US population (Winters, California, USA) and compared them with a public genome dataset from Raleigh (Raleigh, North Carolina, USA). We analyzed nuclear and mitochondrial genomes and describe levels of variation and divergence within and between these two North American D. melanogaster populations. Both populations exhibit negative values of Tajima’s D across the genome, a common signature of demographic expansion. We also detected a low but significant level of genome-wide differentiation between the two populations, as well as multiple allele surfing events, which can be the result of gene drift in local subpopulations on the edge of an expansion wave. In contrast to this genome-wide pattern, we uncovered a 50 kilobases segment in chromosome arm 3L that showed all the hallmarks of a soft selective sweep in both populations. A comparison of allele frequencies within this divergent region among six populations from three continents allowed us to cluster these populations in two differentiated groups, providing evidence for the action of natural selection on a global scale.

BACKGROUNDGWAS of schizophrenia demonstrated that variations in the non-coding regions are responsible for most of common variation heritability of the disease. It is hypothesized that these risk variants alter gene expression. Thus, studying alterations in gene expression in schizophrenia may provide a direct approach to understanding the etiology of the disease. In this study we use Cultured Neural progenitor cells derived from Olfactory Neuroepithelium (CNON) as a genetically unaltered cellular model to elucidate the neurodevelopmental aspects of schizophrenia.METHODSWe performed a gene expression study using RNA-Seq of CNON from 111 controls and 144 individuals with schizophrenia. Differentially expressed (DEX) genes were identified with DESeq2, using covariates to correct for sex, age, library batches and one surrogate variable component.RESULTS80 genes were DEX (FDR<10%), showing enrichment in cell migration, cell adhesion, developmental process, synapse assembly, cell proliferation and related gene ontology categories. Cadherin and Wnt signaling pathways were positive in overrepresentation test, and, in addition, many genes are specifically involved in Wnt5A signaling. The DEX genes were significantly, enriched in the genes overlapping SNPs with genome-wide significant association from the PGC GWAS of schizophrenia (PGC SCZ2). We also found substantial overlap with genes associated with other psychiatric disorders or brain development, enrichment in the same GO categories as genes with mutations de novo in schizophrenia, and studies of iPSC-derived neural progenitor cells.CONCLUSIONSCNON cells are a good model of the neurodevelopmental aspects of schizophrenia and can be used to elucidate the etiology of the disorder.

Applying tissue-specific deconvolution of transcriptional networks to identify their master regulators (MRs) in neuropsychiatric disorders has been largely unexplored. Here, using two schizophrenia (SCZ) case-control RNA-seq datasets, one on postmortem dorsolateral prefrontal cortex (DLPFC) and another on cultured olfactory neuroepithelium, we deconvolved the transcriptional networks and identified TCF4 as a top candidate MR that may be dysregulated in SCZ. We validated TCF4 as a MR through enrichment analysis of TCF4-binding sites in induced pluripotent stem cell (hiPSC)–derived neurons and in neuroblastoma cells. We further validated the predicted TCF4 targets by knocking down TCF4 in hiPSC-derived neural progenitor cells (NPCs) and glutamatergic neurons (Glut_Ns). The perturbed TCF4 gene network in NPCs was more enriched for pathways involved in neuronal activity and SCZ-associated risk genes, compared to Glut_Ns. Our results suggest that TCF4 may serve as a MR of a gene network dysregulated in SCZ at early stages of neurodevelopment.

BackgroundRecently, measurement of RNA at single cell resolution has yielded surprising insights. Methods for single-cell RNA sequencing (scRNA-seq) have received considerable attention, but the broad reliability of single cell methods and the factors governing their performance are still poorly known.ResultsHere, we conducted a large-scale control experiment to assess the transfer function of three scRNA-seq methods and factors modulating the function. All three methods detected greater than 70% of the expected number of genes and had a 50% probability of detecting genes with abundance greater than 2 to 4 molecules. Despite the small number of molecules, sequencing depth significantly affected gene detection. While biases in detection and quantification were qualitatively similar across methods, the degree of bias differed, consistent with differences in molecular protocol. Measurement reliability increased with expression level for all methods and we conservatively estimate measurements to be quantitative at an expression level greater than ~5–10 molecules.ConclusionsBased on these extensive control studies, we propose that RNA-seq of single cells has come of age, yielding quantitative biological information.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3300-3) contains supplementary material, which is available to authorized users.

BackgroundWe examined the performance of three RNA-Sequencing library preparation protocols as a function of RNA integrity, comparing gene expressions between heat-degraded samples to their high-quality counterparts. This work is invaluable given the difficulty of obtaining high-quality RNA from tissues, particularly those from individuals with disease phenotypes.ResultsWith the integrity of total RNA being a critical parameter for RNA-Sequencing analysis, degraded RNA can heavily influence the results of gene expression profiles. We discovered that gene expression read results are influenced by RNA quality when a common library construction protocol is used. These results are based on one technical experiment from a pool of 4 neural progenitor cell lines.ConclusionsThe use of alternative protocols can allow samples with a wider range of RNA qualities to be used, facilitating the investigation of disease tissues.

No abstract