Transcriptome analysis of cortical tissue reveals shared sets of downregulated genes in autism and schizophrenia.

Autism (AUT), schizophrenia (SCZ) and bipolar disorder (BPD) are three highly heritable neuropsychiatric conditions. Clinical similarities and genetic overlap between the three disorders have been reported; however, the causes and the downstream effects of this overlap remain elusive. By analyzing transcriptomic RNA-sequencing data generated from post-mortem cortical brain tissues from AUT, SCZ, BPD and control subjects, we have begun to characterize the extent of gene expression overlap between these disorders. We report that the AUT and SCZ transcriptomes are significantly correlated (P<0.001), whereas the other two cross-disorder comparisons (AUT-BPD and SCZ-BPD) are not. Among AUT and SCZ, we find that the genes differentially expressed across disorders are involved in neurotransmission and synapse regulation. Despite the lack of global transcriptomic overlap across all three disorders, we highlight two genes, IQSEC3 and COPS7A, which are significantly downregulated compared with controls across all three disorders, suggesting either shared etiology or compensatory changes across these neuropsychiatric conditions. Finally, we tested for enrichment of genes differentially expressed across disorders in genetic association signals in AUT, SCZ or BPD, reporting lack of signal in any of the previously published genome-wide association study (GWAS). Together, these studies highlight the importance of examining gene expression from the primary tissue involved in neuropsychiatric conditions-the cortical brain. We identify a shared role for altered neurotransmission and synapse regulation in AUT and SCZ, in addition to two genes that may more generally contribute to neurodevelopmental and neuropsychiatric conditions

Comprehensive structural classification of ligand binding motifs in proteins

Comprehensive knowledge of protein-ligand interactions should provide a useful basis for annotating protein functions, studying protein evolution, engineering enzymatic activity, and designing drugs. To investigate the diversity and universality of ligand binding sites in protein structures, we conducted the all-against-all atomic-level structural comparison of over 180,000 ligand binding sites found in all the known structures in the Protein Data Bank by using a recently developed database search and alignment algorithm. By applying a hybrid top-down-bottom-up clustering analysis to the comparison results, we determined approximately 3000 well-defined structural motifs of ligand binding sites. Apart from a handful of exceptions, most structural motifs were found to be confined within single families or superfamilies, and to be associated with particular ligands. Furthermore, we analyzed the components of the similarity network and enumerated more than 4000 pairs of ligand binding sites that were shared across different protein folds.Comment: 13 pages, 8 figure

In vitro identification and in silico utilization of interspecies sequence similarities using GeneChip(® )technology

BACKGROUND: Genomic approaches in large animal models (canine, ovine etc) are challenging due to insufficient genomic information for these species and the lack of availability of corresponding microarray platforms. To address this problem, we speculated that conserved interspecies genetic sequences can be experimentally detected by cross-species hybridization. The Affymetrix platform probe redundancy offers flexibility in selecting individual probes with high sequence similarities between related species for gene expression analysis. RESULTS: Gene expression profiles of 40 canine samples were generated using the human HG-U133A GeneChip (U133A). Due to interspecies genetic differences, only 14 ± 2% of canine transcripts were detected by U133A probe sets whereas profiling of 40 human samples detected 49 ± 6% of human transcripts. However, when these probe sets were deconstructed into individual probes and examined performance of each probe, we found that 47% of human probes were able to find their targets in canine tissues and generate a detectable hybridization signal. Therefore, we restricted gene expression analysis to these probes and observed the 60% increase in the number of identified canine transcripts. These results were validated by comparison of transcripts identified by our restricted analysis of cross-species hybridization with transcripts identified by hybridization of total lung canine mRNA to new Affymetrix Canine GeneChip(®). CONCLUSION: The experimental identification and restriction of gene expression analysis to probes with detectable hybridization signal drastically increases transcript detection of canine-human hybridization suggesting the possibility of broad utilization of cross-hybridizations of related species using GeneChip technology

Vesivirus 2117 capsids more closely resemble sapovirus and lagovirus particles than other known vesivirus structures

Vesivirus 2117 is an adventitious agent that in 2009, was identified as a contaminant of CHO cells propagated in bioreactors at a pharmaceutical manufacturing plant belonging to Genzyme. The consequent interruption in supply of Fabrazyme and Cerezyme (drugs used to treat Fabry and Gaucher disease respectively), caused significant economic losses. Vesivirus 2117 is a member of the Caliciviridae; a family of small icosahedral viruses encoding a positive sense RNA genome. We have used cryo-electron microscopy and three dimensional image reconstruction to calculate a structure of vesivirus 2117 virus like particles as well as feline calicivirus and a chimeric sapovirus. We present a structural comparison of several members of the Caliciviridae, showing that the distal P domain of vesivirus 2117 is morphologically distinct from that seen in other known vesivirus structures. Furthermore, at intermediate resolutions we found a high level of structural similarity between vesivirus 2117 and Caliciviridae from other genera, such as sapovirus and rabbit haemorrhagic disease virus. Phylogenetic analysis confirms vesivirus 2117 as a vesivirus closely related to canine vesiviruses. We postulate that morphological differences in virion structure seen between vesivirus clades may reflect differences in receptor usage

Intracompartmental and Intercompartmental Transcriptional Networks Coordinate the Expression of Genes for Organellar Functions

Genes for mitochondrial and chloroplast proteins are distributed between the nuclear and organellar genomes. Organelle biogenesis and metabolism, therefore, require appropriate coordination of gene expression in the different compartments to ensure efficient synthesis of essential multiprotein complexes of mixed genetic origin. Whereas organelle-to-nucleus signaling influences nuclear gene expression at the transcriptional level, organellar gene expression (OGE) is thought to be primarily regulated posttranscriptionally. Here, we show that intracompartmental and intercompartmental transcriptional networks coordinate the expression of genes for organellar functions. Nearly 1,300 ATH1 microarray-based transcriptional profiles of nuclear and organellar genes for mitochondrial and chloroplast proteins in the model plant Arabidopsis (Arabidopsis thaliana) were analyzed. The activity of genes involved in organellar energy production (OEP) or OGE in each of the organelles and in the nucleus is highly coordinated. Intracompartmental networks that link the OEP and OGE gene sets serve to synchronize the expression of nucleus- and organelle-encoded proteins. At a higher regulatory level, coexpression of organellar and nuclear OEP/OGE genes typically modulates chloroplast functions but affects mitochondria only when chloroplast functions are perturbed. Under conditions that induce energy shortage, the intercompartmental coregulation of photosynthesis genes can even override intracompartmental networks. We conclude that dynamic intracompartmental and intercompartmental transcriptional networks for OEP and OGE genes adjust the activity of organelles in response to the cellular energy state and environmental stresses, and we identify candidate cis-elements involved in the transcriptional coregulation of nuclear genes. Regarding the transcriptional regulation of chloroplast genes, novel tentative target genes of σ factors are identified

Meta-analysis of massively parallel reporter assays enables prediction of regulatory function across cell types.

Deciphering the potential of noncoding loci to influence gene regulation has been the subject of intense research, with important implications in understanding genetic underpinnings of human diseases. Massively parallel reporter assays (MPRAs) can measure regulatory activity of thousands of DNA sequences and their variants in a single experiment. With increasing number of publically available MPRA data sets, one can now develop data-driven models which, given a DNA sequence, predict its regulatory activity. Here, we performed a comprehensive meta-analysis of several MPRA data sets in a variety of cellular contexts. We first applied an ensemble of methods to predict MPRA output in each context and observed that the most predictive features are consistent across data sets. We then demonstrate that predictive models trained in one cellular context can be used to predict MPRA output in another, with loss of accuracy attributed to cell-type-specific features. Finally, we show that our approach achieves top performance in the Fifth Critical Assessment of Genome Interpretation "Regulation Saturation" Challenge for predicting effects of single-nucleotide variants. Overall, our analysis provides insights into how MPRA data can be leveraged to highlight functional regulatory regions throughout the genome and can guide effective design of future experiments by better prioritizing regions of interest

The Carcinoembryonic Antigen Gene Family

The molecular cloning of carcinoembryonic antigen (CEA) and several cross-reacting antigens reveals a basic domain structure for the whole family, which shows structural similarities to the immunoglobulin superfamily. The CEA family consists of approximately 10 genes which are localized in two clusters on chromosome 19. So far, mRNA species for five of these genes have been identified which show tissue variability in their transcriptional activity. Expression of some of these genes in heterologous systems has been achieved, allowing the localization of some epitopes. The characterization of a CEA gene family in the rat and a comparison with its human counterpart has been utilized in the development of an evolutionary model

SEED: efficient clustering of next-generation sequences.

MotivationSimilarity clustering of next-generation sequences (NGS) is an important computational problem to study the population sizes of DNA/RNA molecules and to reduce the redundancies in NGS data. Currently, most sequence clustering algorithms are limited by their speed and scalability, and thus cannot handle data with tens of millions of reads.ResultsHere, we introduce SEED-an efficient algorithm for clustering very large NGS sets. It joins sequences into clusters that can differ by up to three mismatches and three overhanging residues from their virtual center. It is based on a modified spaced seed method, called block spaced seeds. Its clustering component operates on the hash tables by first identifying virtual center sequences and then finding all their neighboring sequences that meet the similarity parameters. SEED can cluster 100 million short read sequences in <4 h with a linear time and memory performance. When using SEED as a preprocessing tool on genome/transcriptome assembly data, it was able to reduce the time and memory requirements of the Velvet/Oasis assembler for the datasets used in this study by 60-85% and 21-41%, respectively. In addition, the assemblies contained longer contigs than non-preprocessed data as indicated by 12-27% larger N50 values. Compared with other clustering tools, SEED showed the best performance in generating clusters of NGS data similar to true cluster results with a 2- to 10-fold better time performance. While most of SEED's utilities fall into the preprocessing area of NGS data, our tests also demonstrate its efficiency as stand-alone tool for discovering clusters of small RNA sequences in NGS data from unsequenced organisms.AvailabilityThe SEED software can be downloaded for free from this site: http://manuals.bioinformatics.ucr.edu/home/[email protected] informationSupplementary data are available at Bioinformatics online

The hepta-β-glucoside elicitor-binding proteins from legumes represent a putative receptor family

The ability of legumes to recognize and respond to β-glucan elicitors by synthesizing phytoalexins is consistent with the existence of a membrane-bound β-glucan-binding site. Related proteins of approximately 75 kDa and the corresponding mRNAs were detected in various species of legumes which respond to beta-glucans. The cDNAs for the beta-glucan-binding proteins of bean and soybean were cloned. The deduced 75-kDa proteins are predominantly hydrophilic and constitute a unique class of glucan-binding proteins with no currently recognizable functional domains. Heterologous expression of the soybean beta-glucan-binding protein in tomato cells resulted in the generation of a high-affinity binding site for the elicitor-active hepta-β-glucoside conjugate (K-d = 4.5 nM). Ligand competition experiments with the recombinant binding sites demonstrated similar ligand specificities when compared with soybean. In both soybean and transgenic tomato, membrane-bound, active forms of the glucan-binding proteins coexist with immunologically detectable, soluble but inactive forms of the proteins. Reconstitution of a soluble protein fraction into lipid vesicles regained beta-glucoside-binding activity but with lower affinity (K-d = 130 nM). We conclude that the beta-glucan elicitor receptors of legumes are composed of the 75 kDa glucan-binding proteins as the critical components for ligand-recognition, and of an as yet unknown membrane anchor constituting the plasma membrane-associated receptor complex

Evidence for RNA recombination between distinct isolates of Pepino mosaic virus.

Genetic recombination plays an important role in the evolution of virus genomes. In this study we analyzed publicly available genomic sequences of Pepino mosaic virus (PepMV) for recombination events using several bioinformatics tools. The genome-wide analyses not only confirm the presence of previously found recombination events in PepMV but also provide the first evidence for double recombinant origin of the US2 isolate