Prediction of gene expression in embryonic structures of Drosophila melanogaster.

Understanding how sets of genes are coordinately regulated in space and time to generate the diversity of cell types that characterise complex metazoans is a major challenge in modern biology. The use of high-throughput approaches, such as large-scale in situ hybridisation and genome-wide expression profiling via DNA microarrays, is beginning to provide insights into the complexities of development. However, in many organisms the collection and annotation of comprehensive in situ localisation data is a difficult and time-consuming task. Here, we present a widely applicable computational approach, integrating developmental time-course microarray data with annotated in situ hybridisation studies, that facilitates the de novo prediction of tissue-specific expression for genes that have no in vivo gene expression localisation data available. Using a classification approach, trained with data from microarray and in situ hybridisation studies of gene expression during Drosophila embryonic development, we made a set of predictions on the tissue-specific expression of Drosophila genes that have not been systematically characterised by in situ hybridisation experiments. The reliability of our predictions is confirmed by literature-derived annotations in FlyBase, by overrepresentation of Gene Ontology biological process annotations, and, in a selected set, by detailed gene-specific studies from the literature. Our novel organism-independent method will be of considerable utility in enriching the annotation of gene function and expression in complex multicellular organisms

Integrating protein-protein interaction networks with phenotypes reveals signs of interactions

A major objective of systems biology is to organize molecular interactions as networks and to characterize information-flow within networks. We describe a computational framework to integrate protein-protein interaction (PPI) networks and genetic screens to predict the “signs” of interactions (i.e. activation/inhibition relationships). We constructed a Drosophila melanogaster signed PPI network, consisting of 6,125 signed PPIs connecting 3,352 proteins that can be used to identify positive and negative regulators of signaling pathways and protein complexes. We identified an unexpected role for the metabolic enzymes Enolase and Aldo-keto reductase as positive and negative regulators of proteolysis, respectively. Characterization of the activation/inhibition relationships between physically interacting proteins within signaling pathways will impact our understanding of many biological functions, including signal transduction and mechanisms of disease

False negative rates in Drosophila cell-based RNAi screens: a case study

<p>Abstract</p> <p>Background</p> <p>High-throughput screening using RNAi is a powerful gene discovery method but is often complicated by false positive and false negative results. Whereas false positive results associated with RNAi reagents has been a matter of extensive study, the issue of false negatives has received less attention.</p> <p>Results</p> <p>We performed a meta-analysis of several genome-wide, cell-based <it>Drosophila </it>RNAi screens, together with a more focused RNAi screen, and conclude that the rate of false negative results is at least 8%. Further, we demonstrate how knowledge of the cell transcriptome can be used to resolve ambiguous results and how the number of false negative results can be reduced by using multiple, independently-tested RNAi reagents per gene.</p> <p>Conclusions</p> <p>RNAi reagents that target the same gene do not always yield consistent results due to false positives and weak or ineffective reagents. False positive results can be partially minimized by filtering with transcriptome data. RNAi libraries with multiple reagents per gene also reduce false positive and false negative outcomes when inconsistent results are disambiguated carefully.</p

Comparative analysis of the transcriptome across distant species

The transcriptome is the readout of the genome. Identifying common features in it across distant species can reveal fundamental principles. To this end, the ENCODE and modENCODE consortia have generated large amounts of matched RNA-sequencing data for human, worm and fly. Uniform processing and comprehensive annotation of these data allow comparison across metazoan phyla, extending beyond earlier within-phylum transcriptome comparisons and revealing ancient, conserved features. Specifically, we discover co-expression modules shared across animals, many of which are enriched in developmental genes. Moreover, we use expression patterns to align the stages in worm and fly development and find a novel pairing between worm embryo and fly pupae, in addition to the embryo-to-embryo and larvae-to-larvae pairings. Furthermore, we find that the extent of non-canonical, non-coding transcription is similar in each organism, per base pair. Finally, we find in all three organisms that the gene-expression levels, both coding and non-coding, can be quantitatively predicted from chromatin features at the promoter using a 'universal model' based on a single set of organism-independent parameters

A genome-wide association study identifies a gene network associated with paranoid schizophrenia and antipsychotics-induced tardive dyskinesia

In the present study we conducted a genome-wide association study (GWAS) in a cohort of 505 patients with paranoid schizophrenia (SCZ), of which 95 had tardive dyskinesia (TD), and 503 healthy controls. Using data generated by the PsychENCODE Consortium (PEC) and other bioinformatic databases, we revealed a gene network, implicated in neurodevelopment and brain function, associated with both these disorders. Almost all these genes are in gene or isoform co-expression PEC network modules important for the functioning of the brain; the activity of these networks is also altered in SCZ, bipolar disorder and autism spectrum disorders. The associated PEC network modules are enriched for gene ontology terms relevant to the brain development and function (CNS development, neuron development, axon ensheathment, synapse, synaptic vesicle cycle, and signaling receptor activity) and to the immune system (inflammatory response). Results of the present study suggest that orofacial and limbtruncal types of TD seem to share the molecular network with SCZ. Paranoid SCZ and abnormal involuntary movements that indicate the orofacial type of TD are associated with the same genomic loci on chromosomes 3p22.2, 8q21.13, and 13q14.2. The limbtruncal type of TD is associated with a locus on chromosome 3p13 where the best functional candidate is FOXP1, a highconfidence SCZ gene. The results of this study shed light on common pathogenic mechanisms for SCZ and TD, and indicate that the pathogenesis of the orofacial and limbtruncal types of TD might be driven by interacting genes implicated in neurodevelopment

Generation and characterization of interferon-lambda 1-resistant H1N1 influenza A viruses

<div><p>Influenza A viruses pose a constant potential threat to human health. In view of the innate antiviral activity of interferons (IFNs) and their potential use as anti-influenza agents, it is important to know whether viral resistance to these antiviral proteins can arise. To examine the likelihood of emergence of IFN-λ1-resistant H1N1 variants, we serially passaged the A/California/04/09 (H1N1) strain in a human lung epithelial cell line (Calu-3) in the presence of increasing concentrations of recombinant IFN-λ1 protein. To monitor changes associated with adaptation of this virus to growth in Calu-3 cells, we also passaged the wild-type virus in the absence of IFN-λ1. Under IFN-λ1 selective pressure, the parental virus developed two neuraminidase (NA) mutations, S79L and K331N, which significantly reduced NA enzyme activity (↓1.4-fold) and sensitivity to IFN-λ1 (↓˃20-fold), respectively. These changes were not associated with a reduction in viral replication levels. Mutants carrying either K331N alone or S79L and K331N together induced weaker phosphorylation of IFN regulatory factor 3 (IRF3), and, as a consequence, much lower expression of the IFN genes (<i>IFNB1</i>, <i>IFNL1</i> and <i>IFNL2/3</i>) and proteins (IFN-λ1 and IFN-λ2/3). The lower levels of IFN expression correlated with weaker induction of tyrosine-phosphorylated STAT1 and reduced RIG-I protein levels. Our findings demonstrate that influenza viruses can develop increased resistance to the antiviral activity of type III interferons.</p></div

Generation and characterization of interferon-lambda 1-resistant H1N1 influenza A viruses - Fig 6

<p><b>(A, B, C) Influenza-induced IFN gene expression levels in Calu-3 cells.</b> Cells were infected with the indicated recombinant viruses (MOI = 1) and the levels of IFNs were quantified by qPCR at 12, 48, and 72 hpi. Values were determined by comparison to standard curves for each gene and the results are expressed as RNA copy numbers. *<i>P</i> < 0.05; °<i>P</i> < 0.01, compared to the values for the CA/04 virus. <b>(D, E) IFN-λ1 and –λ2/3 protein production in Calu-3 cells.</b> Cells were infected with viruses at a MOI of 5. Supernatants were collected at 24, 48, and 72 hpi, and the levels of secreted IFN-λ1 protein were determined by ELISA. °<i>P</i> < 0.01, compared to the values for the CA/04 virus.</p