BackgroundStructural rearrangements of the genome resulting in genic imbalance due to copy number change are often deleterious at the organismal level, but are common in immortalized cell lines and tumors, where they may be an advantage to cells. In order to explore the biological consequences of copy number changes in the Drosophila genome, we resequenced the genomes of 19 tissue-culture cell lines and generated RNA-Seq profiles.ResultsOur work revealed dramatic duplications and deletions in all cell lines. We found three lines of evidence indicating that copy number changes were due to selection during tissue culture. First, we found that copy numbers correlated to maintain stoichiometric balance in protein complexes and biochemical pathways, consistent with the gene balance hypothesis. Second, while most copy number changes were cell line-specific, we identified some copy number changes shared by many of the independent cell lines. These included dramatic recurrence of increased copy number of the PDGF/VEGF receptor, which is also over-expressed in many cancer cells, and of bantam, an anti-apoptosis miRNA. Third, even when copy number changes seemed distinct between lines, there was strong evidence that they supported a common phenotypic outcome. For example, we found that proto-oncogenes were over-represented in one cell line (S2-DRSC), whereas tumor suppressor genes were under-represented in another (Kc167).ConclusionOur study illustrates how genome structure changes may contribute to selection of cell lines in vitro. This has implications for other cell-level natural selection progressions, including tumorigenesis.Electronic supplementary materialThe online version of this article (doi:10.1186/gb-2014-15-8-r70) contains supplementary material, which is available to authorized users.

BackgroundA generally accepted approach to the analysis of RNA-Seq read count data does not yet exist. We sequenced the mRNA of 726 individuals from the Drosophila Genetic Reference Panel in order to quantify differences in gene expression among single flies. One of our experimental goals was to identify the optimal analysis approach for the detection of differential gene expression among the factors we varied in the experiment: genotype, environment, sex, and their interactions. Here we evaluate three different filtering strategies, eight normalization methods, and two statistical approaches using our data set. We assessed differential gene expression among factors and performed a statistical power analysis using the eight biological replicates per genotype, environment, and sex in our data set.ResultsWe found that the most critical considerations for the analysis of RNA-Seq read count data were the normalization method, underlying data distribution assumption, and numbers of biological replicates, an observation consistent with previous RNA-Seq and microarray analysis comparisons. Some common normalization methods, such as Total Count, Quantile, and RPKM normalization, did not align the data across samples. Furthermore, analyses using the Median, Quantile, and Trimmed Mean of M-values normalization methods were sensitive to the removal of low-expressed genes from the data set. Although it is robust in many types of analysis, the normal data distribution assumption produced results vastly different than the negative binomial distribution. In addition, at least three biological replicates per condition were required in order to have sufficient statistical power to detect expression differences among the three-way interaction of genotype, environment, and sex.ConclusionsThe best analysis approach to our data was to normalize the read counts using the DESeq method and apply a generalized linear model assuming a negative binomial distribution using either edgeR or DESeq software. Genes having very low read counts were removed after normalizing the data and fitting it to the negative binomial distribution. We describe the results of this evaluation and include recommended analysis strategies for RNA-Seq read count data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2353-z) contains supplementary material, which is available to authorized users.

Nuclear pore complexes (NPCs) are important for cellular functions beyond nucleocytoplasmic trafficking, including genome organization and gene expression. This multi-faceted nature and the slow turnover of NPC components complicates investigations of how individual nucleoporins act in these diverse processes. To address this question, we apply an Auxin-Induced Degron (AID) system to distinguish roles of basket nucleoporins NUP153, NUP50 and TPR. Acute depletion of TPR causes rapid and pronounced changes in transcriptomic profiles. These changes are dissimilar to shifts observed after loss of NUP153 or NUP50, but closely related to changes caused by depletion of mRNA export receptor NXF1 or the GANP subunit of the TRanscription-EXport-2 (TREX-2) mRNA export complex. Moreover, TPR depletion disrupts association of TREX-2 subunits (GANP, PCID2, ENY2) to NPCs and results in abnormal RNA transcription and export. Our findings demonstrate a unique and pivotal role of TPR in gene expression through TREX-2- and/or NXF1-dependent mRNA turnover.

Background SARS-CoV-2 causes COVID-19 which has a widely diverse disease profile. The mechanisms underlying its pathogenicity remain unclear. We set out to identify the SARS-CoV-2 pathogenic proteins that through host interactions cause the cellular damages underlying COVID-19 symptomatology. Methods We examined each of the individual SARS-CoV-2 proteins for their cytotoxicity in HEK 293 T cells and their subcellular localization in COS-7 cells. We also used Mass-Spec Affinity purification to identify the host proteins interacting with SARS-CoV-2 Orf6 protein and tested a drug that could inhibit a specific Orf6 and host protein interaction. Results We found that Orf6, Nsp6 and Orf7a induced the highest toxicity when over-expressed in human 293 T cells. All three proteins showed membrane localization in COS-7 cells. We focused on Orf6, which was most cytotoxic and localized to the endoplasmic reticulum, autophagosome and lysosomal membranes. Proteomics revealed Orf6 interacts with nucleopore proteins (RAE1, XPO1, RANBP2 and nucleoporins). Treatment with Selinexor, an FDA-approved inhibitor for XPO1, attenuated Orf6-induced cellular toxicity in human 293 T cells. Conclusions Our study revealed Orf6 as a highly pathogenic protein from the SARS-CoV-2 genome, identified its key host interacting proteins, and Selinexor as a drug candidate for directly targeting Orf6 host protein interaction that leads to cytotoxicity.

Deletions, commonly referred to as deficiencies by Drosophila geneticists, are valuable tools for mapping genes and for genetic pathway discovery via dose-dependent suppressor and enhancer screens. More recently, it has become clear that deviations from normal gene dosage are associated with multiple disorders in a range of species including humans. While we are beginning to understand some of the transcriptional effects brought about by gene dosage changes and the chromosome rearrangement breakpoints associated with them, much of this work relies on isolated examples. We have systematically examined deficiencies of the left arm of chromosome 2 and characterize gene-by-gene dosage responses that vary from collapsed expression through modest partial dosage compensation to full or even over compensation. We found negligible long-range effects of creating novel chromosome domains at deletion breakpoints, suggesting that cases of gene regulation due to altered nuclear architecture are rare. These rare cases include trans de-repression when deficiencies delete chromatin characterized as repressive in other studies. Generally, effects of breakpoints on expression are promoter proximal (~100bp) or in the gene body. Effects of deficiencies genome-wide are in genes with regulatory relationships to genes within the deleted segments, highlighting the subtle expression network defects in these sensitized genetic backgrounds.

Wolbachia is an intracellular bacterium that infects a remarkable range of insect hosts. Insects such as mosquitos act as vectors for many devastating human viruses such as Dengue, West Nile, and Zika. Remarkably, Wolbachia infection provides insect hosts with resistance to many arboviruses thereby rendering the insects ineffective as vectors. To utilize Wolbachia effectively as a tool against vector-borne viruses a better understanding of the host-Wolbachia relationship is needed. To investigate Wolbachia-insect interactions we used the Wolbachia/Drosophila model that provides a genetically tractable system for studying host-pathogen interactions. We coupled genome-wide RNAi screening with a novel high-throughput fluorescence in situ hybridization (FISH) assay to detect changes in Wolbachia levels in a Wolbachia-infected Drosophila cell line JW18. 1117 genes altered Wolbachia levels when knocked down by RNAi of which 329 genes increased and 788 genes decreased the level of Wolbachia. Validation of hits included in depth secondary screening using in vitro RNAi, Drosophila mutants, and Wolbachia-detection by DNA qPCR. A diverse set of host gene networks was identified to regulate Wolbachia levels and unexpectedly revealed that perturbations of host translation components such as the ribosome and translation initiation factors results in increased Wolbachia levels both in vitro using RNAi and in vivo using mutants and a chemical-based translation inhibition assay. This work provides evidence for Wolbachia-host translation interaction and strengthens our general understanding of the Wolbachia-host intracellular relationship.

Gene regulatory networks (GRNs) describe regulatory relationships between transcription factors (TFs) and their target genes. Computational methods to infer GRNs typically combine evidence across different conditions to infer context-agnostic networks. We develop a method, Network Reprogramming using EXpression (NetREX), that constructs a context-specific GRN given context-specific expression data and a context-agnostic prior network. NetREX remodels the prior network to obtain the topology that provides the best explanation for expression data. Because NetREX utilizes prior network topology, we also develop PriorBoost, a method that evaluates a prior network in terms of its consistency with the expression data. We validate NetREX and PriorBoost using the “gold standard” E. coli GRN from the DREAM5 network inference challenge and apply them to construct sex-specific Drosophila GRNs. NetREX constructed sex-specific Drosophila GRNs that, on all applied measures, outperform networks obtained from other methods indicating that NetREX is an important milestone toward building more accurate GRNs.

Spike-in RNAs are valuable controls for a variety of gene expression measurements. The External RNA Controls Consortium developed test sets that were used in a number of published reports. Here we provide an authoritative table that summarizes, updates, and corrects errors in the test version that ultimately resulted in the certified Standard Reference Material 2374. We have noted existence of anti-sense RNA controls in the material, corrected sub-pool memberships, and commented on control RNAs that displayed inconsistent behavior.