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Long noncoding RNAs (lncRNAs) constitute the majority of transcripts in the mammalian genomes, and yet, their functions remain largely unknown. As part of the FANTOM6 project, we systematically knocked down the expression of 285 lncRNAs in human dermal fibroblasts and quantified cellular growth, morphological changes, and transcriptomic responses using Capped Analysis of Gene Expression (CAGE). Antisense oligonucleotides targeting the same lncRNAs exhibited global concordance, and the molecular phenotype, measured by CAGE, recapitulated the observed cellular phenotypes while providing additional insights on the affected genes and pathways. Here, we disseminate the largest-to-date lncRNA knockdown data set with molecular phenotyping (over 1000 CAGE deep-sequencing libraries) for further exploration and highlight functional roles for ZNF213-AS1 and lnc-KHDC3L-2.

SummaryTranscriptomic analysis is used to capture the molecular state of a cell or sample in many biological and medical applications. In addition to identifying alterations in activity at the level of individual genes, understanding changes in the gene networks that regulate fundamental biological mechanisms is also an important objective of molecular analysis. As a result, databases that describe biological pathways are increasingly uesad to assist with the interpretation of results from large-scale genomics studies. Incorporating information from biological pathways and gene regulatory networks into a genomic data analysis is a popular strategy, and there are many methods that provide this functionality for gene expression data. When developing or comparing such methods, it is important to gain an accurate assessment of their performance. Simulation-based validation studies are frequently used for this. This necessitates the use of simulated data that correctly accounts for pathway relationships and correlations. Here we present a versatile statistical framework to simulate correlated gene expression data from biological pathways, by sampling from a multivariate normal distribution derived from a graph structure. This procedure has been released as the graphsim R package on CRAN and GitHub (https://github.com/TomKellyGenetics/graphsim) and is compatible with any graph structure that can be described using the igraph package. This package allows the simulation of biological pathways from a graph structure based on a statistical model of gene expression.

Background The E-cadherin gene (CDH1) is frequently mutated in diffuse gastric cancer and lobular breast cancer, and germline mutations predispose to the cancer syndrome Hereditary Diffuse Gastric Cancer. We are taking a synthetic lethal approach to identify druggable vulnerabilities in CDH1-mutant cancers. Methods Density distributions of cell viability data from a genome-wide RNAi screen of isogenic MCF10A and MCF10A-CDH1−/− cells were used to identify protein classes affected by CDH1 mutation. The synthetic lethal relationship between selected protein classes and E-cadherin was characterised by drug sensitivity assays in both the isogenic breast MCF10A cells and CDH1-isogenic gastric NCI-N87. Endocytosis efficiency was quantified using cholera toxin B uptake. Pathway metagene expression of 415 TCGA gastric tumours was statistically correlated with CDH1 expression. Results MCF10A-CDH1−/− cells showed significantly altered sensitivity to RNAi inhibition of groups of genes including the PI3K/AKT pathway, GPCRs, ion channels, proteosomal subunit proteins and ubiquitinylation enzymes. Both MCF10A-CDH1−/− and NCI-N87-CDH1−/− cells were more sensitive than wild-type cells to compounds that disrupted plasma membrane composition and trafficking, but showed contrasting sensitivities to inhibitors of actin polymerisation and the chloride channel inhibitor NS3728. The MCF10A-CDH1−/− cell lines showed reduced capacity to endocytose cholera toxin B. Pathway metagene analysis identified 20 Reactome pathways that were potentially synthetic lethal in tumours. Genes involved in GPCR signalling, vesicle transport and the metabolism of PI3K and membrane lipids were strongly represented amongst the candidate synthetic lethal genes. Conclusions E-cadherin loss leads to disturbances in receptor signalling and plasma membrane trafficking and organisation, creating druggable vulnerabilities. Electronic supplementary material The online version of this article (10.1007/s10120-018-0859-1) contains supplementary material, which is available to authorised users.

Genomic imprinting is a form of epigenetic modification involving parent-of-origin-dependent gene expression, usually the inactivation of one gene copy in some tissues, at least, for some part of the diploid life cycle. Occurring at a number of loci in mammals and flowering plants, this mode of non-Mendelian expression can be viewed more generally as parentally-specific differential gene expression. The effects of natural selection on genetic variation at imprinted loci have previously been examined in a several population-genetic models. Here we expand the existing one-locus, two-allele population-genetic models of viability selection with genomic imprinting to include sex-limited imprinting, i.e., imprinted expression occurring only in one sex, and differential viability between the sexes. We first consider models of complete inactivation of either parental allele and these models are subsequently generalized to incorporate differential expression. Stable polymorphic equilibrium was possible without heterozygote advantage as observed in some prior models of imprinting in both sexes. In contrast to these latter models, in the sex-limited case it was critical whether the paternally inherited or maternally inherited allele was inactivated. The parental origin of inactivated alleles had a different impact on how the population responded to the different selection pressures between the sexes. Under the same fitness parameters, imprinting in the other sex altered the number of possible equilibrium states and their stability. When the parental origin of imprinted alleles and the sex in which they are inactive differ, an allele cannot be inactivated in consecutive generations. The system dynamics became more complex with more equilibrium points emerging. Our results show that selection can interact with epigenetic factors to maintain genetic variation in previously unanticipated ways.

Long non-coding RNAs (lncRNAs) constitute the majority of transcripts in the mammalian genomes and yet, their functions remain largely unknown. We systematically knockdown 285 lncRNAs expression in human dermal fibroblasts and quantified cellular growth, morphological changes, and transcriptomic responses using Capped Analysis of Gene Expression (CAGE). Antisense oligonucleotides targeting the same lncRNA exhibited global concordance, and the molecular phenotype, measured by CAGE, recapitulated the observed cellular phenotypes while providing additional insights on the affected genes and pathways. Here, we disseminate the largest to-date lncRNA knockdown dataset with molecular phenotyping (over 1,000 CAGE deep-sequencing libraries) for further exploration and highlight functional roles for ZNF213-AS1 and lnc-KHDC3L-2.