Towards Faster Gene Expression Prediction via Dimensionality Reduction and Feature Selection.

The majority of genes have a genetic component to their expression. Elastic nets have been shown effective at predicting tissue-specific, individual-level gene expression from genotype data. We apply principal component analysis (PCA), linkage disequilibrium pruning, or the combination of the two to reduce, or generate, a lower-dimensional representation of the genetic variants used as inputs to the elastic net models for the prediction of gene expression. Our results show that, in general, elastic nets attain their best performance when all genetic variants are included as inputs; however, a relatively low number of principal components can effectively summarize the majority of genetic variation while reducing the overall computation time. Specifically, 100 principal components reduce the computational time of the models by over 80% with only an 8% loss in R2. Finally, linkage disequilibrium pruning does not effectively reduce the genetic variants for predicting gene expression. As predictive models are commonly made for over 27,000 genes for more than 50 tissues, PCA may provide an effective method for reducing the computational burden of gene expression analysis.

Adapting Random Forests to Predict Obesity-Associated Gene Expression.

Random forests (RFs) are effective at predicting gene expression from genotype data. However, a comparison of RF regressors and classifiers, including feature selection and encoding, has been under-explored in the context of gene expression prediction. Specifically, we examine the role of ordinal or one-hot encoding and of data balancing via oversam-pling in the prediction of obesity-associated gene expression. Our work shows that RFs compete with PrediXcan in the prediction of obesity-associated gene expression in subcutaneous adipose tissue, a highly relevant tissue to obesity. Additionally, RFs generate predictions for obesity-associated genes where PrediXcan fails to do so.

Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling.

The role of epidemiological models is crucial for informing public health officials during a public health emergency, such as the COVID-19 pandemic. However, traditional epidemiological models fail to capture the time-varying effects of mitigation strategies and do not account for under-reporting of active cases, thus introducing bias in the estimation of model parameters. To infer more accurate parameter estimates and to reduce the uncertainty of these estimates, we extend the SIR and SEIR epidemiological models with two time-varying parameters that capture the transmission rate and the rate at which active cases are reported to health officials. Using two real data sets of COVID-19 cases, we perform Bayesian inference via our SIR and SEIR models with time-varying transmission and reporting rates and via their standard counterparts with constant rates; our approach provides parameter estimates with more realistic interpretation, and 1-week ahead predictions with reduced uncertainty. Furthermore, we find consistent under-reporting in the number of active cases in the data that we consider, suggesting that the initial phase of the pandemic was more widespread than previously reported.

Lapatinib potentiates cytotoxicity of YM155 in neuroblastoma via inhibition of the ABCB1 efflux transporter.

Adverse side effects of cancer agents are of great concern in the context of childhood tumors where they can reduce the quality of life in young patients and cause life-long adverse effects. Synergistic drug combinations can lessen potential toxic side effects through lower dosing and simultaneously help to overcome drug resistance. Neuroblastoma is the most common cancer in infancy and extremely heterogeneous in clinical presentation and features. Applying a systematic pairwise drug combination screen we observed a highly potent synergy in neuroblastoma cells between the EGFR kinase inhibitor lapatinib and the anticancer compound YM155 that is preserved across several neuroblastoma variants. Mechanistically, the synergy was based on a lapatinib induced inhibition of the multidrug-resistance efflux transporter ABCB1, which is frequently expressed in resistant neuroblastoma cells, which allowed prolonged and elevated cytotoxicity of YM155. In addition, the drug combination (i.e. lapatinib plus YM155) decreased neuroblastoma tumor size in an in vivo model.

Combinatorial Drug Screening Identifies Ewing Sarcoma-specific Sensitivities.

Improvements in survival for Ewing sarcoma pediatric and adolescent patients have been modest over the past 20 years. Combinations of anticancer agents endure as an option to overcome resistance to single treatments caused by compensatory pathways. Moreover, combinations are thought to lessen any associated adverse side effects through reduced dosing, which is particularly important in childhood tumors. Using a parallel phenotypic combinatorial screening approach of cells derived from three pediatric tumor types, we identified Ewing sarcoma-specific interactions of a diverse set of targeted agents including approved drugs. We were able to retrieve highly synergistic drug combinations specific for Ewing sarcoma and identified signaling processes important for Ewing sarcoma cell proliferation determined by EWS-FLI1 We generated a molecular target profile of PKC412, a multikinase inhibitor with strong synergistic propensity in Ewing sarcoma, revealing its targets in critical Ewing sarcoma signaling routes. Using a multilevel experimental approach including quantitative phosphoproteomics, we analyzed the molecular rationale behind the disease-specific synergistic effect of simultaneous application of PKC412 and IGF1R inhibitors. The mechanism of the drug synergy between these inhibitors is different from the sum of the mechanisms of the single agents. The combination effectively inhibited pathway crosstalk and averted feedback loop repression, in EWS-FLI1-dependent manner. Mol Cancer Ther; 16(1); 88-101. ©2016 AACR.

RNA editing generates cellular subsets with diverse sequence within populations.

RNA editing is a mutational mechanism that specifically alters the nucleotide content in transcribed RNA. However, editing rates vary widely, and could result from equivalent editing amongst individual cells, or represent an average of variable editing within a population. Here we present a hierarchical Bayesian model that quantifies the variance of editing rates at specific sites using RNA-seq data from both single cells, and a cognate bulk sample to distinguish between these two possibilities. The model predicts high variance for specific edited sites in murine macrophages and dendritic cells, findings that we validated experimentally by using targeted amplification of specific editable transcripts from single cells. The model also predicts changes in variance in editing rates for specific sites in dendritic cells during the course of LPS stimulation. Our data demonstrate substantial variance in editing signatures amongst single cells, supporting the notion that RNA editing generates diversity within cellular populations.

The second European interdisciplinary Ewing sarcoma research summit--A joint effort to deconstructing the multiple layers of a complex disease.

Despite multimodal treatment, long term outcome for patients with Ewing sarcoma is still poor. The second "European interdisciplinary Ewing sarcoma research summit" assembled a large group of scientific experts in the field to discuss their latest unpublished findings on the way to the identification of novel therapeutic targets and strategies. Ewing sarcoma is characterized by a quiet genome with presence of an EWSR1-ETS gene rearrangement as the only and defining genetic aberration. RNA-sequencing of recently described Ewing-like sarcomas with variant translocations identified them as biologically distinct diseases. Various presentations adressed mechanisms of EWS-ETS fusion protein activities with a focus on EWS-FLI1. Data were presented shedding light on the molecular underpinnings of genetic permissiveness to this disease uncovering interaction of EWS-FLI1 with recently discovered susceptibility loci. Epigenetic context as a consequence of the interaction between the oncoprotein, cell type, developmental stage, and tissue microenvironment emerged as dominant theme in the discussion of the molecular pathogenesis and inter- and intra-tumor heterogeneity of Ewing sarcoma, and the difficulty to generate animal models faithfully recapitulating the human disease. The problem of preclinical development of biologically targeted therapeutics was discussed and promising perspectives were offered from the study of novel in vitro models. Finally, it was concluded that in order to facilitate rapid pre-clinical and clinical development of novel therapies in Ewing sarcoma, the community needs a platform to maintain knowledge of unpublished results, systems and models used in drug testing and to continue the open dialogue initiated at the first two Ewing sarcoma summits.

EWS-FLI1 employs an E2F switch to drive target gene expression.

Cell cycle progression is orchestrated by E2F factors. We previously reported that in ETS-driven cancers of the bone and prostate, activating E2F3 cooperates with ETS on target promoters. The mechanism of target co-regulation remained unknown. Using RNAi and time-resolved chromatin-immunoprecipitation in Ewing sarcoma we report replacement of E2F3/pRB by constitutively expressed repressive E2F4/p130 complexes on target genes upon EWS-FLI1 modulation. Using mathematical modeling we interrogated four alternative explanatory models for the observed EWS-FLI1/E2F3 cooperation based on longitudinal E2F target and regulating transcription factor expression analysis. Bayesian model selection revealed the formation of a synergistic complex between EWS-FLI1 and E2F3 as the by far most likely mechanism explaining the observed kinetics of E2F target induction. Consequently we propose that aberrant cell cycle activation in Ewing sarcoma is due to the de-repression of E2F targets as a consequence of transcriptional induction and physical recruitment of E2F3 by EWS-FLI1 replacing E2F4 on their target promoters.

The silicon trypanosome: a test case of iterative model extension in systems biology.

The African trypanosome, Trypanosoma brucei, is a unicellular parasite causing African Trypanosomiasis (sleeping sickness in humans and nagana in animals). Due to some of its unique properties, it has emerged as a popular model organism in systems biology. A predictive quantitative model of glycolysis in the bloodstream form of the parasite has been constructed and updated several times. The Silicon Trypanosome is a project that brings together modellers and experimentalists to improve and extend this core model with new pathways and additional levels of regulation. These new extensions and analyses use computational methods that explicitly take different levels of uncertainty into account. During this project, numerous tools and techniques have been developed for this purpose, which can now be used for a wide range of different studies in systems biology.

Genetic determinants of lipid homeostasis.

Circulating levels of blood lipids are heritable risk factors for atherosclerosis and heart disease, and are the target of therapeutic intervention. Studies of monogenic disorders and - more recently - genome-wide association studies have identified several important genetic determinants of blood lipid levels. These have the potential to provide new drug targets to alter blood lipid levels and may improve prediction of cardiovascular disease. Better functional validation of lipid loci is required to clarify the biological role of proteins encoded by specific genomic regions and understand how they influence lipid metabolism and confer disease risk.

A genome-wide screen for interactions reveals a new locus on 4p15 modifying the effect of waist-to-hip ratio on total cholesterol.

Recent genome-wide association (GWA) studies described 95 loci controlling serum lipid levels. These common variants explain ∼25% of the heritability of the phenotypes. To date, no unbiased screen for gene-environment interactions for circulating lipids has been reported. We screened for variants that modify the relationship between known epidemiological risk factors and circulating lipid levels in a meta-analysis of genome-wide association (GWA) data from 18 population-based cohorts with European ancestry (maximum N = 32,225). We collected 8 further cohorts (N = 17,102) for replication, and rs6448771 on 4p15 demonstrated genome-wide significant interaction with waist-to-hip-ratio (WHR) on total cholesterol (TC) with a combined P-value of 4.79×10(-9). There were two potential candidate genes in the region, PCDH7 and CCKAR, with differential expression levels for rs6448771 genotypes in adipose tissue. The effect of WHR on TC was strongest for individuals carrying two copies of G allele, for whom a one standard deviation (sd) difference in WHR corresponds to 0.19 sd difference in TC concentration, while for A allele homozygous the difference was 0.12 sd. Our findings may open up possibilities for targeted intervention strategies for people characterized by specific genomic profiles. However, more refined measures of both body-fat distribution and metabolic measures are needed to understand how their joint dynamics are modified by the newly found locus.

Meta analysis of candidate gene variants outside the LPA locus with Lp(a) plasma levels in 14,500 participants of six White European cohorts.

BACKGROUND: Both genome-wide association studies and candidate gene studies have reported that the major determinant of plasma levels of the Lipoprotein (a) [Lp(a)] reside within the LPA locus on chromosome 6. We have used data from the HumanCVD BeadChip to explore the contribution of other candidate genes determining Lp(a) levels. METHODS: 48,032 single nucleotide polymorphisms (SNPs) from the Illumina HumanCVD BeadChip were genotyped in 5059 participants of the Whitehall II study (WHII) of randomly ascertained healthy men and women. SNPs showing association with Lp(a) levels of p<10(-4) outside the LPA locus were selected for replication in a total of an additional 9463 participants of five European based studies (EAS, EPIC-Norfolk, NPHSII, PROCARDIS, and SAPHIR). RESULTS: In Whitehall II, apart from the LPA locus (where p values for several SNPs were <10(-30)) there was significant association at four loci GALNT2, FABP1, PPARGC1A and TNFRSFF11A. However, a meta-analysis of the six studies did not confirm any of these findings. CONCLUSION: Results from this meta analysis of 14,522 participants revealed no candidate genes from the HumanCVD BeadChip outside the LPA locus to have an effect on Lp(a) levels. Further studies with genome-wide and denser SNP coverage are required to confirm or refute this finding.