Unearthing the genome of the earthworm Lumbricus rubellus

The earthworm has long been of interest to biologists, most notably Charles Darwin, who was the first to reveal their true role as eco-engineers of the soil. However, to fully understand an animal one needs to combine observational data with the fundamental building blocks of life, DNA. For many years, sequencing a genome was an incredibly costly and time-consuming process. Recent advances in sequencing technology have led to high quality, high throughput data being available at low cost. Although this provides large amounts of sequence data, the bioinformatics knowledge required to assemble and annotate these new data are still in their infancy. This bottleneck is slowly opening up, and with it come the first glimpses into the new and exciting biology of many new species. This thesis provides the first high quality draft genome assembly and annotation of an earthworm, Lumbricus rubellus. The assembly process and resulting data highlight the complexity of assembling a eukaryotic genome using short read data. To improve assembly, a novel approach was created utilising transcripts to scaffold the genome (https://github.com/elswob/SCUBAT). The annotation of the assembly provides the draft of the complete proteome, which is also supported by the first RNA-Seq generated transcriptome. These annotations have enabled detailed analysis of the protein coding genes including comparative analysis with two other annelids (a leech and a polychaete worm) and a symbiont (Verminephrobacter). This analysis identified four key areas which appear to be either highly enhanced or unique to L. rubellus. Three of these may be related to the unique environment from which the sequenced worms originated and add to the mounting evidence for the use of earthworms as bioindicators of soil quality. All data is stored in relational databases and available to search and browse via a website at www.earthworms.org. It is hoped that this genome will provide a springboard for many future investigations into the earthworm and continue research into this wonderful animal

EpiGraphDB: a database and data mining platform for health data science

Motivation: The wealth of data resources on human phenotypes, risk factors, molecular traits and therapeutic interventions presents new opportunities for population health sciences. These opportunities are paralleled by a growing need for data integration, curation and mining to increase research efficiency, reduce mis-inference and ensure reproducible research. Results: We developed EpiGraphDB (https://epigraphdb.org/), a graph database containing an array of different biomedical and epidemiological relationships and an analytical platform to support their use in human population health data science. In addition, we present three case studies that illustrate the value of this platform. The first uses EpiGraphDB to evaluate potential pleiotropic relationships, addressing mis-inference in systematic causal analysis. In the second case study, we illustrate how protein-protein interaction data offer opportunities to identify new drug targets. The final case study integrates causal inference using Mendelian randomization with relationships mined from the biomedical literature to 'triangulate' evidence from different sources

MELODI:Mining Enriched Literature Objects to Derive Intermediates

Background: The scientific literature contains a wealth of information from different fields on potential disease mechanisms. However, identifying and prioritizing mechanisms for further analytical evaluation presents enormous challenges in terms of the quantity and diversity of published research. The application of data mining approaches to the literature offers the potential to identify and prioritize mechanisms for more focused and detailed analysis. Methods: Here we present MELODI, a literature mining platform that can identify mechanistic pathways between any two biomedical concepts. Results: Two case studies demonstrate the potential uses of MELODI and how it can generate hypotheses for further investigation. First, an analysis of ETS-related gene ERG and prostate cancer derives the intermediate transcription factor SP1, recently confirmed to be physically interacting with ERG. Second, examining the relationship between a new potential risk factor for pancreatic cancer identifies possible mechanistic insights which can be studied in vitro. Conclusions: We have demonstrated the possible applications of MELODI, including two case studies. MELODI has been implemented as a Python/Django web application, and is freely available to use at [www.melodi.biocompute.org.uk]

Stellar Spin-Orbit Misalignment in a Multiplanet System

Stars hosting hot Jupiters are often observed to have high obliquities, whereas stars with multiple co-planar planets have been seen to have low obliquities. This has been interpreted as evidence that hot-Jupiter formation is linked to dynamical disruption, as opposed to planet migration through a protoplanetary disk. We used asteroseismology to measure a large obliquity for Kepler-56, a red giant star hosting two transiting co-planar planets. These observations show that spin-orbit misalignments are not confined to hot-Jupiter systems. Misalignments in a broader class of systems had been predicted as a consequence of torques from wide-orbiting companions, and indeed radial-velocity measurements revealed a third companion in a wide orbit in the Kepler-56 system.Comment: Accepted for publication in Science, published online on October 17 2013; PDF includes main article and supplementary materials (65 pages, 27 figures, 7 tables); v2: small correction to author lis

Multi-ancestry Mendelian randomization of omics traits revealing drug targets of COVID-19 severity

BACKGROUND: Recent omic studies prioritised several drug targets associated with coronavirus disease 2019 (COVID-19) severity. However, little evidence was provided to systematically estimate the effect of drug targets on COVID-19 severity in multiple ancestries. METHODS: In this study, we applied Mendelian randomization (MR) and colocalization approaches to understand the putative causal effects of 16,059 transcripts and 1608 proteins on COVID-19 severity in European and effects of 610 proteins on COVID-19 severity in African ancestry. We further integrated genetics, clinical and literature evidence to prioritise drug targets. Additional sensitivity analyses including multi-trait colocalization and phenome-wide MR were conducted to test for MR assumptions. FINDINGS: MR and colocalization prioritized four protein targets, FCRL3, ICAM5, ENTPD5 and OAS1 that showed effect on COVID-19 severity in European ancestry. One protein target, SERPINA1 showed a stronger effect in African ancestry but much weaker effect in European ancestry (odds ratio [OR] in Africans=0.369, 95%CI=0.203 to 0.668, P = 9.96 × 10(−4); OR in Europeans=1.021, 95%CI=0.901 to 1.157, P = 0.745), which suggested that increased level of SERPINA1 will reduce COVID-19 risk in African ancestry. One protein, ICAM1 showed suggestive effect on COVID-19 severity in both ancestries (OR in Europeans=1.152, 95%CI=1.063 to 1.249, P = 5.94 × 10(−4); OR in Africans=1.481, 95%CI=1.008 to 2.176; P = 0.045). The OAS1, SERPINA1 and ICAM1 effects were replicated using updated COVID-19 severity data in the two ancestries respectively, where alternative splicing events in OAS1 and ICAM1 also showed marginal effects on COVID-19 severity in Europeans. The phenome-wide MR of the prioritised targets on 622 complex traits provided information on potential beneficial effects on other diseases and suggested little evidence of adverse effects on major complications. INTERPRETATION: Our study identified six proteins as showing putative causal effects on COVID-19 severity. OAS1 and SERPINA1 were targets of existing drugs in trials as potential COVID-19 treatments. ICAM1, ICAM5 and FCRL3 are related to the immune system. Across the six targets, OAS1 has no reliable instrument in African ancestry; SERPINA1, FCRL3, ICAM5 and ENTPD5 showed a different level of putative causal evidence in European and African ancestries, which highlights the importance of more powerful ancestry-specific GWAS and value of multi-ancestry MR in informing the effects of drug targets on COVID-19 across different populations. This study provides a first step towards clinical investigation of beneficial and adverse effects of COVID-19 drug targets. FUNDING: No

Discovering cancer vulnerabilities using high-throughput micro-RNA screening

© 2017 The Author(s). Micro-RNAs (miRNAs) are potent regulators of gene expression and cellular phenotype. Each miRNA has the potential to target hundreds of transcripts within the cell thus controlling fundamental cellular processes such as survival and proliferation. Here, we exploit this important feature of miRNA networks to discover vulnerabilities in cancer phenotype, and map miRNA-target relationships across different cancer types. More specifically, we report the results of a functional genomics screen of 1280 miRNA mimics and inhibitors in eight cancer cell lines, and its presentation in a sophisticated interactive data portal. This resource represents the most comprehensive survey of miRNA function in oncology, incorporating breast cancer, prostate cancer and neuroblastoma. A user-friendly web portal couples this experimental data with multiple tools for miRNA target prediction, pathway enrichment analysis and visualization. In addition, the database integrates publicly available gene expression and perturbation data enabling tailored and context-specific analysis of miRNA function in a particular disease. As a proof-of-principle, we use the database and its innovative features to uncover novel determinants of the neuroblastoma malignant phenotype