Biology subjects

Erola, P.

Publications and source records attributed to Erola, P..

3 recordsLinked to original sources

bioRxiv2022

The network of SARS-CoV-2-cancer molecular interactions and pathways

BackgroundRelatively little is known about the long-term impacts of SARS-CoV-2 biology, including whether it increases the risk of cancer. This study aims to identify the molecular interactions between COVID-19 infections and cancer processes. Materials and MethodsWe integrated recent data on SARS-CoV-2 - host protein interactions, risk factors for critical illness, known oncogenes, tumor suppressor genes and cancer drivers in EpiGraphDB, a database of disease biology and epidemiology. We used these data to reconstruct the network of molecular links between SARS-CoV-2 infections and cancer processes in various tissues expressing the angiotensin-converting enzyme 2 (ACE2) receptor. We applied community detection algorithms and Gene Set Enrichment Analysis (GSEA) to identify cancer-relevant pathways that may be perturbed by SARS-CoV-2 infection. ResultsIn lung tissue, the results showed that 4 oncogenes are potentially targeted by SARS-CoV-2, and 92 oncogenes interact with other human genes targeted by SARS-CoV-2. We found evidence of potential SARS-CoV-2 interactions with Wnt and hippo signaling pathways, telomere maintenance, DNA replication, protein ubiquitination and mRNA splicing. Some of these pathways were potentially affected in multiple tissues. ConclusionsThe long-term implications of SARS-CoV-2 infection are still unknown, but our results point to the potential impact of infection on pathways relevant to cancer affecting cell proliferation, development and survival, favoring DNA degradation, preventing the repair of damaging events and impeding the translation of RNA into working proteins. This highlights the need for further research to investigate whether such effects are transient or longer lasting. Our results are openly available in the EpiGraphDB platform at https://epigraphdb.org/covid-cancer and the repository https://github.com/MRCIEU/covid-cancer (https://doi.org/10.5281/zenodo.6391588).

cancer biology↗

bioRxiv2020

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

MotivationThe 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. ResultsWe 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. AvailabilityThe EpiGraphDB platform is openly available at https://epigraphdb.org. Code for replicating case study results is available at https://github.com/MRCIEU/epigraphdb as Jupyter notebooks using the API, and https://mrcieu.github.io/epigraphdb-r using the R package. Contactyi6240.liu@bristol.ac.uk, ben.elsworth@bristol.ac.uk, Tom.Gaunt@bristol.ac.uk

bioinformatics↗

bioRxiv2020

Changes in the gene expression profile during spontaneous migraine attacks

ObjectiveMigraine occurs in clearly defined attacks and thus lends itself to investigate changes during and outside attack. Gene expression fluctuates according to environmental and endogenous events and therefore is likely to reveal changes during a migraine attack. We examined the hypothesis that changes in RNA expression during and outside of a spontaneous migraine attack exist which are specific to the migraine attack. MethodsWe collected blood samples from 27 migraine patients during an attack, two hours after treatment with subcutaneous sumatriptan, on a headache-free day and after a cold pressor test. All patients were deeply phenotyped, including headache characteristics and treatment effect during the sampling. RNA-Sequencing, genotyping, and steroid profiling was performed on all samples. RNA-Sequences were analyzed at gene level (differential expression analysis) and at network level, and we integrated transcriptomic and genomic data. ResultsWe found 29 differentially expressed (DE) genes between attack and after treatment, after subtracting non-migraine specific genes, i.e. genes related to a general pain/stress response. DE genes were functioning in fatty acid oxidation, signaling pathways and immune-related pathways. Network analysis revealed molecular mechanisms affected by change in gene interactions during the migraine attack, e.g. ion transmembrane transport and response to stress. Integration of genomic and transcriptomic data revealed pathways related to sumatriptan treatment, i.e. 5HT1 type receptor mediated signaling pathway. InterpretationUsing a paired-sample design, we uniquely investigated intra-individual changes in the gene expression during a migraine attack. We revealed both genes and pathway potentially involved in the pathophysiology of migraine.

genomics↗