In silico analiza utjecaja toksičnih metala na komplikacije bolesti COVID-19: molekularni uvidi

COVID-19 can cause a range of complications, including cardiovascular, renal, and/or respiratory insufficiencies, yet little is known of its potential effects in persons exposed to toxic metals. The aim of this study was to answer this question with in silico toxicogenomic methods that can provide molecular insights into COVID-19 complications owed to exposure to arsenic, cadmium, lead, mercury, nickel, and chromium. For this purpose we relied on the Comparative Toxicogenomic Database (CTD), GeneMANIA, and ToppGene Suite portal and identified a set of five common genes (IL1B, CXCL8, IL6, IL10, TNF) for the six metals and COVID-19, all of which code for pro-inflammatory and anti-inflammatory cytokines. The list was expanded with additional 20 related genes. Physical interactions are the most common between the genes affected by the six metals (77.64 %), while the dominant interaction between the genes affected by each metal separately is co-expression (As 56.35 %, Cd 64.07 %, Pb 71.5 %, Hg 81.91 %, Ni 64.28 %, Cr 88.51 %). Biological processes, molecular functions, and pathways in which these 25 genes participate are closely related to cytokines and cytokine storm implicated in the development of COVID-19 complications. In other words, our findings confirm that exposure to toxic metals, alone or in combinations, might escalate COVID-19 severity.COVID-19 može izazvati niz komplikacija, uključujući kardiovaskularnu, bubrežnu i/ili respiratornu insuficijenciju, ali se malo zna o njegovim potencijalnim učincima u osoba koje su izložene toksičnim metalima. Cilj ovog istraživanja bio je odgovoriti na to pitanje pomoću in silico toksikogenomske metode, koja može pružiti molekularni uvid u komplikacije bolesti COVID-19 uslijed izloženosti arsenu, kadmiju, olovu, živi, niklu i kromu. U tu su svrhu korišteni Komparativna toksikogenomska baza podataka (CTD), GeneMANIA i ToppGene Suite portal te je identificirana skupina od pet zajedničkih gena (IL1B, CXCL8, IL6, IL10, TNF) za šest metala i COVID-19, koji svi kodiraju proinflamatorne i antiinflamatorne citokine. Lista je proširena s dodatnih 20 srodnih gena. Fizičke interakcije dominirale su između gena na koje utječe kombinacija ispitivanih metala (77,64 %), a koekspresija je dominantna interakcija između gena na koje djeluju pojedinačni metali (As 56,35 %, Cd 64,07 %, Pb 71,5 %, Hg 81,91 %, Ni 64,28 %, Cr 88,51 %). Biološki procesi, molekulske funkcije i putovi u kojima sudjeluje tih 25 gena blisko su povezani s citokinima i citokinskom olujom, koja je uključena u razvoj komplikacija bolesti COVID-19. Drugim riječima, ovi rezultati potvrđuju da izloženost toksičnim metalima, bilo pojedinačno ili u kombinaciji, može dovesti do razvoja težih oblika bolesti COVID-19

Reactome: a database of reactions, pathways and biological processes

Reactome (http://www.reactome.org) is a collaboration among groups at the Ontario Institute for Cancer Research, Cold Spring Harbor Laboratory, New York University School of Medicine and The European Bioinformatics Institute, to develop an open source curated bioinformatics database of human pathways and reactions. Recently, we developed a new web site with improved tools for pathway browsing and data analysis. The Pathway Browser is an Systems Biology Graphical Notation (SBGN)-based visualization system that supports zooming, scrolling and event highlighting. It exploits PSIQUIC web services to overlay our curated pathways with molecular interaction data from the Reactome Functional Interaction Network and external interaction databases such as IntAct, BioGRID, ChEMBL, iRefIndex, MINT and STRING. Our Pathway and Expression Analysis tools enable ID mapping, pathway assignment and overrepresentation analysis of user-supplied data sets. To support pathway annotation and analysis in other species, we continue to make orthology-based inferences of pathways in non-human species, applying Ensembl Compara to identify orthologs of curated human proteins in each of 20 other species. The resulting inferred pathway sets can be browsed and analyzed with our Species Comparison tool. Collaborations are also underway to create manually curated data sets on the Reactome framework for chicken, Drosophila and rice

Zajednički utjecaj ključnih onečišćivača zraka na težinu COVID-a 19 – predviđanje zasnovano na analizi toksikogenomičkih podataka

Considering that some researchers point to a possible influence of air pollution on COVID-19 transmission, severity, and death rate, the aim of our in silico study was to determine the relationship between the key air pollutants [sulphur dioxide (SO2), carbon monoxide (CO), particulate matter (PMx), nitrogen dioxide (NO2), and ozone (O3)] and COVID-19 complications using the publicly available toxicogenomic analytical and prediction tools: (i) Comparative Toxicogenomic Database (CTD) to identify genes common to air pollutants and COVID-19 complications; (ii) GeneMANIA to construct a network of these common and related genes; (iii) ToppGene Suite to extract the most important biological processes and molecular pathways; and (iv) DisGeNET to search for the top gene-disease pairs. SO2, CO, PMx, NO2, and O3 interacted with 6, 6, 18, 9, and 12 COVID-19-related genes, respectively. Four of these are common for all pollutants (IL10, IL6, IL1B, and TNF) and participate in most (77.64 %) physical interactions. Further analysis pointed to cytokine binding and cytokine-mediated signalling pathway as the most important molecular function and biological process, respectively. Other molecular functions and biologica processes are mostly related to cytokine activity and inflammation, which might be connected to the cytokine storm and resulting COVID-19 complications. The final step singled out the link between the CEBPA gene and acute myelocytic leukaemia and between TNFRSF1A and TNF receptor-associated periodic fever syndrome. This indicates possible complications in COVID-19 patients suffering from these diseases, especially those living in urban areas with poor air quality.COVID-19 (engl. coronavirus disease 2019) respiratorna je bolest prouzročena infekcijom SARS-CoV-2 virusom (engl. severe acute respiratory syndrome coronavirus 2). Pretpostavlja se da postoji utjecaj atmosferskih čimbenika, uključujući i onečišćenje zraka, na prenošenje koronavirusa, njegovu težinu i stopu smrtnosti. Stoga je cilj ovoga in silico istraživanja bio utvrditi odnos između ključnih onečišćivača zraka [sumporova dioksida (SO2), ugljikova monoksida (CO), lebdećih čestica (PMx), dušikova dioksida (NO2), ozona (O3)] i komplikacija COVID-a 19 korištenjem: (i) komparativne toksikogenomičke baze podataka (engl. Comparative Toxicogenomic Database, CTD) za dobivanje gena, međusobno povezanih s onečišćivačima zraka i komplikacijama COVID-a 19, (ii) GeneMANIA servera za konstruiranje mreže između dobivenih I srodnih gena, (iii) ToppGene Suite za izdvajanje najvažnijih bioloških procesa/molekularnih puteva i (iv) DisGeNET baze podataka za traženje najvažnijih parova gen-bolest. Za SO2, CO, PMx, NO2 odnosno O3 utvrđena je interakcija sa 6, 6, 18, 9, odnosno 12 gena povezanih s komplikacijama COVID-a 19. Četiri su zajednička (IL10, IL6, IL1B i TNF) i u najvećem postotku (77,64 %) sudjeluju u fizičkim interakcijama. Vezivanje citokina i signalni put posredovan citokinima izdvojeni su kao najvažnija molekularna funkcija i biološki proces. Druge molekularne funkcije i biološki procesi uglavnom su bili povezani s aktivnošću citokina i upalom, što bi se moglo dovesti u vezu s citokinskom olujom i posljedičnim komplikacijama COVID-a 19. Utvrđena je veza između različitih bolesti i ispitivanih gena, posebice između CEBPA i akutne mijelogene leukemije (AML) te između TNFRSF1A i sindroma periodične vrućice povezanoga s TNF receptorom. To upozorava na moguće komplikacije u osoba zaraženih koronavirusom koje boluju od tih bolesti, poglavito kada su dodatno potaknute poremećajem funkcije spomenutih gena

In silico analiza utjecaja toksičnih metala na komplikacije bolesti COVID-19: molekularni uvidi

COVID-19 can cause a range of complications, including cardiovascular, renal, and/or respiratory insufficiencies, yet little is known of its potential effects in persons exposed to toxic metals. The aim of this study was to answer this question with in silico toxicogenomic methods that can provide molecular insights into COVID-19 complications owed to exposure to arsenic, cadmium, lead, mercury, nickel, and chromium. For this purpose we relied on the Comparative Toxicogenomic Database (CTD), GeneMANIA, and ToppGene Suite portal and identified a set of five common genes (IL1B, CXCL8, IL6, IL10, TNF) for the six metals and COVID-19, all of which code for pro-inflammatory and anti-inflammatory cytokines. The list was expanded with additional 20 related genes. Physical interactions are the most common between the genes affected by the six metals (77.64 %), while the dominant interaction between the genes affected by each metal separately is co-expression (As 56.35 %, Cd 64.07 %, Pb 71.5 %, Hg 81.91 %, Ni 64.28 %, Cr 88.51 %). Biological processes, molecular functions, and pathways in which these 25 genes participate are closely related to cytokines and cytokine storm implicated in the development of COVID-19 complications. In other words, our findings confirm that exposure to toxic metals, alone or in combinations, might escalate COVID-19 severity.COVID-19 može izazvati niz komplikacija, uključujući kardiovaskularnu, bubrežnu i/ili respiratornu insuficijenciju, ali se malo zna o njegovim potencijalnim učincima u osoba koje su izložene toksičnim metalima. Cilj ovog istraživanja bio je odgovoriti na to pitanje pomoću in silico toksikogenomske metode, koja može pružiti molekularni uvid u komplikacije bolesti COVID-19 uslijed izloženosti arsenu, kadmiju, olovu, živi, niklu i kromu. U tu su svrhu korišteni Komparativna toksikogenomska baza podataka (CTD), GeneMANIA i ToppGene Suite portal te je identificirana skupina od pet zajedničkih gena (IL1B, CXCL8, IL6, IL10, TNF) za šest metala i COVID-19, koji svi kodiraju proinflamatorne i antiinflamatorne citokine. Lista je proširena s dodatnih 20 srodnih gena. Fizičke interakcije dominirale su između gena na koje utječe kombinacija ispitivanih metala (77,64 %), a koekspresija je dominantna interakcija između gena na koje djeluju pojedinačni metali (As 56,35 %, Cd 64,07 %, Pb 71,5 %, Hg 81,91 %, Ni 64,28 %, Cr 88,51 %). Biološki procesi, molekulske funkcije i putovi u kojima sudjeluje tih 25 gena blisko su povezani s citokinima i citokinskom olujom, koja je uključena u razvoj komplikacija bolesti COVID-19. Drugim riječima, ovi rezultati potvrđuju da izloženost toksičnim metalima, bilo pojedinačno ili u kombinaciji, može dovesti do razvoja težih oblika bolesti COVID-19

Text mining for the biocuration workflow

Molecular biology has become heavily dependent on biological knowledge encoded in expert curated biological databases. As the volume of biological literature increases, biocurators need help in keeping up with the literature; (semi-) automated aids for biocuration would seem to be an ideal application for natural language processing and text mining. However, to date, there have been few documented successes for improving biocuration throughput using text mining. Our initial investigations took place for the workshop on ‘Text Mining for the BioCuration Workflow’ at the third International Biocuration Conference (Berlin, 2009). We interviewed biocurators to obtain workflows from eight biological databases. This initial study revealed high-level commonalities, including (i) selection of documents for curation; (ii) indexing of documents with biologically relevant entities (e.g. genes); and (iii) detailed curation of specific relations (e.g. interactions); however, the detailed workflows also showed many variabilities. Following the workshop, we conducted a survey of biocurators. The survey identified biocurator priorities, including the handling of full text indexed with biological entities and support for the identification and prioritization of documents for curation. It also indicated that two-thirds of the biocuration teams had experimented with text mining and almost half were using text mining at that time. Analysis of our interviews and survey provide a set of requirements for the integration of text mining into the biocuration workflow. These can guide the identification of common needs across curated databases and encourage joint experimentation involving biocurators, text mining developers and the larger biomedical research community

Improving links between literature and biological data with text mining: a case study with GEO, PDB and MEDLINE

High-throughput experiments and bioinformatics techniques are creating an exploding volume of data that are becoming overwhelming to keep track of for biologists and researchers who need to access, analyze and process existing data. Much of the available data are being deposited in specialized databases, such as the Gene Expression Omnibus (GEO) for microarrays or the Protein Data Bank (PDB) for protein structures and coordinates. Data sets are also being described by their authors in publications archived in literature databases such as MEDLINE and PubMed Central. Currently, the curation of links between biological databases and the literature mainly relies on manual labour, which makes it a time-consuming and daunting task. Herein, we analysed the current state of link curation between GEO, PDB and MEDLINE. We found that the link curation is heterogeneous depending on the sources and databases involved, and that overlap between sources is low, <50% for PDB and GEO. Furthermore, we showed that text-mining tools can automatically provide valuable evidence to help curators broaden the scope of articles and database entries that they review. As a result, we made recommendations to improve the coverage of curated links, as well as the consistency of information available from different databases while maintaining high-quality curation

Preferential regulation of miRNA targets by environmental chemicals in the human genome

<p>Abstract</p> <p>Background</p> <p>microRNAs (miRNAs) represent a class of small (typically 22 nucleotides in length) non-coding RNAs that can degrade their target mRNAs or block their translation. Recent disease research showed the exposure to some environmental chemicals (ECs) can regulate the expression patterns of miRNAs, which raises the intriguing question of how miRNAs and their targets cope with the exposure to ECs throughout the genome.</p> <p>Results</p> <p>In this study, we comprehensively analyzed the properties of genes regulated by ECs (EC-genes) and found miRNA targets were significantly enriched among the EC-genes. Compared with the non-miRNA-targets, miRNA targets were roughly twice as likely to be EC-genes. By investigating the collection methods and other properties of the EC-genes, we demonstrated that the enrichment of miRNA targets was not attributed to either the potential collection bias of EC-genes, the presence of paralogs, longer 3'UTRs or more conserved 3'UTRs. Finally, we identified 1,842 significant concurrent interactions between 407 miRNAs and 497 ECs. This association network of miRNAs-ECs was highly modular and could be separated into 14 interconnected modules. In each module, miRNAs and ECs were closely connected, providing a good method to design accurate miRNA markers for ECs in toxicology research.</p> <p>Conclusions</p> <p>Our analyses indicated that miRNAs and their targets played important roles in cellular responses to ECs. Association analyses of miRNAs and ECs will help to broaden the understanding of the pathogenesis of such chemical components.</p

Annotation of phenotypes using ontologies:a gold standard for the training and evaluation of natural language processing systems

Natural language descriptions of organismal phenotypes, a principal object of study in biology, are abundant in the biological literature. Expressing these phenotypes as logical statements using ontologies would enable large-scale analysis on phenotypic information from diverse systems. However, considerable human effort is required to make these phenotype descriptions amenable to machine reasoning. Natural language processing tools have been developed to facilitate this task, and the training and evaluation of these tools depend on the availability of high quality, manually annotated gold standard data sets. We describe the development of an expert-curated gold standard data set of annotated phenotypes for evolutionary biology. The gold standard was developed for the curation of complex comparative phenotypes for the Phenoscape project. It was created by consensus among three curators and consists of entity-quality expressions of varying complexity. We use the gold standard to evaluate annotations created by human curators and those generated by the Semantic CharaParser tool. Using four annotation accuracy metrics that can account for any level of relationship between terms from two phenotype annotations, we found that machine-human consistency, or similarity, was significantly lower than inter-curator (human-human) consistency. Surprisingly, allowing curatorsaccess to external information did not significantly increase the similarity of their annotations to the gold standard or have a significant effect on inter-curator consistency. We found that the similarity of machine annotations to the gold standard increased after new relevant ontology terms had been added. Evaluation by the original authors of the character descriptions indicated that the gold standard annotations came closer to representing their intended meaning than did either the curator or machine annotations. These findings point toward ways to better design software to augment human curators and the use of the gold standard corpus will allow training and assessment of new tools to improve phenotype annotation accuracy at scale