Suchergebnisse

PhylomeDB is a unique knowledge base providing public access to minable and browsable catalogues of pre-computed genome-wide collections of annotated sequences, alignments and phylogenies (i.e. phylomes) of homologous genes, as well as to their corresponding phylogeny-based orthology and paralogy relationships. In addition, PhylomeDB trees and alignments can be downloaded for further processing to detect and date gene duplication events, infer past events of inter-species hybridization and horizontal gene transfer, as well as to uncover footprints of selection, introgression, gene conversion, or other relevant evolutionary processes in the genes and organisms of interest. Here, we describe the latest evolution of PhylomeDB (version 5). This new version includes a newly implemented web interface and several new functionalities such as optimized searching procedures, the possibility to create user-defined phylome collections, and a fully redesigned data structure. This release also represents a significant core data expansion, with the database providing access to 534 phylomes, comprising over 8 million trees, and homology relationships for genes in over 6000 species. This makes PhylomeDB the largest and most comprehensive public repository of gene phylogenies. PhylomeDB is available at http://www.phylomedb.org. ; Spanish Ministry of Science and Innovation (MICINN) [PGC2018-099921-B-I00], cofounded by European Regional Development Fund (ERDF); Catalan Research Agency (AGAUR) [SGR423]; European Union's Horizon 2020 research and innovation programme [ERC-2016–724173]; Gordon and Betty Moore Foundation [GBMF9742]; Instituto de Salud Carlos III [INB Grant PT17/0009/0023 – ISCIII-SGEFI/ERDF]; U.C. was funded in part through H2020 Marie Skłodowska-Curie Actions [H2020-MSCA-IF-2017-793699]; MICINN [IJC2019- 039402-I]. Funding for open access charge: ERC. ; Peer Reviewed ; Postprint (published version)

Phylogenetic trees representing the evolutionary relationships of homologous genes are the entry point for many evolutionary analyses. For instance, the use of a phylogenetic tree can aid in the inference of orthology and paralogy relationships, and in the detection of relevant evolutionary events such as gene family expansions and contractions, horizontal gene transfer, recombination or incomplete lineage sorting. Similarly, given the plurality of evolutionary histories among genes encoded in a given genome, there is a need for the combined analysis of genome-wide collections of phylogenetic trees (phylomes). Here, we introduce a new release of PhylomeDB (http://phylomedb.org), a public repository of phylomes. Currently, PhylomeDB hosts 120 public phylomes, comprising >1.5 million maximum likelihood trees and multiple sequence alignments. In the current release, phylogenetic trees are annotated with taxonomic, protein-domain arrangement, functional and evolutionary information. PhylomeDB is also a major source for phylogeny-based predictions of orthology and paralogy, covering >10 million proteins across 1059 sequenced species. Here we describe newly implemented PhylomeDB features, and discuss a benchmark of the orthology predictions provided by the database, the impact of proteome updates and the use of the phylome approach in the analysis of newly sequenced genomes and transcriptomes. ; Spanish ministry of Economy and Competitiveness [BIO2012-37161]; a Grant from the Qatar National/nResearch Fund [NPRP 5-298-3-086]; a the European Research Council under the European Union's Seventh/nFramework Programme [FP/2007-2013/ERC and ERC-/n2012-StG-310325]; Juan de La Cierva postdoctoral program (to J.H.C.) and La Caixa-CRG International Fellowship Program (to L.P.P.). Funding for open access charge: Internal budget from the CRG

The microbiota of multi-pond solar salterns around the world has been analyzed using a variety of culture-dependent and molecular techniques. However, studies addressing the dynamic nature of these systems are very scarce. Here we have characterized the temporal variation during 1 year of the microbiota of five ponds with increasing salinity (from 18% to >40%), by means of CARD-FISH and DGGE. Microbial community structure was statistically correlated with several environmental parameters, including ionic composition and meteorological factors, indicating that the microbial community was dynamic as specific phylotypes appeared only at certain times of the year. In addition to total salinity, microbial composition was strongly influenced by temperature and specific ionic composition. Remarkably, DGGE analyses unveiled the presence of most phylotypes previously detected in hypersaline systems using metagenomics and other molecular techniques, such as the very abundant Haloquadratum and Salinibacter representatives or the recently described low GC Actinobacteria and Nanohaloarchaeota. In addition, an uncultured group of Bacteroidetes was present along the whole range of salinity. Database searches indicated a previously unrecognized widespread distribution of this phylotype. Single-cell genome analysis of five members of this group suggested a set of metabolic characteristics that could provide competitive advantages in hypersaline environments, such as polymer degradation capabilities, the presence of retinal-binding light-activated proton pumps and arsenate reduction potential. In addition, the fairly high metagenomic fragment recruitment obtained for these single cells in both the intermediate and hypersaline ponds further confirm the DGGE data and point to the generalist lifestyle of this new Bacteroidetes group. ; This work was supported by the projects CGL2012-39627-C03-01 and 02 of the Spanish Ministry of Economy and Competitiveness, which were also co-financed with FEDER support from the European Union. ...

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus, which has spread all over the world leading to a global pandemic. The fast progression of COVID-19 has been mainly related to the high contagion rate of the virus and the worldwide mobility of humans. In the absence of pharmacological therapies, governments from different countries have introduced several non-pharmaceutical interventions to reduce human mobility and social contact. Several studies based on Anonymized Mobile Phone Data have been published analysing the relationship between human mobility and the spread of coronavirus. However, to our knowledge, none of these data-sets integrates cross-referenced geo-localised data on human mobility and COVID-19 cases into one all-inclusive open resource. Herein we present COVID-19 Flow-Maps, a cross-referenced Geographic Information System that integrates regularly updated time-series accounting for population mobility and daily reports of COVID-19 cases in Spain at different scales of time spatial resolution. This integrated and up-to-date data-set can be used to analyse the human dynamics to guide and support the design of more effective non-pharmaceutical interventions. ; This work was supported by the Generalitat de Catalunya through the project PDAD14/20/00001, and by the H2020 programme under Grant Agreement 825070 (INFORE) and the INB Grant (PT17/0009/0001 - ISCIII-SGEFI/ERDF). ; Peer Reviewed ; Postprint (published version)

Cell-specific regulation of protein levels and activity is essential for the distribution of functions among multiple cell types in animals. The finding that many genes involved in these regulatory processes have a premetazoan origin raises the intriguing possibility that the mechanisms required for spatially regulated cell differentiation evolved prior to the appearance of animals. Here, we use high-throughput proteomics in Capsaspora owczarzaki, a close unicellular relative of animals, to characterize the dynamic proteome and phosphoproteome profiles of three temporally distinct cell types in this premetazoan species. We show that life-cycle transitions are linked to extensive proteome and phosphoproteome remodeling and that they affect key genes involved in animal multicellularity, such as transcription factors and tyrosine kinases. The observation of shared features between Capsaspora and metazoans indicates that elaborate and conserved phosphosignaling and proteome regulation supported temporal cell-type differentiation in the unicellular ancestor of animals. ; The CRG/UPF Proteomics Unit is part of the "Plataforma de Recursos Biomoleculares y Bioinformáticos (ProteoRed)" supported by grant PT13/0001 of Instituto de Salud Carlos III (ISCIII). A.S.-P. is supported by an EMBO Long-Term Fellowship (ALTF 841-2014). T.G. group research is funded in part by a grant from the Spanish Ministry of Economy and Competitiveness (BIO2012-37161), a grant from the Qatar National Research Fund (NPRP 5-298-3-086), a grant from the European Union FP7 FP7-PEOPLE-2013-ITN-606786, and a grant from the European Research Council under the European Union's Seventh Framework Program (FP/2007-2013)/ERC (grant agreement no. ERC-2012-StG-310325). I.R.-T. group research is supported by an Institució Catalana de Recerca i Estudis Avançats contract, a European Research Council Consolidator Grant (ERC-2012-Co-616960), and a grant (BFU-2011-23434) from Ministerio de Economía y Competitividad (MINECO). I.R.-T. also acknowledges financial support from Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement de la Generalitat de Catalunya (Project 2014 SGR 619 and Project 2014 SGR 678)

Rett syndrome (RTT) is a rare neurological disorder mostly caused by a genetic variation in MECP2. Making new MECP2 variants and the related phenotypes available provides data for better understanding of disease mechanisms and faster identification of variants for diagnosis. This is, however, currently hampered by the lack of interoperability between genotype-phenotype databases. Here, we demonstrate on the example of MECP2 in RTT that by making the genotype-phenotype data more Findable, Accessible, Interoperable, and Reusable (FAIR), we can facilitate prioritization and analysis of variants. In total, 10,968 MECP2 variants were successfully integrated. Among these variants 863 unique confirmed RTT causing and 209 unique confirmed benign variants were found. This dataset was used for comparison of pathogenicity predicting tools, protein consequences, and identification of ambiguous variants. Prediction tools generally recognised the RTT causing and benign variants, however, there was a broad range of overlap Nineteen variants were identified that were annotated as both disease-causing and benign, suggesting that there are additional factors in these cases contributing to disease development. ; The authors would like to thank the Mutalyzer team for support and feedback, Henk van Kranen for support in liftover of ancient genetic variant descriptions, and Eric Smeets for collection of the Maastricht Rett dataset.This work was funded by ELIXIR (funded by the European Commission within the Research Infrastructures programme of Horizon 2020), the research infrastructure for life-science data (MolData2). FE and LC were also funded by The Dutch Rett Syndrome Foundation (Stichting Terre). CE, AJ, RK, AV, SCG, MB, MRi and MR also received funding from EXCELERATE (H2020, Grant No. 676559). AJ, RK, MR, MB, and SCG also received funding from RD-Connect, European Union Seventh Framework Programme (FP7/2007–2013, Grant No. 305444). FE, CE, AJ, RK, MR, MB, and SCG received funding from the European Union's Horizon 2020 Research and Innovation Program under grant agreement EJP RD N°825575. RK was also funded by NWO in project VWData (grant no. 400.17.605) and BBMRI-NL (NWO, National Roadmap for Large-Scale Research Facilities, grant no. 184.033.111). AV and SCG also received funding from INB Grant (Grant No. PT17/0009/0001 - ISCIII-SGEFI / ERDF). ; Peer Reviewed ; Postprint (published version)

Selenoproteins are proteins that incorporate selenocysteine (Sec), a nonstandard amino acid encoded by UGA, normally a stop codon. Sec synthesis requires the enzyme Selenophosphate synthetase (SPS or SelD), conserved in all prokaryotic and eukaryotic genomes encoding selenoproteins. Here, we study the evolutionary history of SPS genes, providing a map of selenoprotein function spanning the whole tree of life. SPS is itself a selenoprotein in many species, although functionally equivalent homologs that replace the Sec site with cysteine (Cys) are common. Many metazoans, however, possess SPS genes with substitutions other than Sec or Cys (collectively referred to as SPS1). Using complementation assays in fly mutants, we show that these genes share a common function, which appears to be distinct from the synthesis of selenophosphate carried out by the Sec- and Cys- SPS genes (termed SPS2), and unrelated to Sec synthesis. We show here that SPS1 genes originated through a number of independent gene duplications from an ancestral metazoan selenoprotein SPS2 gene that most likely already carried the SPS1 function. Thus, in SPS genes, parallel duplications and subsequent convergent subfunctionalization have resulted in the segregation to different loci of functions initially carried by a single gene. This evolutionary history constitutes a remarkable example of emergence and evolution of gene function, which we have been able to trace thanks to the singular features of SPS genes, wherein the amino acid at a single site determines unequivocally protein function and is intertwined to the evolutionary fate of the entire selenoproteome. ; R.G. group research was funded by grants BIO2011-26205 from the Spanish Ministry of Science and grant SGR-1430 from the Catalan Government. M.M. received a FPU doctoral fellowship AP2008-04334 from the Spanish Ministry of Education. T.G. group research was funded in part by a grant from the Spanish Ministry of Economy and Competitiveness (BIO2012-37161), a grant from the Qatar National Research Fund (Grant No. NPRP 5-298-3-086), and a grant from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC (Grant Agreement n. ERC-2012-StG-310325). R.G. and T.G. acknowledge support of the Spanish Ministry of Economy and Competitiveness, "Centro de Excelencia Severo Ochoa 2013-2017," SEV-2012-0208. V.N.G. group research was supported by the National Institutes of Health (NIH) GM061603.

The identification of orthologs—genes in different species which descended from the same gene in their last common ancestor—is a prerequisite for many analyses in comparative genomics and molecular evolution. Numerous algorithms and resources have been conceived to address this problem, but benchmarking and interpreting them is fraught with difficulties (need to compare them on a common input dataset, absence of ground truth, computational cost of calling orthologs). To address this, the Quest for Orthologs consortium maintains a reference set of proteomes and provides a web server for continuous orthology benchmarking (http://orthology.benchmarkservice.org). Furthermore, consensus ortholog calls derived from public benchmark submissions are provided on the Alliance of Genome Resources website, the joint portal of NIH-funded model organism databases. ; Service and Infrastructure grant from the Swiss Institute of Bioinformatics [to C.D.]; NSF [1917302 to P.D.T.]; NIH NHGRI [HG002273, HG010859 to P.D.T.]; INB [PT17/0009/0001 - ISCIII-SGEFI/ERDF to J.G.V., V.S., J.M.F., L.C., J.L.G. and S.C.G.]; H2020 ELIXIR-EXCELERATE [676559 to J.G.V., V.S., J.M.F., L.C., J.L.G. and S.C.G]; ELIXIR, the research infrastructure for life-science data [to J.G.V., V.S., J.M.F., L.C., J.L.G. and S.C.G]; Ministerio de Ciencia, Innovación y Universidades [PGC2018-098073-A-I00 MCIU/AEI/FEDER, UE to J.H.C.]; European Union's Horizon 2020 Research and Innovation Programme [686070 to J.H.C. and D.S, and 637765 to D.M.E. and S.K.]; Consejería de Educación, Juventud y Deporte de la Comunidad de Madrid and Fondo Social Europeo [PEJ-2017-AI/TIC-7514 to A.H.P.]; IdEX Unistra in the framework of the Investments for the future program of the French government [to O.L.]; National Institutes of Health (NIH) [U24HG007822 to M.J.M.]; European Molecular Biology Laboratory (EMBL) [core funds to M.J.M and M.M.]; Wellcome Trust [WT108749/Z/15/Z to M.M.]; S.K. is a royal Society University Research Fellow; Swiss National Science Foundation [183723 to C.D.]. Funding for open access charge: Swiss National Science Foundation. ; Peer Reviewed ; Postprint (published version)

eTRANSAFE is a research project funded within the Innovative Medicines Initiative (IMI), which aims at developing integrated databases and computational tools (the eTRANSAFE ToxHub) that support the translational safety assessment of new drugs by using legacy data provided by the pharmaceutical companies that participate in the project. The project objectives include the development of databases containing preclinical and clinical data, computational systems for translational analysis including tools for data query, analysis and visualization, as well as computational models to explain and predict drug safety events. ; This research received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreements eTRANSAFE (777365). This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA companies in kind contribution. The Research Programme on Biomedical Informatics (GRIB) of IMIM and DCEXS-UPF is a member of the Spanish National Bioinformatics Institute (INB), PRB2-ISCIII and is supported by grant PT13/0001/0023, of the PE I + D + i 2013–2016, funded by ISCIII and FEDER. The GRIB also receive support from Agència de Gestió D'ajuts Universitaris i de Recerca Generalitat de Catalunya (AGAUR, ref.: 2017SGR01020). The DCEXS is a "Unidad de Excelencia María de Maeztu", funded by the MINECO (ref: MDM-2014-0370). ; Peer Reviewed ; Postprint (published version)