The annotation and the usage of scientific databases could be improved with public issue tracker software

Since the publication of their longtime predecessor The Atlas of Protein Sequences and Structures in 1965 by Margaret Dayhoff, scientific databases have become a key factor in the organization of modern science. All the information and knowledge described in the novel scientific literature is translated into entries in many different scientific databases, making it possible to obtain very accurate information on a biological entity like genes or proteins without having to manually review the literature on it. However, even for the databases with the finest annotation procedures, errors or unclear parts sometimes appear in the publicly released version and influence the research of unaware scientists using them. The researcher that finds an error in a database is often left in a uncertain state, and often abandons the effort of reporting it because of a lack of a standard procedure to do so. In the present work, we propose that the simple adoption of a public error tracker application, as in many open software projects, could improve the quality of the annotations in many databases and encourage feedback from the scientific community on the data annotated publicly. In order to illustrate the situation, we describe a series of errors that we found and helped solve on the genes of a very well-known pathway in various biomedically relevant databases. We would like to show that, even if a majority of the most important scientific databases have procedures for reporting errors, these are usually not publicly visible, making the process of reporting errors time consuming and not useful. Also, the effort made by the user that reports the error often goes unacknowledged, putting him in a discouraging position

A fully integrated machine learning scan of selection in the chimpanzee genome

After diverging, each chimpanzee subspecies has been the target of unique selective pressures. Here, we employ a machine learning approach to classify regions as under positive selection or neutrality genome-wide. The regions determined to be under selection reflect the unique demographic and adaptive history of each subspecies. The results indicate that effective population size is important for determining the proportion of the genome under positive selection. The chimpanzee subspecies share signals of selection in genes associated with immunity and gene regulation. With these results, we have created a selection map for each population that can be displayed in a genome browser (www.hsb.upf.edu/chimp_browser). This study is the first to use a detailed demographic history and machine learning to map selection genome-wide in chimpanzee. The chimpanzee selection map will improve our understanding of the impact of selection on closely related subspecies and will empower future studies of chimpanzee

Signatures of genetic variation in human microRNAs point to processes of positive selection and population-specific disease risks

Data de publicació electrònica: 06-03-2022The occurrence of natural variation in human microRNAs has been the focus of numerous studies during the last 20 years. Most of them have been focused on the role of specific mutations in disease, while a minor proportion seek to analyse microRNA diversity in the genomes of human populations. We analyse the latest human microRNA annotations in the light of the most updated catalogue of genetic variation provided by the 1000 Genomes Project. By means of the in silico analysis of microRNA genetic variation we show that the level of evolutionary constraint of these sequences is governed by the interplay of different factors, like their evolutionary age or genomic location. The role of mutations in the shaping of microRNA-driven regulatory interactions is emphasized with the acknowledgement that, while the whole microRNA sequence is highly conserved, the seed region shows a pattern of higher genetic diversity that appears to be caused by the dramatic frequency shifts of a fraction of human microRNAs. We highlight the participation of these microRNAs in population-specific processes by identifying that not only the seed, but also the loop, are particularly differentiated regions among human populations. The quantitative computational comparison of signatures of population differentiation showed that candidate microRNAs with the largest differences are enriched in variants implicated in gene expression levels (eQTLs), selective sweeps and pathological processes. We explore the implication of these evolutionary-driven microRNAs and their SNPs in human diseases, such as different types of cancer, and discuss their role in population-specific disease risk.This study has been possible thanks to the Chilean Agencia Nacional de Investigación y Desarrollo—ANID (FONDECYT regular nº 1170446), the Chilean Ministry of Education (MAG-1995), grant PID2019-110933 GB-I00/AEI/10. 13039/501100011033 awarded by the Spanish Agencia Estatal de Investigación (AEI), Ministerio de Ciencia, Innovación y Universidades (MCIU, Spain), the support of Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat de Catalunya (GRC 2017 SGR 702), part of the “Unidad de Excelencia María de Maeztu”, funded by the AEI (CEX2018-000792-M). P.V-M is supported by an FPI PhD fellowship (FPI-BES-2016-077706) part of the “Unidad de Excelencia María de Maeztu” funded by MINECO (ref: MDM-2014-0370)

A system-level, molecular evolutionary analysis of mammalian phototransduction

Visual perception is initiated in the photoreceptor cells of the retina via the phototransduction system./nThis system has shown marked evolution during mammalian divergence in such complex attributes as activation time and recovery time. We have performed a molecular evolutionary analysis of proteins involved in mammalian/nphototransduction in order to unravel how the action of natural selection has been distributed throughout the/nsystem to evolve such traits. We found selective pressures to be non-randomly distributed according to both a simple protein classification scheme and a protein-interaction network representation of the signaling pathway. Proteins which are topologically central in the signaling pathway, such as the G proteins, as well as retinoid cycle chaperones and proteins involved in photoreceptor cell-type determination, were found to be more constrained in their evolution. Proteins peripheral to the pathway, such as ion channels and exchangers, as well as the retinoid cycle enzymes, have experienced a relaxation of selective pressures. Furthermore, signals of positive selection were detected in two genes: the short-wave (blue) opsin (OPN1SW) in hominids and the rod-specific Na+/Ca2+,K+ ion exchanger (SLC24A1) in rodents. The functions of the proteins involved in phototransduction and the topology of the interactions between them have imposed non-random constraints on their evolution. Thus, in shaping or conserving system-level phototransduction traits, natural selection has targeted the underlying proteins in a concerted manner.This research was funded by grant BFU2010-19443 (subprogram BMC) awarded by Ministerio de Ciencia y Tecnología (Spain) and by the Direcció General de/nRecerca, Generalitat de Catalunya (Grup de Recerca Consolidat 2009SGR 1101). BI is supported by a FI-DGR from AGAUR, Generalitat de Catalunya (2011F1 B100275). LM acknowledges funding from the Juan de la Cierva Program of the Spanish Ministry of Science and Innovation (MICINN)

A system-level, molecular evolutionary analysis of mammalian phototransduction

Visual perception is initiated in the photoreceptor cells of the retina via the phototransduction system./nThis system has shown marked evolution during mammalian divergence in such complex attributes as activation time and recovery time. We have performed a molecular evolutionary analysis of proteins involved in mammalian/nphototransduction in order to unravel how the action of natural selection has been distributed throughout the/nsystem to evolve such traits. We found selective pressures to be non-randomly distributed according to both a simple protein classification scheme and a protein-interaction network representation of the signaling pathway. Proteins which are topologically central in the signaling pathway, such as the G proteins, as well as retinoid cycle chaperones and proteins involved in photoreceptor cell-type determination, were found to be more constrained in their evolution. Proteins peripheral to the pathway, such as ion channels and exchangers, as well as the retinoid cycle enzymes, have experienced a relaxation of selective pressures. Furthermore, signals of positive selection were detected in two genes: the short-wave (blue) opsin (OPN1SW) in hominids and the rod-specific Na+/Ca2+,K+ ion exchanger (SLC24A1) in rodents. The functions of the proteins involved in phototransduction and the topology of the interactions between them have imposed non-random constraints on their evolution. Thus, in shaping or conserving system-level phototransduction traits, natural selection has targeted the underlying proteins in a concerted manner.This research was funded by grant BFU2010-19443 (subprogram BMC) awarded by Ministerio de Ciencia y Tecnología (Spain) and by the Direcció General de/nRecerca, Generalitat de Catalunya (Grup de Recerca Consolidat 2009SGR 1101). BI is supported by a FI-DGR from AGAUR, Generalitat de Catalunya (2011F1 B100275). LM acknowledges funding from the Juan de la Cierva Program of the Spanish Ministry of Science and Innovation (MICINN)

VCF2Networks: applying genotype networks to single-nucleotide variants data

SUMMARY: A wealth of large-scale genome sequencing projects opens the doors to new approaches to study the relationship between genotype and phenotype. One such opportunity is the possibility to apply genotype networks analysis to population genetics data. Genotype networks are a representation of the set of genotypes associated with a single phenotype, and they allow one to estimate properties such as the robustness of the phenotype to mutations, and the ability of its associated genotypes to evolve new adaptations. So far, though, genotype networks analysis has rarely been applied to population genetics data. To help fill this gap, here we present VCF2Networks, a tool to determine and study genotype network structure from single-nucleotide variant data. AVAILABILITY AND IMPLEMENTATION: VCF2Networks is available at https://bitbucket.org/dalloliogm/vcf2networks. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.This study has been possible thanks the grant BFU2013-43726-P awarded by Ministerio de Economía y Competitividad (Spain) and with the support of Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement de la Generalitat de Catalunya (GRC 2014 SGR 866). GMD was supported by a FPI fellowship BES-2009-017731. AW was supported by the Swiss National Science Foundation and by the URPP Evolutionary Biology at the University of Zurich

Gene connectivity and enzyme evolution in the human metabolic network

Background: Determining the factors involved in the likelihood of a gene being under adaptive selection is still a challenging goal in Evolutionary Biology. Here, we perform an evolutionary analysis of the human metabolic genes to explore the associations between network structure and the presence and strength of natural selection in the genes whose products are involved in metabolism. Purifying and positive selection are estimated at interspecific (among mammals) and intraspecific (among human populations) levels, and the connections between enzymatic reactions are differentiated between incoming (in-degree) and outgoing (out-degree) links. Results: We confirm that purifying selection has been stronger in highly connected genes. Long-term positive selection has targeted poorly connected enzymes, whereas short-term positive selection has targeted different enzymes depending on whether the selective sweep has reached fixation in the population: genes under a complete selective sweep are poorly connected, whereas those under an incomplete selective sweep have high out-degree connectivity. The last steps of pathways are more conserved due to stronger purifying selection, with long-term positive selection targeting preferentially enzymes that catalyze the first steps. However, short-term positive selection has targeted enzymes that catalyze the last steps in the metabolic network. Strong signals of positive selection have been found for metabolic processes involved in lipid transport and membrane fluidity and permeability. Conclusions: Our analysis highlights the importance of analyzing the same biological system at different evolutionary timescales to understand the evolution of metabolic genes and of distinguishing between incoming and outgoing links in a metabolic network. Short-term positive selection has targeted enzymes with a different connectivity profile depending on the completeness of the selective sweep, while long-term positive selection has targeted genes with fewer connections that code for enzymes that catalyze the first steps in the network. Reviewers: This article was reviewed by Diamantis Sellis and Brandon Invergo.This study has been possible thanks to grant BFU2016–77961-P (AEI/FEDER, UE) awarded by the Agencia Estatal de Investigación (Ministerio de Ciencia, Innovación y Universidades, Spain) and with the support of Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat de Catalunya (GRC 2017 527 SGR 702) to JB. Part of the “Unidad de Excelencia María de Maeztu” (MDM-2014-0370), funded by the Ministerio de Economía, Industria y Competividad (MINECO, Spain). JP work is supported in part by the Agencia Estatal de Investigación (Ministerio de Ciencia, Innovación y Universidades, Spain) Helios grant reference: BIO2015–66960-C3–1-R (co-financed by FEDER) and by the European Union through the BioRoboost project (H2020-NMBP-TR-IND-2018-2020/BIOTEC-01-2018 (CSA), Project ID 210491758). BD is supported by F.P.U. grant FPU13/06813 from the Ministerio de Educación, Cultura y Deporte (Spain)

Metabolic flux is a determinant of the evolutionary rates of enzyme-encoding genes

Relationships between evolutionary rates and gene properties on a genomic, functional, pathway, or system level are being explored to unravel the principles of the evolutionary process. In particular, functional network properties have been analyzed to recognize the constraints they may impose on the evolutionary fate of genes. Here we took as a case study the core metabolic network in human erythrocytes and we analyzed the relationship between the evolutionary rates of its genes and the metabolic flux distribution throughout it. We found that metabolic flux correlates with the ratio of nonsynonymous to synonymous substitution rates. Genes encoding enzymes that carry high fluxes have been more constrained in their evolution, while purifying selection is more relaxed in genes encoding enzymes carrying low metabolic fluxes. These results demonstrate the importance of considering the dynamical functioning of gene networks when assessing the action of selection on system-level properties.This research was funded by grants BFU2010-19443 (subprogram BMC) awarded by Ministerio de Ciencia y Tecnología (Spain) and by the Direcció General de Recerca, Generalitat de Catalunya (Grup de Recerca Consolidat 2009 SGR 1101). BI is supported by a FI-DGR from AGAUR, Generalitat de Catalunya (2011F1 B1 00275). LM acknowledges funding from the Juan de la Cierva Program of the Spanish Ministry of Science and Innovation (MICINN

Positive selection in admixed populations from Ethiopia

Background: In the process of adaptation of humans to their environment, positive or adaptive selection has played a main role. Positive selection has, however, been under-studied in African populations, despite their diversity and importance for understanding human history. Results: Here, we have used 119 available whole-genome sequences from five Ethiopian populations (Amhara, Oromo, Somali, Wolayta and Gumuz) to investigate the modes and targets of positive selection in this part of the world. The site frequency spectrum-based test SFselect was applied to idfentify a wide range of events of selection (old and recent), and the haplotype-based statistic integrated haplotype score to detect more recent events, in each case with evaluation of the significance of candidate signals by extensive simulations. Additional insights were provided by considering admixture proportions and functional categories of genes. We identified both individual loci that are likely targets of classic sweeps and groups of genes that may have experienced polygenic adaptation. We found population-specific as well as shared signals of selection, with folate metabolism and the related ultraviolet response and skin pigmentation standing out as a shared pathway, perhaps as a response to the high levels of ultraviolet irradiation, and in addition strong signals in genes such as IFNA, MRC1, immunoglobulins and T-cell receptors which contribute to defend against pathogens. Conclusions: Signals of positive selection were detected in Ethiopian populations revealing novel adaptations in East Africa, and abundant targets for functional follow-up.This study has been possible thanks to the F.P.I. grant BES-2014-068994 to SW, and grant BFU2016–77961-P (AEI/FEDER, UE) both awarded by the Agencia Estatal de Investigación (MINECO, Spain) and with the support of Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat de Catalunya (GRC 2017 SGR 702). Part of the “Unidad de Excelencia María de Maeztu”, funded by the AEI (CEX2018–000792-M). YX and CTS are supported by Wellcome Trust (098051), LP is supported by the European Union through the European Regional Development Fund Project No. 2014–2020.4.01.16–0024, MOBTT53. Publication costs were funded by Wellcome (grant 098051). The funding body has not played any role in the design of the study and collection, analysis, and interpretation of data and in writing of the manuscript

Influence of pathway topology and functional class on the molecular evolution of human metabolic genes

Metabolic networks comprise thousands of enzymatic reactions functioning in a controlled manner and have been shaped by natural selection. Thanks to the genome data, the footprints of adaptive (positive) selection are detectable, and the strength of purifying selection can be measured. This has made possible to know where, in the metabolic network, adaptive selection has acted and where purifying selection is more or less strong and efficient. We have carried out a comprehensive molecular evolutionary study of all the genes involved in the human metabolism. We investigated the type and strength of the selective pressures that acted on the enzyme-coding genes belonging to metabolic pathways during the divergence of primates and rodents. Then, we related those selective pressures to the functional and topological characteristics of the pathways. We have used DNA sequences of all enzymes (956) of the metabolic pathways comprised in the HumanCyc database, using genome data for humans and five other mammalian species. We have found that the evolution of metabolic genes is primarily constrained by the layer of the metabolism in which the genes participate: while genes encoding enzymes of the inner core of metabolism are much conserved, those encoding enzymes participating in the outer layer, mediating the interaction with the environment, are evolutionarily less constrained and more plastic, having experienced faster functional evolution. Genes that have been targeted by adaptive selection are endowed by higher out-degree centralities than non-adaptive genes, while genes with high in-degree centralities are under stronger purifying selection. When the position along the pathway is considered, a funnel-like distribution of the strength of the purifying selection is found. Genes at bottom positions are highly preserved by purifying selection, whereas genes at top positions, catalyzing the first steps, are open to evolutionary changes. These results show how functional and topological characteristics of metabolic pathways contribute to shape the patterns of evolutionary pressures driven by natural selection and how pathway network structure matters in the evolutionary process that shapes the evolution of the system.This work was funded by BFU2016-77961-P and BFU2012-39816-C02-0 (AEI/FEDER, UE) awarded by the Agencia Estatal de Investigación (Spain); and Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat de Catalunya (GRC 2014 SGR 866) Generalitat Valenciana (grant reference: PROMETEOII/2014/065). BD is supported by FPU grant (FPU13/06813) from the Ministerio de Educación, Cultura y Deporte (Spain). KLK was supported by a Fulbright Student Research grant to Spain and by the United States National Science Foundation Graduate Student Research Fellowship under Grant Number DGE-0707424. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript