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Motivation Numerous sequencing studies, including transcriptomics of host-pathogen systems, sequencing of hybrid genomes, xenografts, mixed species systems, metagenomics and meta-transcriptomics, involve samples containing genetic material from divergent organisms. A crucial step in these studies is identifying from which organism each sequencing read originated, and the experimental design should be directed to minimize biases caused by cross-mapping of reads to incorrect source genomes. Additionally, pooling of sufficiently different genetic material into a single sequencing library could significantly reduce experimental costs but requires careful planning and assessment of the impact of cross-mapping. Having these applications in mind we designed Crossmapper, the first to our knowledge tool able to assess cross-mapping prior to sequencing, therefore allowing optimization of experimental design. Results Using any combination of reference genomes, Crossmapper performs read simulation and back-mapping of those reads to the pool of references, quantifies and reports the cross-mapping rates for each organism. Crossmapper performs these analyses with numerous user-specified parameters, including, among others, read length, read layout, coverage, mapping parameters, genomic or transcriptomic data. Additionally, it outputs the results in highly interactive and publication-ready reports. This allows the user to perform multiple comparisons at once and choose the experimental setup minimizing cross-mapping rates. Moreover, Crossmapper can be used for resource optimization in sequencing facilities by pooling different samples into one sequencing library. Availability and implementation Crossmapper is a command line tool implemented in Python 3.6 and available as a conda package, allowing effortless installation. The source code, detailed information and a step-by-step tutorial is available at our GitHub page https://github.com/Gabaldonlab/crossmapper. Supplementary information Supplementary data are available at Bioinformatics online. ; This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) for the EMBL partnership and the grant 'Centro de Excelencia Severo Ochoa' SEV-2012-0208 cofounded by European Regional Development Fund (ERDF); from the CERCA Programme/Generalitat de Catalunya; from the Catalan Research Agency (AGAUR) SGR857 and grants from the European Union's Horizon 2020 research and innovation programme under the grant agreement ERC-2016-724173 and the Marie Sklodowska-Curie grant agreement No H2020-MSCA-ITN-2014-642095. The group also receives support from a INB Grant (PT17/0009/0023–ISCIII-SGEFI/ERDF). ; Peer Reviewed ; Postprint (published version)

MOTIVATION: Numerous sequencing studies, including transcriptomics of host-pathogen systems, sequencing of hybrid genomes, xenografts, mixed species systems, metagenomics and meta-transcriptomics, involve samples containing genetic material from divergent organisms. A crucial step in these studies is identifying from which organism each sequencing read originated, and the experimental design should be directed to minimize biases caused by cross-mapping of reads to incorrect source genomes. Additionally, pooling of sufficiently different genetic material into a single sequencing library could significantly reduce experimental costs but requires careful planning and assessment of the impact of cross-mapping. Having these applications in mind we designed Crossmapper, the first to our knowledge tool able to assess cross-mapping prior to sequencing, therefore allowing optimization of experimental design. RESULTS: Using any combination of reference genomes, Crossmapper performs read simulation and back-mapping of those reads to the pool of references, quantifies and reports the cross-mapping rates for each organism. Crossmapper performs these analyses with numerous user-specified parameters, including, among others, read length, read layout, coverage, mapping parameters, genomic or transcriptomic data. Additionally, it outputs the results in highly interactive and publication-ready reports. This allows the user to perform multiple comparisons at once and choose the experimental setup minimizing cross-mapping rates. Moreover, Crossmapper can be used for resource optimization in sequencing facilities by pooling different samples into one sequencing library. AVAILABILITY AND IMPLEMENTATION: Crossmapper is a command line tool implemented in Python 3.6 and available as a conda package, allowing effortless installation. The source code, detailed information and a step-by-step tutorial is available at our GitHub page https://github.com/Gabaldonlab/crossmapper. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. ; This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) for the EMBL partnership and the grant 'Centro de Excelencia Severo Ochoa' SEV-2012-0208 cofounded by European Regional Development Fund (ERDF); from the CERCA Programme/Generalitat de Catalunya; from the Catalan Research Agency (AGAUR) SGR857 and grants from the European Union's Horizon 2020 research and innovation programme under the grant agreement ERC-2016-724173 and the Marie Sklodowska-Curie grant agreement No H2020-MSCA-ITN-2014-642095. The group also receives support from a INB Grant (PT17/0009/0023–ISCIII-SGEFI/ERDF)

RNA molecules play important roles in virtually every cellular process. These functions are often mediated through the adoption of specific structures that enable RNAs to interact with other molecules. Thus, determining the secondary structures of RNAs is central to understanding their function and evolution. In recent years several sequencing-based approaches have been developed that allow probing structural features of thousands of RNA molecules present in a sample. Here, we describe nextPARS, a novel Illumina-based implementation of in vitro parallel probing of RNA structures. Our approach achieves comparable accuracy to previous implementations, while enabling higher throughput and sample multiplexing. ; This work was supported by the Spanish Ministry of Economy and Competitiveness grants "Centro de Excelencia Severo Ochoa 2013–2017" (SEV-2012-0208); the European Regional Development Fund (ERDF) from the European Union and European Research Council (ERC) Seventh Framework Programme (FP7/2007–2013) (ERC-2012-StG-310325); and the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement (H2020-MSCA-ITN-2014-642095).

Epidemics caused by microbial organisms are part of the natural phenomena of increasing biological complexity. The heterogeneity and constant variability of hosts, in terms of age, immunological status, family structure, lifestyle, work activities, social and leisure habits, daily division of time, and other demographic characteristics make it extremely difficult to predict the evolution of epidemics. Such prediction is, however, critical for implementing intervention measures in due time and with appropriate intensity. General conclusions should be precluded, given that local parameters dominate the flow of local epidemics. Membrane computing models allows us to reproduce the objects (viruses, hosts) and their interactions (stochastic but also with defined probabilities) with an unprecedented level of detail. Our LOIMOS model helps reproduce the demographics and social aspects of a hypothetical town of 10,320 inhabitants in an average European country where COVID-19 is imported from the outside. The above-mentioned characteristics of hosts and their lifestyle are minutely considered. The dynamics of the epidemics are reproduced and include the effects on viral transmission of innate and acquired immunity at various ages. The model predicts the consequences of delaying the adoption of non-pharmaceutical interventions (between 15 and 45 days after the first reported cases) and the effect of those interventions on infection and mortality rates (reducing transmission by 20%, 50%, and 80%) in immunological response groups. The lockdown for the elderly population as a single intervention appears to be effective. This modelling exercise exemplifies the application of membrane computing for designing appropriate interventions in epidemic situations. ; MC and FB were sponsored by the Projects COV20_00067 of the Program SARS-COV-2 and COVID-19 infection of the Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación of Spain, CB06/02/0053 of the Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), and the Regional Government of Madrid (InGeMICS-B2017/BMD-3691). For JCG, this study was partially founded by the Autonomous Community of Madrid, Spain (COVID-19 Grant, 2020) and the Ramón y Cajal Institute for Health Research (IRYCIS), Madrid, Spain. For AM, this study was supported by grants from the Spanish Ministry of Science and Innovation (PID2019-105969GB-I00), the government of Valencia (project Prometeo/2018/A/133) and cofinanced by the European Regional Development Fund (ERDF). ; No

Epidemics caused by microbial organisms are part of the natural phenomena of increasing biological complexity. The heterogeneity and constant variability of hosts, in terms of age, immunological status, family structure, lifestyle, work activities, social and leisure habits, daily division of time and other demographic characteristics make it extremely difficult to predict the evolution of epidemics. Such prediction is, however, critical for implementing intervention measures in due time and with appropriate intensity. General conclusions should be precluded, given that local parameters dominate the flow of local epidemics. Membrane computing models allows us to reproduce the objects (viruses and hosts) and their interactions (stochastic but also with defined probabilities) with an unprecedented level of detail. Our LOIMOS model helps reproduce the demographics and social aspects of a hypothetical town of 10 320 inhabitants in an average European country where COVID-19 is imported from the outside. The above-mentioned characteristics of hosts and their lifestyle are minutely considered. For the data in the Hospital and the ICU we took advantage of the observations at the Nursery Intensive Care Unit of the Consortium University General Hospital, Valencia, Spain (included as author). The dynamics of the epidemics are reproduced and include the effects on viral transmission of innate and acquired immunity at various ages. The model predicts the consequences of delaying the adoption of non-pharmaceutical interventions (between 15 and 45 days after the first reported cases) and the effect of those interventions on infection and mortality rates (reducing transmission by 20, 50 and 80%) in immunological response groups. The lockdown for the elderly population as a single intervention appears to be effective. This modeling exercise exemplifies the application of membrane computing for designing appropriate multilateral interventions in epidemic situations. ; MC and FB were sponsored by the Projects COV20_00067 of the Program SARS-COV-2 and COVID-19 infection of the Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación of Spain, CB06/02/0053 of the Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), and the Regional Government of Madrid (InGeMICS-B2017/BMD-3691). For JCG, this study was partially founded by the Autonomous Community of Madrid, Spain (COVID-19 Grant, 2020) and the Ramón y Cajal Institute for Health Research (IRYCIS), Madrid, Spain. For AM, this study was supported by grants from the Spanish Ministry of Science and Innovation (PID2019-105969GB-I00), the government of Valencia (project Prometeo/2018/A/133) and co-financed by the European Regional Development Fund (ERDF). ; Peer reviewed

Membrane computing is a bio-inspired computing paradigm whose devices are the so-called membrane systems or P systems. The P system designed in this work reproduces complex biological landscapes in the computer world. It uses nested "membrane-surrounded entities" able to divide, propagate, and die; to be transferred into other membranes; to exchange informative material according to flexible rules; and to mutate and be selected by external agents. This allows the exploration of hierarchical interactive dynamics resulting from the probabilistic interaction of genes (phenotypes), clones, species, hosts, environments, and antibiotic challenges. Our model facilitates analysis of several aspects of the rules that govern the multilevel evolutionary biology of antibiotic resistance. We examined a number of selected landscapes where we predict the effects of different rates of patient flow from hospital to the community and vice versa, the cross-transmission rates between patients with bacterial propagules of different sizes, the proportion of patients treated with antibiotics, and the antibiotics and dosing found in the opening spaces in the microbiota where resistant phenotypes multiply. We also evaluated the selective strengths of some drugs and the influence of the time 0 resistance composition of the species and bacterial clones in the evolution of resistance phenotypes. In summary, we provide case studies analyzing the hierarchical dynamics of antibiotic resistance using a novel computing model with reciprocity within and between levels of biological organization, a type of approach that may be expanded in the multilevel analysis of complex microbial landscapes. ; IMPORTANCE The work that we present here represents the culmination of many years of investigation in looking for a suitable methodology to simulate the multihierarchical processes involved in antibiotic resistance. Everything started with our early appreciation of the different independent but embedded biological units that shape the biology, ecology, and evolution of antibiotic-resistant microorganisms. Genes, plasmids carrying these genes, cells hosting plasmids, populations of cells, microbial communities, and host's populations constitute a complex system where changes in one component might influence the other ones. How would it be possible to simulate such a complexity of antibiotic resistance as it occurs in the real world? Can the process be predicted, at least at the local level? A few years ago, and because of their structural resemblance to biological systems, we realized that membrane computing procedures could provide a suitable frame to approach these questions. Our manuscript describes the first application of this modeling methodology to the field of antibiotic resistance and offers a bunch of examples—just a limited number of them in comparison with the possible ones to illustrate its unprecedented explanatory power. ; This work was supported by the European Commission, Seven Framework Program (EVOTAR; FP7-HEALTH-282004) to F. Baquero, T. Coque, V. Fernandez-Lanza, and M. Campos; the Instituto de Salud Carlos III of Spain (Plan Estatal de IDi 2013-2016, grant PI15-00818 and FIS18-1942; CIBERESP, grant CB06/02/0053, and the EU Joint Programming Initiative JPIAMR2016-AC16/00036 to F. Baquero; the Regional Government of Madrid (InGEMICS-C; S2017/BMD-3691) to T. Coque and F. Baquero; and SAF2015-65878-R (MINECO, Spain) and PrometeoII/2014/065 (Generalitat Valenciana, Spain) to A. Moya (all cofinanced by the European Development Regional Fund [ERDF] "A Way to Achieve Europe"). ; Peer reviewed

We report a draft genome assembly for the teleost gilthead sea bream (Sparus aurata), reconstructed by combination of short- and long-read high-throughput sequencing, and genetic linkage maps. The assembly comprised 5,039 scaffolds that span 1.24 Gb of the expected 1.59 Gb complete genome size, with 932 scaffolds (~732 Mb) anchored to 24 chromosomes. This sequencing strategy resulted in a high quality fish genome assembly, as supported by the high N50 (1.07 Mb) and lower L50 (227) values. These long and continuous reads allowed the annotation of a large number of coding genes (55,423), non-coding RNA genes (2,991) and 345 Mb of mobile genetic elements (MGEs). Synteny analysis revealed a high level of homology between gilthead sea bream genes. Phylogenomics relationships with other fish species were established to locate temporal speciation and to assign duplication events. A large degree of recent and lineage-specific gene duplications was found, that were mainly enriched in functions related to genome transposition, immune response and response to stimulus. These duplications and gene family expansions could be related in part to the activity of MGEs. This fact makes gilthead sea bream an interesting model for the study of gene repertoire expansion strategies complementary to fish whole genome duplication events. Transcriptional analyses across six different tissues (anterior and posterior intestine, skeletal muscle, liver, gills, spleen) indicated that gene duplication preferentially affected genes expressed in two or more tissues. These findings highlight the genomic complexity of this species and the importance of selective gene ; This work was financed by Spanish (Intramural-CSIC, 1201530E025; MICINN, BreamAquaINTECH, RTI2018-094128-B-I00) and European Union (AQUAEXCEL2020, 652831) projects. BS was supported by a predoctoral research fellowship (Doctorados industriales, DI-17-09134) from Spanish MINECO.

In this article, we introduce ARES (Antibiotic Resistance Evolution Simulator) a software device that simulates P-system model scenarios with five types of nested computing membranes oriented to emulate a hierarchy of eco-biological compartments, i.e. a) peripheral ecosystem; b) local environment; c) reservoir of supplies; d) animal host; and e) host's associated bacterial organisms (microbiome). Computational objects emulating molecular entities such as plasmids, antibiotic resistance genes, antimicrobials, and/or other substances can be introduced into this framework and may interact and evolve together with the membranes, according to a set of pre-established rules and specifications. ARES has been implemented as an online server and offers additional tools for storage and model editing and downstream analysis ; This work has also been supported by grants BFU2012-39816-C02-01 (co-financed by FEDER funds and the Ministry of Economy and Competitiveness, Spain) to AL and Prometeo/2009/092 (Ministry of Education, Government of Valencia, Spain) and Explora Ciencia y Explora Tecnologia/SAF2013-49788-EXP (Spanish Ministry of Economy and Competitiveness) to AM. IRF is recipient of a "Sara Borrell" postdoctoral fellowship (Ref. CD12/00492) from the Ministry of Economy and Competitiveness (Spain). We are also grateful to the Spanish Network for the Study of Plasmids and Extrachromosomal Elements (REDEEX) for encouraging and funding cooperation among Spanish microbiologists working on the biology of mobile genetic elements (Spanish Ministry of Science and Innovation, reference number BFU2011-14145-E). ; Campos Frances, M.; Llorens, C.; Sempere Luna, JM.; Futami, R.; Rodríguez, I.; Carrasco, P.; Capilla, R. (2015). A membrane computing simulator of trans-hierarchical antibiotic resistance evolution dynamics in nested ecological compartments (ARES). Biology Direct. 10(41):1-13. https://doi.org/10.1186/s13062-015-0070-9 ; S ; 1 ; 13 ; 10 ; 41 ; Baquero F, Coque TM, Canton R. Counteracting antibiotic resistance: breaking ...

Gilthead sea bream is an economically important fish species that is remarkably well-adapted to farming and changing environments. Understanding the genomic basis of this plasticity will serve to orientate domestication and selective breeding toward more robust and efficient fish. To address this goal, a draft genome assembly was reconstructed combining short- and long-read high-throughput sequencing with genetic linkage maps. The assembled unmasked genome spans 1.24 Gb of an expected 1.59 Gb genome size with 932 scaffolds (∼732 Mb) anchored to 24 chromosomes that are available as a karyotype browser at www.nutrigroup-iats.org/seabreamdb. Homology-based functional annotation, supported by RNA-seq transcripts, identified 55,423 actively transcribed genes corresponding to 21,275 unique descriptions with more than 55% of duplicated genes. The mobilome accounts for the 75% of the full genome size and it is mostly constituted by introns (599 Mb), whereas the rest is represented by low complexity repeats, RNA retrotransposons, DNA transposons, and non-coding RNAs. This mobilome also contains a large number of chimeric/composite genes (i.e., loci presenting fragments or exons mostly surrounded by LINEs and Tc1/mariner DNA transposons), whose analysis revealed an enrichment in immune-related functions and processes. Analysis of synteny and gene phylogenies uncovered a high rate of species-specific duplications, resulting from recent independent duplications rather than from genome polyploidization (2.024 duplications per gene; 0.385 excluding gene expansions). These species-specific duplications were enriched in gene families functionally related to genome transposition, immune response, and sensory responses. Additionally, transcriptional analysis of liver, skeletal muscle, intestine, gills, and spleen supported a high number of functionally specialized paralogs under tissue-exclusive regulation. Altogether, these findings suggest a role of recent large-scale gene duplications coupled to tissue expression diversification in the evolution of gilthead sea bream genome during its successful adaptation to a changing and pathogen-rich environment. This issue also underscores a role of evolutionary routes for rapid increase of the gene repertoire in teleost fish that are independent of polyploidization. Since gilthead sea bream has a well-recognized plasticity, the current study will advance our understanding of fish biology and how organisms of this taxon interact with the environment. ; This work was financed by Spanish (Intramural CSIC, 1201530E025; MICINN BreamAquaINTECH, RTI2018-094128-B-I00) and European Union (AQUAEXCEL2020, 652831) projects to JP-S. BS was supported by a predoctoral research fellowship (Doctorados Industriales, DI-17-09134) from Spanish MINECO

Peritoneal hyalinizing vasculopathy (PHV) represents the cornerstone of long-term peritoneal dialysis (PD), and especially characterizes patients associated with encapsulating peritoneal sclerosis. However, the mechanisms of PHV development remain unknown. A cross sectional study was performed in 100 non-selected peritoneal biopsies of PD patients. Clinical data were collected and lesions were evaluated by immunohistochemistry. In selected biopsies a microRNA (miRNA)-sequencing analysis was performed. Only fifteen patients (15%) showed PHV at different degrees. PHV prevalence was significantly lower among patients using PD fluids containing low glucose degradation products (GDP) (5.9% vs. 24.5%), angiotensin converting enzyme inhibitors (ACEIs) (7.5% vs. 23.4%), statins (6.5% vs. 22.6%) or presenting residual renal function, suggesting the existence of several PHV protective factors. Peritoneal biopsies from PHV samples showed loss of endothelial markers and induction of mesenchymal proteins, associated with collagen IV accumulation and wide reduplication of the basement membrane. Moreover, co-expression of endothelial and mesenchymal markers, as well as TGF-β1/Smad3 signaling activation were found in PHV biopsies. These findings suggest that an endothelial-to-mesenchymal transition (EndMT) process was taking place. Additionally, significantly higher levels of miR-7641 were observed in severe PHV compared to non-PHV peritoneal biopsies. Peritoneal damage by GDPs induce miRNA deregulation and an EndMT process in submesothelial vessels, which could contribute to collagen IV accumulation and PHV. ; Funding: This research was funded by grants from the Instituto de Salud Carlos III (ISCIII) and Fondos FEDER European Union (PI15/00120 to R.S, PI18/00882 to M.A.B, PI17/00119 to M.R.-O. and Red de Investigación Renal (REDINREN): RD16/0009, to R.S and M.R-O); "Convocatoria Dinamización Europa Investigación 2019" MINECO (EIN2019-103294 to M.R.-O.); IMPROVE-PD project ("Identification and Management of Patients at Risk–Outcome and Vascular Events in Peritoneal Dialysis") funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Grant Agreement No. 812699 to M.R.-O. and M.L-C. Spanish Ministry of Science and Innovation/Fondo Europeo de Desarrollo Regional (MICINN/FEDER) (PID2019-110132RB-I00) to M.L.-C.