Predicting the accuracy of multiple sequence alignment algorithms by using computational intelligent techniques

Multiple sequence alignments (MSAs) have become one of the most studied approaches in bioinformatics to perform other outstanding tasks such as structure prediction, biological function analysis or next-generation sequencing. However, current MSA algorithms do not always provide consistent solutions, since alignments become increasingly difficult when dealing with low similarity sequences. As widely known, these algorithms directly depend on specific features of the sequences, causing relevant influence on the alignment accuracy. Many MSA tools have been recently designed but it is not possible to know in advance which one is the most suitable for a particular set of sequences. In this work, we analyze some of the most used algorithms presented in the bibliography and their dependences on several features. A novel intelligent algorithm based on least square support vector machine is then developed to predict how accurate each alignment could be, depending on its analyzed features. This algorithm is performed with a dataset of 2180 MSAs. The proposed system first estimates the accuracy of possible alignments. The most promising methodologies are then selected in order to align each set of sequences. Since only one selected algorithm is run, the computational time is not excessively increased

GBPA: una plataforma de investigación en el ámbito de la genómica y la bioinformática en Andalucía

La secuenciación de ácidos nucleicos es una metodología utilizada para determinar el orden exacto de nucleótidos en una molécula de ADN o ARN. Uno de los grandes hitos en la secuenciación de ADN fue la obtención, mediante tecnología Sanger (1), de la secuencia de nucleótidos del genoma humano (2). Dicho proyecto finalizó en 2003 después de 13 años de trabajo y con un coste total de 3.000 millones de dólares. Desde entonces, el desarrollo de nuevas tecnologías de secuenciación masiva o Next Generation Sequencing (NGS) ha permitido realizar de forma cada vez más rápida y más económica la secuenciación de ADN o ARN. Las plataformas de NGS realizan, de forma simultánea, la secuenciación de millones de fragmentos de ADN de forma paralela y sus aplicaciones en el campo de las ciencias biológicas, biomedicina y biotecnología es muy amplio: resecuenciación del genoma, secuenciación del genoma "de novo", resecuenciación dirigida a regiones específicas del genoma, identificación de sitios de unión de proteínas al ADN (ChIP-Seq), detección y comparación de niveles de expresión de genes (RNA-Seq), entre otras (3,4). Debido a las fascinantes oportunidades que ofrece las nuevas tecnologías de secuenciación en el ámbito de la biomedicina, en 2010 se creó la Plataforma de Genómica y Bioinformática de Andalucía (GBPA), impulsada por la Consejería de Salud de la Junta de Andalucía y Roche. GBPA es una plataforma estratégica de secuenciación y genómica computacional con infraestructura de última tecnología, concebida como un espacio de investigación para el desarrollo de la excelencia y de ciencia de alta calidad en el campo de la genómica (principalmente NGS) y la bioinformática. Uno de los proyectos más importantes realizados en GBPA es el denominado Proyecto Genoma Médico (Medical Genome Project, MGP)(5), siendo su objetivo principal la identificación de modificaciones en el genoma humano asociadas a enfermedades raras de componente genético mediante el uso de las nuevas tecnologías de secuenciación masiva. Para dicho fin, se han secuenciado y analizado el exoma (6) de más de 700 muestras entre individuos control e individuos pertenecientes a familias con enfermedades hereditarias monogénicas, generando, como valor añadido, una base de datos de variantes genómicas de 300 individuos control de población española no descritas en ninguna base de datos. El proyecto MGP, ha servido para sentar las bases de la medicina genómica y personalizada en el Sistema Andaluz de Salud

Evolution of the Quorum network and the mobilome (plasmids and bacteriophages) in clinical strains of Acinetobacter baumannii during a decade

In this study, we compared eighteen clinical strains of A. baumannii belonging to the ST-2 clone and isolated from patients in the same intensive care unit (ICU) in 2000 (9 strains referred to collectively as Ab_GEIH-2000) and 2010 (9 strains referred to collectively as Ab_GEIH-2010), during the GEIH-REIPI project (Umbrella BioProject PRJNA422585). We observed two main molecular differences between the Ab_GEIH-2010 and the Ab_GEIH-2000 collections, acquired over the course of the decade long sampling interval and involving the mobilome: i) a plasmid harbouring genes for blaOXA 24/40 ss-lactamase and abKA/abkB proteins of a toxin-antitoxin system; and ii) two temperate bacteriophages, Ab105-1varphi (63 proteins) and Ab105-2varphi (93 proteins), containing important viral defence proteins. Moreover, all Ab_GEIH-2010 strains contained a Quorum functional network of Quorum Sensing (QS) and Quorum Quenching (QQ) mechanisms, including a new QQ enzyme, AidA, which acts as a bacterial defence mechanism against the exogenous 3-oxo-C12-HSL. Interestingly, the infective capacity of the bacteriophages isolated in this study (Ab105-1varphi and Ab105-2varphi) was higher in the Ab_GEIH-2010 strains (carrying a functional Quorum network) than in the Ab_GEIH-2000 strains (carrying a deficient Quorum network), in which the bacteriophages showed little or no infectivity. This is the first study about the evolution of the Quorum network and the mobilome in clinical strains of Acinetobacter baumannii during a decade

Genomic Evolution of Two Acinetobacter baumannii Clinical Strains from ST-2 Clones Isolated in 2000 and 2010 (ST-2_clon_2000 and ST-2_clon_2010)

Acinetobacter baumannii is a successful nosocomial pathogen due to its ability to persist in hospital environments by acquiring mobile elements such as transposons, plasmids, and phages. In this study, we compared two genomes of A. baumannii clinical strains isolated in 2000 (ST-2_clon_2000) and 2010 (ST-2_clon_2010) from GenBank project PRJNA308422

A crowdsourcing database for the copy-number variation of the spanish population

Background: Despite being a very common type of genetic variation, the distribution of copy-number variations (CNVs) in the population is still poorly understood. The knowledge of the genetic variability, especially at the level of the local population, is a critical factor for distinguishing pathogenic from non-pathogenic variation in the discovery of new disease variants. Results: Here, we present the SPAnish Copy Number Alterations Collaborative Server (SPACNACS), which currently contains copy number variation profiles obtained from more than 400 genomes and exomes of unrelated Spanish individuals. By means of a collaborative crowdsourcing effort whole genome and whole exome sequencing data, produced by local genomic projects and for other purposes, is continuously collected. Once checked both, the Spanish ancestry and the lack of kinship with other individuals in the SPACNACS, the CNVs are inferred for these sequences and they are used to populate the database. A web interface allows querying the database with different filters that include ICD10 upper categories. This allows discarding samples from the disease under study and obtaining pseudo-control CNV profiles from the local population. We also show here additional studies on the local impact of CNVs in some phenotypes and on pharmacogenomic variants. SPACNACS can be accessed at: http://csvs.clinbioinfosspa.es/spacnacs/. Conclusion: SPACNACS facilitates disease gene discovery by providing detailed information of the local variability of the population and exemplifies how to reuse genomic data produced for other purposes to build a local reference database.This work is supported by Grants PID2020-117979RB-I00 from the Spanish Ministry of Science and Innovation; by the Institute of Health Carlos III (project IMPaCT-Data, exp. IMP/00019, IMP/00009 and PI20/01305), co-funded by the European Union, European Regional Development Fund (ERDF, “A way to make Europe”)

Melatonin protects rats from radiotherapy-induced small intestine toxicity

Radiotherapy-induced gut toxicity is among the most prevalent dose-limiting toxicities following radiotherapy. Prevention of radiation enteropathy requires protection of the small intestine. However, despite the prevalence and burden of this pathology, there are currently no effective treatments for radiotherapy-induced gut toxicity, and this pathology remains unclear. The present study aimed to investigate the changes induced in the rat small intestine after external irradiation of the tongue, and to explore the potential radio-protective effects of melatonin gel. Male Wistar rats were subjected to irradiation of their tongues with an X-Ray YXLON Y.Tu 320-D03 irradiator, receiving a dose of 7.5 Gy/day for 5 days. For 21 days post-irradiation, rats were treated with 45 mg/day melatonin gel or vehicle, by local application into their mouths. Our results showed that mitochondrial oxidative stress, bioenergetic impairment, and subsequent NLRP3 inflammasome activation were involved in the development of radiotherapy-induced gut toxicity. Oral treatment with melatonin gel had a protective effect in the small intestine, which was associated with mitochondrial protection and, consequently, with a reduced inflammatory response, blunting the NF-κB/NLRP3 inflammasome signaling activation. Thus, rats treated with melatonin gel showed reduced intestinal apoptosis, relieving mucosal dysfunction and facilitating intestinal mucosa recovery. Our findings suggest that oral treatment with melatonin gel may be a potential preventive therapy for radiotherapy-induced gut toxicity in cancer patients.This study was partially supported by grant no. SAF2009-14037 from the Spanish Ministry of Economy and Competitivity (MINECO), GREIB.PT_2010_04 from the CEIBiotic Program of the University of Granada, Spain, and CTS-101 from the Consejería de Innovación, Ciencia y Empresa, Junta de Andalucía, Spain

CIBERER : Spanish national network for research on rare diseases: A highly productive collaborative initiative

Altres ajuts: Instituto de Salud Carlos III (ISCIII); Ministerio de Ciencia e Innovación.CIBER (Center for Biomedical Network Research; Centro de Investigación Biomédica En Red) is a public national consortium created in 2006 under the umbrella of the Spanish National Institute of Health Carlos III (ISCIII). This innovative research structure comprises 11 different specific areas dedicated to the main public health priorities in the National Health System. CIBERER, the thematic area of CIBER focused on rare diseases (RDs) currently consists of 75 research groups belonging to universities, research centers, and hospitals of the entire country. CIBERER's mission is to be a center prioritizing and favoring collaboration and cooperation between biomedical and clinical research groups, with special emphasis on the aspects of genetic, molecular, biochemical, and cellular research of RDs. This research is the basis for providing new tools for the diagnosis and therapy of low-prevalence diseases, in line with the International Rare Diseases Research Consortium (IRDiRC) objectives, thus favoring translational research between the scientific environment of the laboratory and the clinical setting of health centers. In this article, we intend to review CIBERER's 15-year journey and summarize the main results obtained in terms of internationalization, scientific production, contributions toward the discovery of new therapies and novel genes associated to diseases, cooperation with patients' associations and many other topics related to RD research

PAX4 preserves endoplasmic reticulum integrity preventing beta cell degeneration in a mouse model of type 1 diabetes mellitus

Mellado-Gil, José Manuel et al.[Aims/hypothesis]: A strategy to enhance pancreatic islet functional beta cell mass (BCM) while restraining inflammation, through the manipulation of molecular and cellular targets, would provide a means to counteract the deteriorating glycaemic control associated with diabetes mellitus. The aims of the current study were to investigate the therapeutic potential of such a target, the islet-enriched and diabetes-linked transcription factor paired box 4 (PAX4), to restrain experimental autoimmune diabetes (EAD) in the RIP-B7.1 mouse model background and to characterise putative cellular mechanisms associated with preserved BCM. [Methods]: Two groups of RIP-B7.1 mice were genetically engineered to: (1) conditionally express either PAX4 (BPTL) or its diabetes-linked mutant variant R129W (mutBPTL) using doxycycline (DOX); and (2) constitutively express luciferase in beta cells through the use of RIP. Mice were treated or not with DOX, and EAD was induced by immunisation with a murine preproinsulin II cDNA expression plasmid. The development of hyperglycaemia was monitored for up to 4 weeks following immunisation and alterations in the BCM were assessed weekly by non-invasive in vivo bioluminescence intensity (BLI). In parallel, BCM, islet cell proliferation and apoptosis were evaluated by immunocytochemistry. Alterations in PAX4- and PAX4R129W-mediated islet gene expression were investigated by microarray profiling. PAX4 preservation of endoplasmic reticulum (ER) homeostasis was assessed using thapsigargin, electron microscopy and intracellular calcium measurements. [Results]: PAX4 overexpression blunted EAD, whereas the diabetes-linked mutant variant PAX4R129W did not convey protection. PAX4-expressing islets exhibited reduced insulitis and decreased beta cell apoptosis, correlating with diminished DNA damage and increased islet cell proliferation. Microarray profiling revealed that PAX4 but not PAX4R129W targeted expression of genes implicated in cell cycle and ER homeostasis. Consistent with the latter, islets overexpressing PAX4 were protected against thapsigargin-mediated ER-stress-related apoptosis. Luminal swelling associated with ER stress induced by thapsigargin was rescued in PAX4-overexpressing beta cells, correlating with preserved cytosolic calcium oscillations in response to glucose. In contrast, RNA interference mediated repression of PAX4-sensitised MIN6 cells to thapsigargin cell death. [Conclusions/interpretation]: The coordinated regulation of distinct cellular pathways particularly related to ER homeostasis by PAX4 not achieved by the mutant variant PAX4R129W alleviates beta cell degeneration and protects against diabetes mellitus. The raw data for the RNA microarray described herein are accessible in the Gene Expression Omnibus database under accession number GSE62846.This work was funded by grants from the Consejeria de Salud, Fundacion Publica Andaluza Progreso y Salud, Junta de Andalucia (PI-0727-2010 to BRG and PI-0085-2013 to PIL), Consejeria de Economia, Innovacion y Ciencia (P10.CTS.6359 to BRG), Ministerio de Ciencia e Innovacion (BFU2013-42789-P to IQ) and the Ministerio de Economia y Competidividad, Instituto de Salud Carlos III co-funded by Fondos FEDER (PI10/00871 and PI13/00593 to BRG). NC-V is supported by a JDRF subsidy (17-2013-372 to BRG.). AM-M is a recipient of a Miguel Servet grant (CP14/00105) from the Instituto de Salud Carlos III co-funded by Fondos FEDER and EF-M is a recipient of a Juan de la Cierva Fellowship. PM is supported by Swiss National Science Foundation grant 310030-141162, and the European Union grant IMIDIA, C2008-T7. BOB is supported by grants from the Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Republic of Singapore.Peer Reviewe

Predicting the accuracy of multiple sequence alignment algorithms by using computational intelligent techniques

Multiple sequence alignments (MSAs) have become one of the most studied approaches in bioinformatics to perform other outstanding tasks such as structure prediction, biological function analysis or next-generation sequencing. However, current MSA algorithms do not always provide consistent solutions, since alignments become increasingly difficult when dealing with low similarity sequences. As widely known, these algorithms directly depend on specific features of the sequences, causing relevant influence on the alignment accuracy. Many MSA tools have been recently designed but it is not possible to know in advance which one is the most suitable for a particular set of sequences. In this work, we analyze some of the most used algorithms presented in the bibliography and their dependences on several features. A novel intelligent algorithm based on least square support vector machine is then developed to predict how accurate each alignment could be, depending on its analyzed features. This algorithm is performed with a dataset of 2180 MSAs. The proposed system first estimates the accuracy of possible alignments. The most promising methodologies are then selected in order to align each set of sequences. Since only one selected algorithm is run, the computational time is not excessively increased