64,558 research outputs found

    A Path to Implement Precision Child Health Cardiovascular Medicine.

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    Congenital heart defects (CHDs) affect approximately 1% of live births and are a major source of childhood morbidity and mortality even in countries with advanced healthcare systems. Along with phenotypic heterogeneity, the underlying etiology of CHDs is multifactorial, involving genetic, epigenetic, and/or environmental contributors. Clear dissection of the underlying mechanism is a powerful step to establish individualized therapies. However, the majority of CHDs are yet to be clearly diagnosed for the underlying genetic and environmental factors, and even less with effective therapies. Although the survival rate for CHDs is steadily improving, there is still a significant unmet need for refining diagnostic precision and establishing targeted therapies to optimize life quality and to minimize future complications. In particular, proper identification of disease associated genetic variants in humans has been challenging, and this greatly impedes our ability to delineate gene-environment interactions that contribute to the pathogenesis of CHDs. Implementing a systematic multileveled approach can establish a continuum from phenotypic characterization in the clinic to molecular dissection using combined next-generation sequencing platforms and validation studies in suitable models at the bench. Key elements necessary to advance the field are: first, proper delineation of the phenotypic spectrum of CHDs; second, defining the molecular genotype/phenotype by combining whole-exome sequencing and transcriptome analysis; third, integration of phenotypic, genotypic, and molecular datasets to identify molecular network contributing to CHDs; fourth, generation of relevant disease models and multileveled experimental investigations. In order to achieve all these goals, access to high-quality biological specimens from well-defined patient cohorts is a crucial step. Therefore, establishing a CHD BioCore is an essential infrastructure and a critical step on the path toward precision child health cardiovascular medicine

    Editorial : Autophagy : from big data to physiological significance

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    Autophagy is a fundamental catabolic process where cytoplasmic components are sequestered into double-membrane vesicles called autophagosomes, which then fuse with lysosomes and their content is degraded. Our knowledge about autophagy sharply increased during the last decade. This significant progress helped us to understand better the molecular mechanisms of autophagy and to elucidate its role in health and disease. This special issue contains a collection of three original research papers and 12 review articles covering a broad range of topics highlighting how big data and screening approaches can help toward uncovering the molecular mechanisms of autophagy

    Identification of "pathologs" (disease-related genes) from the RIKEN mouse cDNA dataset using human curation plus FACTS, a new biological information extraction system

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    BACKGROUND: A major goal in the post-genomic era is to identify and characterise disease susceptibility genes and to apply this knowledge to disease prevention and treatment. Rodents and humans have remarkably similar genomes and share closely related biochemical, physiological and pathological pathways. In this work we utilised the latest information on the mouse transcriptome as revealed by the RIKEN FANTOM2 project to identify novel human disease-related candidate genes. We define a new term "patholog" to mean a homolog of a human disease-related gene encoding a product (transcript, anti-sense or protein) potentially relevant to disease. Rather than just focus on Mendelian inheritance, we applied the analysis to all potential pathologs regardless of their inheritance pattern. RESULTS: Bioinformatic analysis and human curation of 60,770 RIKEN full-length mouse cDNA clones produced 2,578 sequences that showed similarity (70–85% identity) to known human-disease genes. Using a newly developed biological information extraction and annotation tool (FACTS) in parallel with human expert analysis of 17,051 MEDLINE scientific abstracts we identified 182 novel potential pathologs. Of these, 36 were identified by computational tools only, 49 by human expert analysis only and 97 by both methods. These pathologs were related to neoplastic (53%), hereditary (24%), immunological (5%), cardio-vascular (4%), or other (14%), disorders. CONCLUSIONS: Large scale genome projects continue to produce a vast amount of data with potential application to the study of human disease. For this potential to be realised we need intelligent strategies for data categorisation and the ability to link sequence data with relevant literature. This paper demonstrates the power of combining human expert annotation with FACTS, a newly developed bioinformatics tool, to identify novel pathologs from within large-scale mouse transcript datasets

    Does neuroinflammation turn on the flame in Alzheimer's disease? Focus on astrocytes

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    Data from animal models and Alzheimer's disease (AD) subjects provide clear evidence for an activation of inflammatory pathways during the pathogenetic course of such illness. Biochemical and neuropathological studies highlighted an important cause/effect relationship between inflammation and AD progression, revealing a wide range of genetic, cellular, and molecular changes associated with the pathology. In this context, glial cells have been proved to exert a crucial role. These cells, in fact, undergo important morphological and functional changes and are now considered to be involved in the onset and progression of AD. In particular, astrocytes respond quickly to pathology with changes that have been increasingly recognized as a continuum, with potentially beneficial and/or negative consequences. Although it is now clear that activated astrocytes trigger the neuroinflammatory process, however, the precise mechanisms have not been completely elucidated. Neuroinflammation is certainly a multi-faceted and complex phenomenon and, especially in the early stages, exerts a reparative intent. However, for reasons not yet all well known, this process goes beyond the physiologic control and contributes to the exacerbation of the damage. Here we scrutinize some evidence supporting the role of astrocytes in the neuroinflammatory process and the possibility that these cells could be considered a promising target for future AD therapies

    Deciphering the Mechanisms of Developmental Disorders (DMDD): a new programme for phenotyping embryonic lethal mice

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    International efforts to test gene function in the mouse by the systematic knockout of each gene are creating many lines in which embryonic development is compromised. These homozygous lethal mutants represent a potential treasure trove for the biomedical community. Developmental biologists could exploit them in their studies of tissue differentiation and organogenesis; for clinical researchers they offer a powerful resource for investigating the origins of developmental diseases that affect newborns. Here, we outline a new programme of research in the UK aiming to kick-start research with embryonic lethal mouse lines. The 'Deciphering the Mechanisms of Developmental Disorders' (DMDD) programme has the ambitious goal of identifying all embryonic lethal knockout lines made in the UK over the next 5 years, and will use a combination of comprehensive imaging and transcriptomics to identify abnormalities in embryo structure and development. All data will be made freely available, enabling individual researchers to identify lines relevant to their research. The DMDD programme will coordinate its work with similar international efforts through the umbrella of the International Mouse Phenotyping Consortium [see accompanying Special Article (Adams et al., 2013)] and, together, these programmes will provide a novel database for embryonic development, linking gene identity with molecular profiles and morphology phenotypes

    Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems

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    A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein–protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein–protein interactions, or providing modeled structural data for drug discovery targeting protein–protein interactions.Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft

    Summaries of plenary, symposia, and oral sessions at the XXII World Congress of Psychiatric Genetics, Copenhagen, Denmark, 12-16 October 2014

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    The XXII World Congress of Psychiatric Genetics, sponsored by the International Society of Psychiatric Genetics, took place in Copenhagen, Denmark, on 12-16 October 2014. A total of 883 participants gathered to discuss the latest findings in the field. The following report was written by student and postdoctoral attendees. Each was assigned one or more sessions as a rapporteur. This manuscript represents topics covered in most, but not all of the oral presentations during the conference, and contains some of the major notable new findings reported

    Impact of Sleep and Circadian Disruption on Energy Balance and Diabetes: A Summary of Workshop Discussions

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    A workshop was held at the National Institute for Diabetes and Digestive and Kidney Diseases with a focus on the impact of sleep and circadian disruption on energy balance and diabetes. The workshop identified a number of key principles for research in this area and a number of specific opportunities. Studies in this area would be facilitated by active collaboration between investigators in sleep/circadian research and investigators in metabolism/diabetes. There is a need to translate the elegant findings from basic research into improving the metabolic health of the American public. There is also a need for investigators studying the impact of sleep/circadian disruption in humans to move beyond measurements of insulin and glucose and conduct more in-depth phenotyping. There is also a need for the assessments of sleep and circadian rhythms as well as assessments for sleep-disordered breathing to be incorporated into all ongoing cohort studies related to diabetes risk. Studies in humans need to complement the elegant short-term laboratory-based human studies of simulated short sleep and shift work etc. with studies in subjects in the general population with these disorders. It is conceivable that chronic adaptations occur, and if so, the mechanisms by which they occur needs to be identified and understood. Particular areas of opportunity that are ready for translation are studies to address whether CPAP treatment of patients with pre-diabetes and obstructive sleep apnea (OSA) prevents or delays the onset of diabetes and whether temporal restricted feeding has the same impact on obesity rates in humans as it does in mice
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