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Background: Interactions between transcription factors and DNA lie at the centre of many biological processes including DNA recombination, replication, repair and transcription. Most bacteria encode diverse proteins that act as transcription factors to regulate various traits. Several technologies for identifying protein-DNA interactions at the genomic level have been developed. Bind-n-seq is a high-throughput in vitro method first deployed to analyse DNA interactions associated with eukaryotic zinc-finger proteins. The method has three steps (i) binding protein to a randomised oligonucleotide DNA target library, (ii) deep sequencing of bound oligonucleotides, and (iii) a computational algorithm to define motifs among the sequences. The classical Bind-n-seq strategy suffers from several limitations including a lengthy wet laboratory protocol and a computational algorithm that is difficult to use. We introduce here an improved, rapid, and simplified Bind-n-seq protocol coupled with a user-friendly downstream data analysis and handling algorithm, which has been optimized for bacterial target proteins. We validate this new protocol by showing the successful characterisation of the DNA-binding specificities of YipR (YajQ interacting protein regulator), a well-known transcriptional regulator of virulence genes in the bacterial phytopathogen Xanthomonas campestris pv. campestris (Xcc)., Results: The improved Bind-n-seq approach identified several DNA binding motif sequences for YipR, in particular the CCCTCTC motif, which were located in the promoter regions of 1320 Xcc genes. Informatics analysis revealed that many of these genes regulate functions associated with virulence, motility, and biofilm formation and included genes previously found involved in virulence. Additionally, electromobility shift assays show that YipR binds to the promoter region of XC_2633 in a CCCTCTC motif-dependent manner., Conclusion: We present a new and rapid Bind-n-seq protocol that should be useful to investigate DNA-binding proteins in bacteria. The analysis of YipR DNA binding using this protocol identifies a novel DNA sequence motif in the promoter regions of target genes that define the YipR regulon.

Exposomics is a field of research which is receiving growing attention. In this work, we characterize the exposome research landscape and update our previous study of formal knowledge representation approaches to this field. We applied a deductive analysis using the National Center for Biomedical Ontology Recommender for comparability of the results generated from a literature dataset and newly available ontologies with our previously published work. We highlight the changes in ontology recommendations.

Pre-eclampsia is a pregnancy condition affecting 5-10% of pregnancies, and it is the leading cause of death in pregnancy associated with increased risk of cardiovascular disease later in life. Despite research, the pathogenesis of pre-eclampsia is still poorly understood. In this paper, we investigate the overlapping pathogenic mechanisms between pre-eclampsia and adult hypertension using an integrative biomedical informatics strategy that combined text mining techniques to identify genes and proteins, with geneset analyses, generating knowledge on the pathways and mechanisms involved in these conditions. We identified several overlapping pathogenic pathways/systems including metabolic pathways, developmental biology pathways, immune system, haemostasis, tyrosine kinase pathways, extracellular matrix and oxidative stress pathways. This bioinformatics approach could be applied for investigating mechanistic pathways of other disorders.

Impaired therapeutic responses to anti-inflammatory glucocorticoids (GC) in chronic respiratory diseases are partly attributable to interleukins and transforming growth factor β1 (TGF-β1). However, previous efforts to prevent induction of GC insensitivity by targeting established canonical and non-canonical TGF-β1 pathways have been unsuccessful. Here we elucidate a TGF-β1 signaling pathway modulating GC activity that involves LIM domain kinase 2-mediated phosphorylation of cofilin1. Severe, steroid-resistant asthmatic airway epithelium showed increased levels of immunoreactive phospho-cofilin1. Phospho-cofilin1 was implicated in the activation of phospholipase D (PLD) to generate the effector(s) (lyso)phosphatidic acid, which mimics the TGF-β1-induced GC insensitivity. TGF-β1 induction of the nuclear hormone receptor corepressor, SMRT (NCOR2), was dependent on cofilin1 and PLD activities. Depletion of SMRT prevented GC insensitivity. This pathway for GC insensitivity offers several promising drug targets that potentially enable a safer approach to the modulation of TGF-β1 in chronic inflammatory diseases than is afforded by global TGF-β1 inhibition., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Scientific advancement and the development of new research fields bring uncertainties about what the current topics of research emphasis are and thus, what new knowledge might need to be represented. The exposome is an example of one such new field for which these uncertainties exist. The exposome is the analogue to the genome, from an environmental exposure perspective; research on the exposome has gained momentum only since 2011. In this work, we propose a generally applicable methodology that aims to characterise the landscape of a new research area based on linguistic analysis of its associated publications. Using abstracts of 261 exposome research articles, we illustrate a methodology that combines (1) inductive analysis based on word frequency counts, and term analysis to identify the topics, methods and applications of the new field and (2) deductive analysis using the NCBO Ontology Recommender to identify to what extent this new area is covered by current knowledge representation tools. Applying this method to the exposome literature, we uncover both the current focus of exposome research and the ontologies that are most relevant to the domain.

Biomedical informatics plays a key role in the development of precision medicine and other new technology-enabled health disciplines. In this context, the exposome (defined as the whole set of exposures on an individual) has become a relevant topic. Although most of the research work in the exposome area has been conducted around the physical and chemical world, we sustain that in an increasingly digitised world more attention should be paid to the digital component of the exposome derived from the interactions of individuals with the digital world. We define this "Digital Exposome" as the whole set of tools and platforms that an individual use and the activities and processes that an individual engages with as part of his/her digital life. In addition we support that biomedical informatics can and should lead research in this area.

Background: Left ventricular (LV) hypertrophy is a risk factor for cardiovascular death, but the genetic factors determining LV size and predisposition to hypertrophy are not well understood. We have previously linked the quantitative trait locus cardiac mass 22 (Cm22) on chromosome 2 with cardiac hypertrophy independent of blood pressure in the spontaneously hypertensive rat. From an original cross of spontaneously hypertensive rat with F344 rats, we derived a normotensive polygenic model of spontaneous cardiac hypertrophy, the hypertrophic heart rat (HHR) and its control strain, the normal heart rat (NHR)., Methods and Results: To identify the genes and molecular mechanisms underlying spontaneous LV hypertrophy we sequenced the HHR genome with special focus on quantitative trait locus Cm22. For correlative analyses of function, we measured global RNA transcripts in LV of neonatal HHR and NHR and 198 neonatal rats of an HHR × NHR F2 crossbred population. Only one gene within locus Cm22 was differentially expressed in the parental generation: tripartite motif-containing 55 (Trim55), with mRNA downregulation in HHR (P < 0.05) and reduced protein expression. Trim55 mRNA levels were negatively correlated with LV mass in the F2 cross (r = -0.16, P = 0.025). In exon nine of Trim55 in HHR, we found one missense mutation that functionally alters protein structure. This mutation was strongly associated with Trim55 mRNA expression in F2 rats (F = 10.35, P < 0.0001). Similarly, in humans, we found reduced Trim55 expression in hearts of subjects with idiopathic dilated cardiomyopathy., Conclusion: Our study suggests that the Trim55 gene, located in Cm22, is a novel candidate gene for polygenic LV hypertrophy independent of blood pressure.

The development of new methods, devices and apps for self-monitoring have enabled the extension of the application of these approaches for consumer health and research purposes. The increase in the number and variety of devices has generated a complex scenario where reporting guidelines and data exchange formats will be needed to ensure the quality of the information and the reproducibility of results of the experiments. Based on the Minimal Information for Self Monitoring Experiments (MISME) reporting guideline we have developed an XML format (MISME-ML) to facilitate data exchange for self monitoring experiments. We have also developed a sample instance to illustrate the concept and a Java MISME-ML validation tool. The implementation and adoption of these tools should contribute to the consolidation of a set of methods that ensure the reproducibility of self monitoring experiments for research purposes.

Objectives: Given the quickening speed of discovery of variant disease drivers from combined patient genotype and phenotype data, the objective is to provide methodology using big data technology to support the definition of deep phenotypes in medical records., Methods: As the vast stores of genomic information increase with next generation sequencing, the importance of deep phenotyping increases. The growth of genomic data and adoption of Electronic Health Records (EHR) in medicine provides a unique opportunity to integrate phenotype and genotype data into medical records. The method by which collections of clinical findings and other health related data are leveraged to form meaningful phenotypes is an active area of research. Longitudinal data stored in EHRs provide a wealth of information that can be used to construct phenotypes of patients. We focus on a practical problem around data integration for deep phenotype identification within EHR data. The use of big data approaches are described that enable scalable markup of EHR events that can be used for semantic and temporal similarity analysis to support the identification of phenotype and genotype relationships., Conclusions: Stead and colleagues' 2005 concept of using light standards to increase the productivity of software systems by riding on the wave of hardware/processing power is described as a harbinger for designing future healthcare systems. The big data solution, using flexible markup, provides a route to improved utilization of processing power for organizing patient records in genotype and phenotype research.

The environment's contribution to health has been conceptualized as the exposome. Biomedical research interest in environmental exposures as a determinant of physiopathological processes is rising as such data increasingly become available. The panoply of miniaturized sensing devices now accessible and affordable for individuals to use to monitor a widening range of parameters opens up a new world of research data. Biomedical informatics (BMI) must provide a coherent framework for dealing with multi-scale population data including the phenome, the genome, the exposome, and their interconnections. The combination of these more continuous, comprehensive, and personalized data sources requires new research and development approaches to data management, analysis, and visualization. This article analyzes the implications of a new paradigm for the discipline of BMI, one that recognizes genome, phenome, and exposome data and their intricate interactions as the basis for biomedical research now and for clinical care in the near future.

Every year new databases and tools for the storage and analysis of biological data are developed, updated, and discontinued. For this reason it is very important to have a clear picture of the major repositories providing information about the availability of these databases and tools as well as a brief description of them. This chapter provides an overview of the most important information sources which can guide researchers through the process of selecting databases and tools of interest for immunoinformatics and systems biology in personalized medicine. As an example of a particular resource of interest that combines a curated database and tools for data analysis, this chapter also includes a description of InnateDB. This database offers access to curated information relative to the innate immune response in a systems biology context.

The development of personalized medicine is tightly linked with the correct exploitation of molecular data, especially those associated with the genome sequence along with these use of genomic data there is an increasing demand to share these data for research purposes. Transition of clinical data to research is based in the anonymization of these data so the patient cannot be identified, the use of genomic data poses a great challenge because its nature of identifying data. In this work we have analyzed current methods for genome anonymization and propose a one way encryption method that may enable the process of genomic data sharing accessing only to certain regions of genomes for research purposes.

This work describes a new three-dimensional model for the integration and visualization of comprehensive individual health-related information. The model could be used to represent longitudinal datasets corresponding to any kind of biomedical data (genome, phenome, exposome) relative to a single individual or to populations. Analogously with geographical information systems this model includes different layers at each time point, which are associated with different sets of biomedical data. Each data element represented in this model receives two angular and one linear (temporal) coordinates. This new model could be able to represent virtually any kind of biomedical data including those captured in real-time such as self-monitoring activities or other exposome-related information.

Background: Colon cancer patients with the same stage show diverse clinical behavior due to tumor heterogeneity. We aimed to discover distinct classes of tumors based on microarray expression patterns, to analyze whether the molecular classification correlated with the histopathological stages or other clinical parameters and to study differences in the survival., Methods: Hierarchical clustering was performed for class discovery in 88 colon tumors (stages I to IV). Pathways analysis and correlations between clinical parameters and our classification were analyzed. Tumor subtypes were validated using an external set of 78 patients. A 167 gene signature associated to the main subtype was generated using the 3-Nearest-Neighbor method. Coincidences with other prognostic predictors were assesed., Results: Hierarchical clustering identified four robust tumor subtypes with biologically and clinically distinct behavior. Stromal components (p < 0.001), nuclear β-catenin (p = 0.021), mucinous histology (p = 0.001), microsatellite-instability (p = 0.039) and BRAF mutations (p < 0.001) were associated to this classification but it was independent of Dukes stages (p = 0.646). Molecular subtypes were established from stage I. High-stroma-subtype showed increased levels of genes and altered pathways distinctive of tumour-associated-stroma and components of the extracellular matrix in contrast to Low-stroma-subtype. Mucinous-subtype was reflected by the increased expression of trefoil factors and mucins as well as by a higher proportion of MSI and BRAF mutations. Tumor subtypes were validated using an external set of 78 patients. A 167 gene signature associated to the Low-stroma-subtype distinguished low risk patients from high risk patients in the external cohort (Dukes B and C:HR = 8.56(2.53-29.01); Dukes B,C and D:HR = 1.87(1.07-3.25)). Eight different reported survival gene signatures segregated our tumors into two groups the Low-stroma-subtype and the other tumor subtypes., Conclusions: We have identified novel molecular subtypes in colon cancer with distinct biological and clinical behavior that are established from the initiation of the tumor. Tumor microenvironment is important for the classification and for the malignant power of the tumor. Differential gene sets and biological pathways characterize each tumor subtype reflecting underlying mechanisms of carcinogenesis that may be used for the selection of targeted therapeutic procedures. This classification may contribute to an improvement in the management of the patients with CRC and to a more comprehensive prognosis.

Nanoinformatics has recently emerged to address the need of computing applications at the nano level. In this regard, the authors have participated in various initiatives to identify its concepts, foundations and challenges. While nanomaterials open up the possibility for developing new devices in many industrial and scientific areas, they also offer breakthrough perspectives for the prevention, diagnosis and treatment of diseases. In this paper, we analyze the different aspects of nanoinformatics and suggest five research topics to help catalyze new research and development in the area, particularly focused on nanomedicine. We also encompass the use of informatics to further the biological and clinical applications of basic research in nanoscience and nanotechnology, and the related concept of an extended "nanotype" to coalesce information related to nanoparticles. We suggest how nanoinformatics could accelerate developments in nanomedicine, similarly to what happened with the Human Genome and other -omics projects, on issues like exchanging modeling and simulation methods and tools, linking toxicity information to clinical and personal databases or developing new approaches for scientific ontologies, among many others.

Background: Severe disease caused by 2009 pandemic influenza A/H1N1virus is characterized by the presence of hypercytokinemia. The origin of the exacerbated cytokine response is unclear. As observed previously, uncontrolled influenza virus replication could strongly influence cytokine production. The objective of the present study was to evaluate the relationship between host cytokine responses and viral levels in pandemic influenza critically ill patients., Methods: Twenty three patients admitted to the ICU with primary viral pneumonia were included in this study. A quantitative PCR based method targeting the M1 influenza gene was developed to quantify pharyngeal viral load. In addition, by using a multiplex based assay, we systematically evaluated host cytokine responses to the viral infection at admission to the ICU. Correlation studies between cytokine levels and viral load were done by calculating the Spearman correlation coefficient., Results: Fifteen patients needed of intubation and ventilation, while eight did not need of mechanical ventilation during ICU hospitalization. Viral load in pharyngeal swabs was 300 fold higher in the group of patients with the worst respiratory condition at admission to the ICU. Pharyngeal viral load directly correlated with plasma levels of the pro-inflammatory cytokines IL-6, IL-12p70, IFN-γ, the chemotactic factors MIP-1β, GM-CSF, the angiogenic mediator VEGF and also of the immuno-modulatory cytokine IL-1ra (p < 0.05). Correlation studies demonstrated also the existence of a significant positive association between the levels of these mediators, evidencing that they are simultaneously regulated in response to the virus., Conclusions: Severe respiratory disease caused by the 2009 pandemic influenza virus is characterized by the existence of a direct association between viral replication and host cytokine response, revealing a potential pathogenic link with the severe disease caused by other influenza subtypes such as H5N1.

Introduction: Pandemic A/H1N1/2009 influenza causes severe lower respiratory complications in rare cases. The association between host immune responses and clinical outcome in severe cases is unknown., Methods: We utilized gene expression, cytokine profiles and generation of antibody responses following hospitalization in 19 critically ill patients with primary pandemic A/H1N1/2009 influenza pneumonia for identifying host immune responses associated with clinical outcome. Ingenuity pathway analysis 8.5 (IPA) (Ingenuity Systems, Redwood City, CA) was used to select, annotate and visualize genes by function and pathway (gene ontology). IPA analysis identified those canonical pathways differentially expressed (P < 0.05) between comparison groups. Hierarchical clustering of those genes differentially expressed between groups by IPA analysis was performed using BRB-Array Tools v.3.8.1., Results: The majority of patients were characterized by the presence of comorbidities and the absence of immunosuppressive conditions. pH1N1 specific antibody production was observed around day 9 from disease onset and defined an early period of innate immune response and a late period of adaptive immune response to the virus. The most severe patients (n = 12) showed persistence of viral secretion. Seven of the most severe patients died. During the late phase, the most severe patient group had impaired expression of a number of genes participating in adaptive immune responses when compared to less severe patients. These genes were involved in antigen presentation, B-cell development, T-helper cell differentiation, CD28, granzyme B signaling, apoptosis and protein ubiquitination. Patients with the poorest outcomes were characterized by proinflammatory hypercytokinemia, along with elevated levels of immunosuppressory cytokines (interleukin (IL)-10 and IL-1ra) in serum., Conclusions: Our findings suggest an impaired development of adaptive immunity in the most severe cases of pandemic influenza, leading to an unremitting cycle of viral replication and innate cytokine-chemokine release. Interruption of this deleterious cycle may improve disease outcome.

Recent advances in Nanotechnology are slowly extending their influence in biomedical research and clinical practice (nanomedicine). The authors have recently been granted with an European Commission research project, Action-GRID. This initiative will review current developments in nanomedicine, and analyze the area of nanoinformatics. Its main outcome will be the identification of needs and the discussion of future challenges and priorities for Biomedical Informatics in terms of information processing in nanomedicine and regenerative medicine.

For an effective integration of microarray-based technologies in clinical settings a number of contributions from biomedical informatics technologies and techniques are needed to facilitate the improvement of the phases of experimental design, image analysis, data management, annotation, and analysis. In this communication we briefly present the state-of-the-art in the application of biomedical informatics to laboratories conducting microarray experiments and how our unit is coping with these requirements imposed by the routine clinical work of the National Centre of Microbiology, a reference laboratory for the Spanish Health System.