Bioinformatics applied to human genomics and proteomics: development of algorithms and methods for the discovery of molecular signatures derived from omic data and for the construction of co-expression and interaction networks

[EN] The present PhD dissertation develops and applies Bioinformatic methods and tools to address key current problems in the analysis of human omic data. This PhD has been organised by main objectives into four different chapters focused on: (i) development of an algorithm for the analysis of changes and heterogeneity in large-scale omic data; (ii) development of a method for non-parametric feature selection; (iii) integration and analysis of human protein-protein interaction networks and (iv) integration and analysis of human co-expression networks derived from tissue expression data and evolutionary profiles of proteins. In the first chapter, we developed and tested a new robust algorithm in R, called DECO, for the discovery of subgroups of features and samples within large-scale omic datasets, exploring all feature differences possible heterogeneity, through the integration of both data dispersion and predictor-response information in a new statistic parameter called h (heterogeneity score). In the second chapter, we present a simple non-parametric statistic to measure the cohesiveness of categorical variables along any quantitative variable, applicable to feature selection in all types of big data sets. In the third chapter, we describe an analysis of the human interactome integrating two global datasets from high-quality proteomics technologies: HuRI (a human protein-protein interaction network generated by a systematic experimental screening based on Yeast-Two-Hybrid technology) and Cell-Atlas (a comprehensive map of subcellular localization of human proteins generated by antibody imaging). This analysis aims to create a framework for the subcellular localization characterization supported by the human protein-protein interactome. In the fourth chapter, we developed a full integration of three high-quality proteome-wide resources (Human Protein Atlas, OMA and TimeTree) to generate a robust human co-expression network across tissues assigning each human protein along the evolutionary timeline. In this way, we investigate how old in evolution and how correlated are the different human proteins, and we place all them in a common interaction network. As main general comment, all the work presented in this PhD uses and develops a wide variety of bioinformatic and statistical tools for the analysis, integration and enlighten of molecular signatures and biological networks using human omic data. Most of this data corresponds to sample cohorts generated in recent biomedical studies on specific human diseases

Evolutionary hallmarks of the human proteome: chasing the age and coregulation of protein-coding genes

Abstract Background The development of large-scale technologies for quantitative transcriptomics has enabled comprehensive analysis of the gene expression profiles in complete genomes. RNA-Seq allows the measurement of gene expression levels in a manner far more precise and global than previous methods. Studies using this technology are altering our view about the extent and complexity of the eukaryotic transcriptomes. In this respect, multiple efforts have been done to determine and analyse the gene expression patterns of human cell types in different conditions, either in normal or pathological states. However, until recently, little has been reported about the evolutionary marks present in human protein-coding genes, particularly from the combined perspective of gene expression and protein evolution. Results We present a combined analysis of human protein-coding gene expression profiling and time-scale ancestry mapping, that places the genes in taxonomy clades and reveals eight evolutionary major steps (“hallmarks”), that include clusters of functionally coherent proteins. The human expressed genes are analysed using a RNA-Seq dataset of 116 samples from 32 tissues. The evolutionary analysis of the human proteins is performed combining the information from: (i) a database of orthologous proteins (OMA), (ii) the taxonomy mapping of genes to lineage clades (from NCBI Taxonomy) and (iii) the evolution time-scale mapping provided by TimeTree (Timescale of Life). The human protein-coding genes are also placed in a relational context based in the construction of a robust gene coexpression network, that reveals tighter links between age-related protein-coding genes and finds functionally coherent gene modules. Conclusions Understanding the relational landscape of the human protein-coding genes is essential for interpreting the functional elements and modules of our active genome. Moreover, decoding the evolutionary history of the human genes can provide very valuable information to reveal or uncover their origin and function

Análisis del efecto de las modificaciones post-traduccionales inducidas por óxido nítrico en células troncales embrionarias de ratón.

Motivación: La nitrosilación es un tipo de modificación postraduccional de las proteínas derivada de la presencia de óxido nítrico (NO) en el medio, y cuya acción se centra en residuos de tirosina y cisteína. Sin embargo, no se sabe con certeza como afectan estas modificaciones a la expresión génica en células embrionarias cuando se ven afectados factores de transcripción, histonas o determinados complejos proteicos. De esta forma, podría jugar un papel relevante en procesos de mantenimiento de pluripotencia o de diferenciación celular.En este proyecto se pretende analizar la influencia a nivel genómico de este patrón de expresión diferencial, para así discenir el papel que desempeña la nitrosilación en los mecanismos de regulación transcripcional de células embrionarias. Métodos: Se realizó un ensayo ChIP on chip, para visualizar qué regiones génicas se ven afectadas por el NO en forma de sus dos modificaciones principales (Schnetz MP 2010). Este ensayo permite determinar qué regiones génicas se han visto afectadas por factores de transcripción modificados respecto a una muestra control. Con los resultados de este ensayo, se han iniciado diferentes procedimientos bioinformáticos para la normalización, cribado y visualización de las regiones a las que se unen factores de transcripción nitrosilados, así como identificación de motivos modificados y factores implicados en el proceso. Resultados: Resultados preeliminares demostraron que las proteínas nitrosiladas en residuos de cisteína ocupan regiones reguladoras de genes implicados en procesos de autorenovación y diferenciación celular. Posteriormente, se ha procedido al análisis de genes concretos implicados, motivos y factores de transcripción afectados. Conclusiones: Estamos a la espera de obtener resultados definitivos para realizar las valoraciones oportunas

Motivational approaches to intellectual vice

Despite the now considerable literature on intellectual virtue, there remains relatively little philosophical discussion of intellectual vice. What discussion there is has been shaped by a powerful assumption—that, just as intellectual virtue requires that we are motivated by epistemic goods, intellectual vice requires that we aren't. In this paper, I demonstrate that this assumption is false: motivational approaches cannot explain a range of intuitive cases of intellectual vice. The popularity of the assumption is accounted for by its being a manifestation of a more general understanding of vice as an inversion or mirror image of virtue. I call this the inversion thesis, and argue that the failure of the motivational approach to vice exposes its limitations. I conclude by suggesting that recognizing these limitations can help to encourage philosophical interest in intellectual vice

Evolutionary hallmarks of the human proteome: Chasing the age and coregulation of protein-coding genes

[Background]: The development of large-scale technologies for quantitative transcriptomics has enabled comprehensive analysis of the gene expression profiles in complete genomes. RNA-Seq allows the measurement of gene expression levels in a manner far more precise and global than previous methods. Studies using this technology are altering our view about the extent and complexity of the eukaryotic transcriptomes. In this respect, multiple efforts have been done to determine and analyse the gene expression patterns of human cell types in different conditions, either in normal or pathological states. However, until recently, little has been reported about the evolutionary marks present in human protein-coding genes, particularly from the combined perspective of gene expression and protein evolution. [Results]: We present a combined analysis of human protein-coding gene expression profiling and time-scale ancestry mapping, that places the genes in taxonomy clades and reveals eight evolutionary major steps (>hallmarks>), that include clusters of functionally coherent proteins. The human expressed genes are analysed using a RNA-Seq dataset of 116 samples from 32 tissues. The evolutionary analysis of the human proteins is performed combining the information from: (i) a database of orthologous proteins (OMA), (ii) the taxonomy mapping of genes to lineage clades (from NCBI Taxonomy) and (iii) the evolution time-scale mapping provided by TimeTree (Timescale of Life). The human protein-coding genes are also placed in a relational context based in the construction of a robust gene coexpression network, that reveals tighter links between age-related protein-coding genes and finds functionally coherent gene modules. [Conclusions]: Understanding the relational landscape of the human protein-coding genes is essential for interpreting the functional elements and modules of our active genome. Moreover, decoding the evolutionary history of the human genes can provide very valuable information to reveal or uncover their origin and function.We acknowledge the funding provided to Dr. J. De Las Rivas group by the Local Government, “Junta de Castilla y Leon” (JCyL, Valladolid, Spain, grant number BIO/SA08/14); and by the Spanish Government, “Ministerio de Economia y Competitividad” (MINECO) with grants of the ISCiii co-funded by FEDER (grant references PI12/00624 and PI15/00328). We also acknowledge a research grant to K.P. Lopes as visiting PhD student at the CiC-IMBCC (from January 2015 to January 2016) provided by the Brazilian National Council of Technological and Scientific Development (CNPq). We also acknowledge a PhD research grant to F.J. Campos-Laborie (“Ayudas a la contratación de Personal Investigador”) provided by the JCyL with the support of the “Fondo Social Europeo” (FSE).Peer Reviewe

Additional file 4: of Evolutionary hallmarks of the human proteome: chasing the age and coregulation of protein-coding genes

TABLE (.XLS) including the data and IDs of the 17,437 human protein-coding genes included in each of the eight evolutionary stages. (XLSX 1432 kb