Identificación de módulos asociados a fenotipos patológicos

Trabajo fin de máster en Bioinformática y Biología ComputacionalCopy Number Variations (CNVs) are genomic structural variations frequently observed in healthy individuals, but can also lead to disease. They are the etiological cause of many rare genomic disorders that affect a large number of people in population, constituting a major public health problem. Unlike other small mutations, deleterious CNVs can reach millions of nucleotides containing several genes and other functional DNA regions. Many of these CNVs have yet unknown relationships to the phenotypes observed in patients. Therefore, the identification of the potentially affected molecular and genetical mechanisms in the CNVs and their relation with certain phenotypes in patients with rare deleterious disorders, nowadays, remains as a big challenge for clinical geneticists. Based on different datasets that links phenotypes, patients and genomic loci, two systemic approaches were used to understand the molecular basis that underlie those CNVs. Firstly, a functional analysis of the genes coded in these regions is carried out to realise which are the biological processes affected by the CNVs mutations thus to the phenotypes. Secondly, a network propagation analysis is done to expand the knowledge of the query genes and its interactome context. The results obtained for a cluster of patients and a number of phenotypes of clinical interest are briefly explaine

Chapter Integrative Systems Biology Resources and Approaches in Disease Analytics

Currently, our analytical competences are struggling to keep-up the pace of in-deep analysis of all generated large-scale data resultant of high-throughput omics platforms. While, a substantial effort was spent on methods enhancement regarding technical aspects across many detection omics platforms, the development of integrative down-stream approaches is still challenging. Systems biology has an immense applicability in the biomedical and pharmacological areas since the main goal of those focuses in the translation of measured outputs into potential markers of a Human ailment and/or to provide new compound leads for drug discovery. This approach would become more straightforward and realistic to use in standard analysis workflows if the collation of all available information of every component of a biological system was ensured into a single database framework, instead of search and fetch a single component at time across a scatter of databases resources. Here, we will describe several database resources, standalone and web-based tools applied in disease analytics workflows based in data-driven integration of outputs of multi-omic detection platforms

Transcriptomics of temperature-sensitive R gene-mediated resistance identifies a WAKL10 protein interaction network

© 2024 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Understanding temperature-sensitivity of R gene-mediated resistance against apoplastic pathogens is important for sustainable food production in the face of global warming. Here, we show that resistance of Brassica napus cotyledons against Leptosphaeria maculans was temperature-sensitive in introgression line Topas-Rlm7 but temperature-resilient in Topas-Rlm4. A set of 1,646 host genes was differentially expressed in Topas-Rlm4 and Topas-Rlm7 in response to temperature. Amongst these were three WAKL10 genes, including BnaA07g20220D, representing the temperature-sensitive Rlm7-1 allele and Rlm4. Network analysis identified a WAKL10 protein interaction cluster specifically for Topas-Rlm7 at 25 °C. Diffusion analysis of the Topas-Rlm4 network identified WRKY22 as a putative regulatory target of the ESCRT-III complex-associated protein VPS60.1, which belongs to the WAKL10 protein interaction community. Combined enrichment analysis of gene ontology terms considering gene expression and network data linked vesicle-mediated transport to defence. Thus, dysregulation of effector-triggered defence in Topas-Rlm7 disrupts vesicle-associated resistance against the apoplastic pathogen L. maculans.Peer reviewe

Integrative Systems Biology Resources and Approaches in Disease Analytics

Currently, our analytical competences are struggling to keep-up the pace of in-deep analysis of all generated large-scale data resultant of high-throughput omics platforms. While, a substantial effort was spent on methods enhancement regarding technical aspects across many detection omics platforms, the development of integrative down-stream approaches is still challenging. Systems biology has an immense applicability in the biomedical and pharmacological areas since the main goal of those focuses in the translation of measured outputs into potential markers of a Human ailment and/or to provide new compound leads for drug discovery. This approach would become more straightforward and realistic to use in standard analysis workflows if the collation of all available information of every component of a biological system was ensured into a single database framework, instead of search and fetch a single component at time across a scatter of databases resources. Here, we will describe several database resources, standalone and web-based tools applied in disease analytics workflows based in data-driven integration of outputs of multi-omic detection platforms

Altered Gene Regulatory Networks Are Associated With the Transition From C3 to Crassulacean Acid Metabolism in Erycina (Oncidiinae: Orchidaceae)

Crassulacean acid metabolism (CAM) photosynthesis is a modification of the core C3 photosynthetic pathway that improves the ability of plants to assimilate carbon in water-limited environments. CAM plants fix CO2 mostly at night, when transpiration rates are low. All of the CAM pathway genes exist in ancestral C3 species, but the timing and magnitude of expression are greatly altered between C3 and CAM species. Understanding these regulatory changes is key to elucidating the mechanism by which CAM evolved from C3. Here, we use two closely related species in the Orchidaceae, Erycina pusilla (CAM) and Erycina crista-galli (C3), to conduct comparative transcriptomic analyses across multiple time points. Clustering of genes with expression variation across the diel cycle revealed some canonical CAM pathway genes similarly expressed in both species, regardless of photosynthetic pathway. However, gene network construction indicated that 149 gene families had significant differences in network connectivity and were further explored for these functional enrichments. Genes involved in light sensing and ABA signaling were some of the most differently connected genes between the C3 and CAM Erycina species, in agreement with the contrasting diel patterns of stomatal conductance in C3 and CAM plants. Our results suggest changes to transcriptional cascades are important for the transition from C3 to CAM photosynthesis in Erycina

Understanding longevity sub-networks using network propagation algorithms

Wegweisende Arbeiten an Modellorganismen haben mehrere evolutionär konservierte Signalwege in der Regulation von Langlebigkeit identifiziert. Hochdurchsatz-Experimente haben Gene und Pfadwege identifiziert, die durch diese Langlebigkeits-Interventionen reguliert werden. Diese Hochdurchsatz-Experimente liefern lange Listen von signifikant differentiell regulierten Genen, wodurch die Identifizierung der kausalen Gene des Phänotyps eine Herausforderung darstellt. Netzwerk-Ansätze sind leistungsfähige Methoden zur Entdeckung von Genen und Modulen, die direkt mit dem Phänotypen assoziiert sind. Network Propagation ist ein systembiologischer Ansatz, der darauf basiert, dass Gene, die den gleichen Phänotyp auslösen, dazu neigen, eng zusammenzuarbeiten: das Signal einzelner Gene wird auf ein Netzwerk abgebildet, um die mit dem Phänotypen assoziierten Gene und Module zu verstärken. Network Propagation ist damit eine globale Scoring-Methode mit verschiedenen Anwendungen, wie der Vorhersage von Proteinfunktionen, der Identifizierung von spezifisch veränderten Sub-Netzwerken sowie Genprioritisierung. Es existieren verschiedene mathematische Formulierungen von Network Propagation, unter anderem Random Walks, Random Walks mit Neustart (RWR) und Heat Diffusion (HD). In dieser Arbeit haben wir systematisch die Leistung von RWR und HD Algorithmen unter Verwendung von Rattus norvegicus altersassoziierten mRNA- und Proteinexpressionsdaten in zwei metabolisch aktiven Geweben analysiert. Wir beobachteten, dass die propagierten Scores - abhängig von Netzwerk-Normalisierung und Art der Eingabedaten - von der Topologie des Netzwerkes beeinflusst werden ("Topologie-Bias"). In den Algorithmen der Network Propagation bestimmt der Streukoeffizient ("spreading coefficient", α oder "t") die Stärke und die Reichweite der Signalausbreitung im Netzwerk. Daher ist es wichtig, den Einfluss dieses Parameters auf die propagierten Scores einzuschätzen. In dieser Studie haben wir die beiden Algorithmen unter einer breiten Auswahl von α und ’t’ Parametern verglichen. Wir demonstrieren die Existenz von optimalen Streukoeffizienten und ihre Abhängigkeit von den Eingabewerten (Anfangszustände des Random Walks). Darüber hinaus zeigen wir die Anwendbarkeit und Robustheit von Network Propagation bei der Identifizierung veränderter Subnetzwerke während des Alterns, anhand von Genexpressions- und Proteinexpressionsdaten aus Gehirn- und Lebergeweben. Im Modellorganismus C. elegans haben wir die Antwort des Transkriptoms auf Störungen der Insulin-Signalgebung, der Keimbahn-Signalübertragung, der Kalorienaufnahme und der Hypoxie untersucht. Es ist eine offene Frage, inwiefern diese Pfadwege auf gemeinsame molekulare Endpunkte wirken, die für das Altern oder die Lebensspanne relevant sind. Unter Verwendung traditioneller Methoden zur Interpretation von Transkriptomdaten, welche sich auf die Antwort einzelner Gene konzentrierten, beobachteten wir nur eine geringe Ähnlichkeit zwischen unterschiedlichen Perturbationen. Wir verwendeten daher Network Propagation für die Identifizierung von molekularen Netzwerken, die konsistent durch die betrachteten Faktoren beeinflusst werden. Diese Methode beruht auf der Annahme, dass selbst bei Ansprache derselben zellulären Funktion durch Perturbationen häufig nicht dieselben Gene verändert werden. Stattdessen können verschiedene Perturbationen zur Veränderung verschiedener Gene führen, die in einem gemeinsamen molekularen Subnetzwerk agieren. Unsere Analyse identifizierte molekulare Subnetzwerke, welche die Lebensspanne durch Perturbation verschiedener Signalwege beeinflussen. Diese Netzwerke enthielten Proteine, die an Transkription, tRNA- und rRNA-Prozessierung, Chromatin-Remodellierung, Collagen, Stressresistenz und Reproduktion beteiligt sind, was auf die Existenz gemeinsamer Module und konvergenter Downstream-Mechanismen der Kontrolle der Lebensspanne in C. elegans hindeutet. Ein weiteres Ziel dieser Arbeit war die Identifizierung gewebespezifischer Reaktionen auf reduzierte Aktivität des Insulin-Signalweges IIS (rIIS). Mithilfe von Network Propagation sollten die molekularen Mechanismen, die über den Transkriptionsfaktor dfoxo die Lebensspanne modulieren, präzise bestimmt werden. Dafür wurden Proteine in dfoxo-abhängige und unabhängige Proteine klassifiziert und die stärke der differentiellen Expression (p-Werte) wurden in einem Protein-Protein-Interaktionsnetzwerk von Drosophila propagiert. Anschließend wurden dfoxo-abhängige und unabhängige Netwerkmodule bestimmt. Darüber hinaus wurde Network Propagtion verwendet, um die gemeinsamen molekularen Signaturen von rIIS in zwei mit Langlebigkeit assoziierten genetischen Modellen zu bestimmen

Análise integrativa dos mecanismos de patogênese em doenças lisossômicas

Doenças lisossômicas (DLs) causam acúmulo intracelular de substratos e deficiência no tráfego de macromoléculas. O armazenamento do substrato pode impactar uma ou várias vias que contribuem para o dano celular. Vias morfogênicas e de crescimento como Hedgehog (Hh), mTOR e insulina estão envolvidas na fisiopatologia das DLs. A via Hh é afetada com expressão anormal e alterações nos níveis e distribuição de proteínas Hh. mTOR pode ter um atraso em sua reativação e desregular o término da autofagia e manutenção dos lisossomos. A resistência à insulina causada por mudanças nas jangadas lipídicas também foi descrita em diferentes DLs. Portanto, exploramos como estas vias podem estar relacionadas, mostrando que uma abordagem de medicina de redes pode ser uma ferramenta valiosa para o melhor entendimento da patogênese em DLs. Assim, utilizamos ferramentas de biologia de sistemas para investigar novos elementos associados com a dilatação da aorta em mucopolissacaridoses (MPS). Identificamos genes candidatos associados com processos biológicos, incluindo respostas inflamatórias, deposição de colágeno e metabolismo de lipídeos que podem contribuir para a patogênese da dilatação da aorta em MPS I e MPS VII. Por último, foram identificados novos genes candidatos e vias que convergem em mecanismos funcionais envolvidos nos defeitos de formação precoce do circuito neural, no qual podem indicar pistas sobre o comprometimento cognitivoem pacientes com MPSII. Tais mudanças moleculares durante o neurodesenvolvimento podem preceder as evidências morfológicas e clínicas, destacando aimportância do diagnóstico precoce e do desenvolvimento de novas drogas.Lysosomal storage diseases (LSDs) cause intracellular accumulation of substrates and deficiency in trafficking of macromolecules. The substrate storage can impact one or several pathways which contribute to cell damage. Morphogenic and growth pathways such as hedgehog (Hh), mTOR and insulin are involved in the pathophysiology of LSDs. Hh pathway is affected with abnormal expression and changes in protein levels. mTOR may have a delay in reactivation and deregulate termination of autophagy and reformation of lysosomes. Insulin resistance caused by changes in lipids rafts also has been described in different LSDs. Therefore, we explored how specific signaling pathways can be related to specific LSDs, showing that a system medicine approach could be a valuable tool for the better understanding of LSD pathogenesis. Moreover, we used systems biology tools to investigate new elements that may be involved in aortic dilatation in Mucopolysaccharidoses (MPS) syndrome. We identified candidate genes associated with biological processes related to inflammatory responses, deposition of collagen, and lipid metabolism that may contribute to pathogenesis of aortic dilatation in the MPS I and MPS VII. Finally, we identified new candidate genes and pathways that converge into functional mechanisms involved in early neural circuit formation defects and could indicate clues about cognitive impairment in patients with MPSII. Such molecular changes during neurodevelopment may precede the morphological and clinical evidence, highlighting the importance of an early diagnosis and the development of new drugs

Data Journeys in the Sciences

This groundbreaking, open access volume analyses and compares data practices across several fields through the analysis of specific cases of data journeys. It brings together leading scholars in the philosophy, history and social studies of science to achieve two goals: tracking the travel of data across different spaces, times and domains of research practice; and documenting how such journeys affect the use of data as evidence and the knowledge being produced. The volume captures the opportunities, challenges and concerns involved in making data move from the sites in which they are originally produced to sites where they can be integrated with other data, analysed and re-used for a variety of purposes. The in-depth study of data journeys provides the necessary ground to examine disciplinary, geographical and historical differences and similarities in data management, processing and interpretation, thus identifying the key conditions of possibility for the widespread data sharing associated with Big and Open Data. The chapters are ordered in sections that broadly correspond to different stages of the journeys of data, from their generation to the legitimisation of their use for specific purposes. Additionally, the preface to the volume provides a variety of alternative “roadmaps” aimed to serve the different interests and entry points of readers; and the introduction provides a substantive overview of what data journeys can teach about the methods and epistemology of research

Data journeys in the sciences

This is the final version. Available from Springer via the DOI in this record. This groundbreaking, open access volume analyses and compares data practices across several fields through the analysis of specific cases of data journeys. It brings together leading scholars in the philosophy, history and social studies of science to achieve two goals: tracking the travel of data across different spaces, times and domains of research practice; and documenting how such journeys affect the use of data as evidence and the knowledge being produced. The volume captures the opportunities, challenges and concerns involved in making data move from the sites in which they are originally produced to sites where they can be integrated with other data, analysed and re-used for a variety of purposes. The in-depth study of data journeys provides the necessary ground to examine disciplinary, geographical and historical differences and similarities in data management, processing and interpretation, thus identifying the key conditions of possibility for the widespread data sharing associated with Big and Open Data. The chapters are ordered in sections that broadly correspond to different stages of the journeys of data, from their generation to the legitimisation of their use for specific purposes. Additionally, the preface to the volume provides a variety of alternative “roadmaps” aimed to serve the different interests and entry points of readers; and the introduction provides a substantive overview of what data journeys can teach about the methods and epistemology of research.European CommissionAustralian Research CouncilAlan Turing Institut