Identification of key genes and biological regulatory mechanisms in diabetic nephropathy: Meta-analysis of gene expression datasets / Identificación de genes clave y mecanismos reguladores biológicos en la nefropatía diabética: metaanálisis de conjuntos de datos de expresión génica

Background: Diabetic nephropathy (DN) which refers to the cases with biopsy proven kidney lesions, is one of the main complications of diabetes all around the world; however, the underlying biological changes causing DN remain to be understood. Studying the alterations in gene expression profiles could give a holistic view of the molecular pathogenicity of DN and aid to discover key molecules as potential therapeutic targets. Here, we performed a meta-analysis study that included microarray gene expression profiles coming from glomerular samples of DN patients in order to acquire a list of consensus Differentially Expressed Genes (meta-DEGs) correlated with DN. Methods: After quality control and normalization steps, five gene expression datasets (GES1009, GSE30528, GSE47183, GSE104948, and GSE93804) were entered into the meta-analysis. The meta-analysis was performed by random effect size method and the meta-DEGs were put through network analysis and different pathway enrichment analyses steps. MiRTarBase and TRRUST databases were utilized to predict the meta-DEGs related miRNAs and transcription factors. A co-regulatory network including DEGs, transcription factors and miRNAs was constructed by Cytoscape, and top molecules were identified based on centrality scores in the network.(AU)

Antecedentes: La nefropatía diabética (ND), que se refiere a los casos con lesiones renales comprobadas por biopsia, es una de las principales complicaciones de la diabetes en todo el mundo. Sin embargo, los cambios biológicos subyacentes que causan la ND aún no se han entendido. Aquí realizamos un estudio de metaanálisis que incluyó perfiles de expresión génica de micromatrices provenientes de muestras glomerulares de pacientes con ND para adquirir una lista de genes expresados diferencialmente (meta-DEG) de consenso correlacionados con ND. Métodos: Después de los pasos de control de calidad y normalización, se ingresaron en el metaanálisis cinco conjuntos de datos de expresión génica (GES1009, GSE30528, GSE47183, GSE104948 y GSE93804). El metaanálisis se realizó mediante el método de tamaño de efecto aleatorio y los meta-DEG se sometieron a análisis de red y a diferentes pasos de análisis de enriquecimiento de ruta. Se utilizaron las bases de datos MiRTarBase y TRRUST para predecir los factores de transcripción y los miARN relacionados con los meta-DEG. Cytoscape construyó una red de corregulación que incluye DEG, factores de transcripción y miARN, y las moléculas principales se identificaron en función de las puntuaciones de centralidad en la red. (AU)

Identification of key genes and biological regulatory mechanisms in diabetic nephropathy: Meta-analysis of gene expression datasets.

BACKGROUND: Diabetic nephropathy (DN) which refers to the cases with biopsy proven kidney lesions, is one of the main complications of diabetes all around the world; however, the underlying biological changes causing DN remain to be understood. Studying the alterations in gene expression profiles could give a holistic view of the molecular pathogenicity of DN and aid to discover key molecules as potential therapeutic targets. Here, we performed a meta-analysis study that included microarray gene expression profiles coming from glomerular samples of DN patients in order to acquire a list of consensus Differentially Expressed Genes (meta-DEGs) correlated with DN. METHODS: After quality control and normalization steps, five gene expression datasets (GES1009, GSE30528, GSE47183, GSE104948, and GSE93804) were entered into the meta-analysis. The meta-analysis was performed by random effect size method and the meta-DEGs were put through network analysis and different pathway enrichment analyses steps. MiRTarBase and TRRUST databases were utilized to predict the meta-DEGs related miRNAs and transcription factors. A co-regulatory network including DEGs, transcription factors and miRNAs was constructed by Cytoscape, and top molecules were identified based on centrality scores in the network. RESULTS: The identified meta-DEGs were 1364 DEGs including 665 downregulated and 669 upregulated DEGs. The results of pathway enrichment analysis showed, "immune system", "extracellular matrix organization", "hemostasis", "signal transduction", and "platelet activation" to be the top enriched terms with involvement of the meta-DEGs. After construction of the multilayer regulatory network, several top DEGs (TP53, MYC, BTG2, VEGFA, PTEN, etc.), as well as top miRNAs (miR-335, miR-16, miR-17, miR-20a, and miR-93), and transcription factors (SP1, STAT3, NF-KB1, RELA, E2F1), were introduced as potential therapeutic targets in DN. Among the regulatory molecules, miR-335-5p and SP1 were the most interactive miRNA and transcription factor molecules with the highest degree scores in the constructed network. CONCLUSION: By performing a meta-analysis of available DN-related transcriptomics datasets, we reached a consensus list of DEGs for this complicated disorder. Further enrichment and network analyses steps revealed the involved pathways in the DN pathogenesis and marked the most potential therapeutic targets in this disease.

Modified Oregonator: an Approach from the Complex Networks Theory / Oregonador Modificado: un Enfoque desde la Teoría de Redes Complejas

Abstract: Within the framework of Systems Biology, this paper proposes the complex network theory as a fundamental tool for determining the most critical dynamic variables in complex biochemical mechanisms. The Belousov-Zhabotinsky reaction is proposed as a study model and as a complex bipartite network. By determining the structural property authority, the most relevant dynamic variables are specified, and a mathematical model of the Belousov-Zhabotinsky reaction is obtained. The bidirectional coupling of the proposed model was made with other models associated with biological processes, finding synchronization phenomena when varying the coupling parameter. The time series obtained from the numerical solution of the coupled models were used to construct their images using the Gramian Angular Field technique. In the end, a supervised learning tool is proposed for the classification of the type of coupling by analyzing the images, obtaining score percentages above 94%. The hereby proposed methodology could be extended to the experimental field in order to determine anomalies in the coupling and synchronization of different physiological oscillators.

Resumen: En el marco de la Biología de sistemas, se propone en el presente trabajo a la teoría de redes complejas como una herramienta fundamental para la determinación de las variables dinámicas más importantes en mecanismos bioquímicos complejos. Se emplea como modelo de estudio la reacción de Belousov-Zhabotinsky y se plantea como una red compleja bipartita. Mediante la determinación de la propiedad estructural autoridad, se determinan las variables dinámicas con mayor relevancia y se obtiene un modelo matemático de la reacción de Belousov-Zhabotinsky. Se realizó el acoplamiento bidireccional del modelo planteado con otros modelos asociados a procesos biológicos, encontrándose fenómenos de sincronización al variar el parámetro de acoplamiento. Las series de tiempo obtenidas de la solución numérica de los modelos acoplados se emplearon para construir sus respectivas imágenes mediante la técnica de campo angular gramiano. Finalmente, se propone una herramienta de aprendizaje supervisado para la clasificación del tipo de acoplamiento mediante el análisis de las imágenes, obteniéndose porcentajes de exactitud por encima del 94%. La metodología propuesta en el presente trabajo podría extenderse y trasladarse al campo experimental con la finalidad de determinar anomalías en el acoplamiento y sincronización de distintos osciladores fisiológicos.

Usando a imunologia de sistemas para entender os mecanismos moleculares de doenças e vacinas / Using systems immunology to understand the molecular mechanisms of diseases and vaccines

Nas últimas décadas, dados relacionados com a saúde humana, desde informações clínicas e epidemiológicas até imagens médicas e experimentos ômicos, foram gerados e acumulados em uma quantidade sem precedentes na história. Um campo novo de pesquisa chamado "Imunologia de Sistemas" emergiu para tentar integrar, analisar, interpretar e predizer os mecanismos moleculares de doenças e vacinas. Esta tese mostra diversas aplicações da Imunologia de Sistemas no estudo de arboviroses, vacina da gripe, câncer, tuberculose, pneumonia, artrite, dentre outros. Também mostra o desenvolvimento de ferramentas computacionais amigáveis que permitem com que qualquer cientista, sem conhecimento prévio de bioinformática, possa realizar análises de Imunologia de Sistemas. Os achados das análises forneceram novas hipóteses e insights que, ao serem testados e validados experimentalmente, melhoram nosso entendimento sobre os processos imunológicos por trás da vacinação e de doenças humanas

Análise integrada à biologia de sistemas para avaliação de citocinas pró-inflamatórias e anti-inflamatórias na infecção por Zika vírus / Analysis integrated with the systems biology pro-inflammatory and anti-inflammatory cytokine evalution in Zica Virus infection

Objetivo: O presente estudo objetivou analisar as citocinas pró e anti-inflamatórias em pacientes infectados pelo Zika vírus. Métodos: Através da Biologia de Sistemas, que contribuiu de forma a sumarizar os dados quantitativos complexos. Resultados: Observamos em nossa pesquisa a elevação das citocinas IL-7, IL-9, IL-17a, RANTES, IP-10 e IL-1ra dos indivíduos infectados pelo ZIKV na fase aguda, relacionadas à atividade de linfócitos T e B,que indica uma possível proliferação desses tipos celulares mediante a infecção pelo ZIKV.Conclusão: Constatamos que nas infecções pelo ZIKV não ocorre uma grande liberação de citocinas, o que era esperado devido ao caráter benigno da doença.

Objective: The present study objected to analyze the pro and antiinflammatory cytokines in patients infected with zika vírus. Methods: Through the Systems Biology that contributed in a way to summarize the complex quantitative data. Results: In our study, we observed elevation of the cytokines IL-7, IL-9, IL-17a, RANTES, IP-10 and IL-1ra of the infected individuals by ZIKV in the acute phase, related to T and B lymphocyte activity, which indicate a possible proliferation of these cell types through ZIKV infection. Conclusion: We found that in the infections by the ZIKV, does not occur a great release of cytokines, which was expected due to the benign nature of the disease.

Analysis of the transcriptional regulatory mechanisms mediated by microRNAs in Metabolic Syndrome / Análise dos mecanismos regulatórios transcricionais mediados por microRNAs na Síndrome Metabólica

Metabolic Syndrome (MetS) is a combination of diseases interrelated and associated with increased mortality and risk of cardiovascular events. Among the elucidated molecular mechanisms of MetS, there are several genes regulated by miRNAs - small non-coding RNAs. A large number of transcriptomic studies in public databases integrated with new analysis methods can generate new insights. Therefore, this study aimed to identify circulating miRNAs and their target genes in MetS using a Systems Biology approach. For this, we used GEO-NCBI to download and analyse 26 microarray transcriptome studies of MetS and obesity. After preprocessing, the data underwent differential expression (LIMMA method), gene co-expression (CEMiTool), and enrichment (GSEA, Reactome) analyses. We retrieved a gene expression signature for subcutaneous adipose tissue (SAT) for obese individuals that included 291 consistent differentially expressed genes (DEG). This signature had a positive normalized enrichment score (NES) for adaptive immune system activation responses, and negative NES for metabolic pathways. The consensus co-expression network of SAT revealed 3 communities (CM) of densely interconnected genes. These CMs had a high number of up regulated genes and a consistent positive NES among the studies. The co-expressed genes of these 3 CMs were related to neutrophil degranulation, infiltration of immune system cells, and inflammatory processes. Also, a small brazillian cohort (6 individuals with MetS and 6 controls) underwent a seric miRNA profiling using PCR array. From the 222 miRNAs detected in serum, the differential expression analysis identified 4 upregulated miRNAs (miR-30c-5p, miR-421, miR-542-5p and miR-574) in MetS patients (p<0.01). The integrative miRNAs-mRNAs analysis revealed that the circulating upregulated miRNAs had 12 targets in the SAT, 3 targets in the liver; and no targets in the muscle and blood. Many of these target genes are known modulators of proinflammatory pathways. In conclusion, the use of Systems Biology in the analysis of gene networks and circulating miRNAs identified some potential molecular and pathophysiological mechanisms of the Metabolic Syndrome. The circulating miRNAs identified in this study are potential biomarkers and/or therapeutic targets. However, further studies are needed to validate these miRNAs and their target mRNA

A Síndrome Metabólica (MetS) é um conjunto de doenças inter-relacionadas e associadas ao aumento de mortalidade e risco de eventos cardiovasculares. Entre os mecanismos moleculares elucidados da MetS, existem muitos genes regulados por miRNAs - RNAs pequenos não codificadores. O grande número de estudos transcriptômicos em banco dados públicos integrado a novos métodos de análise podem gerar novas descobertas. Deste modo, o objetivo deste estudo foi identificar miRNAs circulantes e genes alvos na MetS usando a abordagem de Biologia de Sistemas. Para isso, GEO-NCBI foi usado para obter e analisar 26 estudos de transcriptoma por microarray de MetS e obesidade. Após o pré-processamento, realizamos análises de expressão diferencial (método LIMMA), co-expressão gênica (CEMiTool), e enriquecimento (GSEA, Reactome). Identificamos uma assinatura de expressão gênica do tecido adiposo subcutâneo (SAT) de indivíduos obesos, composta por 291 genes consistentemente diferencialmente expressos (DEG). Essa assinatura teve um escore de enriquecimento normalizado (NES) positivo para ativação de respostas do sistema imune adaptativo, e NES negativo para vias de metabolismo. A rede consenso de co-expressão do SAT revelou 3 comunidades (CM) de genes densamente interconectadas. Essas CMs continham muitos genes regulados positivamente e com consistência de NES positivo entre os estudos. Os genes co-expressos dessas 3 comunidades pertenciam a vias de a degranulação de neutrófilos, infiltração de células do sistema imune e processos inflamatórios. Além disso, uma pequena coorte brasileira (6 indivíduos com MetS e 6 controles) foi submetida à dosagem sérica de miRNAs por PCR array. Dos 222 miRNAs detectados no soro, a análise de expressão diferencial identificou 4 miRNAs regulados positivamente (miR-30c-5p, miR-421, miR-542-5p e miR-574) nos pacientes com MetS (p<0.01). A análise integrativa miRNAs-mRNAs revelou que osmiRNAs circulantes superexpressos tinham 12 alvos no SAT, 3 alvos no fígado; e nenhum alvo no músculo e no sangue. Muitos desses alvos são moduladores de vias ró-inflamatórias. Em conclusão, a utilização da Biologia de Sistemas na análise de redes gênicas e miRNAs circulantes identificou alguns potenciais mecanismos moleculares e fisiopatológicos da Síndrome Metabólica. Os miRNAs circulantes identificados neste trabalho são potenciais biomarcadores e/ou alvos terapêuticos. Entretanto, mais estudos são necessários para validar esses miRNAs e seus mRNAs alvos

Systems medicine: a new approach to clinical practice.

Most respiratory diseases are considered complex diseases as their susceptibility and outcomes are determined by the interaction between host-dependent factors (genetic factors, comorbidities, etc.) and environmental factors (exposure to microorganisms or allergens, treatments received, etc.) The reductionist approach in the study of diseases has been of fundamental importance for the understanding of the different components of a system. Systems biology or systems medicine is a complementary approach aimed at analyzing the interactions between the different components within one organizational level (genome, transcriptome, proteome), and then between the different levels. Systems medicine is currently used for the interpretation and understanding of the pathogenesis and pathophysiology of different diseases, biomarker discovery, design of innovative therapeutic targets, and the drawing up of computational models for different biological processes. In this review we discuss the most relevant concepts of the theory underlying systems medicine, as well as its applications in the various biological processes in humans.