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1.
PLoS One ; 9(3): e91886, 2014.
Artículo en Inglés | MEDLINE | ID: mdl-24667684

RESUMEN

Mechanisms governing the inflammatory response during sepsis have been shown to be complex, involving cross-talk between diverse signaling pathways. Current knowledge regarding the mechanisms underlying sepsis provides an incomplete picture of the syndrome, justifying additional efforts to understand this condition. Microarray-based expression profiling is a powerful approach for the investigation of complex clinical conditions such as sepsis. In this study, we investigate whole-genome expression profiles in mononuclear cells from survivors (n = 5) and non-survivors (n = 5) of sepsis. To circumvent the heterogeneity of septic patients, only patients admitted with sepsis caused by community-acquired pneumonia were included. Blood samples were collected at the time of sepsis diagnosis and seven days later to evaluate the role of biological processes or genes possibly involved in patient recovery. Principal Components Analysis (PCA) profiling discriminated between patients with early sepsis and healthy individuals. Genes with differential expression were grouped according to Gene Ontology, and most genes related to immune defense were up-regulated in septic patients. Additionally, PCA in the early stage was able to distinguish survivors from non-survivors. Differences in oxidative phosphorylation seem to be associated with clinical outcome because significant differences in the expression profile of genes related to mitochondrial electron transport chain (ETC) I-V were observed between survivors and non-survivors at the time of patient enrollment. Global gene expression profiles after seven days of sepsis progression seem to reproduce, to a certain extent, patterns collected at the time of diagnosis. Gene expression profiles comparing admission and follow-up samples differed between survivors and non-survivors, with decreased expression of genes related to immune functions in non-survivors. In conclusion, genes related to host defense and inflammatory response ontology were up-regulated during sepsis, consistent with the need for a host response to infection, and the sustainability of their expression in follow-up samples was associated with outcomes.


Asunto(s)
Biomarcadores/análisis , Infecciones Comunitarias Adquiridas/genética , Perfilación de la Expresión Génica , Leucocitos Mononucleares/metabolismo , Neumonía/genética , Sepsis/genética , Adulto , Anciano , Anciano de 80 o más Años , Estudios de Casos y Controles , Infecciones Comunitarias Adquiridas/complicaciones , Infecciones Comunitarias Adquiridas/inmunología , Femenino , Estudios de Seguimiento , Humanos , Leucocitos Mononucleares/citología , Leucocitos Mononucleares/inmunología , Masculino , Persona de Mediana Edad , Análisis de Secuencia por Matrices de Oligonucleótidos , Fosforilación Oxidativa , Neumonía/complicaciones , Neumonía/inmunología , Pronóstico , Sepsis/etiología , Sepsis/patología , Transducción de Señal
2.
Genom Data ; 2: 332-4, 2014 Dec.
Artículo en Inglés | MEDLINE | ID: mdl-26484123

RESUMEN

Mechanisms governing the inflammatory response during sepsis involve crosstalk between diverse signaling pathways, but current knowledge provides an incomplete picture of the syndrome. Microarray-based expression profiling is a powerful approach for the investigation of complex clinical conditions such as sepsis. In this study, we investigated whole-genome expression profiles in mononuclear cells from septic patients admitted in intensive care units with community-acquired pneumonia. Blood samples were collected at the time of sepsis diagnosis and seven days later since we aimed to evaluate the role of biological processes or genes possibly involved in patient recovery. Here we provide a detailed description of the study design, including clinical information, experimental methods and procedures regarding data analysis. Metadata corresponding to microarray results deposited in the database Gene Expression Omnibus (GEO) under the accession number GSE48080 are also described in this report. Our dataset allows the identification of genes possibly associated with host defense to infection as well as gene expression patterns associated with patient outcome.

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