29 research outputs found
Latent physiological factors of complex human diseases revealed by independent component analysis of clinarrays
<p>Abstract</p> <p>Background</p> <p>Diagnosis and treatment of patients in the clinical setting is often driven by known symptomatic factors that distinguish one particular condition from another. Treatment based on noticeable symptoms, however, is limited to the types of clinical biomarkers collected, and is prone to overlooking dysfunctions in physiological factors not easily evident to medical practitioners. We used a vector-based representation of patient clinical biomarkers, or clinarrays, to search for latent physiological factors that underlie human diseases directly from clinical laboratory data. Knowledge of these factors could be used to improve assessment of disease severity and help to refine strategies for diagnosis and monitoring disease progression.</p> <p>Results</p> <p>Applying Independent Component Analysis on clinarrays built from patient laboratory measurements revealed both known and novel concomitant physiological factors for asthma, types 1 and 2 diabetes, cystic fibrosis, and Duchenne muscular dystrophy. Serum sodium was found to be the most significant factor for both type 1 and type 2 diabetes, and was also significant in asthma. TSH3, a measure of thyroid function, and blood urea nitrogen, indicative of kidney function, were factors unique to type 1 diabetes respective to type 2 diabetes. Platelet count was significant across all the diseases analyzed.</p> <p>Conclusions</p> <p>The results demonstrate that large-scale analyses of clinical biomarkers using unsupervised methods can offer novel insights into the pathophysiological basis of human disease, and suggest novel clinical utility of established laboratory measurements.</p
Inflammatory Gene Regulatory Networks in Amnion Cells Following Cytokine Stimulation: Translational Systems Approach to Modeling Human Parturition
A majority of the studies examining the molecular regulation of human labor have
been conducted using single gene approaches. While the technology to produce
multi-dimensional datasets is readily available, the means for facile analysis
of such data are limited. The objective of this study was to develop a systems
approach to infer regulatory mechanisms governing global gene expression in
cytokine-challenged cells in vitro, and to apply these methods
to predict gene regulatory networks (GRNs) in intrauterine tissues during term
parturition. To this end, microarray analysis was applied to human amnion
mesenchymal cells (AMCs) stimulated with interleukin-1β, and differentially
expressed transcripts were subjected to hierarchical clustering, temporal
expression profiling, and motif enrichment analysis, from which a GRN was
constructed. These methods were then applied to fetal membrane specimens
collected in the absence or presence of spontaneous term labor. Analysis of
cytokine-responsive genes in AMCs revealed a sterile immune response signature,
with promoters enriched in response elements for several inflammation-associated
transcription factors. In comparison to the fetal membrane dataset, there were
34 genes commonly upregulated, many of which were part of an acute inflammation
gene expression signature. Binding motifs for nuclear factor-κB were
prominent in the gene interaction and regulatory networks for both datasets;
however, we found little evidence to support the utilization of
pathogen-associated molecular pattern (PAMP) signaling. The tissue specimens
were also enriched for transcripts governed by hypoxia-inducible factor. The
approach presented here provides an uncomplicated means to infer global
relationships among gene clusters involved in cellular responses to
labor-associated signals