Abstract
Gene expression analysis has become a ubiquitous tool for studying a wide range of human diseases. In a typical analysis we compare distinct phenotypic groups and attempt to identify genes that are, on average, significantly different between them. Here we describe an innovative approach to the analysis of gene expression data, one that identifies differences in expression variance between groups as an informative metric of the group phenotype. We find that genes with different expression variance profiles are not randomly distributed across cell signaling networks. Genes with low-expression variance, or higher constraint, are significantly more connected to other network members and tend to function as core members of signal transduction pathways. Genes with higher expression variance have fewer network connections and also tend to sit on the periphery of the cell. Using neural stem cells derived from patients suffering from Schizophrenia (SZ), Parkinson's disease (PD), and a healthy control group, we find marked differences in expression variance in cell signaling pathways that shed new light on potential mechanisms associated with these diverse neurological disorders. In particular, we find that expression variance of core networks in the SZ patient group was considerably constrained, while in contrast the PD patient group demonstrated much greater variance than expected. One hypothesis is that diminished variance in SZ patients corresponds to an increased degree of constraint in these pathways and a corresponding reduction in robustness of the stem cell networks. These results underscore the role that variation plays in biological systems and suggest that analysis of expression variance is far more important in disease than previously recognized. Furthermore, modeling patterns of variability in gene expression could fundamentally alter the way in which we think about how cellular networks are affected by disease processes.
Highlights
In studying biological systems, we tend to think of groups as being defined by specific, measurable parameters, and of the important differences between those groups as being defined by a significant average difference in those parameters
In this study we demonstrated that disease phenotypes were correlated with expression variance
A change in expression variance might infer that the genetic networks representing information flow were less robust— surprisingly, we found that too little and too much variance were detrimental in the context of neurological disease
Summary
We tend to think of groups as being defined by specific, measurable parameters, and of the important differences between those groups as being defined by a significant average difference in those parameters. Much of the language we use in describing biological systems is based on this bias and we talk about genes being expressed in a tissue at a particular level, or about differences in gene expression between groups reflecting the mechanism driving their phenotypic differences. This view of biological systems has been extremely useful in that most of our understanding of biological systems is based on interpreting average behavior. There is evidence that biological sources of variation may play an important role in determining cellular and organismal phenotypes [2,3,4,5,6,7,8], as well as in helping to explain a wide range of biological phenomena ranging from reduced penetrance [9,10] to evolutionary fitness [11]
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