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The Varkud satellite (VS) plasmid, which requires the Varkud (V) plasmid for replication, is found in the mitochondria of several natural isolates of Neurospora. The VS transcript is sufficiently abundant that it might be expected to alter the function of mitochondria; however, previous limited characterization revealed no effect. In this work we have used genetic, biochemical and proteomic approaches to search for effects of the V and VS plasmids. We observed differences in the relative abundance of several mitochondrial proteins between plasmid-containing and plasmid-lacking natural isolates, but subsequently found these not to be due to the plasmids. We constructed a pair of iso-nuclear and iso-mitochondrial strains that differed only by the presence or absence of V and VS, and observed only subtle differences in the abundance of several mitochondrial proteins. We further attempted to detect a cryptic plasmid-related phenotype by growing this pair of strains in the presence of a variety of inhibitors of mitochondrial function or other stress conditions: this also revealed no effect of the plasmids. These observations suggest that, despite the high concentration of VS RNA in the mitochondrion, the V and VS plasmids do not cause substantial changes in the host., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Identifying changes in the relative abundance of proteins between different biological samples is often confounded by technical noise. In this work, we compared eight normalization methods commonly used in two-dimensional gel electrophoresis and difference gel electrophoresis (DIGE) experiments for their ability to reduce noise and for their influence on the list of proteins whose difference in abundance between two samples is determined to be statistically significant. With respect to reducing noise we find that, while all methods improve upon unnormalized data, cyclic linear normalization is the least well suited to gel-based proteomics and the performances of the other methods are similar. We also find in DIGE data that the choice of normalization method has less of an impact on the noise than does the decision to use an internal reference in the experimental design and that both normalization and standardization using the internal reference are required to maximally reduce variance. Despite the similar noise reduction achieved by most normalization methods, the list of proteins whose abundance was determined to differ significantly between biological groups differed depending on the choice of normalization method. This work provides a direct comparison of the impact of normalization methods in the context of common experimental designs.