Does Effective Population Size Govern Evolutionary Differences in Telomere Length?

Lengths of telomeres vary by an order of magnitude across mammalian species. Similarly, age- and sex-standardized telomere lengths differ by up to 1 kb (14%) across human populations. How to explain these differences? Telomeres play a central role in senescence and aging, and genes that affect telomere length are likely under weak selection (i.e. telomere length is a trait that is subject to nearly neutral evolution). Importantly, natural selection is more effective in large populations than in small populations. Here, we propose that observed differences in telomere length across species and populations are largely due to differences in effective population sizes. In this perspective, we present preliminary evolutionary genetic evidence supporting this hypothesis and highlight the need for more data.

A revised phylogeny of Boletaceae using whole genome sequences.

The porcini mushroom family Boletaceae is a diverse, widespread group of ectomycorrhizal (ECM) mushroom-forming fungi that so far has eluded intrafamilial phylogenetic resolution based on morphology and multilocus data sets. In this study, we present a genome-wide molecular data set of 1764 single-copy gene families from a global sampling of 418 Boletaceae specimens. The resulting phylogenetic analysis has strong statistical support for most branches of the tree, including the first statistically robust backbone. The enigmatic Phylloboletellus chloephorus from non-ECM Argentinian subtropical forests was recovered as a new subfamily sister to the core Boletaceae. Time-calibrated branch lengths estimate that the family first arose in the early to mid-Cretaceous and underwent a rapid radiation in the Eocene, possibly when the ECM nutritional mode arose with the emergence and diversification of ECM angiosperms. Biogeographic reconstructions reveal a complex history of vicariance and episodic long-distance dispersal correlated with historical geologic events, including Gondwanan origins and inferred vicariance associated with its disarticulation. Together, this study represents the most comprehensively sampled, data-rich molecular phylogeny of the Boletaceae to date, establishing a foundation for future robust inferences of biogeography in the group.

Development of a computational framework for the analysis of protein correlation profiling and spatial proteomics experiments.

Standard approaches to studying an interactome do not easily allow conditional experiments but in recent years numerous groups have demonstrated the potential for co-fractionation/co-migration based approaches to assess an interactome at a similar sensitivity and specificity yet significantly lower cost and higher speed than traditional approaches. Unfortunately, there is as yet no implementation of the bioinformatics tools required to robustly analyze co-fractionation data in a way that can also integrate the valuable information contained in biological replicates. Here we have developed a freely available, integrated bioinformatics solution for the analysis of protein correlation profiling SILAC data. This modular solution allows the deconvolution of protein chromatograms into individual Gaussian curves enabling the use of these chromatography features to align replicates and assemble a consensus map of features observed across replicates; the chromatograms and individual curves are then used to quantify changes in protein interactions and construct the interactome. We have applied this workflow to the analysis of HeLa cells infected with a Salmonella enterica serovar Typhimurium infection model where we can identify specific interactions that are affected by the infection. These bioinformatics tools simplify the analysis of co-fractionation/co-migration data to the point where there is no specialized knowledge required to measure an interactome in this way. BIOLOGICAL SIGNIFICANCE: We describe a set of software tools for the bioinformatics analysis of co-migration/co-fractionation data that integrates the results from multiple replicates to generate an interactome, including the impact on individual interactions of any external perturbation. This article is part of a Special Issue entitled: Protein dynamics in health and disease. Guest Editors: Pierre Thibault and Anne-Claude Gingras.

Global proteome analysis identifies active immunoproteasome subunits in human platelets.

The discovery of new functions for platelets, particularly in inflammation and immunity, has expanded the role of these anucleate cell fragments beyond their primary hemostatic function. Here, four in-depth human platelet proteomic data sets were generated to explore potential new functions for platelets based on their protein content and this led to the identification of 2559 high confidence proteins. During a more detailed analysis, consistently high expression of the proteasome was discovered, and the composition and function of this complex, whose role in platelets has not been thoroughly investigated, was examined. Data set mining resulted in identification of nearly all members of the 26S proteasome in one or more data sets, except the ß5 subunit. However, ß5i, a component of the immunoproteasome, was identified. Biochemical analyses confirmed the presence of all catalytically active subunits of the standard 20S proteasome and immunoproteasome in human platelets, including ß5, which was predominantly found in its precursor form. It was demonstrated that these components were assembled into the proteasome complex and that standard proteasome as well as immunoproteasome subunits were constitutively active in platelets. These findings suggest potential new roles for platelets in the immune system. For example, the immunoproteasome may be involved in major histocompatibility complex I (MHC I) peptide generation, as the MHC I machinery was also identified in our data sets.