REVIEW There is substantial cytogenetic data indicating that the process of sex determination can evolve relatively rapidly. However, recent molecular studies on the evolution of the regulatory genes that control sex determination in the insect Drosophila melanogaster , the nematode Caenorhabditis elegans , and mammals suggest that, although certain sex determination regulatory genes have evolved relatively rapidly, other sex determination regulatory genes are quite conserved. Thus, studies of the evolution of sex determination, a process that appears to have elements that undergo substantial evolutionary change and others that may be conserved, could provide substantial insights into the kinds of forces that both drive and constrain the evolution of developmental hierarchies.

Motivation: Generation of fast tools of hierarchical clustering to be applied when distances among elements of a set are constrained, causing frequent distance ties, as happens in protein interaction data. Results: We present in this work the program UVCLUSTER, that iteratively explores distance datasets using hierarchical clustering. Once the user selects a group of proteins, UVCLUSTER converts the set of primary distances among them (i.e. the minimum number of steps, or interactions, required to connect two proteins) into secondary distances that measure the strength of the connection between each pair of proteins when the interactions for all the proteins in the group are considered. We show that this novel strategy has advantages over conventional clustering methods to explore protein–protein interaction data. UVCLUSTER easily incorporates the information of the largest available interaction datasets to generate comprehensive primary distance tables. The versatility, simplicity of use and high speed of UVCLUSTER on standard personal computers suggest that it can be a benchmark analytical tool for interactome data analysis. Availability: The program is available upon request from the authors, free for academic users. Additional information available at http://www.uv.es/genomica/UVCLUSTER Contact:ignacio.marin@uv.es

Mutations in the human leucine-rich repeat kinase 2 (LRRK2) gene are associated with both familial and sporadic Parkinson disease (PD). LRRK2 belongs to a gene family known as Roco. Roco genes encode for large proteins with several protein domains. Particularly, all Roco proteins have a characteristic GTPase domain, named Roc, plus a domain of unknown function called COR. In addition, LRRK2 and several other Roco proteins also contain a protein kinase domain. In this study, I use a combination of phylogenetic and structural analyses of the COR, Roc, and kinase domains present in Roco proteins to describe the origin and evolutionary history of LRRK2. Phylogenetic analyses using these domains demonstrate that LRRK2 emerged from a duplication that occurred after the protostome–deuterostome split. The duplication was followed by the acquisition by LRRK2 proteins of a specific type of N-terminal repeat, described here for the first time. This repeat is absent in the proteins encoded by the paralogs of LRRK2, called LRRK1 or in protostome LRRK proteins. These results suggest that Drosophila or Caenorhabditis LRRK genes may not be good models to understand human LRRK2 function. Genes in the slime mold Dictyostelium discoideum with structures very similar to those found in animal LRRK genes, including the protein kinase domain, have been described. However, phylogenetic analyses suggest that this structural similarity is due to independent acquisitions of distantly related protein kinase domains. Finally, I confirm in an extensive sequence analysis that the Roc GTPase domain is related but still substantially different from small GTPases, such as Rab, Ras, or Rho. Modeling based on known kinase structures suggests that mutations in LRRK2 that cause familiar PD may alter the local 3-dimensional folding of the LRRK2 protein without affecting its overall structure.

The analysis of complex networks permeates all sciences, from biology to sociology. A fundamental, unsolved problem is how to characterize the community structure of a network. Here, using both standard and novel benchmarks, we show that maximization of a simple global parameter, which we call Surprise (S), leads to a very efficient characterization of the community structure of complex synthetic networks. Particularly, S qualitatively outperforms the most commonly used criterion to define communities, Newman and Girvan's modularity (Q). Applying S maximization to real networks often provides natural, well-supported partitions, but also sometimes counterintuitive solutions that expose the limitations of our previous knowledge. These results indicate that it is possible to define an effective global criterion for community structure and open new routes for the understanding of complex networks.