Bacterial protein interaction networks: connectivity is ruled by gene conservation, essentiality and function

Protein-protein interaction (PPI) networks are the backbone of all processes in living cells. In this work we relate conservation, essentiality and functional repertoire of a gene to the connectivity $k$ of the corresponding protein in the PPI networks. Focusing on a set of 42 bacterial species with reasonably separated evolutionary trajectories, we investigate three issues: i) whether the distribution of connectivity values changes between PPI subnetworks of essential and nonessential genes; ii) how gene conservation, measured both by the evolutionary retention index (ERI) and by evolutionary pressures (evaluated through the ratio $K_a/K_s$ and ENC plots) is related to the the connectivity of the corresponding protein; iii) how PPI connectivities are modulated by evolutionary and functional relationships, as represented by the Clusters of Orthologous Proteins (COGs). We show that conservation, essentiality and functional specialization of genes control in a quite universal way the topology of the emerging bacterial PPI networks. Noteworthy, a structural transition in the network is observed such that, for connectivities $k\ge40$, bacterial PPI networks are mostly populated by genes that are conserved, essential and which, in most cases, belong to the COG cluster J, related to ribosomal functions and to the processing of genetic information

A new algebraic approach to genome rearrangement models

We present a unified framework for modelling genomes and their rearrangements in a genome algebra, as elements that simultaneously incorporate all physical symmetries. Building on previous work utilising the group algebra of the symmetric group, we explicitly construct the genome algebra for the case of unsigned circular genomes with dihedral symmetry and show that the maximum likelihood estimate (MLE) of genome rearrangement distance can be validly and more efficiently performed in this setting. We then construct the genome algebra for the general case, that is, for genomes represented by elements of an arbitrary group and symmetry group, and show that the MLE computations can be performed entirely within this framework. There is no prescribed model in this framework; that is, it allows any choice of rearrangements with arbitrary weights. Further, since the likelihood function is built from path probabilities -- a generalisation of path counts -- the framework may be utilised for any distance measure that is based on path probabilities.Comment: 35 page

THE CONTRIBUTION OF MOSAIC MUTATION TO AUTISM SPECTRUM DISORDER

Background Genetic variation arises as the result of both spontaneous and artificial processes. While genetic variation and natural selection are the only tools of evolution, genetic variation is also known to also cause disease, including inherited disease and cancer. Here we explore the consequences of human genetic variation first as a tool for testing identity from a mixture of DNA and second as a contributor to autism spectrum disorder. Methods One of the most comprehensive datasets of human genetic variation across all human populations is the 1000 Genomes Project. We use genetic variants from the 1000 Genomes Project to identify polymorphic loci in extant human populations using custom computational tools. For our studies of mosaic mutation in individuals with autism, we use publicly available data and a combination of publically available and custom software. Results Through our analysis of publically available data, we find genomic loci that may be used for identity testing across many human populations. In addition we show that mosaic genetic variation detectable in blood contribute significantly to autism spectrum disorder while mosaic genetic variants that are unique to affected tissues are not frequently detectable from bulk sequence data. Conclusions Our results have implications for the fields of identity testing and disease genetics. Using the genomic loci we identify, it is possible to develop assays to identify DNA from an individual within a mixture even if that individual’s DNA makes up less than one-millionth of the total DNA in the mixture. Our identification of a contribution of mosaic mutations to disease has implications for our understanding of heritability. In classic measurements of heritability, identical twins are used as individuals of constant genetic background and any phenotypic differences between identical twins are assumed to come from the “environment”. While genetic variation unique to a single individual within a twin pair has been identified, our results are the first to indicate that these mutations play a role in the phenotypic differences between identical twins. Our results also have implications for the field of genetic counseling. The parents of probands with high-confidence mosaic mutations may be less likely to have additional children with an autism diagnosis compared to parents with children with ASD overall. Advisor: Jonathan Pevsner Reader: Robert Scharp