Analysis of coevolving gene families using mutually exclusive orthologous modules

Modeling three-dimensional genomic organization in evolution and pathogenesis

The regulation of gene expression is mediated via the complex three-dimensional (3D) conformation of the genetic material and its interactions with various intracellular factors. Various experimental and computational approaches have been developed in recent years for understating the relation between the 3D conformation of the genome and the phenotypes of cells in normal condition and diseases. In this review, we will discuss novel approaches for analyzing and modeling the 3D genomic conformation, focusing on deciphering disease-causing mutations that affect gene expression. We conclude that as this is a very challenging mission, an important direction should involve the comparative analysis of various 3D models from various organisms or cells.

Constraints on lateral gene transfer in promoting fimbrial usher protein diversity and function

Fimbriae are long, adhesive structures widespread throughout members of the family Enterobacteriaceae. They are multimeric extrusions, which are moved out of the bacterial cell through an integral outer membrane protein called usher. The complex folding mechanics of the usher protein were recently revealed to be catalysed by the membrane-embedded translocation and assembly module (TAM). Here, we examine the diversity of usher proteins across a wide range of extraintestinal (ExPEC) and enteropathogenic (EPEC) Escherichia coli, and further focus on a so far undescribed chaperone–usher system, with this usher referred to as UshC. The fimbrial system containing UshC is distributed across a discrete set of EPEC types, including model strains like E2348/67, as well as ExPEC ST131, currently the most prominent multi-drug-resistant uropathogenic E. coli strain worldwide. Deletion of the TAM from a naive strain of E. coli results in a drastic time delay in folding of UshC, which can be observed for a protein from EPEC as well as for two introduced proteins from related organisms, Yersinia and Enterobacter. We suggest that this models why the TAM machinery is essential for efficient folding of proteins acquired via lateral gene transfer.

Tracking the evolution of 3D gene organization demonstrates its connection to phenotypic divergence

It has recently been shown that the organization of genes in eukaryotic genomes, and specifically in 3D, is strongly related to gene expression and function and partially conserved between organisms. However, previous studies of 3D genomic organization analyzed each organism independently from others. Here, we propose an approach for unified inter-organismal analysis of gene organization based on a network representation of Hi-C data. We define and detect four classes of spatially co-evolving orthologous modules (SCOMs), i.e. gene families that co-evolve in their 3D organization, based on patterns of divergence and conservation of distances. We demonstrate our methodology on Hi-C data from Saccharomyces cerevisiae and Schizosaccharomyces pombe, and identify, among others, modules relating to RNA splicing machinery and chromatin silencing by small RNA which are central to S. pombe's lifestyle. Our results emphasize the importance of 3D genomic organization in eukaryotes and suggest that the evolutionary mechanisms that shape gene organization affect the organism fitness and phenotypes. The proposed algorithms can be utilized in future studies of genome evolution and comparative analysis of spatial genomic organization in different tissues, conditions and single cells.

Efficient algorithms for reconstructing gene content by co-evolution

BACKGROUND

In a previous study we demonstrated that co-evolutionary information can be utilized for improving the accuracy of ancestral gene content reconstruction. To this end, we defined a new computational problem, the Ancestral Co-Evolutionary (ACE) problem, and developed algorithms for solving it.

RESULTS

In the current paper we generalize our previous study in various ways. First, we describe new efficient computational approaches for solving the ACE problem. The new approaches are based on reductions to classical methods such as linear programming relaxation, quadratic programming, and min-cut. Second, we report new computational hardness results related to the ACE, including practical cases where it can be solved in polynomial time.Third, we generalize the ACE problem and demonstrate how our approach can be used for inferring parts of the genomes of non-ancestral organisms. To this end, we describe a heuristic for finding the portion of the genome ('dominant set') that can be used to reconstruct the rest of the genome with the lowest error rate. This heuristic utilizes both evolutionary information and co-evolutionary information.We implemented these algorithms on a large input of the ACE problem (95 unicellular organisms, 4,873 protein families, and 10, 576 of co-evolutionary relations), demonstrating that some of these algorithms can outperform the algorithm used in our previous study. In addition, we show that based on our approach a 'dominant set' cab be used reconstruct a major fraction of a genome (up to 79%) with relatively low error-rate (e.g. 0.11). We find that the 'dominant set' tends to include metabolic and regulatory genes, with high evolutionary rate, and low protein abundance and number of protein-protein interactions.

CONCLUSIONS

The ACE problem can be efficiently extended for inferring the genomes of organisms that exist today. In addition, it may be solved in polynomial time in many practical cases. Metabolic and regulatory genes were found to be the most important groups of genes necessary for reconstructing gene content of an organism based on other related genomes.