Differences in evolutionary pressure acting within highly conserved ortholog groups

Recent advances suggest increased influence of selective pressure in allostery

Allosteric regulation of protein functions is ubiquitous in organismal biology, but the principles governing its evolution are not well understood. Here we discuss recent studies supporting the large-scale existence of latent allostery in ancestor proteins of superfamilies. As suggested, the evolution of allostery could be driven by the need for specificity in paralogs of slow evolving protein complexes with conserved active sites. The same slow evolution is displayed by purifying selection exhibited in allosteric proteins with somatic mutations involved in cancer, where disease-associated mutations are enriched in both orthosteric and allosteric sites. Consequently, disease-associated variants can be used to identify druggable allosteric sites that are specific for paralogs in protein superfamilies with otherwise similar functions.

Expression of the epigenetic factor BORIS (CTCFL) in the human genome

BORIS, or CTCFL, the so called Brother of the Regulator of Imprinted Sites because of the extensive homology in the central DNA binding region of the protein to the related regulator, CTCF, is expressed in early gametogenesis and in multiple cancers but not in differentiated somatic cells. Thus it is a member of the cancer testes antigen group (CTAs). Since BORIS and CTCF target common DNA binding sites, these proteins function on two levels, the first level is their regulation via the methylation context of the DNA target site and the second level is their distinct and different epigenetic associations due to differences in the non-homologous termini of the proteins. The regulation on both of these levels is extensive and complex and the sphere of influence of each of these proteins is associated with vastly different cellular signaling processes. On the level of gene expression, BORIS has three known promoters and multiple spliced mRNAs which adds another level of complexity to this intriguing regulator. BORIS expression is observed in the majority of cancer tissues and cell lines analyzed up to today. The expression profile and essential role of BORIS in cancer make this molecule very attractive target for cancer immunotherapy. This review summarizes what is known about BORIS regarding its expression, structure, and function and then presents some theoretical considerations with respect to its genome wide influence and its potential for use as a vaccine for cancer immunotherapy.

Clinical relevance of single nucleotide polymorphisms of the high mobility group box 1 protein gene in patients with major trauma in southwest China

BACKGROUND

High-mobility group box protein 1 (HMGB1) is a pivotal late mediator involved in the development of sepsis and multiple organ dysfunction syndrome (MODS) in critically ill patients. While several single nucleotide polymorphisms (SNPs) have been demonstrated to be critical determinants for outcome of critically ill patients, little is known about the clinical relevance of SNPs of the HMGB1 gene up to date.

METHODS

A total of 3 tag SNPs of the HMGB1 gene were selected using HapMap database and linkage disequilibrium analysis. The tag SNPs were genotyped using a pyrosequencing methodology in 556 unrelated patients with major trauma. Peripheral whole blood samples obtained immediately after admission were determined for HMGB1 production in response to ex vivo lipopolysaccharide (LPS) stimulation.

RESULTS

The rs2249825 SNP and the haplotype TCG were significantly associated with LPS-induced HMGB1 production by peripheral blood leukocytes. There were also significant differences in sepsis morbidity rate and MOD scores among patients with different genotypes of the rs2249825. In addition, the patients with the wild-type haplotype TCG had a lesser sepsis morbidity rate and MOD scores than those without the TCG haplotype.

CONCLUSION

A total of 3 SNPs might act as tag SNPs for the entire HMGB1 gene. The rs2249825 and the haplotype TCG might be used as relevant risk estimate for the development of sepsis and MODS in patients with major trauma.

Discovering local patterns of co-evolution: computational aspects and biological examples

Background

Co-evolution is the process in which two (or more) sets of orthologs exhibit a similar or correlative pattern of evolution. Co-evolution is a powerful way to learn about the functional interdependencies between sets of genes and cellular functions and to predict physical interactions. More generally, it can be used for answering fundamental questions about the evolution of biological systems.

Orthologs that exhibit a strong signal of co-evolution in a certain part of the evolutionary tree may show a mild signal of co-evolution in other branches of the tree. The major reasons for this phenomenon are noise in the biological input, genes that gain or lose functions, and the fact that some measures of co-evolution relate to rare events such as positive selection. Previous publications in the field dealt with the problem of finding sets of genes that co-evolved along an entire underlying phylogenetic tree, without considering the fact that often co-evolution is local.

Results

In this work, we describe a new set of biological problems that are related to finding patterns of local co-evolution. We discuss their computational complexity and design algorithms for solving them. These algorithms outperform other bi-clustering methods as they are designed specifically for solving the set of problems mentioned above.

We use our approach to trace the co-evolution of fungal, eukaryotic, and mammalian genes at high resolution across the different parts of the corresponding phylogenetic trees. Specifically, we discover regions in the fungi tree that are enriched with positive evolution. We show that metabolic genes exhibit a remarkable level of co-evolution and different patterns of co-evolution in various biological datasets.

In addition, we find that protein complexes that are related to gene expression exhibit non-homogenous levels of co-evolution across different parts of the fungi evolutionary line. In the case of mammalian evolution, signaling pathways that are related to neurotransmission exhibit a relatively higher level of co-evolution along the primate subtree.

Conclusions

We show that finding local patterns of co-evolution is a computationally challenging task and we offer novel algorithms that allow us to solve this problem, thus opening a new approach for analyzing the evolution of biological systems.