Evolutionary History of Alzheimer Disease-Causing Protein Family Presenilins with Pathological Implications.

Presenilin proteins make the catalytic component of γ-secretase, a multiprotein transmembrane protease, and are type II transmembrane proteins. Amyloid protein, Notch, and beta catenin are among more than 90 substrates of Presenilins. Mutations in Presenilins lead to defects in proteolytic cleavage of its substrate resulting in some of the most devastating pathological conditions including Alzheimer disease (AD), developmental disorders, and cancer. In addition to catalytic roles, Presenilin protein is also shown to be involved in many non-catalytic roles, i.e., calcium homeostasis, regulation of autophagy, and protein trafficking, etc. These proteolytic proteins are highly conserved and are present in almost all the major eukaryotic groups. Studies, performed on a wide variety of organisms ranging from human to unicellular dictyostelium, have shown the important catalytic and non-catalytic roles of Presenilins. In this study, we infer the evolutionary patterns and history of Presenilins as well as of other γ-secretase proteins. We show that Presenilins are the most ancient of the γ-secretase proteins and that Presenilins may have their origin in the last common ancestor (LCA) of Eukaryotes. We also demonstrate that Presenilin proteins generally lack diversifying selection during the course of their evolution. Through evolutionary trace analysis, we show that Presenilin protein sites that undergo mutations in Familial Alzheimer disease, are highly conserved in metazoans. Finally, we discuss the evolutionary, physiological, and pathological implications of our findings and propose that the evolutionary profile of Presenilins supports the loss of function hypothesis of AD pathogenesis.

Identifying Clusters of High Confidence Homologies in Multiple Sequence Alignments.

Multiple sequence alignment (MSA) is ubiquitous in evolution and bioinformatics. MSAs are usually taken to be a known and fixed quantity on which to perform downstream analysis despite extensive evidence that MSA accuracy and uncertainty affect results. These errors are known to cause a wide range of problems for downstream evolutionary inference, ranging from false inference of positive selection to long branch attraction artifacts. The most popular approach to dealing with this problem is to remove (filter) specific columns in the MSA that are thought to be prone to error. Although popular, this approach has had mixed success and several studies have even suggested that filtering might be detrimental to phylogenetic studies. We present a graph-based clustering method to address MSA uncertainty and error in the software Divvier (available at https://github.com/simonwhelan/Divvier), which uses a probabilistic model to identify clusters of characters that have strong statistical evidence of shared homology. These clusters can then be used to either filter characters from the MSA (partial filtering) or represent each of the clusters in a new column (divvying). We validate Divvier through its performance on real and simulated benchmarks, finding Divvier substantially outperforms existing filtering software by retaining more true pairwise homologies calls and removing more false positive pairwise homologies. We also find that Divvier, in contrast to other filtering tools, can alleviate long branch attraction artifacts induced by MSA and reduces the variation in tree estimates caused by MSA uncertainty.

S18 family of mitochondrial ribosomal proteins: evolutionary history and Gly132 polymorphism in colon carcinoma.

S18 family of mitochondrial ribosomal proteins (MRPS18, S18) consists of three members, S18-1 to -3. Earlier, we found that overexpression of S18-2 protein resulted in immortalization and eventual transformation of primary rat fibroblasts. The S18-1 and -3 have not exhibited such abilities. To understand the differences in protein properties, the evolutionary history of S18 family was analyzed. The S18-3, followed by S18-1 and S18-2 emerged as a result of ancient gene duplication in the root of eukaryotic species tree, followed by two metazoan-specific gene duplications. However, the most conserved metazoan S18 homolog is the S18-1; it shares the most sequence similarity with S18 proteins of bacteria and of other eukaryotic clades. Evolutionarily conserved residues of S18 proteins were analyzed in various cancers. S18-2 is mutated at a higher rate, compared with S18-1 and -3 proteins. Moreover, the evolutionarily conserved residue, Gly132 of S18-2, shows genetic polymorphism in colon adenocarcinomas that was confirmed by direct DNA sequencing.Concluding, S18 family represents the yet unexplored important mitochondrial ribosomal proteins.

Tracing the evolution of FERM domain of Kindlins.

Kindlin proteins represent a novel family of evolutionarily conserved FERM domain containing proteins (FDCPs) and are members of B4.1 superfamily. Kindlins consist of three conserved protein homologs in vertebrates: Kindlin-1, Kindlin-2 and Kindlin-3. All three homologs are associated with focal adhesions and are involved in Integrin activation. FERM domain of each Kindlin is bipartite and plays a key role in Integrin activation. A single ancestral Kindlin protein can be traced back to earliest metazoans, e.g., to Parazoa. This protein underwent multiple rounds of duplication in vertebrates, leading to the present Kindlin family. In this study, we trace phylogenetic and evolutionary history of Kindlin FERM domain with respect to FERM domain of other FDCPs. We show that FERM domain in Kindlin homologs is conserved among Kindlins but amount of conservation is less in comparison with FERM domain of other members in B4.1 superfamily. Furthermore, insertion of Pleckstrin Homology like domain in Kindlin FERM domain has important evolutionary and functional consequences. Important residues in Kindlins are traced and ranked according to their evolutionary significance. The structural and functional significance of high ranked residues is highlighted and validated by their known involvement in Kindlin associated diseases. In light of these findings, we hypothesize that FERM domain originated from a proto-Talin protein in unicellular or proto-multicellular organism and advent of multi-cellularity was accompanied by burst of FDCPs, which supported multi-cellularity functions required for complex organisms. This study helps in developing a better understanding of evolutionary history of FERM domain of FDCPs and the role of FERM domain in metazoan evolution.

Quantitative synteny scoring improves homology inference and partitioning of gene families.

BACKGROUND: Clustering sequences into families has long been an important step in characterization of genes and proteins. There are many algorithms developed for this purpose, most of which are based on either direct similarity between gene pairs or some sort of network structure, where weights on edges of constructed graphs are based on similarity. However, conserved synteny is an important signal that can help distinguish homology and it has not been utilized to its fullest potential. RESULTS: Here, we present GenFamClust, a pipeline that combines the network properties of sequence similarity and synteny to assess homology relationship and merge known homologs into groups of gene families. GenFamClust identifies homologs in a more informed and accurate manner as compared to similarity based approaches. We tested our method against the Neighborhood Correlation method on two diverse datasets consisting of fully sequenced genomes of eukaryotes and synthetic data. CONCLUSIONS: The results obtained from both datasets confirm that synteny helps determine homology and GenFamClust improves on Neighborhood Correlation method. The accuracy as well as the definition of synteny scores is the most valuable contribution of GenFamClust.