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Tracing the evolution of FERM domain of Kindlins
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, Centres, Science for Life Laboratory, SciLifeLab.
2014 (English)In: Molecular Phylogenetics and Evolution, ISSN 1055-7903, E-ISSN 1095-9513, Vol. 80, 193-204 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2014. Vol. 80, 193-204 p.
Keyword [en]
FERM domain, Kindlins, Protein domain evolution, Evolutionary trace analysis, Focal adhesions, PH domain
National Category
Genetics Evolutionary Biology
URN: urn:nbn:se:kth:diva-156436DOI: 10.1016/j.ympev.2014.08.008ISI: 000343742200020ScopusID: 2-s2.0-84906731105OAI: diva2:768554

QC 20141204

Available from: 2014-12-04 Created: 2014-11-28 Last updated: 2016-02-17Bibliographically approved
In thesis
1. From genomes to post-processing of Bayesian inference of phylogeny
Open this publication in new window or tab >>From genomes to post-processing of Bayesian inference of phylogeny
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Life is extremely complex and amazingly diverse; it has taken billions of years of evolution to attain the level of complexity we observe in nature now and ranges from single-celled prokaryotes to multi-cellular human beings. With availability of molecular sequence data, algorithms inferring homology and gene families have emerged and similarity in gene content between two genes has been the major signal utilized for homology inference. Recently there has been a significant rise in number of species with fully sequenced genome, which provides an opportunity to investigate and infer homologs with greater accuracy and in a more informed way. Phylogeny analysis explains the relationship between member genes of a gene family in a simple, graphical and plausible way using a tree representation. Bayesian phylogenetic inference is a probabilistic method used to infer gene phylogenies and posteriors of other evolutionary parameters. Markov chain Monte Carlo (MCMC) algorithm, in particular using Metropolis-Hastings sampling scheme, is the most commonly employed algorithm to determine evolutionary history of genes. There are many softwares available that process results from each MCMC run, and explore the parameter posterior but there is a need for interactive software that can analyse both discrete and real-valued parameters, and which has convergence assessment and burnin estimation diagnostics specifically designed for Bayesian phylogenetic inference.

In this thesis, a synteny-aware approach for gene homology inference, called GenFamClust (GFC), is proposed that uses gene content and gene order conservation to infer homology. The feature which distinguishes GFC from earlier homology inference methods is that local synteny has been combined with gene similarity to infer homologs, without inferring homologous regions. GFC was validated for accuracy on a simulated dataset. Gene families were computed by applying clustering algorithms on homologs inferred from GFC, and compared for accuracy, dependence and similarity with gene families inferred from other popular gene family inference methods on a eukaryotic dataset. Gene families in fungi obtained from GFC were evaluated against pillars from Yeast Gene Order Browser. Genome-wide gene families for some eukaryotic species are computed using this approach.

Another topic focused in this thesis is the processing of MCMC traces for Bayesian phylogenetics inference. We introduce a new software VMCMC which simplifies post-processing of MCMC traces. VMCMC can be used both as a GUI-based application and as a convenient command-line tool. VMCMC supports interactive exploration, is suitable for automated pipelines and can handle both real-valued and discrete parameters observed in a MCMC trace. We propose and implement joint burnin estimators that are specifically applicable to Bayesian phylogenetics inference. These methods have been compared for similarity with some other popular convergence diagnostics. We show that Bayesian phylogenetic inference and VMCMC can be applied to infer valuable evolutionary information for a biological case – the evolutionary history of FERM domain.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. viii, 65 p.
TRITA-CSC-A, ISSN 1653-5723 ; 2016:01
Bayesian inference
National Category
Bioinformatics (Computational Biology)
Research subject
Computer Science
urn:nbn:se:kth:diva-181319 (URN)978-91-7595-849-1 (ISBN)
Public defence
2016-02-25, Fire, Tomtebodavägen 23, 171 65, Solna, 14:00 (English)

QC 20160201

Available from: 2016-02-01 Created: 2016-01-31 Last updated: 2016-02-01Bibliographically approved

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