Showing papers on "Ancestral reconstruction published in 2019"

Highly thermostable carboxylic acid reductases generated by ancestral sequence reconstruction

Abstract: Carboxylic acid reductases (CARs) are biocatalysts of industrial importance. Their properties, especially their poor stability, render them sub-optimal for use in a bioindustrial pipeline. Here, we employed ancestral sequence reconstruction (ASR) – a burgeoning engineering tool that can identify stabilizing but enzymatically neutral mutations throughout a protein. We used a three-algorithm approach to reconstruct functional ancestors of the Mycobacterial and Nocardial CAR1 orthologues. Ancestral CARs (AncCARs) were confirmed to be CAR enzymes with a preference for aromatic carboxylic acids. Ancestors also showed varied tolerances to solvents, pH and in vivo-like salt concentrations. Compared to well-studied extant CARs, AncCARs had a Tm up to 35 °C higher, with half-lives up to nine times longer than the greatest previously observed. Using ancestral reconstruction we have expanded the existing CAR toolbox with three new thermostable CAR enzymes, providing access to the high temperature biosynthesis of aldehydes to drive new applications in biocatalysis. Thomas et al. uses ancestral sequence reconstruction (ASR) tool to reconstruct functional ancestors of the Mycobacterial and Nocardial CAR1 orthologues, representing one of the largest reconstructed proteins to date. These ancestral CARs display varied tolerances to solvents, pH and in vivo-like salt concentrations along with high thermostability compared to well-studied extant CARs.

Ancestral reconstruction reveals mechanisms of ERK regulatory evolution.

Abstract: Protein kinases are crucial to coordinate cellular decisions and therefore their activities are strictly regulated. Previously we used ancestral reconstruction to determine how CMGC group kinase specificity evolved (Howard et al., 2014). In the present study, we reconstructed ancestral kinases to study the evolution of regulation, from the inferred ancestor of CDKs and MAPKs, to modern ERKs. Kinases switched from high to low autophosphorylation activity at the transition to the inferred ancestor of ERKs 1 and 2. Two synergistic amino acid changes were sufficient to induce this change: shortening of the β3-αC loop and mutation of the gatekeeper residue. Restoring these two mutations to their inferred ancestral state led to a loss of dependence of modern ERKs 1 and 2 on the upstream activating kinase MEK in human cells. Our results shed light on the evolutionary mechanisms that led to the tight regulation of a kinase that is central in development and disease.

PARAMO: A Pipeline for Reconstructing Ancestral Anatomies Using Ontologies and Stochastic Mapping

Abstract: Comparative phylogenetics has been largely lacking a method for reconstructing the evolution of phenotypic entities that consist of ensembles of multiple discrete traits -- entire organismal anatomies or organismal body regions. In this study, we provide a new approach named PARAMO (Phylogenetic Ancestral Reconstruction of Anatomy by Mapping Ontologies) that appropriately models anatomical dependencies and uses ontology-informed amalgamation of stochastic maps to reconstruct phenotypic evolution at different levels of anatomical hierarchy including entire phenotypes. This approach provides new opportunities for tracking phenotypic radiations and evolution of organismal anatomies.

Ancestral reconstruction of protein interaction networks.

Abstract: The molecular and cellular basis of novelty is an active area of research in evolutionary biology. Until very recently, the vast majority of cellular phenomena were so difficult to sample that cross-species studies of biochemistry were rare and comparative analysis at the level of biochemical systems was almost impossible. Recent advances in systems biology are changing what is possible, however, and comparative phylogenetic methods that can handle this new data are wanted. Here, we introduce the term "phylogenetic latent variable models" (PLVMs, pronounced "plums") for a class of models that has recently been used to infer the evolution of cellular states from systems-level molecular data, and develop a new parameterization and fitting strategy that is useful for comparative inference of biochemical networks. We deploy this new framework to infer the ancestral states and evolutionary dynamics of protein-interaction networks by analyzing >16,000 predominantly metazoan co-fractionation and affinity-purification mass spectrometry experiments. Based on these data, we estimate ancestral interactions across unikonts, broadly recovering protein complexes involved in translation, transcription, proteostasis, transport, and membrane trafficking. Using these results, we predict an ancient core of the Commander complex made up of CCDC22, CCDC93, C16orf62, and DSCR3, with more recent additions of COMMD-containing proteins in tetrapods. We also use simulations to develop model fitting strategies and discuss future model developments.

PARAMO pipeline: reconstructing ancestral anatomies using ontologies and stochastic mapping

Abstract: Comparative phylogenetics has been largely lacking a method for reconstructing the evolution of phenotypic entities that consist of ensembles of multiple discrete traits -- entire organismal anatomies or organismal body regions. In this study, we provide a new approach named PARAMO (Phylogenetic Ancestral Reconstruction of Anatomy by Mapping Ontologies) that appropriately models anatomical dependencies and uses ontology-informed amalgamation of stochastic maps to reconstruct phenotypic evolution at different levels of anatomical hierarchy including entire phenotypes. This approach provides new opportunities for tracking phenotypic radiations and evolution of organismal anatomies.

Ancestral reconstruction of sunflower karyotypes reveals dramatic chromosomal evolution

Abstract: Mapping the chromosomal rearrangements between species can inform our understanding of genome evolution, reproductive isolation, and speciation. Here we present a systematic survey of chromosomal rearrangements in the annual sunflowers, which is a group known for extreme karyotypic diversity. We build high-density genetic maps for two subspecies of the prairie sunflower, Helianthus petiolaris ssp. petiolaris and H. petiolaris ssp. fallax. Using a novel algorithm implemented in the accompanying R package syntR, we identify blocks of synteny between these two subspecies and previously published high-density genetic maps. We reconstruct ancestral karyotypes for annual sunflowers using those synteny blocks and conservatively estimate that there have been 9.7 chromosomal rearrangements per million years - a high rate of chromosomal evolution. Although the rate of inversion is even higher than the rate of translocation in this group, we further find that every extant karyotype is distinguished by between 1 and 3 translocations involving only 8 of the 17 chromosomes. This non-random sampling suggests that certain chromosomes are prone to translocation and may thus contribute disproportionately to widespread hybrid sterility in sunflowers. These data deepen our understanding of chromosome evolution and confirm that Helianthus has an exceptional rate of chromosomal rearrangement that likely facilitates similarly rapid diversification.

Ancestral Genome Reconstruction

Abstract: Reconstruction of extinct ancestral genomes is an important topic in comparative genomics and has a wide range of applications. By comparing a current-day species against its ancestor, we can deduce how it differs from the ancestor and infer detailed information about the evolution of species. With more and more fully sequenced genomes becoming available, we are able to reconstruct ancestors at the whole genome level by using evolutionary events such as genome rearrangements, gene insertions, deletions and duplications. In this chapter, we will present the concepts related to whole genome evolution and ancestral reconstruction. We will review evolutionary models and algorithms in pairwise comparison of genomes, computing of the median problem and optimizations in inferring phylogenies and ancestors from multiple genomes.

Ambiguity coding allows accurate inference of evolutionary parameters from alignments in an aggregated state-space

Abstract: How can we best learn the history of a proteins evolution? Ideally, a model of sequence evolution should capture both the process that generates genetic variation and the functional constraints determining which changes are fixed. However, in practical terms the most suitable approach may simply be the one that combines the convenience of easily available input data with the ability to return useful parameter estimates. For example, we might be interested in a measure of the strength of selection (typically obtained using a codon model) or an ancestral structure (obtained using structural modelling based on inferred amino acid sequence and side chain configuration).nnBut what if data in the relevant state-space are not readily available? We show that it is possible to obtain accurate estimates of the outputs of interest using an established method for handling missing data. Encoding observed characters in an alignment as ambiguous representations of characters in a larger state-space allows the application of models with the desired features to data that lack the resolution that is normally required. This strategy is viable because the evolutionary path taken through the observed space contains information about states that were likely visited in the "unseen" state-space. To illustrate this, we consider two examples with amino acid sequences as input.nnWe show that{omega} , a parameter describing the relative strength of selection on non-synonymous and synonymous changes, can be estimated in an unbiased manner using an adapted version of a standard 61-state codon model. Using simulated and empirical data, we find that ancestral amino acid side chain configuration can be inferred by applying a 55-state empirical model to 20-state amino acid data. Where feasible, combining inputs from both ambiguity-coded and fully resolved data improves accuracy. Adding structural information to as few as 12.5% of the sequences in an amino acid alignment results in remarkable ancestral reconstruction performance compared to a benchmark that considers the full rotamer state information. These examples show that our methods permit the recovery of evolutionary information from sequences where it has previously been inaccessible.

MapGL: Inferring evolutionary gain and loss of short genomic sequence features by phylogenetic maximum parsimony.

Abstract: Comparative genomics studies are growing in number partly because of their unique ability to provide insight into shared and divergent biology between species. Of particular interest is the use of phylogenetic methods to infer the evolutionary history of cis-regulatory sequence features, which contribute strongly to phenotypic divergence and are frequently gained and lost in eutherian genomes. Understanding the mechanisms by which cis-regulatory element turnover generate emergent phenotypes is crucial to our understanding of adaptive evolution. Ancestral reconstruction methods can place species-specific cis-regulatory features in their evolutionary context, thus increasing our understanding of the process of regulatory sequence turnover. However, applying these methods to gain and loss of cis-regulatory features currently requires complex workflows which represent a potential barrier to widespread adoption by a broad scientific community. MapGL simplifies phylogenetic inference of the evolutionary history of short genomic sequence features by combining the necessary steps into a single piece of software with a simple set of inputs and outputs.

Ancestral Reconstruction and Investigations of Genomic Recombination on some Pentapetalae Chloroplasts.

Abstract: In this article, we propose a semi-automated method to rebuild genome ancestors of chloroplasts by taking into account gene duplication. Two methods have been used in order to achieve this work: a naked eye investigation using homemade scripts, whose results are considered as a basis of knowledge, and a dynamic programming based approach similar to Needleman-Wunsch. The latter fundamentally uses the Gestalt pattern matching method of sequence matcher to evaluate the occurrences probability of each gene in the last common ancestor of two given genomes. The two approaches have been applied on chloroplastic genomes from Apiales, Asterales, and Fabids orders, the latter belonging to Pentapetalae group. We found that Apiales species do not undergo indels, while they occur in the Asterales and Fabids orders. A series of experiments was then carried out to extensively verify our findings by comparing the obtained ancestral reconstruction results with the latest released approach called MLGO (Maximum Likelihood for Gene-Order analysis).

Learning from ancestors.

Abstract: Predicting ancestral sequences of protein kinases reveals the molecular details that underlie different modes of activation.