Category Archives: molecular evolution

Mapping of the Influenza-A Hemagglutinin Serotypes Evolution by the ISSCOR Method

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Mapping of the Influenza-A Hemagglutinin Serotypes Evolution by the ISSCOR Method
Jan P. Radomski, Piotr P. Slonimski, Włodzimierz Zagórski-Ostoja, Piotr Borowicz
(Submitted on 8 Nov 2013)

Analyses and visualizations by the ISSCOR method of influenza virus hemagglutinin genes of different A-subtypes revealed some rather striking temporal relationships between groups of individual gene subsets. Based on these findings we consider application of the ISSCOR-PCA method for analyses of large sets of homologous genes to be a worthwhile addition to a toolbox of genomics – allowing for a rapid diagnostics of trends, and ultimately even aiding an early warning of newly emerging epidemiological threats.

The hemagglutinin mutation E391K of pandemic 2009 influenza revisited

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The hemagglutinin mutation E391K of pandemic 2009 influenza revisited
Jan P. Radomski, Piotr Płoński, Włodzimierz Zagórski-Ostoja
(Submitted on 8 Nov 2013)

Phylogenetic analyses based on small to moderately sized sets of sequential data lead to overestimating mutation rates in influenza hemagglutinin (HA) by at least an order of magnitude. Two major underlying reasons are: the incomplete lineage sorting, and a possible absence in the analyzed sequences set some of key missing ancestors. Additionally, during neighbor joining tree reconstruction each mutation is considered equally important, regardless of its nature. Here we have implemented a heuristic method optimizing site dependent factors weighting differently 1st, 2nd, and 3rd codon position mutations, allowing to extricate incorrectly attributed sub-clades. The least squares regression analysis of distribution of frequencies for all mutations observed on a partially disentangled tree for a large set of unique 3243 HA sequences, along all nucleotide positions, was performed for all mutations as well as for non-equivalent amino acid mutations: in both cases demonstrating almost flat gradients, with a very slight downward slope towards the 3′-end positions. The mean mutation rates per sequence per year were 3.83*10^-4 for the all mutations, and 9.64*10^-5 for the non-equivalent ones.

Can we predict the mutation rate at the single nucleotide scale in the human genome?

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Can we predict the mutation rate at the single nucleotide scale in the human genome?
Adam Eyre-Walker, Ying Chen
(Submitted on 29 Oct 2013)

It has been recently claimed that it is possible to predict the rate of de novo mutation of each site in the human genome with almost perfect accuracy (Michaelson et al. (2012) Cell, 151, 1431-1442). We show that this claim is unwarranted. By considering the correlation between the rate of de novo mutation and the predictions from the model of Michaelson et al., we show that there could be substantial unexplained variance in the mutation rate. We also demonstrate that the model of Michaelson et al. fails to capture a major component of the variation in the mutation rate, that which is local but not associated with simple context.

The epigenome of evolving Drosophila neo-sex chromosomes: dosage compensation and heterochromatin formation

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The epigenome of evolving Drosophila neo-sex chromosomes: dosage compensation and heterochromatin formation
Qi Zhou, Christopher E. Ellison, Vera B. Kaiser, Artyom A. Alekseyenko, Andrey A. Gorchakov, Doris Bachtrog
(Submitted on 26 Sep 2013)

Drosophila Y chromosomes are composed entirely of silent heterochromatin, while male X chromosomes have highly accessible chromatin and are hypertranscribed due to dosage compensation. Here, we dissect the molecular mechanisms and functional pressures driving heterochromatin formation and dosage compensation of the recently formed neo-sex chromosomes of Drosophila miranda. We show that the onset of heterochromatin formation on the neo-Y is triggered by an accumulation of repetitive DNA. The neo-X has evolved partial dosage compensation and we find that diverse mutational paths have been utilized to establish several dozen novel binding consensus motifs for the dosage compensation complex on the neo-X, including simple point mutations at pre-binding sites, insertion and deletion mutations, microsatellite expansions, or tandem amplification of weak binding sites. Spreading of these silencing or activating chromatin modifications to adjacent regions results in massive mis-expression of neo-sex linked genes, and little correspondence between functionality of genes and their silencing on the neo-Y or dosage compensation on the neo-X. Intriguingly, the genomic regions being targeted by the dosage compensation complex on the neo-X and those becoming heterochromatic on the neo-Y show little overlap, possibly reflecting different propensities along the ancestral chromosome to adopt active or repressive chromatin configurations. Our findings have broad implications for current models of sex chromosome evolution, and demonstrate how mechanistic constraints can limit evolutionary adaptations. Our study also highlights how evolution can follow predictable genetic trajectories, by repeatedly acquiring the same 21-bp consensus motif for recruitment of the dosage compensation complex, yet utilizing a diverse array of random mutational changes to attain the same phenotypic outcome.

Predicting protein contact map using evolutionary and physical constraints by integer programming

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Predicting protein contact map using evolutionary and physical constraints by integer programming
Zhiyong Wang, Jinbo Xu
(Submitted on 8 Aug 2013)

Motivation. Protein contact map describes the pairwise spatial and functional relationship of residues in a protein and contains key information for protein 3D structure prediction. Although studied extensively, it remains very challenging to predict contact map using only sequence information. Most existing methods predict the contact map matrix element-by-element, ignoring correlation among contacts and physical feasibility of the whole contact map. A couple of recent methods predict contact map based upon residue co-evolution, taking into consideration contact correlation and enforcing a sparsity restraint, but these methods require a very large number of sequence homologs for the protein under consideration and the resultant contact map may be still physically unfavorable.
Results. This paper presents a novel method PhyCMAP for contact map prediction, integrating both evolutionary and physical restraints by machine learning and integer linear programming (ILP). The evolutionary restraints include sequence profile, residue co-evolution and context-specific statistical potential. The physical restraints specify more concrete relationship among contacts than the sparsity restraint. As such, our method greatly reduces the solution space of the contact map matrix and thus, significantly improves prediction accuracy. Experimental results confirm that PhyCMAP outperforms currently popular methods no matter how many sequence homologs are available for the protein under consideration. PhyCMAP can predict contacts within minutes after PSIBLAST search for sequence homologs is done, much faster than the two recent methods PSICOV and EvFold.

The dynamics of alternative pathways to compensatory substitution

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The dynamics of alternative pathways to compensatory substitution
Chris A. Nasrallah
(Submitted on 9 Aug 2013)

The role of epistatic interactions among loci is a central question in evolutionary biology and is increasingly relevant in the genomic age. While the population genetics of compensatory substitution have received considerable attention, most studies have focused on the case when natural selection is very strong against deleterious intermediates. In the biologically-plausible scenario of weak to moderate selection there exist two alternate pathways for compensatory substitution. In one pathway, a deleterious mutation becomes fixed prior to occurrence of the compensatory mutation. In the other, the two loci are simultaneously polymorphic. The rates of compensatory substitution along these two pathways and their relative probabilities are functions of the population size, selection strength, mutation rate, and recombination rate. In this paper these rates and path probabilities are derived analytically and verified using population genetic simulations. The expected time durations of these two paths are similar when selection is moderate, but not when selection is weak. The effect of recombination on the dynamics of the substitution process are explored using simulation. Using the derived rates, a phylogenetic substitution model of the compensatory evolution process is presented that could be used for inference of population genetic parameters from interspecific data.

The genome of the medieval Black Death agent

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The genome of the medieval Black Death agent (extended abstract)
Ashok Rajaraman, Eric Tannier, Cedric Chauve
(Submitted on 29 Jul 2013)

The genome of a 650 year old Yersinia pestis bacteria, responsible for the medieval Black Death, was recently sequenced and assembled into 2,105 contigs from the main chromosome. According to the point mutation record, the medieval bacteria could be an ancestor of most Yersinia pestis extant species, which opens the way to reconstructing the organization of these contigs using a comparative approach. We show that recent computational paleogenomics methods, aiming at reconstructing the organization of ancestral genomes from the comparison of extant genomes, can be used to correct, order and complete the contig set of the Black Death agent genome, providing a full chromosome sequence, at the nucleotide scale, of this ancient bacteria. This sequence suggests that a burst of mobile elements insertions predated the Black Death, leading to an exceptional genome plasticity and increase in rearrangement rate.

RNA secondary structure prediction from multi-aligned sequences

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RNA secondary structure prediction from multi-aligned sequences
Michiaki Hamada
(Submitted on 8 Jul 2013)

It has been well accepted that the RNA secondary structures of most functional non-coding RNAs (ncRNAs) are closely related to their functions and are conserved during evolution. Hence, prediction of conserved secondary structures from evolutionarily related sequences is one important task in RNA bioinformatics; the methods are useful not only to further functional analyses of ncRNAs but also to improve the accuracy of secondary structure predictions and to find novel functional RNAs from the genome. In this review, I focus on common secondary structure prediction from a given aligned RNA sequence, in which one secondary structure whose length is equal to that of the input alignment is predicted. I systematically review and classify existing tools and algorithms for the problem, by utilizing the information employed in the tools and by adopting a unified viewpoint based on maximum expected gain (MEG) estimators. I believe that this classification will allow a deeper understanding of each tool and provide users with useful information for selecting tools for common secondary structure predictions.

Complete sequence representation across human X and Y centromeric regions

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Complete sequence representation across human X and Y centromeric regions
Karen E. Hayden, Yulia Newton, Miten Jain, Nicolas Altemose, Huntington F. Willard, Jim Kent
(Submitted on 28 Jun 2013)

The human genome remains incomplete, with multi-megabase sized gaps representing the endogenous centromeres and other heterochromatic regions. These regions are commonly enriched with long arrays of near-identical tandem repeats, known as satellite DNAs, that offer a limited number of variant sites to differentiate individual repeat copies across millions of bases. This substantial sequence homogeneity challenges available assembly strategies, and as a result, centromeric regions are omitted from ongoing genomic studies. To address this problem, we present a locally ordered assembly across two haploid human satellite arrays on chromosomes X and Y, resulting in an initial linear representation of 3.83 Mb of centromeric DNA within an individual genome. To further expand the utility of each centromeric reference sequence, we evaluate sites within the arrays for short-read mappability and chromosome specificity. As satellite DNAs evolve in a concerted manner, we use these centromeric assemblies to assess the extent of sequence variation among 372 individuals from distinct human populations. In doing so, we identify two ancient satellite array variants in both X and Y centromeres as determined by array length and sequence composition. This study provides an initial linear representation and comprehensive sequence characterization of a regional centromere and establishes a foundation to extend genomic characterization to these sites as well as to other repeat-rich regions within complex genomes.

Our paper: Integrating influenza antigenic dynamics with molecular evolution

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This guest post is by Trevor Bedford (@trvrb) on his paper (along with coauthors): Bedford et al. Integrating influenza antigenic dynamics with molecular evolution arXived here.

The influenza virus shows a remarkable capacity to evolve to escape human immunity. Many other viruses, like measles, do not have this capacity. After infection with measles, a person gains life-long immunity to the virus, and hence measles has become constrained to be a childhood infection. Continual antigenic evolution in influenza necessitates frequent vaccine updates to provide sufficient protection to circulating strains.

Antigenic differences between strains are commonly quantified using the hemagglutination inhibition (HI) assay, which measures the ability of antibodies created against one strain to interfere with virus from another strain. The resulting HI data is represented as a sparse matrix of comparisons between viruses from strains A, B, C… and sera from strains X, Y, Z… Taken by itself, this matrix is difficult to work with. Experienced virologists can pick up the loss of reactivity between groups of viruses in the noisy HI data, but these patterns are not fully quantified.

In our new paper, available on the arXiv, we extend techniques of multidimensional scaling (MDS) pioneered by Derek Smith and colleagues for the analysis of influenza antigenic data. Here, we attempted to bring the MDS antigenic model into a fully Bayesian framework and refer to the revised technique as Bayesian MDS (BMDS). In this model, viruses and sera are represented as 2D coordinates on an antigenic map in which their pairwise distances yield expectations for the HI titers, with antigenically similar viruses lying close to one another and antigenically distant viruses lying far apart.

By placing antigenic cartography in a Bayesian context, we are able to integrate other data sources, most notably sequence data. In this case, genetic sequences provide an evolutionary tree relating virus strains and we assume that antigenic location evolves along this tree in a 2D diffusion process. This process imposes a prior on antigenic locations in which evolutionary similar viruses have a prior expectation of lying close to one another on the map. In the paper, we use this BMDS / diffusion model to investigate patterns of antigenic evolution in 4 circulating lineages of influenza and show that antigenic drift determines to a large degree incidence patterns across time and across lineages.

The paper is also up on GitHub, which I’ll keep updating as it goes through the review process. The BMDS model is implemented in the software package BEAST and is available in the latest source code. I hope to provide tutorials on running the BMDS model in the not-to-distant future.