Using the Mutation-Selection Framework to Characterize Selection on Protein Sequences

Substitution-Mutation Rate Ratio (c/µ) As Molecular Adaptation Test Beyond Ka/Ks: A SARS-COV-2 Case Study

The Ka/Ks ratio test, which assesses nonsynonymous versus synonymous substitution rates in Translated Region (TR) of a genome, is widely used to quantify fitness changes due to mutations but its critical limits are to be addressed. Ka/Ks can categorize the total fitness change as neutral (Ka/Ks = 1), beneficial (Ka/Ks > 1), or deleterious (Ka/Ks < 1), only if synonymous mutations are neutral. Otherwise, Ka/Ks only provides the fitness change due to protein sequence change. This neutrality assumption also renders this test inapplicable to sites in non-protein-coding UnTranslated Region (UTR). Our previous work introduced a substitution-mutation rate ratio (c/µ) per nucleotide site test (c: substitution rate in UTR/TR or a mean value of Ka and Ks in TR; and µ: mutation rate) as a generalized alternative to detect selection pressure, offering a broader application without forementioned presumptions. This paper derives a general equation linking c/µ with weighted Ks/µ and Ka/µ (c/µ = Ps*(Ks/μ) + Pa*(Ka/μ), Ps and Pa: proportions of synonymous and nonsynonymous sites under a mutation model and a codon table), demonstrating that Ka/Ks infers the same fitness change as c/µ does only if synonymous mutations are neutral (i.e. Ks/µ = 1). Otherwise, Ka/Ks might provide a different assignment from the c/µ test. Indeed, our comparative analysis of the c/µ and Ka/Ks tests across 25 proteins of SARS-COV-2 using three independent genomic sequence datasets shows that Ka/Ks inaccurately reports the type of fitness change for 7 proteins. Our findings advocate for the c/µ test to complement traditional Ka/Ks test to detect the selection pressure at a nucleotide site in a genome.

Enhanced detection and molecular modeling of adaptive mutations in SARS-CoV-2 coding and non-coding regions using the c/µ test

Accurately identifying mutations under beneficial selection in viral genomes is crucial for understanding their molecular evolution and pathogenicity. Traditional methods like the Ka/Ks test, which assesses non-synonymous (Ka) versus synonymous (Ks) substitution rates, assume that synonymous substitutions at synonymous sites are neutral and thus is equal to the mutation rate (µ). Yet, evidence suggests that synonymous sites in translated regions (TRs) and untranslated regions (UTRs) can be under strong beneficial selection (Ks > µ) and strongly conserved (Ks ≈ 0), leading to false predictions of adaptive mutations from codon-by-codon Ka/Ks analysis. Our previous work used a relative substitution rate test (c/µ, c: substitution rate in UTR/TR, and µ: mutation rate) to identify adaptive mutations in SARS-CoV-2 genome without the neutrality assumption of the synonymous sites. This study refines the c/µ test by optimizing µ value, leading to a smaller set of nucleotide and amino acid sites under beneficial selection in both UTR (11 sites with c/µ > 3) and TR (69 nonsynonymous sites: c/µ > 3 and Ka/Ks > 2.5; 107 synonymous sites: Ks/µ > 3). Encouragingly, the top two mutations in UTR and 70% of the top nonsynonymous mutations in TR had reported or predicted effects in the literature. Molecular modeling of top adaptive mutations for some critical proteins (S, NSP11, and NSP5) was carried out to elucidate the possible molecular mechanism of their adaptivity.

Insertions and Deletions: Computational Methods, Evolutionary Dynamics, and Biological Applications

Insertions and deletions constitute the second most important source of natural genomic variation. Insertions and deletions make up to 25% of genomic variants in humans and are involved in complex evolutionary processes including genomic rearrangements, adaptation, and speciation. Recent advances in long-read sequencing technologies allow detailed inference of insertions and deletion variation in species and populations. Yet, despite their importance, evolutionary studies have traditionally ignored or mishandled insertions and deletions due to a lack of comprehensive methodologies and statistical models of insertions and deletion dynamics. Here, we discuss methods for describing insertions and deletion variation and modeling insertions and deletions over evolutionary time. We provide practical advice for tackling insertions and deletions in genomic sequences and illustrate our discussion with examples of insertions and deletion-induced effects in human and other natural populations and their contribution to evolutionary processes. We outline promising directions for future developments in statistical methodologies that would allow researchers to analyze insertions and deletion variation and their effects in large genomic data sets and to incorporate insertions and deletions in evolutionary inference.

PEMPS: a phylogenetic software tool to model the evolution of metabolic pathways

BACKGROUND

Metabolic pathways support the enzyme flux that converts input chemicals into energy and cellular building blocks. With a constant rate of input, steady-state flux is achieved when metabolite concentrations and reaction rates remain constant over time. Individual genes undergo mutation, while selection acts on higher level functions of the pathway, such as steady-state flux where applicable. Modeling the evolution of metabolic pathways through mechanistic sets of ordinary differential equations is a piece of the genotype-phenotype map model for interpreting genetic variation and inter-specific differences. Such models can generate distinct compensatory changes and adaptive changes from directional selection, indicating single nucleotide polymorphisms and fixed differences that could affect phenotype. If used for inference, this would ultimately enable detection of selection on metabolic pathways as well as inference of ancestral states for metabolic pathway function.

RESULTS

A software tool for simulating the evolution of metabolic pathways based upon underlying biochemistry, phylogenetics, and evolutionary considerations is presented. The Python program, Phylogenetic Evolution of Metabolic Pathway Simulator (PEMPS), implements a mutation-selection framework to simulate the evolution of the pathway over a phylogeny by interfacing with COPASI to calculate the steady-state flux of the metabolic network, introducing mutations as alterations in parameter values according to a model, and calculating a fitness score and corresponding probability of fixation based on the change in steady-state flux value(s). Results from simulations are consistent with a priori expectations of fixation probabilities and systematic change in model parameters.

CONCLUSIONS

The PEMPS program simulates the evolution of a metabolic pathway with a mutation-selection modeling framework based on criteria like steady-state flux that is designed to work with SBML-formatted kinetic models, and Newick-formatted phylogenetic trees. The Python software is run on the Linux command line and is available at https://github.com/nmccloskey/PEMPS .

An Improved Codon Modeling Approach for Accurate Estimation of the Mutation Bias

Phylogenetic codon models are routinely used to characterize selective regimes in coding sequences. Their parametric design, however, is still a matter of debate, in particular concerning the question of how to account for differing nucleotide frequencies and substitution rates. This problem relates to the fact that nucleotide composition in protein-coding sequences is the result of the interactions between mutation and selection. In particular, because of the structure of the genetic code, the nucleotide composition differs between the three coding positions, with the third position showing a more extreme composition. Yet, phylogenetic codon models do not correctly capture this phenomenon and instead predict that the nucleotide composition should be the same for all three positions. Alternatively, some models allow for different nucleotide rates at the three positions, an approach conflating the effects of mutation and selection on nucleotide composition. In practice, it results in inaccurate estimation of the strength of selection. Conceptually, the problem comes from the fact that phylogenetic codon models do not correctly capture the fixation bias acting against the mutational pressure at the mutation–selection equilibrium. To address this problem and to more accurately identify mutation rates and selection strength, we present an improved codon modeling approach where the fixation rate is not seen as a scalar, but as a tensor. This approach gives an accurate representation of how mutation and selection oppose each other at equilibrium and yields a reliable estimate of the mutational process, while disentangling the mean fixation probabilities prevailing in different mutational directions.

HLA-H*02:07 Is a Membrane-Bound Ligand of Denisovan Origin That Protects against Lysis by Activated Immune Effectors

The biological relevance of genes initially categorized as “pseudogenes” is slowly emerging, notably in innate immunity. In the HLA region on chromosome 6, HLA-H is one such pseudogene; yet, it is transcribed, and its variation is associated with immune properties. Furthermore, two HLA-H alleles, H*02:07 and H*02:14, putatively encode a complete, membrane-bound HLA protein. Here we thus hypothesized that HLA-H contributes to immune homeostasis similarly to tolerogenic molecules HLA-G, -E, and -F. We tested if HLA-H*02:07 encodes a membrane-bound protein that can inhibit the cytotoxicity of effector cells. We used an HLA-null human erythroblast cell line transduced with HLA-H*02:07 cDNA to demonstrate that HLA-H*02:07 encodes a membrane-bound protein. Additionally, using a cytotoxicity assay, our results support that K562 HLA-H*02:07 inhibits human effector IL-2–activated PBMCs and human IL-2–independent NK92-MI cell line activity. Finally, through in silico genotyping of the Denisovan genome and haplotypic association with Denisovan-derived HLA-A*11, we also show that H*02:07 is of archaic origin. Hence, admixture with archaic humans brought a functional HLA-H allele into modern European and Asian populations.

A computational exploration of resilience and evolvability of protein-protein interaction networks

Protein-protein interaction (PPI) networks represent complex intra-cellular protein interactions, and the presence or absence of such interactions can lead to biological changes in an organism. Recent network-based approaches have shown that a phenotype's PPI network's resilience to environmental perturbations is related to its placement in the tree of life; though we still do not know how or why certain intra-cellular factors can bring about this resilience. Here, we explore the influence of gene expression and network properties on PPI networks' resilience. We use publicly available data of PPIs for E. coli, S. cerevisiae, and H. sapiens, where we compute changes in network resilience as new nodes (proteins) are added to the networks under three node addition mechanisms-random, degree-based, and gene-expression-based attachments. By calculating the resilience of the resulting networks, we estimate the effectiveness of these node addition mechanisms. We demonstrate that adding nodes with gene-expression-based preferential attachment (as opposed to random or degree-based) preserves and can increase the original resilience of PPI network in all three species, regardless of gene expression distribution or network structure. These findings introduce a general notion of prospective resilience, which highlights the key role of network structures in understanding the evolvability of phenotypic traits.

Characterizing Amino Acid Substitution with Complete Linkage of Sites on a Lineage

Amino acid substitution models are commonly used for phylogenetic inference, for ancestral sequence reconstruction, and for the inference of positive selection. All commonly used models explicitly assume that each site evolves independently, an assumption that is violated by both linkage and protein structural and functional constraints. We introduce two new models for amino acid substitution which incorporate linkage between sites, each based on the (population-genetic) Moran model. The first model is a generalized population process tracking arbitrarily many sites which undergo mutation, with individuals replaced according to their fitnesses. This model provides a reasonably complete framework for simulations but is numerically and analytically intractable. We also introduce a second model which includes several simplifying assumptions but for which some theoretical results can be derived. We analyze the simplified model to determine conditions where linkage is likely to have meaningful effects on sitewise substitution probabilities, as well as conditions under which the effects are likely to be negligible. These findings are an important step in the generation of tractable phylogenetic models that parameterize selective coefficients for amino acid substitution while accounting for linkage of sites leading to both hitchhiking and background selection.

Shifts in amino acid preferences as proteins evolve: A synthesis of experimental and theoretical work

Amino acid preferences vary across sites and time. While variation across sites is widely accepted, the extent and frequency of temporal shifts are contentious. Our understanding of the drivers of amino acid preference change is incomplete: To what extent are temporal shifts driven by adaptive versus nonadaptive evolutionary processes? We review phenomena that cause preferences to vary (e.g., evolutionary Stokes shift, contingency, and entrenchment) and clarify how they differ. To determine the extent and prevalence of shifted preferences, we review experimental and theoretical studies. Analyses of natural sequence alignments often detect decreases in homoplasy (convergence and reversions) rates, and variation in replacement rates with time-signals that are consistent with temporally changing preferences. While approaches inferring shifts in preferences from patterns in natural alignments are valuable, they are indirect since multiple mechanisms (both adaptive and nonadaptive) could lead to the observed signal. Alternatively, site-directed mutagenesis experiments allow for a more direct assessment of shifted preferences. They corroborate evidence from multiple sequence alignments, revealing that the preference for an amino acid at a site varies depending on the background sequence. However, shifts in preferences are usually minor in magnitude and sites with significantly shifted preferences are low in frequency. The small yet consistent perturbations in preferences could, nevertheless, jeopardize the accuracy of inference procedures, which assume constant preferences. We conclude by discussing if and how such shifts in preferences might influence widely used time-homogenous inference procedures and potential ways to mitigate such effects.

Ancestral Sequence Reconstruction: From Chemical Paleogenetics to Maximum Likelihood Algorithms and Beyond

As both a computational and an experimental endeavor, ancestral sequence reconstruction remains a timely and important technique. Modern approaches to conduct ancestral sequence reconstruction for proteins are built upon a conceptual framework from journal founder Emile Zuckerkandl. On top of this, work on maximum likelihood phylogenetics published in Journal of Molecular Evolution in 1996 was one of the first approaches for generating maximum likelihood ancestral sequences of proteins. From its computational history, future model development needs as well as potential applications in areas as diverse as computational systems biology, molecular community ecology, infectious disease therapeutics and other biomedical applications, and biotechnology are discussed. From its past in this journal, there is a bright future for ancestral sequence reconstruction in the field of evolutionary biology.

Detecting Signatures of Positive Selection against a Backdrop of Compensatory Processes

There are known limitations in methods of detecting positive selection. Common methods do not enable differentiation between positive selection and compensatory covariation, a major limitation. Further, the traditional method of calculating the ratio of nonsynonymous to synonymous substitutions (dN/dS) does not take into account the 3D structure of biomacromolecules nor differences between amino acids. It also does not account for saturation of synonymous mutations (dS) over long evolutionary time that renders codon-based methods ineffective for older divergences. This work aims to address these shortcomings for detecting positive selection through the development of a statistical model that examines clusters of substitutions in clusters of variable radii. Additionally, it uses a parametric bootstrapping approach to differentiate positive selection from compensatory processes. A previously reported case of positive selection in the leptin protein of primates was reexamined using this methodology.

Evolution and Structure of Proteins and Proteomes

This themed issue centered on the evolution and structure of proteins and proteomes is comprised of seven published manuscripts. [...].