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Cellular costs underpin micronutrient limitation in phytoplankton
McCain, J. Scott P.; Tagliabue, Alessandro; Susko, Edward; Achterberg, Eric P.; Allen, Andrew E.; Bertrand, Erin M. — 2021-06-21 <p>Micronutrients control phytoplankton growth in the ocean, influencing carbon export and fisheries. It is currently unclear how micronutrient scarcity affects cellular processes, and how interdependence across micronutrients arises. We show that proximate causes of micronutrient growth limitation and interdependence are governed by cumulative cellular costs of acquiring and using micronutrients. Using a mechanistic proteomic allocation model of a polar diatom focused on iron and manganese, we demonstrate how cellular processes fundamentally underpin micronutrient limitation, and how they interact and compensate for each other to shape cellular elemental stoichiometry and resource interdependence. We coupled our model with metaproteomic and environmental data, yielding a novel approach for estimating biogeochemical metrics including taxon-specific growth rates. Our results show that cumulative cellular costs govern how environmental conditions modify phytoplankton growth.</p>
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Data from: A phenotype-genotype codon model for detecting adaptive evolution
Jones, Chris; Youssef, Noor; Susko, Edward; Bielawski, Joseph — 2019-11-30 A central objective in biology is to link adaptive evolution in a gene to structural and/or functional phenotypic novelties. Yet most analytic methods make inferences mainly from either phenotypic data or genetic data alone. A small number of models have been developed to infer correlations between the rate of molecular evolution and changes in a discrete or continuous life history trait. But such correlations are not necessarily evidence of adaptation. Here we present a novel approach called the phenotype-genotype branch-site model (PG-BSM) designed to detect evidence of adaptive codon evolution associated with discrete-state phenotype evolution. An episode of adaptation is inferred under standard codon substitution models when there is evidence of positive selection in the form of an elevation in the nonsynonymous-to-synonymous rate ratio ω to a value ω > 1. As it is becoming increasingly clear that ω > 1 can occur without adaptation, the PG-BSM was formulated to infer an instance of adaptive evolution without appealing to evidence of positive selection. The null model makes use of a covarion-like component to account for general heterotachy (i.e., random changes in the evolutionary rate at a site over time). The alternative model employs samples of the phenotypic evolutionary history to test for phenomenological patterns of heterotachy consistent with specific mechanisms of molecular adaptation. These include (i) a persistent increase/decrease in ω at a site following a change in phenotype (the pattern) consistent with an increase/decrease in the functional importance of the site (the mechanism); and (ii) a transient increase in ω at a site along a branch over which the phenotype changed (the pattern) consistent with a change in the site's optimal amino acid (the mechanism). Rejection of the null is followed by post hoc analyses to identify sites with strongest evidence for adaptation in association with changes in the phenotype as well as the most likely evolutionary history of the phenotype. Simulation studies based on a novel method for generating mechanistically realistic signatures of molecular adaptation show that the PG-BSM has good statistical properties. Analyses of three real alignments show that site patterns identified post hoc are consistent with the specific mechanisms of adaptation included in the alternate model. Further simulation studies show that the covarion-like component of the PG-BSM plays a crucial role in mitigating recently discovered statistical pathologies associated with confounding by accounting for heterotachy-by-any-means.
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Supplementary material for: Long branch attraction biases in phylogenetics
Susko, Edward; Roger, Andrew — 2021-10-28 <p>Long branch attraction is a prevalent form of bias in phylogenetic estimation but the reasons for it are only partially understood. We argue here that it is largely due to differences in the sizes of the model spaces corresponding to different trees. Trees with long branches together allow much more flexible internal branch-length parameter estimation. Consequently, although each tree has the same number of parameters, trees with long branches together have larger effective model spaces. The problem of long branch attraction becomes particularly pronounced with partitioned data. Formulation of tree estimation as model selection leads us to propose bootstrap bias corrections as cross-checks on estimation when long branches end up being estimated together. </p>
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Data from: Bayesian long branch attraction bias and corrections
Susko, Edward — 2014-12-02 Previous work on the star-tree paradox has shown that Bayesian methods suffer from a long branch attraction bias. That work is extended to settings involving more taxa and partially resolved trees. The long branch attraction bias is confirmed to arise more broadly and an additional source of bias is found. A by-product of the analysis is methods that correct for biases toward particular topologies. The corrections can be easily calculated using existing Bayesian software. Posterior support for a set of two or more trees can thus be supplemented with corrected versions to cross-check or replace results. Simulations show the corrections to be highly effective.
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Data from: Modeling site heterogeneity with posterior mean site frequency profiles accelerates accurate phylogenomic estimation
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Wang, Huai-Chun; Minh, Bui Quang; Susko, Edward; Roger, Andrew J. — 2017-08-04 Proteins have distinct structural and functional constraints at different sites that lead to site-specific preferences for particular amino acid residues as the sequences evolve. Heterogeneity in the amino acid substitution process between sites is not modeled by commonly used empirical amino acid exchange matrices. Such model misspecification can lead to artefacts in phylogenetic estimation such as long-branch attraction. Although sophisticated site-heterogeneous mixture models have been developed to address this problem in both Bayesian and maximum likelihood (ML) frameworks, their formidable computational time and memory usage severely limits their use in large phylogenomic analyses. Here we propose a posterior mean site frequency (PMSF) method as a rapid and efficient approximation to full empirical profile mixture models for ML analysis. The PMSF approach assigns a conditional mean amino acid frequency profile to each site calculated based on a mixture model fitted to the data using a preliminary guide tree. These PMSF profiles can then be used for in-depth tree-searching in place of the full mixture model. Compared with widely used empirical mixture models with k classes, our implementation of PMSF in IQ-TREE (http://www.iqtree.org) speeds up the computation by approximately k /1.5-fold and requires a small fraction of the RAM. Furthermore, this speedup allows, for the first time, full nonparametric bootstrap analyses to be conducted under complex site-heterogeneous models on large concatenated data matrices. Our simulations and empirical data analyses demonstrate that PMSF can effectively ameliorate long-branch attraction artefacts. In some empirical and simulation settings PMSF provided more accurate estimates of phylogenies than the mixture models from which they derive.

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(Author: Susko, Edward)

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