Biology subjects

Wheeler, L. C.

Publications and source records attributed to Wheeler, L. C..

7 recordsLinked to original sources

bioRxiv2026

Rapid floral syndrome convergence in Penstemon through independent genetic variation

The repeated evolution of certain complex traits within a given lineage is compelling and presents an opportunity for understanding selective and genetic features that promote rapid multi-trait adaptation. The North American plant genus Penstemon is one such example, with numerous evolutionary shifts from ancestral bee syndrome to hummingbird syndrome flowers. Here, we traced the evolution of multi-trait floral phenotypes within a focal clade of Penstemon using a new whole genome phylogenomic estimate and found four independent origins of the hummingbird syndrome. We found strong evolutionary convergence in floral traits across these four origins and observed that the hummingbird syndrome assembled rapidly, without leaving a signal of stepwise modification of traits. Patterns of evolutionary correlations among floral traits in bee syndrome Penstemon species likely enable rapid shifts to hummingbird syndrome. Using phylogenomic tests for introgression, we found no evidence that adaptive introgression has fueled the four repeated origins of hummingbird syndrome flowers. Patterns of allele sharing were instead consistent with substantial levels of incomplete lineage sorting, suggesting repeated complex adaptation involves de novo mutation or adaptation from ancestral variation.

evolutionary biology↗

bioRxiv2023

Predictive links between petal color and pigment quantities in natural Penstemon hybrids

Flowers have evolved remarkable diversity in petal color, in large part due to pollinator-mediated selection. This diversity arises from specialized metabolic pathways that generate conspicuous pigments. Despite the clear link between flower color and floral pigment production, studies determining predictive relationships between pigmentation and petal color are currently lacking. In this study, we analyze a dataset consisting of hundreds of natural Penstemon hybrids that exhibit variation in flower color, including blue, purple, pink, and red. For each individual hybrid, we measured anthocyanin pigment content and petal spectral reflectance. We found that floral pigment quantities are correlated with hue, chroma, and brightness as calculated from petal spectral reflectance data: hue is related to the relative amounts of delphinidin vs. pelargonidin pigmentation, whereas brightness and chroma are correlated with the total anthocyanin pigmentation. We used a partial least squares regression approach to identify predictive relationships between pigment production and petal reflectance. We find that pigment quantity data provide robust predictions of petal reflectance, confirming a pervasive assumption that differences in pigmentation should predictably influence flower color. Moreover, we find that reflectance data enables accurate inferences of pigment quantities, where the full reflectance spectra provide much more accurate inference of pigment quantities than spectral attributes (brightness, chroma, and hue). Our predictive framework provides readily interpretable model coefficients relating spectral attributes of petal reflectance to underlying pigment quantities. These relationships represent key links between genetic changes affecting anthocyanin production and ecological functions of petal coloration.

systems biology↗

bioRxiv2020

Structure and contingency determine mutational hotspots for flower color evolution

Evolutionary genetic studies have uncovered abundant evidence for genomic hotspots of phenotypic evolution, as well as biased patterns of mutations at those loci. However, the theoretical basis for this concentration of particular types of mutations at particular loci remains largely unexplored. In addition, historical contingency is known to play a major role in evolutionary trajectories, but has not been reconciled with the existence of such hotspots. For example, do the appearance of hotspots and the fixation of different types of mutations at those loci depend on the starting state and/or on the nature and direction of selection? Here we use a computational approach to examine these questions, focusing the anthocyanin pigmentation pathway, which has been extensively studied in the context of flower color transitions. We investigate two transitions that are common in nature, the transition from blue to purple pigmentation and from purple to red pigmentation. Both sets of simulated transitions occur with a small number of mutations at just four loci and show strikingly similar peaked shapes of evolutionary trajectories, with the mutations of largest effect occurring early but not first. Nevertheless, the types of mutations (biochemical vs. regulatory) as well as their direction and magnitude are contingent on the particular transition. These simulated color transitions largely mirror findings from natural flower color transitions, which are known to occur via repeated changes at a few hotspot loci. Still, some types of mutations observed in our simulated color evolution are rarely observed in nature, suggesting that pleiotropic effects further limit the trajectories between color phenotypes. Overall, our results indicate that the branching structure of the pathway leads to a predictable concentration of evolutionary change at hotspot loci, but the types of mutations at these loci and their order is contingent on the evolutionary context.

evolutionary biology↗

bioRxiv2020

Learning Peptide Recognition Rules for a Low-Specificity Protein

Many proteins interact with short linear regions of target proteins. For some proteins, however, it is difficult to identify a well-defined sequence motif that defines its target peptides. To overcome this difficulty, we used supervised machine learning to train a model that treats each peptide as a collection of easily-calculated biochemical features rather than as an amino acid sequence. As a test case, we dissected the peptide-recognition rules for human S100A5 (hA5), a low-specificity calcium binding protein. We trained a Random Forest model against a recently released, high-throughput phage display dataset collected for hA5. The model identifies hydrophobicity and shape complementarity, rather than polar contacts, as the primary determinants of peptide binding specificity in hA5. We tested this hypothesis by solving a crystal structure of hA5 and through computational docking studies of diverse peptides onto hA5. These structural studies revealed that peptides exhibit multiple binding modes at the hA5 peptide interface--all of which have few polar contacts with hA5. Finally, we used our trained model to predict new, plausible binding targets in the human proteome. This revealed a fragment of the protein -1-syntrophin binds to hA5. Our work helps better understand the biochemistry and biology of hA5, as well as demonstrating how high-throughput experiments coupled with machine learning of biochemical features can reveal the determinants of binding specificity in low-specificity proteins.

biophysics↗

bioRxiv2020

Were ancestral proteins less specific?

Some have hypothesized that ancestral proteins were, on average, less specific than their descendants. If true, this would provide a universal axis along which to organize protein evolution and suggests that reconstructed ancestral proteins may be uniquely powerful tools for protein engineering. Ancestral sequence reconstruction studies are one line of evidence used to support this hypothesis. Previously, we performed such a study, investigating the evolution of peptide binding specificity for the paralogs S100A5 and S100A6. The modern proteins appeared more specific than their last common ancestor (ancA5/A6), as each paralog bound a subset of the peptides bound by ancA5/A6. In the current study, we revisit this transition, using quantitative phage display to measure the interactions of 19,194 random peptides with human S100A5, S100A6, and ancA5/A6. This unbiased screen reveals a different picture. While S100A5 and S100A6 do indeed bind to a subset of the peptides recognized by ancA5/A6, they also acquired new peptide partners outside of the set recognized by ancA5/A6. Our previous work showed that ancA5/A6 had lower specificity than its descendants when measured against biological targets; our new work shows that ancA5/A6 has similar specificity to the modern proteins when measured against a random set of peptide targets. This demonstrates that altered biological specificity does not necessarily indicate altered intrinsic specificity, and sounds a cautionary note for using ancestral reconstruction studies with biological targets as a means to infer global evolutionary trends in specificity.

evolutionary biology↗

bioRxiv2019

Computational modeling of anthocyanin pathway evolution

Alteration of metabolic pathways is a common mechanism underlying the evolution of new phenotypes. Flower color is a striking example of the importance of metabolic evolution in a complex phenotype, wherein shifts in the activity of the underlying pathway lead to a wide range of pigments. Although experimental work has identified common classes of mutations responsible for transitions among colors, we lack a unifying model that relates pathway function and activity to the evolution of distinct pigment phenotypes. One challenge in creating such a model is the branching structure of pigment pathways, which may lead to evolutionary trade-offs due to competition for shared substrates. In order to predict the effects of shifts in enzyme function and activity on pigment production, we created a simple kinetic model of a major plant pigmentation pathway: the anthocyanin pathway. This model describes the production of the three classes of blue, purple and red anthocyanin pigments, and accordingly, includes multiple branches and substrate competition. We first studied the general behavior of this model using a realistic, functional set of parameters. We then stochastically evolved the pathway toward a defined optimum and analyzed the patterns of fixed mutations. This approach allowed us to quantify the probability density of trajectories through pathway state space and identify the types and number of changes. Finally, we examined whether the observed trajectories and constraints align with experimental observations, i.e., the predominance of mutations which change color by altering the function of branching genes in the pathway. These analyses provide a theoretical framework that can be used to predict the consequences of new mutations in terms of both pigment phenotypes and pleiotropic effects.

evolutionary biology↗

bioRxiv2017

Conservation of specificity in two low-specificity proteins

S100 proteins bind linear peptide regions of target proteins and modulate their activity. The peptide binding interface, however, has remarkably low specificity and can interact with many target peptides. It is not clear if the interface discriminates targets in a biological context, or whether biological specificity is achieved exclusively through external factors such as subcellular localization. To discriminate these possibilities, we used an evolutionary biochemical approach to trace the evolution of paralogs S100A5 and S100A6. We first used isothermal titration calorimetry to study the binding of a collection of peptides with diverse sequence, hydrophobicity, and charge to human S100A5 and S100A6. These proteins bound distinct, but overlapping, sets of peptide targets. We then studied the peptide binding properties of S100A5 and S100A6 orthologs sampled from across five representative amniote species. We found that the pattern of binding specificity was conserved along all lineages, for the last 320 million years, despite the low specificity of each protein. We next used Ancestral Sequence Reconstruction to determine the binding specificity of the last common ancestor of the paralogs. We found the ancestor bound the whole set of peptides bound by modern S100A5 and S100A6 proteins, suggesting that paralog specificity evolved by subfunctionalization. To rule out the possibility that specificity is conserved because it is difficult to modify, we identified a single historical mutation that, when reverted in human S100A5, gave it the ability to bind an S100A6-specific peptide. These results indicate that there are strong evolutionary constraints on peptide binding specificity, and that, despite being able to bind a large number of targets, the specificity of S100 peptide interfaces is indeed important for the biology of these proteins.

biochemistry↗