The effect of epistasis on sexually antagonistic genetic variation

There is increasing evidence of segregating sexually antagonistic (SA) genetic variation for fitness in laboratory and wild populations, yet the conditions for the maintenance of such variation can be restrictive. Epistatic interactions between genes can contribute to the maintenance of genetic variance in fitness and we suggest that epistasis between SA genes should be pervasive. Here, we explore its effect on SA genetic variation in fitness using a two locus model with negative epistasis. Our results demonstrate that epistasis often increases the parameter space showing polymorphism for SA loci. This is because selection in one locus is affected by allele frequencies at the other, which can act to balance net selection in males and females. Increased linkage between SA loci had more marginal effects. We also show that under some conditions, large portions of the parameter space evolve to a state where male benefit alleles are fixed at one locus and female benefit alleles at the other. This novel effect of epistasis on SA loci, which we term the ‘equity effect’, may have important effects on population differentiation and may contribute to speciation. More generally, these results support the suggestion that epistasis contributes to population divergence.     

Sex allocation and sexual conflict in simultaneously hermaphroditic animals

Links between sex allocation (SA) and sexual conflict in simultaneous hermaphrodites have been evident since Charnov''s landmark paper published 30 years ago. We discuss two links, namely the potential for sexual conflict over SA between sperm donor and recipient, and the importance of post-copulatory sexual selection and the resulting sexual conflict for the evolution of SA. We cover the little empirical and theoretical work exploring these links, and present an experimental test of one theoretical prediction. The link between SA and sexual conflict is an interesting field for future empirical and theoretical research.     

Contribution of Fluorophore Dynamics and Solvation to Resonant Energy Transfer in Protein-DNA Complexes: A Molecular-Dynamics Study

In Förster resonance energy transfer (FRET) experiments, extracting accurate structural information about macromolecules depends on knowing the positions and orientations of donor and acceptor fluorophores. Several approaches have been employed to reduce uncertainties in quantitative FRET distance measurements. Fluorophore-position distributions can be estimated by surface accessibility (SA) calculations, which compute the region of space explored by the fluorophore within a static macromolecular structure. However, SA models generally do not take fluorophore shape, dye transition-moment orientation, or dye-specific chemical interactions into account. We present a detailed molecular-dynamics (MD) treatment of fluorophore dynamics for an ATTO donor/acceptor dye pair and specifically consider as case studies dye-labeled protein-DNA intermediates in Cre site-specific recombination. We carried out MD simulations in both an aqueous solution and glycerol/water mixtures to assess the effects of experimental solvent systems on dye dynamics. Our results unequivocally show that MD simulations capture solvent effects and dye-dye interactions that can dramatically affect energy transfer efficiency. We also show that results from SA models and MD simulations strongly diverge in cases where donor and acceptor fluorophores are in close proximity. Although atomistic simulations are computationally more expensive than SA models, explicit MD studies are likely to give more realistic results in both homogeneous and mixed solvents. Our study underscores the model-dependent nature of FRET analyses, but also provides a starting point to develop more realistic in silico approaches for obtaining experimental ensemble and single-molecule FRET data.     

Conflict and competition between model-based and model-free control

A large literature has accumulated suggesting that human and animal decision making is driven by at least two systems, and that important functions of these systems can be captured by reinforcement learning algorithms. The “model-free” system caches and uses stimulus–value or stimulus–response associations, and the “model-based” system implements more flexible planning using a model of the world. However, it is not clear how the two systems interact during deliberation and how a single decision emerges from this process, especially when they disagree. Most previous work has assumed that while the systems operate in parallel, they do so independently, and they combine linearly to influence decisions. Using an integrated reinforcement learning/drift-diffusion model, we tested the hypothesis that the two systems interact in a non-linear fashion similar to other situations with cognitive conflict. We differentiated two forms of conflict: action conflict, a binary state representing whether the systems disagreed on the best action, and value conflict, a continuous measure of the extent to which the two systems disagreed on the difference in value between the available options. We found that decisions with greater value conflict were characterized by reduced model-based control and increased caution both with and without action conflict. Action conflict itself (the binary state) acted in the opposite direction, although its effects were less prominent. We also found that between-system conflict was highly correlated with within-system conflict, and although it is less clear a priori why the latter might influence the strength of each system above its standard linear contribution, we could not rule it out. Our work highlights the importance of non-linear conflict effects, and provides new constraints for more detailed process models of decision making. It also presents new avenues to explore with relation to disorders of compulsivity, where an imbalance between systems has been implicated.     

Coupling Protein Side-Chain and Backbone Flexibility Improves the Re-design of Protein-Ligand Specificity

Interactions between small molecules and proteins play critical roles in regulating and facilitating diverse biological functions, yet our ability to accurately re-engineer the specificity of these interactions using computational approaches has been limited. One main difficulty, in addition to inaccuracies in energy functions, is the exquisite sensitivity of protein–ligand interactions to subtle conformational changes, coupled with the computational problem of sampling the large conformational search space of degrees of freedom of ligands, amino acid side chains, and the protein backbone. Here, we describe two benchmarks for evaluating the accuracy of computational approaches for re-engineering protein-ligand interactions: (i) prediction of enzyme specificity altering mutations and (ii) prediction of sequence tolerance in ligand binding sites. After finding that current state-of-the-art “fixed backbone” design methods perform poorly on these tests, we develop a new “coupled moves” design method in the program Rosetta that couples changes to protein sequence with alterations in both protein side-chain and protein backbone conformations, and allows for changes in ligand rigid-body and torsion degrees of freedom. We show significantly increased accuracy in both predicting ligand specificity altering mutations and binding site sequences. These methodological improvements should be useful for many applications of protein – ligand design. The approach also provides insights into the role of subtle conformational adjustments that enable functional changes not only in engineering applications but also in natural protein evolution.     

Mass spectrometry‐based protein–protein interaction networks for the study of human diseases

A better understanding of the molecular mechanisms underlying disease is key for expediting the development of novel therapeutic interventions. Disease mechanisms are often mediated by interactions between proteins. Insights into the physical rewiring of protein–protein interactions in response to mutations, pathological conditions, or pathogen infection can advance our understanding of disease etiology, progression, and pathogenesis and can lead to the identification of potential druggable targets. Advances in quantitative mass spectrometry (MS)‐based approaches have allowed unbiased mapping of these disease‐mediated changes in protein–protein interactions on a global scale. Here, we review MS techniques that have been instrumental for the identification of protein–protein interactions at a system‐level, and we discuss the challenges associated with these methodologies as well as novel MS advancements that aim to address these challenges. An overview of examples from diverse disease contexts illustrates the potential of MS‐based protein–protein interaction mapping approaches for revealing disease mechanisms, pinpointing new therapeutic targets, and eventually moving toward personalized applications.     

The combined use of photoaffinity labeling and surface plasmon resonance‐based technology identifies multiple salicylic acid‐binding proteins

Salicylic acid (SA) is a small phenolic molecule that not only is the active ingredient in the multi‐functional drug aspirin, but also serves as a plant hormone that affects diverse processes during growth, development, responses to abiotic stresses and disease resistance. Although a number of SA‐binding proteins (SABPs) have been identified, the underlying mechanisms of action of SA remain largely unknown. Efforts to identify additional SA targets, and thereby elucidate the complex SA signaling network in plants, have been hindered by the lack of effective approaches. Here, we report two sensitive approaches that utilize SA analogs in conjunction with either a photoaffinity labeling technique or surface plasmon resonance‐based technology to identify and evaluate candidate SABPs from Arabidopsis. Using these approaches, multiple proteins, including the E2 subunit of α‐ketoglutarate dehydrogenase and the glutathione S‐transferases GSTF2, GSTF8, GSTF10 and GSTF11, were identified as SABPs. Their association with SA was further substantiated by the ability of SA to inhibit their enzymatic activity. The photoaffinity labeling and surface plasmon resonance‐based approaches appear to be more sensitive than the traditional approach for identifying plant SABPs using size‐exclusion chromatography with radiolabeled SA, as these proteins exhibited little to no SA‐binding activity in such an assay. The development of these approaches therefore complements conventional techniques and helps dissect the SA signaling network in plants, and may also help elucidate the mechanisms through which SA acts as a multi‐functional drug in mammalian systems.     

Architecture and Assembly of HIV Integrase Multimers in the Absence of DNA Substrates

We have applied small angle x-ray scattering and protein cross-linking coupled with mass spectrometry to determine the architectures of full-length HIV integrase (IN) dimers in solution. By blocking interactions that stabilize either a core-core domain interface or N-terminal domain intermolecular contacts, we show that full-length HIV IN can form two dimer types. One is an expected dimer, characterized by interactions between two catalytic core domains. The other dimer is stabilized by interactions of the N-terminal domain of one monomer with the C-terminal domain and catalytic core domain of the second monomer as well as direct interactions between the two C-terminal domains. This organization is similar to the “reaching dimer” previously described for wild type ASV apoIN and resembles the inner, substrate binding dimer in the crystal structure of the PFV intasome. Results from our small angle x-ray scattering and modeling studies indicate that in the absence of its DNA substrate, the HIV IN tetramer assembles as two stacked reaching dimers that are stabilized by core-core interactions. These models of full-length HIV IN provide new insight into multimer assembly and suggest additional approaches for enzyme inhibition.     

An Evaluation of High-Throughput Approaches to QTL Mapping in Saccharomyces cerevisiae

Dissecting the molecular basis of quantitative traits is a significant challenge and is essential for understanding complex diseases. Even in model organisms, precisely determining causative genes and their interactions has remained elusive, due in part to difficulty in narrowing intervals to single genes and in detecting epistasis or linked quantitative trait loci. These difficulties are exacerbated by limitations in experimental design, such as low numbers of analyzed individuals or of polymorphisms between parental genomes. We address these challenges by applying three independent high-throughput approaches for QTL mapping to map the genetic variants underlying 11 phenotypes in two genetically distant Saccharomyces cerevisiae strains, namely (1) individual analysis of >700 meiotic segregants, (2) bulk segregant analysis, and (3) reciprocal hemizygosity scanning, a new genome-wide method that we developed. We reveal differences in the performance of each approach and, by combining them, identify eight polymorphic genes that affect eight different phenotypes: colony shape, flocculation, growth on two nonfermentable carbon sources, and resistance to two drugs, salt, and high temperature. Our results demonstrate the power of individual segregant analysis to dissect QTL and address the underestimated contribution of interactions between variants. We also reveal confounding factors like mutations and aneuploidy in pooled approaches, providing valuable lessons for future designs of complex trait mapping studies.     

Multivalent protein–protein interactions are pivotal regulators of eukaryotic Hsp70 complexes

Heat shock protein 70 (Hsp70) is a molecular chaperone and central regulator of protein homeostasis (proteostasis). Paramount to this role is Hsp70’s binding to client proteins and co-chaperones to produce distinct complexes, such that understanding the protein–protein interactions (PPIs) of Hsp70 is foundational to describing its function and dysfunction in disease. Mounting evidence suggests that these PPIs include both “canonical” interactions, which are universally conserved, and “non-canonical” (or “secondary”) contacts that seem to have emerged in eukaryotes. These two categories of interactions involve discrete binding surfaces, such that some clients and co-chaperones engage Hsp70 with at least two points of contact. While the contributions of canonical interactions to chaperone function are becoming increasingly clear, it can be challenging to deconvolute the roles of secondary interactions. Here, we review what is known about non-canonical contacts and highlight examples where their contributions have been parsed, giving rise to a model in which Hsp70’s secondary contacts are not simply sites of additional avidity but are necessary and sufficient to impart unique functions. From this perspective, we propose that further exploration of non-canonical contacts will generate important insights into the evolution of Hsp70 systems and inspire new approaches for developing small molecules that tune Hsp70-mediated proteostasis.     

High affinity nucleic acid aptamers for streptavidin incorporated into bi-specific capture ligands

We have isolated 2′-Fluoro-substituted RNA aptamers that bind to streptavidin (SA) with an affinity around 7 ± 1.8 nM, comparable with that of recently described peptide aptamers. Binding to SA was not prevented by prior saturation with biotin, enabling nucleic acid aptamers to form useful ternary complexes. Mutagenesis, secondary structure analysis, ribonuclease footprinting and deletion analysis provided evidence for the essential structural features of SA-binding aptamers. In order to provide a general method for the exploitation of these aptamers, we produced derivatives in which they were fused to the naturally structured RNA elements, CopT or CopA. In parallel, we produced derivatives of CD4-binding aptamers fused to the complementary CopA or CopT elements. When mixed, these two chimeric aptamers rapidly hybridized, by virtue of CopA–CopT complementarity, to form stable, bi-functional aptamers that we called ‘adaptamers’. We show that a CD4–SA-binding adaptamer can be used to capture CD4 onto a SA-derivatized surface, illustrating their general utility as indirect affinity ligands.     

Does signalling mitigate the cost of agonistic interactions? A test in a cricket that has lost its song

Prevailing models of animal communication assume that signalling during aggressive conflict mitigates the costs of fighting. We tested this assumption by staging dyadic encounters between male field crickets, Teleogryllus oceanicus, under three conditions: (i) both males could sing aggressive songs, (ii) neither male could sing, and (iii) one male could sing but the other could not. We conducted experiments on males from a Hawaiian population from Kauai that has recently evolved signal loss, and males from a Hawaiian population from the Big Island that has not. Among both populations, interactions between two silent males were characterized by higher levels of aggression than interactions involving one or two singing males. Because the level of aggression is strongly related to the cost of fighting, these data demonstrate that signalling mitigates the cost of fighting. In mixed trials, we found no statistically significant differences between the behaviour of calling and non-calling males in either population. We conclude that there is no evidence that the Kauai population exhibits special adaptations to alleviate the costs of signal loss. Finally, we found that males were much more likely to signal after their opponent''s retreat than after their own retreat. Aggressive song therefore meets the definition of a ‘victory display’.     

Using genome-wide measurements for computational prediction of SH2–peptide interactions

Peptide-recognition modules (PRMs) are used throughout biology to mediate protein–protein interactions, and many PRMs are members of large protein domain families. Recent genome-wide measurements describe networks of peptide–PRM interactions. In these networks, very similar PRMs recognize distinct sets of peptides, raising the question of how peptide-recognition specificity is achieved using similar protein domains. The analysis of individual protein complex structures often gives answers that are not easily applicable to other members of the same PRM family. Bioinformatics-based approaches, one the other hand, may be difficult to interpret physically. Here we integrate structural information with a large, quantitative data set of SH2 domain–peptide interactions to study the physical origin of domain–peptide specificity. We develop an energy model, inspired by protein folding, based on interactions between the amino-acid positions in the domain and peptide. We use this model to successfully predict which SH2 domains and peptides interact and uncover the positions in each that are important for specificity. The energy model is general enough that it can be applied to other members of the SH2 family or to new peptides, and the cross-validation results suggest that these energy calculations will be useful for predicting binding interactions. It can also be adapted to study other PRM families, predict optimal peptides for a given SH2 domain, or study other biological interactions, e.g. protein–DNA interactions.     

A gene-specific DNA sequencing chip for exploring molecular evolutionary change

Sequencing by hybridization (SBH) approaches to DNA sequencing face two conflicting constraints. First, in order to ensure that the target DNA binds reliably, the oligonucleotide probes that are attached to the chip array must be >15 bp in length. Secondly, the total number of possible 15 bp oligonucleotides is too large (>4¹⁵) to fit on a chip with current technology. To circumvent the conflict between these two opposing constraints, we present a novel gene-specific DNA chip design. Our design is based on the idea that not all conceivable oligonucleotides need to be placed on a chip— only those that capture sequence combinations occurring in nature. Our approach uses a training set of aligned sequences that code for the gene in question. We compute the minimum number of oligonucleotides (generally 15–30 bp in length) that need to be placed on a DNA chip to capture the variation implied by the training set using a graph search algorithm. We tested the approach in silico using cytochrome-b sequences. Results indicate that on average, 98% of the sequence of an unknown target can be determined using the approach.     

How does breeding system variation modulate sexual antagonism?

The study of sexually antagonistic (SA) traits remains largely limited to dioecious (separate sex), mobile animals. However, the occurrence of sexual conflict is restricted neither by breeding system (the mode of sexual reproduction, e.g. dioecy or hermaphroditism) nor by sessility. Here, we synthesize how variation in breeding system can affect the evolution and expression of intra- and inter-locus sexual conflicts in plants and animals. We predict that, in hermaphrodites, SA traits will (i) display lower levels of polymorphism; (ii) respond more quickly to selection; and (iii) involve unique forms of interlocus conflict over sex allocation, mating roles and selfing rates. Explicit modelling and empirical tests in a broader range of breeding systems are necessary to obtain a general understanding of the evolution of SA traits.     

Structural basis of the methylation specificity of R.DpnI

R.DpnI consists of N-terminal catalytic and C-terminal winged helix domains that are separately specific for the Gm6ATC sequences in Dam-methylated DNA. Here we present a crystal structure of R.DpnI with oligoduplexes bound to the catalytic and winged helix domains and identify the catalytic domain residues that are involved in interactions with the substrate methyl groups. We show that these methyl groups in the Gm6ATC target sequence are positioned very close to each other. We further show that the presence of the two methyl groups requires a deviation from B-DNA conformation to avoid steric conflict. The methylation compatible DNA conformation is complementary with binding sites of both R.DpnI domains. This indirect readout of methylation adds to the specificity mediated by direct favorable interactions with the methyl groups and solvation/desolvation effects. We also present hydrogen/deuterium exchange data that support ‘crosstalk’ between the two domains in the identification of methylated DNA, which should further enhance R.DpnI methylation specificity.     

EEG Source Reconstruction Reveals Frontal-Parietal Dynamics of Spatial Conflict Processing

Cognitive control requires the suppression of distracting information in order to focus on task-relevant information. We applied EEG source reconstruction via time-frequency linear constrained minimum variance beamforming to help elucidate the neural mechanisms involved in spatial conflict processing. Human subjects performed a Simon task, in which conflict was induced by incongruence between spatial location and response hand. We found an early (∼200 ms post-stimulus) conflict modulation in stimulus-contralateral parietal gamma (30–50 Hz), followed by a later alpha-band (8–12 Hz) conflict modulation, suggesting an early detection of spatial conflict and inhibition of spatial location processing. Inter-regional connectivity analyses assessed via cross-frequency coupling of theta (4–8 Hz), alpha, and gamma power revealed conflict-induced shifts in cortical network interactions: Congruent trials (relative to incongruent trials) had stronger coupling between frontal theta and stimulus-contrahemifield parietal alpha/gamma power, whereas incongruent trials had increased theta coupling between medial frontal and lateral frontal regions. These findings shed new light into the large-scale network dynamics of spatial conflict processing, and how those networks are shaped by oscillatory interactions.