Unfinished Business: Evolution of the MHC and the Adaptive Immune System of Jawed Vertebrates.

The major histocompatibility complex (MHC) is a large genetic region with many genes, including the highly polymorphic classical class I and II genes that play crucial roles in adaptive as well as innate immune responses. The organization of the MHC varies enormously among jawed vertebrates, but class I and II genes have not been found in other animals. How did the MHC arise, and are there underlying principles that can help us to understand the evolution of the MHC? This review considers what it means to be an MHC and the potential importance of genome-wide duplication, gene linkage, and gene coevolution for the emergence and evolution of an adaptive immune system. Then it considers what the original antigen-specific receptor and MHC molecule might have looked like, how peptide binding might have evolved, and finally the importance of adaptive immunity in general.

Whitefly hijacks a plant detoxification gene that neutralizes plant toxins.

Plants protect themselves with a vast array of toxic secondary metabolites, yet most plants serve as food for insects. The evolutionary processes that allow herbivorous insects to resist plant defenses remain largely unknown. The whitefly Bemisia tabaci is a cosmopolitan, highly polyphagous agricultural pest that vectors several serious plant pathogenic viruses and is an excellent model to probe the molecular mechanisms involved in overcoming plant defenses. Here, we show that, through an exceptional horizontal gene transfer event, the whitefly has acquired the plant-derived phenolic glucoside malonyltransferase gene BtPMaT1. This gene enables whiteflies to neutralize phenolic glucosides. This was confirmed by genetically transforming tomato plants to produce small interfering RNAs that silence BtPMaT1, thus impairing the whiteflies' detoxification ability. These findings reveal an evolutionary scenario whereby herbivores harness the genetic toolkit of their host plants to develop resistance to plant defenses and how this can be exploited for crop protection.

Cytomegalovirus subverts macrophage identity.

Cytomegaloviruses (CMVs) have co-evolved with their mammalian hosts for millions of years, leading to remarkable host specificity and high infection prevalence. Macrophages, which already populate barrier tissues in the embryo, are the predominant immune cells at potential CMV entry sites. Here we show that, upon CMV infection, macrophages undergo a morphological, immunophenotypic, and metabolic transformation process with features of stemness, altered migration, enhanced invasiveness, and provision of the cell cycle machinery for viral proliferation. This complex process depends on Wnt signaling and the transcription factor ZEB1. In pulmonary infection, mouse CMV primarily targets and reprograms alveolar macrophages, which alters lung physiology and facilitates primary CMV and secondary bacterial infection by attenuating the inflammatory response. Thus, CMV profoundly perturbs macrophage identity beyond established limits of plasticity and rewires specific differentiation processes, allowing viral spread and impairing innate tissue immunity.

Metabolite-Enzyme Coevolution: From Single Enzymes to Metabolic Pathways and Networks.

How individual enzymes evolved is relatively well understood. However, individual enzymes rarely confer a physiological advantage on their own. Judging by its current state, the emergence of metabolism seemingly demanded the simultaneous emergence of many enzymes. Indeed, how multicomponent interlocked systems, like metabolic pathways, evolved is largely an open question. This complexity can be unlocked if we assume that survival of the fittest applies not only to genes and enzymes but also to the metabolites they produce. This review develops our current knowledge of enzyme evolution into a wider hypothesis of pathway and network evolution. We describe the current models for pathway evolution and offer an integrative metabolite-enzyme coevolution hypothesis. Our hypothesis addresses the origins of new metabolites and of new enzymes and the order of their recruitment. We aim to not only survey established knowledge but also present open questions and potential ways of addressing them.

Protein Evolution and Design.

This article introduces the Protein Evolution and Design theme of the Annual Review of Biochemistry Volume 87.

Molecular insights into the activation of Mre11-Rad50 endonuclease activity by Sae2/CtIP.

In Saccharomyces cerevisiae (S. cerevisiae), Mre11-Rad50-Xrs2 (MRX)-Sae2 nuclease activity is required for the resection of DNA breaks with secondary structures or protein blocks, while in humans, the MRE11-RAD50-NBS1 (MRN) homolog with CtIP is needed to initiate DNA end resection of all breaks. Phosphorylated Sae2/CtIP stimulates the endonuclease activity of MRX/N. Structural insights into the activation of the Mre11 nuclease are available only for organisms lacking Sae2/CtIP, so little is known about how Sae2/CtIP activates the nuclease ensemble. Here, we uncover the mechanism of Mre11 activation by Sae2 using a combination of AlphaFold2 structural modeling of biochemical and genetic assays. We show that Sae2 stabilizes the Mre11 nuclease in a conformation poised to cleave substrate DNA. Several designs of compensatory mutations establish how Sae2 activates MRX in vitro and in vivo, supporting the structural model. Finally, our study uncovers how human CtIP, despite considerable sequence divergence, employs a similar mechanism to activate MRN.

Functional Diversification of Chromatin on Rapid Evolutionary Timescales.

Repeat-enriched genomic regions evolve rapidly and yet support strictly conserved functions like faithful chromosome transmission and the preservation of genome integrity. The leading resolution to this paradox is that DNA repeat-packaging proteins evolve adaptively to mitigate deleterious changes in DNA repeat copy number, sequence, and organization. Exciting new research has tested this model of coevolution by engineering evolutionary mismatches between adaptively evolving chromatin proteins of one species and the DNA repeats of a close relative. Here, we review these innovative evolution-guided functional analyses. The studies demonstrate that vital, chromatin-mediated cellular processes, including transposon suppression, faithful chromosome transmission, and chromosome retention depend on species-specific versions of chromatin proteins that package species-specific DNA repeats. In many cases, the ever-evolving repeats are selfish genetic elements, raising the possibility that chromatin is a battleground of intragenomic conflict.

Recent advances in predicting and modeling protein-protein interactions.

Protein-protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale.

ZEPPI: Proteome-scale sequence-based evaluation of protein-protein interaction models.

We introduce ZEPPI (Z-score Evaluation of Protein-Protein Interfaces), a framework to evaluate structural models of a complex based on sequence coevolution and conservation involving residues in protein-protein interfaces. The ZEPPI score is calculated by comparing metrics for an interface to those obtained from randomly chosen residues. Since contacting residues are defined by the structural model, this obviates the need to account for indirect interactions. Further, although ZEPPI relies on species-paired multiple sequence alignments, its focus on interfacial residues allows it to leverage quite shallow alignments. ZEPPI can be implemented on a proteome-wide scale and is applied here to millions of structural models of dimeric complexes in the Escherichia coli and human interactomes found in the PrePPI database. PrePPI's scoring function is based primarily on the evaluation of protein-protein interfaces, and ZEPPI adds a new feature to this analysis through the incorporation of evolutionary information. ZEPPI performance is evaluated through applications to experimentally determined complexes and to decoys from the CASP-CAPRI experiment. As we discuss, the standard CAPRI scores used to evaluate docking models are based on model quality and not on the ability to give yes/no answers as to whether two proteins interact. ZEPPI is able to detect weak signals from PPI models that the CAPRI scores define as incorrect and, similarly, to identify potential PPIs defined as low confidence by the current PrePPI scoring function. A number of examples that illustrate how the combination of PrePPI and ZEPPI can yield functional hypotheses are provided.

A siliceous arms race in pelagic plankton.

Coevolution between predator and prey plays a central role in shaping the pelagic realm and may have significant implications for marine ecosystems and nutrient cycling dynamics. The siliceous diatom frustule is often assumed to have coevolved with the silica-lined teeth of copepods, but empirical evidence of how this relationship drives natural selection and evolution is still lacking. Here, we show that feeding on diatoms causes significant wear and tear on copepod teeth and that this leads to copepods becoming selective feeders. Teeth from copepods feeding on thick-shelled diatoms were more likely to be broken or cracked than those feeding on a dinoflagellate. When fed a large diatom, all analyzed teeth had visible wear. Our results underscore the importance of the predator-prey arms race as a driving force in planktonic evolution and diversity.

Diverse cytomegalovirus US11 antagonism and MHC-A evasion strategies reveal a tit-for-tat coevolutionary arms race in hominids.

Recurrent, ancient arms races between viruses and hosts have shaped both host immunological defense strategies as well as viral countermeasures. One such battle is waged by the glycoprotein US11 encoded by the persisting human cytomegalovirus. US11 mediates degradation of major histocompatibility class I (MHC-I) molecules to prevent CD8+ T-cell activation. Here, we studied the consequences of the arms race between US11 and primate MHC-A proteins, leading us to uncover a tit-for-tat coevolution and its impact on MHC-A diversification. We found that US11 spurred MHC-A adaptation to evade viral antagonism: In an ancestor of great apes, the MHC-A A2 lineage acquired a Pro184Ala mutation, which confers resistance against the ancestral US11 targeting strategy. In response, US11 deployed a unique low-complexity region (LCR), which exploits the MHC-I peptide loading complex to target the MHC-A2 peptide-binding groove. In addition, the global spread of the human HLA-A*02 allelic family prompted US11 to employ a superior LCR strategy with an optimally fitting peptide mimetic that specifically antagonizes HLA-A*02. Thus, despite cytomegaloviruses low pathogenic potential, the increasing commitment of US11 to MHC-A has significantly promoted diversification of MHC-A in hominids.

Decoupled evolution of the Sex Peptide gene family and Sex Peptide Receptor in Drosophilidae.

Across internally fertilising species, males transfer ejaculate proteins that trigger wide-ranging changes in female behaviour and physiology. Much theory has been developed to explore the drivers of ejaculate protein evolution. The accelerating availability of high-quality genomes now allows us to test how these proteins are evolving at fine taxonomic scales. Here, we use genomes from 264 species to chart the evolutionary history of Sex Peptide (SP), a potent regulator of female post-mating responses in Drosophila melanogaster. We infer that SP first evolved in the Drosophilinae subfamily and has since followed markedly different evolutionary trajectories in different lineages. Outside of the Sophophora-Lordiphosa, SP exists largely as a single-copy gene with independent losses in several lineages. Within the Sophophora-Lordiphosa, the SP gene family has repeatedly and independently expanded. Up to seven copies, collectively displaying extensive sequence variation, are present in some species. Despite these changes, SP expression remains restricted to the male reproductive tract. Alongside, we document considerable interspecific variation in the presence and morphology of seminal microcarriers that, despite the critical role SP plays in microcarrier assembly in D. melanogaster, appears to be independent of changes in the presence/absence or sequence of SP. We end by providing evidence that SP's evolution is decoupled from that of its receptor, Sex Peptide Receptor, in which we detect no evidence of correlated diversifying selection. Collectively, our work describes the divergent evolutionary trajectories that a novel gene has taken following its origin and finds a surprisingly weak coevolutionary signal between a supposedly sexually antagonistic protein and its receptor.

Intergenomic signatures of coevolution between Tasmanian devils and an infectious cancer.

Coevolution is common and frequently governs host-pathogen interaction outcomes. Phenotypes underlying these interactions often manifest as the combined products of the genomes of interacting species, yet traditional quantitative trait mapping approaches ignore these intergenomic interactions. Devil facial tumor disease (DFTD), an infectious cancer afflicting Tasmanian devils (Sarcophilus harrisii), has decimated devil populations due to universal host susceptibility and a fatality rate approaching 100%. Here, we used a recently developed joint genome-wide association study (i.e., co-GWAS) approach, 15 y of mark-recapture data, and 960 genomes to identify intergenomic signatures of coevolution between devils and DFTD. Using a traditional GWA approach, we found that both devil and DFTD genomes explained a substantial proportion of variance in how quickly susceptible devils became infected, although genomic architectures differed across devils and DFTD; the devil genome had fewer loci of large effect whereas the DFTD genome had a more polygenic architecture. Using a co-GWA approach, devil-DFTD intergenomic interactions explained ~3× more variation in how quickly susceptible devils became infected than either genome alone, and the top genotype-by-genotype interactions were significantly enriched for cancer genes and signatures of selection. A devil regulatory mutation was associated with differential expression of a candidate cancer gene and showed putative allele matching effects with two DFTD coding sequence variants. Our results highlight the need to account for intergenomic interactions when investigating host-pathogen (co)evolution and emphasize the importance of such interactions when considering devil management strategies.

Pairing interacting protein sequences using masked language modeling.

Predicting which proteins interact together from amino acid sequences is an important task. We develop a method to pair interacting protein sequences which leverages the power of protein language models trained on multiple sequence alignments (MSAs), such as MSA Transformer and the EvoFormer module of AlphaFold. We formulate the problem of pairing interacting partners among the paralogs of two protein families in a differentiable way. We introduce a method called Differentiable Pairing using Alignment-based Language Models (DiffPALM) that solves it by exploiting the ability of MSA Transformer to fill in masked amino acids in multiple sequence alignments using the surrounding context. MSA Transformer encodes coevolution between functionally or structurally coupled amino acids within protein chains. It also captures inter-chain coevolution, despite being trained on single-chain data. Relying on MSA Transformer without fine-tuning, DiffPALM outperforms existing coevolution-based pairing methods on difficult benchmarks of shallow multiple sequence alignments extracted from ubiquitous prokaryotic protein datasets. It also outperforms an alternative method based on a state-of-the-art protein language model trained on single sequences. Paired alignments of interacting protein sequences are a crucial ingredient of supervised deep learning methods to predict the three-dimensional structure of protein complexes. Starting from sequences paired by DiffPALM substantially improves the structure prediction of some eukaryotic protein complexes by AlphaFold-Multimer. It also achieves competitive performance with using orthology-based pairing.

Exploiting enzyme evolution for computational protein design.

Recent years have seen an explosion of interest in understanding the physicochemical parameters that shape enzyme evolution, as well as substantial advances in computational enzyme design. This review discusses three areas where evolutionary information can be used as part of the design process: (i) using ancestral sequence reconstruction (ASR) to generate new starting points for enzyme design efforts; (ii) learning from how nature uses conformational dynamics in enzyme evolution to mimic this process in silico; and (iii) modular design of enzymes from smaller fragments, again mimicking the process by which nature appears to create new protein folds. Using showcase examples, we highlight the importance of incorporating evolutionary information to continue to push forward the boundaries of enzyme design studies.

Mutation pressure, drift, and the pace of molecular coevolution.

Most aspects of the molecular biology of cells involve tightly coordinated intermolecular interactions requiring specific recognition at the nucleotide and/or amino acid levels. This has led to long-standing interest in the degree to which constraints on interacting molecules result in conserved vs. accelerated rates of sequence evolution, with arguments commonly being made that molecular coevolution can proceed at rates exceeding the neutral expectation. Here, a fairly general model is introduced to evaluate the degree to which the rate of evolution at functionally interacting sites is influenced by effective population sizes (Ne), mutation rates, strength of selection, and the magnitude of recombination between sites. This theory is of particular relevance to matters associated with interactions between organelle- and nuclear-encoded proteins, as the two genomic environments often exhibit dramatic differences in the power of mutation and drift. Although genes within low Ne environments can drive the rate of evolution of partner genes experiencing higher Ne, rates exceeding the neutral expectation require that the former also have an elevated mutation rate. Testable predictions, some counterintuitive, are presented on how patterns of coevolutionary rates should depend on the relative intensities of drift, selection, and mutation.

Cell type-specific cytonuclear coevolution in three allopolyploid plant species.

Cytonuclear disruption may accompany allopolyploid evolution as a consequence of the merger of different nuclear genomes in a cellular environment having only one set of progenitor organellar genomes. One path to reconcile potential cytonuclear mismatch is biased expression for maternal gene duplicates (homoeologs) encoding proteins that target to plastids and/or mitochondria. Assessment of this transcriptional form of cytonuclear coevolution at the level of individual cells or cell types remains unexplored. Using single-cell (sc-) and single-nucleus (sn-) RNAseq data from eight tissues in three allopolyploid species, we characterized cell type-specific variations of cytonuclear coevolutionary homoeologous expression and demonstrated the temporal dynamics of expression patterns across development stages during cotton fiber development. Our results provide unique insights into transcriptional cytonuclear coevolution in plant allopolyploids at the single-cell level.

Parasite effects on receivers in animal communication: Hidden impacts on behavior, ecology, and evolution.

Parasites exert a profound effect on biological processes. In animal communication, parasite effects on signalers are well-known drivers of the evolution of communication systems. Receiver behavior is also likely to be altered when they are parasitized or at risk of parasitism, but these effects have received much less attention. Here, we present a broad framework for understanding the consequences of parasitism on receivers for behavioral, ecological, and evolutionary processes. First, we outline the different kinds of effects parasites can have on receivers, including effects on signal processing from the many parasites that inhabit, occlude, or damage the sensory periphery and the central nervous system or that affect physiological processes that support these organs, and effects on receiver response strategies. We then demonstrate how understanding parasite effects on receivers could answer important questions about the mechanistic causes and functional consequences of variation in animal communication systems. Variation in parasitism levels is a likely source of among-individual differences in response to signals, which can affect receiver fitness and, through effects on signaler fitness, impact population levels of signal variability. The prevalence of parasitic effects on specific sensory organs may be an important selective force for the evolution of elaborate and multimodal signals. Finally, host-parasite coevolution across heterogeneous landscapes will generate geographic variation in communication systems, which could ultimately lead to evolutionary divergence. We discuss applications of experimental techniques to manipulate parasitism levels and point the way forward by calling for integrative research collaborations between parasitologists, neurobiologists, and behavioral and evolutionary ecologists.

Tissue-specific plant toxins and adaptation in a specialist root herbivore.

In coevolution between plants and insects, reciprocal selection often leads to phenotype matching between chemical defense and herbivore offense. Nonetheless, it is not well understood whether distinct plant parts are differentially defended and how herbivores adapted to those parts cope with tissue-specific defense. Milkweed plants produce a diversity of cardenolide toxins and specialist herbivores have substitutions in their target enzyme (Na+/K+-ATPase), each playing a central role in milkweed-insect coevolution. The four-eyed milkweed beetle (Tetraopes tetrophthalmus) is an abundant toxin-sequestering herbivore that feeds exclusively on milkweed roots as larvae and less so on milkweed leaves as adults. Accordingly, we tested the tolerance of this beetle's Na+/K+-ATPase to cardenolide extracts from roots versus leaves of its main host (Asclepias syriaca), along with sequestered cardenolides from beetle tissues. We additionally purified and tested the inhibitory activity of dominant cardenolides from roots (syrioside) and leaves (glycosylated aspecioside). Tetraopes' enzyme was threefold more tolerant of root extracts and syrioside than leaf cardenolides. Nonetheless, beetle-sequestered cardenolides were more potent than those in roots, suggesting selective uptake or dependence on compartmentalization of toxins away from the beetle's enzymatic target. Because Tetraopes has two functionally validated amino acid substitutions in its Na+/K+-ATPase compared to the ancestral form in other insects, we compared its cardenolide tolerance to that of wild-type Drosophila and CRISPR-edited Drosophila with Tetraopes' Na+/K+-ATPase genotype. Those two amino acid substitutions accounted for >50% of Tetraopes' enhanced enzymatic tolerance of cardenolides. Thus, milkweed's tissue-specific expression of root toxins is matched by physiological adaptations in its specialist root herbivore.

Identification of hidden associations among eukaryotic genes through statistical analysis of coevolutionary transitions.

Coevolution at the gene level, as reflected by correlated events of gene loss or gain, can be revealed by phylogenetic profile analysis. The optimal method and metric for comparing phylogenetic profiles, especially in eukaryotic genomes, are not yet established. Here, we describe a procedure suitable for large-scale analysis, which can reveal coevolution based on the assessment of the statistical significance of correlated presence/absence transitions between gene pairs. This metric can identify coevolution in profiles with low overall similarities and is not affected by similarities lacking coevolutionary information. We applied the procedure to a large collection of 60,912 orthologous gene groups (orthogroups) in 1,264 eukaryotic genomes extracted from OrthoDB. We found significant cotransition scores for 7,825 orthogroups associated in 2,401 coevolving modules linking known and unknown genes in protein complexes and biological pathways. To demonstrate the ability of the method to predict hidden gene associations, we validated through experiments the involvement of vertebrate malate synthase-like genes in the conversion of (S)-ureidoglycolate into glyoxylate and urea, the last step of purine catabolism. This identification explains the presence of glyoxylate cycle genes in metazoa and suggests an anaplerotic role of purine degradation in early eukaryotes.