The evolution of targeted cannibalism and cannibal-induced defenses in invasive populations of cane toads.

Biotic conflict can create evolutionary arms races, in which innovation in one group increases selective pressure on another, such that organisms must constantly adapt to maintain the same level of fitness. In some cases, this process is driven by conflict among members of the same species. Intraspecific conflict can be an especially important selective force in high-density invasive populations, which may favor the evolution of strategies for outcompeting or eliminating conspecifics. Cannibalism is one such strategy; by killing and consuming their intraspecific competitors, cannibals enhance their own performance. Cannibalistic behaviors may therefore be favored in invasive populations. Here, we show that cane toad tadpoles (Rhinella marina) from invasive Australian populations have evolved an increased propensity to cannibalize younger conspecifics as well as a unique adaptation to cannibalism-a strong attraction to vulnerable hatchlings-that is absent in the native range. In response, vulnerable conspecifics from invasive populations have evolved both stronger constitutive defenses and greater cannibal-induced plastic responses than their native range counterparts (i.e., rapid prefeeding development and inducible developmental acceleration). These inducible defenses are costly, incurring performance reductions during the subsequent life stage, explaining why plasticity is limited in native populations where hatchlings are not targeted by cannibalistic tadpoles. These results demonstrate the importance of intraspecific conflict in driving rapid evolution, highlight how plasticity can facilitate adaptation following shifts in selective pressure, and show that evolutionary processes can produce mechanisms that regulate invasive populations.

Evolutionary interactions between thermal ecology and sexual selection.

Thermal ecology and mate competition are both pervasive features of ecological adaptation. A surge of recent work has uncovered the diversity of ways in which temperature affects mating interactions and sexual selection. However, the potential for thermal biology and reproductive ecology to evolve together as organisms adapt to their thermal environment has been underappreciated. Here, we develop a series of hypotheses regarding (1) not only how thermal ecology affects mating system dynamics, but also how mating dynamics can generate selection on thermal traits; and (2) how the thermal consequences of mate competition favour the reciprocal co-adaptation of thermal biology and sexual traits. We discuss our hypotheses in the context of both pre-copulatory and post-copulatory processes. We also call for future work integrating experimental and phylogenetic comparative approaches to understand evolutionary feedbacks between thermal ecology and sexual selection. Overall, studying reciprocal feedbacks between thermal ecology and sexual selection may be necessary to understand how organisms have adapted to the environments of the past and could persist in the environments of the future.

Adapting a social network intervention for use in secondary mental health services using a collaborative approach with service users, carers/supporters and health professionals in the United Kingdom.

BACKGROUND: Social integration, shared decision-making and personalised care are key elements of mental health and social care policy. Although these elements have been shown to improve service user and service-level outcomes, their translation into practice has been inconsistent and social isolation amongst service users persists. AIM: To co-adapt, with service users, carers/supporters and health professionals, a web-based social network intervention, GENIE™, for use in secondary mental health services. The intervention is designed to support social activity and preference discussions between mental healthcare professionals and service users as a means of connecting individuals to local resources. METHODS: In Phase 1 (LEARN), we completed two systematic reviews to synthesise the existing evidence relating to the i) effectiveness and ii) the implementation of social network interventions for people with mental health difficulties. We undertook semi-structured interviews with a convenience sample of 15 stakeholders previously involved in the implementation of the intervention in physical healthcare settings. Interviews were also conducted with 5 national key stakeholders in mental health (e.g., policy makers, commissioners, third sector leads) to explore wider implementation issues. In Phase 2 (ADAPT), we worked iteratively with eight service users, nine carers, six professionals/volunteers and our patient and public advisory group. We drew on a framework for experience-based co-design, consisting of a series of stakeholder consultation events, to discuss the use of the social network intervention, in mental health services. Participants also considered factors that could serve as enablers, barriers, and challenges to local implementation. RESULTS: Across the stakeholder groups there was broad agreement that the social network intervention had potential to be useful within mental health services. In terms of appropriate and effective implementation, such an intervention was predicted to work best within the care planning process, on discharge from hospital and within early intervention services. There were indications that the social connection mapping and needs assessment components were of most value and feasible to implement which points to the potential utility of a simplified version compared to the one used in this study. The training provided to facilitators was considered to be more important than their profession and there were indications that service users should be offered the opportunity to invite a carer, friend, or family member to join them in the intervention. CONCLUSION: The GENIE™ intervention has been co-adapted for use in mental health services and a plan for optimal implementation has been co-produced. The next phase of the programme of work is to design and implement a randomised controlled trial to evaluate clinical and cost effectiveness of a simplified version of the intervention.

Developing a two-dimensional landscape model of opportunities for penetrative passing in association football - Stage I.

This study investigated a method for modelling a landscape of opportunities for penetrative passing completed on the ground by ball carriers in association football. Analysis of video footage of competitive, professional football performance was undertaken, identifying a sample (n = 20) of attacking sub-phases of gameplay which ended in a penetrative pass being made between defenders to a receiver. Players' relative co-positioning during performance was modelled using bi-dimensional x and y coordinates of each player recorded at 25 fps. Data on player movements during competitive interactions were captured using an automatic video tracking system, recording player co-locations emerging over time, as well as current and estimated running velocities. Results revealed that the half spaces between the midfield and both sidelines were the key locations on field providing most affordances for penetrating passes in the competitive performance sample analysed. Due to the dynamics of players' co-adaptive performance behaviours, it was expected that opportunities for penetrative passing by ball carriers would not display a homogeneous space-time spread across the entire field. Results agreed with these expectations, showing how a landscape of opportunities for penetrative passing might be specified by information emerging from continuous player interactions in competitive performance.

Directional Forgetting for Stable Co-Adaptation in Myoelectric Control.

Conventional myoelectric controllers provide a mapping between electromyographic signals and prosthetic functions. However, due to a number of instabilities continuously challenging this process, an initial mapping may require an extended calibration phase with long periods of user-training in order to ensure satisfactory performance. Recently, studies on co-adaptation have highlighted the benefits of concurrent user learning and machine adaptation where systems can cope with deficiencies in the initial model by learning from newly acquired data. However, the success remains highly dependent on careful weighting of these new data. In this study, we proposed a function driven directional forgetting approach to the recursive least-squares algorithm as opposed to the classic exponential forgetting scheme. By only discounting past information in the same direction of the new data, local corrections to the mapping would induce less distortion to other regions. To validate the approach, subjects performed a set of real-time myoelectric tasks over a range of forgetting factors. Results show that directional forgetting with a forgetting factor of 0.995 outperformed exponential forgetting as well as unassisted user learning. Moreover, myoelectric control remained stable after adaptation with directional forgetting over a range of forgetting factors. These results indicate that a directional approach to discounting past training data can improve performance and alleviate sensitivities to parameter selection in recursive adaptation algorithms.

Evidence for dispersal syndromes in freshwater fishes.

Dispersal is a fundamental process defining the distribution of organisms and has long been a topic of inquiry in ecology and evolution. Emerging research points to an interdependency of dispersal with a diverse suite of traits in terrestrial organisms, however the extent to which such dispersal syndromes exist in freshwater species remains uncertain. Here, we test whether dispersal in freshwater fishes (1) is a fixed property of species, and (2) correlates with life-history, morphological, ecological and behavioural traits, using a global dataset of dispersal distances collected from the literature encompassing 116 riverine species and 196 locations. Our meta-analysis revealed a high degree of repeatability and heritability in the dispersal estimates and strong associations with traits related to life-history strategies, energy allocation to reproduction, ecological specialization and swimming skills. Together, these results demonstrate that similar to terrestrial organisms, the multi-dimensional nature of dispersal syndromes in freshwater species offer opportunities for the development of a unifying paradigm of movement ecology that transcend taxonomic and biogeographical realms. The high explanatory power of the models also suggests that trait-based and phylogenetic approaches hold considerable promises to inform conservation efforts in a rapidly changing world.

microRNAs in the Same Clusters Evolve to Coordinately Regulate Functionally Related Genes.

MicroRNAs (miRNAs) are endogenously expressed small noncoding RNAs. The genomic locations of animal miRNAs are significantly clustered in discrete loci. We found duplication and de novo formation were important mechanisms to create miRNA clusters and the clustered miRNAs tend to be evolutionarily conserved. We proposed a "functional co-adaptation" model to explain how clustering helps newly emerged miRNAs survive and develop functions. We presented evidence that abundance of miRNAs in the same clusters were highly correlated and those miRNAs exerted cooperative repressive effects on target genes in human tissues. By transfecting miRNAs into human and fly cells and extensively profiling the transcriptome alteration with deep-sequencing, we further demonstrated the functional co-adaptation between new and old miRNAs in the miR-17-92 cluster. Our population genomic analysis suggest that positive Darwinian selection might be the driving force underlying the formation and evolution of miRNA clustering. Our model provided novel insights into mechanisms and evolutionary significance of miRNA clustering.

Adaptation to a novel family environment involves both apparent and cryptic phenotypic changes.

Cryptic evolution occurs when evolutionary change is masked by concurrent environmental change. In most cases, evolutionary changes in the phenotype are masked by changing abiotic factors. However, evolutionary change in one trait might also be masked by evolutionary change in another trait, a phenomenon referred to as evolutionary environmental deterioration. Nevertheless, detecting this second type of cryptic evolution is challenging and there are few compelling examples. Here, we describe a likely case of evolutionary environmental deterioration occurring in experimental burying beetle (Nicrophorus vespilloides) populations that are adapting to a novel social environment that lacks post-hatching parental care. We found that populations rapidly adapted to the removal of post-hatching parental care. This adaptation involved clear increases in breeding success and larval density (number of dispersing larvae produced per gram of breeding carcass), which in turn masked a concurrent increase in the mean larval mass across generations. This cryptic increase in larval mass was accomplished through a change in the reaction norm that relates mean larval mass to larval density. Our results suggest that cryptic evolution might be commonplace in animal families, because evolving trophic and social interactions can potentially mask evolutionary change in other traits, like body size.

Specialist and generalist oviposition strategies in butterflies: maternal care or precocious young?

Herbivorous insects specialized on a narrow set of plants are believed to be better adapted to their specific hosts. This hypothesis is supported by observations of herbivorous insect species with a broader diet breadth which seemingly pay a cost through decreased oviposition accuracy. Despite many studies investigating female oviposition behavior, there is a lack of knowledge on how larvae cope behaviorally with their mothers' egg-laying strategies. We have examined a unique system of five nymphalid butterfly species with different host plant ranges that all feed on the same host plant. The study of this system allowed us to compare at the species level how oviposition preference is related to neonate larval responses in several disadvantageous situations. We found a general co-adaptation between female and larval abilities, where species with more discriminating females had larvae that were less able to deal with a suboptimal initial feeding site. Conversely, relatively indiscriminate females had more precocious larvae with better abilities to cope with suboptimal sites. Despite similarities between the tested species with similar host ranges, there were also striking differences. Generalist and specialist species can be found side by side in many clades, with each clade having a specific evolutionary history. Such clade-specific, phylogenetically determined preconditions apparently have affected how precisely a broad or narrow diet breadth can be realized.

Differences in host species relationships and biogeographic influences produce contrasting patterns of prevalence, community composition and genetic structure in two genera of avian malaria parasites in southern Melanesia.

Host-parasite interactions have the potential to influence broadscale ecological and evolutionary processes, levels of endemism, divergence patterns and distributions in host populations. Understanding the mechanisms involved requires identification of the factors that shape parasite distribution and prevalence. A lack of comparative information on community-level host-parasite associations limits our understanding of the role of parasites in host population divergence processes. Avian malaria (haemosporidian) parasites in bird communities offer a tractable model system to examine the potential for pathogens to influence evolutionary processes in natural host populations. Using cytochrome b variation, we characterized phylogenetic diversity and prevalence of two genera of avian haemosporidian parasites, Plasmodium and Haemoproteus, and analysed biogeographic patterns of lineages across islands and avian hosts, in southern Melanesian bird communities to identify factors that explain patterns of infection. Plasmodium spp. displayed isolation-by-distance effects, a significant amount of genetic variation distributed among islands but insignificant amounts among host species and families, and strong local island effects with respect to prevalence. Haemoproteus spp. did not display isolation-by-distance patterns, showed marked structuring of genetic variation among avian host species and families, and significant host species prevalence patterns. These differences suggest that Plasmodium spp. infection patterns were shaped by geography and the abiotic environment, whereas Haemoproteus spp. infection patterns were shaped predominantly by host associations. Heterogeneity in the complement and prevalence of parasite lineages infecting local bird communities likely exposes host species to a mosaic of spatially divergent disease selection pressures across their naturally fragmented distributions in southern Melanesia. Host associations for Haemoproteus spp. indicate a capacity for the formation of locally co-adapted host-parasite relationships, a feature that may limit intraspecific gene flow or range expansions of closely related host species.

A metabolic approach to study algal-bacterial interactions in changing environments.

Increasing evidence exists that bacterial communities interact with and shape the biology of algae and that their evolutionary histories are connected. Despite these findings, physiological studies were and still are generally carried out with axenic or at least antibiotic-treated cultures. Here, we argue that considering interactions between algae and associated bacteria is key to understanding their biology and evolution. To deal with the complexity of the resulting 'holobiont' system, a metabolism-centred approach that uses combined metabolic models for algae and associated bacteria is proposed. We believe that these models will be valuable tools both to study algal-bacterial interactions and to elucidate processes important for the acclimation of the holobiont to environmental changes.

A meta-analysis of the strength and nature of cytoplasmic genetic effects.

Genetic variation in cytoplasmic genomes (i.e. the mitochondrial genome in animals, and the combined mitochondrial and chloroplast genomes in plants) was traditionally assumed to accumulate under a neutral equilibrium model. This view has, however, come under increasing challenge from studies that have experimentally linked cytoplasmic genetic effects to the expression of life history phenotypes. Such results suggest that genetic variance located within the cytoplasm might be of evolutionary importance and potentially involved in shaping population evolutionary trajectories. As a step towards assessing this assertion, here we conduct a formal meta-analytic review to quantitatively assess the extent to which cytoplasmic genetic effects contribute to phenotypic expression across animal and plant kingdoms. We report that cytoplasmic effect sizes are generally moderate in size and associated with variation across a range of factors. Specifically, cytoplasmic effects on morphological traits are generally larger than those on life history or metabolic traits. Cytoplasmic effect sizes estimated at the between-species scale (via interspecies mix-and-matching of cytoplasmic and nuclear genomes) are larger than those at the within-species scale. Furthermore, cytoplasmic effects tied to epistatic interactions with the nuclear genome tend to be stronger than additive cytoplasmic effects, at least when restricting the data set to gonochorous animal species. Our results thus confirm that cytoplasmic genetic variation is commonly tied to phenotypic expression across plants and animals, implicate the cytoplasmic-nuclear interaction as a key unit on which natural selection acts and generally suggest that the genetic variation that lies within the cytoplasm is likely to be entwined in adaptive evolutionary processes.

Genetic diversity, phylogenetic position, and co-phylogenetic relationships of Karyolysus, a common blood parasite of lizards in the western Mediterranean.

The genus Karyolysus was originally proposed to accommodate blood parasites of lacertid lizards in Western Europe. However, recent phylogenetic analyses suggested an inconclusive taxonomic position of these parasites of the order Adeleorina based on the available genetic information. Inconsistencies between molecular phylogeny, morphology, and/or life cycles can reflect lack of enough genetic information of the target group. We therefore surveyed 28 localities and collected blood samples from 828 lizards of 23 species including lacertids, skinks, and geckoes in the western Mediterranean, North Africa, and Macaronesia, where species of Karyolysus and other adeleorine parasites have been described. We combined molecular and microscopic methods to analyze the samples, including those from the host type species and the type locality of Karyolysus bicapsulatus. The phylogenetic relationship of these parasites was analyzed based on the 18S rRNA gene and the co-phylogenetic relationship with their vertebrate hosts was reconstructed. We molecularly detected adeleorine parasites in 37.9% of the blood samples and found 22 new parasite haplotypes. A phylogenetic reconstruction with 132 sequences indicated that 20 of the newly detected haplotypes clustered in a well-supported clade with another 18 sequences that included Karyolysus galloti and Karyolysus lacazei. Morphological evidence also supported that K. bicapsulatus clustered in this monophyletic clade. These results supported the taxonomic validity of the genus. In addition, we found some parasite haplotypes that infected different lizard host genera with ancient diverging histories, which suggested that Karyolysus is less host-specific than other blood parasites of lizards in the region. A co-phylogenetic analysis supported this interpretation because no significant co-speciation signal was shown between Karyolysus and lizard hosts.

Robust Latent Multi-Source Adaptation for Encephalogram-Based Emotion Recognition.

In practical encephalogram (EEG)-based machine learning, different subjects can be represented by many different EEG patterns, which would, in some extent, degrade the performance of extant subject-independent classifiers obtained from cross-subjects datasets. To this end, in this paper, we present a robust Latent Multi-source Adaptation (LMA) framework for cross-subject/dataset emotion recognition with EEG signals by uncovering multiple domain-invariant latent subspaces. Specifically, by jointly aligning the statistical and semantic distribution discrepancies between each source and target pair, multiple domain-invariant classifiers can be trained collaboratively in a unified framework. This framework can fully utilize the correlated knowledge among multiple sources with a novel low-rank regularization term. Comprehensive experiments on DEAP and SEED datasets demonstrate the superior or comparable performance of LMA with the state of the art in the EEG-based emotion recognition.

The PB2 co-adaptation of H10N8 avian influenza virus increases the pathogenicity to chickens and mice.

Avian influenza (AI) is an important zoonotic disease, which can be transmitted across species barriers to other hosts, especially humans, posing a serious threat to the poultry industry and public health. In recent years, human cases infected with the H10N8, H9N2, and H7N9 of avian influenza viruses (AIVs) have been identified frequently as have the internal genes of H7N9 and H10N8, which are derived from H9N2 viruses. The adaptive mutation of the PB2 gene is an important way for the H10N8, H9N2, and H7N9 AIVs to spread across species to adapt to new hosts. Several well-known adaptive mutations in the PB2 gene, such as E627K, D701N, and A588V, significantly enhanced the virulence of the AIVs in mammals. However, the co-adaptation of AIVs to avian and mammalian hosts is rarely studied. In this study, we found that the mutations of PB2-I292V, PB2-R389K, PB2-A588V, PB2-T598M/V, PB2-L648V, and PB2-T676M substitutions significantly increased after 2012. In addition, in our previous studies, we found that the human-origin and avian-origin of H10N8 AIVs with very high homology also have these six mutation differences in PB2 gene, and the avian-origin H10N8 strain known as JX102 with all the key amino acids on the PB2 protein in the pre-evolutionary stage, so JX102 was chosen as a model strain. Among them, PB2-A588V significantly enhanced the activity of polymerase in avian and mammalian cells. Notably, animal experiments showed that PB2-A588V substitution increased the pathogenicity and transmissibility in chickens and the virulence of mice. The combined mutations of PB2-F6 (including PB2-I292V, PB2-R389K, PB2-A588V, PB2-T598M, PB2-L648V, and PB2-T676M) obtained higher adaptability of AIVs in avians and mammals than that of the single mutation of PB2-A588V, which suggested that the PB2 588 site is a key co-adaptation site and that synergies with other mutation sites can further enhance this co-adaptability. The results of this study show that the emergence of co-adaptation not only increases the threat to avians and mammals but may also contribute to a pandemic among avians and cross the interspecies barrier to mammals.

A Method for In Situ Reverse Genetic Analysis of Proteins Involved mtDNA Replication.

The unavailability of tractable reverse genetic analysis approaches represents an obstacle to a better understanding of mitochondrial DNA replication. Here, we used CRISPR-Cas9 mediated gene editing to establish the conditional viability of knockouts in the key proteins involved in mtDNA replication. This observation prompted us to develop a set of tools for reverse genetic analysis in situ, which we called the GeneSwap approach. The technique was validated by identifying 730 amino acid (aa) substitutions in the mature human TFAM that are conditionally permissive for mtDNA replication. We established that HMG domains of TFAM are functionally independent, which opens opportunities for engineering chimeric TFAMs with customized properties for studies on mtDNA replication, mitochondrial transcription, and respiratory chain function. Finally, we present evidence that the HMG2 domain plays the leading role in TFAM species-specificity, thus indicating a potential pathway for TFAM-mtDNA evolutionary co-adaptations.

Identifying Interaction Patterns of Tangible Co-Adaptations in Human-Robot Team Behaviors.

As robots become more ubiquitous, they will increasingly need to behave as our team partners and smoothly adapt to the (adaptive) human team behaviors to establish successful patterns of collaboration over time. A substantial amount of adaptations present themselves through subtle and unconscious interactions, which are difficult to observe. Our research aims to bring about awareness of co-adaptation that enables team learning. This paper presents an experimental paradigm that uses a physical human-robot collaborative task environment to explore emergent human-robot co-adaptions and derive the interaction patterns (i.e., the targeted awareness of co-adaptation). The paradigm provides a tangible human-robot interaction (i.e., a leash) that facilitates the expression of unconscious adaptations, such as "leading" (e.g., pulling the leash) and "following" (e.g., letting go of the leash) in a search-and-navigation task. The task was executed by 18 participants, after which we systematically annotated videos of their behavior. We discovered that their interactions could be described by four types of adaptive interactions: stable situations, sudden adaptations, gradual adaptations and active negotiations. From these types of interactions we have created a language of interaction patterns that can be used to describe tacit co-adaptation in human-robot collaborative contexts. This language can be used to enable communication between collaborating humans and robots in future studies, to let them share what they learned and support them in becoming aware of their implicit adaptations.

Becoming Team Members: Identifying Interaction Patterns of Mutual Adaptation for Human-Robot Co-Learning.

Becoming a well-functioning team requires continuous collaborative learning by all team members. This is called co-learning, conceptualized in this paper as comprising two alternating iterative stages: partners adapting their behavior to the task and to each other (co-adaptation), and partners sustaining successful behavior through communication. This paper focuses on the first stage in human-robot teams, aiming at a method for the identification of recurring behaviors that indicate co-learning. Studying this requires a task context that allows for behavioral adaptation to emerge from the interactions between human and robot. We address the requirements for conducting research into co-adaptation by a human-robot team, and designed a simplified computer simulation of an urban search and rescue task accordingly. A human participant and a virtual robot were instructed to discover how to collaboratively free victims from the rubbles of an earthquake. The virtual robot was designed to be able to real-time learn which actions best contributed to good team performance. The interactions between human participants and robots were recorded. The observations revealed patterns of interaction used by human and robot in order to adapt their behavior to the task and to one another. Results therefore show that our task environment enables us to study co-learning, and suggest that more participant adaptation improved robot learning and thus team level learning. The identified interaction patterns can emerge in similar task contexts, forming a first description and analysis method for co-learning. Moreover, the identification of interaction patterns support awareness among team members, providing the foundation for human-robot communication about the co-adaptation (i.e., the second stage of co-learning). Future research will focus on these human-robot communication processes for co-learning.

A Framework for Optimizing Co-adaptation in Body-Machine Interfaces.

The operation of a human-machine interface is increasingly often referred to as a two-learners problem, where both the human and the interface independently adapt their behavior based on shared information to improve joint performance over a specific task. Drawing inspiration from the field of body-machine interfaces, we take a different perspective and propose a framework for studying co-adaptation in scenarios where the evolution of the interface is dependent on the users' behavior and that do not require task goals to be explicitly defined. Our mathematical description of co-adaptation is built upon the assumption that the interface and the user agents co-adapt toward maximizing the interaction efficiency rather than optimizing task performance. This work describes a mathematical framework for body-machine interfaces where a naïve user interacts with an adaptive interface. The interface, modeled as a linear map from a space with high dimension (the user input) to a lower dimensional feedback, acts as an adaptive "tool" whose goal is to minimize transmission loss following an unsupervised learning procedure and has no knowledge of the task being performed by the user. The user is modeled as a non-stationary multivariate Gaussian generative process that produces a sequence of actions that is either statistically independent or correlated. Dependent data is used to model the output of an action selection module concerned with achieving some unknown goal dictated by the task. The framework assumes that in parallel to this explicit objective, the user is implicitly learning a suitable but not necessarily optimal way to interact with the interface. Implicit learning is modeled as use-dependent learning modulated by a reward-based mechanism acting on the generative distribution. Through simulation, the work quantifies how the system evolves as a function of the learning time scales when a user learns to operate a static vs. an adaptive interface. We show that this novel framework can be directly exploited to readily simulate a variety of interaction scenarios, to facilitate the exploration of the parameters that lead to optimal learning dynamics of the joint system, and to provide an empirical proof for the superiority of human-machine co-adaptation over user adaptation.

The combined influence of codon composition and tRNA copy number regulates translational efficiency by influencing synonymous nucleotide substitution.

Codon usage bias is an important genomic phenomenon, where highly expressed genes use optimal codons for smoother translation with high yield, facilitated by the cognate tRNAs. Here, we presented the tRNA co-adaptation index (co-AI) by correlating tRNA gene copy number and codon composition in Saccharomyces cerevisiae. We observed that this co-AI is positively correlated with protein abundance and translation rate. Considering nucleotide substitutions, co-AI influences synonymous substitutions more than gene expression and protein abundance, the most important determinants of evolutionary rate. Co-AI correlates positively with mRNA secondary structure stability and mRNA half-life, which may lead to protein accumulation under high co-AI. However, the highly expressed proteins encoded by high co-AI genes are assisted by molecular chaperones to attain their proper functional conformation and prevent accumulation.