Thermodynamic stability is a crucial fitness constraint in protein evolution and is a central factor in shaping the sequence landscapes of proteins. The correlation between stability and molecular fitness depends on the mechanism that relates the biophysical property with biological function. In the simplest case, stability and fitness are related by the amount of folded protein. However, when proteins are toxic in the unfolded state, the fitness function shifts, resulting in higher stability under mutation-selection balance. Likewise, a higher population size results in a similar change in protein stability, as it magnifies the effect of the selection pressure in evolutionary dynamics. This study investigates how such factors affect the evolution of protein stability, site-specific mutation rates, and residue-residue covariation. To simulate evolutionary trajectories with realistic modeling of protein energetics, we develop an all-atom simulator of protein evolution, RosettaEvolve. By evolving proteins under different fitness functions, we can study how the fitness function affects the distribution of proposed and accepted mutations, site-specific rates, and the prevalence of correlated amino acid substitutions. We demonstrate that fitness pressure affects the proposal distribution of mutational effects, that changes in stability can largely explain variations in site-specific substitution rates in evolutionary trajectories, and that increased fitness pressure results in a stronger covariation signal. Our results give mechanistic insight into the evolutionary consequences of variation in protein stability and provide a basis to rationalize the strong covariation signal observed in natural sequence alignments. Author summary: Modern-day proteins are the result of the process of evolution. The fate of random substitutions at the nucleotide level is dependent on the fitness of the new gene variant. One of the strongest fitness pressures shaping the sequences of protein is thermodynamic stability; proteins must typically be stable to carry out its function and misfolded proteins can be toxic. To understand the importance of thermodynamic stability in protein evolution and to what extent it can explain natural sequence variation we have developed a method for simulating protein evolution using a three-dimensional structure and structure-based stability calculations. In the simulations, the strength of selection can be varied, and complete phylogenetic trees of a protein family can be generated. Using these simulations, we demonstrate how mutation rates at individual sites in a protein are coupled to the overall stability of the protein, and how the spectrum of accepted mutations is shaped by stability, and how strong interactions between residues in a protein can result in sequence covariation.

A major threat to rice production is the disease epidemics caused by insect-borne viruses that emerge and re-emerge with undefined origins. It is well known that some human viruses have zoonotic origins from wild animals. However, it remains unknown whether native plants host uncharacterized endemic viruses with spillover potential to rice (Oryza sativa) as emerging pathogens. Here, we discovered rice tiller inhibition virus (RTIV), a novel RNA virus species, from colonies of Asian wild rice (O. rufipogon) in a genetic reserve by metagenomic sequencing. We identified the specific aphid vector that is able to transmit RTIV and found that RTIV would cause low-tillering disease in rice cultivar after transmission. We further demonstrated that an infectious molecular clone of RTIV initiated systemic infection and causes low-tillering disease in an elite rice variety after Agrobacterium-mediated inoculation or stable plant transformation, and RTIV can also be transmitted from transgenic rice plant through its aphid vector to cause disease. Finally, global transcriptome analysis indicated that RTIV may disturb defense and tillering pathway to cause low tillering disease in rice cultivar. Thus, our results show that new rice viral pathogens can emerge from native habitats, and RTIV, a rare aphid-transmitted rice viral pathogen from native wild rice, can threaten the production of rice cultivar after spillover. Author summary: Emerging and re-emerging dangerous viruses cause epidemics or pandemics to threaten human and economically important crops and animals. Some of the most dangerous human viruses have zoonotic origins from wild animals. Native plants may also host new endemic viruses that can spread to cause epidemics in cultivated rice, one of the most important staple plants in the world. In the research, we discovered Rice tiller inhibition virus (RTIV), a new insect-borne positive-strand RNA virus, from Asian wild rice plants through metagenomic sequencing, and demonstrated that RTIV could be transmitted by a wide-distributed specific species of aphids to cultivated rice and inhibited tillering, an important agronomic trait of rice plants. We further rescued RTIV by developing infectious clone and confirmed that it can be a dangerous aphid-transmitted viral pathogen of cultivated rice plants. Thus, not only a rare aphid-transmitted viral pathogen of rice plants was discovered in the research, our findings also indicate that novel pathogenic viruses could spread out from native plants to threaten important crop plants.

Phenotypic plasticity, the change in the phenotype of a given genotype in response to its environment of development, is a ubiquitous feature of life, enabling organisms to cope with variation in their environment. Theoretical studies predict that, under stationary environmental variation, the level of plasticity should evolve to match the predictability of selection at the timing of development. However, the extent to which patterns of evolution of plasticity for more integrated traits are mirrored by their underlying molecular mechanisms remains unclear, especially in response to well-characterized selective pressures exerted by environmental predictability. Here, we used experimental evolution with the microalgae Dunaliella salina under controlled environmental fluctuations, to test whether the evolution of phenotypic plasticity in responses to environmental predictability (as measured by the squared autocorrelation ρ2) occurred across biological levels, going from DNA methylation to gene expression to cell morphology. Transcriptomic analysis indicates clear effects of salinity and ρ2 × salinity interaction on gene expression, thus identifying sets of genes involved in plasticity and its evolution. These transcriptomic effects were independent of DNA methylation changes in cis. However, we did find ρ2-specific responses of DNA methylation to salinity change, albeit weaker than for gene expression. Overall, we found consistent evolution of reduced plasticity in less predictable environments for DNA methylation, gene expression, and cell morphology. Our results provide the first clear empirical signature of plasticity evolution at multiple levels in response to environmental predictability, and highlight the importance of experimental evolution to address predictions from evolutionary theory, as well as investigate the molecular basis of plasticity evolution. Experimental evolution of a halophilic alga reveals consistent evolution of reduced plasticity in less predictable environments across three biological levels – epigenomics, transcriptomics, and cell morphology, providing evidence of the mechanisms of plasticity evolution across the genotype-phenotype map.

Social insects demonstrate adaptive behaviour for a given colony size. Remarkably, most species do this even without visual information in a dark environment. However, how they achieve this is yet unknown. Based on individual trait expression, an agent-based simulation was used to identify an explicit mechanism for understanding colony size dependent behaviour. Through repeated physical contact between the queen and individual workers, individual colony members monitor the physiological states of others, reflecting such contact information in their physiology and behaviour. Feedback between the sensing of physiological states and the corresponding behaviour patterns leads to self-organisation with colonies shifting according to their size. We showed (1) the queen can exhibit adaptive behaviour patterns for the increase in colony size while density per space remains unchanged, and (2) such physical constraints can underlie the adaptive switching of colony stages from successful patrol behaviour to unsuccessful patrol behaviour, which leads to constant ovary development (production of reproductive castes). The feedback loops embedded in the queen between the perception of internal states of the workers and behavioural patterns can explain the adaptive behaviour as a function of colony size. Author summary: In the ant Diacamma cf. Indicum (from Japan), the queen spends more effort on queen pheromone-transmitting behaviour (patrolling) in response to the growth of colony size to inhibit worker ovary development. We used an agent-based simulation to understand the mechanism of the colony size dependent behaviour of the queen. The queen simply follows a feedback loop mediated by the mutual contact between her and the workers. In other words, the queen patrols the workers more often when she has recently encountered workers with developed ovaries.

Understanding how pollinators move across space is key to understanding plant mating patterns. Bees are typically assumed to search for flowers randomly or using simple movement rules, so that the probability of discovering a flower should primarily depend on its distance to the nest. However, experimental work shows this is not always the case. Here, we explored the influence of flower size and density on their probability of being discovered by bees by developing a movement model of central place foraging bees, based on experimental data collected on bumblebees. Our model produces realistic bee trajectories by taking into account the autocorrelation of the bee’s angular speed, the attraction to the nest (homing), and a gaussian noise. Simulations revealed a « masking effect » that reduces the detection of flowers close to another, with potential far reaching consequences on plant-pollinator interactions. At the plant level, flowers distant to the nest were more often discovered by bees in low density environments. At the bee colony level, foragers found more flowers when they were small and at medium densities. Our results indicate that the processes of search and discovery of resources are potentially more complex than usually assumed, and question the importance of resource distribution and abundance on bee foraging success and plant pollination. Author summary: Understanding how pollinators move in space is key to understand plant reproduction and its consequences on terrestrial ecosystems. Current models assume simple movement rules that predict flowers are more likely to be visited—and hence pollinated—the closer they are to the pollinators’ nest. Here we developed an explicit movement model that incorporates realistic features of bumblebee behaviour, and calibrated it with experimental data collected in naturalistic conditions. Our model shows that the probability to visit a flower does not only depend on its position, but also on the position of other flowers around that may mask it from the forager. This perceptual masking effect means that pollination efficiency depends on the density and spatial arrangement of flowers around the pollinators’ nest, often in counter-intuitive ways. Taking these effects into account may be key for improving practical actions in precision pollination and pollinator conservation.

Neural mass models (NMMs) are important for helping us interpret observations of brain dynamics. They provide a means to understand data in terms of mechanisms such as synaptic interactions between excitatory and inhibitory neuronal populations. To interpret data using NMMs we need to quantitatively compare the output of NMMs with data, and thereby find parameter values for which the model can produce the observed dynamics. Mapping dynamics to NMM parameter values in this way has the potential to improve our understanding of the brain in health and disease. Though abstract, NMMs still comprise of many parameters that are difficult to constrain a priori. This makes it challenging to explore the dynamics of NMMs and elucidate regions of parameter space in which their dynamics best approximate data. Existing approaches to overcome this challenge use a combination of linearising models, constraining the values they can take and exploring restricted subspaces by fixing the values of many parameters a priori. As such, we have little knowledge of the extent to which different regions of parameter space of NMMs can yield dynamics that approximate data, how nonlinearities in models can affect parameter mapping or how best to quantify similarities between model output and data. These issues need to be addressed in order to fully understand the potential and limitations of NMMs, and to aid the development of new models of brain dynamics in the future. To begin to overcome these issues, we present a global nonlinear approach to recovering parameters of NMMs from data. We use global optimisation to explore all parameters of nonlinear NMMs simultaneously, in a minimally constrained way. We do this using multi-objective optimisation (multi-objective evolutionary algorithm, MOEA) so that multiple data features can be quantified. In particular, we use the weighted horizontal visibility graph (wHVG), which is a flexible framework for quantifying different aspects of time series, by converting them into networks. We study EEG alpha activity recorded during the eyes closed resting state from 20 healthy individuals and demonstrate that the MOEA performs favourably compared to single objective approaches. The addition of the wHVG objective allows us to better constrain the model output, which leads to the recovered parameter values being restricted to smaller regions of parameter space, thus improving the practical identifiability of the model. We then use the MOEA to study differences in the alpha rhythm observed in EEG recorded from 20 people with epilepsy. We find that a small number of parameters can explain this difference and that, counterintuitively, the mean excitatory synaptic gain parameter is reduced in people with epilepsy compared to control. In addition, we propose that the MOEA could be used to mine for the presence of pathological rhythms, and demonstrate the application of this to epileptiform spike-wave discharges. Author summary: EEG is a useful tool to study large scale brain activity. Mathematical models have been developed to help improve the understanding of the generation of signals recorded from the EEG during different brain states. The dynamics of these models are dependent on their inputs (or parameters) and hence it is important to explore the parameter combinations that result in model dynamics that approximate data. This allows us to better understand how the data were generated. However, due to the relative complexity of these models, finding the parameter combinations that explain data can be a cumbersome task and hence many studies make simplifications about how model and data are compared. In this study, we introduce methods that do not require these simplifying assumptions. Using these methods we demonstrate that different choices in the way we compare models and data can lead to differences in what we infer about the underlying mechanisms. However, we find that combining different choices into the same algorithm allows us to better approximate features of the data and better constrain model parameters. We apply our method to try to understand differences observed in the resting EEG between patients with epilepsy and controls. We find that the model explains these differences predominately by a reduced excitatory synaptic gain in patients with epilepsy. We also demonstrate the potential of this method to “mine” for different kinds of dynamics in high dimensional models.

Switch-like responses arising from bistability have been linked to cell signaling processes and memory. Revealing the shape and properties of the set of parameters that lead to bistability is necessary to understand the underlying biological mechanisms, but is a complex mathematical problem. We present an efficient approach to address a basic topological property of the parameter region of multistationary, namely whether it is connected. The connectivity of this region can be interpreted in terms of the biological mechanisms underlying bistability and the switch-like patterns that the system can create. We provide an algorithm to assert that the parameter region of multistationarity is connected, targeting reaction networks with mass-action kinetics. We show that this is the case for numerous relevant cell signaling motifs, previously described to exhibit bistability. The method relies on linear programming and bypasses the expensive computational cost of direct and generic approaches to study parametric polynomial systems. This characteristic makes it suitable for mass-screening of reaction networks. Although the algorithm can only be used to certify connectivity, we illustrate that the ideas behind the algorithm can be adapted on a case-by-case basis to also decide that the region is not connected. In particular, we show that for a motif displaying a phosphorylation cycle with allosteric enzyme regulation, the region of multistationarity has two distinct connected components, corresponding to two different, but symmetric, biological mechanisms. Author summary: This work addresses the challenging problem of studying the set of parameters for which a system of ordinary differential equations has more than one steady state, a property termed multistationarity. In particular, we are interested in systems arising from the study of biochemical networks. The shape of the multistationarity region is linked to different types of switches that the network can display. We provide an algorithm to decide whether this set is path connected, meaning that any two points in the set are joined by a path completely contained in the set. We illustrate the algorithm with numerous relevant networks, for which we can conclude that the parameter region is path connected.

Pasteurella multocida can infect a multitude of wild and domesticated animals, with infections in cattle resulting in hemorrhagic septicemia (HS) or contributing to bovine respiratory disease (BRD) complex. Current cattle vaccines against P. multocida consist of inactivated bacteria, which only offer limited and serogroup specific protection. Here, we describe a newly identified surface lipoprotein, PmSLP, that is present in nearly all annotated P. multocida strains isolated from cattle. Bovine associated variants span three of the four identified phylogenetic clusters, with PmSLP-1 and PmSLP-2 being restricted to BRD associated isolates and PmSLP-3 being restricted to isolates associated with HS. Recombinantly expressed, soluble PmSLP-1 (BRD-PmSLP) and PmSLP-3 (HS-PmSLP) vaccines were both able to provide full protection in a mouse sepsis model against the matched P. multocida strain, however no cross-protection and minimal serum IgG cross-reactivity was identified. Full protection against both challenge strains was achieved with a bivalent vaccine containing both BRD-PmSLP and HS-PmSLP, with serum IgG from immunized mice being highly reactive to both variants. Year-long stability studies with lyophilized antigen stored under various temperatures show no appreciable difference in biophysical properties or loss of efficacy in the mouse challenge model. PmSLP-1 and PmSLP-3 vaccines were each evaluated for immunogenicity in two independent cattle trials involving animals of different age ranges and breeds. In all four trials, vaccination with PmSLP resulted in an increase in antigen specific serum IgG over baseline. In a blinded cattle challenge study with a recently isolated HS strain, the matched HS-PmSLP vaccine showed strong efficacy (75–87.5% survival compared to 0% in the control group). Together, these data suggest that cattle vaccines composed of PmSLP antigens can be a practical and effective solution for preventing HS and BRD related P. multocida infections. Author summary: Surface lipoproteins have been used as components of subunit-based vaccines to combat gram negative bacterial infections. A few years ago, we discovered a gene encoding a predicted lipoprotein (PmSLP) in Pasteurella multocida adjacent to the gene encoding the Slam translocon and showed that it was translocated to the bacterial cell surface. Since Pasteurella multocida had been linked to some devastating cattle diseases, hemorrhagic septicemia (HS) and bovine respiratory disease (BRD), we sought to evaluate PmSLP as a potential vaccine antigen. Upon removal of the lipid anchor, the PmSLP protein was easy to purify and could be reconstituted after lyophilization for long term storage. We investigated whether this protein could provide protection against P. multocida infections by establishing a mouse infection model and demonstrated that PmSLP could provide protect against invasive disease. Finally, to illustrate that the antigen could provide protection in the organism that it naturally infects, we performed large animal vaccine experiments illustrating the protective properties of PmSLP in cattle. Thus, the surface protein PmSLP provides an exciting new protein antigen that could be a cost-effective vaccine to prevent BRD and HS in cattle.

The regularities of the world render an intricate interplay between past and present. Even across independent trials, current-trial perception can be automatically shifted by preceding trials, namely the “serial bias.” Meanwhile, the neural implementation of the spontaneous shift of present by past that operates on multiple features remains unknown. In two auditory categorization experiments with human electrophysiological recordings, we demonstrate that serial bias arises from the co-occurrence of past-trial neural reactivation and the neural encoding of current-trial features. The meeting of past and present shifts the neural representation of current-trial features and modulates serial bias behavior. Critically, past-trial features (i.e., pitch, category choice, motor response) keep their respective identities in memory and are only reactivated by the corresponding features in the current trial, giving rise to dissociated feature-specific serial biases. The feature-specific automatic reactivation might constitute a fundamental mechanism for adaptive past-to-present generalizations over multiple features. The regularities of the world entail an intricate interplay between past and present. This neural imaging study demonstrates that the past automatically shapes the present through the co-occurrence of past information reactivation and encoding of current information.

One of the major pathogenesis mechanisms of SARS-CoV-2 is its potent suppression of innate immunity, including blocking the production of type I interferons. However, it is unknown whether and how the virus interacts with different innate-like T cells, including NKT, MAIT and γδ T cells. Here we reported that upon SARS-CoV-2 infection, invariant NKT (iNKT) cells rapidly trafficked to infected lung tissues from the periphery. We discovered that the envelope (E) protein of SARS-CoV-2 efficiently down-regulated the cell surface expression of the antigen-presenting molecule, CD1d, to suppress the function of iNKT cells. E protein is a small membrane protein and a viroporin that plays important roles in virion packaging and envelopment during viral morphogenesis. We showed that the transmembrane domain of E protein was responsible for suppressing CD1d expression by specifically reducing the level of mature, post-ER forms of CD1d, suggesting that it suppressed the trafficking of CD1d proteins and led to their degradation. Point mutations demonstrated that the putative ion channel function was required for suppression of CD1d expression and inhibition of the ion channel function using small chemicals rescued the CD1d expression. Importantly, we discovered that among seven human coronaviruses, only E proteins from highly pathogenic coronaviruses including SARS-CoV-2, SARS-CoV and MERS suppressed CD1d expression, whereas the E proteins of human common cold coronaviruses, HCoV-OC43, HCoV-229E, HCoV-NL63 and HCoV-HKU1, did not. These results suggested that E protein-mediated evasion of NKT cell function was likely an important pathogenesis factor, enhancing the virulence of these highly pathogenic coronaviruses. Remarkably, activation of iNKT cells with their glycolipid ligands, both prophylactically and therapeutically, overcame the putative viral immune evasion, significantly mitigated viral pathogenesis and improved host survival in mice. Our results suggested a novel NKT cell-based anti-SARS-CoV-2 therapeutic approach. Author summary: The ongoing COVID-19 pandemic is the most severe public health crisis in recent history. Even with efficacious vaccines, it is imperative to better understand the mechanism of viral pathogenesis particularly how SARS-CoV-2 suppresses human innate immunity in order to develop more powerful and effective treatments, not only for the current pandemic, but also for the potential re-emergence of new variants. Here we reported the first in-depth characterization of SARS-CoV-2 interaction with the important arm of innate immunity, CD1d-restricted natural killer T (NKT) cells. We discovered NKT cells were rapidly recruited to lungs post infection. The virus employed its envelope protein to suppress the expression of the key antigen-presenting molecule, CD1d, and inhibited the activation of innate NKT cells. Remarkably, we discovered that this immune evasion was unique to highly pathogenic coronaviruses and absent from common cold coronaviruses, suggesting that this new immune evasion mechanism was an important virulence factor. More importantly, we demonstrated that activation of invariant NKT cells significantly overcame the viral immune evasion and improved the host survival.