搜索结果：Co-evolution

Based on the theory of co-evolution of composite systems development， this study constructs a technology-organization-environment （TOE） framework and chooses 60 cities at the prefecture level within the Yellow River Basin spanning the years 2014 to 2023 for analysis. It also utilizes the entropy-weighted TOPSIS approach to evaluate the advancement of the "four waters" concept （water resource， water environment， water ecology， and water disaster） in each city， employs the Haken model to analyze the co-evolutionary laws of the "four waters" in the cities of the Yellow River Basin， and uses the fuzzy set qualitative comparative analysis （fsQCA） technique to investigate the most effective trajectory for the synchronized development of the "four waters" within the Yellow River Basin. The findings indicated that between 2014 and 2023， the co-evolutionary level of the "four waters" has experienced an overall upward movement with fluctuations， notably with the downstream regions exhibiting a more advanced degree of co-evolution compared to that of the upstream and midstream areas. From the analysis of necessary conditions， none of the seven antecedent variables were necessary conditions for a high level of "four waters" co-evolution， indicating that the improvement of the co-evolution level of the "four waters" in the Yellow River Basin requires a comprehensive consideration of the linkage effects of multiple conditions. Through fsQCA analysis， three main paths of co-evolution were identified： "Technology-Organization-Environment" type， "Technology-Organization" type， and "Organization-Environment" type. These paths showed different characteristics in different regions： The upstream area showed an "environment-driven" development， the middle reaches showed an "organization-driven" development， and the downstream area showed a "technology-environment dual-track" development. This study provides a theoretical basis and practical guidance for the co-evolution of the "four waters" in the Yellow River Basin， which is of great significance for promoting the ecological protection and high-quality development of the Yellow River Basin.

Aquatic photoautotrophs experience strong physicochemical constraints on inorganic carbon acquisition due to low CO2 and O2 diffusion in water producing a strong reduction in the gas conductance. These limitations have driven the widespread evolution of CO2-concentrating mechanisms (CCMs), which enhance CO2 availability around Rubisco and mitigate its catalytic inefficiencies. A compilation of the Rubisco kinetics and apparent photosynthetic CO2 affinities (1/Km CO2) for a wide range of taxa shows that, despite the structural diversity of CCMs, there is an evolutive convergence among cyanobacteria, diatoms, haptophytes, and some rhodophytes and chlorophytes with similarly high 1/Km CO2, therefore operating almost CO2-saturated photosynthesis under ambient conditions. Notably, none of the compiled species achieves CO2 saturation solely through Rubisco kinetics, underscoring the central role of CCMs in aquatic carbon fixation. CCM effectiveness (the ratio between Rubisco CO2 semi-saturation constant under 21% O2 and apparent photosynthetic semi-saturation constant for CO2, Kcair/Km CO2) varies widely across the compiled species but is consistently higher in organisms possessing Rubisco-containing microcompartments, although pyrenoids are not strictly required for concentrating CO2 around Rubisco above ambient levels. Furthermore, an inverse relationship between Rubisco carboxylation efficiency and 1/Km CO2 supports a divergent CCM-Rubisco co-evolutionary trajectory compared to terrestrial plants, likely influenced by intracellular photosynthetic O2 accumulation under submerged conditions. The present review synthesizes current knowledge on CCM diversity and its regulation under future Global Change scenarios, but also reanalyze CCM effectiveness, and its co-evolution with Rubisco kinetic traits across aquatic oxygenic phototrophs, highlighting major knowledge gaps to be filled by future research.

Erwinia amylovora, the causative agent of fire blight, poses a significant threat to global pome fruit production. This study presents a comprehensive genomic analysis of 317 E. amylovora strains and 227 Erwinia phages to elucidate virulence evolution, phage-host dynamics, and the genomic signatures of the co-evolutionary arms race. Our analysis suggests that a substantial portion of E. amylovora's virulence factors (VFs) share evolutionary origins with diverse plant, human, and animal pathogens, underscoring widespread horizontal gene transfer. We identified bacterial phage hydrolases‑like proteins that share phylogenetic and domain-level similarities with phage endolysins. These observations are consistent with the possibility that some bacterial hydrolases originated from phage-derived ancestors, although functional repurposing remains to be experimentally validated. Crucially, our analysis identifies systematic, non-random associations between bacterial defense systems (e.g., RM, CRISPR-Cas, TA) and mobile anti-defense genes. Statistical correlations show strong patterns of co-occurrence and mutual exclusivity, which are consistent with an ongoing phage-bacteria arms race. These patterns provide a genomic basis for generating hypotheses about co-evolutionary dynamics. These findings may advance our understanding of E. amylovora pathogenicity and phage interactions, offering foundational insights for developing targeted phage-based biocontrol strategies against this devastating plant pathogen. Experimental validation of the predicted virulence factors and defense correlations is warranted to confirm their biological roles.

This study characterizes AP-20-A, a lytic podovirus infecting Sinorhizobium meliloti, isolated from agricultural chernozem. Its 49.4 kbp genome shows negligible intergenomic similarity with known rhizobiophages (<2%). Core structural proteins-the major capsid protein (MCP) and terminase large subunit (TerL)-show closest homology to podoviruses infecting Paenibacillus, rather than to alphaproteobacterial viruses, suggesting cross-phylum horizontal gene transfer. This exchange is ecologically plausible, as Paenibacillus and Sinorhizobium co-exist in the rhizosphere. Over 63% of predicted proteins are functionally uncharacterized, with structural homologs detected in bacteria, archaea, and eukaryotes. We report the first identification in a rhizobiophage of a Tad2-like domain, predicted to block the bacterial Thoeris type II anti-phage defense. AP-20-A infected 56% of native S. meliloti strains; agrocenose isolates showed higher resistance than phytocenose isolates, evidence of local co-evolution. Among susceptible strains, 60% entered putative pseudolysogeny (with one strain exhibiting growth stimulation), whereas a symbiotically elite inoculant strain was completely lysed within hours. Some host strains carry additional AbiE systems; whether these independent defense-counterdefense layers interact during infection remains unknown. We conclude that resident phages represent a selective force that can disrupt inoculant establishment, underscoring the need to integrate soil virome assessment into agricultural microbiome management.

Background/Objectives: The unprecedented structural and binding data for antibodies to the SARS-CoV-2 virus taken together with the mutations for the spike protein allows for a broad simulation study of antibody-spike protein binding. This provides an understanding of the co-evolution of human immunity and viral immunity escape. Methods: We utilized the YASARA molecular dynamics program to generate initial structures and simulate to equilibration for six SARS-CoV-2 variants and ten different antibodies sampling two different binding regions to the receptor binding domain of the spike (especially for the Class I antibodies in the same part of the spike that attaches to the ACE2 receptor protein) and one to the N-terminal domain of the spike. Starting structures for antibody binding to variant spike protein domains are perturbatively achieved through point mutations and insertions/deletions in the YASARA program. We employed YASARA to measure interfacial hydrogen bound counts between antibodies and variant spike proteins and the HawkDock MMGBSA program to characterize trends in binding energies with mutation for four of the antibodies. We utilized the VMD program to analyze the time course of hydrogen bond populations. Results: As seen in previous studies, interfacial hydrogen bond counts serve as an excellent proxy for binding energies without the large systematic error inherent in the latter. We find that there is generally a decline in antibody binding strength, as measured by interfacial hydrogen bond counts, with viral evolution, but that a modest re-entrance of binding strength is present for most antibodies studied. Generically, the antibody heavy chain binds more strongly to the spike protein, though for approximately half the antibodies the light chain binding strength converges to the heavy chain strength with viral evolution. Conclusions: The key conclusion is that the identified re-entrant immunity, speculatively arising from a balancing of maintenance of ACE2-spike binding while escaping antibodies through mutation, allows for some maintenance and even strengthening of immunity for later viral strains from early infection or vaccination.

Soil viruses are crucial for microbial life, biogeochemical cycles of carbon and nutrients, and for microbial necromass formation. We hypothesized that the effects of viruses on these processes depend on organic matter and nutrient availability in soils. Here, we combined a 34-year long-term fertilization trial, 150 sequenced soil metagenomes, and microcosm experiments to explore how viruses modulate carbon and nutrient dynamics depending on resource availability. We uncovered 2789 viral populations (vOTUs) grouping into 301 viral clusters, 91% of which were previously unknown. Organically fertilized soils harbored most lytic viruses carrying diverse element cycling-related auxiliary viral genes (AVGs) acquired through co-evolution and horizontal gene transfer. Synthesis and heterologous expression assays further indicated that four AVGs (i.e., cbhA, pel, wbpD, GT2) had higher transcript levels in Escherichia coli under nutrient rich than nutrient poor conditions. Addition of virus particles to soils raised microbial carbon use efficiency (CUE; biomass production relative to carbon uptake) and accelerated microbial turnover leading to boosted microbial necromass formation by 14%. Conversely, in soils without organic fertilizers, viruses facilitate bacterial adaptation to stress (e.g., defense system and interference competition) and accelerate microbial decomposition of organic matter. 35&#x2009;days after virus addition, CO2 and N2O emissions increased by 41% and 52%, respectively. Finally, we propose the Viral Entombing-Priming (VEP) framework to describe the contrasting roles of viruses in carbon and nutrient dynamics depending on soil fertility. This work reveals the viral "Matthew effect" (the rich get richer and the poor get poorer) in resource-rich and resource-poor soils and could unlock nature-based pathways to raise carbon and nutrient retention for sustainable agriculture.

Generative artificial intelligence (GenAI) is poised to transform public health systems through its capacity for data synthesis and predictive modeling. This systematic review, analyzing 119 key studies, adopts a co-evolutionary lens to examine the dynamic interplay between GenAI advancements and public health system adaptation. We demonstrate that the effective integration of GenAI is fundamentally constrained by a system's infrastructural, institutional, and human resource maturity. Our analysis, grounded in the theory of Responsible Innovation, identifies three interconnected governance domains-technical transparency, institutional accountability, and ethical equity-that frame the core challenges. We subsequently propose a three-layer governance framework to navigate these issues, emphasizing that trustworthy AI ecosystems require more than technical excellence; they demand institutional foresight, inclusive governance, and a steadfast commitment to equitable, human-centered health futures.

Enteric methane (CH&#x2084;) emissions from ruminant livestock present dual challenges for agricultural sustainability: contributing to greenhouse gas emissions while reducing feed conversion efficiency and animal productivity. Methane is produced by methanogenic archaea utilizing metabolic hydrogen (H&#x2082;) generated during ruminal fermentation. This hydrogen economy is central to fermentation efficiency, nutrient utilization, and methane formation. Conventional mitigation strategies have primarily focused on inhibiting methanogenesis; however, these approaches often yield inconsistent results across production systems and lack an integrative framework for systematic application. This narrative review proposes a shift in perspective from methane suppression to the management of H&#x2082; flow within the rumen hydrogen economy and introduces two complementary conceptual frameworks to guide this approach. The genetic-microbiome co-evolution framework conceptualizes the rumen microbiome as a partially heritable trait shaped by host genetic and environmental selection, providing a theoretical basis for selecting low-emission, feed-efficient animals. The conceptual fermentation kinetics framework provides a mechanistic basis for understanding how dietary inputs and microbial interactions influence the distribution of hydrogen among competing metabolic pathways, including methanogenesis and propionate formation. Together, these frameworks establish a systems-level perspective that may inform the development of integrated strategies combining host genetic selection, precision nutrition, and microbial management. While substantial validation remains necessary, this approach provides a conceptual foundation for advancing methane mitigation from descriptive observation toward mechanistic interpretation, with the ultimate goal of supporting climate-smart livestock production systems.

The discovery of biotherapeutics is increasingly being driven by generative artificial intelligence (generative AI), marking a paradigm shift from random combinatorial screening to structure-based computational design. Within the field of non-small cell lung cancer (NSCLC), therapeutic development is persistently challenged by biophysical barriers, particularly those associated with neoantigens derived from intracellular oncogenic proteins (e.g., KRAS G12C), conformationally dynamic drug-resistant variants (e.g., EGFR), and structurally complex transmembrane receptors (e.g., CXCR4). Potential pathways for programmable epitope targeting are now being offered by emerging models based on diffusion and multimodal architectures (such as RFdiffusion and Chroma). In this narrative review, the core mechanisms and recent advancements of generative AI in antibody scaffold generation, sequence-structure co-evolution, and the early-stage screening of multi-parameter developability (e.g., solubility, viscosity, and immunogenicity) are evaluated. The utilization of computational methods to address challenging targets is analyzed, and the significant translational gap remaining between algorithmic confidence and clinically relevant biological fidelity is explored. Structural hallucinations, the absence of glycosylation and membrane lipid environment characterizations in training datasets, and binding-function discordance are identified as the primary current technical barriers. Ultimately, the translation of generative AI into clinical oncology will depend on the deep integration of "lab-in-the-loop" platforms, a paradigm where computational physical priors are continuously calibrated by high-throughput experimental data, progressively bridging the gap between in silico design and in vivo efficacy.

Bacteria secrete a diverse arsenal of protein toxins that inhibit the growth of competing species. To avoid self-intoxication, antibacterial toxins are encoded alongside cognate immunity proteins that typically bind to and occlude a toxin's active site, thus neutralizing its activity. Multiple immunity proteins rarely target the same toxin, reflecting the strong co-evolution between toxins and their immunity factors. Here, we identify and characterize an antibacterial toxin in a clinical isolate of Pseudomonas aeruginosa, which we term Tde5, and find that it is inhibited by two structurally distinct immunity proteins, Tdi5a and Tdi5b. Informatic analyses reveal that Tde5 is a member of the &#x3b2;&#x3b2;&#x3b1;-Me DNase superfamily, and accordingly we find that this enzyme inhibits bacterial growth by non-specifically degrading DNA. This antibacterial activity is counteracted by either Tdi5a or Tdi5b, suggesting that these two immunity proteins function independently to protect bacteria against Tde5-mediated toxicity. Using biochemical and biophysical approaches, we show that Tdi5a and Tdi5b interact with Tde5 with sub-nanomolar affinities and that a trimeric complex forms between these three proteins, indicating that the two immunity factors bind to the toxin through non-overlapping interfaces. A 1.73 &#xc5; X-ray crystal structure of the Tde5-Tdi5a complex reveals that Tdi5a binds to Tde5 through a large electrostatic interface and occludes the toxin's active site, whereas structural modelling reveals that Tdi5b binds an exosite distal to the toxin's active site. Together, these findings define a previously unrecognized dual-immunity mechanism and expand our understanding of antibacterial toxin-immunity dynamics, informing future studies of interbacterial competition and virulence.

The evolutionary plasticity of the three-dimensional (3D) genome organization in vertebrates, and its transmission through the germ line, is central to understanding genome function and evolution. Yet, the mechanisms regulating these processes remain poorly characterized across lineages. Here, we integrate fluorescence-activated cell sorting, in situ chromosome capture conformation (Hi-C), and single-cell RNA sequencing to investigate germ line genome architecture in eutherians, marsupials, and reptiles, lineages that last shared a common ancestor ~350 million years ago. We uncover lineage-specific chromatin folding patterns in germ cells, shaped by chromosome morphology and genome size, which constrain DNA loop formation and inter-chromosomal interactions during meiosis. We also explore the relationship between 3D genome remodeling and gene regulation in the context of meiotic sex chromosome inactivation (MSCI). In the tammar wallaby, we identify regions of the X that escape MSCI checkpoint, suggesting incomplete silencing in marsupials. These findings provide high-resolution insights into the evolution of germ line chromatin architecture and the co-evolution of genome structure and function across vertebrates.

To address the challenges of high-dimensional nonlinearity, multimodal landscapes, and stringent constraints prevalent in modern engineering design, traditional meta-heuristic algorithms often suffer from a loss of population diversity and premature convergence. Inspired by the social collaborative predation and collective information interaction behaviors of P. prominens (jumping spiders), this study proposes a novel bio-inspired meta-heuristic optimization algorithm, termed the Experience Exchange Strategy-Enhanced Philoponella Prominens Optimization (EESPPO). The proposed EESPPO integrates an Experience Exchange Strategy framework to reshape the search dynamics of the population through three progressive evolutionary stages: (1) In the Experience Scarcity (ESC) stage, the algorithm focuses on the construction and dynamic maintenance of an experience library to ensure the effective preservation of high-quality historical information; (2) In the Experience Crossover (ECR) stage, a random guidance vector generation mechanism is introduced to significantly enhance population behavioral diversity and the capability to escape local optima; (3) In the Experience Sharing (ESH) stage, an adaptive fusion update strategy is employed to achieve efficient information interaction and co-evolution among individuals. These three stages operate synergistically within the optimization cycle to establish a dynamic balance between global exploration and local exploitation, effectively overcoming the inherent defects of premature convergence in traditional meta-heuristics. Extensive empirical analysis based on the CEC2017 benchmark functions confirms that EESPPO comprehensively outperforms 12 existing advanced algorithms (including PPO, HSO, SGA, PSO, FLO, DE, HO, WOA, KEO, GWO, FDB-AGSK, and IVYPSO) in terms of convergence accuracy and robustness. Furthermore, the application of EESPPO to four challenging engineering design problems confirms its superiority. The experimental results validate the high precision and feasibility of EESPPO in solving complex constrained engineering problems.

RNA viruses form membraneless condensates in host cells to drive replication, but whether these compartments also regulate host RNAs remains unclear. Using MERFISH-based subcellular transcriptomics, we quantified cellular mRNA recruitment into Ebola virus condensates under basal and IFN-stimulated states. We find that in the basal state, cellular RNAs with minimally folded coding regions are selectively recruited. Under IFN-stimulation, however, interferon-stimulated genes (ISGs) with structured 3'UTRs concentrate in viral condensates. We find that both features, minimally folded coding regions and structured 3'UTRs, are conserved in the viral RNA genome, supporting viral genome retention in condensates. In parallel, for cellular mRNAs, we find that partitioning into condensates escapes decay, prolonging RNA-half-life, and amplifying rather than dampening ISG expression. Fruit bats, which do not experience severe disease for RNA viruses, instead have ISGs with reduced 3'UTR folding, and may evade condensate-sequestration, enabling balanced antiviral responses. This selective stabilization links condensate function to RNA regulation as a molecular determinant of viral and host co-evolution and disease pathogenesis.

Enhancing individual awareness and immunization are generally recognized as fundamental strategies for controlling infectious diseases. Awareness is influenced by both mass media and interpersonal interactions, which affect individuals' protective behaviors and their readiness to participate in vaccination programs. This research establishes a hybrid framework combining evolutionary game theory (EGT) and deep neural networks (DNNs) to examine the influence of awareness on epidemic control. Classical epidemic models (ODE-based) often require comprehensive epidemiological datasets to estimate fixed parameters (e.g., infection rate, awareness impact, vaccine efficacy). In reality, however, such extensive data are not always accessible due to limited surveillance, reporting delays, or missing behavioral factors (such as awareness or media effects). Thus, here, we use the DNN framework not as a substitute for epidemiological data, but as a verification and approximation layer: (i) to confirm the consistency of our deterministic model, and (ii) to expand its applicability to scenarios when data is insufficient, or parameters fluctuate over time. Therefore, the proposed method utilizes EGT to encapsulate adaptive individual decision-making influenced by perceived payoffs, vaccination costs, and efficacy, while DNNs model epidemic trajectories and accommodate the nonlinear dynamics arising from awareness-behavior-disease feedback loops. More precisely, DNNs in epidemic models serve as a data-driven corrective layer that enhances deterministic ODE models, making them more flexible, more realistic (by reducing model-data mismatch), and better at capturing concealed social-behavioral feedback. Through numerical simulations, we present epidemic trajectories, two-dimensional heatmaps, and phase portraits to elucidate the co-evolution of awareness, vaccination, and disease transmission. The DNN component improves predicted accuracy by correlating simulated results with real epidemic curve patterns, thereby augmenting theoretical research with empirical confirmation. The findings indicate that effective vaccination initiatives, bolstered by targeted media campaigns and robust peer influence, significantly reduce the severity of the pandemic. The integration of EGT and DNNs underscores the significance of both behavioral adaptation and computational learning in epidemic forecasting and control. This combined effect provides a solid foundation for formulating adaptive policies, enabling policymakers to implement prompt, effective measures during epidemic or pandemic scenarios.

Retrospective diagnosis of past individuals is inherently complex and often misleading. Although ancient pathogens can now be identified with molecular tools, their clinical expressions differed markedly from those observed today. Physiology, immunity, microbiota, and human-environment interactions have changed profoundly over time, altering disease behavior and outcomes. Diseases should therefore be understood as evolving processes shaped by long-term host-pathogen co-evolution rather than fixed entities. Reconstructed ancient microbiota reveal dramatic ecological shifts driven by diet, hygiene, industrialization, antibiotics, and climate. Identical pathogens could thus produce different diseases in different historical periods, complicating paleopathological interpretation. Recent case studies illustrate infections that were once common but are now rare, displaced, or clinically transformed. Ignoring paleoecology and semantic shifts in medical terminology leads to biased and anachronistic diagnoses. The article calls for methodological humility and "context-first" approaches instead of projecting modern disease categories onto the past. This evolutionary perspective not only refines interpretations of ancient remains but also encourages more flexible, personalized, and reflective modern medical practice.