Evolutionary Principles Research Articles

Diffusion Smart-seq3 of breast cancer spheroids to explore spatial tumor biology and test evolutionary principles of tumor heterogeneity.

Combining 3D cultures such as tumor spheroids and organoids with spatial omics holds great potential for tissue biology and cancer research. Yet, this potential is presently limited by technical and financial challenges of spatial omics methods and 3D cultures. To address this, we combine dye diffusion, the Smart-seq3xpress protocol for deep single-cell gene expression profiling, and dedicated probabilistic inference methods into diffusion Smart-seq3 (Smart-seq3D), to reveal the transcriptome of single cells along with their position along the core-periphery axis of spheroids. Applying Smart-seq3D to triple-negative breast tumor spheroids identifies thousands of spatial genes and reveals continuous, ungated spatial gene expression. Spatial gene and pathway expression patterns suggest biologies specific to spheroid regions, which we validate by immunostainings and pharmacological interventions. We use the Smart-seq3D data to test evolutionary principles of spatial tumor heterogeneity. Finally, we characterize aspects of tumor heterogeneity captured by 3D spheroids that are missing from 2D cultures but found in tumors in vivo. Smart-seq3D can offer a cost-efficient approach to explore how cells adapt their transcriptome to different micro-environments, reveal spatial determinants of drug resistance and could serve to characterize spatial interactions between cancer and stromal/immune cells in 3D co-cultures.

Deciphering Aging, Genetic, and Epigenetic Heterogeneity in Cancer Evolution: Toward Personalized Precision Preventative Medicine

ABSTRACTBackgroundCancer's inherent ability to evolve presents significant challenges for its categorization and treatment. Cancer evolution is driven by genetic, epigenetic, and phenotypic diversity influenced by microenvironment changes. Aging plays a crucial role by altering the microenvironment and inducing substantial genetic and epigenetic heterogeneity within an individual's somatic cells even before cancer initiation.ObjectivesThis review highlights the clinical significance of epigenetic mechanisms in cancer evolution, focusing on hematopoietic and solid tumors. The review aims to explore opportunities for integrating evolutionary principles and data science into cancer research.MethodsThe review synthesizes recent advancements in omics technologies, single‐cell sequencing, and genetic barcoding to elucidate epigenetic mechanisms and aging's role in cancer evolution.ResultsEpigenetic mechanisms' high plasticity generates heritable phenotypic diversity, driving malignant evolution toward poor prognosis. Advances in single‐cell sequencing and genetic barcoding enable the precise detection and tracking of biomarkers, allowing early, personalized interventions. Incorporating data science into cancer research has the potential to map, predict, and prevent cancer evolution effectively.ConclusionUnderstanding cancer evolution through novel technologies and data analysis offers a proactive approach to cancer prevention and treatment. By predicting key evolutionary events and leveraging personalized strategies, patient outcomes can be improved, and healthcare burdens reduced, marking a transformative shift in oncology.

Time to start taking time seriously: how to investigate unexpected biological rhythms within infectious disease research.

The discovery of rhythmicity in host and pathogen activities dates back to the Hippocratic era, but the causes and consequences of these biological rhythms have remained poorly understood. Rhythms in infection phenotypes or traits are observed across taxonomically diverse hosts and pathogens, suggesting general evolutionary principles. Understanding these principles may enable rhythms to be leveraged in manners that improve drug and vaccine efficacy or disrupt pathogen timekeeping to reduce virulence and transmission. Explaining and exploiting rhythms in infections require an integrative and multidisciplinary approach, which is a hallmark of research within chronobiology. Many researchers are welcomed into chronobiology from other fields after observing an unexpected rhythm or time-of-day effect in their data. Such findings can launch a rich new research topic, but engaging with the concepts, approaches and dogma in a new discipline can be daunting. Fortunately, chronobiology has well-developed frameworks for interrogating rhythms that can be readily applied in novel contexts. Here, we provide a 'how to' guide for exploring unexpected daily rhythms in infectious disease research. We outline how to establish: whether the rhythm is circadian, to what extent the host and pathogen are responsible, the relevance for host-pathogen interactions, and how to explore therapeutic potential.This article is part of the Theo Murphy meeting issue 'Circadian rhythms in infection and immunity'.

The relevance of the evolutionary approach for understanding health and disease of the human body and mind.

Ultimate and proximate levels of analysis offer synergistic explanations can improve the search for causes of disease and their cures. Here we review how several principles of evolutionary biology such as historical contingencies, mismatches, trade-offs, sexual selection and genomic conflict are applied to problems in medicine and psychiatry. The application of evolutionary principles to many other domains of medicine, among them mental disorders, have not received the same reception from preclinical and clinical researchers. The lack of a well-coordinated interdisciplinarity may be one reason for the slow application of evolutionary principles to biomedicine and psychiatry. This is exemplified by the case of ethopharmacology, an evolutionary approach to psychopharmacology strongly proposed and applied by ethologists but apparently unknown to many evolutionary minded scholars. Another reason has to do with the lack of efforts from many medical schools to integrate evolution and its principles in their curriculum studiorum. Interestingly, this Darwinian approach is generating an important evolutionary epistemology for the study of body and human mind health and diseases.

An explainable few-shot learning model for the directed evolution of antimicrobial peptides

Due to the persistent threat of antibiotic resistance posed by Gram-negative pathogens, the discovery of new antimicrobial agents is of critical importance. In this study, we employed deep learning-guided directed evolution to explore the chemical space of antimicrobial peptides (AMPs), which present promising alternatives to traditional small-molecule antibiotics. Utilizing a fine-tuned protein language model tailored for small dataset learning, we achieved structural modifications of the lipopolysaccharide-binding domain (LBD) derived from Marsupenaeus japonicus, a prawn species of considerable value in aquaculture and commercial fisheries. The engineered LBDs demonstrated exceptional activity against a range of Gram-negative pathogens. Drawing inspiration from evolutionary principles, we elucidated the bactericidal mechanism through molecular dynamics simulations and mapped the directed evolution pathways using a ladderpath framework. This work highlights the efficacy of explainable few-shot learning in the rational design of AMPs through directed evolution.

Chromosome-scale genome assembly reveals how repeat elements shape non-coding RNA landscapes active during newt limb regeneration.

Newts have large genomes harboring many repeat elements. How these elements shape the genome and relate to newts' unique regeneration ability remains unknown. We present here the chromosome-scale assembly of the 20.3 Gb genome of the Iberian ribbed newt, Pleurodeles waltl, with a hitherto unprecedented contiguity and completeness among giant genomes. Utilizing this assembly, we demonstrate conserved synteny as well as genetic rearrangements, such as in the major histocompatibility complex locus. We provide evidence suggesting that intronic repeat elements drive newt-specific circular RNA (circRNA) biogenesis and show their regeneration-specific expression. We also present a comprehensive in-depth annotation and chromosomal mapping of microRNAs, highlighting genomic expansion profiles as well as a distinct regulatory pattern in the regenerating limb. These data reveal links between repeat elements, non-coding RNAs, and adult regeneration and provide key resources for addressing developmental, regenerative, and evolutionary principles.

Cancer evolution: from Darwin to the Extended Evolutionary Synthesis.

The fundamental evolutionary nature of cancer has been recognized for decades. Increasingly powerful genetic and single cell sequencing technologies, as well as preclinical models, continue to unravel the evolution of premalignant cells, and progression to metastatic stages and to drug-resistant end-stage disease. Here, we summarize recent advances and distil evolutionary principles and their relevance for the clinic. We reveal how cancer cell and microenvironmental plasticity are intertwined with Darwinian evolution and demonstrate the need for a conceptual framework that integrates these processes. This warrants the adoption of the recently developed Extended Evolutionary Synthesis (EES).

Deciphering the biosynthetic pathway of triterpene saponins in Prunella vulgaris.

The traditional Chinese medicinal plant Prunella vulgaris contains numerous triterpene saponin metabolites, notably ursolic and oleanolic acid saponins, which have significant pharmacological values. Despite their importance, the genes responsible for synthesizing these triterpene saponins in P. vulgaris remain unidentified. This study used a comprehensive screening methodology, combining phylogenetic analysis, gene expression assessment, metabolome-transcriptome correlation and co-expression analysis, to identify candidate genes involved in triterpene saponins biosynthesis. Nine candidate genes - two OSCs, three CYP716s and four UGT73s - were precisely identified from large gene families comprising hundreds of members. These genes were subjected to heterologous expression and functional characterization, with enzymatic activity assays confirming their roles in the biosynthetic pathway, aligning with bioinformatics predictions. Analysis revealed that these genes originated from a whole-genome duplication (WGD) event in P. vulgaris, highlighting the potential importance of WGD for plant metabolism. This study addresses the knowledge gap in the biosynthesis of triterpene saponins in P. vulgaris, establishing a theoretical foundation for industrial production via synthetic biology. Additionally, we present an efficient methodological protocol that integrates evolutionary principles and bioinformatics techniques in metabolite biosynthesis research. This approach holds significant value for studies focused on unraveling various biosynthetic pathways.

A widespread family of viral sponge proteins reveals specific inhibition of nucleotide signals in anti-phage defense.

Cyclic oligonucleotide-based antiviral signaling systems (CBASS) are bacterial anti-phage defense operons that use nucleotide signals to control immune activation. Here we biochemically screen 57 diverse E. coli and Bacillus phages for the ability to disrupt CBASS immunity and discover anti-CBASS 4 (Acb4) from the Bacillus phage SPO1 as the founding member of a large family of >1,300 immune evasion proteins. A 2.1 Å crystal structure of Acb4 in complex with 3'3'-cGAMP reveals a tetrameric assembly that functions as a sponge to sequester CBASS signals and inhibit immune activation. We demonstrate Acb4 alone is sufficient to disrupt CBASS activation in vitro and enable immune evasion in vivo. Analyzing phages that infect diverse bacteria, we explain how Acb4 selectively targets nucleotide signals in host defense and avoids disruption of cellular homeostasis. Together, our results reveal principles of immune evasion protein evolution and explain a major mechanism phages use to inhibit host immunity.

Зоологічні експозиції природничих музеїв: класичне виконання та сучасний підхід

Traditional natural history museums display elaborate stuffed animals in showcases or as part of dioramas. Classic diorama displays are three-dimensional images, with a combination of natural objects and artworks depicting a natural setting. The basis of a zoological diorama is a realistic image of the landscape (a photograph or painting depicting a certain landscape), which serves as a background for the main objects of the exhibition. To create a realistic atmosphere, natural materials such as soil, stones, plants, and trees are used, and stuffed animals in dioramas are made in dynamic poses typical of the species. This means of recreating a part of the natural environment helps to demonstrate the peculiarities of the ecosystem and the atmosphere itself in which the represented animal species live. The author, who has been visiting zoological museums in different cities and countries for many years, has had the opportunity to observe the emergence of fundamentally new approaches to exhibition activities in modern museums, and the impetus was his acquaintance with the Grande Galerie de l’Évolution in Paris, which is part of the Muséum National d’Histoire Naturelle. The Evolution Gallery exhibition is a vivid example of a new approach that skilfully combines the display of stuffed animals and modern technologies to convey information about the principles of evolution, biodiversity, and the role of animals in ecosystems. The Gallery widely uses such technologies as multimedia displays, interactive panels, mobile applications, etc. In fact, the further development of zoological exhibitions is determined by scientific and technological progress, and the development of digital technologies (high-quality cameras, drone technology, and smartphones) has made the process of nature photography more accessible. This provides endless possibilities for involving such materials in exhibition activities, which is facilitated by the variety of social networks and various aggregators of visual and audio information available for exchange and use. Therefore, the production and display of new biogroups and dioramas requires the mandatory use of modern technologies, which increases the attractiveness of the exposition for the modern museum visitor. The most successful examples of the use of new approaches in the design of the exposition are presented and analysed.

Learning the language of antibody hypervariability

Protein language models (PLMs) have demonstrated impressive success in modeling proteins. However, general-purpose "foundational" PLMs have limited performance in modeling antibodies due to the latter's hypervariable regions, which do not conform to the evolutionary conservation principles that such models rely on. In this study, we propose a transfer learning framework called Antibody Mutagenesis-Augmented Processing (AbMAP), which fine-tunes foundational models for antibody-sequence inputs by supervising on antibody structure and binding specificity examples. Our learned feature representations accurately predict mutational effects on antigen binding, paratope identification, and other key antibody properties. We experimentally validate AbMAP for antibody optimization by applying it to refine a set of antibodies that bind to a SARS-CoV-2 peptide, and obtain an 82% hit-rate and up to 22-fold increase in binding affinity. AbMAP also unlocks large-scale analyses of immune repertoires, revealing that B-cell receptor repertoires of individuals, while remarkably different in sequence, converge toward similar structural and functional coverage. Importantly, AbMAP's transfer learning approach can be readily adapted to advances in foundational PLMs. We anticipate AbMAP will accelerate the efficient design and modeling of antibodies, expedite the discovery of antibody-based therapeutics, and deepen our understanding of humoral immunity.

Comprehensive Analysis and Application Research of Advanced Computational Algorithms in Protein Folding Simulations

Protein engineering stands at the forefront of biotechnology, aiming to modify natural proteins or create new ones tailored to specific functional requirements. The three-dimensional structures of proteins, particularly their folding patterns, are critical in defining their biological roles. Accurate prediction and detailed examination of these protein folding structures are crucial in protein engineering. The close relationship between protein structure and function highlights the importance of understanding protein folding dynamics to successfully manipulate protein designs for intended uses. Genetic algorithms (GA), taking inspiration from natural evolutionary principles, employ a heuristic search approach that integrates elements of randomness. In contrast, simulated annealing (SA) leverages stochastic optimization techniques based on the Monte Carlo method, theoretically capable of approximating the global optimum with a high degree of accuracy. Additionally, generalized ensemble methods are increasingly used to explore protein folding processes. This paper explores the fundamental principles and practical applications of these algorithms in simulating protein folding dynamics, aiming to enhance the methodologies used in protein engineering. This exploration not only aids in the refinement of protein design but also extends the potential applications of engineered proteins in various scientific and industrial fields.

An Evolutionary Psychology Perspective on Athletic Development and Performance: Differences Between Proximate and Ultimate Explanations.

Many areas of mainstream psychology have embraced the notions that understanding human behaviour can be improved by integrating developments from evolutionary science; however, evolutionary principles have not been as widely applied among sport researchers or practitioners, especially those examining athlete development and the psychology of competition and performance. In this paper, we discuss the distinction between ultimate and proximate explanations of psychological outcomes, and the relevance of this distinction for exploring issues related to skill acquisition and athlete development. We use three examples-deliberate practice, early sport play and sustained engagement-to highlight the benefits and challenges of applying evolutionary theories to sport contexts. Embracing our species' evolutionary history has the potential to inform ongoing debates in athlete development and performance, among other areas of sport science.

A novel metaheuristic population algorithm for optimising the connection weights of neural networks

The efficacy of feed-forward multi-layer neural networks relies heavily on their training procedure, where identifying appropriate weights and biases plays a pivotal role. Nonetheless, conventional training algorithms such as backpropagation encounter limitations, including getting trapped in sub-optimal solutions. To rectify these inadequacies, metaheuristic population algorithms are advocated as a dependable alternative. In this paper, we introduce a novel training methodology termed, DDE-OP, which leverages the principles of differential evolution enriched with a division-based scheme and an opposite-direction strategy. Our approach integrates two effective concepts with differential evolution. Initially, the proposed algorithm identifies partitions within the search space through a clustering algorithm and designates the obtained cluster centres to serve as representatives. Subsequently, an updating scheme incorporates these clusters into the current population. Lastly, a quasi-opposite-direction strategy is used to augment search space exploration. Extensive evaluation on diverse classification and approximation tasks demonstrate that DDE-OP surpasses conventional and population-based methodologies.

A new approach based on genetic algorithm for computation off-loading optimization in multi-access edge computing networks

The proliferation of smart devices and the increasing demand for resource- intensive applications present significant challenges in terms of computational efficiency, leading to surge in data traffic. While cloud computing offers partial solutions, its centralized architecture raises concerns about latency. Multi-access edge computing (MEC) emerges as promising alternative by deploying servers at the network edge to bring computations closer to user devices. However, op- timizing computation offloading in the dynamic MEC environment remains a complex challenge. This paper introduces novel genetic algorithm-based ap- proach for efficient computation offloading in MEC, considering processing and transmission delays, user preferences, and system constraints. The pro- posed approach integrates computation offloading and resource allocation al- gorithm based on evolutionary principles, combined with a greedy strategy to maximize overall system performance. By utilizing genetic algorithms, the pro- posed method enables dynamic adaptation to changing conditions, eliminating the need for intricate mathematical models and providing an appealing solution to the complexities inherent in MEC. The urgency of this research arises from the critical need to enhance mobile application performance. Simulation re- sults demonstrate the robustness and efficacy of our approach in achieving near- optimal solutions while efficiently balancing computation offloading, minimiz- ing latency, and maximizing resource utilization. Our approach offers flexibility and adaptability, contributing to advancement of MEC networks and addressing the requirements of latency-sensitive applications.

Evolutionary View of Liver Pathology

ABSTRACTEvolutionary medicine emerged in the late twentieth century, integrating principles of natural selection and adaptation with the health sciences. Today, with a rapidly widening gap between the biology of Homo sapiens and its environment, maladaptation or maladaptive disorders can be detected in almost all diseases, including liver dysfunction. However, in hepatology, as in most medical specialties, evolutionary considerations are neglected because the majority of the medical community is not familiar with evolutionary principles. The aim of this brief review is to highlight an evolutionary approach that may facilitate understanding various liver diseases.

Site-directed mutagenesis of ent-kaurane diterpenoid C-19 oxidase TwKO in Tripterygium wilfordii

Tripterifordin and neotripterifordin are important ent-kaurane diterpenoids in the Chinese medicinal herb Tripterygium wilfordii, possessing significant anti-HIV(human immunodeficiency virus) activity. On the basis of elucidating the natural biosynthetic pathways of these compounds, heterologous production with microbial cell factories can help to alleviate the reliance on plant resources and provide abundant raw materials for sustainable production. TwKO is the first CYP450 enzyme involved in the biosynthesis of tripterifordin and neotripterifordin. This study aimed to enhance the catalytic activity of TwKO by site-directed mutagenesis to benefit the production of tripterifordin and neotripterifordin in yeast. The AlphaFold DB established based on the AlphaFold 2 was employed to obtain the protein model of TwKO. According to multiple sequence alignments and principles of natural evolution, the key residues influencing the binding of TwKO to the substrate were identified. Subsequently, functional characterization of the mutants were conducted in Saccharomyces cerevisiae. A total of 71 mutants were obtained, among which 11 and 11 mutants had the abilities of enhancing the production of 16α-hydroxy-ent-kaurenol and 16α-hydroxy-ent-kaurenoic acid, respectively. In addition, 10 mutants could increase the proportion of the oxidation product of 16α-hydroxy-ent-kaurenol. In particular, R304 was identified as a key residue affecting the catalytic specificity of TwKO, the mutation of which led to the specific prodiction of 16α-hydroxy-ent-kaurenol. This study was the first to reveal the key residue affecting the catalytic activity of TwKO and obtained the mutants with increased TwKO activity, lay a foundation for the biosynthesis of tripterifordin and neotripterifordin.

Experimental and numerical research on jet dynamics of cavitation bubble near dual particles

The current paper delves into the jet dynamics arising from a cavitation bubble in proximity to a dual-particle system, employing both experimental methodology and numerical simulation. The morphological development of a laser-induced bubble as well as the production of jets are captured by utilizing high-speed photography. The principles of bubble morphology evolution and jet formation are revealed by a OpenFOAM solver, which takes into account the effects of two-phase fluid compressibility, phase changes, heat transfer, and surface tension. Fluid temperature variations induced by bubble oscillations are discussed. The results indicate that the jet dynamics can be categorized into three cases, i.e. bubble-splitting double jets, impacting single jet, non-impacting double jets. For bubble-splitting double jets, bubble splitting is induced by an annular pressure gradient towards the bubble axis. This resulted in the production of two unequal-sized sub-bubbles, which subsequently produced double jets in opposite directions. The fluid temperature close to the bubble interface is low, while the bubble center is high. For impacting single jet, it is induced by a conical pressure gradient towards the nearest particle and the jet impacts the particle. The fluid temperature is low near the jet and high near the particle. When the jet penetrates the bubble interface, the temperature inside the bubble reaches its peak. For non-impacting double jets, they are induced by pressure gradients facing each other and they do not impact particles. The temperature inside the bubble increases with the proximity of the two jets.

Thinking on your feet: potentially enhancing phylogenetic tree learning accessibility through a kinaesthetic approach

BackgroundPhylogenetics is one of the main methodologies to understand cross-cutting principles of evolution, such as common ancestry and speciation. Phylogenetic trees, however, are reportedly challenging to teach and learn. Furthermore, phylogenetics teaching methods traditionally rely solely on visual information, creating inaccessibility for people with visual impairment. Sensory learning style models advocate for tailoring teaching to individual preferred sensory learning style. However, recent research suggests that optimal learning, independently of preferred learning style, depends on the types of transmitted information and learning tasks. The lack of empirically-supported education into the effectiveness of teaching phylogenetics through alternative sensory modalities potentially hinders learning. The aim of this study was to determine whether phylogenetic trees could be better understood if presented in kinaesthetic or multisensory teaching modalities.ResultsParticipants (N = 52) self-assessed personal learning style and were randomly assigned to: visual, kinaesthetic or multisensory learning conditions. Phylogeny reading performance was better for both kinaesthetic and multisensory teaching conditions, compared to the visual teaching condition. There was no main effect and no interaction effect of personal learning style.ConclusionsThis study establishes a baseline for further research by suggesting that easy-to-implement kinaesthetic teaching modalities might support phylogenetic tree learning and reading. This has practical implications for evolution education and accessibility for students with visual impairment, underscoring the need to shift from vision-centric teaching paradigms towards evidence-based instructional strategies that accommodate sensory diversity.

Autonomous Systems Control Design Using Neuro Evolution

This paper explores the application of neuro-evolution techniques in the design of control systems for autonomous systems. Neuro-evolution merges neural networks with evolutionary algorithms to develop adaptive, real-time control strategies for autonomous platforms. This approach is particularly effective in dynamic and complex environments, where traditional control methods struggle to ensure optimal performance. By evolving neural network structures, neuro-evolution provides robust and flexible control solutions, with promising applications in fields such as robotics, autonomous vehicles, and drone navigation. The design of autonomous systems has rapidly advanced, fueled by the need for intelligent, adaptive control in environments that are complex and unpredictable. Traditional control systems often lack the flexibility required to handle dynamic changes, making them less effective in highly variable conditions. Neuro-evolution, which combines neural networks with evolutionary algorithms, offers a promising solution by enabling autonomous systems to develop and refine control strategies on their own. This approach leverages evolutionary principles to optimize neural network architectures, producing control systems that adapt in real-time and improve through experience. Neuro-evolution has gained significant attention for applications in robotics, self- driving vehicles, and aerospace, where autonomous, robust control is essential. This paper explores the role of neuro-evolution in enhancing control design, focusing on its advantages over conventional methods and its potential to drive further innovations in autonomous systems.