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The efficiency of analyzing high-throughput data in systems biology has been demonstrated in numerous studies, where molecular data, such as transcriptomics and proteomics, offers great opportunities for understanding the complexity of biological processes. One important aspect of data analysis in systems biology is the shift from a reductionist approach that focuses on individual components to a more integrative perspective that considers the system as a whole, where the emphasis shifted from differential expression of individual genes to determining the activity of gene sets. Here, we present the rROMA software package for fast and accurate computation of the activity of gene sets with coordinated expression. The rROMA package incorporates significant improvements in the calculation algorithm, along with the implementation of several functions for statistical analysis and visualizing results. These additions greatly expand the package's capabilities and offer valuable tools for data analysis and interpretation. It is an open-source package available on github at: www.github.com/sysbio-curie/rROMA. Based on publicly available transcriptomic datasets, we applied rROMA to cystic fibrosis, highlighting biological mechanisms potentially involved in the establishment and progression of the disease and the associated genes. Results indicate that rROMA can detect disease-related active signaling pathways using transcriptomic and proteomic data. The results notably identified a significant mechanism relevant to cystic fibrosis, raised awareness of a possible bias related to cell culture, and uncovered an intriguing gene that warrants further investigation. [ABSTRACT FROM AUTHOR]

Many neuroscientists use the term Blood–Brain Barrier (BBB) to emphasize restrictiveness, often equating or reducing the notion of BBB properties to tight junction molecules physically sealing cerebral endothelial cells, rather than pointing out the complexity of this biological interface with respect to its selectivity and variety of exchange between the general blood circulation and the central nervous tissue. Several authors in the field find it unfortunate that the exquisitely dynamic interfaces between blood and brain continue to be viewed primarily as obstructive barriers to transport. Although the term blood–brain interface is an excellent descriptor that does not convey the idea of a barrier, it is important and preferable for the spreading of an idea beyond specialist communities to try to appeal to well-chosen metaphors. Recent evidence reviewed here indicates that blood–brain interfaces are more than selective semi-permeable membranes in that they display many dynamic processes and complex mechanisms for communication. They are thus more like 'geopolitical borders'. Furthermore, some authors working on blood–brain interface-relevant issues have started to use the word border, for example in border-associated macrophages. Therefore, we suggest adopting the term Blood–Brain Border to better communicate the flexibility of and movement across blood–brain interfaces. [ABSTRACT FROM AUTHOR]

Schistosomiasis, a challenging neglected tropical disease, affects millions of people worldwide. Developing a prophylactic vaccine against Schistosoma mansoni has been hindered by the parasite's biological complexity. In this study, we utilized the innovative phage-display immunoprecipitation followed by a sequencing approach (PhIP-Seq) to screen the immune response of 10 infected rhesus macaques during self-cure and challenge-resistant phases, identifying vaccine candidates. Our high-throughput S. mansoni synthetic DNA phage-display library encoded 99.6% of 119,747 58-mer peptides, providing comprehensive coverage of the parasite's proteome. Library screening with rhesus macaques' antibodies, from the early phase of establishment of parasite infection, identified significantly enriched epitopes of parasite extracellular proteins known to be expressed in the digestive tract, shifting towards intracellular proteins during the late phase of parasite clearance. Immunization of mice with a selected pool of PhIP-Seq-enriched phage-displayed peptides from MEG proteins, cathepsins B, and asparaginyl endopeptidase significantly reduced worm burden in a vaccination assay. These findings enhance our understanding of parasite-host immune responses and provide promising prospects for developing an effective schistosomiasis vaccine. [ABSTRACT FROM AUTHOR]

Motivation In recent years, applying computational modeling to systems biology has caused a substantial surge in both discovery and practical applications and a significant shift in our understanding of the complexity inherent in biological systems. Results In this perspective article, we briefly overview computational modeling in biology, highlighting recent advancements such as multi-scale modeling due to the omics revolution, single-cell technology, and integration of artificial intelligence and machine learning approaches. We also discuss the primary challenges faced: integration, standardization, model complexity, scalability, and interdisciplinary collaboration. Lastly, we highlight the contribution made by the Computational Modeling of Biological Systems (SysMod) Community of Special Interest (COSI) associated with the International Society of Computational Biology (ISCB) in driving progress within this rapidly evolving field through community engagement (via both in person and virtual meetings, social media interactions), webinars, and conferences. Availability and implementation Additional information about SysMod is available at https://sysmod.info. [ABSTRACT FROM AUTHOR]

The advent of single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity and complexity in biological tissues. However, the nature of large, sparse scRNA-seq datasets and privacy regulations present challenges for efficient cell identification. Federated learning provides a solution, allowing efficient and private data use. Here, we introduce scFed, a unified federated learning framework that allows for benchmarking of four classification algorithms without violating data privacy, including single-cell-specific and general-purpose classifiers. We evaluated scFed using eight publicly available scRNA-seq datasets with diverse sizes, species and technologies, assessing its performance via intra-dataset and inter-dataset experimental setups. We find that scFed performs well on a variety of datasets with competitive accuracy to centralized models. Though Transformer-based model excels in centralized training, its performance slightly lags behind single-cell-specific model within the scFed framework, coupled with a notable time complexity concern. Our study not only helps select suitable cell identification methods but also highlights federated learning's potential for privacy-preserving, collaborative biomedical research. [ABSTRACT FROM AUTHOR]

In vitro diagnosis (IVD) is pivotal in modern medicine, enabling early disease detection and treatment optimization. Omics technologies, particularly proteomics and metabolomics, offer profound insights into IVD. Despite its significance, omics analyses for IVD face challenges, including low analyte concentrations and the complexity of biological environments. In addition, the direct omics analysis by mass spectrometry (MS) is often hampered by issues like large sample volume requirements and poor ionization efficiency. Through manipulating their size, surface charge, and functionalization, as well as the nanoparticle‐fluid incubation conditions, nanomaterials have emerged as a promising solution to extract biomolecules and enhance the desorption/ionization efficiency in MS detection. This review delves into the last five years of nanomaterial applications in omics, focusing on their role in the enrichment, separation, and ionization analysis of proteins and metabolites for IVD. It aims to provide a comprehensive update on nanomaterial design and application in omics, highlighting their potential to revolutionize IVD. [ABSTRACT FROM AUTHOR]

Kingianins are pentacyclic polyketides isolated from Endiandra kingiana. They exhibit promising activity in inhibiting anti-apoptotic proteins (i.e Bcl-xL and Mcl-1) and anti-diabetic proteins (a-glucosidase) at micromolar levels. Due to their structural complexity and intriguing biological activity, researchers are increasingly focussing on designing total synthesis routes for these compounds. In this communication, we propose a new non-biomimetic synthesis strategy that utilises a [2+2] ketene cycloaddition reaction as a key-step to access the bicyclo[4.2.0]octane system of kingianins. To the best of our knowledge, this is the first report based on this approach. [ABSTRACT FROM AUTHOR]

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The mathematical study of real-world dynamical systems relies on models composed of differential equations. Numerical methods for solving and analyzing differential equation systems are essential when complex biological problems have to be studied, such as the spreading of a virus, the evolution of competing species in an ecosystem, or the dynamics of neurons in the brain. Here we present PyRates, a Python-based software for modeling and analyzing differential equation systems via numerical methods. PyRates is specifically designed to account for the inherent complexity of biological systems. It provides a new language for defining models that mirrors the modular organization of real-world dynamical systems and thus simplifies the implementation of complex networks of interacting dynamic entities. Furthermore, PyRates provides extensive support for the various forms of interaction delays that can be observed in biological systems. The core of PyRates is a versatile code-generation system that translates user-defined models into "backend" implementations in various languages, including Python, Fortran, Matlab, and Julia. This allows users to apply a wide range of analysis methods for dynamical systems, eliminating the need for manual translation between code bases. PyRates may also be used as a model definition interface for the creation of custom dynamical systems tools. To demonstrate this, we developed two extensions of PyRates for common analyses of dynamic models of biological systems: PyCoBi for bifurcation analysis and RectiPy for parameter fitting. We demonstrate in a series of example models how PyRates can be used in combination with PyCoBi and RectiPy for model analysis and fitting. Together, these tools offer a versatile framework for applying computational modeling and numerical analysis methods to dynamical systems in biology and beyond. Author summary: We present PyRates, a code-generation tool for dynamical systems modeling applied to biological systems. Together with its extensions PyCoBi and RectiPy, PyRates provides a framework for modeling and analyzing complex biological systems via methods such as parameter sweeps, bifurcation analysis, and model fitting. We highlight the main features of this framework, with an emphasis on new features that have been introduced since the initial publication of the software, such as the extensive code generation capacities and widespread support for delay-coupled systems. Using a collection of mathematical models taken from various fields of biology, we demonstrate how PyRates enables analysis of the behavior of complex nonlinear systems using a diverse suite of tools. This includes examples where we use PyRates to interface a bifurcation analysis tool written in Fortran, to optimize model parameters via gradient descent in PyTorch, and to serve as a model definition interface for new dynamical systems analysis tools. [ABSTRACT FROM AUTHOR]

In poultry production, there has been a trend of continuous increase in cost of feed ingredients which represents the major proportion of the production costs. Feed costs can be reduced by improving feed efficiency traits which increase the possibility of using various indigestible feed sources and decrease the environmental impact of the enhanced poultry production. Therefore, feed efficiency has been used as one of the most important economic traits of selection in the breeding program of chickens. Recently, many OMICs experimental studies have been designed to characterize biological differences between the high and low feed efficiency chicken phenotypes. Biological complexity cannot be fully captured by main individual OMICs such as genomics, transcriptomics, proteomics and metabolomics. Therefore, researchers have combined multiple assays from the same set of samples to create multi-OMICs datasets. OMICs findings are crucial in improving existing approaches to poultry breeding. The current review aimed to highlight the components of feed efficiency and general OMICs approaches and technologies. Besides, individual and multi-OMICs based understanding of chicken feed efficiency traits and the application of the acquired knowledge in the chicken breeding program were addressed. [ABSTRACT FROM AUTHOR]

The symposium "Large-scale biological phenomena arising from small-scale biophysical processes" at the SICB 2023 Annual General Meeting focused on the cross-disciplinary exploration of emergent phenomena in biology. Interactions between cells or organisms at small scales within a system can govern patterns occurring at larger scales in space, time, or biological complexity. This theme recurs in many sub-disciplines of biology, including cell and developmental biology, evolution, and ecology. This symposium, and the associated special issue introduced here, showcases a wide range of cross-disciplinary collaborations among biologists, physicists, and engineers. Technological advancements in microscopy and microfluidics, as well as complementary advances in mathematical modeling and associated theory demonstrate the timeliness of this issue. This introduction seeks to provide useful background information to place the studies within this issue in a broader biophysical context and highlight similarities in ideas and approaches across systems and sub-disciplines. We hope to demonstrate that cross-disciplinary research linking small-scale biophysics to larger-scale emergent phenomena can help us understand problems ranging from single-cell behaviors to tissue formation and function, evolution of form, and the dynamics of communities. [ABSTRACT FROM AUTHOR]

Extracellular vesicles (EVs) are naturally occurring nanoparticles released from all eucaryotic and procaryotic cells. While their role was formerly largely underestimated, EVs are now clearly established as key mediators of intercellular communication. Therefore, these vesicles constitute an attractive topic of study for both basic and applied research with great potential, for example, as a new class of biomarkers, as cell‐free therapeutics or as drug delivery systems. However, the complexity and biological origin of EVs sometimes complicate their identification and therapeutic use. Thus, this rapidly expanding research field requires new methods and tools for the production, enrichment, detection, and therapeutic application of EVs. In this review, we have sought to explain how polymer materials actively contributed to overcome some of the limitations associated to EVs. Indeed, thanks to their infinite diversity of composition and properties, polymers can act through a variety of strategies and at different stages of EVs development. Overall, we would like to emphasize the importance of multidisciplinary research involving polymers to address persistent limitations in the field of EVs. [ABSTRACT FROM AUTHOR]

Emphatic claims of a "microbiome revolution" aside, the study of the gut microbiota and its role in organismal development and evolution is a central feature of so-called postgenomics; namely, a conceptual and/or practical turn in contemporary life sciences, which departs from genetic determinism and reductionism to explore holism, emergentism and complexity in biological knowledge-production. This paper analyses the making of postgenomic knowledge about developmental symbiosis in Drosophila melanogaster by a specific group of microbiome scientists. Drawing from both practical philosophy of science and Science and Technology Studies, the paper documents epistemological questions of artefactuality and representativeness of model organisms as they emerge in the day-to-day labour producing and being produced by the "microbiome revolution." Specifically, the paper builds on all the written and editorial exchanges involved in the troubled publication of a research paper studying the symbiotic role of the microbiota in the flies' development. These written materials permit us to delimit the network of justifications, evidence, standards of knowledge-production, trust in the tools and research designs that make up the conditions of possibility of a postgenomic fact. More than reframing the organism as a radically novel multiplicity of reactive genomes, we conclude, doing postgenomic research on the microbiota and symbiosis means producing a story that deviates from the scripts embedded into the sociotechnical experimental systems of post-Human Genome Project life sciences. [ABSTRACT FROM AUTHOR]

The vasculature of the brain consists of specialized endothelial cells that form a blood-brain barrier (BBB). This barrier, in conjunction with supporting cell types, forms the neurovascular unit (NVU). The NVU restricts the passage of certain substances from the bloodstream while selectively permitting essential nutrients and molecules to enter the brain. This protective role is crucial for optimal brain function, but presents a significant obstacle in treating neurological conditions, necessitating chemical modifications or advanced drug delivery methods for most drugs to cross the NVU. A deeper understanding of NVU in health and disease will aid in the identification of new therapeutic targets and drug delivery strategies for improved treatment of neurological disorders. To achieve this goal, we need models that reflect the human BBB and NVU in health and disease. Although animal models of the brain's vasculature have proven valuable, they are often of limited translational relevance due to interspecies differences or inability to faithfully mimic human disease conditions. For this reason, human in vitro models are essential to improve our understanding of the brain's vasculature under healthy and diseased conditions. This review delves into the advancements in in vitro modeling of the BBB and NVU, with a particular focus on microfluidic models. After providing a historical overview of the field, we shift our focus to recent developments, offering insights into the latest achievements and their associated constraints. We briefly examine the importance of chip materials and methods to facilitate fluid flow, emphasizing their critical roles in achieving the necessary throughput for the integration of microfluidic models into routine experimentation. Subsequently, we highlight the recent strides made in enhancing the biological complexity of microfluidic NVU models and propose recommendations for elevating the biological relevance of future iterations. Importantly, the NVU is an intricate structure and it is improbable that any model will fully encompass all its aspects. Fit-for-purpose models offer a valuable compromise between physiological relevance and ease-of-use and hold the future of NVU modeling: as simple as possible, as complex as needed. [ABSTRACT FROM AUTHOR]

tRNA function is based on unique structures that enable mRNA decoding using anticodon trinucleotides. These structures interact with specific aminoacyl-tRNA synthetases and ribosomes using 3D shape and sequence signatures. Beyond translation, tRNAs serve as versatile signaling molecules interacting with other RNAs and proteins. Through evolutionary processes, tRNA fragmentation emerges as not merely random degradation but an act of recreation, generating specific shorter molecules called tRNA-derived small RNAs (tsRNAs). These tsRNAs exploit their linear sequences and newly arranged 3D structures for unexpected biological functions, epitomizing the tRNA "renovatio" (from Latin, meaning renewal, renovation, and rebirth). Emerging methods to uncover full tRNA/tsRNA sequences and modifications, combined with techniques to study RNA structures and to integrate AI-powered predictions, will enable comprehensive investigations of tRNA fragmentation products and new interaction potentials in relation to their biological functions. We anticipate that these directions will herald a new era for understanding biological complexity and advancing pharmaceutical engineering. Kuhle et al. explore tRNA-derived small RNAs (tsRNAs) as critical cellular regulators. Originating from tRNA "renovatio" (Latin for "renewal and rebirth"), tsRNA functions are tied to 3D structures and dynamically regulated by RNA modifications and binding partners, highlighting the promise of experimental and AI-based approaches to tackle their biological complexity. [ABSTRACT FROM AUTHOR]

Understanding phenotype-to-genotype relationships is a grand challenge of 21st century biology with translational implications. The recently proposed "omnigenic" model postulates that effects of genetic variation on traits are mediated by core-genes and -proteins whose activities mechanistically influence the phenotype, whereas peripheral genes encode a regulatory network that indirectly affects phenotypes via core gene products. Here, we develop a positive-unlabeled graph representation-learning ensemble-approach based on a nested cross-validation to predict core-like genes for diverse diseases using Mendelian disorder genes for training. Employing mouse knockout phenotypes for external validations, we demonstrate that core-like genes display several key properties of core genes: Mouse knockouts of genes corresponding to our most confident predictions give rise to relevant mouse phenotypes at rates on par with the Mendelian disorder genes, and all candidates exhibit core gene properties like transcriptional deregulation in disease and loss-of-function intolerance. Moreover, as predicted for core genes, our candidates are enriched for drug targets and druggable proteins. In contrast to Mendelian disorder genes the new core-like genes are enriched for druggable yet untargeted gene products, which are therefore attractive targets for drug development. Interpretation of the underlying deep learning model suggests plausible explanations for our core gene predictions in form of molecular mechanisms and physical interactions. Our results demonstrate the potential of graph representation learning for the interpretation of biological complexity and pave the way for studying core gene properties and future drug development. Understanding phenotype-genotype relationships is a grand challenge of current biological research. Here, the authors use graph representation learning to identify human genes which display key characteristics of core genes for five complex diseases. [ABSTRACT FROM AUTHOR]

The Stara planina tourist region is one of the most attractive regions for tourism in Bulgaria and one of the few that have almost year-round tourist activities. The outline of the area includes the mountain of the same name, but also the Pre-Balkan. This fact predetermines the exceptional richness and diversity of natural tourist resources. Here are some of the largest broad-leaved forests in the country, waterfalls, bizarre rock formations, etc. The relatively well-preserved natural environment is a prerequisite for the presence of exceptional biodiversity, as well as a significant number of protected areas. The region is specialized mainly in mountain tourism, including recreation, mountain trekking, rural and ecotourism. Due to the scarce tourist infrastructure, ski tourism still lags behind in its development compared to other mountain regions in the country. Very often, the Stara planina Region is primarily identified with its cultural and historical heritage, while its natural features remain in the background. Regardless of this, and as a result of the more intense tourist load, the problem of protecting natural tourist resources from the growing anthropogenic pressure is gaining special relevance. There is also an urgent need to identify and evaluate the prospects for sustainable development of the tourism industry in the region. The purpose of the present study is to valorize the natural tourist resources of the Stara planina tourist region and to provide guidelines for their sustainable use. [ABSTRACT FROM AUTHOR]

Despite the advent of tissue engineering (TE) for the remodeling, restoring, and replacing damaged cardiovascular tissues, the progress is hindered by the optimal mechanical and chemical properties required to induce cardiac tissue-specific cellular behaviors including migration, adhesion, proliferation, and differentiation. Cardiac extracellular matrix (ECM) consists of numerous structural and functional molecules and tissue-specific cells, therefore it plays an important role in stimulating cell proliferation and differentiation, guiding cell migration, and activating regulatory signaling pathways. With the improvement and modification of cell removal methods, decellularized ECM (dECM) preserves biochemical complexity, and bio-inductive properties of the native matrix and improves the process of generating functional tissue. In this review, we first provide an overview of the latest advancements in the utilization of dECM in in vitro model systems for disease and tissue modeling, as well as drug screening. Then, we explore the role of dECM-based biomaterials in cardiovascular regenerative medicine (RM), including both invasive and non-invasive methods. In the next step, we elucidate the engineering and material considerations in the preparation of dECM-based biomaterials, namely various decellularization techniques, dECM sources, modulation, characterizations, and fabrication approaches. Finally, we discuss the limitations and future directions in fabrication of dECM-based biomaterials for cardiovascular modeling, RM, and clinical translation. Graphical Abstract [ABSTRACT FROM AUTHOR]

Soybean is a globally significant crop, playing a vital role in human nutrition and agriculture. Its complex genetic structure and wide trait variation, however, pose challenges for breeders and researchers aiming to optimize its yield and quality. Addressing this biological complexity requires innovative and accurate tools for trait prediction. In response to this challenge, we have developed SoyDNGP, a deep learning-based model that offers significant advancements in the field of soybean trait prediction. Compared to existing methods, such as DeepGS and DNNGP, SoyDNGP boasts a distinct advantage due to its minimal increase in parameter volume and superior predictive accuracy. Through rigorous performance comparison, including prediction accuracy and model complexity, SoyDNGP represents improved performance to its counterparts. Furthermore, it effectively predicted complex traits with remarkable precision, demonstrating robust performance across different sample sizes and trait complexities. We also tested the versatility of SoyDNGP across multiple crop species, including cotton, maize, rice and tomato. Our results showed its consistent and comparable performance, emphasizing SoyDNGP's potential as a versatile tool for genomic prediction across a broad range of crops. To enhance its accessibility to users without extensive programming experience, we designed a user-friendly web server, available at http://xtlab.hzau.edu.cn/SoyDNGP. The server provides two features: 'Trait Lookup', offering users the ability to access pre-existing trait predictions for over 500 soybean accessions, and 'Trait Prediction', allowing for the upload of VCF files for trait estimation. By providing a high-performing, accessible tool for trait prediction, SoyDNGP opens up new possibilities in the quest for optimized soybean breeding. [ABSTRACT FROM AUTHOR]

The emergence of massive datasets exploring the multiple levels of molecular biology has made their analysis and knowledge transfer more complex. Flexible tools to manage big biological datasets could be of great help for standardizing the usage of developed data visualizations and integration methods. Business intelligence (BI) tools have been used in many fields as exploratory tools. They have numerous connectors to link numerous data repositories with a unified graphic interface, offering an overview of data and facilitating interpretation for decision makers. BI tools could be a flexible and user-friendly way of handling molecular biological data with interactive visualizations. However, it is rather uncommon to see such tools used for the exploration of massive and complex datasets in biological fields. We believe that two main obstacles could be the reason. Firstly, we posit that the way to import data into BI tools are not compatible with biological databases. Secondly, BI tools may not be adapted to certain particularities of complex biological data, namely, the size, the variability of datasets and the availability of specialized visualizations. This paper highlights the use of five BI tools (Elastic Kibana, Siren Investigate, Microsoft Power BI, Salesforce Tableau and Apache Superset) onto which the massive data management repository engine called Elasticsearch is compatible. Four case studies will be discussed in which these BI tools were applied on biological datasets with different characteristics. We conclude that the performance of the tools depends on the complexity of the biological questions and the size of the datasets. [ABSTRACT FROM AUTHOR]

The Moon has returned into the focus of human endeavors regarding human spaceflight, e.g., with NASA's Artemis program, ESA's Moon Village, and the Russian/Chinese International Lunar Research Station. In difference to the pathfinding missions of the Apollo -era, the goal for these future missions is to stay on the lunar surface for longer durations and inhabit the lunar environment (near-)permanently. This requires a different approach to be affordable, i.e., instead of resupply as mostly used on e.g., the International Space Station, resource management has to include recycling and in-situ utilization. The former especially calls for the application of so-called BLSS to allow providing essential life-support services to the crew without prohibitive resource consumption, which is economically not feasible to achieve with resupplies. Bio-regenerative-life-support systems have been researched for decades, yet the system complexity, technology advancements, and singular aspects as e.g., plant biology require more research, especially if combined as in a greenhouse. For instance, the understanding of how a microbiome develops in a closed environment and what implications the microbiome has on plant growth is still insufficient. Within the EDEN project, the German Aerospace Center built a lunar analogue greenhouse and operated it at the Neumayer-III research station in Antarctica for four years, testing the technology – which was not space hardware – and operations. Derived from this experience the next step in the project is to design and subsequently operate a ground test demonstrator for a lunar greenhouse, as close as possible to the actual space hardware and operations. This paper explains the current design and trade-offs that led to it. Furthermore, the concept of operations is shown to illustrate the demonstrator's utility for researching bioregenerative-life-support. Overall, the system presented is feasible and useful to close the gaps, currently still existing in this field of research, and thus a mission enabler for future long-duration human space exploration missions. • Design of an integrated greenhouse module test demonstrator. • Extensive science program definition. • Mission scenario definition for lunar greenhouse. • Discussion of benefits and open issues. [ABSTRACT FROM AUTHOR]

Genetic diversity is a core aspect of biodiversity that is increasingly being considered in global conservation policy. However, genetic data are lacking for most species. Monitoring and reporting genetic diversity trends and developing conservation policies that are generalizable across species is therefore difficult. Macrogenetics is an emerging field that leverages publicly archived genetic data to understand patterns of genetic diversity and drivers of change in hundreds to thousands of species at regional to global extents. Macrogenetics offers new opportunities to fill data gaps for conservation. We discuss the most promising paths for conservation macrogenetics to support new policies adopted for genetic diversity by providing essential goals for research and data infrastructure, and technological pathways for monitoring genetic biodiversity in natural wildlife populations. Genetic biodiversity is rapidly gaining attention in global conservation policy. However, for almost all species, conservation relevant, population-level genetic data are lacking, limiting the extent to which genetic diversity can be used for conservation policy and decision-making. Macrogenetics is an emerging discipline that explores the patterns and processes underlying population genetic composition at broad taxonomic and spatial scales by aggregating and reanalyzing thousands of published genetic datasets. Here we argue that focusing macrogenetic tools on conservation needs, or conservation macrogenetics, will enhance decision-making for conservation practice and fill key data gaps for global policy. Conservation macrogenetics provides an empirical basis for better understanding the complexity and resilience of biological systems and, thus, how anthropogenic drivers and policy decisions affect biodiversity. [ABSTRACT FROM AUTHOR]

The primary objective of the key biodiversity areas (KBA) approach is to identify significant global areas for species conservation. Although it has been successful in identifying Important Bird Areas using a similar methodology, we have identified five limitations that may be applicable when considering other species: The KBA approach is excessively rigid in determining important conservation features, lacks adaptability in addressing landscape connectivity, introduces errors by applying global criteria without input from local experts, relying on post-analysis consideration of implementation opportunities and limitations, and does not automatically involve implementation agencies in the assessment process. To address these limitations, we propose three modifications to the existing approach: (1) Providing local stakeholders with training in regional conservation planning, (2) expanding the Alliance for Zero Extinction program to encompass a wider range of threatened species, and (3) allowing local stakeholders to nominate KBAs based on their own regional conservation assessments. These modifications would leverage the expertise of proponents of the KBA approach and contribute to the diversity of methods necessary for effective biodiversity conservation. [ABSTRACT FROM AUTHOR]

In this text I address one of the ontological, epistemic and methodological proposals that best synthesizes the work of feminist epistemologies on how to conceptualize the notions of gender and sex and their relationship in the biological realm. I describe the scope and limitations of this proposal and show that the very notion of sex implies an androcentric bias from which it legitimizes the endogenous/exogenous dichotomy, a functional reductionism, the idea of internal coherence, and the reduction of our biological complexity to features linked to reproduction with respect to biological differences in general and, as I will address, in the biomedical field in particular when characterizing the prevalence and development of diseases. In contrast, I propose the notion of epigenealogy for a historical reconceptualization of our biological materiality capable of delving into how our plasticity through epigenetic processes dances synchronically with our social, contingent and situated experience. [ABSTRACT FROM AUTHOR]

Biological membranes (biomembranes) are one of the most complicated structures that allow life to exist. Investigating their structure, dynamics, and function is crucial for advancing our knowledge of cellular mechanisms and developing novel therapeutic strategies. However, experimental investigation of many biomembrane phenomena is challenging due to their compositional and structural complexity, as well as the inherently multi‐scalar features. Computational approaches, particularly molecular dynamics (MD) simulations, have emerged as powerful tools for addressing the atomic details of biomembrane systems, driving breakthroughs in our understanding of biomembranes and their roles in cellular function. This review presents an overview of the latest advancements in related computational approaches, from force fields and model construction to MD simulations and trajectory analysis. We also discussed current hot research topics and challenges. Finally, we outline future directions, emphasizing the integration of force field development, enhanced sampling techniques, and data‐driven approaches to accelerate the growth of this field in the years to come. We aim to equip readers with an understanding of the promise and limitations of emerging computational technologies in biomembrane systems and offer valuable recommendations for future research endeavors. This article is categorized under:Structure and Mechanism > Computational Biochemistry and BiophysicsMolecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods [ABSTRACT FROM AUTHOR]

Advances in imaging technologies combined with artificial intelligence (AI) are transforming dental, oral, and craniofacial research. This editorial highlights breakthroughs ranging from gene expression mapping to visualizing the availability of global AI data, providing new insights into biological complexity and clinical applications. [ABSTRACT FROM AUTHOR]

All extant life is descended from a common ancestor, which, despite being very ancient, appears to have been a complex cellular organism. A new study by Moody et al. shows that this ancestor was not only a complex cell, but also lived within a microbial ecology likely inhabited by other complex cells. [ABSTRACT FROM AUTHOR]

The physiological loss of muscle mass and strength with aging is referred to as “sarcopenia”, whose combined effect with osteoporosis is a serious threat to the elderly, accounting for decreased mobility and increased risk of falls with consequent fractures. In previous studies, we observed a high degree of inter-individual variability in paraspinal muscle fatty infiltration, one of the most relevant indices of muscle wasting. This aspect led us to develop a computerized method to quantitatively characterize muscle fatty infiltration in aging and diseases. Magnetic resonance images of paraspinal muscles from 58 women of different ages (age range of 23–85 years) and physio-pathological status (healthy young, pre-menopause, menopause, and osteoporosis) were used to set up a method based on fractal-derived texture analysis of lean muscle area (contractile muscle) to estimate muscle fatty infiltration. In particular, lacunarity was computed by parameter β from the GBA (gliding box algorithm) curvilinear plot fitted by our hyperbola model function. Succolarity was estimated by parameter µ, for the four main directions through an algorithm implemented with this purpose. The results show that lacunarity, by quantifying muscle fatty infiltration, can discriminate between osteoporosis and healthy aging, while succolarity can separate the other three groups showing similar lacunarity. Therefore, fractal-derived features of contractile muscle, by measuring fatty infiltration, can represent good indices of sarcopenia in aging and disease.

Mathematical modeling is a pivotal tool for deciphering the complexities of biological systems and their control mechanisms, providing substantial benefits for industrial applications and answering relevant biological questions. BioModels' Model of the Year 2023 competition was established to recognize and highlight exciting modeling-based research in the life sciences, particularly by non-independent early-career researchers. It further aims to endorse reproducibility and FAIR principles of model sharing among these researchers. We here delineate the competition's criteria for participation and selection, introduce the award recipients, and provide an overview of their contributions. Their models provide crucial insights into cell division regulation, protein stability, and cell fate determination, illustrating the role of mathematical modeling in advancing biological research. [ABSTRACT FROM AUTHOR]

Colorectal cancer represents an important oncological challenge both for its incidence, which makes it an important health problem, and for its biological complexity, which has made clinical results very difficult in terms of outcome for this category of patients. To date these diseases should not be treated as a single entity but it is necessary to distinguish colorectal cancers based on characteristics that nowadays are essential to have greater therapeutic benefits. These include the sideness of the disease, the state of microsatellites, the presence of prognostic and predictive mutations of response to treatments currently available in clinical practice, which are associated with new therapeutic targets. The greatest challenge in the future will be to circumvent the resistance mechanisms that make this disease very difficult to treat with good long-term results by studying effective combination treatments with a good toxicity profile. Once such combinations or targeted treatments are consolidated, it will be desirable to shift the best therapies to the first line treatment to make them immediately accessible to the patient. It will also be essential to refine the selection of patients who can benefit from these treatments. [ABSTRACT FROM AUTHOR]

Interstitial lung diseases (ILDs) are complex and heterogeneous diseases. The use of traditional diagnostic classification in ILD can lead to suboptimal management, which is worsened by not considering the molecular pathways, biological complexity, and disease phenotypes. The identification of specific "treatable traits" in ILDs, which are clinically relevant and modifiable disease characteristics, may improve patient's outcomes. Treatable traits in ILDs may be classified into four different domains (pulmonary, aetiological, comorbidities, and lifestyle), which will facilitate identification of related assessment tools, treatment options, and expected benefits. A multidisciplinary care team model is a potential way to implement a "treatable traits" strategy into clinical practice with the aim of improving patients' outcomes. Multidisciplinary models of care, international registries, and the use of artificial intelligence may facilitate the implementation of the "treatable traits" approach into clinical practice. Prospective studies are needed to test potential therapies for a variety of treatable traits to further advance care of patients with ILD. [ABSTRACT FROM AUTHOR]

The proper functioning of diverse biological systems depends on the spatial organization of their cells, a critical factor for biological processes like shaping intricate tissue functions and precisely determining cell fate. Nonetheless, conventional bulk or single-cell RNA sequencing methods were incapable of simultaneously capturing both gene expression profiles and the spatial locations of cells. Hence, a multitude of spatially resolved technologies have emerged, offering a novel dimension for investigating regional gene expression, spatial domains, and interactions between cells. Spatial transcriptomics (ST) is a method that maps gene expression in tissue while preserving spatial information. It can reveal cellular heterogeneity, spatial organization and functional interactions in complex biological systems. ST can also complement and integrate with other omics methods to provide a more comprehensive and holistic view of biological systems at multiple levels of resolution. Since the advent of ST, new methods offering higher throughput and resolution have become available, holding significant potential to expedite fresh insights into comprehending biological complexity. Consequently, a rapid increase in associated research has occurred, using these technologies to unravel the spatial complexity during developmental processes or disease conditions. In this review, we summarize the recent advancement of ST in historical, technical, and application contexts. We compare different types of ST methods based on their principles and workflows, and present the bioinformatics tools for analyzing and integrating ST data with other modalities. We also highlight the applications of ST in various domains of biomedical research, especially development and diseases. Finally, we discuss the current limitations and challenges in the field, and propose the future directions of ST. [ABSTRACT FROM AUTHOR]

Background: Neurosphere medium (NSM) and self-renewal medium (SRM) were widely used to isolate enteric neural stem cells (ENSCs) in the form of neurospheres. ENSCs or their neurosphere forms were neurogenic and gliogenic, but the compelling evidence for their capacity of assembling enteric neural networks remained lacking, raising the question of their aptitude for rebuilding the enteric nervous system (ENS) in ENSC therapeutics. It prompted us to explore an effective culture protocol or strategy for assembling ENS networks, which might also be employed as an in vitro model to simplify the biological complexity of ENS embedded in gut walls. Methods: NSM and SRM were examined for their capacity to generate neurospheres in mass culture of dispersed murine fetal enterocytes at serially diluted doses and assemble enteric neural networks in two- and three-dimensional cell culture systems and ex vivo on gut explants. Time-lapse microphotography was employed to capture cell activities of assembled neural networks. Neurosphere transplantation was performed via rectal submucosal injection. Results: In mass culture of dispersed enterocytes, NSM generated discrete units of neurospheres, whereas SRM promoted neural network assembly with neurospheres akin to enteric ganglia. Both were highly affected by seeding cell doses. SRM had similar ENSC mitosis-driving capacity to NSM, but was superior in driving ENSC differentiation in company with heightened ENSC apoptosis. Enteric neurospheres were motile, capable of merging together. It argued against their clonal entities. When nurtured in SRM, enteric neurospheres proved competent to assemble neural networks on two-dimensional coverslips, in three-dimensional hydrogels and on gut explants. In the course of neural network assembly from enteric neurospheres, neurite extension was preceded by migratory expansion of gliocytes. Assembled neural networks contained motile ganglia and gliocytes that constantly underwent shapeshift. Neurospheres transplanted into rectal submucosa might reconstitute myenteric plexuses of recipients' rectum. Conclusion: Enteric neurospheres mass-produced in NSM might assemble neural networks in SRM-immersed two- or three-dimensional environments and on gut explants, and reconstitute myenteric plexuses of the colon after rectal submucosal transplantation. Our results also shed first light on the dynamic entity of ENS and open the experimental avenues to explore cellular activities of ENS and facilitate ENS demystification. [ABSTRACT FROM AUTHOR]

Zanzibar is among the few places in sub-Saharan Africa where interruption of Schistosoma transmission seems an achievable goal. Our systematic review identifies and discusses milestones in schistosomiasis research, control and elimination efforts in Zanzibar over the past 100 years. The search in online databases, libraries, and the World Health Organization Archives revealed 153 records published between May 1928 and August 2022. The content of records was summarised to highlight the pivotal work leading towards urogenital schistosomiasis elimination and remaining research gaps. The greatest achievement following 100 years of schistosomiasis interventions and research is undoubtedly the improved health of Zanzibaris, exemplified by the reduction in Schistosoma haematobium prevalence from>50% historically down to<5% in 2020, and the absence of severe morbidities. Experiences from Zanzibar have contributed to global schistosomiasis guidelines, whilst also revealing challenges that impede progression towards elimination. Challenges include: transmission heterogeneity requiring micro-targeting of interventions, post-treatment recrudescence of infections in transmission hotspots, biological complexity of intermediate host snails, emergence of livestock Schistosoma species complicating surveillance whilst creating the risk for interspecies hybridisation, insufficient diagnostics performance for light intensity infections and female genital schistosomiasis, and a lack of acceptable sanitary alternatives to freshwater bodies. Our analysis of the past revealed that much can be achieved in the future with practical implementation of integrated interventions, alongside operational research. With continuing national and international commitments, interruption of S. haematobium transmission across both islands is within reach by 2030, signposting the future demise of urogenital schistosomiasis across other parts of sub-Saharan Africa. [ABSTRACT FROM AUTHOR]

Understanding wildlife responses to novel threats is vital in counteracting biodiversity loss. The emerging pathogen Batrachochytrium salamandrivorans (Bsal) causes dramatic declines in European salamander populations, and is considered an imminent threat to global amphibian biodiversity. However, real-life disease outcomes remain largely uncharacterized. We performed a multidisciplinary assessment of the longer-term impacts of Bsal on highly susceptible fire salamander (Salamandra salamandra) populations, by comparing four of the earliest known outbreak sites to uninfected sites. Based on large-scale monitoring efforts, we found population persistence in strongly reduced abundances to over a decade after Bsal invasion, but also the extinction of an initially small-sized population. In turn, we found that host responses varied, and Bsal detection remained low, within surviving populations. Demographic analyses indicated an ongoing scarcity of large reproductive adults with potential for recruitment failure, while spatial comparisons indicated a population remnant persisting within aberrant habitat. Additionally, we detected no early signs of severe genetic deterioration, yet nor of increased host resistance. Beyond offering additional context to Bsal-driven salamander declines, results highlight how the impacts of emerging hypervirulent pathogens can be unpredictable and vary across different levels of biological complexity, and how limited pathogen detectability after population declines may complicate surveillance efforts. [ABSTRACT FROM AUTHOR]

Species richness is strikingly uneven across taxonomic groups at all hierarchical levels, but the reasons for this heterogeneity are poorly understood. It is well established that morphological diversity (disparity) is decoupled from taxonomic diversity, both between clades and across geological time. Morphological complexity has been much less studied, but there is theory linking complexity with differential diversity across groups. Here we devise an index of complexity from the differentiation of the fore and hind limb pairs for a sample of 983 species of extant birds. We test the null hypothesis that this index of morphological complexity is uncorrelated with clade diversity, revealing a significant and negative correlation between the species richness of clades and the mean morphological complexity of those clades. Further, we find that more complex clades tend to occupy a smaller number of dietary and habitat niches, and that this proxy for greater ecological specialisation correlates with lower species richness. Greater morphological complexity in the appendicular skeleton therefore appears to hinder the generation and maintenance of species diversity. This may result from entrenchment into morphologies and ecologies that are less capable of yielding further diversity. Biodiversity is unevenly distributed across the tree of life, and theoretical work suggests that biological complexity may be influential in forming these patterns. Here, Brinkworth et al. have shown that bird clades with more complex appendicular skeletons tend to have lower extant species richness. [ABSTRACT FROM AUTHOR]

The calcitonin gene-related peptide (CGRP) is a neuropeptide widely distributed throughout the human body. While primarily recognized as a nociceptive mediator, CGRP antagonists are currently utilized for migraine treatment. However, its role extends far beyond this, acting as a regulator of numerous biological processes. Indeed, CGRP plays a crucial role in vasodilation, inflammation, intestinal motility, and apoptosis. In this review, we explore the non-nociceptive effects of CGRP in various body systems, revealing actions that can be contradictory at times. In the cardiovascular system, it functions as a potent vasodilator, yet its antagonists do not induce arterial hypertension, suggesting concurrent modulation by other molecules. As an immunomodulator, CGRP exhibits intriguing complexity, displaying both anti-inflammatory and pro-inflammatory effects. Furthermore, CGRP appears to be involved in obesity development while paradoxically reducing appetite. A thorough investigation of CGRP's biological effects is crucial for anticipating potential side effects associated with its antagonists' use and for developing novel therapies in other medical fields. In summary, CGRP represents a neuropeptide with a complex systemic impact, extending well beyond nociception, thus offering new perspectives in medical research and therapeutics [ABSTRACT FROM AUTHOR]

This paper critically examines Mitchell's integrative pluralism. Integrative pluralism is the view that scientific explanations should primarily aim to integrate descriptions from different ontological levels. We contend that, while integrative pluralism is a fundamental strategy in contemporary science, there are specific reasons why one should not expect integration in the sense developed by Mitchell to be the optimal strategy and the one that scientists should always aim for. Drawing on some examples from contemporary biology, we argue that integration is sometimes neither epistemically desirable, nor ontologically achievable. We conclude that integrative pluralism should thus be limited to a specific class of complex systems but cannot be generalised as the preferable research strategy without further information about the epis-temic practices of the scientific community or the ontology of the system under investigation. [ABSTRACT FROM AUTHOR]

Many clinical and experimental studies have implicated the growth hormone (GH)-- insulin-like growth factor (IGF-1) axis with the progression of cancer. The epidemiological finding that patients with Laron syndrome (LS), the best-characterized disease under the spectrum of congenital IGF-1 deficiencies, do not develop cancer is of major scientific and translational relevance. The evasion of LS patients from cancer emphasizes the central role of the GH--IGF-1 system in cancer biology. To identify genes that are differentially expressed in LS and that might provide a biological foundation for cancer protection, we have recently conducted genome-wide profiling of LS patients and normal controls. Analyses were performed on immortalized lymphoblastoid cell lines derived from individual patients. Bioinformatic analyses identified a series of genes that are either over- or under-represented in LS. Differential expression was demonstrated in a number of gene families, including cell cycle, metabolic control, cytokine--cytokine receptor interaction, Jak-STAT and PI3K-AKT signaling, etc. Major differences between LS and controls were also noticed in pathways associated with cell cycle distribution, apoptosis, and autophagy. The identification of novel downstream targets of the GH--IGF-1 network highlights the biological complexity of this hormonal system and sheds light on previously unrecognized mechanistic aspects associated with GH--IGF-1 action in the cancer cell. [ABSTRACT FROM AUTHOR]

Protein–protein interactions (PPIs) form the foundation of any cell signaling network. Considering that PPIs are highly dynamic processes, cellular assays are often essential for their study because they closely mimic the biological complexities of cellular environments. However, incongruity may be observed across different PPI assays when investigating a protein partner of interest; these discrepancies can be partially attributed to the fusion of different large functional moieties, such as fluorescent proteins or enzymes, which can yield disparate perturbations to the protein’s stability, subcellular localization, and interaction partners depending on the given cellular assay. Owing to their smaller size, epitope tags may exhibit a diminished susceptibility to instigate such perturbations. However, while they have been widely used for detecting or manipulating proteins in vitro, epitope tags lack the in vivo traceability and functionality needed for intracellular biosensors. Herein, we develop NbV5, an intracellular nanobody binding the V5-tag, which is suitable for use in cellular assays commonly used to study PPIs such as BRET, NanoBiT, and Tango. The NbV5:V5 tag system has been applied to interrogate G protein–coupled receptor signaling, specifically by replacing larger functional moieties attached to the protein interactors, such as fluorescent or luminescent proteins ((30 kDa), by the significantly smaller V5-tag peptide (1.4 kDa), and for microscopy imaging which is successfully detected by NbV5-based biosensors. Therefore, the NbV5:V5 tag system presents itself as a versatile tool for livecell imaging and a befitting adaptation to existing cellular assays dedicated to probing PPIs. [ABSTRACT FROM AUTHOR]

A full understanding concerning the photophysical properties of a fluorescent label is crucial for a reliable and predictable performance in biolabelling applications. This holds true not only for the choice of a fluorophore in general, but also for the correct interpretation of data, considering the complexity of biological environments. In the frame of a case study involving inflammation imaging, we report the photophysical characterization of four fluorescent S100A9-targeting compounds in terms of UV–vis absorption and photoluminescence spectroscopy, fluorescence quantum yields (ΦF) and excited state lifetimes (τ) as well as the evaluation of the radiative and non-radiative rate constants (kr and knr, respectively). The probes were synthesized based on a 2-amino benzimidazole-based lead structure in combination with commercially available dyes, covering a broad color range from green (6-FAM) over orange (BODIPY-TMR) to red (BODIPY-TR) and near-infrared (Cy5.5) emission. The effect of conjugation with the targeting structure was addressed by comparison of the probes with their corresponding dye-azide precursors. Additionally, the 6-FAM and Cy5.5 probes were measured in the presence of murine S100A9 to determine whether protein binding influences their photophysical properties. An interesting rise in ΦF upon binding of 6-FAM-SST177 to murine S100A9 enabled the determination of its dissociation equilibrium constant, reaching up to KD = 324 nM. This result gives an outlook for potential applications of our compounds in S100A9 inflammation imaging and fluorescence assay developments. With respect to the other dyes, this study demonstrates how diverse microenvironmental factors can severely impair their performance while rendering them poor performers in biological media, showing that a preliminary photophysical screening is key to assess the suitability of a particular luminophore. [ABSTRACT FROM AUTHOR]

Numerous computational methods based on sequences or structures have been developed for the characterization of protein function, but they are still unsatisfactory to deal with the multiple functions of multi-domain protein families. Here we propose an original approach based on 1) the detection of conserved sequence modules using partial local multiple alignment, 2) the phylogenetic inference of species/genes/modules/functions evolutionary histories, and 3) the identification of co-appearances of modules and functions. Applying our framework to the multidomain ADAMTS-TSL family including ADAMTS (A Disintegrin-like and Metalloproteinase with ThromboSpondin motif) and ADAMTS-like proteins over nine species including human, we identify 45 sequence module signatures that are associated with the occurrence of 278 Protein-Protein Interactions in ancestral genes. Some of these signatures are supported by published experimental data and the others provide new insights (e.g. ADAMTS-5). The module signatures of ADAMTS ancestors notably highlight the dual variability of the propeptide and ancillary regions suggesting the importance of these two regions in the specialization of ADAMTS during evolution. Our analyses further indicate convergent interactions of ADAMTS with COMP and CCN2 proteins. Overall, our study provides 186 sequence module signatures that discriminate distinct subgroups of ADAMTS and ADAMTSL and that may result from selective pressures on novel functions and phenotypes. Author summary: The vast majority of proteins have a modular multidomain organization. Domains are conserved building blocks of proteins that are widely used to characterize and predict protein functions. However, the organization of multidomain proteins underlies a great complexity and their biological role is generally not the sum of the functions attributed to each domain. To address this problem, it is necessary to develop new methods to better identify functional signatures. Here, we developed a framework based on partial local multiple alignments (to find conserved sequence modules) and phylogenetic reconciliation methods (to integrate the evolution of species, genes, sequence modules, and Protein-Protein Interactions). Application of our framework to the search for functional sequence modules in extracellular matrix proteins from the ADAMTS (A Disintegrin-like and Metalloproteinase with ThromboSpondin motif) and ADAMTSL (ADAMTS-like) family, enabled to highlight sequence signatures potentially involved in Protein-Protein Interactions. [ABSTRACT FROM AUTHOR]

In recent years, the application of advanced analytics, especially artificial intelligence (AI), to digital H&E images, and other histological image types, has begun to radically change how histological images are used in the clinic. Alongside the recognition that the tumour microenvironment (TME) has a profound impact on tumour phenotype, the technical development of highly multiplexed immunofluorescence platforms has enhanced the biological complexity that can be captured in the TME with high precision. AI has an increasingly powerful role in the recognition and quantitation of image features and the association of such features with clinically important outcomes, as occurs in distinct stages in conventional machine learning. Deep‐learning algorithms are able to elucidate TME patterns inherent in the input data with minimum levels of human intelligence and, hence, have the potential to achieve clinically relevant predictions and discovery of important TME features. Furthermore, the diverse repertoire of deep‐learning algorithms able to interrogate TME patterns extends beyond convolutional neural networks to include attention‐based models, graph neural networks, and multimodal models. To date, AI models have largely been evaluated retrospectively, outside the well‐established rigour of prospective clinical trials, in part because traditional clinical trial methodology may not always be suitable for the assessment of AI technology. However, to enable digital pathology‐based advanced analytics to meaningfully impact clinical care, specific measures of 'added benefit' to the current standard of care and validation in a prospective setting are important. This will need to be accompanied by adequate measures of explainability and interpretability. Despite such challenges, the combination of expanding datasets, increased computational power, and the possibility of integration of pre‐clinical experimental insights into model development means there is exciting potential for the future progress of these AI applications. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]

As development of gene drive systems accelerates and diversifies, predicting outcomes for target populations and the potential for human and environmental risks requires accounting for numerous eco-evolutionary processes. Gene drive dynamic models quantify the influence of features across genetics (e.g., resistance development and standing genetic diversity), demographics (e.g., mating systems and inbreeding), spatial ecology (e.g., dispersal and competition), biotic and abiotic environments (e.g., climate variation and landscape structure), and implementation strategies (e.g., introduction size and timing) on gene drive outcomes. Synthesizing published gene drive models reveals research trends, knowledge gaps, and emergent principles. Modeling limitations and tradeoffs are discussed. Integrating an iterative modeling approach within the existing phased pathway for gene drive research improves utility for risk assessment. Engineered gene drives create potential for both widespread benefits and irreversible harms to ecosystems. CRISPR-based systems of allelic conversion have rapidly accelerated gene drive research across diverse taxa, putting field trials and their necessary risk assessments on the horizon. Dynamic process-based models provide flexible quantitative platforms to predict gene drive outcomes in the context of system-specific ecological and evolutionary features. Here, we synthesize gene drive dynamic modeling studies to highlight research trends, knowledge gaps, and emergent principles, organized around their genetic, demographic, spatial, environmental, and implementation features. We identify the phenomena that most significantly influence model predictions, discuss limitations of biological complexity and uncertainty, and provide insights to promote responsible development and model-assisted risk assessment of gene drives. [ABSTRACT FROM AUTHOR]

Neuron models exist in different levels of complexity and biological modeling depth. The Hindmarsh–Rose model offers a rich repertoire of neuronal dynamics while being moderately mathematically complex. Existing circuit realizations of this neuron model, however, require a large amount of operational amplifiers due to the model's quadratic and cubic nonlinearity. In contrast to hardware realizations of simpler neuron models, this leads to a higher power consumption. In this work, the Hindmarsh–Rose model is approximated by an ideal electrical circuit that relies mostly on passive circuit elements and thus reduces the power consumption. For this purpose, we analyze the power flows of an equivalent electrical circuit of the Hindmarsh–Rose model and replace several nonlinear circuit elements by constant ones. Moreover, we approximate the cubic nonlinearity by three memristors in combination with a negative impedance converter. This negative impedance converter represents the only active circuit element required for the complete circuit, leading to an increased energy efficiency compared to the existing circuit realizations. Simulations verify the circuit's ability to generate spiking and bursting dynamics comparable to the original Hindmarsh–Rose model. [ABSTRACT FROM AUTHOR]

Rothia is an opportunistic pathogen, particularly life-threatening for the immunocompromised. It is associated with pneumonia, endocarditis, peritonitis and many other serious infections, including septicemia. Of note, Rothia mucilaginousa produces metabolites that support and increase overgrowth of Pseudomonas aeruginosa, one of the ESKAPE bacteria. Endolysins are considered as antibacterial enzymes derived from bacteriophages that selectively and efficiently kill susceptible bacteria without harming human cells or the normal microbiome. Here, we applied a computational analysis of metagenomic sequencing data of the gastric mucosa phageome extracted from human patients' stomach biopsies. A selected candidate anti-Rothia sequence was produced in an expression system, purified and confirmed as a Rothia mucilaginosa- and Rothia dentocariosa-specific endolysin PolaR, able to destroy bacterial cells even when aggregated, as in a biofilm. PolaR had no cytotoxic or antiproliferative effects on mammalian cells. PolaR is the first described endolysin selectively targeting Rothia species, with a high potential to combat infections caused by Rothia mucilaginosa and Rothia dentocariosa, and possibly other bacterial groups. PolaR is the first antibacterial enzyme selected from the gastric mucosa phageome, which underlines the biological complexity and probably underestimated biological role of the phageome in the human gastric mucosa. [ABSTRACT FROM AUTHOR]

Liver diseases are the primary reason for morbidity and mortality in the world. Owing to a shortage of organ donors and postoperative immune rejection, patients routinely suffer from liver failure. Unlike 2D cell models, animal models, and organoids, 3D bioprinting can be successfully employed to print living tissues and organs that contain blood vessels, bone, and kidney, heart, and liver tissues and so on. 3D bioprinting is mainly classified into four types: inkjet 3D bioprinting, extrusion-based 3D bioprinting, laser-assisted bioprinting (LAB), and vat photopolymerization. Bioinks for 3D bioprinting are composed of hydrogels and cells. For liver 3D bioprinting, hepatic parenchymal cells (hepatocytes) and liver nonparenchymal cells (hepatic stellate cells, hepatic sinusoidal endothelial cells, and Kupffer cells) are commonly used. Compared to conventional scaffold-based approaches, marked by limited functionality and complexity, 3D bioprinting can achieve accurate cell settlement, a high resolution, and more efficient usage of biomaterials, better mimicking the complex microstructures of native tissues. This method will make contributions to disease modeling, drug discovery, and even regenerative medicine. However, the limitations and challenges of this method cannot be ignored. Limitation include the requirement of diverse fabrication technologies, observation of drug dynamic response under perfusion culture, the resolution to reproduce complex hepatic microenvironment, and so on. Despite this, 3D bioprinting is still a promising and innovative biofabrication strategy for the creation of artificial multi-cellular tissues/organs. [ABSTRACT FROM AUTHOR]

Altered river flows and fragmented habitats often simplify riverine communities and favor non-native fishes, but their influence on life-history expression and survival is less clear. Here, we quantified the expression and ultimate success of diverse salmon emigration behaviors in an anthropogenically altered California river system. We analyzed two decades of Chinook salmon monitoring data to explore the influence of regulated flows on juvenile emigration phenology, abundance, and recruitment. We then followed seven cohorts into adulthood using otolith (ear stone) chemical archives to identify patterns in time- and size-selective mortality along the migratory corridor. Suppressed winter flow cues were associated with delayed emigration timing, particularly in warm, dry years, which was also when selection against late migrants was the most extreme. Lower, less variable flows were also associated with reduced juvenile and adult production, highlighting the importance of streamflow for cohort success in these southernmost populations. While most juveniles emigrated from the natal stream as fry or smolts, the survivors were dominated by the rare few that left at intermediate sizes and times, coinciding with managed flows released before extreme summer temperatures. The consistent selection against early (small) and late (large) migrants counters prevailing ecological theory that predicts different traits to be favored under varying environmental conditions. Yet, even with this weakened portfolio, maintaining a broad distribution in migration traits still increased adult production and reduced variance. In years exhibiting large fry pulses, even marginal increases in their survival would have significantly boosted recruitment. However, management actions favoring any single phenotype could have negative evolutionary and demographic consequences, potentially reducing adaptability and population stability. To recover fish populations and support viable fisheries in a warming and increasingly unpredictable climate, coordinating flow and habitat management within and among watersheds will be critical to balance trait optimization versus diversification.

Microphysiological systems (MPS) are drawing increasing interest from academia and from biomedical industry due to their improved capability to capture human physiology. MPS offer an advanced in vitro platform that can be used to study human organ and tissue level functions in health and in diseased states more accurately than traditional single cell cultures or even animal models. Key features in MPS include microenvironmental control and monitoring as well as high biological complexity of the target tissue. To reach these qualities, cross-disciplinary collaboration from multiple fields of science is required to build MPS. Here, we review different areas of expertise and describe essential building blocks of heart MPS including relevant cardiac cell types, supporting matrix, mechanical stimulation, functional measurements, and computational modelling. The review presents current methods in cardiac MPS and provides insights for future MPS development with improved recapitulation of human physiology. [ABSTRACT FROM AUTHOR]