Biological Principles Research Articles

From Nature to Design: Tailoring Pure Mycelial Materials for the Needs of Tomorrow.

Modern efforts to influence materials science with principles of biology have allowed fungal mycelial materials to take a foothold and develop novel solutions for the circular bioeconomy of tomorrow. However, recent studies have shown that the value of tomorrow's green materials is not determined simply by their environmental viability, but rather by their ability to make the polluting materials of today obsolete. With an inherently strong structure of chitin and β-glucan, the ever-adaptable mycelia of fungi can compete at the highest levels with a litany of materials from leather to polyurethane foam to paper to wood. There are significant efforts to optimize pure mycelial materials (PMMs) through the entire process of species and strain selection, mycelial growth, and fabrication. Indeed, the promising investigations of novel species demonstrate how the diversity of fungi can be leveraged to create uniquely specialized materials. This review aims to highlight PMMs' current trajectory, evaluate the successes in technology, and explore how these new materials can help shape a better tomorrow.

Conceptual breakthroughs of the long noncoding RNA functional system and its endogenous regulatory role in the cancerous regime.

Long noncoding RNAs (lncRNAs) derived from noncoding regions in the human genome were once regarded as junks with no biological significance, but recent studies have shown that these molecules are highly functional, prompting an explosion of studies on their biology. However, these recent efforts have only begun to recognize the biological significance of a small fraction (< 1%) of the lncRNAs. The basic concept of these lncRNA functions remains controversial. This controversy arises primarily from conventional biased observations based on limited datasets. Fortunately, emerging big data provides a promising path to circumvent conventional bias to understand an unbiased big picture of lncRNA biology and advance the fundamental principles of lncRNA biology. This review focuses on big data studies that break through the critical concepts of the lncRNA functional system and its endogenous regulatory roles in all cancers. lncRNAs have unique functional systems distinct from proteins, such as transcriptional initiation and regulation, and they abundantly interact with mitochondria and consume less energy. lncRNAs, rather than proteins as traditionally thought, function as the most critical endogenous regulators of all cancers. lncRNAs regulate the cancer regulatory regime by governing the endogenous regulatory network of all cancers. This is accomplished by dominating the regulatory network module and serving as a key hub and top inducer. These critical conceptual breakthroughs lay a blueprint for a comprehensive functional picture of the human genome. They also lay a blueprint for combating human diseases that are regulated by lncRNAs.

In Vitro Biocompatibility Assessment of a Novel Membrane Containing Magnesium-Chitosan/Carboxymethyl Cellulose and Alginate Intended for Bone Tissue Regeneration.

Bone tissue engineering (BTE) is an emerging interdisciplinary field that aims to develop new strategies and materials for repairing, regenerating, or replacing damaged bone tissues. This field combines engineering, biology, and medicine principles to create functional bone tissues in the laboratory and in vivo. The main goal of BTE is to create biological substitutes that mimic the structure, function, and properties of natural bone tissue, thereby promoting the regeneration of bone defects caused by trauma, disease, or aging. In this study, we developed a biocomposite membrane using magnesium-chitosan, carboxymethyl cellulose, and alginate through a simple cast drying method. The biocompatibility of the membrane was evaluated using human osteoblastic cells, and it was found to be nontoxic to these cells. Both metabolic activity measurements (24 and 48 hours) and the lactate dehydrogenase release assay (72 hours) indicated that the membrane was biocompatible and did not exert significant toxic effects. These results suggest that the developed biocomposite membrane has the potential to be used as a safe and effective biomaterial for various biomedical applications, such as BTE, wound healing, and drug delivery. Further studies are warranted to explore the full potential of this membrane and its performance in different biological environments.

Biofilms for Production of Chemicals and Energy.

The twenty-first century will be the century of biology. This is not only because of breakthrough advances in molecular biology tools but also because we need to reinvent our economy based on the biological principles of energy efficiency and sustainability. Consequently, new tools for production routines must be developed to help produce platform chemicals and energy sources based on sustainable resources. In this context, biofilm-based processes have the potential to impact future production processes, because they can be carried out continuously and with robust stationary biocatalysts embedded in an extracellular matrix with different properties. We review productive biofilm systems used for heterotrophic and lithoautotrophic production and attempt to identify fundamental reasons why they may be particularly suitable as future production systems.

Conservation of genetic resources of farm animals

The problem of preserving the genetic resources of farm animals and breed diversity is one of the priorities in the global livestock industry. As of September 1, 2023, the State Breeding Register of the Russian Federation registered 83 gene pool farms for breeding 63 breeds of 16 species of farm animals, of which 42 are domestic breeds bred in 23 of the 89 constituents of the Russian Federation. As a consequence, it requires the creation of a national system for preserving the gene pool of farm animals based on legislative, economic and biological principles for them. The main centers of breeds formation and the current state of the local breeds in the Russian Federation have been examined in detail in the article. Ways to improve the legislative framework in the fi eld of preserving breed diversity and economic stimulation of gene pool farms were proposed. At the same time, the optimal approach for the selection of small in number local breeds is the use of traditional evaluation of their breeding value together with genotyping for a number of specific genetic markers. The authors have developed appropriate proposals for the conservation of the gene pool of national breeds of farm animals such as: 1) the adoption of the Federal Law on the Conservation of Agrobiodiversity and the creation of an Advisory Council under the Ministry of Agriculture of the Russian Federation and the Russian Academy of Sciences for the conservation of gene pool breeds of animals; 2) the formation of a network of gene pool farms for the main types of farm animals, with their inventory at the genetic level, the creation of information bases and the Federal Bank for the cryopreservation of semen, embryos and DNA of animals of gene pool breeds with budgetary fi nancing of gene pool livestock.

Geometry for low-inertia aerosol capture: Lessons from fog-basking beetles.

Water in the form of windborne fog droplets supports life in many coastal arid regions, where natural selection has driven nontrivial physical adaptation toward its separation and collection. For two species of Namib desert beetle whose body geometry makes for a poor filter, subtle modifications in shape and texture have been previously associated with improved performance by facilitating water drainage from its collecting surface. However, little is known about the relevance of these modifications to the flow physics that underlies droplets' impaction in the first place. We find, through coupled experiments and simulations, that such alterations can produce large relative gains in water collection by encouraging droplets to "slip" toward targets at the millimetric scale, and by disrupting boundary and lubrication layer effects at the microscopic scale. Our results offer a lesson in biological fog collection and design principles for controlling particle separation beyond the specific case of fog-basking beetles.

Patient-Derived Models of Cancer in the NCI PDMC Consortium: Selection, Pitfalls, and Practical Recommendations.

For over a century, early researchers sought to study biological organisms in a laboratory setting, leading to the generation of both in vitro and in vivo model systems. Patient-derived models of cancer (PDMCs) have more recently come to the forefront of preclinical cancer models and are even finding their way into clinical practice as part of functional precision medicine programs. The PDMC Consortium, supported by the Division of Cancer Biology in the National Cancer Institute of the National Institutes of Health, seeks to understand the biological principles that govern the various PDMC behaviors, particularly in response to perturbagens, such as cancer therapeutics. Based on collective experience from the consortium groups, we provide insight regarding PDMCs established both in vitro and in vivo, with a focus on practical matters related to developing and maintaining key cancer models through a series of vignettes. Although every model has the potential to offer valuable insights, the choice of the right model should be guided by the research question. However, recognizing the inherent constraints in each model is crucial. Our objective here is to delineate the strengths and limitations of each model as established by individual vignettes. Further advances in PDMCs and the development of novel model systems will enable us to better understand human biology and improve the study of human pathology in the lab.

Biological processes and factors involved in soft and hard tissue healing.

Wound healing is a complex and iterative process involving myriad cellular and biologic processes that are highly regulated to allow satisfactory repair and regeneration of damaged tissues. This review is intended to be an introductory chapter in a volume focusing on the use of platelet concentrates for tissue regeneration. In order to fully appreciate the clinical utility of these preparations, a sound understanding of the processes and factors involved in soft and hard tissue healing. This encompasses an appreciation of the cellular and biological mediators of both soft and hard tissues in general as well as specific consideration of the periodontal tissues. In light of good advances in this basic knowledge, there have been improvements in clinical strategies and therapeutic management of wound repair and regeneration. The use of platelet concentrates for tissue regeneration offers one such strategy and is based on the principles of cellular and biologic principles of wound repair discussed in this review.

The Giardia lamblia ribosome structure reveals divergence in several biological pathways and the mode of emetine function

Giardia lamblia is a deeply branching protist and a human pathogen. Its unusual biology presents the opportunity to explore conserved and fundamental molecular mechanisms. We determined the structure of the G.lamblia 80S ribosome bound to tRNA, mRNA, and the antibiotic emetine by cryo-electron microscopy, to an overall resolution of 2.49Å. The structure reveals rapidly evolving protein and nucleotide regions, differences in the peptide exit tunnel, and likely altered ribosome quality control pathways. Examination of translation initiation factor binding sites suggests these interactions are conserved despite a divergent initiation mechanism. Highlighting the potential of G. lamblia to resolve conserved biological principles; our structure reveals the interactions of the translation inhibitor emetine with the ribosome and mRNA, thus providing insight into the mechanism of action for this widely used antibiotic. Our work defines key questions in G. lamblia and motivates future experiments to explore the diversity of eukaryotic gene regulation.

Decoding the DNA methylome of central nervous system tumors: An emerging modality for integrated diagnosis.

The definitive diagnosis and classification of individual cancers are crucial for patient care and cancer research. To achieve a robust diagnosis of central nervous system (CNS) tumors, a genotype-phenotype integrated diagnostic approach was introduced in recent versions of the World Health Organization classification, followed by the incorporation of a genome-wide DNA methylome-based classification. Microarray-based platforms are widely used to obtain DNA methylome data, and the German Cancer Research Center (Deutsches Krebsforschungszentrum [DKFZ]) has a webtool for a DNA methylation-based classifier (DKFZ classifier). Integration of DNA methylome will further enhance the precision of CNS tumor classification, especially in diagnostically challenging cases. However, in the clinical application of DNA methylome-based classification, challenges related to data interpretation persist, in addition to technical caveats, regulations, and limited accessibility. Dimensionality reduction (DMR) can complement integrated diagnosis by visualizing a profile and comparing it with other known samples. Therefore, DNA methylome-based classification is a highly useful research tool for auxiliary analysis in challenging diagnostic and rare disease cases, and for establishing novel tumor concepts. Decoding the DNA methylome, especially by DMR in addition to DKFZ classifier, emphasizes the capability of grasping the fundamental biological principles that provide new perspectives on CNS tumors.

From Homogeneity to Turing Pattern: Kinetically Controlled Self-Organization of Transmembrane Protein.

Understanding the spatial organization of membrane proteins is crucial for unraveling key principles in cell biology. The reaction-diffusion model is commonly used to understand biochemical patterning; however, applying reaction-diffusion models to subcellular phenomena is challenging because of the difficulty in measuring protein diffusivity and interaction kinetics in the living cell. In this work, we investigated the self-organization of the plasmalemma vesicle-associated protein (PLVAP), which creates regular arrangements of fenestrated ultrastructures, using single-molecule tracking. We demonstrated that the spatial organization of the ultrastructures is associated with a decrease in the association rate by actin destabilization. We also constructed a reaction-diffusion model that accurately generates a hexagonal array with the same 130 nm spacing as the actual scale and informs the stoichiometry of the ultrastructure, which can be discerned only through electron microscopy. Through this study, we integrated single-molecule experiments and reaction-diffusion modeling to surpass the limitations of static imaging tools and proposed emergent properties of the PLVAP ultrastructure.

Sustainable Product Lifecycle Management – Implementation of a Digital Twin of a Biologically Transformed Product-Ecosystem

Biological transformation is the transfer of principles of natural systems to technical materials, processes and systems. The essential concepts of biological transformation - inspiration (learning from the observation of nature for the adaptation of technology), integration (symbiosis of technical systems based on biological principles) and interaction (interweaving of nature with technology) - can be transferred to Industry 4.0 solutions. One of these solutions is the Digital Twin, which can be used to provide transparency of an individual product’s environmental impact and to positively influence it throughout all lifecycle phases. A Digital Twin can be seen a digital ecosystem that inherits various biological principles itself, but also serves as an enabler of biologically transformed products. The application of the principles of biological transformation to Industry 4.0 helps achieve sustainable value creation. Manufacturing and operating companies, leveraging operational data with the help of Digital Twins, are equipped to better understand a product ecosystem’s performance and identify constraining resources that determine its lifecycle sustainability. This paper demonstrates the concept development of a Digital Twin for a biologically transformed product-ecosystem with integrated sustainability assessment focused on the production phase and its corresponding user interface.

A Physiological-Informed Generative Model for Improving Breast Lesion Classification in Small DCE-MRI Datasets.

In biomedical image processing, Deep Learning (DL) is increasingly exploited in various forms and for diverse purposes. Despite unprecedented results, the huge number of parameters to learn, which necessitates a substantial number of annotated samples, remains a significant challenge. In medical domains, obtaining high-quality labelled datasets is still a challenging task. In recent years, several works have leveraged data augmentation to face this issue, mostly thanks to the introduction of generative models able to produce artificial samples having the same characteristics as the acquired ones. However, we claim that biological principles must be considered in this process, as all medical imaging techniques exploit one or more physical laws or properties directly associated with the physiological characteristics of the tissues under analysis. A notable example is the Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI), in which the kinetic of the contrast agent (CA) highlights both morphological and physiological aspects. In this paper, we introduce a novel generative approach explicitly relying on Physiologically Based Pharmacokinetic (PBPK) modelling and on an Intrinsic Deforming Autoencoder (DAE) to implement a physiologically-aware data augmentation strategy. As a case of study, we consider breast DCE-MRI. In particular, we tested our proposal on two private and one public datasets with different acquisition protocols, demonstrating that the proposed method significantly improves the performance of several DL-based lesion classifiers.

Current research on mechanisms of limb bud development, and challenges for the next decade.

The developmental mechanisms of limb buds have been studied in developmental biology as an excellent model of pattern formation. Chick embryos have contributed to the discovery of new principles in developmental biology, as it is easy to observe live embryos and manipulate embryonic tissues. Herein, I outline recent findings and future issues over the next decade regarding three themes, based on my research: limb positioning, proximal-distal limb elongation and digit identity determination. First, how hindlimb position is determined at the molecular level is described, with a focus on the transforming growth factor-β signaling molecule GDF11. Second, I explain how the cell population in the limb bud deforms with developmental progress, shaping the limb bud with elongation along the proximal-distal axis. Finally, I describe the developmental mechanisms that determine digit identity through the interdigits.

Proliferative activity of normal tissues and malignant tumors during infestation of the organism by a biotic ecopathogen

The article shows the role of metabolites of metacercariae and opisthorchiae in the induction of proliferative activity of somatic cells of the hepatobiliary system and other organs. The importance of cellular and tissue bioregulators in increasing the level of proliferative activity of various tissues in organisms infested by the trematode Opisthorchis felineus is considered. It follows from the data obtained that the biological function of a tissue-specific keilon–containing effector produced by differentiated cells and inhibiting cell proliferation based on the biological feedback principle, in comparison with the background of prolonged parasitization of this ecopathogenic helminth in the hepatobiliary system of the body, decreases. There is also a decrease in the sensitivity of somatic cells to a tissue-specific growth inhibiting factor. It follows that the activation of cellular and tissue proliferation of animals longterm infested by opisthorchiasis is also due to a violation of the mechanisms of regulation of cellular and tissue homeostasis. This is confirmed by an increase in the growth rate of syngenic malignant tumors, as well as a modification of the effect of homologous leukocyte interferon from an inhibitor of the malignant process to an activator.

LINgroups as a Robust Principled Approach to Compare and Integrate Multiple Bacterial Taxonomies.

As a central organizing principle of biology, bacteria and archaea are classified into a hierarchical structure across taxonomic ranks from kingdom to subspecies. Traditionally, this organization was based on observable characteristics of form and chemistry but recently, bacterial taxonomy has been robustly quantified using comparisons of sequenced genomes, as exemplified in the Genome Taxonomy Database (GTDB). Such genome-based taxonomies resolve genomes down to genera and species and are useful in many contexts yet lack the flexibility and resolution of a fine-grained approach. The Life Identification Number (LIN) approach is a common, quantitative framework to tie existing (and future) bacterial taxonomies together, increase the resolution of genome-based discrimination of taxa, and extend taxonomic identification below the species level in a principled way. Utilizing LINgroup as an organizational concept helps resolve some of the confusion and unforeseen negative effects resulting from nomenclature changes of microorganisms that are closely related by overall genomic similarity (often due to genome-based reclassification). Our experimental results demonstrate the value of LINs and LINgroups in mapping between taxonomies, translating between different nomenclatures, and integrating them into a single taxonomic framework. They also reveal the robustness of LIN assignment to hyper-parameter changes when considering within-species taxonomic groups.

Tillage techniques in grass crop rotations in Western Kazakhstan

The transition to new technologies is predetermined by global and domestic scientific and production experience and general trends in the development of agriculture. The basis of grass crop rotations is the general biological principle of the different effects of perennial grasses on the soil. To the problem of forage production for the conditions of the arid steppe of Western Kazakhstan with an annual rainfall of 250-300 mm, a method of using forage crops based on grass crop rotation is proposed. Periodic change of different crops in crop rotation plays a positive phytosanitary role in clearing the soil of weeds. The article discusses three types of soil cultivation techniques based on the principles of sustainable production of grain crops and stabilization of soil fertility in the conditions of Western Kazakhstan. The research was carried out on the basis of the Ural Agricultural Experimental Station LLP. The purpose of the research is to develop methods of soil cultivation in grass crop rotations for the accumulation, conservation and rational use of soil moisture in resource-saving technologies for cultivating basic field crops, ensuring the preservation of soil fertility, improving the phytosanitary condition of crops in the dry steppe zone of Western Kazakhstan. The objective of the research is to study the influence of methods for processing turf of perennial grasses in the structure of crop rotations in order to stabilize soil fertility and the phytosanitary condition of crops. Three types of processing were used in the studies: flat cutter, slotting + cultivation, cultivation. Grass crop rotation was sown over a layer of perennial grasses and included the following forage crops: sorghum, corn and millet. The best results in terms of yield were observed based on the joint interaction of technologies for factor B (slotting + cultivation).

Promoters and Synthetic Promoters in Trichoderma reesei.

Trichoderma reesei holds immense promise for large-scale protein production, rendering it an excellent subject for deeper exploration using genetic engineering methods to achieve a comprehensive grasp of its cellular physiology. Understanding the genetic factors governing its intrinsic regulatory network is crucial, as lacking this knowledge could impede the expression of target genes. Prior and ongoing studies have concentrated on advancing new expression systems grounded in synthetic biology principles. These methodologies involve utilizing established potent promoters or engineered variations. Genomic and transcriptomic analyses have played a pivotal role in identifying robust promoters and expression systems, including light-responsive, copper-inducible, L-methionine-inducible, and Tet-On systems, among others. This chapter seeks to highlight various research endeavors focusing on tunable and constitutive promoters, the impact of different promoters on both native and foreign protein expression, the discovery of fresh promoters, and strategies conducive to future research aimed at refining and enhancing protein expression in T. reesei. Characterizing new promoters and adopting innovative expression systems hold the potential to significantly expand the molecular toolkit accessible for genetically engineering T. reesei strains. For instance, modifying potent inducible promoters such as Pcbh1 by replacing transcriptional repressors (cre1, ace1) with activators (xyr1, ace2, ace3, hap2/3/5) and integrating synthetic expression systems can result in increased production of crucial enzymes such as endoglucanases (EGLs), β-glucosidases (BGLs), and cellobiohydrolases (CBHs). Similarly, robust constitutive promoters such as Pcdna1 can be converted into synthetic hybrid promoters by incorporating activation elements from potent inducible promoters, facilitating cellulase induction and expression even under repressive conditions. Nevertheless, further efforts are necessary to uncover innovative promoters and devise novel expression strategies to enhance the production of desired proteins on an industrial scale.

Parylene-MoOx crossbar memristors as a volatile reservoir and non-volatile readout: a homogeneous reservoir computing system.

From the very beginning, the emulation of biological principles has been the primary avenue for the development of energy-efficient artificial intelligence systems. Reservoir computing, which has a solid biological basis, is particularly appealing due to its simplicity and efficiency. So-called memristors, resistive switching elements with complex dynamics, have proved beneficial for replicating both principal parts of a reservoir computing system. However, these parts require distinct behaviors found in differing memristive structures. The development of a homogeneous memristive reservoir computing system will significantly facilitate and reduce the fabrication process cost. The following work employs the co-existence of volatile and non-volatile regimes in parylene-MoOx crossbar memristors controlled by compliance current for this aim. The stable operation of the memristors under study is confirmed by low cycle-to-cycle and device-to-device variations of the switching voltages. For the transition between the volatile and non-volatile regimes, factors such as compliance current and reading voltage along with possible intrinsic origins are discussed. The results provide a foundation for the future hardware development of a homogeneous parylene-based reservoir computing system, considering high MNIST dataset classification accuracy (∼96%).

SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System for GNN-based Synthetic Lethal Prediction.

Synthetic Lethal (SL) relationships, though rare among the vast array of gene combinations, hold substantial promise for targeted cancer therapy. Despite advancements in AI model accuracy, there is still a significant need among domain experts for interpretive paths and mechanism explorations that align better with domain-specific knowledge, particularly due to the high costs of experimentation. To address this gap, we propose an iterative Human-AI collaborative framework with two key components: 1) HumanEngaged Knowledge Graph Refinement based on Metapath Strategies, which leverages insights from interpretive paths and domain expertise to refine the knowledge graph through metapath strategies with appropriate granularity. 2) Cross-Granularity SL Interpretation Enhancement and Mechanism Analysis, which aids experts in organizing and comparing predictions and interpretive paths across different granularities, uncovering new SL relationships, enhancing result interpretation, and elucidating potential mechanisms inferred by Graph Neural Network (GNN) models. These components cyclically optimize model predictions and mechanism explorations, enhancing expert involvement and intervention to build trust. Facilitated by SLInterpreter, this framework ensures that newly generated interpretive paths increasingly align with domain knowledge and adhere more closely to real-world biological principles through iterative Human-AI collaboration. We evaluate the framework's efficacy through a case study and expert interviews.