Biological Systems Research Articles

Unravelling the biotoxicity of graphdiyne: molecular dynamics simulation of the interaction between villin headpiece protein and graphdiyne

Abstract Recently, there has been a growing prevalence in the utilization of graphdiyne (GDY) in the field of biomedicine, attributed to its distinctive physical structure and chemical properties. Additionally, its biocompatibility has garnered increasing attention. However, there is a lack of research on the biological effects and physical mechanisms of GDY-protein interactions at the molecular scale. In this study, the villin headpiece subdomain (HP35) served as a representative protein model. Molecular dynamics simulations were employed to investigate the interaction process between the HP35 protein and GDY, as well as the structural evolution of the protein. The data presented in our study demonstrate that GDY can rapidly adsorb HP35 protein and induce denaturation to one of the α-helix structures of HP35 protein. This implies a potential cytotoxicity concern of GDY for biological systems. Compared to graphene, GDY induced less disruption to HP35 protein. This can be attributed to the presence of natural triangular vacancies in GDY, which prevents π-π stacking action and the limited interaction of GDY with HP35 protein is not conducive to the expansion of protein structures. These findings unveil the biological effects of GDY at the molecular level and provide valuable insights for the application of GDY in biomedicine.

147 The ‘horse-side’ of host-microbe interactions and gastrointestinal homeostasis in the equine hindgut

Abstract Homeostasis is a complex physiological process that enables biological systems to maintain balanced internal environments in response to internal and external stimuli. In the equine gastrointestinal (GI) tract, homeostasis involves a network of factors that regulate gene expression, maintain the integrity of the mucosal barrier, and modulate immune responses to pathogenic microorganisms while supporting commensal bacteria that provide benefits to the host (Figure 1). These factors work together and respond to changes in diet, environment, external stress, and challenges by infectious agents or toxins. Proper gastrointestinal function is dependent on maintaining homeostasis in the system, and the dynamic interactions between the microbial population (the microbiota) and the equine host are central to this equilibrium. The regulation and interplay between the equine host and GI microbiota are evident in the distribution of distinct microbial communities within the various compartments of the equine hindgut. The contributions of each compartment to digestive physiology and the overlap with various stages of microbial fermentation in the equine hindgut are well-researched. What remains to be documented is gene expression in tissues of the equine GI and the underlying role this might play in the processes of digestion, physiology, and homeostasis in the GI. To better understand the roles that host gene expression plays in the interactions with the microbiota, it is critical that we investigate gene expression in the different tissues along the intestinal tract. Protein-coding genes are crucial in maintaining the health of the gastrointestinal tract. They direct the synthesis of various structural elements, molecular transporters, and digestive enzymes, which support digestion, nutrient absorption, and gut integrity. Non-coding genes also play an important role in regulating protein-coding gene expression, modulating immune responses, and maintaining gut barrier function. Moreover, there is evidence that non-coding transcripts regulate the composition of microbial communities in the GI tract. The presentation will focus on the current research and future directions for investigating protein-coding and non-coding gene expression to understand the mechanisms involved in the host-microbe interaction in the equine hindgut and their potential role in maintaining GI homeostasis.

Do concepts of individuality account for individuation practices in studies of host-parasite systems? A modeling account of biological individuality.

In recent discussions, the widespread conviction that scientific individuation practices are governed by theories and concepts of biological individuality has been challenged, particularly by advocates of practice-based approaches. This discussion raises questions about the relationship between individuation practices and concepts of individuality. In this paper, I discuss four studies of host-parasite systems and analyze the respective individuation practices to see whether they correspond to established concepts of biological individuality. My analysis suggests that scientists individuate biological systems on different levels of organization and that the researchers' respective emphasis on one of the levels depends on the explanandum and research context as well as epistemic aims and purposes. It thus makes sense to use different concepts of individuality to account for different individuation practices. However, not all individuation practices are represented equally well by concepts of biological individuality. To account for this observation, I propose that concepts of individuality should be understood as abstracted, idealized, or simplified models that represent only certain aspects of scientific practice. A modeling account suggests a pluralistic view of concepts of biological individuality that not only allows the coexistence of different kinds of individuality (e.g., evolutionary individuality, immunological individuality, ecological individuality) but also of normative and descriptive concepts.

Sensitive colorimetric detection of heparin using reverse modulation of peroxidase- and oxidase-mimetic activities in Fe3O4@PDA@MnO2 nanocomposites

Heparin, a widely studied glycosaminoglycan, plays crucial roles in the regulation of various physiological and pathological processes. Therefore, it's important to develop highly selective and sensitive methods for convenient monitoring of heparin levels in biological systems. We report the design and synthesis of Fe3O4@PDA@MnO2 nanoparticles (FPM-NPs), which exhibit dual enzymatic activities, enabling quantitative detection of heparin. The FPM-NPs feature a unique tri-layer spherical shell structure, possessing both peroxidase-like and oxidase-like activities, and catalyze the oxidation of 3,3′,5,5′-tetramethylbenzidine (TMB) in the presence or absence of H2O2. Remarkably, upon co-incubated with heparin, the oxidase activity of FPM-NPs decreases, while the peroxidase activity increases. By leveraging these dual enzymatic properties of FPM-NPs, a highly sensitive and specific colorimetric detection of heparin is achieved, with a detection limit reaching 6.51 nM and a good linear response to quantify heparin ranging 10–800 nM. Additionally, the developed FPM-NPs are successfully applied to measure heparin in fetal bovine serum samples. We also extend this detection method to a paper-based chip, enabling portable detection of heparin through grayscale analysis of mobile phone photographs. The multi-nanozyme-based heparin detection approach provides a new perspective for future research on expanding the application of nanocomposite materials in biomedical detection and analysis.

Molecular Communication-Based Intelligent Dopamine Rate Modulator for Parkinson's Disease Treatment.

Parkinson's disease (PD) is a progressive neurodegenerative disease, and it is caused by the loss of dopaminergic neurons in the basal ganglia (BG). Currently, there is no definite cure for PD, and available treatments mainly aim to alleviate its symptoms. Due to impaired neurotransmitter-based information transmission in PD, molecular communication-based approaches can be employed as potential solutions to address this issue. Molecular Communications (MC) is a bio-inspired communication method utilizing molecules to carry information. This mode of communication stands out for developing biocompatible nanomachines for diagnosing and treating, particularly in addressing neurodegenerative diseases like PD, due to its compatibility with biological systems. This study presents a novel treatment method that introduces an Intelligent Dopamine Rate Modulator (IDRM), which is located in the synaptic gap between the substantia nigra pars compacta (SNc) and striatum to compensate for insufficiency dopamine release in BG caused by PD. For storing dopamine in the IDRM, dopamine compound (DAC) is swallowed and crossed through the digestive system, blood circulatory system, blood-brain barrier (BBB), and brain extracellular matrix uptakes with IDRMs. Here, the DAC concentration is calculated in these regions, revealing that the required exogenous dopamine consistently reaches IDRM. Therefore, the perpetual dopamine insufficiency in BG associated with PD can be compensated. This method reduces drug side effects because dopamine is not released in other brain regions. Unlike other treatments, this approach targets the root cause of PD rather than just reducing symptoms.

A multi-objective Immune Balancing Algorithm for Distributed Heterogeneous Batching-integrated Assembly Hybrid Flowshop Scheduling

Driven by economic globalization, global supply chain collaboration has gained significant importance, fostering the emergence of distributed manufacturing. This paper addresses the Distributed Heterogeneous Batching-integrated Assembly Hybrid Flowshop Scheduling (DHBIAHFS) problem within the pharmaceutical industry. Jobs are allocated to factories for processing, batched within defined lot sizes for transportation, and subsequently assembled into products to minimize the maximum completion time and tardy product count. Effective lot sizing during transport is emphasized between factories and assembly machines. Drawing inspiration from the biological immune system’s balancing mechanisms, we propose a Multi-objective Immune Balancing Algorithm (MOIBA) equipped with learning and repairing mechanisms. Each solution is structured with three nested sequences, and composite heuristic evaluations are employed to generate high-quality initial solutions. The performance of each solution is assessed based on both fitness and diversity metrics. Customized crossover and mutation operators are introduced with dynamically adjusted probabilities reflective of immune response dynamics. Quantitative analysis validates our mathematical model and the distinct components of MOIBA. We compare MOIBA’s efficiency against six other effective multi-objective strategies using three performance metrics. Stability and robustness assessments, conducted through variance examination and statistical testing, offer insights into MOIBA’s consistency and reliability across diverse problem instances.

The relationship between sleep and interoception.

The present study aimed to characterise the relationship between sleep and interoception in two independent studies. Theoretical grounds for such a relationship include a weak relationship between subjectively- and objectively-measured sleep, the covariance of sleep with pain, as well as the mass regulation of a number of visceral biological systems. In addition, such a relationship is often reflected in our language (i.e., 'feeling' tired). In both studies (Study 1: N = 118, 73% female, mean age 20.98 years; Study 2: N = 830, 49% female, mean age 38.04 years) sleep over the last month was self-reported. Confidence in interoceptive accuracy, which is the precision with which a person can monitor visceral signals, was measured using the Interoceptive Accuracy Scale, while interoceptive attention, which is the dispositional tendency to attend to bodily signals, was measured using the Body Perception Questionnaire. Study 1 revealed a relationship between long sleep latency and low confidence in interoceptive accuracy (β = -0.222, p = 0.021) and poor sleep quality and less interoceptive attention (β = -0.226, p = 0.016). Study 2 replicated these results as well as made clear a more general pattern whereby poor sleep-characterised by short duration, poor quality, less efficiency, long latency, and more frequent nighttime awakenings-related to an interoceptive phenotype of less confidence in interoceptive accuracy and more interoceptive attention (all β ≥ 0.047, p ≤ 0.17). In conclusion, results from these two independent studies provide robust cross-sectional evidence for associations between various dimensions of poor sleep and greater interoceptive attention, but less confidence in interoceptive accuracy.

CHARMM36 All-Atom Gas Model for Lipid Nanobubble Simulation.

Lipid nanobubbles with different gas cores may integrate the biocompatibility of lipids, powerful physicochemical properties of nanobubbles, and therapeutic effects of gas molecules, which thus promote enormous biomedical applications such as ultrasound molecular imaging, gene/drug delivery, and gas therapy. In order for further more precise applications, the exact molecular mechanisms for the interactions between lipid nanobubbles and biological systems should be studied. Molecular dynamics (MD) simulation provides a powerful computational tool for this purpose. However, previous state-of-the-art MD simulations of free gas nanobubble/lipid nanobubble employed the vacuum as their gas cores, which is not suitable for studying the interactions between functional lipid nanobubbles and biological systems and revealing the biological roles of gas molecules. Hence, in this work, we developed and optimized the CHARMM36 all-atom gas parameters for six gases including N2, O2, H2, CO, CO2, and SO2, which accurately reproduced the gas density at different pressures as well as the spontaneous formation of gas nanobubbles. Subsequent applications of these gas parameters for lipid nanobubble simulations also reproduced the self-assembly process of the lipid nanobubble. We further developed a Python script to generate all-atom lipid nanobubble simulation systems, which was proven to be efficient for all-atom MD simulations of lipid nanobubbles and to be able to capture the exact dynamics of gas molecules at the gas-lipid and lipid-water interfaces of the lipid nanobubble. In summary, the all-atom gas models proposed in this work are suitable for simulating free gas nanobubbles and lipid nanobubbles, which are supposed to overcome the shortcomings of previous state-of-the-art MD simulations with the vacuum replacing the gas core and play key roles in revealing the molecular-level interactions between lipid nanobubbles and biological systems.

NIR-II light-activated and Cu nanocatalyst-enabled bioorthogonal reaction in living systems for efficient tumor therapy

Bioorthogonal reaction refers to chemical reactions that occur within a biological system without interfering the normal biochemical process, offering the unprecedented versatility in engineering chemical reactions within cells. However, the precise regulation of bioorthogonal reaction in living systems is mired by the complexity of the physiological environment and the toxicity of catalysts. Herein, considering the deeper tissue penetration and reduced phototoxicity compared to visible light and ultraviolet, a second near infrared (NIR-II) light-activated Cu-based bioorthogonal reaction is developed to achieve precise spatiotemporal control and effective switching for Cu(I)-catalyzed azide-alkyne cycloaddition (CuAAC) mediated chemical transformations in tumor, reducing the off-target effects. The catalytic activity of Cu catalyst through valence state interconversion between Cu(II) and Cu(I) can be precisely regulated in a reversible manner under NIR-II light irradiation-induced photoelectron transfer, which controls the extent of desired drug synthesis in bioorthogonal reaction. Meanwhile, the adverse effects of Cu(I) can be substantially mitigated within normal tissues due to their oxygen-rich condition. By utilizing NIR-II light and oxygen level, the Cu bioorthogonal catalyst achieves a balance between catalytic activity and biocompatibility. The ability to achieve precise spatiotemporal control and reversible catalysis makes this NIR-II light-mediated CuAAC platform an efficient and adaptable tool for bioorthogonal chemistry in living systems.

Evaluation and imaging of biodistribution of magnetic nanoparticles in a model of hepatic cirrhosis via Alternating Current Biosusceptometry.

In recent years, magnetic nanoparticles (MNPs) have exhibited theranostic characteristics which confer a wide range of applications in the biomedical field. Consequently, through Alternating Current Biosusceptometry (ACB), magnetic nanoparticles can be used as tracers, allowing the study of healthy and cirrhotic livers and providing the ability to differentiate them through the reconstruction of quantitative images. The ACB system consists of a developing biomagnetic technique that has the ability to magnetize and measure the magnetic susceptibility of a material such as MNPs, thereby offering quantitative information about biological systems with magnetic tracers.&#xD.

Highly sensitive near-infrared fluorescent probe for monitoring peroxynitrite in nonalcoholic fatty liver disease: Toward early diagnosis and therapeutic evaluation

Nonalcoholic fatty liver disease (NAFLD) poses a significant global health concern, necessitating precise diagnostic tools and effective treatment strategies. Peroxynitrite (ONOO−), a reactive oxygen species, plays a pivotal role in NAFLD pathogenesis, highlighting its potential as a biomarker for disease diagnosis and therapeutic evaluation. This study reports on the development of a near-infrared (NIR) fluorescent probe, designated DRP-O, for the selective detection of ONOO− with high sensitivity and photostability. DRP-O exhibits rapid response kinetics (within 2 min) and an impressive detection limit of 2.3 nM, enabling real-time monitoring of ONOO− dynamics in living cells. Notably, DRP-O demonstrates excellent photostability under continuous laser irradiation, ensuring reliable long-term monitoring in complex biological systems. We apply DRP-O to visualize endogenous ONOO− in living cells, demonstrating its potential for diagnosing and monitoring NAFLD-related oxidative stress. Furthermore, DRP-O effectively evaluates the efficacy of therapeutic drugs in NAFLD cell models, underscoring its potential utility in drug screening studies. Moreover, we confirm DRP-O to enable selective identification of fatty liver tissues in a mouse model of NAFLD, indicating its potential for the early diagnosis of NAFLD. Collectively, DRP-O represents a valuable tool for studying ONOO− dynamics, evaluating drug efficacy, and diagnosing NAFLD, offering insights into novel therapeutic strategies for this prevalent liver disorder.

Toward the Integration of Machine Learning and Molecular Modeling for Designing Drug Delivery Nanocarriers.

The pioneering work on liposomes in the 1960s and subsequent research in controlled drug release systems significantly advances the development of nanocarriers (NCs) for drug delivery. This field is evolved to include a diverse array of nanocarriers such as liposomes, polymeric nanoparticles, dendrimers, and more, each tailored to specific therapeutic applications. Despite significant achievements, the clinical translation of nanocarriers is limited, primarily due to the low efficiency of drug delivery and an incomplete understanding of nanocarrier interactions with biological systems. Addressing these challenges requires interdisciplinary collaboration and a deep understanding of the nano-bio interface. To enhance nanocarrier design, scientists employ both physics-based and data-driven models. Physics-based models provide detailed insights into chemical reactions and interactions at atomic and molecular scales, while data-driven models leverage machine learning to analyze large datasets and uncover hidden mechanisms. The integration of these models presents challenges such as harmonizing different modeling approaches and ensuring model validation and generalization across biological systems. However, this integration is crucial for developing effective and targeted nanocarrier systems. By integrating these approaches with enhanced data infrastructure, explainable AI, computational advances, and machine learning potentials, researchers can develop innovative nanomedicine solutions, ultimately improving therapeutic outcomes.

The Atypical Dual Specificity Phosphatase DUSP15 Regulates Jak1-Mediated STAT3 Activation.

The signal transducer and activator of transcription 3 (STAT3) protein is a key regulator of cell differentiation, proliferation, and survival in hematopoiesis, immune responses, and other biological systems. STAT3 transcriptional activity is strictly regulated through various mechanisms, such as phosphorylation and dephosphorylation. In this study, we attempted to identify novel phosphatases which regulate STAT3 activity in response to cytokine stimulations. To this end, leukemia inhibitory factor (LIF)/STAT3 dependent phosphatase induction was evaluated in the mouse hepatoma cell line Hepa1-6. After LIF stimulation, the expression of several atypical dual specific phosphatases (aDUSPs) was upregulated in Hepa1-6 cells. Among the LIF-induced aDUSPs, we focused on DUSP15 and clarified its functions in LIF/STAT3 signaling using RNA interference. DUSP15 knockdown decreased LIF-induced Socs3 mRNA expression and STAT3 translocation. Furthermore, loss of DUSP15 reduced the phosphorylation of STAT3 at Tyr705 and Janus family tyrosine kinase 1 (Jak1) at Tyr1034/1035 in response to LIF. The interaction between Jak1 and DUSP15 was observed in LIF-stimulated Hepa1-6 cells. We also demonstrated the suppression of granulocyte colony-stimulating factor (G-CSF)-mediated gp130/STAT3-dependent cell growth of Ba/F-G133 cells via DUSP15 knockdown. Therefore, DUSP15 functions as a positive feedback regulator in the Jak1/STAT3 signaling cascade.

Replicating biological 3D root and hyphal networks in transparent glass chips

Replicating the complex 3D microvascular architectures found in biological systems is a critical challenge in tissue engineering and other fields requiring efficient mass transport. Conventional microfabrication techniques often face limitations in creating extensive hierarchical networks, especially within bulk materials. Here, we report a versatile bioinspired approach to generate optimized 3D microvascular networks within transparent glass matrix by transcribing the natural growth patterns of plants and fungi. Plant seeds or fungal spores are first cultivated on nanoparticle-based culture media. Subsequent heat treatment removes the biological species while sintering the surrounding compound into a solidified chip with replica root/hyphal architectures as open microchannels. A diverse range of architectures, including the hierarchical branching of plant roots and the intricate networks formed by fungal hyphae, can be faithfully replicated. The resultant glass microvascular networks exhibit high chemical and thermal stability, enabling applications under harsh conditions. Fluid flow experiments validate the functionalities of the fabricated channels. By co-cultivating plants and fungi, hierarchical multi-scale architectures mimicking natural vascular systems are achieved. This bioinspired manufacturing technique leverages autonomous biological growth for architectural optimization, offering a complementary approach to existing microfabrication methods. The transparent nature of the glass chips allows for direct optical inspection, potentially facilitating integration with imaging components. This versatile platform holds promise for various engineering applications, such as microreactors, heat exchangers, and advanced filtration systems.

Combining Zhegalkin Polynomials and SAT Solving for Context-specific Boolean Modeling of Biological Systems.

Large amounts of knowledge regarding biological processes are readily available in the literature and aggregated in diverse databases. Boolean networks are powerful tools to render that knowledge into models that can mimic and simulate biological phenomena at multiple scales. Yet, when a model is required to understand or predict the behavior of a biological system in given conditions, existing information often does not completely match this context. Networks built from only prior knowledge can overlook mechanisms, lack specificity, and just partially recapitulate experimental observations. To address this limitation, context-specific data needs to be integrated. However, the brute-force identification of qualitative rules matching these data becomes infeasible as the number of candidates explodes for increasingly complex systems. Here, we used Zhegalkin polynomials to transform this identification into a binary value assignment for exponentially fewer variables, which we addressed with a state-of-the-art SAT solver. We evaluated our implemented method alongside two widely recognized tools, CellNetOptimizer and Caspo-ts, on both artificial toy models and large-scale models based on experimental data from the HPN-DREAM challenge. Our approach demonstrated benchmark-leading capabilities on networks of significant size and intricate complexity. It thus appears promising for the in silico modeling of ever more comprehensive biological systems.

Multifunctional Magnetic Muscles for Soft Robotics

Despite recent advancements, artificial muscles have not yet been able to strike the right balance between exceptional mechanical properties and dexterous actuation abilities that are found in biological systems. Here, we present an artificial magnetic muscle that exhibits multiple remarkable mechanical properties and demonstrates comprehensive actuating performance, surpassing those of biological muscles. This artificial muscle utilizes a composite configuration, integrating a phase-change polymer and ferromagnetic particles, enabling active control over mechanical properties and complex actuating motions through remote laser heating and magnetic field manipulation. Consequently, the magnetic composite muscle can dynamically adjust its stiffness as needed, achieving a switching ratio exceeding 2.7 × 10³. This remarkable adaptability facilitates substantial load-bearing capacity, with specific load capacities of up to 1000 and 3690 for tensile and compressive stresses, respectively. Moreover, it demonstrates reversible extension, contraction, bending, and twisting, with stretchability exceeding 800%. We leverage these distinctive attributes to showcase the versatility of this composite muscle as a soft continuum robotic manipulator. It adeptly executes various programmable responses and performs complex tasks while minimizing mechanical vibrations. Furthermore, we demonstrate that this composite muscle excels across multiple mechanical and actuation aspects compared to existing actuators.

On the Special Issue “Soft-nanotechnology for Brain Science and Biological Systems”

On the Special Issue “Soft-nanotechnology for Brain Science and Biological Systems”

Enhancing Chiral Molecule Detection: A Weak Measurement Approach Utilizing Two-Dimensional Information

This study addresses the critical need for high purity chiral molecules in biological systems by overcoming the challenges associated with the quantitative detection of chiral molecules and their enantiomeric mixtures. We developed an innovative detection approach that leverages the two-dimensional information gleaned from natural optical rotation (NOR) and Faraday optical rotation (FOR) under magnetic fields in chiral molecules, combined with an ultrahigh-resolution weak measurement sensor. This novel weak measurement system achieves unparalleled accuracy in detecting spin angles, with a precision of 1.86×10-5°. Notably, our method introduces no chemical reactions or interference with the substances under test. It offers enhanced discrimination capabilities through the dual-dimensional analysis of both natural and Faraday optical rotation, alongside a simple and compact sensor design. Conclusively, our study introduces a novel, high-precision, and multi-dimensional optical detection paradigm for chiral molecules. By incorporating Faraday rotation in the presence of a magnetic field, we expand the informational dimensionality accessible to the original weak measurement sensor, facilitating the quantitative analysis of chiral molecules and their enantiomers. This breakthrough not only furnishes a novel instrument for the exploration and development of chiral pharmaceuticals but also propels the advancement of weak measurement sensing technology forward.

Behavioural stochastic resonance across the lifespan.

Stochastic resonance (SR) is the phenomenon wherein the introduction of a suitable level of noise enhances the detection of subthreshold signalsin non linear systems. It manifests across various physical and biological systems, including the human brain. Psychophysical experiments have confirmed the behavioural impact of stochastic resonance on auditory, somatic, and visual perception. Aging renders the brain more susceptible to noise, possibly causing differences in the SR phenomenon between young and elderly individuals. This study investigates the impact of noise on motion detection accuracy throughout the lifespan, with 214 participants ranging in age from 18 to 82. Our objective was to determine the optimal noise level to induce an SR-like response in both young and old populations. Consistent with existing literature, our findings reveal a diminishing advantage with age, indicating that the efficacy of noise addition progressively diminishes. Additionally, as individuals age, peak performance is achieved with lower levels of noise. This study provides the first insight into how SR changes across the lifespan of healthy adults and establishes a foundation for understanding the pathological alterations in perceptual processes associated with aging.

Special Issue on “Soft-nanotechnology for Brain Science and Biological Systems”

Special Issue on “Soft-nanotechnology for Brain Science and Biological Systems”