Biological Model Research Articles

Axially multifocal metalens for 3D volumetric photoacoustic imaging of neuromelanin in live brain organoid.

Optical resolution photoacoustic imaging of uneven samples without z-scanning is transformative for the fast analysis and diagnosis of diseases. However, current approaches to elongate the depth of field (DOF) typically imply cumbersome postprocessing procedures, bulky optical element ensembles, or substantial excitation beam side lobes. Metasurface technology allows for the phase modulation of light and the miniaturization of imaging systems to wavelength-size thickness. Here, we propose a metalens composed of submicrometer-thick titanium oxide nanopillars, which generates an elongated beam of diffraction-limited diameter with an aspect ratio of 286 and a uniform intensity throughout the DOF. The metalens enhances visualization of phantom samples with tilted surfaces compared to conventional lenses. Moreover, the volumetric imaging of neuromelanin is facilitated for depths of up to 500 micrometers within the human midbrain and forebrain organoids that are 3D biological models of human brain regions. This approach provides a miniaturized platform for neurodegenerative disease diagnosis and drug discovery.

Robust estimation of skin physiological parameters from hyperspectral images using Bayesian neural networks.

Machine learning models for the direct extraction of tissue parameters from hyperspectral images have been extensively researched recently, as they represent a faster alternative to the well-known iterative methods such as inverse Monte Carlo and inverse adding-doubling (IAD). We aim to develop a Bayesian neural network model for robust prediction of physiological parameters from hyperspectral images. We propose a two-component system for extracting physiological parameters from hyperspectral images. First, our system models the relationship between the measured spectra and the tissue parameters as a distribution rather than a point estimate and is thus able to generate multiple possible solutions. Second, the proposed tissue parameters are then refined using the neural network that approximates the biological tissue model. The proposed model was tested on simulated and in vivo data. It outperformed current models with an overall mean absolute error of 0.0141 and can be used as a faster alternative to the IAD algorithm. Results suggest that Bayesian neural networks coupled with the approximation of a biological tissue model can be used to reliably and accurately extract tissue properties from hyperspectral images on the fly.

Plasma Proteomic Signature as a Predictor of Age Advancement in People Living With HIV.

Due to the increased burden of non-AIDS-related comorbidities in people living with HIV (PLHIV), identifying biomarkers and mechanisms underlying premature aging and the risk of developing age-related comorbidities is a priority. Evidence suggests that the plasma proteome is an accurate source for measuring biological age and predicting age-related clinical outcomes. To investigate whether PLHIV on antiretroviral therapy (ART) exhibit a premature aging phenotype, we profiled the plasma proteome of two independent cohorts of virally suppressed PLHIV (200HIV and 2000HIV) and one cohort of people without HIV (200FG) using O-link technology. Next, we built a biological age-prediction model and correlated age advancement (the deviation of the predicted age from the chronological age) with HIV-related factors, comorbidities, and cytokines secreted by immune cells. We identified a common signature of 77 proteins associated with chronological age across all cohorts, most of which were involved in inflammatory and senescence-related processes. PLHIV showed increased age advancement compared to people without HIV. In addition, age advancement in the 2000HIV cohort was positively associated with prior hepatitis C and cytomegalovirus (CMV) infections, non-AIDS-related comorbidities, ART duration, cumulative exposure to the protease inhibitor Ritonavir, as well as higher production of monocyte-derived proinflammatory cytokines and chemokines and lower secretion of T-cell derived cytokines. Our proteome-based predictive model is a promising approach for calculating the age advancement in PLHIV. This will potentially allow for further characterization of the pathophysiological mechanisms linked to accelerated aging and enable monitoring the effectiveness of novel therapies aimed at reducing age-related diseases in PLHIV.

In Vivo and In Vitro Models of Hepatic Fibrosis for Pharmacodynamic Evaluation and Pathology Exploration

Hepatic fibrosis (HF) is an important pathological state in the progression of chronic liver disease to end-stage liver disease and is usually triggered by alcohol, nonalcoholic fatty liver, chronic hepatitis viruses, autoimmune hepatitis (AIH), or cholestatic liver disease. Research on novel therapies has become a hot topic due to the reversibility of HF. Research into the molecular mechanisms of the pathology of HF and potential drug screening relies on reliable and rational biological models, mainly including animals and cells. Hence, a number of modeling approaches have been attempted based on human dietary, pathological, and physiological factors in the development of HF. In this review, classical and novel methods of modeling HF in the last 10 years were collected from electronic databases, including Web of Science, PubMed, ScienceDirect, ResearchGate, Baidu Scholar, and CNKI. Animal models of HF are usually induced by chemical toxicants, special diets, pathogenic microorganisms, surgical operations, and gene editing. The advantages and limitations of hepatic stellate cells (HSCs), organoids, and 3D coculture-based HF modeling methods established in vitro were also proposed and summarized. This information provides a scientific basis for the discovery of the pathological mechanism and treatment of HF.

A library of proton lineal energy spectra spanning the full range of clinically relevant energies.

In locations where the proton energy spectrum is broad, lineal energy spectrum-based proton biological effects models may be more accurate than dose-averaged linear energy transfer (LETd) based models. However, the development of microdosimetric spectrum-based biological effects models is hampered by the extreme computational difficulty of calculating microdosimetric spectra. Given a precomputed library of lineal energy spectra for monoenergetic protons, a weighted summation can be performed which yields the lineal energy spectrum of an arbitrary polyenergetic beam. Using this approach, lineal energy spectra can be rapidly calculated on a voxel-by-voxel level. Monoenergetic proton tracks generated using Geant4-DNA were imported into SuperTrack, a GPU-accelerated software for calculation of microdosimetric spectra. Libraries of proton lineal energy spectra which span the energy range of 0-300MeV were computed. The libraries were validated by comparison to Monte Carlo calculations in the literature, as well as lineal energy spectra measured experimentally with a tissue equivalent proportional counter. The lineal energy libraries have been made available in three data formats, two are plain-text,.csv and.les, and one binary encoded, root. Library files include the lineal energy bin abscissa in keV per micron and the unnormalized number of counts occurring within that bin. A computational technique for summation of the library files to yield the lineal energy of a polyenergetic beam is described in this work. The lineal energy libraries can be used to rapidly determine the lineal energy spectra at the location of cell cultures for in-vitro experiments and in each voxel of a treatment plan for in-vivo outcome modelling. These libraries have already been incorporated into RayStation 2023B-IonPG for lineal energy spectra calculation and we anticipate they will be incorporated into further dose calculation engines and Monte Carlo toolkits.

Deciphering the interplay between biology and physics with a finite element method-implemented vertex organoid model: A tool for the mechanical analysis of cell behavior on a spherical organoid shell.

Understanding the interplay between biology and mechanics in tissue architecture is challenging, particularly in terms of 3D tissue organization. Addressing this challenge requires a biological model enabling observations at multiple levels from cell to tissue, as well as theoretical and computational approaches enabling the generation of a synthetic model that is relevant to the biological model and allowing for investigation of the mechanical stresses experienced by the tissue. Using a monolayer human colon epithelium organoid as a biological model, freely available tools (Fiji, Cellpose, Napari, Morphonet, or Tyssue library), and the commercially available Abaqus FEM solver, we combined vertex and FEM approaches to generate a comprehensive viscoelastic finite element model of the human colon organoid and demonstrated its flexibility. We imaged human colon organoid development for 120 hours, following the evolution of the organoids from an immature to a mature morphology. According to the extracted architectural/geometric parameters of human colon organoids at various stages of tissue architecture establishment, we generated organoid active vertex models. However, this approach did not consider the mechanical aspects involved in the organoids' morphological evolution. Therefore, we applied a finite element method considering mechanical loads mimicking osmotic pressure, external solicitation, or active contraction in the vertex model by using the Abaqus FEM solver. Integration of finite element analysis (FEA) into the vertex model achieved a better fit with the biological model. Therefore, the FEM model provides a basis for depicting cell shape, tissue deformation, and cellular-level strain due to imposed stresses. In conclusion, we demonstrated that a combination of vertex and FEM approaches, combining geometrical and mechanical parameters, improves modeling of alterations in organoid morphology over time and enables better assessment of the mechanical cues involved in establishing the architecture of the human colon epithelium.

Long-term dynamics of placozoan culture: emerging models for population and space biology

Long-term dynamics of placozoan culture: emerging models for population and space biology

Protein carbamylation and proteomics: from artifacts to elucidation of biological functions

Lysine carbamylation is a non-enzymatic protein post-translational modification (PTM) that plays important roles in regulating enzymatic activity and the pathogenesis of diseases such as atherosclerosis, rheumatoid arthritis, and uremia. The progress of understanding the roles of carbamylation in biological systems has been delayed due to lack of systematic assays to study its functions. To aggravate this scenario, carbamylation is a major artifact in proteomics analysis given that urea, which is used during sample preparation, induces carbamylation. In addition, anti-acetyllysine antibodies co-purify carbamylated and acetylated peptides. In a recent paper, we leveraged co-purification with anti-acetyllysine antibodies to develop a method for analyzing carbamylated proteomes. In this perspective article, we discuss how this method may be applied to characterize the physiological functions of carbamylation in humans and other biological models, as well as the utility of establishing novel disease biomarkers.

Dynamic intelligent prediction approach for landslide displacement based on biological growth models and CNN-LSTM

Dynamic intelligent prediction approach for landslide displacement based on biological growth models and CNN-LSTM

Toxicological studies of fungicides frequently detected in drinking water using in vitro biological models

Toxicological studies of fungicides frequently detected in drinking water using in vitro biological models

Enhancing human ACE2 expression in mouse models to improve COVID-19 research.

Mice are one of the most common biological models for laboratory use. However, wild-type mice are not susceptible to COVID-19 infection due to the low affinity of mouse ACE2, the entry protein for SARS-CoV-2. Although mice with human ACE2 (hACE2) driven by Ace2 promoter reflect its tissue specificity, these animals exhibit low ACE2 expression, potentially limiting their fidelity in mimicking COVID-19 manifestations and their utility in viral studies. Here, we created and compared hACE2 mouse models generated with different strategies. Our findings show that distinct β-globin insertion within hACE2 cassette significantly influences its expression, with downstream placement enhancing transcription. Moreover, optimizing hACE2 codons (opt-hACE2) improves translation efficiency in multiple tissues. Notably, opt-hACE2 mice displayed more active immune responses and severe COVID-19 phenotypes following SARS-CoV-2 challenge compared to other models. Our study demonstrates the dual regulatory role of β-globin element in transgene transcription and suggests that opt-hACE2 mice might serve as valuable tools for SARS-CoV-2 research.

Metabolic profiling and antibacterial activity of tree wood extracts obtained under variable extraction conditions

IntroductionTree bacterial diseases are a threat in forestry due to their increasing incidence and severity. Understanding tree defence mechanisms requires evaluating metabolic changes arising during infection. Metabolite extraction affects the chemical diversity of the samples and, therefore, the biological relevance of the data. Metabolite extraction has been standardized for several biological models. However, little information is available regarding how it influences wood extract’s chemical diversity.ObjectivesThis study aimed to develop a methodological approach to obtain extracts from different tree species with the highest reproducibility and chemical diversity possible, to ensure proper coverage of the trees’ metabolome.MethodsA full factorial design was used to evaluate the effect of solvent type, extraction temperature and number of extraction cycles on the metabolic profile, chemical diversity and antibacterial activity of four tree species.ResultsSolvent, temperature and their interaction significantly affected the extracts’ chemical diversity, while the number of extraction cycles positively correlated with yield and antibacterial activity. Although 60% of the features were recovered in all the tested conditions, differences in the presence and abundance of specific chemical classes per tree were observed, including organooxygen compounds, prenol lipids, carboxylic acids, and flavonoids.ConclusionsEach tree species has a unique metabolic profile, which means that no single protocol is universally effective. Extraction at 50 °C for three cycles using 80% methanol or chloroform/methanol/water showed the best results and is suggested for studying wood metabolome. These observations highlight the need to tailor extraction protocols to each tree species to ensure comprehensive metabolome coverage for metabolic profiling.

QSP Modeling Shows Pathological Synergism Between Insulin Resistance and Amyloid-Beta Exposure in Upregulating VCAM1 Expression at the BBB Endothelium.

Type 2 diabetes mellitus (T2DM), characterized by insulin resistance, is closely associated with Alzheimer's disease (AD). Cerebrovascular dysfunction is manifested in both T2DM and AD, and is often considered as a pathological link between the two diseases. Insulin signaling regulates critical functions of the blood-brain barrier (BBB), and endothelial insulin resistance could lead to BBB dysfunction, aggravating AD pathology. However, insulin signaling is intrinsically dynamic and involves interactions among numerous molecular mediators. Hence, a mechanistic systems biology model is needed to understand how insulin regulates BBB physiology and the consequences of its impairment in T2DM and AD. In this study, we investigated the pharmacodynamic effect of insulin on the expression of vascular cell adhesion molecule 1 (VCAM1), a marker of cerebrovascular inflammation. Intriguingly, normal insulin concentrations selectively activated the PI3K-AKT pathway, leading to decreased VCAM1 expression. However, exposure to supraphysiological insulin levels, which is present in insulin resistance, activated both PI3K-AKT and MEK-ERK pathways, and increased VCAM1 expression. We developed a mathematical model that adequately described the dynamics of various insulin signaling nodes and VCAM1 expression. Further, the model was integrated with invitro proteomics and transcriptomics data from AD patients to simulate VCAM1 expression under pathological conditions. This approach allowed us to establish a quantitative systems pharmacology framework to investigate BBB dysfunction in AD and metabolic syndrome, thereby offering opportunities to identify specific disruptions in molecular networks that will enable us to identify novel therapeutic targets.

Building Foundation Models in Biology

Building Foundation Models in Biology

Parametric Design and Mechanical Characterization of a Selective Laser Sintering Additively Manufactured Biomimetic Ribbed Dome Inspired by the Chorion of Lepidopteran Eggs

The current research aims to analyze the shape and structural features of the eggs of the lepidoptera species Melitaea sp. (Lepidoptera, Nympalidae) and develop design solutions through the implementation of a novel strategy of biomimetic design. Scanning electron microscopy (SEM) analysis of the chorion reveals a medial zone that forms an arachnoid grid resembling a ribbed dome with convex longitudinal ribs and concave transverse ring members. A parametric design algorithm was created with the aid of computer-aided design (CAD) software Rhinoceros 3D and Grasshopper3D in order to abstract and emulate the biological model. A series of physical models were manufactured with variations in geometric parameters like the number of ribs and rings, their thickness, and curvature. Selective laser sintering (SLS) technology and Polyamide12 (nylon) material were utilized for the prototyping process. Quasi-static compression testing was carried out in conjunction with finite element analysis (FEA) to investigate the deformation patterns and stress dispersion of the models. The biomimetic ribbed dome appears to significantly dampen the snap-through behavior that is observed in typical solid and lattice domes, decreasing dynamic stresses developed during the response and preventing catastrophic failure of the structure. Increasing the curvature of the ring segments further reduces the snap-through phenomenon and improves the overall strength. However, excessive curvature has a negative effect on the maximum sustained load. Increasing the number and thickness of the transverse rings and the number of the longitudinal ribs also increases the strength of the dome. However, excessive increase in the rib radius leads to more acute snap-through behavior and an earlier failure. The above results were validated using respective finite element analyses.

Dynamical behavior of a time-fractional biological model via an efficient numerical method

Dynamical behavior of a time-fractional biological model via an efficient numerical method

Analysis of Stability in a Delay Differential Equation Model for Malaria InfectionWith Treatment

In this paper, we introduce a biological model employing delay differential equations to explore the evolution of malaria within a host undergoing drug treatment. Our analysis focuses on the stability of equilibrium points, leveraging the critical case theorem, an extension of the Lyapunov-Malkin theorem, which is particularly useful for scenarios involving zero roots in the characteristic equation. By determining equilibrium points and assessing their stability through the eigenvalues of the linearized system, we ensure the applicability of the theorem via translations to zero. The results highlight the significant influence of treatment-induced delays on the stability of malaria dynamics, offering valuable insights for optimizing control strategies and improving disease management.

Solution biophysics identifies lipid nanoparticle non-sphericity, polydispersity, and dependence on internal ordering for efficacious mRNA delivery.

Lipid nanoparticles (LNPs) are the most advanced delivery system currently available for RNA therapeutics. Their development has accelerated since the success of Patisiran, the first siRNA-LNP therapeutic, and the mRNA vaccines that emerged during the COVID-19 pandemic. Designing LNPs with specific targeting, high potency, and minimal side effects is crucial for their successful clinical use. These characteristics have been improved through the development of microfluidic platforms, which have enhanced the efficacy and uniformity of LNP batches. However, our understanding of how the composition and mixing method influences the structural, biophysical, and biological properties of the resulting particles remains limited, hindering the development of LNPs. Our lack of structural understanding extends from the physical and compositional polydispersity of LNPs, which render traditional characterization methods, such as dynamic light scattering (DLS), unable to accurately quantitate the physicochemical characteristics of LNPs. In this study, we address the challenge of structurally characterizing polydisperse LNP formulations using emerging solution-based biophysical methods that have higher resolution and provide biophysical data beyond size and polydispersity. These techniques include sedimentation velocity analytical ultracentrifugation (SV-AUC), field flow fractionation followed by multi-angle light scattering (FFF-MALS), and size-exclusion chromatography in-line with synchrotron small-angle X-ray scattering (SEC-SAXS). Here, we show that the LNPs have intrinsic polydispersity in size, RNA loading, and shape, and that these parameters are dependent on both the formulation technique and lipid composition. Lastly, we demonstrate that these biophysical methods can be employed to predict transfection in three biological models by examining the relationship between mRNA translation and physicochemical characteristics. We envision that employing solution-based biophysical methods will be essential for determining LNP structure-function relationships, facilitating the creation of new design rules for future LNPs.

Efficient mRNA Delivery In Vitro and In Vivo Using a Polycharged Biodegradable Nanomaterial.

As RNA rises as one of the most significant modalities for clinical applications and life science research, efficient tools for delivering and integrating RNA molecules into biological systems become essential. Herein, we report a formulation using a polycharged biodegradable nano-carrier, N1-501, which demonstrates superior efficiency and versatility in mRNA encapsulation and delivery in both cell and animal models. N1-501 is a polymeric material designed to function through a facile one-step formulation process suitable for various research settings. Its capability for mRNA transfection is investigated across a wide range of mRNA doses and in different biological models, including 18 tested cell lines and mouse models. This study also comprehensively analyzes N1-501's application for mRNA transfection by examining factors such as buffer composition and pH, incubation condition, and media type. Additionally, N1-501's superior in vivo mRNA transfection capability ensures its potential as an efficient and consistent tool for advancing mRNA-based therapies and genetic research.

City as a filter: urban density affects taxonomic and functional diversity of foliage dwelling spiders

ContextUrbanization affects landscape structure, functions and local environmental conditions, with major impacts on biodiversity. An evaluation of its effects on biodiversity, including both taxonomic and functional diversity, is thus compelling, with a specific focus on taxonomic groups providing fundamental ecosystem services. Spiders are ideal biological models for urban ecology studies because they are renowned bioindicators and can be found abundantly along urbanization gradients.ObjectivesIn this work, we aim at evaluating the filtering role exerted by urbanization at landscape scale on foliage-dwelling spider communities both at functional and taxonomic level.MethodsWe assessed the response of foliage-dwelling spiders to urbanization in Torino (NW-Italy), by sampling their communities in urban parks along an urbanization gradient and in a control area located in a nearby natural park. We tested their response in terms of taxonomic and functional diversity to urban density and six landscape metrics. Results of statistical models (GLMMs) were used to predict values of the current biodiversity in the city and its values under different future scenarios of urbanization (i.e. 2040, 2050).ResultsSpider abundance and species richness decreased in the city compared to the control area and along the urbanization gradient. Variation in the community composition was mostly due to species replacement (67%) within the control area, and to species loss in the urban area (62%). This pattern was mostly due to the loss of specialized foraging guilds, such as pollinator-feeding spiders. Functional dissimilarity among samples within the urban area was mostly explained by functional loss (69%), suggesting an environmental filter favoring species preadapted to urban conditions. By projecting biodiversity measures in two “greener city” scenarios, we identified 8 priority areas in the city where management actions should be implemented.ConclusionsOur findings underscore the role of urbanization in shaping spider communities, favoring generalist species and specific functional traits. The prediction on future scenarios proved to be useful to identify areas where increasing the surface of urban parks may contribute most effectively to spider biodiversity.