Cross-Domain Text Mining of Pathophysiological Processes Associated with Diabetic Kidney Disease.

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease worldwide. This study's goal was to identify the signaling drivers and pathways that modulate glomerular endothelial dysfunction in DKD via artificial intelligence-enabled literature-based discovery. Cross-domain text mining of 33+ million PubMed articles was performed with SemNet 2.0 to identify and rank multi-scalar and multi-factorial pathophysiological concepts related to DKD. A set of identified relevant genes and proteins that regulate different pathological events associated with DKD were analyzed and ranked using normalized mean HeteSim scores. High-ranking genes and proteins intersected three domains-DKD, the immune response, and glomerular endothelial cells. The top 10% of ranked concepts were mapped to the following biological functions: angiogenesis, apoptotic processes, cell adhesion, chemotaxis, growth factor signaling, vascular permeability, the nitric oxide response, oxidative stress, the cytokine response, macrophage signaling, NFκB factor activity, the TLR pathway, glucose metabolism, the inflammatory response, the ERK/MAPK signaling response, the JAK/STAT pathway, the T-cell-mediated response, the WNT/ß-catenin pathway, the renin-angiotensin system, and NADPH oxidase activity. High-ranking genes and proteins were used to generate a protein-protein interaction network. The study results prioritized interactions or molecules involved in dysregulated signaling in DKD, which can be further assessed through biochemical network models or experiments.

Fructooligosaccharides act on the gut-bone axis to improve bone independent of Tregs and alter osteocytes in young adult C57BL/6 female mice.

Targeting the gut-bone axis with probiotics and prebiotics is considered as a promising strategy to reduce the risk of osteoporosis. Gut-derived short chain fatty acids (SCFA) mediate the effects of probiotics on bone via Tregs, but it is not known whether prebiotics act through a similar mechanism. We investigated how 2 different prebiotics, tart cherry (TC) and fructooligosaccharide (FOS), affect bone, and whether Tregs are required for this response. Eight-wk-old C57BL/6 female mice were fed with diets supplemented with 10% w/w TC, FOS, or a control diet (Con; AIN-93M) diet, and they received an isotype control or CD25 Ab to suppress Tregs. The FOS diet increased BMC, density, and trabecular bone volume in the vertebra (~40%) and proximal tibia (~30%) compared to the TC and control diets (Con), irrespective of CD25 treatment. Both prebiotics increased (P < .01) fecal SCFAs, but the response was greater with FOS. To determine how FOS affected bone cells, we examined genes involved in osteoblast and osteoclast differentiation and activity as well as genes expressed by osteocytes. The FOS increased the expression of regulators of osteoblast differentiation (bone morphogenetic protein 2 [Bmp2], Wnt family member 10b [Wnt10b] and Osterix [Osx]) and type 1 collagen). Osteoclasts regulators were unaltered. The FOS also increased the expression of genes associated with osteocytes, including (Phex), matrix extracellular phosphoglycoprotein (Mepe), and dentin matrix acidic phosphoprotein 1 (Dmp-1). However, Sost, the gene that encodes for sclerostin was also increased by FOS as the number and density of osteocytes increased. These findings demonstrate that FOS has a greater effect on the bone mass and structure in young adult female mice than TC and that its influence on osteoblasts and osteocytes is not dependent on Tregs.

An agent-based modeling approach for lung fibrosis in response to COVID-19.

The severity of the COVID-19 pandemic has created an emerging need to investigate the long-term effects of infection on patients. Many individuals are at risk of suffering pulmonary fibrosis due to the pathogenesis of lung injury and impairment in the healing mechanism. Fibroblasts are the central mediators of extracellular matrix (ECM) deposition during tissue regeneration, regulated by anti-inflammatory cytokines including transforming growth factor beta (TGF-ß). The TGF-ß-dependent accumulation of fibroblasts at the damaged site and excess fibrillar collagen deposition lead to fibrosis. We developed an open-source, multiscale tissue simulator to investigate the role of TGF-ß sources in the progression of lung fibrosis after SARS-CoV-2 exposure, intracellular viral replication, infection of epithelial cells, and host immune response. Using the model, we predicted the dynamics of fibroblasts, TGF-ß, and collagen deposition for 15 days post-infection in virtual lung tissue. Our results showed variation in collagen area fractions between 2% and 40% depending on the spatial behavior of the sources (stationary or mobile), the rate of activation of TGF-ß, and the duration of TGF-ß sources. We identified M2 macrophages as primary contributors to higher collagen area fraction. Our simulation results also predicted fibrotic outcomes even with lower collagen area fraction when spatially-localized latent TGF-ß sources were active for longer times. We validated our model by comparing simulated dynamics for TGF-ß, collagen area fraction, and macrophage cell population with independent experimental data from mouse models. Our results showed that partial removal of TGF-ß sources changed the fibrotic patterns; in the presence of persistent TGF-ß sources, partial removal of TGF-ß from the ECM significantly increased collagen area fraction due to maintenance of chemotactic gradients driving fibroblast movement. The computational findings are consistent with independent experimental and clinical observations of collagen area fractions and cell population dynamics not used in developing the model. These critical insights into the activity of TGF-ß sources may find applications in the current clinical trials targeting TGF-ß for the resolution of lung fibrosis.

Computational fluid dynamics modeling of aerosol particle transport through lung airway mucosa.

Delivery of aerosols to the lung can treat various lung diseases. However, the conducting airways are coated by a protective mucus layer with complex properties that make this form of delivery difficult. Mucus is a non-Newtonian fluid and is cleared from the lungs over time by ciliated cells. Further, its gel-like structure hinders the diffusion of particles through it. Any aerosolized treatment of lung diseases must penetrate the mucosal barrier. Using computational fluid dynamics, a model of the airway mucus and periciliary layer was constructed to simulate the transport of impacted aerosol particles. The model predicts the dosage fraction of particles of a certain size that penetrate the mucus and reach the underlying tissue, as well as the distance downstream of the dosage site where tissue concentration is maximized. Reactions that may occur in the mucus are also considered, with simulated data for the interaction of a model virus and an antibody.

Experimental and mathematical approaches for drug delivery for the treatment of wet age-related macular degeneration.

Several chronic eye diseases affect the posterior segment of the eye. Among them age-related macular degeneration can cause vision loss if left untreated and is one of the leading causes of visual impairment in the world. Most treatments are based on intravitreally injected therapeutics that inhibit the action of vascular endothelial growth factor. However, due to the need for monthly injections, this method is associated with poor patient compliance. To address this problem, numerous drug delivery systems (DDSs) have been developed. This review covers a selection of particulate systems, non-stimuli responsive hydrogels, implants, and composite systems that have been developed in the last few decades. Depending on the type of DDS, polymer material, and preparation method, different mechanical properties and drug release profiles can be achieved. Furthermore, DDS development can be optimized by implementing mathematical modeling of both drug release and pharmacokinetic aspects. Several existing mathematical models for diffusion-controlled, swelling-controlled, and erosion-controlled drug delivery from polymeric systems are summarized. Compartmental and physiologically based models for ocular drug transport and pharmacokinetics that have studied drug concentration profiles after intravitreal delivery or release from a DDS are also reviewed. The coupling of drug release models with ocular pharmacokinetic models can lead to obtaining much more efficient DDSs for the treatment of age-related macular degeneration and other diseases of the posterior segment of the eye.

Pathophysiology of mesangial expansion in diabetic nephropathy: mesangial structure, glomerular biomechanics, and biochemical signaling and regulation.

Diabetic nephropathy, a kidney complication arising from diabetes, is the leading cause of death in diabetic patients. Unabated, the growing epidemic of diabetes is increasing instances of diabetic nephropathy. Although the main causes of diabetic nephropathy have been determined, the mechanisms of their combined effects on cellular and tissue function are not fully established. One of many damages of diabetic nephropathy is the development of fibrosis within the kidneys, termed mesangial expansion. Mesangial expansion is an important structural lesion that is characterized by the aberrant proliferation of mesangial cells and excess production of matrix proteins. Mesangial expansion is involved in the progression of kidney failure in diabetic nephropathy, yet its causes and mechanism of impact on kidney function are not well defined. Here, we review the literature on the causes of mesangial expansion and its impacts on cell and tissue function. We highlight the gaps that still remain and the potential areas where bioengineering studies can bring insight to mesangial expansion in diabetic nephropathy.

Iterative community-driven development of a SARS-CoV-2 tissue simulator.

The 2019 novel coronavirus, SARS-CoV-2, is a pathogen of critical significance to international public health. Knowledge of the interplay between molecular-scale virus-receptor interactions, single-cell viral replication, intracellular-scale viral transport, and emergent tissue-scale viral propagation is limited. Moreover, little is known about immune system-virus-tissue interactions and how these can result in low-level (asymptomatic) infections in some cases and acute respiratory distress syndrome (ARDS) in others, particularly with respect to presentation in different age groups or pre-existing inflammatory risk factors. Given the nonlinear interactions within and among each of these processes, multiscale simulation models can shed light on the emergent dynamics that lead to divergent outcomes, identify actionable "choke points" for pharmacologic interventions, screen potential therapies, and identify potential biomarkers that differentiate patient outcomes. Given the complexity of the problem and the acute need for an actionable model to guide therapy discovery and optimization, we introduce and iteratively refine a prototype of a multiscale model of SARS-CoV-2 dynamics in lung tissue. The first prototype model was built and shared internationally as open source code and an online interactive model in under 12 hours, and community domain expertise is driving regular refinements. In a sustained community effort, this consortium is integrating data and expertise across virology, immunology, mathematical biology, quantitative systems physiology, cloud and high performance computing, and other domains to accelerate our response to this critical threat to international health. More broadly, this effort is creating a reusable, modular framework for studying viral replication and immune response in tissues, which can also potentially be adapted to related problems in immunology and immunotherapy.

Multiscale modeling in disease.

Multiscale computational modeling aims to connect the complex networks of effects at different length and/or time scales. For example, these networks often include intracellular molecular signaling, crosstalk, and other interactions between neighboring cell populations, and higher levels of emergent phenomena across different regions of tissues and among collections of tissues or organs interacting with each other in the whole body. Recent applications of multiscale modeling across intracellular, cellular, and/or tissue levels are highlighted here. These models incorporated the roles of biochemical and biomechanical modulation in processes that are implicated in the mechanisms of several diseases including fibrosis, joint and bone diseases, respiratory infectious diseases, and cancers.

Mathematical Modeling of the Gut-Bone Axis and Implications of Butyrate Treatment on Osteoimmunology.

Butyrate, a short-chain fatty acid produced by the gut microbiota, has pivotal roles in the regulation of the immune system. Recent studies have revealed that butyrate increases the differentiation of peripheral regulatory T cells in the gut-bone axis and promotes osteoblasts' bone forming activity. However, the mechanism of the therapeutic benefit of butyrate in bone remodeling remains incompletely understood. Here, we develop a multicompartment mathematical model to quantitatively predict the contribution of butyrate on the expansion of regulatory T cells in the gut, blood, and bone compartments. We investigate the interplay between regulatory T cell-derived TGF-ß and CD8+ T cell-derived Wnt-10b with changes in gut butyrate concentration. In addition, we connect our model to a detailed model of bone metabolism to study the impacts of butyrate and Wnt-10b on trabecular bone volume. Our results indicate both direct and indirect immune-mediated impacts of butyrate on bone metabolism.

Neonicotinoid exposure disrupts bumblebee nest behavior, social networks, and thermoregulation.

Neonicotinoid pesticides can negatively affect bee colonies, but the behavioral mechanisms by which these compounds impair colony growth remain unclear. Here, we investigate imidacloprid's effects on bumblebee worker behavior within the nest, using an automated, robotic platform for continuous, multicolony monitoring of uniquely identified workers. We find that exposure to field-realistic levels of imidacloprid impairs nursing and alters social and spatial dynamics within nests, but that these effects vary substantially with time of day. In the field, imidacloprid impairs colony thermoregulation, including the construction of an insulating wax canopy. Our results show that neonicotinoids induce widespread disruption of within-nest worker behavior that may contribute to impaired growth, highlighting the potential of automated techniques for characterizing the multifaceted, dynamic impacts of stressors on behavior in bee colonies.

Mathematical Model for Glucose Dependence of the Local Renin-Angiotensin System in Podocytes.

Diabetic kidney disease (DKD) is the primary cause of kidney failure. Diabetic hyperglycemia primarily damages podocyte cells. Podocytes express a local renin-angiotensin system (RAS) that produces angiotensin II (ANG II). ANG II levels are elevated by hyperglycemia, triggering podocyte injury. Quantitative descriptions of glucose dose dependency of ANG II are scarce in the literature. For better understanding of the mechanism of glycemic injury in DKD, a mathematical model is developed to describe the glucose-stimulated local RAS in podocytes. The model of the RAS signaling pathway in podocytes tracks peptides and enzymes without explicit glucose dependence. Local and global sensitivity analyses are used to identify the key parameters to be estimated in the model. Three approaches are explored to incorporate glucose dependency through linear ramp functions for the sensitive parameters. The first approach uses inferences from literature data to estimate the parameter values, while the other approaches reduce the number of assumptions by using least-squares regression to estimate all or a subset of the parameters. Physiological parameter values and RAS peptide concentrations ranges are used to discriminate between plausible models for the glucose dose dependency. This is the first model of the theory of the local RAS mechanism specific to podocyte cells to track ANG II levels in a range of glycemic conditions that may contribute to podocyte damage in DKD. The ability to track ANG II behavior could enable prediction of its downstream effects on podocytes and provide opportunities to better characterize pathophysiological features of DKD progression.

A review of mathematical modeling and simulation of controlled-release fertilizers.

Nutrients released into soils from uncoated fertilizer granules are lost continuously due to volatilization, leaching, denitrification, and surface run-off. These issues have caused economic loss due to low nutrient absorption efficiency and environmental pollution due to hazardous emissions and water eutrophication. Controlled-release fertilizers (CRFs) can change the release kinetics of the fertilizer nutrients through an abatement strategy to offset these issues by providing the fertilizer content in synchrony with the metabolic needs of the plants. Parametric analysis of release characteristics of CRFs is of paramount importance for the design and development of new CRFs. However, the experimental approaches are not only time consuming, but they are also cumbersome and expensive. Scientists have introduced mathematical modeling techniques to predict the release of nutrients from the CRFs to elucidate fundamental understanding of the dynamics of the release processes and to design new CRFs in a shorter time and with relatively lower cost. This paper reviews and critically analyzes the latest developments in the mathematical modeling and simulation techniques that have been reported for the characteristics and mechanisms of nutrient release from CRFs. The scope of this review includes the modeling and simulations techniques used for coated, controlled-release fertilizers.

Mathematical Modeling of Tuberculosis Granuloma Activation.

Tuberculosis (TB) is one of the most common infectious diseases worldwide. It is estimated that one-third of the world's population is infected with TB. Most have the latent stage of the disease that can later transition to active TB disease. TB is spread by aerosol droplets containing Mycobacterium tuberculosis (Mtb). Mtb bacteria enter through the respiratory system and are attacked by the immune system in the lungs. The bacteria are clustered and contained by macrophages into cellular aggregates called granulomas. These granulomas can hold the bacteria dormant for long periods of time in latent TB. The bacteria can be perturbed from latency to active TB disease in a process called granuloma activation when the granulomas are compromised by other immune response events in a host, such as HIV, cancer, or aging. Dysregulation of matrix metalloproteinase 1 (MMP-1) has been recently implicated in granuloma activation through experimental studies, but the mechanism is not well understood. Animal and human studies currently cannot probe the dynamics of activation, so a computational model is developed to fill this gap. This dynamic mathematical model focuses specifically on the latent to active transition after the initial immune response has successfully formed a granuloma. Bacterial leakage from latent granulomas is successfully simulated in response to the MMP-1 dynamics under several scenarios for granuloma activation.

Derivation of an Analytical Solution to a Reaction-Diffusion Model for Autocatalytic Degradation and Erosion in Polymer Microspheres.

A mathematical reaction-diffusion model is defined to describe the gradual decomposition of polymer microspheres composed of poly(D,L-lactic-co-glycolic acid) (PLGA) that are used for pharmaceutical drug delivery over extended periods of time. The partial differential equation (PDE) model treats simultaneous first-order generation due to chemical reaction and diffusion of reaction products in spherical geometry to capture the microsphere-size-dependent effects of autocatalysis on PLGA erosion that occurs when the microspheres are exposed to aqueous media such as biological fluids. The model is solved analytically for the concentration of the autocatalytic carboxylic acid end groups of the polymer chains that comprise the microspheres as a function of radial position and time. The analytical solution for the reaction and transport of the autocatalytic chemical species is useful for predicting the conditions under which drug release from PLGA microspheres transitions from diffusion-controlled to erosion-controlled release, for understanding the dynamic coupling between the PLGA degradation and erosion mechanisms, and for designing drug release particles. The model is the first to provide an analytical prediction for the dynamics and spatial heterogeneities of PLGA degradation and erosion within a spherical particle. The analytical solution is applicable to other spherical systems with simultaneous diffusive transport and first-order generation by reaction.

Bifurcation study of blood flow control in the kidney.

Renal blood flow is maintained within a narrow window by a set of intrinsic autoregulatory mechanisms. Here, a mathematical model of renal hemodynamics control in the rat kidney is used to understand the interactions between two major renal autoregulatory mechanisms: the myogenic response and tubuloglomerular feedback. A bifurcation analysis of the model equations is performed to assess the effects of the delay and sensitivity of the feedback system and the time constants governing the response of vessel diameter and smooth muscle tone. The results of the bifurcation analysis are verified using numerical simulations of the full nonlinear model. Both the analytical and numerical results predict the generation of limit cycle oscillations under certain physiologically relevant conditions, as observed in vivo.

Mathematical modeling of drug delivery from autocatalytically degradable PLGA microspheres--a review.

PLGA microspheres are widely studied for controlled release drug delivery applications, and many models have been proposed to describe PLGA degradation and erosion and drug release from the bulk polymer. Autocatalysis is known to have a complex role in the dynamics of PLGA erosion and drug transport and can lead to size-dependent heterogeneities in otherwise uniformly bulk-eroding polymer microspheres. The aim of this review is to highlight mechanistic, mathematical models for drug release from PLGA microspheres that specifically address interactions between phenomena generally attributed to autocatalytic hydrolysis and mass transfer limitation effects. Predictions of drug release profiles by mechanistic models are useful for understanding mechanisms and designing drug release particles.

Optimal Control of One-dimensional Cellular Uptake in Tissue Engineering.

A control problem motivated by tissue engineering is formulated and solved in which control of the uptake of growth factors (signaling molecules) is necessary to spatially and temporally regulate cellular processes for the desired growth or regeneration of a tissue. Four approaches are compared for determining 1D optimal boundary control trajectories for a distributed parameter model with reaction, diffusion, and convection: (i) basis function expansion, (ii) method of moments, (iii) internal model control (IMC), and (iv) model predictive control (MPC). The proposed method-of-moments approach is computationally efficient while enforcing a non-negativity constraint on the control input. While more computationally expensive than methods (i)-(iii), the MPC formulation significantly reduced the computational cost compared to simultaneous optimization of the entire control trajectory. A comparison of the pros and cons of each of the four approaches suggests that an algorithm that combines multiple approaches is most promising for solving the optimal control problem for multiple spatial dimensions.