On the role of membrane embedding, protein rigidity and transmembrane length in lipid membrane fusion.

The fusion of biological membranes is ubiquitous in natural processes like exo- and endocytosis, intracellular trafficking and viral entry. Membrane fusion is also utilized in artificial biomimetic fusion systems, e.g. for drug delivery. Both the natural and the biomimetic fusion systems rely on a wide range of (artificial) proteins mediating the fusion process. Although the exact mechanisms of these proteins differ, clear analogies in their general behavior can be observed in bringing the membranes in close proximity and mediating the fusion reaction. In our study, we use molecular dynamics simulations with coarse grained models, mimicking the general behavior of fusion proteins (spikes), to systematically examine the effects of specific characteristics of these proteins on the fusion process. The protein characteristics considered are (i) the type of membrane embedding, i.e., either transmembrane or not, (ii) the rigidity, and (iii) the transmembrane domain (TMD) length. The results show essential differences in fusion pathway between monotopic and transmembrane spikes, in which transmembrane spikes seem to inhibit the formation of hemifusion diaphragms, leading to a faster fusion development. Furthermore, we observed that an increased rigidity and a decreased TMD length both proved to contribute to a faster fusion development. Finally, we show that a single spike may suffice to successfully induce a fusion reaction, provided that the spike is sufficiently rigid and attractive. Not only does this shed light on biological fusion of membranes, it also provides clear design rules for artificial membrane fusion systems.

Simulating Metabolic Flexibility in Low Energy Expenditure Conditions Using Genome-Scale Metabolic Models.

Metabolic flexibility is the ability of an organism to adapt its energy source based on nutrient availability and energy requirements. In humans, this ability has been linked to cardio-metabolic health and healthy aging. Genome-scale metabolic models have been employed to simulate metabolic flexibility by computing the Respiratory Quotient (RQ), which is defined as the ratio of carbon dioxide produced to oxygen consumed, and varies between values of 0.7 for pure fat metabolism and 1.0 for pure carbohydrate metabolism. While the nutritional determinants of metabolic flexibility are known, the role of low energy expenditure and sedentary behavior in the development of metabolic inflexibility is less studied. In this study, we present a new description of metabolic flexibility in genome-scale metabolic models which accounts for energy expenditure, and we study the interactions between physical activity and nutrition in a set of patient-derived models of skeletal muscle metabolism in older adults. The simulations show that fuel choice is sensitive to ATP consumption rate in all models tested. The ability to adapt fuel utilization to energy demands is an intrinsic property of the metabolic network.

Interpretable systems biomarkers predict response to immune-checkpoint inhibitors.

Cancer cells can leverage several cell-intrinsic and -extrinsic mechanisms to escape immune system recognition. The inherent complexity of the tumor microenvironment, with its multicellular and dynamic nature, poses great challenges for the extraction of biomarkers of immune response and immunotherapy efficacy. Here, we use RNA-sequencing (RNA-seq) data combined with different sources of prior knowledge to derive system-based signatures of the tumor microenvironment, quantifying immune-cell composition and intra- and intercellular communications. We applied multi-task learning to these signatures to predict different hallmarks of immune responses and derive cancer-type-specific models based on interpretable systems biomarkers. By applying our models to independent RNA-seq data from cancer patients treated with PD-1/PD-L1 inhibitors, we demonstrated that our method to Estimate Systems Immune Response (EaSIeR) accurately predicts therapeutic outcome. We anticipate that EaSIeR will be a valuable tool to provide a holistic description of immune responses in complex and dynamic systems such as tumors using available RNA-seq data.

Erratum: A Distance-Based Framework for the Characterization of Metabolic Heterogeneity in Large Sets of Genome-Scale Metabolic Models.

[This corrects the article DOI: 10.1016/j.patter.2020.100080.].

A Distance-Based Framework for the Characterization of Metabolic Heterogeneity in Large Sets of Genome-Scale Metabolic Models.

Gene expression and protein abundance data of cells or tissues belonging to healthy and diseased individuals can be integrated and mapped onto genome-scale metabolic networks to produce patient-derived models. As the number of available and newly developed genome-scale metabolic models increases, new methods are needed to objectively analyze large sets of models and to identify the determinants of metabolic heterogeneity. We developed a distance-based workflow that combines consensus machine learning and metabolic modeling techniques and used it to apply pattern recognition algorithms to collections of genome-scale metabolic models, both microbial and human. Model composition, network topology and flux distribution provide complementary aspects of metabolic heterogeneity in patient-specific genome-scale models of skeletal muscle. Using consensus clustering analysis we identified the metabolic processes involved in the individual responses to endurance training in older adults.

Metabolic Modeling Combined With Machine Learning Integrates Longitudinal Data and Identifies the Origin of LXR-Induced Hepatic Steatosis.

Temporal multi-omics data can provide information about the dynamics of disease development and therapeutic response. However, statistical analysis of high-dimensional time-series data is challenging. Here we develop a novel approach to model temporal metabolomic and transcriptomic data by combining machine learning with metabolic models. ADAPT (Analysis of Dynamic Adaptations in Parameter Trajectories) performs metabolic trajectory modeling by introducing time-dependent parameters in differential equation models of metabolic systems. ADAPT translates structural uncertainty in the model, such as missing information about regulation, into a parameter estimation problem that is solved by iterative learning. We have now extended ADAPT to include both metabolic and transcriptomic time-series data by introducing a regularization function in the learning algorithm. The ADAPT learning algorithm was (re)formulated as a multi-objective optimization problem in which the estimation of trajectories of metabolic parameters is constrained by the metabolite data and refined by gene expression data. ADAPT was applied to a model of hepatic lipid and plasma lipoprotein metabolism to predict metabolic adaptations that are induced upon pharmacological treatment of mice by a Liver X receptor (LXR) agonist. We investigated the excessive accumulation of triglycerides (TG) in the liver resulting in the development of hepatic steatosis. ADAPT predicted that hepatic TG accumulation after LXR activation originates for 80% from an increased influx of free fatty acids. The model also correctly estimated that TG was stored in the cytosol rather than transferred to nascent very-low density lipoproteins. Through model-based integration of temporal metabolic and gene expression data we discovered that increased free fatty acid influx instead of de novo lipogenesis is the main driver of LXR-induced hepatic steatosis. This study illustrates how ADAPT provides estimates for biomedically important parameters that cannot be measured directly, explaining (side-)effects of pharmacological treatment with LXR agonists.

Counterion-Dependent Mechanisms of DNA Origami Nanostructure Stabilization Revealed by Atomistic Molecular Simulation.

The DNA origami technique has proven to have tremendous potential for therapeutic and diagnostic applications like drug delivery, but the relatively low concentrations of cations in physiological fluids cause destabilization and degradation of DNA origami constructs preventing in vivo applications. To reveal the mechanisms behind DNA origami stabilization by cations, we performed atomistic molecular dynamics simulations of a DNA origami rectangle in aqueous solvent with varying concentrations of magnesium and sodium as well as polyamines like oligolysine and spermine. We explored the binding of these ions to DNA origami in detail and found that the mechanism of stabilization differs between ion types considerably. While sodium binds weakly and quickly exchanges with the solvent, magnesium and spermine bind close to the origami with spermine also located in between helices, stabilizing the crossovers characteristic for DNA origami and reducing repulsion of parallel helices. In contrast, oligolysine of length ten prevents helix repulsion by binding to adjacent helices with its flexible side chains, spanning the gap between the helices. Shorter oligolysine molecules with four subunits are weak stabilizers as they lack both the ability to connect helices and to prevent helix repulsion. This work thus shows how the binding modes of ions influence the stabilization of DNA origami nanostructures on a molecular level.

Characterization of disease-specific cellular abundance profiles of chronic inflammatory skin conditions from deconvolution of biopsy samples.

BACKGROUND: Psoriasis and atopic dermatitis are two inflammatory skin diseases with a high prevalence and a significant burden on the patients. Underlying molecular mechanisms include chronic inflammation and abnormal proliferation. However, the cell types contributing to these molecular mechanisms are much less understood. Recently, deconvolution methodologies have allowed the digital quantification of cell types in bulk tissue based on mRNA expression data from biopsies. Using these methods to study the cellular composition of the skin enables the rapid enumeration of multiple cell types, providing insight into the numerical changes of cell types associated with chronic inflammatory skin conditions. Here, we use deconvolution to enumerate the cellular composition of the skin and estimate changes related to onset, progress, and treatment of these skin diseases. METHODS: A novel signature matrix, i.e. DerM22, containing expression data from 22 reference cell types, is used, in combination with the CIBERSORT algorithm, to identify and quantify the cellular subsets within whole skin biopsy samples. We apply the approach to public microarray mRNA expression data from the skin layers and 648 samples from healthy subjects and patients with psoriasis or atopic dermatitis. The methodology is validated by comparison to experimental results from flow cytometry and immunohistochemistry studies, and the deconvolution of independent data from isolated cell types. RESULTS: We derived the relative abundance of cell types from healthy, lesional, and non-lesional skin and observed a marked increase in the abundance of keratinocytes and leukocytes in the lesions of both inflammatory dermatological conditions. The relative fraction of these cells varied from healthy to diseased skin and from non-lesional to lesional skin. We show that changes in the relative abundance of skin-related cell types can be used to distinguish between mild and severe cases of psoriasis and atopic dermatitis, and trace the effect of treatment. CONCLUSIONS: Our analysis demonstrates the value of this new resource in interpreting skin-derived transcriptomics data by enabling the direct quantification of cell types in a skin sample and the characterization of pathological changes in tissue composition.

Multivalency in a Dendritic Host-Guest System.

Multivalency is an important instrument in the supramolecular chemistry toolkit for the creation of strong specific interactions. In this paper we investigate the multivalency effect in a dendritic host-guest system using molecular dynamics simulations. Specifically, we consider urea-adamantyl decorated poly(propyleneimine) dendrimers that together with compatible mono-, bi-, and tetravalent ureidoacetic acid guests can form dynamic patchy nanoparticles. First, we simulate the self-assembly of these particles into macromolecular nanostructures, showing guest-controlled reduction of dendrimer aggregation. Subsequently, we systematically study guest concentration dependent multivalent binding. At low guest concentrations multivalency of the guests clearly increases relative binding as tethered headgroups bind more often than free guests' headgroups. We find that despite an abundance of binding sites, most of the tethered headgroups bind in close proximity, irrespective of the spacer length; nevertheless, longer spacers do increase binding. At high guest concentrations the dendrimer becomes saturated with bound headgroups, independent of guest valency. However, in direct competition the tetravalent guests prevail over the monovalent ones. This demonstrates the benefit of multivalency at high as well as low concentrations.

Computational modelling of energy balance in individuals with Metabolic Syndrome.

BACKGROUND: A positive energy balance is considered to be the primary cause of the development of obesity-related diseases. Treatment often consists of a combination of reducing energy intake and increasing energy expenditure. Here we use an existing computational modelling framework describing the long-term development of Metabolic Syndrome (MetS) in APOE3L.CETP mice fed a high-fat diet containing cholesterol with a human-like metabolic system. This model was used to analyze energy expenditure and energy balance in a large set of individual model realizations. RESULTS: We developed and applied a strategy to select specific individual models for a detailed analysis of heterogeneity in energy metabolism. Models were stratified based on energy expenditure. A substantial surplus of energy was found to be present during MetS development, which explains the weight gain during MetS development. In the majority of the models, energy was mainly expended in the peripheral tissues, but also distinctly different subgroups were identified. In silico perturbation of the system to induce increased peripheral energy expenditure implied changes in lipid metabolism, but not in carbohydrate metabolism. In silico analysis provided predictions for which individual models increase of peripheral energy expenditure would be an effective treatment. CONCLUSION: The computational analysis confirmed that the energy imbalance plays an important role in the development of obesity. Furthermore, the model is capable to predict whether an increase in peripheral energy expenditure - for instance by cold exposure to activate brown adipose tissue (BAT) - could resolve MetS symptoms.

In Silico Analysis Identifies Intestinal Transit as a Key Determinant of Systemic Bile Acid Metabolism.

Bile acids fulfill a variety of metabolic functions including regulation of glucose and lipid metabolism. Since changes of bile acid metabolism accompany obesity, Type 2 Diabetes Mellitus and bariatric surgery, there is great interest in their role in metabolic health. Here, we developed a mathematical model of systemic bile acid metabolism, and subsequently performed in silico analyses to gain quantitative insight into the factors determining plasma bile acid measurements. Intestinal transit was found to have a surprisingly central role in plasma bile acid appearance, as was evidenced by both the necessity of detailed intestinal transit functions for a physiological description of bile acid metabolism as well as the importance of the intestinal transit parameters in determining plasma measurements. The central role of intestinal transit is further highlighted by the dependency of the early phase of the dynamic response of plasma bile acids after a meal to intestinal propulsion.

In vivo and in silico dynamics of the development of Metabolic Syndrome.

The Metabolic Syndrome (MetS) is a complex, multifactorial disorder that develops slowly over time presenting itself with large differences among MetS patients. We applied a systems biology approach to describe and predict the onset and progressive development of MetS, in a study that combined in vivo and in silico models. A new data-driven, physiological model (MINGLeD: Model INtegrating Glucose and Lipid Dynamics) was developed, describing glucose, lipid and cholesterol metabolism. Since classic kinetic models cannot describe slowly progressing disorders, a simulation method (ADAPT) was used to describe longitudinal dynamics and to predict metabolic concentrations and fluxes. This approach yielded a novel model that can describe long-term MetS development and progression. This model was integrated with longitudinal in vivo data that was obtained from male APOE*3-Leiden.CETP mice fed a high-fat, high-cholesterol diet for three months and that developed MetS as reflected by classical symptoms including obesity and glucose intolerance. Two distinct subgroups were identified: those who developed dyslipidemia, and those who did not. The combination of MINGLeD with ADAPT could correctly predict both phenotypes, without making any prior assumptions about changes in kinetic rates or metabolic regulation. Modeling and flux trajectory analysis revealed that differences in liver fluxes and dietary cholesterol absorption could explain this occurrence of the two different phenotypes. In individual mice with dyslipidemia dietary cholesterol absorption and hepatic turnover of metabolites, including lipid fluxes, were higher compared to those without dyslipidemia. Predicted differences were also observed in gene expression data, and consistent with the emergence of insulin resistance and hepatic steatosis, two well-known MetS co-morbidities. Whereas MINGLeD specifically models the metabolic derangements underlying MetS, the simulation method ADAPT is generic and can be applied to other diseases where dynamic modeling and longitudinal data are available.

Identifying User Preferences for a Digital Educational Solution for Young Seniors With Diabetes.

The Eindhoven Diabetes Education Simulator project was initiated to develop an educational solution that helps diabetes patients understand and learn more about their diabetes. This article describes the identification of user preferences for the development of such solutions. Young seniors (aged 50-65 years) with type 2 diabetes were chosen as the target group because they are likely to have more affinity with digital devices than older people and because 88% of the Dutch diabetes population is >50 years of age. Data about the target group were gathered through literature research and interviews. The literature research covered data about their device use and education preferences. To gain insight into the daily life of diabetes patients and current diabetes education processes, 20 diabetes patients and 10 medical experts were interviewed. The interviews were analyzed using affinity diagrams. Those diagrams, together with the literature data, formed the basis for two personas and corresponding customer journey maps. Literature showed that diabetes prevalence is inversely correlated to educational level. Computer and device use is relatively low within the target group, but is growing. The interviews showed that young seniors like to play board, card, and computer games, with others or alone. Family and loved ones play an important role in their lives. Medical experts are crucial in the diabetes education of young senior diabetes patients. These findings are translated into a list of design aspects that can be used for creating educational solutions.

A Dynamic Model for Prediction of Psoriasis Management by Blue Light Irradiation.

Clinical investigations prove that blue light irradiation reduces the severity of psoriasis vulgaris. Nevertheless, the mechanisms involved in the management of this condition remain poorly defined. Despite the encouraging results of the clinical studies, no clear guidelines are specified in the literature for the irradiation scheme regime of blue light-based therapy for psoriasis. We investigated the underlying mechanism of blue light irradiation of psoriatic skin, and tested the hypothesis that regulation of proliferation is a key process. We implemented a mechanistic model of cellular epidermal dynamics to analyze whether a temporary decrease of keratinocytes hyper-proliferation can explain the outcome of phototherapy with blue light. Our results suggest that the main effect of blue light on keratinocytes impacts the proliferative cells. They show that the decrease in the keratinocytes proliferative capacity is sufficient to induce a transient decrease in the severity of psoriasis. To study the impact of the therapeutic regime on the efficacy of psoriasis treatment, we performed simulations for different combinations of the treatment parameters, i.e., length of treatment, fluence (also referred to as dose), and intensity. These simulations indicate that high efficacy is achieved by regimes with long duration and high fluence levels, regardless of the chosen intensity. Our modeling approach constitutes a framework for testing diverse hypotheses on the underlying mechanism of blue light-based phototherapy, and for designing effective strategies for the treatment of psoriasis.

Fragmentation and Coagulation in Supramolecular (Co)polymerization Kinetics.

The self-assembly of molecular building blocks into one-dimensional supramolecular architectures has opened up new frontiers in materials science. Due to the noncovalent interactions between the monomeric units, these architectures are intrinsically dynamic, and understanding their kinetic driving forces is key to rationally programming their morphology and function. To understand the self-assembly dynamics of supramolecular polymerizations (SP), kinetic models based on aggregate growth by sequential monomer association and dissociation have been analyzed. However, fragmentation and coagulation events can also play a role, as evident from studies on peptide self-assembly and the fact that aggregations can be sensitive to mechanical agitations. Here, we analyze how fragmentation and coagulation events influence SP kinetics by theoretical analysis of self-assembling systems of increasing complexity. Our analysis starts with single-component systems in which aggregates are able to grow via an isodesmic or cooperative nucleation-elongation mechanism. Subsequently, equilibration dynamics in cooperative two-component supramolecular copolymerizations are investigated. In the final part, we reveal how aggregate growth in the presence of competing, kinetically controlled pathways is influenced by fragmentation and coagulation reactions and reveal how seed-induced growth can give rise to block copolymers. Our analysis shows how fragmentation and coagulation reactions are able to modulate SP kinetics in ways that are highly system dependent.

Flux Balance Analysis of Plant Metabolism: The Effect of Biomass Composition and Model Structure on Model Predictions.

The biomass composition represented in constraint-based metabolic models is a key component for predicting cellular metabolism using flux balance analysis (FBA). Despite major advances in analytical technologies, it is often challenging to obtain a detailed composition of all major biomass components experimentally. Studies examining the influence of the biomass composition on the predictions of metabolic models have so far mostly been done on models of microorganisms. Little is known about the impact of varying biomass composition on flux prediction in FBA models of plants, whose metabolism is very versatile and complex because of the presence of multiple subcellular compartments. Also, the published metabolic models of plants differ in size and complexity. In this study, we examined the sensitivity of the predicted fluxes of plant metabolic models to biomass composition and model structure. These questions were addressed by evaluating the sensitivity of predictions of growth rates and central carbon metabolic fluxes to varying biomass compositions in three different genome-/large-scale metabolic models of Arabidopsis thaliana. Our results showed that fluxes through the central carbon metabolism were robust to changes in biomass composition. Nevertheless, comparisons between the predictions from three models using identical modeling constraints and objective function showed that model predictions were sensitive to the structure of the models, highlighting large discrepancies between the published models.

Concept Development of the Eindhoven Diabetes Education Simulator Project.

OBJECTIVE: This study was designed to define the concept of an educational diabetes game following a user-centered design approach. MATERIALS AND METHODS: The concept development of the Eindhoven Diabetes Education Simulator (E-DES) project can be divided in two phases: concept generation and concept evaluation. Four concepts were designed by the multidisciplinary development team based on the outcomes of user interviews. Four other concepts resulted from the Diabetes Game Jam. Several users and experts evaluated the concepts. These user evaluations and a feasibility analysis served as input for an overall evaluation and discussion by the development team resulting in the final concept choice. RESULTS: The four concepts of the development team are a digital board game, a quiz platform, a lifestyle simulator, and a puzzle game. The Diabetes Game Jam resulted in another digital board game, two mobile swipe games, and a fairy tale-themed adventure game. The combined user evaluations and feasibility analysis ranked the quiz platform and the digital board game equally high. Each of these games fits one specific subgroup of users best: the quiz platform best fits an eager-to-learn, more individualistic patient, whereas the board game best fits a less-eager-to-learn, family-oriented patient. The choice for a specific concept is therefore highly dependent on the choice of our specific target audience. CONCLUSIONS: The user-centered design approach with multiple evaluations has enabled us to choose the most promising concept from eight different options. A digital board game is chosen for further development because the target audience for E-DES is the less-motivated, family-oriented patients.

A genome-scale metabolic network reconstruction of tomato (Solanum lycopersicum L.) and its application to photorespiratory metabolism.

Tomato (Solanum lycopersicum L.) has been studied extensively due to its high economic value in the market, and high content in health-promoting antioxidant compounds. Tomato is also considered as an excellent model organism for studying the development and metabolism of fleshy fruits. However, the growth, yield and fruit quality of tomatoes can be affected by drought stress, a common abiotic stress for tomato. To investigate the potential metabolic response of tomato plants to drought, we reconstructed iHY3410, a genome-scale metabolic model of tomato leaf, and used this metabolic network to simulate tomato leaf metabolism. The resulting model includes 3410 genes and 2143 biochemical and transport reactions distributed across five intracellular organelles including cytosol, plastid, mitochondrion, peroxisome and vacuole. The model successfully described the known metabolic behaviour of tomato leaf under heterotrophic and phototrophic conditions. The in silico investigation of the metabolic characteristics for photorespiration and other relevant metabolic processes under drought stress suggested that: (i) the flux distributions through the mevalonate (MVA) pathway under drought were distinct from that under normal conditions; and (ii) the changes in fluxes through core metabolic pathways with varying flux ratio of RubisCO carboxylase to oxygenase may contribute to the adaptive stress response of plants. In addition, we improved on previous studies of reaction essentiality analysis for leaf metabolism by including potential alternative routes for compensating reaction knockouts. Altogether, the genome-scale model provides a sound framework for investigating tomato metabolism and gives valuable insights into the functional consequences of abiotic stresses.

Model-Based Quantification of the Systemic Interplay between Glucose and Fatty Acids in the Postprandial State.

In metabolic diseases such as Type 2 Diabetes and Non-Alcoholic Fatty Liver Disease, the systemic regulation of postprandial metabolite concentrations is disturbed. To understand this dysregulation, a quantitative and temporal understanding of systemic postprandial metabolite handling is needed. Of particular interest is the intertwined regulation of glucose and non-esterified fatty acids (NEFA), due to the association between disturbed NEFA metabolism and insulin resistance. However, postprandial glucose metabolism is characterized by a dynamic interplay of simultaneously responding regulatory mechanisms, which have proven difficult to measure directly. Therefore, we propose a mathematical modelling approach to untangle the systemic interplay between glucose and NEFA in the postprandial period. The developed model integrates data of both the perturbation of glucose metabolism by NEFA as measured under clamp conditions, and postprandial time-series of glucose, insulin, and NEFA. The model can describe independent data not used for fitting, and perturbations of NEFA metabolism result in an increased insulin, but not glucose, response, demonstrating that glucose homeostasis is maintained. Finally, the model is used to show that NEFA may mediate up to 30-45% of the postprandial increase in insulin-dependent glucose uptake at two hours after a glucose meal. In conclusion, the presented model can quantify the systemic interactions of glucose and NEFA in the postprandial state, and may therefore provide a new method to evaluate the disturbance of this interplay in metabolic disease.

Programmable chemical reaction networks: emulating regulatory functions in living cells using a bottom-up approach.

Living cells are able to produce a wide variety of biological responses when subjected to biochemical stimuli. It has become apparent that these biological responses are regulated by complex chemical reaction networks (CRNs). Unravelling the function of these circuits is a key topic of both systems biology and synthetic biology. Recent progress at the interface of chemistry and biology together with the realisation that current experimental tools are insufficient to quantitatively understand the molecular logic of pathways inside living cells has triggered renewed interest in the bottom-up development of CRNs. This builds upon earlier work of physical chemists who extensively studied inorganic CRNs and showed how a system of chemical reactions can give rise to complex spatiotemporal responses such as oscillations and pattern formation. Using purified biochemical components, in vitro synthetic biologists have started to engineer simplified model systems with the goal of mimicking biological responses of intracellular circuits. Emulation and reconstruction of system-level properties of intracellular networks using simplified circuits are able to reveal key design principles and molecular programs that underlie the biological function of interest. In this Tutorial Review, we present an accessible overview of this emerging field starting with key studies on inorganic CRNs followed by a discussion of recent work involving purified biochemical components. Finally, we review recent work showing the versatility of programmable biochemical reaction networks (BRNs) in analytical and diagnostic applications.