In silico analysis of design of experiment methods for metabolic pathway optimization.

Microbial cell factories allow the production of chemicals presenting an alternative to traditional fossil fuel-dependent production. However, finding the optimal expression of production pathway genes is crucial for the development of efficient production strains. Unlike sequential experimentation, combinatorial optimization captures the relationships between pathway genes and production, albeit at the cost of conducting multiple experiments. Fractional factorial designs followed by linear modeling and statistical analysis reduce the experimental workload while maximizing the information gained during experimentation. Although tools to perform and analyze these designs are available, guidelines for selecting appropriate factorial designs for pathway optimization are missing. In this study, we leverage a kinetic model of a seven-genes pathway to simulate the performance of a full factorial strain library. We compare this approach to resolution V, IV, III, and Plackett Burman (PB) designs. Additionally, we evaluate the performance of these designs as training sets for a random forest algorithm aimed at identifying best-producing strains. Evaluating the robustness of these designs to noise and missing data, traits inherent to biological datasets, we find that while resolution V designs capture most information present in full factorial data, they necessitate the construction of a large number of strains. On the other hand, resolution III and PB designs fall short in identifying optimal strains and miss relevant information. Besides, given the small number of experiments required for the optimization of a pathway with seven genes, linear models outperform random forest. Consequently, we propose the use of resolution IV designs followed by linear modeling in Design-Build-Test-Learn (DBTL) cycles targeting the screening of multiple factors. These designs enable the identification of optimal strains and provide valuable guidance for subsequent optimization cycles.

Combinatorial optimization of pathway, process and media for the production of p-coumaric acid by Saccharomyces cerevisiae.

Microbial cell factories are instrumental in transitioning towards a sustainable bio-based economy, offering alternatives to conventional chemical processes. However, fulfilling their potential requires simultaneous screening for optimal media composition, process and genetic factors, acknowledging the complex interplay between the organism's genotype and its environment. This study employs statistical design of experiments to systematically explore these relationships and optimize the production of p-coumaric acid (pCA) in Saccharomyces cerevisiae. Two rounds of fractional factorial designs were used to identify factors with a significant effect on pCA production, which resulted in a 168-fold variation in pCA titre. Moreover, a significant interaction between the culture temperature and expression of ARO4 highlighted the importance of simultaneous process and strain optimization. The presented approach leverages the strengths of experimental design and statistical analysis and could be systematically applied during strain and bioprocess design efforts to unlock the full potential of microbial cell factories.

Machine Learning-Guided Optimization of p-Coumaric Acid Production in Yeast.

Industrial biotechnology uses Design-Build-Test-Learn (DBTL) cycles to accelerate the development of microbial cell factories, required for the transition to a biobased economy. To use them effectively, appropriate connections between the phases of the cycle are crucial. Using p-coumaric acid (pCA) production in Saccharomyces cerevisiae as a case study, we propose the use of one-pot library generation, random screening, targeted sequencing, and machine learning (ML) as links during DBTL cycles. We showed that the robustness and flexibility of the ML models strongly enable pathway optimization and propose feature importance and Shapley additive explanation values as a guide to expand the design space of original libraries. This approach allowed a 68% increased production of pCA within two DBTL cycles, leading to a 0.52 g/L titer and a 0.03 g/g yield on glucose.

Carboxylic acid reductase-dependent biosynthesis of eugenol and related allylphenols.

BACKGROUND: (Hydroxy)cinnamyl alcohols and allylphenols, including coniferyl alcohol and eugenol, are naturally occurring aromatic compounds widely utilised in pharmaceuticals, flavours, and fragrances. Traditionally, the heterologous biosynthesis of (hydroxy)cinnamyl alcohols from (hydroxy)cinnamic acids involved CoA-dependent activation of the substrate. However, a recently explored alternative pathway involving carboxylic acid reductase (CAR) has proven efficient in generating the (hydroxy)cinnamyl aldehyde intermediate without the need for CoA activation. In this study, we investigated the application of the CAR pathway for whole-cell bioconversion of a range of (hydroxy)cinnamic acids into their corresponding (hydroxy)cinnamyl alcohols. Furthermore, we sought to extend the pathway to enable the production of a variety of allylphenols and allylbenzene. RESULTS: By screening the activity of several heterologously expressed enzymes in crude cell lysates, we identified the combination of Segniliparus rugosus CAR (SrCAR) and Medicago sativa cinnamyl alcohol dehydrogenase (MsCAD2) as the most efficient enzymatic cascade for the two-step reduction of ferulic acid to coniferyl alcohol. To optimise the whole-cell bioconversion in Escherichia coli, we implemented a combinatorial approach to balance the gene expression levels of SrCAR and MsCAD2. This optimisation resulted in a coniferyl alcohol yield of almost 100%. Furthermore, we extended the pathway by incorporating coniferyl alcohol acyltransferase and eugenol synthase, which allowed for the production of eugenol with a titre of up to 1.61 mM (264 mg/L) from 3 mM ferulic acid. This improvement in titre surpasses previous achievements in the field employing a CoA-dependent coniferyl alcohol biosynthesis pathway. Our study not only demonstrated the successful utilisation of the CAR pathway for the biosynthesis of diverse (hydroxy)cinnamyl alcohols, such as p-coumaryl alcohol, caffeyl alcohol, cinnamyl alcohol, and sinapyl alcohol, from their corresponding (hydroxy)cinnamic acid precursors but also extended the pathway to produce allylphenols, including chavicol, hydroxychavicol, and methoxyeugenol. Notably, the microbial production of methoxyeugenol from sinapic acid represents a novel achievement. CONCLUSION: The combination of SrCAR and MsCAD2 enzymes offers an efficient enzymatic cascade for the production of a wide array of (hydroxy)cinnamyl alcohols and, ultimately, allylphenols from their respective (hydroxy)cinnamic acids. This expands the range of value-added molecules that can be generated using microbial cell factories and creates new possibilities for applications in industries such as pharmaceuticals, flavours, and fragrances. These findings underscore the versatility of the CAR pathway, emphasising its potential in various biotechnological applications.

Simulated Design-Build-Test-Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering.

Combinatorial pathway optimization is an important tool in metabolic flux optimization. Simultaneous optimization of a large number of pathway genes often leads to combinatorial explosions. Strain optimization is therefore often performed using iterative design-build-test-learn (DBTL) cycles. The aim of these cycles is to develop a product strain iteratively, every time incorporating learning from the previous cycle. Machine learning methods provide a potentially powerful tool to learn from data and propose new designs for the next DBTL cycle. However, due to the lack of a framework for consistently testing the performance of machine learning methods over multiple DBTL cycles, evaluating the effectiveness of these methods remains a challenge. In this work, we propose a mechanistic kinetic model-based framework to test and optimize machine learning for iterative combinatorial pathway optimization. Using this framework, we show that gradient boosting and random forest models outperform the other tested methods in the low-data regime. We demonstrate that these methods are robust for training set biases and experimental noise. Finally, we introduce an algorithm for recommending new designs using machine learning model predictions. We show that when the number of strains to be built is limited, starting with a large initial DBTL cycle is favorable over building the same number of strains for every cycle.

Design of Four Small-Molecule-Inducible Systems in the Yeast Chromosome, Applied to Optimize Terpene Biosynthesis.

The optimization of cellular functions often requires the balancing of gene expression, but the physical construction and screening of alternative designs are costly and time-consuming. Here, we construct a strain of Saccharomyces cerevisiae that contains a "sensor array" containing bacterial regulators that respond to four small-molecule inducers (vanillic acid, xylose, aTc, IPTG). Four promoters can be independently controlled with low background and a 40- to 5000-fold dynamic range. These systems can be used to study the impact of changing the level and timing of gene expression without requiring the construction of multiple strains. We apply this approach to the optimization of a four-gene heterologous pathway to the terpene linalool, which is a flavor and precursor to energetic materials. Using this approach, we identify bottlenecks in the metabolic pathway. This work can aid the rapid automated strain development of yeasts for the bio-manufacturing of diverse products, including chemicals, materials, fuels, and food ingredients.

Elucidating yeast glycolytic dynamics at steady state growth and glucose pulses through kinetic metabolic modeling.

Microbial cell factories face changing environments during industrial fermentations. Kinetic metabolic models enable the simulation of the dynamic metabolic response to these perturbations, but their development is challenging due to model complexity and experimental data requirements. An example of this is the well-established microbial cell factory Saccharomyces cerevisiae, for which no consensus kinetic model of central metabolism has been developed and implemented in industry. Here, we aim to bring the academic and industrial communities closer to this consensus model. We developed a physiology informed kinetic model of yeast glycolysis connected to central carbon metabolism by including the effect of anabolic reactions precursors, mitochondria and the trehalose cycle. To parametrize such a large model, a parameter estimation pipeline was developed, consisting of a divide and conquer approach, supplemented with regularization and global optimization. Additionally, we show how this first mechanistic description of a growing yeast cell captures experimental dynamics at different growth rates and under a strong glucose perturbation, is robust to parametric uncertainty and explains the contribution of the different pathways in the network. Such a comprehensive model could not have been developed without using steady state and glucose perturbation data sets. The resulting metabolic reconstruction and parameter estimation pipeline can be applied in the future to study other industrially-relevant scenarios. We show this by generating a hybrid CFD-metabolic model to explore intracellular glycolytic dynamics for the first time. The model suggests that all intracellular metabolites oscillate within a physiological range, except carbon storage metabolism, which is sensitive to the extracellular environment.

Kinetic Modeling of Saccharomyces cerevisiae Central Carbon Metabolism: Achievements, Limitations, and Opportunities.

Central carbon metabolism comprises the metabolic pathways in the cell that process nutrients into energy, building blocks and byproducts. To unravel the regulation of this network upon glucose perturbation, several metabolic models have been developed for the microorganism Saccharomyces cerevisiae. These dynamic representations have focused on glycolysis and answered multiple research questions, but no commonly applicable model has been presented. This review systematically evaluates the literature to describe the current advances, limitations, and opportunities. Different kinetic models have unraveled key kinetic glycolytic mechanisms. Nevertheless, some uncertainties regarding model topology and parameter values still limit the application to specific cases. Progressive improvements in experimental measurement technologies as well as advances in computational tools create new opportunities to further extend the model scale. Notably, models need to be made more complex to consider the multiple layers of glycolytic regulation and external physiological variables regulating the bioprocess, opening new possibilities for extrapolation and validation. Finally, the onset of new data representative of individual cells will cause these models to evolve from depicting an average cell in an industrial fermenter, to characterizing the heterogeneity of the population, opening new and unseen possibilities for industrial fermentation improvement.

Enzyme-constrained models predict the dynamics of Saccharomyces cerevisiae growth in continuous, batch and fed-batch bioreactors.

Genome-scale, constraint-based models (GEM) and their derivatives are commonly used to model and gain insights into microbial metabolism. Often, however, their accuracy and predictive power are limited and enable only approximate designs. To improve their usefulness for strain and bioprocess design, we studied here their capacity to accurately predict metabolic changes in response to operational conditions in a bioreactor, as well as intracellular, active reactions. We used flux balance analysis (FBA) and dynamic FBA (dFBA) to predict growth dynamics of the model organism Saccharomyces cerevisiae under different industrially relevant conditions. We compared simulations with the latest developed GEM for this organism (Yeast8) and its enzyme-constrained version (ecYeast8) herein described with experimental data and found that ecYeast8 outperforms Yeast8 in all the simulations. EcYeast8 was able to predict well-known traits of yeast metabolism including the onset of the Crabtree effect, the order of substrate consumption during mixed carbon cultivation and production of a target metabolite. We showed how the combination of ecGEM and dFBA links reactor operation and genetic modifications to flux predictions, enabling the prediction of yields and productivities of different strains and (dynamic) production processes. Additionally, we present flux sampling as a tool to analyse flux predictions of ecGEM, of major importance for strain design applications. We showed that constraining protein availability substantially improves accuracy of the description of the metabolic state of the cell under dynamic conditions. This therefore enables more realistic and faithful designs of industrially relevant cell-based processes and, thus, the usefulness of such models.

Dietary nitrate does not reduce oxygen cost of exercise or improve muscle mitochondrial function in patients with mitochondrial myopathy.

Muscle weakness and exercise intolerance negatively affect the quality of life of patients with mitochondrial myopathy. Short-term dietary nitrate supplementation has been shown to improve exercise performance and reduce oxygen cost of exercise in healthy humans and trained athletes. We investigated whether 1 wk of dietary inorganic nitrate supplementation decreases the oxygen cost of exercise and improves mitochondrial function in patients with mitochondrial myopathy. Ten patients with mitochondrial myopathy (40 ± 5 yr, maximal whole body oxygen uptake = 21.2 ± 3.2 ml·min-1·kg body wt-1, maximal work load = 122 ± 26 W) received 8.5 mg·kg body wt-1·day-1 inorganic nitrate (~7 mmol) for 8 days. Whole body oxygen consumption at 50% of the maximal work load, in vivo skeletal muscle oxidative capacity (evaluated from postexercise phosphocreatine recovery using 31P-magnetic resonance spectroscopy), and ex vivo mitochondrial oxidative capacity in permeabilized skinned muscle fibers (measured with high-resolution respirometry) were determined before and after nitrate supplementation. Despite a sixfold increase in plasma nitrate levels, nitrate supplementation did not affect whole body oxygen cost during submaximal exercise. Additionally, no beneficial effects of nitrate were found on in vivo or ex vivo muscle mitochondrial oxidative capacity. This is the first time that the therapeutic potential of dietary nitrate for patients with mitochondrial myopathy was evaluated. We conclude that 1 wk of dietary nitrate supplementation does not reduce oxygen cost of exercise or improve mitochondrial function in the group of patients tested.

Muscle Metabolic Responses During Dynamic In-Magnet Exercise Testing: A Pilot Study in Children with an Idiopathic Inflammatory Myopathy.

RATIONALE AND OBJECTIVES: The clinical utility of supine in-magnet bicycling in combination with phosphorus magnetic resonance spectroscopy ((31)P MRS) to evaluate quadriceps muscle metabolism was examined in four children with juvenile dermatomyositis (JDM) in remission and healthy age- and gender-matched controls. MATERIALS AND METHODS: Two identical maximal supine bicycling tests were performed using a magnetic resonance-compatible ergometer. During the first test, cardiopulmonary performance was established in the exercise laboratory. During the second test, quadriceps energy balance and acid/base balance during incremental exercise and phosphocreatine recovery were determined using (31)P MRS. RESULTS: During the first test, no significant differences were found between patients with JDM and their healthy peers regarding cardiopulmonary performance. The outcomes of the first test indicate that both groups attained maximal performance. During the second test, quadriceps phosphocreatine and pH time courses were similar in all but one patient experiencing idiopathic postexercise pain. This patient demonstrated faster phosphocreatine depletion and acidification during exercise, yet postexercise mitochondrial adenosine triphosphate synthesis rate measured by phosphocreatine recovery kinetics was approximately twofold faster than control (time constant 23 seconds vs 43 ± 7 seconds, respectively). CONCLUSIONS: These results highlight the utility of in-magnet cycle ergometry in combination with (31)P MRS to assess and monitor muscle energetic patterns in pediatric patients with inflammatory myopathies.

31P MR spectroscopy and computational modeling identify a direct relation between Pi content of an alkaline compartment in resting muscle and phosphocreatine resynthesis kinetics in active muscle in humans.

The assessment of mitochondrial properties in skeletal muscle is important in clinical research, for instance in the study of diabetes. The gold standard to measure mitochondrial capacity non-invasively is the phosphocreatine (PCr) recovery rate after exercise, measured by (31)P Magnetic Resonance spectroscopy ((31)P MRS). Here, we sought to expand the evidence base for an alternative method to assess mitochondrial properties which uses (31)P MRS measurement of the Pi content of an alkaline compartment attributed to mitochondria (Pi2; as opposed to cytosolic Pi (Pi1)) in resting muscle at high magnetic field. Specifically, the PCr recovery rate in human quadriceps muscle was compared with the signal intensity of the Pi2 peak in subjects with varying mitochondrial content of the quadriceps muscle as a result of athletic training, and the results were entered into a mechanistic computational model of mitochondrial metabolism in muscle to test if the empirical relation between Pi2/Pi1 ratio and the PCr recovery was consistent with theory. Localized (31)P spectra were obtained at 7T from resting vastus lateralis muscle to measure the intensity of the Pi2 peak. In the endurance trained athletes a Pi2/Pi1 ratio of 0.07 ± 0.01 was found, compared to a significantly lower (p<0.05) Pi2/Pi1 ratio of 0.03 ± 0.01 in the normally active group. Next, PCr recovery kinetics after in magnet bicycle exercise were measured at 1.5T. For the endurance trained athletes, a time constant τPCr 12 ± 3 s was found, compared to 24 ± 5s in normally active subjects. Without any parameter optimization the computational model prediction matched the experimental data well (r(2) of 0.75). Taken together, these results suggest that the Pi2 resonance in resting human skeletal muscle observed at 7T provides a quantitative MR-based functional measure of mitochondrial density.

Prediction of muscle energy states at low metabolic rates requires feedback control of mitochondrial respiratory chain activity by inorganic phosphate.

The regulation of the 100-fold dynamic range of mitochondrial ATP synthesis flux in skeletal muscle was investigated. Hypotheses of key control mechanisms were included in a biophysical model of oxidative phosphorylation and tested against metabolite dynamics recorded by (31)P nuclear magnetic resonance spectroscopy ((31)P MRS). Simulations of the initial model featuring only ADP and Pi feedback control of flux failed in reproducing the experimentally sampled relation between myoplasmic free energy of ATP hydrolysis (ΔG(p) = ΔG(p)(o')+RT ln ([ADP][Pi]/[ATP]) and the rate of mitochondrial ATP synthesis at low fluxes (<0.2 mM/s). Model analyses including Monte Carlo simulation approaches and metabolic control analysis (MCA) showed that this problem could not be amended by model re-parameterization, but instead required reformulation of ADP and Pi feedback control or introduction of additional control mechanisms (feed forward activation), specifically at respiratory Complex III. Both hypotheses were implemented and tested against time course data of phosphocreatine (PCr), Pi and ATP dynamics during post-exercise recovery and validation data obtained by (31)P MRS of sedentary subjects and track athletes. The results rejected the hypothesis of regulation by feed forward activation. Instead, it was concluded that feedback control of respiratory chain complexes by inorganic phosphate is essential to explain the regulation of mitochondrial ATP synthesis flux in skeletal muscle throughout its full dynamic range.

Similar mitochondrial activation kinetics in wild-type and creatine kinase-deficient fast-twitch muscle indicate significant Pi control of respiration.

Past simulations of oxidative ATP metabolism in skeletal muscle have predicted that elimination of the creatine kinase (CK) reaction should result in dramatically faster oxygen consumption dynamics during transitions in ATP turnover rate. This hypothesis was investigated. Oxygen consumption of fast-twitch (FT) muscle isolated from wild-type (WT) and transgenic mice deficient in the myoplasmic (M) and mitochondrial (Mi) CK isoforms (MiM CK(-/-)) were measured at 20°C at rest and during electrical stimulation. MiM CK(-/-) muscle oxygen consumption activation kinetics during a step change in contraction rate were 30% faster than WT (time constant 53 ± 3 vs. 69 ± 4 s, respectively; mean ± SE, n = 8 and 6, respectively). MiM CK(-/-) muscle oxygen consumption deactivation kinetics were 380% faster than WT (time constant 74 ± 4 s vs. 264 ± 4 s, respectively). Next, the experiments were simulated using a computational model of the oxidative ATP metabolic network in FT muscle featuring ADP and Pi feedback control of mitochondrial respiration (J. A. L. Jeneson, J. P. Schmitz, N. A. van den Broek, N. A. van Riel, P. A. Hilbers, K. Nicolay, J. J. Prompers. Am J Physiol Endocrinol Metab 297: E774-E784, 2009) that was reparameterized for 20°C. Elimination of Pi control via clamping of the mitochondrial Pi concentration at 10 mM reproduced past simulation results of dramatically faster kinetics in CK(-/-) muscle, while inclusion of Pi control qualitatively explained the experimental observations. On this basis, it was concluded that previous studies of the CK-deficient FT muscle phenotype underestimated the contribution of Pi to mitochondrial respiratory control.

An MR-compatible bicycle ergometer for in-magnet whole-body human exercise testing.

An MR-compatible ergometer was developed for in-magnet whole-body human exercise testing. Designed on the basis of conventional mechanically braked bicycle ergometers and constructed from nonferrous materials, the ergometer was implemented on a 1.5-T whole-body MR scanner. A spectrometer interface was constructed using standard scanner hardware, complemented with custom-built parts and software to enable gated data acquisition during exercise. High-quality 31P NMR spectra were reproducibly obtained from the medial head of the quadriceps muscle of the right leg of eight healthy subjects during two-legged high-frequency pedaling (80 revolutions per minute) at three incremental workloads, including maximal. Muscle phosphocreatine content dropped 82%, from 32.2+/-1.0 mM at rest to 5.7+/-1.1 mM at maximal workload (mean+/-standard error; n=8), indicating that the majority of quadriceps motor units were recruited. The cardiovascular load of the exercise was likewise significant, as evidenced by heart rates of 150 (+/-10%) beats per minute, measured immediately afterward. As such, the newly developed MR bicycling exercise equipment offers a powerful new tool for clinical musculoskeletal and cardiovascular MR investigation. The basic design of the ergometer is highly generic and adaptable for application on a wide selection of whole-body MR scanners.

Silencing of glycolysis in muscle: experimental observation and numerical analysis.

The longstanding problem of rapid inactivation of the glycolytic pathway in skeletal muscle after contraction was investigated using (31)P NMR spectroscopy and computational modelling. Accumulation of phosphorylated glycolytic intermediates (hexose monophosphates) during cyclic contraction and subsequent turnover during metabolic recovery was measured in vivo in human quadriceps muscle using dynamic (31)P NMR spectroscopy. The concentration of hexose monophosphates in muscle peaked 40 s into metabolic recovery from maximal contractile work at 6.9 +/- 1.3 mm (mean +/- s.d.; n = 8) and subsequently declined at a rate of 0.009 +/- 0.001 mm s(1). It was next tested whether the current knowledge of the kinetic controls in the glycolytic pathway in muscle integrated in the Lambeth and Kushmerick computational model of skeletal muscle glycolysis explained the experimental data. It was found that the model underestimated the magnitude of deactivation of the glycolytic pathway in resting muscle, resulting in depletion of glycolytic intermediates and substrate for oxidative ATP synthesis. Numerical analysis of the model identified phosphofructokinase and pyruvate kinase as the kinetic control sites involved in deactivation of the glycolytic pathway. Ancillary 100-fold inhibition of both phosphofructokinase and pyruvate kinase was found necessary to predict glycolytic intermediate and ADP concentrations correctly in resting human muscle. Incorporation of this information into the model resulted in highly improved agreement between predicted and measured in vivo dynamics of hexose monophosphates in muscle following contraction. We concluded that silencing of the glycolytic pathway in muscle following contraction is most likely to be mediated by phosphofructokinase and pyruvate kinase inactivation on a time scale of seconds and minutes, respectively, and is necessary to prevent depletion of vital cellular substrates.

Magnitude and control of mitochondrial sensitivity to ADP.

The transduction function for ADP stimulation of mitochondrial ATP synthesis in skeletal muscle was reconstructed in vivo and in silico to investigate the magnitude and origin of mitochondrial sensitivity to cytoplasmic ADP concentration changes. Dynamic in vivo measurements of human leg muscle phosphocreatine (PCr) content during metabolic recovery from contractions were performed by (31)P-NMR spectroscopy. The cytoplasmic ADP concentration ([ADP]) and rate of oxidative ATP synthesis (Jp) at each time point were calculated from creatine kinase equilibrium and the derivative of a monoexponential fit to the PCr recovery data, respectively. Reconstructed [ADP]-Jp relations for individual muscles containing more than 100 data points were kinetically characterized by nonlinear curve fitting yielding an apparent kinetic order and ADP affinity of 1.9 +/- 0.2 and 0.022 +/- 0.003 mM, respectively (means +/- SD; n = 6). Next, in silico [ADP]-Jp relations for skeletal muscle were generated using a computational model of muscle oxidative ATP metabolism whereby model parameters corresponding to mitochondrial enzymes were randomly changed by 50-150% to determine control of mitochondrial ADP sensitivity. The multiparametric sensitivity analysis showed that mitochondrial ADP ultrasensitivity is an emergent property of the integrated mitochondrial enzyme network controlled primarily by kinetic properties of the adenine nucleotide translocator.

Stress related collagen ultrastructure in human aortic valves--implications for tissue engineering.

Understanding the response of tissue structures to mechanical stress is crucial for optimization of mechanical conditioning protocols in the field of heart valve tissue engineering. In heart valve tissue, it is unclear to what extent mechanical loading affects the collagen fibril morphology. To determine if local stress affects the collagen fibril morphology, in terms of fibril diameter, its distribution, and the fibril density, this was investigated in adult native human aortic valve leaflets. Transmission electron microscopy images of collagen fibrils were analyzed at three locations: the commissures, the belly, and the fixed edge of the leaflets. Subsequently, the mechanical behavior of human aortic valves was used in a computational model to predict the stress distribution in the valve leaflet during the diastolic phase of the cardiac cycle. The local stresses at the three locations were related to the collagen fibril morphology. The fibril diameter and density varied significantly between the measured locations, and appeared inversely related. The average fibril diameter increased from the fixed edge, to the belly, and to the commissures of the leaflets, while fibril density decreased. Interestingly, these differences corresponded well with the level of stress at the locations. The presented data showed that large tissue stress is associated with greater average fibril diameter, lower fibril density, and wider fibril size distribution compared with low stress locations in the leaflets. The findings here provide insight in the effect of mechanical loading on the collagen ultrastructure, and are valuable to improve conditioning protocols for tissue engineering.