Bifunctional enzyme provides absolute concentration robustness in multisite covalent modification networks.

Biochemical covalent modification networks exhibit a remarkable suite of steady state and dynamical properties such as multistationarity, oscillations, ultrasensitivity and absolute concentration robustness. This paper focuses on conditions required for a network of this type to have a species with absolute concentration robustness. We find that the robustness in a substrate is endowed by its interaction with a bifunctional enzyme, which is an enzyme that has different roles when isolated versus when bound as a substrate-enzyme complex. When isolated, the bifunctional enzyme promotes production of more molecules of the robust species while when bound, the same enzyme facilitates degradation of the robust species. These dual actions produce robustness in the large class of covalent modification networks. For each network of this type, we find the network conditions for the presence of robustness, the species that has robustness, and its robustness value. The unified approach of simultaneously analyzing a large class of networks for a single property, i.e. absolute concentration robustness, reveals the underlying mechanism of the action of bifunctional enzyme while simultaneously providing a precise mathematical description of bifunctionality.

Impact of resource distributions on the competition of species in stream environment.

Our earlier work in Nguyen et al. (Maximizing metapopulation growth rate and biomass in stream networks. arXiv preprint arXiv:2306.05555 , 2023) shows that concentrating resources on the upstream end tends to maximize the total biomass in a metapopulation model for a stream species. In this paper, we continue our research direction by further considering a Lotka-Volterra competition patch model for two stream species. We show that the species whose resource allocations maximize the total biomass has the competitive advantage.

Clinical effects of physiologic lesion testing in influencing treatment strategy for multi-vessel coronary artery disease.

Background: The application of fractional flow reserve (FFR) and instantaneous wave-free ratio (iFR) in multivessel coronary artery disease (CAD) patients has not been definitively explored. We herein assessed how treatment strategies were decided based on FFR/iFR values in vessels selected clinically. Specifically, we sought to determine whether treatment selection was based on whether the vessel tested was the clinical target stenosis. Methods: 270 consecutive patients with angiographically determined multivessel disease who underwent FFR/iFR testing were included. Patients were classified initially based on their angiographic findings, then re-evaluated from FFR/iFR results (normal or abnormal). Tested lesions were classified into target or non-target lesions based on clinical and non-invasive evaluations. Results: Abnormal FFR/iFR values were demonstrated in 51.9 % of patients, in whom 51.4 % received coronary stenting (PCI) and 44.3 % had bypass surgery (CABG). With two-vessel CAD patients, medical therapy was preferred when the target lesion was normal (72.6 %), while PCI was preferred when it was abnormal (78.4 %). In non-target lesions, PCI was preferred regardless of FFR/iFR results (78.0 %). With three-vessel CAD patients, CABG was preferred when the target lesion was abnormal (68.5 %), and there was no difference in the selected modality when it was normal. Furthermore, the incidence of tested lesions was higher in the left anterior descending (LAD) compared to other coronary arteries, and two-vessel CAD patients with LAD stenoses were more frequently treated by PCI. Conclusion: The use of invasive physiologic testing in multivessel CAD patients may alter the preferred treatment strategy, leading to an overall increase in PCI selection.

Nucleoporin93 limits Yap activity to prevent endothelial cell senescence.

As the innermost lining of the vasculature, endothelial cells (ECs) are constantly subjected to systemic inflammation and particularly vulnerable to aging. Endothelial health is hence vital to prevent age-related vascular disease. Healthy ECs rely on the proper localization of transcription factors via nuclear pore complexes (NPCs) to govern cellular behavior. Emerging studies report NPC degradation with natural aging, suggesting impaired nucleocytoplasmic transport in age-associated EC dysfunction. We herein identify nucleoporin93 (Nup93), a crucial structural NPC protein, as an indispensable player in vascular protection. Endothelial Nup93 protein levels are significantly reduced in the vasculature of aged mice, paralleling observations of Nup93 loss when using in vitro models of EC senescence. The loss of Nup93 in human ECs induces cell senescence and promotes the expression of inflammatory adhesion molecules, where restoring Nup93 protein in senescent ECs reverses features of endothelial aging. Mechanistically, we find that both senescence and loss of Nup93 impair endothelial NPC transport, leading to nuclear accumulation of Yap and downstream inflammation. Pharmacological studies indicate Yap hyperactivation as the primary consequence of senescence and Nup93 loss in ECs. Collectively, our findings indicate that the maintenance of endothelial Nup93 is a key determinant of EC health, where aging targets endothelial Nup93 levels to impair NPC function as a novel mechanism of EC senescence and vascular aging.

Endothelial nitric oxide synthase (eNOS) S1176 phosphorylation status governs atherosclerotic lesion formation.

Objective: We have previously demonstrated the in vivo importance of the Akt-eNOS substrate-kinase relationship, as defective postnatal angiogenesis characteristic of global Akt1-null mice is rescued when bred to 'gain-of-function' eNOS S1176D mutant mice. While multiple studies support the vascular protective role of endothelial NO generation, the causal role of Akt1-dependent eNOS S1176 phosphorylation during atherosclerotic plaque formation is not yet clear. Approach and results: We herein bred congenic 'loss-of-function' eNOS S1176A and 'gain-of-function' eNOS S1176D mutant mice to the exacerbated atherogenic Akt1-/-; ApoE-/- double knockout mice to definitively test the importance of Akt-mediated eNOS S1176 phosphorylation during atherogenesis. We find that a single amino acid substitution at the eNOS S1176 phosphorylation site yields divergent effects on atherosclerotic plaque formation, as an eNOS phospho-mimic aspartate (D) substitution at S1176 leads to favorable lipid profiles and decreased indices of atherosclerosis, even when on a proatherogenic Akt1 global deletion background. Conversely, mice harboring an unphosphorylatable mutation to alanine (S1176A) result in increased plasma lipids, increased lesion formation and cellular apoptosis, phenocopying the physiological consequence of eNOS deletion and/or impaired enzyme function. Furthermore, gene expression analyses of whole aortas indicate a combinatorial detriment from NO deficiency and Western Diet challenge, as 'loss-of-function' eNOS S1176A mice on a Western Diet present a unique expression pattern indicative of augmented T-cell activity when compared to eNOS S1176D mice. Conclusions: By using genetic epistasis approaches, we conclusively demonstrate that Akt-mediated eNOS S1176 phosphorylation and subsequent eNOS activation remains to be the most physiologically relevant method of NO production to promote athero-protective effects.

Predicting opioid use disorder before and after the opioid prescribing peak in the United States: A machine learning tool using electronic healthcare records.

Existing predictive models of opioid use disorder (OUD) may change as the rate of opioid prescribing decreases. Using Veterans Administration's EHR data, we developed machine-learning predictive models of new OUD diagnoses and ranked the importance of patient features based on their ability to predict a new OUD diagnosis in 2000-2012 and 2013-2021. Using patient characteristics, the three separate machine learning techniques were comparable in predicting OUD, achieving an accuracy of >80%. Using the random forest classifier, opioid prescription features such as early refills and length of prescription consistently ranked among the top five factors that predict new OUD. Younger age was positively associated with new OUD, and older age inversely associated with new OUD. Age stratification revealed prior substance abuse and alcohol dependency as more impactful in predicting OUD for younger patients. There was no significant difference in the set of factors associated with new OUD in 2000-2012 compared to 2013-2021. Characteristics of opioid prescriptions are the most impactful variables that predict new OUD both before and after the peak in opioid prescribing rates. Predictive models should be tailored to age groups. Further research is warranted to determine if machine learning models perform better when tailored to other patient subgroups.

Nucleoporin93 (Nup93) Limits Yap Activity to Prevent Endothelial Cell Senescence.

Endothelial cells (ECs) form the innermost lining of the vasculature and serve a pivotal role in preventing age-related vascular disease. Endothelial health relies on the proper nucleocytoplasmic shuttling of transcription factors via nuclear pore complexes (NPCs). Emerging studies report NPC degradation with natural aging, suggesting impaired nucleocytoplasmic transport in age-related EC dysfunction. We herein identify nucleoporin93 (Nup93), a crucial structural NPC protein, as an indispensable player for vascular protection. Endothelial Nup93 protein levels are significantly reduced in the vasculature of aged mice, paralleling observations of Nup93 loss when using in vitro models of endothelial aging. Mechanistically, we find that loss of Nup93 impairs NPC transport, leading to the nuclear accumulation of Yap and downstream inflammation. Collectively, our findings indicate maintenance of endothelial Nup93 as a key determinant of EC health, where aging targets endothelial Nup93 levels to impair NPC function as a novel mechanism for EC senescence and vascular aging.

Deep learning generates synthetic cancer histology for explainability and education.

Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology.

Results on stochastic reaction networks with non-mass action kinetics.

In 2010, Anderson, Craciun, and Kurtz showed that if a deterministically modeled reaction network is complex balanced, then the associated stochastic model admits a stationary distribution that is a product of Poissons [1]. That work spurred a number of followup analyses. In 2015, Anderson, Craciun, Gopalkrishnan, and Wiuf considered a particular scaling limit of the stationary distribution detailed in [1], and proved it is a well known Lyapunov function [2]. In 2016, Cappelletti and Wiuf showed the converse of the main result in [1]: if a reaction network with stochastic mass action kinetics admits a stationary distribution that is a product of Poissons, then the deterministic model is complex balanced [3]. In 2017, Anderson, Koyama, Cappelletti, and Kurtz showed that the mass action models considered in [1] are non-explosive (so the stationary distribution characterizes the limiting behavior). In this paper, we generalize each of the three followup results detailed above to the case when the stochastic model has a particular form of non-mass action kinetics.