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General General

Right Ventricular Sarcomere Contractile Depression and the Role of Thick Filament Activation in Human Heart Failure with Pulmonary Hypertension.

In bioRxiv : the preprint server for biology

RATIONALE : Right ventricular (RV) contractile dysfunction commonly occurs and worsens outcomes in heart failure patients with reduced ejection fraction and pulmonary hypertension (HFrEF-PH). However, such dysfunction often goes undetected by standard clinical RV indices, raising concerns that they may not reflect aspects of underlying myocyte dysfunction.

OBJECTIVE : To determine components of myocyte contractile depression in HFrEF-PH, identify those reflected by clinical RV indices, and elucidate their underlying biophysical mechanisms.

METHODS AND RESULTS : Resting, calcium- and load-dependent mechanics were measured in permeabilized RV cardiomyocytes isolated from explanted hearts from 23 HFrEF-PH patients undergoing cardiac transplantation and 9 organ-donor controls. Unsupervised machine learning using myocyte mechanical data with the highest variance yielded two HFrEF-PH subgroups that in turn mapped to patients with depressed (RVd) or compensated (RVc) clinical RV function. This correspondence was driven by reduced calcium-activated isometric tension in RVd, while surprisingly, many other major myocyte contractile measures including peak power, maximum unloaded shortening velocity, and myocyte active stiffness were similarly depressed in both groups. Similar results were obtained when subgroups were first defined by clinical indices, and then myocyte mechanical properties in each group compared. To test the role of thick-filament defects, myofibrillar structure was assessed by X-ray diffraction of muscle fibers. This revealed more myosin heads associated with the thick filament backbone in RVd but not RVc, as compared to controls. This corresponded to reduced myosin ATP turnover in RVd myocytes, indicating less myosin in a cross-bridge ready disordered-relaxed (DRX) state. Altering DRX proportion (%DRX) affected peak calcium-activated tension in the patient groups differently, depending on their basal %DRX, highlighting potential roles for precision-guided therapeutics. Lastly, increasing myocyte preload (sarcomere length) increased %DRX 1.5-fold in controls but only 1.2-fold in both HFrEF-PH groups, revealing a novel mechanism for reduced myocyte active stiffness and by extension Frank-Starling reserve in human HF.

CONCLUSIONS : While there are multiple RV myocyte contractile deficits In HFrEF-PH, clinical indices primarily detect reduced isometric calcium-stimulated force related to deficits in basal and recruitable %DRX myosin. Our results support use of therapies to increase %DRX and enhance length-dependent recruitment of DRX myosin heads in such patients.

Jani Vivek, Aslam M Imran, Fenwick Axel J, Ma Weikang, Gong Henry, Milburn Gregory, Nissen Devin, Salazar Ilton Cubero, Hanselman Olivia, Mukherjee Monica, Halushka Marc K, Margulies Kenneth B, Campbell Kenneth, Irving Thomas C, Kass David A, Hsu Steven

2023-Mar-12

General General

MAHOMES II: A webserver for predicting if a metal binding site is enzymatic.

In bioRxiv : the preprint server for biology

UNLABELLED : Recent advances have enabled high-quality computationally generated structures for proteins with no solved crystal structures. However, protein function data remains largely limited to experimental methods and homology mapping. Since structure determines function, it is natural that methods capable of using computationally generated structures for functional annotations need to be advanced. Our laboratory recently developed a method to distinguish between metalloenzyme and non-enzyme sites. Here we report improvements to this method by upgrading our physicochemical features to alleviate the need for structures with sub-angstrom precision and using machine learning to reduce training data labeling error. Our improved classifier identifies protein bound metal sites as enzymatic or non-enzymatic with 94% precision and 92% recall. We demonstrate that both adjustments increased predictive performance and reliability on sites with sub-angstrom variations. We constructed a set of predicted metalloprotein structures with no solved crystal structures and no detectable homology to our training data. Our model had an accuracy of 90 - 97.5% depending on the quality of the predicted structures included in our test. Finally, we found the physicochemical trends that drove this model's successful performance were local protein density, second shell ionizable residue burial, and the pocket's accessibility to the site. We anticipate that our model's ability to correctly identify catalytic metal sites could enable identification of new enzymatic mechanisms and improve de novo metalloenzyme design success rates.

SIGNIFICANCE STATEMENT : Identification of enzyme active sites on proteins with unsolved crystallographic structures can accelerate discovery of novel biochemical reactions, which can impact healthcare, industrial processes, and environmental remediation. Our lab has developed an ML tool for predicting sites on computationally generated protein structures as enzymatic and non-enzymatic. We have made our tool available on a webserver, allowing the scientific community to rapidly search previously unknown protein function space.

Feehan Ryan, Copeland Matthew, Franklin Meghan W, Slusky Joanna S G

2023-Mar-12

General General

Structure-Kinetics Relationships of Opioids from Metadynamics and Machine Learning.

In bioRxiv : the preprint server for biology

The nation's opioid overdose deaths reached an all-time high in 2021. The majority of deaths are due to synthetic opioids represented by fentanyl. Naloxone, which is an FDA-approved reversal agent, antagonizes opioids through competitive binding at the mu-opioid receptor (mOR). Thus, knowledge of opioid's residence time is important for assessing the effectiveness of naloxone. Here we estimated the residence times of 15 fentanyl and 4 morphine analogs using metadynamics, and compared them with the most recent measurement of the opioid kinetic, dissociation, and naloxone inhibitory constants (Mann, Li et al, Clin. Pharmacol. Therapeut. 2022). Importantly, the microscopic simulations offered a glimpse at the common binding mechanism and molecular determinants of dissociation kinetics for fentanyl analogs. The insights inspired us to develop a machine learning (ML) approach to analyze the kinetic impact of fentanyl's substituents based on the interactions with mOR residues. This proof-of-concept approach is general; for example, it may be used to tune ligand residence times in computer-aided drug discovery.

Mahinthichaichan Paween, Liu Ruibin, Vo Quynh N, Ellis Christopher R, Stavitskaya Lidiya, Shen Jana

2023-Mar-07

Cardiology Cardiology

Clinical and genetic associations of deep learning-derived cardiac magnetic resonance-based left ventricular mass.

In Nature communications ; h5-index 260.0

Left ventricular mass is a risk marker for cardiovascular events, and may indicate an underlying cardiomyopathy. Cardiac magnetic resonance is the gold-standard for left ventricular mass estimation, but is challenging to obtain at scale. Here, we use deep learning to enable genome-wide association study of cardiac magnetic resonance-derived left ventricular mass indexed to body surface area within 43,230 UK Biobank participants. We identify 12 genome-wide associations (1 known at TTN and 11 novel for left ventricular mass), implicating genes previously associated with cardiac contractility and cardiomyopathy. Cardiac magnetic resonance-derived indexed left ventricular mass is associated with incident dilated and hypertrophic cardiomyopathies, and implantable cardioverter-defibrillator implant. An indexed left ventricular mass polygenic risk score ≥90th percentile is also associated with incident implantable cardioverter-defibrillator implant in separate UK Biobank (hazard ratio 1.22, 95% CI 1.05-1.44) and Mass General Brigham (hazard ratio 1.75, 95% CI 1.12-2.74) samples. Here, we perform a genome-wide association study of cardiac magnetic resonance-derived indexed left ventricular mass to identify 11 novel variants and demonstrate that cardiac magnetic resonance-derived and genetically predicted indexed left ventricular mass are associated with incident cardiomyopathy.

Khurshid Shaan, Lazarte Julieta, Pirruccello James P, Weng Lu-Chen, Choi Seung Hoan, Hall Amelia W, Wang Xin, Friedman Samuel F, Nauffal Victor, Biddinger Kiran J, Aragam Krishna G, Batra Puneet, Ho Jennifer E, Philippakis Anthony A, Ellinor Patrick T, Lubitz Steven A

2023-Mar-21

General General

A ventilation early warning system (VEWS) for diaphanous workspaces considering COVID-19 and future pandemics scenarios.

In Heliyon

The COVID-19 pandemic has generated new needs due to the associated health risks and, more specifically, its rapid infection rate. Prevention measures to avoid contagions in indoor spaces, especially in office and public buildings (e.g., hospitals, public administration, educational centres, etc.), have led to the need for adequate ventilation to dilute the possible concentration of the virus. This article presents our contribution to this new challenge, namely the Ventilation Early Warning System (VEWS) which has aims to adapt the operation of the current Heating, Ventilating and Air Conditioning (HVAC) systems to the ventilation needs of diaphanous workspaces, based on a Smart Campus Digital Twin (SCDT) framework approach, while maintaining sustainability. Different technologies such as the Internet of Things (IoT), Building Information Modelling (BIM) and Artificial Intelligence (AI) algorithms are combined to collect and integrate monitoring data (historical records, real-time information, and location-related patterns) to carry out forecasting simulations in this digital twin. The generated outputs serve to assist facility managers in their building governance, considering the appropriate application of health measures to reduce the risk of coronavirus contagion in combination with sustainability criteria. The article also provides the results of the implementation of the VEWS in a university workspace as a case study. Its application has made it possible to detect and warn of inadequate ventilation situations for the daily flow of people in the different controlled zones.

Costa Gonçal, Arroyo Oriol, Rueda Pablo, Briones Alan

2023-Mar

BIM, Building digital twin, COVID-19, Facilities management, IoT, Simulation, Smart building

General General

Fault Prognosis of Turbofan Engines: Eventual Failure Prediction and Remaining Useful Life Estimation

ArXiv Preprint

In the era of industrial big data, prognostics and health management is essential to improve the prediction of future failures to minimize inventory, maintenance, and human costs. Used for the 2021 PHM Data Challenge, the new Commercial Modular Aero-Propulsion System Simulation dataset from NASA is an open-source benchmark containing simulated turbofan engine units flown under realistic flight conditions. Deep learning approaches implemented previously for this application attempt to predict the remaining useful life of the engine units, but have not utilized labeled failure mode information, impeding practical usage and explainability. To address these limitations, a new prognostics approach is formulated with a customized loss function to simultaneously predict the current health state, the eventual failing component(s), and the remaining useful life. The proposed method incorporates principal component analysis to orthogonalize statistical time-domain features, which are inputs into supervised regressors such as random forests, extreme random forests, XGBoost, and artificial neural networks. The highest performing algorithm, ANN-Flux, achieves AUROC and AUPR scores exceeding 0.95 for each classification. In addition, ANN-Flux reduces the remaining useful life RMSE by 38% for the same test split of the dataset compared to past work, with significantly less computational cost.

Joseph Cohen, Xun Huan, Jun Ni

2023-03-23