Inhibition of Eicosanoid Degradation Mitigates Fibrosis of the Heart.

BACKGROUND: Organ fibrosis due to excessive production of extracellular matrix by resident fibroblasts is estimated to contribute to >45% of deaths in the Western world, including those due to cardiovascular diseases such as heart failure. Here, we screened for small molecule inhibitors with a common ability to suppress activation of fibroblasts across organ systems. METHODS: High-content imaging of cultured cardiac, pulmonary, and renal fibroblasts was used to identify nontoxic compounds that blocked induction of markers of activation in response to the profibrotic stimulus, transforming growth factor-ß1. SW033291, which inhibits the eicosanoid-degrading enzyme, 15-hydroxyprostaglandin dehydrogenase, was chosen for follow-up studies with cultured adult rat ventricular fibroblasts and human cardiac fibroblasts (CF), and for evaluation in mouse models of cardiac fibrosis and diastolic dysfunction. Additional mechanistic studies were performed with CFs treated with exogenous eicosanoids. RESULTS: Nine compounds, including SW033291, shared a common ability to suppress transforming growth factor-ß1-mediated activation of cardiac, pulmonary, and renal fibroblasts. SW033291 dose-dependently inhibited transforming growth factor-ß1-induced expression of activation markers (eg, α-smooth muscle actin and periostin) in adult rat ventricular fibroblasts and normal human CFs, and reduced contractile capacity of the cells. Remarkably, the 15-hydroxyprostaglandin dehydrogenase inhibitor also reversed constitutive activation of fibroblasts obtained from explanted hearts from patients with heart failure. SW033291 blocked cardiac fibrosis induced by angiotensin II infusion and ameliorated diastolic dysfunction in an alternative model of systemic hypertension driven by combined uninephrectomy and deoxycorticosterone acetate administration. Mechanistically, SW033291-mediated stimulation of extracellular signal-regulated kinase 1/2 mitogen-activated protein kinase signaling was required for the compound to block CF activation. Of the 12 exogenous eicosanoids that were tested, only 12(S)-hydroxyeicosatetraenoic acid, which signals through the G protein-coupled receptor, GPR31, recapitulated the suppressive effects of SW033291 on CF activation. CONCLUSIONS: Inhibition of degradation of eicosanoids, arachidonic acid-derived fatty acids that signal through G protein-coupled receptors, is a potential therapeutic strategy for suppression of pathological organ fibrosis. In the heart, we propose that 15-hydroxyprostaglandin dehydrogenase inhibition triggers CF-derived autocrine/paracrine signaling by eicosanoids, including 12(S)-hydroxyeicosatetraenoic acid, to stimulate extracellular signal-regulated kinase 1/2 and block conversion of fibroblasts into activated cells that secrete excessive amounts of extracellular matrix and contribute to heart failure pathogenesis.

Disruption of Phosphodiesterase 3A Binding to SERCA2 Increases SERCA2 Activity and Reduces Mortality in Mice With Chronic Heart Failure.

BACKGROUND: Increasing SERCA2 (sarco[endo]-plasmic reticulum Ca2+ ATPase 2) activity is suggested to be beneficial in chronic heart failure, but no selective SERCA2-activating drugs are available. PDE3A (phosphodiesterase 3A) is proposed to be present in the SERCA2 interactome and limit SERCA2 activity. Disruption of PDE3A from SERCA2 might thus be a strategy to develop SERCA2 activators. METHODS: Confocal microscopy, 2-color direct stochastic optical reconstruction microscopy, proximity ligation assays, immunoprecipitations, peptide arrays, and surface plasmon resonance were used to investigate colocalization between SERCA2 and PDE3A in cardiomyocytes, map the SERCA2/PDE3A interaction sites, and optimize disruptor peptides that release PDE3A from SERCA2. Functional experiments assessing the effect of PDE3A-binding to SERCA2 were performed in cardiomyocytes and HEK293 vesicles. The effect of SERCA2/PDE3A disruption by the disruptor peptide OptF (optimized peptide F) on cardiac mortality and function was evaluated during 20 weeks in 2 consecutive randomized, blinded, and controlled preclinical trials in a total of 148 mice injected with recombinant adeno-associated virus 9 (rAAV9)-OptF, rAAV9-control (Ctrl), or PBS, before undergoing aortic banding (AB) or sham surgery and subsequent phenotyping with serial echocardiography, cardiac magnetic resonance imaging, histology, and functional and molecular assays. RESULTS: PDE3A colocalized with SERCA2 in human nonfailing, human failing, and rodent myocardium. Amino acids 277-402 of PDE3A bound directly to amino acids 169-216 within the actuator domain of SERCA2. Disruption of PDE3A from SERCA2 increased SERCA2 activity in normal and failing cardiomyocytes. SERCA2/PDE3A disruptor peptides increased SERCA2 activity also in the presence of protein kinase A inhibitors and in phospholamban-deficient mice, and had no effect in mice with cardiomyocyte-specific inactivation of SERCA2. Cotransfection of PDE3A reduced SERCA2 activity in HEK293 vesicles. Treatment with rAAV9-OptF reduced cardiac mortality compared with rAAV9-Ctrl (hazard ratio, 0.26 [95% CI, 0.11 to 0.63]) and PBS (hazard ratio, 0.28 [95% CI, 0.09 to 0.90]) 20 weeks after AB. Mice injected with rAAV9-OptF had improved contractility and no difference in cardiac remodeling compared with rAAV9-Ctrl after aortic banding. CONCLUSIONS: Our results suggest that PDE3A regulates SERCA2 activity through direct binding, independently of the catalytic activity of PDE3A. Targeting the SERCA2/PDE3A interaction prevented cardiac mortality after AB, most likely by improving cardiac contractility.

Rapid Approximate Subset-Based Spectra Prediction for Electron Ionization-Mass Spectrometry.

Mass spectrometry is a vital tool in the analytical chemist's toolkit, commonly used to identify the presence of known compounds and elucidate unknown chemical structures. All of these applications rely on having previously measured spectra for known substances. Computational methods for predicting mass spectra from chemical structures can be used to augment existing spectral databases with predicted spectra from previously unmeasured molecules. In this paper, we present a method for prediction of electron ionization-mass spectra (EI-MS) of small molecules that combines physically plausible substructure enumeration and deep learning, which we term rapid approximate subset-based spectra prediction (RASSP). The first of our two models, FormulaNet, produces a probability distribution over chemical subformulae to achieve a state-of-the-art forward prediction accuracy of 92.9% weighted (Stein) dot product and database lookup recall (within top 10 ranked spectra) of 98.0% when evaluated against the NIST 2017 Mass Spectral Library. The second model, SubsetNet, produces a probability distribution over vertex subsets of the original molecule graph to achieve similar forward prediction accuracy and superior generalization in the high-resolution, low-data regime. Spectra predicted by our best model improve upon the previous state-of-the-art spectral database lookup error rate by a factor of 2.9×, reducing the lookup error (top 10) from 5.7 to 2.0%. Both models can train on and predict spectral data at arbitrary resolution. Source code and predicted EI-MS spectra for 73.2M small molecules from PubChem will be made freely accessible online.

Prediction of chemical shift in NMR: A review.

Calculation of solution-state NMR parameters, including chemical shift values and scalar coupling constants, is often a crucial step for unambiguous structure assignment. Data-driven (sometimes called empirical) methods leverage databases of known parameter values to estimate parameters for unknown or novel molecules. This is in contrast to popular ab initio techniques that use detailed quantum computational chemistry calculations to arrive at parameter estimates. Data-driven methods have the potential to be considerably faster than ab inito techniques and have been the subject of renewed interest over the past decade with the rise of high-quality databases of NMR parameters and novel machine learning methods. Here, we review these methods, their strengths and pitfalls, and the databases they are built on.

Could a Neuroscientist Understand a Microprocessor?

There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. Additionally, we argue for scientists using complex non-linear dynamical systems with known ground truth, such as the microprocessor as a validation platform for time-series and structure discovery methods.

3D imaging in volumetric scattering media using phase-space measurements.

We demonstrate the use of phase-space imaging for 3D localization of multiple point sources inside scattering material. The effect of scattering is to spread angular (spatial frequency) information, which can be measured by phase space imaging. We derive a multi-slice forward model for homogenous volumetric scattering, then develop a reconstruction algorithm that exploits sparsity in order to further constrain the problem. By using 4D measurements for 3D reconstruction, the dimensionality mismatch provides significant robustness to multiple scattering, with either static or dynamic diffusers. Experimentally, our high-resolution 4D phase-space data is collected by a spectrogram setup, with results successfully recovering the 3D positions of multiple LEDs embedded in turbid scattering media.

Rapid prediction of full spin systems using uncertainty-aware machine learning.

Accurate simulation of solution NMR spectra requires knowledge of all chemical shift and scalar coupling parameters, traditionally accomplished by heuristic-based techniques or ab initio computational chemistry methods. Here we present a novel machine learning technique which combines uncertainty-aware deep learning with rapid estimates of conformational geometries to generate Full Spin System Predictions with UnCertainty (FullSSPrUCe). We improve on previous state of the art in accuracy on chemical shift values, predicting protons to within 0.209 ppm and carbons to within 1.213 ppm. Further, we are able to predict all scalar coupling values, unlike previous GNN models, achieving 3JHH accuracies between 0.838 Hz and 1.392 Hz on small experimental datasets. Our uncertainty quantification shows a strong, useful correlation with accuracy, with the most confident predictions having significantly reduced error, including our top-80% most confident proton shift predictions having an average error of only 0.140 ppm. We also properly handle stereoisomerism and intelligently augment experimental data with ab initio data through disagreement regularization to account for deficiencies in training data.

An extensive ß1-adrenergic receptor gene signaling network regulates molecular remodeling in dilated cardiomyopathies.

We investigated the extent, biologic characterization, phenotypic specificity, and possible regulation of a ß1-adrenergic receptor-linked (ß1-AR-linked) gene signaling network (ß1-GSN) involved in left ventricular (LV) eccentric pathologic remodeling. A 430-member ß1-GSN was identified by mRNA expression in transgenic mice overexpressing human ß1-ARs or from literature curation, which exhibited opposite directional behavior in interventricular septum endomyocardial biopsies taken from patients with beta-blocker-treated, reverse remodeled dilated cardiomyopathies. With reverse remodeling, the major biologic categories and percentage of the dominant directional change were as follows: metabolic (19.3%, 81% upregulated); gene regulation (14.9%, 78% upregulated); extracellular matrix/fibrosis (9.1%, 92% downregulated); and cell homeostasis (13.3%, 60% upregulated). Regarding the comparison of ß1-GSN categories with expression from 19,243 nonnetwork genes, phenotypic selection for major ß1-GSN categories was exhibited for LV end systolic volume (contractility measure), ejection fraction (remodeling index), and pulmonary wedge pressure (wall tension surrogate), beginning at 3 months and persisting to study completion at 12 months. In addition, 121 lncRNAs were identified as possibly involved in cis-acting regulation of ß1-GSN members. We conclude that an extensive 430-member gene network downstream from the ß1-AR is involved in pathologic ventricular remodeling, with metabolic genes as the most prevalent category.

Myocardial Injury and Altered Gene Expression Associated With SARS-CoV-2 Infection or mRNA Vaccination.

SARS CoV-2 enters host cells via its Spike protein moiety binding to the essential cardiac enzyme angiotensin-converting enzyme (ACE) 2, followed by internalization. COVID-19 mRNA vaccines are RNA sequences that are translated into Spike protein, which follows the same ACE2-binding route as the intact virion. In model systems, isolated Spike protein can produce cell damage and altered gene expression, and myocardial injury or myocarditis can occur during COVID-19 or after mRNA vaccination. We investigated 7 COVID-19 and 6 post-mRNA vaccination patients with myocardial injury and found nearly identical alterations in gene expression that would predispose to inflammation, coagulopathy, and myocardial dysfunction.

Rapid prediction of NMR spectral properties with quantified uncertainty.

Accurate calculation of specific spectral properties for NMR is an important step for molecular structure elucidation. Here we report the development of a novel machine learning technique for accurately predicting chemical shifts of both [Formula: see text]   and [Formula: see text] nuclei which exceeds DFT-accessible accuracy for [Formula: see text] and [Formula: see text] for a subset of nuclei, while being orders of magnitude more performant. Our method produces estimates of uncertainty, allowing for robust and confident predictions, and suggests future avenues for improved performance.

Automatic discovery of cell types and microcircuitry from neural connectomics.

Neural connectomics has begun producing massive amounts of data, necessitating new analysis methods to discover the biological and computational structure. It has long been assumed that discovering neuron types and their relation to microcircuitry is crucial to understanding neural function. Here we developed a non-parametric Bayesian technique that identifies neuron types and microcircuitry patterns in connectomics data. It combines the information traditionally used by biologists in a principled and probabilistically coherent manner, including connectivity, cell body location, and the spatial distribution of synapses. We show that the approach recovers known neuron types in the retina and enables predictions of connectivity, better than simpler algorithms. It also can reveal interesting structure in the nervous system of Caenorhabditis elegans and an old man-made microprocessor. Our approach extracts structural meaning from connectomics, enabling new approaches of automatically deriving anatomical insights from these emerging datasets.