CPMDS Highlights
Clancy to present at the Division of Cardiovascular Sciences (DCVS) Strategic Vision Webinar Series
Colleen Clancy, Ph.D., professor and director of Precision Medicine and Data Sciences, has been invited to present in a virtual seminar series, “Strategic Vision Webinar Series”, hosted by the Division of Cardiovascular Sciences (DCVS). Clancy will present at the session dedicated to the Enhancing resilience. The topic of the presentation is "Revolutionizing Precision Medicine with Digital Twins: Prediction Through Computational Data Integration".
Clancy Presented at the Sixth Annual Cedar Sinai Precision Health Research Symposium
On November 6, 2024, Director Clancy joined other esteemed scientists to present at the sixth annual Cedar Sinai Precision Health Research Symposium. This year’s topic was focused on “Integrative Multi Mix and Translational Research for Precision Health”. It was a lively session with thrilling presentations about the future of precision medicine from Professors Steve Finkbeiner from The Gladstone and UCSF and Megan Hitchins from Cedars-Sinai. Spirited discussion during the moderated session.
Mechanisms of Chemical Atrial Defibrillation by Flecainide and Ibutilide
Abstract
Pharmacological approaches to atrial defibrillation offer noninvasive, low-risk, and cost-effective alternatives to ablation, promoting health equity. This study investigates how ibutilide-mediated action potential prolongation enhances use-dependent effects of flecainide by shortening the diastolic interval, reducing drug unbinding, and suppressing atrial excitability to terminate re-entrant arrhythmia. Using computational modeling, we predict optimal sodium- and potassium-channel blocker combinations for chemical atrial defibrillation. The results suggest that acute flecainide-ibutilide application is a viable drug-repurposing strategy. Additionally, we assess the safety pharmacology of this combination on ventricular electrophysiology.
Gonzalo Hernandez-Hernandez Recognized at UC Davis Chancellor’s Postdoctoral Reception
Gonzalo Hernandez-Hernandez has officially begun his UC Davis Chancellor’s Postdoctoral Fellowship through the University of California President’s Postdoctoral Fellowship Program. The fellowship provides scholars with exceptional professional development and faculty mentorship opportunities. Aimed at individuals with unique perspectives shaped by non-traditional educational backgrounds or a deep understanding of the experiences of historically underrepresented groups in higher education, the program works to enhance diversity within the academic community at the University of California.
Hernandez-Hernandez was recognized for his contributions to promoting diversity, including research focused on increasing equitable access in fields where women and minorities remain underrepresented. His project, titled “Predicting the Impact of Sex-Specific Differences in Vascular Smooth Muscle Cells in Mechanisms of Hypertension,” explores the development of tailored antihypertensive treatments based on sex. Guided by mentors Professor Fernando Santana and Professor Colleen Clancy, his work aims to advance precision medicine in hypertension treatment.
Gonzalo was honored during the Chancellor’s Postdoctoral Fellow reception, which kicked off Postdoctoral Appreciation Week, alongside his mentor, Professor Santana, and Provost Mary Croughan.
An artificial intelligence accelerated virtual screening platform for drug discovery
Abstract
Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding pose and binding affinity predicted by computational docking. Here we develop a highly accurate structure-based virtual screen method, RosettaVS, for predicting docking poses and binding affinities. Our approach outperforms other state-of-the-art methods on a wide range of benchmarks, partially due to our ability to model receptor flexibility. We incorporate this into a new open-source artificial intelligence accelerated virtual screening platform for drug discovery. Using this platform, we screen multi-billion compound libraries against two unrelated targets, a ubiquitin ligase target KLHDC2 and the human voltage-gated sodium channel NaV1.7. For both targets, we discover hit compounds, including seven hits (14% hit rate) to KLHDC2 and four hits (44% hit rate) to NaV1.7, all with single digit micromolar binding affinities. Screening in both cases is completed in less than seven days. Finally, a high resolution X-ray crystallographic structure validates the predicted docking pose for the KLHDC2 ligand complex, demonstrating the effectiveness of our method in lead discovery.
Colleen Clancy Presented the State of the Art Lecture at the ISHR-NAS Meeting
Colleen Clancy, Ph.D., professor and director of Precision Medicine and Data Sciences, presented the state of the art lecture about her recent work on computational digital twins at the International Society for Heart Research (ISHR) - NAS meeting.
Fred Meyers Begins Term as Chair of City Year Sacramento Board
Fred Meyers, M.D., Distinguished Professor Emeritus of Internal Medicine/Hematology-Oncology at UC Davis School of Medicine, has begun his term as Board Chair for City Year Sacramento (CYSac). CYSac, an AmeriCorps education non-profit, recruits young adults for a year of service as Student Success Coaches (SSCs) in under-resourced schools within the Oak Park and South Sacramento areas of the Sacramento City Unified School District.
"My passion for City Year Sacramento stems from its mission to embrace education as the surest path to good health for individuals and our communities," said Fred Meyers.
Structural Modeling of Ion Channels Using AlphaFold2, RoseTTAFold2, and ESMFold
Abstract
Ion channels play key roles in human physiology and are important targets in drug discovery. The atomic-scale structures of ion channels provide invaluable insights into a fundamental understanding of the molecular mechanisms of channel gating and modulation. Recent breakthroughs in deep learning-based computational methods, such as AlphaFold, RoseTTAFold, and ESMFold have transformed research in protein structure prediction and design. We review the application of AlphaFold, RoseTTAFold, and ESMFold to structural modeling of ion channels using representative voltage-gated ion channels, including human voltage-gated sodium (NaV) channel - NaV1.8, human voltage-gated calcium (CaV) channel – CaV1.1, and human voltage-gated potassium (KV) channel – KV1.3. We compared AlphaFold, RoseTTAFold, and ESMFold structural models of NaV1.8, CaV1.1, and KV1.3 with corresponding cryo-EM structures to assess details of their similarities and differences. Our findings shed light on the strengths and limitations of the current state-of-the-art deep learning-based computational methods for modeling ion channel structures, offering valuable insights to guide their future applications for ion channel research.
Toward High-resolution Modeling of Small Molecule–ion Channel Interactions
Abstract
Ion channels are critical drug targets for a range of pathologies, such as epilepsy, pain, itch, autoimmunity, and cardiac arrhythmias. To develop effective and safe therapeutics, it is necessary to design small molecules with high potency and selectivity for specific ion channel subtypes. There has been increasing implementation of structure-guided drug design for the development of small molecules targeting ion channels. We evaluated the performance of two RosettaLigand docking methods, RosettaLigand and GALigandDock, on the structures of known ligand–cation channel complexes. Ligands were docked to voltage-gated sodium (NaV), voltage-gated calcium (CaV), and transient receptor potential vanilloid (TRPV) channel families. For each test case, RosettaLigand and GALigandDock methods frequently sampled a ligand-binding pose within a root mean square deviation (RMSD) of 1–2 Å relative to the experimental ligand coordinates. However, RosettaLigand and GALigandDock scoring functions cannot consistently identify experimental ligand coordinates as top-scoring models. Our study reveals that the proper scoring criteria for RosettaLigand and GALigandDock modeling of ligand–ion channel complexes should be assessed on a case-by-case basis using sufficient ligand and receptor interface sampling, knowledge about state-specific interactions of the ion channel, and inherent receptor site flexibility that could influence ligand binding.
Advances in Induced Pluripotent Stem Cell-derived Cardiac Myocytes: Technological Breakthroughs, Key Discoveries and New Applications
Abstract
A transformation is underway in precision and patient-specific medicine. Rapid progress has been enabled by multiple new technologies including induced pluripotent stem cell-derived cardiac myocytes (iPSC-CMs). Here, we delve into these advancements and their future promise, focusing on the efficiency of reprogramming techniques, the fidelity of differentiation into the cardiac lineage, the functional characterization of the resulting cardiac myocytes, and the many applications of in silico models to understand general and patient-specific mechanisms controlling excitation–contraction coupling in health and disease. Furthermore, we explore the current and potential applications of iPSC-CMs in both research and clinical settings, underscoring the far-reaching implications of this rapidly evolving field.
Reaching Across the Causeway Award 2024
Co-PIs: Kermit L. Carraway, James M. Angelastro
Abstract
The non-canonical (β-catenin-independent) Wnt/planar cell polarity (Wnt/PCP) pathway plays central roles in embryonic development by mediating cellular motility events required for proper tissue structuring. Accumulating evidence suggests that the Wnt/PCP pathway is exploited by some solid tumors to promote their growth and invasiveness. Glioblastoma (GBM) is the most common and lethal form of brain cancer in humans. Efforts to thwart GBM malignancy with therapeutic agents targeting presumed oncogenic drivers have not been successful, suggesting that unknown or unexplored pathways might additionally contribute to malignancy. We have found that key components of the Wnt/PCP pathway are overexpressed in GBM relative to normal brain tissue independent of GBM subtype, and overexpression correlates with poorer patient outcomes. Moreover, we have observed that suppression of Fzd7 and Vangl1, core components of the Wnt/PCP signaling complex, reduce GBM cell growth and invasiveness in vitro and in vivo. Together these observations suggest that aberrant Wnt/PCP engagement may contribute to the malignant properties of a wide range of GBM patients’ tumors. The driving hypothesis for the project is that Wnt/PCP signaling contributes to the malignant properties of GBM largely independent of specific subtypes and suspected molecular drivers, and that mechanistic vulnerabilities may be identified that could ultimately aid in the development of novel therapeutic strategies and agents. Our overarching aims for the project are to validate Wnt/PCP as a viable target for multiple GBM subtypes using a series of patient-derived xenograft (PDX) models, and to develop a more thorough mechanistic understanding of the interactions among pathway components. In Aim 1 we will assess the Wnt/PCP dependence of five patient-derived xenograft models of primary GBM from the Mayo Brain Tumor PDX National Resource that represent an array of oncogenic drivers using cellular proliferation and invasiveness assays. In addition, we will determine the impact of Wnt/PCP component ablation on the growth of orthotopically xenografted tumors derived from the five genetically diverse PDX lines in NOD SCID mice, as well as in tumors from a retrovirus-induced model of GBM in immune-intact mice. We anticipate that these studies will begin to highlight the GBM subtype-agnostic nature of Wnt/PCP dependence. In Aim 2, we will unravel the molecular mechanisms underlying Wnt/PCP-induced motility/invasiveness and proliferation. Specifically, we will assess the role of a newly identified Vangl1/Fzd7 complex in signaling to the actin cytoskeleton, and examine the mechanism of Wnt/PCP-induced cross-regulation of the Akt kinase in mediating GBM cell proliferation. The successful completion of the proposed studies will validate Wnt/PCP as target for multiple GBM subtypes, and will develop a mechanistic platform upon which Wnt-PCP targeting therapeutics may ultimately be developed.
Structure-Activity Relationship Study Identifies a Novel Lipophilic Amiloride Derivative that Efficiently Kills Chemoresistant Breast Cancer Cells
Abstract
Derivatives of the potassium-sparing diuretic amiloride are preferentially cytotoxic toward tumor cells relative to normal cells, and have the capacity to target tumor cell populations resistant to currently employed therapeutic agents. However, a major barrier to clinical translation of the amilorides is their modest cytotoxic potency, with estimated IC50 values in the high micromolar range. Here we report the synthesis of ten novel amiloride derivatives and the characterization of their cytotoxic potency toward MCF7 (ER/PR-positive), SKBR3 (HER2-positive) and MDA-MB-231 (triple negative) cell line models of breast cancer. Comparisons of derivative structure with cytotoxic potency toward these cell lines underscore the importance of an intact guanidine group, and uncover a strong link between drug-induced cytotoxicity and drug lipophilicity. We demonstrate that our most potent derivative called LLC1 is preferentially cytotoxic toward mouse mammary tumor over normal epithelial organoids, acts in the single digit micromolar range on breast cancer cell line models representing all major subtypes, acts on cell lines that exhibit both transient and sustained resistance to chemotherapeutic agents, but exhibits limited anti-tumor effects in a mouse model of metastatic breast cancer. Nonetheless, our observations offer a roadmap for the future optimization of amiloride-based compounds with preferential cytotoxicity toward breast tumor cells.
Association of Neighborhood and Environmental Factors With Clinical Phenotypes and Outcomes in Heart Failure With Preserved Ejection Fraction
Heart failure with preserved ejection fraction (HFpEF) is heterogeneous with multiple comorbidities and limited therapeutic options.1 Multiple pathologies contribute to the development of distinct clinical HFpEF phenogroups. Evidence suggests that social determinants of health (SDoH) are pivotal in the pathogenesis of cardiovascular disease. Defined by the Centers for Disease Control and Prevention and the World Health Organization, SDoH refers to the conditions in the environments where people are born, live, learn, work, play, worship, and age, influencing health outcomes and quality-of-life risks. SDoH encompasses 5 domains: (1) Education Access and Quality, (2) Economic Stability, (3) Social and Community Context, (4) Health Care Access and Quality, (5) Neighborhood and Built Environment.
A Computational Model Predicts Sex-specific Responses to Calcium Channel Blockers in Mammalian Mesenteric Vascular Smooth Muscle
Abstract
The function of the smooth muscle cells lining the walls of mammalian systemic arteries and arterioles is to regulate the diameter of the vessels to control blood flow and blood pressure. Here, we describe an in silico model, which we call the 'Hernandez-Hernandez model', of electrical and Ca2+ signaling in arterial myocytes based on new experimental data indicating sex-specific differences in male and female arterial myocytes from murine resistance arteries. The model suggests the fundamental ionic mechanisms underlying membrane potential and intracellular Ca2+ signaling during the development of myogenic tone in arterial blood vessels.
Elucidating Molecular Mechanisms of Protoxin-II State-specific Binding to the Human NaV1.7 Channel
Abstract
Human voltage-gated sodium (hNaV) channels are responsible for initiating and propagating action potentials in excitable cells, and mutations have been associated with numerous cardiac and neurological disorders. hNaV1.7 channels are expressed in peripheral neurons and are promising targets for pain therapy. The tarantula venom peptide protoxin-II (PTx2) has high selectivity for hNaV1.7 and is a valuable scaffold for designing novel therapeutics to treat pain. Here, we used computational modeling to study the molecular mechanisms of the state-dependent binding of PTx2 to hNaV1.7 voltage-sensing domains (VSDs).
New Extramural Funding
New extramural funding including NIH grant R01HL128537, “Digital Twins from the Atom to the Rhythm” (R01), and R01HL152681 “Metabolic Control of Cardiac Pacemaking” (R01) from the National Institutes of Health. Heart, Blood and Lung Institute, have been awarded.
UC Davis Health Team Uses AI to Predict Risk of Liver Cancer
New algorithms developed by clinicians and data scientists could help personalize treatment
A team of UC Davis Health clinicians and data scientists have developed a machine-learning model to better predict which patients are at greater risk of developing a common type of liver cancer, hepatocellular carcinoma (HCC).
The findings of their research — published in the journal Gastro Hep Advances — describe how predictive-learning can aid physicians in providing early HCC risk assessments for patients diagnosed with metabolic dysfunction-associated steatotic liver disease, or MASLD. The pilot technology may be able to give physicians critical information to screen patients more closely and thus offer more personalized care.
Toward Digital Twin Technology for Precision Pharmacology
Abstract
The authors demonstrate the feasibility of technological innovation for personalized medicine in the context of drug-induced arrhythmia. The authors use atomistic-scale structural models to predict rates of drug interaction with ion channels and make predictions of their effects in digital twins of induced pluripotent stem cell-derived cardiac myocytes. The authors construct a simplified multilayer, 1-dimensional ring model with sufficient path length to enable the prediction of arrhythmogenic dispersion of repolarization. Finally, the authors validate the computational pipeline prediction of drug effects with data and quantify drug-induced propensity to repolarization abnormalities in cardiac tissue. The technology is high throughput, computationally efficient, and low cost toward personalized pharmacologic prediction.
A Multiscale Predictive Digital Twin for Neurocardiac Modulation
Abstract
Cardiac function is tightly regulated by the autonomic nervous system (ANS). Activation of the sympathetic nervous system increases cardiac output by increasing heart rate and stroke volume, while parasympathetic nerve stimulation instantly slows heart rate. Importantly, imbalance in autonomic control of the heart has been implicated in the development of arrhythmias and heart failure. Understanding of the mechanisms and effects of autonomic stimulation is a major challenge because synapses in different regions of the heart result in multiple changes to heart function. For example, nerve synapses on the sinoatrial node (SAN) impact pacemaking, while synapses on contractile cells alter contraction and arrhythmia vulnerability. Here, we present a multiscale neurocardiac modelling and simulator tool that predicts the effect of efferent stimulation of the sympathetic and parasympathetic branches of the ANS on the cardiac SAN and ventricular myocardium. The model includes a layered representation of the ANS and reproduces firing properties measured experimentally. Model parameters are derived from experiments and atomistic simulations. The model is a first prototype of a digital twin that is applied to make predictions across all system scales, from subcellular signalling to pacemaker frequency to tissue level responses. We predict conditions under which autonomic imbalance induces proarrhythmia and can be modified to prevent or inhibit arrhythmia. In summary, the multiscale model constitutes a predictive digital twin framework to test and guide high-throughput prediction of novel neuromodulatory therapy.
