Patent Application
20250101383
Atrial fibrillation (AF) is a common form of sustained cardiac arrhythmia in humans (Du et al., 2017). At the whole heart level, a central feature of AF is a very rapid and uncoordinated atrial activity while at the cellular level, the mechanism maintaining arrhythmia often arises from a “vulnerable substrate”, which consists of either action potential duration (APD) prolongation or shortening events (Christophersen et al., 2013). Such vulnerable substrates have been linked to genetic predispositions, cardiac remodeling caused by heart disease, aging, and/or altered regulation by neurohormonal factors. linkage analysis in familial cases of AF as well as genome-wide associated studies (GWAS) in the general population have elucidated some of the genetic underpinnings associated with the disease with close to 140 genetic loci linked to >200 genes have been identified, however none of these genes have been validated as disease-causing in the general population, limiting drug discovery efforts. In this context, a major barrier to progress is the lack of experimental platforms/strategies enabling rapidly establishment of causal links between gene function and AF-associated phenotypes (electrical remodeling, arrhythmia).
The four-chambered mouse heart has been used to establish functional links between genes or genetic loci and rhythm phenotypes (Lozano-Velasco et al., 2016; Nadadur et al., 2016; Temple et al., 2005; van Ouwerkerk et al., 2019; Wang et al., 2010; Zhang et al., 2019). However, despite proteome homology with humans and ability to manipulate the genome, the substantial electrophysiological differences (fast resting rate, short AP duration and triangular shape, species-specific K+ channels (Kaese and Verheule, 2012)), relatively long lifespan (years) and low throughput capacity of methods to retrieve electrophysiological parameters, limit the use of mice as a primary model for gene discovery related to AF. In contrast to mice, flies have a short generation time (˜10 days) and established automated kinetic imaging techniques (Fink et al., 2009; Klassen et al., 2017), coupled with available functional genomic resources (e.g. Flybase.org; VDRC (Mohr et al., 2014)), enabling the rapid evaluation of gene function on rhythm parameters at the whole heart level, although a limitation to this model is the lack of atrial specificity.
Although human iPSC-derived atrial-like cardiomyocytes (ACMs) can be used identify atrial-specific and cell autonomous rhythm-regulating mechanisms, the relative immaturity of hiPSCs-derived CMs and inherent lack of tissue level integration, might limit translation of the findings to the adult human heart. In sum, single model approaches are limited in their ability to validate large cohorts of AF-associated genes, indicating the necessity to develop alternative strategies to improve AF gene validation.
Combining assays with human, atrial, and whole organ relevance that also have HT functional genomics capacity could enhance our ability to rapidly establish causal links between AF-associated genes and arrhythmia phenotypes. The disclosure provided herein established such a platform. A human-relevant assay was developed that measures APD in ACMs with single cell resolution. In parallel, a fly cardiac function assay was optimized that measures contraction duration (systolic interval (SI)), as a surrogate measurement for APD. A cohort of 20 AF-associated genes was screened, and Phospholamban (PLN) loss of function was identified as a conserved gene that surprisingly and significantly shortens action potential duration in ACMs, HAMs and fly cardiomyocytes. Remarkably, addition of environmental stressors (i.e fibroblasts, β-adrenergic stimulation), further increased the generation of irregular beat to beat intervals, delayed after depolarizations, and triggered action potentials, in PLN knockdown cells as compared to controls. To delineate the mechanism underlying PLN KD-dependent arrhythmia, a logistic regression approach was used in HAM populations which predicted that PLN functionally interacts with both NCX (loss of function) and L-type calcium channels (gain of function) to mediate these arrhythmic phenotypes. Co-KD of PLN and NCX in ACMs and flies led to increased arrhythmic events, while treatment of ACMs with L-type calcium channel inhibitor, verapamil, reverted these phenotypes. The platform described herein provides in-depth resolution of cardiac electrophysiology metrics that can be used in various applications such as, for example, (1) performing large-scale functional genomic screens to identify novel gene regulatory networks governing cardiac rhythm, (2) creating new arrhythmia models to phenotypically characterize rhythm-associated cardiac diseases and stressors that affect the disease; (3) screening small-molecules to discovery new anti-arrhythmic therapeutics; and (4) determining the effects of the cardio microenvironment (e.g. by co-culturing with fibroblasts) on rhythm-associated cardiac diseases.
In an aspect, the present disclosure describes an in vitro-generated cardiomyocyte. In some embodiments, the in vitro-generated cardiomyocyte is generated from a reprogrammed cell in vitro. In some embodiments, the in vitro-generated cardiomyocyte comprises at least one gene associated with a cardiac rhythm disorder having an altered expression status. In some embodiments, the in vitro-generated cardiomyocyte displays a phenotype associated with the cardiac rhythm disorder. In some embodiments, the cardiac rhythm disorder is atrial fibrillation (AF). In some embodiments, the cardiomyocyte is an atrial-like cardiomyocyte (ACM). In some embodiments, the gene associated with the cardiac rhythm disorder is selected from a group comprising GATA5, GATA6, PITX2, KCNA5, GATA4, KCNJ5, HCN4, GJA1, TBX5, SYNE2, NKX2-6, SH3PXD2A, KCNN3, NPPA, ZFHX3, NKX2-5, HAND2, GJA5, KCND3, and PLN.
In some embodiments, the phenotype associated with the cardiac rhythm disorder is an alteration in a cardiac rhythm parameter. In some embodiments, the cardiac rhythm parameter may be selected from a group comprising action potential duration (APD), systolic interval, beat rate, beat refractory period, peak-to-peak interval, early afterdepolarization, delayed afterdepolarization, and Arrythmia Index (AI) value. In some embodiments, the phenotype is an AI value greater than 20. In some embodiments, the alteration in the cardiac rhythm parameter is a change in the APD75 value, wherein the APD75 value is APD measured at 75% repolarization. In some embodiments, the alteration in the cardiac rhythm parameter is a change in the APD90 value, wherein APD90 is APD measured at 90% repolarization. In some embodiments, the alteration in the cardiac rhythm parameter is a shortening of the APD as compared to a reference cardiomyocyte. In some embodiments, the alteration in the cardiac rhythm parameter is an increase in the beat refractory period as compared to a reference cardiomyocyte. In some embodiments, the alteration in the cardiac rhythm parameter is an increase in beat rate as compared to a reference cardiomyocyte. In some embodiments, the alteration in the cardiac rhythm parameter is a reduction in the systolic interval as compared to a reference cardiomyocyte. In some embodiments, the alteration in the cardiac rhythm parameter is a shortening of the Ca2+ transient duration as compared to a reference cardiomyocyte. In some embodiments, the altered expression status is overexpression of the at least one gene associated with the cardiac rhythm disorder as compared to the expression level of the gene in a reference cardiomyocyte. In some embodiments, the altered expression status is reduced expression of the at least one gene associated with the cardiac rhythm disorder as compared to the expression level in a reference cardiomyocyte.
In some embodiments, the in vitro-generated cardiomyocyte further comprises a nucleic acid molecule capable of modulating the expression of the at least one gene associated with the cardiac rhythm disorder. In some embodiments, the nucleic acid molecule is siRNA.
In some embodiments, the reprogrammed cell is a cardiac progenitor cell. In some embodiments, the cardiac progenitor cell overexpresses Id1. In some embodiments, the reprogrammed cell is an induced pluripotent stem cell (iPSC).
In some embodiments, the cardiomyocyte expresses one or more genes selected from a group comprising NR2F2, TBX5, ZNF385B, KCNJ3, KCNA5, NPPA, NPPB, EGR1/2 and PDGFRA.
In an aspect, the present disclosure describes a cell population comprising at least two in vitro-generated cardiomyocytes of the present disclosure. In some embodiments, the percentage of the cell population that exhibits an AI value of at least 20 is greater than 20%, 30%, 40%, 50%, 60%, 70% or 80%. In some embodiments, the APD75 value measured using a Kolmogorov-Smirnov scale is at least 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, or 0.9 when compared to the APD75 value of a reference cardiomyocyte cell population.
In an aspect, the present disclosure describes a cell co-culture model of cardiac fibrosis comprising the in vitro-generated cardiomyocyte of the present disclosure and a fibroblast cell. In some embodiments, the ratio of the fibroblast cell to the cardiomyocyte cell is 3:1.
In an aspect, the present disclosure describes a cell co-culture model of cardiac arrhythmia comprising the in vitro-generated cardiomyocyte of the present disclosure and a pharmaceutical compound. In some embodiments, the pharmaceutical compound is isoproterenol. In some embodiments, the pharmaceutical compound is dofetilide.
In an aspect, the present disclosure provides a method for screening a candidate agent for the treatment of a cardiac rhythm disorder comprising contacting the cardiomyocyte of the present disclosure with the candidate agent and detecting an effect of the candidate agent on the phenotype associated with the cardiac rhythm disorder. In some embodiments, the method further comprises culturing the cardiomyocyte with a fibroblast cell prior to contacting with the candidate agent. In some embodiments, the method further comprises labeling the cardiomyocyte with a voltage dye or a nuclear dye. In some embodiments, the method further comprises quantifying the voltage-dependent fluorescence variation over time. In some embodiments, the method further comprises automatically processing the action potential trace. In some embodiments, the method further comprises measuring parameters selected from the group comprising: APD-10, 25, 50, 75, 90; T25-75, T75-25; Vmax up and down; beat rate; peak-to-peak interval; and rhythm regularity index. In some embodiments, the candidate agent is a nucleic acid. In some embodiments, the candidate agent is a small molecule. In some embodiments, the candidate agent is a protein. In some embodiments, the nucleic acid recognizes the gene phospholamban (PLN). In some embodiments, the nucleic acid recognizes the gene NCX. In some embodiments, the candidate agent is isoproterenol. In some embodiments, the candidate agent is dofetilide. In some embodiments, the candidate agent is verapamil. In some embodiments, the detecting is conducted at single cell resolution.
In an aspect, the present disclosure provides a method of determining an increased risk for atrial fibrillation (AF) in a human subject comprising collecting a biological sample from the human subject and determining by an assay a level of a gene or gene product associated with AF in the biological sample. In some embodiments, the assay further comprises contacting the biological sample with a reagent that recognizes the gene or gene product associated with AF. In some embodiments, the biological sample is blood from the human subject. In some embodiments, the biological sample is a DNA sample. In some embodiments, the assay comprises genome sequencing of the human subject. In some embodiments, the assay comprises a proteomic assay. In some embodiments, the method further comprises administering a therapeutic agent to the human subject, wherein the therapeutic agent is configured to mitigate or alleviate one or more symptoms of AF in the human subject. In some embodiments, the gene is phospholamban (PLN). In some embodiments, the gene is NCX. In some embodiments, the gene product is a L-type Calcium channel. In some embodiments, the therapeutic agent is verapamil.
In an aspect, the present disclosure provides a method for high-throughput identification of a gene underlying a cardiac rhythm disorder comprising evaluating the effect of the loss-of-function and gain-of-function of the gene on the in vitro-generated cardiomyocyte of the present disclosure, evaluating the effect of the loss-of-function and gain-of-function of the gene on a Drosophila heart, or computational modeling of the effect of knockdown of the gene on a computational model of heterogenous adult human atrial myocytes (HAMs). In some embodiments, the cardiac rhythm disorder is AF. In some embodiments, the evaluating comprises measuring a phenotype associated with the cardiac rhythm disorder, wherein the phenotype is an alteration in a cardiac rhythm parameter. In some embodiments, the cardiac rhythm parameter is selected from a group comprising action potential duration (APD), systolic interval, beat rate, beat refractory period, peak-to-peak interval, early afterdepolarization, delayed afterdepolarization, and Arrythmia Index (AI) value.
In some embodiments, the evaluating comprises measuring parameters of Drosophila heart function selected from the group comprising: heart period (R-R interval), systolic interval (SI), arrhythmicity, fractional shortening, and contractility. In some embodiments, the modeling further comprises simulating parameters selected from a group comprising: affinity of the sarco/endoplasmic reticulum Ca2+-ATPase (SERCA) for cytosolic Ca2+, maximum ion channel conductances, rates for membrane transporters, and Ca+2 handling fluxes. In some embodiments, the method further comprises simulating the parameters on a cell population basis.
All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. Additionally, this application is related to the following Disease Models & Mechanisms research article: Kervadec A, et al. Multiplatform modeling of atrial fibrillation identifies phospholamban as a central regulator of cardiac rhythm. Dis Model Mech. 2023 Jul. 1; 16 (7):dmm049962. doi: 10.1242/dmm.049962. Epub 2023 Jul. 17, which became available on Jul. 17, 2023, and which is incorporated herein by reference in its entirety.
Atrial fibrillation (AF) is a common and genetically inheritable form of cardiac arrhythmia; however, it is currently not known how these genetic predispositions contribute to the initiation and/or maintenance of AF-associated phenotypes. One major barrier to progress is the lack of experimental systems enabling to rapidly explore gene function on rhythm parameters in models with human atrial and whole organ relevance. The disclosure provided herein describes a multi-model platform enabling 1) the high-throughput characterization of gene function on action potential duration and rhythm parameters using human iPSC-derived atrial-like cardiomyocytes and the Drosophila heart model, and 2) the validation of the findings using computational models of human adult atrial myocytes and tissue. Mechanistically, the present disclosure reveals that Phospholamban regulates rhythm homeostasis by functionally interacting with L-type calcium channels and NCX. In summary, the novel multi-model system approach illustrated herein paves the way for the discovery and molecular delineation of gene regulatory networks controlling atrial rhythm with application to AF.
Several aspects are described below with reference to example applications for illustration. It should be understood that numerous specific details, relationships, and methods are set forth to provide a full understanding of the features described herein. One having ordinary skill in the relevant art, however, will readily recognize that the features described herein can be practiced without one or more of the specific details or with other methods. The features described herein are not limited by the illustrated ordering of acts or events, as some acts can occur in different orders and/or concurrently with other acts or events. Furthermore, not all illustrated acts or events are required to implement a methodology in accordance with the features described herein.
The terminology used herein is for the purpose of describing particular cases only and is not intended to be limiting. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. When ranges are used herein for physical properties, all combinations and subcombinations of ranges and specific embodiments therein are intended to be included. The term “about” when referring to a number or a numerical range means that the number or numerical range referred to is an approximation within experimental variability (or within statistical experimental error), and thus the number or numerical range may vary between 1% and 15% of the stated number or numerical range. The term “comprising” (and related terms such as “comprise” or “comprises” or “having” or “including”) is not intended to exclude that in other certain embodiments, for example, an embodiment of any composition of matter, composition, method, or process, or the like, described herein, may “consist of” or “consist essentially of” the described features.
In an aspect, the present disclosure describes an in vitro-generated cardiomyocyte. The in vitro-generated cardiomyocyte is generated from a reprogrammed cell in vitro. The in vitro-generated cardiomyocyte comprises at least one gene associated with a cardiac rhythm disorder having an altered expression status. The in vitro-generated cardiomyocyte displays a phenotype associated with the cardiac rhythm disorder. In some embodiments, the cardiac rhythm disorder is atrial fibrillation (AF). In some embodiments, the cardiomyocyte is an atrial-like cardiomyocyte (ACM).
In some embodiments, the gene associated with the cardiac rhythm disorder is selected from a group comprising GATA5, GATA6, PITX2, KCNA5, GATA4, KCNJ5, HCN4, GJA1, TBX5, SYNE2, NKX2-6, SH3PXD2A, KCNN3, NPPA, ZFHX3, NKX2-5, HAND2, GJA5, KCND3, and PLN. In some embodiments, the gene associated with the cardiac rhythm disorder is selected from a group comprising but not limited to ABCC9, C9ORF3, CAND2, CAV1, CEP68, CUX2, GATA1, GATA2, GATA3, GATA4, GATA5, GATA6, GJA1, GJA5, GREM2, Hand, HAND2, HCN2-4, JPH2, KCNA1-5, KCND1-3, KCNE1-5, KCNH2, KCNJ10, KCNJ12, KCNJ15, KCNJ18, KCNJ2, KCNJ4, KCNJ5, KCNJ8, KCNK3, KCNMA, KCNN1-3, KCNN2, KCNN3, KCNQ1, LMNA, MYH6, MYL4, NEBL, NEURL, NKX2.1, NKX2.4, NKX2.5, NKX2-5, NKX2-6, NPPA, PITX2, PLN, PRRX1, Ptx1-3, RYR2, SCN1-5, SH3KBP1, SH3PXD2A, SHOX2, SOX5, SYNE1, SYNE2, SYNPO2L, TBX2, TBX3, TBX5, TBX6, TWIST1, TWIST2, ZFH2-4, and ZFHX3.
In some embodiments, the phenotype associated with the cardiac rhythm disorder is an alteration in a cardiac rhythm parameter. The cardiac rhythm parameter may be selected from a group comprising action potential duration (APD), systolic interval, beat rate, beat refractory period, peak-to-peak interval, early afterdepolarization, delayed afterdepolarization, and Arrythmia Index (AI) value. In some embodiments, the phenotype is an AI value greater than 20. In some embodiments, the alteration in the cardiac rhythm parameter is a change in the APD75 value, wherein the APD75 value is APD measured at 75% repolarization. In some embodiments, the alteration in the cardiac rhythm parameter is a change in the APD90 value, wherein APD90 is APD measured at 90% repolarization. In some embodiments, the alteration in the cardiac rhythm parameter is a shortening of the APD as compared to a reference cardiomyocyte. In some embodiments, the alteration in the cardiac rhythm parameter is an increase in the beat refractory period as compared to a reference cardiomyocyte. In some embodiments, the alteration in the cardiac rhythm parameter is an increase in beat rate as compared to a reference cardiomyocyte. In some embodiments, the alteration in the cardiac rhythm parameter is a reduction in the systolic interval as compared to a reference cardiomyocyte. In some embodiments, the alteration in the cardiac rhythm parameter is a shortening of the Ca2+ transient duration as compared to a reference cardiomyocyte.
In some embodiments, the altered expression status is overexpression of the at least one gene associated with the cardiac rhythm disorder as compared to the expression level of the gene in a reference cardiomyocyte. Genes associated with a cardiac rhythm disorder may be selected from the group comprising but not limited to ABCC9, C9ORF3, CAND2, CAV1, CEP68, CUX2, GATA1, GATA2, GATA3, GATA4, GATA5, GATA6, GJA1, GJA5, GREM2, Hand, HAND2, HCN2-4, JPH2, KCNA1-5, KCND1-3, KCNE1-5, KCNH2, KCNJ10, KCNJ12, KCNJ15, KCNJ18, KCNJ2, KCNJ4, KCNJ5, KCNJ8, KCNK3, KCNMA, KCNN1-3, KCNN2, KCNN3, KCNQ1, LMNA, MYH6, MYL4, NEBL, NEURL, NKX2.1, NKX2.4, NKX2.5, NKX2-5, NKX2-6, NPPA, PITX2, PLN, PRRX1, Ptx1-3, RYR2, SCN1-5, SH3KBP1, SH3PXD2A, SHOX2, SOX5, SYNE1, SYNE2, SYNPO2L, TBX2, TBX3, TBX5, TBX6, TWIST1, TWIST2, ZFH2-4, and ZFHX3.
In some embodiments, the altered expression status is reduced expression of the at least one gene associated with the cardiac rhythm disorder as compared to the expression level in a reference cardiomyocyte. In some embodiments, the in vitro-generated cardiomyocyte further comprises a nucleic acid molecule capable of modulating the expression of the at least one gene associated with the cardiac rhythm disorder. The nucleic acid molecule may comprise but is not limited to small interfering ribonucleic acid (siRNA), short hairpin RNA (shRNA), a nucleic acid expressing CRISPR/Cas9, a nucleic acid expressing a TALEN, a nucleic acid expressing a zinc finger nuclease, a nucleic acid expressing a meganuclease, a nucleic acid expressing an endonuclease, or any combination thereof.
In some embodiments, the reprogrammed cell is a cardiac progenitor cell. In some embodiments, the cardiac progenitor cell overexpresses Id1. In some embodiments, the reprogrammed cell is an induced pluripotent stem cell (iPSC). In some embodiments, the reprogrammed cell is a stem cell. In some embodiments, the cardiomyocyte expresses one or more genes selected from a group comprising but not limited to NR2F2, TBX5, ZNF385B, KCNJ3, KCNA5, NPPA, NPPB, EGR 1/2 and PDGFRA.
In an aspect, the present disclosure describes a cell population comprising at least two in vitro-generated cardiomyocytes of the present disclosure. In some embodiments, the percentage of the cell population that exhibits an AI value of at least 20 is greater than 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 99%, or any integer in between. In some embodiments, the APD75 value measured using a Kolmogorov-Smirnov scale is at least 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, or 0.9 when compared to the APD75 value of a reference cardiomyocyte cell population.
In an aspect, the present disclosure describes a cell co-culture model of cardiac fibrosis comprising the in vitro-generated cardiomyocyte of the present disclosure and a fibroblast cell. In some embodiments, the ratio of the fibroblast cell to the cardiomyocyte cell is 3:1. In some embodiments, the ratio of the fibroblast cell to the cardiomyocyte is 2:1, 1:1, or 0.5:1.
In an aspect, the present disclosure describes a cell co-culture model of cardiac arrhythmia comprising the in vitro-generated cardiomyocyte of the present disclosure and a pharmaceutical compound. In some embodiments, the pharmaceutical compound is isoproterenol. In some embodiments, the pharmaceutical compound is dofetilide.
In an aspect, the present disclosure provides a method for screening a candidate agent for the treatment of a cardiac rhythm disorder comprising contacting the cardiomyocyte of the present disclosure with the candidate agent and detecting an effect of the candidate agent on the phenotype associated with the cardiac rhythm disorder. In some embodiments, the method further comprises culturing the cardiomyocyte with a fibroblast cell prior to contacting with the candidate agent. In some embodiments, the method further comprises labeling the cardiomyocyte with a voltage dye or a nuclear dye. In some embodiments, the method further comprises quantifying the voltage-dependent fluorescence variation over time. In some embodiments, the method further comprises automatically processing the action potential trace. In some embodiments, the method further comprises measuring parameters selected from the group comprising: APD-10, 25, 50, 75, 90; T25-75, T75-25; Vmax up and down; beat rate; peak-to-peak interval; and rhythm regularity index. In some embodiments, the candidate agent is a nucleic acid. In some embodiments, the candidate agent is a small molecule. In some embodiments, the candidate agent is a protein. In some embodiments, the nucleic acid recognizes the gene phospholamban (PLN). In some embodiments, the nucleic acid recognizes the gene NCX. In some embodiments, the candidate agent is isoproterenol. In some embodiments, the candidate agent is dofetilide. In some embodiments, the candidate agent is verapamil. In some embodiments, the detecting is conducted at single cell resolution.
In an aspect, the present disclosure provides a method of determining an increased risk for atrial fibrillation (AF) in a human subject comprising collecting a biological sample from the human subject and determining by an assay a level of a gene or gene product associated with AF in the biological sample. In some embodiments, the assay further comprises contacting the biological sample with a reagent that recognizes the gene or gene product associated with AF. In some embodiments, the biological sample is blood from the human subject. In some embodiments, the biological sample is a DNA sample. In some embodiments, the assay comprises genome sequencing of the human subject. In some embodiments, the assay comprises a proteomic assay. In some embodiments, the method further comprises administering a therapeutic agent to the human subject, wherein the therapeutic agent is configured to mitigate or alleviate one or more symptoms of AF in the human subject. In some embodiments, the gene is phospholamban (PLN). In some embodiments, the gene is NCX. In some embodiments, the gene product is a L-type Calcium channel. In some embodiments, the therapeutic agent is verapamil.
In an aspect, the present disclosure provides a method for high-throughput identification of a gene underlying a cardiac rhythm disorder comprising evaluating the effect of the loss-of-function and gain-of-function of the gene on the in vitro-generated cardiomyocyte of the present disclosure, evaluating the effect of the loss-of-function and gain-of-function of the gene on a Drosophila heart, or computational modeling of the effect of knockdown of the gene on a computational model of heterogenous adult human atrial myocytes (HAMs). In some embodiments, the cardiac rhythm disorder is AF. In some embodiments, the evaluating comprises measuring a phenotype associated with the cardiac rhythm disorder, wherein the phenotype is an alteration in a cardiac rhythm parameter. In some embodiments, the cardiac rhythm parameter is selected from a group comprising action potential duration (APD), systolic interval, beat rate, beat refractory period, peak-to-peak interval, early afterdepolarization, delayed afterdepolarization, and Arrythmia Index (AI) value.
In some embodiments, the evaluating comprises measuring parameters of Drosophila heart function selected from the group comprising: heart period (R-R interval), systolic interval (SI), arrhythmicity, fractional shortening, and contractility. In some embodiments, the modeling further comprises simulating parameters selected from a group comprising: affinity of the sarco/endoplasmic reticulum Ca2+-ATPase (SERCA) for cytosolic Ca2+, maximum ion channel conductances, rates for membrane transporters, and Ca+2 handling fluxes. In some embodiments, the method further comprises simulating the parameters on a cell population basis. The cell population may comprise at least 100 HAMs, at least 200 HAMs, at least 300 HAMs, at least 400 HAMs, 500 HAMs, at least 600 HAMs, at least 700 HAMs, at least 800 HAMs, at least 900 HAMs, or at least 1000 HAMs.
The following examples further illustrate aspects of the disclosure, but should not be construed as in any way limiting its scope. All references cited herein are each hereby incorporated by reference in their entirety, For example, Kervadec A, et al. Multiplatform modeling of atrial fibrillation identifies phospholamban as a central regulator of cardiac rhythm. Dis Model Mech. 2023 Jul. 1; 16 (7): dmm049962. doi: 10.1242/dmm.049962. Epub 2023 Jul. 17. PMID: 37293707; PMCID: PMC10387351 is incorporated herein by reference in its entirety.
In this example, a novel phenotypic platform was established to phenotypically assess atrial fibrillation (AF) associated genes, enabling the study of gene function on action potential duration (APD) and rhythm parameters in high throughput (HT) in atrial-like cardiomyocytes (ACMs).
To study the molecular basis of chamber-specific electrical disorders such as atrial fibrillation, Id1-overexpressing cardiac progenitors (CPs) were used to generate ACMs (Cunningham et al., 2017, and Yu et al., 2018). Treatment of Id1-induced CPs with a single dose of retinoic acid (300 nM) efficiently promoted the generation of atrial-like, NR2F2+ beating CMs (˜80% were NR2F2+, ACTN2+) (
Next, to facilitate the characterization of AF-associated arrhythmia phenotypes in ACMs, an imaging platform was developed that automatically tracks and quantifies action potential (AP) and rhythm parameters in HT with single cell resolution (
To test the platform's ability to identify APD modulators, ACMs of the present disclosure were infused with isoproterenol, a non-selective β-adrenergic agonist, known to both shorten APD and increase beat rate in hiPSC-CMs. Escalating doses of isoproterenol caused a dose-dependent shortening of median APD75 values, from 121.3 ms (untreated) to 108.6 ms (1 μM) and 91.2 ms (9 μM) (
In this example, the HT phenotypic platform of the present disclosure was used to assess APD and rhythm parameters in flies.
To assess AF-associated mechanisms at the whole organ level using the Drosophila model, high-speed video recording of heart movements was used in in situ preparations. Heart function was quantified (Fink et al., 2009; Vogler and Ocorr, 2009) providing precise measurements of heart period (R-R interval), systolic interval (SI), as well as arrhythmicity and fractional shortening/contractility in a functioning heart. Most of the key cardiac ion channels present in human hearts are also present and functional in the fly heart (Ocorr et al., 2007c; Ocorr et al., 2017) (Table 1). Importantly, simultaneous optical and intracellular recordings demonstrated a direct 1:1 correlation between myocardial cell depolarization and heart wall movement. It is important to note that the fly heart is composed of a single layer of myocardial cells and any heart wall movement is an immediate reflection of the contractile state of component myocardial cells. Thus, APD was quantified and the corresponding systolic interval (SI) from simultaneous electrical and optical recordings from hearts of middle-aged wildtype controls and KCNQ mutants. A strong correlation (r=0.96, p<0.0001) was found between APD and SI (
Table 1 below lists Human AF candidate genes tested in ACMs (
ABCC9
dSur
HCN4
Ih
JPH2
junctophilin
KCNA5
Shaker
KCND3
Shal
KCNH2
seizure
KCNJ2, 5,
Irk
8
KCNK3
ork/sandman
KCNN2, 3
SK
KCNMA
BK-not tested
in ACMs?
KCNQ1
KCNQ
RYR2
RyR
SCN1-5
nap
CUX2
cut
GATA4/5/
pnr/grn/
6
GATAd
HAND2
Hand
NKX2-5/2-
tin
6
SOX5
Sox102F
TBX5
Bifid/
Doc1/Doc2/
Doc3
Drosophila Genome
GJA1
CG11459/26-
29-p/CG4847
LMNA
LamC/Lam
MYH6
Mhc
MYL4
Mlc-c/Mlc1
NEBL
Lasp
SYNE2
Msp300
SYNPO2L
CG1674
C9ORF3
CG10602
CAND2
Cand1
NEURL
neur
SH3PXD2A
cindr/Nipped-
A
Table 2 below shows human AF candidate genes tested in the fly heart listed with their human orthologs and stock center ID number.
In this example, gene expression in ACMs was assessed by RNA-sequencing (RNA-seq) to identify and target AF-associated genes.
To evaluate the ability of the platform to identify AF-associated genes and mechanisms, the expression of genes previously associated with AF (Fatkin et al., 2017) was assessed by RNA-seq of day 12 and day 25 ACMs. The result revealed that most AF candidate genes were expressed in ACMs at moderate-to-high levels (from 0.1 to >100 RPKM) (
In parallel, 24 fly genes were screened that were orthologous to 17 of the 20 AF-associated genes. Genes were knocked down using a heart-specific driver (Hand-Gal4 (Sellin et al., 2006)) crossed to UAS-candidate gene-RNAi lines. Progeny of the crosses were aged to three-weeks old (middle aged) and heart function was characterized. Thirteen of the genes tested exhibited significantly altered systolic intervals and/or rhythm phenotypes in the fly cardiac model and 7 of these overlapped with the genes affecting APD in the ACMs (p-value <0.001;
Although there is evidence that APD prolongation is associated with AF (Nielsen et al., 2013; Olson et al., 2006), APD shortening is thought to be the most common mechanism underlying the onset and maintenance of AF (Teh et al., 2012; Wakili et al., 2011). The functional screen of the present disclosure therefore focused on the gene KD that induced the strongest APD shortening phenotype. Remarkably, in both ACM and fly heart platforms, reduced PLN/SclA function consistently led to the strongest APD and SI shortening phenotype. In ACMs, PLN KD caused a significant shortening of median APD75 values, from 118.1 ms to 78.5 ms (˜−40 ms) (KS-D=0.3305, p-value <0.0001), and shortened calcium transient duration (
To validate the phenotypic platform findings, the functional screen of the present disclosure employed a computational modeling approach of adult human atrial myocytes (HAMs) (
Table 3 below shows a selection of 20 AF-genes harboring a rare variant in familial AF studies and/or SNPs reported in GWAS studies.
Table 4 below shows numerical values of data presented in a heat map in
Table 5 below shows screen results in ACMs. The table displays median, normalized median, KS-D, and P-values related to
Table 6 below shows screen results in flies. The table displays normalized systolic interval (SI), standard deviations, and P values related to
In this example, the effects of PLN knockdown were assessed in ACMs and flies. To determine if loss of PLN function alone is sufficient to induce arrhythmia-like phenotypes, the beat-to-beat interval variance (arrhythmia index, AI) was measured in ACMs upon PLN KD, and no difference was found as compared to siControl (
Conversely, treating ACMs with Isoproterenol alone did not increase the percentage of arrhythmic cells, while exposing ACMs to Isoproterenol along with PLN KD, increased the percentage of arrhythmic cells from 13% to 22% (
In fly hearts, despite significant changes in SI, neither AI nor MAD arrhythmia parameters were significantly altered by cardiac-specific PLN/SclA KD. To add an adrenergic stress, the fly hearts were exposed to octopamine (OA), the fruit fly version of norepinephrine/adrenaline (Sujkowski et al., 2017). Acute OA exposure significantly elevated heart rate by significantly shortening systolic intervals in both control and KD lines with a maximal effect at 100 nM, which was the dose used for subsequent pharmacological pacing of the fly heart (
In this example, computational modeling was used to validate PLN as a key regulator of rhythm in human adult atria. To validate the phenotypic platform findings, models were used of both isolated HAMs and two-dimensional atrial tissue that allows to modulate cell-cell electrical coupling (
Table 7 below shows conduction velocity measured in 2D tissue.
In this example, regression analysis and genetic perturbations were conducted to further characterize the role of PLN in the regulation of atrial rhythm.
To characterize how PLN control rhythm homeostasis in atrial myocytes, it was noted by the inventors that at Kmf 50%, only half of HAMs generated DADs (see
To validate these predictions, two parameters were selected that were most positively (ICaL conductance) or negatively (NCX maximal transport rate) correlated with DAD incidence. First, in ACMs it was tested whether reduced expression of the sodium-calcium exchanger NCX in the background of PLN KD would further increase the percentage arrhythmic cells. Consistent with the model prediction, combined KD of PLN and NCX in the presence of perturbagens (fibroblasts co-culture and isoproterenol infusion) significantly increased the occurrence of arrhythmia-like phenotypes in ACMs as compared to single PLN KD, from 37.5% to 43.2% of cells with AI>20 (
Finally, the regression analysis also revealed that DAD-generating HAMs had increased L-type calcium channel (GCaL) currents. Thus, to test whether inhibition of L-type calcium channels activity might reduce PLN-induced arrhythmia phenotypes, ACMs were treated with a calcium channel blocker verapamil and the percentage of arrhythmic cells was quantified in response to PLN KD+Fib+isoproterenol treatment. Remarkably, ACMs treated with verapamil were 1.7 fold less arrhythmic than DMSO control (from 31.2% to 18%,
Table 8 below shows a glossary for model parameters that were perturbed for constructing populations of human atrial models.
In this example, cardiomyocytes of the present disclosure are infused with a candidate agent as described in Example 1, and an AI value is generated to describe the effect of the candidate agent on a phenotype associated with a cardiac rhythm disorder. In certain embodiments, the cardiomyocytes are ACMs of the present disclosure. In some embodiments, the cardiomyocytes are cultured with fibroblasts prior to being contacted with the candidate agent. In many embodiments, the cardiomyocytes are further labeled with a voltage dye or a nuclear dye. In many embodiments, the voltage-dependent fluorescence variation of the voltage dye is quantified over time. In some cases, action potential traces of the cardiomyocytes are automatically processed. In many cases, parameters associated with infusion of the candidate agent are measured. The parameters are selected from the group comprising: APD-10, 25, 50, 75, 90; T25-75, T75-25; Vmax up and down; beat rate; peak-to-peak interval; and rhythm regularity index. In some embodiments, the candidate agent is a nucleic acid. In other embodiments, the candidate agent is a small molecule. In certain embodiments, the candidate agent is a protein. In some cases, the nucleic acid recognizes the gene phospholamban (PLN). In many cases, the nucleic acid recognizes the gene NCX. In some embodiments, the cardiomyocytes are assessed at single cell resolution.
In this example, analyses of the present disclosure are used to determine an increased risk for AF in a human subject. In some embodiments, a biological sample is collected from a human subject. In other embodiments, a level of a gene or gene product associated with AF in the biological sample is determined by an assay of the present disclosure. In embodiments, the gene or gene product is identified using the novel multiplatform modeling approach of the present disclosure. In some cases, the assay further comprises contacting the biological sample with a reagent that recognizes the gene or gene product associated with AF. In some embodiments, the biological sample is blood from a human subject. In certain embodiments, the biological sample is a DNA sample. In some cases, the assay comprises genomic sequencing of the human subject. In certain cases, the assay comprises a proteomic assay. In certain embodiments, a therapeutic agent is administered to the human subject. In some cases, the therapeutic agent is configured to alleviate or mitigate one or more symptoms of AF in the human subject. In some embodiments, the gene is PLN. In other embodiments, the gene is NCX. In some cases, the gene product is an L-type Calcium channel. In certain embodiments, the therapeutic agent is verapamil.
The integrated use of model systems combining functional screening capacity and human atrial and whole organ relevance represents a novel approach enabling the identification and characterization of new genes affecting AF-associated rhythm biology with unprecedented throughput as shown in
In this example, mathematical modeling is used to assess the behavior of atria tissue. Two-dimensional (2D) models were created to understand the dynamic behaviors of atrial AP and Ca2+ in tissue using a monodomain equation (Clayton et al., 2011) to describe the tissue electrical coupling (see Ni et al., 2017):
where Vm is the membrane potential of cardiomyocytes, iion represents total ionic current, Cm is the capacitance of the cell membrane, and D indicates the isotropic diffusion coefficient describing the cell-to-cell coupling strength. The 2D model comprises 120×125 grids with a spatial interval of 0.25 mm. To account for the intrinsic variabilities in tissue, the population of 600 models was mapped to the tissue based on a heterogeneous pattern dividing the tissue into 600 blocks consisting of 5×5 grids. Under normal coupling, D=0.1485 mm2 ms, so that the conduction velocity under normal conditions is aligned with previous experimental observations and consistent with modeling studies. To assess how tissue coupling affects the arrhythmic events, a reduced coupling (scale to 25% of tissue conductivity) condition was also simulated. The resulting conduction velocity with normal or reduced tissue coupling is given in Table 7. The simulations of the present disclosure showed that the APD variations seen at the single-cell level are reduced in coupled tissue (
ACMs for the study were developed by dissociating Id1 overexpressing hiPSCsl with 0.5 mM EDTA (ThermoFisher Scientific) in PBS without CaCl2 and MgCl2 (Corning) for 7 min at room temperature. hiPSC were resuspended in mTeSR-1 media (StemCell Technologies) supplemented with 2 μM Thiazovivin (StemCell Technologies) and plated in a Matrigel-coated 12-well plate at a density of 3×105 cells per well. After 24 hours after passage, cells were fed daily with mTeSR-1 media (without Thiazovivin) for 3-5 days until they reached ≥90% confluence to begin differentiation. hiPSC-ACMs were differentiated as previously described2. At day 0, cells were treated with 6 μM CHIR99021 (Selleck Chemicals) in S12 media3 for 48 hours. At day 2, cells were treated with 2 μM Wnt-C59 (Selleck Chemicals), an inhibitor of WNT pathway, in S12 media. 48 hours later (at day 4), S12 media is fully changed. At day 5, cells were dissociated with TrypLE Express (Gibco) for 2 min and blocked with RPMI (Gibco) supplemented with 10% FBS (Omega Scientific). Cells were resuspended in S12 media supplemented with 4 mg/L Recombinant Human Insulin (Gibco) (S12+ media), 300 nM retinoic acid (R2625-50 MG) and 2 μM Thiazovivin and plated onto a Matrigel-coated 12-well plate at a density of 9×105 cells per well. S12+ media was changed at day 8 and replaced at day 10 with RPMI (Gibco) media supplemented with 213 μg/μL L-ascorbic acid (Sigma), 500 mg/L BSA-FV (Gibco), 0.5 mM L-carnitine (Sigma) and 8 g/L AlbuMAX Lipid-Rich BSA (Gibco) (CM media). Typically, hiPSC-ACMs start to beat around day 9-10. At day 15, cells were purified with lactate media (RPMI without glucose, 213 μg/μL L-ascorbic acid, 500 mg/L BSA-FV and 8 mM Sodium-DL-Lactate (Sigma)), for 4 days. At day 19, media was replaced with CM media.
Voltage assay was performed using the labeling protocol described herein. Briefly, hiPSC-ACMs at day 25 of differentiation were dissociated with TrypLE Select 10× for up to 10 min and the action of TrypLE was neutralized with RPMI supplemented with 10% FBS. Cells were resuspended in RPMI with 2% KOSR (Gibco) and 2% B27 50× with vitamin A (Life Technologies) supplemented with 2 μM Thiazovivin and plated at a density of 6,000 cells per well in a Matrigel-coated 384-well plate. hiPSC-ACMs were then transfected with siRNAs directed against AFib-associated genes (ON-TARGETplus Human, siGATA4: J-008244-05-0002, siGATA5: J-010324-06-0005, siGATA6: J-008351-06-0005, siGJA1: J-011042-05-0002, siGJA5: J-017368-05-0002, siHAND2: J-008698-06-0005, siHCN4: J-006203-05-0002, siKCNA5: J-006215-06-0005, siKCND3: L-006226-00-0005, siKCNJ5: J-006250-06-0002, siKCNN3: J-006270-06-0002, siNKX2-5: J-019795-07-0002, siNKX2-6: J-025793-17-0002, siNPPA: J-012729-05-0002, siPITX2: J-017315-05-0005, siPLN: J-011754-05-0005, siSH3PXD2A: J-006657-07-0002, siSYNE2: J-019259-09-0002, siTBX5: J-013410-5-0002, siZFHX3: J-015410-5-0002) using lipofectamine RNAi Max (ThermoFisher). Each siRNA was tested in biological quadruplicates for each differentiation and differences between experimental conditions and controls were replicated in at least 2 independent differentiations. Every three days post-transfection, cells were first washed with pre-warmed Tyrode's solution (Sigma) by removing 50 μL of media and adding 50 μL of Tyrode's solution 5 times using a 16-channel pipette. After the fifth wash, 50 μL of 2× dye solution consisting in voltage-sensitive dye Vf2.1 Cl (Fluovolt, 1:2000, ThermoFisher) diluted in Tyrode's solution supplemented with 1 μL of 10% Pluronic F127 (diluted in water, ThermoFisher) and 20 μg/mL Hoescht 33258 (diluted in water, ThermoFisher) was added to each well. The plate was placed back in the 37° C. 5% CO2 incubator for 45 min. After incubation time, cells were washed 4 times with fresh pre-warmed Tyrode's solution using the same method described above. hiPSC-ACMs were then automatically imaged with ImageXpress Micro XLS microscope at an acquisition frequency of 100 Hz for a duration of 5 sec with an excitation wavelength of 485/20 nm and emission filter 525/30 nm. A single image of Hoescht was acquired before the time series. Fluorescence over time quantification and trace analysis were quantified using custom software packages. Although, cells were not paced during the APD measurement process, beat rate was controlled in silico by only comparing APDs between conditions where peak trains had similar beat rate (+/−10%), thereby minimizing the effect of beat rate on APD.
hiPSC-ACMs were dissociated, plated in 384-well plate and transfected with siRNA-associated AFib (ON-TARGETplus Human, siNCX: J-007620-05-0002). 24 hours post-transfection, 2,000 primary human fibroblasts were added per well to the hiPSC-ACMs. 48 hours later (the day of the imaging), cells were dyed with the voltage sensitive dye Vf2.1 Cl as described above, then treated with 50 μL of 2× solution of isoproterenol (1 μM final) diluted in Tyrode alone and in combination with 2× solution of Verapamil (30 nM final), diluted in Tyrode at the 5th wash. After 20 minutes of compound incubation time, cells were imaged, and single-cell traces analyzed as described previously.
Cardiac ion currents were recorded from single cardiomyocytes using the whole-cell patch-clamp method. Briefly, coverslips with ACMs or VCMs were transferred into electrophysiological perfused recording chamber (RC-25-F, Warner Instruments, Hamden, CT) mounted on the stage of an inverted Olympus microscope. Patch pipettes were pulled from thin-wall borosilicate glass capillaries (CORNING 7740, 1.65 mm) with a P-2000 laser pipette puller (Sutter Instruments, California, USA) and had electrode tip resistances between 1.5 and 5.5 M2 with access resistance of <8M (2 for whole-cell patch recordings. Series resistance and cell capacitance were compensated to between 30 and 60% in some voltage-clamp recordings. For current-clamp recordings, pipettes 1 contained (in mM): K aspartate 76, KCl 20, MgCl 2.5, HEPES 10, NaCl 4, CaCl2 6, K4EGTA 10, K2ATP 5 and Na-GTP 0.1 (pH 7.2; 310 mOsm). All recordings were performed at room temperature in Tyrode's solution. Current response traces were acquired using the Axon 200B amplifier. Currents were digitally sampled at 10 kHz using Digidata 1322A digitizer hardware and pClamp 10.2 software (Molecular Devices, California, USA). For both ACMs and VCMs, n=5.
The Hand 4.2-Gal4 fly line was used as a heart-specific driver line (Brand and Perrimon, 1993). Virgin Hand-Gal4 females were crossed to male flies from UAS-RNAi lines for each AF gene candidate. UAS-RNAi lines and their respective control lines were acquired from the Bloomington Drosophila Stock Center (BDSC, Indiana, United States of America) and Vienna Drosophila Resource Center (VDRC, Vienna, Austria). For each gene candidate, at least 2 different RNAi lines were used; GD and KK were the genetic background lines for stocks from VDRC lines and ATTP2 and ATTP40 were the genetic background lines for stocks from BDSC).
The PLN (fly ortholog: Sarcolamban A/SclA) sensitized fly line was made by recombining the USA-SclA RNAi with the Hand 4.2-Gal4 heart-specific driver line. Virgin females from the Hand-Gal4 or the SclA-sensitized, Hand-Gal4 driver lines were crossed to males of the desired UAS-RNAi lines. Adult female flies for all crosses were collected upon eclosion and raised at 25° C. on a 12-hour light-dark cycle. Flies were fed a standard yeast-cornmeal diet, with food replaced every other day.
Cardiac phenotypes of middle-aged (3-weeks old) female flies from each cross were characterized using denervated, semi-intact preparations as previously described in (Ocorr et al., 2009; Vogler and Ocorr, 2009). Briefly, hearts from 20-25 flies were examined for each genotype and age. Adult female flies were exposed to FlyNap, a triethylamine-based anesthetic, for at least one minute until no movement was detected. Hearts were exposed by dissection in room temperature, air bubbled, artificial hemolymph (AHL, Ocorr et al, 2007). High-speed video recordings were filmed with a Hamamatsu EM-CCD camera and using HC Image capture software (Hamamatsu Corp). Heart movements were analyzed using the Semi-automated Optical Heartbeat Analysis (SOHA) software (sohasoftware.com). Movies were recorded at speeds of 140+ fps with pixel resolution ˜ 1 micron/pixel allowing very precise temporal and spatial measurements, including heart period (HP) and rate (1/HP), diastolic and systolic intervals, and fractional shortening/contractility. To quantitate arrhythmia, median absolute deviation (MAD) was first calculated. The median value of the absolute deviations of each heart period (Xi) from the median heart period ((X)) was calculated and then multiplied by a constant (k=1.4826 assuming data is normally distributed).
To normalize the MAD index (nMAD), the MAD value was divided by the median heart period. Qualitative records of heart wall movements (M-modes/kymographs) were produced by electronically excising a 1 pixel horizontal “slice” from each movie frame and aligning them horizontally providing an edge trace displaying heart wall movements in the X-axis over time along the Y-axis (Ocorr et al., 2009; Ocorr et al., 2007c).
Octopamine (OA) pacing experiments were performed in situ on the semi-intact fly preparation. OA (Sigma-Aldrich #00250) stock solution (10 mM) was freshly prepared by dissolving in water and was further diluted in AHL. A dose-response curve was generated using doses ranging from 0.1 nM OA to 500 nM OA (Supplemental
Simultaneous optical and intracellular electrical recordings were performed as previously described in (Ocorr et al., 2017). Briefly, a semi-intact preparation was prepared that was incubated in artificial hemolymph. Optical recordings were done as described above; electrical potentials were recorded using sharp glass electrodes (20±50M (2) filled with 3M KCl and standard intracellular electrophysiological techniques. Data were acquired using an Axon-700B Multiclamp amplifier, signals were digitized using the DIGIDATA 1322A and data were captured and analyzed using PClamp 9.0 and Clampfit 10.0 software respectively (all from Molecular Devices). Data was quantified from representative 30 s recordings where the resting membrane potential had remained stable for at least 30 s. To coordinate the optical and electrical recordings a TTL pulse was sent by the image capture software to the Digitizer. The pulse duration lasted for the entire period of optical recording and was recorded in a separate channel by the PClamp software allowing us to delineate the beginning and the end of the optical recording and directly align it with the electrical record.
ACMs-Population distribution of control and siRNA-treated hiPSC-ACMs was generated with GraphPad Prism software (2019) using nonlinear regression. Unpaired nonparametric Kolmogorov-Smirnov test was used to compare each treated conditions to control using APD75 of every measured cells. To determine any statistical significance between experimental and control groups, two-sided p-values were calculated with Student's t-test using GraphPad Prism software.
Flies-Data that exhibited a normal distribution (Shapiro-Wilk test) was evaluated for significance using a 1-way ANOVA (for simple comparisons) or a 2-way ANOVA (for multiple manipulations) followed by multiple comparisons post-hoc tests as indicated in figure legends. Data sets that did not show a normal distribution (typically heart period, systolic interval and diastolic interval, and arrhythmia parameters) were analyzed using a nonparametric Wilcoxon Rank Sum test or Kruskal-Wallis test followed by Dunn multiple comparisons post-hoc tests. For acute octopamine stress experiments, repeated measures were used, 2-way ANOVA with a Geisser-Greenhouse correction to address potential lack of sphericity, followed by Sidak's multiple comparisons test. If data did not meet assumptions of normality, the data was log-transformed and repeated measures were repeated, 2-way ANOVA. Statistical analysis and data visualization was completed with GraphPad Prism (v8.0.0; graphpad.com), R (v3.6; r-project.org), and Rstudio (v1.3.959; rstudio.com).
The computational model of the present disclosure was employed (Grandi et al., 2011) of human atrial myocytes to simulate human action potential (AP) and Ca2+. PLN regulates the function of sarco/endoplasmic reticulum Ca2+-ATPase (SERCA) function by decreasing the apparent affinity of SERCA for Ca2+ ions (Periasamy et al., 2008; Simmerman and Jones, 1998). Accordingly, the effects of PLN knockdown on SERCA were simulated by various degrees of reduction in the SERCA affinity parameter (Kmf) for cytosolic Ca2+: Kmf was scaled by 75%, 50%, or 25% to cover a wide parameter space of change. These changes were made based on a previous study showing that applying PLN antibody shifted the affinity from 0.8 μM to 0.2 μM (Cantilina et al., 1993).
To describe the intrinsic cell-to-cell variabilities in atrial electrophysiology and uncover the uncertainty of the modeling results, a population-based approach was applied (Ni et al., 2018; Sobie, 2009) and populations of 600 human atrial model variants were built by randomly perturbing key model parameters (e.g., the maximum ion channel conductances, rates for membrane transporters, and Ca2+ handling fluxes) by a lognormal distribution (φ=0.2).
Logistic regression analysis was performed (Morotti et al., 2017) to understand the influence of each model parameter on the arrhythmic outcome in human atrial myocytes. For each cell of the population of models, a binary code (yes/no) was applied to describe the presence/absence of delayed afterdepolarizations (DADs). Logistic regression coefficients were obtained using MATLAB (R2019b) scripts (Morotti and Grandi, 2017).
A constant pacing-and-pause protocol was applied to evaluate the physiological effects of PLN knockdown. Specifically, single cells were paced at 2 Hz for 290 s prior to a 10-s period of pause without stimulation. In tissue simulations, stimuli were applied at the left side of the 2D tissue at 2 Hz for 10 s, which was followed by a 10-s period without stimulation. AP and Ca2+ traces from the last four stimuli and the non-paced period were recorded for data analysis. Logistic regression analysis was applied to uncover the influence of model parameters on the incidence of arrhythmogenic events.
While preferred embodiments of the present disclosure have been shown and described herein, it will be understood by those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions can be made without departing from the invention. It should be understood that various alternatives to the embodiments described herein can be employed. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.
Any and all priority claims identified in the Application Data sheet, or any correction thereto, are hereby incorporated by reference under 37 CFR 1.57. For example, this Application claims the benefit of U.S. Provisional App. No. 63/584,370 filed on Sep. 21, 2023, which is incorporated by reference in its entirety herein.
This invention was made with government support under R01 HL153645, R01 HL148827, R01 HL149992, and R01 AG071464 awarded by the National Institutes of Health. The government has certain rights in the invention.
| Number | Date | Country | |
|---|---|---|---|
| 63584370 | Sep 2023 | US |
