Functional profiling of KCNE1 variants informs population carrier frequency of Jervell and Lange-Nielsen syndrome type 2

Functional profiling of KCNE1 variants informs population carrier frequency of Jervell and Lange-Nielsen syndrome type 2 | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Functional profiling of KCNE1 variants informs population carrier frequency of Jervell and Lange-Nielsen syndrome type 2 Carlos G. Vanoye , Reshma R. Desai , Jordan D. John , Steven C. Hoffman , Nicolas Fink , Yue Zhang , Omkar G. Venkatesh , Jonathan Roe , Sneha Adusumilli , Nirvani P. Jairam , View ORCID Profile Charles R. Sanders , Adam S. Gordon , View ORCID Profile Alfred L. George Jr. doi: https://doi.org/10.1101/2025.03.28.646046 Carlos G. Vanoye a Department of Pharmacology, Northwestern University Feinberg School of Medicine , Chicago, IL USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Reshma R. Desai a Department of Pharmacology, Northwestern University Feinberg School of Medicine , Chicago, IL USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jordan D. John b Northwestern University Feinberg School of Medicine , Chicago, IL USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Steven C. Hoffman b Northwestern University Feinberg School of Medicine , Chicago, IL USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nicolas Fink b Northwestern University Feinberg School of Medicine , Chicago, IL USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yue Zhang b Northwestern University Feinberg School of Medicine , Chicago, IL USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Omkar G. Venkatesh b Northwestern University Feinberg School of Medicine , Chicago, IL USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jonathan Roe b Northwestern University Feinberg School of Medicine , Chicago, IL USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sneha Adusumilli a Department of Pharmacology, Northwestern University Feinberg School of Medicine , Chicago, IL USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nirvani P. Jairam a Department of Pharmacology, Northwestern University Feinberg School of Medicine , Chicago, IL USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Charles R. Sanders c Center for Structural Biology and Department of Biochemistry, Vanderbilt University School of Medicine Nashville , TN USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Charles R. Sanders Adam S. Gordon a Department of Pharmacology, Northwestern University Feinberg School of Medicine , Chicago, IL USA d Center for Genetic Medicine, Northwestern University Feinberg School of Medicine , Chicago, IL USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alfred L. George Jr. a Department of Pharmacology, Northwestern University Feinberg School of Medicine , Chicago, IL USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alfred L. George Jr. For correspondence: al.george{at}northwestern.edu Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Congenital long-QT syndrome (LQTS) is most often associated with pathogenic variants in KCNQ1 encoding the pore-forming voltage-gated potassium channel subunit of the slow delayed rectifier current ( I Ks ). Generation of I Ks requires assembly of KCNQ1 with an auxiliary subunit encoded by KCNE1 , which is also associated with LQTS but causality of autosomal dominant disease is disputed. By contrast, KCNE1 is an accepted cause of recessive type 2 Jervell and Lange-Nielson syndrome (JLN2). The functional consequences of most KCNE1 variants have not been determined and the population prevalence of JLN2 is unknown. Methods : We determined the functional properties of 95 KCNE1 variants co-expressed with KCNQ1 in heterologous cells using high-throughput voltage-clamp recording. Experiments were conducted with each KCNE1 variant expressed in the homozygous state and then a subset was studied in the heterozygous state. The carrier frequency of JLN2 was estimated by considering the population prevalence of dysfunctional variants. Results : There is substantial overlap between disease-associated and population KCNE1 variants. When examined in the homozygous state, 68 KCNE1 variants exhibited significant differences in at least one functional property compared to WT KCNE1, whereas 27 variants did not significantly affect function. Most dysfunctional variants exhibited loss-of-function properties. We observed no evidence of dominant-negative effects. Most variants were scored as variants of uncertain significance (VUS) and inclusion of functional data resulted in revised classifications for only 14 variants. The population carrier frequency of JLN2 was calculated as 1 in 1034. Peak current density and activation voltage-dependence but no other biophysical properties were correlated with findings from a mutational scan of KCNE1. Conclusions : Among 95 disease-associated or population KCNE1 variants, many exhibit abnormal functional properties but there was no evidence of dominant-negative behaviors. Using functional data, we inferred a population carrier frequency for recessive JLN2. This work helps clarify the pathogenicity of KCNE1 variants. Introduction Congenital cardiac arrhythmia susceptibility including monogenic etiologies of the long-QT syndrome (LQTS) are important and recognizable causes of sudden death in the young. 1 , 2 Autosomal dominant and recessive LQTS are most often associated with pathogenic variants in genes encoding voltage-gated potassium, sodium and calcium channels or channel auxiliary subunits or specific modulatory proteins. 3 Determining the functional consequence of LQTS-associated variants helps define disease mechanisms at the molecular level and can assist in discriminating pathogenic from benign protein changes. Variants in KCNQ1 are the most frequently discovered cause of autosomal dominant LQTS (designated as the LQT1 subtype). 4 This gene encodes the KCNQ1 (K V 7.1) voltage-gated potassium channel, which is the pore-forming subunit responsible for generating the slow delayed rectifier current ( I Ks ), a major repolarizing current in cardiac myocytes. The generation of I Ks depends on a molecular partnership between KCNQ1 and an auxiliary subunit encoded by KCNE1 . 5 , 6 Heterozygous KCNE1 variants are associated with genetic LQTS in sporadic cases and small families (designated LQT5), but an international LQTS gene curation working group of the Clinical Genome Resource (ClinGen) found limited evidence supporting disease causality. 7 However, KCNE1 is a recognized cause of the autosomal recessive Jervell and Lange-Nielson Syndrome type 2 (JLN2) featuring LQTS combined with congenital sensorineural hearing impairment. 8 , 9 JLN2 is a rare disorder with an uncertain population prevalence. An international multicenter study evaluated the clinical features and electrocardiographic penetrance of KCNE1 variants in several genotype-positive individuals including 229 who were heterozygous and 19 who were diagnosed with JLN2. 10 This study also noted that many of the 32 unique disease-associated KCNE1 variants among the study population were also found in individuals from the Genome Aggregation Database (gnomAD). 11 Penetrance and arrhythmia event frequencies were low in both groups but experimental evidence of deleterious consequences was lacking for the majority of the variants raising the possibility that some non-pathogenic missense variants were falsely associated with LQTS in some heterozygous probands. 12 Additional experimental evidence may assist in determining pathogenicity for KCNE1 variants. The most widely used approach for determining the functional consequences of an ion channel variant involves heterologous expression of a recombinant channel coupled with voltage-clamp electrophysiological recording. The typical experimental approach (e.g., manual patch clamp recording) is slow, tedious and has limited throughput. Automated patch clamp recording enables much higher throughput and enables study of large numbers of channel variants including those in LQTS genes. 13 - 17 We previously optimized automated patch clamp recording methods to study dozens of KCNQ1 variants, which are also suitable for examining the functional effects of KCNE1 variants. 13 , 18 Beyond electrophysiological studies, other approaches (e.g., deep mutational scanning) for screening large numbers of engineered variants have been applied to specific ion channels or regulatory proteins. 17 , 19 - 21 Muhammad and colleagues reported use of high-throughput variant effect mapping of KCNE1 combining a cell viability assay with a flow-cytometry enabled cell surface expression analysis to assign functional classes to the majority of all possible missense KCNE1 variants. 21 How the findings from such high-throughput screening assays correlate with detailed electrophysiological analyses is not well studied. In this study, we determined the functional consequences of a large number of KCNE1 variants using high-throughput voltage-clamp recording. These data provide detailed biophysical properties of disease-associated and population variants in this gene. We assessed the impact of incorporating variant function on assignment of pathogenicity. We also used these data to estimate the population carrier frequency of JLN2, and to benchmark results from a deep mutational scan of KCNE1. Methods Plasmids and mutagenesis Full length cDNA encoding WT KCNQ1 (GenBank accession number AF000571 ) was engineered in the mammalian expression vector pIRES-CyOFP1 (pIRES-CyOFP1-KCNQ1, AddGene #173162). Human KCNE1 cDNA (GenBank accession number EF514883 ) was engineered in the mammalian expression vector pIRES2-EGFP and included a 53 base pair cassette (GGATCCACCGGTCATCCACGCCTGAGATCTAG ACAAACTTCCTAGACTGCATG) inserted immediately after the stop codon at a Bam HI site to facilitate cloning (pIRES-EGFP-GA-KCNE1, AddGene #173160). Both vectors enabled co-expression of untagged channel subunits with either orange or green fluorescent proteins as a means for tracking successful cell transfection. KCNE1 variants were introduced into the WT coding sequence using one of two methods. For some variants, the complete KCNE1 open reading frame with individual variants was synthesized using a Synthetic Genomics BioXp 3200 workstation (SGI-DNA, La Jolla, CA) and cloned into pIRES-EGFP-GA-KCNE1 using Gibson assembly. Other KCNE1 variants were generated by site-directed PCR mutagenesis using Q5 Hot Start High Fidelity DNA polymerase (New England Biolabs, Ipswich, MA) as previously described. 13 Mutagenic primers for PCR mutagenesis were designed using custom software (Q5 primer designer; available at https://prism.northwestern.edu/records/tcsgw-z4295 ), and are presented in Supplemental Table S1 . Complete sequence of each variant plasmid was performed with nanopore sequencing (Primordium Labs, Arcadia, CA) and analyzed using a custom multiple sequence alignment tool (MuSIC; available at https://prism.northwestern.edu/records/h6hc6-n0j20 ). View this table: View inline View popup Download powerpoint Supplemental Table S1. Primer sequences for site-directed mutagenesis of KCNE1 (29 variants). Cell culture and electroporation CHO-K1 cells (CCL-61, American Type Culture Collection, Manassas, VA) or CHO-K1 cells constitutively expressing human KCNE1 (designated CHO-KCNE1 cells) 13 were used. Cells were grown in F-12 nutrient medium (Thermo Fisher Scientific, Waltham, MA) supplemented with 10% fetal bovine serum (Atlanta Biologicals, Flowery Branch, GA), penicillin (50 units/mL), streptomycin (50 μg/mL) at 37°C in 5% CO 2 and maintained under selection with hygromycin B (600 μg/mL). Plasmids encoding WT KCNQ1 and WT or mutant KCNE1 were transiently expressed by electroporation using the MaxCyte STX system (MaxCyte Inc., Rockville, MD). 14 Cells were grown to 70-80% confluence then harvested using 0.25% trypsin (Thermo Fisher Scientific, Waltham, MA). A 500 µl aliquot of cell suspension was used to determine cell number and viability using an automated cell counter (ViCell, Beckman Coulter, Brea, CA). Remaining cells were collected by centrifugation at 160 x g for 4 minutes, washed with electroporation buffer (EBR100, MaxCyte Inc.), and re-suspended in electroporation buffer at a density of 100 x 10 6 viable cells/ml. Each electroporation was performed using 100 µl of cell suspension. CHO-K1 or CHO-KCNE1 cells were electroporated with 10 µg of WT KCNQ1 cDNA and 20 µg of WT or mutant KCNE1. The DNA-cell suspension mix was transferred to an OC-100×2 processing assembly (MaxCyte Inc.) and electroporated using the preset CHO-PE (CHO Protein Expression) protocol. Immediately after electroporation, 10 µl of recombinant human deoxyribonuclease I (dornase alpha, Pulmozyme ® , Genentech, Inc., San Francisco, CA) was added to the DNA-cell suspension and the entire mixture was transferred to a 35 mm tissue culture dish for a 30 min incubation at 37°C in 5% CO 2 . Following incubation, cells were gently suspended in culture media, transferred to a T75 tissue culture flask and grown for 24 hours at 37°C in 5% CO 2 . Following incubation, cells were harvested, counted, transfection efficiency determined by flow cytometry (see below), and then frozen in 1 ml aliquots at 1.8 x 10 6 viable cells/ml in liquid nitrogen until they were used in experiments. Transfection efficiency was evaluated before freezing using a benchtop flow cytometer (CytoFLEX, Beckman Coulter). Forward scatter (FSC), side scatter (SSC), and orange and green fluorescence were recorded. FSC and SSC were used to gate single viable cells and to eliminate doublets, dead cells and debris. Ten thousand events were recorded for each sample. Non-electroporated cells were assayed as a control for all parameters and used to set the gates for each experiment. The percentage of fluorescent cells was determined from the gated population. Electrophysiology The day before automated patch clamp recording, electroporated cells were thawed, plated and incubated for 10 hours at 37°C in 5% CO 2 . The cells were then grown overnight at 28°C in 5% CO 2 . Prior to experiments, cells were passaged using 0.25% trypsin in cell culture media. Cell aliquots (500 µl) were used to determine cell number and viability by automated cell counting and transfection efficiency by flow cytometry. Cells were then diluted to 200,000 cells/ml with external bath solution (see below) and allowed to recover 60 minutes at 15°C while shaking on a rotating platform at 200 rpm. Automated patch clamp experiments were performed using the Syncropatch 768 PE platform (Nanion Technologies, Munich, Germany). Single-hole, 384-well medium resistance (4-5 MΩ) or S-Type (2.5-3.5 MΩ) recording chips were used. Pulse generation and data collection were carried out with PatchController384 V.1.3.0 and DataController384 V1.2.1 software (Nanion Technologies). Whole-cell currents were recorded at room temperature in the whole-cell configuration, filtered at 3 kHz and acquired at 10 kHz. The access resistance and apparent membrane capacitance were estimated using built-in protocols. The external bath solution contained: 140 mM NaCl, 4 mM KCl, 2 mM CaCl 2 , 1 mM MgCl 2 , 10 mM HEPES, 5 mM glucose, pH 7.4. The internal solution contained: 60 mM KF, 50 mM KCl, 10 mM NaCl, 10 mM HEPES, 10 mM EGTA, 2 mM ATP-Mg, pH 7.2. Whole-cell currents were elicited from a holding potential of -80 mV using 2000 ms depolarizing pulses (from -80 mV to +60 mV in +10mV steps, every 10 secs) followed by a 2000 ms step to - 30 mV to analyze tail currents and channel deactivation rate. Non-specific currents were eliminated by recording whole-cell currents before and after addition of the I Ks selective blocker JNJ-303 (2 μM; Tocris Bioscience, Minneapolis, MN). 22 Only JNJ-303 sensitive currents and recordings meeting the following criteria were used in data analysis: seal resistance ≥ 0.5 GΩ, series resistance ≤ 20 MΩ, capacitance ≥ 1 pF, voltage-clamp stability (defined as the standard error for the baseline current measured at the holding potential for all test pulses being <10% of the mean baseline current). Data were analyzed and plotted using DataController384 V1.8 (Nanion Technologies), Excel (Microsoft Office 2013), SigmaPlot 2000 (Systat Software, Inc.) and Prism 8 (GraphPad Software) software packages. Additional custom semi-automated data handling routines were used for rapid analysis of current density and voltage-dependence of activation. Whole-cell currents were normalized for membrane capacitance and results expressed as mean ± 95% confidence interval (95% CI). Peak currents were recorded at 1990 ms after the start of the voltage pulse, while tail currents were measured 10 ms after changing the membrane potential to -30 mV. The voltage-dependence of activation was calculated by fitting the normalized G-V curves with a Boltzmann function (tail currents measured at -30 mV). The voltage-dependence of activation was determined only for cells with mean current density greater than the background current amplitude. The rate of channel deactivation was determined by fitting tail currents pre-activated with the +60 mV pulse to a single exponential function. Typical experiments compared five variants to the WT channels assayed on the same plate with up to 64 replicate recordings. Properties of each variant are presented relative to the WT channel assayed in parallel as percent current density measured at +60 mV, difference (Δ) in voltage-dependence of activation V ½ , and the ratio of activation or deactivation time-constants (variant / WT τ act, or τ deact ). The number of cells used for each experimental condition is given in supplemental datasets. Statistical analysis was performed using one-way analysis of variance (3 or more groups) and P ≤ 0.02 was considered significant based on a Bonferroni correction for typical experiments in which 5 variants were measured in parallel with WT. Classification of KCNE1 variants KCNE1 variants were classified as benign (B), likely benign (LB), variant of uncertain significance (VUS), likely pathogenic (LP), or pathogenic (P) in accordance with the American College of Medical Genetics and Genomics (ACMG) guidelines. 23 Six reviewers (J.J., S.H., N.F., Y.Z., O.V., J.R.) independently classified each variant based on existing evidence and then arrived at a consensus. ACMG criteria that could be applied for one or more variants included PVS1, PM2, PP1, PP3 and BP4. In addition to these criteria, we also used results from our functional assay to incorporate the relevant PS3 criterion under three scenarios: 1) classification of variants without applying PS3; 2) classification of variants with PS3 determined from our experiments demonstrating loss-of-function (see below); and 3) classification of variants using PS3 at varying strengths to reflect the full spectrum of electrophysiological phenotypes (see below). Evidence pertaining to KCNE1 missense variants was gathered using multiple sources including published literature, ClinVar ( https://www.ncbi.nlm.nih.gov/clinvar ), 24 InterVar ( http://wintervar.wglab.org ), 25 gnomAD v.2.1.1 ( https://gnomad.broadinstitute.org/ ), 11 and ENSEMBL ( http://www.ensembl.org ). 26 The PVS1 criterion, reflecting loss-of-function, was applied for any KCNE1 variant with deletion of more than the last 10% of the protein product consistent with Clinical Genome Resource (ClinGen) Sequence Variant Information Workgroup. 27 For deletions within the final 13 residues of the 129 amino acid protein (i.e., those that may escape nonsense mediated mRNA decay), PVS1 was applied only if loss-of-function was demonstrated by our functional study. The PM2 allele frequency criteria was applied if the highest single population allele frequency in gnomAD (Grpmax) did not exceed 0.001. The PP1 co-segregation criterion was applied at different strengths according to the number of informative meiosis among published family data as proposed by Jarvik and Browning. 28 Published pedigrees were only included in the PP1 calculation if they confirmed co-segregation of disease phenotype with a heterozygous KCNE1 variant, if genotypes were confirmed in all family members, if no more than one KCNE1 variant was present in affected family members, and if no KCNQ1 variants were present. The PP3 and BP4 criteria, reflecting in silico evidence, were derived from prediction scores generated by SIFT ( https://sift.bii.a-star.edu.sg/ ) 29 and PolyPhen-2 ( http://genetics.bwh.harvard.edu/pph2/ ). 30 PP3 or BP4 was assigned to a variant if SIFT and PolyPhen scores were concordant (deleterious or probably damaging for PP3; tolerated or benign for BP4), and neither were assigned if SIFT and PolyPhen2 were discordant. The PS3 criterion was applied based on current density measured in our study relative to WT KCNE1. Functional data from previously published literature were not used. Variants exhibiting mild (<25%), moderate (25-74%) or severe (≥ 75%) reductions in current density in the homozygous state relative to KCNE1 WT were assigned PS3 with evidence strengths of supporting (PS3[P]), moderate (PS3[M]), or strong (PS3[S]), respectively. In an expanded analysis, PS3(M) was applied for variants exhibiting >150% current density relative to KCNE1 WT consistent with gain-of-function, and BS3[P] was applied if a variant exhibited 100 ± 10% current density relative to KCNE1 WT consistent with normal to near-normal activity. Estimation of JLN2 carrier frequency To estimate the population carrier frequency for JLN2, we applied principles of Hardy-Weinberg equilibrium to publicly available, population-scale allele frequency data, adapting a previously described method. 31 , 32 For this analysis, we used functional criteria to define a carrier as any individual who is heterozygous for a variant generating current density less than 50% of WT ( Supplemental Table S2 ). We extracted the global counts of homozygous and heterozygous individuals for these specific variants from gnomAD, v2.1.1 (original source of population variants we studied), along with the total count of chromosomes assessed for each of these variants. Based on the rarity of these variants, we assumed linkage equilibrium among them – that is, we assumed that individuals are unlikely to carry a haplotype with more than one of these variants in cis . We therefore calculated carrier rate (2pq in the Hardy-Weinberg equilibrium) according to the following equation: in which k is the number of variants with current density less than 50% of WT, p represents the frequency of the most common allele, and q represents the frequency of the variant allele. View this table: View inline View popup Download powerpoint Supplemental Table S2. Summary of KCNE1 variant information and classifications. Results The primary objective of this study was to determine detailed functional properties of a large number of KCNE1 variants co-expressed with KCNQ1 in a heterologous cell line with rigorous and standardized voltage-clamp protocols performed using automated patch clamp recording. 13 , 14 This approach provided a uniform experimental platform with a high degree of replication and lack of operator bias, which overcomes the heterogeneity of conventional voltage-clamp recording performed in diverse laboratory settings. Secondary goals were to determine if functional data can contribute to classifying the pathogenicity of KCNE1 variants, and to test if findings from a deep mutation scan correlate with direct electrophysiological assessments. Finally, our knowledge of dysfunctional KCNE1 variants enabled us to estimate the population carrier frequency of KCNE1 -associated JLN2. Functional properties of KCNE1 variants We determined the functional properties of all known disease-associated KCNE1 variants reported prior to 2022 along with an approximately equal number of rare and ultra-rare KCNE1 missense variants. Reported disease-associated variants were acquired from the professional version of the Human Gene Mutation Database (HGMD). 33 Additional disease-associated variants were obtained from the literature and the ClinVar database entered prior to 2022. Population variants with minor allele frequencies less than 0.001 were ascertained from gnomAD (version 2.1.1). There was substantial overlap between these two groups of variants. In gnomAD, 22 variants were absent whereas 23 were heterozygous in a single individual ( Supplemental Table S2 ). All other KCNE1 variants we studied were heterozygous in 2 to 73 individuals in gnomAD. Each variant was engineered in a recombinant human KCNE1 cDNA and co-expressed with WT human KCNQ1 in CHO-K1 cells to generate the slow delayed rectifying current ( I Ks ), which we pharmacologically isolated using a selective inhibitor (JNJ-303). In separate experiments, we mimicked the heterozygous state by expressing KCNE1 variants with WT KCNQ1 in a CHO-K1 cell line stably expressing WT KCNE1. Two prominent functional properties were measured including peak current density and voltage-dependence of activation quantified as the voltage-midpoint (V ½ ) of conductance-voltage relationships. We also quantified activation and deactivation kinetics as time constants derived from exponential fits to the time course of these gating events. Functional results are presented separately for the three main KCNE1 structural domains (N-terminus, transmembrane, C-terminus). We first determined functional properties of variants expressed in the absence of WT KCNE1 (homozygous state), and then for those variants exhibiting abnormal function we separately measured properties in the presence of WT KCNE1 (mimicking the heterozygous state). Functional properties of KCNE1 variants in the homozygous state KCNE1 variants analyzed in the homozygous state were distributed approximately equally among the three major protein domains: 36 in the N-terminus, 29 in the transmembrane domain and 30 in the C-terminus ( Supplemental Figure S1 ) . Averaged whole-cell currents (normalized to WT channel peak amplitude) recorded from CHO-K1 cells co-expressing WT KCNQ1 with each of the 95 KCNE1 variants are presented in Supplemental Figure S2 . We designated four groups based on current density: severe loss-of-function (peak current density ≤0.25 fold of WT I Ks ), partial loss-of-function (peak current density 0.25-0.75 fold of WT I Ks ), WT-like, and gain-of-function (peak current density >1.5 fold of WT I Ks ). Complete quantitative data are presented in Supplemental Dataset 1 . Download figure Open in new tab Supplemental Fig. S1. Distribution of variants in the KCNE1 protein. Approximate location of KCNE1 variants within the N-terminus, transmembrane (TM) domain, and C-terminus. Red ‘lollipops’ projecting above the horizontal line represent variants found 0 or 1 times in gnomAD. Blue ‘lollipops’ projecting below the horizontal line represent variants found 2 or more times in gnomAD. The height of each lollipop corresponds to the number of variants at that location. Details about specific variants is presented in Supplemental Table S2 . Download figure Open in new tab Download figure Open in new tab Download figure Open in new tab Supplemental Fig. S2. Averaged whole cell current traces from cells expressing KCNE1 variants in the homozygous state. Each trace represents JNJ-303 sensitive currents from an average of 21-126 cells co-expressing KCNQ1 with the indicated KCNE1 variant. Currents were normalized to peak current in cells expressing WT KCNE1 and WT KCNQ1 recorded in parallel. Scale bars represent 25% of WT (vertical) and 500 ms (horizontal). Location of variants is indicated by the colored labels (green = N-terminus; orange = TM domain; blue = C-terminus). Figure 1 . illustrates peak current density relative to WT I Ks for each of the 95 KCNE1 variants grouped by their location in the KCNE1 protein and presented as scatter plots consisting of all individual measurements, mean values and 95% confidence intervals. Figure 2 summarizes mean peak current density data in volcano plots illustrating variants with statistically significant differences from WT I Ks (plotted above the horizontal dashed line representing P = 0.02) either to the left or right of a vertical horizontal line indicating loss- or gain-of-function, respectively. Approximately half (47 of 95) of the KCNE1 variants tested in the homozygous state exhibited peak current densities significantly different from WT I Ks . Severe and partial loss-of-function were most common for variants in the transmembrane domain. Six variants (R32C, S34P, E83K, Q88K, Q96R, V99L) exhibited peak current density significantly greater than WT I Ks and none were in the transmembrane domain. Download figure Open in new tab Figure 1. Peak current density for KCNE1 variants in the homozygous state. JNJ-303 sensitive peak whole-cell currents measured at +60 mV from CHO-K1 cells co-expressing WT KCNQ1 with KCNE1 variants plotted as fold difference from WT channels recorded in parallel. ( A ) Variants in the N-terminus (green symbols). ( B ) Variants in the transmembrane domain (orange symbols). ( C ) Variants in the C-terminus (blue symbols). All individual data points are plotted as open symbols and mean values are shown as filled symbols with error bars representing the 95% CI. Values to the right or left of the vertical dashed line (normalized WT value) represent current density larger (gain-of-function) or smaller (loss-of-function) than WT, respectively. The number of recorded cells for each variant and individual P -values are presented in Supplemental Dataset 1 . Download figure Open in new tab Figure 2. Volcano plots of current density for KCNE1 variants in the homozygous state. Volcano plots of mean JNJ-303 sensitive peak whole-cell currents from Figure 1 . ( A ) Variants in the N-terminus (green symbols). ( B ) Variants in the transmembrane domain (orange symbols). ( C ) Variants in the C-terminus (blue symbols). Only variants with peak current density significantly different from WT ( P < 0.02, horizontal dashed line) are labeled. Black symbols represent variants with no significant difference from WT. Symbols to the left of the vertical dashed line denote smaller (loss-of-function) and symbols to the right indicate larger (gain-of-function) current density. Tail current amplitudes were sufficiently large to calculate voltage-dependence of activation parameters reliably for 78 variants. About one third (34%, 27/78) of variants predominantly in the transmembrane domain or C-terminus induced significantly hyperpolarizing (10 variants) or depolarizing (17 variants) shifts of activation V 1/2 ( Figure 3 ; Supplemental Figure S3 ). For these 78 variants, we also determined the voltage sensitivity of channel activation ( k , the slope of the Boltzmann fit curve, Supplemental Figure S4 ). Interestingly, for all variants with significant shifts in activation V ½ , hyperpolarizing shifts were accompanied by larger slope values (10 variants), while depolarizing shifts had smaller slope values (17 variants). This association was independent of variant location. Download figure Open in new tab Supplemental Fig. S3. Activation voltage-dependence of KCNE1 variants (homozygous state). Averaged voltage-dependence of activation measured in KCNE1 variant-expressing cells plotted as the difference (ΔV½ in mV) from the averaged V½ for WT KCNE1 channels recorded in parallel. ( A ) Variants in the N-terminus (green symbols). ( B ) Variants in the transmembrane domain (orange symbols). ( C ) Variants in the C-terminus (blue symbols). All individual data points are plotted as open symbols and mean values are shown as filled symbols with error bars representing the 95% CI. Values to the right or left of the vertical dashed line (indicating no difference from WT) represent hyperpolarized (gain-of-function) or depolarized (loss-of-function) activation V½, respectively. For some variants, activation V½ could not be determined (nd). The number of recorded cells for each variant and individual P -values are presented in Dataset 1 . Download figure Open in new tab Supplemental Fig. S4. Activation slope factors of KCNE1 variants (homozygous state). Average slope for voltage-dependence of activation curves ( k ) determined from fitting the data for each KCNE1 variant-expressing cell to a Boltzmann function and plotted as ratio from the averaged k for WT KCNE1 channels recorded in parallel. ( A ) Variants in the N-terminus (green symbols). ( B ) Variants in the transmembrane domain (orange symbols). ( C ) Variants in the C-terminus (blue symbols). All individual data points are plotted in panels A - C as open symbols and mean values are shown as filled symbols with error bars representing the 95% CI. Values to the right or left of the vertical dashed line (indicating no difference from WT) represent steeper (i.e., smaller voltage change for increase in function) or shallower (i.e., larger voltage change for increase in function) activation curve slope, respectively. ( D-F ) Volcano plots of mean values for KCNE1 variants with k values significantly different from WT ( P < 0.02, horizontal dashed line). Symbols to the left of the vertical dashed line denote larger k values (loss-of-function), while symbols to the right indicate smaller k values (gain-of-function). Symbol color denotes location with KCNE1 domains as described for panels A - C . Unlabeled black symbols represent variants with no significant difference from WT. The number of recorded cells for each variant and individual P -values are presented in Dataset 1 . Download figure Open in new tab Figure 3. Activation voltage-dependence for KCNE1 variants in the homozygous state. Voltage-dependence of activation measured in KCNE1 variant-expressing cells plotted as the difference (ΔV½ in mV) from the averaged V½ for WT KCNE1 channels recorded in parallel and presented as volcano plots. ( A ) Variants in the N-terminus (green symbols). ( B ) Variants in the transmembrane domain (orange symbols). ( C ) Variants in the C-terminus (blue symbols). Only variants with V½ significantly different from WT ( P < 0.02, horizontal dashed line) are labeled. Black symbols represent variants with no significant difference from WT. Symbols to the left of the vertical dashed line denote depolarized V½ values (loss-of-function), while symbols to the right indicate hyperpolarized V½ values (gain-of-function). Black symbols represent variants with no significant difference from WT. The number of recorded cells for each variant and individual P -values are presented in Supplemental Dataset 1 and scatter plots of individual data are presented in Supplemental Figure S3 . We also determined the effect of homozygous KCNE1 variants on activation and deactivation kinetics. Sixteen variants caused significant changes in the rate of channel activation ( Figure 4 ; Supplemental Figure S5 ). Five variants located in the N-terminus (T10M, R32C, P35S, A43K, A44T) and 7 in the transmembrane domain (M49I, V47F, L48F, G55S, T58I, I61V, R67G) caused faster activation relative to WT channel, while C-terminal variants caused either faster (S74W, Q88K, Q96R) or slower (D91E, R98Q, A114T) activation. Twenty-four KCNE1 variants evoked altered deactivation kinetics ( Figure 5 ; Supplemental Figure S6 ). Variants in the transmembrane domain and C-terminus mostly induced faster deactivation relative to the WT channel. Four transmembrane variants (L48F, M49I, G55S, T58I) accelerated both activation and deactivation. Two variants in either the N-terminus (E43K) or C-terminus (D91E) affected both activation and deactivation in opposite directions, faster activation with slower deactivation for E43K, and slower activation with faster deactivation for D91E. Download figure Open in new tab Supplemental Fig. S5. Activation time constants of KCNE1 variants (homozygous state). Activation time constants determined for each KCNE1 variant expressed as a ratio to the averaged activation time constant of WT channels recorded in parallel. ( A ) Variants in the N-terminus (green symbols). ( B ) Variants in the transmembrane domain (orange symbols). ( C ) Variants in the C-terminus (blue symbols). All individual data points are plotted as open symbols and mean values are shown as filled symbols with error bars representing the 95% CI. Values to the right or left of the vertical dashed line (indicating no difference from WT) represent smaller (faster activation, gain-of-function) or larger (slower activation, loss-of-function) activation time constants, respectively. For some variants, activation time constants could not be determined (nd). The number of recorded cells for each variant and individual P -values are presented in Dataset 1 . Download figure Open in new tab Supplemental Fig. S6. Deactivation time constants of KCNE1 variants (homozygous state). Deactivation time constants determined for each KCNE1 variant expressed as a ratio to the averaged deactivation time constant of WT channels recorded in parallel. ( A ) Variants in the N-terminus (green symbols). ( B ) Variants in the transmembrane domain (orange symbols). ( C ) Variants in the C-terminus (blue symbols). All individual data points are plotted as open symbols and mean values are shown as filled symbols with error bars representing the 95% CI. Values to the right or left of the vertical dashed line (indicating no difference from WT) represent larger (slower deactivation, gain-of-function) or smaller (faster deactivation, loss-of-function) deactivation time constants, respectively. For some variants, deactivation time constants could not be determined (nd). The number of recorded cells for each variant and individual P -values are presented in Dataset 1 . Download figure Open in new tab Figure 4. Activation kinetics for KCNE1 variants in the homozygous state. Activation time constants determined for each KCNE1 variant expressed as a ratio to the averaged value of WT channels recorded in parallel and presented as volcano plots. ( A ) Variants in the N-terminus (green symbols). ( B ) Variants in the transmembrane domain (orange symbols). ( C ) Variants in the C-terminus (blue symbols). Only variants with time constants significantly different from WT ( P < 0.02, horizontal dashed line) are labeled. Black symbols represent variants with no significant difference from WT. Values to the right or left of the vertical dashed line (indicating no difference from WT) represent smaller (faster activation, gain-of-function) or larger (slower activation, loss-of-function) activation time constants, respectively. The number of recorded cells for each variant and individual P -values are presented in Supplemental Dataset 1 . Scatter plots of individual data are presented in Supplemental Figure S5 . Download figure Open in new tab Figure 5. Deactivation kinetics for KCNE1 variants in the homozygous state. Deactivation time constants determined for each KCNE1 variant expressed as a ratio to the averaged value of WT channels recorded in parallel and presented as volcano plots. ( A ) Variants in the N-terminus (green symbols). ( B ) Variants in the transmembrane domain (orange symbols). ( C ) Variants in the C-terminus (blue symbols). Only variants with time constants significantly different from WT ( P < 0.02, horizontal dashed line) are labeled. Black symbols represent variants with no significant difference from WT. Values to the right or left of the vertical dashed line (indicating no difference from WT) represent larger (slower deactivation, gain-of-function) or smaller (faster deactivation, loss-of-function) deactivation time constants, respectively. The number of recorded cells for each variant and individual P -values are presented in Supplemental Dataset 1 . Scatter plots of individual data are presented in Supplemental Figure S6 . In summary, among the 95 KCNE1 variants tested in the homozygous state, 68 induced significant differences in the measured functional properties, while 27 variants did not significantly affect I Ks function. Transmembrane domain variants exerted the largest number of significant effects on functional properties with 100% of these variants causing at least one significantly different functional property compared to WT. In contrast, variants in the N-terminus induced the least number of significant functional effects with approximately half (53%) exhibiting WT-like function. In the C-terminus, 22 variants (73%) affected at least one functional property. Functional properties of KCNE1 variants in the heterozygous state To determine if any variants with significant dysfunction exerted dominant effects, we repeated voltage clamp experiments in the presence of WT KCNE1. The KCNE1 variants analyzed in the heterozygous state included 18 severe loss-of-function, 8 with partial loss-of-function, and 2 gain-of-function variants. Averaged normalized whole-cell currents recorded from CHO-E1 cells co-expressed with WT KCNQ1 and each of the 28 KCNE1 variants are presented in Supplemental Figure S7 . Download figure Open in new tab Supplemental Fig. S7. Averaged whole cell current traces from cells expressing KCNE1 variants in the heterozygous state. Each trace represents JNJ-303 sensitive currents from an average of 38-131 CHO-E1 cells coexpressing KCNQ1 with the indicated KCNE1 variant. Currents were normalized to peak current in CHO-E1 cells co-expressing WT KCNE1 and WT KCNQ1 recorded in parallel. Scale bars represent 25% of WT (vertical) and 500 ms (horizontal). Location of variants is indicated by the colored labels (green = N-terminus; orange = TM domain; blue = C-terminus). Figure 6 . and Supplemental Figure S8 illustrate the functional properties of KCNE1 variants expressed in the heterozygous state and complete quantitative data are presented in Supplemental Dataset 2 . Only one variant (Q96R) (gain-of-function in the homozygous state) exhibited significantly different peak current density relative to the WT channel. None of the other 27 variants were significantly different from WT. In the heterozygous state, only 2 variants, both located in the C-terminus, caused significant differences in activation V 1/2 ; D76N (severe loss-of-function in homozygous state) caused a depolarizing shift, while Q96R generated a hyperpolarizing shift, similar to when the variant was expressed in the homozygous state. Unlike in the homozygous state, no significant differences were observed for activation curve slope ( Supplemental Dataset 2 ). Download figure Open in new tab Supplemental Fig. S8. Functional properties of KCNE1 variants in the heterozygous state. ( A ) Average JNJ-303-sensitive peak whole-cell current density measured at +60 mV from CHO-E1 cells co-expressing WT KCNQ1 with each KCNE1 variant. Data are displayed as fold divergence from WT channels recorded in parallel. Values to the right or left of the vertical dashed line (normalized WT value) represent current density larger or smaller than WT, respectively. ( B ) Activation voltage-dependence for each heterozygous KCNE1 variant plotted as the difference (ΔV½ in mV) between the averaged V½ for WT channels recorded in parallel. Values to the right or left of the vertical dashed line indicate hyperpolarized or depolarized activation V½, respectively. ( C ) Activation time constants (τ) determined for each heterozygous KCNE1 variant plotted as the ratio to the averaged activation time constant for WT channels recorded in parallel. Values to the right or left of the vertical dashed line (no difference from WT) indicate faster or slower activation, respectively. ( D ) Deactivation time constants (τ) determined for each heterozygous KCNE1 variant plotted as the ratio to the averaged deactivation time constant for WT channels recorded in parallel. Values to the right or left of the vertical dashed line indicate slower or faster deactivation, respectively. Location of variants is indicated by the colored symbols (green = N-terminus; orange = TM domain; blue = C-terminus). The number of recorded cells for each variant and individual P -values are presented in Dataset 2 . Analysis of the whole-cell current kinetics revealed that three transmembrane variants (G52R, F53S, T58P) in the heterozygous state induced significantly slower activation. All three variants exhibited severe loss-of-function in the homozygous state based on current density ( Figures 1 and 2 ). Three variants in the transmembrane domain (G52R, F53S, L59P; all severe loss-of-function in the homozygous state) and three in the C-terminus (D76N, Y81C, W87R; severe or partial loss-of-function in the homozygous state) accelerated deactivation. Two transmembrane domain variants (F53S and T58P) slowed activation while also causing faster deactivation in the heterozygous state. Download figure Open in new tab Figure 6. Functional properties of KCNE1 variants in the heterozygous state. ( A ) Volcano plots of mean JNJ-303 sensitive peak whole-cell current density measured at +60 mV from CHO-E1 cells co-expressing WT KCNQ1 with each KCNE1 variant. Data are displayed as fold divergence from WT channels recorded in parallel. Only variants with peak current density significantly different from WT ( P < 0.02, horizontal dashed line) are labeled. Values to the right or left of the vertical dashed line (normalized WT value) represent current density larger or smaller than WT, respectively. ( B ) Volcano plots of mean difference (ΔV½ in mV) between the averaged V½ for variant and WT channels recorded in parallel. Only variants with peak current density significantly different from WT (P<0.02, horizontal dashed line) are labeled. Values to the right or left of the vertical dashed line indicate hyperpolarized or depolarized activation V½, respectively. ( C ) Volcano plots of mean activation time constants (τ) determined for each heterozygous KCNE1 variant plotted as the ratio to the averaged activation time constant for WT channels recorded in parallel. Values to the right or left of the vertical dashed line (no difference from WT) indicate faster or slower activation, respectively. ( D ) Volcano plots of mean deactivation time constants (τ) determined for each heterozygous KCNE1 variant plotted as the ratio to the averaged deactivation time constant for WT channels recorded in parallel. Values to the right or left of the vertical dashed line indicate slower or faster deactivation, respectively. Symbols colors are defined in previous figures. The number of recorded cells for each variant and individual P -values are presented in Supplemental Dataset 2 . Scatter plots of individual data are presented in Supplemental Figure S8 . In summary of our functional study, all KCNE1 variants with severe loss-of-function in the homozygous state exhibited peak current densities there were not significantly different from WT channels when tested in the heterozygous state, indicating that none of these variants exert overt dominant-negative effects. However, some of those variants induced slower activation and faster deactivation rates suggesting that co-expression of WT KCNE1 does not fully rescue some variants. In the C-terminus, the variant Q96R exerts a gain-of-function phenotype. This variant causes significant increase in peak current density and hyperpolarizing shift in V ½ of activation of similar magnitude in both the homozygous and heterozygous state ( Supplemental Datasets 1 and 2) . Impact of functional results on variant classification To ascertain genotype-phenotype correlations and evaluate evidence of disease co-segregation, we performed an extensive review of published reports in which KCNE1 was reported to be associated with long-QT syndrome or other clinical phenotypes ( Supplemental Table S3 ). We found limited evidence supporting disease co-segregation with most reports describing KCNE1 variants in isolated cases lacking a detailed family history. This is consistent with the findings of the ClinGen expert panel, which classified the association between KCNE1 and autosomal dominant LQTS as ‘limited’. 7 View this table: View inline View popup Supplemental Table S3. Literature review of KCNE1 genotype-phenotype correlations. References to original research concerning each variant investigated in our study. Literature was obtained from KCNE1 variant entries in ClinVar including their corresponding LitVar findings, Bibliome.ai browser, and Mastermind Genomic Search Engine by Genomenon. Additional literature was obtained via Google Search using keywords in the following search string: (KCNE1 OR ISK OR JLNS OR JLNS2 OR LQT2/5 OR LQT5 OR MinK OR “potassium voltage-gated channel subfamily E regulatory subunit 1”) AND ([single letter code amino acid variant] OR [three letter code amino acid variant] OR p.[three letter code amino acid variant] OR “[complementary DNA change]” OR “[dbSNP identity]”) with aliases for KCNE1 in the first set of parentheses and variant-specific identifiers in the second set of parentheses, with bracketed terms adjusted per variant. Of the variants investigated in this study that do not possess a dbSNP ID (G25V, L51H, F54V, I82V, and V99L), search strings were entered as above without the dbSNP ID. An example of such a search string would be the following for KCNE1 D76N: (KCNE1 OR ISK OR JLNS OR JLNS2 OR LQT2/5 OR LQT5 OR MinK OR “potassium voltage-gated channel subfamily E regulatory subunit 1”) AND (D76N OR Asp76Asn OR p.Asp76Asn OR “c.226G>A” OR “rs74315445”) Sources were included provided they list specific germline nucleotide changes and/or amino acid changes. Sources were excluded if they included identifiers for multiple missense variants without specifying nucleotide and/or protein changes (eg, a source includes rs199473348 without distinguishing between A8V or A8E). Sources that investigated cDNA changes not used in constructs in this study were excluded (eg, both c.273C>A and c.273C>G code for D91E but only the sources investigating c.273C>A were included). References with somatic mutations were excluded. Application of ACMG criteria prior to consideration of functional data classified 91 as VUS, 2 as likely pathogenic (LP; A8V, G52R) and 2 as pathogenic (P; W17X, Y46X) ( Supplemental Table S2 ). The inclusion of functional data resulted in revised ACMG classifications with 77 VUS, 15 LP, and 3 P ( Supplemental Table S2 ). The 13 variants reclassified from VUS to LP exhibited severe loss-of-function in the homozygous state. Among 33 variants with partial loss-of-function, 32 remained classified as VUS and one was reclassified as LP (S74L). We initially applied functional data without considering gain-of-function as pathogenic or WT-like current density as benign criteria. In an expanded analysis we scored gain-of-function as PS3[M] and WT-like current density as BS3[P]. This resulted in additional reclassification of 5 VUS to LB (P11S, A31T, G40S, V80I, T120I) and 1 LP to VUS (A8V; Supplemental Table S2 ). There were no additional reclassifications from VUS to LP in this expanded analysis. Among the 22 variants absent in gnomAD, 21 were initially classified as VUS and six could be reclassified from VUS to LP (V47F, L51H, F53S, F54V, T58P-L59P, W87R) and one from LP to P (G52R) after consideration of expanded functional data. Estimation of JLN2 carrier frequency KCNE1 is associated with autosomal recessive LQTS with deafness (type 2 Jervell and Lange-Nielsen syndrome; JLN2). Co-inheritance of dysfunctional KCNE1 variants as either homozygous or compound heterozygous genotypes would be compatible with this condition. Based on our functional study, we posited that variants exhibiting peak current densities less than 50% of WT I Ks would be consistent with a pathogenic carrier allele. Therefore, we used functional data from our study combined with allele counts in gnomAD to estimate a population JLN2 carrier frequency (see Methods). Using this approach, we calculated the prevalence of JLN2 carriers in the population to be 0.000967 (1 in 1034). Correlation of voltage clamp recordings with KCNE1 deep mutational scan Voltage clamp recording is widely considered a ‘gold’ standard experimental approach for determining the functional consequences of ion channel variants, but there are limitations in scale even with automation. A recent study using high throughput variant effect mapping of an epitope-tagged KCNE1 reported both cell surface expression and cell fitness in cells co-expressing WT KCNQ1 or a gain-of-function KCNQ1 variant (S140G) as proxies for function for a large number of single amino acid variants. 21 We compared our findings to results from this deep mutational scan to determine the extent of correlation between the two approaches. Figure 7 illustrates the correlation between peak current density measured in cells expressing WT KCNQ1 and different KCNE1 variants with the functional and trafficking scores deduced from the mutational scanning study. Both scores showed statistically significant linear correlations with measured current density although many variants were well outside 95% confidence intervals. The correlation was strongest with functional score (r 2 = 0.304, P < 0.0001) but was also significantly correlated with trafficking scores (r 2 = 0.092, P = 0.003) albeit less strong. The weaker correlation with trafficking scores suggests that current density can be influenced by factors other than cell surface expression (e.g., channel open probability, activation or deactivation kinetics). We also examined correlation with biophysical properties of the expressed channels ( Figure 8 ). Activation voltage dependence quantified as the absolute difference between WT and variant activation V ½ (ΔV ½ in mV) was significantly correlated with functional scores (r 2 = 0307, P < 0.0001), but there was no correlation with activation or deactivation kinetics. Discussion KCNE1 encodes a single transmembrane domain protein identified originally by expression cloning using Xenopus laevis oocytes. 34 and initially thought to represent a novel minimal potassium channel sequence (dubbed minK). 35 However, further investigations revealed that it was a modulator of KCNQ1 (originally named K V LQT1), 36 the gene for type 1 LQTS implicating KCNE1 as an essential component of the I Ks channel complex. 5 , 6 The recognition of this physiologically important molecular partnership identified KCNE1 as a candidate gene for genetic arrhythmia susceptibility and lead to discovery of homozygous and compound heterozygous KCNE1 variants in a few families with JLN. 8 The addition of KCNE1 to genetic testing panels for cardiac arrhythmia led to discoveries of heterozygous variants in a small subset of congenital LQTS cases designated as LQT5, 9 which accounts for 1% or less of all genotype-positive LQTS cases. 37 Download figure Open in new tab Figure 7. Correlation of peak current density with functional and trafficking scores from a KCNE1 deep mutation scan. ( A ) Plot of peak current density measured in cells co-expressing WT KCNQ1 with KCNE1 variants (homozygous state) compared with the Functional Score reported for each variant determined by a cell fitness assay. 21 ( B ) Plot of peak current density measured in cells co-expressing WT KCNQ1 with KCNE1 variants (homozygous state) compared with the reported Trafficking Score for each variant. 21 In both plots, solid blue lines represent a linear regression fit to the data with 95% CI shown as light blue shadow. Download figure Open in new tab Figure 8. Correlation of biophysical properties with functional scores from a KCNE1 deep mutation scan. ( A ) Plot of differences in activation V½ (ΔV½ in mV) measured in cells co-expressing WT KCNQ1 with KCNE1 variants (homozygous state) compared with the Functional Score reported for each variant determined by a cell fitness assay. 21 ( B ) Plot of differences in activation time constants measured in cells co-expressing WT KCNQ1 with KCNE1 variants (homozygous state) compared with the reported Functional Score. ( C ) Plot of differences in deactivation time constants measured in cells co-expressing WT KCNQ1 with KCNE1 variants (homozygous state) compared with the reported Functional Score. While genetic evidence for association between KCNE1 and JLN, a recessive disease, appears strong, the evidence supporting the association of KCNE1 with autosomal dominant LQTS only reached the strength of limited when reviewed by a ClinGen expert panel. 7 Additionally, an international multicenter study concluded that heterozygous loss-of-function KCNE1 variants were low penetrance contributors to arrhythmia susceptibility with most carriers exhibiting mild to no clinical phenotypes. 10 Moreover, the emergence of population exome and genome sequencing data revealed the presence of many rare KCNE1 variants that had been implicated in LQT5, further supporting the assertion of low penetrance. However, a stated limitation of this conclusion was the absence of more complete functional assessments of putative disease-causing variants. 10 In this study, we sought to evaluate the functional consequences of many human KCNE1 variants associated with either cardiac arrhythmia or discovered in a population sample (gnomAD). We used a scalable approach based on automated patch clamp recording that was validated previously for study of KCNQ1 variants. 13 , 18 We found extensive overlap in functional behavior between disease-associated and population KCNE1 missense variants. Furthermore, none of the studied variants exhibited dominant-negative effects that would be consistent with highly penetrant disease-causing variants in a heterozygous state. We did observe that several KCNE1 variants exhibited substantial loss-of-function effects in the homozygous state that were able to drive reclassification of some VUS to pathogenic categories. We also used these data to infer a population carrier frequency of JLN2. Overall, our study contributes to clarifying the genetic contributions of KCNE1 to congenital arrhythmia susceptibility. Our findings coupled with a careful review of the literature are consistent with the notion that KCNE1 contributes to clinically significant arrhythmia susceptibility mainly as a recessive gene. Our estimate of the JLN2 carrier frequency (1 in 1034) indicates that KCNE1 is not among the most frequent recessive genes in the population with a carrier frequency similar to MYH7 , a rare inherited cause of cardiomyopathy. 32 Previous reports of severe arrhythmia phenotypes associated with heterozygous KCNE1 variants may be explained by a reporting bias that highlights the most severe cases recognized before availability of population variant frequencies. An alternative viewpoint as suggested by Roberts et al., 10 is that KCNE1 variants act as modifiers with low penetrance individually, requiring other concurrent genetic or acquired factors to manifest as LQTS. The idea of concurrent factors is supported by evidence that a common KCNE1 variant (D85N) is a risk factor for drug-induced LQTS. 38 Other unrecognized factors including undetected pathogenic variants in other known or unknown arrhythmia susceptibility genes may exist in LQT5 cases, and arrhythmia susceptibility in these cases may be oligogenic rather than monogenic. Inclusion of KCNE1 as a potential monogenic cause of autosomal dominant LQTS on clinical genetic testing panels may warrant reconsideration as suggested by the ClinGen panel. 7 We also examined the correlation between our direct electrophysiological assessments of 95 KCNE1 variants and data from a deep mutational scan of this gene that used indirect proxies of function. 21 As illustrated in Figure 7 , there was significant correlation between direct measurement of peak current density and a functional score derived from a cell viability assay. This result is encouraging and supports the validity of the mutational scanning data for general functional effects. We also highlight a significant correlation between the functional score and activation voltage-dependence ( Figure 8A ) that further underscores the potential value of the approach. By contrast, the cell viability assay did not correlate with more nuanced functional consequences of KCNE1 variants (activation and deactivation kinetics; Figure 8B , C ) that may contribute to arrhythmia susceptibility. Importantly, the reported deep mutational scan did not assess KCNE1 variants in the heterozygous state, but refinement of the assay (e.g., constitutive expression of WT KCNE1 in the cellular platform) might be possible to address this point. Study Limitations We did not consider other potential interacting partners of KCNE1 such as the human ether-á-go-go related gene (hERG), which might be relevant. 39 We did not consider performing such studies given the uncertain physiological relevance of hERG interactions with KCNE1 that were demonstrated only in overexpression studies in vitro . We also did not use the most relevant cell type (e.g., cardiac myocytes) to conduct our study because of the challenges inherent in culturing, transfecting and recording from such cells that would have severely limited throughput. Finally, in our heterozygous assay, we cannot control stoichiometry of WT and variant KCNE1 in transiently transfected cells. However, using automated patch clamp, we were able to record from a large number of replicates that should sample the range of subunit ratios that arise from random assortment analogous to expression in native cells. Conclusions In this study, we determined the functional consequences of 95 KCNE1 variants that enabled us to demonstrate absence of a dominant-negative molecular mechanism, infer a population carrier frequency of recessive JLN2, and benchmark findings from an orthogonal deep mutational scan. This work helps clarify the pathogenicity of KCNE1 , contributes to emerging doubt regarding this gene as a monogenic cause of dominant cardiac arrhythmia, and validates deep mutational scanning for assessing some functional properties of variants in this gene. Acknowledgements This work was supported by NIH grant HL122010. References 1. ↵ George AL , Jr. Molecular and genetic basis of sudden cardiac death . J Clin Invest . 2013 ; 123 : 75 – 83 . OpenUrl CrossRef PubMed 2. ↵ Ackerman M , Atkins DL and Triedman JK . Sudden cardiac death in the young . Circulation . 2016 ; 133 : 1006 – 26 . OpenUrl Abstract / FREE Full Text 3. ↵ Schwartz PJ , Ackerman MJ , Antzelevitch C , et al. Inherited cardiac arrhythmias . Nat Rev Dis Primers . 2020 ; 6 : 58 . OpenUrl PubMed 4. ↵ Schwartz PJ , Crotti L and Insolia R. Long-QT syndrome: from genetics to management . Circ Arrhythm Electrophysiol . 2012 ; 5 : 868 – 877 . OpenUrl FREE Full Text 5. ↵ Sanguinetti MC , Curran ME , Zou A , et al. 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