107 research outputs found
Electrophysiological and cellular analysis of filamin-C mutations causing cardiomyopathy using human iPSC-derived cardiomyocytes
Background: Arrhythmogenic Cardiomyopathy (AC) is a genetic
cardiac disease resulting from different mutations within proteins
constituting the intercalated disc, including desmosomal and nondesmosomal proteins. Recent studies have revealed that mutations in
filamin-C (FLNC) may lead to AC. The arrhythmogenesis and
electrophysiological effects of FLNC-related AC are incompletely
understood. Therefore, the aim of this study is to assess the potential
electrophysiological consequences of FLNC loss as occurs in AC in
human induced pluripotent stem cell-derived cardiomyocytes (hiPSCCMs). Specifically, I aimed to characterise abnormal electrical activity
and the expression and function of key proteins in cardiac electrical
activity such as gap junction protein connexin 43 (Cx43).//
Methods: hiPSC-CMs were differentiated and observed by
immunofluorescence microscopy. Small interfering RNA (siRNA)
transfection was utilised to knockdown the expression of FLNC in
hiPSC-CMs. Protein analysis was performed using western blotting to
confirm the knockdown efficiency. Electrophysiological properties were
recorded using a multielectrode array and manual patch clamping.
Optical recording of membrane potential and calcium activity from
hiPSC-CMs were also carried out using parameter sensitive dyes.//
Results: Silencing of FLNC led to markedly decreased
immunofluorescence signals of FLNC, Cx43, desmoplakin, and
junctional plakoglobin. No significant reductions were noted in the immunofluorescence signals of voltage-gated sodium channel (Nav1.5) and plakophilin-2 compared with control hiPSC-CMs. Western blotting showed the reduction of FLNC and Cx43 expression following silencing
of FLNC. Knockdown of FLNC resulted in disturbances to the recorded
action and field potential signals of hiPSC-CMs and arrhythmic likeevents. Transfected hiPSC-CMs with siRNA-FLNC were associated
with prolongation of calcium transient durations, optical action potential
duration, and action potentials measured with patch clamping.//
Conclusion: The current findings indicated that loss of FLNC resulted in a complex arrhythmogenic phenotype in hiPSC-CM
Effect of TBBPA on arterial contractile regulation and possible implications for the development of hypertensive diseases
The endocrine disruptor (EDCs) is a compound that has been defined as “an exogenous agent that interferes with the production, release, transport, metabolism, binding, action or elimination of natural hormones in the body responsible for the maintenance of homeostasis and the regulation of developmental processes.” This compound can affect the endocrine function via interference with hormone pathways (e.g., oestrogen, androgen, or thyroid hormone). The constant human exposure to endocrine disruptors has raised some concerns. Some of these components are suspected of being harmful to human health.
Brominated flame retardants (BFRs) are chemicals widely used in consumer products, including electronics, vehicles, plastics, and textiles, to reduce flammability. These compounds can interfere with hormone homeostasis, so they are considered endocrine disruptors. Tetrabromobisphenol A (TBBPA) is the most studied BFRs due to its toxicity and presence in a variety of environmental media and the human being. The exposure to this compound is associated with several health risks: thyroid disorders, diabetes, reproductive health, cancer, and neurobehavioral development disorders. In addition, TBBPA exposure can be correlated with some cardiovascular disorders, such as diabetes and obesity. This compound has also been detected in biological samples such as human serum, urine, and breast milk. Moreover, TBBPA has also been detected in the umbilical cord of Japanese pregnant women, proving a prenatal exposure to this compound. This observation suggests that TBBPA can cross the human placenta. In this scenario, it is important to understand how the TBBPA exposure effects the vascular tonus and if the endocrine disrupting effects from that exposure can be detected in future generations.
In this project, organ bath and patch clamp techniques were developed and applied to achieve the main goal of this doctoral thesis: to study the effect of TBBPA on arterial contractility and analyse the mode of action of TBBPA as a human EDCs and understand its involvement in vascular disorders. This study was performed in two different study models: in the human umbilical artery (HUA) and in the rat aorta.
Additionally, the cGMP compartmentation in human vascular smooth muscle was also analysed. Therefore, in the first research work presented, we infected smooth muscle cells with adenovirus containing mutants of the rat olfactory cyclic nucleotide-gated (CNG) channel-subunit to understand how the cGMP conveys different information and we recorded the associated cGMP-gated current (ICNG). The whole cell configuration of the patch clamp technique was used to measure the ICNG and the potassium current (IK) in human umbilical artery smooth muscle cells (HUASMC). Atrial Natriuretic Peptide (ANP) induced an activation of basal ICNG, whereas sodium nitroprusside (SNP) had a slight effect. IBMX (nonselective PDE inhibitor), T0-156 (PDE5 inhibitor), and cilostamide (PDE3 inhibitor) all had a small effect on the basal ICNG current. Concerning potassium channels, we observed that ANP and testosterone induced activation of IK and this effect is bigger than that induced by SNP, cilostamide and T0-156. Cilostamide and T0-156 decreased the ICNG stimulation induced by ANP and testosterone, suggesting that the pGC pool is controlled by PDE3 and PDE5. Thus, the effects of SNP show the presence of two separated pools, one next to the plasma membrane and controlled by the PDE5 and PDE3, and a second pool in the cytosol of the cells that is regulated mainly by PDE3. These findings show the existence of cGMP compartmentalization in human vascular smooth muscle cells, and this phenomenon is controlled by PDE3 and PDE5.
The second research work evaluated the direct effects and the 24 h exposure of TBBPA on the HUA and also its mode of action (MOA). The viability of HUASMC was analysed using MTT assay and the cells exposed to high concentrations of TBBPA (500 and 1000 μM) showed a decrease in cell viability. Using the organ bath technique, endothelium-denuded HUA rings were contracted with serotonin (5-HT), histamine (His), and potassium chloride (KCl), and then the direct effects of TBBPA (0.01- 100 μM) were analysed. The effects of 24 hours TBBPA exposure (1, 10, and 50 μM) were also analysed on contractile responses of HUA to 5-HT, His, and KCl. Furthermore, the vascular MOA of TBBPA was studied through the analysis of cGMP and calcium (Ca2+) channels activity, these pathways are involved in the relaxation and contraction of HUA, respectively. Our results demonstrated that the direct effects of TBBPA induce a vasorelaxation of HUA. The 24h TBBPA exposure changed the vasoconstrictor response pattern of 5-HT, His and KCl and the vasorelaxant response pattern of SNP and nifedipine. This effect is due to the involvement of TBBPA with the NO/sGC/cGMP/PKG pathway and the interference in Ca2+ influx. Furthermore, using the real-time quantitative polymerase chain reaction (RT-qPCR), TBBPA clearly modulates L-type Ca2+ and large-conductance Ca2+ 1.1 α- and β1 subunit channels, and soluble guanylyl cyclase (sGC) and protein Kinase G. In this sense, our data demonstrated that TBBPA induces changes in the vascular homeostasis of HUA.
In the last part of this work, the effect of TBBPA in rat aortic smooth muscle and the possible mechanisms involved were investigated and to achieve these goals, we started with the analysis of A7r5 cells viability. These cells were exposed to different TBBPA concentrations, and the results showed that the high concentrations of TBBPA (500 and 1000 μM) decreased the viability of the A7r5 cells. Then, using the organ bath technique, rat aorta rings without endothelium were contracted with Phenylephrine, Noradrenaline, and isosmotic KCl solution to evaluate the vascular effect of TBBPA (0.01–100 μM). Furthermore, MOA of TBBPA was studied through Nifedipine (specific blocker of L-type VGCC), tetraethylammonium (TEA), 4-aminopyridine (4-AP), and glybenclamide (Gly) (K+ channel inhibitors). Our results suggest that the direct effects of TBBPA induced vasorelaxation of rat aorta, involving the inhibition of Ca2+ channels and activation of potassium channels. Moreover, through RT-qPCR, it was demonstrated that TBBPA clearly modulates L-type Ca2+ and large-conductance Ca2+ 1.1 α- and β1 subunit channels, and sGC and protein Kinase G. Overall, it was shown that TBBPA exposure also interferes with vascular homeostasis of rat aorta through Ca2+ and K+ channels.
In conclusion, the main findings of this thesis confirmed the crucial actions of TBBPA in vascular smooth muscle. These effects demonstrate that TBBPA induces smooth muscle relaxation through an endothelium-independent MOA. Due to sGC activation that increases the cGMP intracellular levels, inhibition of L-Type VGCC and activation of K+ channels were verified. Another innovative result of the present thesis was the identification of cGMP compartmentalization in human vascular smooth muscle cells. Further understanding and targeting of these results might be exploited in future studies to acknowledge the effects of TBBPA at the vascular level and its complexity in environmental and human exposure.Disruptores endócrinos (EDCs) são substâncias exógenas ao corpo humano que podem interferir na síntese, secreção, transporte, metabolismo ou eliminação das diferentes hormonas, que são responsáveis pela manutenção da homeostase corporal, reprodução, desenvolvimento ou comportamento. Os EDC são um grupo muito heterogéneo de compostos, que vão desde químicos sintéticos a alguns produtos constituintes naturais de algumas plantas. A avaliação do seu impacto na saúde é extremamente difícil, assim a constante exposição humana a EDCs tem suscitado algumas preocupações. Sabe-se atualmente que existem diversas patologias em que estas substâncias podem ter um papel determinante como causadoras ou amplificadoras das suas manifestações, uma vez que esses compostos afetam a função endócrina interferindo nas vias hormonais (por exemplo: estrogénio, androgénio ou hormonas tiroidianas). Os retardadores de chama bromados (BFRs) são produtos químicos omnipresentes usados amplamente pela indústria. Estes compostos são frequentemente usados em eletrónica, veículos motorizados, brinquedos, plásticos e têxteis para reduzir a inflamabilidade. Os BFRs são lipofílicos e persistentes, e infelizmente muitas destas substâncias químicas não permanecem fixas no produto que as contém, sendo lentamente libertadas para o ar, para as partículas de pó e água, e terminam entrando nos alimentos e no nosso organismo. Os efeitos nocivos para a saúde destes produtos químicos podem estar relacionados à sua persistência e bioacumulação em humanos. As principais vias de exposição são fontes alimentares, inalação e ingestão através de pó, como por exemplo o pó doméstico. Dos retardadores de chama o TBBPA (Tetrabromobisphenol A) é o composto mais estudado devido à sua toxicidade e deteção em diversos meios ambientes e no ser humano. A sua incidência e/ou prevalência de problemas de saúde associados à perturbação endócrina tem aumentado ao longo dos anos. Estudos recentes têm sugerido que o TBBPA contribui para uma série de problemas de saúde, que envolvem não só, doenças como o cancro da mama ou dos rins, mas também incluem doenças metabólicas, como a obesidade. Adicionalmente foram reportados efeitos a nível do desenvolvimento e reprodução humana, assim como a nível da tiroide, sistema cardiovascular e sistema neuro-endócrino. O TBBPA foi identificado em amostras abióticas, como água, ar e poeira, solo, sedimentos e polímeros plásticos, e em amostras bióticas, como soro humano, plasma, urina e leite materno. Além disso, o TBBPA também foi detetado no cordão umbilical de grávidas, comprovando uma exposição pré-natal a este composto. Este resultado sugere que o TBBPA pode atravessar a placenta humana. Então, os efeitos da disrupção endócrina resultantes da exposição ao TBBPA podem ser detetados em gerações futuras? Estudos em embriões e larvas de peixes-zebra, evidenciaram que o TBBPA pode causar toxicidade no sistema cardíaco comprometendo o seu desenvolvimento, resultado do stress oxidativo e consequentemente apoptose celular provocado por este composto. Foi também demonstrado que o TBBPA pode induzir hiperemia e pericardite (edema pericárdico) em embriões e larvas de peixes-zebra. Assim, estes resultados mostraram que há uma relação dose-resposta significativa entre os parâmetros de toxicidade (taxa de eclosão, taxa de sobrevivência, taxa de malformação e taxa de crescimento) e a concentração do TBBPA, nas gerações futuras.
A nível vascular, não existem dados publicados relativamente aos efeitos do TBBPA, nem sobre o seu possível papel no desenvolvimento de desordens vasculares. Neste sentido, o trabalho desenvolvido nesta tese de doutoramento teve como principal objetivo o estudo dos efeitos do TBBPA a nível da contratilidade arterial e a análise dos possíveis mecanismos envolvidos nesses efeitos, para futuramente se estudar se o efeito do TBBPA pode ou não estar associado ao desenvolvimento de patologias vasculares, de forma a minimizar o impacto da exposição ao TBBPA nas gerações futuras. Esta análise foi realizada em dois modelos de estudo diferentes, nomeadamente em artérias umbilicais humana (HUA) e em aortas de rato.
A HUA é facilmente obtida a partir do cordão umbilical, está implicada na circulação feto-placentária e é uma excelente fonte de células musculares lisas vasculares (VSMC). Mecanismos endócrinos e parácrinos que regulam o estado contrátil das VSMC na HUA são muito importantes para permitir as trocas gasosas e de nutrientes entre o feto e a placenta, uma vez que os vasos sanguíneos do cordão umbilical não são inervados. Estas características torna a HUA um bom modelo para analisar os efeitos dos EDCs no sistema vascular e compreender possíveis implicações vasculares da exposição a esses compostos na gravidez. Em relação à aorta de rato, é um modelo que tem sido utilizado ao longo dos anos devido a uma boa extrapolação dos resultados para o humano.
Para além do objetivo principal, anteriormente mencionado, no primeiro trabalho apresentado nesta tese, analisou-se a compartimentação do cGMP (cyclic Guanosine 3,5’- Monophosphate) a nível vascular através da ativação dos CNG (cyclic nucleotide gated channels) sensíveis a cGMP e a possível implicação das fosfodiesterases (PDE). A técnica patch clamp na configuração whole cell foi usada para medir o sinal de ativação dos canais de CNG. As células musculares lisas da artéria umbilical humana (HUASMC) foram infetadas com o adenovírus WT-CNGA2. Os compostos utilizados foram: peptídeo natriurético atrial (ANP), nitroprussiato de sódio (SNP), 3-isobutil-1-metilxantina (IBMX) (inibidor não seletivo das PDE), To-156 (inibidor específico da PDE5), ciloestamida (inibidor específico da PDE3) e Sp-8 (análogo da molécula cGMP). Analisando os resultados obtidos, observa-se que o ANP e o SNP induzem um diferente aumento da ICNG. O sinal do cGMP induzido pelo ANP parece ser controlado pela PDE5 e pela PDE3. Contudo, a administração do SNP parece criar dois efeitos separados, um mais localizado junto à membrana plasmática que é controlado pela PDE3 e pela PDE5, e o outro efeito localizado no interior das células que é regulado apenas pela PDE3. Em suma, a distribuição temporal e espacial diferente do cGMP pode contribuir para efeitos específicos do ANP e de dadores de oxido nítrico (NO) na função vascular, confirmando que a regulação e a síntese do cGMP são compartimentadas nas HUASMC.
No segundo trabalho apresentado foram avaliados os efeitos diretos e os efeitos após 24h de exposição ao TBBPA na HUA e analisado o seu possível modo de ação (MOA). O ensaio de MTT foi usado para analisar viabilidade celular das células musculares lisas da artéria umbilical humana (HUASMC), estas células foram expostas a diferentes concentrações de TBBPA, e observou-se uma diminuição da viabilidade celular nas concentrações maiores (500 e 1000 μM). Usando a técnica de banho de órgãos, anéis HUA sem endotélio foram contraídos com serotonina (5-HT), histamina (His) e cloreto de potássio (KCl) e, em seguida, os efeitos diretos do TBBPA (0,01-100 μM) foram analisados. Após 24 horas de exposição ao TBBPA (1, 10 e 50 μM) foram avaliadas as respostas contráteis da HUA à aplicação dos agentes contráteis, 5-HT His e KCl. Para investigar mais detalhadamente o modo de ação vascular do TBBPA, através do qual ele prejudica a homeostase vascular do HUA. Além disso, o mecanismo de ação vascular do TBBPA foi estudado através da análise da atividade dos nucleótidos cíclicos e dos canais de cálcio (Ca2+), vias envolvidas respetivamente, no relaxamento e na contração da HUA. Os resultados obtidos demonstraram que os efeitos diretos do TBBPA induzem um vasorelaxamento da HUA e que a exposição de 24 horas ao TBBPA altera o padrão de resposta vasoconstritora de 5-HT, His e KCl e o padrão de resposta vasorelaxante do SNP e da nifedipina (Nif). Este efeito é devido ao envolvimento do TBBPA com a via NO/sGC/cGMP/PKG e com a interferência no influxo de Ca2+. Além disso, usando a reação em cadeia da polimerase quantitativa em tempo real (qPCR), observou-se que o TBBPA modifica a expressão dos canais de Ca2+ tipo L, das subunidades α- e β1 dos canais de potássio ativados por cálcio (BKCa), da guanilato ciclase solúvel (sGC) e da proteína cinase G (PKG). Assim, estes resultados apontam para alterações na homeostase vascular da HUA provocadas pela exposição ao TBBPA.
No terceiro trabalho apresentado foi analisado o efeito do TBBPA no músculo liso da aorta de ratos, para investigar a sua via de sinalização. Para atingir este objetivo, começamos também com a análise da viabilidade celular das células A7r5, usando o ensaio de MTT. As células A7r5 foram expostas durante 24 horas a diferentes concentrações de TBBPA, e os resultados obtidos mostraram que as maiores concentrações de TBBPA (500 e 1000 μM) diminuíram a viabilidade celular. Em seguida, pela técnica do banho de órgãos, os anéis de aorta de rato sem endotélio foram contraídos com Fenilefrina (Phenyl), Noradrenalina (NA) e com uma solução KCl isosmótico para avaliar o efeito vascular do TBBPA (0,01–100 μM). Para além disso, o mecanismo de ação do TBBPA foi estudado através de inibidores específicos, nomeadamente, a Nif, um inibidor de canais de cálcio dependentes de voltagem tipo L, o tetraetilamonio (TEA), um inibidor de canais de potássio dependentes de cálcio (BKCa), a 4-aminopiridina (4-AP), um inibidor de canais de potássio dependentes de voltagem (Kv) e a glibenclamida (Gly), um inibidor de canais de potássio dependentes de ATP (KATP). Os resultados mostraram que estes inibidores reduziram o efeito vasorelaxante do TBBPA, sugerindo que os efeitos vasculares do TBBPA envolvem os canais de Ca2+ e de K+. Para avaliar a atividade dos canais de cálcio dependentes de voltagem (VGCC) tipo L em células A7r5, aplicou-se a técnica patch clamp na configuração de whole cell, e observou-se uma diminuição da corrente de Ca2+. Estes resultados suportam a ideia que os efeitos do TBBPA induzem vasorelaxamento na aorta de ratos, devido à inibição dos canais de Ca2+ e ativação dos canais de K+. Também, por qPCR, observou-se que o TBBPA modula a expressão dos canais de Ca2+ tipo L, das subunidades α- e β1 dos BKCa, da sGC e da PKG.
Em suma, os resultados obtidos nesta tese, durante o desenvolvimento deste projeto, confirmaram as ações cruciais do TBBPA no músculo liso vascular. Estes resultados demonstram que o TBBPA induz um relaxamento do músculo liso agindo através de um mecanismo independente do endotélio. Este mecanismo de ação do TBBPA envolve ativação de sGC, o aumento os níveis intracelulares de cGMP, uma inibição dos VGCC tipo L e uma ativação dos canais de K+. Outro resultado inovador da presente tese foi a identificação da existência de compartimentação de cGMP em células musculares lisas vasculares humanas.
Devido à alteração da homeostase vascular induzida por TBBPA, este composto pode ser um possível indutor de doenças hipertensivas. Nesta linha de investigação, os resultados obtidos parecem muito promissores, neste sentido estudos adicionais são necessários para conhecer melhor o mecanismo de ação do TBBPA a nível vascular e compreender a sua complexidade na exposição humana e ambiental, de forma a minimizar o risco em gerações futuras
Computational modeling of human atrial cardiomyocytes: integration of electro-mechanical & mechano-electric feedback pathways
The cardiomyocytes are very complex consisting of many interlinked non-linear regulatory mechanisms between electrical excitation and mechanical contraction. Thus given a integrated electromechanically coupled system it becomes hard to understand the individual contributor of cardiac electrics and mechanics under both physiological and pathological conditions. Hence, to identify the causal relationship or to predict the responses in a integrated system the use of computational modeling can be beneficial. Computational modeling is a powerful tool that provides complete control of parameters along with the visibility of all the individual components of the integrated system. The advancement of computational power has made it possible to simulate the models in a short timeframe, providing the possibility of increased predictive power of the integrated system. My doctoral thesis is focused on the development of electromechanically integrated human atrial cardiomyocyte model with proper consideration of feedforward and feedback pathways
Utvrđivanje povezanosti genotipa i fenotipa hipertrofične kardiomiopatije primenom mašinskog učenja
Hypertrophic cardiomyopathy (HCM) is the most prevailing heritable cardiomyopathy. HCM is diagnosed by the existence of left ventricular hypertrophy despite the lack of abnormal loading conditions causing it. HCM is a heterogeneous disease regarding genetic mutations. Clinical manifestations and prognosis vary widely as well. Some patients are completely asymptomatic, in some others, severe heart failure and sudden cardiac death may arise. Definitive genotype-phenotype associations are still unknown. Machine learning (ML) is a subdiscipline of artificial intelligence, wherein computer algorithms are used for learning complex patterns from data. The aim of this research was to decipher genotype-phenotype associations in HCM using ML. The study was multi-centric and retroprospective, and involved 143 adult HCM patients. Medical and family history, anthropometric measurements, genetic testing, blood markers, transthoracic echocardiography with Doppler, cardiopulmonary exercise testing (CPET), ECG and ECG-holter-monitoring data were collected and further analysed. HCM subphenotypes were identified using clustering. Associations of genotype and phenotype were evaluated used Python modules Scikit-learn and SHapley Additive exPlanation (SHAP). Genotype-specific echocardiogram findings were identified using Python deep learning (DL) and computer vision library Fast AI, by generation of DL models for classification of ultrasonic images, and later analysis of the most decisive image regions. Four HCM subtypes were identified based on the overall phenotypic appearance: cluster 0 (“AHOLD”), distinguishable by aortic root diameter (AO) and lactate dehydrogenase (LDH), with values mostly AO > 30 mm, and LDH > 300 U/L; cluster 1 (“RVSP ASCAOVS”), distinguishable by right ventricle systolic pressure (RVSP), diameter of ascending aorta (AscAO), and aortic leaflet separation diameter (AOvs), with the values of RVSP 27 m/s; cluster 2 (“weight”), recognizable by weight, wherein values being mostly > 95 kg; and cluster 3 (“AV LVOT PG”) distinguishable by aortic valve mean pressure gradient (AV meanPG), aortic valve peak pressure gradient (AV maxPG), and left ventricular outflow tract peak gradient (LVOT maxPG) wherein AV maxPG > 15 mmHg, AV meanPG > 6 mmHg, and LVOT maxPG > 15 mmHg. ML algorithms confirmed that the determination of genotype-phenotype associations in HCM is a cumbersome task. Two phenotypic outcomes that can be predicted from mutated genes are the absence or presence of sinus rhythm and the absence or presence of myocardial injury. Models predicting the absence or presence of sinus rhythm had similar performance when they were built using only causative genes and when using all analyzed genes, indicating potential importance of causative genes and irrelevance of non-causative genes for that outcome. On the other hand, models predicting myocardial injury — infarction had better performance when they were built using all analyzed genes (and not just causative ones), indicating a potentially significant role of non-causative genes in that outcome. The ML algorithms were able to predict phenotypic outcomes — fatigue, dyspnea, chest pain, palpitations, syncope, heart murmur, pretibial edema, systolic anterior motion, papillary muscle abnormalities, hypokinesia, atrial fibrillation (AF), first-degree atrioventricular (AV) block, left bundle branch block (LBBB), right bundle branch block (RBBB), left anterior hemiblock, ST segment abnormalities, and negative T wave — using genotypic and phenotypic data. The combination of a mutation in TNNT2 and peak respiratory exchange ratio (RER) contributed the most in predicting fatigue. The combination of a mutation in MYBPC3 and peak VO2 contributed the most in predicting dyspnea. The combination of a mutation in TNNI3 and high-density lipoprotein (HDL) level contributed the most in predicting chest pain. The combination of a mutation in MYH7 and pacemaker/defibrillator implants in family history, as well as the combination of a mutation in TNNT2 and left atrial volume (LAV), contributed the most in predicting heart murmur. Lastly, the combination of a mutation in MYBPC3 and transmitral maximal pressure gradient (MV maxPG) aided the most in predicting negative T wave. Genotype-specific echocardiogram findings were identified: for mutations in the MYH7 gene (vs. mutation not detected), the most discriminative structures are the left ventricular outflow tract, septum, anterior wall, apex, right ventricle, and mitral apparatus; for mutations in the TNNT2 gene (vs. mutation not detected), the most discriminative structures are septum and right ventricle; while for mutations in MYBPC3 gene (vs. mutation not detected) these are septum, left ventricle, and left ventricle chamber. ML has thus been demonstrated to be useful in deciphering genotype-phenotype associations in HCM.Hipertrofična kardiomiopatija (HCM) je najčešća nasledna kardiomiopatija. Dijagnoza HCM se postavlja na osnovu prisustva hipertrofije leve komore, uz isključivanje drugih uzroka hipertrofije. U pogledu genetičkih mutacija, HCM je heterogena bolest. Kliničke manifestacije i prognoza takođe mogu da budu veoma različite. Kod nekih pacijenata HCM je potpuno asimptomatska, dok kod drugih mogu da se razviju teška srčana insuficijencija i iznenadna srčana smrt. Povezanost genotipa i fenotipa HCM još uvek nije u potpunosti utvrđena. Mašinsko učenje je subdisciplina veštačke inteligencije u kojoj se kompjuterski algoritmi koriste za učenje kompleksnih šablona iz podataka. Cilj ovog istraživanja je bilo utvrđivanje povezanosti genotipa i fenotipa HCM primenom mašinskog učenja. Studija je bila multicentrična i retroprospektivna, obuhvatila je 143 odrasla pacijenta sa potvrđenom dijagnozom HCM. Anamnestički podaci, antropometrijska merenja, rezultati genetičkog testiranja, biohemijskih analiza, nalazi transtorakalne ehokardiografije sa doplerom, kardiopulmonalnog testa fizičkim opterećenjem, elektrokardiograma (EKG) i EKG-holter-monitoringa su prikupljeni i korišćeni u daljoj analizi. HCM subfenotipi su identifikovani klasterizacijom. Povezanost genotipa i fenotipa je evaluirana korišćenjem Python modula Scikit-learn i SHapley Additive exPlanation (SHAP). Genotip-specifični nalazi ehokardiograma su identifikovani korišćenjem Python biblioteke za duboko učenje i računarski vid Fast AI, izradom modela za klasifikaciju ehokardiograma i naknadnom analizom regiona koji su najviše doprineli razlikovanju klasa. Četiri podtipa HCM su identifikovana na osnovu svih dostupnih podataka o fenotipu: klaster 0 (“AHOLD”), koji se razlikuje od ostalih na osnovu prečnika korena aorte (AO) i laktat dehidrogenaze (LDH), pri čemu su vrednosti AO > 30 mm i LDH > 300 U/L; klaster 1 (“RVSP ASCAOVS”), koji se razlikuje od ostalih na osnovu sistolnog pritiska desne komore (RVSP), dijametra ascedentne aorte (AscAO), i separacije aortnih kuspisa (AOvs), pri čemu su vrednosti AOvs > 27 m/s, AscAO 95 kg; i klaster 3 (“AV LVOT PG”) koji se razlikuje od ostalih na osnovu srednjeg gradijenta pritisaka nad aortnom valvulom (AV meanPG), maksimalnog gradijenta pritisaka nad aortnom valvulom (AV maxPG), i maksimalnog gradijenta pritisaka nad izlaznim traktom leve komore (LVOT maxPG), pri čemu su vrednosti AV maxPG > 15 mmHg, AV meanPG > 6 mmHg, i LVOT maxPG > 15 mmHg. Algoritmi mašinskog učenja su potvrdili da utvrđivanje povezanosti genotipa i fenotipa HCM nije jednostavan zadatak. Predikcija ishoda fenotipa na osnovu informacije o mutiranim genima je moguća za prisustvo ili odsustvo sinusnog ritma i prisustvo ili odsustvo oštećenja miokarda. Modeli koji vrše predikciju prisustva ili odsustva sinusnog ritma su imali slične performanse kada su izrađeni samo na osnovu uzročnih gena za HCM i kada su izrađeni na osnovu svih analiziranih gena što sugeriše mogući značaj uzročnih gena za HCM i irelevantnost drugih analiziranih gena za ovaj ishod. Modeli koji vrše predikciju oštećenja miokarda su imali bolje performanse kada su korišćeni podaci o svim analiziranim genima (a ne samo o uzročnim genima za HCM), što sugeriše moguću važnu ulogu gena koji nisu uzročni, za ovaj ishod. Algoritmi mašinskog učenja su izvršili predikciju sledećih ishoda na osnovu podataka o genotipu i fenotipu: zamor, dispneja, bol u grudima, palpitacije, sinkopa, šum na srcu, pretibijalni edem, pokretanje mitralnog zalistka unapred (SAM), abnormalnost papilarnih mišića, hipokinezija, atrijalna fibrilacija, atrioventrikularni blok prvog stepena, blok leve grane (LBBB), blok desne grane (RBBB), prednji levi hemiblok, abnormalnosti ST segmenta, i negativni T talas. Prilikom predikcije zamora, najveći doprinos je imala kombinacija mutacije u TNNT2 i maksimalnog odnosa disajne razmene (RER). Prilikom predikcije dispneje najveći doprinos imala je kombinacija mutacije u MYBPC3 i vršne potrošnje kiseonika (peak VO2). Prilikom predikcije bola u grudima, najveći doprinos je imala kombinacija mutacije u TNNI3 i koncentracije lipoproteina visoke gustine (eng. high-density lipoprotein, HDL). Prilikom predikcije šuma na srcu najveći doprinos imala je kombinacija mutacije u MYH7 i podatka o implantiranju pejsmejkera/defibrilatora u porodičnoj istoriji, kao i kombinacija mutacije u TNNT2 i zapremine leve pretkomore (LAV). Prilikom predikcije negativnog T talasa, najveći doprinos imala je kombinacija mutacije u MYBPC3 i vrednosti transmitralnog maksimalnog gradijenta pritiska (MV maxPG). Identifikovani su genotip-specifični nalazi ehokardiograma: za mutaciju u MYH7 genu (nasuprot negativnom rezultatu na mutacije u analiziranim genima), strukture koje najviše utiču na raspoznavanje su septum, izlazni trakt leve komore (LVOT), prednji zid, vrh srca, desna komora i mitralni aparat; za mutaciju u TNNT2 genu (nasuprot negativnom rezultatu na mutacije u analiziranim genima) strukture koje najviše utiču na raspoznavanje su septum i desna komora; dok su za mutaciju u MYBPC3 genu (nasuprot negativnom rezultatu na mutacije u analiziranim genima) ove strukture septum, leva komora i šupljina leve komore. Mašinsko učenje je na ovaj način doprinelo u određenoj meri izučavanju povezanosti genotipa i fenotipa HCM
Palmitoylation and regulation of the funny current HCN4 channel
The sinoatrial node (SAN) acts as the primary pacemaker of the heart as it spontaneously generates electrical activity that propagate through the cardiac conduction system, underpinning automaticity of the heart. A network of surface membrane ion currents (“membrane clock”) and the rhythmic oscillation of local Ca²⁺ release from the sarcoplasmic reticulum (“calcium clock”) work interdependently to form a coupled-clock system that drives pacemaker automaticity and its regulation on a beat-to-beat basis. The “funny current” (If) is a key component of the membrane clock contributing to the diastolic depolarisation of the SAN. Hyperpolarisation-activated cyclic nucleotide-gated channel HCN4 is the predominant isoform responsible for almost 70% of the sinoatrial If. HCN4 channels localise to lipid rafts in the SAN and disorganisation of these raft membrane microdomains result in channel redistribution, thus altering its kinetic properties.
Ion channels are an integral component of the complex sinoatrial pacemaking network, and their regulation is therefore central to controlling the heart rate. S-palmitoylation is a form of lipidation that involves the covalent addition of a 16-carbon palmitate to a thiol group of a cysteine residue in a protein. Unlike most lipid modifications, palmitoylation is unique due to its reversible nature, allowing the dynamic regulation of both soluble and integral proteins. In recent years, palmitoylation has emerged as an important regulator of cardiac electrophysiology as it influences the function and membrane microdomain localisation of key cardiac Na⁺ and Ca²⁺ handling proteins.
The present in-vitro study was adopted to characterise palmitoylation of HCN4 channels and to establish its functional consequences. Site-specific resin assisted capture (acyl-RAC) was used to assess palmitoylation of HCN4 in human embryonic kidney (HEK) cells as well as endogenous HCN4 in isolated neonatal rat whole heart and atrial myocytes. HCN4 was sub-stoichiometrically palmitoylated in all experimental systems examined. Truncated HCN4 intracellular amino and carboxyl termini fused to YFP and cysteine-to-alanine mutations of the palmitoylation sites in HEK-293 cells mapped HCN4 palmitoylation sites to a pair of cysteines (C93 and C179) in the HCN4 N-terminus domain. A double cysteine-to-alanine mutation C93/179AA of both palmitoylation sites reduced palmitoylation of full-length HCN4 by ~67% in comparison to wild type HCN4. Membrane impermeable biotinylation of cell surface HCN4 revealed that palmitoylation did not influence its trafficking to the cell surface or cell surface turnover rate. Standard discontinuous sucrose gradient indicated that HCN4 channels did not require palmitoylation to localise to lipid rafts in HEK-293 cells.
Whole-cell patch clamp was used to investigate IHCN4 in HEK-293 cells engineered to stably express wild type and mutant HCN4. Loss of palmitoylation at the N-terminus significantly reduced HCN4 current magnitude by ~5 to 8-fold across a range of voltages. However, it did not alter its half-maximal activation voltage (V₀.₅: -90.4 ± 2.5 mV for WT vs -90.4 ± 1.6 mV for C93/179AA), nor its activation slope factor (k: 7.1 ± 0.5 mV for WT vs 6.0 ± 0.2 mV for C93/179AA). Phylogenetic analysis was used to evaluate the evolutionary acquisition of HCN4 palmitoylation within the pre-metazoan and metazoan lineage. While cysteine 93 was broadly conserved within all classes of HCN4 vertebrate orthologs, conservation of cysteine 179 was confined to placental mammals.
Together, this study demonstrated the importance of palmitoylation as a regulator of HCN4 channel function by enhancing HCN4-mediated currents. Palmitoylation of the HCN4 amino terminus is likely to significantly enhance If in the SAN, accelerating diastolic depolarisation, and increasing heart rate
Utvrđivanje povezanosti genotipa i fenotipa hipertrofične kardiomiopatije primenom mašinskog učenja
Hypertrophic cardiomyopathy (HCM) is the most prevailing heritable cardiomyopathy. HCM is diagnosed by the existence of left ventricular hypertrophy despite the lack of abnormal loading conditions causing it. HCM is a heterogeneous disease regarding genetic mutations. Clinical manifestations and prognosis vary widely as well. Some patients are completely asymptomatic, in some others, severe heart failure and sudden cardiac death may arise. Definitive genotype-phenotype associations are still unknown. Machine learning (ML) is a subdiscipline of artificial intelligence, wherein computer algorithms are used for learning complex patterns from data. The aim of this research was to decipher genotype-phenotype associations in HCM using ML. The study was multi-centric and retroprospective, and involved 143 adult HCM patients. Medical and family history, anthropometric measurements, genetic testing, blood markers, transthoracic echocardiography with Doppler, cardiopulmonary exercise testing (CPET), ECG and ECG-holter-monitoring data were collected and further analysed. HCM subphenotypes were identified using clustering. Associations of genotype and phenotype were evaluated used Python modules Scikit-learn and SHapley Additive exPlanation (SHAP). Genotype-specific echocardiogram findings were identified using Python deep learning (DL) and computer vision library Fast AI, by generation of DL models for classification of ultrasonic images, and later analysis of the most decisive image regions. Four HCM subtypes were identified based on the overall phenotypic appearance: cluster 0 (“AHOLD”), distinguishable by aortic root diameter (AO) and lactate dehydrogenase (LDH), with values mostly AO > 30 mm, and LDH > 300 U/L; cluster 1 (“RVSP ASCAOVS”), distinguishable by right ventricle systolic pressure (RVSP), diameter of ascending aorta (AscAO), and aortic leaflet separation diameter (AOvs), with the values of RVSP 27 m/s; cluster 2 (“weight”), recognizable by weight, wherein values being mostly > 95 kg; and cluster 3 (“AV LVOT PG”) distinguishable by aortic valve mean pressure gradient (AV meanPG), aortic valve peak pressure gradient (AV maxPG), and left ventricular outflow tract peak gradient (LVOT maxPG) wherein AV maxPG > 15 mmHg, AV meanPG > 6 mmHg, and LVOT maxPG > 15 mmHg. ML algorithms confirmed that the determination of genotype-phenotype associations in HCM is a cumbersome task. Two phenotypic outcomes that can be predicted from mutated genes are the absence or presence of sinus rhythm and the absence or presence of myocardial injury. Models predicting the absence or presence of sinus rhythm had similar performance when they were built using only causative genes and when using all analyzed genes, indicating potential importance of causative genes and irrelevance of non-causative genes for that outcome. On the other hand, models predicting myocardial injury — infarction had better performance when they were built using all analyzed genes (and not just causative ones), indicating a potentially significant role of non-causative genes in that outcome. The ML algorithms were able to predict phenotypic outcomes — fatigue, dyspnea, chest pain, palpitations, syncope, heart murmur, pretibial edema, systolic anterior motion, papillary muscle abnormalities, hypokinesia, atrial fibrillation (AF), first-degree atrioventricular (AV) block, left bundle branch block (LBBB), right bundle branch block (RBBB), left anterior hemiblock, ST segment abnormalities, and negative T wave — using genotypic and phenotypic data. The combination of a mutation in TNNT2 and peak respiratory exchange ratio (RER) contributed the most in predicting fatigue. The combination of a mutation in MYBPC3 and peak VO2 contributed the most in predicting dyspnea. The combination of a mutation in TNNI3 and high-density lipoprotein (HDL) level contributed the most in predicting chest pain. The combination of a mutation in MYH7 and pacemaker/defibrillator implants in family history, as well as the combination of a mutation in TNNT2 and left atrial volume (LAV), contributed the most in predicting heart murmur. Lastly, the combination of a mutation in MYBPC3 and transmitral maximal pressure gradient (MV maxPG) aided the most in predicting negative T wave. Genotype-specific echocardiogram findings were identified: for mutations in the MYH7 gene (vs. mutation not detected), the most discriminative structures are the left ventricular outflow tract, septum, anterior wall, apex, right ventricle, and mitral apparatus; for mutations in the TNNT2 gene (vs. mutation not detected), the most discriminative structures are septum and right ventricle; while for mutations in MYBPC3 gene (vs. mutation not detected) these are septum, left ventricle, and left ventricle chamber. ML has thus been demonstrated to be useful in deciphering genotype-phenotype associations in HCM.Hipertrofična kardiomiopatija (HCM) je najčešća nasledna kardiomiopatija. Dijagnoza HCM se postavlja na osnovu prisustva hipertrofije leve komore, uz isključivanje drugih uzroka hipertrofije. U pogledu genetičkih mutacija, HCM je heterogena bolest. Kliničke manifestacije i prognoza takođe mogu da budu veoma različite. Kod nekih pacijenata HCM je potpuno asimptomatska, dok kod drugih mogu da se razviju teška srčana insuficijencija i iznenadna srčana smrt. Povezanost genotipa i fenotipa HCM još uvek nije u potpunosti utvrđena. Mašinsko učenje je subdisciplina veštačke inteligencije u kojoj se kompjuterski algoritmi koriste za učenje kompleksnih šablona iz podataka. Cilj ovog istraživanja je bilo utvrđivanje povezanosti genotipa i fenotipa HCM primenom mašinskog učenja. Studija je bila multicentrična i retroprospektivna, obuhvatila je 143 odrasla pacijenta sa potvrđenom dijagnozom HCM. Anamnestički podaci, antropometrijska merenja, rezultati genetičkog testiranja, biohemijskih analiza, nalazi transtorakalne ehokardiografije sa doplerom, kardiopulmonalnog testa fizičkim opterećenjem, elektrokardiograma (EKG) i EKG-holter-monitoringa su prikupljeni i korišćeni u daljoj analizi. HCM subfenotipi su identifikovani klasterizacijom. Povezanost genotipa i fenotipa je evaluirana korišćenjem Python modula Scikit-learn i SHapley Additive exPlanation (SHAP). Genotip-specifični nalazi ehokardiograma su identifikovani korišćenjem Python biblioteke za duboko učenje i računarski vid Fast AI, izradom modela za klasifikaciju ehokardiograma i naknadnom analizom regiona koji su najviše doprineli razlikovanju klasa. Četiri podtipa HCM su identifikovana na osnovu svih dostupnih podataka o fenotipu: klaster 0 (“AHOLD”), koji se razlikuje od ostalih na osnovu prečnika korena aorte (AO) i laktat dehidrogenaze (LDH), pri čemu su vrednosti AO > 30 mm i LDH > 300 U/L; klaster 1 (“RVSP ASCAOVS”), koji se razlikuje od ostalih na osnovu sistolnog pritiska desne komore (RVSP), dijametra ascedentne aorte (AscAO), i separacije aortnih kuspisa (AOvs), pri čemu su vrednosti AOvs > 27 m/s, AscAO 95 kg; i klaster 3 (“AV LVOT PG”) koji se razlikuje od ostalih na osnovu srednjeg gradijenta pritisaka nad aortnom valvulom (AV meanPG), maksimalnog gradijenta pritisaka nad aortnom valvulom (AV maxPG), i maksimalnog gradijenta pritisaka nad izlaznim traktom leve komore (LVOT maxPG), pri čemu su vrednosti AV maxPG > 15 mmHg, AV meanPG > 6 mmHg, i LVOT maxPG > 15 mmHg. Algoritmi mašinskog učenja su potvrdili da utvrđivanje povezanosti genotipa i fenotipa HCM nije jednostavan zadatak. Predikcija ishoda fenotipa na osnovu informacije o mutiranim genima je moguća za prisustvo ili odsustvo sinusnog ritma i prisustvo ili odsustvo oštećenja miokarda. Modeli koji vrše predikciju prisustva ili odsustva sinusnog ritma su imali slične performanse kada su izrađeni samo na osnovu uzročnih gena za HCM i kada su izrađeni na osnovu svih analiziranih gena što sugeriše mogući značaj uzročnih gena za HCM i irelevantnost drugih analiziranih gena za ovaj ishod. Modeli koji vrše predikciju oštećenja miokarda su imali bolje performanse kada su korišćeni podaci o svim analiziranim genima (a ne samo o uzročnim genima za HCM), što sugeriše moguću važnu ulogu gena koji nisu uzročni, za ovaj ishod. Algoritmi mašinskog učenja su izvršili predikciju sledećih ishoda na osnovu podataka o genotipu i fenotipu: zamor, dispneja, bol u grudima, palpitacije, sinkopa, šum na srcu, pretibijalni edem, pokretanje mitralnog zalistka unapred (SAM), abnormalnost papilarnih mišića, hipokinezija, atrijalna fibrilacija, atrioventrikularni blok prvog stepena, blok leve grane (LBBB), blok desne grane (RBBB), prednji levi hemiblok, abnormalnosti ST segmenta, i negativni T talas. Prilikom predikcije zamora, najveći doprinos je imala kombinacija mutacije u TNNT2 i maksimalnog odnosa disajne razmene (RER). Prilikom predikcije dispneje najveći doprinos imala je kombinacija mutacije u MYBPC3 i vršne potrošnje kiseonika (peak VO2). Prilikom predikcije bola u grudima, najveći doprinos je imala kombinacija mutacije u TNNI3 i koncentracije lipoproteina visoke gustine (eng. high-density lipoprotein, HDL). Prilikom predikcije šuma na srcu najveći doprinos imala je kombinacija mutacije u MYH7 i podatka o implantiranju pejsmejkera/defibrilatora u porodičnoj istoriji, kao i kombinacija mutacije u TNNT2 i zapremine leve pretkomore (LAV). Prilikom predikcije negativnog T talasa, najveći doprinos imala je kombinacija mutacije u MYBPC3 i vrednosti transmitralnog maksimalnog gradijenta pritiska (MV maxPG). Identifikovani su genotip-specifični nalazi ehokardiograma: za mutaciju u MYH7 genu (nasuprot negativnom rezultatu na mutacije u analiziranim genima), strukture koje najviše utiču na raspoznavanje su septum, izlazni trakt leve komore (LVOT), prednji zid, vrh srca, desna komora i mitralni aparat; za mutaciju u TNNT2 genu (nasuprot negativnom rezultatu na mutacije u analiziranim genima) strukture koje najviše utiču na raspoznavanje su septum i desna komora; dok su za mutaciju u MYBPC3 genu (nasuprot negativnom rezultatu na mutacije u analiziranim genima) ove strukture septum, leva komora i šupljina leve komore. Mašinsko učenje je na ovaj način doprinelo u određenoj meri izučavanju povezanosti genotipa i fenotipa HCM
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