👤 Thomas P Burghardt

Human ventriculum myosin (βmys) powers contraction sometimes in complex with myosin binding protein C (MYBPC3). The latter regulates βmys activity and impacts cardiac function. Single residue variants (SRVs) change protein sequence in βmys or MYBPC3 causing inheritable heart diseases by affecting the βmys/MYBPC3 complex. Muscle genetics encode instructions for contraction informing native protein construction, functional integration, and inheritable disease impairment. A digital model decodes these instructions and evolves by processing new information content from diverse data modalities using a human partner-driven virtuous cycle optimization. A general neural-network contraction model characterizes SRV impacts on human health. It rationalizes phenotype and pathogenicity assignment given the SRVs characteristics and, in this sense, decodes βmys/MYBPC3 complex genetics and implicitly captures ventricular muscle functionality. When an SRV modified domain locates to an inter-protein contact in βmys/MYBPC3 it affects complex coordination. Domains involved, one in βmys and the other in MYBPC3, form coordinated domains (co-domains). Bilateral co-domains imply potential for their SRV modification probabilities to respond jointly to a common perturbation revealing location. Human genetic diversity from the serial founder effect is the common systemic perturbation coupling co-domains subsequently mapped by a method called 2-dimensional correlation genetics (2D-CG). Interpreting general neural-network contraction model output involves 2D-CG co-domain mapping providing structural insights with natural language expression. It aligns machine-learned intelligence from the neural network model with human provided structural insight from the 2D-CG map, and other data from the literature, to form a neural-symbolic hybrid model integrating genetic and protein-interaction data into a nascent digital twin. The process forms a template for combining new information content from diverse data modalities into an evolving digital model. This nascent digital twin interprets SRV implications for disease mechanism discovery. Show less

Anorexia nervosa (AN) is a mental disorder marked by a significantly low body weight. Differentially methylated CpG sites have been reported to be involved in body weight regulation. Methylation pattern may change during considerable weight gain by in-patient treatment. Consequently, we aimed to (1) replicate the hypomethylation at the NR1H3 gene locus (identified in our previous epigenome-wide association study) in independent study groups of 189 female patients with AN and 67 healthy-lean female controls, and (2) identify regions associated with large weight gain associated DNA methylation changes in three patients with AN through whole-genome bisulfite sequencing in CD14 Show less

Mutations leading to a reduced or loss of function in genes of the leptin-melanocortin system confer a risk for monogenic forms of obesity. Yet, gain of function variants in the melanocortin-4-receptor (MC4R) gene predispose to a lower BMI. In individuals with reduced body weight, we thus expected mutations leading to an enhanced function in the respective genes, like leptin (LEP) and MC4R. Therefore, we have Sanger sequenced the coding regions of LEP and MC4R in 462 female patients with anorexia nervosa (AN), and 445 healthy-lean controls. In total, we have observed four and eight variants in LEP and MC4R, respectively. Previous studies showed different functional in vitro effects for the detected frameshift and non-synonymous variants: (1) LEP: reduced/loss of function (p.Val94Met), (2) MC4R: gain of function (p.Val103Ile, p.Ile251Leu), reduced or loss of function (p.Thr112Met, p.Ser127Leu, p.Leu211fsX) and without functional in vitro data (p.Val50Leut). In LEP, the variant p.Val94Met was detected in one patient with AN. For MC4R variants, one patient with AN carried the frameshift variant p.Leu211fsX. One patient with AN was heterozygous for two variants at the MC4R (p.Val103Ile and p.Ser127Leu). All other functionally relevant variants were detected in similar frequencies in patients with AN and lean individuals. Show less

The bone-derived adipokine lipocalin-2 is relevant for body weight regulation by stimulating the leptin-melanocortin pathway. We aimed to (i) detect variants in the lipocalin-2 gene ( Sanger sequencing of the coding region of Fourteen Lipocalin-2 levels are positively associated with body mass index (BMI). Single Show less

Coordinated sarcomere proteins produce contraction force for muscle shortening. In human ventriculum they include the cardiac myosin motor (βmys), repetitively converting ATP free energy into work, and myosin binding protein C (MYBPC3) that in complex with βmys is regulatory. Single nucleotide variants (SNVs) causing hereditary heart diseases frequently target this protein pair. The βmys/MYBPC3 complex models a regulated motor and is used here to study how the proteins couple. SNVs in βmys or MYBPC3 survey human populations worldwide. Their protein expression modifies domain structure affecting phenotype and pathogenicity outcomes. When the SNV modified domain locates to inter-protein contacts it could affect complex coordination. Domains involved, one in βmys the other in MYBPC3, form coordinated domains (co-domains). Co-domain bilateral structure implies the possibility for a shared impact from SNV modification in either domain suggesting a correlated response to a common perturbation could identify their location. Genetic divergence over human populations is proposed to perturb SNV probability coupling that is detected by cross-correlation in 2D correlation genetics (2D-CG). SNV probability data and 2D-CG identify three critical sites, two in MYBPC3 with links to several domains across the βmys motor, and, one in βmys with links to the MYBPC3 regulatory domain. MYBPC3 sites are hinges sterically enabling regulatory interactions with βmys. The βmys site is the actin binding C-loop (residues 359-377). The C-loop is a trigger for actin-activated myosin ATPase and a contraction velocity modulator. Co-domain identification implies their spatial proximity suggesting a novel approach for in vivo protein complex structure determination. Show less

The cardiac muscle proteins, generating and regulating energy transduction during a heartbeat, assemble in the sarcomere into a cyclical machine repetitively translating actin relative to myosin filaments. Myosin is the motor transducing ATP free energy into actin movement against resisting force. Cardiac myosin binding protein C (mybpc3) regulates shortening velocity probably by transient N-terminus binding to actin while its C-terminus strongly binds the myosin filament. Inheritable heart disease associated mutants frequently modify these proteins involving them in disease mechanisms. Nonsynonymous single nucleotide polymorphisms (SNPs) cause single residue substitutions with independent characteristics (sequence location, residue substitution, human demographic, and allele frequency) hypothesized to decide dependent phenotype and pathogenicity characteristics in a feed-forward neural network model. Trial models train and validate on a dynamic worldwide SNP database for cardiac muscle proteins then predict phenotype and pathogenicity for any single residue substitution in myosin, mybpc3, or actin. A separate Bayesian model formulates conditional probabilities for phenotype or pathogenicity given independent SNP characteristics. Neural/Bayes forecasting tests SNP pathogenicity vs (in)dependent SNP characteristics to assess individualized disease risk and in particular to elucidate gender and human subpopulation bias in disease. Evident subpopulation bias in myosin SNP pathogenicities imply myosin normally engages multiple sarcomere proteins functionally. Consistent with this observation, mybpc3 forms a third actomyosin interaction competing with myosin essential light chain N-terminus suggesting a novel strain-dependent mechanism adapting myosin force-velocity to load dynamics. The working models, and the integral myosin/mybpc3 motor concept, portends the wider considerations involved in understanding heart disease as a systemic maladaptation. Show less

The cardiac muscle sarcomere contains multiple proteins contributing to contraction energy transduction and its regulation during a heartbeat. Inheritable heart disease mutants affect most of them but none more frequently than the ventricular myosin motor and cardiac myosin binding protein c (mybpc3). These co-localizing proteins have mybpc3 playing a regulatory role to the energy transducing motor. Residue substitution and functional domain assignment of each mutation in the protein sequence decides, under the direction of a sensible disease model, phenotype and pathogenicity. The unknown model mechanism is decided here using a method combing neural and Bayes networks. Missense single nucleotide polymorphisms (SNPs) are clues for the disease mechanism summarized in an extensive database collecting mutant sequence location and residue substitution as independent variables that imply the dependent disease phenotype and pathogenicity characteristics in 4 dimensional data points (4ddps). The SNP database contains entries with the majority having one or both dependent data entries unfulfilled. A neural network relating causes (mutant residue location and substitution) and effects (phenotype and pathogenicity) is trained, validated, and optimized using fulfilled 4ddps. It then predicts unfulfilled 4ddps providing the implicit disease model. A discrete Bayes network interprets fulfilled and predicted 4ddps with conditional probabilities for phenotype and pathogenicity given mutation location and residue substitution thus relating the neural network implicit model to explicit features of the motor and mybpc3 sequence and structural domains. Neural/Bayes network forecasting automates disease mechanism modeling by leveraging the world wide human missense SNP database that is in place and expanding. Show less

Atypical antipsychotics have become a common therapeutic option in both schizophrenia and bipolar disorder. However, these medications come with a high risk of metabolic side effects, particularly dyslipidemia and insulin resistance. Therefore, identification of patients who are at increased risk for metabolic side effects is of great importance. The genetics of fatty acid metabolism is one area of research that may help identify such patients. Therefore, in this present study, we aimed to determine the effect of one commonly studied genetic polymorphism from both fatty acid desaturase 1 (FADS1) and FADS2 gene on a surrogate measure of insulin resistance and lipid levels in a metabolically high-risk population of patients largely exposed to atypical antipsychotics. This study used a cross-sectional design, fasting blood draws, and genetic analysis to investigate associations between polymorphisms, haplotypes, and metabolic measures. A total of 320 subjects with schizophrenia (n = 226) or bipolar disorder (n = 94) were included in this study. The mean age of the population was 42.5 years and 45% were male. A significant association between FADS1 and FADS2 haplotypes was found with insulin resistance while controlling for confounders. Further investigation is required to replicate this finding. Show less