Molecular modeling of LDLR aids interpretation of genomic variants

Assessing the clinical utility of protein structural analysis in genomic variant classification: experiences from a diagnostic laboratory

BACKGROUND

The widespread clinical application of genome-wide sequencing has resulted in many new diagnoses for rare genetic conditions, but testing regularly identifies variants of uncertain significance (VUS). The remarkable rise in the amount of genomic data has been paralleled by a rise in the number of protein structures that are now publicly available, which may have clinical utility for the interpretation of missense and in-frame insertions or deletions.

METHODS

Within a UK National Health Service genomic medicine diagnostic laboratory, we investigated the number of VUS over a 5-year period that were evaluated using protein structural analysis and how often this analysis aided variant classification.

RESULTS

We found 99 novel missense and in-frame variants across 67 genes that were initially classified as VUS by our diagnostic laboratory using standard variant classification guidelines and for which further analysis of protein structure was requested. Evidence from protein structural analysis was used in the re-assessment of 64 variants, of which 47 were subsequently reclassified as pathogenic or likely pathogenic and 17 remained as VUS. We identified several case studies where protein structural analysis aided variant interpretation by predicting disease mechanisms that were consistent with the observed phenotypes, including loss-of-function through thermodynamic destabilisation or disruption of ligand binding, and gain-of-function through de-repression or escape from proteasomal degradation.

CONCLUSIONS

We have shown that using in silico protein structural analysis can aid classification of VUS and give insights into the mechanisms of pathogenicity. Based on our experience, we propose a generic evidence-based workflow for incorporating protein structural information into diagnostic practice to facilitate variant classification.

Molecular Modeling is an Enabling Approach to Complement and Enhance Channelopathy Research

Hundreds of human membrane proteins form channels that transport necessary ions and compounds, including drugs and metabolites, yet details of their normal function or how function is altered by genetic variants to cause diseases are often unknown. Without this knowledge, researchers are less equipped to develop approaches to diagnose and treat channelopathies. High-resolution computational approaches such as molecular modeling enable researchers to investigate channelopathy protein function, facilitate detailed hypothesis generation, and produce data that is difficult to gather experimentally. Molecular modeling can be tailored to each physiologic context that a protein may act within, some of which may currently be difficult or impossible to assay experimentally. Because many genomic variants are observed in channelopathy proteins from high-throughput sequencing studies, methods with mechanistic value are needed to interpret their effects. The eminent field of structural bioinformatics integrates techniques from multiple disciplines including molecular modeling, computational chemistry, biophysics, and biochemistry, to develop mechanistic hypotheses and enhance the information available for understanding function. Molecular modeling and simulation access 3D and time-dependent information, not currently predictable from sequence. Thus, molecular modeling is valuable for increasing the resolution with which the natural function of protein channels can be investigated, and for interpreting how genomic variants alter them to produce physiologic changes that manifest as channelopathies. © 2022 American Physiological Society. Compr Physiol 12:3141-3166, 2022.

Structural bioinformatics enhances mechanistic interpretation of genomic variation, demonstrated through the analyses of 935 distinct RAS family mutations

MOTIVATION

Protein-coding genetic alterations are frequently observed in Clinical Genetics, but the high yield of variants of uncertain significance remains a limitation in decision making. RAS-family GTPases are cancer drivers, but only 54 variants, across all family members, fall within well-known hotspots. However, extensive sequencing has identified 881 non-hotspot variants for which significance remains to be investigated.

RESULTS

Here, we evaluate 935 missense variants from seven RAS genes, observed in cancer, RASopathies and the healthy adult population. We characterized hotspot variants, previously studied experimentally, using 63 sequence- and 3D structure-based scores, chosen by their breadth of biophysical properties. Applying scores that display best correlation with experimental measures, we report new valuable mechanistic inferences for both hot-spot and non-hotspot variants. Moreover, we demonstrate that 3D scores have little-to-no correlation with those based on DNA sequence, which are commonly used in Clinical Genetics. Thus, combined, these new knowledge bear significant relevance.

AVAILABILITY AND IMPLEMENTATION

All genomic and 3D scores, and markdown for generating figures, are provided in our supplemental data.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Interpreting Sequence Variation in PDAC-Predisposing Genes Using a Multi-Tier Annotation Approach Performed at the Gene, Patient, and Cohort Level

We investigated germline variation in pancreatic ductal adenocarcinoma (PDAC) predisposition genes in 535 patients, using a custom-built panel and a new complementary bioinformatic approach. Our panel assessed genes belonging to DNA repair, cell cycle checkpoints, migration, and preneoplastic pancreatic conditions. Our bioinformatics approach integrated annotations of variants by using data derived from both germline and somatic references. This integrated approach with expanded evidence enabled us to consider patterns even among private mutations, supporting a functional role for certain alleles, which we believe enhances individualized medicine beyond classic gene-centric approaches. Concurrent evaluation of three levels of evidence, at the gene, sample, and cohort level, has not been previously done. Overall, we identified in PDAC patient germline samples, 12% with mutations previously observed in pancreatic cancers, 23% with mutations previously discovered by sequencing other human tumors, and 46% with mutations with germline associations to cancer. Non-polymorphic protein-coding pathogenic variants were found in 18.4% of patient samples. Moreover, among patients with metastatic PDAC, 16% carried at least one pathogenic variant, and this subgroup was found to have an improved overall survival (22.0 months versus 9.8; p=0.008) despite a higher pre-treatment CA19-9 level (p=0.02). Genetic alterations in DNA damage repair genes were associated with longer overall survival among patients who underwent resection surgery (92 months vs. 46; p=0.06). ATM alterations were associated with more frequent metastatic stage (p = 0.04) while patients with BRCA1 or BRCA2 alterations had improved overall survival (79 months vs. 39; p=0.05). We found that mutations in genes associated with chronic pancreatitis were more common in non-white patients (p<0.001) and associated with longer overall survival (52 months vs. 26; p=0.004), indicating the need for greater study of the relationship among these factors. More than 90% of patients were found to have variants of uncertain significance, which is higher than previously reported. Furthermore, we generated 3D models for selected mutant proteins, which suggested distinct mechanisms underlying their dysfunction, likely caused by genetic alterations. Notably, this type of information is not predictable from sequence alone, underscoring the value of structural bioinformatics to improve genomic interpretation. In conclusion, the variation in PDAC predisposition genes appears to be more extensive than anticipated. This information adds to the growing body of literature on the genomic landscape of PDAC and brings us closer to a more widespread use of precision medicine for this challenging disease.

Phenotypic characterization and predictive analysis of p.Asp47Asn LDL receptor mutation associated with Familial Hypercholesterolemia in a Chilean population

BACKGROUND

Familial hypercholesterolemia (FH) is an inherited disorder mainly caused by mutations in the LDL receptor (LDL-R) and characterized by elevation of low-density lipoprotein cholesterol (LDL-C) levels and premature cardiovascular disease.

OBJECTIVE

In this study, we evaluated the clinical phenotype of the p.Asp47Asn, described as an uncertain pathogenic variant, and its effect on the structure of LDL-R and ligand interactions with apolipoproteins.

METHODS

27 children and adolescents with suspected FH diagnosis were recruited from a pediatric endocrinology outpatient clinic. Blood samples were collected after 12 h fasting for lipid profile analysis. DNA sequencing was performed for six FH-related genes by Ion Torrent PGM platform and copy number variation by MLPA. For index cases, a familial cascade screening was done restricted to the same mutation found in the index case. In silico analysis were developed to evaluate the binding capacity of LDL-R to apolipoproteins B100 and E.

RESULTS

Lipid profile in children and adolescents demonstrated higher LDL-C levels in p.Asp47Asn carriers compared to the wild type genotype. In silico analysis predicted a reduction in the binding capacity of the ligand-binding modules LA1-2 of p.Asp47Asn LDL-R for ApoB100 and ApoE, which was not produced by local structural changes or folding defects but as a consequence of a decreased apparent affinity for both apolipoproteins.

CONCLUSION

The clinical phenotype and the structural effects of p.Asp47Asn LDL-R mutation suggest that this variant associates to FH.

Proteostasis Regulation in the Endoplasmic Reticulum: An Emerging Theme in the Molecular Pathology and Therapeutic Management of Familial Hypercholesterolemia

Familial hypercholesterolemia (FH) is an autosomal genetic disease characterized by high serum low-density lipoprotein (LDL) content leading to premature coronary artery disease. The main genetic and molecular causes of FH are mutations in low-density lipoprotein receptor gene (LDLR) resulting in the non-clearance of LDL from the blood by hepatocytes and consequently the formation of plaques. LDLR is synthesized and glycosylated in the endoplasmic reticulum (ER) and then transported to the plasma membrane via Golgi. It is estimated that more than 50% of reported FH-causing mutations in LDLR result in misfolded proteins that are transport-defective and hence retained in ER. ER accumulation of misfolded proteins causes ER-stress and activates unfolded protein response (UPR). UPR aids protein folding, blocks further protein synthesis, and eliminates misfolded proteins via ER-associated degradation (ERAD) to alleviate ER stress. Various studies demonstrated that ER-retained LDLR mutants are subjected to ERAD. Interestingly, chemical chaperones and genetic or pharmacological inhibition of ERAD have been reported to rescue the transport defective mutant LDLR alleles from ERAD and restore their ER-Golgi transport resulting in the expression of functional plasma membrane LDLR. This suggests the possibility of pharmacological modulation of proteostasis in the ER as a therapeutic strategy for FH. In this review, we picture a detailed analysis of UPR and the ERAD processes activated by ER-retained LDLR mutants associated with FH. In addition, we discuss and critically evaluate the potential role of chemical chaperones and ERAD modulators in the therapeutic management of FH.