Assessing Human Genetic Variations in Glucose Transporter SLC2A10 and Their Role in Altering Structural and Functional Properties

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.

Impact of integrated translational research on clinical exome sequencing

PURPOSE

Exome sequencing often identifies pathogenic genetic variants in patients with undiagnosed diseases. Nevertheless, frequent findings of variants of uncertain significance necessitate additional efforts to establish causality before reaching a conclusive diagnosis. To provide comprehensive genomic testing to patients with undiagnosed disease, we established an Individualized Medicine Clinic, which offered clinical exome testing and included a Translational Omics Program (TOP) that provided variant curation, research activities, or research exome sequencing.

METHODS

From 2012 to 2018, 1101 unselected patients with undiagnosed diseases received exome testing. Outcomes were reviewed to assess impact of the TOP and patient characteristics on diagnostic rates through descriptive and multivariate analyses.

RESULTS

The overall diagnostic yield was 24.9% (274 of 1101 patients), with 174 (15.8% of 1101) diagnosed on the basis of clinical exome sequencing alone. Four hundred twenty-three patients with nondiagnostic or without access to clinical exome sequencing were evaluated by the TOP, with 100 (9% of 1101) patients receiving a diagnosis, accounting for 36.5% of the diagnostic yield. The identification of a genetic diagnosis was influenced by the age at time of testing and the disease phenotype of the patient.

CONCLUSION

Integration of translational research activities into clinical practice of a tertiary medical center can significantly increase the diagnostic yield of patients with undiagnosed disease.

Molecular mechanics and dynamic simulations of well-known Kabuki syndrome-associated KDM6A variants reveal putative mechanisms of dysfunction

BACKGROUND

Kabuki syndrome is a genetic disorder that affects several body systems and presents with variations in symptoms and severity. The syndrome is named for a common phenotype of faces resembling stage makeup used in a Japanese traditional theatrical art named kabuki. The most frequent cause of this syndrome is mutations in the H3K4 family of histone methyltransferases while a smaller percentage results from genetic alterations affecting the histone demethylase, KDM6A. Because of the rare presentation of the latter form of the disease, little is known about how missense changes in the KDM6A protein sequence impact protein function.

RESULTS

In this study, we use molecular mechanic and molecular dynamic simulations to enhance the annotation and mechanistic interpretation of the potential impact of eleven KDM6A missense variants found in Kabuki syndrome patients. These variants (N910S, D980V, S1025G, C1153R, C1153Y, P1195L, L1200F, Q1212R, Q1248R, R1255W, and R1351Q) are predicted to be pathogenic, likely pathogenic or of uncertain significance by sequence-based analysis. Here, we demonstrate, for the first time, that although Kabuki syndrome missense variants are found outside the functionally critical regions, they could affect overall function by significantly disrupting global and local conformation (C1153R, C1153Y, P1195L, L1200F, Q1212R, Q1248R, R1255W and R1351Q), chemical environment (C1153R, C1153Y, P1195L, L1200F, Q1212R, Q1248R, R1255W and R1351Q), and/or molecular dynamics of the catalytic domain (all variants). In addition, our approaches predict that many mutations, in particular C1153R, could allosterically disrupt the key enzymatic interactions of KDM6A.

CONCLUSIONS

Our study demonstrates that the KDM6A Kabuki syndrome variants may impair histone demethylase function through various mechanisms that include altered protein integrity, local environment, molecular interactions and protein dynamics. Molecular dynamics simulations of the wild type and the variants are critical to gain a better understanding of molecular dysfunction. This type of comprehensive structure- and MD-based analyses should help develop improved impact scoring systems to interpret the damaging effects of variants in this protein and other related proteins as well as provide detailed mechanistic insight that is not currently predictable from sequence alone.

Glucose Transport and Transporters in the Endomembranes

Glucose is a basic nutrient in most of the creatures; its transport through biological membranes is an absolute requirement of life. This role is fulfilled by glucose transporters, mediating the transport of glucose by facilitated diffusion or by secondary active transport. GLUT (glucose transporter) or SLC2A (Solute carrier 2A) families represent the main glucose transporters in mammalian cells, originally described as plasma membrane transporters. Glucose transport through intracellular membranes has not been elucidated yet; however, glucose is formed in the lumen of various organelles. The glucose-6-phosphatase system catalyzing the last common step of gluconeogenesis and glycogenolysis generates glucose within the lumen of the endoplasmic reticulum. Posttranslational processing of the oligosaccharide moiety of glycoproteins also results in intraluminal glucose formation in the endoplasmic reticulum (ER) and Golgi. Autophagic degradation of polysaccharides, glycoproteins, and glycolipids leads to glucose accumulation in lysosomes. Despite the obvious necessity, the mechanism of glucose transport and the molecular nature of mediating proteins in the endomembranes have been hardly elucidated for the last few years. However, recent studies revealed the intracellular localization and functional features of some glucose transporters; the aim of the present paper was to summarize the collected knowledge.

Modeling post-translational modifications and cancer-associated mutations that impact the heterochromatin protein 1α-importin α heterodimers

Heterochromatin protein 1α (HP1α) is a protein that mediates cancer-associated processes in the cell nucleus. Proteomic experiments, reported here, demonstrate that HP1α complexes with importin α (IMPα), a protein necessary for its nuclear transport. This data is congruent with Simple Linear Motif (SLiM) analyses that identify an IMPα-binding motif within the linker that joins the two globular domains of this protein. Using molecular modeling and dynamics simulations, we develop a model of the IMPα-HP1α complex and investigate the impact of phosphorylation and genomic variants on their interaction. We demonstrate that phosphorylation of the HP1α linker likely regulates its association with IMPα, which has implications for HP1α access to the nucleus, where it functions. Cancer-associated genomic variants do not abolish the interaction of HP1α but instead lead to rearrangements where the variant proteins maintain interaction with IMPα, but with less specificity. Combined, this new mechanistic insight bears biochemical, cell biological, and biomedical relevance.

[Genetic characteristics of stroke]

In the recent years there is a growing interest in identification of additional genetic factors of stroke. A growing body of evidence supports the role of genetic factors in determining the risk of both hemorrhagic and ischemic stroke. The article considers the main genes associated with susceptibility to stroke and genetic polymorphisms associated with the disease. Genetic factors, modulating inflammation process, coagulation, lipid metabolism, NO formation, renin-angiotensin-aldosterone system and homeostasis play a significant role in stroke development. A comprehensive analysis of different genes associated with stroke may help to detect individuals with extremely high risk of stroke and implement timely preventive measures to decrease stroke burden.

The Importance of Biologic Knowledge and Gene Expression Context for Genomic Data Interpretation

Background: Genomic sequencing, including whole exome sequencing (WES), is enabling a higher resolution for defining diseases, understand mechanisms, and improving the practice of clinical care. However, WES routinely identifies genomic variants with uncertain functional effects. Furthering uncertainty in WES data interpretation is that many genes can express multiple transcripts and their relative expression may differ by body tissue. In order to interpret WES data, we not only need to understand which transcript is most relevant, but what tissue is most relevant. Methods: In this work, we quantify how frequently differences in transcript and tissue expression affect WES data interpretation at gene, pathway, disease, and biologic network levels. We combined and analyzed multiple large and publically available datasets to inform genomic data interpretation. Results: Across well-established biologic pathways and genes with pathogenic disease variants, 54 and 40% have a different protein coding effect by transcript selection for, respectively, 25 and 50% of the genes contained. Additionally, strong differences in human tissue expression levels affect 33 and 19% of the same set of pathways and diseases for, respectively, 25 and 50% of the genes contained. Conclusion: Whole exome sequencing identifies genomic variants, but to interpret the functional effects of those variants in high-resolution, we recommend building transcript selection and cross-tissue gene expression levels into hypotheses and analyses. Using current large-scale data, we show how extensively interpretation of genomic variants may differ according to transcript and tissue, across most pathways and disease. Thus, their inclusion is necessary for WES data interpretation.