A comprehensive map of human glucokinase variant activity

Inhibition of Hmbox1 Promotes Cardiomyocyte Survival and Glucose Metabolism Through Gck Activation in Ischemia/Reperfusion Injury

BACKGROUND

Exercise-induced physiological cardiac growth regulators may protect the heart from ischemia/reperfusion (I/R) injury. Homeobox-containing 1 (Hmbox1), a homeobox family member, has been identified as a putative transcriptional repressor and is downregulated in the exercised heart. However, its roles in exercise-induced physiological cardiac growth and its potential protective effects against cardiac I/R injury remain largely unexplored.

METHODS

We studied the function of Hmbox1 in exercise-induced physiological cardiac growth in mice after 4 weeks of swimming exercise. Hmbox1 expression was then evaluated in human heart samples from deceased patients with myocardial infarction and in the animal cardiac I/R injury model. Its role in cardiac I/R injury was examined in mice with adeno-associated virus 9 (AAV9) vector-mediated Hmbox1 knockdown and in those with cardiac myocyte-specific Hmbox1 ablation. We performed RNA sequencing, promoter prediction, and binding assays and identified glucokinase (Gck) as a downstream effector of Hmbox1. The effects of Hmbox1 together with Gck were examined in cardiomyocytes to evaluate their cell size, proliferation, apoptosis, mitochondrial respiration, and glycolysis. The function of upstream regulator of Hmbox1, ETS1, was investigated through ETS1 overexpression in cardiac I/R mice in vivo.

RESULTS

We demonstrated that Hmbox1 downregulation was required for exercise-induced physiological cardiac growth. Inhibition of Hmbox1 increased cardiomyocyte size in isolated neonatal rat cardiomyocytes and human embryonic stem cell-derived cardiomyocytes but did not affect cardiomyocyte proliferation. Under pathological conditions, Hmbox1 was upregulated in both human and animal postinfarct cardiac tissues. Furthermore, both cardiac myocyte-specific Hmbox1 knockout and AAV9-mediated Hmbox1 knockdown protected against cardiac I/R injury and heart failure. Therapeutic effects were observed when sh-Hmbox1 AAV9 was administered after I/R injury. Inhibition of Hmbox1 activated the Akt/mTOR/P70S6K pathway and transcriptionally upregulated Gck, leading to reduced apoptosis and improved mitochondrial respiration and glycolysis in cardiomyocytes. ETS1 functioned as an upstream negative regulator of Hmbox1 transcription, and its overexpression was protective against cardiac I/R injury.

CONCLUSIONS

Our studies unravel a new role for the transcriptional repressor Hmbox1 in exercise-induced physiological cardiac growth. They also highlight the therapeutic potential of targeting Hmbox1 to improve myocardial survival and glucose metabolism after I/R injury.

A power-based sliding window approach to evaluate the clinical impact of rare genetic variants in the nucleotide sequence or the spatial position of the folded protein

Systematic determination of novel variant pathogenicity remains a major challenge, even when there is an established association between a gene and phenotype. Here we present Power Window (PW), a sliding window technique that identifies the impactful regions of a gene using population-scale clinico-genomic datasets. By sizing analysis windows on the number of variant carriers, rather than the number of variants or nucleotides, statistical power is held constant, enabling the localization of clinical phenotypes and removal of unassociated gene regions. The windows can be built by sliding across either the nucleotide sequence of the gene (through 1D space) or the positions of the amino acids in the folded protein (through 3D space). Using a training set of 350k exomes from the UK Biobank (UKB), we developed PW models for well-established gene-disease associations and tested their accuracy in two independent cohorts (117k UKB exomes and 65k exomes sequenced at Helix in the Healthy Nevada Project, myGenetics, or In Our DNA SC studies). The significant models retained a median of 49% of the qualifying variant carriers in each gene (range 2%-98%), with quantitative traits showing a median effect size improvement of 66% compared with aggregating variants across the entire gene, and binary traits' odds ratios improving by a median of 2.2-fold. PW showcases that electronic health record-based statistical analyses can accurately distinguish between novel coding variants in established genes that will have high phenotypic penetrance and those that will not, unlocking new potential for human genomics research, drug development, variant interpretation, and precision medicine.

Destabilization and Degradation of a Disease-Linked PGM1 Protein Variant

PGM1-linked congenital disorder of glycosylation (PGM1-CDG) is an autosomal recessive disease characterized by several phenotypes, some of which are life-threatening. Research focusing on the disease-related variants of the α-D-phosphoglucomutase 1 (PGM1) protein has shown that several are insoluble in vitro and expressed at low levels in patient fibroblasts. Due to these observations, we hypothesized that some disease-linked PGM1 protein variants are structurally destabilized and subject to protein quality control (PQC) and rapid intracellular degradation. Employing yeast-based assays, we show that a disease-associated human variant, PGM1 L516P, is insoluble, inactive, and highly susceptible to ubiquitylation and rapid degradation by the proteasome. In addition, we show that PGM1 L516P forms aggregates in S. cerevisiae and that both the aggregation pattern and the abundance of PGM1 L516P are chaperone-dependent. Finally, using computational methods, we perform saturation mutagenesis to assess the impact of all possible single residue substitutions in the PGM1 protein. These analyses identify numerous missense variants with predicted detrimental effects on protein function and stability. We suggest that many disease-linked PGM1 variants are subject to PQC-linked degradation and that our in silico site-saturated data set may assist in the mechanistic interpretation of PGM1 variants.

Characterizing glucokinase variant mechanisms using a multiplexed abundance assay

BACKGROUND

Amino acid substitutions can perturb protein activity in multiple ways. Understanding their mechanistic basis may pinpoint how residues contribute to protein function. Here, we characterize the mechanisms underlying variant effects in human glucokinase (GCK) variants, building on our previous comprehensive study on GCK variant activity.

RESULTS

Using a yeast growth-based assay, we score the abundance of 95% of GCK missense and nonsense variants. When combining the abundance scores with our previously determined activity scores, we find that 43% of hypoactive variants also decrease cellular protein abundance. The low-abundance variants are enriched in the large domain, while residues in the small domain are tolerant to mutations with respect to abundance. Instead, many variants in the small domain perturb GCK conformational dynamics which are essential for appropriate activity.

CONCLUSIONS

In this study, we identify residues important for GCK metabolic stability and conformational dynamics. These residues could be targeted to modulate GCK activity, and thereby affect glucose homeostasis.

FiTMuSiC: leveraging structural and (co)evolutionary data for protein fitness prediction

Systematically predicting the effects of mutations on protein fitness is essential for the understanding of genetic diseases. Indeed, predictions complement experimental efforts in analyzing how variants lead to dysfunctional proteins that in turn can cause diseases. Here we present our new fitness predictor, FiTMuSiC, which leverages structural, evolutionary and coevolutionary information. We show that FiTMuSiC predicts fitness with high accuracy despite the simplicity of its underlying model: it was among the top predictors on the hydroxymethylbilane synthase (HMBS) target of the sixth round of the Critical Assessment of Genome Interpretation challenge (CAGI6) and performs as well as much more complex deep learning models such as AlphaMissense. To further demonstrate FiTMuSiC's robustness, we compared its predictions with in vitro activity data on HMBS, variant fitness data on human glucokinase (GCK), and variant deleteriousness data on HMBS and GCK. These analyses further confirm FiTMuSiC's qualities and accuracy, which compare favorably with those of other predictors. Additionally, FiTMuSiC returns two scores that separately describe the functional and structural effects of the variant, thus providing mechanistic insight into why the variant leads to fitness loss or gain. We also provide an easy-to-use webserver at https://babylone.ulb.ac.be/FiTMuSiC , which is freely available for academic use and does not require any bioinformatics expertise, which simplifies the accessibility of our tool for the entire scientific community.

Cellular and molecular mechanisms of aspartoacylase and its role in Canavan disease

Canavan disease is an autosomal recessive and lethal neurological disorder, characterized by the spongy degeneration of the white matter in the brain. The disease is caused by a deficiency of the cytosolic aspartoacylase (ASPA) enzyme, which catalyzes the hydrolysis of N-acetyl-aspartate (NAA), an abundant brain metabolite, into aspartate and acetate. On the physiological level, the mechanism of pathogenicity remains somewhat obscure, with multiple, not mutually exclusive, suggested hypotheses. At the molecular level, recent studies have shown that most disease linked ASPA gene variants lead to a structural destabilization and subsequent proteasomal degradation of the ASPA protein variants, and accordingly Canavan disease should in general be considered a protein misfolding disorder. Here, we comprehensively summarize the molecular and cell biology of ASPA, with a particular focus on disease-linked gene variants and the pathophysiology of Canavan disease. We highlight the importance of high-throughput technologies and computational prediction tools for making genotype-phenotype predictions as we await the results of ongoing trials with gene therapy for Canavan disease.

A probabilistic graphical model for estimating selection coefficient of missense variants from human population sequence data

Accurately predicting the effect of missense variants is a central problem in interpretation of genomic variation. Commonly used computational methods does not capture the quantitative impact on fitness in populations. We developed MisFit to estimate missense fitness effect using biobank-scale human population genome data. MisFit jointly models the effect at molecular level ( d ) and population level (selection coefficient, s ), assuming that in the same gene, missense variants with similar d have similar s . MisFit is a probabilistic graphical model that integrates deep neural network components and population genetics models efficiently with inductive bias based on biological causality of variant effect. We trained it by maximizing probability of observed allele counts in 236,017 European individuals. We show that s is informative in predicting frequency across ancestries and consistent with the fraction of de novo mutations given s . Finally, MisFit outperforms previous methods in prioritizing missense variants in individuals with neurodevelopmental disorders.

Alignment-based Protein Mutational Landscape Prediction: Doing More with Less

The wealth of genomic data has boosted the development of computational methods predicting the phenotypic outcomes of missense variants. The most accurate ones exploit multiple sequence alignments, which can be costly to generate. Recent efforts for democratizing protein structure prediction have overcome this bottleneck by leveraging the fast homology search of MMseqs2. Here, we show the usefulness of this strategy for mutational outcome prediction through a large-scale assessment of 1.5M missense variants across 72 protein families. Our study demonstrates the feasibility of producing alignment-based mutational landscape predictions that are both high-quality and compute-efficient for entire proteomes. We provide the community with the whole human proteome mutational landscape and simplified access to our predictive pipeline.

Accurate proteome-wide missense variant effect prediction with AlphaMissense

The vast majority of missense variants observed in the human genome are of unknown clinical significance. We present AlphaMissense, an adaptation of AlphaFold fine-tuned on human and primate variant population frequency databases to predict missense variant pathogenicity. By combining structural context and evolutionary conservation, our model achieves state-of-the-art results across a wide range of genetic and experimental benchmarks, all without explicitly training on such data. The average pathogenicity score of genes is also predictive for their cell essentiality, capable of identifying short essential genes that existing statistical approaches are underpowered to detect. As a resource to the community, we provide a database of predictions for all possible human single amino acid substitutions and classify 89% of missense variants as either likely benign or likely pathogenic.

Diffuse, Adult-Onset Nesidioblastosis/Non-Insulinoma Pancreatogenous Hypoglycemia Syndrome (NIPHS): Review of the Literature of a Rare Cause of Hyperinsulinemic Hypoglycemia

Differential diagnosis of hypoglycemia in the non-diabetic adult patient is complex and comprises various diseases, including endogenous hyperinsulinism caused by functional β-cell disorders. The latter is also designated as nesidioblastosis or non-insulinoma pancreatogenous hypoglycemia syndrome (NIPHS). Clinically, this rare disease presents with unspecific adrenergic and neuroglycopenic symptoms and is, therefore, often overlooked. A combination of careful clinical assessment, oral glucose tolerance testing, 72 h fasting, sectional and functional imaging, and invasive insulin measurements can lead to the correct diagnosis. Due to a lack of a pathophysiological understanding of the condition, conservative treatment options are limited and mostly ineffective. Therefore, nearly all patients currently undergo surgical resection of parts or the entire pancreas. Consequently, apart from faster diagnosis, more elaborate and less invasive treatment options are needed to relieve the patients from the dangerous and devastating symptoms. Based on a case of a 23-year-old man presenting with this disease in our department, we performed an extensive review of the medical literature dealing with this condition and herein presented a comprehensive discussion of this interesting disease, including all aspects from epidemiology to therapy.

Mind the gap

Amino acid deletions are high-risk, high-reward mutations, yet structural consequences are poorly understood. In this issue of Structure, Woods et al. (2023) individually deleted 65 residues from a small α-helical protein, structurally assayed the 17 soluble variants, and developed a computational model of deletion solubility combining Rosetta and AlphaFold2.