In SilicoPrediction of the Effects of Mutations in the Human Mevalonate Kinase Gene: Towards a Predictive Framework for Mevalonate Kinase Deficiency

Increased core body temperature exacerbates defective protein prenylation in mouse models of mevalonate kinase deficiency

Mevalonate kinase deficiency (MKD) is characterized by recurrent fevers and flares of systemic inflammation, caused by biallelic loss-of-function mutations in MVK. The underlying disease mechanisms and triggers of inflammatory flares are poorly understood because of the lack of in vivo models. We describe genetically modified mice bearing the hypomorphic mutation p.Val377Ile (the commonest variant in patients with MKD) and amorphic, frameshift mutations in Mvk. Compound heterozygous mice recapitulated the characteristic biochemical phenotype of MKD, with increased plasma mevalonic acid and clear buildup of unprenylated GTPases in PBMCs, splenocytes, and bone marrow. The inflammatory response to LPS was enhanced in compound heterozygous mice and treatment with the NLRP3 inflammasome inhibitor MCC950 prevented the elevation of circulating IL-1β, thus identifying a potential inflammasome target for future therapeutic approaches. Furthermore, lines of mice with a range of deficiencies in mevalonate kinase and abnormal prenylation mirrored the genotype-phenotype relationship in human MKD. Importantly, these mice allowed the determination of a threshold level of residual enzyme activity, below which protein prenylation is impaired. Elevated temperature dramatically but reversibly exacerbated the deficit in the mevalonate pathway and the defective prenylation in vitro and in vivo, highlighting increased body temperature as a likely trigger of inflammatory flares.

Computationally-Aided Modeling of Hsp70-Client Interactions: Past, Present, and Future

Hsp70 molecular chaperones play central roles in maintaining a healthy cellular proteome. Hsp70s function by binding to short peptide sequences in incompletely folded client proteins, thus preventing them from misfolding and/or aggregating, and in many cases holding them in a state that is competent for subsequent processes like translocation across membranes. There is considerable interest in predicting the sites where Hsp70s may bind their clients, as the ability to do so sheds light on the cellular functions of the chaperone. In addition, the capacity of the Hsp70 chaperone family to bind to a broad array of clients and to identify accessible sequences that enable discrimination of those that are folded from those that are not fully folded, which is essential to their cellular roles, is a fascinating puzzle in molecular recognition. In this article we discuss efforts to harness computational modeling with input from experimental data to develop a predictive understanding of the promiscuous yet selective binding of Hsp70 molecular chaperones to accessible sequences within their client proteins. We trace how an increasing understanding of the complexities of Hsp70-client interactions has led computational modeling to new underlying assumptions and design features. We describe the trend from purely data-driven analysis toward increased reliance on physics-based modeling that deeply integrates structural information and sequence-based functional data with physics-based binding energies. Notably, new experimental insights are adding to our understanding of the molecular origins of "selective promiscuity" in substrate binding by Hsp70 chaperones and challenging the underlying assumptions and design used in earlier predictive models. Taking the new experimental findings together with exciting progress in computational modeling of protein structures leads us to foresee a bright future for a predictive understanding of selective-yet-promiscuous binding exploited by Hsp70 molecular chaperones; the resulting new insights will also apply to substrate binding by other chaperones and by signaling proteins.

New Developments and Possibilities in Reanalysis and Reinterpretation of Whole Exome Sequencing Datasets for Unsolved Rare Diseases Using Machine Learning Approaches

Rare diseases impact the lives of 300 million people in the world. Rapid advances in bioinformatics and genomic technologies have enabled the discovery of causes of 20–30% of rare diseases. However, most rare diseases have remained as unsolved enigmas to date. Newer tools and availability of high throughput sequencing data have enabled the reanalysis of previously undiagnosed patients. In this review, we have systematically compiled the latest developments in the discovery of the genetic causes of rare diseases using machine learning methods. Importantly, we have detailed methods available to reanalyze existing whole exome sequencing data of unsolved rare diseases. We have identified different reanalysis methodologies to solve problems associated with sequence alterations/mutations, variation re-annotation, protein stability, splice isoform malfunctions and oligogenic analysis. In addition, we give an overview of new developments in the field of rare disease research using whole genome sequencing data and other omics.

Mevalonate Kinase-Associated Diseases: Hunting for Phenotype-Genotype Correlation

Mevalonate kinase-associated diseases (MKAD) are caused by pathogenic mutations in the mevalonate kinase gene (MVK) and encompass several phenotypically different rare and hereditary autoinflammatory conditions. The most serious is a recessive systemic metabolic disease called mevalonic aciduria, and the most recently recognized is disseminated superficial actinic porokeratosis, a dominant disease limited to the skin. To evaluate a possible correlation between genotypes and (1) the different MKAD clinical subtypes or (2) the occurrence of severe manifestations, data were reviewed for all patients with MVK variants described in the literature (N = 346), as well as those referred to our center (N = 51). The genotypes including p.(Val377Ile) (homozygous or compound heterozygous) were more frequent in mild systemic forms but were also sometimes encountered with severe disease. We confirmed that amyloidosis was more prevalent in patients compound heterozygous for p.(Ile268Thr) and p.(Val377Ile) than in others and revealed new associations. Patients homozygous for p.(Leu264Phe), p.(Ala334Thr) or compound heterozygous for p.(His20Pro) and p.(Ala334Thr) had increased risk of severe neurological or ocular symptoms. All patients homozygous for p.(Leu264Phe) had a cataract. The variants associated with porokeratosis were relatively specific and more frequently caused a frameshift than in patients with other clinical forms (26% vs. 6%). We provide practical recommendations focusing on phenotype-genotype correlation in MKAD that could be helpful for prophylactic management.

In Silico Analysis of the Effects of Point Mutations on α-Globin: Implications for α-Thalassemia

Hemoglobinopathies are inherited diseases that impair the structure and function of the oxygen-carrying pigment hemoglobin (Hb). Adult Hb consists of two α and two β subunits. α-Thalassemia (α-thal) affects the genes that code for the α-globin chains, HBA1 and HBA2. Mutations can result in asymptomatic, mild or severe outcomes depending on several factors, such as mutation type, number of mutations and the location at which they occur. PredictSNP was used to estimate whether every possible single nucleotide polymorphism (SNP) would have a neutral or deleterious effect on the protein. These results were then used to create a plot of predicted tolerance to change for each residue in the protein. Tolerance to change was negatively correlated with the residue's sequence conservation score. The PredictSNP data were compared to clinical reports of 110 selected variants in the literature. There were 29 disagreements between the two data types. Some of these could be resolved by considering the role of the affected residue in binding other molecules. The three-dimensional structures of some of these variant proteins were modeled. These models helped explain variants which affect heme binding. We predict that where a point mutation alters a residue that is intolerant to change, is well conserved and or involved in interactions, it is likely to be associated with disease. Overall, the data from this study could be used alongside biochemical and clinical data to assess novel α-globin variants.

Videira P, Valadão G. Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

The amount of data collected and managed in (bio)medicine is ever-increasing. Thus, there is a need to rapidly and efficiently collect, analyze, and characterize all this information. Artificial intelligence (AI), with an emphasis on deep learning, holds great promise in this area and is already being successfully applied to basic research, diagnosis, drug discovery, and clinical trials. Rare diseases (RDs), which are severely underrepresented in basic and clinical research, can particularly benefit from AI technologies. Of the more than 7000 RDs described worldwide, only 5% have a treatment. The ability of AI technologies to integrate and analyze data from different sources (e.g., multi-omics, patient registries, and so on) can be used to overcome RDs’ challenges (e.g., low diagnostic rates, reduced number of patients, geographical dispersion, and so on). Ultimately, RDs’ AI-mediated knowledge could significantly boost therapy development. Presently, there are AI approaches being used in RDs and this review aims to collect and summarize these advances. A section dedicated to congenital disorders of glycosylation (CDG), a particular group of orphan RDs that can serve as a potential study model for other common diseases and RDs, has also been included.

Defective Protein Prenylation in a Spectrum of Patients With Mevalonate Kinase Deficiency

The rare autoinflammatory disease mevalonate kinase deficiency (MKD, which includes HIDS and mevalonic aciduria) is caused by recessive, pathogenic variants in the MVK gene encoding mevalonate kinase. Deficiency of this enzyme decreases the synthesis of isoprenoid lipids and thus prevents the normal post-translational prenylation of small GTPase proteins, which then accumulate in their unprenylated form. We recently optimized a sensitive assay capable of detecting unprenylated Rab GTPase proteins in peripheral blood mononuclear cells (PBMCs) and showed that this assay distinguished MKD from other autoinflammatory diseases. We have now analyzed PBMCs from an additional six patients with genetically-confirmed MKD (with different compound heterozygous MVK genotypes), and compared these with PBMCs from three healthy volunteers and four unaffected control individuals heterozygous for the commonest pathogenic variant, MVK^V377I. We detected a clear accumulation of unprenylated Rab proteins, as well as unprenylated Rap1A by western blotting, in all six genetically-confirmed MKD patients compared to heterozygous controls and healthy volunteers. Furthermore, in the three subjects for whom measurements of residual mevalonate kinase activity was available, enzymatic activity inversely correlated with the extent of the defect in protein prenylation. Finally, a heterozygous MVK^V377I patient presenting with autoinflammatory symptoms did not have defective prenylation, indicating a different cause of disease. These findings support the notion that the extent of loss of enzyme function caused by biallelic MVK variants determines the severity of defective protein prenylation, and the accumulation of unprenylated proteins in PBMCs may be a sensitive and consistent biomarker that could be used to aid, or help rule out, diagnosis of MKD.

Catalytic mechanism of mevalonate kinase revisited, a QM/MM study

Mevalonate Kinase (MVK) catalyses the ATP-Mg2+ mediated phosphate transfer of mevalonate to produce mevalonate 5-phosphate and is a key kinase in the mevalonate pathway in the biosynthesis of isopentenyl diphosphate, the precursor of isoprenoid-based biofuels. However, the crystal structure in complex with the native substrate mevalonate, ATP and Mg2+ has not been resolved, which has limited the understanding of its reaction mechanism and therefore its application in the production of isoprenoid-based biofuels. Here using molecular docking, molecular dynamics (MD) simulations and a hybrid QM/MM study, we revisited the location of Mg2+ resolved in the crystal structure of MVK and determined a catalytically competent MVK structure in complex with the native substrate mevalonate and ATP. We demonstrated that significant conformational change on a flexible loop connecting the α6 and α7 helix is induced by the substrate binding. Further, we found that Asp204 is coordinated to the Mg2+ ion. Arg241 plays a crucial role in organizing the triphosphoryl tail of ATP for in-line phosphate transfer and stabilizing the negative charge that accumulates at the β,γ-bridging oxygen of ATP upon bond cleavage. Remarkably, we revealed that the phosphorylation of mevalonate catalyzed by MVK occurs via a direct phosphorylation mechanism, instead of the conventionally postulated catalytic base mechanism. The catalytically competent complex structure of MVK as well as the mechanism of reaction will pave the way for the rational engineering of MVK to exploit its applications in the production of biofuels.

MMAB, a novel candidate gene to be screened in the molecular diagnosis of Mevalonate Kinase Deficiency

Mevalonate kinase deficiency (MKD) is an autosomal recessive inflammatory disease. Mutations in MVK gene are associated with MKD with modest genotype-phenotype correlation. In spite of recent guidelines indicating specific MVK mutations for the more severe form or the milder one, little is known about MVK variability within and between populations. The aim of this work is to provide supplementary information about MVK variability useful in the molecular diagnosis of MKD, as well as to unravel the presence of novel genes potentially involved as involved in the clinical heterogeneity of MKD phenotype. We used a population-based approach, coupled with Combined Annotation-Dependent Depletion (CADD) score, to analyze the level of genetic variability for common and putatively deleterious MVK variants. We also performed Exome screening with the Illumina Human Exome Bead Chip on 21 MKD patients to double-check our in silico findings. Haplotype block detection in different populations revealed the existence of two blocks in MVK; interestingly, the first haploblock comprises the promoter region shared with MMAB gene. Analyses of MMAB and MVK genetic variants in 21 MKD patients strengthen our observations showing a novel scenario in which the same mutations commonly associated with MKD are found coupled with different combination of MMAB rs7134594 SNP was already described as associated with HDL cholesterol level and present in the haploblock promoter region. The rs7134594 SNP is reported as an eQTL for MVK and MMAB. Hypothesizing the presence of genetic variants modulating the complex phenotypic spectrum of MKD, we suggest that future directions in screening for MKD pathogenic variants should focus both MMAB and MVK genes.

Polygenic determinants in extremes of high-density lipoprotein cholesterol

HDL cholesterol (HDL-C) remains a superior biochemical predictor of CVD risk, but its genetic basis is incompletely defined. In patients with extreme HDL-C concentrations, we concurrently evaluated the contributions of multiple large- and small-effect genetic variants. In a discovery cohort of 255 unrelated lipid clinic patients with extreme HDL-C levels, we used a targeted next-generation sequencing panel to evaluate rare variants in known HDL metabolism genes, simultaneously with common variants bundled into a polygenic trait score. Two additional cohorts were used for validation and included 1,746 individuals from the Montréal Heart Institute Biobank and 1,048 individuals from the University of Pennsylvania. Findings were consistent between cohorts: we found rare heterozygous large-effect variants in 18.7% and 10.9% of low- and high-HDL-C patients, respectively. We also found common variant accumulation, indicated by extreme polygenic trait scores, in an additional 12.8% and 19.3% of overall cases of low- and high-HDL-C extremes, respectively. Thus, the genetic basis of extreme HDL-C concentrations encountered clinically is frequently polygenic, with contributions from both rare large-effect and common small-effect variants. Multiple types of genetic variants should be considered as contributing factors in patients with extreme dyslipidemia.

25-Hydroxycholesterol and inflammation in Lovastatin-deregulated mevalonate pathway

Mevalonate pathway deregulation has been observed in several diseases, including Mevalonate kinase deficiency (MKD). MKD is a hereditary auto-inflammatory disorder, due to mutations at mevalonate kinase gene (MVK), encoding mevalonate kinase (MK) enzyme. MVK mutations have been reported as associated with impairment of mevalonate pathway with consequent decrease of protein prenylation levels, defective autophagy and increase of IL-1β secretion, followed by cell death. Since 25-hydroxycholesterol (25-HC), a metabolite of cholesterol, can suppress IL-1β production, thus reducing inflammation, we evaluated the effect of 25-HC in an in vitro model of mevalonate pathway alteration, obtained using Lovastatin. Human glioblastoma cell line (U87-MG) was chosen to mimic, at least in part, the central nervous system impairment observed in MKD; 25-HC effects were evaluated aimed at disclosing if this compound could be considered as novel potential drug for MKD. Our results showed that 25-HC is able to reduce inflammation but it is ineffective to restore autophagy flux and to decrease apoptosis levels, both caused by lower protein prenylation; so, in spite of its anti-inflammatory action it is not useful to rescue defective prenylation/autophagy impairment-driven apoptosis in Lovastatin impaired mevalonate pathway. We hypothesize the presence in the mevalonate pathway of alternative mechanisms acting between inflammation and apoptotic autophagy impairment.

In silico prediction of the effects of mutations in the human triose phosphate isomerase gene: Towards a predictive framework for TPI deficiency

Triose phosphate isomerase (TPI) deficiency is a rare, but highly debilitating, inherited metabolic disease. Almost all patients suffer severe neurological effects and the most severely affected are unlikely to live beyond early childhood. Here, we describe an in silico study into well-characterised variants which are associated with the disease alongside an investigation into 79 currently uncharacterised TPI variants which are known to occur in the human population. The majority of the disease-associated mutations affected amino acid residues close to the dimer interface or the active site. However, the location of the altered amino acid residue did not predict the severity of the resulting disease. Prediction of the effect on protein stability using a range of different programs suggested a relationship between the degree of instability caused by the sequence variation and the severity of the resulting disease. Disease-associated variations tended to affect well-conserved residues in the protein's sequence. However, the degree of conservation of the residue was not predictive of disease severity. The majority of the 79 uncharacterised variants are potentially associated with disease since they were predicted to destabilise the protein and often occur in well-conserved residues. We predict that individuals homozygous for the corresponding mutations would be likely to suffer from TPI deficiency.

Modulating Mobility: a Paradigm for Protein Engineering?

Proteins are highly mobile structures. In addition to gross conformational changes occurring on, for example, ligand binding, they are also subject to constant thermal motion. The mobility of a protein varies through its structure and can be modulated by ligand binding and other events. It is becoming increasingly clear that this mobility plays an important role in key functions of proteins including catalysis, allostery, cooperativity, and regulation. Thus, in addition to an optimum structure, proteins most likely also require an optimal dynamic state. Alteration of this dynamic state through protein engineering will affect protein function. A dramatic example of this is seen in some inherited metabolic diseases where alternation of residues distant from the active site affects the mobility of the protein and impairs function. We postulate that using molecular dynamics simulations, experimental data or a combination of the two, it should be possible to engineer the mobility of active sites. This may be useful in, for example, increasing the promiscuity of enzymes. Thus, a paradigm for protein engineering is suggested in which the mobility of the active site is rationally modified. This might be combined with more "traditional" approaches such as altering functional groups in the active site.