The role of protein complexes in human genetic disease

Construction of a proximity labeling vector to identify protein-protein interactions in human stem cells

Identification of protein-protein interactions is essential for understanding protein functions in biological processes. While immunoprecipitation has traditionally been used to isolate proteins and their partners, it faces limitations in capturing transient interactions. Proximity labeling, particularly with the biotin ligase TurboID, addresses this challenge by enabling rapid and efficient identification of interacting proteins in vivo. Human induced pluripotent stem cells are valuable models for studying human development, however certain biological processes, such as differentiation, can be difficult to analyze because conventional transfection methods are challenging. Therefore, an alternative strategy for detection of interacting proteins is necessary. Here, we developed a novel system employing TurboID-fusion proteins within an integrative and inducible expression vector to investigate the interactome during stem cell differentiation. We validated our system by using U2AF2 and GFP as bait proteins, generated two distinct cell lines, and determining the minimum induction time required for optimal protein expression. Our results confirmed that the system did not alter the expected localization of U2AF2. Applying our system, we identified significant differences in the interactome of U2AF2 between the pluripotent and mesodermal differentiation stages, demonstrating that U2AF2 interacts with distinct protein sets following cell fate commitment. Our study successfully unveils a new tool for studying protein-protein interaction in human stem cells.

Recent progress and future challenges in structure-based protein-protein interaction prediction

Protein-protein interactions (PPIs) play a fundamental role in cellular processes, and understanding these interactions is crucial for advances in both basic biological science and biomedical applications. This review presents an overview of recent progress in computational methods for modeling protein complexes and predicting PPIs based on 3D structures, focusing on the transformative role of artificial intelligence-based approaches. We further discuss the expanding biomedical applications of PPI research, including the elucidation of disease mechanisms, drug discovery, and therapeutic design. Despite these advances, significant challenges remain in predicting host-pathogen interactions, interactions between intrinsically disordered regions, and interactions related to immune responses. These challenges are worthwhile for future explorations and represent the frontier of research in this field.

A random forest-based predictive model for classifying BRCA1 missense variants: a novel approach for evaluating the missense mutations effect

The right classification of variants is the key to pre-symptomatic detection of disease and conducting preventive actions. Since BRCA1 has a high incidence and penetrance in breast and ovarian cancers, a high-performance predictive tool can be employed to classify the clinical significance of its variants. Several tools have previously been developed for this purpose which poorly classify the significance in specific cases. The proposed tools commonly assign a score without providing any interpretation behind it. To reach an accurate predictive tool with interpretation abilities, in this study, we propose BRCA1-Forest which works based on random forest as a well-known machine learning technique for making interpretable decisions with high specificity and sensitivity in variants classification. The method involves narrowing down available options until reaching the final decision. To this end, a set of BRCA1 benign and pathogenic missense variants was collected first, and then, the dataset was prepared based on the effect of each variant on the protein sequence. The dataset was enriched by adding physicochemical changes and the conservation score of the amino acid position as pathogenicity criteria. The proposed model was trained based on the dataset to classify the clinical significance of variants. The performance of BRCA1-Forest was compared to four state-of-the-art methods, SIFT, PolyPhen2, CADD, and DANN, in terms of different evaluation metrics including precision, recall, false positive rate (FPR), the area under the receiver operator curve (AUC ROC), the area under the precision-recall curve (AUC-PR), and Mathew correlation coefficient (MCC). The results reveal that the proposed model outperforms the abovementioned tools in all metrics except for recall. The software of BRCA1-Forest is available at https://github.com/HamedKAAC/BRCA1Forest .

The conductance of KCNQ2 and its pathogenic variants is determined by individual subunit gating

KCNQ2 channel subunits form part of the M-current and underlie one of the major potassium currents throughout the human nervous system, regulating resting membrane potentials, shaping action potentials, and impeding repetitive neuronal firing. However, how individual subunits within tetramers control channel functionality remains unresolved. Here, we investigate (i) whether opening of KCNQ2 channels requires a concerted step or can result from independent subunit activation and (ii) how individual subunits regulate gate opening and conductance. The E140R mutation in the S2 segment prevents activated voltage sensor conformations, but concatemeric constructs containing up to three E140R subunits retain KCNQ2-like currents. The underlying single-channel currents show subconductance levels resulting from limitations in inner gate dimensions, determined by the number of activated subunits and their spatial arrangement. Channel opening is allosteric and requires activation of only a single subunit, which can accentuate the influence of clinically relevant heterozygous mutations at threshold voltages.

Glaucomatous retinal ganglion cells: death and protection

Glaucoma is a group of diseases characterized by progressive optic nerve degeneration, with the characteristic pathological change being death of retinal ganglion cells (RGCs), which ultimately causes visual field loss and irreversible blindness. Elevated intraocular pressure (IOP) remains the most important risk factor for glaucoma, but the exact mechanism responsible for the death of RGCs is currently unknown. Neurotrophic factor deficiency, impaired mitochondrial structure and function, disrupted axonal transport, disturbed Ca2+ homeostasis, and activation of apoptotic and autophagic pathways play important roles in RGC death in glaucoma. This review was conducted using Web of Science, PubMed, Project, and other databases to summarize the relevant mechanisms of death of RGCs in glaucoma, in addition to outlining protective treatments to improve the degradation of RGCs.

Dual inheritance patterns: A spectrum of non-syndromic inherited retinal disease phenotypes with varying molecular mechanisms

Inherited retinal diseases (IRDs) encompass a variety of disease phenotypes and are known to display both clinical and genetic heterogeneity. A further complexity is that for several IRD-associated genes, pathogenic variants have been reported to cause either autosomal dominant (AD) or autosomal recessive (AR) diseases. The possibility of dual inheritance can create a challenge for variant interpretation as well as the genetic counselling of patients. This review aims to determine whether the molecular mechanisms behind the dual inheritance of each IRD-associated gene is well established, not yet properly understood, or if the association is questionable. Each gene is discussed individually in detail due to different protein structures and functions, but there are overlapping characteristics. For example, eight genes only have a limited number of reported pathogenic variants or a hotspot region implicated in the second inheritance pattern. Whereas CRX and RP1 display distinct spatial patterns for AR and AD pathogenic variants based on the variant type and/or location. The genes with a questionable dual inheritance, namely AIPL1, CRB1, and RCBTB1 highlight the importance of carefully considering allele frequency data. Finally, the crucial role relevant functional studies in animal and cell models play in validating a variant's biochemical or molecular effect is emphasised.

An mRNA-encoded dominant-negative inhibitor of transcription factor RUNX1 suppresses vitreoretinal disease in experimental models

Messenger RNA (mRNA)-based therapies are a promising approach to medical treatment. Except for infectious diseases, no other disease has mRNA-based therapies available. The eye is an ideal model for mRNA therapeutic development because it requires limited dosing. Proliferative vitreoretinopathy (PVR) is a blinding condition caused by retinal detachment that now lacks available medical treatment, with surgery as the only treatment option. We previously implicated runt-related transcription factor-1 (RUNX1) as a driver of epithelial-to-mesenchymal transition (EMT) in PVR and as a critical mediator of aberrant ocular angiogenesis when up-regulated. On the basis of these findings, an mRNA was designed to express a dominant-negative inhibitor of RUNX1 (RUNX1-Trap). We show that RUNX1-Trap delivered in polymer-lipidoid complexes or lipid nanoparticles sequestered RUNX1 in the cytosol and strongly reduced proliferation in primary cell cultures established from fibrotic membranes derived from patients with PVR. We assessed the preclinical efficacy of intraocular delivery of mRNA-encoded RUNX1-Trap in a rabbit model of PVR and in a laser-induced mouse model of aberrant angiogenesis often used to study wet age-related macular degeneration. mRNA-encoded RUNX1-Trap suppressed ocular pathology, measured as pathological scores in the rabbit PVR model and leakage and lesion size in the laser-induced choroidal neovascularization mouse model. mRNA-encoded RUNX1-Trap also strongly reduced proliferation in a human ex vivo explant model of PVR. These data demonstrate the therapeutic potential of mRNA-encoded therapeutic molecules with dominant-negative properties, highlighting the potential of mRNA-based therapies beyond standard gene supplementation approaches.

Biochemical, biophysical, and structural investigations of two mutants (C154Y and R312H) of the human Kir2.1 channel involved in the Andersen-Tawil syndrome

Inwardly rectifying potassium (Kir) channels play a pivotal role in physiology by establishing, maintaining, and regulating the resting membrane potential of the cells, particularly contributing to the cellular repolarization of many excitable cells. Dysfunction in Kir2.1 channels is implicated in several chronic and debilitating human diseases for which there are currently no effective treatments. Specifically, Kir2.1-R312H and Kir2.1-C154Y mutations are associated with Andersen-Tawil syndrome (ATS) in humans. We have investigated the impact of these two mutants in the trafficking of the channel to the cell membrane and function in Xenopus laevis oocytes. Despite both mutations being trafficked to the cell membrane at different extents and capable of binding PIP2 (phosphatidylinositol-4,5-bisphosphate), the main modulator for channel activity, they resulted in defective channels that do not display K+ current, albeit through different molecular mechanisms. Coexpression studies showed that R312H and C154Y are expressed and associated with the WT subunits. While WT subunits could rescue R312H dysfunction, the presence of a unique C154Y subunit disrupts the function of the entire complex, which is a typical feature of mutations with a dominant-negative effect. Molecular dynamics simulations showed that Kir2.1-C154Y mutation induces a loss in the structural plasticity of the selectivity filter, impairing the K+ flow. In addition, the cryo-EM structure of the Kir2.1-R312H mutant has been reconstructed. This study identified the molecular mechanisms by which two ATS-causing mutations impact Kir2.1 channel function and provide valuable insights that can guide potential strategies for the development of future therapeutic interventions for ATS.

The role of patient-specific variables in protein corona formation and therapeutic efficacy in nanomedicine

Despite their potential, the adoption of nanotechnology in therapeutics remains limited, with only around eighty nanomedicines approved in the past 30 years. This disparity is partly due to the "one-size-fits-all" approach in medical design, which often overlooks patient-specific variables such as biological sex, genetic ancestry, disease state, environment, and age that influence nanoparticle behavior. Nanoparticles (NPs) must be transported through systemic, microenvironmental, and cellular barriers that vary across heterogeneous patient populations. Key patient-dependent properties impacting NP delivery include blood flow rates, body fat distribution, reproductive organ vascularization, hormone and protein levels, immune responses, and chromosomal differences. Understanding these variables is crucial for developing effective, patient-specific nanotechnologies. The formation of a protein corona around NPs upon exposure to biological fluids significantly alters NP properties, affecting biodistribution, pharmacokinetics, cytotoxicity, and organ targeting. The dynamics of the protein corona, such as time-dependent composition and formation of soft and hard coronas, depend on NP characteristics and patient-specific serum components. This review highlights the importance of understanding protein corona formation across different patient backgrounds and its implications for NP design, including sex, ancestry, age, environment, and disease state. By exploring these variables, we aim to advance the development of personalized nanomedicine, improving therapeutic efficacy and patient outcomes.

Epigenetic Reprogramming and Inheritance of the Cellular Differentiation Status Following Transient Expression of a Nonfunctional Dominant-Negative Retinoblastoma Mutant in Murine Mesenchymal Stem Cells

The retinoblastoma gene product (Rb1), a master regulator of the cell cycle, plays a prominent role in cell differentiation. Previously, by analyzing the differentiation of cells transiently overexpressing the ΔS/N DN Rb1 mutant, we demonstrated that these cells fail to differentiate into mature adipocytes and that they constitutively silence Pparγ2 through CpG methylation. Here, we demonstrate that the consequences of the transient expression of ΔS/N DN Rb1 are accompanied by the retention of Cebpa promoter methylation near the TSS under adipogenic differentiation, thereby preventing its expression. The CGIs of the promoters of the Rb1, Ezh2, Mll4, Utx, and Tet2 genes, which are essential for adipogenic differentiation, have an unmethylated status regardless of the cell differentiation state. Moreover, Dnmt3a, a de novo DNA methyltransferase, is overexpressed in undifferentiated ΔS/N cells compared with wild-type cells and, in addition to Dnmt1, Dnmt3a is significantly upregulated by adipogenic stimuli in both wild-type and ΔS/N cells. Notably, the chromatin modifier Ezh2, which is also involved in epigenetic reprogramming, is highly induced in ΔS/N cells. Overall, we demonstrate that two major genes, Pparγ2 and Cebpa, which are responsible for terminal adipocyte differentiation, are selectively epigenetically reprogrammed to constitutively silent states. We hypothesize that the activation of Dnmt3a, Rb1, and Ezh2 observed in ΔS/N cells may be a consequence of a stress response caused by the accumulation and malfunctioning of Rb1-interacting complexes for the epigenetic reprogramming of Pparγ2/Cebpa and prevention of adipogenesis in an inappropriate cellular context. The failure of ΔS/N cells to differentiate and express Pparγ2 and Cebpa in culture following the expression of the DN Rb1 mutant may indicate the creation of epigenetic memory for new reprogrammed epigenetic states of genes.

De novo monoallelic Reelin missense variants cause dominant neuronal migration disorders via a dominant-negative mechanism

Reelin (RELN) is a secreted glycoprotein essential for cerebral cortex development. In humans, recessive RELN variants cause cortical and cerebellar malformations, while heterozygous variants were associated with epilepsy, autism, and mild cortical abnormalities. However, the functional effects of RELN variants remain unknown. We identified inherited and de novo RELN missense variants in heterozygous patients with neuronal migration disorders (NMDs) as diverse as pachygyria and polymicrogyria. We investigated in culture and in the developing mouse cerebral cortex how different variants impacted RELN function. Polymicrogyria-associated variants behaved as gain-of-function, showing an enhanced ability to induce neuronal aggregation, while those linked to pachygyria behaved as loss-of-function, leading to defective neuronal aggregation/migration. The pachygyria-associated de novo heterozygous RELN variants acted as dominant-negative by preventing WT RELN secretion in culture, animal models, and patients, thereby causing dominant NMDs. We demonstrated how mutant RELN proteins in vitro and in vivo predict cortical malformation phenotypes, providing valuable insights into the pathogenesis of such disorders.

A circle of life: platelet and megakaryocyte cytoskeleton dynamics in health and disease

Platelets are blood cells derived from megakaryocytes that play a central role in regulating haemostasis and vascular integrity. The microtubule cytoskeleton of megakaryocytes undergoes a critical dynamic reorganization during cycles of endomitosis and platelet biogenesis. Quiescent platelets have a discoid shape maintained by a marginal band composed of microtubule bundles, which undergoes remarkable remodelling during platelet activation, driving shape change and platelet function. Disrupting or enhancing this process can cause platelet dysfunction such as bleeding disorders or thrombosis. However, little is known about the molecular mechanisms underlying the reorganization of the cytoskeleton in the platelet lineage. Recent studies indicate that the emergence of a unique platelet tubulin code and specific pathogenic tubulin mutations cause platelet defects and bleeding disorders. Frequently, these mutations exhibit dominant negative effects, offering valuable insights into both platelet disease mechanisms and the functioning of tubulins. This review will highlight our current understanding of the role of the microtubule cytoskeleton in the life and death of platelets, along with its relevance to platelet disorders.

Analyzing the functional effects of DNA variants with gene editing

Continual advancements in genomics have led to an ever-widening disparity between the rate of discovery of genetic variants and our current understanding of their functions and potential roles in disease. Systematic methods for phenotyping DNA variants are required to effectively translate genomics data into improved outcomes for patients with genetic diseases. To make the biggest impact, these approaches must be scalable and accurate, faithfully reflect disease biology, and define complex disease mechanisms. We compare current methods to analyze the function of variants in their endogenous DNA context using genome editing strategies, such as saturation genome editing, base editing and prime editing. We discuss how these technologies can be linked to high-content readouts to gain deep mechanistic insights into variant effects. Finally, we highlight key challenges that need to be addressed to bridge the genotype to phenotype gap, and ultimately improve the diagnosis and treatment of genetic diseases.

Differential functional consequences of GRIN2A mutations associated with schizophrenia and neurodevelopmental disorders

Human genetic studies have revealed rare missense and protein-truncating variants in GRIN2A, encoding for the GluN2A subunit of the NMDA receptors, that confer significant risk for schizophrenia (SCZ). Mutations in GRIN2A are also associated with epilepsy and developmental delay/intellectual disability (DD/ID). However, it remains enigmatic how alterations to the same protein can result in diverse clinical phenotypes. Here, we performed functional characterization of human GluN1/GluN2A heteromeric NMDA receptors that contain SCZ-linked GluN2A variants, and compared them to NMDA receptors with GluN2A variants associated with epilepsy or DD/ID. Our findings demonstrate that SCZ-associated GRIN2A variants were predominantly loss-of-function (LoF), whereas epilepsy and DD/ID-associated variants resulted in both gain- and loss-of-function phenotypes. We additionally show that M653I and S809R, LoF GRIN2A variants associated with DD/ID, exert a dominant-negative effect when co-expressed with a wild-type GluN2A, whereas E58Ter and Y698C, SCZ-linked LoF variants, and A727T, an epilepsy-linked LoF variant, do not. These data offer a potential mechanism by which SCZ/epilepsy and DD/ID-linked variants can cause different effects on receptor function and therefore result in divergent pathological outcomes.

Free-Docking and Template-Based Docking: Physics Versus Knowledge-Based Docking

Docking methods can be used to predict the orientations of two or more molecules with respect of each other using a plethora of various algorithms, which can be based on the physics of interactions or can use information from databases and templates. The usability of these approaches depends on the type and size of the molecules, whose relative orientation will be estimated. The two most important limitations are (i) the computational cost of the prediction and (ii) the availability of the structural information for similar complexes. In general, if there is enough information about similar systems, knowledge-based and template-based methods can significantly reduce the computational cost while providing high accuracy of the prediction. However, if the information about the system topology and interactions between its partners is scarce, physics-based methods are more reliable or even the only choice. In this chapter, knowledge-, template-, and physics-based methods will be compared and briefly discussed providing examples of their usability with a special emphasis on physics-based protein-protein, protein-peptide, and protein-fullerene docking in the UNRES coarse-grained model.

Computational approaches for identifying disease-causing mutations in proteins

Advancements in genome sequencing have expanded the scope of investigating mutations in proteins across different diseases. Amino acid mutations in a protein alter its structure, stability and function and some of them lead to diseases. Identification of disease-causing mutations is a challenging task and it will be helpful for designing therapeutic strategies. Hence, mutation data available in the literature have been curated and stored in several databases, which have been effectively utilized for developing computational methods to identify deleterious mutations (drivers), using sequence and structure-based properties of proteins. In this chapter, we describe the contents of specific databases that have information on disease-causing and neutral mutations followed by sequence and structure-based properties. Further, characteristic features of disease-causing mutations will be discussed along with computational methods for identifying cancer hotspot residues and disease-causing mutations in proteins.

An unsupervised deep learning framework for predicting human essential genes from population and functional genomic data

BACKGROUND

The ability to accurately predict essential genes intolerant to loss-of-function (LOF) mutations can dramatically improve the identification of disease-associated genes. Recently, there have been numerous computational methods developed to predict human essential genes from population genomic data. While the existing methods are highly predictive of essential genes of long length, they have limited power in pinpointing short essential genes due to the sparsity of polymorphisms in the human genome.

RESULTS

Motivated by the premise that population and functional genomic data may provide complementary evidence for gene essentiality, here we present an evolution-based deep learning model, DeepLOF, to predict essential genes in an unsupervised manner. Unlike previous population genetic methods, DeepLOF utilizes a novel deep learning framework to integrate both population and functional genomic data, allowing us to pinpoint short essential genes that can hardly be predicted from population genomic data alone. Compared with previous methods, DeepLOF shows unmatched performance in predicting ClinGen haploinsufficient genes, mouse essential genes, and essential genes in human cell lines. Notably, at a false positive rate of 5%, DeepLOF detects 50% more ClinGen haploinsufficient genes than previous methods. Furthermore, DeepLOF discovers 109 novel essential genes that are too short to be identified by previous methods.

CONCLUSION

The predictive power of DeepLOF shows that it is a compelling computational method to aid in the discovery of essential genes.

Investigation of the impact of nonsynonymous mutations on thyroid peroxidase dimer

Congenital hypothyroidism is one of the most common preventable endocrine disorders associated with thyroid dysgenesis or dyshormonogenesis. Thyroid peroxidase (TPO) gene defect is mainly responsible for dyshormonogenesis; a defect in the thyroid hormone biosynthesis pathway. In Bangladesh, there is limited data regarding the genetic etiology of Congenital Hypothyroidism (CH). The present study investigates the impact of the detected mutations (p.Ala373Ser, and p.Thr725Pro) on the TPO dimer protein. We have performed sequential molecular docking of H2O2 and I- ligands with both monomers of TPO dimer to understand the iodination process in thyroid hormone biosynthesis. Understanding homodimer interactions at the atomic level is a critical challenge to elucidate their biological mechanisms of action. The docking results reveal that mutations in the dimer severely disrupt its catalytic interaction with essential ligands. Molecular dynamics simulation has been performed to validate the docking results, thus realizing the consequence of the mutation in the biological system's mimic. The dynamics results expose that mutations destabilize the TPO dimer protein. Finally, principal component analysis exhibits structural and energy profile discrepancies in wild-type and mutant dimers. The findings of this study highlight that the mutations in TPO protein can critically affect the dimer structure and loss of enzymatic activity is persistent. Other factors also might influence the hormone synthesis pathway, which is under investigation.

Impaired activity and membrane association of most calpain-5 mutants causal for neovascular inflammatory vitreoretinopathy

Neovascular inflammatory vitreoretinopathy (NIV) is a rare eye disease that ultimately leads to complete blindness and is caused by mutations in the gene encoding calpain-5 (CAPN5), with six pathogenic mutations identified. In transfected SH-SY5Y cells, five of the mutations resulted in decreased membrane association, diminished S-acylation, and reduced calcium-induced autoproteolysis of CAPN5. CAPN5 proteolysis of the autoimmune regulator AIRE was impacted by several NIV mutations. R243, L244, K250 and the adjacent V249 are on β-strands in the protease core 2 domain. Conformational changes induced by Ca2+binding result in these β-strands forming a β-sheet and a hydrophobic pocket which docks W286 side chain away from the catalytic cleft, enabling calpain activation based on comparison with the Ca2+-bound CAPN1 protease core. The pathologic variants R243L, L244P, K250N, and R289W are predicted to disrupt the β-strands, β-sheet, and hydrophobic pocket, impairing calpain activation. The mechanism by which these variants impair membrane association is unclear. G376S impacts a conserved residue in the CBSW domain and is predicted to disrupt a loop containing acidic residues which may contribute to membrane binding. G267S did not impair membrane association and resulted in a slight but significant increase in autoproteolytic and proteolytic activity. However, G267S is also identified in individuals without NIV. Combined with the autosomal dominant pattern of NIV inheritance and evidence that CAPN5 may dimerize, the results are consistent with a dominant negative mechanism for the five pathogenic variants which resulted in impaired CAPN5 activity and membrane association and a gain-of-function for the G267S variant.

Base editor screens for in situ mutational scanning at scale

A fundamental challenge in biology is understanding the molecular details of protein function. How mutations alter protein activity, regulation, and response to drugs is of critical importance to human health. Recent years have seen the emergence of pooled base editor screens for in situ mutational scanning: the interrogation of protein sequence-function relationships by directly perturbing endogenous proteins in live cells. These studies have revealed the effects of disease-associated mutations, discovered novel drug resistance mechanisms, and generated biochemical insights into protein function. Here, we discuss how this "base editor scanning" approach has been applied to diverse biological questions, compare it with alternative techniques, and describe the emerging challenges that must be addressed to maximize its utility. Given its broad applicability toward profiling mutations across the proteome, base editor scanning promises to revolutionize the investigation of proteins in their native contexts.

Glaucomatous optic neuropathy: Mitochondrial dynamics, dysfunction and protection in retinal ganglion cells

Glaucoma is a leading cause of irreversible blindness worldwide and is characterized by a slow, progressive, and multifactorial degeneration of retinal ganglion cells (RGCs) and their axons, resulting in vision loss. Despite its high prevalence in individuals 60 years of age and older, the causing factors contributing to glaucoma progression are currently not well characterized. Intraocular pressure (IOP) is the only proven treatable risk factor. However, lowering IOP is insufficient for preventing disease progression. One of the significant interests in glaucoma pathogenesis is understanding the structural and functional impairment of mitochondria in RGCs and their axons and synapses. Glaucomatous risk factors such as IOP elevation, aging, genetic variation, neuroinflammation, neurotrophic factor deprivation, and vascular dysregulation, are potential inducers for mitochondrial dysfunction in glaucoma. Because oxidative phosphorylation stress-mediated mitochondrial dysfunction is associated with structural and functional impairment of mitochondria in glaucomatous RGCs, understanding the underlying mechanisms and relationship between structural and functional alterations in mitochondria would be beneficial to developing mitochondria-related neuroprotection in RGCs and their axons and synapses against glaucomatous neurodegeneration. Here, we review the current studies focusing on mitochondrial dynamics-based structural and functional alterations in the mitochondria of glaucomatous RGCs and therapeutic strategies to protect RGCs against glaucomatous neurodegeneration.

Peptide-mediated inhibition of the transcriptional regulator Elongin BC induces apoptosis in cancer cells

Inhibition of protein-protein interactions (PPIs) via designed peptides is an effective strategy to perturb their biological functions. The Elongin BC heterodimer (ELOB/C) binds to a BC-box motif and is essential for cancer cell growth. Here, we report a peptide that mimics the high-affinity BC-box of the PRC2-associated protein EPOP. This peptide tightly binds to the ELOB/C dimer (k_D = 0.46 ± 0.02 nM) and blocks the association of ELOB/C with its interaction partners, both in vitro and in the cellular environment. Cancer cells treated with our peptide inhibitor showed decreased cell viability, increased apoptosis, and perturbed gene expression. Therefore, our work proposes that blocking the BC-box-binding pocket of ELOB/C is a feasible strategy to impair its function and inhibit cancer cell growth. Our peptide inhibitor promises novel mechanistic insights into the biological function of the ELOB/C dimer and offers a starting point for therapeutics linked to ELOB/C dysfunction.

Buffering of genetic dominance by allele-specific protein complex assembly

Protein complex assembly often occurs while subunits are being translated, resulting in complexes whose subunits were translated from the same mRNA in an allele-specific manner. It has thus been hypothesized that such cotranslational assembly may counter the assembly-mediated dominant-negative effect, whereby co-assembly of mutant and wild-type subunits "poisons" complex activity. Here, we show that cotranslationally assembling subunits are much less likely to be associated with autosomal dominant relative to recessive disorders, and that subunits with dominant-negative disease mutations are significantly depleted in cotranslational assembly compared to those associated with loss-of-function mutations. We also find that complexes with known dominant-negative effects tend to expose their interfaces late during translation, lessening the likelihood of cotranslational assembly. Finally, by combining complex properties with other features, we trained a computational model for predicting proteins likely to be associated with non-loss-of-function disease mechanisms, which we believe will be of considerable utility for protein variant interpretation.

BK channels of five different subunit combinations underlie the de novo KCNMA1 G375R channelopathy

The molecular basis of a severe developmental and neurological disorder associated with a de novo G375R variant of the tetrameric BK channel is unknown. Here, we address this question by recording from single BK channels expressed to mimic a G375R mutation heterozygous with a WT allele. Five different types of functional BK channels were expressed: 3% were consistent with WT, 12% with homotetrameric mutant, and 85% with three different types of hybrid (heterotetrameric) channels assembled from both mutant and WT subunits. All channel types except WT showed a marked gain-of-function in voltage activation and a smaller decrease-of-function in single-channel conductance, with both changes in function becoming more pronounced as the number of mutant subunits per tetrameric channel increased. The net cellular response from the five different types of channels comprising the molecular phenotype was a shift of -120 mV in the voltage required to activate half of the maximal current through BK channels, giving a net gain-of-function. The WT and homotetrameric mutant channels in the molecular phenotype were consistent with genetic codominance as each displayed properties of a channel arising from only one of the two alleles. The three types of hybrid channels in the molecular phenotype were consistent with partial dominance as their properties were intermediate between those of mutant and WT channels. A model in which BK channels randomly assemble from mutant and WT subunits, with each subunit contributing increments of activation and conductance, approximated the molecular phenotype of the heterozygous G375R mutation.

Characterization of Native COMPASS Complex in Urothelial Carcinoma Cells by Size Exclusion Chromatography

The human COMPASS complexes regulate gene expression during development and cell differentiation. Three distinct subunits, KMT2C, KMT2D, and KDM6A (also known as UTX), are frequently mutated in urothelial carcinoma, possibly disrupting the formation of functional COMPASS complexes. Here, we describe methods to evaluate the formation of these large native protein complexes in urothelial carcinoma (UC) cell lines harboring different mutations in KMT2C/D. To this end COMPASS complexes were purified from nuclear extracts by size exclusion chromatography (SEC) using a Sepharose 6 column. SEC fractions were then separated by 3-8% Tris-acetate gradient polyacrylamide gel and the COMPASS complex subunits KMT2C, UTX, WDR5, and RBBP5 were detected by immunoblotting. In this fashion, the formation of a COMPASS complex could be observed in UC cells with wild-type but not in cells with mutant KMT2C and KMTD.

Specific gene expression signatures of low grade meningiomas

Introduction

Meningiomas are the most common primary central nervous system (CNS) tumors in adults, representing approximately one-third of all primary adult CNS tumors. Although several recent publications have proposed alternative grading systems of meningiomas that incorporate genomic and/or epigenomic data to better predict meningioma recurrence and progression-free survival, our understanding of driving forces of meningioma development is still limited.

Objective

To define gene expression signatures of the most common subtypes of meningiomas to better understand cellular processes and signaling pathways specific for each tumor genotype.

Methods

We used RNA sequencing (RNA-seq) to determine whole transcriptome profiles of twenty meningiomas with genomic alterations including NF2 inactivation, loss of chr1p, and missense mutations in TRAF7, AKT1 and KLF4.

Results

The analysis revealed that meningiomas with NF2 gene inactivation expressed higher levels of BCL2 and GLI1 compared with tumors harboring TRAF7 missense mutations. Moreover, NF2 meningiomas were subdivided into two distinct groups based on additional loss of chr1p. NF2 tumors with intact chr1p were characterized by the high expression of tumor suppressor PTCH2 compared to NF2 tumors with chr1p loss. Taken together with the high expression of BCL2 and GLI1, these results suggest that activation of Sonic Hedgehog pathway may contribute to NF2 meningioma development. In contrast, NF2 tumors with chr1p loss expressed high levels of transcription factor FOXD3 and its antisense RNA FOXD3-AS1. Examination of TRAF7 tumors demonstrated that TRAF7 regulates a number of biomechanically responsive genes (KRT6a, KRT16, IL1RL1, and AQP3 among others). Interestingly, AKT1 and KLF4 meningiomas expressed genes specific for PI3K/AKT signaling pathway, suggesting overlapping gene signatures between the two subtypes. In addition, KLF4 meningiomas had high expression of carcinoembryonic antigen family members CEACAM6 and CEACAM5.

Conclusions

Each group of meningiomas displayed a unique gene expression signature suggesting signaling pathways potentially implicated in tumorigenesis. These findings will improve our understanding of meningioma tumorigenesis and prognosis.

Two neuronal peptides encoded from a single transcript regulate mitochondrial complex III in Drosophila

Naturally produced peptides (<100 amino acids) are important regulators of physiology, development, and metabolism. Recent studies have predicted that thousands of peptides may be translated from transcripts containing small open-reading frames (smORFs). Here, we describe two peptides in Drosophila encoded by conserved smORFs, Sloth1 and Sloth2. These peptides are translated from the same bicistronic transcript and share sequence similarities, suggesting that they encode paralogs. Yet, Sloth1 and Sloth2 are not functionally redundant, and loss of either peptide causes animal lethality, reduced neuronal function, impaired mitochondrial function, and neurodegeneration. We provide evidence that Sloth1/2 are highly expressed in neurons, imported to mitochondria, and regulate mitochondrial complex III assembly. These results suggest that phenotypic analysis of smORF genes in Drosophila can provide a wealth of information on the biological functions of this poorly characterized class of genes.

CTBP1 and CTBP2 mutations underpinning neurological disorders: a systematic review

C-terminal binding proteins (CtBP1/2) are transcriptional coregulators that play a significant role during vertebrate neurodevelopment. This systematic review aims to identify case reports with genetic variants in CTBP1 and CTBP2 associated with brain development syndromes.

We screened different databases (PubMed, Scopus, Google Scholar, LILACS) by systematically searching journals and checking reference lists and citations of background papers. We found fourteen cases (10 males) from five papers carrying two pathogenic, heterozygous variants in the CTBP1 gene (13 individuals carried the missense mutation c.991C T, p.Arg342Trp, and one subject carrying the 2-base pair deletion c.1315_1316delCA, p.Gln439ValfsTer84). These mutations were de novo in 13 cases and one case of maternal germinal mosaicism. Two variants are in the same domain of the protein: Pro-Leu-Asp-Leu-Ser (PLDLS) C terminal. Patients with these mutations exhibit a phenotype with intellectual disability, HADDTS syndrome (hypotonia, ataxia, developmental delay, and tooth enamel defects), and cerebellar volume loss. We did not identify reported cases associated with homozygous mutations harbored in CTBP1. We did not identify any report of neurodevelopment phenotypes associated with heterozygous or homozygous CTBP2 mutations. Due to CTBP2/RIBEYE being a gene with dual function, identifying and interpreting the potential pathogenic variants is challenging.

Further, homozygous mutations in the CTBP2 gene may be lethal. The mechanisms involved in the pathogenesis of neurodevelopment due to variants of these proteins have not yet been elucidated, despite some functional evidence. Further studies should be conducted to understand these transcription factors and their interaction with each other and their partners.

Intellectual developmental disorder with dysmorphic facies and ptosis caused by copy number variation including the BRPF1 gene in Peruvian patient

Background

Intellectual developmental disorder with dysmorphic facies and ptosis (MIM #617333) is a very rare condition, characterized by more than 80% by language delay, intellectual disability, gross motor development delay, broad nasal bridge, hypertelorism, and hypotonia. This condition exhibits as autosomal dominant inheritance and is caused by a heterozygous variant in the BRPF1 gene. Additionally, the copy number variation in the terminal region of chromosome 3p (MIM #613792) has been shown to manifest in most patients as intellectual disability, motor delay, and hypotonia.

Case presentation

We present an 18-year-old male patient with facial dysmorphism, intellectual disability, ptosis, and congenital heart disease. Using chromosomal microarray analysis, a previously unreported 90 kb deletion involving seven genes was found.

Conclusion

When comparing our findings with 39 previous reports, we found that the common clinical features of this syndrome, such as gross motor delay, hypotonia, and congenital spinal cord abnormalities, were not observed in this patient. From the seven genes implicated in the deletion, only BRPF1 could be strongly correlated with the phenotype, according to its function and haploinsufficiency coefficients.

Evolving Experimental Techniques for Structure-Based Drug Design

Structure-based drug design (SBDD) is a prominent method in rational drug development and has traditionally benefitted from the atomic models of protein targets obtained using X-ray crystallography at cryogenic temperatures. In this perspective, we highlight recent advances in the development of structural techniques that are capable of probing dynamic information about protein targets. First, we discuss advances in the field of X-ray crystallography including serial room-temperature crystallography as a method for obtaining high-resolution conformational dynamics of protein-inhibitor complexes. Next, we look at cryogenic electron microscopy (cryoEM), another high-resolution technique that has recently been used to study proteins and protein complexes that are too difficult to crystallize. Finally, we present small-angle X-ray scattering (SAXS) as a potential high-throughput screening tool to identify inhibitors that target protein complexes and protein oligomerization.

Large protein complex interfaces have evolved to promote cotranslational assembly

Assembly pathways of protein complexes should be precise and efficient to minimise misfolding and unwanted interactions with other proteins in the cell. One way to achieve this efficiency is by seeding assembly pathways during translation via the cotranslational assembly of subunits. While recent evidence suggests that such cotranslational assembly is widespread, little is known about the properties of protein complexes associated with the phenomenon. Here, using a combination of proteome-specific protein complex structures and publicly available ribosome profiling data, we show that cotranslational assembly is particularly common between subunits that form large intermolecular interfaces. To test whether large interfaces have evolved to promote cotranslational assembly, as opposed to cotranslational assembly being a non-adaptive consequence of large interfaces, we compared the sizes of first and last translated interfaces of heteromeric subunits in bacterial, yeast, and human complexes. When considering all together, we observe the N-terminal interface to be larger than the C-terminal interface 54% of the time, increasing to 64% when we exclude subunits with only small interfaces, which are unlikely to cotranslationally assemble. This strongly suggests that large interfaces have evolved as a means to maximise the chance of successful cotranslational subunit binding.

Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure

Most known pathogenic mutations occur in protein-coding regions of DNA and change the way proteins are made. Taking protein structure into account has therefore provided great insight into the molecular mechanisms underlying human genetic disease. While there has been much focus on how mutations can disrupt protein structure and thus cause a loss of function (LOF), alternative mechanisms, specifically dominant-negative (DN) and gain-of-function (GOF) effects, are less understood. Here, we investigate the protein-level effects of pathogenic missense mutations associated with different molecular mechanisms. We observe striking differences between recessive vs dominant, and LOF vs non-LOF mutations, with dominant, non-LOF disease mutations having much milder effects on protein structure, and DN mutations being highly enriched at protein interfaces. We also find that nearly all computational variant effect predictors, even those based solely on sequence conservation, underperform on non-LOF mutations. However, we do show that non-LOF mutations could potentially be identified by their tendency to cluster in three-dimensional space. Overall, our work suggests that many pathogenic mutations that act via DN and GOF mechanisms are likely being missed by current variant prioritisation strategies, but that there is considerable scope to improve computational predictions through consideration of molecular disease mechanisms.

Most known pathogenic mutations occur in protein-coding regions of DNA and change the way proteins are made. Here the authors analyse the locations of thousands of human disease mutations and their predicted effects on protein structure and show that,while loss-of-function mutations tend to be highly disruptive, non-loss-of-function mutations are in general much milder at a protein structural level.

Network Pharmacology Approach for Medicinal Plants: Review and Assessment

Natural products have played a critical role in medicine due to their ability to bind and modulate cellular targets involved in disease. Medicinal plants hold a variety of bioactive scaffolds for the treatment of multiple disorders. The less adverse effects, affordability, and easy accessibility highlight their potential in traditional remedies. Identifying pharmacological targets from active ingredients of medicinal plants has become a hot topic for biomedical research to generate innovative therapies. By developing an unprecedented opportunity for the systematic investigation of traditional medicines, network pharmacology is evolving as a systematic paradigm and becoming a frontier research field of drug discovery and development. The advancement of network pharmacology has opened up new avenues for understanding the complex bioactive components found in various medicinal plants. This study is attributed to a comprehensive summary of network pharmacology based on current research, highlighting various active ingredients, related techniques/tools/databases, and drug discovery and development applications. Moreover, this study would serve as a protocol for discovering novel compounds to explore the full range of biological potential of traditionally used plants. We have attempted to cover this vast topic in the review form. We hope it will serve as a significant pioneer for researchers working with medicinal plants by employing network pharmacology approaches.

Application of molecular dynamics simulation in biomedicine

Molecular dynamics (MD) simulation has been widely used in the field of biomedicine to study the conformational transition of proteins caused by mutation or ligand binding/unbinding. It provides some perspectives those are difficult to find in traditional biochemical or pathological experiments, for example, detailed effects of mutations on protein structure and protein-protein/ligand interaction at the atomic level. In this review, a broad overview on conformation changes and drug discovery by MD simulation is given. We first discuss the preparation of protein structure for MD simulation, which is a key step that determines the accuracy of the simulation. Then, we summarize the applications of commonly used force fields and MD simulations in scientific research. Finally, enhanced sampling methods and common applications of these methods are introduced. In brief, MD simulation is a powerful tool and it can be used to guide experimental study. The combination of MD simulation and experimental techniques is an a priori means to solve the biomedical problems and give a deep understanding on the relationship between protein structure and function.

Diverse Molecular Mechanisms Underlying Pathogenic Protein Mutations: Beyond the Loss-of-Function Paradigm

Most known disease-causing mutations occur in protein-coding regions of DNA. While some of these involve a loss of protein function (e.g., through premature stop codons or missense changes that destabilize protein folding), many act via alternative molecular mechanisms and have dominant-negative or gain-of-function effects. In nearly all cases, these non-loss-of-function mutations can be understood by considering interactions of the wild-type and mutant protein with other molecules, such as proteins, nucleic acids, or small ligands and substrates. Here, we review the diverse molecular mechanisms by which pathogenic mutations can have non-loss-of-function effects, including by disrupting interactions, increasing binding affinity, changing binding specificity, causing assembly-mediated dominant-negative and dominant-positive effects, creating novel interactions, and promoting aggregation and phase separation. We believe that increased awareness of these diverse molecular disease mechanisms will lead to improved diagnosis (and ultimately treatment) of human genetic disorders. Expected final online publication date for the Annual Review of Genomics and Human Genetics, Volume 23 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Known allosteric proteins have central roles in genetic disease

Allostery is a form of protein regulation, where ligands that bind sites located apart from the active site can modify the activity of the protein. The molecular mechanisms of allostery have been extensively studied, because allosteric sites are less conserved than active sites, and drugs targeting them are more specific than drugs binding the active sites. Here we quantify the importance of allostery in genetic disease. We show that 1) known allosteric proteins are central in disease networks, contribute to genetic disease and comorbidities much more than non-allosteric proteins, and there is an association between being allosteric and involvement in disease; 2) they are enriched in many major disease types like hematopoietic diseases, cardiovascular diseases, cancers, diabetes, or diseases of the central nervous system; 3) variants from cancer genome-wide association studies are enriched near allosteric proteins, indicating their importance to polygenic traits; and 4) the importance of allosteric proteins in disease is due, at least partly, to their central positions in protein-protein interaction networks, and less due to their dynamical properties.

Dynamics of huntingtin protein interactions in the striatum identifies candidate modifiers of Huntington disease

Huntington disease (HD) is a monogenic neurodegenerative disorder with one causative gene, huntingtin (HTT). Yet, HD pathobiology is multifactorial, suggesting that cellular factors influence disease progression. Here, we define HTT protein-protein interactions (PPIs) perturbed by the mutant protein with expanded polyglutamine in the mouse striatum, a brain region with selective HD vulnerability. Using metabolically labeled tissues and immunoaffinity purification-mass spectrometry, we establish that polyglutamine-dependent modulation of HTT PPI abundances and relative stability starts at an early stage of pathogenesis in a Q140 HD mouse model. We identify direct and indirect PPIs that are also genetic disease modifiers using in-cell two-hybrid and behavioral assays in HD human cell and Drosophila models, respectively. Validated, disease-relevant mHTT-dependent interactions encompass mediators of synaptic neurotransmission (SNAREs and glutamate receptors) and lysosomal acidification (V-ATPase). Our study provides a resource for understanding mHTT-dependent dysfunction in cortico-striatal cellular networks, partly through impaired synaptic communication and endosomal-lysosomal system. A record of this paper's Transparent Peer Review process is included in the supplemental information.

Protein Structure Prediction with Mass Spectrometry Data

Knowledge of protein structure is crucial to our understanding of biological function and is routinely used in drug discovery. High-resolution techniques to determine the three-dimensional atomic coordinates of proteins are available. However, such methods are frequently limited by experimental challenges such as sample quantity, target size, and efficiency. Structural mass spectrometry (MS) is a technique in which structural features of proteins are elucidated quickly and relatively easily. Computational techniques that convert sparse MS data into protein models that demonstrate agreement with the data are needed. This review features cutting-edge computational methods that predict protein structure from MS data such as chemical cross-linking, hydrogen-deuterium exchange, hydroxyl radical protein footprinting, limited proteolysis, ion mobility, and surface-induced dissociation. Additionally, we address future directions for protein structure prediction with sparse MS data. Expected final online publication date for the Annual Review of Physical Chemistry, Volume 73 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Mutation location and IKs regulation in the arrhythmic risk of long QT syndrome type 1: the importance of the KCNQ1 S6 region

AIMS

Mutation type, location, dominant-negative IKs reduction, and possibly loss of cyclic adenosine monophosphate (cAMP)-dependent IKs stimulation via protein kinase A (PKA) influence the clinical severity of long QT syndrome type 1 (LQT1). Given the malignancy of KCNQ1-p.A341V, we assessed whether mutations neighbouring p.A341V in the S6 channel segment could also increase arrhythmic risk.

METHODS AND RESULTS

Clinical and genetic data were obtained from 1316 LQT1 patients [450 families, 166 unique KCNQ1 mutations, including 277 p.A341V-positive subjects, 139 patients with p.A341-neighbouring mutations (91 missense, 48 non-missense), and 900 other LQT1 subjects]. A first cardiac event represented the primary endpoint. S6 segment missense variant characteristics, particularly cAMP stimulation responses, were analysed by cellular electrophysiology. p.A341-neighbouring mutation carriers had a QTc shorter than p.A341V carriers (477 ± 33 vs. 490 ± 44 ms) but longer than the remaining LQT1 patient population (467 ± 41 ms) (P < 0.05 for both). Similarly, the frequency of symptomatic subjects in the p.A341-neighbouring subgroup was intermediate between the other two groups (43% vs. 73% vs. 20%; P < 0.001). These differences in clinical severity can be explained, for p.A341V vs. p.A341-neighbouring mutations, by the p.A341V-specific impairment of IKs regulation. The differences between the p.A341-neighbouring subgroup and the rest of LQT1 mutations may be explained by the functional importance of the S6 segment for channel activation.

CONCLUSION

KCNQ1 S6 segment mutations surrounding p.A341 increase arrhythmic risk. p.A341V-specific loss of PKA-dependent IKs enhancement correlates with its phenotypic severity. Cellular studies providing further insights into IKs-channel regulation and knowledge of structure-function relationships could improve risk stratification. These findings impact on clinical management.

Complexome Profiling: Assembly and Remodeling of Protein Complexes

Many proteins have been found to operate in a complex with various biomolecules such as proteins, nucleic acids, carbohydrates, or lipids. Protein complexes can be transient, stable or dynamic and their association is controlled under variable cellular conditions. Complexome profiling is a recently developed mass spectrometry-based method that combines mild separation techniques, native gel electrophoresis, and density gradient centrifugation with quantitative mass spectrometry to generate inventories of protein assemblies within a cell or subcellular fraction. This review summarizes applications of complexome profiling with respect to assembly ranging from single subunits to large macromolecular complexes, as well as their stability, and remodeling in health and disease.

Discovery-Versus Hypothesis-Driven Detection of Protein-Protein Interactions and Complexes

Protein complexes are the main functional modules in the cell that coordinate and perform the vast majority of molecular functions. The main approaches to identify and quantify the interactome to date are based on mass spectrometry (MS). Here I summarize the benefits and limitations of different MS-based interactome screens, with a focus on untargeted interactome acquisition, such as co-fractionation MS. Specific emphasis is given to the discussion of discovery- versus hypothesis-driven data analysis concepts and their applicability to large, proteome-wide interactome screens. Hypothesis-driven analysis approaches, i.e., complex- or network-centric, are highlighted as promising strategies for comparative studies. While these approaches require prior information from public databases, also reviewed herein, the available wealth of interactomic data continuously increases, thereby providing more exhaustive information for future studies. Finally, guidance on the selection of interactome acquisition and analysis methods is provided to aid the reader in the design of protein-protein interaction studies.

Heterozygous lamin B1 and lamin B2 variants cause primary microcephaly and define a novel laminopathy

PURPOSE

Lamins are the major component of nuclear lamina, maintaining structural integrity of the nucleus. Lamin A/C variants are well established to cause a spectrum of disorders ranging from myopathies to progeria, termed laminopathies. Phenotypes resulting from variants in LMNB1 and LMNB2 have been much less clearly defined.

METHODS

We investigated exome and genome sequencing from the Deciphering Developmental Disorders Study and the 100,000 Genomes Project to identify novel microcephaly genes.

RESULTS

Starting from a cohort of patients with extreme microcephaly, 13 individuals with heterozygous variants in the two human B-type lamins were identified. Recurrent variants were established to be de novo in nine cases and shown to affect highly conserved residues within the lamin ɑ-helical rod domain, likely disrupting interactions required for higher-order assembly of lamin filaments.

CONCLUSION

We identify dominant pathogenic variants in LMNB1 and LMNB2 as a genetic cause of primary microcephaly, implicating a major structural component of the nuclear envelope in its etiology and defining a new form of laminopathy. The distinct nature of this lamin B-associated phenotype highlights the strikingly different developmental requirements for lamin paralogs and suggests a novel mechanism for primary microcephaly warranting future investigation.

Of numbers and movement - understanding transcription factor pathogenesis by advanced microscopy

Transcription factors (TFs) are life-sustaining and, therefore, the subject of intensive research. By regulating gene expression, TFs control a plethora of developmental and physiological processes, and their abnormal function commonly leads to various developmental defects and diseases in humans. Normal TF function often depends on gene dosage, which can be altered by copy-number variation or loss-of-function mutations. This explains why TF haploinsufficiency (HI) can lead to disease. Since aberrant TF numbers frequently result in pathogenic abnormalities of gene expression, quantitative analyses of TFs are a priority in the field. In vitro single-molecule methodologies have significantly aided the identification of links between TF gene dosage and transcriptional outcomes. Additionally, advances in quantitative microscopy have contributed mechanistic insights into normal and aberrant TF function. However, to understand TF biology, TF-chromatin interactions must be characterised in vivo, in a tissue-specific manner and in the context of both normal and altered TF numbers. Here, we summarise the advanced microscopy methodologies most frequently used to link TF abundance to function and dissect the molecular mechanisms underlying TF HIs. Increased application of advanced single-molecule and super-resolution microscopy modalities will improve our understanding of how TF HIs drive disease.

RNA-protein interactions: Central players in coordination of regulatory networks

Changes in the abundance of protein and RNA molecules can impair the formation of complexes in the cell leading to toxicity and death. Here we exploit the information contained in protein, RNA and DNA interaction networks to provide a comprehensive view of the regulation layers controlling the concentration-dependent formation of assemblies in the cell. We present the emerging concept that RNAs can act as scaffolds to promote the formation ribonucleoprotein complexes and coordinate the post-transcriptional layer of gene regulation. We describe the structural and interaction network properties that characterize the ability of protein and RNA molecules to interact and phase separate in liquid-like compartments. Finally, we show that presence of structurally disordered regions in proteins correlate with the propensity to undergo liquid-to-solid phase transitions and cause human diseases. Also see the video abstract here https://youtu.be/kfpqibsNfS0.

Insight into the Autosomal-Dominant Inheritance Pattern of SOD1-Associated ALS from Native Mass Spectrometry

About 20% of all familial amyotrophic lateral sclerosis (ALS) cases are associated with mutations in superoxide dismutase (SOD1), a homodimeric protein. The disease has an autosomal-dominant inheritance pattern. It is, therefore, important to determine whether wild-type and mutant SOD1 subunits self-associate randomly or preferentially. A measure for the extent of bias in subunit association is the coupling constant determined in a double-mutant cycle type analysis. Here, cell lysates containing co-expressed wild-type and mutant SOD1 subunits were analyzed by native mass spectrometry to determine these coupling constants. Strikingly, we find a linear positive correlation between the coupling constant and the reported average duration of the disease. Our results indicate that inter-subunit communication and a preference for heterodimerization greatly increase the disease severity.

Identification of pathogenic missense mutations using protein stability predictors

Attempts at using protein structures to identify disease-causing mutations have been dominated by the idea that most pathogenic mutations are disruptive at a structural level. Therefore, computational stability predictors, which assess whether a mutation is likely to be stabilising or destabilising to protein structure, have been commonly used when evaluating new candidate disease variants, despite not having been developed specifically for this purpose. We therefore tested 13 different stability predictors for their ability to discriminate between pathogenic and putatively benign missense variants. We find that one method, FoldX, significantly outperforms all other predictors in the identification of disease variants. Moreover, we demonstrate that employing predicted absolute energy change scores improves performance of nearly all predictors in distinguishing pathogenic from benign variants. Importantly, however, we observe that the utility of computational stability predictors is highly heterogeneous across different proteins, and that they are all inferior to the best performing variant effect predictors for identifying pathogenic mutations. We suggest that this is largely due to alternate molecular mechanisms other than protein destabilisation underlying many pathogenic mutations. Thus, better ways of incorporating protein structural information and molecular mechanisms into computational variant effect predictors will be required for improved disease variant prioritisation.

The Consequences of Abnormal Gene Dosage: Lessons from Chromosome 18

Accurate interpretation of genomic copy number variation (CNV) remains a challenge and has important consequences for both congenital and late-onset disease. Hemizygosity dosage characterization of the genes on chromosome 18 reveals a spectrum of outcomes ranging from no clinical effect, to risk factors for disease, to both low- and high-penetrance disease. These data are important for accurate and predictive clinical management. Additionally, the potential mechanisms of reduced penetrance due to dosage compensation are discussed as a key to understanding avenues for potential treatment. This review describes the chromosome 18 findings, and discusses the molecular mechanisms that allow haploinsufficiency, reduced penetrance, and dosage compensation.

Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations

To deal with the huge number of novel protein-coding variants identified by genome and exome sequencing studies, many computational variant effect predictors (VEPs) have been developed. Such predictors are often trained and evaluated using different variant data sets, making a direct comparison between VEPs difficult. In this study, we use 31 previously published deep mutational scanning (DMS) experiments, which provide quantitative, independent phenotypic measurements for large numbers of single amino acid substitutions, in order to benchmark and compare 46 different VEPs. We also evaluate the ability of DMS measurements and VEPs to discriminate between pathogenic and benign missense variants. We find that DMS experiments tend to be superior to the top-ranking predictors, demonstrating the tremendous potential of DMS for identifying novel human disease mutations. Among the VEPs, DeepSequence clearly stood out, showing both the strongest correlations with DMS data and having the best ability to predict pathogenic mutations, which is especially remarkable given that it is an unsupervised method. We further recommend SNAP2, DEOGEN2, SNPs&GO, SuSPect and REVEL based upon their performance in these analyses.

A novel missense variant in MYO3A is associated with autosomal dominant high-frequency hearing loss in a German family

Background

MYO3A, encoding the myosin IIIA protein, is associated with autosomal recessive and autosomal dominant nonsyndromic hearing loss. To date, only two missense variants located in the motor‐head domain of MYO3A have been described in autosomal dominant families with progressive, mild‐to‐profound sensorineural hearing loss. These variants alter the ATPase activity of myosin IIIA.

Methods

Exome sequencing of a proband from a three‐generation German family with prelingual, moderate‐to‐profound, high‐frequency hearing loss was performed. Segregation analysis confirmed a dominant inheritance pattern. Regression analysis of mean hearing level thresholds per individual and ear was performed at high‐, mid‐, and low‐frequencies.

Results

A novel heterozygous missense variant c.716T>C, p.(Leu239Pro) in the kinase domain of MYO3A was identified that is predicted in silico as disease causing. High‐frequency, progressive hearing loss was identified.

Conclusion

Correlation analysis of pure‐tone hearing thresholds revealed progressive hearing loss, especially in the high‐frequencies. In the present study, we report the first dominant likely pathogenic variant in MYO3A in a European family and further support MYO3A as an autosomal dominant hearing loss gene.