En masse organoid phenotyping informs metabolic-associated genetic susceptibility to NASH.

Genotype-phenotype associations for common diseases are often compounded by pleiotropy and metabolic state. Here, we devised a pooled human organoid-panel of steatohepatitis to investigate the impact of metabolic status on genotype-phenotype association. En masse population-based phenotypic analysis under insulin insensitive conditions predicted key non-alcoholic steatohepatitis (NASH)-genetic factors including the glucokinase regulatory protein (GCKR)-rs1260326:C>T. Analysis of NASH clinical cohorts revealed that GCKR-rs1260326-T allele elevates disease severity only under diabetic state but protects from fibrosis under non-diabetic states. Transcriptomic, metabolomic, and pharmacological analyses indicate significant mitochondrial dysfunction incurred by GCKR-rs1260326, which was not reversed with metformin. Uncoupling oxidative mechanisms mitigated mitochondrial dysfunction and permitted adaptation to increased fatty acid supply while protecting against oxidant stress, forming a basis for future therapeutic approaches for diabetic NASH. Thus, "in-a-dish" genotype-phenotype association strategies disentangle the opposing roles of metabolic-associated gene variant functions and offer a rich mechanistic, diagnostic, and therapeutic inference toolbox toward precision hepatology. VIDEO ABSTRACT.

Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq.

A central goal of genetics is to define the relationships between genotypes and phenotypes. High-content phenotypic screens such as Perturb-seq (CRISPR-based screens with single-cell RNA-sequencing readouts) enable massively parallel functional genomic mapping but, to date, have been used at limited scales. Here, we perform genome-scale Perturb-seq targeting all expressed genes with CRISPR interference (CRISPRi) across >2.5 million human cells. We use transcriptional phenotypes to predict the function of poorly characterized genes, uncovering new regulators of ribosome biogenesis (including CCDC86, ZNF236, and SPATA5L1), transcription (C7orf26), and mitochondrial respiration (TMEM242). In addition to assigning gene function, single-cell transcriptional phenotypes allow for in-depth dissection of complex cellular phenomena-from RNA processing to differentiation. We leverage this ability to systematically identify genetic drivers and consequences of aneuploidy and to discover an unanticipated layer of stress-specific regulation of the mitochondrial genome. Our information-rich genotype-phenotype map reveals a multidimensional portrait of gene and cellular function.

The genetic architecture of behavioral canalization.

Behaviors are components of fitness and contribute to adaptive evolution. Behaviors represent the interactions of an organism with its environment, yet innate behaviors display robustness in the face of environmental change, which we refer to as 'behavioral canalization'. We hypothesize that positive selection of hub genes of genetic networks stabilizes the genetic architecture for innate behaviors by reducing variation in the expression of interconnected network genes. Robustness of these stabilized networks would be protected from deleterious mutations by purifying selection or suppressing epistasis. We propose that, together with newly emerging favorable mutations, epistatically suppressed mutations can generate a reservoir of cryptic genetic variation that could give rise to decanalization when genetic backgrounds or environmental conditions change to allow behavioral adaptation.

DECIPHER: Improving Genetic Diagnosis Through Dynamic Integration of Genomic and Clinical Data.

DECIPHER (Database of Genomic Variation and Phenotype in Humans Using Ensembl Resources) shares candidate diagnostic variants and phenotypic data from patients with genetic disorders to facilitate research and improve the diagnosis, management, and therapy of rare diseases. The platform sits at the boundary between genomic research and the clinical community. DECIPHER aims to ensure that the most up-to-date data are made rapidly available within its interpretation interfaces to improve clinical care. Newly integrated cardiac case-control data that provide evidence of gene-disease associations and inform variant interpretation exemplify this mission. New research resources are presented in a format optimized for use by a broad range of professionals supporting the delivery of genomic medicine. The interfaces within DECIPHER integrate and contextualize variant and phenotypic data, helping to determine a robust clinico-molecular diagnosis for rare-disease patients, which combines both variant classification and clinical fit. DECIPHER supports discovery research, connecting individuals within the rare-disease community to pursue hypothesis-driven research.

Natural selection of immune and metabolic genes associated with health in two lowland Bolivian populations.

A growing body of work has addressed human adaptations to diverse environments using genomic data, but few studies have connected putatively selected alleles to phenotypes, much less among underrepresented populations such as Amerindians. Studies of natural selection and genotype-phenotype relationships in underrepresented populations hold potential to uncover previously undescribed loci underlying evolutionarily and biomedically relevant traits. Here, we worked with the Tsimane and the Moseten, two Amerindian populations inhabiting the Bolivian lowlands. We focused most intensively on the Tsimane, because long-term anthropological work with this group has shown that they have a high burden of both macro and microparasites, as well as minimal cardiometabolic disease or dementia. We therefore generated genome-wide genotype data for Tsimane individuals to study natural selection, and paired this with blood mRNA-seq as well as cardiometabolic and immune biomarker data generated from a larger sample that included both populations. In the Tsimane, we identified 21 regions that are candidates for selective sweeps, as well as 5 immune traits that show evidence for polygenic selection (e.g., C-reactive protein levels and the response to coronaviruses). Genes overlapping candidate regions were strongly enriched for known involvement in immune-related traits, such as abundance of lymphocytes and eosinophils. Importantly, we were also able to draw on extensive phenotype information for the Tsimane and Moseten and link five regions (containing PSD4, MUC21 and MUC22, TOX2, ANXA6, and ABCA1) with biomarkers of immune and metabolic function. Together, our work highlights the utility of pairing evolutionary analyses with anthropological and biomedical data to gain insight into the genetic basis of health-related traits.

Non-Poissonian Bursts in the Arrival of Phenotypic Variation Can Strongly Affect the Dynamics of Adaptation.

Modeling the rate at which adaptive phenotypes appear in a population is a key to predicting evolutionary processes. Given random mutations, should this rate be modeled by a simple Poisson process, or is a more complex dynamics needed? Here we use analytic calculations and simulations of evolving populations on explicit genotype-phenotype maps to show that the introduction of novel phenotypes can be "bursty" or overdispersed. In other words, a novel phenotype either appears multiple times in quick succession or not at all for many generations. These bursts are fundamentally caused by statistical fluctuations and other structure in the map from genotypes to phenotypes. Their strength depends on population parameters, being highest for "monomorphic" populations with low mutation rates. They can also be enhanced by additional inhomogeneities in the mapping from genotypes to phenotypes. We mainly investigate the effect of bursts using the well-studied genotype-phenotype map for RNA secondary structure, but find similar behavior in a lattice protein model and in Richard Dawkins's biomorphs model of morphological development. Bursts can profoundly affect adaptive dynamics. Most notably, they imply that fitness differences play a smaller role in determining which phenotype fixes than would be the case for a Poisson process without bursts.

Personalized genetic counseling for Stargardt disease: Offspring risk estimates based on variant severity.

Recurrence risk calculations in autosomal recessive diseases are complicated when the effect of genetic variants and their population frequencies and penetrances are unknown. An example of this is Stargardt disease (STGD1), a frequent recessive retinal disease caused by bi-allelic pathogenic variants in ABCA4. In this cross-sectional study, 1,619 ABCA4 variants from 5,579 individuals with STGD1 were collected and categorized by (1) severity based on statistical comparisons of their frequencies in STGD1-affected individuals versus the general population, (2) their observed versus expected homozygous occurrence in STGD1-affected individuals, (3) their occurrence in combination with established mild alleles in STGD1-affected individuals, and (4) previous functional and clinical studies. We used the sum allele frequencies of these severity categories to estimate recurrence risks for offspring of STGD1-affected individuals and carriers of pathogenic ABCA4 variants. The risk for offspring of an STGD1-affected individual with the "severe|severe" genotype or a "severe|mild with complete penetrance" genotype to develop STGD1 at some moment in life was estimated at 2.8%-3.1% (1 in 36-32 individuals) and 1.6%-1.8% (1 in 62-57 individuals), respectively. The risk to develop STGD1 in childhood was estimated to be 2- to 4-fold lower: 0.68%-0.79% (1 in 148-126) and 0.34%-0.39% (1 in 296-252), respectively. In conclusion, we established personalized recurrence risk calculations for STGD1-affected individuals with different combinations of variants. We thus propose an expanded genotype-based personalized counseling to appreciate the variable recurrence risks for STGD1-affected individuals. This represents a conceptual breakthrough because risk calculations for STGD1 may be exemplary for many other inherited diseases.

Deciphering the impact of coding and non-coding SCN1A gene variants on RNA splicing.

Variants that disrupt normal pre-mRNA splicing are increasingly being recognized as a major cause of monogenic disorders. The SCN1A gene, a key epilepsy gene that is linked to various epilepsy phenotypes, is no exception. Approximately 10% of all reported variants in the SCN1A gene are designated as splicing variants, with many located outside of the canonical donor and acceptor splice sites, and most have not been functionally investigated. However, given its restricted expression pattern, functional analysis of splicing variants in the SCN1A gene could not be routinely performed. In this study, we conducted a comprehensive analysis of all reported SCN1A variants and their potential to impact SCN1A splicing and conclude that splicing variants are substantially misannotated and under-represented. We created a splicing reporter system consisting of 18 splicing vectors covering all 26 protein-coding exons with different genomic contexts and several promoters of varying strengths in order to reproduce the wild-type splicing pattern of the SCN1A gene, revealing cis-regulatory elements essential for proper recognition of SCN1A exons. Functional analysis of 95 SCN1A variants was carried out, including all 68 intronic variants reported in the literature, located outside of the splice sites canonical dinucleotides; 21 exonic variants of different classes (synonymous, missense, nonsense and in-frame deletion) and six variants observed in patients with epilepsy. Interestingly, almost 20% of tested intronic variants had no influence on SCN1A splicing, despite being reported as causative in the literature. Moreover, we confirmed that the majority of predicted exonic variants affect splicing unravelling their true molecular mechanism. We used functional data to perform genotype-phenotype correlation, revealing distinct distribution patterns for missense and splice-affecting 'missense' variants and observed no difference in the phenotype severity of variants leading to in-frame and out-of-frame isoforms, indicating that the Nav1.1 protein is highly intolerant to structural variations. Our work demonstrates the importance of functional analysis in proper variant annotation and provides a tool for high-throughput delineation of splice-affecting variants in SCN1A in a whole-gene manner.

Interpretable modeling of genotype-phenotype landscapes with state-of-the-art predictive power.

Large-scale measurements linking genetic background to biological function have driven a need for models that can incorporate these data for reliable predictions and insight into the underlying biophysical system. Recent modeling efforts, however, prioritize predictive accuracy at the expense of model interpretability. Here, we present LANTERN (landscape interpretable nonparametric model, https://github.com/usnistgov/lantern), a hierarchical Bayesian model that distills genotype-phenotype landscape (GPL) measurements into a low-dimensional feature space that represents the fundamental biological mechanisms of the system while also enabling straightforward, explainable predictions. Across a benchmark of large-scale datasets, LANTERN equals or outperforms all alternative approaches, including deep neural networks. LANTERN furthermore extracts useful insights of the landscape, including its inherent dimensionality, a latent space of additive mutational effects, and metrics of landscape structure. LANTERN facilitates straightforward discovery of fundamental mechanisms in GPLs, while also reliably extrapolating to unexplored regions of genotypic space.

Gain of function due to increased opening probability by two KCNQ5 pore variants causing developmental and epileptic encephalopathy.

Developmental and epileptic encephalopathies (DEEs) are neurodevelopmental diseases characterized by refractory epilepsy, distinct electroencephalographic and neuroradiological features, and various degrees of developmental delay. Mutations in KCNQ2, KCNQ3, and, more rarely, KCNQ5 genes encoding voltage-gated potassium channel subunits variably contributing to excitability control of specific neuronal populations at distinct developmental stages have been associated to DEEs. In the present work, the clinical features of two DEE patients carrying de novo KCNQ5 variants affecting the same residue in the pore region of the Kv7.5 subunit (G347S/A) are described. The in vitro functional properties of channels incorporating these variants were investigated with electrophysiological and biochemical techniques to highlight pathophysiological disease mechanisms. Currents carried by Kv7.5 G347 S/A channels displayed: 1) large (>10 times) increases in maximal current density, 2) the occurrence of a voltage-independent component, 3) slower deactivation kinetics, and 4) hyperpolarization shift in activation. All these functional features are consistent with a gain-of-function (GoF) pathogenetic mechanism. Similar functional changes were also observed when the same variants were introduced at the corresponding position in Kv7.2 subunits. Nonstationary noise analysis revealed that GoF effects observed for both Kv7.2 and Kv7.5 variants were mainly attributable to an increase in single-channel open probability, without changes in membrane abundance or single-channel conductance. The mutation-induced increase in channel opening probability was insensitive to manipulation of membrane levels of the critical Kv7 channel regulator PIP2. These results reveal a pathophysiological mechanism for KCNQ5-related DEEs, which might be exploited to implement personalized treatments.

Mutant libraries reveal negative design shielding proteins from supramolecular self-assembly and relocalization in cells.

Understanding the molecular consequences of mutations in proteins is essential to map genotypes to phenotypes and interpret the increasing wealth of genomic data. While mutations are known to disrupt protein structure and function, their potential to create new structures and localization phenotypes has not yet been mapped to a sequence space. To map this relationship, we employed two homo-oligomeric protein complexes in which the internal symmetry exacerbates the impact of mutations. We mutagenized three surface residues of each complex and monitored the mutations' effect on localization and assembly phenotypes in yeast cells. While surface mutations are classically viewed as benign, our analysis of several hundred mutants revealed they often trigger three main phenotypes in these proteins: nuclear localization, the formation of puncta, and fibers. Strikingly, more than 50% of random mutants induced one of these phenotypes in both complexes. Analyzing the mutant's sequences showed that surface stickiness and net charge are two key physicochemical properties associated with these changes. In one complex, more than 60% of mutants self-assembled into fibers. Such a high frequency is explained by negative design: charged residues shield the complex from self-interacting with copies of itself, and the sole removal of the charges induces its supramolecular self-assembly. A subsequent analysis of several other complexes targeted with alanine mutations suggested that such negative design is common. These results highlight that minimal perturbations in protein surfaces' physicochemical properties can frequently drive assembly and localization changes in a cellular context.

Higher-order epistasis and phenotypic prediction.

Contemporary high-throughput mutagenesis experiments are providing an increasingly detailed view of the complex patterns of genetic interaction that occur between multiple mutations within a single protein or regulatory element. By simultaneously measuring the effects of thousands of combinations of mutations, these experiments have revealed that the genotype-phenotype relationship typically reflects not only genetic interactions between pairs of sites but also higher-order interactions among larger numbers of sites. However, modeling and understanding these higher-order interactions remains challenging. Here we present a method for reconstructing sequence-to-function mappings from partially observed data that can accommodate all orders of genetic interaction. The main idea is to make predictions for unobserved genotypes that match the type and extent of epistasis found in the observed data. This information on the type and extent of epistasis can be extracted by considering how phenotypic correlations change as a function of mutational distance, which is equivalent to estimating the fraction of phenotypic variance due to each order of genetic interaction (additive, pairwise, three-way, etc.). Using these estimated variance components, we then define an empirical Bayes prior that in expectation matches the observed pattern of epistasis and reconstruct the genotype-phenotype mapping by conducting Gaussian process regression under this prior. To demonstrate the power of this approach, we present an application to the antibody-binding domain GB1 and also provide a detailed exploration of a dataset consisting of high-throughput measurements for the splicing efficiency of human pre-mRNA [Formula: see text] splice sites, for which we also validate our model predictions via additional low-throughput experiments.

GenIA, the Genetic Immunology Advisor database for inborn errors of immunity.

BACKGROUND: To date, no publicly accessible platform has captured and synthesized all of the layered dimensions of genotypic, phenotypic, and mechanistic information published in the field of inborn errors of immunity (IEIs). Such a platform would represent the extensive and complex landscape of IEIs and could increase the rate of diagnosis in patients with a suspected IEI, which remains unacceptably low. OBJECTIVE: Our aim was to create an expertly curated, patient-centered, multidimensional IEI database that enables aggregation and sophisticated data interrogation and promotes involvement from diverse stakeholders across the community. METHODS: The database structure was designed following a subject-centered model and written in Structured Query Language (SQL). The web application is written in Hypertext Preprocessor (PHP), Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), and JavaScript. All data stored in the Genetic Immunology Advisor (GenIA) are extracted by manually reviewing published research articles. RESULTS: We completed data collection and curation for 24 pilot genes. Using these data, we have exemplified how GenIA can provide quick access to structured, longitudinal, more thorough, comprehensive, and up-to-date IEI knowledge than do currently existing databases, such as ClinGen, Human Phenotype Ontology (HPO), ClinVar, or Online Mendelian Inheritance in Man (OMIM), with which GenIA intends to dovetail. CONCLUSIONS: GenIA strives to accurately capture the extensive genetic, mechanistic, and phenotypic heterogeneity found across IEIs, as well as genetic paradigms and diagnostic pitfalls associated with individual genes and conditions. The IEI community's involvement will help promote GenIA as an enduring resource that supports and improves knowledge sharing, research, diagnosis, and care for patients with genetic immune disease.

A mutation in Themis contributes to anaphylaxis severity following oral peanut challenge in CC027 mice.

BACKGROUND: The development of peanut allergy is due to a combination of genetic and environmental factors, although specific genes have proven difficult to identify. Previously, we reported that peanut-sensitized Collaborative Cross strain CC027/GeniUnc (CC027) mice develop anaphylaxis upon oral challenge to peanut, in contrast to C3H/HeJ (C3H) mice. OBJECTIVE: This study aimed to determine the genetic basis of orally induced anaphylaxis to peanut in CC027 mice. METHODS: A genetic mapping population between CC027 and C3H mice was designed to identify the genetic factors that drive oral anaphylaxis. A total of 356 CC027xC3H backcrossed mice were generated, sensitized to peanut, then challenged to peanut by oral gavage. Anaphylaxis and peanut-specific IgE were quantified for all mice. T-cell phenotyping was conducted on CC027 mice and 5 additional Collaborative Cross strains. RESULTS: Anaphylaxis to peanut was absent in 77% of backcrossed mice, with 19% showing moderate anaphylaxis and 4% having severe anaphylaxis. There were 8 genetic loci associated with variation in response to peanut challenge-6 associated with anaphylaxis (temperature decrease) and 2 associated with peanut-specific IgE levels. There were 2 major loci that impacted multiple aspects of the severity of acute anaphylaxis, at which the CC027 allele was associated with worse outcome. At one of these loci, CC027 has a private genetic variant in the Themis gene. Consistent with described functions of Themis, we found that CC027 mice have more immature T cells with fewer CD8+, CD4+, and CD4+CD25+CD127- regulatory T cells. CONCLUSIONS: Our results demonstrate a key role for Themis in the orally reactive CC027 mouse model of peanut allergy.

The W792R HCM missense mutation in the C6 domain of cardiac myosin binding protein-C increases contractility in neonatal mouse myocardium.

Missense mutations in cardiac myosin binding protein C (cMyBP-C) are known to cause hypertrophic cardiomyopathy (HCM). The W792R mutation in the C6 domain of cMyBP-C causes severe, early onset HCM in humans, yet its impact on the function of cMyBP-C and the mechanism through which it causes disease remain unknown. To fully characterize the effect of the W792R mutation on cardiac morphology and function in vivo, we generated a murine knock-in model. We crossed heterozygous W792RWR mice to produce homozygous mutant W792RRR, heterozygous W792RWR, and control W792RWW mice. W792RRR mice present with cardiac hypertrophy, myofibrillar disarray and fibrosis by postnatal day 10 (PND10), and do not survive past PND21. Full-length cMyBP-C is present at similar levels in W792RWW, W792RWR and W792RRR mice and is properly incorporated into the sarcomere. Heterozygous W792RWR mice displayed normal heart morphology and contractility. Permeabilized myocardium from PND10 W792RRR mice showed increased Ca2+ sensitivity, accelerated cross-bridge cycling kinetics, decreased cooperativity in the activation of force, and increased expression of hypertrophy-related genes. In silico modeling suggests that the W792R mutation destabilizes the fold of the C6 domain and increases torsion in the C5-C7 region, possibly impacting regulatory interactions of cMyBP-C with myosin and actin. Based on the data presented here, we propose a model in which mutant W792R cMyBP-C preferentially forms Ca2+ sensitizing interactions with actin, rather than inhibitory interactions with myosin. The W792R-cMyBP-C mouse model provides mechanistic insights into the pathology of this mutation and may provide a mechanism by which other central domain missense mutations in cMyBP-C may alter contractility, leading to HCM.

The Architecture of Metabolic Networks Constrains the Evolution of Microbial Resource Hierarchies.

Microbial strategies for resource use are an essential determinant of their fitness in complex habitats. When facing environments with multiple nutrients, microbes often use them sequentially according to a preference hierarchy, resulting in well-known patterns of diauxic growth. In theory, the evolutionary diversification of metabolic hierarchies could represent a mechanism supporting coexistence and biodiversity by enabling temporal segregation of niches. Despite this ecologically critical role, the extent to which substrate preference hierarchies can evolve and diversify remains largely unexplored. Here, we used genome-scale metabolic modeling to systematically explore the evolution of metabolic hierarchies across a vast space of metabolic network genotypes. We find that only a limited number of metabolic hierarchies can readily evolve, corresponding to the most commonly observed hierarchies in genome-derived models. We further show how the evolution of novel hierarchies is constrained by the architecture of central metabolism, which determines both the propensity to change ranks between pairs of substrates and the effect of specific reactions on hierarchy evolution. Our analysis sheds light on the genetic and mechanistic determinants of microbial metabolic hierarchies, opening new research avenues to understand their evolution, evolvability, and ecology.

Stepwise Evolution of E. coli C and ΦX174 Reveals Unexpected Lipopolysaccharide (LPS) Diversity.

Phage therapy is a promising method for the treatment of multidrug-resistant bacterial infections. However, its long-term efficacy depends on understanding the evolutionary effects of the treatment. Current knowledge of such evolutionary effects is lacking, even in well-studied systems. We used the bacterium Escherichia coli C and its bacteriophage ΦX174, which infects cells using host lipopolysaccharide (LPS) molecules. We first generated 31 bacterial mutants resistant to ΦX174 infection. Based on the genes disrupted by these mutations, we predicted that these E. coli C mutants collectively produce eight unique LPS structures. We then developed a series of evolution experiments to select for ΦX174 mutants capable of infecting the resistant strains. During phage adaptation, we distinguished two types of phage resistance: one that was easily overcome by ΦX174 with few mutational steps ("easy" resistance) and one that was more difficult to overcome ("hard" resistance). We found that increasing the diversity of the host and phage populations could accelerate the adaptation of phage ΦX174 to overcome the hard resistance phenotype. From these experiments, we isolated 16 ΦX174 mutants that, together, can infect all 31 initially resistant E. coli C mutants. Upon determining the infectivity profiles of these 16 evolved phages, we uncovered 14 distinct profiles. Given that only eight profiles are anticipated if the LPS predictions are correct, our findings highlight that the current understanding of LPS biology is insufficient to accurately forecast the evolutionary outcomes of bacterial populations infected by phage.

SCN1A-Characterization of the Gene's Variants in the Polish Cohort of Patients with Dravet Syndrome: One Center Experience.

The aim of this study was to characterize the genotype and phenotype heterogeneity of patients with SCN1A gene mutations in the Polish population, fulfilling the criteria for the diagnosis of Dravet syndrome (DRVT). Particularly important was the analysis of the clinical course, the type of epileptic seizures and the co-occurrence of additional features such as intellectual disability, autism or neurological symptoms such as ataxia or gait disturbances. Based on their results and the available literature, the authors discuss potential predictors for DRVT. Identifying these early symptoms has important clinical significance, affecting the course and disease prognosis. 50 patients of the Pediatric Neurology Clinic of the Institute of Mother and Child in Warsaw clinically diagnosed with DRVT and carriers of SCN1A pathogenic variants were included. Clinical data were retrospectively collected from caregivers and available medical records. Patients in the study group did not differ significantly in parameters such as type of first seizure and typical epileptic seizures from those described in other studies. The age of onset of the first epileptic seizure was 2-9 months. The co-occurrence of intellectual disability was confirmed in 71% of patients and autism in 18%. The study did not show a correlation between genotype and phenotype, considering the severity of the disease course, clinical symptoms, response to treatment, the presence of intellectual disability, autism symptoms or ataxia. From the clinical course, a significant problem was the differentiation between complex febrile convulsions and symptoms of DRVT. The authors suggest that parameters such as the age of the first seizure, less than one year of age, the onset of a seizure up to 72 h after vaccination and the presence of more than two features of complex febrile seizures are more typical of DRVT, which should translate into adequate diagnostic and clinical management. The substantial decrease in the age of genetic verification of the diagnosis, as well as the decline in the use of sodium channel inhibitors, underscores the growing attention of pediatric neurologists in Poland to the diagnosis of DRVT.

Role of NR5A1 Gene Mutations in Disorders of Sex Development: Molecular and Clinical Features.

Disorders/differences of sex development (DSDs) are defined as broad, heterogenous groups of congenital conditions characterized by atypical development of genetic, gonadal, or phenotypic sex accompanied by abnormal development of internal and/or external genitalia. NR5A1 gene mutation is one of the principal genetic alterations implicated in causing DSD. This review outlines the role of NR5A1 gene during the process of gonadal development in humans, provides an overview of the molecular and functional characteristics of NR5A1 gene, and discusses potential clinical phenotypes and additional organ diseases due to NR5A1 mutations. NR5A1 mutations were analyzed in patients with 46,XY DSD and 46,XX DSD both during the neonatal and pubertal periods. Loss of function of the NR5A1 gene causes several different phenotypes, including some associated with disease in additional organs. Clinical phenotypes may vary, even among patients carrying the same NR5A1 variant, indicating that there is no specific genotype-phenotype correlation. Genetic tests are crucial diagnostic tools that should be used early in the diagnostic pathway, as early as the neonatal period, when gonadal dysgenesis is the main manifestation of NR5A1 mutation. NR5A1 gene mutations could be mainly associated with amenorrhea, ovarian failure, hypogonadism, and infertility during puberty. Fertility preservation techniques should be considered as early as possible.

Multiorgan manifestations of COL4A1 and COL4A2 variants and proposal for a clinical management protocol.

COL4A1/2 variants are associated with highly variable multiorgan manifestations. Depicting the whole clinical spectrum of COL4A1/2-related manifestations is challenging, and there is no consensus on management and preventative strategies. Based on a systematic review of current evidence on COL4A1/2-related disease, we developed a clinical questionnaire that we administered to 43 individuals from 23 distinct families carrying pathogenic variants. In this cohort, we extended ophthalmological and cardiological examinations to asymptomatic individuals and those with only limited or mild, often nonspecific, clinical signs commonly occurring in the general population (i.e., oligosymptomatic). The most frequent clinical findings emerging from both the literature review and the questionnaire included stroke (203/685, 29.6%), seizures or epilepsy (199/685, 29.0%), intellectual disability or developmental delay (168/685, 24.5%), porencephaly/schizencephaly (168/685, 24.5%), motor impairment (162/685, 23.6%), cataract (124/685, 18.1%), hematuria (63/685, 9.2%), and retinal arterial tortuosity (58/685, 8.5%). In oligosymptomatic and asymptomatic carriers, ophthalmological investigations detected retinal vascular tortuosity (5/13, 38.5%), dysgenesis of the anterior segment (4/13, 30.8%), and cataract (2/13, 15.4%), while cardiological investigations were unremarkable except for mild ascending aortic ectasia in 1/8 (12.5%). Our multimodal approach confirms highly variable penetrance and expressivity in COL4A1/2-related conditions, even at the intrafamilial level with neurological involvement being the most frequent and severe finding in both children and adults. We propose a protocol for prevention and management based on individualized risk estimation and periodic multiorgan evaluations.