IFAP syndrome is a rare genetic disorder characterized by ichthyosis follicularis, atrichia, and photophobia. Previous research found that mutations in MBTPS2, encoding site-2-protease (S2P), underlie X-linked IFAP syndrome. The present report describes the identification via whole-exome sequencing of three heterozygous mutations in SREBF1 in 11 unrelated, ethnically diverse individuals with autosomaldominant IFAP syndrome. SREBF1 encodes sterol regulatory element-binding protein 1 (SREBP1), which promotes the transcription of lipogenes involved in the biosynthesis of fatty acids and cholesterols. This process requires cleavage of SREBP1 by site-1-protease (S1P) and S2P and subsequent translocation into the nucleus where it binds to sterol regulatory elements (SRE). The three detected SREBF1 mutations caused substitution or deletion of residues 527, 528, and 530, which are crucial for S1P cleavage. In vitro investigation of SREBP1 variants demonstrated impaired S1P cleavage, which prohibited nuclear translocation of the transcriptionally active form of SREBP1. As a result, SREBP1 variants exhibited significantly lower transcriptional activity compared to the wild-type, as demonstrated via luciferase reporter assay. RNA sequencing of the scalp skin from IFAP-affected individuals revealed a dramatic reduction in transcript levels of low-density lipoprotein receptor (LDLR) and of keratin genes known to be expressed in the outer root sheath of hair follicles. An increased rate of in situ keratinocyte apoptosis, which might contribute to skin hyperkeratosis and hypotrichosis, was also detected in scalp samples from affected individuals. Together with previous research, the present findings suggest that SREBP signaling plays an essential role in epidermal differentiation, skin barrier formation, hair growth, and eye function.

ImportanceUncombable hair syndrome (UHS) is a rare hair shaft anomaly that manifests during infancy and is characterized by dry, frizzy, and wiry hair that cannot be combed flat. Only about 100 known cases have been reported so far.ObjectiveTo elucidate the genetic spectrum of UHS.Design, Setting, and ParticipantsThis cohort study includes 107 unrelated index patients with a suspected diagnosis of UHS and family members who were recruited worldwide from January 2013 to December 2021. Participants of all ages, races, and ethnicities were recruited at referral centers or were enrolled on their own initiative following personal contact with the authors. Genetic analyses were conducted in Germany from January 2014 to December 2021.Main Outcomes and MeasuresClinical photographs, Sanger or whole-exome sequencing and array-based genotyping of DNA extracted from blood or saliva samples, and 3-dimensional protein modeling. Descriptive statistics, such as frequency counts, were used to describe the distribution of identified pathogenic variants and genotypes.ResultsThe genetic characteristics of patients with UHS were established in 80 of 107 (74.8%) index patients (82 [76.6%] female) who carried biallelic pathogenic variants in PADI3, TGM3, or TCHH (ie, genes that encode functionally related hair shaft proteins). Molecular genetic findings from 11 of these 80 individuals were previously published. In 76 (71.0%) individuals, the UHS phenotype were associated with pathogenic variants in PADI3. The 2 most commonly observed PADI3 variants account for 73 (48.0%) and 57 (37.5%) of the 152 variant PADI3 alleles in total, respectively. Two individuals carried pathogenic variants in TGM3, and 2 others carried pathogenic variants in TCHH. Haplotype analyses suggested a founder effect for the 4 most commonly observed pathogenic variants in the PADI3 gene.Conclusions and RelevanceThis cohort study extends and gives an overview of the genetic variant spectrum of UHS based on molecular genetic analyses of the largest worldwide collective of affected individuals, to our knowledge. Formerly, a diagnosis of UHS could only be made by physical examination of the patient and confirmed by microscopical examination of the hair shaft. The discovery of pathogenic variants in PADI3, TCHH, and TGM3 may open a new avenue for clinicians and affected individuals by introducing molecular diagnostics for UHS.

Most individuals with rare diseases initially consult their primary care physician. For a subset of rare diseases, efficient diagnostic pathways are available. However, ultra-rare diseases often require both expert clinical knowledge and comprehensive genetic diagnostics, which poses structural challenges for public healthcare systems. To address these challenges within Germany, a novel structured diagnostic concept, based on multidisciplinary expertise at established university hospital centers for rare diseases (CRDs), was evaluated in the three year prospective study TRANSLATE NAMSE. A key goal of TRANSLATE NAMSE was to assess the clinical value of exome sequencing (ES) in the ultra-rare disease population. The aims of the present study were to perform a systematic investigation of the phenotypic and molecular genetic data of TRANSLATE NAMSE patients who had undergone ES in order to determine the yield of both ultra-rare diagnoses and novel gene-disease associations; and determine whether the complementary use of machine learning and artificial intelligence (AI) tools improved diagnostic effectiveness and efficiency. ES was performed for 1,577 patients (268 adult and 1,309 pediatric). Molecular genetic diagnoses were established in 499 patients (74 adult and 425 pediatric). A total of 370 distinct molecular genetic causes were established. The majority of these concerned known disorders, most of which were ultra-rare. During the diagnostic process, 34 novel and 23 candidate genotype-phenotype associations were delineated, mainly in individuals with neurodevelopmental disorders. To determine the likelihood that ES will lead to a molecular diagnosis in a given patient, based on the respective clinical features only, we developed a statistical framework called YieldPred. The genetic data of a subcohort of 224 individuals that also gave consent to the computer-assisted analysis of their facial images were processed with the AI tool Prioritization of Exome Data by Image Analysis (PEDIA) and showed superior performance in variant prioritization. The present analyses demonstrated that the novel structured diagnostic concept facilitated the identification of ultra-rare genetic disorders and novel gene-disease associations on a national level and that the machine learning and AI tools improved diagnostic effectiveness and efficiency for ultra-rare genetic disorders.