Recently, an intronic biallelic (AAGGG)_n repeat expansion in RFC1 was shown to be a cause of CANVAS and adult-onset ataxia in multiple populations. As the prevalence of the RFC1 repeat expansion in Dutch cases was unknown, we retrospectively tested 9 putative CANVAS cases and two independent cohorts (A and B) of 395 and 222 adult-onset ataxia cases, respectively, using the previously published protocol and, for the first time optical genome mapping to determine the size of the expanded RFC1 repeat. We identified the biallelic (AAGGG)_n repeat expansion in 5/9 (55%) putative CANVAS patients and in 10/617 (1.6%; cohorts A + B) adult-onset ataxia patients. In addition to the AAGGG repeat motif, we observed a putative GAAGG repeat motif in the repeat expansion with unknown significance in two adult-onset ataxia patients. All the expanded (AAGGG)_n repeats identified were in the range of 800–1299 repeat units. The intronic biallelic RFC1 repeat expansion thus explains a number of the Dutch adult-onset ataxia cases that display the main clinical features of CANVAS, and particularly when ataxia is combined with neuropathy. The yield of screening for RFC1 expansions in unselected cohorts is relatively low. To increase the current diagnostic yield in ataxia patients, we suggest adding RFC1 screening to the genetic diagnostic workflow by using advanced techniques that attain long fragments.

Currently, routine diagnostics for spinocerebellar ataxia (SCA) look for polyQ repeat expansions and conventional variations affecting the proteins encoded by known SCA genes. However, ~40% of the patients still remain without a genetic diagnosis after routine tests. Increasing evidence suggests that variations in the enhancer regions of genes involved in neurodegenerative disorders can also cause disease. Since the enhancers of SCA genes are not yet known, it remains to be determined whether variations in these regions are a cause of SCA. In this pilot project, we aimed to identify the enhancers of the SCA genes ATXN1, ATXN3, TBP and ITPR1 in the human cerebellum using 4C-seq, publicly available datasets, reciprocal 4C-seq, and luciferase assays. We then screened these enhancers for copy number variants (CNVs) in a cohort of genetically undiagnosed SCA patients. We identified two active enhancers for each of the four SCA genes. CNV analysis did not reveal any CNVs in the enhancers of the four SCA genes in the genetically undiagnosed SCA patients. However, in one patient, we noted a CNV deletion with an unknown clinical significance near one of the ITPR1 enhancers. These results not only reveal elements involved in SCA gene regulation but can also lead to the discovery of novel SCA-causing genetic variants. As enhancer variations are being increasingly recognized as a cause of brain disorders, screening the enhancers of ATXN1, ATXN3, TBP and ITPR1 for variations other than CNVs and identifying and screening enhancers of other SCA genes might elucidate the genetic cause in undiagnosed patients.

Purpose: To investigates the gut microbiota in patients with vernal keratoconjunctivitis (VKC) compared with normal subjects. Method: A case–control study was conducted involving 32 patients with VKC and 16 age- and sex-matched healthy controls. Fecal samples were collected and analyzed using quantitative PCR to measure the abundance of 5 bacterial groups: Prevotella , Firmicutes , Faecalibacterium , Bifidobacterium , and Bacteroides . In addition, clinical severity scores for VKC symptoms and signs were recorded and analyzed. Results: Patients with VKC exhibited significant microbial dysbiosis compared with controls, including an upregulation of Prevotella (mean fold change 21.579; P <0.001) and downregulation of Firmicutes , Faecalibacterium , and Bifidobacterium ( P < 0.01 for all). No significant correlations were observed between microbial levels and clinical severity scores. Conclusions: The study identifies significant gut microbial imbalances in patients with VKC, suggesting that dysbiosis may play a role in the disease. Microbiota-targeted interventions, such as probiotics and dietary modifications, may offer novel strategies for managing VKC.

Aging is a multidimensional process, yet most aging models reduce it to a single score to estimate biological aging and predict health outcomes, disease risk, or mortality. Here, we introduce PUMA (Phenotypic Unsupervised Model of Aging), a framework that characterizes aging through multiple phenotypic dimensions. Applying PUMA to more than 1,000 traits spanning behavioral, psychological, social, physical, environmental, and biomedical domains in over 150,000 individuals from the Lifelines cohort, we identified seven distinct phenotypic aging dimensions. These dimensions were significantly associated with the future incidence of major age-related diseases, including cancer, diabetes, COPD, heart failure, stroke, and Parkinson's disease, demonstrating the potential of PUMA to stratify individuals by disease risk and identify phenotypic domains for targeted intervention. Notably, dimensions reflecting psychosocial factors, particularly cumulative life stress, predicted disease risk as strongly as, or more strongly than, traditional biomedical risk factors, highlighting the importance of psychological and social influences in aging and disease risk. The observation that phenotypic aging dimensions are differentially associated with the future risk of age-related diseases supports a multidimensional model of aging, indicating that aging is not a single uniform process. Because PUMA relies on accessible phenotypic data, it provides an interpretable framework for disease risk stratification and targeted preventive strategies.