Patients with nonketotic hyperglycinemia and deficient glycine cleavage enzyme activity, but without mutations in AMT, GLDC or GCSH, the genes encoding its constituent proteins, constitute a clinical group which we call 'variant nonketotic hyperglycinemia'. We hypothesize that in some patients the aetiology involves genetic mutations that result in a deficiency of the cofactor lipoate, and sequenced genes involved in lipoate synthesis and iron-sulphur cluster biogenesis. Of 11 individuals identified with variant nonketotic hyperglycinemia, we were able to determine the genetic aetiology in eight patients and delineate the clinical and biochemical phenotypes. Mutations were identified in the genes for lipoate synthase (LIAS), BolA type 3 (BOLA3), and a novel gene glutaredoxin 5 (GLRX5). Patients with GLRX5-associated variant nonketotic hyperglycinemia had normal development with childhood-onset spastic paraplegia, spinal lesion, and optic atrophy. Clinical features of BOLA3-associated variant nonketotic hyperglycinemia include severe neurodegeneration after a period of normal development. Additional features include leukodystrophy, cardiomyopathy and optic atrophy. Patients with lipoate synthase-deficient variant nonketotic hyperglycinemia varied in severity from mild static encephalopathy to Leigh disease and cortical involvement. All patients had high serum and borderline elevated cerebrospinal fluid glycine and cerebrospinal fluid:plasma glycine ratio, and deficient glycine cleavage enzyme activity. They had low pyruvate dehydrogenase enzyme activity but most did not have lactic acidosis. Patients were deficient in lipoylation of mitochondrial proteins. There were minimal and inconsistent changes in cellular iron handling, and respiratory chain activity was unaffected. Identified mutations were phylogenetically conserved, and transfection with native genes corrected the biochemical deficiency proving pathogenicity. Treatments of cells with lipoate and with mitochondrially-targeted lipoate were unsuccessful at correcting the deficiency. The recognition of variant nonketotic hyperglycinemia is important for physicians evaluating patients with abnormalities in glycine as this will affect the genetic causation and genetic counselling, and provide prognostic information on the expected phenotypic course.

A novel chromosome simulator recapitulates the position and dynamics of centromeric chromatin in a model composed of cross-linked intramolecular loops. Simulations reveal that chromatin loops stiffen the centromere and dictate the distribution of pericentric cohesin.

Purpose: RNA-seq is a promising approach to improve diagnoses by detecting pathogenic aberrations in RNA splicing that are missed by DNA sequencing. RNA-seq is typically performed on clinically-accessible tissues (CATs) from blood and skin. RNA tissue-specificity makes it difficult to identify aberrations in relevant but non-accessible tissues (non-CATs). We determined how RNA-seq from CATs represent splicing in and across genes and non-CATs. Methods:We quantified RNA splicing in 801 RNA-seq samples from 56 different adult and fetal tissues from GTEx and ArrayExpress. We identified genes and splicing events in each non-CAT and determined when RNA-seq in each CAT would inadequately represent them. We developed an online resource, MAJIQ-CAT, for exploring our analysis for specific genes and tissues. Results:In non-CATs, 40.2% of genes have splicing that is inadequately represented by at least one CAT. 6.3% of genes have splicing inadequately represented by all CATs. A majority (52.1%) of inadequately represented genes are lowly expressed in CATs (TPM < 1), but 5.8% are inadequately represented despite being well expressed (TPM > 10). Conclusion:Many splicing events in non-CATs are inadequately evaluated using RNA-seq from CATs. MAJIQ-CAT allows users to explore which accessible tissues, if any, best represent splicing in genes and tissues of interest.

Despite the success and fast adaptation of deep learning models in biomedical domains, their lack of interpretability remains an issue. Here, we introduce Enhanced Integrated Gradients (EIG), a method to identify significant features associated with a specific prediction task. Using RNA splicing prediction as well as digit classification as case studies, we demonstrate that EIG improves upon the original Integrated Gradients method and produces sets of informative features. We then apply EIG to identify A1CF as a key regulator of liver-specific alternative splicing, supporting this finding with subsequent analysis of relevant A1CF functional (RNA-seq) and binding data (PAR-CLIP).