Tyrosine hydroxylase variants influence protein expression, cellular localization, stability, enzymatic activity and the physical interaction between tyrosine hydroxylase and GTP cyclohydrolase 1.

Tyrosine hydroxylase (TH) is the rate-limiting enzyme in dopamine biosynthesis catalyzing the tetrahydrobiopterin (BH4 )-dependent hydroxylation of tyrosine to L-DOPA. Here, we analyzed 25 TH variants associated with various degrees of dopa-responsive dystonia and evaluate the effect of each variant on protein stability, activity and cellular localization. Furthermore, we investigated the physical interaction between TH and human wildtype (wt) GTP cyclohydrolase 1 (GTPCH) and the effect of variants on this interaction. Our in vitro results classify variants according to their resistance to proteinase K digestion into three groups (stable, intermediate, unstable). Based on their cellular localization, two groups of variants can be identified, variant group one with cytoplasmic distribution and variant group two forming aggregates. These aggregates do not correlate with loss of enzymatic activity but nevertheless might be a good target for molecular chaperones. Unfortunately, no obvious correlation between the half-life of a variant and its enzymatic activity or between solubility, stability and enzymatic activity of a given variant could be found. Excitingly, some variants disrupt the physical interaction between TH and human wildtype GTPCH, thereby interfering with enzymatic activity and offering new druggable targets for therapy. Taken together, our results highlight the importance of an in-depth molecular analysis of each variant in order to be able to classify groups of disease variants and to find specific therapies for each subgroup. Stand-alone in silico analyses predict less precise the effect of specific variants and should be combined with other in vitro analyses in cellular model systems.

Defining clinical subgroups and genotype-phenotype correlations in NBAS-associated disease across 110 patients.

PURPOSE: Pathogenic variants in neuroblastoma-amplified sequence (NBAS) cause an autosomal recessive disorder with a wide range of symptoms affecting liver, skeletal system, and brain, among others. There is a continuously growing number of patients but a lack of systematic and quantitative analysis. METHODS: Individuals with biallelic variants in NBAS were recruited within an international, multicenter study, including novel and previously published patients. Clinical variables were analyzed with log-linear models and visualized by mosaic plots; facial profiles were investigated via DeepGestalt. The structure of the NBAS protein was predicted using computational methods. RESULTS: One hundred ten individuals from 97 families with biallelic pathogenic NBAS variants were identified, including 26 novel patients with 19 previously unreported variants, giving a total number of 86 variants. Protein modeling redefined the ß-propeller domain of NBAS. Based on the localization of missense variants and in-frame deletions, three clinical subgroups arise that differ significantly regarding main clinical features and are directly related to the affected region of the NBAS protein: ß-propeller (combined phenotype), Sec39 (infantile liver failure syndrome type 2/ILFS2), and C-terminal (short stature, optic atrophy, and Pelger-Huët anomaly/SOPH). CONCLUSION: We define clinical subgroups of NBAS-associated disease that can guide patient management and point to domain-specific functions of NBAS.

Modeling dose-response functions for combination treatments with log-logistic or Weibull functions.

Dose-response curves of new substances in toxicology and related areas are commonly fitted using log-logistic functions. In more advanced studies, an additional interest is often how these substances will behave when applied in combination with a second substance. Here, an essential question for both design and analysis of these combination experiments is whether the resulting dose-response function will still be a member of the class of log-logistic functions, and, if so, what function parameters will result for the combined substances. Different scenarios might be considered in regard to whether a true interaction between the substances is expected, or whether the combination will simply be additive. In this paper, it is shown that the resulting function will in general not be a log-logistic function, but can be approximated very closely with one. Parameters for this approximation can be predicted from the parameters of both ingredients. Furthermore, some simple interaction structures can still be represented with a single log-logistic function. The approach can also be applied to Weibull-type dose-response functions, and similar results are obtained. Finally, the results were applied to a real data set obtained from cell culture experiments involving two cancer treatments, and the dose-response curve of a combination treatment was predicted from the properties of the singular substances.