MYORG Mutation Heterozygosity Is Associated With Brain Calcification.

BACKGROUND: Biallelic mutations in the MYORG gene were first identified as the cause of recessively inherited primary familial brain calcification. Interestingly, some heterozygous carriers also exhibited brain calcifications. OBJECTIVES: To further investigate the role of single heterozygous MYORG mutations in the development of brain calcifications. METHODS: A nation-wide cohort of Chinese primary familial brain calcification probands was enrolled from March 2016 through September 2019. Mutational analysis of MYORG was performed in 435 primary familial brain calcification probands who were negative for mutations in the other four known primary familial brain calcification-causative genes (SLC20A2, PDGFRB, PDGFB, and XPR1). RESULTS: Biallelic MYORG mutations were identified in 14 primary familial brain calcification patients from 10 unrelated families. Interestingly, 12 heterozygous carriers from seven of these families also exhibited mild-to-moderate brain calcifications. Moreover, single heterozygous mutations were detected in an additional 9 probands and in 7 of their family members affected with brain calcifications. In our cohort, clinical and imaging penetrance of individuals with biallelic mutations were 100%, whereas among individuals with heterozygous mutations, penetrance of imaging phenotype was reduced to 73.7% (28 of 38) and clinical penetrance was much lower. Most (34 of 38) remained asymptomatic whereas 4 carriers had symptoms of uncertain clinical significance (nonspecific depression, epilepsy and late-onset parkinsonism). Compared with individuals with biallelic MYORG mutations, individuals with heterozygous mutations had brain calcifications with much lower calcification scores (P < 2e-16). CONCLUSIONS: Presence of brain calcifications in individuals with heterozygous MYORG mutations suggested a semidominant inheritance pattern with incomplete penetrance. This finding further expanded the genotype-phenotype correlations of MYORG-related primary familial brain calcification. © 2020 International Parkinson and Movement Disorder Society.

Genepleio software for effective estimation of gene pleiotropy from protein sequences.

Though pleiotropy, which refers to the phenomenon of a gene affecting multiple traits, has long played a central role in genetics, development, and evolution, estimation of the number of pleiotropy components remains a hard mission to accomplish. In this paper, we report a newly developed software package, Genepleio, to estimate the effective gene pleiotropy from phylogenetic analysis of protein sequences. Since this estimate can be interpreted as the minimum pleiotropy of a gene, it is used to play a role of reference for many empirical pleiotropy measures. This work would facilitate our understanding of how gene pleiotropy affects the pattern of genotype-phenotype map and the consequence of organismal evolution.

EOLA1 protects lipopolysaccharide induced IL-6 production and apoptosis by regulation of MT2A in human umbilical vein endothelial cells.

Endothelial cell (EC) injury or dysfunction is believed to be mediated at least in part by lipopolysaccharide (LPS). Recent studies have shown that LPS induces apoptosis in different types of endothelium, including HUVEC. Previously we used EOLA1 (endothelial-overexpressed LPS-associated factor 1) cDNA as a bait and performed a yeast two-hybrid screening of a human liver cDNA library and identified metallothionein 2a (MT2a) as the associated protein. EOLA1 protein plays a role as a signal transduction factor. But the mechanism of EOLA1 mediated the protection of cell production of IL-6 and apopotosis in HUVEC is not known. MT2a is expressed in many kinds of cells and plays a role in inflammation. In this study, we demonstrated that LPS could induce EOLA1 expression in time-dependent and apparently contributed to the inhibition of IL-6 production and apoptosis induced by LPS treatment. We also found that deletion of EOLA1 promoted IL-6 production and apoptosis in the treatment of LPS in HUVEC. Furthermore, we demonstrated that MT2a was activated by LPS, and played a key role in LPS-induced IL-6 expression in HUVEC. We further provided the evidence that EOLA1 functioned as a negative regulator for LPS response by regulation of MT2a. These findings suggest that EOLA1 may have an important regulatory role during EC inflammatory responses.

A computational analysis framework for molecular cell dynamics: case-study of exocytosis.

One difficulty in conducting biologically meaningful dynamic analysis at the systems biology level is that in vivo system regulation is complex. Meanwhile, many kinetic rates are unknown, making global system analysis intractable in practice. In this article, we demonstrate a computational pipeline to help solve this problem, using the exocytotic process as an example. Exocytosis is an essential process in all eukaryotic cells that allows communication in cells through vesicles that contain a wide range of intracellular molecules. During this process a set of proteins called SNAREs acts as an engine in this vesicle-membrane fusion, by forming four-helical bundle complex between (membrane) target-specific and vesicle-specific SNAREs. As expected, the regulatory network for exocytosis is very complex. Based on the current understanding of the protein-protein interaction network related to exocytosis, we mathematically formulated the whole system, by the ordinary differential equations (ODE). We then applied a mathematical approach (called inverse problem) to estimating the kinetic parameters in the fundamental subsystem (without regulation) from limited in vitro experimental data, which fit well with the reports by the conventional assay. These estimates allowed us to conduct an efficient stability analysis under a specified parameter space for the exocytotic process with or without regulation. Finally, we discuss the potential of this approach to explain experimental observations and to make testable hypotheses for further experimentation.

The Impact of Model Uncertainty on Benchmark Dose Estimation.

We study the popular benchmark dose (BMD) approach for estimation of low exposure levels in toxicological risk assessment, focusing on dose-response experiments with quantal data. In such settings, representations of the risk are traditionally based on a specified, parametric, dose-response model. It is a well-known concern, however, that uncertainty can exist in specification and selection of the model. If the chosen parametric form is in fact misspecified, this can lead to inaccurate, and possibly unsafe, lowdose inferences. We study the effects of model selection and possible misspecification on the BMD, on its corresponding lower confidence limit (BMDL), and on the associated extra risks achieved at these values, via large-scale Monte Carlo simulation. It is seen that an uncomfortably high percentage of instances can occur where the true extra risk at the BMDL under a misspecified or incorrectly selected model can surpass the target BMR, exposing potential dangers of traditional strategies for model selection when calculating BMDs and BMDLs.