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Munter, H. M.

Publications and source records attributed to Munter, H. M..

2 recordsLinked to original sources

Loss of the APP regulator RHBDL4 preserves memory in an Alzheimer's disease mouse model.

Characteristic cerebral pathological changes of Alzheimers disease (AD) such as glucose hypometabolism or the accumulation of cleavage products of the amyloid precursor protein (APP), known as A{beta} peptides, lead to sustained endoplasmic reticulum (ER) stress and neurodegeneration. To preserve ER homeostasis, cells activate their unfolded protein response (UPR). The rhomboid-like-protease 4 (RHBDL4) is an enzyme that participates in the UPR by targeting proteins for proteasomal degradation. We demonstrated previously that RHBLD4 cleaves APP in HEK293T cells, leading to decreased total APP and A{beta}. More recently, we showed that RHBDL4 processes APP in mouse primary mixed cortical cultures as well. Here, we aim to examine the physiological relevance of RHBDL4 in the brain. We first found that brain samples from AD patients and an AD mouse model (APPtg) showed increased RHBDL4 mRNA and protein expression. To determine the effects of RHBDL4s absence on APP physiology in vivo, we crossed APPtg mice to a RHBDL4 knockout (R4-/-) model. RHBDL4 deficiency in APPtg mice led to increased total cerebral APP and amyloidogenic processing when compared to APPtg controls. Contrary to expectations, as assessed by cognitive tests, RHBDL4 absence rescued cognition in 5-month-old female APPtg mice. Informed by unbiased RNAseq data, we demonstrated in vitro and in vivo that RHBDL4 absence leads to greater levels of active {beta}-catenin due to decreased proteasomal clearance. Decreased {beta}-catenin activity is known to underlie cognitive defects in APPtg mice and AD. Our work suggests that RHBDL4s increased expression in AD, in addition to regulating APP levels, leads to aberrant degradation of {beta}-catenin, contributing to cognitive impairment.

animal behavior and cognition↗

Characterizing the quality metric in genotype imputation

Large-scale imputation reference panels are now available and have contributed to efficient genome-wide association studies through genotype imputation. However, it is still under debate whether large-size multi-ancestry or small-size population-specific reference panels are the optimal choices for under-represented populations. We imputed genotypes of East Asian (EAS; 180k Japanese) subjects using the Trans-Omics for Precision Medicine (TOPMed) reference panel and found that the standard imputation quality metric (Rsq) substantially overestimated the dosage r2 (squared correlation between imputed dosage and true genotype). Variance component analysis of Rsq revealed that the increased imputed-genotype certainty (dosages closer to 0, 1, or 2) caused upward bias, indicating some systemic bias in the imputation. Through systematic simulations using different template switching rates ({theta} value) in the hidden Markov model, we uncovered that the lower {theta} value increased the imputed-genotype certainty and Rsq; however, dosage r2 was insensitive to the {theta} value, thereby causing a deviation. In simulated reference panels with different sizes and ancestral diversities, the {theta} value estimates from Minimac decreased with the size of a single ancestry and increased with the ancestral diversity. Thus, Rsq could overestimate or underestimate dosage r2 for a subpopulation in the multi-ancestry panel and the deviation represents different imputed-dosage distributions. Finally, despite the impact of {theta} value, distant ancestries in the reference panel contributed only a few additional variants passing a predefined Rsq threshold. We conclude that the {theta} value has a substantial impact on the imputed dosage and the imputation quality metric value.

bioinformatics↗