Hsiung, Ging‐Yuek Robin

AbstractMotivationDiseases that progress slowly are often studied by observing cohorts at different stages of disease for short periods of time. The Alzheimer's Disease Neuroimaging Initiative (ADNI) follows elders with various degrees of cognitive impairment, from normal to impaired. The study includes a rich panel of novel cognitive tests, biomarkers, and brain images collected every 6 months for as long as 6 years. The relative timing of the observations with respect to disease pathology is unknown. We propose a general semiparametric model and iterative estimation procedure to estimate simultaneously the pathological timing and long‐term growth curves. The resulting estimates of long‐term progression are fine‐tuned using cognitive trajectories derived from the long‐term “Personnes Agées Quid” study.ResultsWe demonstrate with simulations that the method can recover long‐term disease trends from short‐term observations. The method also estimates temporal ordering of individuals with respect to disease pathology, providing subject‐specific prognostic estimates of the time until onset of symptoms. When the method is applied to ADNI data, the estimated growth curves are in general agreement with prevailing theories of the Alzheimer's disease cascade. Other data sets with common outcome measures can be combined using the proposed algorithm.AvailabilitySoftware to fit the model and reproduce results with the statistical software R is available as the grace package. ADNI data can be downloaded from the Laboratory of NeuroImaging.

AbstractFrontotemporal lobar degeneration (FTLD) is the second leading cause of dementia in individuals under age 65. In many patients, the predominant pathology includes neuronal cytoplasmic or intranuclear inclusions of ubiquitinated TAR DNA binding protein 43 (FTLD‐TDP). Recently, a genome‐wide association study identified the first FTLD‐TDP genetic risk factor, in which variants in and around the TMEM106B gene (top SNP rs1990622) were significantly associated with FTLD‐TDP risk. Intriguingly, the most significant association was in FTLD‐TDP patients carrying progranulin (GRN) mutations. Here, we investigated to what extent the coding variant, rs3173615 (p.T185S) in linkage disequilibrium with rs1990622, affects progranulin protein (PGRN) biology and transmembrane protein 106 B (TMEM106B) regulation. First, we confirmed the association of TMEM106B variants with FTLD‐TDP in a new cohort of GRN mutation carriers. We next generated and characterized a TMEM106B‐specific antibody for investigation of this protein. Enzyme‐linked immunoassay analysis of progranulin protein levels showed similar effects upon T185 and S185 TMEM106B over‐expression. However, over‐expression of T185 consistently led to higher TMEM106B protein levels than S185. Cycloheximide treatment experiments revealed that S185 degrades faster than T185 TMEM106B, potentially due to differences in N‐glycosylation at residue N183. Together, our results provide a potential mechanism by which TMEM106B variants lead to differences in FTLD‐TDP risk. image We studied the p.T185S TMEM106B genetic variant previously implicated in frontotemporal dementia with TAR DNA binding protein 43 pathology caused by progranulin mutations. Our cell culture studies provide evidence that the protective S185 isoform is degraded more rapidly than T185 TMEM106B, potentially due to differences in glycosylation. These findings suggest that low TMEM106B levels might protect against FTLD‐TDP in these patients.