Mutations in leucine-rich repeat kinase 2 (LRRK2) cause autosomal-dominant familial Parkinson's disease. We generated lines of Caenorhabditis elegans expressing neuronally directed human LRRK2. Expressing human LRRK2 increased nematode survival in response to rotenone or paraquat, which are agents that cause mitochondrial dysfunction. Protection by G2019S, R1441C, or kinase-dead LRRK2 was less than protection by wild-type LRRK2. Knockdown of lrk-1, the endogenous ortholog of LRRK2 in C. elegans, reduced survival associated with mitochondrial dysfunction. C. elegans expressing LRRK2 showed rapid loss of dopaminergic markers (DAT::GFP fluorescence and dopamine levels) beginning in early adulthood. Loss of dopaminergic markers was greater for the G2019S LRRK2 line than for the wild-type line. Rotenone treatment induced a larger loss of dopamine markers in C. elegans expressing G2019S LRRK2 than in C. elegans expressing wild-type LRRK2; however, loss of dopaminergic markers in the G2019S LRRK2 nematode lines was not statistically different from that in the control line. These data suggest that LRRK2 plays an important role in modulating the response to mitochondrial inhibition and raises the possibility that mutations in LRRK2 selectively enhance the vulnerability of dopaminergic neurons to a stressor associated with Parkinson's disease.

A pattern of components from brain event-related potentials (ERPs) (cognitive non-invasive electrical brain measures) performed well in separating early-stage Alzheimer's disease (AD) subjects from normal-aging control subjects and shows promise for developing a clinical diagnostic for probable AD. A Number-Letter task elicited brain activity related to cognitive processes. In response to the task stimuli, brain activity was recorded as ERPs, whose components were measured by principal components analysis (PCA). The ERP component scores to relevant and irrelevant stimuli were used in discriminant analyses to develop functions that successfully classified individuals as belonging to an early-stage Alzheimer's disease group or a like-aged Control group, with probabilities of an individual belonging to each group. Applying the discriminant function to the developmental half of the data showed 92% of the subjects were correctly classified into either the AD group or the Control group with a sensitivity of 1.00. The two crossvalidation results were good with sensitivities of 0.83 and classification accuracies of 0.75-0.79. P3 and CNV components, as well as other, earlier ERP components, e.g. C145 and the memory "Storage" component, were useful in the discriminant functions.

Mutations in leucine-rich repeat kinase 2 (LRRK2) are prevalent causes of late-onset Parkinson's disease. Here, we show that LRRK2 binds to MAPK kinases (MKK) 3, 6, and 7, and that LRRK2 is able to phosphorylate MKK3, 6 and 7. Over-expression of LRRK2 and MKK6 increased the steady state levels of each protein beyond that observed with overexpression of either protein alone. Co-expression increased levels of MKK6 in the membrane more than in the cytoplasm. The increased expression of LRRK2 and MKK6 requires MKK6 activity. The disease-linked LRRK2 mutations, G2019S, R1441C and I2020T, enhance binding of LRRK2 to MKK6. This interaction was further supported by in vivo studies in C. elegans. RNAi knockdown in C. elegans of the endogenous orthologs for MKK6 or p38, sek-1 and pmk-1, abolishes LRRK2-mediated protection against mitochondrial stress. These results were confirmed by deletion of sek-1 in C.elegans. These data demonstrate that MKKs and LRRK2 function in similar biological pathways, and support a role for LRRK2 in modulating the cellular stress response.

Behavioral markers measured through neuropsychological testing in Mild Cognitive Impairment (MCI) were analyzed and combined in multivariate ways to predict conversion to Alzheimer’s disease (AD) in a longitudinal study of 43 MCI patients. The test measures taken at a baseline evaluation were first reduced to underlying components (Principal Components Analysis, PCA) and then the component scores were used in discriminant analysis to classify MCI individuals as likely to convert or not. When empirically weighted and combined, episodic memory, speeded executive functioning, recognition memory (false and true positives), visuospatial memory processing speed, and visuospatial episodic memory were together strong predictors of conversion to AD. These multivariate combinations of the test measures achieved through the PCA were good, statistically significant predictors of MCI conversion to AD (84% accuracy, 86% sensitivity, and 83% specificity). Importantly, the posterior probabilities of group membership that accompanied the binary prediction for each participant indicated the confidence of the prediction. Most of the subjects (81%) were in the highly confident probability bins (0.70 – 1.00), where the obtained prediction accuracy was more than 90%. The strength and reliability of this multivariate prediction method were tested by cross-validation and randomized resampling.

Predicting which individuals will progress to Alzheimer’s disease (AD) is important in both clinical and research settings. We used brain Event-Related Potentials (ERPs) obtained in a perceptual/cognitive paradigm with various processing demands to predict which individual Mild Cognitive Impairment (MCI) subjects will develop AD versus which will not. ERP components, including P3, memory “storage” component, and other earlier and later components, were identified and measured by Principal Components Analysis. When measured for particular task conditions, a weighted set of eight ERP component_conditions performed well in discriminant analysis at predicting later AD progression with good accuracy, sensitivity, and specificity. The predictions for most individuals (79%) had high posterior probabilities and were accurate (88%). This method, supported by a cross-validation where the prediction accuracy was 70–78%, features the posterior probability for each individual as a method of determining the likelihood of progression to AD. Empirically obtained prediction accuracies rose to 94% when the computed posterior probabilities for individuals were 0.90 or higher (which was found for 40% of our MCI sample).

Mutations in LRRK2 are one of the primary genetic causes of Parkinson's disease (PD). LRRK2 contains a kinase and a GTPase domain, and familial PD mutations affect both enzymatic activities. However, the signaling mechanisms regulating LRRK2 and the pathogenic effects of familial mutations remain unknown. Identifying the signaling proteins that regulate LRRK2 function and toxicity remains a critical goal for the development of effective therapeutic strategies. In this study, we apply systems biology tools to human PD brain and blood transcriptomes to reverse-engineer a LRRK2-centered gene regulatory network. This network identifies several putative master regulators of LRRK2 function. In particular, the signaling gene RGS2, which encodes for a GTPase-activating protein (GAP), is a key regulatory hub connecting the familial PD-associated genes DJ-1 and PINK1 with LRRK2 in the network. RGS2 expression levels are reduced in the striata of LRRK2 and sporadic PD patients. We identify RGS2 as a novel interacting partner of LRRK2 in vivo. RGS2 regulates both the GTPase and kinase activities of LRRK2. We show in mammalian neurons that RGS2 regulates LRRK2 function in the control of neuronal process length. RGS2 is also protective against neuronal toxicity of the most prevalent mutation in LRRK2, G2019S. We find that RGS2 regulates LRRK2 function and neuronal toxicity through its effects on kinase activity and independently of GTPase activity, which reveals a novel mode of action for GAP proteins. This work identifies RGS2 as a promising target for interfering with neurodegeneration due to LRRK2 mutations in PD patients.

We analyzed verbal episodic memory learning and recall using the Logical Memory (LM) subtest of the Wechsler Memory Scale-III in order to determine how gender differences in AD compare to those seen in normal elderly and whether or not these differences impact assessment of AD. We administered the LM to both an AD and a Control group, each comprised of 21 men and 21 women, and found a large drop in performance from normal elders to AD. Of interest was a gender interaction whereby the women’s scores dropped 1.6 times more than the men’s did. Control women on average outperformed Control men on every aspect of the test, including immediate recall, delayed recall, and learning. Conversely, AD women tended to perform worse than AD men. Additionally, the LM achieved perfect diagnostic accuracy in discriminant analysis of AD vs. Control women, a statistically significantly higher result than for men. The results indicate the LM is a more powerful and reliable tool in detecting AD in women than in men.

Neuropsychological assessment aids in the diagnosis of Alzheimer’s disease (AD) by objectively establishing cognitive impairment from standardized tests. We present new criteria for diagnosis that use weighted combined scores from multiple tests. Our method employs two multivariate analyses: Principal Components Analysis (PCA) and discriminant analysis. PCA (N = 216 subjects) created more interpretable cognitive dimensions by resolving 49 test measures in our neuropsychological battery to 13 component scores for each subject. The component scores were used to build discriminant functions that classified each participant as either an early-stage AD (N = 55) or normal elderly (N = 78). Our discriminant function performed with high accuracy, sensitivity, and specificity (nearly all >90%) in the development, a cross-validation, and a new subjects validation. When contrasted to two different traditional empirical methods for diagnosis (using cutscores and defining AD as falling below 5% on two or more test domains), our results suggested that the multivariate method was superior in classification (approximately 20% more accurate).