Fernandez, B. | Search bioRxiv

bioRxiv2020

Distinct roles for RAB10 and RAB29 in pathogenic LRRK2-mediated endolysosomal trafficking alterations

Mutations in the gene encoding leucine-rich repeat kinase 2 (LRRK2) cause familial Parkinsons disease, and sequence variations are associated with the sporadic form of the disease. LRRK2 phosphorylates a subset of RAB proteins implicated in secretory and recycling trafficking pathways, including RAB8A and RAB10. Another RAB protein, RAB29, has been reported to recruit LRRK2 to the Golgi where it stimulates its kinase activity. Our previous studies revealed that G2019S LRRK2 expression or knockdown of RAB8A deregulate epidermal growth factor receptor (EGFR) trafficking, with a concomitant accumulation of the receptor in a RAB4-positive recycling compartment. Here, we show that the G2019S LRRK2-mediated EGFR deficits are mimicked by knockdown of RAB10 and rescued by expression of active RAB10. By contrast, RAB29 knockdown is without effect, but expression of RAB29 also rescues the pathogenic LRRK2-mediated trafficking deficits independently of Golgi integrity. Our data suggest that G2019S LRRK2 deregulates endolysosomal trafficking by impairing the function of RAB8A and RAB10, whilst RAB29 positively modulates non-Golgi-related trafficking events impaired by pathogenic LRRK2.

cell biology↗

bioRxiv2020

Weight Sensitivity of Temporal SNR Metrics in multi-echo fMRI

PurposeIn multi-echo fMRI (ME-fMRI), various weighting schemes have been proposed for the combination of the data across echoes. Here we introduce a framework that facilitates a deeper understanding of the weight dependence of temporal SNR measures in ME-fMRI. Theory and MethodsWe examine two metrics that have been used to characterize ME-fMRI performance: temporal SNR (tSNR) and multi-echo temporal (metSNR). Both metrics can be described using the generalized Rayleigh quotient (GRQ) and are predicted to be relatively insensitive to the weights when there is a high degree of similarity between a metric-specific matrix in the GRQ numerator and a metricindependent covariance matrix in the GRQ denominator. The application of the GRQ framework to experimental data is demonstrated using a resting-state fMRI dataset acquired with a multi-echo multi-band EPI sequence. ResultsIn the example dataset, similarities between the covariance matrix and the metSNR and tSNR numerator matrices are highest in grey matter (GM) and cerebrospinal fluid (CSF) voxels, respectively. For representative GM and CSF voxels that exhibit high matrix similarity values, the metSNR and tSNR values, respectively, are both within 4% of their optimal values across a range of weighting schemes. However, there is a fundamental tradeoff, with a high degree of weight sensitivity in the tSNR and metSNR metrics for the representative GM and CSF voxels, respectively. Geometric insight into the observed weight dependencies is provided through a graphical interpretation of the GRQ. ConclusionA GRQ framework can provide insight into the factors that determine the weight sensitivity of tSNR and metSNR measures in ME-fMRI.

neuroscience↗