Mild Chronic Colitis Triggers Parkinsonism in LRRK2 Mutant Mice Through Activating TNF-α Pathway.

BACKGROUND: Leucine-rich repeat kinase 2 (LRRK2) is a common risk gene for Parkinson's disease (PD) and inflammatory bowel disorders. However, the penetrance of the most prevalent LRRK2 mutation, G2019S, is <50%. Factors other than genetic mutations are needed in PD process. OBJECTIVES: To examine whether and how gut inflammation may act as an environmental trigger to neurodegeneration in PD. METHODS: A mild and chronic dextran sodium sulfate (DSS)-induced colitis mice model harboring LRRK2 G2019S mutation was established. The colitis severity, immune responses, locomotor function, dopaminergic neuron, and microglia integrity were compared between littermate controls, transgenic LRRK2 wild type (WT), and LRRK2 G2019S mice. RESULTS: The LRRK2 G2019S mice are more vulnerable to DSS-induced colitis than littermate controls or LRRK2 WT animals with increased intestinal expressions of pattern-recognition receptors, including toll-like receptors (TLRs), nuclear factor (NF)-κB activation, and pro-inflammatory cytokines secretion, especially tumor necrosis factor (TNF)-α. Notably, the colonic expression of α-synuclein was significantly increased in LRRK2 G2019S colitis mice. We subsequently observed more aggravated locomotor defect, microglia activation, and dopaminergic neuron loss in LRRK2 G2019S colitis mice than control animals. Treatment with anti-TNF-α monoclonal antibody, adalimumab, abrogated both gut and neuroinflammation, mitigated neurodegeneration, and improved locomotor function in LRRK2 G2019S colitis mice. Finally, we validated increased colonic expressions of LRRK2, TLRs, and NF-κB pathway proteins and elevated plasma TNF-α level in PD patients compared to controls, especially in those with LRRK2 risk variants. CONCLUSIONS: Our findings demonstrate that chronic colitis promotes parkinsonism in genetically susceptible mice and TNF-α plays a detrimental role in the gut-brain axis of PD. © 2021 International Parkinson and Movement Disorder Society.

Isotropic multi-scale neuronal reconstruction from high-ratio expansion microscopy with contrastive unsupervised deep generative models.

BACKGROUND AND OBJECTIVE: Current methods for imaging reconstruction from high-ratio expansion microscopy (ExM) data are limited by anisotropic optical resolution and the requirement for extensive manual annotation, creating a significant bottleneck in the analysis of complex neuronal structures. METHODS: We devised an innovative approach called the IsoGAN model, which utilizes a contrastive unsupervised generative adversarial network to sidestep these constraints. This model leverages multi-scale and isotropic neuron/protein/blood vessel morphology data to generate high-fidelity 3D representations of these structures, eliminating the need for rigorous manual annotation and supervision. The IsoGAN model introduces simplified structures with idealized morphologies as shape priors to ensure high consistency in the generated neuronal profiles across all points in space and scalability for arbitrarily large volumes. RESULTS: The efficacy of the IsoGAN model in accurately reconstructing complex neuronal structures was quantitatively assessed by examining the consistency between the axial and lateral views and identifying a reduction in erroneous imaging artifacts. The IsoGAN model accurately reconstructed complex neuronal structures, as evidenced by the consistency between the axial and lateral views and a reduction in erroneous imaging artifacts, and can be further applied to various biological samples. CONCLUSION: With its ability to generate detailed 3D neurons/proteins/blood vessel structures using significantly fewer axial view images, IsoGAN can streamline the process of imaging reconstruction while maintaining the necessary detail, offering a transformative solution to the existing limitations in high-throughput morphology analysis across different structures.