The pathological hallmark of synucleinopathies, including Lewy body dementia and Parkinson’s disease (PD), is the presence of Lewy bodies, which are primarily composed of intracellular inclusions of misfolded α-synuclein (α-syn) among other proteins. α-Syn is found in extracellular biological fluids in PD patients and has been implicated in modulating immune responses in the central nervous system (CNS) and the periphery. Natural killer (NK) cells are innate effector lymphocytes that are present in the CNS in homeostatic and pathological conditions. NK cell numbers are increased in the blood of PD patients and their activity is associated with disease severity; however, the role of NK cells in the context of α-synucleinopathies has never been explored. Here, we show that human NK cells can efficiently internalize and degrade α-syn aggregates via the endosomal/lysosomal pathway. We demonstrate that α-syn aggregates attenuate NK cell cytotoxicity in a dose-dependent manner and decrease the release of the proinflammatory cytokine, IFN-γ. To address the role of NK cells in PD pathogenesis, NK cell function was investigated in a preformed fibril α-syn–induced mouse PD model. Our studies demonstrate that in vivo depletion of NK cells in a preclinical mouse PD model resulted in exacerbated motor deficits and increased phosphorylated α-syn deposits. Collectively, our data provide a role of NK cells in modulating synuclein pathology and motor symptoms in a preclinical mouse model of PD, which could be developed into a therapeutic for PD and other synucleinopathies.

Parkinson’s disease (PD) is characterized by the accumulation of alpha-synuclein (α-syn) inclusions, the major component of Lewy bodies. Extracellular α-syn aggregates act as a damage-associated molecular pattern (DAMP) and the presence of autoantibodies against α-syn species in the cerebrospinal fluid and the serum of PD patients implicate the involvement of innate and adaptive immune responses. In non-transgenic (Tg) mice, intrastriatal injection of preformed fibril (PFF) α-syn results in widespread pathologic α-syn inclusions in the CNS. While the PFF model has been broadly utilized to study the mechanistic relationship between α-syn transmission and other neuropathological phenotypes, the immune phenotypes in this model are not clearly demonstrated. This study aimed to characterize the immune phenotypes during pathologic α-syn propagation by utilizing PFF α-syn–injected non-tg mice. Here, we showed that pathologic α-syn inclusions are prevalent in various brain regions and the gut at 5 months post injection (p.i.), preceding the degeneration of dopaminergic neurons in substantia nigra (SN). We discovered a distinct inflammatory response involving both activation of microglia and astrocytes and infiltration of B, CD4+ T, CD8+ T, and natural killer cells in the brain at 5 months p.i. Moreover, PFF α-syn–injected mice display significant alterations in the frequency and number of leukocyte subsets in the spleen and lymph nodes with minimum alterations in the blood. Our data provide primary evidence that intracerebral-initiated synucleinopathies in non-tg mice alter immune cell profiles both in the CNS and peripheral lymphoid organs. Furthermore, our data provides support for utilizing this mouse model to assess the mechanistic connection between immune responses and synuclein pathology.

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The process of combining, via mathematical software tools, a set of low resolution images into a single high resolution image is often referred to as super-resolution. Algorithms for super-resolution involve two key steps: registration and reconstruction. Most approaches proposed in the literature decouple these steps, solving each independently. This can be effective if there are very simple, linear displacements between the low resolution images. However, for more complex, nonlinear, nonuniform transformations, estimating the displacements can be very difficult, leading to severe inaccuracies in the reconstructed high resolution image. This paper presents a mathematical framework and optimization algorithms that can be used to jointly estimate these quantities. Efficient implementation details are considered, and numerical experiments are provided to illustrate the effectiveness of our approach.

Microglia are the brain-resident macrophages responsible for immune surveillance that become activated in response to injury, infection, environmental toxins, and other stimuli that threaten neuronal survival. Previous work from our group demonstrated that mice deficient in Regulator of G-protein Signaling 10 (RGS10), a microglia-enriched GTPase activating protein (GAP) for G-protein alpha subunits, displayed increased microglial burden in the CNS at birth and developed a parkinsonian phenotype after exposure to chronic systemic inflammation, implicating a neuroprotective role for RGS10 in the nigrostriatal pathway. While it is known that RGS10 is expressed in both microglia and certain subsets of neurons, it is not known whether RGS10 functions similarly in both cells types. In this study we sought to delineate the specific role of RGS10 in microglia and identify the molecular pathway(s) required for RGS10 to exert its actions in microglia. Here, we identify RGS10 as a negative regulator of the NF-κB pathway in microglia and demonstrate that the pro-inflammatory and cytotoxic phenotype of Rgs10-null microglia can be reversed by lentiviral-mediated restoration of RGS10 expression. In vivo gene transfer of RGS10 into the substantia nigra pars compacta (SNpc) of rats reduced microgliosis and protected against 6-OHDA neurotoxin-induced death of dopaminergic (DA) neurons. Together, our findings suggest that modulation of RGS10 activity in microglia may afford therapeutic benefit in the treatment of chronic neuroinflammatory conditions as well as neuroprotection against inflammation-related degeneration in Parkinson’s disease (PD), the second most common neurodegenerative disorder affecting individuals over age 65.

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In this paper we develop flexible Krylov methods for efficiently computing regularized solutions to large-scale linear inverse problems with an ℓ 2 fit-to-data term and an ℓ p penalization term, for p ≥ 1. First we approximate the p-norm penalization term as a sequence of 2-norm penalization terms using adaptive regularization matrices in an iterative reweighted norm fashion, and then we exploit flexible preconditioning techniques to efficiently incorporate the weight updates. To handle general (non-square) ℓ p -regularized least-squares problems, we introduce a flexible Golub-Kahan approach and exploit it within a Krylov-Tikhonov hybrid framework. The key benefits of our approach compared to existing optimization methods for ℓ p regularization are that efficient projection methods replace inner-outer schemes and that expensive regularization parameter selection techniques can be avoided. Theoretical insights are provided, and numerical results from image deblurring and tomographic reconstruction illustrate the benefits of this approach, compared to well-established methods. Furthermore, we show that our approach for p = 1 can be used to efficiently compute solutions that are sparse with respect to some transformations. 1where b ∈ R m is the observed data, A ∈ R m×n models the forward process, x true ∈ R n is the desired solution, and e ∈ R m represents noise or errors in the observation. Due to the ill-posedness of the underlying problem [18], regularization should be applied to recover a meaningful approximation of x true in (1). In this paper, we are interested in problems of the form minwhere · p for p ≥ 1 is the vectorial p-norm, λ > 0 is a regularization parameter, and Ψ ∈ R n×n is a nonsingular matrix. For p = 2 and Ψ = I, (2) is the standard Tikhonov regularization problem, and many efficient techniques, including hybrid iterative methods, have been proposed, see, e.g., [5,10,28,22]. However, optimization problems (2) for p = 2 can be significantly more challenging. For example, for p = 1, the so-called ℓ 1 -regularized problem suffers from non-differentiability at the origin; moreover, in some situations, one may wish to consider 0 < p < 1, which results in a nonconvex optimization problem, see, e.g., [20,24,25]. In this paper, we will focus on p ≥ 1, and henceforth we will refer to problem (2) with Ψ = I as an "ℓ p -regularized problem" and problem (2) with Ψ = I will be dubbed the "transformed ℓ p -regularized" problem. Typically the transformed ℓ p -regularized problem arises in cases where sparsity in some frequency domain (e.g., in a wavelet domain) is desired. Depending on the application, a sparsity transform may be included in both the fit-to-data and the regularization term. This was considered in [3] for image deblurring problems, where the resulting minimization problem was solved with an inner-outer iteration scheme.Most of the previously developed methods for ℓ p minimization utilize nonlinear optimization schemes or iteratively reweighted optimization schemes, which can get very expensive d...