An IFNγ/CXCL2 regulatory pathway determines lesion localization during EAE

Abstract Background Myelin oligodendrocyte glycoprotein (MOG)-reactive T-helper (Th)1 cells induce conventional experimental autoimmune encephalomyelitis (cEAE), characterized by ascending paralysis and monocyte-predominant spinal cord infiltrates, in C57BL/6 wildtype (WT) hosts. The same T cells induce an atypical form of EAE (aEAE), characterized by ataxia and neutrophil-predominant brainstem infiltrates, in syngeneic IFNγ receptor (IFNγR)-deficient hosts. Production of ELR+ CXC chemokines within the CNS is required for the development of aEAE, but not cEAE. The cellular source(s) and localization of ELR+ CXC chemokines in the CNS and the IFNγ-dependent pathways that regulate their production remain to be elucidated. Methods The spatial distribution of inflammatory lesions and CNS expression of the ELR+ CXC chemokines, CXCL1 and CXCL2, were determined via immunohistochemistry and/or in situ hybridization. Levels of CXCL1 and CXCL2, and their cognate receptor CXCR2, were measured in/on leukocyte subsets by flow cytometric and quantitative PCR (qPCR) analysis. Bone marrow neutrophils and macrophages were cultured with inflammatory stimuli in vitro prior to measurement of CXCL2 and CXCR2 by qPCR or flow cytometry. Results CNS-infiltrating neutrophils and monocytes, and resident microglia, are a prominent source of CXCL2 in the brainstem of IFNγRKO adoptive transfer recipients during aEAE. In WT transfer recipients, IFNγ directly suppresses CXCL2 transcription in microglia and myeloid cells, and CXCR2 transcription in CNS-infiltrating neutrophils. Consequently, infiltration of the brainstem parenchyma from the adjacent meninges is blocked during cEAE. CXCL2 directly stimulates its own expression in cultured neutrophils, which is enhanced by IL-1 and suppressed by IFNγ. Conclusions We provide evidence for an IFNγ-regulated CXCR2/CXCL2 autocrine/paracrine feedback loop in innate immune cells that determines the location of CNS infiltrates during Th1-mediated EAE. When IFNγ signaling is impaired, myeloid cell production of CXCL2 increases, which promotes brainstem inflammation and results in clinical ataxia. IFNγ, produced within the CNS of WT recipients, suppresses myeloid cell CXCR2 and CXCL2 production, thereby skewing the location of neuroinflammatory infiltrates to the spinal cord and the clinical phenotype to an ascending paralysis. These data reveal a novel mechanism by which IFNγ and CXCL2 interact to direct regional recruitment of leukocytes in the CNS, resulting in distinct clinical presentations.https://deepblue.lib.umich.edu/bitstream/2027.42/145159/1/12974_2018_Article_1237.pd

Differential diagnosis of suspected multiple sclerosis: a consensus approach

BACKGROUND AND OBJECTIVES: Diagnosis of multiple sclerosis (MS) requires exclusion of diseases that could better explain the clinical and paraclinical findings. A systematic process for exclusion of alternative diagnoses has not been defined. An International Panel of MS experts developed consensus perspectives on MS differential diagnosis. METHODS: Using available literature and consensus, we developed guidelines for MS differential diagnosis, focusing on exclusion of potential MS mimics, diagnosis of common initial isolated clinical syndromes, and differentiating between MS and non-MS idiopathic inflammatory demyelinating diseases. RESULTS: We present recommendations for 1) clinical and paraclinical red flags suggesting alternative diagnoses to MS; 2) more precise definition of "clinically isolated syndromes" (CIS), often the first presentations of MS or its alternatives; 3) algorithms for diagnosis of three common CISs related to MS in the optic nerves, brainstem, and spinal cord; and 4) a classification scheme and diagnosis criteria for idiopathic inflammatory demyelinating disorders of the central nervous system. CONCLUSIONS: Differential diagnosis leading to MS or alternatives is complex and a strong evidence base is lacking. Consensus-determined guidelines provide a practical path for diagnosis and will be useful for the non-MS specialist neurologist. Recommendations are made for future research to validate and support these guidelines. Guidance on the differential diagnosis process when MS is under consideration will enhance diagnostic accuracy and precision

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given