16 research outputs found
Quantitative Proteome Profiling of CNS-Infiltrating Autoreactive CD4<sup>+</sup> Cells Reveals Selective Changes during Experimental Autoimmune Encephalomyelitis
Experimental autoimmune encephalomyelitis
(EAE) is a murine model
of multiple sclerosis, a chronic neurodegenerative and inflammatory
autoimmune condition of the central nervous system (CNS). Pathology
is driven by the infiltration of autoreactive CD4<sup>+</sup> lymphocytes
into the CNS, where they attack neuronal sheaths causing ascending
paralysis. We used an isotope-coded protein labeling approach to investigate
the proteome of CD4<sup>+</sup> cells isolated from the spinal cord
and brain of mice at various stages of EAE progression in two EAE
disease models: PLP<sub>139–151</sub>-induced relapsing-remitting
EAE and MOG<sub>35–55</sub>-induced chronic EAE, which emulate
the two forms of human multiple sclerosis. A total of 1120 proteins
were quantified across disease onset, peak-disease, and remission
phases of disease, and of these 13 up-regulated proteins of interest
were identified with functions relating to the regulation of inflammation,
leukocyte adhesion and migration, tissue repair, and the regulation
of transcription/translation. Proteins implicated in processes such
as inflammation (S100A4 and S100A9) and tissue repair (annexin A1),
which represent key events during EAE progression, were validated
by quantitative PCR. This is the first targeted analysis of autoreactive
cells purified from the CNS during EAE, highlighting fundamental CD4<sup>+</sup> cell-driven processes that occur during the initiation of
relapse and remission stages of disease
Subgroup analyses of the performance of the machine-learned predictor.
Subgroup analyses of the performance of the machine-learned predictor.</p
Performance of the machine-learned predictor.
Performance of the machine-learned predictor.</p
Performance of the machine-learned model when propensity matching based on age and gender.
Performance of the machine-learned model when propensity matching based on age and gender.</p