Itay Raphael

T cell subsets and their signature cytokines in autoimmune and inflammatory diseases

CD4(+) T helper (Th) cells are critical for proper immune cell homeostasis and host defense, but are also major contributors to pathology of autoimmune and inflammatory diseases. Since the discovery of the Th1/Th2 dichotomy, many additional Th subsets were discovered, each with a unique cytokine profile, functional properties, and presumed role in autoimmune tissue pathology. This includes Th1, Th2, Th17, Th22, Th9, and Treg cells which are characterized by specific cytokine profiles. Cytokines produced by these Th subsets play a critical role in immune cell differentiation, effector subset commitment, and in directing the effector response. Cytokines are often categorized into proinflammatory and anti-inflammatory cytokines and linked to Th subsets expressing them. This article reviews the different Th subsets in terms of cytokine profiles, how these cytokines influence and shape the immune response, and their relative roles in promoting pathology in autoimmune and inflammatory diseases. Furthermore, we will discuss whether Th cell pathogenicity can be defined solely based on their cytokine profiles and whether rigid definition of a Th cell subset by its cytokine profile is helpful.

Body fluid biomarkers in multiple sclerosis: how far we have come and how they could affect the clinic now and in the future

Multiple sclerosis (MS) is an autoimmune inflammatory disease of the central nervous system, which affects over 2.5 million people worldwide. Although MS has been extensively studied, many challenges still remain in regards to treatment, diagnosis and prognosis. Typically, prognosis and individual responses to treatment are evaluated by clinical tests such as the expanded disability status scale, MRI and presence of oligoclonal bands in the cerebrospinal fluid. However, none of these measures correlates strongly with treatment efficacy or disease progression across heterogeneous patient populations and subtypes of MS. Numerous studies over the past decades have attempted to identify sensitive and specific biomarkers for diagnosis, prognosis and treatment efficacy of MS. The objective of this article is to review and discuss the current literature on body fluid biomarkers in MS, including research on potential biomarker candidates in the areas of miRNA, mRNA, lipids and proteins.

Microwave and magnetic (M2) proteomics of the experimental autoimmune encephalomyelitis animal model of multiple sclerosis

We hypothesized that quantitative MS/MS‐based proteomics at multiple time points, incorporating rapid microwave and magnetic (M2) sample preparation, could enable relative protein expression to be correlated to disease progression in the experimental autoimmune encephalomyelitis (EAE) animal model of multiple sclerosis. To test our hypothesis, microwave‐assisted reduction/alkylation/digestion of proteins from brain tissue lysates bound to C8 magnetic beads and microwave‐assisted isobaric chemical labeling were performed of released peptides, in 90 s prior to unbiased proteomic analysis. Disease progression in EAE was assessed by scoring clinical EAE disease severity and confirmed by histopathologic evaluation for central nervous system inflammation. Decoding the expression of 283 top‐ranked proteins (p <0.05) at each time point relative to their expression at the peak of disease, from a total of 1191 proteins observed in four technical replicates, revealed a strong statistical correlation to EAE disease score, particularly for the following four proteins that closely mirror disease progression: 14‐3‐3ε (p = 3.4E‐6); GPI (p = 2.1E‐5); PLP1 (p = 8.0E‐4); PRX1 (p = 1.7E‐4). These results were confirmed by Western blotting, signaling pathway analysis, and hierarchical clustering of EAE risk groups. While validation in a larger cohort is underway, we conclude that M2 proteomics is a rapid method to quantify putative prognostic/predictive protein biomarkers and therapeutic targets of disease progression in the EAE animal model of multiple sclerosis.

Immunoenrichment microwave and magnetic proteomics for quantifying CD47 in the experimental autoimmune encephalomyelitis model of multiple sclerosis

We hypothesized that quantitative MS/MS‐based proteomics at multiple time points, incorporating immunoenrichment prior to rapid microwave and magnetic (IM2) sample preparation, might enable correlation of the relative expression of CD47 and other low abundance proteins to disease progression in the experimental autoimmune encephalomyelitis (EAE) animal model of multiple sclerosis. To test our hypothesis, anti‐CD47 antibodies were used to enrich for low abundance CD47 prior to microwave and magnetic proteomics in EAE. Decoding protein expression at each time point, with CD47‐immunoenriched samples and targeted proteomic analysis, enabled peptides from the low abundance proteins to be precisely quantified throughout disease progression, including: CD47: 86–99, corresponding to the “marker of self” overexpressed by myelin that prevents phagocytosis, or “cellular devouring,” by microglia and macrophages; myelin basic protein: 223–228, corresponding to myelin basic protein; and migration inhibitory factor: 79–87, corresponding to a proinflammatory cytokine that inhibits macrophage migration. While validation in a larger cohort is underway, we conclude that IM2 proteomics is a rapid method to precisely quantify peptides from CD47 and other low abundance proteins throughout disease progression in EAE. This is likely due to improvements in selectivity and sensitivity, necessary to partially overcome masking of low abundance proteins by high abundance proteins and improve dynamic range.We hypothesized that quantitative tandem mass spectrometry-based proteomics at multiple time points, incorporating immunoenrichment prior to rapid microwave and magnetic (IM2) sample preparation, might enable correlation of the relative expression of CD47 and other low abundance proteins to disease progression in the experimental autoimmune encephalomyelitis (EAE) animal model of multiple sclerosis. To test our hypothesis, anti-CD47 antibodies were used to enrich for low abundance CD47 prior to microwave and magnetic (M2) proteomics in EAE. Decoding protein expression at each time point, with CD47-immunoenriched samples and targeted proteomic analysis, enabled peptides from the low abundance proteins to be precisely quantified throughout disease progression, including: CD47: 86-99, corresponding to the “marker of self” overexpressed by myelin that prevents phagocytosis, or “cellular devouring”, by microglia and macrophages; MBP: 223-228, corresponding to myelin basic protein; and MIF: 79-87, corresponding to a proinflammatory cytokine that inhibits macrophage migration. While validation in a larger cohort is underway, we conclude that IM2 proteomics is a rapid method to precisely quantify peptides from CD47 and other low abundance proteins throughout disease progression in EAE. This is likely due to improvements in selectivity and sensitivity, necessary to partially overcome masking of low abundance proteins by high abundance proteins and improve dynamic range.

Microwave & Magnetic (M2) Proteomics Reveals CNS-Specific Protein Expression Waves that Precede Clinical Symptoms of Experimental Autoimmune Encephalomyelitis

Central nervous system-specific proteins (CSPs), transported across the damaged blood-brain-barrier (BBB) to cerebrospinal fluid (CSF) and blood (serum), might be promising diagnostic, prognostic and predictive protein biomarkers of disease in individual multiple sclerosis (MS) patients because they are not expected to be present at appreciable levels in the circulation of healthy subjects. We hypothesized that microwave & magnetic (M2) proteomics of CSPs in brain tissue might be an effective means to prioritize putative CSP biomarkers for future immunoassays in serum. To test this hypothesis, we used M2 proteomics to longitudinally assess CSP expression in brain tissue from mice during experimental autoimmune encephalomyelitis (EAE), a mouse model of MS. Confirmation of central nervous system (CNS)-infiltrating inflammatory cell response and CSP expression in serum was achieved with cytokine ELISPOT and ELISA immunoassays, respectively, for selected CSPs. M2 proteomics (and ELISA) revealed characteristic CSP expression waves, including synapsin-1 and α-II-spectrin, which peaked at day 7 in brain tissue (and serum) and preceded clinical EAE symptoms that began at day 10 and peaked at day 20. Moreover, M2 proteomics supports the concept that relatively few CNS-infiltrating inflammatory cells can have a disproportionally large impact on CSP expression prior to clinical manifestation of EAE.

Early disease correlated protein detection using early response index (ERI)

Finding disease correlated proteins with significant expression changes before the clinical onset stage of disease is of great interest to researchers for developing early disease diagnosis biomarkers. Since disease correlated proteins have relatively low level of abundance change at early stages, it is hard to find them using existing bioinformatic tools in high throughput data, which has limited dynamic range and significant noise. Most existing biomarker discovery algorithms can only detect proteins with high abundance changes, and early disease diagnostic markers are frequently missed. We proposed a new ranking score called early response index (ERI) for prioritizing disease correlated protein as potential early diagnostic markers. Rather than classification accuracy, ERI estimates the average classification accuracy improvement achievable by proteins when they are combined with other proteins as features of classifiers. ERI is more sensitive to abundance changes than other ranking statistics. In a validation study to detect proteins with sustained expression changes from the pre-clinical onset to the clinical onset stage of multiples sclerosis using a mouse model, the proposed algorithm outperforms other tested algorithms in both sensitivity and specificity.

Stability of T-cell lineages in autoimmune diseases

Life is about change and instability – so should we be surprised that our favorite T-cell lineages are not as stable as we desperately hoped they would be? Probably not, and as much as this complicates our scientific lives, we need to understand this and learn to interpret our data accordingly, no matter how little we like it. But let’s start at the beginning.

Early response index: a statistic to discover potential early stage disease biomarkers

BackgroundIdentifying disease correlated features early before large number of molecules are impacted by disease progression with significant abundance change is very advantageous to biologists for developing early disease diagnosis biomarkers. Disease correlated features have relatively low level of abundance change at early stages. Finding them using existing bioinformatic tools in high throughput data is a challenging task since the technology suffers from limited dynamic range and significant noise. Most existing biomarker discovery algorithms can only detect molecules with high abundance changes, frequently missing early disease diagnostic markers.ResultsWe present a new statistic called early response index (ERI) to prioritize disease correlated molecules as potential early biomarkers. Instead of classification accuracy, ERI measures the average classification accuracy improvement attainable by a feature when it is united with other counterparts for classification. ERI is more sensitive to abundance changes than other ranking statistics. We have shown that ERI significantly outperforms SAM and Localfdr in detecting early responding molecules in a proteomics study of a mouse model of multiple sclerosis. Importantly, ERI was able to detect many disease relevant proteins before those algorithms detect them at a later time point.ConclusionsERI method is more sensitive for significant feature detection during early stage of disease development. It potentially has a higher specificity for biomarker discovery, and can be used to identify critical time frame for disease intervention.

Identification of candidate predictive protein biomarkers by M2 proteomics for clinical onset and treatment efficacy of multiple sclerosis

important biomarker in neuromyelitis optica. Furthermore, autoantibodies to myelin oligodendrocyte glycoprotein (MOG) are detected in pediatric demyelinating disorders. We examined a cohort of adults with AQP4 antibody-negative neuromyelitis optica spectrum disorder (NMOSD) for antibodies to MOG. Methods: We performed a flow cytometry cell-based assay using live human embryonic kidney 293 cells that were lentivirus-transduced cells to express full-length surface MOG. Serum was tested in 23 AQP4 antibody-negative NMOSD patients with bilateral and/or recurrent optic neuritis (BON, n = 11), longitudinally extensive transverse myelitis (LETM, n = 10), and sequential BON and LETM (n= 2). Control cohorts included patients with clinically definite multiple sclerosis (MS, n = 76), as well as age matched healthy and other neurological disease controls (n= 52). Results: MOG antibodies were detected in 9/23 AQP4 antibodynegative NMOSD patients compared to 1/76 MS patients and 0/52 controls (P b 0.001). In all patients, the MOG antibodies were of the IgG rather than the IgM isotype. MOG antibodies were detected in 8/11 BON, 0/10 LETM, and 1/2 sequential BON and LETM patients. 6/9 MOG antibody-positive AQP4 antibody-negative patients had a relapsing course. MOG antibody-positive patients were more often female, of younger age at disease onset, and had a preceding viral prodrome compared to MOG antibody-negative patients (P values not significant). MOG antibody-positive patients had prominent bilateral optic disc swelling at presentation, andweremore likely to have a rapid response to steroid therapy and relapse on steroid cessation than MOG antibody-negative patients (P= 0.034, P = 0.029, respectively). While 8/9 MOG antibody-positive patients had good follow-up visual acuity, one experienced sustained impairments in visual acuity and visual field testing. Furthermore, three patients had retinal nerve fiber layer atrophy on optical coherence tomography at follow-up, and one had residual spinal disability. Conclusions: MOG antibodies have a strong association with BON and may be a useful clinical biomarker. MOG antibody-associated BON is a relapsing disorder that is frequently steroid responsive and often steroid dependent. Failure of early recognition and institution of immunotherapy may be associated with sustained impairment.

Immunoenrichment microwave and magnetic proteomics for quantifying CD47 in the experimental autoimmune encephalomyelitis model of multiple sclerosis: Proteomics and 2DE

We hypothesized that quantitative MS/MS‐based proteomics at multiple time points, incorporating immunoenrichment prior to rapid microwave and magnetic (IM2) sample preparation, might enable correlation of the relative expression of CD47 and other low abundance proteins to disease progression in the experimental autoimmune encephalomyelitis (EAE) animal model of multiple sclerosis. To test our hypothesis, anti‐CD47 antibodies were used to enrich for low abundance CD47 prior to microwave and magnetic proteomics in EAE. Decoding protein expression at each time point, with CD47‐immunoenriched samples and targeted proteomic analysis, enabled peptides from the low abundance proteins to be precisely quantified throughout disease progression, including: CD47: 86–99, corresponding to the “marker of self” overexpressed by myelin that prevents phagocytosis, or “cellular devouring,” by microglia and macrophages; myelin basic protein: 223–228, corresponding to myelin basic protein; and migration inhibitory factor: 79–87, corresponding to a proinflammatory cytokine that inhibits macrophage migration. While validation in a larger cohort is underway, we conclude that IM2 proteomics is a rapid method to precisely quantify peptides from CD47 and other low abundance proteins throughout disease progression in EAE. This is likely due to improvements in selectivity and sensitivity, necessary to partially overcome masking of low abundance proteins by high abundance proteins and improve dynamic range.We hypothesized that quantitative tandem mass spectrometry-based proteomics at multiple time points, incorporating immunoenrichment prior to rapid microwave and magnetic (IM2) sample preparation, might enable correlation of the relative expression of CD47 and other low abundance proteins to disease progression in the experimental autoimmune encephalomyelitis (EAE) animal model of multiple sclerosis. To test our hypothesis, anti-CD47 antibodies were used to enrich for low abundance CD47 prior to microwave and magnetic (M2) proteomics in EAE. Decoding protein expression at each time point, with CD47-immunoenriched samples and targeted proteomic analysis, enabled peptides from the low abundance proteins to be precisely quantified throughout disease progression, including: CD47: 86-99, corresponding to the “marker of self” overexpressed by myelin that prevents phagocytosis, or “cellular devouring”, by microglia and macrophages; MBP: 223-228, corresponding to myelin basic protein; and MIF: 79-87, corresponding to a proinflammatory cytokine that inhibits macrophage migration. While validation in a larger cohort is underway, we conclude that IM2 proteomics is a rapid method to precisely quantify peptides from CD47 and other low abundance proteins throughout disease progression in EAE. This is likely due to improvements in selectivity and sensitivity, necessary to partially overcome masking of low abundance proteins by high abundance proteins and improve dynamic range.