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Janus-like opposing roles of CD47 in autoimmune brain inflammation in humans and mice.

Han MH, Lundgren DH, Jaiswal S, Chao M, Graham KL, Garris CS, Axtell RC, Ho PP, Lock CB, Woodard JI, Brownell SE, Zoudilova M, Hunt JF, Baranzini SE, Butcher EC, Raine CS, Sobel RA, Han DK, Weissman I, Steinman L - J. Exp. Med. (2012)

Bottom Line: Immunohistochemical studies demonstrate that CD47 is expressed in normal myelin and in foamy macrophages and reactive astrocytes within active MS lesions.In vitro assays demonstrate that blocking CD47 also promotes phagocytosis of myelin and that this effect is dependent on signal regulatory protein α (SIRP-α).Depending on the cell type, location, and disease stage, CD47 has Janus-like roles, with opposing effects on EAE pathogenesis.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA. mayhan@stanford.edu

ABSTRACT
Comparison of transcriptomic and proteomic data from pathologically similar multiple sclerosis (MS) lesions reveals down-regulation of CD47 at the messenger RNA level and low abundance at the protein level. Immunohistochemical studies demonstrate that CD47 is expressed in normal myelin and in foamy macrophages and reactive astrocytes within active MS lesions. We demonstrate that CD47(-/-) mice are refractory to experimental autoimmune encephalomyelitis (EAE), primarily as the result of failure of immune cell activation after immunization with myelin antigen. In contrast, blocking with a monoclonal antibody against CD47 in mice at the peak of paralysis worsens EAE severity and enhances immune activation in the peripheral immune system. In vitro assays demonstrate that blocking CD47 also promotes phagocytosis of myelin and that this effect is dependent on signal regulatory protein α (SIRP-α). Immune regulation and phagocytosis are mechanisms for CD47 signaling in autoimmune neuroinflammation. Depending on the cell type, location, and disease stage, CD47 has Janus-like roles, with opposing effects on EAE pathogenesis.

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Identification and comparison of transcriptomic and proteomic landscapes. Comparative expression levels from the overlap of microarray and proteomic analysis of MS lesions. 834 MS UniProt IDs were jointly detected in both microarray and mass spectrometry analysis. RNA and protein expression levels of targets were measured by fluorescent intensity and spectral counts, respectively. Global transcriptomic and proteomic landscapes were compared for the 834 overlapping targets using logs (base 10) of mean relative abundance normalized to a mean of zero. Those proteins with absolute log difference <1 for each of the four lesion types (control [CTL], AP, CAP, and CP) were called inliers (i), <2 midliers (ii), and >2 outliers (iii). Note that an absolute log difference of 1 denotes a one order of magnitude difference in relative abundance.
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fig1: Identification and comparison of transcriptomic and proteomic landscapes. Comparative expression levels from the overlap of microarray and proteomic analysis of MS lesions. 834 MS UniProt IDs were jointly detected in both microarray and mass spectrometry analysis. RNA and protein expression levels of targets were measured by fluorescent intensity and spectral counts, respectively. Global transcriptomic and proteomic landscapes were compared for the 834 overlapping targets using logs (base 10) of mean relative abundance normalized to a mean of zero. Those proteins with absolute log difference <1 for each of the four lesion types (control [CTL], AP, CAP, and CP) were called inliers (i), <2 midliers (ii), and >2 outliers (iii). Note that an absolute log difference of 1 denotes a one order of magnitude difference in relative abundance.

Mentions: We compared transcriptomic and proteomic profiles from the same MS brain tissue to study differential expression of RNA transcripts and proteins during disease progression. Microarray analysis was newly performed for this study. Proteomic experiments were based on the MS brain lesion proteome dataset from our previously published work (Han et al., 2008). Tissue containing acute plaque (AP), chronic active plaque (CAP), and chronic plaque (CP) were analyzed by microarray analysis and by mass spectrometry (Fig. S1). Microarray analysis identified 6,601 RNA targets (Table S1), whereas the corresponding proteomic study identified 2,404 protein targets (Table S2). Only 1,229 RNA targets (of the 6,601 total, ∼20% of identified) mapped to 834 proteins identified in the proteomic study (∼30% of all proteins identified). The majority of the targets (5,372 RNA targets and 1,570 proteins) had no overlap between the two platforms (Fig. S2 and Table S3). We then grouped 834 common targets (identified in both microarray and proteomic platforms) into inliers (RNA expression levels correlate with protein expression levels; relative abundance difference between RNA probe intensities and protein spectral counts were less than one order of magnitude), midliers (RNA expression levels correlate with protein expression levels; relative abundance less than two orders of magnitude), and outliers (RNA expression levels do not correlate with protein expression levels; relative abundance greater than two orders of magnitude) to study concordance between messenger RNA (mRNA) and protein expression (Lu et al., 2007). We identified 374 inliers (45%), 407 midliers (49%), and 53 outliers (6%) using this criteria (Fig. 1 and Table S3). This data for the first time suggested that only a fraction (30% of proteins and 20% of RNA) of the targets were present as both RNA and protein forms in a given tissue. However, RNA and protein expression levels correlated reasonably well when the same target was identified in both types of analyses (Gygi et al., 2000).


Janus-like opposing roles of CD47 in autoimmune brain inflammation in humans and mice.

Han MH, Lundgren DH, Jaiswal S, Chao M, Graham KL, Garris CS, Axtell RC, Ho PP, Lock CB, Woodard JI, Brownell SE, Zoudilova M, Hunt JF, Baranzini SE, Butcher EC, Raine CS, Sobel RA, Han DK, Weissman I, Steinman L - J. Exp. Med. (2012)

Identification and comparison of transcriptomic and proteomic landscapes. Comparative expression levels from the overlap of microarray and proteomic analysis of MS lesions. 834 MS UniProt IDs were jointly detected in both microarray and mass spectrometry analysis. RNA and protein expression levels of targets were measured by fluorescent intensity and spectral counts, respectively. Global transcriptomic and proteomic landscapes were compared for the 834 overlapping targets using logs (base 10) of mean relative abundance normalized to a mean of zero. Those proteins with absolute log difference <1 for each of the four lesion types (control [CTL], AP, CAP, and CP) were called inliers (i), <2 midliers (ii), and >2 outliers (iii). Note that an absolute log difference of 1 denotes a one order of magnitude difference in relative abundance.
© Copyright Policy - openaccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC3405500&req=5

fig1: Identification and comparison of transcriptomic and proteomic landscapes. Comparative expression levels from the overlap of microarray and proteomic analysis of MS lesions. 834 MS UniProt IDs were jointly detected in both microarray and mass spectrometry analysis. RNA and protein expression levels of targets were measured by fluorescent intensity and spectral counts, respectively. Global transcriptomic and proteomic landscapes were compared for the 834 overlapping targets using logs (base 10) of mean relative abundance normalized to a mean of zero. Those proteins with absolute log difference <1 for each of the four lesion types (control [CTL], AP, CAP, and CP) were called inliers (i), <2 midliers (ii), and >2 outliers (iii). Note that an absolute log difference of 1 denotes a one order of magnitude difference in relative abundance.
Mentions: We compared transcriptomic and proteomic profiles from the same MS brain tissue to study differential expression of RNA transcripts and proteins during disease progression. Microarray analysis was newly performed for this study. Proteomic experiments were based on the MS brain lesion proteome dataset from our previously published work (Han et al., 2008). Tissue containing acute plaque (AP), chronic active plaque (CAP), and chronic plaque (CP) were analyzed by microarray analysis and by mass spectrometry (Fig. S1). Microarray analysis identified 6,601 RNA targets (Table S1), whereas the corresponding proteomic study identified 2,404 protein targets (Table S2). Only 1,229 RNA targets (of the 6,601 total, ∼20% of identified) mapped to 834 proteins identified in the proteomic study (∼30% of all proteins identified). The majority of the targets (5,372 RNA targets and 1,570 proteins) had no overlap between the two platforms (Fig. S2 and Table S3). We then grouped 834 common targets (identified in both microarray and proteomic platforms) into inliers (RNA expression levels correlate with protein expression levels; relative abundance difference between RNA probe intensities and protein spectral counts were less than one order of magnitude), midliers (RNA expression levels correlate with protein expression levels; relative abundance less than two orders of magnitude), and outliers (RNA expression levels do not correlate with protein expression levels; relative abundance greater than two orders of magnitude) to study concordance between messenger RNA (mRNA) and protein expression (Lu et al., 2007). We identified 374 inliers (45%), 407 midliers (49%), and 53 outliers (6%) using this criteria (Fig. 1 and Table S3). This data for the first time suggested that only a fraction (30% of proteins and 20% of RNA) of the targets were present as both RNA and protein forms in a given tissue. However, RNA and protein expression levels correlated reasonably well when the same target was identified in both types of analyses (Gygi et al., 2000).

Bottom Line: Immunohistochemical studies demonstrate that CD47 is expressed in normal myelin and in foamy macrophages and reactive astrocytes within active MS lesions.In vitro assays demonstrate that blocking CD47 also promotes phagocytosis of myelin and that this effect is dependent on signal regulatory protein α (SIRP-α).Depending on the cell type, location, and disease stage, CD47 has Janus-like roles, with opposing effects on EAE pathogenesis.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA. mayhan@stanford.edu

ABSTRACT
Comparison of transcriptomic and proteomic data from pathologically similar multiple sclerosis (MS) lesions reveals down-regulation of CD47 at the messenger RNA level and low abundance at the protein level. Immunohistochemical studies demonstrate that CD47 is expressed in normal myelin and in foamy macrophages and reactive astrocytes within active MS lesions. We demonstrate that CD47(-/-) mice are refractory to experimental autoimmune encephalomyelitis (EAE), primarily as the result of failure of immune cell activation after immunization with myelin antigen. In contrast, blocking with a monoclonal antibody against CD47 in mice at the peak of paralysis worsens EAE severity and enhances immune activation in the peripheral immune system. In vitro assays demonstrate that blocking CD47 also promotes phagocytosis of myelin and that this effect is dependent on signal regulatory protein α (SIRP-α). Immune regulation and phagocytosis are mechanisms for CD47 signaling in autoimmune neuroinflammation. Depending on the cell type, location, and disease stage, CD47 has Janus-like roles, with opposing effects on EAE pathogenesis.

Show MeSH
Related in: MedlinePlus