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lipid uptake receptor cd36  (MedChemExpress)


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    Structured Review

    MedChemExpress lipid uptake receptor cd36
    Preparation and anti‐atherosclerotic mechanisms of the CuPB@HA nanozyme. (A) Synthesis procedure of CuPB@HA. PB nanoparticles were prepared through a PVP/HCl‐assisted thermal reaction using K 3 [Fe(CN) 6 ] as the precursor, followed by Cu incorporation to obtain CuPB and subsequent HA functionalization to form CuPB@HA. (B) Therapeutic mechanisms. Systemically administered CuPB@HA selectively targets lesional CD44 + macrophages. Upon internalization, it synergistically remodels the plaque microenvironment by scavenging ROS to promote a shift toward an anti‐inflammatory macrophage phenotype and improving macrophage lipid‐handling profiles by downregulating <t>CD36</t> and upregulating ABCA1/ABCG1‐related cholesterol transport mediators, thereby attenuating foam‐cell lipid accumulation. Some elements in the image were sourced from BioRender ( https://app.biorender.com/illustrations/69c95a7d8bdf29a2ebf6bea4 ).
    Lipid Uptake Receptor Cd36, supplied by MedChemExpress, used in various techniques. Bioz Stars score: 94/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/cd36+receptor/CD36+Antibody/pmc13440190-279-28-32
    Average 94 stars, based on 1 article reviews
    lipid uptake receptor cd36 - by Bioz Stars, 2026-10
    94/100 stars

    Images

    1) Product Images from "Copper‐Doped Prussian Blue Nanozymes With Hyaluronic Acid‐Mediated Targeting Alleviate Oxidative Stress and Regulate Cholesterol Handling for Atherosclerosis Therapy"

    Article Title: Copper‐Doped Prussian Blue Nanozymes With Hyaluronic Acid‐Mediated Targeting Alleviate Oxidative Stress and Regulate Cholesterol Handling for Atherosclerosis Therapy

    Journal: Advanced Science

    doi: 10.1002/advs.76976

    Preparation and anti‐atherosclerotic mechanisms of the CuPB@HA nanozyme. (A) Synthesis procedure of CuPB@HA. PB nanoparticles were prepared through a PVP/HCl‐assisted thermal reaction using K 3 [Fe(CN) 6 ] as the precursor, followed by Cu incorporation to obtain CuPB and subsequent HA functionalization to form CuPB@HA. (B) Therapeutic mechanisms. Systemically administered CuPB@HA selectively targets lesional CD44 + macrophages. Upon internalization, it synergistically remodels the plaque microenvironment by scavenging ROS to promote a shift toward an anti‐inflammatory macrophage phenotype and improving macrophage lipid‐handling profiles by downregulating CD36 and upregulating ABCA1/ABCG1‐related cholesterol transport mediators, thereby attenuating foam‐cell lipid accumulation. Some elements in the image were sourced from BioRender ( https://app.biorender.com/illustrations/69c95a7d8bdf29a2ebf6bea4 ).
    Figure Legend Snippet: Preparation and anti‐atherosclerotic mechanisms of the CuPB@HA nanozyme. (A) Synthesis procedure of CuPB@HA. PB nanoparticles were prepared through a PVP/HCl‐assisted thermal reaction using K 3 [Fe(CN) 6 ] as the precursor, followed by Cu incorporation to obtain CuPB and subsequent HA functionalization to form CuPB@HA. (B) Therapeutic mechanisms. Systemically administered CuPB@HA selectively targets lesional CD44 + macrophages. Upon internalization, it synergistically remodels the plaque microenvironment by scavenging ROS to promote a shift toward an anti‐inflammatory macrophage phenotype and improving macrophage lipid‐handling profiles by downregulating CD36 and upregulating ABCA1/ABCG1‐related cholesterol transport mediators, thereby attenuating foam‐cell lipid accumulation. Some elements in the image were sourced from BioRender ( https://app.biorender.com/illustrations/69c95a7d8bdf29a2ebf6bea4 ).

    Techniques Used:

    Single‐cell transcriptomics identifies CD44 as a potential targeting receptor on pathogenic macrophages in atherosclerotic lesions. (A) UMAP projection of the human carotid plaque single‐cell transcriptomic dataset ( GSE253903 ), illustrating the distinct clustering of major immune and stromal cell lineages. (B) Dot plot depicting the expression profiles of cell‐type‐specific marker genes across all identified clusters. (C) Density Plot showing the high expression of CD44. (D) Violin plots demonstrate significantly elevated CD44 expression in macrophages from symptomatic patients compared to asymptomatic patients. (E) UMAP sub‐clustering of the macrophage population into distinct functional subsets. (F) Bar graph showing an increased proportion of inflammatory macrophages and a decreased proportion of Foamy_Trem2 macrophages in symptomatic lesions. (G) Violin plots detailing the differential expression of CD44 across macrophage subtypes between the two clinical groups. (H) Density Plot illustrating the strong co‐expression of CD44 with pathogenic markers (IL1B, NFE2L2, and CD36).
    Figure Legend Snippet: Single‐cell transcriptomics identifies CD44 as a potential targeting receptor on pathogenic macrophages in atherosclerotic lesions. (A) UMAP projection of the human carotid plaque single‐cell transcriptomic dataset ( GSE253903 ), illustrating the distinct clustering of major immune and stromal cell lineages. (B) Dot plot depicting the expression profiles of cell‐type‐specific marker genes across all identified clusters. (C) Density Plot showing the high expression of CD44. (D) Violin plots demonstrate significantly elevated CD44 expression in macrophages from symptomatic patients compared to asymptomatic patients. (E) UMAP sub‐clustering of the macrophage population into distinct functional subsets. (F) Bar graph showing an increased proportion of inflammatory macrophages and a decreased proportion of Foamy_Trem2 macrophages in symptomatic lesions. (G) Violin plots detailing the differential expression of CD44 across macrophage subtypes between the two clinical groups. (H) Density Plot illustrating the strong co‐expression of CD44 with pathogenic markers (IL1B, NFE2L2, and CD36).

    Techniques Used: Single-cell Transcriptomics, Single Cell, Expressing, Marker, Functional Assay, Quantitative Proteomics

    CuPB@HA nanozymes accumulate in atherosclerotic plaques and synergistically remodel lipid metabolism, oxidative stress, and inflammatory polarization in macrophages. (A) Representative in vivo fluorescence images of HFD‐fed ApoE −/− atherosclerotic mice after intravenous administration of Cy5.5‐labeled CuPB or CuPB@HA at 12 and 24 h post‐injection. (B) Ex vivo fluorescence images of major organs, including heart, liver, spleen, lung, and kidney, harvested at corresponding time points after nanozyme administration. (C) Representative confocal fluorescence images of atherosclerotic plaque sections from HFD‐fed ApoE −/− mice showing the spatial association of Cy5.5‐labeled CuPB@HA with CD68‐positive macrophage‐rich regions and CD44‐positive regions. Cy5.5‐labeled CuPB@HA is pseudo‐colored red, CD68 or CD44 is shown in green. (D) Fluorescence microscopy images showing the time‐dependent cellular uptake of FITC‐labeled CuPB and CuPB@HA by macrophages, with or without excess free HA pre‐incubation. FITC‐labeled nanozymes are shown in green, and nuclei are stained with DAPI in blue. (E) Western blot analysis of proteins related to lipid metabolism, oxidative stress, and inflammatory polarization in RAW264.7 macrophages after different treatments. (F) RT‐qPCR analysis of genes related to lipid metabolism, oxidative stress, and inflammatory polarization in RAW264.7 macrophages after different treatments. (G) Representative Oil Red O staining images showing intracellular lipid accumulation in RAW264.7 macrophages after different treatments. (H–J) Representative immunofluorescence images showing the expression of ARG1 (H), iNOS (I), and CD36 (J) in RAW264.7 macrophages after different treatments. (K) Quantitative analysis of cellular uptake fluorescence intensity in Figure 4D. (L) Quantitative analysis of Oil Red O‐positive areas in Figure 4G (n = 3). (M–O) Quantitative fluorescence analysis of ARG1 (M), iNOS (N), and CD36 (O) staining in Figure 4H–J ( n = 5). Quantitative data are presented as the mean ± SD. Statistical significance was assessed via one‐way ANOVA (* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001).
    Figure Legend Snippet: CuPB@HA nanozymes accumulate in atherosclerotic plaques and synergistically remodel lipid metabolism, oxidative stress, and inflammatory polarization in macrophages. (A) Representative in vivo fluorescence images of HFD‐fed ApoE −/− atherosclerotic mice after intravenous administration of Cy5.5‐labeled CuPB or CuPB@HA at 12 and 24 h post‐injection. (B) Ex vivo fluorescence images of major organs, including heart, liver, spleen, lung, and kidney, harvested at corresponding time points after nanozyme administration. (C) Representative confocal fluorescence images of atherosclerotic plaque sections from HFD‐fed ApoE −/− mice showing the spatial association of Cy5.5‐labeled CuPB@HA with CD68‐positive macrophage‐rich regions and CD44‐positive regions. Cy5.5‐labeled CuPB@HA is pseudo‐colored red, CD68 or CD44 is shown in green. (D) Fluorescence microscopy images showing the time‐dependent cellular uptake of FITC‐labeled CuPB and CuPB@HA by macrophages, with or without excess free HA pre‐incubation. FITC‐labeled nanozymes are shown in green, and nuclei are stained with DAPI in blue. (E) Western blot analysis of proteins related to lipid metabolism, oxidative stress, and inflammatory polarization in RAW264.7 macrophages after different treatments. (F) RT‐qPCR analysis of genes related to lipid metabolism, oxidative stress, and inflammatory polarization in RAW264.7 macrophages after different treatments. (G) Representative Oil Red O staining images showing intracellular lipid accumulation in RAW264.7 macrophages after different treatments. (H–J) Representative immunofluorescence images showing the expression of ARG1 (H), iNOS (I), and CD36 (J) in RAW264.7 macrophages after different treatments. (K) Quantitative analysis of cellular uptake fluorescence intensity in Figure 4D. (L) Quantitative analysis of Oil Red O‐positive areas in Figure 4G (n = 3). (M–O) Quantitative fluorescence analysis of ARG1 (M), iNOS (N), and CD36 (O) staining in Figure 4H–J ( n = 5). Quantitative data are presented as the mean ± SD. Statistical significance was assessed via one‐way ANOVA (* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001).

    Techniques Used: In Vivo, Fluorescence, Labeling, Injection, Ex Vivo, Microscopy, Incubation, Staining, Western Blot, Quantitative RT-PCR, Immunofluorescence, Expressing

    Transcriptomic reprogramming of pathogenic macrophages by CuPB@HA nanozymes. (A) Differential expression scatter plot of Model vs. Control, highlighting upregulated DEGs (red, Fold Change > 1.5, FDR < 0.05). (B) GO biological process enrichment of the upregulated DEGs from (A). (C) Differential expression scatter plot of Treat vs. Model, highlighting downregulated DEGs (blue, Fold Change > 1.5, FDR < 0.05). (D) GO biological process enrichment of the downregulated DEGs from (C). (E) Heatmap of representative DEGs for lipid uptake, cholesterol efflux, oxidative stress, and inflammation. (F) Quantitative expression profiles of essential genes selected from (E). Data are mean ± SD ( n = 3). (G) UpSet plot showing the intersection of DEGs between the disease progression and treatment sets. (H) Protein‐protein interaction (PPI) network of the key intersected DEGs. (I) Core PPI sub‐network of highly interconnected hub genes (Cd36, Il1b, Tnf, Il10, Nos2, Arg1, Mmp9).
    Figure Legend Snippet: Transcriptomic reprogramming of pathogenic macrophages by CuPB@HA nanozymes. (A) Differential expression scatter plot of Model vs. Control, highlighting upregulated DEGs (red, Fold Change > 1.5, FDR < 0.05). (B) GO biological process enrichment of the upregulated DEGs from (A). (C) Differential expression scatter plot of Treat vs. Model, highlighting downregulated DEGs (blue, Fold Change > 1.5, FDR < 0.05). (D) GO biological process enrichment of the downregulated DEGs from (C). (E) Heatmap of representative DEGs for lipid uptake, cholesterol efflux, oxidative stress, and inflammation. (F) Quantitative expression profiles of essential genes selected from (E). Data are mean ± SD ( n = 3). (G) UpSet plot showing the intersection of DEGs between the disease progression and treatment sets. (H) Protein‐protein interaction (PPI) network of the key intersected DEGs. (I) Core PPI sub‐network of highly interconnected hub genes (Cd36, Il1b, Tnf, Il10, Nos2, Arg1, Mmp9).

    Techniques Used: Quantitative Proteomics, Control, Expressing, Biomarker Discovery

    CuPB@HA attenuates atherosclerotic plaque burden and promotes plaque stability in HFD‐fed ApoE −/− mice. (A) Schematic of the in vivo experimental design and treatment timeline. (B–E) Serum lipid profiles of mice in different treatment groups, including (B) total cholesterol (TC), (C) triglycerides (TG), (D) low‐density lipoprotein cholesterol (LDL‐C), and (E) high‐density lipoprotein cholesterol (HDL‐C). Data are mean ± SD ( n = 6). Significance was assessed via one‐way ANOVA with Tukey's post hoc test (* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001). (F–J) Representative histological and immunohistochemical images of aortic root cross‐sections (scale bars: 100 µm): (F) Representative Oil Red O (ORO) staining of aortas. (G) ORO staining for lipid accumulation; (H) H&E staining for necrotic core and plaque morphology; (I) Masson's trichrome staining for collagen deposition; and (J) IHC staining for CD36 expression. (K–O) Quantification of lesional characteristics across treatment groups: (K) relative plaque area ( en face ORO), (L) lipid area (aortic root ORO), (M) necrotic core area (H&E), (N) collagen‐to‐plaque ratio (Masson's trichrome), and (O) CD36‐positive area (IHC). Data are presented as the mean ± SD ( n = 6). Significance was assessed via one‐way ANOVA with Tukey's post hoc test (* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001).
    Figure Legend Snippet: CuPB@HA attenuates atherosclerotic plaque burden and promotes plaque stability in HFD‐fed ApoE −/− mice. (A) Schematic of the in vivo experimental design and treatment timeline. (B–E) Serum lipid profiles of mice in different treatment groups, including (B) total cholesterol (TC), (C) triglycerides (TG), (D) low‐density lipoprotein cholesterol (LDL‐C), and (E) high‐density lipoprotein cholesterol (HDL‐C). Data are mean ± SD ( n = 6). Significance was assessed via one‐way ANOVA with Tukey's post hoc test (* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001). (F–J) Representative histological and immunohistochemical images of aortic root cross‐sections (scale bars: 100 µm): (F) Representative Oil Red O (ORO) staining of aortas. (G) ORO staining for lipid accumulation; (H) H&E staining for necrotic core and plaque morphology; (I) Masson's trichrome staining for collagen deposition; and (J) IHC staining for CD36 expression. (K–O) Quantification of lesional characteristics across treatment groups: (K) relative plaque area ( en face ORO), (L) lipid area (aortic root ORO), (M) necrotic core area (H&E), (N) collagen‐to‐plaque ratio (Masson's trichrome), and (O) CD36‐positive area (IHC). Data are presented as the mean ± SD ( n = 6). Significance was assessed via one‐way ANOVA with Tukey's post hoc test (* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001).

    Techniques Used: In Vivo, Immunohistochemical staining, Staining, Immunohistochemistry, Expressing

    Related Articles

    Protein-Protein interactions:

    Article Title: Fatty acid binding protein 4 (FABP4): a key player in neuroinflammation and neuropathic pain.
    Article Snippet: Neuropathic pain (NP) is caused by lesions or diseases of the somatosensory system.. Emerging evidence implicates adipokines in NP pathogenesis, yet the role of fatty acid-binding protein 4 (FABP4) remains unclear.. Using a mouse model of sciatic nerve crush injury, we found that wild-type (WT) mice developed robust NP behaviors, concomitant with significant FABP4 upregulation and extensive macrophage infiltration in the injured nerve.

    In Vitro:

    Article Title: Fatty acid binding protein 4 (FABP4): a key player in neuroinflammation and neuropathic pain.
    Article Snippet: Neuropathic pain (NP) is caused by lesions or diseases of the somatosensory system.. Emerging evidence implicates adipokines in NP pathogenesis, yet the role of fatty acid-binding protein 4 (FABP4) remains unclear.. Using a mouse model of sciatic nerve crush injury, we found that wild-type (WT) mice developed robust NP behaviors, concomitant with significant FABP4 upregulation and extensive macrophage infiltration in the injured nerve.

    Blocking Assay:

    Article Title: Fatty acid binding protein 4 (FABP4): a key player in neuroinflammation and neuropathic pain.
    Article Snippet: Neuropathic pain (NP) is caused by lesions or diseases of the somatosensory system.. Emerging evidence implicates adipokines in NP pathogenesis, yet the role of fatty acid-binding protein 4 (FABP4) remains unclear.. Using a mouse model of sciatic nerve crush injury, we found that wild-type (WT) mice developed robust NP behaviors, concomitant with significant FABP4 upregulation and extensive macrophage infiltration in the injured nerve.

    Translocation Assay:

    Article Title: Fatty acid binding protein 4 (FABP4): a key player in neuroinflammation and neuropathic pain.
    Article Snippet: Neuropathic pain (NP) is caused by lesions or diseases of the somatosensory system.. Emerging evidence implicates adipokines in NP pathogenesis, yet the role of fatty acid-binding protein 4 (FABP4) remains unclear.. Using a mouse model of sciatic nerve crush injury, we found that wild-type (WT) mice developed robust NP behaviors, concomitant with significant FABP4 upregulation and extensive macrophage infiltration in the injured nerve.



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    Image Search Results


    Preparation and anti‐atherosclerotic mechanisms of the CuPB@HA nanozyme. (A) Synthesis procedure of CuPB@HA. PB nanoparticles were prepared through a PVP/HCl‐assisted thermal reaction using K 3 [Fe(CN) 6 ] as the precursor, followed by Cu incorporation to obtain CuPB and subsequent HA functionalization to form CuPB@HA. (B) Therapeutic mechanisms. Systemically administered CuPB@HA selectively targets lesional CD44 + macrophages. Upon internalization, it synergistically remodels the plaque microenvironment by scavenging ROS to promote a shift toward an anti‐inflammatory macrophage phenotype and improving macrophage lipid‐handling profiles by downregulating CD36 and upregulating ABCA1/ABCG1‐related cholesterol transport mediators, thereby attenuating foam‐cell lipid accumulation. Some elements in the image were sourced from BioRender ( https://app.biorender.com/illustrations/69c95a7d8bdf29a2ebf6bea4 ).

    Journal: Advanced Science

    Article Title: Copper‐Doped Prussian Blue Nanozymes With Hyaluronic Acid‐Mediated Targeting Alleviate Oxidative Stress and Regulate Cholesterol Handling for Atherosclerosis Therapy

    doi: 10.1002/advs.76976

    Figure Lengend Snippet: Preparation and anti‐atherosclerotic mechanisms of the CuPB@HA nanozyme. (A) Synthesis procedure of CuPB@HA. PB nanoparticles were prepared through a PVP/HCl‐assisted thermal reaction using K 3 [Fe(CN) 6 ] as the precursor, followed by Cu incorporation to obtain CuPB and subsequent HA functionalization to form CuPB@HA. (B) Therapeutic mechanisms. Systemically administered CuPB@HA selectively targets lesional CD44 + macrophages. Upon internalization, it synergistically remodels the plaque microenvironment by scavenging ROS to promote a shift toward an anti‐inflammatory macrophage phenotype and improving macrophage lipid‐handling profiles by downregulating CD36 and upregulating ABCA1/ABCG1‐related cholesterol transport mediators, thereby attenuating foam‐cell lipid accumulation. Some elements in the image were sourced from BioRender ( https://app.biorender.com/illustrations/69c95a7d8bdf29a2ebf6bea4 ).

    Article Snippet: Subsequently, the samples were incubated overnight at 4°C with specific primary antibodies targeting the M1 marker iNOS (Proteintech, 22226‐1‐AP,1:400), the M2 marker ARG1 (Proteintech, 16001‐1‐AP, 1:400), or the lipid uptake receptor CD36 (MedChemExpress, HY‐P86458, 1:400).

    Techniques:

    Single‐cell transcriptomics identifies CD44 as a potential targeting receptor on pathogenic macrophages in atherosclerotic lesions. (A) UMAP projection of the human carotid plaque single‐cell transcriptomic dataset ( GSE253903 ), illustrating the distinct clustering of major immune and stromal cell lineages. (B) Dot plot depicting the expression profiles of cell‐type‐specific marker genes across all identified clusters. (C) Density Plot showing the high expression of CD44. (D) Violin plots demonstrate significantly elevated CD44 expression in macrophages from symptomatic patients compared to asymptomatic patients. (E) UMAP sub‐clustering of the macrophage population into distinct functional subsets. (F) Bar graph showing an increased proportion of inflammatory macrophages and a decreased proportion of Foamy_Trem2 macrophages in symptomatic lesions. (G) Violin plots detailing the differential expression of CD44 across macrophage subtypes between the two clinical groups. (H) Density Plot illustrating the strong co‐expression of CD44 with pathogenic markers (IL1B, NFE2L2, and CD36).

    Journal: Advanced Science

    Article Title: Copper‐Doped Prussian Blue Nanozymes With Hyaluronic Acid‐Mediated Targeting Alleviate Oxidative Stress and Regulate Cholesterol Handling for Atherosclerosis Therapy

    doi: 10.1002/advs.76976

    Figure Lengend Snippet: Single‐cell transcriptomics identifies CD44 as a potential targeting receptor on pathogenic macrophages in atherosclerotic lesions. (A) UMAP projection of the human carotid plaque single‐cell transcriptomic dataset ( GSE253903 ), illustrating the distinct clustering of major immune and stromal cell lineages. (B) Dot plot depicting the expression profiles of cell‐type‐specific marker genes across all identified clusters. (C) Density Plot showing the high expression of CD44. (D) Violin plots demonstrate significantly elevated CD44 expression in macrophages from symptomatic patients compared to asymptomatic patients. (E) UMAP sub‐clustering of the macrophage population into distinct functional subsets. (F) Bar graph showing an increased proportion of inflammatory macrophages and a decreased proportion of Foamy_Trem2 macrophages in symptomatic lesions. (G) Violin plots detailing the differential expression of CD44 across macrophage subtypes between the two clinical groups. (H) Density Plot illustrating the strong co‐expression of CD44 with pathogenic markers (IL1B, NFE2L2, and CD36).

    Article Snippet: Subsequently, the samples were incubated overnight at 4°C with specific primary antibodies targeting the M1 marker iNOS (Proteintech, 22226‐1‐AP,1:400), the M2 marker ARG1 (Proteintech, 16001‐1‐AP, 1:400), or the lipid uptake receptor CD36 (MedChemExpress, HY‐P86458, 1:400).

    Techniques: Single-cell Transcriptomics, Single Cell, Expressing, Marker, Functional Assay, Quantitative Proteomics

    CuPB@HA nanozymes accumulate in atherosclerotic plaques and synergistically remodel lipid metabolism, oxidative stress, and inflammatory polarization in macrophages. (A) Representative in vivo fluorescence images of HFD‐fed ApoE −/− atherosclerotic mice after intravenous administration of Cy5.5‐labeled CuPB or CuPB@HA at 12 and 24 h post‐injection. (B) Ex vivo fluorescence images of major organs, including heart, liver, spleen, lung, and kidney, harvested at corresponding time points after nanozyme administration. (C) Representative confocal fluorescence images of atherosclerotic plaque sections from HFD‐fed ApoE −/− mice showing the spatial association of Cy5.5‐labeled CuPB@HA with CD68‐positive macrophage‐rich regions and CD44‐positive regions. Cy5.5‐labeled CuPB@HA is pseudo‐colored red, CD68 or CD44 is shown in green. (D) Fluorescence microscopy images showing the time‐dependent cellular uptake of FITC‐labeled CuPB and CuPB@HA by macrophages, with or without excess free HA pre‐incubation. FITC‐labeled nanozymes are shown in green, and nuclei are stained with DAPI in blue. (E) Western blot analysis of proteins related to lipid metabolism, oxidative stress, and inflammatory polarization in RAW264.7 macrophages after different treatments. (F) RT‐qPCR analysis of genes related to lipid metabolism, oxidative stress, and inflammatory polarization in RAW264.7 macrophages after different treatments. (G) Representative Oil Red O staining images showing intracellular lipid accumulation in RAW264.7 macrophages after different treatments. (H–J) Representative immunofluorescence images showing the expression of ARG1 (H), iNOS (I), and CD36 (J) in RAW264.7 macrophages after different treatments. (K) Quantitative analysis of cellular uptake fluorescence intensity in Figure 4D. (L) Quantitative analysis of Oil Red O‐positive areas in Figure 4G (n = 3). (M–O) Quantitative fluorescence analysis of ARG1 (M), iNOS (N), and CD36 (O) staining in Figure 4H–J ( n = 5). Quantitative data are presented as the mean ± SD. Statistical significance was assessed via one‐way ANOVA (* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001).

    Journal: Advanced Science

    Article Title: Copper‐Doped Prussian Blue Nanozymes With Hyaluronic Acid‐Mediated Targeting Alleviate Oxidative Stress and Regulate Cholesterol Handling for Atherosclerosis Therapy

    doi: 10.1002/advs.76976

    Figure Lengend Snippet: CuPB@HA nanozymes accumulate in atherosclerotic plaques and synergistically remodel lipid metabolism, oxidative stress, and inflammatory polarization in macrophages. (A) Representative in vivo fluorescence images of HFD‐fed ApoE −/− atherosclerotic mice after intravenous administration of Cy5.5‐labeled CuPB or CuPB@HA at 12 and 24 h post‐injection. (B) Ex vivo fluorescence images of major organs, including heart, liver, spleen, lung, and kidney, harvested at corresponding time points after nanozyme administration. (C) Representative confocal fluorescence images of atherosclerotic plaque sections from HFD‐fed ApoE −/− mice showing the spatial association of Cy5.5‐labeled CuPB@HA with CD68‐positive macrophage‐rich regions and CD44‐positive regions. Cy5.5‐labeled CuPB@HA is pseudo‐colored red, CD68 or CD44 is shown in green. (D) Fluorescence microscopy images showing the time‐dependent cellular uptake of FITC‐labeled CuPB and CuPB@HA by macrophages, with or without excess free HA pre‐incubation. FITC‐labeled nanozymes are shown in green, and nuclei are stained with DAPI in blue. (E) Western blot analysis of proteins related to lipid metabolism, oxidative stress, and inflammatory polarization in RAW264.7 macrophages after different treatments. (F) RT‐qPCR analysis of genes related to lipid metabolism, oxidative stress, and inflammatory polarization in RAW264.7 macrophages after different treatments. (G) Representative Oil Red O staining images showing intracellular lipid accumulation in RAW264.7 macrophages after different treatments. (H–J) Representative immunofluorescence images showing the expression of ARG1 (H), iNOS (I), and CD36 (J) in RAW264.7 macrophages after different treatments. (K) Quantitative analysis of cellular uptake fluorescence intensity in Figure 4D. (L) Quantitative analysis of Oil Red O‐positive areas in Figure 4G (n = 3). (M–O) Quantitative fluorescence analysis of ARG1 (M), iNOS (N), and CD36 (O) staining in Figure 4H–J ( n = 5). Quantitative data are presented as the mean ± SD. Statistical significance was assessed via one‐way ANOVA (* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001).

    Article Snippet: Subsequently, the samples were incubated overnight at 4°C with specific primary antibodies targeting the M1 marker iNOS (Proteintech, 22226‐1‐AP,1:400), the M2 marker ARG1 (Proteintech, 16001‐1‐AP, 1:400), or the lipid uptake receptor CD36 (MedChemExpress, HY‐P86458, 1:400).

    Techniques: In Vivo, Fluorescence, Labeling, Injection, Ex Vivo, Microscopy, Incubation, Staining, Western Blot, Quantitative RT-PCR, Immunofluorescence, Expressing

    Transcriptomic reprogramming of pathogenic macrophages by CuPB@HA nanozymes. (A) Differential expression scatter plot of Model vs. Control, highlighting upregulated DEGs (red, Fold Change > 1.5, FDR < 0.05). (B) GO biological process enrichment of the upregulated DEGs from (A). (C) Differential expression scatter plot of Treat vs. Model, highlighting downregulated DEGs (blue, Fold Change > 1.5, FDR < 0.05). (D) GO biological process enrichment of the downregulated DEGs from (C). (E) Heatmap of representative DEGs for lipid uptake, cholesterol efflux, oxidative stress, and inflammation. (F) Quantitative expression profiles of essential genes selected from (E). Data are mean ± SD ( n = 3). (G) UpSet plot showing the intersection of DEGs between the disease progression and treatment sets. (H) Protein‐protein interaction (PPI) network of the key intersected DEGs. (I) Core PPI sub‐network of highly interconnected hub genes (Cd36, Il1b, Tnf, Il10, Nos2, Arg1, Mmp9).

    Journal: Advanced Science

    Article Title: Copper‐Doped Prussian Blue Nanozymes With Hyaluronic Acid‐Mediated Targeting Alleviate Oxidative Stress and Regulate Cholesterol Handling for Atherosclerosis Therapy

    doi: 10.1002/advs.76976

    Figure Lengend Snippet: Transcriptomic reprogramming of pathogenic macrophages by CuPB@HA nanozymes. (A) Differential expression scatter plot of Model vs. Control, highlighting upregulated DEGs (red, Fold Change > 1.5, FDR < 0.05). (B) GO biological process enrichment of the upregulated DEGs from (A). (C) Differential expression scatter plot of Treat vs. Model, highlighting downregulated DEGs (blue, Fold Change > 1.5, FDR < 0.05). (D) GO biological process enrichment of the downregulated DEGs from (C). (E) Heatmap of representative DEGs for lipid uptake, cholesterol efflux, oxidative stress, and inflammation. (F) Quantitative expression profiles of essential genes selected from (E). Data are mean ± SD ( n = 3). (G) UpSet plot showing the intersection of DEGs between the disease progression and treatment sets. (H) Protein‐protein interaction (PPI) network of the key intersected DEGs. (I) Core PPI sub‐network of highly interconnected hub genes (Cd36, Il1b, Tnf, Il10, Nos2, Arg1, Mmp9).

    Article Snippet: Subsequently, the samples were incubated overnight at 4°C with specific primary antibodies targeting the M1 marker iNOS (Proteintech, 22226‐1‐AP,1:400), the M2 marker ARG1 (Proteintech, 16001‐1‐AP, 1:400), or the lipid uptake receptor CD36 (MedChemExpress, HY‐P86458, 1:400).

    Techniques: Quantitative Proteomics, Control, Expressing, Biomarker Discovery

    CuPB@HA attenuates atherosclerotic plaque burden and promotes plaque stability in HFD‐fed ApoE −/− mice. (A) Schematic of the in vivo experimental design and treatment timeline. (B–E) Serum lipid profiles of mice in different treatment groups, including (B) total cholesterol (TC), (C) triglycerides (TG), (D) low‐density lipoprotein cholesterol (LDL‐C), and (E) high‐density lipoprotein cholesterol (HDL‐C). Data are mean ± SD ( n = 6). Significance was assessed via one‐way ANOVA with Tukey's post hoc test (* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001). (F–J) Representative histological and immunohistochemical images of aortic root cross‐sections (scale bars: 100 µm): (F) Representative Oil Red O (ORO) staining of aortas. (G) ORO staining for lipid accumulation; (H) H&E staining for necrotic core and plaque morphology; (I) Masson's trichrome staining for collagen deposition; and (J) IHC staining for CD36 expression. (K–O) Quantification of lesional characteristics across treatment groups: (K) relative plaque area ( en face ORO), (L) lipid area (aortic root ORO), (M) necrotic core area (H&E), (N) collagen‐to‐plaque ratio (Masson's trichrome), and (O) CD36‐positive area (IHC). Data are presented as the mean ± SD ( n = 6). Significance was assessed via one‐way ANOVA with Tukey's post hoc test (* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001).

    Journal: Advanced Science

    Article Title: Copper‐Doped Prussian Blue Nanozymes With Hyaluronic Acid‐Mediated Targeting Alleviate Oxidative Stress and Regulate Cholesterol Handling for Atherosclerosis Therapy

    doi: 10.1002/advs.76976

    Figure Lengend Snippet: CuPB@HA attenuates atherosclerotic plaque burden and promotes plaque stability in HFD‐fed ApoE −/− mice. (A) Schematic of the in vivo experimental design and treatment timeline. (B–E) Serum lipid profiles of mice in different treatment groups, including (B) total cholesterol (TC), (C) triglycerides (TG), (D) low‐density lipoprotein cholesterol (LDL‐C), and (E) high‐density lipoprotein cholesterol (HDL‐C). Data are mean ± SD ( n = 6). Significance was assessed via one‐way ANOVA with Tukey's post hoc test (* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001). (F–J) Representative histological and immunohistochemical images of aortic root cross‐sections (scale bars: 100 µm): (F) Representative Oil Red O (ORO) staining of aortas. (G) ORO staining for lipid accumulation; (H) H&E staining for necrotic core and plaque morphology; (I) Masson's trichrome staining for collagen deposition; and (J) IHC staining for CD36 expression. (K–O) Quantification of lesional characteristics across treatment groups: (K) relative plaque area ( en face ORO), (L) lipid area (aortic root ORO), (M) necrotic core area (H&E), (N) collagen‐to‐plaque ratio (Masson's trichrome), and (O) CD36‐positive area (IHC). Data are presented as the mean ± SD ( n = 6). Significance was assessed via one‐way ANOVA with Tukey's post hoc test (* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001).

    Article Snippet: Subsequently, the samples were incubated overnight at 4°C with specific primary antibodies targeting the M1 marker iNOS (Proteintech, 22226‐1‐AP,1:400), the M2 marker ARG1 (Proteintech, 16001‐1‐AP, 1:400), or the lipid uptake receptor CD36 (MedChemExpress, HY‐P86458, 1:400).

    Techniques: In Vivo, Immunohistochemical staining, Staining, Immunohistochemistry, Expressing

    Journal: Cell Reports Medicine

    Article Title: Lipolysis engages CD36 to promote ZBP1-mediated necroptosis-impairing lung regeneration in COPD

    doi: 10.1016/j.xcrm.2024.101732

    Figure Lengend Snippet:

    Article Snippet: Scavenger Receptor B2/CD36 Monoclonal Antibody (Clone JC63.1) , cayman , Cat#10009893; RRID: AB_10613953.

    Techniques: Control, Recombinant, Selection, Membrane, Enzyme-linked Immunosorbent Assay, Quantitation Assay, Software

    Real-time bioluminescence imaging of theTLR2 signals after LPS challenge. ( a ) Schematic presentation of experimental protocol used for the treatment with anti-CD36 antibodies in P8-P9 mice. ( b – j ) Representative images of the bioluminescent signals recorded from the brains of living P10–P12 TLR2-luc-GFP mice treated with anti-CD36 antibody ( b – d ), LPS ( e – g ) or pre-treated with anti-CD36 antibody followed by systemic LPS injection ( h – j ). Note that a robust TLR2 induction 24 h post-LPS treatment is significantly reduced when the pups are pre-treated with anti-CD36. The scales on the right are the colour maps for source intensity. The scale ranges differ at individual ages. ( k ) Quantification of the in vivo bioluminescence data from P8–P12 (in photons per second, p/s). One way ANOVA (anti-CD36 vs LPS *p ≤ 0.05, LPS vs anti-CD36 + LPS *p ≤ 0.05).

    Journal: Scientific Reports

    Article Title: CD36 neutralisation blunts TLR2-IRF7 but not IRF3 pathway in neonatal mouse brain and immature human microglia following innate immune challenge

    doi: 10.1038/s41598-023-29423-0

    Figure Lengend Snippet: Real-time bioluminescence imaging of theTLR2 signals after LPS challenge. ( a ) Schematic presentation of experimental protocol used for the treatment with anti-CD36 antibodies in P8-P9 mice. ( b – j ) Representative images of the bioluminescent signals recorded from the brains of living P10–P12 TLR2-luc-GFP mice treated with anti-CD36 antibody ( b – d ), LPS ( e – g ) or pre-treated with anti-CD36 antibody followed by systemic LPS injection ( h – j ). Note that a robust TLR2 induction 24 h post-LPS treatment is significantly reduced when the pups are pre-treated with anti-CD36. The scales on the right are the colour maps for source intensity. The scale ranges differ at individual ages. ( k ) Quantification of the in vivo bioluminescence data from P8–P12 (in photons per second, p/s). One way ANOVA (anti-CD36 vs LPS *p ≤ 0.05, LPS vs anti-CD36 + LPS *p ≤ 0.05).

    Article Snippet: By using a specific CD36 receptor blocking antibody in conjunction with biophotonic/bioluminescence imaging in the live TLR2-luc GFP mice, here we show that systemic delivery of an anti-CD36 antibody completely blunts TLR2 induction following LPS-mediated innate immune challenge in P9 brain.

    Techniques: Imaging, Injection, In Vivo

    Neonatal microglia demonstrate a reduced classical phenotype when treated with anti-CD36 antibody. ( a – v ) Immunostaining of neonatal brain sections with classical and alternative microglia phenotypic markers ( a – r ), with astrocyte marker GFAP ( s , t ) and IB4 ( u , v ). ( w ) Area of the cortical section analysed and quantification of CD68, MHC II, GFAP and IB4 expression ( x ). Microglial cells (Iba1 +) show increased expression of the classical activation markers CD86 ( a – f ) and MHCII ( g – l ) after systemic LPS injection (x). Their expression is significantly diminished when the pups are pre-treated with anti-CD36 antibody ( d – f and j – l ). Quantification of the signals in arbitrary units shows an overall decrease in expression of the classical markers when the pups are injected with anti-CD36 before the LPS treatment ( x ). Microglia in both the groups did not express the alternative microglial activation marker CD206 ( m – r ). No significant decrease was observed with the astrocytic marker GFAP in LPS and anti-CD36 + LPS treated pups ( s , t , x ). A non-significant decrease in the expression of IB4 is observed in the anti-CD36 + LPS pups when compared to the LPS treated pups ( u , v , x ). Scale bar is 100 µm. Unpaired t test with Welch's correction ***p ≤ 0.001; **p ≤ 0.01.

    Journal: Scientific Reports

    Article Title: CD36 neutralisation blunts TLR2-IRF7 but not IRF3 pathway in neonatal mouse brain and immature human microglia following innate immune challenge

    doi: 10.1038/s41598-023-29423-0

    Figure Lengend Snippet: Neonatal microglia demonstrate a reduced classical phenotype when treated with anti-CD36 antibody. ( a – v ) Immunostaining of neonatal brain sections with classical and alternative microglia phenotypic markers ( a – r ), with astrocyte marker GFAP ( s , t ) and IB4 ( u , v ). ( w ) Area of the cortical section analysed and quantification of CD68, MHC II, GFAP and IB4 expression ( x ). Microglial cells (Iba1 +) show increased expression of the classical activation markers CD86 ( a – f ) and MHCII ( g – l ) after systemic LPS injection (x). Their expression is significantly diminished when the pups are pre-treated with anti-CD36 antibody ( d – f and j – l ). Quantification of the signals in arbitrary units shows an overall decrease in expression of the classical markers when the pups are injected with anti-CD36 before the LPS treatment ( x ). Microglia in both the groups did not express the alternative microglial activation marker CD206 ( m – r ). No significant decrease was observed with the astrocytic marker GFAP in LPS and anti-CD36 + LPS treated pups ( s , t , x ). A non-significant decrease in the expression of IB4 is observed in the anti-CD36 + LPS pups when compared to the LPS treated pups ( u , v , x ). Scale bar is 100 µm. Unpaired t test with Welch's correction ***p ≤ 0.001; **p ≤ 0.01.

    Article Snippet: By using a specific CD36 receptor blocking antibody in conjunction with biophotonic/bioluminescence imaging in the live TLR2-luc GFP mice, here we show that systemic delivery of an anti-CD36 antibody completely blunts TLR2 induction following LPS-mediated innate immune challenge in P9 brain.

    Techniques: Immunostaining, Marker, Expressing, Activation Assay, Injection

    Treatment with anti-CD36 antibody reduces expression of inflammatory mediators after LPS challenge. ( a – x ) Protein expression of 24 inflammatory cytokines reveals a significant reduction in cytokines ( a ) TNF-a, ( b ) IFN-g, ( c ) IL-1b, ( e ) IL-6, ( f ) IL-10, ( h ) IL-13, ( i ) IL-17, along with ( k ) GCSF, ( l ) GM-CSF, ( m ) MCP-1 and chemokines ( p ) CCL3, ( r ) CCL11 ( s ) CCL24, ( u ) CXCL1, ( v ) CXCL9 and ( x ) CXCL13 when the pups are treated with anti-CD36 before LPS. Unpaired t-test ***p ≤ 0.001; **p ≤ 0.01 and *p ≤ 0.05.

    Journal: Scientific Reports

    Article Title: CD36 neutralisation blunts TLR2-IRF7 but not IRF3 pathway in neonatal mouse brain and immature human microglia following innate immune challenge

    doi: 10.1038/s41598-023-29423-0

    Figure Lengend Snippet: Treatment with anti-CD36 antibody reduces expression of inflammatory mediators after LPS challenge. ( a – x ) Protein expression of 24 inflammatory cytokines reveals a significant reduction in cytokines ( a ) TNF-a, ( b ) IFN-g, ( c ) IL-1b, ( e ) IL-6, ( f ) IL-10, ( h ) IL-13, ( i ) IL-17, along with ( k ) GCSF, ( l ) GM-CSF, ( m ) MCP-1 and chemokines ( p ) CCL3, ( r ) CCL11 ( s ) CCL24, ( u ) CXCL1, ( v ) CXCL9 and ( x ) CXCL13 when the pups are treated with anti-CD36 before LPS. Unpaired t-test ***p ≤ 0.001; **p ≤ 0.01 and *p ≤ 0.05.

    Article Snippet: By using a specific CD36 receptor blocking antibody in conjunction with biophotonic/bioluminescence imaging in the live TLR2-luc GFP mice, here we show that systemic delivery of an anti-CD36 antibody completely blunts TLR2 induction following LPS-mediated innate immune challenge in P9 brain.

    Techniques: Expressing

    Blocking CD36 significantly alters TLR-2 and TLR-3 signalling. ( a ) Western blot of whole brain protein lysates from the control (saline), anti-CD36, LPS and anti-CD36 + LPS injected pups. ( b ) Iba1, ( c ) TLR2, ( d ) TLR3, ( e ) IRF7, ( f ) phosrpho-P65 expression levels were quantified in different conditions. GAPDH was used as loading control A significant increase in the levels of all the tested proteins is observed after LPS injection. Treating the pups with anti-CD36 before LPS injections considerably reduces their expression to the level as observed in the control pups. ( g ) Western blot of total protein lysates from the control, anti-CD36, LPS and anti-CD36 + LPS injected mice. ( h ) TLR4, ( i ) IRF3, ( j ) iNOS expression levels were quantified in above-described conditions. GAPDH is used as loading control. A significant increase in the levels of all the proteins tested is observed after LPS injection when compared to control and anti-CD36 treated mice. Of note, LPS-mediated increase in expression of TLR4/IRF3 is maintained in the anti-CD36 + LPS treated group. The entire data was presented as mean ± SEM and statistical significance between the groups was achieved using one-way ANOVA with Tukey’s multiple comparison test (ctl vs LPS, ctl vs anti-CD36, LPS vs anti-CD36 + LPS) and depicted as ****p ≤ 0.0001; ***p ≤ 0.001; **p ≤ 0.01 and *p ≤ 0.05.

    Journal: Scientific Reports

    Article Title: CD36 neutralisation blunts TLR2-IRF7 but not IRF3 pathway in neonatal mouse brain and immature human microglia following innate immune challenge

    doi: 10.1038/s41598-023-29423-0

    Figure Lengend Snippet: Blocking CD36 significantly alters TLR-2 and TLR-3 signalling. ( a ) Western blot of whole brain protein lysates from the control (saline), anti-CD36, LPS and anti-CD36 + LPS injected pups. ( b ) Iba1, ( c ) TLR2, ( d ) TLR3, ( e ) IRF7, ( f ) phosrpho-P65 expression levels were quantified in different conditions. GAPDH was used as loading control A significant increase in the levels of all the tested proteins is observed after LPS injection. Treating the pups with anti-CD36 before LPS injections considerably reduces their expression to the level as observed in the control pups. ( g ) Western blot of total protein lysates from the control, anti-CD36, LPS and anti-CD36 + LPS injected mice. ( h ) TLR4, ( i ) IRF3, ( j ) iNOS expression levels were quantified in above-described conditions. GAPDH is used as loading control. A significant increase in the levels of all the proteins tested is observed after LPS injection when compared to control and anti-CD36 treated mice. Of note, LPS-mediated increase in expression of TLR4/IRF3 is maintained in the anti-CD36 + LPS treated group. The entire data was presented as mean ± SEM and statistical significance between the groups was achieved using one-way ANOVA with Tukey’s multiple comparison test (ctl vs LPS, ctl vs anti-CD36, LPS vs anti-CD36 + LPS) and depicted as ****p ≤ 0.0001; ***p ≤ 0.001; **p ≤ 0.01 and *p ≤ 0.05.

    Article Snippet: By using a specific CD36 receptor blocking antibody in conjunction with biophotonic/bioluminescence imaging in the live TLR2-luc GFP mice, here we show that systemic delivery of an anti-CD36 antibody completely blunts TLR2 induction following LPS-mediated innate immune challenge in P9 brain.

    Techniques: Blocking Assay, Western Blot, Control, Saline, Injection, Expressing, Comparison

    Blocking CD36 in human immature microglia affects the TLR3, TLR2-IRF7 but not TLR4/IRF-3 pathway. ( a ) Western blot of total protein lysates of HMC3 cells treated with anti-CD36 antibody, followed by LPS treatment after 24 h. Expression levels of ( b ) Iba-1, ( c ) TLR2, ( d ) TLR 3 along with ( e ) IRF 7, ( f ) p-P65, ( g ) TLR 4, ( h ) IRF-3, ( i ) iNOS were analysed and quantified. GAPDH was used as loading control. LPS treatment increases expression levels of all measured proteins while pre-treatment withanti-CD36 antibody prevented increase in their expression levels. Expressions of ( g ) TLR 4 and ( h ) IRF-3 ( i ) were not affected by anti-CD36 antibody pre-treatment.. Entire data in the figure was presented as mean ± SEM and statistical significance between the groups was achieved using one-way ANOVA with Tukey multiple comparison test and depicted as ****p ≤ 0.0001; ***p ≤ 0.001; **p ≤ 0.01 and *p ≤ 0.05.

    Journal: Scientific Reports

    Article Title: CD36 neutralisation blunts TLR2-IRF7 but not IRF3 pathway in neonatal mouse brain and immature human microglia following innate immune challenge

    doi: 10.1038/s41598-023-29423-0

    Figure Lengend Snippet: Blocking CD36 in human immature microglia affects the TLR3, TLR2-IRF7 but not TLR4/IRF-3 pathway. ( a ) Western blot of total protein lysates of HMC3 cells treated with anti-CD36 antibody, followed by LPS treatment after 24 h. Expression levels of ( b ) Iba-1, ( c ) TLR2, ( d ) TLR 3 along with ( e ) IRF 7, ( f ) p-P65, ( g ) TLR 4, ( h ) IRF-3, ( i ) iNOS were analysed and quantified. GAPDH was used as loading control. LPS treatment increases expression levels of all measured proteins while pre-treatment withanti-CD36 antibody prevented increase in their expression levels. Expressions of ( g ) TLR 4 and ( h ) IRF-3 ( i ) were not affected by anti-CD36 antibody pre-treatment.. Entire data in the figure was presented as mean ± SEM and statistical significance between the groups was achieved using one-way ANOVA with Tukey multiple comparison test and depicted as ****p ≤ 0.0001; ***p ≤ 0.001; **p ≤ 0.01 and *p ≤ 0.05.

    Article Snippet: By using a specific CD36 receptor blocking antibody in conjunction with biophotonic/bioluminescence imaging in the live TLR2-luc GFP mice, here we show that systemic delivery of an anti-CD36 antibody completely blunts TLR2 induction following LPS-mediated innate immune challenge in P9 brain.

    Techniques: Blocking Assay, Western Blot, Expressing, Control, Comparison

    CD36 mediated-effects on TLR signalling pathways in neonatal brain and immature human microglia. A diagram showing the mechanism of the scavenger receptor CD36-TLR mediated signalling in neonatal mouse brain as well as human microglia. ( a ) schematic representation of the methodology used. Neonatal mice and HMC3 cells were treated with LPS or anti-CD36 + LPS. ( b ) Upon activation by various ligands, the TLR2-CD36 mediated MyD88 dependent pathway activates transcription of various inflammatory cytokines. Activated endosomal TLR3 activates IRF3, which promotes transcription of the genes related to type 1 Interferons. Interestingly, IRF3 is also activated by TLR4, through the TRAM-TRIFF complex mediated cascade. ( c ) Blocking CD36 with the antibody prior to the LPS stimulation affects the TLR2 and TLR3 mediated signalling while the TLR4-IRF3 pathway continues to contribute to inflammation in both neonatal mouse as well as in human microglial cells. The schematic was created with BioRender.com.

    Journal: Scientific Reports

    Article Title: CD36 neutralisation blunts TLR2-IRF7 but not IRF3 pathway in neonatal mouse brain and immature human microglia following innate immune challenge

    doi: 10.1038/s41598-023-29423-0

    Figure Lengend Snippet: CD36 mediated-effects on TLR signalling pathways in neonatal brain and immature human microglia. A diagram showing the mechanism of the scavenger receptor CD36-TLR mediated signalling in neonatal mouse brain as well as human microglia. ( a ) schematic representation of the methodology used. Neonatal mice and HMC3 cells were treated with LPS or anti-CD36 + LPS. ( b ) Upon activation by various ligands, the TLR2-CD36 mediated MyD88 dependent pathway activates transcription of various inflammatory cytokines. Activated endosomal TLR3 activates IRF3, which promotes transcription of the genes related to type 1 Interferons. Interestingly, IRF3 is also activated by TLR4, through the TRAM-TRIFF complex mediated cascade. ( c ) Blocking CD36 with the antibody prior to the LPS stimulation affects the TLR2 and TLR3 mediated signalling while the TLR4-IRF3 pathway continues to contribute to inflammation in both neonatal mouse as well as in human microglial cells. The schematic was created with BioRender.com.

    Article Snippet: By using a specific CD36 receptor blocking antibody in conjunction with biophotonic/bioluminescence imaging in the live TLR2-luc GFP mice, here we show that systemic delivery of an anti-CD36 antibody completely blunts TLR2 induction following LPS-mediated innate immune challenge in P9 brain.

    Techniques: Activation Assay, Blocking Assay