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single donor human umbilical vein endothelial cell huvec lines  (PromoCell)


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    PromoCell single donor human umbilical vein endothelial cell huvec lines
    Machine Learning predicts the effect of genetic variants in coronary artery cell types. A. REnformer was trained on publicly available scATAC data from primary coronary artery tissue samples. REnformer was then used to predict the genome accessibility for 9 coronary artery cell types. An example locus around the MMP2 gene is shown. The tracks show the ground truth scATAC data (labelled “ATAC” followed by the cell type), and directly below the REnformer prediction for that cell type. B. The effect on genome accessibility of 21,348 CAD associated lead SNPs and high linkage disequilibrium candidate causal SNPs is predicted by REnformer across 9 coronary artery cell types as well as <t>HUVECs.</t> Predicted coverage (Y-axis) shows the predicted chromatin accessibility at a given SNP position. Alt-Ref prediction (X-axis) shows the predicted effect size of the candidate CAD SNP. Empirical significance cut-offs of 0.01 were calculated for each cell type; variants below the cut-off (loss of function) are coloured blue, variants above the cut-off (gain of function) are coloured red. C. Example REnformer predictions at the chr1:q25.1 locus for SNP rs1879454 (reference allele = C, alternate allele =A). The position of rs1879454 is shown by a blue triangle. D. The prediction difference for rs1879454 where the reference allele has been subtracted from the alternate allele. E. Feature attribution for the REnformer predictions in panel C. The relative contribution of each base pair in the reference and alternate genome to the prediction is shown by height (positive or negative) of the relevant letter. The variant position is shown by a blue triangle.
    Single Donor Human Umbilical Vein Endothelial Cell Huvec Lines, supplied by PromoCell, used in various techniques. Bioz Stars score: 99/100, based on 2211 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    1) Product Images from "Machine Learning and Micro Capture-C resolve GWAS associations revealing endothelial stress pathways in Coronary Artery Disease"

    Article Title: Machine Learning and Micro Capture-C resolve GWAS associations revealing endothelial stress pathways in Coronary Artery Disease

    Journal: medRxiv

    doi: 10.64898/2025.12.18.25342557

    Machine Learning predicts the effect of genetic variants in coronary artery cell types. A. REnformer was trained on publicly available scATAC data from primary coronary artery tissue samples. REnformer was then used to predict the genome accessibility for 9 coronary artery cell types. An example locus around the MMP2 gene is shown. The tracks show the ground truth scATAC data (labelled “ATAC” followed by the cell type), and directly below the REnformer prediction for that cell type. B. The effect on genome accessibility of 21,348 CAD associated lead SNPs and high linkage disequilibrium candidate causal SNPs is predicted by REnformer across 9 coronary artery cell types as well as HUVECs. Predicted coverage (Y-axis) shows the predicted chromatin accessibility at a given SNP position. Alt-Ref prediction (X-axis) shows the predicted effect size of the candidate CAD SNP. Empirical significance cut-offs of 0.01 were calculated for each cell type; variants below the cut-off (loss of function) are coloured blue, variants above the cut-off (gain of function) are coloured red. C. Example REnformer predictions at the chr1:q25.1 locus for SNP rs1879454 (reference allele = C, alternate allele =A). The position of rs1879454 is shown by a blue triangle. D. The prediction difference for rs1879454 where the reference allele has been subtracted from the alternate allele. E. Feature attribution for the REnformer predictions in panel C. The relative contribution of each base pair in the reference and alternate genome to the prediction is shown by height (positive or negative) of the relevant letter. The variant position is shown by a blue triangle.
    Figure Legend Snippet: Machine Learning predicts the effect of genetic variants in coronary artery cell types. A. REnformer was trained on publicly available scATAC data from primary coronary artery tissue samples. REnformer was then used to predict the genome accessibility for 9 coronary artery cell types. An example locus around the MMP2 gene is shown. The tracks show the ground truth scATAC data (labelled “ATAC” followed by the cell type), and directly below the REnformer prediction for that cell type. B. The effect on genome accessibility of 21,348 CAD associated lead SNPs and high linkage disequilibrium candidate causal SNPs is predicted by REnformer across 9 coronary artery cell types as well as HUVECs. Predicted coverage (Y-axis) shows the predicted chromatin accessibility at a given SNP position. Alt-Ref prediction (X-axis) shows the predicted effect size of the candidate CAD SNP. Empirical significance cut-offs of 0.01 were calculated for each cell type; variants below the cut-off (loss of function) are coloured blue, variants above the cut-off (gain of function) are coloured red. C. Example REnformer predictions at the chr1:q25.1 locus for SNP rs1879454 (reference allele = C, alternate allele =A). The position of rs1879454 is shown by a blue triangle. D. The prediction difference for rs1879454 where the reference allele has been subtracted from the alternate allele. E. Feature attribution for the REnformer predictions in panel C. The relative contribution of each base pair in the reference and alternate genome to the prediction is shown by height (positive or negative) of the relevant letter. The variant position is shown by a blue triangle.

    Techniques Used: Variant Assay

    A. Genome regulatory elements including enhancers (Enh), CTCF sites, and promoters (Pr) were determined for HUVECs using REgulamentary. B. Chart showing the number of intersections between REgulamentary defined regulatory elements and candidate causal variants.
    Figure Legend Snippet: A. Genome regulatory elements including enhancers (Enh), CTCF sites, and promoters (Pr) were determined for HUVECs using REgulamentary. B. Chart showing the number of intersections between REgulamentary defined regulatory elements and candidate causal variants.

    Techniques Used:

    REnformer prioritises candidate causal variants in HUVECs. REnformer predictions are shown for 4 example candidate causal variants: rs604723 (A), rs2508619 (B), rs582384 (C), rs17293632 (D). In each panel, the prioritised SNP is labelled above the gene annotation, and other non-prioritised SNPs are shown as vertical black lines. The REnformer prediction (alt-ref) is shown below the gene annotation and directly above ATACseq data for HUVECs. Publicly available ChIPseq tracks (H3K4me3, H3K4me1, H3K27ac, CTCF) are also shown to aid identification of the genome regulatory elements. REnformer feature attribution for the 40bp around the prioritised SNP (yellow highlight) is shown below the genome tracks in each panel; the reference allele feature attribution is shown above the alternate allele. Statistically significant transcription factor binding motifs are shown below the feature attribution in B, C, and D. The identity of the transcription factor binding the affected motif in A is unknown.
    Figure Legend Snippet: REnformer prioritises candidate causal variants in HUVECs. REnformer predictions are shown for 4 example candidate causal variants: rs604723 (A), rs2508619 (B), rs582384 (C), rs17293632 (D). In each panel, the prioritised SNP is labelled above the gene annotation, and other non-prioritised SNPs are shown as vertical black lines. The REnformer prediction (alt-ref) is shown below the gene annotation and directly above ATACseq data for HUVECs. Publicly available ChIPseq tracks (H3K4me3, H3K4me1, H3K27ac, CTCF) are also shown to aid identification of the genome regulatory elements. REnformer feature attribution for the 40bp around the prioritised SNP (yellow highlight) is shown below the genome tracks in each panel; the reference allele feature attribution is shown above the alternate allele. Statistically significant transcription factor binding motifs are shown below the feature attribution in B, C, and D. The identity of the transcription factor binding the affected motif in A is unknown.

    Techniques Used: Binding Assay

    Analysis of the endothelial enhancer-promoter map for CAD genetics. A. String diagram showing genes linked by MCC to a HUVEC enhancer containing a candidate causal CAD SNP. Interactions between a captured MCC enhancer and gene promoters were called using the bespoke MCC peakcaller MCC_LoTron. Gene promoters were defined using REgulamentary. Nodes which are enriched for specified GO terms are highlighted by colour. The thickness of the connecting line corresponds to the strength of the interaction evidence between two nodes. B. Gene Ontology analysis (biological processes enrichment) of the MCC derived gene network.
    Figure Legend Snippet: Analysis of the endothelial enhancer-promoter map for CAD genetics. A. String diagram showing genes linked by MCC to a HUVEC enhancer containing a candidate causal CAD SNP. Interactions between a captured MCC enhancer and gene promoters were called using the bespoke MCC peakcaller MCC_LoTron. Gene promoters were defined using REgulamentary. Nodes which are enriched for specified GO terms are highlighted by colour. The thickness of the connecting line corresponds to the strength of the interaction evidence between two nodes. B. Gene Ontology analysis (biological processes enrichment) of the MCC derived gene network.

    Techniques Used: Derivative Assay

    POINTseq detects the changes in MCC linked output genes. A. Schematic overview of experimental approach. MCC links candidate causal enhancer SNPs to the promoters of output genes. Long read sequencing is used to phase SNPs and reconstruct the alleles from the donor cells. ATACseq and POINTseq data is re-mapped to the reconstructed alleles and reads are assigned to the allele they are derived from based on corresponding SNPs. The amount of signal coming from each allele can then be measured and compared, demonstrating the effect of a casual SNP in an enhancer on gene transcriptional output. B. Example POINTseq data tracks compared to traditional RNAseq. POINTseq, polyA-, and polyA+ RNAseq were performed on 3 HUVEC donors. Reads are split between forward and reverse DNA strand, mapped to the hg38 reference genome, and displayed as bigwig files. POINTseq offers far greater transcriptional gene coverage as introns are retained. D. Plot of POINTseq measured allelic skew in genes linked by MCC to candidate causal enhancer SNPs. Direction of change is displayed with respect to the reference allele versus the alternate allele (x-axis). Significance (log10 p-value) is displayed on the y-axis. Detected significant genes are labelled.
    Figure Legend Snippet: POINTseq detects the changes in MCC linked output genes. A. Schematic overview of experimental approach. MCC links candidate causal enhancer SNPs to the promoters of output genes. Long read sequencing is used to phase SNPs and reconstruct the alleles from the donor cells. ATACseq and POINTseq data is re-mapped to the reconstructed alleles and reads are assigned to the allele they are derived from based on corresponding SNPs. The amount of signal coming from each allele can then be measured and compared, demonstrating the effect of a casual SNP in an enhancer on gene transcriptional output. B. Example POINTseq data tracks compared to traditional RNAseq. POINTseq, polyA-, and polyA+ RNAseq were performed on 3 HUVEC donors. Reads are split between forward and reverse DNA strand, mapped to the hg38 reference genome, and displayed as bigwig files. POINTseq offers far greater transcriptional gene coverage as introns are retained. D. Plot of POINTseq measured allelic skew in genes linked by MCC to candidate causal enhancer SNPs. Direction of change is displayed with respect to the reference allele versus the alternate allele (x-axis). Significance (log10 p-value) is displayed on the y-axis. Detected significant genes are labelled.

    Techniques Used: Sequencing, Derivative Assay



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    Image Search Results


    Endothelial tube formation assay using conditioned media from HCT116 cells overexpressing RRAS mutants and appropriate controls. ( A ) Representative micrographs of HUVEC (green) obtained through high-content imaging using a montage of z-stacks at 4× magnification. Scale bar = 300 μm. Representative data (mean ± s.d.) of ( B ) total mesh area, ( C ) number of isolated segments, ( D ) number of extremities, ( E ) mesh index, and ( F ) mean mesh size across three independent trials, each done in quadruplicate, are shown. Statistical analysis was performed using one-way ANOVA and Tukey’s HSD post hoc test. * p < 0.05; ns = not significant. PTT: pTarget™ empty vector; WT: wild type.

    Journal: Cells

    Article Title: Ras-Related Mutants Identified in Young-Onset Colorectal Cancer Display Divergent Phenotypes and Retain Their Pro-Angiogenic Effects

    doi: 10.3390/cells15040349

    Figure Lengend Snippet: Endothelial tube formation assay using conditioned media from HCT116 cells overexpressing RRAS mutants and appropriate controls. ( A ) Representative micrographs of HUVEC (green) obtained through high-content imaging using a montage of z-stacks at 4× magnification. Scale bar = 300 μm. Representative data (mean ± s.d.) of ( B ) total mesh area, ( C ) number of isolated segments, ( D ) number of extremities, ( E ) mesh index, and ( F ) mean mesh size across three independent trials, each done in quadruplicate, are shown. Statistical analysis was performed using one-way ANOVA and Tukey’s HSD post hoc test. * p < 0.05; ns = not significant. PTT: pTarget™ empty vector; WT: wild type.

    Article Snippet: Human umbilical vein endothelial cell (HUVEC; ATCC ® CRL-1730TM, ATCC) was propagated in Ham’s F-12K medium supplemented with 10% FBS, 50 μg/mL endothelial cell growth supplement, and 100 μg/mL heparin.

    Techniques: Endothelial Tube Formation Assay, Imaging, Isolation, Plasmid Preparation

    Machine Learning predicts the effect of genetic variants in coronary artery cell types. A. REnformer was trained on publicly available scATAC data from primary coronary artery tissue samples. REnformer was then used to predict the genome accessibility for 9 coronary artery cell types. An example locus around the MMP2 gene is shown. The tracks show the ground truth scATAC data (labelled “ATAC” followed by the cell type), and directly below the REnformer prediction for that cell type. B. The effect on genome accessibility of 21,348 CAD associated lead SNPs and high linkage disequilibrium candidate causal SNPs is predicted by REnformer across 9 coronary artery cell types as well as HUVECs. Predicted coverage (Y-axis) shows the predicted chromatin accessibility at a given SNP position. Alt-Ref prediction (X-axis) shows the predicted effect size of the candidate CAD SNP. Empirical significance cut-offs of 0.01 were calculated for each cell type; variants below the cut-off (loss of function) are coloured blue, variants above the cut-off (gain of function) are coloured red. C. Example REnformer predictions at the chr1:q25.1 locus for SNP rs1879454 (reference allele = C, alternate allele =A). The position of rs1879454 is shown by a blue triangle. D. The prediction difference for rs1879454 where the reference allele has been subtracted from the alternate allele. E. Feature attribution for the REnformer predictions in panel C. The relative contribution of each base pair in the reference and alternate genome to the prediction is shown by height (positive or negative) of the relevant letter. The variant position is shown by a blue triangle.

    Journal: medRxiv

    Article Title: Machine Learning and Micro Capture-C resolve GWAS associations revealing endothelial stress pathways in Coronary Artery Disease

    doi: 10.64898/2025.12.18.25342557

    Figure Lengend Snippet: Machine Learning predicts the effect of genetic variants in coronary artery cell types. A. REnformer was trained on publicly available scATAC data from primary coronary artery tissue samples. REnformer was then used to predict the genome accessibility for 9 coronary artery cell types. An example locus around the MMP2 gene is shown. The tracks show the ground truth scATAC data (labelled “ATAC” followed by the cell type), and directly below the REnformer prediction for that cell type. B. The effect on genome accessibility of 21,348 CAD associated lead SNPs and high linkage disequilibrium candidate causal SNPs is predicted by REnformer across 9 coronary artery cell types as well as HUVECs. Predicted coverage (Y-axis) shows the predicted chromatin accessibility at a given SNP position. Alt-Ref prediction (X-axis) shows the predicted effect size of the candidate CAD SNP. Empirical significance cut-offs of 0.01 were calculated for each cell type; variants below the cut-off (loss of function) are coloured blue, variants above the cut-off (gain of function) are coloured red. C. Example REnformer predictions at the chr1:q25.1 locus for SNP rs1879454 (reference allele = C, alternate allele =A). The position of rs1879454 is shown by a blue triangle. D. The prediction difference for rs1879454 where the reference allele has been subtracted from the alternate allele. E. Feature attribution for the REnformer predictions in panel C. The relative contribution of each base pair in the reference and alternate genome to the prediction is shown by height (positive or negative) of the relevant letter. The variant position is shown by a blue triangle.

    Article Snippet: Single donor human umbilical vein endothelial cell (HUVEC) lines were purchased from Promocell (C-12200) or were isolated from umbilical cord tissue samples provided by the Anthony Nolan Trust, who received ethical approval from the East Midlands-Derby, UK Research Ethics Committee and written, informed consent from the donor’s parents.

    Techniques: Variant Assay

    A. Genome regulatory elements including enhancers (Enh), CTCF sites, and promoters (Pr) were determined for HUVECs using REgulamentary. B. Chart showing the number of intersections between REgulamentary defined regulatory elements and candidate causal variants.

    Journal: medRxiv

    Article Title: Machine Learning and Micro Capture-C resolve GWAS associations revealing endothelial stress pathways in Coronary Artery Disease

    doi: 10.64898/2025.12.18.25342557

    Figure Lengend Snippet: A. Genome regulatory elements including enhancers (Enh), CTCF sites, and promoters (Pr) were determined for HUVECs using REgulamentary. B. Chart showing the number of intersections between REgulamentary defined regulatory elements and candidate causal variants.

    Article Snippet: Single donor human umbilical vein endothelial cell (HUVEC) lines were purchased from Promocell (C-12200) or were isolated from umbilical cord tissue samples provided by the Anthony Nolan Trust, who received ethical approval from the East Midlands-Derby, UK Research Ethics Committee and written, informed consent from the donor’s parents.

    Techniques:

    REnformer prioritises candidate causal variants in HUVECs. REnformer predictions are shown for 4 example candidate causal variants: rs604723 (A), rs2508619 (B), rs582384 (C), rs17293632 (D). In each panel, the prioritised SNP is labelled above the gene annotation, and other non-prioritised SNPs are shown as vertical black lines. The REnformer prediction (alt-ref) is shown below the gene annotation and directly above ATACseq data for HUVECs. Publicly available ChIPseq tracks (H3K4me3, H3K4me1, H3K27ac, CTCF) are also shown to aid identification of the genome regulatory elements. REnformer feature attribution for the 40bp around the prioritised SNP (yellow highlight) is shown below the genome tracks in each panel; the reference allele feature attribution is shown above the alternate allele. Statistically significant transcription factor binding motifs are shown below the feature attribution in B, C, and D. The identity of the transcription factor binding the affected motif in A is unknown.

    Journal: medRxiv

    Article Title: Machine Learning and Micro Capture-C resolve GWAS associations revealing endothelial stress pathways in Coronary Artery Disease

    doi: 10.64898/2025.12.18.25342557

    Figure Lengend Snippet: REnformer prioritises candidate causal variants in HUVECs. REnformer predictions are shown for 4 example candidate causal variants: rs604723 (A), rs2508619 (B), rs582384 (C), rs17293632 (D). In each panel, the prioritised SNP is labelled above the gene annotation, and other non-prioritised SNPs are shown as vertical black lines. The REnformer prediction (alt-ref) is shown below the gene annotation and directly above ATACseq data for HUVECs. Publicly available ChIPseq tracks (H3K4me3, H3K4me1, H3K27ac, CTCF) are also shown to aid identification of the genome regulatory elements. REnformer feature attribution for the 40bp around the prioritised SNP (yellow highlight) is shown below the genome tracks in each panel; the reference allele feature attribution is shown above the alternate allele. Statistically significant transcription factor binding motifs are shown below the feature attribution in B, C, and D. The identity of the transcription factor binding the affected motif in A is unknown.

    Article Snippet: Single donor human umbilical vein endothelial cell (HUVEC) lines were purchased from Promocell (C-12200) or were isolated from umbilical cord tissue samples provided by the Anthony Nolan Trust, who received ethical approval from the East Midlands-Derby, UK Research Ethics Committee and written, informed consent from the donor’s parents.

    Techniques: Binding Assay

    Analysis of the endothelial enhancer-promoter map for CAD genetics. A. String diagram showing genes linked by MCC to a HUVEC enhancer containing a candidate causal CAD SNP. Interactions between a captured MCC enhancer and gene promoters were called using the bespoke MCC peakcaller MCC_LoTron. Gene promoters were defined using REgulamentary. Nodes which are enriched for specified GO terms are highlighted by colour. The thickness of the connecting line corresponds to the strength of the interaction evidence between two nodes. B. Gene Ontology analysis (biological processes enrichment) of the MCC derived gene network.

    Journal: medRxiv

    Article Title: Machine Learning and Micro Capture-C resolve GWAS associations revealing endothelial stress pathways in Coronary Artery Disease

    doi: 10.64898/2025.12.18.25342557

    Figure Lengend Snippet: Analysis of the endothelial enhancer-promoter map for CAD genetics. A. String diagram showing genes linked by MCC to a HUVEC enhancer containing a candidate causal CAD SNP. Interactions between a captured MCC enhancer and gene promoters were called using the bespoke MCC peakcaller MCC_LoTron. Gene promoters were defined using REgulamentary. Nodes which are enriched for specified GO terms are highlighted by colour. The thickness of the connecting line corresponds to the strength of the interaction evidence between two nodes. B. Gene Ontology analysis (biological processes enrichment) of the MCC derived gene network.

    Article Snippet: Single donor human umbilical vein endothelial cell (HUVEC) lines were purchased from Promocell (C-12200) or were isolated from umbilical cord tissue samples provided by the Anthony Nolan Trust, who received ethical approval from the East Midlands-Derby, UK Research Ethics Committee and written, informed consent from the donor’s parents.

    Techniques: Derivative Assay

    POINTseq detects the changes in MCC linked output genes. A. Schematic overview of experimental approach. MCC links candidate causal enhancer SNPs to the promoters of output genes. Long read sequencing is used to phase SNPs and reconstruct the alleles from the donor cells. ATACseq and POINTseq data is re-mapped to the reconstructed alleles and reads are assigned to the allele they are derived from based on corresponding SNPs. The amount of signal coming from each allele can then be measured and compared, demonstrating the effect of a casual SNP in an enhancer on gene transcriptional output. B. Example POINTseq data tracks compared to traditional RNAseq. POINTseq, polyA-, and polyA+ RNAseq were performed on 3 HUVEC donors. Reads are split between forward and reverse DNA strand, mapped to the hg38 reference genome, and displayed as bigwig files. POINTseq offers far greater transcriptional gene coverage as introns are retained. D. Plot of POINTseq measured allelic skew in genes linked by MCC to candidate causal enhancer SNPs. Direction of change is displayed with respect to the reference allele versus the alternate allele (x-axis). Significance (log10 p-value) is displayed on the y-axis. Detected significant genes are labelled.

    Journal: medRxiv

    Article Title: Machine Learning and Micro Capture-C resolve GWAS associations revealing endothelial stress pathways in Coronary Artery Disease

    doi: 10.64898/2025.12.18.25342557

    Figure Lengend Snippet: POINTseq detects the changes in MCC linked output genes. A. Schematic overview of experimental approach. MCC links candidate causal enhancer SNPs to the promoters of output genes. Long read sequencing is used to phase SNPs and reconstruct the alleles from the donor cells. ATACseq and POINTseq data is re-mapped to the reconstructed alleles and reads are assigned to the allele they are derived from based on corresponding SNPs. The amount of signal coming from each allele can then be measured and compared, demonstrating the effect of a casual SNP in an enhancer on gene transcriptional output. B. Example POINTseq data tracks compared to traditional RNAseq. POINTseq, polyA-, and polyA+ RNAseq were performed on 3 HUVEC donors. Reads are split between forward and reverse DNA strand, mapped to the hg38 reference genome, and displayed as bigwig files. POINTseq offers far greater transcriptional gene coverage as introns are retained. D. Plot of POINTseq measured allelic skew in genes linked by MCC to candidate causal enhancer SNPs. Direction of change is displayed with respect to the reference allele versus the alternate allele (x-axis). Significance (log10 p-value) is displayed on the y-axis. Detected significant genes are labelled.

    Article Snippet: Single donor human umbilical vein endothelial cell (HUVEC) lines were purchased from Promocell (C-12200) or were isolated from umbilical cord tissue samples provided by the Anthony Nolan Trust, who received ethical approval from the East Midlands-Derby, UK Research Ethics Committee and written, informed consent from the donor’s parents.

    Techniques: Sequencing, Derivative Assay

    Serum FHR1 concentrations correlate with non-HDL cholesterol levels in ACVD patients. ( A ) serum FHR1 concentrations in ACVD patients correlate with total cholesterol (n=192) and ( B ) non-HDL cholesterol (n=211). ( C ) Although FHR1 concentrations are substantially lower in serum of aged matched healthy control individuals compared to ACVD patients, they correlate with non-HDL cholesterol levels (n=50). ( D ) In addition, FHR1 serum concentrations of ACVD patients correlate with serum triglycerides (n=213), ( E ) lipid peroxidation levels (n=33), and ( F ) serum concentrations of ApoB100 (n=76). ( G ) FHR1 binding to ApoB100 and LDL is significantly enhanced upon oxidization. No enhancement was detected upon binding to oxidized ApoA1 (***p<0.001, one-way ANOVA, n=3-4). ( H ) Expression of pro-inflammatory cytokines MCP-1, CXCL10, and IL-1β in HUVEC cells is decreased upon incubation with HDL isolated from normal human sera not containing FHR1 (***p<0.001, p**<0.01, *p<0.05, two-way ANOVA, n=3). A-E: Spearman correlations.

    Journal: International Journal of Medical Sciences

    Article Title: Deletion of the Mouse Homolog of Human FHR1 (muFHR1) Alleviates Atherosclerosis in ApoE-/- mice

    doi: 10.7150/ijms.114990

    Figure Lengend Snippet: Serum FHR1 concentrations correlate with non-HDL cholesterol levels in ACVD patients. ( A ) serum FHR1 concentrations in ACVD patients correlate with total cholesterol (n=192) and ( B ) non-HDL cholesterol (n=211). ( C ) Although FHR1 concentrations are substantially lower in serum of aged matched healthy control individuals compared to ACVD patients, they correlate with non-HDL cholesterol levels (n=50). ( D ) In addition, FHR1 serum concentrations of ACVD patients correlate with serum triglycerides (n=213), ( E ) lipid peroxidation levels (n=33), and ( F ) serum concentrations of ApoB100 (n=76). ( G ) FHR1 binding to ApoB100 and LDL is significantly enhanced upon oxidization. No enhancement was detected upon binding to oxidized ApoA1 (***p<0.001, one-way ANOVA, n=3-4). ( H ) Expression of pro-inflammatory cytokines MCP-1, CXCL10, and IL-1β in HUVEC cells is decreased upon incubation with HDL isolated from normal human sera not containing FHR1 (***p<0.001, p**<0.01, *p<0.05, two-way ANOVA, n=3). A-E: Spearman correlations.

    Article Snippet: Human umbilical vein endothelial (HUVEC) cells were obtained from the American Type Culture Collection (ATCC).

    Techniques: Control, Binding Assay, Expressing, Incubation, Isolation