Research ArticleVASCULAR BIOLOGY

Genome-wide functional analysis reveals central signaling regulators of lymphatic endothelial cell migration and remodeling

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Science Signaling  03 Oct 2017:
Vol. 10, Issue 499, eaal2987
DOI: 10.1126/scisignal.aal2987
  • Fig. 1 Overview of LEC migration screen and analyses.

    (A) HDLEC monolayers were transfected with CDC42 or CDH5 siRNA pools (as positive controls) and subjected to the scratch wounding assay. Values represent the percentage migration relative to the mean of mock-transfected (Control) cells. Scale bars, 500 μm. (B) Plot of the primary screen results for each siRNA pool. Migration scores plotted on the x axis are expressed as robust z scores. Each point represents the average of two replicate wells per gene-specific siRNA pool. Results with |robust z score| > 2 (dashed vertical lines) were considered as accelerated or impaired migration candidates, and those with cell density < 60% of the median per field (dashed horizontal line) were classified as low cell count. (C) Schematic representation of the relationships between data sets, experimentally derived gene lists, and specific analyses within this study. Blue boxes, complete data sets. Red boxes, summaries of the siRNA migration screens. Other boxes, specific analyses. Bullet points, the gene list input into the given analysis or data file. The gene list size and the data file containing the results are indicated. MIARE, Minimum Information About an RNAi Experiment; HSV-1, herpes simplex virus–1; GEO, Gene Expression Omnibus.

  • Fig. 2 Functional categorization of highly validated genes that promote LEC migration.

    Annotation of the 154 highly validated candidates into groups reflecting their functional role in the cell, based on literature and text mining and MetaCore analysis (fig. S4 and data files S5 and S6). Connecting lines represent protein-protein interactions. GPCRs, G protein–coupled receptors.

  • Fig. 3 Morphological changes induced by siRNAs targeting validated candidate genes.

    (A) Fluorescent microscopy images of cells stained with CellTracker Green (whole cell) and phalloidin CF555 (filamentous actin) were subjected to automated high-content analysis of cellular regions. Green lines delineate valid cell segmentation boundaries. Scale bars, 250 μm. (B) Unsupervised clustering of candidate genes and cell morphological parameters (such as size, shape, and actin intensity) allows identification of genes that regulate similar aspects of cell morphology. Selected example candidates are indicated below the heatmap. (C) Examples of morphological changes observed after transfection with siRNA pools in cluster 2 (see also fig. S5). Scale bar, 250 μm. (D) Comparison of four distinguishing morphological parameters across morphology clusters. A schematic diagram of representative morphology, with actin in orange, is depicted for each cluster.

  • Fig. 4 Evaluation of highly validated candidate migration genes in HMBECs.

    Migration was assessed in HMBECs and HDLECs transfected with siRNA pools targeting the 154 highly validated candidate genes. (A) Dot plot comparing the migration screen results in HDLECs (x axis) compared to HMBECs (y axis). Each data point represents a gene-specific siRNA pool, represented as the average migration score of two biological replicates (each comprising technical duplicate wells), relative to mock-transfected controls. Results with migration scores below 0.65 (dashed lines) in HDLECs and/or HMBECs were classified into the indicated categories (quadrants). (B) Migration phenotypes resulting from transfection of HMBECs or HDLECs with the indicated siRNA pools. Cells are labeled with CellTracker Green. Scale bars, 500 μm. (C) Lists of the genes identified as having LEC-dominant and BEC-dominant effects on migration. Inset numbers represent the morphology clusters as defined in Fig. 3. (D) Heatmap of HMBEC morphology parameters. Gene order and parameter order were not subjected to hierarchical clustering but were kept the same as in Fig. 3B to highlight the similarities and differences in morphological phenotypes observed between the two cell types. Selected example candidates are labeled below the heatmap. (E) Comparison of the morphological changes that are induced by transfection of HMBECs and HDLECs with a siRNA pool targeting LPL (see also fig. S7C). Scale bars, 250 μm.

  • Fig. 5 The EC migratome.

    The 68 common EC migration candidates and 20 LEC-dominant candidates were analyzed for protein-protein interactions, identifying two-step pathways between many validated EC migration genes. Migration candidates are color-coded to match Fig. 4 (A and C). Colored boxes with dashed outlines indicate functional categories. NFκB, nuclear factor κB.

  • Fig. 6 Overlap between migration candidate genes and genes differentially expressed during lymphatic remodeling in vivo.

    (A) Immunofluorescence staining of LNs from uninfected control mice (day 0) and 5 days after subcutaneous HSV-1 infection (day 5) to identify lymphatic vessels (LYVE1), high endothelial venules [peripheral node addressin (PNAd)], and all other endothelium (CD31). Scale bars, 200 μm. (B) The list of genes differentially expressed (HSV-1 day 6 compared to day 0) in LN LECs in the microarray analysis was compared to the expanded migration candidate list from the primary siRNA screen (excluding low cell count genes) to determine the number and significance of overlapping genes. Significance of overlap was defined against a simulated null distribution derived from 10,000 random pairs of gene lists of equivalent size as described in Materials and Methods. (C) Venn diagram of the number of input and overlapping genes from the analysis in (B). ***Empirical P < 0.001; hypergeometric P = 0.0078. (D) Filtering of differentially expressed genes into cell-selective and shared categories, and comparison of these gene lists to the expanded migration candidate list. Empirical P values were determined according to simulated null distributions as in (B); hypergeometric P values are indicated in footnote b. (E) Venn diagram of the number of overlapping genes identified between the different categories of differentially expressed genes in (D) with genes identified in the primary siRNA screen. Intersections are exclusive of one another and color-coded to match (D). *P < 0.05 and **P < 0.01, empirical P values. (F) Venn diagram of overlapping genes between those differentially expressed in dermal LECs during CHS (24 hours compared to unstimulated) and the expanded migration candidate list. *P < 0.05, empirical P value; hypergeometric P = 0.0547.

  • Fig. 7 Gal-1 regulates LEC migration in vitro and in vivo.

    (A and B) Western blotting for Gal-1 in cultured HDLECs and HMBECs (A) and in HDLECs transfected with LGALS1 siRNA pool (B). “Control” indicates mock-transfected cells in (B) and (D) to (G) and non-targeting siRNA in (C). GAPDH, glyceraldehyde-3-phosphate dehydrogenase. (C) Quantitation of Gal-1 knockdown by Western blotting. Mean ± SEM of n = 4 independent experiments; ****P < 0.0001 by Student’s t test. (D) Tube network formation under collagen I gel by HDLECs. Scale bar, 250 μm. Mean ± SEM of three independent experiments shown; *P < 0.05 by Student’s t test. (E) HDLECs with or without LGALS1 knockdown were grown on a confluent fibroblast monolayer before CD31 and LYVE1 immunofluorescence was performed to visualize HDLECs and smooth muscle actin (SMActin) to stain fibroblasts. Scale bars, 250 μm. (F and G) Tubule networks from (E) were quantified by the parameters indicated. Mean ± SEM of three independent experiments; (F) *P < 0.05 by analysis of variance (ANOVA) with Tukey post hoc test; (G) P = 0.05 by Student’s t test. A.U., arbitrary units. (H) Immunofluorescence for LYVE1 (lymphatics) and CD146 (blood vessels) on mouse ears injected with the indicated proteins in Matrigel, with or without the Gal-1 inhibitor anginex. Scale bars, 250 μm. Lymphatic vessel width and density were quantitated. n = 9 mice with 9 to 18 ears per condition across two independent experiments. Mean ± SEM; *P < 0.05, **P < 0.01, and ***P < 0.001 by ANOVA with Tukey post hoc test. (I) Immunohistochemistry on serial sections of human LN. White arrows: lymphatic vessel (LV). Black arrowhead: blood vessel (BV). Scale bars, 50 μm. (J) Bioinformatic interrogation of two published gene expression data sets (100, 101) for LGALS1 expression in normal tissues and cancer stroma. Central line, median; box, interquartile range; whiskers, 90th/10th percentiles; dots, minimum/maximum; P values determined in Oncomine using two-sample t test.

  • Fig. 8 Gal-1 signaling maintains LEC phenotype.

    (A) HDLECs transfected with LGALS1 siRNA pools were serum-starved before stimulation with VEGFA (20 ng/ml) and then analyzed by immunoblotting. Control, nontargeting siRNA; Src, Src family kinases. (B) Quantification of phospho (p)-VEGFR2, p-ERK 1/2, and p-Akt bands in (A) normalized to the total respective protein and expressed relative to unstimulated Control-transfected cells. Mean ± SEM; n = 3 to 6 independent experiments; *P < 0.05 and **P < 0.01 by ANOVA with Tukey post hoc test. (C) Quantification of total VEGFR3 bands in (A) normalized to loading control; mean ± SEM shown; n = 3 independent experiments; *P < 0.05 by Student’s t test. (D) Twenty-four hours after scratch wounding, siRNA-treated HDLECs were examined by immunofluorescence for indicated proteins; nuclei were counterstained with Hoechst 33342 (Hoechst). Scale bars, 250 μm. For quantification, see fig. S12A. (E and F) Lysates of HDLECs transfected as indicated were analyzed after 72 hours by Western blotting for LEC, BEC, or common endothelial lineage marker proteins. (G) Quantitation of Western blot analyses of respective LEC and BEC marker proteins in (E) and (F) normalized to loading control. Mean ± SEM; VEGFR2, n = 8 independent experiments; VEGFR3, n = 10 independent experiments; CD146, n = 6 independent experiments; CEACAM-1, n = 5 independent experiments; podoplanin, n = 3 independent experiments; and Prox1, n = 5 independent experiments. *P < 0.05 and **P < 0.01 by ANOVA with Dunnett post hoc test.

Supplementary Materials

  • www.sciencesignaling.org/cgi/content/full/10/499/eaal2987/DC1

    Fig. S1. Optimization of LEC scratch wound migration assay.

    Fig. S2. High-throughput scratch wound migration assay.

    Fig. S3. Genome-wide siRNA screen assay timing.

    Fig. S4. GO classification of validated regulators of HDLEC migration.

    Fig. S5. HDLEC morphology cluster phenotypes and corresponding migration scores.

    Fig. S6. BEC migration assay.

    Fig. S7. Comparison of selected HMBEC and HDLEC morphology phenotypes.

    Fig. S8. Validation of a role for LGALS1 in LEC migration and remodeling.

    Fig. S9. Validation of a role for MICAL2 in LEC migration and remodeling.

    Fig. S10. Effect of Gal-1 on angiogenesis and blood vessel remodeling.

    Fig. S11. VEGFR signaling and lineage marker abundance in HDLECs.

    Fig. S12. Immunofluorescence analysis of LEC and BEC marker proteins in HDLECs.

    Fig. S13. Immunofluorescence analysis of LEC and common endothelial marker proteins in LGALS1-depleted HDLECs.

    Data file S1. MIARE siRNA screen descriptors.

    Data file S2. Aggregated siRNA screen and LN microarray results.

    Data file S3. Primary screen pathway enrichment analysis.

    Data file S4. Primary screen GO enrichment analysis.

    Data file S5. Secondary screen GO enrichment analysis.

    Data file S6. Secondary screen pathway enrichment analysis.

    Data file S7. Overlapping genes detected in LECs in siRNA screen and LN microarray.

    Data file S8. Overlapping genes detected in LECs in siRNA screen and dermal CHS.

  • Supplementary Materials for:

    Genome-wide functional analysis reveals central signaling regulators of lymphatic endothelial cell migration and remodeling

    Steven P. Williams, Adam F. Odell, Tara Karnezis, Rae H. Farnsworth, Cathryn M. Gould, Jason Li, Sophie Paquet-Fifield, Nicole C. Harris, Anne Walter, Julia L. Gregory, Sara F. Lamont, Ruofei Liu, Elena A. Takano, Cameron J. Nowell, Neil I. Bower, Daniel Resnick, Gordon K. Smyth, Leigh Coultas, Benjamin M. Hogan, Stephen B. Fox, Scott N. Mueller, Kaylene J. Simpson, Marc G. Achen, Steven A. Stacker*

    *Corresponding author. Email: steven.stacker{at}petermac.org

    This PDF file includes:

    • Fig. S1. Optimization of LEC scratch wound migration assay.
    • Fig. S2. High-throughput scratch wound migration assay.
    • Fig. S3. Genome-wide siRNA screen assay timing.
    • Fig. S4. GO classification of validated regulators of HDLEC migration.
    • Fig. S5. HDLEC morphology cluster phenotypes and corresponding migration scores.
    • Fig. S6. BEC migration assay.
    • Fig. S7. Comparison of selected HMBEC and HDLEC morphology phenotypes.
    • Fig. S8. Validation of a role for LGALS1 in LEC migration and remodeling.
    • Fig. S9. Validation of a role for MICAL2 in LEC migration and remodeling.
    • Fig. S10. Effect of Gal-1 on angiogenesis and blood vessel remodeling.
    • Fig. S11. VEGFR signaling and lineage marker abundance in HDLECs.
    • Fig. S12. Immunofluorescence analysis of LEC and BEC marker proteins in HDLECs.
    • Fig. S13. Immunofluorescence analysis of LEC and common endothelial marker proteins in LGALS1-depleted HDLECs.
    • Legends for data files S1 to S8

    [Download PDF]

    Technical Details

    Format: Adobe Acrobat PDF

    Size: 3.27 MB

    Other Supplementary Material for this manuscript includes the following:

    • Data file S1 (Microsoft Excel format). MIARE siRNA screen descriptors.
    • Data file S2 (Microsoft Excel format). Aggregated siRNA screen and LN microarray results.
    • Data file S3 (Microsoft Excel format). Primary screen pathway enrichment analysis.
    • Data file S4 (Microsoft Excel format). Primary screen GO enrichment analysis.
    • Data file S5 (Microsoft Excel format). Secondary screen GO enrichment analysis.
    • Data file S6 (Microsoft Excel format). Secondary screen pathway enrichment analysis.
    • Data file S7 (Microsoft Excel format). Overlapping genes detected in LECs in siRNA screen and LN microarray.
    • Data file S8 (Microsoft Excel format). Overlapping genes detected in LECs in siRNA screen and dermal CHS.

    [Download Data files S1 to S8]


    Citation: S. P. Williams, A. F. Odell, T. Karnezis, R. H. Farnsworth, C. M. Gould, J. Li, S. Paquet-Fifield, N. C. Harris, A. Walter, J. L. Gregory, S. F. Lamont, R. Liu, E. A. Takano, C. J. Nowell, N. I. Bower, D. Resnick, G. K. Smyth, L. Coultas, B. M. Hogan, S. B. Fox, S. N. Mueller, K. J. Simpson, M. G. Achen, S. A. Stacker, Genome-wide functional analysis reveals central signaling regulators of lymphatic endothelial cell migration and remodeling. Sci. Signal. 10, eaal2987 (2017).

    © 2017 American Association for the Advancement of Science

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