13 research outputs found
IntLIM: integration using linear models of metabolomics and gene expression data
Abstract Background Integration of transcriptomic and metabolomic data improves functional interpretation of disease-related metabolomic phenotypes, and facilitates discovery of putative metabolite biomarkers and gene targets. For this reason, these data are increasingly collected in large (> 100 participants) cohorts, thereby driving a need for the development of user-friendly and open-source methods/tools for their integration. Of note, clinical/translational studies typically provide snapshot (e.g. one time point) gene and metabolite profiles and, oftentimes, most metabolites measured are not identified. Thus, in these types of studies, pathway/network approaches that take into account the complexity of transcript-metabolite relationships may neither be applicable nor readily uncover novel relationships. With this in mind, we propose a simple linear modeling approach to capture disease-(or other phenotype) specific gene-metabolite associations, with the assumption that co-regulation patterns reflect functionally related genes and metabolites. Results The proposed linear model, metabolite ~ gene + phenotype + gene:phenotype, specifically evaluates whether gene-metabolite relationships differ by phenotype, by testing whether the relationship in one phenotype is significantly different from the relationship in another phenotype (via a statistical interaction gene:phenotype p-value). Statistical interaction p-values for all possible gene-metabolite pairs are computed and significant pairs are then clustered by the directionality of associations (e.g. strong positive association in one phenotype, strong negative association in another phenotype). We implemented our approach as an R package, IntLIM, which includes a user-friendly R Shiny web interface, thereby making the integrative analyses accessible to non-computational experts. We applied IntLIM to two previously published datasets, collected in the NCI-60 cancer cell lines and in human breast tumor and non-tumor tissue, for which transcriptomic and metabolomic data are available. We demonstrate that IntLIM captures relevant tumor-specific gene-metabolite associations involved in known cancer-related pathways, including glutamine metabolism. Using IntLIM, we also uncover biologically relevant novel relationships that could be further tested experimentally. Conclusions IntLIM provides a user-friendly, reproducible framework to integrate transcriptomic and metabolomic data and help interpret metabolomic data and uncover novel gene-metabolite relationships. The IntLIM R package is publicly available in GitHub (https://github.com/mathelab/IntLIM) and includes a user-friendly web application, vignettes, sample data and data/code to reproduce results
Ets2 in Tumor Fibroblasts Promotes Angiogenesis in Breast Cancer
<div><p>Tumor fibroblasts are active partners in tumor progression, but the genes and pathways that mediate this collaboration are ill-defined. Previous work demonstrates that <i>Ets2</i> function in stromal cells significantly contributes to breast tumor progression. Conditional mouse models were used to study the function of <i>Ets2</i> in both mammary stromal fibroblasts and epithelial cells. Conditional inactivation of <i>Ets2</i> in stromal fibroblasts in <i>PyMT</i> and <i>ErbB2</i> driven tumors significantly reduced tumor growth, however deletion of <i>Ets2</i> in epithelial cells in the <i>PyMT</i> model had no significant effect. Analysis of gene expression in fibroblasts revealed a tumor- and <i>Ets2-</i>dependent gene signature that was enriched in genes important for ECM remodeling, cell migration, and angiogenesis in both <i>PyMT</i> and <i>ErbB2</i> driven-tumors. Consistent with these results, <i>PyMT</i> and <i>ErbB2</i> tumors lacking <i>Ets2</i> in fibroblasts had fewer functional blood vessels, and <i>Ets2</i> in fibroblasts elicited changes in gene expression in tumor endothelial cells consistent with this phenotype. An <i>in vivo</i> angiogenesis assay revealed the ability of <i>Ets2</i> in fibroblasts to promote blood vessel formation in the absence of tumor cells. Importantly, the <i>Ets2</i>-dependent gene expression signatures from both mouse models were able to distinguish human breast tumor stroma from normal stroma, and correlated with patient outcomes in two whole tumor breast cancer data sets. The data reveals a key function for <i>Ets2</i> in tumor fibroblasts in signaling to endothelial cells to promote tumor angiogenesis. The results highlight the collaborative networks that orchestrate communication between stromal cells and tumor cells, and suggest that targeting tumor fibroblasts may be an effective strategy for developing novel anti-angiogenic therapies.</p></div
<i>Ets2</i> deletion in stromal fibroblasts reduces size and number of tumor lesions in <i>ErbB2</i> breast cancer model.
<p><b>A.</b> Left: graph represents carcinoma lesion sizes in mm<sup>2</sup> from 16 week old <i>ErbB2;Ets2<sup>db/loxP</sup></i> (n = 5, 2.28±4.16) and <i>ErbB2;Fsp-Cre;Ets2<sup>db/loxP</sup></i> (n = 5, 1.83±7.96) mice (**P<0.01, Non-parametric Mann-Whitney test). Right: graph represents number of lesions larger than one mm<sup>2</sup> in mammary glands of 16 week old <i>ErbB2;Ets2<sup>db/loxP</sup></i> (n = 5) and <i>ErbB2;Fsp-Cre;Ets2<sup>db/loxP</sup></i> (n = 5) mice (**P<0.01, Chi Square test). <b>B.</b> Representative H&E stained mammary glands from 16 week old <i>ErbB2;Ets2<sup>db/loxP</sup></i> and <i>ErbB2;Fsp-Cre;Ets2<sup>db/loxP</sup></i> mice. Scale bar, 2mm. LN, lymph node. T, tumor. <b>C.</b> Representative histological sections from mammary glands of 16 week old <i>ErbB2;Ets2<sup>db/loxP</sup></i> and <i>ErbB2;Fsp-Cre;Ets2<sup>db/loxP</sup></i> mice. Scale bar, 50 µm. <b>D.</b> Left: representative IHC staining for Ki67 in mammary glands from 16 week old <i>ErbB2;Ets2<sup>db/loxP</sup></i> and <i>ErbB2;Fsp-Cre;Ets2<sup>db/loxP</sup></i> mice. Scale bar, 50 µm. Right: graph represents percentage of Ki67 positive epithelial cells (n = 3, bars represent means ± SD, *<i>*</i>P<0.01, Welch’s t-test assuming unequal variance).</p
<i>Ets2</i> regulation of angiogenesis and ECM remodeling is tumor cell independent.
<p><b>A.</b> Experimental strategy of matrigel plug assay. <b>B.</b> Left: CD31 staining (red) in matrigel plugs containing either <i>PyMT;Ets2<sup>db/loxP</sup></i> or <i>PyMT;Fsp-Cre;Ets2<sup>db/loxP</sup></i> tumor fibroblasts. Scale bar, 200 µm. Slides were counterstained with DAPI (blue). Right: graph represents percent CD31 positive area quantified using MetaMorph Software (n = 4, bars represent means ± SD, *<i>*</i>P<0.01, Welch’s t-test assuming unequal variance). <b>C.</b> Right: gelatinase <i>in situ</i> zymography for MMP9 activity (green) of matrigel plugs containing primary fibroblasts of indicated genetic groups. Scale bar, 50 µm. Right: graph representing fluorescence intensity quantified using MetaMorph Software (n = 4, bars represent means ± SD, *<i>*</i>P<0.01, Welch’s t-test assuming unequal variance).</p
<i>Ets2</i> regulates tumor fibroblast specific transcription program.
<p><b>A.</b> Heatmap representing expression levels of 107 upregulated and downregulated genes in <i>PyMT;Ets2<sup>db/loxP</sup></i> vs. <i>PyMT;Fsp-Cre;Ets2<sup>db/loxP</sup></i> fibroblasts in all indicated genotypes harvested from 9 week old mice (n = 1, Log fold change>2, Negative Log P value (NLP)>4.5). <b>B.</b> GSEA plots depicting ECM remodeling, angiogenesis, cell growth and cell migration to be enriched in <i>PyMT;Ets2<sup>db/loxP</sup></i> fibroblasts as compared to <i>PyMT;Fsp-Cre;Ets2<sup>db/loxP</sup></i> fibroblasts. NES: normalized enrichment score. <b>C.</b> Heatmap representing expression levels of 69 upregulated and downregulated genes in <i>ErbB2;Ets2<sup>db/loxP</sup></i> vs. <i>ErbB2;Fsp-Cre;Ets2<sup>db/loxP</sup></i> fibroblasts in all indicated genotypes harvested from 16 week old mice (n = 1, Log fold change>2). <b>D.</b> GSEA plots depicting ECM remodeling, angiogenesis, cell growth and cell migration to be enriched in <i>ErbB2;Ets2<sup>db/loxP</sup></i> fibroblasts as compared to <i>ErbB2;Fsp-Cre;Ets2<sup>db/loxP</sup></i> fibroblasts. NES: normalized enrichment score. <b>E.</b> Venn diagrams depicting the number of common genes in the leading edge subset of indicated GO categories enriched in <i>PyMT;Ets2<sup>db/loxP</sup></i> fibroblasts (gray circles) and <i>ErbB2;Ets2<sup>db/loxP</sup></i> fibroblasts (white circles).</p
<i>Ets2</i> in fibroblasts controls tumor angiogenesis.
<p><b>A.</b> Left: <i>in vivo</i> tumor vasculature visualized by intracardiac injection of FITC lectin (green). Scale bar, 200 µm. Right: graph represents percent lectin positive area quantified using ImageJ, Fiji(n = 3, bars represent means ± SD, *<i>*</i> P<0.01, Welch’s t-test assuming unequal variance). <b>B.</b> Left: immunofluorescence staining for CD31 (red) in mammary gland tumors from 10 week old <i>PyMT;Ets2<sup>db/loxP</sup></i> and <i>PyMT;Fsp-Cre;Ets2<sup>db/loxP</sup></i> mice. Scale bar, 200 µm. Slides were counterstained with DAPI (blue). Right: graph represents percent CD31 positive area quantified using ImageJ, Fiji (n = 3, bars represent means ± SD, *<i>*</i>P<0.01, Welch’s t-test assuming unequal variance). <b>C.</b> Left: i<i>n vivo</i> tumor vasculature visualized by intrafemoral injection of DyLight 594 lectin (green). Scale bar, 200 µm. Right: graph represents percent lectin positive area quantified using ImageJ, Fiji (n = 3, bars represent means ± SD, *<i>*</i> P<0.01, Welch’s t-test assuming unequal variance). <b>D.</b> Left: immunofluorescence staining for CD31 (red) in mammary gland tumors from 16 week old <i>ErbB2;Ets2<sup>db/loxP</sup></i> and <i>ErbB2;Fsp-Cre;Ets2<sup>db/loxP</sup></i> mice. Scale bar, 200 µm. Slides were counterstained with DAPI (blue). Right: graph represents percent CD31 positive area quantified using ImageJ, Fiji (n = 3, bars represent means ± SD, *<i>*</i>P<0.01, Welch’s t-test assuming unequal variance). <b>E.</b> Heatmap depicting the genes significantly differentially regulated in endothelial cells isolated from <i>PyMT;Ets2<sup>db/loxP</sup></i> vs. <i>PyMT;Fsp-Cre;Ets2<sup>db/loxP</sup></i> mammary glands harvested at 9 weeks (n = 3, fold change>2, *P≤0.05). <b>F.</b> GSEA plots depicting ECM remodeling, cell adhesion and cell chemotaxis to be enriched in endothelial cells isolated from <i>PyMT;Ets2<sup>db/loxP</sup></i> mammary glands as compared to <i>PyMT;Fsp-Cre;Ets2<sup>db/loxP</sup></i> mammary glands. NES: normalized enrichment score.</p
<i>Ets2</i> gene signatures are represented in human breast tumor stroma.
<p><b>A.</b> Heat map displaying the expression of the human orthologs of the <i>PyMT</i> derived 38-gene <i>Ets2</i> signature in normal- and tumor-stroma from human breast cancer patients. 16 genes were upregulated (denoted by red bar on the y-axis) and 22 genes were downregulated (denoted by the blue bar on the y-axis) in <i>Ets2</i> null tumor fibroblasts. Red and blue regions inside the heat map indicate relative gene expression levels (red, increased and blue, decreased) between the normal and tumor stroma (P = 0.00085, one-sided Wilcoxon rank sum test, based on 10,000 permutations). <b>B.</b> Heat map displaying the expression of the human orthologs of the <i>ErbB2</i> derived 36-gene <i>Ets2</i> signature in normal- and tumor-stroma from human breast cancer patients. 3 genes were upregulated (denoted by red bar on the y-axis) and 33 genes were downregulated (denoted by the blue bar on the y-axis) in <i>Ets2</i> null tumor fibroblasts. Red and blue regions inside the heat map indicate relative gene expression levels (red, increased and blue, decreased) between the normal and tumor stroma (P = 0.00004, one-sided Wilcoxon rank sum test, based on 10,000 permutations). <b>C.</b> Expression of the 29 <i>PyMT</i>-driven <i>Ets2</i>-dependent genes present in the NKI whole tumor data set correlates with patient outcome. Kaplan-Meier curves of high risk and low risk groups based on expression of the 29 <i>Ets2</i> tumor specific genes (**P<0.0001). <b>D.</b> Expression of the 30 <i>ErbB2</i>-driven <i>Ets2</i>-dependent genes present in the NKI whole tumor data set correlates with patient outcome. Kaplan-Meier curves of high risk and low risk groups based on expression of the 30 <i>Ets2</i> tumor specific genes (**P<0.0001). <b>E.</b> Expression of the 7 endothelial cell genes dependent on <i>Ets2</i> fibroblast signaling present in the NKI whole tumor data set correlates with patient outcome. Kaplan-Meier curves of high risk and low risk groups based on expression of the 7 endothelial cell genes (**P<0.0001).</p
<i>Ets2</i> ablation in stromal fibroblasts restricts <i>PyMT</i> driven mammary tumorigenesis.
<p><b>A.</b> Dissected tumor weight in grams isolated from 14 week old <i>PyMT;Ets2<sup>db/loxP</sup></i> (n = 21, 12.36±3.8), <i>PyMT;Fsp-Cre;Ets2<sup>db/loxP</sup></i> (n = 17, 7.83±2.7), and <i>PyMT;MMTV-Cre;Ets2<sup>db/loxP</sup></i> (n = 15, 11.3±4.7) mice (**P<0.01, *P<0.05, adjusted P-values were obtained from an ANOVA model assuming unequal variance with Holm’s methods). <b>B.</b> Representative H&E stained mammary glands from 14 week old <i>PyMT;Ets2<sup>db/loxP</sup></i> and <i>PyMT;Fsp-Cre;Ets2<sup>db/loxP</sup></i> mice. Area outlined in yellow represents less advanced tumor progression. Scale bar, 2mm. <b>C.</b> Representative histological sections from mammary glands of 10 week old <i>PyMT;Ets2<sup>db/loxP</sup></i> and <i>PyMT;Fsp-Cre;Ets2<sup>db/loxP</sup></i> mice. Scale bar, 50 µm. <b>D.</b> Left: representative IHC staining for Ki67 in mammary glands of 10 week old <i>PyMT;Ets2<sup>db/loxP</sup></i> and <i>PyMT;Fsp-Cre;Ets2<sup>db/loxP</sup></i> mice. Scale bar, 50 µm. Right: graph represents percentage of Ki67 positive epithelial cells (n = 3, bars represent means ± SD, **P<0.01, unpaired Welch’s t-test assuming unequal variance).</p
Ets1 and Ets2 are required for endothelial cell survival during embryonic angiogenesis
The ras/Raf/Mek/Erk pathway plays a central role in coordinating endothelial cell activities during angiogenesis. Transcription factors Ets1 and Ets2 are targets of ras/Erk signaling pathways that have been implicated in endothelial cell function in vitro, but their precise role in vascular formation and function in vivo remains ill-defined. In this work, mutation of both Ets1 and Ets2 resulted in embryonic lethality at midgestation, with striking defects in vascular branching having been observed. The action of these factors was endothelial cell autonomous as demonstrated using Cre/loxP technology. Analysis of Ets1/Ets2 target genes in isolated embryonic endothelial cells demonstrated down-regulation of Mmp9, Bcl-XL, and cIAP2 in double mutants versus controls, and chromatin immunoprecipitation revealed that both Ets1 and Ets2 were loaded at target promoters. Consistent with these observations, endothelial cell apoptosis was significantly increased both in vivo and in vitro when both Ets1 and Ets2 were mutated. These results establish essential and overlapping functions for Ets1 and Ets2 in coordinating endothelial cell functions with survival during embryonic angiogenesis