UGDH_Publication_2021

A description of the figures in this "UGDH,2021" paper.
For details:
Proteomics_UGDH_2021_Manuscript_FigureLegends_V3.docx

Figure 1

Study workflow and a summary of the three EMT breast cell models.
Generated in Excel and powerpoint.

D492:
Isolation, immortalization, and characterization of a human breast epithelial cell line with stem cell properties.
D492M:
Endothelial Induced EMT in Breast Epithelial Cells with Stem Cell Properties
ECM1 secreted by HER2-overexpressing breast cancer cells promotes formation of a vascular niche accelerating cancer cell migration and invasion. (non-tumorigenic)

HMLE:
ISOLATION AND GROWTH OF HUMAN MAMMARY EPITHELIAL CELLS (Stampfer 1985)
Human breast cancer cells generated by oncogenic transformation of primary mammary epithelial cells.
HMLEM:
Protein Kinase C α Is a Central Signaling Node and Therapeutic Target for Breast Cancer Stem Cells.

PMC42ET:
A New Human Breast Carcinoma Cell Line (PMC42) With Stem Cell Characteristics. I. Morphologic Characterization.
PMC42LA:
PMC42, A Novel Model for the Differentiated Human Breast

Figure 2A

PCA plot of the proteomic dataset

Figure 2B

Dendrogram plot of the proteomic dataset

Figure 2C-D

RNA expression of CDH1 and CDH2

Figure 2E-G

Proteomic expression of the consistent changes in EMT
http://dbemt.bioinfo-minzhao.org/

Figure 2H

Proteomic expression of the consistent changes in EMT- PKP3

Figure 3A-B

Analysis of the proteomic datasets.
Generated in Excel and powerpoint.

Figure 3C-E

Volcano plots of the proteomic datasets

Figure 3F

Heatmap of the consistent proteomic changes.
R version: 4.0.0
ComplexHeatmap version: ComplexHeatmap_2.3.4

Figure 4A-C

GO annotation - BP
DAVID Bioinformatics Resources 6.8
Date: 2021.02.09

Procedures:

1. Go to Perseus Output data: “C:\Users\lenovo\OneDrive - Háskóli Íslands\PC-HI\5-2 ProteomicPaper-UGDH\Perseus\” “PerseusOutputData-NTNU-ThreeEMT-1.6.14.0-2021.01.11.xlsx”
2. Find genes with significance "+" for each EMT model.
3. Copy these genes into DAVID for "Functional Annotation Chart" analysis, for GO-BP, GO-CC and GO-MF separately
4. Export the output and copy the output .txt file into an Excel file.
5. Use R file for plotting:
   “C:\Users\lenovo\OneDrive - Háskóli Íslands\PC-HI\5-2 ProteomicPaper-UGDH\Rfiles”
   “BarPlot-DoxPlot-GO-Annotation-2021.02.08.R”

Figure 4D-E

Reactome pathway analysis
Reactome Version:
Reactome Database Release: 75
Pathway Browser Version: 3.7
Date: 2021.02.09

Procedures:

1. Go to Perseus Output data: “C:\Users\lenovo\OneDrive - Háskóli Íslands\PC-HI\5-2 ProteomicPaper-UGDH\Perseus\” “PerseusOutputData-NTNU-ThreeEMT-1.6.14.0-2021.01.11.xlsx”
2. Find genes with significance "+" for each EMT model.
3. Copy these genes into Reactome for pathway enrichment.
4. Export the output .csv file
5. Use R file for plotting:
   “C:\Users\lenovo\OneDrive - Háskóli Íslands\PC-HI\5-2 ProteomicPaper-UGDH\Rfiles\”
   “TreePlot_Reactome_Pathway_Enrichment_2021.02.09.R”
   

Figure 5A-D

Consistent metabolic EMT markers - proteomics

Figure 5E

RNA expression of UGDH

Figure 5F

UGDH proteomic expression confirmed in another study

Figure 6A

PCA plot of the metabolomic dataset

Figure 6B

Dendrogram plot of the metabolomic dataset

Figure 6C-E

Metabolite levels
UDP-Glc: BASIC mode - Phenylalanine IS; Lysine IS; Adenine IS
UDP-GlcA: BASIC mode - Cysteine IS; Glutamine IS; Glutamic acid IS
Glycerophosphocholine: Acidic negative mode - Lysine IS.

Figure 6F

Glycerophosphhocholine level in D492, D492M and D492HER2
Acidic negative mode - Glutamic acid IS.
Generated in Excel and powerpoint.

Figure 6G

IPA analysis of the phosphoproteoimc datasets

Figure 7A

Patient survival analysis of UGDH
Generated in https://kmplot.com/analysis/index.php?p=service&cancer=breast.

Go to website: https://kmplot.com/analysis/index.php?p=service
input gene UGDH
Auto select best cutoff
intrinsic subtype: basal

Figure 7B-C

Proliferation - siUGDH (only one siRNA)

Figure 7D-E

Invasion - siUGDH (first siRNA)
Generated in Excel and powerpoint.

Figure 7F-G

SNAI1 - siUGDH (first siRNA)

Figure 8A-B

Generated in Excel and powerpoint.

Figure 8C

Motif enrichment analysis from Perseus version 1.6.14.0

Figure 8D-F

siPDGFRB in D492M (only one siRNA)

Figure 8G-H

siRELA in D492M with the first siRNA

Supplementary figure 1

Cell images

Supplementary figure 2

Scatter plot of the datasets consistency
Dundee vs. NTNU (D492 vs. D492M)

Supplementary figure 3

Inconsistent EMT markers; http://dbemt.bioinfo-minzhao.org/

Supplementary figure 4

GO annotation - CC and MF

DAVID Bioinformatics Resources 6.8
Date: 2021.02.09

Procedures:

1. Go to Perseus Output data:
   “C:\Users\lenovo\OneDrive - Háskóli Íslands\PC-HI\5-2 ProteomicPaper-UGDH\Perseus\” “PerseusOutputData-NTNU-ThreeEMT-1.6.14.0-2021.01.11.xlsx”
2. Find genes with significance "+" for each EMT model.
3. Copy these genes into DAVID for "Functional Annotation Chart" analysis, for GO-BP, GO-CC and GO-MF separately
4. Export the output and copy the output .txt file into an Excel file.
5. Use R file for plotting:
   “C:\Users\lenovo\OneDrive - Háskóli Íslands\PC-HI\5-2 ProteomicPaper-UGDH\Rfiles”
   “BarPlot-DoxPlot-GO-Annotation-2021.02.08.R”
   

Supplementary figure 5A

KD efficiency of UGDH in the metabolomic study

Supplementary figure 5B-E

KD efficiency of UGDH in the other experiments

Supplementary figure 6A-C

Patient survival analysis
Generated in https://kmplot.com/analysis/index.php?p=service&cancer=breast.

Supplementary figure 6D

GPC level changes with KD in D492HER2
Generated in Excel and powerpoint.

Supplementary figure 6E-F

Invasion - siUGDH (second siRNA)
Generated in Excel and powerpoint.

Supplementary figure 6G-H

SNAI1- siUGDH (second siRNA)

Supplementary figure 7A

Generated in Excel and powerpoint.

Supplementary figure 7B-D

siPDGFRB in D492HER2 (only one siRNA)

Supplementary figure 7E-F

siRELA in D492M with the second siRNA

Supplementary figure 7G-J

siRELA in D492HER2 (with two siRNAs)