supplemental information table of contents supplemental...
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Supplemental Information Table of Contents
Supplemental Figure 1, related to Figure 1
Supplemental Figure 2, related to Figure 2
Supplemental Figure 3, related to Figure 3
Supplemental Figure 4, related to Figure 4
Supplemental Figure 5, related to Figure 5
Supplemental Figure 6, related to Figure 6
Supplemental Methods
Supplemental References
Qiao_Supplemental Materials
C
pancreas
shRedd1
shCtrl xx
p48-Cre;
LSL-KrasG12D
primary
epithelial cells
(KPECs)x
B
A
D
F
E
e.
* *
Pancreas
Liver Lung
PDAC
Qiao_Supplemental Figure 1, related to Figure 1.
Lung KrasG12D;
Redd1+/+
KrasG12D;
Redd1-/-
Metastasis 0/16 2/23
p48-C
re; K
ras
G12D;
Redd1
+/+p
48-C
re; K
ras
G12D;
Redd1
-/-
Pancreas Lung
Intratracheal
Ad-Cre
LoxP LoxP
Kras *G12D
p48 Cre
STOP 1 2*
Redd1 Neoβ-gal(exon 1)
LoxP LoxP
Kras *G12D
Redd1 Neoβ-gal(exon 1)
STOP 1 2*
G
H
Qiao_Supplemental Figure 1, related to Figure 1.
(A) Schematic of genetic alleles employed. The REDD1-/- allele replaces the entire Redd1 coding
region with the bgal/neo fusion cDNA.
(B) Tumor-free survival of pancreas cohort. Kaplan-Meier analysis of p48-Cre;KrasG12D;Redd-/-
(p48KR) mice (n = 6) and p48-Cre;KrasG12D;Redd1+/+ (p48K) mice (n = 12) based on the
presence of invasive tumors (PDAC). Mice reaching euthanasia endpoint without PDAC were
censored (tic marks). P value by log-rank test.
(C) Representative photomicrographs of H&E stained tissue sections showing metastases to liver
and lung (indicated by *) from primary PDAC arising in p48KR mice. Scale bar, 100m.
(D) Schematic representation of orthotopic pancreatic tumor model. Primary KRAS-activated
pancreatic epithelial cells (KPECs) expressing control (shCtrl) or REDD1-deficient shRNA
(shREDD1) are re-implanted into the pancreas of host mice.
(E) Representative H&E stained tissue sections of pancreas following orthotopic implantation,
showing histology of poorly differentiated PDAC.
(F) Wet weight of mouse pancreas following orthotopic implantation of REDD1 knockdown or
control primary KPECs. Horizontal lines indicate mean SD. * p = 0.038 by two tailed t-test.
(G) Analysis of the subset of AdK and AdKR mice whose lungs were prepped by perfusion at
necropsy for whole-mount sectioning. Graph at left shows the percentage of lobes with invasive
disease in the respective genotypes, as assessed with H+E (representative sections shown at
right) and with elastic stain (not shown) by an expert lung pathologist (MMK). Pre-invasive
lesions (AdK) are characterized by papillary architecture and condensed nuclei, while invasive
lesions (AdKR) lack papillary morphology and have nuclei with open chromatin and prominent
nucleoli. Scale bar, 250m.
(H) Summary of metastases identified in the lung cancer cohort, by genotype.
0 2 4 6 8
Steroid biosynthesis
Cell adhesion molecules (CAMs)
ECM-receptor interaction
Pathways in cancer
PPAR signaling pathway
Fatty Acid Biosynthesis
Biosynthesis of unsaturated fatty acids
-log 10 (P-Value)
-log 10 (P-Value)
(Gene Number)
KEGG Pathway
PPAR signaling pathway
Fatty acids biosynthesis
Pathways in cancer
ECM-receptor interaction
Cell adhesion molecules (CAMs)
Steroids biosynthesis
7
5
68
11
18
37
11
-log10 (p value)
Biosynthesis of unsaturated fatty acids
KEGG Pathway Gene number
0 2 4 6 8
A
Qiao_Supplemental Figure 2, related to Figure 2.
B
D E
F G
J
H
+/++/+
FA
bio
synth
esis
FA
transport
KrasG12D
min max
normoxia hypoxia
Redd1
Fabp3Fabp4Fabp7Slc27a1FasnScd1AcacaHmgcs1Acly
–/– –/–
β-actin
REDD1
shCtrl
shREDD1
1 2 N H
293TA549
K
Fluorescence intensity
Redd1 +/+
norm
oxia
Redd1 -/- n
ormoxia
0.0
0.5
1.0
1.5
2.0
lipid
dro
plet
s le
vel [
LD54
0]
Copy of normoxia
Redd1 +/+ normoxiaRedd1 -/- normoxia*
LD
540
[RF
U]
1.5
2.0
1.0
0.5
0.0
–/–Redd1 +/+
Redd1+/+ Redd1-/-
Counts
104 105 106
400
200
LD540 L
I
shCrl
shRedd1 (E3)
shRedd1 (F9)
0.0
0.5
1.0
1.5
re-plot A549 LPC
*
**1.5
1.0
0.5
0.0LP
C u
pta
ke [R
FU
] A549
shRedd1_1
shRedd1_2
shCtrl ++
--
-
-- +-
M
C
Qiao_Supplemental Figure 2, related to Figure 2.
(A) Loss of REDD1 confers tumorigenesis in the setting of mutant RAS expression. Primary Redd1-/-
or Redd1+/+ MEFs were co-infected with lentiviral constructs expressing the adenoviral E1A
protein and KRAS G12V, then injected into immunodeficient (nude) mice. Primary Redd1-/-
MEFs are non-tumorigenic with or without E1A expression alone (not shown). N=8 mice per
genotype. Error bars denote SD. p-value by multiple measures ANOVA.
(B) Schematic of genetic alleles employed in Figure 2.
(C) GSEA plots of RNA-seq data showing suppression of “Horton SREBF targets” signature
(systematic signature M3009) in paired primary KrasG12D;Redd1-/- versus KrasG12D;Redd1+/+
primary cells, cultured under normoxia.
(D) DAVID analysis (https://david.ncifcrf.gov/) of KEGG pathway signatures, showing an
unselected ranking by p-value of the top canonical pathways altered in RNA-seq data of
KrasG12D;Redd1-/- versus KrasG12D;Redd1+/+ primary MEFs (KRMEFs and KMEFs,
respectively). Differential expression was defined as mean > two-fold difference in triplicate
samples of each genotype when normalized by DeSeq. Cells from three mice per genotype were
analyzed.
(E) Enrichment of lipid transport genes and suppression of de novo lipogenesis genes in KRMEFs as
compared to KMEFs cultured under normoxia or hypoxia (1% O2, 18 hr). RNA-seq data from
three independent pairs of KMEFs and KRMEFs.
(F) Increased phospholipids in KRMEFs compared to KMEFs during growth under normoxia as
detected by UHPLC-MS lipidomics analysis.
(G) Uptake of Top-Fluor-LPC in immortalized Redd1-/- as compared to Redd1+/+ MEFs cultured
under normoxia and hypoxia (1% O2, 18 hr). Bar graph (right) represents quantification from
three independent experiments. Error bars indicate SD.
(H) Knockdown of REDD1 in KRAS-activated primary pancreatic epithelial cells (KPECs) by
lentiviral shRNA (shRedd1) or control vector (shCtrl), assessed by qRT-PCR analysis.
(I) TAGs enriched in KRMEFs under normoxia.
(J) TAGs enriched in KRMEFs under hypoxia remain unchanged in the corresponding culture
medium as detected by lipidomics analysis. For (I) and (J), box-and-whisker plots denote
normalized abundance of metabolite. Whiskers denote range and boxes denote SD. Triplicate
samples from each of two mice per genotype were analyzed.
(K) Increased LD540 staining of neutral lipid droplets in in immortalized Redd1-/- MEFs as
measured by flow cytometry analysis. Bar graph at right represents summary of three
independent experiments from paired Redd1+/+ and Redd1-/- MEFs.
(L) Immunoblot for REDD1 confirming shRNA knockdown of REDD1 in A549 cells. Whole cell
lysates from 293T cells cultured under normoxia (N) or hypoxia (H, 1% O2, 4h) were used as
positive control.
(M) Uptake of Top-Fluor-LPC in A549 cells with shCtrl or shREDD1 cultured under normoxia. Bar
graph represents quantification from three independent experiments. Error bars indicate SD.
For all panels, * p< 0.05, ** p < 0.01.
A
00.
10.
20.
40.
60.
8
0
50
100
150
H2O2 [mM]
Copy of Copy of Data 4
AH375 pLKOAH375 D11 shRedd1
H2O2 (mM)
0.0 0.1 0.2 0.4 0.6 0.8
Cell
via
bili
ty (
%) shRedd1
shCtrl
50
100
p < 0.001, ****
Lamin B164
98Nrf2
KrasG12D
+/+ -/-Redd1
C
Qiao_Supplemental Figure 3, related to Figure 3.
Vector Redd1-HA
Counts
DCF Fluorescence intensity
300
200
100
KPEC shRedd1
DC
F [
RF
U]
2.5
2.0
0.0
0.5
1.5
1.0
Kra
sG
12D ; R
edd1
+/+
Kra
sG12
D ; Red
d1 -/-
0.0
0.5
1.0
1.5
2.0
2.5
DCF RD1-HA
n.s.
103 104 105
DKra
s G12
D ; Red
d1 +/
+
Kra
s G12
D ; Red
d1 -/-
0.0
0.5
1.0
1.5
DC
F [R
FU
]
KRAS PMEFs DCF Ratio
DC
F[R
FU
]
1.0
0.5
0.0
*
+/+ -/-
KrasG12D
Redd1
Fluorescence intensity
102 103 105 106
KrasG12D; Redd1 +/+
KrasG12D; Redd1 -/-
Counts
100
50
DCF
104
B
(A) Decreased ROS in KRMEFS as compared to KMEFs, assessed by staining with CM-H2DCFDA.
Right: Summary from four independent experiments/mice measured in triplicate.
(B) Transfection of REDD1 induces H2O2 as compared to vector control in KPECs with stable
knockdown of endogenous REDD1. Errors bars denote SD. Graph at right shows mean of two
experiments. Error bars denote SD.
(C) Immunoblot showing equal NRF2 protein levels in nuclear extracts of KMEFs and KRMEFs.
TBHQ (Tert-butylhydroquinone) treatment serves as a positive control for NRF2 induction.
(D) Ablation of REDD1 in primary KPECs induces resistance to oxidative stress (H2O2 treatment).
Bars indicate mean of two experiments performed in duplicate. p-value by repeated
measurements of ANOVA.
Unless otherwise noted, for all panels error bars denote SD.
Qiao_Supplemental Figure 4, related to Figure 4.
Vehic
leB
SO
KrasG12D;Redd -/- (AdKR) visible orthotopic tumors were harvested from mice treated with either
vehicle or glutathione synthase inhibitor buthionine sulfoximine (BSO) at the end of the experiment.
Qiao_Supplemental Figure 5, related to Figure 5.
ED
F H
D11
norm
oxia
D11
hyp
oxia
24hr
D11
hyp
oxia
24hr +
LW
6 20
µM
D11
hyp
oxia
24hr +
PX47
8 10
µM
0.0
0.5
1.0
1.5
Copy of only high dose
normoxiahypoxia 24hrhypoxia 24hr + LW6 hypoxia 24hr + PX478
1.5
1.0
0.5
0.0
Rela
tive m
RN
A
**
***PparƔ
+Hypoxia 18hr
-Normoxia
+LW6
--+
+
-
---
- --+
PX478
+
G
KrasG
12D ; Redd1 +
/+ norm
oxia
KrasG
12D ; Redd1 -
/- norm
oxia
KrasG
12D ; Redd1 +
/+ hypoxia
KrasG
12D ; Redd1 -
/- hypoxia
0
2
4
6
Nor
mal
ized
[log
2]
*Normalized PPARg_061918
Normoxia Hypoxia
**
**6
4
2
0
PparƔ
Rela
tive m
RN
A
KrasG12D; Redd1+/+
shRed
d1_1
shRed
d1_2
0.0
0.5
1.0
1.5
Copy of LPC SSO_summary
VechicleSSO KrasG12D; Redd1-/-
I
4
2
0
Normoxia Hypoxia
CD36
Rela
tive m
RN
A
6
Kra
sG12
D ; Red
d1 +/
+ norm
oxia
Kra
sG
12D ; R
edd1
-/- norm
oxia
Kra
sG
12D ; R
edd1
+/+ hyp
oxia
Kra
sG12
D ; Red
d1 -/- h
ypoxi
a
0
2
4
6
Norm
aliz
ed [lo
g 2
]
*Normalized CD36_061918
*
*
shRed
d1_1
shRed
d1_2
0.0
0.5
1.0
1.5
Ah375 replot GW9662
Vechicle
GW9662
LP
C u
pta
ke [R
FU
]
1.0
0.5
0.0
VehicleGW9662
shRedd1_2 +-
shRedd1_1 + -
**
shRed
d1_1
shRed
d1_2
0.0
0.5
1.0
1.5
Copy of LPC SSO_summary
VechicleSSO
1.5
Fluorescence intensity
Counts
GW9662Vehicle
TopFluor-LPC
150
50
100
104 105
148HIF1α
+/+ -/- Redd1
KrasG12D
Lamin B1
Nuclear extract
B C
Kra
s;Red
d1 +/+
Kra
s;Red
d1 -/-
0
1
2
3
mR
NA
fo
ld in
du
cti
on
[vs. A
CT
B]
qRT-PCR of GLUT1
KrasG12D;Redd1 +/+KrasG12D;Redd1 -/-
Rela
tive m
RN
A
3
2
1
0
**
Glut1
-/-Redd1 +/+
KrasG12D
J
A
K
Qiao_Supplemental Figure 5, related to Figure 5.
(A) Knockdown of REDD1 in primary KPECs increases protein level of HIFα as detected by
IP/Western analysis under hypoxic conditions (1% O2). IgG serves as a control for IP.
(B) Immunoblot detection of HIF1 protein level in nuclear extracts from paired primary KRMEFs
and KMEFs (normoxia). Two independent experiments were performed.
(C) Expression of Glut1 in KMEFs and KRMEFs, assessed by qRT-PCR analysis. Graph shows
mean of three experiments. Error bars denote SD.
(D) Decreased basal O2 consumption rate (OCR), mitochondrial ATP synthesis (ATP) and maximal
respiratory capacity (MRC) in KRMEFs as measured via Seahorse XFe96. Shown is a
representative experiment, performed three times.
(E) Heatmap depicting significantly altered glycolytic, PPP and TCA metabolite levels in KRMEFs
versus KMEFs as analyzed by UHPLC-MS. Steady-state metabolomics data were normalized to
sample median by MetaboAnalyst. PPP: Pentose phosphate pathway, TCA: tricarboxylic acid
cycle. Triplicate samples from two mice per genotype were analyzed.
(F) Quantification of PPARγ expression levels in KRMEFs and KMEFs cultured under normoxia
and hypoxia (1% O2, 18 hr) as analyzed by RNA-seq analysis. n = 4 independent cultures for
normoxia; n = 3 for hypoxia. Error bars denote SEM.
(G) Induction of PPARγ mRNA under hypoxia (1% O2, 18 hr) in KPECs expressing a REDD1
shRNA can be suppressed by co-treatment with HIF1 inhibitors including LW6 (20M) and
PX478 (10M), as assessed by qRT-PCR analysis. Data represent two experiments performed in
duplicate.
(H) Topfluor-LPC uptake is inhibited by the PPARγ antagonist GW9662 treatment (20M, 12 hr) in
REDD1-ablated KPECs.
(I) Quantification of three independent experiments showing Topfluor-LPC uptake inhibited by
GW9662 treatment as described in (H).
(J) Quantification of CD36 expression levels in KMEFs and KRMEFs cultured under normoxia and
hypoxia (1% O2, 18 hr), as analyzed by RNAseq analysis. n=4 independent cultures for
normoxia; n=3 for hypoxia.
(K) CD36 expression is negatively correlated with REDD1 expression in Patient-Derived Xenograft
(PDX) models of pancreas carcinoma. Values reflect tumor-intrinsic REDD1 levels, as non-
tumor elements are largely murine-derived. Data were derived from the Mouse Models of
Human Cancer Database (MMHC, formerly MTB), Mouse Genome Informatics, The Jackson
Laboratory, Bar Harbor, Maine (http://tumor.informatics.jax.org/). Pearson r and p value (two-
tailed) are shown.
Unless otherwise specified, error bars denote SD.
For all panels, * p < 0.05, ** p < 0.01, *** p < 0.001 by two-tailed t-test.
Qiao_Supplemental Figure 6, related to Figure 6.
B
A
(A) REDD1 expression in normal human cells/tissues. Data were ranked from the highest (left) to
the lowest (right) according to REDD1 expression. Mean fold-difference in expression versus
normal lung or pancreas is shown at top. Data were obtained from the Genotype-Tissue
Expression (GTEx) Project database (https://gtexportal.org/home/).
(B) Proportion of smokers with RAS-mutant lung adenocarcinoma is not significantly different
based on REDD1 gene expression signature. Shown is the TCGA RAS-MUT LUAD population,
stratified into top (high) and bottom (low) quartiles of the signature metagene values as in Fig.
6A. P value by chi-square test.
Analysis of REDD1 expression in human cells/tissues
Data for Supplemental Figure S6A were derived from The Genotype-Tissue Expression (GTEx)
Project. The GTEx Project was supported by the Common Fund of the Office of the Director of
the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS.
The data used for the analyses were obtained from the GTEx Portal
(https://gtexportal.org/home/ ) on 01/17/2020.
Generation of the REDD1-associated gene expression signature
Gene expression values were derived from RNA-Seq data for KRMEFs compared to KMEFs (4
vs. 4 samples). Reads were aligned to the mm10 reference genome with BWA by the MGH
sequencing core. Bam to Sam file conversion, sorting, indexing, and file merging was done
with SamTools (Li et al., 2009). FPKM values (Fragments per Kilobase of transcript Per
Million mapped reads) were calculated by Cufflinks (Trapnell et al., 2010) version 2.2.1 suit of
tools. First, cufflinks was used to normalize the results for the assembled isoforms using flags
to normalize using only compatible hits that map to the transcriptome and only those in the
upper quartile (and also masked chrM and rRNA). Second, cuffquant was run to quantize reads
with rRNA masking. Finally, cuffnorm was run to produce normalized FPKM. Cufflinks
FPKM was loaded into a matrix in R, quantile normalized, and then a variation filter was
applied to remove genes with less than 1.5 fold minimum variation and 2 minimum absolute
variation (leaving 4124 out of 23235 genes). A t-test was then performed to find genes
significantly varying between KRMEF and KMEF and corrected for multiple hypothesis testing
Qiao_Supplemental Methods.
using the Benjamini-Hochberg (Benjamini & Hochberg, 1995) step-up FDR-controlling
procedure. Genes from the KRMEFs vs. KMEFs with a p-value less than 0.05 were selected
leaving 415 genes (197 up in KRAS null and 218 down in KRAS null), then X and Y
chromosome genes were removed from the signatures leaving 187 up and 210 down genes,
which were used to make a meta-gene to analyze the signature in patient tumor samples. These
genes were mapped to genes in TCGA RNASeqV2 data leaving 159 genes up and 180 genes
down. The meta-gene was made from mean of the log2 of KRAS null up genes thresholded to a
minimum of 0.1 minus the mean of the log2 of KRAS null down up genes thresholded to a
minimum of 0.1.
Qiao_Supplemental Methods.
Benjamini Y, Hochberg Y. 1995. Controlling the False Discovery Rate: a Practical and Powerful
Approach to Multiple Testing. Journal of the Royal Statistical Society 57: 11.
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R,
Genome Project Data Processing S. 2009. The Sequence Alignment/Map format and
SAMtools. Bioinformatics 25: 2078-2079.
Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold
BJ, Pachter L. 2010. Transcript assembly and quantification by RNA-Seq reveals
unannotated transcripts and isoform switching during cell differentiation. Nat
Biotechnol 28: 511-515.
Qiao_Supplemental References.