Supplementary Material
A. Data used in this study
ATP consumption data for turtle brain cortical cells and hepatocytes, frog skeletal muscle cell,
naked mole rat cell, and Wistar rat thymocyte cells are collected from the published literature
1-5
. We
have also collected the matching gene-expression data of these cells used in the same studies on
proteins of the following biological processes: translation (GO0006412), sodium-potassium exchange
ATPase activity (GO0005391), calcium transporting ATPase activity (GO0005388), gluconeogenesis
(GO0006094), actin-activated ATPase activity (GO00030898) and urea genesis from the GO and
KEGG databases
6, 7
. In addition, we have collected gene-expression data for the same six groups of
proteins in the human breast epithelial cell, renal proximal tubule epithelial cell, B lymphocyte,
endothelial cell, smooth muscle cell, breast cancer cell line, rat hippocampus cell and mouse brain,
heart, lung and muscle cells as well as RNA-seq data of naked mole rats under both hypoxia and
matching normoxia from the GEO database. Also we have collected gene-expression data for human
breast tissues with and without BRCA mutations, inflammatory and normal colon tissue, precancerous
colon tissue and TCGA RNA-seq data of colon carcinoma. The following table provides the detailed
information about these datasets.
Supplementary Table 1: Gene expression data used in this study
GEO ID
Species
Number of
samples
Conditions
Description
Cell lines of human breast epithelial cell, renal proximal tubule
GSE3537
Homo sapiens
69
Hypoxia vs normoxia
GSE4086
Homo sapiens
4
Hypoxia vs normoxia
Cell lines of human B lymphocyte
GSE480
Mus musculus
20
Hypoxia vs normoxia
Mouse brain, heart, lung and muscle cells
GSE3763
Blind mole rats
12
Hypoxia vs normoxia
Muscle tissue
24
Hypoxia by ischemia
GSE1357
Rattus
norvegicus
epithelial cell, endothelial cell, and smooth muscle cell
Hippocampus cell of hypoxia-sensitive and hypoxia-tolerant rat
tissue
GSE13671
Homo sapiens
14
BRCA1 mutation
Normal breast tissue with and without BRCA mutations
GSE17072
Homo sapiens
20
BRCA1 mutation
Normal breast tissue with BRCA mutation from breast cancer patients
GSE19338
Mus musculus
24
Normoxia
Mouse colon tissue with and without APC mutation
GSE7307
Homo sapiens
677
Normoxia
Collection of 70 human cell types
GSE30337
Naked mole rats
13
Hypoxia vs normoxia
Transcriptome sequencing of naked mole rat tissue
GSE9649
Homo sapiens
30
Control vs Lactate/hypoxia
Human mammalian epithelial cells
GSE29406
Homo sapiens
12
Control vs Lactate/hypoxia
MCF7 cells
GSE11341
Homo sapiens
23
GSE4183
Homo sapiens
15
Precancerous vs normal
Colon inflammatory bowel diseases vs normal colon tissue
GSE4183
Homo sapiens
15
Precancerous vs normal
Colon adenoma vs normal colon tissue
GSE12391
Homo sapiens
114
Precancerouse vs cancer
Dysplastic nevus, radial and vertical growth phase melanoma vs
Hypoxia for extended hours
vs normoxia
1
Human Lung endothelial and cardiac cells
GSE11223
TCGA data
archive
Homo sapiens
Homo sapiens
and normal
common melanocytic nevus
202
Inflammatory vs normal
Ulcerative colitis inflamed colon tissue vs uninflamed colon tissue
254
Cancer vs normal
Colon carcionoma vs normal colon tissue
B. Definition of substantially reduced expression levels
We have used a non-parametric method introduced in
8
to assess if a specified set of genes are
differentially expressed under hypoxic versus normoxic conditions. Consider a data set D of
expression levels of N genes collected on a set of samples. We rank its genes {g1, …, gN} in the
increasing order of their expression levels averaged over all the samples. In the following analysis in
this section and Supplementary Section C, we assess if a subset of genes S of D is differentially
expressed under the above two conditions based on the overall change in the rankings of the genes of
S in the whole ranking lists of D’s genes under the two conditions. Previous studies have shown that
such a non-parametric method is highly stable and reliable for assessing changes in gene-expressions
under different conditions and even across different organisms 9, 10.
For a target gene set S of D, define
where
. The expression level score (ELS) of S is defined as
.
The ELS(S) value reflects the overall rankings of the expression levels of the genes in gene set S
among all the genes in D. Let ELS(S, C) represent the expression-level score of S in the data set of
condition C; we use the value
to assess the altered overall expression levels of S under a hypoxic versus the matching normoxic
condition.
C. A regression model for ATP-consumption reduction versus reduced expression levels
We have derived a linear regression model between the reduction percentages in ATP
consumption in hypoxic condition, defined by
2
,
and the reduced gene expressions (
) of the six groups of proteins defined in Supplementary
Section A using publicly available data for naked mole rats and hypoxia-tolerant rats measured using
the
values. From the available data, we noted that the ATP consumption reduction percentage
is smaller and the
value is larger for hypoxia-tolerant rats than those for naked mole rats.
For the linear regression model, we assume
when
=0, namely the ATP consumption
will not have significant changes when the expression level of ATP consumption genes remain at the
same level. Then we estimated the parameters a and b in the linear model:
which optimally match the collected
assumption of
when
and
,
values for the two organisms and the
=0. The following gives the model parameters we derived:
Supplementary Table 2: Regression coefficient estimates
Translation
-5.056
1.009
Sodium-potassium exchange ATPase activity
-0.6026
1.110
Calcium transporting ATPase activity
-0.8246
0.997
Gluconeogenesis
-1.919
1.002
Urea genesis
-1.012
0.995
Actin-activated ATPase activity
-0.979
1.004
We then applied this model to the
values estimated for human, mouse and rats based on
their gene expression data under hypoxic versus normoxic conditions as in Supplementary Section B.
Figure 1 of the main text shows the estimated reduction percentages in ATP consumption values for
these organisms. In the analysis, we assumed that the proportions of the ATP consumption by each
relevant process are comparable across all the species examined.
Based on our literature search, turtle and frogs are known to substantially repress their ATP
consumptions during hypoxia. It is also known that blind mole rats have a larger reduction than that of
hypoxia-tolerant rats and naked mole rats and further than those of human, mouse and
hypoxia-sensitive rats. These qualitative data supports the data shown in Figure 1 in the main text.
D. Validation of the ATP reduction regression model
Here we provide a rationale as well as supporting evidence for our regression model for
3
prediction of reduced ATP consumption based on the reduced gene expression levels of the relevant
proteins.
1. The foundation for our regression model to be meaningful is that there is a strong correlation
between the reduced gene expression levels of the six groups of proteins (see Supplementary Section
A) and the reduced activity levels of these proteins. Only when such correlation exists will the derived
regression model be statistically meaningful. To see this, note that it has been well established that
there is a strong correlation between changes in protein abundance and in the mRNA level of the
corresponding gene
11
in general. For these six groups of proteins, we note that the majority of their
mRNA and proteins have relatively long half-lives, which is one essential factor for highly correlated
mRNA expression and protein abundance 12, confirming that the above general observation on mRNA
and protein abundance levels applies to these six groups of proteins. In addition, Michaelis–Menten
kinetics directly indicates that increased enzyme abundance will result in increased enzymatic
activities 13.
2. The relative order among the eight organisms under consideration in terms of their reduction
percentage in ATP consumptions predicted by our model is consistent with a number of published
data, hence providing supporting evidence that our model is meaningful. For example, the predicted
reduction in ATP demand by naked mole rats is smaller than that by blind mole rats, which is smaller
than those by frog and turtle. It is known that the naked mole rats typically require a minimum level
of 10% oxygen in its habitat while blind mole rats can stay viable at 3% oxygen while turtles and
frogs can survive months of anoxia (<1% oxygen)
14-16
. Hence the predicted reduction percentages in
ATP consumption are highly consistent with this knowledge about these organisms.
3. Note from Figure 1 of the main text that the proteins with the largest reductions are translation
and Na+/K+ ATPase activity that are consistent to the previous experimental observations
3, 17
. In
comparison, there is a significant reduction in Ca2+ ATPase activity in turtle comparing to the naked
mole rats and blind mole rats. This comparison result is consistent with the knowledge that turtle can
reduce its Ca2+ ATPase during months of anoxia while those hypoxia tolerant rats and mole rats can
temporally deal with altered calcium regulation and human cannot deal with it 18-20.
Based on these evidences, we assert that our regression model can accurately predict the ATP
demand reduction percentages in hypoxic versus normoxic conditions.
E. Accumulation of amino acid and fatty acid metabolites during hypoxia
Our analysis of the transcriptomic data of two human epithelial cell lines shows that multiple
transporters for amino acids are up-regulated during hypoxia, particularly transporters for glutamate
(see Supplementary Material Section F). Previous studies have shown that protein-synthesis decreases
during hypoxia, which suggests that amino acids are taken up for ATP production 21. Recent studies
have identified a connection between the cellular oxygen level and nitric oxide signaling, which can
trigger the uptake of amino acids
22
. Multiple studies have observed increased amino-acid
4
accumulation in hypoxic cells, indicating that the cells take in more amino acids than they can handle
as in the case of glucose
23
. We can see from Supplementary Figure 1 why some amino acids get
accumulated in hypoxic cells since their hydrolysis to ATP goes through portions of the glycolysis
pathway, which are already congested as discussed earlier. In addition, the activity of the TCA cycle
will diminish due to the reduced supply of NAD+ from the electron transfer chain caused by hypoxia.
It is worth noting that glycolysis pathway can also be fueled by glutamate and glutamine through
glutaminolysis via a section of the TCA cycle 24. Our analysis clearly shows that the accumulation of
glucose metabolites along the glycolysis pathway is not caused by these nutrients since key
glutaminolysis genes such as malate dehydrogenase (MDH1, MDH2) and succinate dehydrogenase
(SDHA), are down-regulated in precancerous and early cancer stages (see Supplementary Material
Section F and G and Supplementary figure 2).
A similar analysis shows that a number of fatty acid transporters are up-regulated (see
Supplementary Material Section F) by hypoxia, which is consistent with a previous study showing
that hypoxia can lead to the accumulation of glycerol and lipids 25. From the above discussion, we can
see that the two pathways for hydrolyzing fatty acids to ATP, i.e., β-oxidation to acetyl-CoA and
conversion to dehydroxyacetone, are already congested by glucose metabolism or reduced by the lack
of NAD+, hence leading to the accumulation of fatty acids observed in hypoxic cells.
Because of the overlapping nature among the pathways for energy metabolism for glucose,
amino acids and fatty acids, the accumulation of one nutrient type can lead to the accumulation of
another. It is known that some cancers rely more on lipids rather than glucose for ATP production such
as prostate cancer
26
. Discussion here suggests that the glycolysis pathway can be congested under
hypoxic conditions even if only limited glucose metabolism is being used due to the other
metabolisms.
F.
Up-regulated transporters for amino acids and fatty acids and accumulations in hypoxia
We have examined a set of transcriptomic data of the human breast epithelial cells under hypoxic
versus normoxic conditions (GEO dataset GSE3537), and found that the glutamate transporter
SLC1A6 and the fatty acid transporters SLC27A1-SLC27A4 are substantially up-regulated under
hypoxia. Two additional amino-acid transporters SLC7A2 (for arginine, lysine and ornithine)
and SLC7A8 (for alanine, serine, threonine, cysteine, phenylalanine, tyrosine, leucine, arginine and
tryptophan) are moderately up-regulated.
We did a similar analysis on a set of transcriptomic data of the human renal proximal tubule
epithelial cell under hypoxic versus normoxic conditions (GEO dataset GSE3537) and found that the
following transporters are up-regulated: SLC1A6 (for glutamate), SLC1A3 (for glutamate), SLC7A2
(for arginine, lysine and ornithine), SLC7A5 (for phenylalanine, tyrosine, leucine, arginine and
tryptophan) and SLC7A9 (for cysteine and neutral and dibasic amino acids) as well as fatty acid
transporters SLC27A1 and SLC27A3. In addition, transporters SLC7A1 (for arginine, lysine and
5
ornithine) and SLC7A11 (specific for cysteine and glutamate) are up-regulated in a later time than the
other transporters.
We have collected metabolomic data of human cell line under hypoxic (5% oxygen or lower)
versus normoxic (21% oxygen) conditions using nuclear magnetic resonance (a manuscript in
preparation). Increased cellular abundances of valine, leucine, isoleucine, alanine, glutamate,
glutamine, creatine and choline were observed while decreased abundances of threonine, glycine,
tyrosine, succinate and phosphocholine were found under hypoxia 23.
Supplementary Figure 1: A schematic for energy metabolisms. The pathways colored in blue, orange
and green are for glycolysis, fatty acid energy metabolism and glutaminolysis, respectively.
Our analysis of gene-expression data collected on human renal proximal tubule epithelial cells
and human breast epithelial cells under hypoxia suggests that 10 of the 20 amino acid types are
6
accumulated due to increased transportation and increased biosynthesis. Statistical analysis along with
flux distribution analysis reveals that (i) increased synthesis of glutamate from histidine, proline and
glutamine, (ii) increased metabolism from glutamate to succinate, (iii) increased furmarate production
from aspartate, ornithine, citruline, arginine, and tyrosine, (iv) increased glycine production from
serine and choline, and (v) increased reaction rate from pyruvate to alanine. In addition, decreased
metabolism rates were found for valine, leucine and isoleucine. Differentially expressed genes are
evaluated by using moderated t-test using 0.05 as the statistical significance cutoff 27.
G. Transcriptomic data analysis of hyaluronic acid synthesis induced by hypoxia
We have collected and used three sets of gene-expression data, GSE3537, GSE9649 and
GSE29406, of human cells treated by hypoxic condition. Our analysis clearly shows that hypoxia can
lead the production of hyaluronic acid through over-expression of HAS1, and other enzymes in
hyaluronic acid synthesis pathways including HK1, PGM3, GPI, GFPT2 and UAP1 (Supplementary
Figure 2).
Although several glutamine transporters show up regulation in hypoxic condition, the enzymes,
MDH and SDH, of the key steps in glutaminolysis pathway are significantly down regulated,
indicating the glycolytic accumulation in hypoxic condition is not fueled by glutamine.
7
Supplementary Figure 2: Differentially expressed genes in hyaluronic acid synthesis pathway,
hypoxia response genes, glutaminolysis and TCA cycle in different tissue types. The columns from
left to right represent (1) human hypoxia vs normoxia (GSE3537), (2) human hypoxia vs normoxia
(GSE9649), (3) human hypoxia vs normoxia (GSE29406), (4) human inflammatory colon tissue with
ulcerative colitis vs normal colon, (5) human colon inflammatory bowel disease vs normal colon, and
(6) human colon adenoma vs normal colon tissue. Differentially expressed genes are examined by
using SAM and 0.05 as the statistical significant cutoff 27.
H. Transcriptomic data analysis of inflammatory and precancerous tissues
We have collected and used two sets of gene-expression data, GSE11341 and GSE4183, of human
colon ulcerative colitis, inflammatory bowel disease and adenoma tissues with human normal colon
8
tissues as control group. Our analysis shows up-regulation by hypoxia-response genes and hyaluronic
acid synthesis genes, including HIF1A, LDHA, SLC2A1, SLC16A1, HK1, PGM3, GPI, GFPT2 and
HAS1, and down-regulation by TCA cycle genes, MDH and SDH, in the inflammatory and
precancerous tissue (Supplementary Figure 2). It is worth noting that the up-regulation of HIF1A is
consistently observed in all conditions while MYC is only over-expressed in one hypoxic experiment,
indicating that the up-regulation of the above hypoxia-response genes are induced by HIF rather than
MYC in the inflammatory and precancerous tissues.
I. Transcriptomic data analysis of melanoma samples of different stage.
We have collected and used the gene-expression data set, GSE12391, of human melanoma
samples of different stages including dysplastic nevus, radial growth phase melanoma, and vertical
growth phase melanoma with normal melanocytic nevus as control group. Our analysis shows
up-regulation by hyaluronic acid synthesis genes and related signals, including PGM3, UGDH,
GFPT2, GNPNAT1, UAP1, UAP1L, HAS3, and TGFB1 in the precancerous tissues (dysplastic nevus)
and invasive melanoma tissues (vertical growth phase melanoma) (Supplementary Figure 3). This
expression pattern is highly consistent with the corresponding genes in precancerous and cancerous
colon tissues shown in Figure 3 in the main text. Particularly this is consistent with the general
knowledge that hyaluronic acid plays key roles in cancer invasion and metastasis
28
and our model
here on cancer initiation. Note that no exporter gene is included here since no hyaluronic
acid-exporter gene has been identified in melanycytes unlike that the exporter gene for hyaluronic
acid in fibroblasts is known to be ABCC5 (ATP-Binding Cassette, Sub-Family C (CFTR/MRP),
Member 5) 29 and the exporter gene is CFTR (Cystic Fibrosis Transmembrane Conductance Regulator
(ATP-Binding Cassette Sub-Family C, Member 7)) in epithelial cells 30.
9
Supplementary Figure 3: Differentially expressed genes in hyaluronic acid synthesis pathway and
related signaling genes in dysplastic nevus, radial growth phase melanoma, and vertical growth phase
melanoma (from top to bottom). Blue and red are for significantly up and down regulation in the
disease samples comparing to normal melanocytic nevus, respectively while gray represents no
significant changes.
J.
Statistical inference of the occurrence order of cancer hallmark events
We have previously analyzed the gene expression data of precancerous colon tissue, early stage
colon cancer tissues along with other six other solid-cancer types, namely renal cell carcinoma,
10
hepatocellular carcinoma, non-small cell lung cancer pancreatic cancer, prostate cancer, gastric
adenocarcinoma. Our analysis results indicate that (1) HIF1α and HIF1α-regulated genes, such as
glucose/lactate transporters, glycolysis, and oxidative phosphorylate, show substantial up-regulation
in the majority of the samples under consideration; and (2) the percentage of early stage cancer
samples with such up-regulated genes is consistently higher than the percentage of early stage cancer
samples with the marker genes in each of the other cancer hallmark events such as angiogenesis,
apoptosis, immune responses and metastasis. A detailed Bayesian analysis based on these data shows
with high confidence that cellular hypoxia takes places at least as early as the dysregulation of genes
involved in all the other cancer hallmark events. A manuscript is ready for submission and available at
http://csbl.bmb.uga.edu/publications/materials/zhangchi/comparative_cancer_study.pdf.
11
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