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 . 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 Conditions Description samples GSE3537 Homo sapiens 69 Hypoxia vs normoxia Cell lines of human breast epithelial cell, renal proximal tubule epithelial cell, endothelial cell, and smooth muscle cell 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 GSE1357 Rattus 24 Hypoxia by ischemia Hippocampus cell of hypoxia-sensitive and hypoxia-tolerant rat norvegicus 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 Hypoxia for extended hours Human Lung endothelial and cardiac cells vs normoxia 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

1 and normal common melanocytic nevus GSE11223 Homo sapiens 202 Inflammatory vs normal Ulcerative colitis inflamed colon tissue vs uninflamed colon tissue TCGA data Homo sapiens 254 Cancer vs normal Colon carcionoma vs normal colon tissue archive

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 dif ferentially expressed under hypoxic versus normoxic conditions. Consider a data set D of expression l evels of N genes collected on a set of samples. We rank its genes {g1, …, gN} in the increasing order o f their expression levels averaged over all the samples. In the following analysis in this section and Su pplementary Section C, we assess if a subset of genes S of D is differentially expressed under the abo ve two conditions based on the overall change in the rankings of the genes of S in the whole ranking li sts of D’s genes under the two conditions. Previous studies have shown that such a non-parametric me thod is highly stable and reliable for assessing changes in gene-expressions under different conditions and even across different organisms . 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 consumptio n in hypoxic condition, defined by

2 ,

and the reduced gene expressions ( ) of the six groups of proteins defined in Supplementary Secti on 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 th e 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 lev el. Then we estimated the parameters a and b in the linear model: , which optim ally match the collected and values for the two organisms and the assumption of when =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 the ir gene expression data under hypoxic versus normoxic conditions as in Supplementary Section B. Fig ure 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 consu mptions during hypoxia. It is also known that blind mole rats have a larger reduction than that of hypo xia-tolerant rats and naked mole rats and further than those of human, mouse and hypoxia-sensitive ra ts. 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 prediction of reduced ATP consumption based on the reduced gene expression levels of the relevant proteins.

3 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 the re is a strong correlation between changes in protein abundance and in the mRNA level of the corresp onding gene 11 in general. For these six groups of proteins, we note that the majority of their mRNA a nd proteins have relatively long half-lives, which is one essential factor for highly correlated mRNA e xpression and protein abundance 12, confirming that the above general observation on mRNA and prot ein abundance levels applies to these six groups of proteins. In addition, Michaelis–Menten kinetics di rectly 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 reduct ion in ATP demand by naked mole rats is smaller than that by blind mole rats, which is smaller than th ose 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 su rvive months of anoxia (<1% oxygen) 14-16. Hence the predicted reduction percentages in ATP consum ption 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 translatio n and Na+/K+ ATPase activity that are consistent to the previous experimental observations . In comp arison, there is a significant reduction in Ca2+ ATPase activity in turtle comparing to the naked mole r ats 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 tempora lly 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 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

4 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 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) ver

5 sus normoxic (21% oxygen) conditions using nuclear magnetic resonance (a manuscript in preparatio n). Increased cellular abundances of valine, leucine, isoleucine, alanine, glutamate, glutamine, creatin e and choline were observed while decreased abundances of threonine, glycine, tyrosine, succinate an d 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 an d human breast epithelial cells under hypoxia suggests that 10 of the 20 amino acid types are accumul ated due to increased transportation and increased biosynthesis. Statistical analysis along with flux dis tribution analysis reveals that (i) increased synthesis of glutamate from histidine, proline and glutamin e, (ii) increased metabolism from glutamate to succinate, (iii) increased furmarate production from asp

6 artate, ornithine, citruline, arginine, and tyrosine, (iv) increased glycine production from serine and ch oline, and (v) increased reaction rate from pyruvate to alanine. In addition, decreased metabolism rate s 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

8 colon ulcerative colitis, inflammatory bowel disease and adenoma tissues with human normal colon 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.

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