DOWN-REGULATION OF AQUAPORIN 9 BY NATURAL PRODUCT PHLORETIN MEDIATES

LUMINAL BLADDER CANCER MIGRATION IN VITRO

by Justin Hui

A thesis submitted to Johns Hopkins University in conformity with the requirements for

the degree of Master of Science in Engineering

Baltimore, Maryland May 2020

© 2020 Justin Hui

All rights reserved

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Abstract

An altered redox balance is a common hallmark of cancer progression and drug

resistance. There is a known controversy behind the use of antioxidants or pro-oxidants as

treatments to limit cancer progression. The role of reactive oxygen species (ROS) is a largely

unexplored field in the context of the intrinsic subtypes of bladder cancer. In this study, we

used the bladder cancer dataset acquired from The Cancer Genome Atlas to identify differences

in the expression profile of ROS-associated mRNA between the intrinsic subtypes, basal and

luminal. Interestingly, pathway enrichment identified oxidant detoxification as an upregulated

pathway in basal tumours. Further analysis of a curated list of genes involved in H2O2 illustrated

marked contrasts between the redox balance of basal and luminal bladder cancers. We

identified a 2.5 and 1.95 fold higher expression of the H2O2 producing genes SOD2 and SOD3 in

basal tumours than luminal tumours. Additionally, there was a 2.69 fold increase in

transcription of the H2O2 transport protein AQP9 in basal tumours. Basal and luminal cell lines

exposed to the AQP9 inhibitor Phloretin showed strong efficacy to inhibit cell migration.

Luminal cell line, RT112, experienced a 26% reduced wound closure in response to 20M of

Phloretin. In contrast, basal cell line, T24, showed no response to 20M of Phloretin. This

matched the lack of AQP9 expression in basal cancer cell lines. The limited migration of T24 in

the presence of Phloretin indicates better intracellular antioxidation as Phloretin limits

extracellular release of H2O2. Without a strong antioxidation, enhanced intracellular

accumulation of H2O2 would lead to cytotoxicity. We speculate that luminal bladder cancers are

more susceptible to increased H2O2 levels leading to reduced migration and proliferation.

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Advisor: Dr. Anirudha Singh; Readers: Dr. Kevin Yarema, Dr. David Gracias

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Table of Contents

ABSTRACT ........................................................................................................................................................ II

LIST OF TABLES ................................................................................................................................................V

LIST OF FIGURES .............................................................................................................................................. VI

INTRODUCTION ............................................................................................................................................... 1

MATERIALS AND METHODS .............................................................................................................................. 6

ANALYSIS OF TCGA BLADDER CANCER DATASET ................................................................................................................... 6

ANALYSIS MRNA EXPRESSION PROFILE OF BLADDER CANCER CELL LINES .................................................................................... 6

CELL CULTURE ............................................................................................................................................................... 7

IMMUNOFLUORESCENCE (NO DATA)................................................................................................................................... 7

FLOW CYTOMETRY (NO DATA) .......................................................................................................................................... 7

PEROXIDE ASSAY (NO DATA) ............................................................................................................................................. 8

WOUND HEALING ASSAY ................................................................................................................................................. 8

STATISTICAL ANALYSIS ..................................................................................................................................................... 9

RESULTS ........................................................................................................................................................ 12

DIFFERENTIAL EXPRESSION ANALYSIS OF ROS RELATED MRNA BETWEEN TCGA SUBTYPES ......................................................... 12

MIGRATION................................................................................................................................................................. 17

DISCUSSION ................................................................................................................................................... 18

CONCLUSION ................................................................................................................................................. 21

REFERENCES .................................................................................................................................................. 22

CURRICULUM VITAE ....................................................................................................................................... 29

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List of Tables

Table 1. List of self-curated H2O2 related genes that are differentially expressed between basal

and luminal tumours ..................................................................................................................... 13

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List of Figures

Figure 1 ROS metabolic pathway. Image adapted from Lennicke et al. Ref 16 ............................. 2

Figure 2 Clustered BLCA TCGA dataset with redox-associated genes. ......................................... 10

Figure 3 Pathway enrichment of gene clusters in Figure 2 .......................................................... 11

Figure 4 Curated ROS associated mRNA expression profile of BLCA TCGA dataset..................... 16

Figure 5 Curated ROS associated mRNA expression profile in 30 bladder cancer cell lines ........ 17

Figure 6 Wound closure of RT112 and T24 cell lines in response to Phloretin over 24h. ............ 18

1

Introduction

Bladder cancer is the fourth most common cancer in men and cause nearly 18,000

deaths in the United States1. Bladder cancer tumours are staged based on the tumour-node-

metastasis (TNM) system and directly translate to the extent of invasion2. Bladder cancer can

be largely categorized into non-muscle invasive (NMIBC) and muscle invasive (MIBC). NMIBCs,

stage Ta, rarely progress to invasion which results in a five-year survival of ~90% whereas

MIBCs, stage T2 and above, have a five-year survival of <50%3. Thus, MIBC is of particular

interest due to its propensity for invasion that leads to metastasis. Over the past couple years,

research conducted on MIBCs have shown considerable genomic and cellular heterogeneity.

Much like breast cancer, transcriptomic studies on bladder cancer have shown distinct intrinsic

molecular subtypes. According to a study conducted by the University of North Carolina (UNC),

MIBCs can be subtyped to either a basal or luminal phenotype4. It is generally accepted that

basal MIBCs are more aggressive than luminal MIBCs. Basal MIBCs are associated with

advanced stages, metastatic disease at diagnosis, and poor overall survival5–8. Currently, efforts

are primarily focused on further characterizing the subtypes to identify better suited molecular

targets for drug therapy. However, with recent trends identifying the many roles of the tumour

microenvironment in cancer behaviour we aimed to further understand how cancer cells are

able to remodel and alter normal tissue homeostasis for their own benefit.

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Figure 1 ROS metabolic pathway. Image adapted from Lennicke et al. Ref 16

It has long been known that excessive elevated reactive oxygen species (ROS) is

oncogenic and promotes tumourigenesis by causing DNA damage or secondary signalling 9–12.

There are many proteins in the ROS metabolic pathway that contribute to the production,

dismutation, and transport of ROS (Figure 1).The most heavily studied ROS molecules are

superoxide (O2-) and hydrogen peroxide (H2O2). H2O2 is unique in that it oxidizes nucleic acids

and cysteine residues on proteins while O2- is mostly generated in the mitochondria during

aerobic metabolism through NADPH oxidases (NOX), dual oxidases (DUOX), and

cyclooxygenases (COX) among others. Superoxides are then dismutated to H2O2 by the

superoxide dismutase (SOD) family. The produced H2O2 is converted to H2O and O2 by several

molecules such as catalase (CAT), glutathione peroxidase (GPX), peroxiredoxins (PRX), and

thioredoxin (TRX). Overproduction and dysregulation of ROS continues in cancer cells, further

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causing pro-tumourigenic effects such as enhanced proliferation, DNA damage, and drug

resistance13–15. However, increased localization of ROS can promote cell toxicity and induce

apoptotic pathways inhibiting cancer progression16. The effect of increased localization of ROS

in cancer is highly dependent on the genetic, environmental, and tissue-of-origin of the tumour.

These contradicting consequences imply that increased antioxidant levels can promote

tumourigenesis, depending on the cancer type. A recent study conducted by Sarmiento-Salinas

et al. showed the differential response of luminal and basal breast cancer cell lines to either

H2O2 or the H2O2 scavenging molecule, N-acetyl cysteine (NAC)17. The specific response of

breast cancer subtypes to hydrogen peroxide modulators reinforce the potential for their use in

personalized cancer treatments.

Unconverted H2O2 is easily diffused through the mitochondrial membrane and the

cytosol, into the extracellular space for further downstream signalling. H2O2 has been shown to

influence the tumour microenvironment (TME) to promote tumourigenesis through enhanced

angiogenesis, fibroblast recruitment and their differentiation into myofibroblasts18,19. High ROS

levels in the TME leads to immunosuppression and inhibit T-cell proliferation20. The ability for

cancer cells to influence the TME through ROS and H2O2 can be critical to our understanding of

tumour behaviour and methods to inhibit their growth. One such way that cancer cells can

remodel their environment is through H2O2 transport using the cell membrane protein family

aquaporins (AQP)21–24. Aquaporins are a family of membrane bound proteins that control the

flux of water, small-uncharged molecules, and glycerol. In addition to small molecule transport,

aquaporins are thought to contribute to non-canonical cell migration modes through water

transfer from the front to the rear of the cell25,26. Researchers have identified 13 distinct

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aquaporins in mammalian cells (AQP0-12) which can be further subcategorized into three

groups orthodox aquaporins (AQP0,1,2,4,5,6,8), aquaglyceroporins (AQP3,7,9,10), and

unorthodox aquaporins (AQP11,12)22. Subsequently, there are several AQPs (AQP3,5,8,9) that

have been shown to allow movement of considerable amount of H2O2; aptly named

peroxiporins27. Changes in aquaporin expression has been shown in several cancers such as

lung28,29, colorectal30,31, and prostate32.

Aquaporin 3 has been linked to advanced stages of bladder cancer and as an indicator

for worst progression-free survival33,34. Increased AQP3 expression resulted in higher

exogenous H2O2 uptake in HEK293 cells35. AQP9, which has been shown to be present in the

human urothelium in situ and in vitro, is of particular interest because of its multifaceted role as

a transporter of peroxides and glycerol36,37. Several studies have demonstrated that AQP9

modulation leads to inhibition of migration/invasion32, enhanced H2O2 toxicity24, and reduced

growth38 depending on the cancer type.

Phloretin is a naturally occurring flavonoid that have been used as an inhibitor of

AQP939,40. Past studies have used this small molecule to inhibit migration and proliferation in

breast cancer41, and induce apoptosis in esophageal and colon cancer42,43. Molecular dynamics

simulations conducted by Wacker et al, demonstrated that single point mutations on human

AQP9 altered Phloretin efficacy. Point mutations introduced to the intracellular pore entrance

led to the greatest reduction of Phloretin efficacy and affinity. This led the authors to conclude

that Phloretin inhibits AQP9 efficacy by binding to the intracellular region to reduce transport.

The aim of this study is to assess the differential response of bladder cancer subtypes to

AQP9 inhibition through intracellular H2O2 accumulation, extracellular H2O2 release, and the

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effect on their migratory capabilities. We hypothesize that reactive oxygen species as

secondary messengers to control stromal cell interactions to enhance cancer cell invasive

capabilities. ROS and water transport protein expression was assessed through differential gene

expression analysis of ROS associated mRNA using bladder tumour datasets provided by The

Cancer Genome Atlas (TCGA). We then observed the response of T24 and RT112 bladder cancer

cell lines to varying concentrations of Phloretin.

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Materials and Methods

Analysis of TCGA bladder cancer dataset

Initial screening of ROS expression in bladder cancer subtypes was conducted using the

bladder cancer RNAseq expression dataset collected through The Cancer Genome Atlas (TCGA)

using the GenomicDataCommons R package. The samples used for RNAseq were then

annotated according to the UNC molecular subtypes as in previously reported research6. This

dataset consisted of 406 patient derived tumours (203 basal and 203 luminal) and 19 healthy

tissues. Differential expression analysis was performed between basal and luminal tumours

using the DESeq2 package44. The list of differentially expressed genes were filtered for those

with padj < 0.05. ROS-related genes were further selected based on GSEA

(GO_OXIDATION_REDUCTION_PROCESS, ANTIOXIDANT_ACTIVITY,

REACTOME_BIOLOGICAL_OXIDATION, AND GO_WATER_TRANSPORT) yielding 1112 genes of

interest. The full list was filtered for those that were differentially expressed with a p.adj < 0.01

resulting in 724 genes that were used for unsupervised hierarchical clustering (Figure 2). K-

means an unsupervised hierarchical clustering was used to separate the dataset into 2 sample

clusters and 5 gene clusters. Biological process enrichment was conducted using the

reactomePA45 package on the 5 individual clusters (Figure 3). Additionally, a set of self-curated

H2O2 related genes (Table 1) were identified for further analyses.

Analysis mRNA expression profile of bladder cancer cell lines

Cell line ROS expression screening was conducted on thirty RNAseq expression datasets

of bladder cancer cell lines obtained through the Gene Expression Omnibus (GSE97768). The

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cell lines were annotated according to MD Anderson Cancer Center classification and a study

conducted by Robertson et al46. Expression counts underwent variance stabilizing

transformation using DESeq2 for visualization.

Cell Culture

Human bladder cancer cell lines used were: T24 (ATCC HTB-4) and RT112 was

generously donated from the McConkey Lab, Johns Hopkins University. Cells were cultured in

Modified Eagle Medium (MEM) supplemented with 10% fetal bovine serum (FBS), 2% MEM

vitamin solution (100X), 500U/mL penicillin-streptomycin, 1% MEM non-essential amino acids

solution (100X), 1% HEPES (1M), and 1% sodium pyruvate (100mM) procured from Gibco.

Immunofluorescence (no data)

Protein expression and localization of AQP9 was first determined using

immunofluorescence. The cells were grown in well plates for 24h and fixed with 4%

paraformaldehyde, permeabilized using 0.1% Triton-X100 in PBS and blocked with 5% BSA in

PBS. Cells were stained with primary AQP9 antibodies (1:300, Santa Cruz Biotech), Phalloidin,

and Hoescht 33342. Images were captured using an Evos FLAuto.

Flow Cytometry (no data)

Baseline and altered intracellular H2O2 levels were quantified using flow cytometry and

pentafluorobenzenesulfonyl fluorescein (Cayman Chemicals) as the fluorescent marker for

H2O2. Pentafluorobenzenesulfonyl fluorescein is a cell permeable molecule which emits

fluorescence in the presence of hydrogen peroxide. When this molecule is taken up by cells it

emits fluorescence that is indicative of intracellular H2O2 levels. Briefly, 5x104 cells were plated

on 24 well plates and incubated for 24h with varying concentrations of the test material.

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Cultures were then stained with 10µM pentafluorobenzenesulfonyl fluorescein stain for 30min

at room temperature. Cultures were then washed with PBS twice, trypsinized, and resuspended

in ice cold PBS for immediate analysis using a BD Accuri flow cytometer. Data was processed

using FlowJo software.

Peroxide Assay (no data)

The Amplex Red Assay (Thermo Fischer) was used to determine extracellular H2O2

levels. This indicated the amount of H2O2 transported out of the cells or produced through

extracellular enzymes such as SOD3. This is also indicative of the release of H2O2 through

diffusion or transport proteins in the presence of the AQP9 inhibitor phloretin. In brief, cells

were seeded into 96-well plates and incubated overnight. The media was then removed and

replaced with 20mM HEPES in HBSS with 10µM of Amplex Red. Measurements were taken

every 30min for 6 hours.

Wound Healing Assay

Functional behaviour of cancer cell in the presence or absence of phloretin was assessed

in the conventional scratch test assay. Cells (1x105/well) were seeded into a 24-well plate and

incubated for 48h to reach 100% confluence. The cultures were then mechanically wounded

using a 1000µl pipette tip and incubated in serum-free media with varying concentrations of

test material. Images were captured every 2 hours for 16 hours and then at 24 hours using a

phase-contrast microscope. The gap area was analysed using the MRI Wound Healing Tool

macro for ImageJ. Data points were averaged and a line was fit using the loess method on

RStudio.

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Statistical Analysis

According to DESeq2 package, differentially expressed genes were assessed using the

Wald Test and adjusted for multiple testing using Benjamini and Hochberg. Experiments were

completed in triplicate and assessed using student’s T-test.

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Figure 2 Hierarchical and kmeans clustering of the BLCA TCGA dataset with redox-associated genes.

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Figure 3 Pathway enrichment using Reactome Pathway Analysis of gene clusters in Figure 2

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Results

Differential expression analysis of ROS related mRNA between TCGA subtypes

Expression analysis of ROS-related genes in bladder cancer tumours from TCGA showed

that the dataset can be very distinctly clustered into their subtypes using unsupervised

hierarchical clustering, k-means clustering, and a list of oxidation/reduction genes (Figure 2).

Clusters I and II were heavily enriched in basal and luminal tumours respectively. As seen in

Figure 2, gene cluster III (178 genes) and IV (196 genes) are upregulated in basal and luminal

tumours respectively. Clusters I (85 genes) and II (161 genes) are downregulated in both basal

and luminal tumours while genes in cluster V (104 genes) have slightly higher expression in

luminal tumours. Notably in Figure 3D, “Detoxification of Reactive Oxygen Species” was among

the top 8 enriched biological pathways in cluster III. Cluster IV and V showed strong connections

with processes involving electron transport.

Differential analysis of the genes in our pipeline further illustrate the importance of

H2O2 response between bladder cancer subtypes, shown in Table 1. Initially, both SOD2 and

SOD3 are both upregulated in basal tumours by 2.41 and 1.84 times respectively. In contrast,

SOD1 was weakly upregulated in luminal tumours with slight statistical significance. H2O2

degrading proteins namely the GPX family is mostly downregulated in basal tumours. Those

within the GPX family with padj <0.01, GPX2,5, and 6 had 2.22 times greater expression in

luminal tumours compared to basal tumours. Only GPX7 and 8 are upregulated in basal

tumours with 1.57 and 3.25 times greater expression respectively. Catalase (CAT) another

notable enzyme that breaks down H2O2 exhibited slightly higher expression in luminal tumours.

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DUOX1 and DUOX2 both had 1.47 times higher expression in luminal tumours. Lastly, the

majority of the aquaporins are upregulated in basal tumours; AQP1,2,4,5,6, and 9. Peroxiporins,

AQP 5 and 9 had 3.46 and 2.64 times higher expression in basal tumours respectively. AQP3 and

8 were upregulated in luminal tumours with 1.42 and 1.49 times higher expression. The curated

gene set was then used for unsupervised hierarchical clustering and then subdivided using k-

means, shown in Figure 4A. The samples were clustered into 3 distinct groups with a primarily

basal group (cluster I), primarily luminal group (cluster II), and a mixture (cluster III). Gene

cluster III is seen to be very distinctly upregulated in the basal cluster and downregulated in the

luminal cluster. This cluster consists of some of the key genes mentioned above (GPX8, SOD2,3,

and AQP9). Likewise, gene cluster I consisted of GPX2, AQP3, DUOX1, and DUOX2 which are

distinctly upregulated in the luminal cluster.

Table 1. List of self-curated H2O2 related genes that are differentially expressed between basal and luminal tumours

Basal-Luminal Basal-Normal Luminal-Normal

log2FC p-value p-adj log2FC p-value p-adj log2FC p-value p-adj

SOD1 -0.14 1.55E-02 2.61E-02 * 0.03 8.21E-01 8.87E-01 n.s 0.17 2.20E-01 3.25E-01 n.s

SOD2 1.36 3.20E-36 1.14E-34 **** -0.46 7.77E-02 1.60E-01 n.s -1.82 3.31E-12 6.14E-11 ****

SOD3 0.97 1.66E-09 6.85E-09 **** -1.44 2.24E-04 1.16E-03 ** -2.41 6.23E-10 7.82E-09 ****

GPX1 0.10 2.09E-01 2.71E-01 n.s 0.72 1.81E-04 9.61E-04 *** 0.62 1.26E-03 4.00E-03 **

GPX2 -1.29 4.48E-09 1.77E-08 **** -0.97 6.96E-02 1.46E-01 n.s 0.33 5.40E-01 6.52E-01 n.s

GPX3 -0.53 6.39E-03 1.15E-02 * -1.40 2.73E-03 1.00E-02 ** -0.87 6.17E-02 1.15E-01 n.s

GPX4 -0.13 7.28E-02 1.06E-01 n.s 0.22 1.94E-01 3.24E-01 n.s 0.34 4.12E-02 8.21E-02 n.s

GPX5 -1.93 2.75E-04 6.11E-04 *** 0.36 7.83E-01 8.60E-01 n.s 2.29 7.94E-02 1.42E-01 n.s

GPX6 -1.99 1.31E-04 3.04E-04 *** 0.49 7.07E-01 8.06E-01 n.s 2.48 5.50E-02 1.05E-01 n.s

GPX7 0.65 6.00E-09 2.34E-08 **** 0.59 2.98E-02 7.40E-02 n.s -0.06 8.16E-01 8.73E-01 n.s

GPX8 1.70 5.75E-64 1.53E-61 **** 1.08 8.91E-06 6.83E-05 **** -0.62 1.10E-02 2.65E-02 *

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CAT -0.23 6.85E-03 1.23E-02 * -0.82 7.37E-05 4.39E-04 *** -0.59 4.44E-03 1.20E-02 *

NOX1 -1.20 1.93E-18 1.78E-17 **** -0.08 8.09E-01 8.79E-01 n.s 1.12 7.36E-04 2.49E-03 **

NOX4 1.22 1.14E-21 1.36E-20 **** 1.09 4.02E-04 1.93E-03 ** -0.13 6.82E-01 7.70E-01 n.s

NOX5 0.65 9.57E-05 2.27E-04 *** 1.26 1.85E-03 7.19E-03 ** 0.61 1.32E-01 2.16E-01 n.s

DUOX1 -0.64 2.25E-06 6.60E-06 **** -1.00 1.98E-03 7.62E-03 ** -0.37 2.56E-01 3.68E-01 n.s

DUOX2 -0.56 9.83E-03 1.72E-02 * -1.33 1.12E-02 3.28E-02 * -0.77 1.42E-01 2.30E-01 n.s

AQP1 0.26 2.53E-02 4.10E-02 * -2.34 2.80E-17 2.01E-15 **** -2.59 6.58E-21 4.88E-19 ****

AQP2 1.34 3.07E-04 6.79E-04 *** -1.70 4.59E-02 1.05E-01 n.s -3.04 3.79E-04 1.38E-03 **

AQP3 -0.51 1.20E-02 2.07E-02 * -1.75 3.13E-04 1.55E-03 ** -1.25 1.03E-02 2.51E-02 *

AQP4 1.57 8.98E-05 2.14E-04 *** -0.33 7.33E-01 8.24E-01 n.s -1.89 4.90E-02 9.49E-02 n.s

AQP5 1.79 1.77E-11 8.86E-11 **** 0.66 3.05E-01 4.53E-01 n.s -1.13 7.84E-02 1.41E-01 n.s

AQP6 1.06 2.99E-07 9.72E-07 **** 0.79 1.17E-01 2.20E-01 n.s -0.27 6.00E-01 7.01E-01 n.s

AQP7 -1.68 8.96E-15 6.09E-14 **** -1.96 1.74E-04 9.30E-04 *** -0.28 5.87E-01 6.91E-01 n.s

AQP8 -0.58 8.50E-04 1.76E-03 ** -0.19 6.62E-01 7.71E-01 n.s 0.39 3.61E-01 4.80E-01 n.s

AQP9 1.40 6.89E-15 4.73E-14 **** 1.76 5.51E-05 3.41E-04 *** 0.36 4.14E-01 5.33E-01 n.s

AQP10 -0.61 1.17E-02 2.02E-02 * 0.93 1.24E-0.1 2.31E-01 n.s 1.54 1.08E-02 2.6E-02 *

HK2 0.08 4.48E-01 5.21E-01 n.s 0.60 1.71E-02 4.65E-02 * 0.52 3.83E-02 7.72E-02 n.s

HIF1A 0.95 1.39E-28 2.80E-27 **** 0.46 2.51E-02 6.42E-02 n.s -0.49 1.88E-02 4.21E-02 *

HIF3A 0.52 2.11E-02 3.47E-02 * -2.99 3.79E-08 4.92E-07 **** -3.51 1.08E-10 1.54E-09 ****

HIF1AN 0.08 1.07E-01 1.50E-01 n.s -0.47 3.85E-05 2.49E-04 *** -0.54 1.72E-06 1.08E-05 ****

PRDX1 0.32 2.97E-06 8.56E-06 **** 0.39 1.55E-02 4.29E-02 * 0.08 6.26E-01 7.25E-01 n.s

PRDX2 -0.38 1.50E-09 6.21E-09 **** -0.08 5.95E-01 7.17E-01 n.s 0.30 4.90E-02 9.49E-02 n.s

PRDX3 -0.10 4.82E-02 7.35E-02 n.s -0.19 1.14E-01 2.16E-01 n.s -0.09 4.46E-01 5.63E-01 n.s

PRDX4 0.60 1.54E-18 1.44E-17 **** 1.15 5.09E-12 1.42E-10 **** 0.54 1.08E-03 3.48E-03 **

PRDX5 -0.29 4.35E-05 1.08E-04 *** 0.15 3.75E-01 5.24E-01 n.s 0.45 9.92E-03 2.42E-02 *

PRDX6 0.61 8.14E-24 1.14E-22 **** 0.47 1.50E-03 6.02E-03 ** -0.15 3.23E-01 4.42E-01 n.s

TXN 0.63 3.07E-13 1.81E-12 **** 0.60 4.09E-03 1.41E-02 * -0.03 8.84E-01 9.22E-01 n.s

TXN2 -0.35 4.14E-10 1.82E-09 **** -0.22 9.81E-02 1.92E-01 n.s 0.13 3.52E-01 4.72E-01 n.s

NFE2L2 0.06 3.53E-01 4.25E-01 n.s -0.90 1.96E-08 2.69E-07 **** -0.97 1.97E-09 2.25E-08 ****

Using the same curated list, expression counts were compared between the subtypes

and a set of 19 healthy tissues taken adjacent to tumour sites; displayed in Figure 4B-J.

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Expression of the 4 identified aquaglyceroporins (AQP3,7,9,10) are shown in Figure 4B-E. There

was low AQP10 expression in all tumours and showed no statistical significance between the

basal and normal tissue. A marked downregulation of AQP3 was noted in basal tumours,

consistent with previous studies.33,34 Similarly, there was a dramatic decrease in AQP7

expression in basal tumours seen in Figure 4D. Peroxiporins 3 and 8 (AQP3,8) were

downregulated in basal tumours compared to luminal tumours by 0.5 fold while peroxiporins 5

and 9 were upregulated 1.79 and 1.4 fold respectively. Likewise, basal tumours expressed

higher levels of AQP5 and 9 than normal tissues, 0.66 and 1.76 fold increase respectively.

Luminal tumours exhibited downregulation of AQP3 and 5 (1.25 and 1.13 respectively) and

slight upregulation of AQP8 and 9 (0.39 and 0.36 fold respectively) compared to normal tissues.

DUOX1 and DUOX2 showed minimal statistical significance compared to normal tissues (Figure

4F,G). There were no significant changes in expression of SOD1 expression between tumour

and normal tissue. Surprisingly, basal and luminal tumours had a lower expression of SOD3

compared to healthy tissues. SOD2 displayed similar trends except there was no statistical

changes between basal tumours and healthy tissue.

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Figure 4 A) Visualization of ROS pipeline mRNA expression in TCGA-BLCA dataset B-J) mRNA counts of various genes of interest TCGA p>0.05 (n.s); p <= 0.05 (*); p <=0.01 (**); p<= 0.001 (***); p<= 0.0001 (****)

Analysis of 30 bladder cancer cell lines with the same curated gene list shows marked

differences between basal and luminal cell lines. As seen in Figure 5A, there are no marked

genes that are distinctly upregulated in either subtype. As expected there was no statistical

significance among in most of genes of interest, Figure 5C,E-J. The only gene that had

expression counts that were statistically different was AQP3. Basal cell lines had a lower

expression of AQP3 compared to luminal cell lines. Surprisingly, basal cell lines had nearly no

expression of AQP9. However, there was a notable higher expression of SOD3 in basal cells and

higher expression of DUOX1 and DUOX2 in luminal cells.

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Figure 5 A) Visualization of ROS pipeline mRNA expression in 30 bladder cancer cell lines B-J) mRNA counts of various genes of interest. p>0.05 (n.s); p <= 0.05 (*); p <=0.01 (**); p<= 0.001 (***); p<= 0.0001 (****)

Migration

Cancer cell response to the AQP9 inhibitor phloretin was functionally measured using a

migration assay in the presence at varying concentrations. RT112 showed a marked dose

response as their migration was drastically hindered with increasing concentrations of Phloretin

as shown in Figure 6A. In the presence of 0.1% DMSO there was approximately 16% wound

closure at 24h compared to the lowest tested concentration of 20M with 11% wound closure.

The percent wound closure continued to decrease with increasing concentration reaching 3.6%

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at the 100M. Basal cancer cell line, T24, did not show a dose response to increasing

concentrations of Phloretin as seen in Figure 6B. Phloretin at 20M and 40M behaved

similarly to 0.1% DMSO. Although 60M, 80M, and 100M showed a marked decrease in

wound closure there was no discernable trend that higher concentration of Phloretin decreased

wound closure.

Figure 6 Wound closure of A) RT112 and B) T24 cell lines in response to varying concentrations of Phloretin over 24h.

Discussion

An altered redox expression profile have been shown to accompany the progression of

several cancers10,11,19,47–50. Previous studies have confirmed similar trends in bladder cancer

with higher H2O2 production through SOD2 linked to high-grade bladder tumours10. Further,

one study showed increase expression of GPX and CAT in bladder cancer tissues whereas others

reported lower CAT expression in cancer tissues relative to benign tissues51,52. The present

study explored the expression profiles of redox-associated genes in bladder cancer using the

19

BLCA dataset acquired from TCGA. Doing so, highlighted heterogeneity within bladder cancer

subtypes with respect to oxidation and reduction capabilities. Redox-related gene expression

clustered bladder cancer tumours according to their intrinsic subtypes (Figure 2), basal and

luminal. Pathway enrichment on these gene clusters suggest a higher number of genes related

to detoxification of oxidants in cluster 3 which was upregulated in basal tumours. This pathway

was not enriched in the other clusters, suggesting a more efficient antioxidative response of

basal tumours to cytotoxic levels of oxidants. In contrast, SOD2 and SOD3 are upregulated in

basal tumours which suggest an increased H2O2 production (Figure 4I-J). SOD2 is exclusively

found in the mitochondrial matrix while SOD3 is excreted into the extracellular space53. These

findings suggest that basal cells could be better equipped to reduce excess levels of H2O2.

Therefore, it can be speculated that they are less susceptible to increased H2O2 concentrations

similar to past studies which have suggested a higher threshold of cancer cells to H2O2

compared to normal cells54. Likewise, the upregulation of oxidant detoxifying genes in basal

phenotype tumours indicate their resistance to ROS mediated cytotoxicity compared to luminal

tumours.

Our curated list of H2O2 associated genes indicated marked expression differences

between basal and luminal subtypes. AQP9 was chosen because of its involvement in

transporting water, glycerol, and H2O2 across the cell membrane. AQP9 silencing through siRNA

was shown to reduce prostate cancer cell migration and invasion32. Human basal tumours had a

2.69 fold higher expression of AQP9 than luminal tumours. This was in contrast to AQP9

expression in basal cancer cell lines which had no expression of AQP9 mRNA. Comparisons

between luminal tumours and normal tissues showed a stark reduction in other peroxiporin

20

expression in luminal tumours. This further suggests the stunted capability of cells in luminal

tumours to control intracellular accumulation of H2O2. These differences were highlighted in

the migration assays in the presence of Phloretin. Luminal bladder cancer cell line, RT112,

exhibited reduced migration as a response to as little as 20M of Phloretin. Although basal

bladder cancer cell line, T24, had reduced migration at higher concentrations 60M, it did not

show a dose dependent response. This behaviour is in line with a study conducted by Kim et

al.55 who demonstrated a decreased proliferation and migration of prostate cancer cells when

exposed to Phloretin. The authors concluded that the suppression of proliferation and

migration was accompanied with a decrease in antioxidant enzyme mRNA (CAT, GPX1,3).

Similarly, Chen et al.32 demonstrated reduced motility in AQP9 silenced prostate cancer cells.

Based on RNAseq analysis of bladder cancer cell lines (Figure 5D) there is virtually no expression

of AQP9 in basal cancer cell lines. This follows previous reported literature on Phloretin and its

non-specific inhibition of the aquaglyceroporins AQP3 and AQP956. This compounds Phloretin’s

efficacy to limit migration of RT112 as luminal cancer cell lines had increased expression of both

AQP3 and AQP9 in luminal cell lines compared to basal cell lines (Figure 5B and D). Inhibition of

these aquaglyceroporins likely leads to enhanced intracellular H2O2 accumulation leading to

cytotoxicity.

21

Conclusion

Our analysis further demonstrates functional contrasts between basal and luminal

subtyped bladder cancers. Transcriptome-wide analysis identified marked differences in ROS-

associated mRNA, notably the high activity of SOD2 and SOD3 indicating greater H2O2 levels.

The altered redox state can affect many downstream processes such as hypoxia, angiogenesis,

and matrix degradation. We’ve also shown that mediating cancer cell’s ability to facilitate

transport of H2O2 through AQP9 and potentially AQP3 resulted in reduced migration

capabilities. Additionally, pathway enrichment identified oxidant detoxification as an enriched

gene set in basal tumours. We speculate that this finding suggests basal tumours are more

equipped to tolerate and ameliorate excess H2O2 compared to luminal tumours, however

additional studies are required. These findings show that the migratory behaviour of luminal

cancer cells can be attenuated by limiting H2O2 leading to excess intracellular accumulation and

a reduction of stromal cell interactions. Further studies will elucidate the potential of transport

protein inhibitors to limit cancer-stromal interactions and their role in treatment of invasive

luminal bladder carcinoma. The results shown here indicate the potential of phloretin and other

pharmacological inhibitors of aquaporins as treatment regiments for luminal bladder

carcinomas.

22

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Curriculum Vitae

JUSTIN HUI

Education MSE in Biomedical Engineering 2018-2020

Johns Hopkins University, Baltimore, MD, USA

BASc in Mechanical Engineering with Distinction 2014-2018 University of British Columbia, Kelowna, BC, Canada

Research Interests Cancer Models Tumour Microenvironment/Stroma Mechanobiology Biomaterials Tissue Engineering

Research Experience Graduate Research Assistant (PI: Anirudha Singh Ph.D.) 2018-2020 Urology, Johns Hopkins Hospital, Baltimore, MD - Study the relationship between ROS and bladder cancer subtypes through TCGA and scRNAseq analyses - Mechanobiology of fibroblasts and secreted collagen structure Undergraduate Research Assistant (PI: Mina Hoorfar Ph.D.) 2016-2018 Advanced Thermofluidics Lab, University of British Columbia, Kelowna, BC - Electrospinning polymeric nanofibers for microfluidic surface modification - Electrospinning polymer-oxide composite nanofibers for gas detection - Porous hydrogel design

Publications

Mehrabi, P.; Hui, J.; Montazeri, M. M.; Nguyen, K. T.; Logel, A.; O’Brian, A.; Hoorfar, M. Smelling Through Microfluidic Olfaction Technology; 2018.

Li, Y.; Hui, J.; Kawchuk, J.; O’Brien, A.; Jiang, Z.; Hoorfar, M. Composite Membranes of PVDF Nanofibers Impregnated with Nafion for Increased Fuel Concentrations in Direct Methanol Fuel Cells. Fuel Cells 2019.

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Mehrabi, P.; Hui, J.; Janfaza, S.; O’Brien, A.; Tasnim, N.; Najjaran, H.; Hoorfar, M. Fabrication of SnO2 Composite Nanofiber-Based Gas Sensor Using the Electrospinning Method for Tetrahydrocannabinol (THC) Detection. Micromachines 2020.

Hui, J.; Rajani, S.; Sharma, S.; Singh, A.; (2020) Biochemical and Biomechanical characterization of Eleutherodactylus Coqui vocal sac tissue for bioinspired micro-architectured elastic materials. (Submitted) Hui, J.; Sharma, S.; Rajani, S.; Singh, A.; (2020) Down-regulation of Aquaporin 9 by Natural Product Phloretin Mediates Luminal Bladder Cancer Migration in vitro. (In Progress)

Skills Flow Cytometry: Beckman Coulter Cytoflex, BD Accuri Sequencing: scRNAseq, MULTIseq, qPCR Programming Languages: R, Matlab, Arduino Manufacturing: Machining, 3D Printing, Prototyping Microscopy: Zeiss 800 Confocal, Zeiss Axio Observer, SEM, TEM

Certificates Introduction to Cancer Biology taught by JHU 2019 Understanding Cancer Metastasis taught by JHU 2019 Statistics for Genomic Data Science taught by JHU 2020