Regulation of Micrornas Targeting the Angiogenic Switch Molecule Fibroblast Growth Factor Binding Protein 1 by Retinoic Acid Receptor Activation

Regulation of MicroRNAs Targeting the Angiogenic Switch Molecule Fibroblast Growth Factor Binding Protein 1 by Retinoic Acid Receptor Activation

ADissertation submitted to the Faculty of the Graduate School of Arts and Sciences of Georgetown University in partial fulﬁllment of the requirements for the degree of Doctor of Philosophy in Tumor Biology

Tabari M. Baker, M.S.

ii Regulation of MicroRNAs Targeting the Angiogenic Switch Molecule Fibroblast Growth Factor Binding Protein 1 by Retinoic Acid Receptor Activation

Tabari M. Baker, M.S.

Dissertation Advisor: Anton Wellstein, M.D., Ph.D.

Abstract

This dissertation examines the role of retinoic acid receptor activation in the post-transcriptional regulation of a fibroblast growth factor binding protein. Previous work showed that all-trans retinoic acid (ATRA) reduces mRNA expression of the angiogenic switch molecule, Fibroblast Growth Factor Binding Protein 1 (FGFBP1 or FGF-BP), independent of an effect on transcription of the FGFBP1 mRNA. I hypothesized that a retinoid-induced microRNA was involved in FGFBP1 mRNA loss. MicroRNAs (miRs) are 19–22 nucleotide (nt) single stranded non-coding RNAs that post-transcriptionally repress mature mRNA function, thereby reducing expression of their target proteins. The current dogma suggests that miRs canonically bind to the 3’ untranslated region (UTR) of mRNA through a 7-nt seed-matched site. However, recent data indicate that miRs may also bind the open reading frame (ORF) of mR- NAs. In this dissertation, I show that miR-27b-3p and miR-125a-5p are induced by ATRA and target FGFBP1. Overexpression of miR-27b-3p and miR-125a-5p rapidly reduced FGFBP1 mRNA levels through a target site in the open reading frame of the FGFBP1 mRNA. Both microRNAs showed specificity for regions within the ORF of FGFBP1, suggesting that these microRNAs may also be involved in inhibiting translation of the FGFBP1 protein. Next generation sequencing data from The Cancer Genome Atlas shows that loss of these microRNAs is characteristic of several epithelial cancers, including head and neck, lung, and cervical squamous cell carcinomas,

iii suggesting a tumor suppressor role for miRs 27a-3p and 125a-5p. In total, these data suggest an important regulatory role for miRs 27b-3p and 125a-5p in the oncogenesis of squamous cell carcinomas, through modulation of FGFBP1 expression.

Index words: MicroRNA, Vitamin A, Retinoic Acid, FGFBP1, Fibroblast Growth Factor Binding Proteins, Post-Transcriptional Gene Regulation

iv Dedication

This dissertation is dedicated to my families; the Bakers’, the Ellisons’, the Croslans’, and the Roseboros’. Without your individual support and contributions, my life and this work would not have been possible. This dissertation is as much yours as it is mine. I also dedicate this work to my wife, DaJoie Croslan Baker, whose outrageous support, unconditional love, and enduring patience were instrumental in completing this work. Thanks, Bae. Finally, and most importantly, I dedicate this work to those who have been touched, directly or indirectly, by the emperor of all maladies, cancer. It is my hope that this work makes an incremental diﬀerence in increasing our understanding of cancers, modifying our treatment regimes, and someday eradicating these diseases. If this work assists in improving the quality of life of patients by giving them another day to love, cry, laugh, or think deeply, I am grateful. It is your circumstance that drives me to develop ways to give you one more day.

v Acknowledgments

Firstly, I would like to acknowldege my advisor, Dr. Anton Wellstein, for providing thoughtful criticisms and a supportive environment to learn how to be a successful scientist. I would also like to thank Dr. Claudius Malerczyk, for his assistance in providing the Northern blots integral to this work. I would like to thank my committee members, Drs. Michael Johnson, Aykut Üren, Aiwu He, G. Ian Gallicano, and Patricio Ray. Your guidance was critical for the completion of this project. IwouldalsoliketothankDr.AnnaRiegel,DirectoroftheTumorBiologyTraining program, and her administrative staﬀ. Her tireless leadership, and their support, were instrumental in my professional growth. Finally, I would like to thank Georgetown University, and her support staﬀ, for maintaining a great environment to learn and grow.

vi Contents

Chapter 1Introduction...... 1 2Background...... 4 2.1 MicroRNAs: Discovery, Function, and Role in Cancer ...... 4 2.2 Retinoid Signaling in Normal and Pathologic States ...... 17 2.3 Fibroblast Growth Factors, Their Binding Proteins, and Their Role in Oncogenesis ...... 24 3 All-Trans Retinoic Acid Reduces Expression of FGFBP1 mRNA in a time-dependent and ORF-dependent manner ...... 30 3.1 Aim and Hypothesis ...... 30 3.2 MaterialsandMethods ...... 30 3.3 Results and Discussion ...... 33 4 A Region in the ORF is Necessary for ATRA-mediated degradation of FGFBP1...... 36 4.1 Aims and Hypothesis ...... 36 4.2 MaterialsandMethods ...... 36 4.3 Results and Discussion ...... 38 5 All-Trans Retinoic Acid Regulates the Expression of MicroRNAs Tar- geting FGFBP1...... 42 5.1 Aim and Hypothesis ...... 42 5.2 MaterialsandMethods ...... 42 5.3 Results and Discussion ...... 43 6 MicroRNAs 27b-3p and 125a-5p target the ORF of FGFBP1 and reduce mRNA and protein levels...... 47 6.1 Aim and Hypothesis ...... 47 6.2 MaterialsandMethods ...... 47 6.3 Results and Discussion ...... 50 7 MicroRNAs 27b-3p and 125a-5p are reduced in squamous cell carcinoma tissue samples...... 56 7.1 Aim and Hypothesis ...... 56 7.2 MaterialsandMethods ...... 56

vii 7.3 Results and Discussion ...... 56 8 ConclusionsandFutureDirections...... 59 Bibliography ...... 68

viii List of Figures

2.1 MicroRNA Biogenesis Occurs Via Three Distinct Pathways ...... 7 2.2 Retinoic Acid Receptor Signaling Pathway ...... 22 2.3 The Fibroblast Growth Factor Signaling Pathway ...... 26 2.4 MechanismsofFGFBPAction...... 29 3.1 ATRA reduces expression of FGFBP1 mRNA in a time- and ORF- dependent manner ...... 34 4.1 The FGFBP1 ORF is necessary for mRNA degradation and can be targeted by microRNAs...... 39 5.1 Combined in vitro and in silico analyses suggest hundreds of microR- NAstargettheORFofFGFBP1...... 44 6.1 Gene set enrichment analysis (GSEA) reveals several retinoid-induced microRNAs with overrepresented targets in ME180 cells...... 51 6.2 ME180 cells express FGFBPs and microRNAs 27b-3p and 125a-5p reduce FGFBP1 mRNA and protein levels through ORF homology. . 53 7.1 MicroRNAs 27b-3p and 125a-5p are reduced in several squamous cell carcinoma tissue samples...... 57

ix List of Abbreviations

-3p 3-prime

-5p 5-prime

ADH alcohol dehydrogenase

Ago2 Argonaute 2

AKT v-AKT murine thymoma viral oncogene homolog 1; also known as AKT, CWS6, PKB, PKB-ALPHA, PRKBA, RAC, RAC- ALPHA

ApoA-I apolipoprotein A1

ARF6 ADP-ribosylation factor 6

ATRA all-trans retinoic acid

BAD BCL2-associated agonist of cell death; also known as BBC2, BCL2L8

BAX BCL2-associated X protein; also known as BCL2L4

CD63 CD63 molecule; also known as MLA1, ME491, LAMP-3, OMA81H, TSPAN30

CD81 CD81 molecule; also known as S5.7, DVID6, TAPA1, TSPAN28

x CD9 CD9 molecule; also known as MIC3, MRP-1, BTCC-1, DRAP- 27, TSPAN29

CDS coding sequence

CpG -C-phosphate-G-

CRABP cellular retinoic acid binding protein

CRBP cellular retinol binding protein; also known as RBP1, RBPC, CRBP1, CRBPI, BRABP-I

CYP26 cytochrome P450, family 26; also known as CP26

DAG diacylglycerol

DGCR8 DiGeorge syndrome critical region gene 8

DMA N-dimethyl amiloride

DNA deoxyribonucleic acid

DNMT1 DNA (cytosine-5-)-methyltransferase 1; also known as AIM,DNMT, MCMT, CXXC9, HSN1E, ADCADN

DNMT3B DNA (cytosine-5-)-methyltransferase 3; also known as ICF, ICF1, M.HsaIIIB

DR1 direct repeat 1

DR2 direct repeat 2

DR5 direct repeat 5 dsRNA double-stranded RNA

xi ECM extracellular matrix

EFNA3 ephrin-A3; also known as EFL2, EPLG3, LERK3, Ehk1-L

ERK extracellular signal regulated kinase; also known as MAPK1

ERK1 extracellular signal regulated kinase 1; also known as ERT2, PRKM3, P44ERK1, P44MAPK, HS44KDAP, HUMKER1A, p44-ERK1, p44-MAPK

ESCRT endosomal sorting complex

FABP5 fatty acid binding protein 5(psoriasis-associated); also known as E-FABP, EFABP, KFABP, PA-FABP, PAFABP

FGF1 ﬁbroblast growth factor 1; also known as AFGF, ECGF, FGFA, ECGFA, ECGFB, FGF-1, HBGF-1, HBGF1, GLIO703, ECGF- ,FGF-↵

FGF2 ﬁbroblast growth factor 2; also known as BFGF, FGFB, FGF-2, HBGF-2

FGFBP1 ﬁbroblast growth factor binding protein 1; also known as FGF- BP, FGF-BP1, FGFBP, FGFBP-1, HBP17, FGFBP1

FGFBP2 ﬁbroblast growth factor binding protein 2; also known as UNQ425/PRO1065, HBP17RP, KSP37

FGFBP3 ﬁbroblast growth factor binding protein 3; also known as FGF- BP3; C10orf13

FGFR1–4 ﬁbroblast growth factor receptors 1–4

xii FGFRs ﬁbroblast growth factor receptors

FGFs ﬁbroblast growth factors

FOXO1 forkhead box O1; also known as FKH1, FKHR, FOXO1A

FPLC fast protein liquid chromatography

FRS2↵ ﬁbroblast growth factor receptor substrate 2; also known as FRS2A, FRS2↵, SNT, SNT-1, SNT1

G1 Growth 1

GAB1 GRB2-associated binding protein 1(GAB1)

GRB2 growth factor receptor-boud protein 2; also known as AHS, EGFRBP-GRB2, Grb3-3, MST084, MSTP084, NCKAP2

HDL high-density lipoprotein

HITS-CLIP high-throughput sequencing cross-linking immunoprecipitation

HOX homeobox

HSP heat shock proteins

HSPG heparin sulfate proteoglycans

HSPGs heparin sulfate proteoglycans

IP3 inositol 1,4,5-triphosphate

KRAB Krüppel associated box

LDL low-density lipoprotein

xiii MAP mitogen associated protein mG methylguanine

MgCl magnesium chloride miR(s) microRNA(s)

MLCK myosin light chain kinase mRNA messenger Ribonucleic acid

MVB multivesicular bodies

NaCl sodium chloride

NaOAc sodium acetate

NCoRs nuclear corepressor proteins

NFAT nuclear factor of activated T cells

NPM1 nucleophosmin 1 nSMase2 neutral sphingomyelinase 2; also known as ISC1, NSMASE, NS- MASE1 nt(s) nucleotide(s)

P-body processing body p16 cyclin-dependent kinase inhibitor 2A; also known as ARF, MLM, P14, P16, P19, CMM2, INK4, MTS1, TP16, CDK4I, CDKN2, INK4A, MTS-1, P14ARF, P16INK4, P16INK4A, P16-INK4A

xiv p21 cyclin-dependent kinase inhibitor 1A; also known as P21, CIP1, SDI1, WAF1, CAP20, CDKN1, MDA-6, p21CIP1 p72 DEAD (Asp-Glu-Ala-Asp) box helicase 17; also known as RP3- 434P1.1, P72, RH70

PABP poly(A)bindingprotein

PCD6IP programmed cell death 6 interacting protein; also known as ALIX

PDPK1 3-phosphinositide-dependent protein kinase; also known as PDK1, PDPK2, PRO0461

PI3K phosphatidylinositol-4,5-bisphosphate 3-kinase

PLC1phospholipaseC1; also known as PLCG1, PLC1, NCKAP3, PLC-II, PLC148, PLCgamma1

PML promyelocytic leukemia; also known as MYL, PP8675, RNF71, TRIM19 poly(A) polyadenylated

PPAR\ peroxisome proliferator-activated receptors and premiR precursor microRNA pri-miR primary microRNA

RA retinoic acid

RALDH1-3 retinaldehyde dehydrogenases 1-3

RAR↵ retinoic acid receptor ↵

xv RAREs retinoic acid response elements

RARs retinoic acid receptors

RBP4 retinol binding protein 4; also known as RDCCAS

RDH retinol dehydrogenase

RDH10 retinol dehydrogenase 10; also known as SDR16C4

RISC RNA-induced Silencing Complex

RXRs retinoid X receptors

SDS sodium dodecyl sulfate

SOS sonofsevenless;alsoknownasGF1,HGF,NS4,GGF1,GINGF

SSC saline-socium citrate

STRA6 stimulated by retinoic acid 6; also known as MCOPS9, NCOPCB8, PP14296

TIS Target Island Score

Tris-HCl tris(hydroxymethyl)aminomethane hydrochloride

Tsg101 tumorsusceptibilitygene101

UTR untranslated region

YAP Yes-associated protein

ZnSO4 Zinc sulfate

xvi Chapter 1

Introduction

Cancer is a pervasive disease that has debilitating effects on both the individuals with the disease and their supporting families. It is estimated that cancer has affected approximately 13.7 million Americans, with an estimated 1.7 million new cases expected this year. Defined simply as abnormal and uncontrolled cell growth, most cancers have successfully avoided therapy due to the cancer cell’s inherent ability to manipulate the cellular machinery. This ability allows cancers to change their phenotype in order to continue their aberrant proliferation. The most aggressive cancers acquire the additional capacities of modulating the surrounding environment to suit optimal growth and colonizing distant sites via movement through the circulation, further complicat- ing the treatment of the disease. Understanding the cellular and molecular biology driving the initiation, continued growth, and subsequent metastasis of this disease is necessary to establish usable clinical prognostic and diagnostic indicators of disease regression. This dissertation aims to highlight a particular molecular mechanism, mi- croRNA induced messenger RNA (mRNA) degradation, and how this mechanism can be exploited to prevent tumor growth. The impetus behind this work is based on previous studies completed by Claudius Malerczyk, Emmanuel Liaudet-Coopman, and Bryan Boyle. These studies, centered on the observation that the mRNA of a Fibroblast Growth Factor Binding Pro- tein (FGF-BP or FGFBP1) is degraded after treatment with all-trans retinoic acid

1 (ATRA), serve as the foundation for the speciﬁc aims of this dissertation. The previous works showed that 1) FGFBP1 expression is regulated at a post-transcriptional level by ATRA, 2) ATRA modulates FGFBP1 expression through the transcription of a gene product, and 3) the gene product is sequence-speciﬁc. The logical extension of these initial data led to the hypothesis of this dissertation: ATRA induces transcription of (a) microRNA(s) that target(s) FGFBP1. Through this work, I attempted to answer three major questions:

1. Which microRNAs are regulated by ATRA?

2. Which microRNAs target FGFBP1?

3. Which region(s) of FGFBP1 is(are) being targeted by microRNAs?

To contextualize the data presented in this dissertation, a comprehensive background of microRNAs, retinoid signaling, and fibroblast growth factor signaling is provided. In detail, a description of the intial discovery, canonical function, and oncogenic potential of microRNAs. Subsequently, retinoid receptor signaling in normal and pathologic states is described. I provide an overview of both normal and oncogenic fibroblast growth factor signaling. Finally, I briefly discuss fibroblast growth factor receptor-retinoid receptor crosstalk in development and tissue patterning. Following this background information, I present the results containing the data generated. I conclude by placing the results into the appropriate context and describing any future work necessary to further characterize our findings. Finally, I discuss asetofexperimentsundertaken,butnotpresentedasapartofthisdissertation. These experiments, which aimed to identify the individual microRNAs binding to the FGFBP1 mRNA through RNA binding protein immunoprecipitation, did not glean

2 usable data for this work. However, a signiﬁcant amount of time was spent optimiz- ing and attempting these experiments. As such, I will discuss the lessons learned and possible implications of those experiments.

3 Chapter 2

Background

2.1 MicroRNAs: Discovery, Function, and Role in Cancer

2.1.1 MicroRNA Discovery

Roy Britten and Eric Davidson first suggested the existence of regulatory RNA in their 1969 theory describing a mechanism for RNA regulation of cell fate decisions. In short, they hypothesized that an ’activator RNA’ regulates gene expression through sequence-specific binding with a double-stranded DNA termed a ’receptor gene’ [19]. The theoretical activator RNA, characterized from data published by several groups, had several similarities to the later discovered miRs or their precursors; they were transcribed from short, redundant DNA sequences (40–180 nts), showed sequence- specificity to DNA, appeared resistant to RNase enzymes, and exhibited a short half-life [12, 17, 19, 26, 144]. While these activator RNAs differed from miRs in their dimerization partner (DNA vs. RNA), their localization (nuclear vs. cytoplasmic), and their action (gene activation vs. gene repression), Britten and Davidson introduced amechanismthatuncoupledgeneexpressionfromdirecttranscriptionalmodulation. The initial discovery of miRs is largely attributed to the work of Victor Ambros, Gary Ruvkun and their colleagues who described the existence of a small antisense RNA that regulated the expression of the LIN-14 protein [89, 171]. Combined, the two groups showed that a) Lin-4 was a transcribed gene that produced no protein product, b) the Lin-4 RNA was between 22 and 61 nt in length and had partial

4 complementarity to the 3’ UTR of the Lin-14 transcript, and c) Lin-4 expression was temporal. Seven years later, studies by Gary Ruvkun’s and Dave Bartel’s groups independently identified another miR, let-7, and the mechanism for miR-mediated cleavage of mRNA [132, 178]. Subsequent works by Phillip Sharp, Carlo Croce, George Calin, Oliver Voinnet and others expanded the role of miRs in several contexts, including cancer, stem cell differentiation, immune cell regulation, and viral evolution [38, 68, 104, 168, 182] The two decades of work following has been dedicated to discov- ering new miRs, identifying miR targets, and determining various contexts in which specific miRs regulate gene expression.

2.1.2 MicroRNA Biogenesis

While speciﬁc miRs have diﬀerent transcripts, targets, and expression patterns, their mechanisms of biogenesis is the same. Canonically, miRs are RNase-III generated single-stranded RNAs of approximately 22 nucleotides [5]. MicroRNAs are RNA polymerase II transcribed, either intergenically or as independent miR transcripts. The majority of miR genes appear to have similar conserved regulatory regions as protein coding genes including CpG islands; however, there is a miR population whose transcription start sites and promoter regions are not clear [28, 124, 135]. The initial precursor miR transcript, called a primary miR transcript (pri-miR), is between 100–120 nucleotides in length and contains a 5’ methylguanine (mG) cap structure, several local hairpin structures, and a 3’ poly(A) tail (see Figure 2.1) [10]. This pri- miR is sequestered in the nucleus until it is processed by the Microprocessor complex, which contains the RNase-III enzyme Drosha and the dsRNA binding protein Pasha (or DGCR8) [36, 52, 62]. The complex cleaves the large hairpin pri-miR into a 70 nt precursor miR (pre-miR) and guides exportation of the pre-miR through a nuclear pore characterized by the protein Exportin-5 [16, 101, 176]. Once released from

5 the nucleus, the pre-miR is processed into an 18-24nt mature miR by a cytoplasmic RNase-III enzyme Dicer [40, 71, 77, 80]. The pre-miR structure contains two strands which, during Dicer processing, leads to the generation of two mature miRs; a 5-prime (-5p; also known as the guide strand) and a 3-prime (-3p) miR. The -5p strand of the miR is preferentially loaded into the RNA-induced Silencing Complex (RISC), comprised mainly of the endonuclease Argonaute 2 (Ago2 in eukaryotes).

6 pre-miR Exportin-5 Drosha

Pasha Dicer Drosha

Pasha pri-miR

Canonical 5’mG poly(A) Processing pre-miR Ago2 Spliceosome Mirtron miR Processing miR Gene Gene A Gene B mRNA 3’UTR

7 Exon Exon

Simtron Drosha Processing Drosha Exon Exon ???? ???? ???? Nucleus Cytoplasm

Figure 2.1: MicroRNA biogenesis can occur via three distinct pathways. (Caption continued on the following page.) Figure 2.1: MicroRNA biogenesis can occur via three distinct pathways. Canonical Processing Pathway (black arrows) - Canonically, miR genes are transcribed into a 120nt, hairpin-structured pre-microRNA (pre-miR) transcripts containing a 5’- methylguanine cap and a 3’ poly(A) tail. Nuclear processing by the RNase enzymes Drosha and Pasha (or DGCR8) trims the pre-miR to a 70nt primary microRNA (pri-miR) transcript, which is subsequently exported from the nucleus by the nuclear pore protein Exportin-5. Cytosolic processing by the RNase III enzyme Dicer further trims the miR transcript into a double-stranded, mature miR transcript containing a 5-prime(-5p) (maroon) and a 3-prime(-3p) (blue) strand. Either the -5p or -3p strand is then shuttled into the RNA Induced Silencing Complex (RISC), characterized by the endonuclease Argonaute 2 Ago2). The Ago2-miR complex mediates mRNA degradation through homology with the 3’UTR of an mRNA. Mirtron Pro- cessing Pathway (green arrows) - Alternatively, miRs can reside within intronic sequences of protein coding genes. These are liberated, as pre-miRs, via the splicing machinery and exported from the nucleus by Exportin-5. They are processed in the cytosol using Dicer and RISC, as in the canonical pathway. Simtron Pathway (red arrows) -Simtronsarealsotranscribedfromtheintronsofprotein-codinggenes. However, they are released from the introns by the nuclear RNase enzyme Drosha. Remaining nuclear and cytosolic processing occurs through an unknown mechanism. Messenger RNA targeting, however, occurs through RISC Ago2 mediated cleavage.

There is also significant evidence of alternate mechanisms of miR biogenesis. Most well characterized are the mirtrons, miRs that are spliced and processed from introns of protein-coding genes (see Figure 2.1) [11, 13, 122]. These miRs were first observed in Drosophila melongaster and Caenorhabditis elegans after examination of high-throughput sequencing data, and are now thought to comprise approximately 40% of all characterized miRs [134]. In contrast with the canonical pathway, mirtrons avoid Microprocessor-mediated cleavage by Drosha and are instead generated by the splicing machinery, particularly the lariat debranching enzyme DBR1 [122, 134]. Sub- squent nuclear export, cytoplasmic processing, and RISC loading are identical to the canonical pathway. Interestingly, there exist 3 classes of mirtrons; a conventional mirtron, with exons immediately flanking the hairpin-structured pri-miR transcript,

8 and a 5’- and 3’-tailed mirtron, which has some additional unstructured sequence 5’ or 3’, respectively, to the pri-miR transcript [87]. A related mechanism involves Drosha processing of an intronic miR independent of the spliceosome and the Microprocessor complex (see Figure 2.1). MicroRNAs generated in this fashion are simtrons, recently discovered by Michelle Hastings’ group [63]. Each of these mechanisms contributes to the total miR pool available for intracellular gene regulation or secretion.

2.1.3 MicroRNA Function: Intracellular Targeting and Turnover / Intercellular Actions

Since their discovery in the early 1990s, miRs have been extensively studied for their role in intracellular gene regulation. As such, an increasing body of knowledge is being developed on the targets of specific miRs. To date, there are currently 30,424 mature miR species identified in 206 different organisms [53, 54, 55, 82]. While the majority of these miRs may have some homology within the vital seed sequence across several species (e.g. a large majority of human and mouse miRs are homologous within the seed sequence) suggesting that they target identical mRNAs, only a small portion have been completely characterized with some targets identified in any given species. Here, I describe the mechanisms involved with miR targeting, with particular focus on miR processing in eukaryotes. Once released into the cytosol, processed by the Microprocessor, and loaded into RISC, the mature miR is active and available for target acquisition. The target, canonically the 3’ UTR of a particular mRNA, must have homology to nucleotides 2-7/8 of the 5’ end of the mature miR. This region of the miR is called the ’seed sequence,’ and overwhelming data suggests that homology in this region is necessary for mRNA degradation. Lim et. al. initially described this 6-mer interaction, however, it was Robert Darnell’s group that definitively characterized this necessary

9 seed interaction [95]. Using cross-linked immunoprecipitation of Argonaute followed by high-throughput sequencing (called HITS-CLIP), Chi et. al. demonstrated native miR-mRNA interactions [25]. They generated binding maps for multiple miRs, focusing on miR-124, in simultaneous experiments on mouse brain and cultured HeLa cells. Their data showed that miRs suppress hundreds of transcripts, and that miR-124 targets several genes within the neuronal differentiation pathway [25]. Others have demonstrated the relevance of seed interaction in miR targeting using other cross-linking techniques, including formaldehyde, or photoreactive nucleoside- modified RNAs [61, 79]. The complex containing RISC and the mRNA:miR dsRNA is then bound by the mammalian processing body (P-body) protein GW182, and mRNA poly(A) binding protein (PABP) [46, 78, 96, 141]. The homology-mediated binding of nucleotides 2-7/8 of the miR to the 3’UTR of target mRNAs, sequestration of the RISC complex in mammalian P-bodies, and degradation of the mRNA by either the Slicer activity of RISC or decapping and deadenylation, miRs reduce expression levels of specific mRNAs and their proteins [6, 58, 72, 83]. Transcript degradation is thought to occur rapidly, while translation inhibition is thought to be dependent on the number of GW182-RISC complexes sequestered in P-bodies. Interestingly, data also exists for a noncanonical targeting of miRs to other regions of a gene besides the 3’UTR. In fact, the Chi et. al. data reveals that a significant portion of Ago2:miR:mRNA interactions that do not occur within the 3’UTR of any gene and, alternatively, are found within the ORF or, to a lesser extent, the 5’UTR of their target genes [25]. Several studies support this fact. Lytle et. al. described efficient miR repression through 5’UTR targeting miRs [102]. Using the well-described let- 7 :lin-41 interaction as a model system, they suggested that targeting of a miR to any region of the gene is sufficient to repress translation [102]. Another study, completed by Schnall-Levin et. al., describes four miRs targeting repeat regions within the coding

10 sequence (CDS) of a family of transcription factors. They observe consistent targeting of miRs-23a/b, -181a/b/c/d, -188-3p, and -199a/b-5p to 8-mer sites characteristic of

C2H2 zinc finger domains [139]. The targets of the four miRs overlap for 23 genes, all of which contain Krüppel associated box (KRAB) domains found within the CDS of their respective transcription factors [139]. Other groups have shown the combinatorial effect of CDS and 3’UTR miR targeting sites in the miR-mediated post-transcriptional regulation of genes. Ott and colleagues demonstrate a role for miR-29 for targeting the CDS and 3’UTR of elastin through 14 target sites [123]. Work of several groups have also described a deviation from the canonical rule that miRs have full seed-sequence homology to their target mRNAs in order to affect mRNA degradation or translational repression. Work following the initial HITS-CLIP analyses by Chi et. al. revealed that 15% of the microRNA response elements (MREs; by definition, regions of homology to a particular miR) for miR-124 did not have complete seed sequence homology, suggesting that a bulge at position 5 of the miR may still allow for appropriate gene repression [24]. Hafner et. al. described a similar phenomenon using photoactivatable nucleotide Ago2 crosslinking, suggesting 6% of all interactions measured had some degree of mismatching within the seed sequence [61]. A third group, using a crosslinking and ligation approach, also showed that only 37% of miR-mRNA interactions involved full Watson-Crick base pairing in the seed sequence [64]. Finally, some have suggested ’seedless’ interactions between miRs and mRNAs. Lal et. al. described miR-24 regulation of several genes in the E2F family without little to no homology between the miR and the target [88]. With such complexities involving miR biogenesis, miR targeting, and combinatorial miR actions, significant work is necessary to understand the role that these small RNAs play in post-transcriptional gene repression.

11 While the majority of microRNAs generated are involved in regulating intracellular gene expression, several groups have presented evidence suggesting that some are packaged into secretory vesicles and released into the extracellular milleu. Cir- culating microRNAs have been observed in several bodily fluids, including blood, urine, saliva, tears, milk, semen, amniotic fluid, cerebrospinal fluid, and malignant ascites of several tumors [47, 91, 112, 170]. This begs two questions: how are these microRNAs released from cells, and how are they protected from endogenous ribonu- cleases (RNases)? Evidence suggests that several mechanisms exist for packaging and secretion of microRNAs from cells, including association with RNA binding proteins, lipoproteins, exosomes, microvesicles, or apoptotic bodies [166, 167, 177, 179]. The first four mechanisms will be discussed here, as these involve active processing and release mechanisms. The fifth, apoptosis-induced microRNA secretion, will not be discussed as microRNAs released through this mechanism are passively shed and indiscriminate. The simplest mechanism of microRNA secretion involves release of a microRNA, complexed with either an RNA-binding protein or a lipoprotein, from the cell. After cytoplasmic generation of the microRNA via one of the aforementioned mechanisms, the microRNA is bound by a RNA-binding protein and released into the extracellular space. This is evidenced by studies describing circulating microRNAs bound to Ago2 or Nucleophosmin (NPM1)(see [7] and [169]). Arroyo et. al. first described the existence of circulating microRNAs whose stability was sensitive to treatment with proteases [7]. Size-exclusion chromatography revealed 77 miRs associated with late fractions, suggesting that these miRs were not vesicle-enclosed. Further characterization revealed that several of these plasma microRNAs were exclusively associated with Ago2, and they hypothesized that these Ago2-miR complexes could be functional at some distant site. The authors highlighted miR-122, a liver-specific microRNA that

12 appeared only in the protein fractions of the fractionated plasma. They suggested that this miR may be released through the protein carrier pathway. Subsequently, other groups have also observed the enrichment of this microRNA in the plasma and have therapeutically exploited its expression levels in the blood as a surrogate for liver disease and dysfunction [9, 39, 113] Wang et. al. also explored vesicle-free miR secretion from several different cell lines [169]. In a series of experiments using rotenone (a respiratory chain inhibitor), brefeldin A (a Golgi inhibitor), and N-dimethyl amiloride (DMA) (an exosome secretion inhibitor), they showed that miR secretion was energy dependent, Gogli-independent, and exosome-independent, respectively [169]. In addition, they showed that several RNA-binding proteins, including NPM1, were found extracellularly. While the group could only show that NPM1 was protective of free miRs in a cell-free system and not in an in vitro context, the cumulative data suggests that miRs are actively secreted with RNA-binding proteins. Data has also suggested that lipoproteins can serve as carriers for extracellular RNA. In the exploration for biomarkers for familial hypercholesterolemia, Vickers et. al. made the seminal observation that functional miRs are bound to both high-density and low-density lipoproteins (HDL and LDL, respectively), that HDL-miR complexes can be isolated from the blood, and that a distinct secreted HDL-miR signature is defined for familal hypercholesterolemia patients [167]. Through ultracentrifugation, fractionation by fast protein liquid chromatography fractionation (FPLC), and immunoprecipitation using the HDL-specific protein apolipoprotein A1 (ApoA-I), they demonstrated that plasma HDLs have a specific microRNA signature. They also show that these HDL-conjugated miRs are functional, delivering miRs-375 and -223 to hep- atocytes [167]. The authors also confirm that the targets of HDL-coupled microRNAs are reduced and that this reduction is signature of an artherosclerotic phenotype. The paper focused on the role of HDL-miR complexes in artherosclerosis, however,

13 their data also showed that miRs can be found conjugated to LDLs. Comparisions of microRNA profiles of LDL, HDL, and exosomes derived from human plasma showed that all three macromolecules can carry miRs. While the expression profiles between exosomes and LDL were not shown to be statistically significant, this data suggests that LDL is also a viable carrier of circulating miRs. In total, this work demonstrates that lipoproteins are involved in extracellular miR transport. Controversy still exists on the pathways involved with generation and secretion of these lipoproteins. The authors here suggest that release is mediated by the ceramide and neutral sphingomyelinase (nSMase) pathways, following the evidence for the role of this pathway in the release of several other bioactive lipids [51, 167]. The study of this packaging and secretion mechanism is still in its infancy and more work is necessary to fully characterize the role of these lipoproteins in miR delivery. More complex and seemingly more selective mechanisms of miR secretion exist. These pathways involve enclosing the microRNA cargo into lipid bilayer-protected vesicles and releasing these vesicles from the membrane, allowing them to freely travel to their respective targets. The two most studied and well-characterized are the microvesicle-(also called ectosomes, microparticles, and shedding vesicles) packaging and exosomal-packaging and secretion pathways. These two shed vesicles are morphologically different from each other, with distinct sizes (microvesicles range from 200nm to 1µmindiameter;exosomesrangefrom50–100nmindiameter),sedi- mentation rates (microvesicles pellet at 10,000g; exosomes pellet at 100,000g), and membrane features (microvesicles are enriched in phosphatidylserine, integrins, se- lectins, major histocompatibility complex (MHC) proteins, and CD40; exosomes are enriched in CD9, CD63, CD81, heat shock proteins (HSPs), and endosomal sorting complex (ESCRT) components) [116, 121]. Their formation, release, extracellular half-life, and targets are also drastically different. Microvesicle formation and release

14 involves the budding and detachment of exocytosed cytoplasm from the donor cell, typically induced by an extracellular stimulus (see reviews [27] and [121]). Evidence suggests that the extracellular half-life of these microvesicles is relatively short, between 5-60 minutes in vivo [56, 129, 155]. The short half-life of these vesicles suggests rapid uptake and cargo release into surrounding cells in close proximity to the donor cell. As such, these vesicles are likely involved in perturbing the immediate surrounding stroma, enhancing the tumor microenvironment for optimal growth. In contrast to the microvesicle processing pathway, exosomes are generated through a perversion of the late endosome processing pathway. Upon final processing, the miR can be packaged into multivesicular bodies (MVB) through a currently underdescribed mechanism. It is known that neutral sphingomyelinase 2 (nSMase2), ceramide, and ESCRT are necessary for exosomal mediated secretion of miRs, but the mechanisms involved in the packaging of specific miRs in these bodies have not been fully elucidated [81, 156, 172]. The data has hinted at several possible proteins regulating miR packaging in exosomes. It is known that the ESCRT machinery, particularly ESCRT-0, captures ubiquitinated proteins for packaging into MVBs [173]. The programmed cell death 6 interacting protein (PCD6IP, which is also known as ALIX), the Rab family of small GTPase proteins, and the tumor susceptibility gene 101 (Tsg101) have all been extensively characterized as key players in exosome pro- duction [120, 131, 146]. Gibbings et. al. showed that GW182, a protein with a known role in P-body mediated degradation of miR-mRNA complexes, may also be involved in trafficking miRs into MVBs for secretion [49]. These proteins interact within the MVBs to form intraluminal vesicles (ILVs) containing HSPs, miRs, Ago2, and other macromolecules. The MVBs release the ILVs extracellularly through exvagination into the extracellular space, where they typically have a half-life of up to 4 hours [138, 143, 149].

15 Data has suggested that several cancer-specific stimuli can induce release of both microparticles and exosomes. Muralidharan-Chari et. al. described an ARF6 mediated mechanism which leads to microvesicle release. They observe that ARF6 activation leads to phosphorylation of ERK, which subsequently activates myosin light chain kinase (MLCK) [115]. MLCK activation is necessary for microvesicle release. This attributes the well-characterized tyrosine kinase pathway, particularly the downstream mitogen associated protein kinase (MAPK) pathway, to an additional role in the pathophysiology of cancer through microenvironment manipulation. In addition, hypoxia has been shown to stimulate release of microvesicles and exosomes from various cell types. Tadokoro et. al. examined the role of hypoxia-induced exosomes from leukemic cells on the formation of endothelial cell tubes [148]. They observed an increase in human umbilical vein endothelial cell (HUVEC) tube formation when introduced to exosomes from hypoxic K562 cells, a human leukemia cell line. Further miR array analysis suggested that the leukemic exosome miR profile was dramatically altered under hypoxia. They showed that an exosome-specific, hypoxia-induced miR, miR-210, could attenuate endothelial tube formation through targeting of the angiogenic factor Ephrin-A3 (EFNA3) [148]. These data suggest that microvesicle and exosome secretion is activated through several cancer-specific stimuli.

2.1.4 Role of MicroRNAs in Cancer

Assessment of miR biogenesis and regulation in cancer has been challenging, as miR profiling data is confounded by the number of targets for each individual miR and the target mRNA-miR stoichiometry effect [14, 15, 44, 76]. However, data does suggest aberrant miR expression in oncogenesis. Early studies profiling miR expression in cancer suggested that microRNAs were globally underexpressed in tumors, with misexpression being attributed to CpG island methylation, loss of heterozygosity in

16 either a miR gene or a gene housing a mirtron, and dysregulation or loss of miR- processing components [84, 85, 100, 106, 147]. The data initially suggested that miRs served as tumor suppressor genes whose regulation was lost in oncogenesis. How- ever, several groups have identified overexpressed miRs involved in maintenance of an oncogenic phenotype. Most well characterized is miR-21, which has been shown to be upregulated in several different cancers [8, 103, 128, 150, 175]. Other miRs have been independently recognized for their roles in various cancer subtypes, through either gain or loss of expression. Thus, each miR must be assessed individually for its particular role in cancer, whether as oncogene or tumor suppressor. Large scale studies of miR expression profiles must be done to determine the roles of specific miRs in the cancer context. Promising, however, has been the utility of circulating miRs as markers of disease and therapy response. Circulating microRNAs have been used as prognostic and diagnostic indicators for artherosclerosis, liver disease, viral infection, and several cancers [9, 20, 125, 167]. While this field is still at its infancy and much more work needs to be done to identify accurate miR biomarkers, circulating miRs as biomarkers is a promising field of research.

2.2 Retinoid Signaling in Normal and Pathologic States

2.2.1 Retinoids, Retinoic Acid Receptor Pharmacology and Signaling

Retinoids are a family of lipophilic molecules derived from vitamin A, with roles in regulating cell proliferation, determining cell fate, and modifying immune and neuronal function [4]. Characterized by a -ionone ring, a polyene side chain, and a terminal hyrdoxyl group, these molecules cannot be synthesized and must be taken up through nutritional means. Ingested as retinyl esters, the molecules are hydrolyzed

17 into retinol in the small intestine, solubilized and taken up into the cells through binding of the retinol-sequestering protein retinol binding protein 4 (RBP4) and the membrane protein stimulated by retinoic acid 6 (STRA6) (see Figure 2.2 for diagram). Once inside, retinol is sequestered by the cellular retinol binding protein (CRBP), which serves to protect the oxidation-sensitive retinol molecule from even- tual degradation by cytochrome P450 family members. After release from CRBPs, the retinol molecule is metabolized to all-trans retinalaldehyde (retinal) by either alcohol or retinol dehydrogenases (ADH or RDH) , particularly retinol dehydrogenase 10 (RDH10). Retinal is further metabolized by retinaldehyde dehydrogenases, specif- ically retinaldehyde dehydrogenases 1-3 (RALDH1-3), into ATRA. ATRA can also be sequestered in the cytoplasm by a cellular retinoic acid binding protein (CRABP), which provides additional protection from oxidation. Final elimination occurs via the cytochrome P450 pathway, particularly CYP26 [41], which also generates the active secondary metabolites all-trans 4-oxo retinoic acid, 9-cis retinoic acid, and 11-cis retinoic acid, and 13-cis retinoic acid [151]. Each of these secondary metabolites is considered a minor product, however, several have also been shown to be involved in retinoid mediated signaling. 9-cis retinoic acid has been shown to be a ligand for the retinoid X receptor (RXR; to be discussed later) [65]. 9-cis retinoic acid, and 11-cis retinoic acid, and 13-cis retinoic acid has been shown to be essential for mediating visual perception through their opsin-binding capabilities [18, 67]. Retinoids, as a class of molecules, exist in several different structures due to rotation around the bonds in the polyene side chains. This rotation allows retinoids some specificity for different nuclear receptors. As such, retinoids are classified by both their structure and their ability to bind particular nuclear receptors. First (non-aromatic) and second generation (mono-aromatic) retinoids are the most flexible, allowing binding to several retinoic acid receptors. Third generation (poly-aromatic) retinoids have

18 the fewest aliphatic side chains, therefore, are the most rigid and have the greatest specificity for particular RARs. The first ageneration retinoids, which include retinol, tretinoin (retinoic acid), isotretinoin, and alitretinoin, have all been clinically useful for several indications including several cancers and skin disorders (see the clinical trials [33, 59, 66, 105, 107, 108, 109, 110, 118, 119, 126, 158, 159, 160, 161, 162, 163] or review by [151]). Second generation and third retinoids, which include etretinate, acitretin, tazarotene, and motretinate, have been mostly indicated for psoriasis and acne [137, 165]. Classically, retinoids mediate their actions through retinoic acid receptors (RARs), aclassofnuclearreceptorsthatdirectlyregulatetranscription.Retinoicacidrecep- tors exist in two families; the RARs, which have three isotypes (↵, ,and)and; the retinoid X receptors (RXRs), which also have three isotypes (↵, ,and)[23]. The structure of both families of receptors are similar, characterized by a C-terminal ligand binding domain, a hinge region, a central DNA-binding domain and a variable N-terminal domain. The ligand binding domain of the retinoid receptors is flexible to allow for variety in ligand binding, receptor interaction with various nuclear protein machinery, and dimerization with other retinoid receptors. The DNA-binding domain contains a zinc finger motif, which allows for sequence-specific DNA binding through retinoic acid response elements (RAREs) found genomically. These RAREs, charcterized by direct repetition of hexameric sequence with either a 1, 2, or 5 base spacer between each repeat (called DR1, DR2, or DR5, respectively; commonly no- tated as PuG(G/T)TCA(X)nPu(G/T)TCA), are typically found upstream of the transcription start site of retinoid-responsive genes [23]. These sequences are commonly degenerate, allowing for significant base substitution for targeting by RARs. This sequence degeneracy, as well as the variability in spacer length, is partially responsible for the pleiotrophic nature of RAR signaling. As such, accurately identifying

19 retinoid responsive genes has remained challenging. In the absence of any RAR lig- and, RARs heterodimerize with RXRs in the nucleus, bind to RAREs, and recruit corepressor complexes, characterized by the nuclear corepressor proteins (NCoRs) and silencing mediator of retinoic acid and thyroid hormone receptor (SMRT) [4]. These RAR:RXR:corepressor complexes actively prevent expression of RAR target genes. In the presence of an RAR ligand, RAR:RXR heterodimers alternatively recruit coactivator complexes, containing members of the p160 family of steroid receptor coactivators (SRC1-3), and RNApolII. Once activated, transcription of downstream genes (e.g. the well-characterized homeobox (HOX) family of transcription factors) occurs until the stimulus is removed, or until further metabolization occurs. At the termination of the retinoid response, these RAR:RXR complexes are degraded via the proteosome. Alternatively, retinoids can mediate their effects through binding to other cytosolic proteins. Schug et. al. described a mechanism for RA binding to fatty acid binding proteins [140]. Their work, in contrast to the classical RA activation outcomes, showed RA signaling inducing cell proliferation and promoting cell survival. They suggested that CRABP and fatty acid binding protein 5 (FABP5) compete for RA binding in the cytosol. Therefore, stoichiometric expression differences between these two proteins in particular cell types define the pathway by which RA signals. In a transgenic mammary tumor model, they show that RA signaling induces tumor growth in a retinoid-dependent manner. Further analyses show that RA binds to FABP5, which translocates to the nucleus and subsequently activates the peroxisome proliferator- activated receptors beta and delta (PPAR\). PPAR\ then heterodimerizes with RXRs, mediating transcription of cell survival related genes, particularly the AKT pathway [140]. FABP5 knockdown experiments suggest that altering CRABP/FABP5

20 ratios in cells can directly eﬀect the pathway used by RA. In total, these data further reinforce the pleiotrophic nature of RA signaling.

2.2.2 Retinoids as a Treatment for Cancer

Of late, a number of retinoids have been studied, and have shown clinical utility as both adjuvant and neoadjuvant therapies in several cancer subtypes, including acute promyelocytic leukemia, acute myelogenous leukemia, neuroblastoma, cervical, breast, and both small and non-small cell lung cancers [33, 59, 66, 105, 107, 108, 109, 110, 118, 119, 126, 158, 159, 160, 161, 162, 163]. They intervene in the progression of cancers typically through either cell cycle inhibition or apoptosis activation. Data has shown that retinoids can prevent a cell’s continuation through the cell cycle arrest in G1 phase [142]. Retionids have been most successful in acute myelogenous leukemias (of which acute promyelocytic leukemia is a subtype), as this disease is characterized by a chromosomal translocation between chromosomes 15 and 17. This translocation, denoted t(15;17)(q22;q21), fuses the promyelocytic leukemia (PML) gene to the retinoic acid receptor-alpha (RAR↵) gene. This fusion allows for a constituitively active RAR, which induces dedifferentiation of leukocytes to promyelocytes. Retinoids, ATRA particularly, induces differentiation of these promyelocytes to terminally differ- entiated granulocytes, allowing for complete remission of the disease [70]. In squamous cell carcinomas, ATRA has been shown to regulate cancer proliferation by decreasing expression of the extracellular signal-regulated kinase, ERK1 [29]. While this mechanism is through direct RA:RAR binding, epigenetic mechanisms of RA action have also been described and been useful in reducing the proliferative capacity of cancer cells. Several independent studies have demonstrated a role for RA in reducing the methylation marks characteristic of active chromatin. Das et. al.

21 Retinol Retinoic Acid RBP4 STRA6

CRBP CRABP

ADH RDH Raldh

Retinol Retinaldehyde Retinoic Acid

Co-Activator Complex

RAR RXR Nucleus RARE

Figure 2.2: Mechanisms of Retinoic Acid Receptor Signaling. See text (Chapter 2.2.1) for details.

22 describe retinoid-induced miR expression involved in DNA demthylation in neuroblastoma cells, a model in which retinoids have shown clinical promise. Using methylated DNA immunoprecipitation, they showed that the overwhelming response to RAR activation (via ATRA) in neuroblastoma cells was promoter demethylation (402 gene promoters demethylated vs. 88 gene promoters hypermethylated) [35]. Subsequently, they showed the downregulation of two key DNA methyltransferases, DNMT1, and DNMT3B, as a result of ATRA treatment. Interestingly, they show that a microRNA, miR-151, is responsible for the ATRA-induced loss of DNMT1, suggesting an epigenetic role for RA signaling. A follow-up study done by Lim et. al. also described RA induced epigenetic responses in neuroblastomas. They describe retinoid induced promoter hypomethylation of p16 and p21 in neuroblastoma cells. In their system, Lim et. al. described RA activation inducing cellular senescence through the upregu- lation of the cell cycle proteins p16 and p21 [94]. Interestingly, this mechanism also involves miRs. Das et. al. further characterized ATRA’s role in the epigenetic regulation of genes, particularly miRs, in neuroblastomas. Using bioinformatic analyses of DNA methylation, mRNA expression, and miR expression, they showed epigenetic silencing of 67 miR genes [34]. Gene expression analysis revealed these epigentically regulated miRs targeted the 3’UTR of several genes, and the targets of those 67 miRs were increased. Finally, they showed that overexpression of those miRs reduced the viability of neuroblastoma cells. In total, these results suggest a role for ATRA in reducing the proliferation of cancers, through direct transcriptional regulation, or through secondary, epigenetic eﬀects.

23 2.3 Fibroblast Growth Factors, Their Binding Proteins, and Their Role in Oncogenesis

2.3.1 Fibroblast Growth Factor Signaling

Growth factors are secreted proteins that are involved in cell proliferation, tissue development, and wound healing. Growth factors also play important roles in facilitat- ing communication between cell types, particularly epithelial and mesenchymal cells. Aberrant expression of these proteins leads to malignant states, such as uncontrolled proliferation and pathological angiogenesis. Therefore, control of growth factors must be tightly regulated and understanding the mechanisms involved in regulating growth factor signaling. One of the most characterized growth factors is the family of fibroblast growth factors (FGFs). Initially discovered in the brain and pituitary gland as acidic and basic FGFs, these 150-250 amino acid (18–28kDa MW) proteins play apivotalroleinembryonicdevelopmentandadulttissuehomeostasis[74].Atthe cellular level, they are known to promote cell proliferation, survival, motility, and differentiation [127]. As such, these molecules and their obligate receptors have been extensively discussed and their pathways targeted in several pathologies, including aberrant development and carcinogenesis. Disheartening, however, is the complexity of this signaling pathway. To date, 22 different FGFs have been characterized, with FGF1 and FGF2 having been most extensively studied [74]. The FGFs are divided into 7 families based on phylogenetic and structural analysis [50, 75]. Five of the FGF families contain members that function in a paracrine role, while the remaining families contain members that function in either an endocrine or intracrine fashion based on the existence of a secretory signal sequence and/or a heparin-binding domain [75]. FGFs serve as ligands to activate four different fibroblast growth factor receptors (FGFRs; FGFR1-4) , 3 of which have alternately spliced isoforms (FGFR1-3) [127].

24 More confounding is the recent discovery of a role for Klotho, a transmembrane protein characterized as a cofactor for endocrine FGFs [164]. As such, much work has been undertaken to delineate the details of this signaling pathway. The amino acid sequence for the paracrine FGFs contains a secretory signal, allowing for release extracellularly [75]. Commonly seqeustered in the extracellular matrix by HSPGs, FGFs must be in typically be in close proximity to cells in order to activate FGFRs. FGF signaling occurs after ligand binding to the extracellular domain of the receptor, assisted by HSPGs [37]. FGF ligand binding induces homodimeriza- tion of the receptor, which brings the intracellular kinase domains within appropriate distance to ensure transphosphorylation between the C-terminal kinase domains of the FGFR dimers. Once phosphorylated, the receptor complex binds and activates one of two constituitively associated adaptor proteins; FGFR substrate 2↵ (FRS2↵), or phospholipase C1 (PLC1) [22, 60]. Activation of FRS2↵ by phosphorylation activates and binds the protein growth factor receptor-bound 2 (GRB2). GRB2 can activate either son of sevenless (SOS) or GRB2-associated binding protein 1(GAB1), inducing activation of either the MAP kinase cascade or PI3K signaling, respectively [45]. Activation of the MAP kinase cascade, through Ras, Raf, MEK, and ERK leads to activation of a transcriptional program that induces cell proliferation. Activation of PI3K signaling, through 3-phosphinositide-dependent protein kinase(PDPK1), activates AKT [157]. AKT activation inhibits apoptosis through inhibition of both the mitochondrial pore proteins BAD and BAX and the exocutioner caspase, caspase 9. AKT also inhibits the forkhead box class O (FOXO) transcription factors, whose transcriptional targets are associated with cell proliferation and survival. PLC1activa- tion by FGFRs induces an alternate signaling pathway. Once phosphorylated, PLC1 can induce hydrolysis of the membrane protein phosphatidylinositol-4,5-bisphosphate

(PIP2), generating inositol triphosphate (IP3)anddiacylglycerol(DAG).IP3 mediates

25 Ligand Binding Map Kinase AKT Activation Calcium Release 1 3 Activation 4 5 FGFR

HSPG

FGF FGF

FRS2α SOS GAB1 PLCγ1 P GRB2 P P P Ras PI3K PIP2 Transphosphorylation 2 and Adaptor Binding Raf PDK IP3

AKT MEK Ca2+

Erk Calcineurin

NFAT

6 Gene Transcription Cell Proliferation FOS FOXO NFAT Cell Survival Cell Motility

Figure 2.3: Mechanisms of FGF Signaling. See text (Chapter 2.3.1) for details. Mod- iﬁed from [50].

calcium release from intracellular stores, while DAG activates the protein kinase C (PKC) pathway. Calcium release activates the cytosolic calcium binding protein calcineurin, which activates the transcription factor nuclear factor of activated T cells (NFAT) [50]. This transcription factor is responsible for transcribing mRNAs involved in cell motility.

26 2.3.2 Fibroblast Growth Factor Binding Proteins and their Role in Cancer

Paracrine FGFs bind to heparin sulfate proteoglycans (HSPGs) in the extracellular matrix (ECM), requiring the action of proteases or heparinases for their mobilization and activation. Binding proteins, which serve to either sequester or release growth factors, play a critical role in fine-tuning signaling mechanisms. As an alternative mode of mobilization, our lab has characterized a secreted carrier protein, namely FGFBP, which chaperones and activates FGFs from the ECM to the FGFRs (see 2.4 for cartoon.)[152]. FGFBPs are a family of three proteins (FGFBPs -1, -2, and -3) involved in the solubilization of FGFs from the extracellular matrix. FGFBP1 is a 234 amino acid protein that has been evolutionarily conserved across several species, including bovine, rat, mouse, and human [1]. FGFBP1 binds to FGF-1, 2, 7, 10 and 22, enhancing FGF-mediated biochemical and biological effects, such as cell proliferation, survival, differentiation and motility in a variety of cell models [152, 154, 174]. FGFBP2, also called killer specific protein of 37kDa (KSP37), is a 223 amino acid protein that has been shown to be associated with early stage clear cell carcinoma [43]. FGFBP3 has also been shown to be integral in determining vascular permeability [181]. Previous data has also shown FGFBP1 to be highly expressed in several cancers, including head and neck, colon, pancreas, and, breast [154]. Recent observations have described a significant role for FGFBP1 in transformed phenotypes, and as such, have groups have begun to therapeutically target this protein. Data from our lab have described a role for FGFBPs in tumor-stromal crosstalk [2]. Increased expression of FGFBP1 increases cellular tyrosine kinase activity, which has been shown to be a driver of oncogenesis [133]. Additionally, FGFBP1 has been shown to play a role as an angiogenic switch molecule in several cancers [31, 32]. As such, groups have identified

27 FGFBPs as therapeutic targets and have attempted to reduce FGFBP1 expression levels through several means. Our lab showed that expression of FGFBP1 was shown to be lost by retinoic acid treatment [3, 92]. This work is a logical extension of these original studies.

28 Figure 2.4: FGFBPs enhance FGF signaling through the solubilization of extracellular bound FGFs. - After release from epithelial cells (or carcinomas) into the extracellular matrix, FGFBPs solubilize sequestered FGFs. Once freed from the ECM, the FGFs can activate FGFRs, thereby modulating expression of genes involved in cell proliferation, motility, and survival. These liberated FGFs can act in a paracrine fashion, activating adjacent epithelial cells, adjacent transformed cells, or surrounding stromal cells. In addition, freed FGFs can act in an autocrine fashion, activating resident FGFRs. Courtesy of A. Wellstein.

29 Chapter 3

All-Trans Retinoic Acid Reduces Expression of FGFBP1 mRNA in a time-dependent and ORF-dependent manner

3.1 Aim and Hypothesis

The aim of this study was to validate the ATRA-mediated reduction of FGFBP1 in ME180 cells and determine the time-course for this reduction. I hypothesized that ATRA would rapidly reduce FGFBP1 levels.

3.2 Materials and Methods

RNA Isolation, cDNA Synthesis, and quantitative real-time PCR –TotalRNAwas isolated using the RNA-Stat reagent (Tel-Test; Friendswood, TX) and precipitated using the sodium acetate-ethanol method. After assessing RNA concentration and pu- rity using absorbance readings acquired on a Nanodrop spectrophotometer (Thermo Scientific; Waltham, MA) (260nm/280nm ratio), 500ng was used for cDNA synthesis (iScript cDNA Synthesis Kit, Bio-Rad; Hercules, CA). Subsequent qPCR analyses were done using iQ SYBR Green Master Mix (Bio-Rad; Hercules, CA), gene-specific primers [human FGFBP1 and human Actin], and 2µL of newly synthesized cDNA. The PCR conditions were as follows: initial denaturation - 95 C for 10 minutes; 40 cycles of amplification, consisting of denaturation at 95 C for 30 seconds, annealing at 55 C for 30 seconds, and extension at 72 C for 30 seconds.

30 Generation of the FGFBP1 Expression Vectors âĂŞ- Generation of the full length FGFBP1 cDNA from ME180 total RNA template was performed using the Ac- cess RT-PCR System (Promega; Madison, WI). First strand cDNA synthesis and PCR was performed in a single reaction using 5µgoftemplateRNA,50unitsof Avian Myeloblastosis Virus (AMV) reverse transcriptase, 50 units of Tfl DNA polymerase, 0.2mM dNTPs, 1µM each FGFBP1 sense and antisense primers FGFBP1 sense primer: 5’-CTGTTGAATAGTCTACCCC-3’ (phosphorylated to ensure unidirectional cloning), FGFBP1 antisense primer: 5’-CACCCAGAGTTTTATTACC-3’), and 1mM MgSO4.SynthesisoffirststrandcDNAwasperformedat48Cfor45 minutes. After inactivation of the reverse transcriptase, cDNA amplification was performed over 40 cycles of PCR [denaturation = 94 C (2 minutes), annealing =

52 C (1 minute), and extension = 72 C (2 minutes)]. The two amplified fragments, a full-length (BP-FL) and frame shift mutant (BP-FS), were subcloned into the pCR3.1-Uni vector, generating the pCR3.1-Uni-BP-FL and pCR3.1-Uni-BP-FS vectors, respectively(Life Technologies; Grand Island, NY). Subsequent bacterial colonies were screened using a [ 32P-ATP] 5’ end-labeled antisense oligonucleotide probe (5’- CGTGTCCTGCACTATGCTG-3’), sequenced by Sanger sequencing, and subcloned into the pRC/RSV vector using restriction enzymes HinDIII and XbaI. This resulted in two pRC/RSV-BP-FL vectors; one containing the full-length sequence of human FGFBP1 (pRC/RSV-BP-FL), and another containing a frameshift deletion mutation at position 184 (G) (pRC/RSV-BP-FS). Generation of the 5’UTR deleted FGFBP1 vector (pRC/RSV-BP-5’) was completed by digesting the pRC/RSV-BP-FL vector with BglII and filling in with the Klenow fragment. The resulting vector was digested with XbaI, purified by agarose gel electrophoresis, and the fragment FGFBP1 128–1166, was subcloned into the HincII and XbaI sites of the pSP64-poly(A) vector(Promega; Madison, WI). Double digestion

31 of this vector with HinDIII and XbaI released the 5’UTR deleted FGFBP1 fragment, which was subcloned into the pRC/RSV vector (pRC/RSV-BP-5’). Generation of the 3’UTR deleted FGFBP1 vector (pRC/RSV-BP-3’) was completed by digesting the pCR3.1-Uni/BP-FL with HinDIII and XmnI, gel purifying the BP cDNA insert (containing BP nucleotides 1-846), and ligating the insert into the shuttle vector pSP64-Poly(A). This vector was subsequently double digested with HinDIII and XbaI, gel purified, and subcloned into the pRC/RSV vector, generating the final pRC/RSV- BP-3’ vector. Generation of the ORF FGFBP1 vector (pRC/RSV-BP-ORF) was completed by subcloning the HinDIII-digested insert from pRC/RSV-BP-FL into the pRC/RSV plasmid. Northern Blot Analyses Stably transfected SW13 cells were grown to 80% conflu- ence, washed twice in serum-free IMEM, and treated with 10µM ATRA for 6 hours. Total RNA was isolated using RNA STAT-60 according to the manufacturer’s pro- tocol (Tel-Test Inc.; Friendswood, TX). Thirty µgoftotalRNAwereseparatedby gel electrophoresis (1.2% formaldehyde-agarose gel), blotted onto nylon membranes (Schleicher and Schuell; Keene, NH), and prehybridized in 5X SSC (0.75 M sodium chloride, 0.075 M sodium citrate (pH 7.0), 0.5% (w/v) SDS, and 5X Denhardt’s solution (Life Technologies; Grand Island, NY)) for 4 hours. Hybridization with either

FGFBP1-speciﬁc or GAPDH-speciﬁc DNA probes was carried out overnight at 42 C in 5X SSC. Three, ten-minute post-hybridization washes were conducted using 2X

SSC and 0.1% SDS at 42 C.Afinal,20minutepost-hybridiztionwashin1XSSC at 65 C was completed, and autoradiography of the blots was performed using in- tensifying screens at 70 C.QuantificationofmRNAlevelswasperformedusinga phosphor-imager (GE Healthcare Bio-Sciences; Piscataway, NJ). Hybridization probes were prepared by random-primed DNA labeling (Boehringer Mannheim; Gaithersburg, MD) of purified insert fragments from human FGFBP-ORF

32 and human GAPDH (Clontech; Mountain View, CA). The ﬁnal concentration of the labeled probes was always greater than 2 x 106 cpm/mL of the hybridization solution.

3.3 Results and Discussion

To determine the time course for ATRA induced FGFBP1 mRNA degradation, I treated ME180 cells, an HPV-16 positive cervical squamous cell carcinoma cell line,

5 with 10 M ATRA for different time intervals and measured FGFBP1 mRNA expression by quantitative real-time PCR (qPCR). ATRA reduces FGFBP1 mRNA levels to 35% of an untreated control as early as 1 hour post-treatment (Figure 3.1A). In the continued presence of ATRA for 24 hours, FGFBP1 mRNA levels remained between 15% and 24% showing a persistent downregulation in ME180 cells. We were interested in whether a specific portion of the FGFBP1 gene was necessary for the ATRA-mediated degradation we observed. Accordingly, we constructed several plasmids that would generate either the full-length FGFBP1 transcript, a 3’UTR-deleted FGFBP1 transcript (3’UTR), a 5’UTR-deleted FGFBP1 transcript (5’UTR), or a ORF-only transcript. After transfecting these constructs into an FGFBP1-negative SW13 cell line, we treated with ATRA for 6 hours and performed Northern blotting to determine the mRNA levels. We observed that, in the presence of ATRA, mRNA levels for the full length, open reading frame, and individual UTR- deleted FGFBP1 were reduced by 50% (Figures 3.1B & C). These data suggest that the ORF of FGFBP1 is sufficient for the ATRA-induced degradation. These experiments served to validate the previously published data by our lab [93]. They showed a significant reduction in FGFBP1 mRNA levels after treatment with ATRA. Here, we wanted to recapitulate that data using qPCR. We show that this phenomenon was detectable by qPCR and as such, this method could be used

33 100 A

50 mRNA (% Ctrl) (% mRNA

25 FGFBP1 0 06121824 ATRA Treatment (hr)

B Vector FGFBP1 Full FGFBP1 Δ5’UTR FGFBP1 Δ3’UTR FGFBP1 ORF

ATRA -- ++ -- ++ -- ++ -- ++

FGFBP1

GAPDH

1.5

n o

ssi *****

1.0 Expre

0.5

FGFBP1 FGFBP1 Untreated

ATRA Fold Fold 0.0 FGFBP1 Full Length Δ5’UTR Δ3’UTR ORF

Figure 3.1: All-trans retinoic acid treatment induces stable loss of FGFBP1 mRNA in a transcription-dependent manner. A. Degradation of FGFBP1 mRNA by ATRA (10µM) is time-dependent and detectable by qRT-PCR. ATRA induces a 75% decrease in FGFBP1 mRNA levels in ME180 cells. This decrease is maintained for 24 hours. B. ATRA induced degradation of FGFBP1 mRNA is dependent on the ORF. Constructs containing either the full length (Full),openreadingframe(ORF), or UTR-deleted (5’UTR) or (3’UTR) regions of FGFBP1 mRNA were transfected into FGFBP1-naive SW13 cells and treated with (10µM) ATRA. Northern blots and deletion constructs were generated by C. Malerczyk, M.D. using FGFBP1 and GAPDH -speciﬁc probes. C. Quantiﬁcation of 1 B. * = p <0.05, ** = p <0.01.

34 as a surrogate to Northern blotting. Similar to Liaudet-Coopman et. al., we show that retinoids rapidly degrade FGFBP1 mRNA levels. With the recent discovery of miRs, and evidence suggesting ATRA regulation of miRs could be involved in post-transcriptional regulation of genes, we designed FGFBP1 constructs containing either a full-length gene, a deleted 5’UTR fragment, a deleted 3’UTR fragment, or an ORF only fragment, hypothesizing that loss of the 3’UTR would abrogate ATRA- mediated FGFBP1 loss. After transfecting these constructs into SW13 cells, which do not express FGFBP1, we treated with ATRA for 6 hours and assessed FGFBP1 fragment mRNA levels. To our surprise, the FGFBP1 ORF was necessary for ATRA- mediated degradation. Initially, we thought that this ﬁnding was odd. However, this ﬁnding corroborates recent studies suggesting that miRs can target the ORF of genes and reduce their mRNA levels [25, 123, 139]. As such, we attempted to determine the sequence necessary for ATRA induced FGFBP1 degradation.

35 Chapter 4

A Region in the ORF is Necessary for ATRA-mediated degradation of FGFBP1

4.1 Aims and Hypothesis

The aim of this study was to determine the sequence speciﬁcity of ATRA-mediated loss of FGFBP1, particularly, which nucleotides within the ORF of FGFBP1 were required for the degradation. The goal was to use deletion constructs to further isolate the nucleotide sequence necessary for FGFBP1 mRNA targeting by ATRA.

4.2 Materials and Methods

Generation of the truncated FGFBP1 Expression Vectors -GenerationofthethreeBP deletion vectors was completed by unidirectional nested deletions of the pRC/RSV- BP-FL vector. In short, the pRC/RSV-BP-FL vector was double digested with Bst1107I and BstXI. The resulting fragment was phenol extracted, ethanol precipitated, and incubated with 100 U of exonuclease III in the appropriate buffer (66mM Tris-HCL, pH 8.0, 0.66 mM MgCl) at 37 C. At 10 second intervals, 2µLaliquotsof the digest were removed. The aliquots were incubated with 3 U Mung Bean nuclease in the appropriate buffer (500 mM NaOAc, pH 5.0, 300mM NaCl, 10mM ZnSO4) at 30 C for 30 minutes, phenol extracted, ethanol precipitated, and ligated into the pRC/RSV vector, generating pRC/RSV-BP(1-204bp), pRC/RSV-BP(1-353bp), and pRC/RSV-BP(1-631bp) vectors. The sequence for the vectors was confirmed by

36 Sanger sequencing using the antisense primer 5’-CAACAGATGGCTGGCAACTAG- 3’. The G(179) vector was generated as a result of the cloning of the aforementioned vectors. Vectors were generated by C. Malerczyk, Ph.D. Northern Blot Analyses - Stably transfected SW13 cells were grown to 80% conﬂu- ence, washed twice in serum-free IMEM, and treated with 10µM ATRA for 6 hours. Total RNA was isolated using RNA STAT-60 according to the manufacturer’s pro- tocol (Tel-Test Inc.; Friendswood, TX). Thirty µgoftotalRNAwereseparatedby gel electrophoresis (1.2% formaldehyde-agarose gel), blotted onto nylon membranes (Schleicher and Schuell; Keene, NH), and prehybridized in 5X SSC (0.75 M sodium chloride, 0.075 M sodium citrate (pH 7.0), 0.5% (w/v) SDS, and 5X Denhardt’s solution (Life Technologies; Grand Island, NY)) for 4 hours. Hybridization with either

FGFBP1-speciﬁc or GAPDH-speciﬁc DNA probes was carried out overnight at 42 C in 5X SSC. Three, ten-minute post-hybridization washes were conducted using 2X

SSC and 0.1% SDS at 42 C.Afinal,20minutepost-hybridiztionwashin1XSSC at 65 C was completed, and autoradiography of the blots was performed using in- tensifying screens at -70 C.QuantificationofmRNAlevelswasperformedusinga phosphor-imager (GE Healthcare Bio-Sciences; Piscataway, NJ). Northern blot analyses were completed by C. Malerczyk, Ph.D. Hybridization probes were prepared by random-primed DNA labeling (Boehringer Mannheim; Gaithersburg, MD) of purified insert fragments from human BP-ORF and human GAPDH (Clontech; Mountain View, CA). The final concentration of the labeled probes was always greater than 2 x 106 cpm/mL of the hybridization solution. In-Silico Analysis of microRNA Target Sites in FGFBP1 - Putative microRNA target sites were discovered using both the miRanda (Computational Biology, Memo- rial Sloan Kettering Cancer Center; New York, NY) and rna22 (IBM; Armonk, NY) algorithms. The sequences for all mature microRNAs and human FGFBP1 were down-

37 loaded from the publically available databases miRBase (version 19) and NCBI RefSeq (NM005130.4 - human FGFBP1) , respectively. Graphs of microRNA Target Island Scores (TIS) were generated using the statistical and graphics software packages R and Adobe Illustrator CS6 (Adobe Systems; San Jose, CA), respectively. Scores that were greater than three standard deviations from the mean score across the entire gene (miR TIS > 66) were graphed.

4.3 Results and Discussion

After determining that the ORF of FGFBP1 was sufficient for the ATRA-mediated mRNA degradation we observed, I asked whether a specific sequence within the ORF of FGFBP1 was necessary for the mRNA degradation effect. Accordingly, we generated constructs containing fragments of the FGFBP1 ORF. These plasmids were transfected into SW13 cells and the cells were subsequently treated with ATRA for 6 hours. As shown in Figure 4.1A, mRNA from constructs containing the first 353 nucleotides of FGFBP1 (1-353bp) and the first 631 nucleotides of FGFBP1 (1-631bp) were significantly reduced after treatment with ATRA, as compared to an untreated control. ATRA treatment of SW13 cells transfected with the construct containing the first 204 nucleotides (1-204bp) of FGFBP1 showed a reduction that did not reach statistical significance. These data suggest that the region of FGFBP1 ORF necessary for degradation is between nucleotides 204 and 631. We also made constructs containing a frameshift deletion mutation at position 179, which inserted a stop codon (TGA) at position 198. We transfected this plasmid into SW13 cells and treated with ATRA for 6 hours. As shown in Figure 4.1B, the frameshift deletion mutant (G(179)) abrogated the effects of ATRA on FGFBP1 loss.

38 A Vector FGFBP1 Full FGFBP1 (1-204bp) FGFBP1 (1-353bp) FGFBP1 (1-631bp)

ATRA -- ++ -- ++ -- ++ -- ++

FGFBP1

GAPDH

1.5

1.0 ns

Expression ** *** *

0.5 Untreated

FGFBP1 FGFBP1 ATRA Fold 0.0 FGFBP1 Full Length 1-204bp 1-353bp 1-631bp

B 1.5 Vector FGFBP1 Full FGFBP1 ΔG(179) ns ATRA -- ++ -- ++ 1.0

Expression ** FGFBP1 Untreated 0.5 ATRA

GAPDH FGFBP1

Fold 0.0 FGFBP1 Full Length ΔG(179) 160 C 140

120

100

miR Target Island Score miR Target 60 5’UTR FGFBP1 ORF 3’UTR 1 200 400 600 800 1000 1200 N ucleotide Position

D 1-631bp 1-353bp 140 1-204bp

120

100

miR Target Island Score miR Target 60 FGFBP1 ORF

Figure 4.1: The FGFBP1 ORF is necessary for mRNA degradation and can be targeted by microRNAs. (Caption continued on the following page.)

39 Figure 4.1: The FGFBP1 ORF is necessary for mRNA degradation and can be targeted by microRNAs. A. Constructs containing either full length (Full) or short ORF fragments of FGFBP1 mRNA were transfected into SW13 cells. ATRA treatment and Northern blots were done as described in Materials and Methods. Below. Quantification of above blot. *=p<0.05,**=p<0.01,***=p<0.005.B. Deletion of a G at position 179 abrogates ATRA-mediated loss of FGFBP1. Constructs containing either full length (Full) or the frameshift mutant (G(179)) were transfected into SW13 cells and treated as above. Northern blot analyses and deletion constructs were generated by C. Malerczyk, M.D. Below. Quantification of blot to the left. ** = p <0.01. C. I nsilico assessment of microRNA susceptible sites in the FGFBP1 ORF by rna22 suggests extensive microRNA targeting. The miR Target Island Score quantifies microRNA targeting (through seed homology) within a 21-nt window within the sequence. Several regions within the FGFBP1 ORF have high miR Target Island scores, suggesting possible microRNA targeting regions. D. The region of the FGFBP1 ORF between 205 bp and 631 bp is highly targetable by microRNAs. This region contains peaks with the highest microRNA Target Island Scores within the ORF.

As our data has suggested that an ATRA-induced, transcribed molecule with sequence speciﬁcity to a region within the ORF of FGFBP1 is essential for the mRNA loss that I observed, I hypothesized that a microRNA (or several microR- NAs) was/were involved. To further examine the validity of this hypothesis, I used both in silico and in vitro methods to determine the microRNAs putatively targeting FGFBP1. Using rna22,amicroRNAtargetpredictionalgorithmdevelopedbyIBM [111], I explored the prediction of microRNAs targeting the ORF of FGFBP1. Using mature sequences of all human microRNAs in miRBase 19, I searched for regions with homology to several microRNAs. Nucleotide sequences with target island scores greater than 60 suggested regions of high targetablity by several microRNAs. Sur- prisingly, I found that the FGFBP1 ORF contained a number of regions with high target island scores (Figure 4.1C). Having generated data suggesting that the region

40 between nucleotides 204 and 631 were necessary for ATRA-induced mRNA degradation, I focused on this region in our in silico analyses. As shown in figure 4.1D, I found that the region between 204-631 bp contained numerous areas with high target island scores (Figure 4.1D -green and blue regions). Interestingly, the region between 1-204 bp (Figure 4.1D - orange region) had very few areas of high target island scoring, and concommitantly, was not significantly downregulated by ATRA (Figure A). Thus, these in silico data supported our experimental observations; therefore, we initiated an in silico and in vitro "microRNome" screen to determine which microR- NAs could target the FGFBP1 ORF. Using a locally run installation of the miRanda microRNA target prediction algorithm, we searched for microRNAs with significant seed-sequence homology to the ORF of FGFBP1, with particular emphasis on the region between nucleotides 204 and 631. As expected, several hundred microRNAs had some homology to this 437-nt region of FGFBP1.

41 Chapter 5

All-Trans Retinoic Acid Regulates the Expression of MicroRNAs

Targeting FGFBP1.

5.1 Aim and Hypothesis

The aim of this study was to constrain the number of putative microRNAs studied by determing ATRA’s ability to modulate individual miR expression. I hoped to validate these miR expression studies using gene set enrichment analyses to highlight miRs with strong regulatory capabilities. I hypothesized that several miRs could target the ORF of FGFBP1 between nucleotides 204 and 631, however, I had no basis for determining the exact number. The goal of this study was to reveal a testable number of miRs.

5.2 Materials and Methods

MicroRNA Expression Analysis of ATRA-treated ME180 cells -Analysisofthetotal microRNA proﬁle was completed on ATRA treated ME180 cells. MicroRNAs were further isolated from the total RNA using a miR isolation kit (SA Biosciences; Frederick, MD). 500ng of the microRNA fraction was converted to cDNA using poly(A)-tailing followed by universal priming with miR First Strand Kit and quantitated using pre- designed miR speciﬁc qPCR primers (SA Biosciences; Frederick, MD) on an ABI 7900 HT Real-Time PCR system (Applied Biosystems, Foster City, CA). Cycle threshold levels were acquired and normalized to the geometric mean of three loading control

42 small RNAs (U6, RNU43 small nucleolar RNA, and U1 spliceosomal small nuclear RNA). Z-scores for each microRNA across all treatments were calculated using R statistical software and a parallel coordinates plot was generated using the d3 JavaScript library. Venn diagrams were generated using R statistical software.

5.3 Results and Discussion

In order to reduce the number of putative FGFBP1-targeting microRNAs, I treated

5 ME180 cells for 1, 3, and 6 hours with 10 M ATRA to determine which microRNAs were retinoid responsive. I calculated the z scores across all treatments for each mi- croRNA and visualized microRNA z score proﬁles using a parallel coordinates plot. In order to identify ATRA-induced microRNAs, I highlighted microRNAs whose zscores were greater than 1.0 (1 SD from the mean) for each ATRA treatment. I found that 141 microRNAs (18.5%)wereupregulatedafteronehour,197microRNAs(25.8%) were upregulated after three hours, and 83 microRNAs (10.9%)wereupregulatedaf- ter 6 hours (see Figure 5.1A). Comparing these upregulated microRNAs with the in silico mined list reduced the number FGFBP1-targeting microRNAs to 281 (Figure 5.1B; total Venn diagram overlap). Considering that the ATRA-mediated downregulation of FGFBP1 is a rapid event, I only further analyzed the 220 microRNAs found in the intersection of the 1 hour and 3 hour Venn diagrams. I further reduced the number of possible microRNAs targeting the FGFBP1 ORF through gene expression/gene set enrichment analyses. I hypothesized that microR- NAs with signiﬁcant impact on the phenotype of the cell would be exposed through

5 the negative regulation of known targets. As such, I treated ME180 cells with 10 M ATRA for 1, 3, and 6 hours and performed Illumina-based gene expression arrays. Af-

43 A 10 M ATRA Control 1 hr 3 hrs 6 hrs

1.5 1.5 1.5 1.5

1.0 1.0 1.0 1.0

0.5 0.5 0.5 0.5 1 hour

3 hours 0.0 0.0 0.0 0.0 6 hours

Z-Score

-0.5 -0.5 -0.5 -0.5

-1.0 -1.0 -1.0 -1.0

-1.5 -1.5 -1.5 -1.5

1 hr ATRA 3 hr ATRA 6 hr ATRA

51 69 23 B

91 129 61

395 357 425

miRanda miRanda miRanda

Figure 5.1: Combined in Vitro and in silico analyses suggest hundreds of microRNAs target the ORF of FGFBP1. A. Whole miRNome quantitative PCR analysis suggests that several microRNAs are regulated by ATRA in a temporal fashion. Of the 751 microRNAs assessed, 423 (56.3%)showedincreasedexpressioninresponseto10µM ATRA treatment. 18.9% (142 - orange), 26.3% (198 - yellow), and 11.1% (83 - green) of the microRNAs were upregulated after 1 hour, 3 hours and 6 hours of 10µM ATRA, respectively. B. Venn diagrams of ATRA-responsive miRs and miRanda identiﬁed miRs reduces the putative number of microRNAs targeting FGFBP1.

44 ter normalization, I used the Gene Set Enrichment Analysis algorithm by the Broad Institute to further interrogate the gene expression profiles. The strength in this analysis lies in the biological relevance of the curated gene sets provided, which are derived from current biological data. Using a gene set that contains all genes that share a 3’UTR microRNA binding motif, I compared gene sets whose microRNA targets were downregulated in ATRA-treated cells. This analysis returned several microR- NAs whose targets had reduced gene expression levels and were overrepresented in the gene expression arrays. As there was already significant work published on mi- croRNAs 27b and 125a in squamous cell carcinomas and the GSEA analyses further validated our hypothesis, I explored the role of these two microRNAs in modulating FGFBP1 expression. ATRA regulation of miRs has been extensively studied in several cell types, however, most studies have allowed for long exposures to ATRA (see Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/) and [21] for examples). While these studies provide a glimpse into retinoid regulation of miRs, they are confounded by the evident role of ATRA in mediating other epigenetic responses, particularly hi- stone modifications. Our study was interested in a relatively rapidly acting miR. This suggests that the microRNAs targeting this gene must be directly transcribed through a RAR:RXR mediated mechanism. Interestingly, I noticed a fluctuation in the number miRs with high z scores over time. I hypothesize that this is due to sim- ple drug metabolism mechanisms. As a polar molecule, ATRA is rapidly degraded by cytochrome P450 enzymes. As such, it must be protected through CRABP binding.

5 The addition of 10 M ATRA could overwhelm the available RARs and CRABPs in the system, leaving any unbound ATRA as a target for degradation. The initial (1hr) number of ATRA-induced miRs could be due to initial activation of all available RARs. At the 3 hour time point, additional RAR activation could be due to subse-

45 quent release of ATRA from CRABP. The reduction in the number of ATRA induced miRs at 6 hours could be due to the loss of ATRA due to cytochrome mediated degradation. However, I do concede that a secondary pathway activated by ATRA could be involved in modulating miR expression. Data has suggested that ATRA can also induce PPAR\ pathway activation through the binding of ATRA to FABP5 [140]. This pathway induces activation of cell proliferation and cell survival genes. Addition of ATRA could, in theory, activate both pathways and the expression levels detected could be a sum of the combinatorial regulation of PPAR:RXR and RAR:RXR to particular miRs. In order to alleviate that concern, I assessed FABP5/CRABPII levels in ME180 cells via qPCR. Interestingly, ME180 cells have slightly higher expression levels for CRABPII than FABP5, suggesting that these cells preferentially use the RAR:RXR mechanism of activation (data not shown).

46 Chapter 6

MicroRNAs 27b-3p and 125a-5p target the ORF of FGFBP1 and reduce mRNA and protein levels.

6.1 Aim and Hypothesis

The aim of these studies was to determine the targetability of FGFBP1 by miR-27b- 3p and 125a-5p. I hypothesized that these miRs had target regions within the ORF of FGFBP1.

6.2 Materials and Methods

RNA Isolation, cDNA Synthesis, and quantitative real-time PCR -TotalRNAwas isolated using the RNA-Stat reagent (Tel-Test; Friendswood, TX) and precipitated using the sodium acetate-ethanol method. After assessing RNA concentration and pu- rity using absorbance readings acquired on a Nanodrop spectrophotometer (Thermo Scientiﬁc; Waltham, MA) (260nm/280nm ratio), 500ng was used for cDNA synthesis (iScript cDNA Synthesis Kit, Bio-Rad; Hercules, CA). Subsequent qPCR analyses were done using iQ SYBR Green Master Mix (Bio-Rad; Hercules, CA), gene-speciﬁc primers [human FGFBP1 and human Actin], and 2µL of newly synthesized cDNA.

The PCR conditions were as follows: initial denaturation - 95 C for 10 minutes; 40 cycles of ampliﬁcation, consisting of denaturation at 95 C for 30 seconds, annealing at 55 C for 30 seconds, and extension at 72 C for 30 seconds.

47 Gene Expression Analysis -Geneexpressionanalyseswereperformedbythe UCLA Neuroscience Genomics Core. To describe briefly, ME180 cells treated with 10 µM ATRA for 1, 3, or 6 hours (in quadruplicate) were rinsed with sterile PBS and total RNA was isolated as described above. After quantification using both the Nanodrop spectrophotometer and Quant-iT RiboGreen RNA Assay kit (Life Tech- nologies), the total RNA was prepared using the Illumina TotalPrep RNA Amplifi- cation Kit (Applied Biosystems/Ambion; Foster City, CA) according to the provided instructions. cDNA generated from this kit was column purified, in vitro transcription was performed to generate biotinylated cRNA, and the cRNA was column purified and quantified using the Experion system (Bio-Rad). 1.5µgofthelabeledcRNAwas hybridized to an Illumina Human HT-12 v4 BeadChip (Illumina; San Diego, CA). The arrays were scanned using an Illumina BeadStation and intensities were measured using the BeadStudio software (Illumina). The data was subsequently analyzed using the R software packages lumi and limma.Genespecificbeadintensitieswere background corrected and normalized using the variance-stabilizing transformation

(VST). Differential gene expression analysis and log2 fold changes were calculated using limma. MicroRNA-specific gene set enrichment analyses between untreated and treated samples were run using a local instance of the GSEA algorithm developed and maintained by the Broad Institute of Massachusetts Institute of Technology and Harvard University (Boston, MA). Transient Transfections of microRNA Mimics - Mimics for microRNAs 27b-3p, and 125a-5p (Qiagen; Germantown, MD) were transfected into ME180 cells using HiPerFect transfection reagent (Qiagen) according to provided instructions. For all experiments, 50,000 cells were plated into each well of a 24-well plate and allowed to incubate for 30 minutes while preparing the lipid complexes. The final concentration of the mimics were 5nM. The cell-lipid complex mixture was allowed to incubate

48 overnight at 37 C. Subsequently, the cells were treated with either 10µM ATRA (Sigma-Aldrich; St. Louis, MO) for 1, 6, or 24 hours. Actin-normalized FGFBP1 mRNA expression was assessed via quantitative real-time PCR (qPCR) using the

Ct method. Each transfection experiment was done in quadruplicate and mean expression levels were calculated. Differences between mock-, mimic-, and sponge- transfected cells in the presence or absence of retinoic acid were described using Student’s t-test. Protein Isolation and Western Blotting - ME180 cells were grown to 50-60% con- fluence in Improved Minimum Essential Medium in a 6-well plate (IMEM; Life Tech- nologies; Grand Island, NY). Cells were rinsed twice with ice-cold PBS and scraped from the cell surface in 500µL cold lysis buffer (20mM Tris pH 7.5, 200mM NaCl,

2.5mM MgCl2,0.5% NP40 (Igepal), 60 units/mL Superase-IN, 1mM dithiothreitol, 1mg/mL Pefabloc SC). Lysates were centrifuged at 14,000xg to remove membrane fractions and the supernatants were transferred to a clean microcentrifuge tube. Pro- tein concentration was quantified using 280nm absorbance readings from a Nanodrop spectrophotometer (Thermo Scientific; Waltham, MA). Fifty micrograms of protein were loaded onto 4-12% Bis-Tris gels (Life Technologies; Grand Island, NY) and electrophoresed at 175 V for 40 min. The proteins were transferred onto PVDF membranes using the iBlot dry blotting system (Life Technologies), blocked with 5% nonfat dry milk in PBST, and incubated with antibodies against human FGFBP1 (1:500 in 1% nonfat dry milk in PBST; Sigma-Aldrich) overnight at 4 C. Blots were rinsed three times in PBST and incubated for 1 hour with horseradish peroxidase-conjugated goat anti-rabbit secondary antibodies (1:10,000; GE Healthcare-Bio-sciences; Piscataway, NJ). Chemiluminescent detection was catalyzed using Immobilon Chemiluminescent HRP Substrate (EMD Millipore; Billerica, MA) and visualized using x-ray film. The blots were stripped, reprobed for human beta-Actin using a mouse monoclonal an-

49 tibody(Cell Signaling Technology; Danvers, MA), and visualized as described previously. Densitometric quantiﬁcation of the x-ray ﬁlm was completed using ImageJ software (National Institutes of Health; Bethesda, MD).

6.3 Results and Discussion

I further reduced the number of possible microRNAs targeting the FGFBP1 ORF through gene expression/gene set enrichment analyses. I hypothesized that microR- NAs with signiﬁcant impact on the phenotype of the cell would be exposed through

5 the negative regulation of known targets. As such, I treated ME180 cells with 10 M ATRA for 1, 3, and 6 hours and performed Illumina-based gene expression arrays. Af- ter normalization, I used the Gene Set Enrichment Analysis algorithm by the Broad Institute to further interrogate the gene expression proﬁles. The strength in this analysis lies in the biological relevance of the curated gene sets provided, which are derived from current biological data. Using a gene set that contains all genes that share a 3’UTR microRNA binding motif, I compared gene sets whose microRNA targets were downregulated in ATRA-treated cells. This analysis returned several microR- NAs whose targets had reduced gene expression levels and were overrepresented in the gene expression arrays (Figures 6.1A and 6.1B). I further explored miRs-27b-3p and -125a-5p for two reasons. Our gene set enrichment analyses had revealed that targets for these miRs were highly enriched in a set of genes whose expression was negatively correlated with miR expression (Figure 6.1B). In addition, these miRs had been shown by others to have a retinoic acid response element (RARE) in relatively close proximity to the putative TSS of the miRs, ﬁtting with our hypothesis that this miRs must be directly transcribe through a RAR:RXR mechanism [69]. As such, I explored the role of these two microRNAs in modulating FGFBP1 expression.

50 A

ATRA miR 1hr 3hr 6hr ES p-value FDR ES p-value FDR ES p-value FDR let-7 -1.11 0.0 0.4 ------family 10a/b -1.21 0.0 0.16 -1.48 0.008 0.06 -1.43 0.009 0.23

23a/b -1.1 0.33 0.41 -1.45 0.0 0.06 -1.19 0.07 0.31

99a/b -1.78 0 0 ------

150 -0.995 0.5 0.72 ------1.02 0.4 0.53

212/132 -0.996 0.5 0.72 -1.23 0.09 0.16 -1.29 0.06 0.28

ATRA 1hr 3hr 6hr ES p-value FDR ES p-value FDR ES p-value FDR

-1.41 0.0 0.105 -1.07 0.15 1 ------

ATRA 1hr 3hr 6hr ES p-value FDR ES p-value FDR ES p-value FDR

-0.23 0.79 1.0 -1.52 0.0 0.053 -1.1 0.18 0.39

Figure 6.1: Gene set enrichment analysis (GSEA) reveals several retinoid-induced microRNAs with overrepresented targets in ME180 cells. (Caption continued on the following page.)

51 Figure 6.1: Gene set enrichment analysis (GSEA) reveals several retinoid-induced microRNAs with overrepresented targets in ME180 cells. A. MicroRNAs whose targets are enriched and are associated with several cancers are regulated by ATRA. The table displays the key statistics reported by GSEA. The enrichment score (ES) represents the degree to which a gene set is overrepresented in a ranked list of genes. A positive ES suggests that the gene set is at the top of the list (expression level of the microRNA is positively correlated with the expression level of the set of genes). A negative ES suggests that the gene set is at the bottom of the list (expression level of the microRNA is negatively correlated with the expression level of the set of genes). The p-value estimates the statistical significance of the ES for the gene set. The false discovery rate (FDR) represents the probability that the ES represents a false positive finding. In this analysis, the FDR is adjusted for gene set size and multiple hypothesis testing, while the p-value is not. As such, the FDR, particularly an FDR of < 25%,isamorereliablemeasureofthe significance of a gene’s ES than the p-value. Each of the microRNAs in this table have well-established roles in carcinogenesis, are upregulated by ATRA, and reduce expression levels of several of their targets as shown by GSEA. B. ATRA treated ME180 cells enrich targets of microRNAs 27b and 125a, as shown by GSEA. Left. Shown are example enrichment plots of microRNAs 27a/b and 125a/b. Right. Tables displaying GSEA-calculated ES, p-value, and FDR for miRs 27a/b and 125a/b at 1, 3 and 6 hours, respectively. MicroRNA 27a/b has its lowest ES (-1.41) and best FDR (0.105) at 1 hour post-treatment, suggesting miR-27a/b is rapidly induced by ATRA. MicroRNA 125a/5p has its lowest ES (-1.52) and best FDR (0.053) at 3 hours post-treatment, suggesting that the effect of miR-125a/b is a slower, possibly longer lasting effect. ES = Enrichment Score, FDR = False Discovery Rate. Data is archived as accession number GSE54464 at NCBI’s Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/).

52 10 A C 6 ** 1 5 ** 0.1 4 Expression

3 Mock 0.01 ** * miR-27b-3p miR-125a-5p FGFBP1 2 0.001 FGFBP1

Fold * ns FGFBP2 1 ** FGFBP3 *** 0.0001 0

Fold Change from Mock Transfected ME180 Cells Change from Fold Mock Transfected FGFBP1 FGFBP2 FGFBP3 53

B hsa-miR-27b-3p 3’ UCGGUGACACUU 5’ D 698 704 tRA Syn-miR-27b-3p _ + _ + FGFBP1 ORF 5’CTGTGAA 3’ _ _ Unt 1h 3h Syn-miR-125a-5p + + FGFBP1 hsa-miR-125a-5p 3’ UCCAGAGUCCU 5’ FGFBP1 344 349 FGFBP1 ORF 5’CTCAGG 3’ Actin Actin 1.0 0.5 0.7 1.0 0.8 0.9 0.3

Figure 6.2: ME180 cells express FGFBPs and microRNAs 27b-3p and 125a-5p reduce FGFBP1 mRNA and protein levels through ORF homology. A. Quantitative real-time PCR (qRT-PCR) using FGFBP subtype specific primers reveals that ME180 cells preferentially express FGFBP1. ** = p <0.01, n=6. B. MicroRNAs 27b-3p and 125a-5p have sites of seed sequence homology within the ORF of FGFBP1. MicroRNA 27b-3p is homologous to a 7-nt region between 698–704 nts in the FGFBP1 ORF, while microRNA 125a-5p is homologous to a 6-nt region between 344–349 nts in the FGFBP1 ORF. The 3’UTR of FGFBP1 does not have significant homology to the seed sequence of either of these microRNAs. (Caption continued on next page.) Figure 6.2: ME180 cells express FGFBPs and microRNAs 27b-3p and 125a- 5p reduce FGFBP1 mRNA and protein levels through ORF homology. C. Quantitative real-time PCR of FGFBP1 mRNA confirms the role of microRNAs 27b-3p and 125a-5p in reducing FGFBP1 levels. Transfection of synthetic microRNAs 27b-3p and 125a-5p into ME180 cells significantly reduces FGFBP1 mRNA levels. Mi- croRNA 27b-3p appears to increase FGFBP2 and FGFBP3 levels, while microRNA 125a-5p appears to only slightly reduce FGFBP2 levels. * = p <0.05, ** = p <0.01., *** = p <0.005, n=3. D. Left panel. Western blotting of ME180 cells after treatment with 10µM ATRA for 1 and 3 hours shows a reduction in FGFBP1 protein levels. n=3. Right panel. MicroRNAs 27b-3p and 125a-5p work in tandem to reduce FGFBP1 protein levels. Dual transfection of ME180 cells with 5nM synthetic microR- NAs 27b-3p and 125a-5p reduce FGFBP1 levels, while not changing expression levels independently. Dually transfected cells reduce FGFBP1 levels by 70%. Images shown are representative of 3 biological replicates.

MicroRNAs targeting the ORF of genes have not been fully characterized. As such, IwantedtoestablishthefunctionalroleofmiRs-27b-3pand125a-5pinregulating FGFBP1. Searching within the ORF of FGFBP1 for regions with homology to the seed sequence for either miR-27b-3p or 125a-5p revealed sites for each microRNA. Mi- croRNA 27b-3p has homology to a 7-nucleotide site between nucleotides 698 and 704 of FGFBP1 (Figure 6.3B). MicroRNA 125a-5p has homology to a 6-nucleotide site between nucleotides 344 and 349 of FGFBP1 (Figure 6.3B). While the seed homology site for microRNA 125a-5p fell outside of our expected region, it did still target a site inside the ORF of FGFBP1, suggesting that ORF targeting microRNAs can reduce mRNA levels. With our data showing that these microRNAs were increased by ATRA and gene targets of these microRNAs were reduced, as shown by gene set enrichment analyses, I hypothesized that these two microRNAs were involved in reducing FGFBP1 mRNA levels by targeting the ORF. It is hypothesized by several groups that ORF targeting microRNAs rapidly reduce mRNA levels through mRNA

54 degradation. In order to demonstrate that microRNAs 27b-3p and 125a-5p were func- tioning in this same capacity for FGFBP1, I transfected synthetic microRNAs 27b-3p and 125a-5p into ME180 cells and assessed FGFBP1 mRNA levels after 24 hours. Mi- croRNAs 27b-3p and 125a-5p signiﬁcantly reduce FGFBP1 mRNA levels, as shown in Figure 6.1C. In addition, I assessed FGFBP1 protein levels after synthetic microRNA transfection. I show that miRs-27b-3p and 125a-5p, when transfected independently, do not change FGFBP1 protein levels. When transfected together, however, these microRNAs reduce FGFBP1 protein levels by 70%.

55 Chapter 7

MicroRNAs 27b-3p and 125a-5p are reduced in squamous cell carcinoma tissue samples.

7.1 Aim and Hypothesis

The aim of this study was to explore the expression levels of miRs-27b-3p and - 125a-5p in squamous cell carcinoma tissues. As I had previously demonstrated that these miRs are upregulated after ATRA treatment and reduced FGFBP1 levels, I hypothesized that squamous cell carcinomas would have reduced expression of these miRs.

7.2 Materials and Methods

Next-generation sequencing data for lung, cervical, and head and neck squamous cell carcinomas was downloaded from The Cancer Genome Atlas Data Portal (https://tcga-data.nci.nih.gov/tcga/tcgaHome2.jsp). The data was retrieved as a data matrix and parsed for miR expression, using reads per million microRNA mapped (RPMMM) as an indicator of expression. Statistical analyses (unpaired t-tests) were done using Graphpad Prism.

7.3 Results and Discussion

In order to determine the generalizability of ATRA regulation of FGFBP1 through microRNAs 125a-5p and 27b-3p, we analyzed The Cancer Genome Atlas database

56 400000

*** *** *** 300000

200000

(RPMMM) Controls (n=22) HNSCC (n=37)

ped LUSC (n=45) p 100000 CESC (n=52)

***p < 0.0001 Unpaired t-test

RNA ma RNA 0

ro hsa-miR-1-1 hsa-miR-21

i 4000

n ***

llio

i *** 3000 ***

2000 Controls (n=22) ads p HNSCC (n=37)

Re LUSC (n=45) CESC (n=52) 1000 *** *** ***p < 0.0001 *** Unpaired t-test

0 hsa-miR-125a-5p hsa-miR-27b-3p

Figure 7.1: MicroRNAs 27b-3p and 125a-5p are reduced in several squamous cell carcinoma tissue samples. Next generation sequencing data downloaded from The Cancer Genome Atlas suggests that head and neck, lung, and cervical squamous cell carcinomas all lose microRNA 27b-3p and 125a-5p expression.

57 to assess the expression levels of these microRNAs in squamous cell carcinomas. The database contained normalized next generation sequencing data for head and neck squamous cell carcinoma (HNSCC), lung squamous cell carcinoma (LUSC), and cervical squamous cell carcinoma (CESC). We plotted reads per million microRNA mapped (RPMMM) for microRNAs 27b-3p and 125a-5p, as well as microRNAs 1-1 and 21 as controls. As expected, microRNA 1-1 has undetectable reads due to its well- established lack of expression in epithelial cells. Conversely, microRNA 21 is highly expressed in all squamous cell carcinoma cell lines, as has been published by several independent groups. Interestingly, expression of microRNAs 27b-3p and 125a-5p are signiﬁcantly reduced, as compared to control tissue, suggesting a role for these microRNAs in this disease.

58 Chapter 8

Conclusions and Future Directions

Here, I have demonstrated a role for ATRA in modulating expression of a FGF- binding protein through a pair of miRs. This work was characterized by a series of molecular experiments which suggest miR binding to the open reading frame of FGFBP1, in contrast with the current dogma that suggests primarily 3’UTR targeting. Initially, through qRT-PCR, we showed the ATRA reduces expression levels of FGFBP1 mRNA. This reduction is sustained over 24 hours in the presence of ATRA. Interestingly, FGFBP1 mRNA is reduced by 75% very rapidly by ATRA, as early as 1 hour after treatment. The magnitude of this reduction was not consistent across several biological replicates, suggesting that other mechanisms were involved in FGFBP1 mRNA mediated reduction. I explored one of those mechanisms in data not shown in this thesis. I performed an experiment to determine the role of confluency on the expression of FGFBP1 in ME180 cells. As cells became more confluent and filled more space on the dish, they expressed less FGFBP1. This reduction in FGFBP1 levels could be due to several hypothesized reasons; 1) FGFBP1 could be a necessary factor for initiating early proliferation, but is not necessary for continued growth or, 2) A microRNA (or microRNAs) targeting FGFBP1 could be more highly expressed in individual cells as they become more confluent. Characterization of FGFBP1 has shown that is an effective angiogenic switch molecule, suggesting that it is necessary in the initial seeding and early proliferation of tumor cells in vivo [93]. As angiogenesis is an early event in the establishment of a tumor, FGFBP1 would also need to be

59 expressed in high levels when fewer cells are present. Once a tumor is established, and the vasculature has been successfully co-opted for tumor growth, FGFBP1 expression, in theory, could be reduced. In an in vitro model, this seems plausible, as there is an abundance of nutrients, growth factors, etc. in growth medium. In an in vivo model, however, this does not seem to be the case. Studies done by our lab have shown sustained overexpression of FGFBP1 in colon, pancreatic and skin cancers [86, 130, 153]. It is likely that the aberrant proliferation rapidly outgrows the blood supply providing nutrients to the tumor cell. Therefore, the tumor constantly needs new blood vessels to supply it. However, the ME180 cells are most likely mimicking the in vitro behavior discussed. This hypothesis would necessitate an appropriate extracellular sensor for simultaneously monitoring cell-cell contact and inhibiting transcription of FGFBP1. Interestingly, NOTCH signaling allows for cell-contact specific transcriptional regulation and has been shown to reduce expression of several genes in endothelial cells. In fact, Liu et. al. described a mechanism for NOTCH-mediated reduction of another angiogenic switch molecule, VEGFR2 [98]. This mechanism could be involved in also regulating FGFBP1 at a transcriptional level, based on cell density. As such, exploration of the role of this mechanism in modulating FGFBP1 expression would be an interesting set of follow up experiments. Using RNA interference to reduce levels of NOTCH would be an interesting set of experiments to determine it’s role in regulating cell-density dependent FGFBP1 expression. A second hypothesis for reduced FGFBP1 expression in high density cell cultures is that confluency affects the expression of microRNAs, particularly FGFBP1-specific microRNAs. Increased expression of these microRNAs reduces the expression of their downstream targets, thereby displaying a net loss of gene expression. This observation was initially made by Joshua Mendell’s group in 2009. They showed that cell-cell contact globally increases microRNA expression in NIH3T3 fibroblasts MCF7 breast

60 carcinoma, HCT116 colorectal carcinoma, MiaPaCa-2 pancreatic adenocarcinoma, Hepa1-6 hepatocellular carcinoma, and HEK293 embryonic kidney cell lines [73]. The Mendell group used both microRNA arrays and individual Northern probes to show increased miR expression as cells went from 50% to 100% confluent. While the Mendell group did not characterize any downstream targets of the microRNAs they showed to be overexpressed, it is reasonable to assume that miR overexpression resulted in alossofmRNAtargets.Asbefore,anextracellularsensorwouldbenecessaryfor inducing transcription of the miRs. Interestingly, a recent publication has suggested such a sensor, and has implicated it in regulating miR expression in a cell density- dependent manner. Mori et. al., in their 2014 Cell paper, showed that the Hippo/YAP pathway was responsible for regulating the Microprocessor complex, thereby affecting microRNA levels in cancer cells[114]. By showing that Hippo activation and YAP nuclear translocation prevents p72’s association with the Microprocessor complex, it provides a mechanism for cell-density mediated miR expression. They propose that, in a low-density context, YAP translocates to the nucleus and prevents p72 from associating with the Microprocessor complex, thereby reducing miR expression. At high densities, YAP is retained in the cytoplasm by Hippo, allowing appropriate miR processing. Mori et. al. caveat their work by stating that this mechanism is likely only specific for tumors with impaired Hippo signaling and aberrant YAP translocation. However, this mechanism explains the cell-density dependent global increase in miR expression. While no one has studied the role of Hippo/YAP signaling in ME180 cells particularly, several studies have suggested a role for Hippo/YAP signaling in squamous cell carcinomas [42, 48, 97, 117, 145, 180]. In total, these data suggest that miRs are directly regulated by cell contact. As such, further studies identifying whether miRs targeting FGFBP1 are also upregulated due to cell-cell interactions

61 would be interesting. Also, assessing the role of the Hippo/YAP signaling pathway in regulating FGFBP1 expression would be an interesting set of experiments. After determining that the ORF of FGFBP1 was necessary for ATRA mediated degradation by Northern blotting and establishing the hypothesis that a miR may be involved, I used in silico techniques to further characterize the putative binding sites in the FGFBP1 ORF for miRs. I used two different, publically available algorithms; rna22,developedbyIBMandMiranda, developed by Memorial Sloan Kettering Can- cer Center. Each of these algorithms has their strengths and weaknesses. Rna22 is the most robust, allowing the user to determine regions of miR targeting called target islands. However, at the time of writing this dissertation, the servers running rna22 had been shut down. Miranda is the most commonly used algorithm, being the backend target identification algorithm for microRNA website microrna.org. Its most recent online iteration is built to determine miR binding sites in 3’UTRs of genes using both homology and a conservation score, which quantifies the conservation of a target site across species. As such, for this work, a stand-alone version of the algorithm is run. This algorithm allows the user to input the miRs and sequence that they wish to query, and scores putative target sites based on homology. The stand-alone version used in this dissertation does not include the conservation score, therefore, is slightly less informative. With these caveats, both provided some information as to which miRs target the FGFBP1 ORF. Using rna22, I found a number of sites with high TISs, suggesting that microRNAs target these regions. These data are astounding, in that most studies that assess global miR targeting using RNA precipitation methods only present anecdotal data about miRs targeting ORFs (see [25] for example). I cross-examined the ORF of FGFBP1 with the miranda algorithm to determine the particular miRs and their target sites. Not surprising, I show that hundreds of miRs can target the ORF of FGFBP1. Herein lies the weakness of in silico analysis. My

62 expectation was that these analyses would significantly reduce the number of miRs studied. However, with miRs only needing homology to an 8-nt seed sequence (with some data now showing that some bulges and mismatches may be allowed in that sequence), the dimensionality of the data was not reduced. However, these analyses did reveal some interesting information about the FGFBP1 mRNA. Firstly, the ORF seemed to be highly targeted, in fact, much more than the 5’UTR and the 3’UTR. This has interesting connotations for FGFBPs. The mRNA for the FGFBP family is more highly homologous in its regulatory regions than in its ORF, with BP1:BP2 being 46.61% homologous in the 3’UTR, BP2:BP3 being 46.52% homologous in the 3’UTR, and BP1:BP3 being 29.46% homologous in the 3’UTR. This suggests that there are fewer miRs that could regulate expression of this family of genes independently through their 3’UTRs. As such, the ORF would serve as an alternative site for targeting a specific FGFBP family member. As an exercise, I compared miR TIS between FGFBPs 1 & 2 in data not presented here, and observe that the 3’UTRs have nearly overlapping high TISs. However, the ORF TISs between the two mR- NAs have a dramatically different profile, suggesting that the targetability of ORF in these mRNAs is different. When I ran miranda to determine the individual miR species targeting these mRNAs, there was significant overlap between the lists of miRs targeting the 3’UTRs of FGFBPs 1 & 2. There was little overlap between the lists of miRs targeting the ORF. I would hypothesize that, as an evolutionarily conserved mechanism, miRs targeting the ORF allow additional target specificity not always allocated by the 3’UTR, particularly in the case of the FGFBP family. This is an interesting hypothesis left for others to explore. Identification of the two miRs 27b-3p, and 125a-5p with significant homology to the ORF of FGFBP1 was a novel finding. Confirming our findings, others have shown that these miRs are regulated by ATRA. MicroRNA-27b-3p and miR-125a-5p, have

63 been shown to contain RAREs 5’ to the TSS in a paper highlighting RAR/ER antagonism [69]. Interestingly, these miRs have various actions in different cell types. This pleiotrophic effect seems to be due to the abundance of targets for a particular miR. Pierre Paolo Pandolfi’s group called this the competitive endogenous RNA hypothesis, where the overall effect of a miR on a particular gene is titrated by the number of total mRNA targets that exist for that miR at any given time [136]. The breadth of targeting by a particular miR is currently unknown. It is feasible that these miRs may be targeting other genes involved with mediating FGFBP1 levels, including but not limited to, molecules in the transcriptional, translational, or splicing machinery. Impairing efficient transcription would reduce expression levels of FGFBP1 mRNA. This is not likely, as previous evidence from the lab showed that in the presence of actinomycin D, a transcription inhibitor, the FGFBP1 mRNA levels remained high [93], suggesting a long half-life mRNA. Inhibition of translation through loss of necessary translational machinery could slow down translation of the nascent FGFBP1 mRNA enough to allow for miR targeting. Evidence does not suggest that this is the case, as we previously showed that inhibition of translation via cycloheximide in the presence of ATRA did not reduce FGFBP1 levels [93]. While daunting, an exciting set of experiments would involve assessing the targets of these microRNAs using a cross-linking based approach. It is feasible to use a photoactivatable nucleoside to tag the miRs, then use next-generation sequencing to assess actual targets, similar to the CLASH technique used by Helwak et. al. [64] This work would require the assistance of a bioinformatician to properly assess true interactions versus false positives. It is my belief that individual miR target assessment is the next step in understanding the roles of these small RNAs in mediating gene expression levels. What is also unknown is the role that these miRs play in the development of squamous cell carcinomas. However, it is evident that there is an association between

64 loss of these miRs and carcinogenesis, as shown by the TCGA data presented in this dissertation. These data are confirmed by groups observing a loss of miR-27b appears to be characteristic of several squamous cell carcinoma subtypes, including esophogeal and oral carcinomas [57, 99]. Future directions would include assessing expression of both of these miRs in a normal versus transformed context to determine their roles. An interesting study would involve using an inducible underexpression model, either in vitro or in vivo to assess if these loss of these miRs can drive the transformation of normal cells. Alternatively, genomic editing using either the CRISPR/Cas or Tale nuclease systems, could be useful in assessing the role that the loss of these miRs play in oncogenesis. During this project, a significant amount of time was spent attempting to identify the specific miRs binding to the ORF of FGFBP1. Data previously generated in the lab had suggested that position 179 in the ORF of FGFBP1 may be involved in miR targeting. Deletion of the nucleotide in that position (G179) had been shown to abrogate the effects of ATRA-induced degradation. As such, the original hypothesis was that the seed sequence of a miR must contain that site and loss of that nucleotide induced subsequent loss of miR targeting. I used the in silico tools mentioned above to reduce the number of putative targets. I found 6 miRs that had the appropriate seed sequence to target G179. In order to molecularly characterize this targeting, I attempted to sequentially precipitate the FGFBP1 binding site using AGO2 and abiotinylatedoligonucleotidecomplementarytoasite3’toG179. This was not successful. After thinking critically about this set of experiments, I believe that it was unlikely that this experiment would work for several reasons. Firstly, I believe that the abundance of a particular miR:mRNA combination in the RISC complex at any given point is relatively low. This is due to the downstream mechanism of RISC targeting. RISC mediates deadenylation and degradation of an mRNA target when

65 loaded with a miR. As has been previously published, RISC recruits PABP and the CCR4-NOT complex to deadenylate mRNA targets. The subsequent degradation of the mRNA occurs in a 3’ to 5’ direction. As the biotinylated oligonucleotide designed for this assay was generated against a region at the 3’ end, it is likely that either the homologous sequence had already been degraded and was not available for targeting or was being sterically hindered by the PABP:CCR4-NOT complex positioned at the 3’ end of the mRNA. Steric hinderance would not likely aﬀect a long transcript, however, the full FGFBP1 is 1369 bases long with a CDS of only 704 bases. Sec- ondly, I believe that the shotgun cloning technique that I used to identify putative miRs targeting the FGFBP1 ORF was not sensitive enough to detect AGO2 captured miRs. After sequentially precipitating the FGFBP1 mRNA and associated miRs, I attempted to insert these miRs into suitable vectors for sequencing. As such, several hundred colonies were picked, their plasmid DNA isolated, and Sanger sequenced. The majority of the bacterial clones contained copies of the linker sequences used to amplify the isolated miR cDNA. Clean up attempts using gel electophoresis did not allow for recovery of enough starting material for cloning. Alternatively, next generation sequencing libraries could have been generated and assessed for miR expression. However, I was able to observe, via AGO2 alone precipitation, that several miRs were found associated with AGO2 in the presence of ATRA (data not shown). This suggests that, while the miR is protected by the AGO2 protein, the targeted mRNA is not and is rapidly degraded. I believe, however, that miR:ORF interactions are interesting and should be studied at the molecular level. It is my hope that an appropriate technique will be developed to do that. While many studies have been done to tease out the role of ATRA in various contexts, few have assessed miR expression after an ATRA challenge. With the abundance of gene expression data available on public databases, an interesting bioinformatics

66 approach could be used to narrow down the role of ATRA on miR expression. Once again, the collaboration between a talented bioinformatician and a capable bench scientist would need to be recruited to delineate between false positives and actual ATRA-targeted miRs. However, a well-designed study could increase the understanding of ATRA and partially explain the pleiotrophic actions of this signaling pathway. Finally, these data connect the retinoid and FGF signaling pathways through miR regulation. It has been well established that these pathways are intrinsically tied in embryonic development and cell fate decisions. While not a focus of this dissertation, I observed that one miR, miR-205, is upregulated by ATRA in ME180 cells. Others have also suggested that ATRA regulates expression levels of miR-205 and miR-205 is highly regulated embryonically [90]. Interestingly, this miR has a conserved target region within the 3’UTR of several FGFs and several regulatory genes in the FGF signaling pathway (i.e. FGF2 and Sprouty). With the well-characterized antagonism between retinoid and FGF signaling mechanisms, this microRNA and others regulated by ATRA, could serve as an innovative mechanism to ensure maintenance retinoid-FGF antagonism in limb development, bone development, and branching morphogenesis [30].

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