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Investigation of Myc-regulated long non-coding RNAs in cell cycle and Myc-dependent transformation
by
Matthew Steven MacDougall
A thesis submitted in conformity with the requirements for the degree of Master of Science
Graduate Department of Laboratory Medicine and Pathobiology University of Toronto
© Copyright by Matthew Steven MacDougall (2012)
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Investigation of Myc-regulated long non-coding RNAs in cell cycle
and Myc-dependent transformation
Matthew Steven MacDougall
Master of Science
Graduate Department of Laboratory Medicine and Pathobiology University of Toronto
2012
Myc deregulation critically contributes to many cancer etiologies. Recent work suggests that
Myc and its direct interactors can confer a distinct epigenetic state. Our goal is to better
understand the Myc-conferred epigenetic status of cells. We have previously identified the long
non-coding RNA (lncRNA), H19, as a target of Myc regulation and shown it to be important for
transformation in lung and breast cells. These results prompted further analysis to identify
similarly important Myc-regulated lncRNAs. Myc-regulated lncRNAs associated with the cell
cycle and transformation have been identified by microarray analysis. A small number of
candidate lncRNAs that were differentially expressed in both the cell cycle and transformation
have been validated. Given the increasing importance of lncRNAs and epigenetics to cancer
biology, the discovery of Myc-induced, growth associated lncRNAs could provide insight into
the mechanisms behind Myc-related epigenetic signatures in both normal and disease states.
Abstract
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Acknowledgments To my supervisor, Dr. Linda Penn: thank you for your mentorship; you have truly taught me to
always challenge myself. Your guidance has been invaluable to my interest in pursuing a career
in academic medicine.
To my supervisor, Dr. Philip Marsden: your guidance goes far beyond the bench as you always
find new ways to challenge me to think critically. Your mentorship, from both a scientific and
clinical perspective, has helped me greatly.
To both of my supervisors: this opportunity to be co-supervised has been wonderful, thank you.
To my committee members: Dr. Cheryl Arrowsmith, Dr. Senthil Muthuswamy, and Dr. Rod
Bremner. Thanks for providing the knowledge and suggestions to keep me on track.
To the Penn lab members past and present: thank you for all of your support. In particular, I
thank Sam Kim, Christina Bros, and Romi Ponzielli for helpful discussions and suggestions
when it mattered most.
To the Marsden lab members past and present: thank you for all of your support. In particular, I
thank Jeff Man, Paul Turgeon, Matt Yan, and David Ho for intellectual discussions and
contributions. A big thank you goes to Maria Chalsev for her continued support in plasmid
preparation.
To my family and friends: thank you for all of your endless support and encouragement as I
pursue my goals.
Attribution of Work:
The work on the MCF-10As and the cell cycle could not have been achieved without the
optimization and experiments performed previously by Andrew Rust. Moreover, the work
completed using the MCF-10As and the 3D model of transformation was an extension of
Amanda Wasylishen’s work on Myc phosphorylation mutants in that model. Therefore, much of
the optimization and generation of stable expressing cell lines in that model were established
previously by her.
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Table of Contents Abstract ........................................................................................................................................... iiAcknowledgments .......................................................................................................................... iiiTable of Contents ........................................................................................................................... ivList of Figures ................................................................................................................................ viAbbreviations ................................................................................................................................ viiChapter 1 Introduction .................................................................................................................... 1
1.1 Myc Structure/Function ...................................................................................................... 21.2 Myc Transcriptional Activity and the Cell Cycle ............................................................... 41.3 Myc and Epigenetics ........................................................................................................... 61.4 H19 – a Myc-induced regulatory, non-coding RNA .......................................................... 81.5 Long non-coding RNA Function ...................................................................................... 111.6 Rationale for the investigation of Myc-regulated lncRNAs ............................................. 141.7 Working Model ................................................................................................................. 151.8 Assumptions ...................................................................................................................... 151.9 Hypothesis ......................................................................................................................... 161.10 Objective ........................................................................................................................... 16
Chapter 2 Results .......................................................................................................................... 172.1 MCF-10A Cell-based model development ....................................................................... 17
Introduction to MCF-10A cells ......................................................................................... 17Mitogen starved and stimulated MCF-10A cells synchronously enter the cell cycle ....... 17Expression profiling of Myc and Myc targets in MCF-10A cells within the cell cycle ... 19MCF-10A cells, grown on Matrigel, can undergo Myc-dependent transformation ......... 22
2.2 Global long non-coding gene expression profiling ........................................................... 27Identification of cell cycle associated lncRNAs ............................................................... 29Identification of Myc-induced transformation associated lncRNAs ................................ 29Identification of lncRNA genes common to Myc-induced transformation and the cell
cycle ...................................................................................................................... 322.3 Application of Inclusion Criteria ...................................................................................... 342.4 Candidate Expression and Selection ................................................................................. 372.5 Expression Validation ....................................................................................................... 402.6 Validation of Publicly Available Myc-ChIP data in MCF-10A cells ............................... 402.7 Functional Validation and Expression Profiling of Candidates lncRNA-LY6E and
lncRNA-FZD6 .................................................................................................................. 42Chapter 3 Discussion .................................................................................................................... 47
3.1 Candidate lncRNA profiling ............................................................................................. 473.2 Large scale lncRNA profiling ........................................................................................... 483.3 MCF-10A cell system ....................................................................................................... 503.4 Future Directions .............................................................................................................. 513.5 Conclusions and Implications ........................................................................................... 52
Chapter 4 Methods ........................................................................................................................ 544.1 Cell Culture ....................................................................................................................... 54
Reagents: ........................................................................................................................... 54MCF-10A cells: ................................................................................................................ 54
4.2 Immunoblotting ................................................................................................................. 54Whole Cell Extracts .......................................................................................................... 54
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SDS-PAGE ....................................................................................................................... 55Antibodies ......................................................................................................................... 55
4.3 Quantitative Real-time PCR ............................................................................................. 56RNA Isolation ................................................................................................................... 56cDNA Synthesis ................................................................................................................ 56Primer Design ................................................................................................................... 56Relative and Absolute Quantification ............................................................................... 57
4.4 Flow Cytometry ................................................................................................................ 594.5 MCF-10A: Model of Cell Cycle Entry ............................................................................. 60
Seeding Density: ............................................................................................................... 604.6 MCF-10A: Myc-dependent Model of Transformation ..................................................... 614.7 MCF-10A: Model of Myc-dependent Gene Regulation in the absence of other stimuli .. 624.8 Gene Expression Array ..................................................................................................... 62
Sample Preparation ........................................................................................................... 62Arraystar Microarray Analysis ......................................................................................... 63
4.9 Bioinformatic Analysis ..................................................................................................... 63Preprocessing: Array Background Adjustment, raw mRNA data normalization, and
low intensity filtering ............................................................................................ 63Inclusion Criteria .............................................................................................................. 64Candidate Selection Criteria ............................................................................................. 65
4.10 Nuclear-Cytoplasmic Partitioning .................................................................................... 684.11 ChIP-qRT-PCR ................................................................................................................. 694.12 Statistical Analysis ............................................................................................................ 71
References ..................................................................................................................................... 72Appendices .................................................................................................................................... 87
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List of Figures Figure 1: Pictoral representation and hypotheses of Myc’s role in the epigenome. ...................... 9Figure 2: MCF-10A cells respond rapidly to mitogen withdrawal and induction ....................... 18Figure 3: MCF-10A cells synchronously entering the cell cycle show coordinated Myc and Myc target gene expression ................................................................................................................... 20Figure 4: MCF-10A cells acinar morphogenesis ......................................................................... 23Figure 5: MCF-10A cells stably over expressing Myc-T58A form transformed, multiacinar structures ....................................................................................................................................... 26Figure 6: Flow Chart of the strategy for systematic identification of cell cycle associated and Myc-induced transformation associated lncRNAs ....................................................................... 28Figure 7: Identification of cell cycle associated lncRNAs ........................................................... 30Figure 8: Identification of Myc-induced transformation associated lncRNAs ............................ 31Figure 9: Identification of lncRNAs common to G0/G1 to S phase progression of the cell cycle and Myc-induced transformation .................................................................................................. 33Figure 10: Expression validation of 6 candidate lncRNAs .......................................................... 38Figure 11: Myc binds the promoter of lncRNA-LY6E ................................................................ 41Figure 12: lncRNA-LY6E is dynamically regulated in cell cycle and Myc-dependent transformation ............................................................................................................................... 43Figure 13: Schematic working model of how Myc-repression contributes to epigenetic regulation ...................................................................................................................................... 49Figure 14: Distribution of differentially expressed genes by normalized array intensity ........... 67 Table 1: Characteristics of candidate lncRNAs from the expression profiling union between cell cycle and transformation ............................................................................................................... 35Table 2: Primer List ..................................................................................................................... 58
Supplemental Figure 1: Cell cycle seeding density optimization .............................................. 88Supplemental Figure 2: Model of Myc-dependent Gene Regulation ........................................ 89Supplemental Figure 3: lncRNA-FZD6 is dynamically regulated in cell cycle and Myc-dependent transformation .............................................................................................................. 90Supplemental Figure 4: Candidate lncRNAs are not nuclear retained under asynchronous growing conditions ........................................................................................................................ 92Supplemental Figure 5: 8 hours of Myc induction under starvation conditions does not lead to significant changes in candidate lncRNA expression ................................................................... 93
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Abbreviations AOMF Advanced Optical Microscopy Facility APC adenomatous polyposis coli bHLH-LZ basic helix-loop-helix-leucine zipper BLAST basic local alignment search tool CCNB1 cyclin B1 CCND1/D2 cyclin D1/D2 CCNE cyclin E cDNA complementary DNA ChIP chromatin immunoprecipitation c-Myc cellular Myc CNV copy number variation CoREST/REST corepressor/repressor element-1 silencing transcription
factor COSMIC Catalogue of Somatic Mutations in Cancer CTCF CCCTC-binding factor CTD C-terminal domain DNMT3a DNA methyltransferase 3a EDTA Ethylenediaminetetraacetic acid EGF epidermal growth factor ENCODE encyclopedia of DNA elements ENSEMBL EMBL-EBI and the Sanger Centre Genome Browser EST expressed sequence tag EZH2 enhancer of zeste 2 Fos c-Fos, FBJ murine osteosarcoma viral oncogene homolog FZD6 frizzled 6 GADD45 growth arrest and DNA damage inducible protein, 45 GAS1 growth arrest specific protein 1 GCN5 general control of amino acid synthesis protein 5 GEO Genome Expression Omnibus GFP green fluorescent protein GSK3β Glycogen Synthase Kinase 3 beta H19 imprinted maternally expressed transcript H2A.Z histone family H2A, member Z HAT histone acetyl transferase HOX Homeobox ICR imprinting control region Igf2 insulin-like growth factor 2 Jun c-Jun, jun proto-oncogene KDM5A/B lysine demethylase 5A/5B, JARID 1A/1B lncRNA long non-coding RNA LSD1 lysine demethylase 1
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LY6E lymphocyte antigen 6 complex, locus E miRNA microRNA Miz1 Myc-interacting zinc finger protein 1 MTA1 metastasis associated 1 NCBI National Center for Biotechnology Information ncRNA non-coding RNA NTD N-terminal domain NuRD nucleosome remodeling deacetylase ORF open reading frame p21 cyclin-dependent kinase inhibitor 1A p27 cyclin-dependent kinase inhibitor 1B PBS phosphate buffered saline PCA3 prostate cancer antigen 3 PRC2 polycomb repressive complex 2 pTEFb positive transcription elongation factor b qRT-PCR quantitative real-time polymerase chain reaction SDS-PAGE sodium dodecyl sulfate polyacrylamide gel electrophoresis SNP single nucleotide polymorphism SUZ12 suppressor of zeste 12 TCF T-cell factor TRRAP transformation/transcription domain-associated protein UCSC University of California at Santa Cruz Genome Browser UHN University Health Network Wnt wingless-type MMTC integration site family Xist X inactive-specific transcript
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Chapter 1 Introduction
c-Myc (Myc) is a major player in many functions of the cell. As a proto-oncogene, Myc has
prominent roles in cellular functions ranging from cell cycle, growth, and metabolism to cell
death, differentiation, and angiogenesis (1, 2). Given that Myc is involved in such diverse
processes, it is also highly regulated within the cell. Myc is regulated at the transcriptional, post-
transcriptional, and post-translational levels (3, 4, 5). With its short mRNA and protein half-lives
as well its tight transcriptional regulation, Myc is the convergence point of many cellular
signaling pathways and thus responds rapidly to cues sensed by the cell in the extracellular
milieu.
When the tight regulation of Myc is lost, cellular transformation often follows (2, 6). The loss of
Myc’s tight regulation, or its deregulation, can occur by several mechanisms that affect the level
and activity of Myc in different ways. Among these mechanisms can be genomic events that
include insertional mutagenesis near the Myc promoter (7, 8, 9), chromosomal translocation (10),
and amplification (11) leading to constitutive Myc transcription. Other mechanisms of
deregulation include upstream signaling pathway dysregulation. An example includes loss of
APC from the Wnt pathway resulting in Myc transcription induced by TCF and β-catenin (12).
The diverse means of Myc deregulation lead to a broad range of cancer types that originate from
many different cell types. The first example of the importance of Myc in human cancer came
with the discovery of Myc translocation in Burkitt Lymphoma (13, 14). As an extension of this,
Myc amplification is commonly seen in many human solid tumours (15). Aside from the genetic
events that directly implicate Myc, the deregulation that can occur at the level of upstream
signaling on Myc is also relevant to cancer. For example, APC/Wnt deregulation of Myc is
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common to colon cancer (16) whereas dysregulation involving the NOTCH pathway is
prominent in T-cell acute lymphoblastic leukemia (17, 18, 19). Myc deregulation is therefore
commonly observed in many human cancers. Clinically, Myc’s inhibition could be an important
avenue for future therapeutic development. The feasibility of this approach has been
demonstrated using a dominant-negative inhibitor of Myc that, when expressed throughout the
whole animal in vivo, inhibited Myc’s activity and led to tumour eradication (20). Therefore the
study of Myc in both normal and disease states can provide a greater understanding of Myc’s
normal functions and their role in the genesis of human cancer.
1.1 Myc Structure/Function The first member of the Myc family to be discovered was v-myc. It was one of the first
transforming oncogenes isolated from avian tumour viruses that specifically led to a leukemia
called my
The MYC gene encodes the Myc protein. Myc regulates its broad cellular functions through its
action as a transcription factor. It is therefore largely nuclear retained and localized to chromatin.
From the classical transcription factor functional perspective, Myc has the ability to either
directly activate or repress transcription (30). It achieves this as a member of the basic helix-
elocytomatosis (21, 22, 23). The MC29 viral genome where Myc was discovered
transduced the gene from the host genome, which is now called cellular Myc (c-Myc or Myc)
(24). The family of Myc also includes c-Myc, L-Myc, N-Myc, S-Myc, and B-Myc (25, 26, 27,
28, 28, 29). The transforming family members, c-Myc, L-Myc, and N-Myc, are expressed during
fetal development of organisms (2). c-Myc is the only transforming member that is also
expressed in the normal adult making it the primary focus of this thesis. It is important to note
that each of the Myc transforming family members is deregulated in cancer including N-Myc and
L-Myc (25, 26, 27).
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loop-helix leucine zipper(bHLH-LZ) family of DNA-binding transcription factors (31, 32). The
DNA-binding abilities of this family are dependent on dimerization with other family members.
Myc dimerization mostly occurs with Max (33).
Together as a heterodimer, Myc and Max target DNA in a sequence specific manner at the
canonical E-box cis-regulatory element whose sequence is 5’-CACGTG-3’ (34, 35). Like most
proteins, Myc is modular and requires the heterodimerization and DNA-binding capabilities
within its C-terminal domain (CTD). This region contains the bHLH-LZ domain of Myc. The
helix-loop-helix-leucine zipper (HLH-LZ) is essential for dimerization with Max. The basic
region (b) of the bHLH-LZ is responsible for DNA binding in the major groove. Lastly, this
CTD region also contains Myc’s nuclear localization signal (36). Together, all of the modules
within the CTD of Myc are required for all of its functions (37).
As a transcription factor, Myc recruits cofactors and the basic transcriptional machinery for
proper function. Myc achieves this via through its CTD and an N-terminal domain (NTD).
Within the NTD are several modules important for Myc function called Myc-homology boxes I-
IV (MBI, MBII, MBIII, MBIV). Some of these regions have been demonstrated to be important
for both transcriptional activation and repression (30, 38). Although this has not been
investigated in full, it is thought that these regions are functionally important due to their ability
to interact with other proteins (39). Some examples of Myc’s cofactors that bind to the NTD are
histone acetyltransferases (HATs) such as TRRAP-GCN5 (40). The NTD is not the only protein
interaction domain though, cofactors can bind elsewhere in Myc in the CTD such as the histone
demethylase, KDM5A/B (41).
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1.2 Myc Transcriptional Activity and the Cell Cycle Myc expression largely correlates with the growth state of the cell so an important measure of
Myc’s function in the cell is its ability to regulate the cell growth and division cycle. Sustained
and dysregulated signaling in the cell cycle is a hallmark of cancer and often implicates Myc;
therefore it is a primary focus of this thesis (42). In particular, cell cycle regulation by Myc
involves its transcription activating and repressing activities. Myc functions as an immediate-
early response gene by directly regulating genes that are important in cell growth during G1/G0
to S transition (43, 44). This includes the activation of cyclin D genes (CCND1 and CCND2) and
cyclin dependent kinase 4 as well as the repression of growth arrest associated genes such as
p21, p27, GADD45, and GAS1 (45). Induction or repression of these genes may or may not be
mitogen dependent as it has also been suggested that Myc induction alone under mitogen
withdraw conditions can induce cell cycle entry (46). Regardless, through the ability to regulate
transcription, Myc can orchestrate the necessary gene expression changes for cell cycle entry.
Myc function in the cell cycle context necessitates a description of how Myc can facilitate
transcription in greater detail. Myc transactivation activity first requires DNA binding.
Subsequent activation of transcription can then occur by several different pathways including
chromatin remodeling and polymerase-pause release. These functions are likely linked. It is
believed that Myc transcriptional activation involves, in part, its control of histone acetylation
through interaction with HATs (47). Thus, the cases of transactivation mechanisms are best
represented by an example; the cell cycle gene, CCND2. Mitogen stimulation leads to the
recruitment of TRRAP by Myc, which in turn recruits GCN5, a HAT, that leads to CCND2
promoter acetylation and activation (Figure 1A) (48, 49). Furthermore, Myc can also recruit the
cyclin dependent kinase complex, P-TEFb, which has been implicated in mediating the
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phosphorylation of the CTD of RNA polymerase II and release of the stalled polymerase at the
CCND2 promoter (50).
Converse to Myc transactivation, Myc transrepression does not necessarily require DNA binding
as Myc can indirectly access the DNA through protein-protein interactions (51, 52). Therefore, it
is predominantly E-box independent (53, 54). Like transactivation, repression can also occur
through several, albeit less well-characterized, mechanisms. In general these require Max as well
as Miz1, a zinc finger protein (55, 56). A primary cell cycle-related example is the
transrepression of the growth arrest gene, p21. Myc, in complex with Max and Miz1, can recruit
the DNA methyltransferase (DMT), DNMT3a, which leads to DNA methylation at the promoter
and repression of p21 transcription (57). Therefore, this mechanism may be particularly relevant
for Myc target genes with CpG island promoters. Similar to transactivation, chromatin
remodeling via alteration of histone modifications can occur at the p21 promoter. Once again the
complex of Myc and Miz1 can recruit KDM5B, which is a histone demethylase (KDM, Lysine
demethylase). Recruitment results in the demethylation of histone H3 at lysine 4 (H3K4) and the
subsequent down regulation of p21 transcription (Figure 1B) (58).
Taken together, transcriptional activation and repression by Myc occurs through diverse
mechanisms in order to control and orchestrate its many functions. In particular, emphasis has
been placed on Myc regulation of cell cycle associated genes. These genes represent an
important class of Myc-regulated target genes as their over-expression or –repression can lead to
profound cellular transformation (59). The mechanisms of how Myc regulates the transcription
of its targets and the function of those downstream targets have been the focus of therapeutic
target development efforts as these could have broad clinical utility. Therefore, this thesis aims to
emphasize novel perspectives on Myc’s target gene function and regulation.
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1.3 Myc and Epigenetics A description of Myc-activated and Myc-repressed targets in the cell cycle yields the observation
that Myc-mediated transcriptional regulation requires the recruitment of many different
cofactors. The types of these cofactors vary by target genes, but a theme emerges in the types of
proteins that Myc can recruit. These are enriched for proteins that regulate chromatin structure
and histone modification. It is known from large scale genomic studies that Myc can bind large
numbers of genes with some reports suggesting Myc binds 10-15% (1, 60) of the genome.
Regardless of number, Myc binding is widespread (61). Of these Myc-bound targets, it seems
that Myc is a rather weak transcription factor in that it very modestly affects target gene
expression by about two-fold (45, 60). These characteristics set it apart from many classical
transcription factors and those within the bHLH-LZ superfamily in that these proteins tend to
restrict their binding to a distinct set of genes and induce or repress their expression to great
magnitude (62, 63). Thus, it seems that Myc’s subtle effects on expression and its ability to act
globally set it at the interface between classical transcription factor and epigenetic gene
regulation.
Subtle effects on gene expression, globally, could have massive implications on cell biology.
Myc may achieve this by dynamic interaction with and alteration of chromatin state (64). As a
DNA binding transcription factor, the accessibility of DNA is a critical component of Myc
genome binding. This more open, accessible DNA encompasses euchromatin whereas
heterochromatin is characterized as repressed and less accessible. The chromatin structure is
regulated, in part, by histone tail modifications. Myc DNA binding is associated with active
histone modifications such as H3K4me3 that open chromatin (65). Furthermore, it seems that E-
box sequence specificity of Myc is less of a priority over chromatin context recognition as the E-
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boxes in regions of repressed chromatin are not bound by Myc. As well, within euchromatic
regions, Myc associates with DNA in the absence of E-boxes directly or indirectly through
protein complexes (Figure 1C) (66). Overall, Myc’s dynamic interaction with the chromatin
landscape may be a means of the cell to direct specific gene expression programs of Myc in a
context dependent manner.
Once bound to the DNA, Myc can then alter histone modifications through its protein-protein
interactions (66, 67). It can achieve this locally by enhancing acetylation as a means of direct
transcriptional regulation, described previously (49). Myc can also act on chromatin more
globally, in particular Myc induction is positively correlated with global euchromatic marks and
H2A.Z variant exchange and negatively correlated with heterochromatic marks (68). More
interestingly, some Myc-dependent chromatin marks can occur in the absence of local Myc
binding suggesting that Myc can alter chromatin state in an indirect fashion (69). There are
several postulates that could explain this phenomenon. Myc binding in a specific region could
mediate distal chromatin modification by chromosomal looping and other higher order DNA
structures. Additionally, Myc could be associated with DNA indirectly through interaction with
other DNA binding transcription factors. In this way, Myc could have access to chromatin
without being directly bound to DNA (64).
Myc’s ability to indirectly interact with and alter chromatin structure seems to be best explained
by the idea that Myc’s own target genes could function to directly alter chromatin. The primary
example of Myc’s role in the epigenome involves the induction of GCN5, an activating, HAT
previously described as a Myc interactor (70). The induction of GCN5 leads to the strong
increase in overall, genome-wide acetylation (67). Since Myc can also directly bind GCN5, this
may be occurring through a feed forward mechanism (Figure 1E, left). Other induced target
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genes that support this concept and demonstrate that this observation is not limited to chromatin
activating modifiers are CCCTC-Binding factor (CTCF) and Metastasis Associated 1 (MTA1).
CTCF is an example of an insulator protein. It has been implicated in blocking the spread of
heterchromatic, repressive histone marks. Myc’s induction of this gene could result in the
facilitation of Myc’s role in the maintenance of active chromatin context as well (71). Lastly,
MTA1 is part of the repressive NuRD complex, which functions to deactylate and remodel
histones (Figure 1D, Left). Altogether, the most feasible explanation of Myc’s indirect regulation
of chromatin is that it does so through its target genes’ inherent epigenetic activating, repressing,
and maintenance functions (Figure 1D & E).
Thus far, Myc-induced transcriptional targets have been emphasized, but Myc mediated gene
repression may also play a role in this model. Myc has been shown to regulate regulatory, non-
coding RNAs (ncRNA) on a large scale (2, 6). In particular, Myc exhibits widespread repression
of micro RNA (miRNA) expression (72). These genes primarily exhibit post-transcriptional
control of gene expression through RNA interference and some reports have suggested
interaction with epigenetic pathways (73). Therefore, the repressed targets of c-Myc as well as its
ncRNA targets may also play a critical role in Myc’s regulation of the epigenome.
1.4 H19 – a Myc-induced regulatory, non-coding RNA A finding that began to integrate the idea that Myc depended on epigenetic context for binding
and could regulate ncRNAs other than miRNAs came with the discovery that Myc could regulate
H19 (74). H19 is an imprinted regulatory RNA. As part of the Igf2-H19 locus, H19 is maternally
expressed in early development and only differentiated cardiac and skeletal muscle have
measurable expression after this time, in a fully developed organism (75). It has been
demonstrated that parent-of-origin specific expression of H19 is regulated epigenetically by
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Figure 1: Pictoral representation and hypotheses of Myc’s role in the epigenome.
A schematic representation of Myc’s direct and known roles in regulating the epigenome in
transcriptional activation (A), transcriptional repression (B), and intergenic regulation (C). D &
E) Left, A schematic representation of Myc’s indirect roles in regulating the epigenome through
its target genes or direct interaction with its own target genes (feed forward). Right,
Demonstrates the overall hypothesis that Myc’s role in the epigenome may, in part, be regulated
by lncRNAs by similar mechanisms.
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differential methylation at an imprinting control region (ICR). Specifically, this ICR is upstream
of the H19 promoter (76). Since Igf2 is further upstream of the ICR from H19, when the ICR is
unmethylated on the maternal allele CTCF can bind and allow enhancers, downstream of the
Igf2-H19 locus, to enhance H19 expression. Conversely, when the ICR is methylated, expression
of Igf2 is activated in lieu of H19 by the locus enhancers (77). Myc has been shown to bind and
induce H19 at the maternal allele under the unmethylated ICR conditions suggesting methylation
status as another means of Myc’s interaction with the epigenome (74). This is consistent with
later findings that Myc’s interaction with E-boxes is methylation dependent outside of imprinted
loci (78).
The function of H19 in normal biology is not well understood, but its expression is associated
with positive and negative regulators of growth. H19 has also been shown to be important to
tumour biology (Reviewed in Ref(77)). Knockdown of H19 leads to reduced colony formation of
cells grown in soft agar as a measure of transformation (74). Several theories have been
suggested; most relevant to this thesis is the concept that H19 is a transcriptional regulator. The
discovery that Xist, another ncRNA, can coat the inactive X chromosome and mediate gene
repression by chromatin modification and DNA methylation led to the hypothesis that H19 could
regulate gene expression in a similarly epigenetic fashion (79). To date, the only piece of
evidence to support this is that the deletion of H19 leads to altered methylation at the Igf2 locus
(80). This would further support the idea that Myc could regulate epigenetic effector genes as a
means of controlling gene expression globally. Furthermore, it may suggest that Myc can
achieve this through ncRNA regulation and that this regulation could have important
implications for cancer biology.
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1.5 Long non-coding RNA Function Interestingly, H19 and Xist are examples of a newly recognized class of regulatory ncRNAs
called long non-coding RNAs (lncRNAs). For many years, this type of regulatory RNA was
thought to be rare; H19 and Xist being unique examples. After their discovery in the early
1990’s, they added evidence to an already ongoing debate over the function of the non-coding
genome. This debate largely stems from the conundrum that a mere 2-3% of the genome codes
for protein, once thought to be the primary effector molecule of the cell. Elucidating the role of
the remaining 97-98% of the genome has been fraught with many challenges generally focused
around limitations of current technologies (81). The ENCODE project has yielded evidence that
suggests that 93% of the genome is actively or “pervasively” transcribed (82). This has added to
the number of “dark matter” transcripts, those RNA molecules with unknown function (83), and
led to the hypothesis that there may be biological relevance to these transcripts. Recent evidence
using high throughput sequencing technology suggests that the complexity of the RNA
transcribed from these regions should not be underestimated (84). Counter arguments to the
existence of the transcription from the so called “junk DNA” regions include the concept that it
could be biological noise (81, 85).
The debate has begun to shift away from the existence of these transcripts towards a dispute over
how to define pervasive transcription of the genome and what it entails (86, 87, 88). Amidst the
shift in debate, a window has opened in which numerous groups have identified transcripts,
distinct from coding genes, with important functions in the cell much like H19 and Xist.
(Reviewed in (89)) As such, this will function as the definition of lncRNAs in this thesis.
Early experiments that began to establish that there were many lncRNAs interspersed throughout
the genome utilized chromatin marks. In particular, since histone 3 trimethylation at lysine 4 and
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lysine 36 (H3K4 and H3K36) marked active sites of protein coding gene transcription, it was
hypothesized that intergenic regions containing this mark would provide a means of
systematically identifying lncRNAs. Through the use of tiling arrays across these H3K4-K36
marked intergenic regions, over 1000 new lncRNAs were discovered (90). The discovery opened
the field to more widespread studies such as one particularly relevant to this work; the discovery
of extensive transcription of lncRNAs in cell cycle promoters. This work showed numerous
lncRNAs in the vicinity of known cell cycle gene promoters and demonstrated that some exhibit
temporal-specific expression in different phases of the cell cycle (91). The class of genes
identified by Hung T et al.(91) represents a unique class of lncRNAs in cell cycle promoter
regions. The group does not address their mechanisms of action in the cell cycle or the idea that
more lncRNAs outside of cell cycle protein coding gene promoters could also be functional in
the cell cycle. As such, these experiments set the stage for this thesis. Overall, the lncRNA class
is broad reaching and many of its functions, ranging from development to cancer, have been
demonstrated through the use of knockdown and over expression genetic studies, which provides
evidence in favor of their relevance.
Advances in understanding these novel lncRNA genes have been made in spite of their poor
sequence conservation and low abundance when compared to their protein coding counterparts.
To this end, Ulitsky et al. (92) have suggested through studies of lncRNA genes in zebrafish that
conservation of synteny, or genomic co-localization with neighboring genes, is the most relevant
form of conservation for lncRNAs. This was achieved by showing that zebrafish lncRNA
knockout phenotypes could be rescued with the expression of their human or mouse counterparts
that were conserved in synteny, but minimally at the sequence level (92). Therefore, the lncRNA
genes may require some reconsideration of our criteria for evaluating a gene’s functional and
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disease relevance. In light of these considerations, this thesis aims to explore the functions of
these lncRNAs and the role they play as Myc target genes.
lncRNAs have diverse cellular roles. As described with H19 and Xist, they can function in
imprinting and dosage compensation, respectively. Moreover, they can play important roles in
development and differentiation among many other functions (93, 94). They are differentially
expressed in human disease and, in particular, cancer and thus could provide novel insight in
cancer biology and as therapeutic targets (95). Excitingly, in these contexts they display highly
tissue specific expression (96, 97) (Marsden Lab, unpublished). The mechanisms through which
these lncRNAs act to regulate their diverse functions in distinct cellular and tissue compartments
seem to be primarily associated with transcription and chromatin regulation (98).
One such lncRNA that best illustrates the diverse epigenetic roles of this class of genes is called
HOTAIR. HOTAIR was discovered using high resolution tiling arrays in the HOX loci (99).
Rinn and colleagues demonstrated anatomic specific expression of the HOX genes and their
neighboring lncRNAs, including HOTAIR. More specifically, they demonstrated that HOTAIR
that was expressed at the boundary of active and inactive chromatin in the HOXC locus acted to
down regulate, in trans, the HOXD locus through an interaction with SUZ12, a part of the
polycomb repressive complex 2(PRC2). Therefore, knockdown of HOTAIR led to increased
expression of HOXD genes. HOTAIR also interacts with LSD1, a part of the CoREST/REST
lysine demethylase, repressor complex (100). Specifically, it seems that HOTAIR can act as a
scaffold between SUZ12 -PRC2 and LSD1-CoREST/REST through HOTAIR’s 5’ and 3’ends,
respectively. Therefore, it seems that HOTAIR acts to integrate the removal of active
methylation histone marks with the addition of repressive methylated histone marks.
Additionally, it seems that there may be some cell cycle dependence for the interaction of
14
HOTAIR with these protein complexes. Specifically, cell cycle specific phosphorylation of
EZH2, another component of PRC2, leads to increased HOTAIR binding (101). Taken together,
HOTAIR is an lncRNA that can integrate the writing and erasing functions of two important
chromatin modifiers. In turn this complex regulates a specific set of genes and may be
coordinated in the cell cycle by phosphorylation of PRC2 components (102).
Much like H19, HOTAIR also provides an example of an lncRNA that can be functionally
important in cancer biology. Given HOTAIR’s localization to the HOX locus and its potentially
cell cycle coordinated roles, it has been shown that HOTAIR deregulation can alter chromatin
states of breast tissue in such a way that promotes metastasis (103). Additionally, HOTAIR
overexpression and associated chromatin structure alterations have been observed as a
characteristic in many other cancers including colorectal and hepatocellular carcinoma (104,
105).
1.6 Rationale for the investigation of Myc-regulated lncRNAs In conclusion, the lncRNA class has many functionally similar genes that can regulate gene
expression at the level of the epigenome by interacting with, directing, and integrating the
functions of chromatin modifying complexes genome wide like HOTAIR. This function can
have important implications in human diseases including cancer. Importantly, these genes are
regulated much like mRNAs and preliminary data from the ENCODE project suggests that Myc
can bind the regulatory regions of a large proportion of these genes much like H19. As Myc
induces chromatin modulating, protein coding genes, I believe that Myc can regulate the
expression of lncRNAs and that these lncRNAs can similarly contribute to Myc’s indirect
regulation of epigenetics (Figure 1D, right) or a feed forward mechanism wherein Myc induces
the transcription of, and binds to, a target lncRNA to regulate chromatin changes (Figure 1E,
15
right). Therefore, gaining an understanding of Myc-regulated lncRNAs could aid in better
understanding Myc’s role in the epigenome and may unveil novel therapeutic targets.
Given that Myc plays such a prominent role in many cancers, another challenging aspect of
studying Myc-regulated lncRNAs was selection of a model. Our desire to study normal functions
of Myc and how their deregulation could lead to cancer led us to a desire to discover Myc-
regulated lncRNAs in a near-normal cell line model that when Myc was overexpressed
transformed to cells with the characteristics of cancer. Since Myc has been demonstrated to be
associated with breast cancer progression and late-stage aggressive breast tumours (Reviewed in
Ref(106)), a mammary epithelial cell line model called MCF-10A, previously characterized in
the lab, was selected for these purposes.
1.7 Working Model Myc deregulation critically contributes to many cancer etiologies in part through its regulation of
cell cycle and cell growth related genes. Less known is Myc’s role in epigenetic gene regulation
both in the cell cycle and at large. Though this epigenetic gene regulation could occur through a
variety different mechanisms, Myc’s ability to induce the expression of H19 suggests that the
long non-coding RNA genes could figure in prominently. As such, lncRNA biology could
contribute to our understanding of Myc’s normal and cancer epigenetic functions.
1.8 Assumptions Several working assumptions have been made regarding this work. The first is that Myc
functions as an epigenetic regulator in the cell cycle and that it achieves this through an as yet
unknown group of lncRNAs. Based on the first assumption, it is then assumed that cell cycle
16
associated lncRNAs will have overlapping roles in the process of transformation founded upon
our knowledge of the dysregulation of the cell cycle as a hallmark of cancer.
1.9 Hypothesis I hypothesize that Myc can regulate lncRNAs in the cell cycle and that these could be important
in Myc-dependent transformation.
1.10 Objective Evaluation of this hypothesis will make use gene expression microarray technology to discover
and profile gene expression of Myc-regulated lncRNAs. Using this technology, the objective is
to profile lncRNA expression in the cell cycle. lncRNA expression will then be profiled in a
Myc-dependent model of transformation, which will be used to inform and prioritize the cell
cycle lncRNAs that are potentially Myc and cancer associated. Functional characterization of
these lncRNAs will then ensue.
17
Chapter 2 Results
2.1 MCF-10A Cell-based model development
Introduction to MCF-10A cells
The MCF-10A cell line was isolated from a patient with benign, fibrocystic disease. Cells from
the mastectomy, initially characterized as cytogenetically normal, were cultured in low calcium
medium for an extended amount of time and gave rise to spontaneously immortalized, adherent
MCF-10A cells (107). The MCF-10A cells have been confirmed as mammary epithelial cells
that bear resemblance to basal cells and are estrogen receptor negative (108). These cells, though
viewed as normal breast epithelial cells, were later cytogenetically characterized as not normal.
They are near diploid with few genomic lesions (109, 110). These cells have a wild type p53
status and are non-transformed as is evidenced by their inability to form tumours in nude mice
xenograft experiments (107, 111). Importantly, Myc levels are elevated, but they remain highly
responsive to extracellular cues. Therefore, these cells provide the approximate model of non-
transformed mammary epithelial cells to study the role and regulation of Myc in the context
breast tumourigenesis.
Mitogen starved and stimulated MCF-10A cells synchronously enter the cell
cycle
Gaining an understanding of Myc’s roles in normal cell biology is essential to better evaluating
its role in the deregulated state commonly observed in neoplasia and malignancy. One of Myc’s
prominent functions that can go awry is its role in the cell cycle. The nature of MCF-10A cells
being non-transformed means that they are still responsive to extracellular stimuli and a good fit
18
Figure 2: MCF-10A cells respond rapidly to mitogen withdrawal and induction
A) Fixed propidium iodide(PI) flow cytometry was conducted on MCF-10A cells that were
asynchronously growing, starved of mitogens for 24 hours, or starved for 24 hours and
subsequently exposed to mitogens for 8 to 24 hours. Bars represent the mean fraction of the cell
population with a given amount PI stain for N = 2-6 where the error bars represent standard
deviation. Since PI stains DNA, ‘pre-G1’ represents <2N DNA content, ‘G1’ – 2N, ‘S’ – 2N to
4N, and ‘G2/M’ – 4N. B) MCF-10A cells were monitored for phenotypic changes by light
microscopy. MCF-10A cells that were asynchronously growing (left panel) and starved of
mitogens for 24 hours (right panel), as in A, are shown to highlight their qualitative differences.
19
for studying normal cellular functions like the cell cycle. Given that MCF-10A cells are
epithelial, they require and are highly responsive to mitogens like epidermal growth factor
(EGF).(107) Similarly, they maintain their ability to contact inhibit cell cycle progression
through the down-regulation of EGF-dependent signaling (112). Their EGF dependence for cell
cycle progression has been established (113). Therefore, the goal was to use the MCF-10A cells,
taking advantage of their mitogen and EGF dependence, to study Myc’s role in the cell cycle.
MCF-10As that were asycnchronously growing at 40-50% confluent, low density (Figure 2A,
‘Growing’; Figure 2B, Left; Supp. Figure 1) were withdrawn from EGF, horse serum, cholera
toxin, and hydrocortisone, which are components of their normal growth medium (See Methods).
On average greater than 80% of the cells respond by arrest in a 2N, G1/G0 phase of the cell cycle
as measured by fixed propidium iodide flow cytometry (Figure 2A, ‘Starved’). Phenotypically,
this leads to changes in cellular morphology marked by a rounded appearance of the islands of
epithelial cells on a two dimensional surface (Figure 2B, Right) as compared to the
asynchronously growing cells (Figure 2B, Left). Subsequent release of these cells from arrest by
reintroducing all of the aforementioned mitogen media components leads to a concerted and
synchronous reentry into the cell cycle. In particular over the 8-24 hour time course, this entails
the decrease of the 2N, G1 peak to a minimum at 16 hours. Concurrently, the S phase and G2/M
populations approach their maximum values around 16-18 hours (Figure 2A, ‘8h-24h’).
Expression profiling of Myc and Myc targets in MCF-10A cells within the
cell cycle
In order to confirm that Myc is playing a role in the observed entry into the cell cycle, expression
profiling at the protein and transcript level was performed for Myc, its transcriptional targets and
20
Figure 3: MCF-10A cells synchronously entering the cell cycle show coordinated Myc and
Myc target gene expression
A) Immunoblotting with antibody targeting Myc and other indicated cell cycle markers was
performed on protein isolates from MCF-10A cells asynchronously grown (Asy), starved of
mitogens (Stv), or starved and subsequently exposed to mitogens to release the cells from arrest.
The cell cycle markers probed were Cyclin E (CCNE), c-Jun, and c-Fos. α-actin was used as a
loading control. A representative image of N=1 blots is shown. B) RNA was isolated from cells
identically treated to those in A. qRT-PCR quantification of Myc, Cyclin D2 (CCND2), p21, and
Cyclin B1 (CCNB1) relative transcript levels was conducted utilizing the ΔΔCt method with
ribosomal protein, large, P0 (RPLP0) as the endogenous control. Bar height represents the
21
average of N=2 biological replicates except for CCNB1 which is N=1. Error bars represent
standard deviation of those experiments with 2 biological replicates. It should be noted that these
data are only preliminary to demonstrate the aspects of MCF-10A cell cycle entry through
molecular markers.
22
other cell cycle markers. In the cell cycle experiment, lysates were harvested from similar time
points as previously described. As the cells prepare to divide and consistent with Myc being an
immediate early response gene, its protein level is detectable by about 1 hour post re-stimulation
of starved cells with mitogens as measured by immunoblot (Figure 3A). This is consistent with
other immediate early response genes, Fos and Jun (Figure 3A) Similarly, Myc mRNA is up-
regulated early by 8 hours, prior to the full release of cells from G1 arrest (Figure 3B, Top Left).
Cyclin D2 (CCND2) and Cyclin B1 (CCNB1) are important Myc-induced markers of G1/S
transition and G2/M, respectively. Consistent with the fixed propidium iodide flow cytometry
data, CCND2 mRNA is induced approximately 3 fold by 8 hours when the cells are preparing to
transition from G1 to S phase (Figure 3B, Top Right). Similarly, the protein levels of another
marker of G1/S transition, CCNE, increase until around 12 hours post stimulation (Figure 3A).
Conversely, CCNB1 mRNA level is upregulated at 24h with an approximately 30 fold induction
from starvation which is consistent with its role in the G2/M phase of the cell cycle (Figure 3B,
Bottom Right). Lastly, the mRNA of an important Myc-repressed marker of growth arrest, p21,
is at its maximum in the starved cells and is repressed approximately 2 fold in response to
mitogen induction by 8 hours (Figure 3B, Bottom Left). Given Myc’s well known role in
regulating the G1/S transition, these preliminary data suggest that, in the MCF-10A cells,
elevated functional levels of Myc occur by 8 hours of exposure to mitogen stimulation.
MCF-10A cells, grown on Matrigel, can undergo Myc-dependent
transformation
The MCF-10A cell system is also responsive to over expression of known oncogenes. This
concept holds true for Myc over expression as well. Wildtype Myc can induce the transformation
23
Figure 4: MCF-10A cell acinar morphogenesis
Normal MCF-10A (top) cells plated on basement membrane mimicking extracellular membrane
substrate proliferate for approximately 8 days. Over this time they form spherical cell masses
with cells in contact with the basement membrane polarizing and initiating the signaling
dichotomy that occurs around day 8 with the initiation of cell death pathways of the inner cells
and the growth arrest of outer, polarized cells. Over the next 8 days, inner cells die and are
cleared to form the lumen of the highly ordered acinar structures. MCF-10A cells that over
express oncogenes (bottom), like Myc, form abnormal, transformed multiacinar structures that
do not undergo luminal clearing. Reprinted and Adapted by permission from Macmillan
Publishers Ltd: Nature Reviews Cancer, Debnath,J. and Brugge,J.S, 2005 (117).
24
of the MCF-10A cells, at least as measured by anchorage independent colony formation in soft
agar (114). Similarly, within the Myc protein, threonine 58 is a site that, when phosphorylated by
GSK3β, destabilizes Myc and targets it to proteasomal degradation (115, 116). Therefore, a Myc
phosphorylation mutant at threonine 58 (T58A) is predicted to stabilize Myc and give the protein
a dominant positive function when over expressed in MCF-10A cells. To this end, Myc-T58A
cells grown in soft agar form significantly more colonies than cells over expressing wildtype
Myc.(114) Given that Myc and Myc-T58A over expression can lead to the transformation of
MCF-10A cells, a Myc-dependent model of transformation to complement the cell cycle was
needed.
The complementary model chosen was a 3D culture model that made use of the unique
characteristic of MCF-10A cells being an immortalized cell line that has maintained its ability to
form polarized structures and tight junction cell-to-cell interactions when grown on extracellular
matrix substrates (110). In fact, these glandular epithelial cells have retained their ability to form
the well-ordered, gland-like acinar structures when grown on Matrigel™ (Figure 4) (118). These
acinar structures mimic mammary gland architecture with hollowed lumens and apicobasal
polarity; some even progress to milk formation (110, 117, 119, 120). Thus, oncogenes that
interrupt the normal morphogenesis and architecture of these mammary epithelial acinar
structures could be important for understanding carcinoma formation from the early to late stages
(Figure 4).
In this model of MCF-10A acinar formation, over-expression of the potent oncogene Myc and its
more transforming Myc-T58A leads to disruption of the normal acinar morphogenesis with
differing extent (Wasylishen AR and Penn LZ, In Preparation). If transformation in this model is
defined as the formation of disordered, multiacinar structures, then transformation can be
25
26
Figure 5: MCF-10A cells stably over expressing Myc-T58A form transformed, multiacinar
structures
A) MCF-10A cells stably expressing empty vector pMN-GFP, pMN-MYC, or pMN-T58A-MYC
grown on matrigel extracellular matrix form normal and transformed acinar structures. These
have been phenotyped and counted on day 8 of morphogenesis in a blinded fashion. The plot
shows transformation as a percentage of all acini counted (>100). The mean and the standard
deviation are shown for N=5 biological replicates; *** indicates p<0.001 by one-way ANOVA
with Bonferroni post-test for the T58A to GFP comparison. B) Representative light microscopy
images are shown for day 4 and day 8 acini. An example of a transformed, multiacinar structure
is delineated by the arrow.
27
measured relative to the total population of both normal and transformed acini (Figure 4,
Bottom). Using this single parameter as a measure, Myc over-expression results in a trend
towards increased transformation. More significantly, Myc-T58A over-expression leads to ~30%
transformation, which is about 3 fold over basal transformation that occurs in the empty vector
GFP cells (Figure 5A). Qualitatively, these differences are not apparent until day 8 when the
proliferative phase of morphogenesis is nearing completion (Figure 5B, ‘Day 8’). Therefore, the
working assumption is that by day 4 the necessary transcriptional programs may be in place to
establish the phenotypic differences observed on day 8 (Figure 5B, ‘Day 4’).
2.2 Global long non-coding gene expression profiling MCF-10A cells were used as the model system to study Myc and its ability to regulate lncRNAs
in both the normal and transformation disease context. Primarily, the global expression profiling
made use of a commercial expression microarray from Arraystar™ that has gene specific probes
that target both protein coding and lncRNAs. The lncRNAs contained on this array are
assembled from several different sources, as detailed in the Methods section of this thesis and
include both known and putative lncRNAs. The function or disease relevance of these lncRNAs
is not well known to date. In order to address this question and how Myc coordinates their
expression and function, the first step is to identify Myc lncRNA targets. Therefore, the
microarray experiment made use of both the cell cycle and Myc-induced transformation models
in MCF-10As. The rationale for profiling lncRNA gene expression in both models is that it may
provide a means of finding those genes commonly regulated in both processes (Figure 6).
28
Figure 6: Flow Chart of the strategy for systematic identification of cell cycle associated
and Myc-induced transformation associated lncRNAs
A) A flow chart that represents the overall strategy of identifying lncRNAs in the cell cycle and
prioritizing them through comparison with lncRNAs identified in transformation. B)The boxes in
the flow diagram represent each step of data creation of handling used for the handling of
microarray data and candidate identification. Below each box indicates how each step selects for
a specific subset of genes targeted by probes on the array platform. Above several boxes, the
corresponding figure is indicated.
29
Identification of cell cycle associated lncRNAs
To profile the lncRNA gene expression changes in the cell cycle, the MCF-10A model of
synchronous cell cycle entry was used. To enrich for those lncRNAs regulated by Myc directly
in the cell cycle, it was hypothesized that, since Myc is an immediate-early response gene that
functions to regulate G1 to S transition, the prudent time for lncRNA profiling would be just
prior to cells beginning to enter S phase. This would be a time at which established Myc target
genes show regulated expression changes. The first noted increase in the S phase population after
mitogen induced release from arrest was at 10 hours and corresponded with changes in
expression of CCND2 and p21 (Figure 2A, Figure 3B). Therefore, the time point chosen for
microarray gene expression profiling was at 8 hours. The analysis of gene expression changes
was performed by Arraystar using a fold change cutoff of 1.5 fold and a p-value cutoff of 0.05
(Figure 7). When 8h mitogen induced cells were compared cells in a starved state, there were
1,123 differentially expressed lncRNA genes (Figure 7). Of note, there seemed to be widespread
down-regulation (~70%) of lncRNA transcripts in response to mitogen stimulation. Importantly,
Myc-regulated internal control genes such as cyclin E and gadd45γ, were up- and down-
regulated, respectively, as expected.
Identification of Myc-induced transformation associated lncRNAs
To address the questions of disease relevance and how Myc coordinates these lncRNAs, the
MCF-10A model of Myc-dependent transformation was used. As mentioned previously, the
transformed phenotype of the MCF-10A cells is only morphologically measurable by day 8. It
was hypothesized that the transcriptional programs required for establishing any differences in
phenotype were set earlier in the proliferative phase of morphogenesis around day 4. Therefore,
30
Figure 7: Identification of cell cycle associated lncRNAs
A) The volcano plot of all significant and non-significant lncRNA expression changes that occur
between MCF-10A cells stimulated for 8 hours with mitogens post 24 hours of starvation and
MCF-10A cells starved of mitogens for 24 hours only. Each plotted point represents an lncRNA
gene targeting probe above the low intensity filtering threshold (B, ‘Present Probes’). The
horizontal line represents the p value cutoff of 0.05 and the two vertical lines represent the fold
change cutoff of 1.5 fold in either direction of change. lncRNA genes that fit the cutoff criteria
are in black and those that do not are in grey. B) A table summary of the important aspects of the
volcano plot in A.
31
Figure 8: Identification of Myc-induced transformation associated lncRNAs
A & C) The volcano plots of all significant and non-significant lncRNA expression changes that
occur between MCF-10A-Myc (A) or MCF-10A-Myc-T58A (C) and MCF-10A-GFP empty
vector cells grown on Matrigel™. Each plotted point represents an lncRNA targeting probe
above the low intensity filtering threshold (B and D, ‘Present Probes’, respectively). The
horizontal line in each plot represents the p value cutoff of 0.05 and the two vertical lines
represent the fold change cutoff of 1.5 fold in either direction of change. lncRNA genes that fit
the cutoff criteria are in black and those that do not are in grey. B & D) The table summaries of
the important aspects of the volcano plot in either A or C, respectively.
32
RNA for lncRNA gene expression analysis was isolated at the day 4 time point for MCF-10A
GFP (empty vector), MYC, and MYC-T58A cells. Gene expression changes would therefore be
a measure of population gene expression including both transformed and non-transformed acini.
Myc over-expressing MCF-10A cells form acini that differentially express only 89 lncRNAs
when compared the GFP expressing acini (Figure 8A & 8B). Consistent with more phenotypic
transformation (Figure 5), Myc-T58A over-expressing MCF-10A cells form acini that
differentially express 407 lncRNAs compared to GFP (Figure 8C). As was previously noted in
the cell cycle, these transformation associated lncRNA genes also seem to undergo widespread
down-regulation (Figure 8D). These will be identified as transformation associated lncRNAs
from this point forward.
Identification of lncRNA genes common to Myc-induced transformation and
the cell cycle
Since many Myc, cell cycle regulated coding genes have been shown to be key regulators of
cancer development (ie Cyclin D1), it was hypothesized that identifying cell cycle regulated
lncRNAs that were similarly regulated in the T58A-Myc transformation model would likely
identify Myc regulated lncRNAs important in cancer. Therefore, the differentially expressed
transformation and cell cycle associated lncRNAs have been compared for commonality. This
comparison begins to address the initially posed challenges of inferring lncRNA function,
disease relevance, and Myc’s role. As such, there were 33 lncRNAs common among those
differentially expressed genes identified (Figure 9A). This small population of genes in the union
is statistically more likely to occur than random chance alone (p =0.007, hypergeometric
distribution).
33
Figure 9: Identification of lncRNAs common to G0
A) Venn diagram that highlights the union between the cell cycle associated lncRNAs (1,123)
and the Myc-T58A transformation associated lncRNAs (407). B) Of the 33 genes identified in
the union of the venn diagram in A, 20 fit the inclusion criteria highlighted in the text. B is a heat
map of the relative expression changes in those 20 candidate lncRNAs across the treatment
conditions indicated. The order starts with coordinately up-regulated in the conditions and
progresses to coordinately down-regulated in the conditions.
/G1 to S phase progression of the cell
cycle and Myc-induced transformation
34
2.3 Application of Inclusion Criteria Given that the array contains predicted genes, a set of inclusion criteria was applied to yield a list
of 20 candidates. The following criteria were applied to better define the transcript candidates.
Primarily, application of these criteria made use of Ensembl (121) and University of California
Santa Cruz(UCSC) Genome Browser (122). These databases provide an interconnected interface
to easily view genomic data in non-coding regions of the genome including predicted transcript
and pseudogene information as well as data from the Encyclopedia of DNA Elements
(ENCODE) project (123). Information cited below that could be useful for understanding the
relevance of these candidate lncRNAs was gathered and collated in Table 1.
Gene prediction by Ensembl is achieved through the use of expressed sequence tags(EST)
information (124). First the ESTs, are aligned to the genome using the programs Exonerate
(125), BLAST (126), and EST2Genome (127). This is then followed by all redundant,
overlapping regions being consolidated into a single transcript or a set of variants. GenomeWise
(128), a program that predicts all the of the open reading frames(ORFs) in a given genomic
contig, is then used to assess the ORFs present in the predicted transcripts. The absence of a
significantly sized ORF results in the annotation as a non-coding processed transcript by
Ensembl. Probes on the microarray that directly overlap these predicted genes as well as those
annotated as lincRNAs and antisense RNAs were selected.
Evidence for Presence of a Transcript
These transcripts were then confirmed across databases in the UCSC Genome Browser using the
EST information deposited there. Potential spliced ESTs that spanned the full length of a known
or predicted transcript were annotated. Similarly, the UCSC Genome Browser is the depository
35
Table 1:
Characteristics of
candidate lncRNAs
from the expression
profiling union
between cell cycle and
transformation
ID1
Coordinates2
StrandExons
Type3
Gene U
pstreamG
ene Dow
nstreamM
yc Bound4A
bundance5
Database
FCAbsolute M
itogenregulation
p-valueFCA
bsolute 3D T58A
-GFP
regulationp-value
ENST00000423943
1:159931014-159948851+
3Intergenic
SLAM
F9PIG
M+
+Ensem
bl1.590
up0.017
1.908up
0.018BC035759
16:3700637-3701704-
1A
ntisenseTRA
P1D
NA
SEI+
+U
CSC2.527
up0.026
2.090up
0.037EN
ST00000504601.117:48261457-48262313
+2
Antisense
HILS1
COL1A
-+
Ensembl
1.823up
0.0382.887
down
0.044EN
ST0000052658511:76491664-76495723
-2
Antisense
TSKULRRC32
++
Ensembl
5.443dow
n0.003
1.717dow
n0.003
ENST00000522060
8:144063466-144099798-
3Intergenic
LY6ECYP11B2
++
Ensembl
10.334dow
n0.002
1.584dow
n0.044
ENST00000443631
9:131486724-131495473+
2A
ntisenseZD
HH
C12ZER1
++
Ensembl
2.210dow
n0.013
1.754dow
n0.031
ENST00000499203
5:139483881-139487940-
3A
ntisensePU
RAN
RG2
+-
Ensembl
3.155dow
n0.048
1.954dow
n0.039
AK055958
14:91314206-91316246-
1Intergenic
RPS6KA5
TTC7B-
+U
CSC8.505
down
0.0031.647
down
0.029EN
ST000004405742:209118795-209122854
+2
Antisense
IDH
1PIKFYV
E+
+Ensem
bl2.720
down
0.0401.570
down
0.048EN
ST0000055690415:91565852-91574370
+4
IntergenicV
PS33BSV
2B+
-Ensem
bl2.031
down
0.0232.317
down
0.018BC037839
15:32,828,960-32,872,810+
3Intergenic
FAM
7A1
ARG
HA
P11A+
+U
CSC3.748
down
0.0242.738
down
0.049A
F14731714:23456112-23456486
+1
Antisense
JUB
C14ORF93
+-
UCSC
2.863dow
n0.012
1.534dow
n0.036
AK026750
8:142363507-142365465-
1Intergenic
GPR20
SLC45A4
--
UCSC
3.794dow
n0.005
2.093dow
n0.007
ENST00000522856
8:104258701-104310991-
2Intergenic
FZD6
BAA
LC+
+Ensem
bl1.879
down
0.0011.667
down
0.013EN
ST000004256882:166649530-166653589
+3
IntergenicG
ALN
T3TTC21B
--
Ensembl
1.988dow
n0.010
1.688dow
n0.002
AL832163
8:117945114-117953474-
1A
ntisenseC8O
RF85RA
D21
-+
UCSC
2.655dow
n0.024
2.716dow
n0.038
ENST00000429269
1:234765057-234770526+
2Intergenic
IRF2BP2TO
MM
20-
-Ensem
bl1.786
down
0.0481.971
down
0.048A
K0273838:144840686-144842682
+1
IntergenicM
APK15
SCRIB+
+U
CSC4.091
down
0.0001.843
down
0.039EN
ST000004729433:156465135-156534823
-3
IntergenicLEKR1
TIPARP
++
Ensembl
3.342dow
n0.010
2.234dow
n0.038
ENST00000434627
9:34665662-34681295+
3A
ntisenseRP11-195F19.5
CCL16+
+Ensem
bl6.598
down
0.0002.221
down
0.012
5, Abundance m
easurement using norm
alized hybridization intensity, + represents a lncRNA
whose norm
alized hybridation intensity is greater than the median of the array experim
ent
Table 1: Characteristics of candidate lncRNA
s from the expression profiling union betw
een cell cycle and transformation
1, Database Identification for the database indicated
2, Genom
ic Coordinates in the form of: Chr: Start - Finish
3, Type of transcript Identified where antisense
represents any lncRNA
that lies on the opposite strand of a known gene and w
here intergenic represents any lncRNA
that does not overlap any other known gene
4, Myc bound annotation derived from
an analysis of ENCO
DE M
yc-ChIP seq experiment; + represents M
yc bound in any human cell line assayed w
ith 5kb of the lncRNA
's annotated transcription start site
36
for ENCODE data as well other large scale genomic experiments. The information provided by
the publicly available RNA-seq information can support the predicted gene structure of the gene
of interest. The chromatin mark information can provide information about regions of the gene
marked with promoter associated histone marks as well as gene body associated marks, which
provides further evidence for the transcript.
Transcripts that fit the above criteria were then subjected to classification based on their
orientation with respect to neighboring protein coding genes. Specifically, a transcript could be
classified in one of three ways, for simiplicity. First, it could be defined as an antisense
transcript, which is a transcript that overlaps the gene body of a known protein coding gene, but
is located on the opposite strand. The second classification is bidirectional transcripts, which are
defined as transcripts oriented in a “head-to-head” fashion with a known protein coding gene and
thus share a common promoter region. The last classification is intergenic transcripts, which are
those transcripts that are not in the vicinity of any known protein coding gene. Importantly, those
unclassified transcripts that did not fit any of the previous criteria were removed. In particular,
these primarily included transcripts that were intragenic or interspersed within a known protein
coding gene body and oriented in the same direction of transcription.
Gene Structure Selection
Additionally, those transcripts that were spliced were annotated by the number exons they
contained as spliced transcripts would be preferred in the candidate selection process. From this,
evidence that the cell is regulating the gene of interest as well as evidence for gene orientation
can be inferred.
37
As described above, intragenic transcripts were removed from analysis as this was considered
evidence that may confound the non-coding nature of a given transcript. This concept has been
applied as a primary means of the discovery of these lncRNA transcripts in the intergenic regions
of the genome.
Bioinformatic Evidence for Non-coding Nature of Transcript
Genes that were not parsed out of consideration in this manner were then subjected to translation
in all six reading frames using the ExPASy Translate Tool in order to predict any significant
open reading frames longer than 100 bases or ~30 amino acids.
Pseudogenes are a class of lncRNAs that are similar to known protein coding genes. Recent
reports have demonstrated that there may be some functional importance to the presence of
pseudogenes, but that does not change the fact that pseudogenes have high sequence identity
with known protein coding genes (129). For this reason, this analysis has filtered them.
Pseudogene Filtering
2.4 Candidate Expression and Selection The 20 candidate lncRNAs after application of inclusion criteria show expression, consistent
with the global trends in each of their parent datasets. Therefore they are primarily coordinately
down-regulated in response to mitogen stimulation of MCF-10As or in reponse to stable over-
expression of Myc-T58A MCF-10As grown on Matrigel™ (Figure 9B).
To prioritize the list of 20 candidate lncRNAs, candidate selection criteria were required. The
candidate selection criteria are highlighted in the methods section. Myc-binding status, the
relative abundance based on array hybridization intensity, and the splicing status of the candidate
38
Figure 10: Expression validation of 6 candidate lncRNAs
A) The fold expression changes in response to mitogen of the annotated lncRNAs on the array
are plotted against the fold change measured independently by qRT-PCR. The line of best fit is
plotted with the coefficient of determination noted. B) Similarly, fold expression changes in
response to over-expression of Myc-T58A are plotted for the array and independent qRT-PCR.
Annotations for both plots indicate the accession ID. The 6 digit numbers are to be preceded by
ENST00000 to complete their Ensembl ID. The qRT-PCR fold changes are the averages of 4
39
biological replicates consistent with the microarray. C & D) Represent the individual expression
changes in each of the treatments; 8h mitogen (C) and Myc/Myc-T58A overexpression (D).
Bars represent the average of 3 biological replicates with error bars indicating standard deviation.
The 8h mitogen-starved comparison utilizes a paired t-test, where the T58A comparison makes
use of a one-way ANOVA with a bonferroni post test. * is p<0.05, ** is p<0.01, *** is p<0.001.
40
genes were given the most weight because abundant, Myc-regulated lncRNAs are the goal of this
experiment. Foremost, 14 of 20 candidates were bound by Myc under growing conditions in any
one of the seven cell types where Myc ChIP-seq was performed as part of the ENCODE project
(82, 123). Of these 14, six were selected for expression verification.
2.5 Expression Validation Expression validation within the mitogen responsive experiment yielded a positive trend with
respect to the comparison of array fold change and qRT-PCR fold change (Figure10A). The
coefficient of determination (R2
2.6 Validation of Publicly Available Myc-ChIP data in MCF-10A cells
) was 0.7192. Given the small range of verified expression in the
Myc-T58A treatment condition, no positive trend was observed for the array expression profiling
and the qRT-PCR expression profiling (Figure 10B). In general, though, there was statistically
significant down regulation of the candidate lncRNAs in response to Myc-T58A stable over-
expression when grown on Matrigel™ as measured by qRT-PCR. The down-regulation observed
by qRT-PCR is consistent with the array direction of regulation and is best observed in the
individual gene expression changes (Figure 10C & D) Therefore, increasing the number of
lncRNAs validated and the range of their fold changes would yield a similar correlation to the
mitogen responsive verifications.
Publicly available Myc-ChIP-seq data from the ENCODE project was performed in diverse
human cell types that did not include MCF-10A cells. The potential for Myc to bind and regulate
a specific target gene may be cell-type specific, therefore validation of Myc binding was
performed in MCF-10A cells. Of the six coordinately downregulated lncRNAs selected for
validation, two were in regions of the genome that frequently undergo loss of heterozygosity in
41
Figure 11: Myc binds the promoter of lncRNA-LY6E
Chromatin immunoprecipitation using the antibody, N262, targeting Myc was performed using
crosslinked, asynchronously growing MCF-10A cells. The mean log2
ratios of N262 to IgG
were calculated for a non-Myc bound E-box negative control, CCND2 positive control, lncRNA-
FZD6, and lncRNA-LY6E. Paired t-tests were performed across N = 4 replicates; * represents p
< 0.05 and error bars represent standard deviation.
42
breast cancer cell lines as annotated in the COSMIC database (130). These candidate lncRNAs,
lncRNA-LY6E (ENST00000522060) and lncRNA-FZD6 (ENST00000521383), were both
downregulated in response to mitogen stimulation as well as Myc-T58A over-expression thus it
was hypothesized that these lncRNAs were targets of Myc repression. MCF-10A cells were
cross-linked and immunoprecipitated with Myc N262 antibody or paired rabbit IgG under
asynchronous growing conditions, when it was proposed that Myc would be actively repressing
these candidates. With a non-Myc-bound E-box as a negative control and the cyclin D2 promoter
as a positive control, there was a significant differential in Myc binding at the cyclin D2
promoter versus the negative control suggesting that the experiment is valid. The same samples
were then used to evaluate the Myc-binding status of lncRNA-FZD6 and lncRNA-LY6E. Only
lncRNA-LY6E had significantly enriched Myc-binding at its promoter under asynchronously
growing conditions (Figure 11).
2.7 Functional Validation and Expression Profiling of Candidates lncRNA-LY6E and lncRNA-FZD6
lncRNA-LY6E is located in a simple bidirectional locus with LY6E with Myc bound at the
bidirectional promoter region between the two genes (Figure 12A). This lncRNA is a 995bp,
poly-adenylated transcript with 2 exons. Its promoter contains both a canonical E-box and a
TATAA box. In order to develop a more clear view of the function and regulation of the
candidate lncRNAs, several experiments were performed. These experiments included the array
expression validation as well as expression profiling of lncRNA and pre-lncRNA species across
the cell cycle, after Myc induction, and in nuclear/cytoplasmic compartments. Array expression
validation experiments reveals that mitogen stimulation in 2D and Myc-T58A-overexpression in
3D yield significantly decreased mRNA levels consistent with array data (Figure 12B & 12C).
43
Figure 12: lncRNA-LY6E is dynamically regulated in cell cycle and Myc-dependent
transformation
44
Scale model of the lncRNA-LY6E locus with annotation of mRNA expression and Myc ChIP
primers. B) Verification of mitogen dependent repression of lncRNA-LY6E by qRT-PCR. ***
represents p < 0.001 by paired t-test. C) Verification of the decrease in expression of lncRNA-
LY6E in response to Myc-T58A overexpression in MCF-10A cells grown on Matrigel. **
represents p< 0.01 by one-way ANOVA and Bonferoni post-test. D) Expression profiling of
mature lncRNA-LY6E and pre-lncRNA-LY6E throughout MCF-10A cells synchronized by
mitogen starvation followed by induction. Error bars in B, C, D represent standard deviation.
45
Expression profiling in the cell cycle of mRNA revealed elevated transcript levels that were cell
cycle arrest-associated (Figure 12D, left). To evaluate whether these expression changes could be
attributed to transcriptional regulation of lncRNA-LY6E, primers targeting the heterogenous
nuclear RNA or pre-lncRNA were designed within the intron of this lncRNA. Pre-lncRNA
expression profiling in the cell cycle mimicked the trends seen with the mature lncRNA-LY6E
levels being elevated in cell cycle arrest (Figure 12D, right). This supports the view that the
changes in expression are occurring due to changes in transcription and thus are consistent with
the hypothesis that Myc is acting to repress the transcription of lncRNA-LY6E.
Similar analysis was performed for lncRNA-FZD6, which is a larger lncRNA that is located in a
similarly bidirectional locus with the Wnt pathway receptor, FZD6 (Supplemental Figure 3a).
This lncRNA has multiple predicted splice variants of various size that are poly-adenylated
transcript. Its promoter contains both a canonical E-box and a TATAA box. Though Myc is not
bound at this bidirectional promoter under growing conditions in MCF-10A cells, the array
expression changes still showed significantly decreased transcript levels under the two
conditions (Supp. Figure 3B & 3C). The cell cycle profiling of lncRNA-FZD6 mRNA showed a
similar trend in with cell cycle arrest-associated increase in transcript (Supp. Figure 3D, Left).
Conversely, measurement of pre-lncRNA levels revealed no appreciable change across the time
points measured (Supp. Figure 3D, Right).
Nuclear/Cytoplasmic partitioning was performed for both candidate lncRNAs in MCF-10A cells
under asynchronous growing conditions to evaluate whether they are nuclear retained under
conditions where their expression is low. Upon evaluation of candidate lncRNAs, their
expression was not particularly enriched in any given cellular compartment under these
asynchronously growing conditions where their expression is at or below the level of detection.
46
(Supp. Figure 4, Note scale differences). Xist lncRNA was used as a positive control for the
nuclear compartment and RPLP0 was used as a characteristic protein coding gene. Xist was
enriched in the nuclear extracted RNA and RPLP0 was enriched in the cytoplasmic RNA, as
expected.
Lastly, Myc’s ability to repress these candidate lncRNAs under starvation conditions was
evaluated after 8 hours of Myc induction in the absence of mitogens. Cyclin D2, a Myc induction
control, was significantly induced under these conditions whereas p21, a Myc repression control,
was not significantly repressed (Supp. Figure 5A & 5B). Similar to p21, there was no significant
repression of mature lncRNA-LY6E and pre-lncRNA levels (Supp. Figure 5C & 5D). lncRNA-
FZD6 showed significant repression for its mature transcript, but not for its hnRNA (Supp.
Figure 5E & 5F). Altogether, this suggests that longer Myc-induction for this experiment may be
necessary to see the effects of Myc repression and additional positive controls would add weight
to this experiment and help with data interpretation.
47
Chapter 3 Discussion
3.1 Candidate lncRNA profiling Selection and validation of Myc-bound candidate lncRNAs has revealed a promising lncRNA
that has been called lncRNA-LY6E for its nearest neighbour gene. LY6E stands for lymphocyte
antigen 6 complex, locus E with other aliases including retinoic acid inducible gene E protein
(RIG-E) and stem cell antigen 2 (SCA2). LY6E’s function is not well known, but it seems to be
broadly expressed and interferon responsive (131). Importantly, its loss of expression has been
implicated in hepatocellular carcinogenesis (132). Similarly, as previously stated, loss of
heterozygosity is observed in this locus in breast cancer cell lines. Therefore, lncRNA-LY6E
may act to coordinate the functions of LY6E and may also function as a tumour suppressor. An
interaction or interplay between lncRNA-LY6E and LY6E remains to be evaluated. Another
model that could be tested is the concept the lncRNA-LY6E is the important gene in the locus
for epigenetic regulation is cis or trans.
lncRNA-LY6E’s expression has been profiled in several different states. These data suggest that
it is a cell cycle arrest-associated transcript and that, upon induction with mitogen, may be
transcriptionally repressed by Myc. Two possible models could explain this, the first is that Myc
could be bound basally, as has been demonstrated, and actively repressing lncRNA-LY6E under
conditions permissive to cell growth. The second model is that Myc induction around 1-2 hours
post mitogen stimulation is what directly represses lncRNA-LY6E in the cell cycle.
Under growing conditions, lncRNA-LY6E is not nuclear retained while its localization under
starved conditions remains to be seen. Similarly, lncRNA-LY6E shows no significant change in
response to Myc induction alone for 8 hours, but a trend toward decreased expression is observed
48
at the mature lncRNA and pre-lncRNA levels suggesting that a longer Myc induction may be
warranted to observe repression by Myc in isolation in this model. Altogether, lncRNA-LY6E
may be a novel Myc-repressed lncRNA that could function, in part, to regulate growth arrest and
cell cycle progression.
The model of Myc’s indirect role in the epigenome through its target genes suggests that Myc’s
induced genes have chromatin modifying functions, but this model does not propose a role for
Myc-repressed genes. Recent work from Geiseler et al. (133)may suggest a potential model to
test in terms of how the transcriptional or post-transcriptional down-regulation of lncRNAs can
be important in epigenetic changes. Specifically they showed that the decapping and degradation
of lncRNA in the galactosidase locus of yeast in response to food source changes could lead to
the induction of neighboring galactosidase genes for metabolism of the newly acquired food
source (133). Therefore, we propose that Myc-mediated transcriptional repression of lncRNAs
can function to regulate inducible gene expression in a similar manner (Figure 13).
3.2 Large scale lncRNA profiling Using the developed MCF-10A models, microarray lncRNA expression profiling was
undertaken. First, lncRNA expression changes were profiled in the cell cycle model comparing
the 8h time point to the starved control. Numerous gene expression changes occurred suggesting
that lncRNAs may play an as yet unknown, but critical role in the cell cycle. Also observed was
widespread downregulation of lncRNAs, this observation seems to be consistent with Myc’s
ability to downregulate a large number of miRNAs (6). This can occur in the cell cycle through a
dynamic interplay between Myc and miRNAs, for example through the repression of let-7 and
miR-34a (134).
49
Figure 13: Schematic working model of how Myc-repression contributes to epigenetic
regulation
A stimulus like growth induction or growth arrest can lead to the specific Myc functions at a
given cell-cycle associated lncRNA promoter. Growth induction favors the repression of the cell
cycle arrest-associated transcript which in turn leads to the activation of inducible cell cycle
genes.
50
In the 3D culture model of Myc-dependent transformation, fewer gene expression changes were
observed overall. In this assay, wildtype Myc may therefore require further signaling deficiencies
to potentiate transformation. Consistent with this, in MCF-10A Myc-T58A over expressing cells,
larger scale repression was also observed in the lncRNAs. This is again consistent with the
observed importance of Myc-mediated repression of miRNAs in tumourigenesis (72). These data
seem to suggest some consistency between Myc’s regulation of non-coding genes, but this claim
must be further addressed as this work is limited by array technology in that probes are designed
in a biased way targeting known or predicted lncRNAs.
3.3 MCF-10A cell system Two models for analyzing Myc function were developed using non-transformed MCF-10A cell
system. Both take advantage of their exquisite sensitivity to external stimuli. The first makes use
of mitogen withdrawal, specifically EGF, to arrest cells followed by mitogen add-back to release
the cells from arrest synchronously into the cell cycle. Since Myc is an immediate early response
gene, as confirmed in these cells, it was hypothesized that Myc begins its control of
transcriptional programs early, prior to G1-S transition. As such, 8h post mitogen add-back was
selected as a time point to assay for gene expression in the hope of enriching for Myc target
genes. Similarly, this time point was selected because it allowed for some inference of function
of differentially expressed genes as growth related. For these reasons, the MCF-10A cell cycle
model demonstrated here is an important model of human cell cycle regulation. It is important to
note that although there is arrest and synchronous cell cycle progression, each process isn’t
complete and represents only population level changes.
MCF-10A cells, epithelial in origin, also respond to extracellular matrix mimicking substrates in
a well defined manner. Normal, non-transformed MCF-10A cells grown on Matrigel form highly
51
ordered, polarized acinar structures. This was demonstrated here and shown previously (110,
117). Similarly shown, over expression of oncogenes that transform the MCF-10A cells yield a
loss of polarity and result in a phenotype change apparent in 3D culture. Typically, this
phenotypic change is the formation of multiacinar structures, which is consistent with our model
of Myc-dependent transformation in Myc-T58A over expressing cells. This 3D culture model
mimics breast ductal histology and pathology remarkably well.
A caveat to this breast acinar model is that the system to quantify the level of transformation of
MCF-10A cells is rudimentary and not complete because it does not take into account other
parameters such as the heterogeneity of acinar size. Therefore, it should only be used to indicate
population changes. Any gene expression changes between Myc-T58A and GFP over expressing
MCF-10A cells are considered in light of this.
3.4 Future Directions A priority for future experiments would be to evaluate the function of lncRNA-LY6E through
knockdown or over expression experiments. First, the interplay between lncRNA-LY6E and its
neighbour LY6E would be addressed. This could then be extended to a larger scale by testing the
global gene expression changes in cells over expressing lncRNA-LY6E. If a global pattern of
gene expression changes was observed, a mechanism of gene regulation could be investigated by
evaluating chromatin marks on nearby genes with expression changes. Similarly, over-
expression experiments could be used to begin to evaluate the model that lncRNA-LY6E is a
tumour suppressor by over-expressing it in Myc-T58A transformed MCF-10A acini with the
hypothesis that lncRNA-LY6E would reduce Myc-T58A induced transformation. Other Myc-
dependent, breast cancer cell lines like MDA-MB-231 cells could be similarly used.
52
Upstream of lncRNA-LY6E, the direct regulation by Myc would need to be shown. This could
involve any of the following three experiments. The first would be the extension of the Myc-
inducible experiment shown in supplemental figure 5 to investigate if Myc induction, in the
absence of other signaling, can regulate lncRNA-LY6E. This could be achieved by extending the
Myc-induction time. The second experiment could be the use of a promoter-reporter construct
that contains the lncRNA-LY6E promoter in the presence or absence of Myc. Lastly, addressing
direct Myc repression in the cell cycle would require the knockdown of Myc expression during
synchronous cell cycle entry assaying for the changes in lncRNA-LY6E expression.
In general, these experiments provide the necessary rationale to pursue the hypothesis that Myc
is a global repressor of non-coding regulatory RNA, including lncRNAs. Specifically,
transcriptome-wide RNA-seq experiments would be a logical step to begin to address this
hypothesis as they are not limited by prediction based approaches and allow for de novo
transcript assembly (135).
3.5 Conclusions and Implications In all, this thesis is the first attempt, to our knowledge, to profile Myc regulated lncRNAs on a
large scale. Given the challenges of Myc knockdown experiments and their effects on cell
viability, an approach was taken that utilized a model of cell cycle progression and a model of
Myc-dependent transformation. Due to the largely unknown functions of lncRNA genes, the cell
cycle model was aimed at inferring cell cycle related functions of a large group of lncRNAs. The
profiling of gene expression changes in the Myc dependent model of transformation was aimed
at adding Myc- and cancer-dependence to a set of lncRNAs. Together, these gene sets combined
with publicly available Myc-ChIP-seq data provided the first view of a novel set of Myc
regulated lncRNA candidates. One of the candidates, lncRNA-LY6E provides promising
53
validation of this approach but its function and relevance to cancer biology remain to be
elucidated.
Indeed, as the details of these candidate lncRNAs begin to be elucidated they could be utilized in
the clinic as diagnostic tools. Much like the HOTAIR example, expression profiling of tumours
could aid in staging and treatment. Similarly, lncRNAs could be biomarkers in bodily fluids
(136). A strikingly relevant example is of the lncRNA, Prostate Cancer Antigen 3 (PCA3), which
can be found in the urine of men with prostate cancer (137). Therefore, the lncRNAs identified
could provide useful new diagnostic tools for management and treatment of cancer. Therapeutic
relevance is not limited to biomarkers. A prevailing therapeutic question with transcription
factors and Myc specifically is whether the transcription targets are “targetable” for disease
therapy (70). The lncRNAs identified in this study could provide a novel class of therapeutic
targets among Myc’s many target genes. Recent work has shown that cancers with specific
lncRNAs can be targeted by antisense-oligonucleotide photomolecular beacons for
photodynamic therapy (138). Overall, Myc regulated lncRNAs could not only provide a better
understanding of Myc’s roles in the epigenome, but could provide novel therapeutic targets and
tools for Myc-dependent cancers.
54
Chapter 4 Methods
4.1 Cell Culture
Reagents:
Media, supplemented with penicillin and streptomycin, and phosphate-buffered saline (PBS)
were supplied by the UHN Tissue Culture Media Facility. Trypsin-EDTA was purchased from
Gibco at 10x concentration and diluted to 1x in PBS.
MCF-10A cells:
MCF-10A cells were a kind gift from Senthil Muthuswamy, Cold Spring Harbor Laboratory,
Cold Spring Harbor, NY, USA.(107) These cells were grown in 1:1 DMEM H21/HAM F12
growth media supplemented with 5% [v/v] horse serum (Gibco# 16050-122, Lot# 8178102),
20ng/ml epidermal growth factor(EGF) (Cedarlane# 236EG), 0.5µg/mL hydrocortisone,
0.1µg/mL cholera toxin, 10µg/mL insulin (Sigma#19278).
4.2 Immunoblotting
Whole Cell Extracts
MCF-10A cells under various growth conditions were lysed by aspirating media, washed with
PBS, followed by the direct addition of boiling 1x SDS loading buffer (1%SDS [w/v], 11% [v/v]
glycerol, 0.1M Tris-HCL pH 6.8). Lysed cells were collected with cell scrapers and 10% β-
mercaptoethanol plus loading dye was added. These extracts were then boiled for 5 minutes and
placed at -20°C.
55
SDS-PAGE
Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) was carried out using
8% polyacrylamide. Electrophoresis was performed at 120V for approximately one and a half
hours. Subsequently, gels were transferred to nitrocellulose membranes (Whatman). Membranes
were blocked for 1 hour in 5% milk dissolved [w/v] in 1xPBS with 0.01% TWEEN-20(Sigma,
cat. no.) at room temperature. Immunoblotting with primary antibody was achieved by overnight
incubation at 4°C in the aforementioned 5% milk solution. Washing was performed using 1xPBS
with 0.01% TWEEN-20(PBS-T) for 5 minutes, three times. Secondary antibodies were applied
for one hour at room temperature in the 5% milk solution. As previously, membranes were
washed for 5 minutes, three times in PBS-T followed by once with 1xPBS for 5 minutes.
Antibodies
The primary antibodies used for overnight incubation were diluted as follows: 1/1,000 anti-Myc
(9E10 – monoclonal, mouse hybridoma), 1/3,000 anti-actin (Sigma, cat. no. A2066), 1/1,000
anti-Cyclin E (Santa Cruz, Sc-247), 1/1,000 anti-c-Jun (Cell Signaling, 60A8), 1/1,000 anti-c-
Fos (Santa Cruz, Sc-52). Secondary antibodies for fluorescent imaging using an Odyssey IR
Imaging system (LICOR) were used as 1/20,000 dilutions of IRDye 680 conjugated goat
(polyclonal) anti-rabbit (LICOR, cat. no. 926-32221) and IRDye 800 CW conjugated goat
(polyclonal) anti-mouse (LICOR, cat. no. 926-32210).
56
4.3 Quantitative Real-time PCR
RNA Isolation
MCF-10A cellular lysis for RNA isolation was achieved through the use of the TRIZOL®
reagent from Invitrogen (Cat. No. 12183-555). TRIZOL is a guanidinium thiocyanate-phenol-
chloroform based method of RNA isolation that utilizes the soluble properties of RNA at a low
pH of 4 to perform a phase extraction (139). This phase extraction was combined with silica-
based column purification (Purelink™ RNA Mini Kit) for greater recovery of target RNA. While
RNA was bound to the column, an on-column, deoxyribonuclease treatment was performed
using the Purelink™ DNase Set in order to remove contaminating genomic DNA. The
concentration of the isolated RNA was then measured using the NanoDrop-1000
spectrophotometer (ThermoScientific).
cDNA Synthesis
One microgram of RNA was subjected to first-strand cDNA synthesis using the SuperScript® III
First-Strand synthesis system from Invitrogen (Cat. No. 18080-051). The manufacturer’s
protocol was followed specifically using random hexamers for non-specific, unbiased priming of
DNA synthesis. The final reaction product of cDNA synthesis was diluted 5-fold for
quantification purposes.
Primer Design
Primer design for cDNA quantification was achieved through the use of OLIGO 7 Primer
Analysis Software from Molecular Biology Insights Inc (140). Specifically, gene specific
primers were designed across exon-exon boundaries with preference given to introns greater than
57
500bp in length. Exons for inclusion in primer design were screened for interspersed repeats or
low complexity DNA using the program RepeatMasker (www.repeatmasker.org) as these
regions were to be excluded for amplification. Primers were designed to have a 40-60% GC-
content, an optimal annealing temperature of 50-60°C, and a melting temperature difference of
<1°C. More specifically the 5’ end of primers were designed to have a greater internal stability
(lower ΔG) than the 3’ end in order to optimize priming specificity. Lastly, and more subtly, all
primer-dimer and -hairpin formations were screened bioinformatically for the most energetically
stable subsets. Those primers that did not contain significant secondary structure or dimer
stability were selected. BLAST was used to compare primer sequences to the genome to ensure
there was no non-specific primer binding. Successfully designed primers are annotated in Table
2.
Relative and Absolute Quantification
Quantitative real-time polymerase chain reaction (qRT-PCR) was performed using the ABI
PRISM® 7900HT Sequence Detection System. Reactions were carried out in the 384-well
format using MicroAmp® optical 384-Well reaction plates (Applied Biosystems, part no.
4343814) and MicroAmp® Optical Adhesive Film (Applied Biosystems, part no. 4311971).
Specifically, reactions used a 10µl total volume that included 2µl of cDNA and 8µl of each gene
specific master mix. The gene specific master mixes contained forward and reverse primers as
well as Power SYBR® Green PCR Master Mix (Applied Biosystems, part no. 4367659). The
cycling program used an initial denaturation step of 95°C for 10 minutes followed by 40 cycles
of 95°C for 15 seconds and 60°C for 1 minute. The program was completed with a dissociation
step for product analysis. 60S acidic ribosomal protein P0 (RPLP0) was used as an endogenous
control for normalization in all experiments. Therefore, relative quantification was achieved
58
Table 2: Primer List
Application Target Gene Orientation Primer Sequence
qRT-PCR
RPLP0 F CAGATTGGCTACCCAACTGTT R GGGAAGGTGTAATCCGTCTCC
CCND2 F CTGTGTGCCACCGACTTTAAG R GATGGCTGCTCCCACACTTC
Myc F AGGGTCAAGTTGGACAGTGTC R TGGTGCATTTTCGGTTGTGG
CCNB1 F GAGGGAGCAGTGCGGGGTTT R AAGCAGAACACCGGAGGCCC
p21 F GGCGGTTGAATGAGAGGTTC R CCTCCGGGAGAGAGGAAAAG
lncRNA-LY6E F ACCGTCACTGACACCTGGA R TCAGCCCTGAGGCTTTGAT
lncRNA-FZD6 F CGGGGAGCCTGGTCACCAA R TGGGCTGCTCAGGGTTCCATC
hnRNA lncRNA-LY6E
F CACTGTGTCAGGGGTGTGTA R CAGGAGGGCTCTGGAATGG
hnRNA lncRNA-FZD6
F GGCTGAGCAGAGGCATAGA R ACTGCTTCCTCCCAAGTTC
Luciferase F ACTCCTCTGGATCTACTGGTC R GTAATGAAGGCTCCTCA
ENST00000443631 F CTGTGGTGACAGCTTTACC R TCGTCGAACGACCTTGTTT
ENST00000499203 F GACAAGCTGAACCAAATGTA R ATGCAACAAAGTTCAATAGT
ENST00000440574 F AGAGCACCACAGAGTGTTT R GGTGGCTCACGCTTGTAAT
AK027383 F AGCCCTGCACCAGCAAATC R TGTGCAAGGGAAGCTCCTCAT
ENST00000434627 F CCTGCAGACGGCCTATTGTG R GTGGGTCACACAGCCATAC
ChIP-qPCR
CCND2 F CCTTGACTCAAGGATGCGTTAGA R GAGCCGACTGCGGTGAAGT
HNT1 Exonic E-box Neg. Control
F CCAAACGCAGTACAGCATGG R GTTGTCTGTCTGCACCGAGC
lncRNA-LY6E F AGTGCTGCTACGTAAGAAGGA R CGAGGAGATGTCACAGAGATT
lncRNA-FZD6 F GCCCGCACCTGAGTTTCCTC R CCCGGCATCGCCTTCAGAG
59
using the ΔΔCt method where cDNA from genes of interest was measured relative to the
endogenous control and across experimental states being measured. Fold change within this
method was calculated using 𝐹𝐶 = 2−∆∆𝐶𝑡. For absolute quantification, expressed sequence tag
(EST) clones for genes of interest were ordered. High purity plasmids of known copy number
were then used to create a standard curve. Experimental samples were then compared to the
standard curve of known copy number to establish the amount of transcript in a given amount of
cDNA. Additionally, for these experiments in vitro transcribed luciferase RNA (0.025ng,
2.73x107
4.4 Flow Cytometry
copies) was added to lysed TRIZOL samples prior to purification in order to control for
RNA isolation and first-strand synthesis efficiency.
Flow cytometry was completed at the Flow Cytometry Facility of Ontario Cancer Institute.
Specifically, MCF-10A cells were placed in non-sterile 1x trypsin to create a suspension of both
adherent and floating cells and cellular fragments. Suspensions were spun at 2000rpm for 2
minutes. Pellets were isolated by decanting the supernatant and subsequently washed and
resuspended with non-sterile PBS. Suspensions were once again pelleted as before and isolated
by decanting the supernatant. Subsequently, cells were fixed by resuspension in cold 70%
ethanol and placed at -20°C for a minimum of 4 hours and a maximum of 14 days.
Fixed cells were then spun as before, the supernatant ethanol was discarded, and the pellets were
rehydrated in PBS. Cells were similarly pelleted and resuspended in a solution of RNase,
DNase-free(Roche#11119915001) and placed at 37°C for 1 hour. Lastly, the cells were stained
with a solution of the nucleic acid stain, propidium iodide (PI) (Sigma #P4170), and allowed to
incubate for 15 minutes.
60
The fixed, PI-stained cells were assessed using the BectonDickson Biosciences FACSCalibur
flow cytometer. Ten-thousand events were acquired as representative of the total cell population
specifically gating for those single cellular events that were PI-positive. The distribution of PI
positive cells was then plotted in two dimensions with number of events on the y-axis and
relative PI-staining on the x-axis. This allowed for visualization of cell cycle profiles of cells
changing their DNA content from 2N to 4N.
4.5 MCF-10A: Model of Cell Cycle Entry MCF-10A cells grown in two-dimensions are remarkably responsive to extracellular stimuli. In
order to study MCF-10A cells undergoing the process of cell cycle entry from a quiescent state.
This was achieved by depriving MCF-10A cells of mitogens through exposure to starvation
media that contained 0.05% horse serum [v/v] and 10µg/mL insulin. Most importantly this
reduces MCF-10A exposure to EGF, a mitogen that they are very dependent upon. Cells in
starvation media arrest in 24 hours in G1/G0 of the cell cycle as measured by previously
described fixed propidium iodide flow cytometry. After arrest, cells are placed back into full
MCF-10A growth media. In response, these cells re-enter the cell cycle in near complete
synchrony through the first round of replication which begins occurring around 14 hours after
addition of full mitogen-containing media.
Seeding Density:
Due to the constantly changing cell number, seeding density was an important variable for
optimization in order to prevent any contact related inhibition of cell growth that could skew any
cell cycle related analysis. This optimization was achieved through seeding between 50,000 and
500,000 cells/10cm plate and harvesting for fixed PI flow cytometry at 24 and 48 hours. The
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goal was to use the fewest cells so as to maintain a sub-confluent population of cells at the end of
the experiment. In the end, an initial seeding density of 300,000cells/10cm was used
(Supplemental Figure 1)
4.6 MCF-10A: Myc-dependent Model of Transformation MCF-10A cells can also be effectively grown on substrates that mimic the basement membrane.
For this model, cells were grown on Matrigel™(BD Biosciences, cat no. 356230) that is a
solubilized basement membrane derived from Engelbreth-Holm-Swarm mouse sarcoma.
Specifically, we use Matrigel that has been growth factor reduced. This protocol is adapted from
Debnath J. et al (110). Briefly, 25,000 cells/mL of MCF-10A cells in a 2.5% Matrigel [v/v] are
seeded on a layer of 100% Matrigel that has been solidified. Cells proliferate in the matrigel
through about 10 days in culture while forming highly ordered acinar structure and beginning to
undergo luminal cell death around day 8 (Figure 4). In order to study transformation of the MCF-
10A cells in this context, stable cell lines of pMN-GFP, -Myc, and T58A-Myc were generated by
Amanda Wasylishen.
Imaging of MCF-10A cells grown in 3D on matrigel was performed every 4 days up to 16 days
using the Zeiss AxioObserver equipped with the Roper Scientific Coolsnap HQ camera for
image acquisition at the Advanced Optical Microscopy Facility (AOMF), Ontario Cancer
Institute, Princess Margaret Hospital, Toronto, ON, Canada. Images were acquired to monitor
acinar morphogenesis as well as for quantification of transformation at day 8. Transformation
was defined as disordered, multi-acinar structures and was quantified as percentage of the total
population of all sizes of acini (>100) by blinded counts of cells in 3D culture for 8 days.
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4.7 MCF-10A: Model of Myc-dependent Gene Regulation in the absence of other stimuli
Stable inducible MCF-10A cell lines created by Amanda Wasylishen utilize a two plasmid, 4-
hydroxytamoxifen (4-OHT) inducible system. Briefly, the synthetic fusion GEV16 transcription
factor under the control of the ubiquitin promoter in the first plasmid is constitutively expressed
and localized to the cytoplasm. Upon addition of 4-OHT, GEV16 is translocated to the nucleus
where it binds to the Gal4 upstream activating sequences in the second plasmid, which in turn
activates the gene of interest(Supp. Figure 2) (141).
Using this system for inducible gene expression of Myc, we have used MCF-10A cells in
starvation media in the absence of mitogens for 24 hours and induced Myc expression.
Subsequently, RNA was harvested 8 hours after induction to analyze gene expression changes as
a result of Myc induction alone.
4.8 Gene Expression Array
Sample Preparation
Utilizing the model of cell cycle entry in the MCF-10A cells grown in 2D, RNA was harvested
from cells deprived of mitogens for 24 hours as well as cells deprived of mitogens for 24 hours
and subsequently reintroduced to them for 8 hours. As a control, cDNA was made from isolated
RNA and the relative changes of genes associated with early cell cycle events, p21 and CCND2,
were measured (Data not shown).
Similarly, RNA was harvested from MCF-10A-GFP, -MYC, and -MYC-T58A cells grown in 3D
on Matrigel for 4 days. It should be noted that TRIZOL reagent for RNA isolation placed
directly on the cell and Matrigel mixture can effectively lyse cells.
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Four replicates of each of the five aforementioned samples were obtained for a total of twenty
RNA samples for array analysis.
Arraystar Microarray Analysis
Microarray analysis employed Arraystar Inc., a fully integrated microarray service, and their
Human lncRNA Microarray V2.0. This custom lncRNA array utilized an Agilent array platform.
The sample preparation and microarray hybridization were performed based on the
manufacturer’s standard protocols with minor modifications. Briefly, mRNA was purified from 1
μg total RNA after removal of rRNA (mRNA-ONLY™ Eukaryotic mRNA Isolation Kit,
Epicentre). Then, each sample was amplified and transcribed into fluorescent cRNA along the
entire length of the transcripts without 3’ bias utilizing a random priming method. The labeled
cRNAs were hybridized onto the Human LncRNA Array v2.0 (8 x 60K, Arraystar). After having
washed the slides, the arrays were scanned by the Agilent Scanner G2505B.
Specifically this platform is intended for parallel analysis of lncRNA and protein coding gene
expression. There are probes targeting 33,045 LncRNAs and 30,215 coding transcripts. The
lncRNA predicted and known genes were collected from databases such as Refseq, UCSC, and
Ensembl as well as literature sources.
4.9 Bioinformatic Analysis
Preprocessing: Array Background Adjustment, raw mRNA data
normalization, and low intensity filtering
In order to ensure that observed differential expression between treatments is not a result of
systematic variation and artifacts several preprocessing steps were utilized. The first involved
64
microarray spot selection, outlier pixel removal and identification of background pixels utilizing
Agilent Feature Extraction software (version 10.7.3.1) for array image processing and a set of
control spots on each microarray (Includes no probe and endogenous controls). Briefly, the raw
signal intensities after spot selection are background subtracted and corrected for signal biases to
yield processed signal intensities corrected for optical noise and cross hybridization.
The processed signal intensities from each microarray must then be subjected to normalization
between arrays. Using the GeneSpring GX v11.5.1 software package (Agilent Technologies),
quantile normalization was performed which assumes that the distribution of probe intensities is
the same across all arrays/samples and makes them the same. Low intensity filtering was then
performed as follows. lncRNAs were called as ‘Present,’ ‘Marginal,’ or ‘Absent’ based on their
normalized intensity. lncRNAs in the 2D samples that have at least 4 out of 8 flags as Present or
Marginal were chosen for further data analysis. Similarly, lncRNAs and mRNAs in the 3D
samples that have at least 8 out of 12 flags as Present or Marginal were also chosen. Probe
intensities can then be directly compared between experiments in a paired analysis within
experiments to yield differentially expressed gene fold changes and p-values. Statistically
significant differentially expressed genes were then identified through Volcano Plot filtering.
Inclusion Criteria
The nature of the microarray approach making use of a combination of probes targeting known
and predicted transcripts made it necessary to develop a means a selecting those transcripts that
fit the criteria of a long non-coding RNA, unambiguously. A long non-coding RNA was defined
as any transcriptional unit that contained no evidence of a significant open reading frame and did
not coincide with the location of known protein coding genes or pseudogenes. The criteria are
detailed in the results section of this thesis.
65
Candidate Selection Criteria
Application of the inclusion criteria and annotating transcripts as described created a uniform list
of predicted and known lncRNAs. To establish their importance and relevance to this study on
Myc-regulated lncRNAs and their importance to cancer, the candidate selection criteria below
were applied. These criteria were aimed at created a manageable and relevant list of candidate
lncRNAs for expression and functional validation.
Myc Binding Status
The ENCODE project contains information pertaining the transcription factor
binding through the use of chromatin immunoprecipitation followed by high-
throughput, next-generation sequencing (ChIP-seq) (123). The sequencing read
peaks from Myc ChIP-seq experiments performed in cell lines of diverse origins
can be visualized in the UCSC Genome Browser. As such, the Myc binding status
of each lncRNA candidate could be evaluated with preference given to those that
were Myc-bound within 5kb of their annotated transcriptional start site.
EST expression status
The National Center for Biotechnology Information (NCBI) contains many
resources pertaining to genes and expression including one called Unigene (142).
Unigene clusters expressed sequence tags that seem to be transcribed from the same
locus (143). The tissue of origin of each EST taken relative to all ESTs in a given
cluster can be used to suggest the tissue type restricted expression of the cluster.
Restricted expression in mammary glands as well as mammary gland tumours was
enriched and given preference.
66
GEO evidence
The Gene Expression Omnibus (GEO) is also a part of NCBI and is interfaced with
the EST clusters as annotated by Unigene. NCBI GEO is an archive of expression
data acquired through both microarray and high-throughput sequencing
technologies (144). GEO Profiles, a portion of this database, provides the
information of a single gene or EST cluster across array experiments. The profiles
of individual EST clusters that represented candidate lncRNAs were used to provide
expression evidence relevant to the biological question.
Abundance
The abundance of lncRNA transcript species has been a debated issue, but in
general the lncRNA class of genes is less expressed at steady state than their protein
coding gene counterparts in whole cell RNA isolates. A relative means of assessing
the abundance of these transcripts was needed in order to select those transcripts of
higher relative abundance in the array experiment. Therefore, the normalized
expression values, those values that were used to directly calculate fold change after
all pre-processing, of all probes targeting significantly changed lncRNAs in a given
experiment on the array were plotted as a function of their p-values in order to show
the distribution (Figure 14). The median value of the normalized expression in the
dataset was then plotted as a line. Any probes that had normalized expression
values in the compared conditions above the median values were considered
abundant by this analysis and given preference.
67
Figure 14: Distribution of differentially expressed genes by normalized array intensity
A representative distribution of significant, differentially expressed lncRNA genes’ normalized
intensity as a function of their p-value. The T58A and GFP normalized intensities of the 33
genes from figure 9 in the union of T58A and mitogen are plotted. The median of 6.496 is
plotted on the y-axis. This plot was used to demonstrate the relative abundance of transcript in a
given comparison.
68
COSMIC CNV evidence
The Catalogue of Somatic Mutations in Cancer (COSMIC) is a database that allows
for the analysis of somatic mutations in cancer using a simple user interface (130).
Making use of this resource, candidate lncRNA loci were viewed and analyzed for
the presence of small nucleotide polymorphisms (SNPs) or larger scale, copy
number variations (CNVs). Candidates that were mutated, gained, or lost in human
breast cancer were given preference.
4.10 Nuclear-Cytoplasmic Partitioning This protocol was adapted from Fish et al (145). MCF-10A cells were grown to 70-80%
confluence, the media transferred to a separate tube and the cells were washed two times with
1xPBS warmed to 37°C. Cells were detached using 1mL of 1x trypsin incubated at 37°C for 20-
30 minutes. Residual trypsin was used to collect and suspend cells, which were then added to the
previously collected media to inactivate trypsin. Cells were centrifuged at 800g for 5 minutes at
4°C. After discarding the supernatant, cell pellets were then washed with cold 1xPBS and
centrifuged as above. Pellets were resuspended in 1mL of cold 1xPBS and transferred to a sterile
Eppendorf tube. Cells were once again centrifuged as above, supernatants were discarded.
To the cell pellet, 175µl of fresh buffer RLN (50mM Tris-HCl, pH 8.0; 140mM NaCl; 1.5mM
MgCl2; 0.5% Nonidet P-40 or IGEPAL CA-630; 0.2units/ul RNaseOUT; 1mM dithiothreitol
[DTT]) was added and used to carefully resuspend cells. Cell suspension in buffer RLN was
incubated for 5 minutes on ice. Suspension was subjected to centrifugation at 300g for 2 minutes
at 4°C. The supernatant (cytoplasmic fraction) was removed and kept on ice. The nuclear pellet
was washed with 500µl of cold 1xPBS and centrifuged at 300g for 5 minutes at 4°C, supernatant
69
discarded. Lastly, 1mL of TRIZOL was added to both fractions for RNA isolation. Subsequently,
RNA extraction, cDNA synthesis, and absolute quantification by qRT-PCR was performed as
described previously.
4.11 ChIP-qRT-PCR Sub-confluent MCF10A cells were cross-linked with 1% formaldehyde for 10 minutes at room
temperature. To quench the cross-linking reaction, 1M glycine was added to a final concentration
of 0.125M for 10 minutes at room temperature. Two washes with cold 1xPBS followed. One
milliliter of cold 1xPBS + protease inhibitors was added and cells were harvested with a cell
scraper. Cells were centrifuged at 425g and cell pellets were resuspended in 1ml nuclei lysis
buffer(1% SDS, 10mM EDTA and 50mM Tris, pH 8.1) with protease inhibitors per 2.0x107
Sonicated cells were centrifuged at 15000g at 4°C for 10 minutes and supernatants were removed
and pooled. Using IP dilution buffer (0.01% SDS, 1.1% Triton X- 100, 1.2mM EDTA, 16.7mM
Tris-HCl, pH 8.1, 167mM NaCl), pooled supernatants were diluted ~10 times. Previously,
protein G agarose beads were washed two times with sonication buffer and then blocked in
sonication buffer containing salmon sperm DNA(50µg/ml) overnight at 4°C. 60µl of prepared
protein G agarose beads were used to pre-clear the chromatin by incubating at 4°C for 1 hours
with rotation. Beads were then centrifuged at 5000g for 1 minute. The supernatant was collected
and aliquoted according to relative number of cells per reaction with the corresponding antibody
added to each. Reactions were incubated overnight at 4°C with rotation.
cells.
Resuspended cells were incubated on ice for 10 minutes and then sonicated with 20 pulses
(setting high, 30s per pulse, 30s on ice between pulses) using a BioRuptor Sonicator (Diagenode,
BioRuptor 200, UCD-200 TM-EX) to generate fragments between 100 and 1000bp confirmed by
agarose gel electrophoresis.
70
60µl of blocked protein G agarose was added to the reactions and incubated for 3 hours at 4°C
with rotation. The beads were pulled down by centrifugation at 5000g for 1 minutes. The
supernatant was discarded except for 200µl of from the IgG sample, kept on ice, to use as a total
input control. The beads were then washed with 1ml of each cold buffer below by resuspending
the beads in the buffer, incubating for 3-5 minutes with rotation, and centrifuging at 5000g for 1
minute. The supernatant was removed prior to the addition of the next buffer.
1X low salt immune complex wash buffer (0.1% SDS, 1% Triton X-100, 2mM EDTA,
20mM Tris-HCl, pH 8.1, 150mM NaCl)
1X high salt immune complex wash buffer (0.1% SDS, 1% Triton X-100, 2mM EDTA,
20mM Tris-HCl, pH 8.1, 500mM NaCl)
1X lithium chloride wash buffer (0.25M LiCl, 1% IGEPAL CA630, 1% deoxycholic acid
[sodium salt], 1mM EDTA, 10mM Tris, pH 8.1)
2X Tris-EDTA (TE) buffer (10mM Tris-HCl, pH 8.0, 1mM EDTA)
After washes, make fresh elution buffer (1% SDS, 100mM NaHCO3); 200µl is required per
tube. Add 100µl to all tubes and mix by gently flicking. Incubate at 65°C for 15 minutes. Pull
down bead by centrifuging at 5000g for 1 minute and collect the supernatant to new tubes and
repeat with remaining 100µl/tube, combining eluates. To all tubes, including the total input
control, add 8µl of 5M NaCl and incubate overnight to begin reverse cross-linking. Add RNase
A and incubate for 30 minutes at 37°C and complete the reverse cross-linking by adding 4µl of
500mM EDTA, 8µl of 1M Tris-HCL (pH 6.5), and 1µl of Proteinase K followed by incubation
at 45°C for 1-2 hours.
71
5µl of 3M NaOAc was added to adjust pH for purification of the DNA which utilized the silica
column based QIAquick PCR Purification Kit (QIAGEN, cat. no. 28106) according to
manufacturer’s protocol. DNA was eluted from the columns with 60µl of RNase-, DNase-free
water and frozen at -20°C for further use
Relative DNA amounts were measured by qRT-PCR as described previously with primers
designed around Myc ChIP-seq peaks as described in the Candidate Selection section.
4.12 Statistical Analysis Statistical analysis was performed using Graph Pad Prism software. As well, calculation of the
hypergeometric distribution utilized R statistical programming.
72
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Appendices
88
Supplemental Figure 1: Cell cycle seeding density optimization
MCF-10A cells were seeded at the various low density amounts indicated at left and allowed to
recover for 24 and 48 hours in full serum. The fixed propidium iodide flow cytometric cell cycle
profiles are shown at right. The red box indicates the selected density and recovery time after
seeding.
89
Supplemental Figure 2: Model of Myc-dependent Gene Regulation
A) Diagram representation of the Myc-inducible expression system that uses the constitutively
expressed GEV16 transcription factor that is estrogen agonist responsive. Upon induction with 4-
hydroxytamoxifen (4HT) GEV16 localizes to the nucleus and binds the 5xUAS sequences
upstream of Myc for induction of transcription. Reprinted by permission from Macmillan
Publishers Ltd: Cell Death and Differentiation, Callus, BA et al., 2008 (141). B) Immunoblot
showing the 8 hour inducible expression of Myc in MCF-10A pF-Myc cells with 4HT compared
with empty vector(pF) and ethanol treated, vehicle controls. C) Inducible expression of Myc in
MCF-10A cells that have been starved for 24 hours and subsequently induced with 4HT and
maintained under starvation conditions were analyzed by fixed propidium iodide flow cytometry.
D) RNA lysates from the cells in C were isolated and analyzed for changes in Myc, Myc-
induced CCND2 and CCNB1, and Myc-repressed p21 mRNA. All date presented here are
representative of 2 independent experiments.
90
Supplemental Figure 3: lncRNA-FZD6 is dynamically regulated in cell cycle and Myc-
dependent transformation
91
A) Scale model of the lncRNA-FZD6 locus with annotation of mRNA expression and Myc ChIP
primers. B) Verification of mitogen dependent repression of lncRNA-FZD6 by qRT-PCR. ***
represents p < 0.001 by paired t-test of three replicates. C) Verification of the decrease in
expression of lncRNA-FZD6 in response to Myc-T58A overexpression in MCF-10A cells grown
on Matrigel. *** represents p< 0.001 by one-way ANOVA and Bonferroni post-test of 3
replicates. D) Expression profiling of mature lncRNA-FZD6 and pre-lncRNA-FZD6 throughout
MCF-10A cells synchronized by mitogen starvation followed by induction. The mean relative
expression of three replicates is shown. Error bars in B, C, D represent standard deviation.
92
Supplemental Figure 4: Candidate lncRNAs are not nuclear retained under asynchronous
growing conditions
Nuclear-cytoplasmic partitioning was performed using NP-40 lysis of MCF-10A cells growing
asynchronously. Absolute quantification of RPLP0 (A), Xist positive control (B), lncRNA-LY6E
(C), and lncRNA-FZD6 (D) was achieved using standard curves of known copy number
plasmids of each respective target measured by qRT-PCR. Error bars represent standard
deviation of four replicates. Please note differences in scale.
93
Supplemental Figure 5: 8 hours of Myc induction under starvation conditions does not lead
to significant changes in candidate lncRNA expression
Using the inducible system described in supplemental figure 2 with Myc and empty vector (pF),
the expression of CCND2 (A), p21 (B), lncRNA-LY6E (C) and pre-lncRNA-LY6E (D), as well
as lncRNA-FZD6 (E) and pre-lncRNA-FZD6 (F) was measured by qRT-PCR relative to starved
cells. The primary comparison of 8 hours ethanol, vehicle treated and 8 hours 4-
hydroxytamoxifen treated MCF-10A cells starved for 24 hours was evaluated by two-way
ANOVA. ** represents p < 0.01 and error bars represent standard deviation of three replicates.