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5-2010
The Important Importins: Nuclear Import of the Thyroid Hormone Receptor alpha
Laura Elizabeth Parente College of William and Mary
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Recommended Citation Parente, Laura Elizabeth, "The Important Importins: Nuclear Import of the Thyroid Hormone Receptor alpha" (2010). Undergraduate Honors Theses. Paper 712. https://scholarworks.wm.edu/honorstheses/712
This Honors Thesis is brought to you for free and open access by the Theses, Dissertations, & Master Projects at W&M ScholarWorks. It has been accepted for inclusion in Undergraduate Honors Theses by an authorized administrator of W&M ScholarWorks. For more information, please contact [email protected]. The Important Importins: Nuclear Import of the Thyroid Hormone Receptor α
A thesis submitted in partial fulfillment of the requirement for the degree of Bachelor of Science with Honors in Biology from the College of William and Mary in Virginia
By
Laura Elizabeth Parente
Accepted for Honors
______Dr. Lizabeth Allison, Director
______Dr. Diane Shakes
______Dr. Patty Zwollo
______Dr. Rowan Lockwood
Williamsburg, Virginia
April 28, 2010 Abstract
The thyroid hormone receptor α1 (TRα) is a nuclear receptor for the thyroid hormone, and acts to either activate or repress transcription of thyroid hormone-responsive genes. While TRα carries out its function as a transctiption factor in the nucleus, TRα actually shuttles rapidly between the nucleus and cytoplasm. An important aspect of this shuttling activity, and to its role as a nuclear transcription factor, is the process by which TRα is imported into the nucleus. Previous research has shown that in mammalian cells, TRα’s nuclear import follows a signal-mediated pathway that is temperature and energy- dependent, and can be mediated in vitro by importins α1 and β. This thesis research investigated which importins are able to mediate nuclear import of TRα in HeLa (human) cells in vivo. RNA interference (RNAi) was used to knock down the expression of individual importins in the cells, after which the subcellular localization of TRα was examined for any changes. RNAi knockdown was validated using reverse transcriptase real-time PCR (RT-qPCR). Total RNA was purified from RNAi-treated cells, and was shown to be of high quality through UV spectrophotometry and electrophoresis. This RNA was then reverse-transcribed into cDNA and analyzed by qPCR. RT-qPCR was used to evaluate eight importins (Imps β, α1, α2, α3, α4, α5, α6, and Ipo 7) for knockdown of their corresponding mRNA levels following transfection with importin-specific RNAi. The data show that RNAi knockdown was validated, with seven out of the eight importins showing knockdown at levels of 70% or higher. RNAi import assays were used to directly evaluate the role of seven individual importins (Imps β, α1, α2, α3, α4, α5 and Ipo 7) in mediating TRα’s nuclear import. The qPCR results confirmed that importin-specific short hairpin RNA (shRNA) molecules effectively reduced the level of those importins in the cell. Following RNAi treatment, cells were examined by fluorescence microscopy to evaluate the subcellular localization of TRα. A shift from the normal, primarily nuclear localization of TRα to a more whole-cell distribution was seen as evidence of the RNAi-targeted importin mediating TRα import. Knockdown of Ipo 7 resulted in the most significant cytoplasmic shift of TRα, suggesting that TRα’s nuclear import is mediated primarily by Ipo 7. Knockdown of Imp β caused the second most significant shift in TRα distribution, indicating that Imp β also plays a substantial role in TRα’s nuclear import. The importin α isoforms varied in the extent to which they were able to mediate TRα import, with the data indicating that Imp α1 is the main adaptor importin acting with Imp β to import TRα into the nucleus. Imp α3 also appears to play a lesser role in TRα’s import. Taken together, the data presented in this thesis research suggest that TRα utilizes multiple import pathways, with import facilitated primarily by Ipo 7 and Imp β/Imp α1, and potentially Impβ/Imp α3 to a lesser extent. Table of Contents
Figures & Tables ...... iii
Acknowledgements ...... iv
General Introduction ...... 1 Thyroid Hormone Receptor ...... 2 Thyroid Hormone ...... 2 Thyroid Hormone Receptor ...... 4 TRα Structure ...... 5 TRα Regulatory Activity ...... 7 Nucleocytoplasmic Transport ...... 9 Nuclear Import ...... 9 Nuclear Pore Complex ...... 10 Nucleocytoplasmic Transport Cycle ...... 11 Ran Gradient ...... 14 Importins ...... 15 Imp β and β-like importins ...... 15 Imp α ...... 19 Study of Importins ...... 24 Nuclear import of TRα ...... 25 Import of Nuclear Receptors ...... 25 TRα’s nuclear import pathway ...... 26 Gene Expression Analysis using RNAi and qPCR ...... 28 RNAi ...... 28 Mechanism ...... 29 Study of Gene Function ...... 31 RNAi in the study of nuclear import ...... 34 Real-time quantitative PCR ...... 35 Mechanism ...... 36 Gene Expression Analyses ...... 39 Thesis Objectives ...... 40
Methods ...... 44 Plasmids ...... 44 Cell Culture ...... 45 Transient Transfection ...... 45 Fluorescence Microscopy ...... 46 Cell Scoring and Statistical Analysis ...... 46 RNA Purification and Quality Analysis ...... 47 qPCR ...... 48
Results ...... 50 Real-time PCR Validation of RNAi knockdown ...... 50 RNA Quality Control ...... 50
i qPCR Quality ...... 51 Gene Expression Ratios Confirm Knockdown ...... 54 Import Assays ...... 61 Importin β mediates TRα nuclear import ...... 62 Analysis of alpha importins ...... 65 Ipo 7 appears to play the greatest role in TRα nuclear import ...... 68
Discussion ...... 71 TRα nuclear import is mediated mainly by Ipo 7 ...... 71 Factors affecting accuracy of data collected ...... 72 Support for multiple import pathways of TRα...... 74 Substrate specificity of importins and the importin α family ...... 79 Appropriateness of the Livak method for qPCR analysis ...... 81 Future Directions ...... 86 Significance and Conclusions ...... 88
Appendix ...... 90
References ...... 132
ii Figures & Tables
Figure 1. Nucleocytoplasmic transport of proteins
Figure 2. Domain structure of Imp β and Imp α
Figure 3. Importin α families
Figure 4. Mechanism of RNA interference using short hairpin RNA (shRNA)
Figure 5. Real-time PCR amplification and detection
Figure 6. Overall methods
Figure 7. BioAnalyzer data indicates high-quality RNA
Figure 8. qPCR runs did not show environmental or genomic contamination
Figure 9. The Livak method
Figure 10. RQ Data confirms importin knockdown
Figure 11. Scoring categories for TRα localization
Figure 12. Importins vary in their ability to mediate TRα import
Figure 13. Knockdown of Imp β, Imp α1, and Imp α3 cause an increase in the percentage of cells with whole-cell distribution of TRα
Figure 14. Knockdown of Ipo 7 causes an increase in the percentage of cells with whole-cell distribution of TRα
Table 1. Importins in the β-Karyopherin family and their cargo proteins
Table 2. Importin α isoforms and their cargo proteins
Table 3. NanoDrop data indicates high-quality RNA
Table 4. Proteins with Multiple Import Pathways
iii Acknowledgements
I would like to first thank Dr. Allison for her help and guidance these past three years, and for giving me the opportunity to work on such an amazing project. She has been unparalleled as a research advisor, and under her direction I’ve developed a love for research. Without her support, this thesis would not have been possible. I would also like to thank my committee members, Dr. Zwollo, Dr. Shakes, and Dr. Lockwood, for their support during the writing of this thesis.
Special thanks goes to the members of the Awesome Allison Lab, especially Wes, Jim, Michelle, and John. I couldn’t have asked for a better lab atmosphere or group of friends. I’d like to thank Vinny Roggero for his constant guidance and patience, and for letting me take on a project that continues his research. Finally, I would like to thank my family for their love and support.
iv General Introduction
The thyroid hormone is a tyrosine-based hormone that affects a wide
range of tissues, and has important effects on metabolism, growth, and
development. These effects are mediated through hormone binding to the thyroid
hormone receptor (TR) in the nucleus. TR is a transcription factor in the nuclear
receptor superfamily that can either activate or repress transcription of thyroid-
hormone responsive genes depending on its liganded state. As a transcription factor, TR carries out its function through binding DNA in the nucleus; however, research has shown that TR shuttles between the nucleus and the cytoplasm and therefore must cross the nuclear membrane via nuclear pore complexes
(NPCs). An important aspect of this shuttling, and to its role as a nuclear transcription factor, is the process by which TR is imported into the nucleus from the cytoplasm.
Nuclear import of small molecules (less than 40 kD) occurs by passive diffusion, while import of larger molecules requires the use of an active, signal- mediated pathway (Stewart, 2007). At about 46 kD, TR is close to the size limit, and previous work has shown that TR import in Xenopus oocytes can proceed using both of these mechanisms (Bunn et al., 2001). Subsequent research in mammalian cells demonstrated that TR utilizes an energy-dependent, signal- mediated import pathway that requires soluble transport factors to gain entry into the nucleus (Roggero, 2008). These soluble transport factors are known as importins, which bind to a cargo protein and facilitate translocation of the cargo
through the NPC by interacting with particular NPC proteins. The specific
1 importins that are able to mediate TR import are still unknown. TR’s nuclear
import is an important aspect of the regulation of the thyroid hormone system,
and it is important to understand all levels of thyroid hormone regulation given its
physiological significance.
The primary objective of this thesis was to identify which importins
mediate nuclear import of TR, using an in vivo methodology in mammalian cell
lines. The following introduction will provide background information on TR and
nucleocytoplasmic transport, as well as an explanation of the main methods used
and the specific objectives of this thesis.
Thyroid Hormone Receptor
Thyroid Hormone
The thyroid hormones (TH), thyroxine (T4) and triiodothyronine (T3), are tyrosine-based hormones produced by the thyroid gland (Cheng et al., 2010).
The thyroid hormones are synthesized by a hormone cascade that begins when metabolic signals signal the hypothalamus to produce and secret thyrotropin- releasing hormone (TRH) into the blood (Cheng et al., 2010; Sugrue et al., 2010).
TRH then travels to the pituitary gland, where it positively regulates the production of thyrotropin (TSH) (Vella and Hollenberg, 2009). TSH in turn stimulates the thyroid gland to produce the thyroid hormones (Chiamolera and
Wondisford, 2009). Most TH secreted by the thyroid gland is in the form of T4, while T3 is secreted to a much lesser extent (Gereben et al., 2008b). Free T3 in
2 circulation also takes part in a negative feedback loop that downregulates the
production of TRH and TSH, thereby limiting the secretion of the thyroid
hormones (Chiamolera and Wondisford, 2009).
Most of the thyroid hormone circulating in the blood is bound to transport
proteins such as thyroxine-binding globulin (TBG) (Gereben et al., 2008a;
Gereben et al., 2008b). The major form of thyroid hormone in the blood is T4,
which is stable but relatively inactive. T4 is converted to T3 in body tissues by
deiodinases, enabling the hormone to carry out its physiological effects (Gereben et al., 2008a; Gereben et al., 2008b; Koenig, 2003). The thyroid hormone system affects nearly every system in the body, including endocrine, metabolic,
gastrointestinal, reproductive and cardiopulmonary, as T3 is a physiologically
significant hormone with many important roles (Bernal and Refetoff, 2008; Cheng
et al., 2010; Machado et al., 2009; Oetting and Yen, 2007). T3 functions to
maintain homeostasis in adult mammals in response to changing environmental
conditions (Klieverik et al., 2009). T3 activity is also crucial to the growth and
development of many animal species, regarding both fetal and adult development (Bernal and Refetoff, 2008; Huang et al., 2008; Zhang and Lazar,
2000). For example, neonatal brain development depends in large part upon the
correct availability of TH in the CNS (Ahmed et al., 2008). The thyroid hormone also functions to increase sympathetic activity along with catecholamines and is an important regulator of cardiac function, cardiac contractility, and heart rate
(Galli et al., 2010; Klein and Ojamaa, 2001; Mittag et al., 2010).
3 Thyroid Hormone Receptor
These physiological effects of TH are mediated through the interaction of
TH with its cellular receptors, known collectively as thyroid hormone receptors
(TRs) (Yen et al., 2006). TRs are members of the nuclear receptor superfamily, which share common structural characteristics key to their regulatory function
(Aranda and Pascual, 2001; Tsai and O'Malley, 1994). There are two main forms of TR, TRα and TRβ, encoded by two genes located on human chromosomes 17 and 3 respectively (Davis et al., 2008b). Both TRα and TRβ exist in two major isoforms. TRβ’s isoforms occur through multiple promoter usage, while TRα’s occur through alternative splicing of a common mRNA transcript under the control of a single promoter (Oetting and Yen, 2007). This thesis work focuses on
TRα1, which, like both the TRβ forms, is capable of binding target DNA and its ligand T3. TRα2 is unable to bind T3 and can only weakly associate with DNA due to an amino acid substitution within the carboxyl terminal (Moore and Guy, 2005).
TRα1 (hereafter referred to as TRα for simplicity) binds DNA at regulatory elements within the promoters of TH-responsive genes known as thyroid hormone response elements (TREs) and activates or represses transcription of target genes in relation to the state of ligand binding (Bonamy et al., 2005; Davis et al., 2008). Generally, TRα bound to T3 will activate transcription of target genes while unbound TRα will repress transcription, but in certain cases ligand- bound TRα will repress transcription while unbound TRα will activate transcription (Apriletti et al., 1998; Oetting and Yen, 2007).
4 TRα Structure
TRα is composed of four domains: an amino-terminal transactivation (A/B)
domain, a DNA-binding domain (C domain), a small hinge region (D domain) and
a carboxy-terminal ligand binding domain (E domain) (Bain et al., 2007; Shank
and Paschal, 2005). Each domain serves a unique function important to TRα
activity, as well as providing regions for interaction with positive and negative
regulatory factors and components of the basal transcriptional machinery.
The N-terminal transactivation domain is the least conserved and most
variable region (Cheng, 2005a). It plays a role in transcriptional activation, and
research has also shown involvement in coregulator binding (Iwasaki et al.,
2006), as well as the recruitment of coregulators to additional TRα domains (Tian
et al., 2006). The high level of amino acid variation in the transactivation domain
sequence likely corresponds to its role in coregulator interaction, suggesting that
this region may be important in regulating transcription in response to changing
environmental conditions (Aranda and Pascual, 2001). In addition, this domain
has been found to contain a novel nuclear localization sequence (NLS) that
targets TRα for nuclear import (M.S. Mavinakere and L.A. Allison, manuscript in
preparation). The fact that this region contains an NLS is particularly important
since this thesis research focuses on nuclear import of TRα.
The central DNA-binding domain (DBD) is the most highly conserved domain with about 88% sequence similarity among TR isoforms (Laudet and
Gronemeyer, 2002). The region responsible for binding TREs is formed by two zinc finger motifs connected by a short DNA recognition helix (Aranda and
5 Pascual, 2001). The P box of the first zinc finger contains amino acids that interact with nitrogenous bases and phosphate groups on the major groove of target TREs, facilitating TRα-DNA binding (Oetting and Yen, 2007). The D box of the downstream zinc finger binds to the minor groove of the TREs. In addition, the DBD also contains regions that enable TRα to stably bind to the retinoid X receptor (RXR), a nuclear receptor which commonly dimerizes with TR
(Khorasanizadeh and Rastinejad, 2001; Rastinejad et al., 1995). This dimer formation strongly enhances TRα’s ability to bind to specific DNA sequences and contributes to the specificity of TRα (Tagami et al., 2009; Velasco et al., 2007).
Much of TRα binding to TREs is thought to occur through TRα:RXR heterodimerization, although TRα monomers and TRα:TRα homodimers are both able to bind TREs as well (Collingwood et al., 1997; Figueira et al., 2007).
The hinge region connects the DBD’s C-terminal end to the ligand-binding domain (Nascimento et al., 2006). The hinge region notably contains a lysine-rich sequence which extends into the N-terminus of the DBD. This sequence acts as an NLS, recognizing and binding karyopherin proteins involved in nuclear import
(Zhu et al., 1998). The hinge region is also likely to play some role in binding of transcriptional coregulators (Nascimento et al., 2006; Reutrakul et al., 2000).
The primary function of the C-terminal ligand-binding domain (LBD) is to bind T3. Discontinuous stretches of amino acids along the length of the domain
form a hydrophobic ligand-binding pocket (Apriletti et al., 1998). This pocket is
able to bind both T3 and T4, although the larger TRα-T4 complex is less stable
than the TRα-T3 complex. The binding of T3 causes a conformational change that
6 encloses the hormone inside the hydrophobic helices of the pocket (Apriletti et
al., 1998; Wagner et al., 1995). This conformational change is important because
it allows coactivators to bind to the receptor-ligand complex and regulate
transcription. In addition to this main function, the LBD is involved in
transcriptional activation (Qi et al., 1995) and dimerization (Tagami et al., 2009).
In addition, the LBD contains multiple nuclear export sequences that facilitate passage of TRα out of the nucleus (M.S. Mavinakere and L.A. Allison, manuscript in preparation).
All of these structural domains allow TRα to interact in some way with a range of different regulatory proteins. These regulatory proteins, in the form of coactivators and corepressors, allow TRα to positively or negatively regulate target genes in a ligand-dependent fashion.
TRα Regulatory Activity
TRα is relatively unique among nuclear receptors in that it can bind target genes regardless of whether it is bound to its ligand (Chassande, 2003; Lazar,
1993). Most TRα binding occurs at positive TREs, which are activated by ligand- bound TRα (Aranda and Pascual, 2001). Gene activation or repression depends both on the ligand state and on the presence of coregulatory proteins.
When TRα binds a positive TRE in the absence of T3, transcription is
repressed. This is due to an active corepressor site present in TRα’s unliganded state, which allows corepressor proteins to associate with TRα (Oetting and Yen,
2007; Sanchez-Pacheco and Aranda, 2003). Corepessors act to prevent the
7 efficient association of the transcriptional machinery with promoter regions
(Chassande, 2003; Moore and Guy, 2005). Two of the corepressor proteins most
extensively studied with TRα are NCoR and SMRT (Astapova et al., 2008; Choi
et al., 2008). While their exact mode of transcriptional repression remains under study, they are known to interact with the hinge region and general transcriptional
machinery (such as TBP and TFIIB), which inhibits the formation of the
transcriptional preinitiation complex (Astapova et al., 2009). In addition, histone
deacetylases (HDACs) also play a role in repressing transcription by removing
acetyl groups from histone N-terminal tails, maintaining tight chromatin coiling
and ultimately preventing the binding of coactivators to DNA (Aranda and
Pascual, 2001; Chassande, 2003; Nagy et al., 1997).
Alternatively, when TRα is bound to T3, binding a positive TRE leads to
activation of TH-responsive genes (Das et al., 2010). T3 binding causes a
conformational change that disrupts the corepressor binding site while simultaneously forming a site for coactivator binding (Moore and Guy, 2005;
Oetting and Yen, 2007). These coactivators include members of the p150 family known as steroid receptor co-activators (SRCs) (Moore and Guy, 2005).
Coactivator recruitment promotes association of the basal transcriptional machinery with the promoter regions of target genes (Huang et al., 2008). In contrast to corepressors, which include HDACs, coactivators often associate with histone acetyltransferases (HATs) (Baumann et al., 2001; Glass and Rosenfeld,
2000). HATs acetylate histone N-terminal tails, which loosens the DNA:histone
8 association and allows more efficient transcription of genes in the acetylated regions (Wolffe et al., 2000).
These regulatory proteins affect TRα’s role as a transcription factor when bound to DNA in the nucleus. As a transcription factor, TRα is primarily localized in the nucleus both in the presence and absence of ligand (Baumann et al.,
2001). While originally thought to reside solely in the nucleus, research has shown that TRα is actually a shuttling protein that crosses rapidly between the nucleus and cytoplasm (Baumann et al., 2001; Bunn et al., 2001; Davis et al.,
2008a; Grespin et al., 2008). This nucleocytoplasmic shuttling adds an additional level to TRα’s control of target gene regulation.
Nucleocytoplasmic Transport
Nuclear Import
TRα carries out its regulatory activity by binding target sequences on
DNA, meaning that it must first gain access to the nucleus from the cytoplasm by crossing the nuclear envelope. The nuclear envelope creates an intracellular compartmentalization that is a hallmark of eukaryotic cells, and physically separates DNA in the nucleus from the translational machinery in the cytoplasm
(Cook et al., 2007; Debler et al., 2008). This compartmentalization enables spatial regulation of gene expression, but also necessitates a method for key molecules to enter and exit the nucleus (Fried and Kutay, 2003; Lange et al.,
2007). The success of a cell is highly dependent on its ability to regulate
9 bidirectional transport across the nuclear membrane. The process by which proteins, RNA, and ribonucleoproteins enter and exit the nucleus is called nucleocytoplasmic transport (Fried and Kutay, 2003; Macara, 2001). This process localizes proteins destined to the nucleus or cytoplasm, and in doing so plays a key role in signal transduction pathways, gene activation or repression,
and in the regulation of major cellular process such as cell cycle progression
(Lange et al., 2007; Stewart, 2007). For this reason, understanding the
mechanisms of nucleocytoplasmic transport is important as any malfunctioning of
this process can lead to pathological conditions and disease (McLane and
Corbett, 2009).
Nuclear Pore Complex
The channels through which import and export occur are called nuclear pore complexes (NPCs) (Dange et al., 2008). The NPC regulates facilitated entry of large macromolecules and passive diffusion of smaller molecules and
ions between the nuclear compartment and the cytoplasm (Alber et al., 2007; Lim
et al., 2007). While the number of NPCs varies considerably across cell type and
species due to differing metabolic and transcriptional needs, the structure of the
NPC is highly conserved and indicates the importance of nuclear transport among eukaryotes (Debler et al., 2008; Elad et al., 2009).
The NPC exhibits 8-fold symmetry and consists of a central core channel with filaments extending into the cytoplasm, as well as nuclear fibrils converging into a basket shape (Dange et al., 2008). The NPC is composed of multiple
10 copies of about 30 different proteins, collectively called nucleoporins (Nups)
(Alber et al., 2007). Nups fall into three categories: transmembrane Nups, which
anchor the NPC in the nuclear envelope, structural Nups, and FG Nups. FG
Nups contain domains with extensive repeats of phenylalanine-glycine (FG),
which act as binding sites for the import machinery and facilitate the actual
translocation of molecules into the nucleus (Isgro and Schulten, 2007; Terry and
Wente, 2009).
Nucleocytoplasmic Transport Cycle
There are two main modes of nucleocytoplasmic transport: passive
diffusion and active transport. Ions, small metabolites, and small molecules less
than about 40 kD can diffuse across the nuclear membrane, while larger
molecules must use an energy-dependent, signal-mediated process for transport
(Lim et al., 2007).
Active nuclear import and export are mediated by a group of soluble
transport proteins collectively called karyopherins, which bind to and enable
macromolecules to reach their target location within the cell (Cook et al., 2007).
Karyopherins are known as either importins or exportins, depending on whether they aid in nuclear import or export. Transport across the nuclear membrane relies on the interaction of these karyopherins with specific amino acid sequences called nuclear localization sequences (NLSs) and nuclear export sequences (NESs) (Lange et al., 2007; Sorokin et al., 2007).
11 NLSs consist of basic, usually lysine or arginine-rich sequences in either one cluster (monopartite) or two clusters separated by 10-12 amino acids
(bipartite) (Lange et al., 2009). NESs are hydrophobic, typically leucine-rich sequences (Fried and Kutay, 2003). NLSs are recognized by importins and target the protein for import, while NESs are recognized by exportins and facilitate export. In both cases, the karyopherin:cargo complex is translocated across the nuclear membrane by interactions with FG repeats in the NPC (Macara, 2001;
Stewart, 2007).
The Beta-karyopherin family is responsible for the majority of nuclear transport and interacts with the NPC to enable translocation (Cook et al., 2007;
Strom and Weis, 2001). Although most nuclear import occurs via direct binding of a Beta-karyopherin receptor to a cargo, some use an adaptor molecule to bind certain proteins. In the classical nuclear import model, importin α (Imp α) acts as an adaptor receptor that facilitates interaction between the cargo protein of interest and Importin β (Imp β) (Lange et al., 2007; Riddick and Macara, 2007). In this way, molecules which cannot directly bind Imp β can still take advantage of its ability to enable passage into the nucleus.
The classical import model involves the interaction of Imp α and Imp β to facilitate entry of the cargo protein (Figure 1). Imp α recognizes and binds to a specific class of NLS motifs, called classical NLSs (cNLS) on the cargo, and then binds Imp β (Lange et al., 2007). The trimeric complex docks to the NPC via Imp
β and translocates into the nucleus by means of hydrophobic interactions between Imp β and the nucleoporin FG repeats (Fried and Kutay, 2003; Riddick
12 β α β
CAS β α α RanGTP
Nucleus
Cytoplasm CAS β β α α
α RanGDP β
Figure 1. Nucleocytoplasmic transport of proteins The classical nuclear import cycle begins in the cytoplasm, where Imp α recognizes and binds the NLS of a cargo protein. Imp α then binds to Imp β, acting as an adaptor protein to create a trimeric import complex. Imp β then mediates the translocation of the whole complex through the nuclear pore complex (NPC) by interacting directly with phenylalanine-glycine repeats in the NPC. Upon RanGTP binding to Imp β in the nucleus, the trimeric complex dissociates and the cargo is released to carry out its function in the nucleus. CAS binding to Imp α in the nucleus facilitates Imp α export, while Imp β is exported to the cyto- plasm complexed with RanGTP. Hydrolysis of RanGTP to RanGDP in the cytoplasm releases Imp β and the importins are then able to be used for another round of import.
(Adapted from Allison, 2007)
13 and Macara, 2007). Once inside the nucleus, Imp β is bound by the small
GTPase RanGTP, the complex dissociates and the cargo is free to carry out its
function in the nucleus (Cook et al., 2007). Since nucleocytoplasmic transport is a continuous process, Imps α and β are then recycled out to the cytoplasm where they can be used for further rounds of import.
Ran Gradient
The success of the nucleocytoplasmic transport cycle depends on Ran, a
24kD GTPase that cycles between binding GTP and GDP (Cook et al., 2007).
GTP hydrolysis by Ran provides the energy required for the transport cycle, while an asymmetric distribution of RanGTP/GDP provides the directionality of the cycle (Lui and Huang, 2009; Riddick and Macara, 2005). Ran is distributed such that the nucleus has a high concentration of RanGTP while the cytoplasm contains a high concentration of RanGDP (Clarke, 2008). When the trimeric importin:cargo complex enters the nucleus, RanGTP binds Imp β and causes a conformational change in Imp β that dissociates Imp β from Imp α (Lui and
Huang, 2009; Stewart, 2007). The cargo protein is then displaced from Imp α,
and importins β and α are each exported out of the nucleus complexed with
RanGTP. Once in cytoplasm, RanGTP is hydrolyzed to RanGDP, which releases
the importins to be used again in another cycle of import (Lui and Huang, 2009).
Since Ran has a low intrinsic activity regarding nucleotide exchange and
GTP hydrolysis, this crucial RanGTP/GDP gradient between the cytoplasm and
nucleus is maintained by compartment-specific nucleotide exchange factors
14 (Hutten et al., 2008). When RanGTP is exported out of the nucleus with
importins, Ran GTPase-activating protein (RanGAP) stimulates GTP hydrolysis
(Hutten et al., 2008). Cytoplasmic RanGDP binds nuclear transport factor-2
(NTF2) and is imported into the nucleus (Quimby et al., 2000). There, the Ran guanine nucleotide-exchange factor (RanGEF), which exists associated with
chromatin histones, catalyzes nucleotide exchange to generate RanGTP
(Lonhienne et al., 2009).
Importins
Karyopherins, which include both importins and exportins, are a
conserved family of mobile transport proteins that mediate the bidirectional
trafficking of macromolecules across the nuclear envelope. The karyopherin-Beta
family is responsible for the majority of nucleocytoplasmic transport in a cell
(Pemberton and Paschal, 2005; Stewart, 2007; Strom and Weis, 2001).
Imp β and β-like importins
The human genome encodes over 22 β-karyopherins (Strom and Weis,
2001). They are characterized by their ability to bind cargo directly as well as
indirectly via an adaptor karyopherin (Cingolani et al., 2002; Palmeri and Malim,
1999). The β-karyopherins are also directly responsible for mediating
translocation through domains that bind FG repeats in the NPC (Chook and
Blobel, 2001; Otsuka et al., 2008). While there are many models as to how
15 exactly this interaction works, it is clear that there is a substantial amount of
FG:importin interaction occurring somehow in the central channel of the NPC
(Frey and Gorlich, 2007).
Importin β1 (Importin β) is the most well-studied member of the
karyopherin-β family and is encoded by only one gene in humans
(Mosammaparast and Pemberton, 2004). However, there are multiple importin β-
like receptors that compose the rest of the family, including Importin 7, Importin
13, and transportin (Table 1) (Pemberton and Paschal, 2005; Strom and Weis,
2001).
Imp β is a 97 kD superhelix of 19 HEAT repeats that assumes different
conformations in different functional states (Figure 2A) (Zachariae and
Grubmuller, 2008). A HEAT repeat unit consists of a hairpin made of two β-
helices connected by a linker region (Fried and Kutay, 2003; Strom and Weis,
2001). Imp β’s outer surface contains hydrophobic FG-repeat binding pockets located in an N-terminal/central region of Imp β (repeats 4-8) (Chook and Blobel,
2001; Mosammaparast and Pemberton, 2004). The N-terminal domain (HEAT erepeats 1-8) binds RanGTP, while the C-terminal domain binds cargo or Imp α
by coiling around the α-helical IBB domain (HEAT repeats 7-19) (Lott et al.,
2010). HEAT repeat 8 also contains an “acidic loop” that forms part of a mutually
exclusive binding site, selectively accepting either cargo or RanGTP as a way to
mediate substrate release (Conti et al., 2006). RanGTP binding Imp β in the nucleus causes a conformational change, resulting in dissociation of the import
complex and cargo release (Lott et al., 2010; Strom and Weis, 2001).
16
Table 1. Importins in the β-Karyopherin family and their cargo proteins
IBB domain of Imp α, RDRP NS5 (dengue virus) Rex (human T-cell leukemia virus type 1), ribosomal proteins, Imp β1 snurportin, cyclin B1, Smad3, PTHrP
Imp 4 histones, ribosomal proteins, Transition Protein 2
Imp 5 histones, ribosomal proteins
HIV Rev, HIV RTC, glucocorticoid receptor (GR), Imp 7 ribosomal proteins
Imp 8 SRP19
Imp 9 histones, ribosomal proteins
Imp 11 UbcM2, rpL12
Imp 13 SUMO-conjugating enzyme hUBC9, RBM8, Pax6
ribosomal proteins, c-Fos, HIV integrase, CyPA, histones, Transportin hnRNP proteins (A1, F) Transportin SR SR domain proteins
Transportin SR 2 SR domain proteins
Transportin 2 HuR
17 A
Acidic Importin β1 Loop
19 HEAT repeats N- 8 -C
RanGTP NPC Cargo
B Importin α 10th ARM Auto inhibition ARM repeats repeat › N- KRR -C Importin-β CAS NLS-cargo binding binding binding
Figure 2. Domain structure of Imp β and Imp α A. Imp β. Imp β is composed of 19 HEAT repeats, each of which consists of two β-helices connected by a short turn. In HEAT 8, this turn is replaced by an acidic loop that regulates substrate binding and release. HEAT repeats 1-8 bind RanGTP in the nucleus of cells. Cargo proteins or the IBB domain of Imp α bind HEAT repeats 7-19. Interaction with the NPC involves HEAT repeats 4-8. B. Imp α. Imp α is composed of a flexible N-terminal domain and a highly structured domain consisting of ten ARM repeats. The hydrophobic ARM repeats form a twisted structure that creates specific binding pockets for a cargo’s NLS The N-terminal domain is the Imp-β -binding (IBB) domain, and also contains an autoinhibitory region that interacts with the NLS-binding groove to inhibit cargo binding. The exportin CAS binds to the tenth ARM repeat to facilitate the export of Imp α from the nucleus.
Adapted from Strom and Weiss, 2001; and Goldfarb et al., 2004
18 The flexibility of Imp β’s solenoid structure allows it to switch between a cytoplasmic and nuclear conformation, alternately binding and releasing its cargo depending on Ran and cargo binding (Conti et al., 2006; Sorokin et al., 2007).
This also lets Imp β and β-like karyopherins bind different substrate cargo proteins, explaining how a limited number of importins can transport a large number of different proteins. The other members of the β-karyopherin family share the same general structure and characteristics, but differ in their helical pitch, overall shape, and in the way they bind different cargo (Conti et al., 2006;
Pemberton and Paschal, 2005). Only Imp β is known to interact with the importin
α adaptors (Lange et al., 2007; Stewart, 2007). As will be explained later, the interaction of Imp β with various importin α adaptors will be a major focus of this thesis.
Imp α
The alpha importins act as adaptor proteins, binding both cargo and Imp
β. The human genome encodes at least six importin α isoforms: α1, α3, α4, α5,
α6, and α7 (Friedrich et al., 2006). Importin α8 has been recently discovered in cattle and has been found to have a human orthologous gene with 75% sequence identity (Tejomurtula et al., 2009). The alpha importins are classified into three groups based on sequence homology, which share about 50% sequence similarity (Figure 3) (Kohler et al., 1999). The first group consists of
Imp α1 alone, since its most closely related homologue, Imp α2, has been characterized in Xenopus and Drosophila but has not been found in mammals
19 Importin α1
50%
44%
Importin α5 47% Importin α3
80% 87% 85%
Importin α6 84% Importin α7 Importin α4
Figure 3. Importin α families. In mammals, six importin α isoforms exist and are separated into three families based on sequence identity. The numerical value indicates percent homology.
Adapted from Yoneda, 2000.
20 (Quensel et al., 2004). The second group includes Imp α3 and Imp α4, while
Imps α5, α6, and α7 compose the third subfamily (Friedrich et al., 2006;
Tejomurtula et al., 2009). Within a group, the sequence similarity increases to
80% or more. Each importin α isoform is responsible for binding to and facilitating import of several different cargo proteins in conjunction with Imp β (Cook et al.,
2007; Goldfarb et al., 2004; Lange et al., 2007).
Analysis of Imp α mRNA and protein expression in different tissues shows that the alpha importins show cell and tissue-specific expression patterns, with levels of each isoform varying among tissues depending on tissue-specific needs
(Friedrich et al., 2006). However, all isoforms can be found at some level in the various tissues, with the exception of importin α6 which is limited to the testis
(Kohler et al., 1999; Sorokin et al., 2007). The existence of at least six distinct importin α isoforms in mammals, including humans, implies that different isoforms could recognize unique target cargoes. While the six characterized alpha importins share significant homology, research utilizing many experimental approaches has clearly shown that there are in fact distinct substrate specificities for each importin α isoform (Table 2) (Friedrich et al., 2006; Kohler et al., 1999;
Miyamoto et al., 2002; Quensel et al., 2004). It is thought that the characteristics of a protein’s NLS along with its structural features as a whole contribute to its high specificity for a particular alpha importin isoform (Friedrich et al., 2006). As a result, the alpha importins differ in their functional relevance for any particular protein’s nuclear import pathway.
21
Table 2. Importin α isoforms and their cargo proteins
Imp α1 cNLS, BRCA1, p53, HIV Integrase
Imp α3 cNLS, RCC1, RNA helicase A, RanBP3, DNA helicase Q1, p53
Imp α4 cNLS, NF-kB
Imp α5 cNLS, STAT-1, STAT-3
Imp α6 unknown (testis specific)
Imp α7 STAT-1, STAT-3
22 The alpha importins are 58 kD proteins that consist of a flexible N-terminal importin-β-binding (IBB) domain and a highly structured domain consisting of ten tandem armadillo (ARM) repeats (Figure 2B) (Chook and Blobel, 2001; Stewart,
2007). Each ARM repeat is constructed from three α-repeats connected by loops
(Goldfarb et al., 2004). The tenth ARM repeat on the C-terminal region includes a binding site for cellular apoptosis susceptibility (CAS) protein, an export factor in the Imp β-like family. The hydrophobic ARM repeats located between the IBB domain and the CAS binding site form a twisted structure which serves as the cNLS binding pocket (Goldfarb et al., 2004; Lange et al., 2007). A protein’s cNLS binds to two sites within a helical surface groove of the ARM repeats; ARM repeats 2-4 comprise the N-terminal NLS binding site, while ARM repeats 7-9 act as the C-terminal NLS binding site (Lange et al., 2009; Miyamoto et al., 2002).
The IBB domain plays a dual regulatory role because it interacts in trans with Imp β and in cis with the cNLS-binding groove (Goldfarb et al., 2004;
Harreman et al., 2003). When Imp α is not bound to Imp β, the KRR residues in the IBB domain interact with the cNLS-binding pocket, acting as a competitive inhibitor to regulate cargo binding (Cardarelli et al., 2009; Stewart, 2007). Imp α could interact first with Imp β to free the NLS binding pocket, or with the cargo protein first to release the IBB domain to bind Imp β, or the two events could occur in a concerted fashion (Goldfarb et al., 2004). It has also been recently proposed that the IBB domain controls the degree of tertiary structure strain of
Imp β, which regulates the affinity of the trimeric import complex for the FG repeats of the NPC (Lott et al., 2010).
23 The short, acidic CAS domain facilitates nuclear export of Imp α. Once the
Imp α/β/cargo complex has dissociated upon entering the nucleus, Imp α binds
CAS and RanGTP (Sorokin et al., 2007). CAS then mediates importin α’s passage out of the nucleus, so it can bind more substrate and be used again in further import cycles (Chook and Blobel, 2001).
Study of Importins
Mechanisms of nucleocytoplasmic transport have been a focus of much research in recent years (Cook et al., 2007; Mosammaparast and Pemberton,
2004; Riddick and Macara, 2007). The number of studies investigating the import pathways of different proteins has grown, and increasingly, cargoes are being matched with their importin (Miki et al., 2009; Sorokin et al., 2007; Strom and
Weis, 2001). The study of nuclear import is complex, and trying to determine specific importin:cargo pairs is particularly difficult for a number of reasons. There are a large number of β karyopherins and Imp α adaptors, and new importins continue to be discovered (Kohler et al., 1999; Strom and Weis, 2001;
Tejomurtula et al., 2009). Each karyopherin transports many proteins, and some proteins can be recognized by several different karyopherins (Mosammaparast and Pemberton, 2004; Muhlhausser et al., 2001). In addition, Imp-independent nuclear import pathways exist, and non-conventional nuclear transport mechanisms, such as calmodulin-mediated import, continue to be discovered
(Fabbro and Henderson, 2003; Wagstaff and Jans, 2009). Finally, the substrate specificity of the alpha importins suggests that each Imp α isoform plays a role in
24 only certain specific cellular pathways, as mentioned previously and illustrated in
Table 2.
Nuclear import of TRα
Import of Nuclear Receptors
This research focuses specifically on the import of TRα. A key aspect that must be considered in experiments characterizing the importins that mediate nuclear receptor import is that of NLSs. Clearly, nuclear receptors of a certain size must contain an NLS. However, nuclear receptors are not limited to one
NLS, a factor which complicates the study of their nuclear import. In fact, nuclear receptors generally have several NLSs found in different domains (Pemberton and Paschal, 2005). For example, the glucocorticoid receptor (GR) has two
NLSs, and the estrogen receptor (ER), mineralcorticoid receptor (MR), androgen receptor (AR), and progesterone receptor (PR) have all been shown to contain one or more NLS (Freedman and Yamamoto, 2004; Kaku et al., 2008; Savory et al., 1999; Shank and Paschal, 2005; Ylikomi et al., 1992). In addition, these
NLSs exhibit differences in import function depending on the presence or absence of the ligand. Ligand binding can induce conformational changes in the
NLS, further varying its import characteristics.
Until recently, there has been little research into determining exactly which importins interact with each nuclear receptor to facilitate nuclear import.
Research has shown that GR is imported by both Ipo 7 and Imp α/β, and that the
25 androgen receptor (AR) is imported via the Imp α/β complex as well (Freedman and Yamamoto, 2004; Kaku et al., 2008). In addition, the retinoid X receptor
(RXR) interacts with Imp β, while the vitamin D receptor (VDR) is associated with
Imp α1 (Yasmin et al., 2005). The import mechanisms used by other nuclear
receptors are still under study. The specific importins able to mediate TRα remain
unknown, although past work has shown that Imp α1 and Imp β are able to
facilitate import in an in vitro system (Roggero, 2008).
TRα’s nuclear import pathway
The TRα protein has a size of 46kD, meaning that it is close to the size limit differentiating passive and active nuclear transport through the nuclear pore complex. Previous research using Xenopus oocytes has shown that in these highly specialized cells, TRα is capable of gaining entry to the nucleus in two coexisting but distinct ways: a passive diffusion pathway and a signal-mediated pathway (Bunn et al., 2001). However, there is no evidence for passive diffusion in mammalian cells. Consistent with the idea of a signal-mediated pathway in mammalian cells is that fact that TRα does in fact contain an NLS. Previous research suggests that the NLS located in the hinge region of TRα is a classical
NLS (Lee and Mahdavi, 1993). Nuclear import from a classical NLS indicates import is mediated by the Imp α/β import pathway. Recently, it has been shown that TRα actually contains two NLSs, the original cNLS in the hinge domain, and a newly discovered second NLS in the A/B transactivation domain (M.S.
Mavinakere and L.A. Allison, manuscript in preparation). While this study
26 provided in vivo evidence for an active import pathway, it was possible that these
results were cell or species-specific due to the use of specialized Xenopus cells.
Subsequently, research was done using permeabilized cell in vitro nuclear import assays to determine the general mechanism of TRα nuclear import in mammalian cells (Roggero, 2008). Using human HeLa cells, three criteria were examined to provide insight into the requirements of TRα’s active nuclear import pathway: requirement of soluble factors, temperature dependence, and energy dependence. It was found that TRα import requires one or more soluble factors, since the presence of cytosol was necessary to achieve nuclear entry. Second, it was shown that TRα import is temperature-dependent, since nuclear import was inhibited by chilling to 4°C versus the normal temperature of 30°C. Third, to test for energy dependence, apyrase treatment was used to hydrolyze and deplete
ATP (and therefore GTP). This was found to strongly inhibit nuclear import, clearly showing that the TRα import pathway is indeed dependent on metabolic energy. Finally, to characterize the soluble factors required for nuclear import, recombinant proteins were used to rebuild the components of the import pathway. An import reaction mixture containing RanGDP, NTF2, Imp α1, and Imp
β, along with a RanGDP energy regeneration system was able to mediate TRα
nuclear import.
Taken together, this research shows that in mammalian cells, TRα follows a signal-mediated import pathway (Roggero, 2008). TRα import is temperature and energy-dependent, and classical import factors (Imps α1/β) are able to facilitate TRα import. While these experiments were carried out in human cells,
27 the methodology leaves open the possibility that results are influenced by the controlled in vitro environment (Fried and Kutay, 2003). Regardless, the precise import pathway used by TRα remains unknown. As mentioned previously, there are multiple nuclear import pathways mediated by a variety of karyopherins. TRα could use a nonclassical pathway where TRα recognition and import are mediated by Imp β or a β-like importin alone. Even if TRα is thought to follow a classical import pathway, as this previous research suggests, human cells contain at least 6 importin α isoforms that interact with Imp β. The substrate specificity of each isoform means it is highly likely that TRα would only be recognized by one or a small subset of importin α isoforms. While it is known that
Imp α1/Imp β can facilitate the nuclear import of TRα, the question remains as to whether this is the main import pathway followed by TRα in vivo. In addition, at this point it is unknown which of the other importin α adaptors, if any, are able to mediate TRα import in vivo.
Gene Expression Analysis using RNAi and qPCR
RNAi
RNA interference (RNAi) is a technique commonly used to examine intracellular protein function, and is utilized in this research to test the role of individual importins in the nuclear import pathway of TRα. RNAi is a natural regulatory mechanism for sequence-specific gene suppression through mRNA knockdown (Obbard et al., 2009). Knockdown is the reduction of a specific
28 mRNA in a cell as compared to a control cell, which means fewer mRNAs are available for translation of the corresponding protein. The conserved pathway uses short pieces (~21 nt) of dsRNA, termed short interfering RNAs (siRNAs), to induce degradation of complementary mRNAs, thus preventing their expression
(Aagaard and Rossi, 2007).
Mechanism
RNAi as a specific mechanism for downregulating gene expression was first described in 1998, and knowledge of its mechanistic aspects has progressed rapidly since then (Fire et al., 1998). RNAi effects post-transcriptional gene suppression in the cytoplasm of cells. The basic mechanism involves short dsRNA molecules binding to the complementary sequence of mRNA, resulting in that mRNA’s degradation and thus inhibition of protein synthesis (Figure 4).
Endogenous dsRNA is cleaved by the RNAseIII endonuclease Dicer to form siRNAs, the effector molecules of RNAi that direct sequence-specific gene silencing (Hannon, 2002; Macrae et al., 2006; Scherr and Eder, 2007). siRNAs are RNA duplexes of 21–23 nucleotides that contain a characteristic two nucleotide 3' overhang at the end of each strand (Kim and Rossi, 2009; Sandy et al., 2005). These overhangs allow the siRNAs to assemble with cellular proteins into the RNA-induced silencing complex (RISC), which includes Dicer, the
“slicing” protein Ago-2, and TAR RNA-binding protein (TRBP) (Castanotto and
Rossi, 2009; Wang et al., 2009a). RISC unwinds the siRNA to expose its antisense strand, which directs the specificity of corresponding target mRNA recognition through intermolecular base pairing (Wang et al., 2008).
29 shRNA Plasmids
ne bra em l M el ne C bra em M r a le c u N
Dicer Cleavage
SiRNAs RISC Complex RISC
mRNA
mRNA Cleavage
Figure 4. Mechanism of RNA interference using short hairpin RNA (shRNA) shRNAs are expressed from plasmid vectors and are cleaved into the effector siRNA molecules by the RNase Dicer in the cytoplasm. siRNAs are dsRNA molecules 21–23 nucleotides in length that contain characteristic two nucleotide 3' overhangs, which are recognized by the RISC complex. The RISC complex unwinds the siRNA to expose its antisense strand, which binds to target mRNA via complementary base pairing. The RISC Ago-2 protein then cleaves the mRNA, preventing expression of the corresponding protein in the cell. In this figure, 4 shRNA plasmids are expressed, each targeting a different sequence of the same mRNA.
30 Target mRNA is cleaved by the RISC Ago-2 protein, a member of the
highly conserved Argonaut protein family. Of the ubiquitously expressed Ago
subfamily members, in humans only Ago-2 exhibits slicing endonuclease activity
(Liu and Li, 2004; Wang et al., 2009b). Ago proteins and Dicer both possess a
PIWI-Argonaute-Zwille (PAZ) domain, which recognizes the 2-nt 3’ overhangs on siRNAs (Kim and Rossi, 2009; Sashital and Doudna, 2010). The single-stranded antisense (or guide) strand of the siRNA targets mRNA for this direct, sequence- specific cleavage that results in mRNA degradation. RISC can direct multiple rounds of mRNA slicing between the 10th and 11th base relative to the 5’ end of
the antisense siRNA strand (Kim and Rossi, 2009; Wang et al., 2009b).
RNAi-induced knockdown of a target gene results in fewer mRNA
sequences, preventing expression of the product protein and causing a reduction
of the level of that protein in the cell (Scherr and Eder, 2007). The proteins
already present in the cell, having been translated prior to RNAi treatment, will degrade over time according to their individual half-lives. This results in an overall reduction of a specific protein in the cell, and the consequences of that reduction can then be studied. Altered cellular activities can be attributed to the suppression of the target gene (Bantounas et al., 2004; Cullen, 2006; Sandy et al., 2005).
Study of Gene Function
The discovery and study of RNAi has had tremendous implications for both basic and applied research. In particular, it is possible to exploit this natural
31 gene silencing pathway to regulate nearly any gene of interest. As a result, the ability of RNAi to suppress gene expression has provided a powerful tool with which to study gene function (Bantounas et al., 2004; Bonaldi et al., 2008;
Dykxhoorn and Lieberman, 2005). Cellular genes whose sequences are known can be targeted for knockdown by the exogenous introduction of complementary siRNAs. These siRNAs act as artificial RNAi triggers and utilize the cellular machinery to suppress the expression of the target gene of interest. The cell can then be evaluated for any effects caused by knockdown of that gene. RNAi’s characteristic potency, specificity, and simplicity in knocking down expression of target genes make it an attractive technique for the study of gene function
(Aagaard and Rossi, 2007; Dykxhoorn and Lieberman, 2005; Hannon, 2002;
Huppi et al., 2005). Initial attempts to use RNAi in mammalian cells were unsuccessful due to the activation of the cellular interferon response. The discovery that efficient silencing occurred with the use of 21-23 nt siRNAs, which were much smaller than the RNA molecules originally used, enabled mammalian systems to be extensively studied using RNAi (Elbashir et al., 2001; Grimm,
2009; Huppi et al., 2005).
Working through the mechanistic aspects of the RNAi pathway, outlined above, has yielded great insight into the design of RNA molecules that effectively trigger gene suppression. MicroRNAs (miRNAs) are the endogenous substrate for the RNAi machinery (Kim and Rossi, 2009; Sandy et al., 2005). Scientific research takes advantage of the cellular RNAi machinery in order to control the activity of genes of interest.
32 RNAi can be triggered using two different methods: an RNA-based approach using delivered synthesized siRNAs, and a DNA-based approach using expressed short hairpin RNAs (shRNAs) (Aagaard and Rossi, 2007; Paddison et al., 2002). Both methods take advantage of the endogenous RNAi machinery by introducing siRNAs, the effector molecules of the RNAi pathway, and result in knockdown of expression of the target host genes (Rao et al., 2009).
Synthesized siRNAs are delivered to the cytoplasm where they then directly incorporate into the RISC complex to initiate mRNA knockdown (Bantounas et al., 2004). Alternatively, short hairpin RNAs (shRNAs) are expressed using a
DNA vector and exported out of the nucleus by exportin 5, where they are cleaved into the effector siRNAs by Dicer (refer to Figure 4) (Grimm, 2009). The shRNA expression system consists of an RNA polymerase III promoter followed by at least 19 nucleotides of sense (or antisense) target sequences, a 4-10 nucleotide single-stranded loop, the complementary antisense (or sense) target sequence, and finally 4-6 U’s as a terminator (Aagaard and Rossi, 2007)
Regardless of the method, the use of RNAi targeting multiple sequences of the target gene is generally expected in the literature (Cullen, 2006; Huppi et al.,
2005; Stein and Krieg, 1994).
This thesis research uses shRNA to trigger knockdown for several reasons. Plasmid-expressed shRNA are more potent inducers of RNAi than siRNAs and can trigger more effective knockdown, possibly because they more closely resemble the endogenous miRNA (Kim and Rossi, 2009; Siolas et al.,
2005). As an expressed system, they enable longer-term, stable knockdown of
33 their target genes for up to about 72 hours following transient transfection (Scherr and Eder, 2007). This is an important characteristic given the relatively long half- life of importins. They are also cost-effective, provide a renewable source of
RNAi, and the techniques used to transfect shRNA plasmids are the same as those used for any other transfection of cultured cells. For the purposes of this research in particular, the use of shRNA plasmids allowed straightforward and effective co-transfection along with GFP-TRα.
RNAi in the study of nuclear import
RNAi has been used in a diverse array of gene expression studies
(Bantounas et al., 2004; Dykxhoorn and Lieberman, 2005; Holmes et al., 2010).
Of particular importance to this thesis are those studies which use RNAi to investigate the nuclear import pathways of different proteins. In this line of research, RNAi is used to discern which importins are necessary for nuclear import of a particular protein in vivo, especially after an in vitro interaction has been observed. For example, in vitro nuclear transport assays indicated that the protein mDia2 bound to Imp α and was imported into the nucleus by the Imp
α/Imp β complex (Miki et al., 2009). Subsequent depletion of Imp β with RNAi suppressed the nuclear localization of the protein mDia2, providing in vivo evidence that mDia2 is imported into the nucleus in an Imp β-dependent manner.
This technique is also often used to study the import of proteins important in the medical field; for example, knockdown of transportin SR2 was found to interfere with HIV replication by blocking import of the nucleoprotein preintegration
34 complex (Christ et al., 2008). Numerous studies have used RNAi to confirm
import pathways in vivo (Fries et al., 2007; Kaku et al., 2008; Liu et al., 2005;
Nakahara et al., 2006; Pan et al., 2008; Tao et al., 2006).
The purpose of this research was to characterize TRα’s import mechanism
by determining which importin(s) are able to mediate its nuclear import. RNAi
was used to decrease the levels of individual importins in the cell, after which a
fluorescently-tagged version of TRα was examined for any changes in localization in the cell. As a DNA-binding transcription factor, TRα is primarily
localized to the nucleus of the cell in a steady state, at levels of about 90%
nuclear localization or higher. Therefore, it was predicted that if RNAi knocks
down the expression of an importin that mediates TRα’s nuclear import, TRα
would not be imported into the nucleus as efficiently and some protein would
remain in the cytoplasm. This thesis research examined whether the subcellular
localization of TRα would change from a primarily nuclear to more whole cell
distribution, and thus provide evidence of a particular importin’s role in TRα’s
nuclear import mechanism.
Real-time quantitative PCR
Knockdown must be confirmed in order for RNAi experiments to have any
significance (Holmes et al., 2010; Huppi et al., 2005). Real-time quantitative
polymerase chain reaction (qPCR) is considered the best method for validation of
RNAi knockdown because it allows highly accurate quantification of gene
35 expression (Bustin et al., 2009; Derveaux et al., 2010; Provenzano and Mocellin,
2007).
Both conventional and real-time PCR work by amplifying the number of copies of a target DNA segment. The PCR system requires a template DNA to be amplified, primers complementary to the 3' ends of the target sequence strands, nucleotides, a heat-stable DNA polymerase, and a buffer solution
(Altshuler, 2006). This reaction mixture is placed in a thermal cycler and undergoes about 40 cycles of heating and cooling that result in the template DNA being copied exponentially. Each cycle consists of three steps: a hot denaturation step (around 95°C), which unwinds the DNA template; a cooler annealing step (50-65°C) to allow primers to bind to the single stranded DNA; and finally an extension phase (70-74°C), in which DNA polymerase synthesizes a new DNA strand complementary to the DNA template strand (McPherson and
Moller, 2006; (Kubista et al., 2006). Under ideal conditions, the existing target
DNA is doubled with each cycle until components become limiting.
Mechanism
Real-time PCR is unique because it allows the accumulation of amplified
DNA product to be detected and measured in “real time” as the reaction progresses (Dorak, 2008). This is made possible by the use of a fluorescent reporter molecule; as the amount of DNA increases, there is a proportional increase in the strength of the fluorescent signal (Figure 5) (Fraga et al., 2006).
36 Denaturation
Primer Annealing
Extension
Figure 5. Real-time PCR amplification and detection
Real-time PCR enables the simultaneous amplification and quantification of a DNA template through the use of fluorescent reporter molecules, such as SYBR Green, which bind nonspecifically to double-stranded DNA. Upon DNA binding, SYBR green fluorescence increases 1000-fold and can be detected and measured in real time as the reaction progresses. The fluorescent signal is proportional to the amount of dsDNA present and directly reflects the amount of amplified DNA product in each cycle.
37 Thermal cyclers equipped with fluorescence detection modules measure the
increasing fluorescence while amplification occurs.
There are two types of fluorescent chemistries used in qPCR: DNA-
binding dyes and fluorescently-labeled sequence-specific primers or probes. The
DNA-binding dye SYBR Green was used in this thesis research for its ease of
use and cost-effectiveness. SYBR Green binds non-specifically to the minor
groove of double-stranded DNA. When SYBR Green is free in solution its
fluorescence is almost nonexistent, but when bound to dsDNA its fluorescence
increases up to 1000-fold (Fraga et al., 2006). Therefore, the fluorescent signal is
proportional to the amount of dsDNA present and directly reflects the amount of
amplified product in each cycle (Fraga et al., 2006; Dorak, 2008; Kubista et al.,
2006). In the initial phase of amplification, fluorescence is undetectable even
though the product is accumulating exponentially. At some point – and this point
depends on the amount of starting material – the amplified product has
accumulated enough so that there is finally a detectable fluorescent signal. The
cycle number at which this occurs is called the threshold cycle (Ct) and is determined by the amount of input DNA template (Provenzano and Mocellin,
2007; VanGuilder et al., 2008). If a larger amount of template is put into the reaction mixture, then relatively few cycles are needed to reach the threshold level and the reaction will have a low Ct value. On the other hand, if there is a
small amount of template present, more amplification cycles will be required and
the reaction will have a higher Ct value.
38 Gene Expression Analyses
Quantification of gene expression by qPCR is used to validate RNAi
knockdown, since the protein of interest must in fact show a reduced level of
expression in order for any results obtained from RNAi studies to carry weight
(Bustin et al., 2009; Derveaux et al., 2010; Holmes et al., 2010). The first step in this procedure is reverse transcription, which is the synthesis of cDNA from its
complementary mRNA template (Dorak, 2008). For RNAi validation, it is this
cDNA that serves as the template for qPCR. The mRNA template has been
purified from RNAi-treated cells, and in successfully treated cells, there should be
lower levels of mRNA as compared to control cells. The less mRNA (and
therefore cDNA template) that is present, the longer it takes for this template to
amplify to a large enough quantity to reach the threshold fluorescence value
(Nolan et al., 2006).
The type of analysis done for RNAi validation is known as relative
quantification, which measures the changes in a gene’s expression in response
to RNAi versus control treatments (Dorak, 2008; (Schmittgen and Livak, 2008).
Results are given as a ratio, or fold change, of the amount of target cDNA in experimental cells versus control cells. All methods of gene expression analysis aim to convert the threshold cycle into a relative quantity of starting template and involve a comparison of a target gene’s threshold cycles in target and control samples (Cikos et al., 2007; Rieu and Powers, 2009; VanGuilder et al., 2008).
RNAi-treated cells should have a higher Ct value, indicative of a lower amount of
starting material, when compared to the Ct value of control, untreated cells.
39 This comparison must also be normalized to an internal standard to
control for differences in template starting amounts and human loading errors.
Housekeeping (or reference) genes are used as these internal standards (Dorak,
2008; (Udvardi et al., 2008). Ideal reference genes for qPCR are those whose
expression level does not vary across test samples and under different
experimental conditions. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and Beta-actin are two of the most common housekeeping genes used in qPCR as reference genes (Bustin, 2000; Bustin et al., 2009; Suzuki et al., 2000). There are several mathematical models available for calculating relative expression, each with their own assumptions, strengths, and weaknesses (Cikos et al., 2007;
Rebrikov and Trofimov, 2006). In general, however, they calculate the ratio of a target gene’s expression in experimental versus control cells, and this ratio is then normalized using the housekeeping gene’s expression in the same samples.
Thesis Objectives
The balance between nuclear import, export, and retention of key
molecules is crucial to gene regulation in a cell. Nucleocytoplasmic transport
processes controls this balance because it allows for the localization of
transcription factors and other important molecules to where they are needed in a
cell at that time (Cook et al., 2007). The interaction of importins with their cargo
protein, and how this interaction facilitates nuclear import, is of great importance
for a complete understanding of associated gene regulation. For TRα and all
40 other transcription factors, the process of nuclear receptor trafficking provides a
form of transcriptional regulation. The transcription of nuclear receptor-
responsive genes, such as TREs, is affected significantly if the nuclear receptor
cannot enter the nucleus in the first place, or cannot enter efficiently so that export dominates. As emphasized in this introduction, determining which
importins TRα interacts with in order to enter the nucleus will further our
knowledge of all levels of thyroid hormone function and regulation. A more
complete understanding of TRα’s nuclear import mechanism will provide valuable
knowledge to the study of TH-related diseases and their corresponding
pharmacological therapies.
There has been a significant amount of research looking into the effects of
TH and TRα, especially regarding TH’s physiological effects on humans.
However, there has traditionally been less research on the mechanisms of TRα
import and export. Recently, our lab investigated the export mechanisms of TRα
and showed that TRα uses, in part, a cooperative CRT-CRM1 mediated export
pathway (Grespin et al., 2008). So while our knowledge of TR export has
expanded, there is still little understanding of the import mechanism of TRα.
Given the importance of TH to humans, this is clearly a knowledge gap that
would be beneficial to fill.
Past work on TRα import showed that TRα follows an energy-dependent,
signal-mediated import pathway in mammalian cells; however, the importins able
to mediate this entry remain uncharacterized (Roggero, 2008). This thesis
research continues from this line of work with the overall goal of determining the
41 precise nuclear import pathway utilized by TRα, since importins differ in their functional relevance for distinct import pathways. The use here of an in vivo approach in the form of RNAi testing of gene function allowed for study of nuclear import under physiological conditions.
The specific objectives of this thesis were to:
1. Validate RNAi knockdown of importins using real-time PCR in human
(HeLa) cells.
2. Determine which importins are used by TRα in its nuclear import
pathway in HeLa cells by examining changes in TRα’s subcellular
localization following RNAi knockdown of importins
As indicated by the objectives, there are two distinct parts to this thesis research: RNAi import assays to evaluate the role of specific importins, and qPCR to confirm that RNAi knockdown was successful (Figure 6). The results from this research provide insight into the specific mechanism by which TRα enters the nucleus. This in turn allows a greater understanding of TRα itself and its role in regulation of T3-responsive genes, as well as adding to the general body of knowledge on the import pathways of nuclear receptors.
42 R NA s RNAi i V ay a s lid s a A t rt io o n p
m I
Transfection
RNAi knockdown RNA purification
RNA Quality Control Fluorescence Microscopy Agilent NanoDrop Bioalalyzer 1000
cDNA
qPCR/Analysis
Figure 6. Overall methods There are two main parts to this thesis research: RNAi import assays and RNAi validation. The RNAi import assays are used to evaluate the role of different importins in TRα’s nuclear import pathway. This involves trans- fecting human HeLa cells with shRNA plasmids targeting the importins, then scoring cells for localization of TRα using fluorescence microscopy. RNAi validation by real-time PCR is required to confirm that the knock- down of importins was successful; otherwise, import assay results would not be significant.
43 Methods
Plasmids
The plasmid pGFP-TRα encodes a functional GFP-TRα fusion protein expressed under a human cytomegalovirus (CMV) control. This plasmid was constructed previously in the lab by subcloning the PCR product of rTRα1 (rat) cDNA into the enhanced GFP expression plasmid pEGFP-C1 (CLONTECH
Laboratories, Inc. Palo Alto, CA) using SacI and BamHI enzymes (Bunn et al.,
2001).
For RNAi, a commercial kit from SuperArray Biosciences was used.
SureSilencing shRNA Plasmids specific for Imp β, Imp α1, Imp α3, Imp α4, Imp
α5, Imp α6, Imp α7, Ipo 7, and a negative control scrambled sequence shRNA were used. For each gene, the kit provided four separate shRNA designs packaged in a plasmid backbone, each of which targets a different sequence of that importin’s mRNA. The shRNA control was a single plasmid containing a single scrambled sequence, for a total of 33 plasmids.
All plasmids were propagated in E. coli-DH5α using 2 µl of each stock plasmid solution to transform host bacteria. Plasmids propogated in DH5α were purified using the standard procedure for a Qiagen Plasmid Midi Kit (Qiagen Inc.
Valencia, CA). A NanoDrop® ND-1000 full-spectrum UV/Vis Spectrophotometer was then used to measure DNA purity and concentration. Once the plasmids were purified, the 4 separate variants per importin-targeted shRNA were
44 combined into a single mix of plasmids. This mix, containing shRNA targeting 4
different sequences, was then diluted to 1µg/µl for ease of use in transfection.
Cell Culture
HeLa (human) cells (ATCC CCL-2) were cultured in Minimum Essential
Medium (MEM) supplemented with 10% fetal bovine serum (FBS) (Invitrogen,
Carlsbad, CA) at 37°C under 5% CO2 and 98% humidity. Cells were grown to 70-
90% confluency before the transfection procedures.
Transient Transfection
HeLa cells were seeded at a concentration of 2-2.5 x 105 cells per well on
22 mm Coverslips for Cell GrowthTM (Fisher) in 6 well culture dishes (Nunc,
Rochester, NY). Twenty-four hours after seeding, each well containing 2 ml MEM
supplemented with 10% FBS was transiently transfected with 1 µg GFP-TRα
plasmid DNA, 1 µg of the appropriate shRNA plasmid DNA, and 3 µl
Lipofectamine 2000 reagent (Invitrogen) diluted in Opti-MEM I Reduced Serum
Medium and incubated for 8 hours. Two controls were used: a TRα-only control,
and a scrambled sequence shRNA negative control. The Lipofectamine
2000/MEM transfection media was replaced with fresh MEM containing 10% fetal
bovine serum at 8 hours post-transfection. Twenty-six hours post-transfection,
cells were fixed at in 3.7% formaledehyde (Fisher) for 10 minutes followed by
three 5 minutes washes with Dulbecco’s phosphate buffered saline (DPBS).
45 The coverslips were subsequently mounted on microscope slides using 8
µl GelMount mounting media with DAPI (0.5 µg/ml, Sigma) and examined by
fluorescence microscopy.
Fluorescence Microscopy
Direct fluorescence microscopy was used to localize GFP-TRα protein in fixed HeLa cells after importin knockdown. Cells were viewed using either of two microscopes. An Olympus BX60 microscope with U-MNU filter cube for DAPI, and Omega Optical XF100-2 for GFP were used with an Olympus 40x/0.75 objective. Primary image acquisition and processing were done using a Cooke
SenisCamQE camera and IPlab software (BD BioSciences Bioimaging, Rockville,
MD). For other analyses, an inverted Nikon ECLIPSE TE 2000-E fluorescence
microscope, with Nikon Ultraviolet Excitation: UV-2E/C filter block for DAPI
visualization, and a Blue Excitation: B-2E/C filter block for GFP/FITC visualization
was used with a Nikon Plan Apo 40x/0.95 objective. A CoolSNAP HQ2 CCD
camera (Photometrics, Tucson, AZ) and NIS-Elements AR software (Nikon) were
used for image acquisition and primary image processing. For both microscopes,
secondary image processing was done using Adobe
PhotoshipCS3/IllustratorCS3.
Cell Scoring and Statistical Analysis
The localization of fluorescence in cells was scored as one of three
categories: completely nuclear, nuclear and cytoplasmic, and whole cell. Cells
46 with varying levels of fluorescence between the nucleus and cytoplasm were
counted as nuclear and cytoplasmic, as long as there was clearly fluorescence
present in the cytoplasm. All experiments consisted of a minimum of three
replicates, and each replicate counted one hundred cells with healthy nuclei. The
state of the nuclei was assessed by visually examining the integrity and
morphology of each nucleus using the DAPI stain. All cell counts were performed
blind, without knowledge of the transfection or shRNA treatment.
Once all cells were scored all the data were entered into Microsoft Excel.
Replicate counts were combined and graphed, and the mean and standard error
of the mean were calculated for each.
RNA Purification and Quality Analysis
HeLa cells were grown to 70-90% confluency and seeded on 100 mm
Thermo Scientific Nunc plates (Fisher Scientific) at a concentration of 6 x 105
cells per plate. Twenty-four hours after seeding, each plate containing 10 ml
MEM supplemented with 10% FBS was transfected with 10 µg of shRNA plasmid shRNA using 20 µl Lipofectamine reagent (Invitrogen) diluted in 2.5 ml Opti-MEM
I Reduced Serum Medium. Plates were incubated for 8 hours. The Lipofectamine
2000/MEM transfection media was replaced with fresh MEM containing 10% fetal bovine serum at 8 hours post-transfection, to ensure the same treatment conditions as used during transient transfection for fluorescence microscopy
(described above).
47 R NA s RNAi i V ay a s lid s a A t rt io o n p
m I
Transfection
RNAi knockdown RNA purification
RNA Quality Control Fluorescence Microscopy Agilent NanoDrop Bioalalyzer 1000
cDNA
qPCR/Analysis
Figure 6. Overall methods There are two main parts to this thesis research: RNAi import assays and RNAi validation. The RNAi import assays are used to evaluate the role of different importins in TRα’s nuclear import pathway. This involves trans- fecting human HeLa cells with shRNA plasmids targeting the importins, then scoring cells for localization of TRα using fluorescence microscopy. RNAi validation by real-time PCR is required to confirm that the knock- down of importins was successful; otherwise, import assay results would not be significant.
43 At 26 hours post-transfection, RNA purification was performed using the
AurumTM Total RNA Mini Kit and following the Spin Protocol for Cultured
Mammalian Cells. The DNase I digest was extended from 15 minutes to 30
minutes to reduce levels of genomic DNA contamination. A detailed explanation
of methods can be found in the Appendix. Total RNA was extracted from cells
transfected with each of the 9 plasmids tested: Imp β, Imp α1, Imp α3, Imp α4,
Imp α5, Imp α6, Imp α7, Ipo 7, and shRNA control plasmid.
RNA concentration and initial determination of integrity was done using
the NanoDrop® ND-1000 full-spectrum UV/Vis Spectrophotometer. Only RNA
with a A260: A280 ratio greater than 2.0, and a A260:A230 ratio greater than 1.7 was
utilized. RNA quality and integrity was further analyzed using the Agilent 2100
BioAnalyzer’s Lab-on-a-Chip technology. Purified RNA samples were run using the RNA 6000 Pico Total RNA Assay with the sample RNA diluted to 50-5000
pg/µl in sterile double-deionized water.
qPCR
cDNA was synthesized using SuperArray Bioscience’s RT2 First Strand Kit
using 0.735 µg total RNA. The RT2 first strand kit includes a genomic DNA elimination step to remove residual contamination in the RNA samples before reverse transcription.
Applied Biosystems’ StepOneTM Real-Time PCR machine was used for
real-time PCR. RT-qPCR reactions were set up on a 48-well plate. A single
reaction (total volume 25 µl) contained 12.5 µl RT2 SYBR Green/Fluorescein
48 qPCR Master Mix specifically designed for ABI instruments, 11.5 µl RNase-free
water, 1.5 µl cDNA template, and 1 µl appropriate primer. For each experimental
sample, a set of reactions was prepared testing each gene of interest and a
housekeeping gene (GAPDH) to normalize the raw data. A No Template Control containing water instead of template was included to detect any environmental contamination introduced during reaction setup. No Reverse Transcription (NRT) controls were included for each sample (each gene of interest and housekeeping gene in each sample) in order to detect any genomic contamination. Plates were
centrifuged for 90 seconds at 1300 rpm prior to beginning the run. After enzyme
activation (10 min, 95°C), thermocycling consisted of 40 cycles of 15 sec at 95°C,
35 sec at 55°C, and 30 sec at 72°C. SYBR Green fluorescence was detected
and recorded during the annealing step of each cycle. A melting curve was
performed as a quality control measure. Real-time PCR data were analyzed by
the ΔΔCt (Livak) method using the ABI StepOne software. Detailed methods for
cDNA preparation and qPCR plate setup are found in the Appendix.
49 Results
Real-time PCR Validation of RNAi knockdown
When using RNA interference (RNAi) to study gene function, the first step must be to ensure that the target knockdown was effective. Reverse transcription followed by real-time PCR (RT-qPCR) represents the most sensitive and reliable method for amplifying and quantifying mRNA levels, and is considered the gold standard for gene expression analyses of this kind (Bustin et al., 2009).
Therefore, after shRNA expression plasmid transfection, the effect of RNAi on the corresponding importin mRNA levels was evaluated by qPCR.
RNA Quality Control
The most important prerequisite for any experiment involving gene expression analysis is consistent, high quality RNA from every experimental sample, since high quality RNA is essential for obtaining accurate, good-quality qPCR results (Bustin et al., 2009). This fact is constantly reinforced in the literature, and poor-quality RNA is one of the most frequently stated points of qPCR complications (Becker et al., 2010; Fleige and Pfaffl, 2006).Therefore, all nine of the purified RNA samples (representing the 8 individual importin knockdown plasmids and the control shRNA plasmid) were tested for quality prior to reverse transcription and use in qPCR.
RNA concentration and purity was first tested by UV spectrophotometry.
For the best qPCR results, the SA Bioscience RT2 qPCR system requires that the
50 RNA samples demonstrate quality consistent with the following criteria:
Concentration by A260 greater than 4 µg/ml total RNA, a A260: A280 ratio greater than 2.0, and an A260:A230 ratio greater than 1.7. All RNA samples used demonstrated quality according to these standards (Table 3).
The eight RNA samples were then examined by electrophoresis on the
Agilent 2100 BioAnalyzer using an RNA 6000 Pico LabChip. The BioAnalyzer runs very high-sensitivity assays, and is accepted in the literature as the industry standard for RNA quality control (Bustin and Nolan, 2004). The resulting electropherogram verified the high quality of the RNA, in that there is a sharp distinction in 18S and 28S ribosomal RNA (rRNA) peaks (Figure 7). Results did not show any smearing or unwanted peaks, which would indicate degradation of the RNA sample. Therefore, RNA for each of the nine samples (eight importins and one shRNA control) was verified to be of high quality and suitable for use in cDNA preparation and real-time PCR.
qPCR Quality
To test for decreased levels of importin mRNA, we measured the target transcript levels in cells transfected with gene-specific shRNAs versus negative
51 Table 3.RNA Quality Control: NanoDrop Data*
[ng/µl] A260: A280 A260:A230 Imp β 207.3 2.1 2.16
Imp α1 150.6 2.11 1.55
Imp α3 168.9 2.08 2.05
Imp α4 91.9 2.08 1.97
Imp α5 100 2.05 1.7
Imp α6 97.7 2.09 2.02
Imp α7 115.6 2.06 1.74
Ipo 7 189.6 2.06 2.12 shRNA control 161.5 2.09 2.04
*The ratios of the absorptions at 260 nm versus 280 nm, and 260 nm versus 230 nm, are commonly used to assess the purity of RNA. The RT 2 qPCR system requires that the RNA samples demonstrate quality consistent with the following criteria before being used in cDNA preparation and real-time PCR: Concentration- greater than 4 ng/µl total RNA, a A260: A280 ratio greater than 2.0, and an A260:A230 ratio greater than 1.7.
52 A
Ladder shRNA ControlImp β1 Imp α1 Imp α3 Imp α4 Imp α5 Imp α6 Imp α7 Ipo 7
B Ladder shRNA Control Imp β1
Imp α1 Imp α3 Imp α4
Imp α5 Imp α6 Imp α7
Ipo7
Figure 7. BioAnalyzer data indicate high-quality RNA
Purified total RNA samples were evaluated by high-sensitivity electrophoresis using the Agilent BioAnalyzer. RNA samples were run on an RNA 6000 Pico LabChip. A. Results given as a composite gel that shows sharp bands especially at the bottom of each band for the 28S (top) and 18S (bottom) ribosomal RNA. B. Results given as electropherograms show two peaks indicating 18S (left peak) and 28S (right peak) ribosomal RNA. Peaks are sharply distinct without shoulders, indicating high-quality RNA without degradation.
53 control shRNAs using RT-qPCR. To evaluate the true level of knockdown
present, it was important that cells were exposed to the same experimental
conditions for both the RNAi import assays and qPCR validation. Cells were
transfected with equivalent amounts of plasmid and transfection reagent and
received the same media change. RNA was extracted at 26 hours post-
transfection, identical to when cells were fixed for fluorescence microscopy, to
ensure we were evaluating mRNA knockdown at the same levels present when
the cells were scored for TRα localization.
The qPCR was of good quality and showed very little contamination of samples that would negatively affect the data analysis. The no template control
(NTC) included in each run never showed amplification, meaning that there was little to no environmental contamination introduced during reaction setup. The
PCR runs also included no reverse transcription (NRT) controls for each gene of interest and the housekeeping gene in both experimental and control cell samples. These NRT control reactions showed slight amplification very late in the
PCR reaction; i.e. at cycle numbers in the late thirties (Figure 8). This represents only slight genomic contamination that is very unlikely to have affected the amplification and quantification of the genes of interest or housekeeping gene.
Gene Expression Ratios Confirm Knockdown
Once the qPCR data were shown to be of good quality, data were evaluated to determine the level of importin knockdown. The levels of expressed genes can be analyzed by two methods, absolute quantification and relative
54 Figure 8. qPCR runs did not show environmental or genomic con- tamination
Controls were included in each qPCR run to test for contamination of samples that would negatively affect the data analysis. No template control (NTC) reactions were included to test for any environmental contamination introduced during the reaction setup. No reverse transcriptase (NRT) controls were included to test for the presence of genomic DNA contamination. The amplification plot shown here is representative of all collected data, in that the NTC control never showed amplification and its fluorescence did not rise above the baseline level. The NRT controls showed very slight amplification late in the qPCR reaction (cycle numbers greater than 37), which does not have a negative impact on data analysis.
55 quantification. Absolute quantification attempts to determine the exact copy
number present, while relative quantification describes the change in expression
of the target gene relative to an untreated control as a ratio or fold change. For
gene expression analyses such as RNAi validation, relative quantification is the
most useful and appropriate method to use.
Relative quantification determines the changes in steady-state mRNA
levels of a gene of interest between treated and control samples, and expresses
it relative to the level of a reference gene (Dorak, 2008). This reference gene
serves as an endogenous control that normalizes differences in the amount and
quality of starting template, as well as in reaction efficiency. Glyceraldehyde-3-
phosphate dehydrogenase (GAPDH) was used as a reference gene for these
calculations and is the housekeeping gene recommended by the RT2 Primer
Assay kit. GAPDH is accepted in the literature as a standard housekeeping gene
in the literature since it is ubiquitously expressed in cells and tissues, and its
transcription generally does not vary with experimental condition (Bustin, 2000;
Suzuki et al., 2000). Given the requirements for relative quantification, a set of
reactions was prepared testing the gene of interest and the housekeeping gene
in both experimental and control samples. The amount of nucleic acid used as
PCR template was standardized among all samples, and the reference gene
served to further normalize the data.
The most common method for relative quantification, and the one
2 recommended for use by the RT Primer Assay, is the Livak (ΔΔCt) method
(VanGuilder et al., 2008). The Livak method enables relative quantification by
56 comparing the threshold cycles (Ct) of target and reference genes in both experimental and control samples (Figure 9) (Livak and Schmittgen, 2001;
Schmittgen and Livak, 2008):
First, the Ct of the gene of interest (GOI) is normalized to that of the
housekeeping gene (HKG) in both samples:
ΔCt (experimental) = CtGOI, exp - CtHKG, exp
ΔCt (control) = CtGOI, ctl - CtHKG, ctl
Then the Ct of the experimental sample is normalized to that of the calibrator:
ΔΔCt = ΔCt (exp) – ΔCt (ctl)
Finally, the expression ratio (fold change) is calculated:
2-ΔΔCt = Relative expression (fold change)
For this research, the control sample is the sample transfected with the negative control shRNA plasmid, while the experimental sample is the sample transfected with an importin-specific shRNA plasmid. In each, the threshold cycles of the importin gene of interest and the GAPDH reference gene have been measured. Since we used fluorescence microscopy to evaluate the ability of eight different importins to mediate TRα’s entry into the nucleus, there were eight different experimental samples to be analyzed by qPCR and compared relative to the control sample. Transfection of HeLa cells with shRNA plasmids specific for each of those importins should cause degradation of the target importin mRNA.
Therefore, relative to a control cell not experiencing knockdown, we predicted that importin mRNA levels would be reduced. To determine the relative
57 Livak (ΔΔCt) Method
-ΔΔC -(ΔC (exp) - ΔC (ctl)) RQ = 2 t = 2 t t
ΔCt (exp) = CtGOI, exp - CtHKG, exp
ΔCt (ctl) = CtGOI, ctl - CtHKG, ctl Fluorescence Ct
Cycle #
Red = Control sample, HKG Orange = Experimental sample, HKG Green = Control sample, GOI Blue = Experimental sample, GOI
Figure 9. The Livak method.
The Livak method compares the threshold cycles of a gene of interest and a reference gene in both experimental and control samples. The ΔΔCt calculation determines the relative ratio of mRNA levels of a gene of interest between treated and control samples, and expresses it relative to the level of a reference gene. Experimental samples were treated with importin-specific shRNA plasmids, while control samples were treated with negative control shRNA plasmids.
58 difference in expression level of the target importin genes in knockdown and control samples, we set up the ABI StepOne software to analyze the qPCR run data using the Livak method. At least three runs of PCR were performed for each plasmid as is recommended, and most plasmids were tested four times or more
(Wang et al., 2010).
The results demonstrate knockdown of all importin mRNA levels (Figure
10). The ABI software gives the fold change in expression as an “RQ” or relative quantity value. For each importin, the RQ represents the ratio of importin mRNA expressed in experimental cells, which should have experienced knockdown, versus control cells, and was normalized to the levels of the housekeeping gene
GAPDH. All RQ values for these data are decimals less than 1.0, with 1.0 being the level of that importin mRNA found in an untreated cell.
Although all experimental samples showed knockdown, there were slight differences in the extent of knockdown. Six of the shRNA plasmids tested (Imp
α1, Imp α3, Imp α4, Imp α7, Imp β, and Ipo 7) had an RQ value of less than 0.3.
This means that the level of mRNA for these importins in shRNA-treated cells is less than a third of the level found the control samples. Put another way, these seven importins demonstrated knockdown at levels of 70% or greater. In contrast, the RQ value for Imp α5 was only 0.684, which means the level of this importin was only reduced by about 30%.
In summary, real-time PCR confirmed the effectiveness of using importin- specific shRNA plasmids for RNAi. With the exception of Imp α5, all importins showed mRNA knockdown at levels of ≥70%. These levels of knockdown are
59 1.0
0.9
0.8
0.7
0.6
0.5 RQ 0.4
0.3
0.2
0.1
0.0 Imp β1 Imp α5 Imp α1 Imp α4 Imp α3 Imp α7 Ipo 7
Figure 10. RQ data confirm importin knockdown
Real-time PCR was used to confirm importin knockdown. Bars indicate mean RQ values, and error bars indicate standard error of the mean for at least 3 replicates. The RQ value was determined by the Livak method and represents the ratio of importin mRNA expressed in experimental cells versus control cells normalized to the levels of the housekeeping gene GAPDH. An RQ value of 1.0 indicates the level of that importin mRNA found in an untreated cell. With the exception of Imp α5, all importins showed mRNA knockdown at levels of ≥70%.
60 considered standard for RNAi validation (Bustin et al., 2009; Derveaux et al.,
2010).
Import Assays
The goal of this thesis research was to determine which importins are able
to mediate nuclear import of TRα. As a DNA-binding transcription factor, nearly all TRα is localized in the nucleus of cells at a steady state. Typically, TRα is found primarily in the nucleus in 90% or more of cells in culture. In order to enter the nucleus, TRα must be recognized by importins in the cytoplasm. RNAi was
used to inhibit the expression of importins, thus decreasing their levels in the cell,
after which TRα was examined for any changes to this nuclear localization. To
examine the subcellular localization of TRα, we expressed TRα as a GFP fusion protein in HeLa cells and examined its localization by fluorescence microscopy.
The key concept behind using RNAi as a tool to study gene function is that upon suppression, altered activities in the cell can be attributed to the function of the targeted gene. It was predicted that if RNAi knocks down the expression of an importin that does mediate TRα’s nuclear import, then TRα will not be imported into the nucleus as efficiently and some protein will remain in the cytoplasm. TRα is a shuttling protein, so any TRα exported from the nucleus will stay in the cytoplasm since levels of its importin are too low to mediate effective import. Any newly synthesized TRα will remain cytoplasmic for the same reason.
61 For this thesis research, any change in localization from primarily nuclear to more
whole-cell could be attributed to the reduced expression of a particular importin.
Importin β mediates TRα nuclear import
During active nuclear import of proteins, the NLS of the cargo protein is
recognized by transport receptors called importins. The karyopherin-Beta family is responsible for the majority of nuclear import in a cell. Importin β1 (Imp β) is the most well-studied member of this family and interacts with the nuclear pore complex to directly facilitate import. Importin β is known to be responsible for the import of many cargos, and was shown to be involved in TRα’s nuclear import through in vitro transport assays (Roggero, 2008). To investigate whether the nuclear import of TR is mediated in vivo by Imp β, we reduced the levels of Imp β in HeLa cells using RNAi. This decreased the amount of Imp β mRNA effectively as shown by qPCR to levels of about 30% of normal. HeLa cells depleted of Imp
β were examined for localization of expressed GFP-TRα and cells were scored into one of three categories: nuclear, nuclear and cytoplasmic, and whole cell
(Figure 11).
The results show that the nuclear accumulation of TRα was inhibited by
Imp β knockdown (Figure 12). Data show the mean value of TRα distribution in each of the three scoring categories obtained from three replicate transfections, each of which scored 200 cells. Transfection efficiency of all replicates was 80% or higher. Control cells transfected only with GFP-TRα showed an expected level
62 GFP DAPI Merge
Whole Cell
Nuclear & Cytoplasmic
Nuclear
Figure 11. Scoring categories for TRα localization
GFP-TRα localization in HeLa cells was scored as one of three categories: nuclear, nuclear and cytoplasmic, and whole cell. These categories were used to score the variation in subcellular distribution of GFP-TRα following RNAi-mediated knockdown of each importin and DNA staining with DAPI to reveal the nucleus. Under normal conditions, TRα is localized primarily to the nucleus of most cells. If RNAi knocks down the expression of an importin that mediates TRα’s nuclear import, then TRα will not be imported into the nucleus as efficiently. In this case, TRα localization would be expected to shift and become more cytoplasmic.
63 100.0 TRα shRNA Control 90.0 Imp α4 Imp α7 80.0 Imp α5 Imp α3
70.0 Imp α1 Imp β1
60.0 Ipo 7
50.0
40.0
30.0 % Cellular Localization
20.0
10.0
0.0 N N+C WC
Figure 12. Importins vary in their ability to mediate TRα import HeLa cells were transfected with importin-specific shRNA plasmids, resulting in effective knockdown of importin mRNA as determined by qPCR. Cells were fixed and scored for TRα localization using the 3 scoring catego- ries. Bars indicate mean cell counts in each of the three categories of localiza- tion, and error bars indicate standard error of the mean for 3 replicates. Knockdown of Ipo 7 caused the most significant cytoplasmic shift in TRα distribution out of all the importins tested. Knockdown of Imp β, Imp α1, and Imp α3 also caused a decrease in the number of cells with nuclear localization of TRα.
64 of 86% of cells with a primarily nuclear localization of TRα. In contrast, cells
transfected with Imp β-specific shRNA showed only 72% of cells with nuclear
localization of TRα. Visually, it was readily apparent that cultures transfected with
Imp β shRNA showed more cells with a whole-cell distribution of TRα (Figure 13).
Transfection with scrambled-sequence shRNA was used as a negative control to evaluate any nonspecific effects caused by small dsRNA transfection, and was found to not affect nuclear localization of TRα. Negative control cells showed
85% of cells had TRα localized in the nucleus, which was only 1% lower than the
86% nuclear localization found in control cells transfected only with TRα. The change from 86% to 72% nuclear localization can be attributed to the reduced expression of Imp β in the cells. These results suggest that TRα can be transported into the nucleus in an Imp β-dependent manner.
Analysis of alpha importins
Imp β recognizes cargo protein NLS in one of two ways: by direct binding,
or by indirect interaction using an adaptor importin. In the classical nuclear import
model, an alpha importin acts as the adaptor to facilitate interaction between the
cargo protein of interest and Imp β. Having already demonstrated that Imp β
plays a role in mediating TRα’s nuclear import, the next step was to investigate if
TRα is recognized directly by Imp β or indirectly by an alpha importin. To this
end, five different importin α isoforms were examined for their role in TRα import.
This was done using the same rationale and methodology as for the testing of
65 GFP-TRα DAPI Merge
No shRNA
Imp β shRNA
Imp α1 shRNA
Imp α3 shRNA
Figure 13. Knockdown of Imp β, Imp α1, and Imp α3 cause an increase in the percentage of cells with whole-cell distribution of TRα
HeLa cell cultures treated with Imp β, Imp α1, and Imp α3-specific shRNA are observed to show a greater percentage of cells with a whole-cell distribution of TRα. Cell pictures shown are representative of the whole-cell distributions seen following shRNA treatment when examined by fluorescence microscopy after stain- ing with the DNA stain DAPI to visualize the nucleus. In the absence of shRNA treatment, TRα is localized primarily to the nucleus in the majority of cells.
66 Imp β. The alpha importins tested using RNAi were importins α1, α3, α4, α5, and
α7.
The resulting data show that knockdown of the importin α isoforms varied in the extent to which they caused a change in TRα nuclear localization (Figure
12). As mentioned before, the TRα-only control and the shRNA negative control cells both showed high values for nuclear localization, at 86% and 85% of cells respectively. Among the importin α isoforms, knockdown of importin α1 showed the largest resulting shift in nuclear localization. Cells transfected with Imp α1- specific shRNA showed that 77% of cells had TRα localized in the nucleus, a drop from the near-86% of cells with TRα nuclear localization found in the controls. Knockdown of Imp α3 also appears to cause a more cytoplasmic shift of
TRα, as only 81% of Imp α3-depleted cells showed nuclear localization. The other importin α isoforms (α4, α5 and α7) did not appear to play a large role in nuclear import. Cells experiencing knockdown of these isoforms remained primarily nuclear in TRα localization; for example, when depleted of Imp α4, 88% of cells demonstrated nuclear localization of TRα.
As a whole these data suggest that the importin α isoforms vary in the extent to which they were able to mediate TRα nuclear import. Knockdown of Imp
α1 caused the most significant shift in standard TRα nuclear localization, suggesting that Imp α1 is the main adaptor importin acting with Imp β to import
TRα into the nucleus. Imp α3 also appears to play a role in TRα import, having caused the second largest shift in TRα localization. Therefore, it is possible that
67 Imp α3 is involved in a second nuclear import pathway for TRα that also acts
through binding Imp β.
Ipo 7 appears to play the greatest role in TRα nuclear import
The classical nuclear import pathway involves the interaction of Imp β and the Imp α isoforms to facilitate entry of cargo proteins into the nucleus. While the classical mechanism has been extensively studied, other importins and import mechanisms exist. The β -karyopherin family, which includes Imp β, consists of importins that are able to directly bind cargo without requiring the use of adaptor importins, and it is this family that mediates the majority of nuclear import in a cell
(Strom and Weis, 2001). In investigating TRα’s import pathway, it is logical to continue from the study of the classical importins and study β-like importins as well. In addition, TRα’s second NLS is known to be a nonclassical NLS and is therefore not likely to use the classical importins. For these reasons, Importin 7
(referred to as Ipo 7 to avoid confusion with Imp α7), an importin in the β- karyopherin family, was evaluated for its role in mediating TRα import. RNAi suppressed levels of Ipo 7 mRNA effectively as determined by qPCR, and TRα was examined for its localization in cells in the same way as described previously for the other importins.
The results show that knockdown of Ipo 7 caused the most significant cytoplasmic shift in TRα distribution out of all the importins tested (Figure 12).
Cells depleted of Ipo 7 showed only 66% of cells had TRα localized to the nucleus. The significant drop from 86% of control cells demonstrating nuclear
68 localization to just 66% can be attributed to the reduced levels of Ipo 7 in the
cells. Visually, it was evident that an increased number of cells had whole-cell
distribution of TRα. In addition, many of these cells were marked by the presence
of small foci or aggregates of TRα in the cytoplasm around the nucleus (Figure
14). This accumulation of TRα was not observed to this extent upon knockdown of any of the other importins. If Ipo 7 does mediate TRα import, this pattern could
be a consequence of suppressing Ipo 7 in the cell, which would effectively
prevent TRα from entering the nucleus at its normal rate.
The nuclear import and accumulation of TRα was markedly inhibited by
knockdown of Ipo 7, and many cells showed a noticeably whole-cell distribution.
The fact that knockdown of Ipo 7 resulted in the most significant cytoplasmic shift
of TRα, even more so than occurred with knockdown of Imp β, suggests that
TRα’s nuclear import is mediated primarily by Ipo 7.
69 GFP-TRα DAPI DIC Merge
A
GFP-TRα DAPI DIC Merge
B
Figure 14. Knockdown of Ipo 7 causes an increase in the per- centage of cells with whole-cell distribution of TRα
A. The images depict HeLa cell cultures transfected with a GFP-TRα expression vector and Ipo 7-specific shRNA and analyzed by fluorescence microscopy after staining with DAPI to visualize the nucleus. White arrow heads indicate the TRα aggregates observed in the cytoplasm of many cells after knockdown of Ipo 7.
B. HeLa cell cultures treated with Ipo 7-specific shRNA are observed to show the highest percentage of cells with a whole-cell distribution of TRα, indicating the most significant shift in TRα localization from that seen with the control cells. In the absence of shRNA treatment, TRα is localized primarily to the nucleus in the majority of cells.
70 Discussion
TRα nuclear import is mediated mainly by Ipo 7
The data presented in this thesis research indicate that knockdown of Ipo
7, Imp β/α1, and Imp β/α3 caused TRα to shift from a primarily nuclear to more
cytoplasmic distribution. This shift in TRα distribution is due to the fact that an importin involved in TRα’s import pathway has been depleted from the cell using
RNA interference; therefore, TRα is not able to enter the nucleus as efficiently.
Based on the data, these importins vary in the extent to which they are able to mediate TRα import. The evidence presented in this thesis suggests that Ipo 7 is the major importin used by TRα to enter the nucleus. This is supported by the findings that transient transfections of Ipo 7-specific shRNA produced the most significant changes to the localization of GFP-TRα. The Impα1/Imp β complex was also shown to play a considerable role in TRα import, since knockdown of
Imp α1 caused a shift in localization similar to but slightly less apparent than the shift that occurred after knockdown of Ipo 7. It is also possible that Impα3/Imp β could act as a more minor nuclear import pathway for TRα, as cells depleted of
Imp α3 also exhibited a cytoplasmic shift. Taken together, these results suggest that the nuclear import of TRα is facilitated primarily by Ipo 7 and Imp β/Imp α1, and potentially Impβ/Imp α3 to a lesser extent.
71 Factors affecting accuracy of data collected
The data presented indicate that knockdown of Ipo 7, Imp β/Imp α1, and
Imp β/Imp α3 caused TRα to shift to a significantly more cytoplasmic distribution.
However, it is likely that the data collected underestimate the role these importins are playing in nuclear import. The use of RNAi to test the role of importins was likely to have contributed to this underestimation, because by the nature of its mechanism, RNAi only causes knockdown, not knockout, of the target importins.
This means that a portion of the target importin mRNA is still being translated into protein. Since importins have a long half-life, it is assumed that the majority of the target importins found in the cell prior to RNAi treatment were still present
(Villanyi et al., 2008). Therefore, due to the combination of knockdown and long importin half-lives, there was still some level of target importin present in the cell at the time the cells were fixed and analyzed for TRα distribution. As a result of this, it was not expected that cells would ever show a fully cytoplasmic distribution of TRα, since there would still be importins present to interact with and transport TRα into the nucleus. In addition, a wholly cytoplasmic distribution would depend on the complete export of all nuclear TRα, which is highly unlikely as it is quite strongly retained in the nucleus. For these reasons, the nuclear/cytoplasmic and whole cell scoring categories were likely to be underrepresented.
Cells were fixed 26 hours after shRNA transfection, which was shown to be long enough for RNAi to effectively reduce the levels of importins in the cells.
Allowing the cells to incubate a longer time before fixing would increase their
72 exposure to RNAi and would theoretically increase any subsequent shift in TRα localization, but was found to lead to cell death. These shRNAs are targeting importins, which are required for the import of many vital proteins involved in major cellular processes. Reducing the level of an importin in a cell prevents the cell from effectively importing any of that importin’s cargo molecules, and quickly
produces detrimental consequences. In addition, unintended side effects of RNAi
can result in toxicity to cells (Scherr and Eder, 2007). These toxic side effects
include saturation of the endogenous RNAi pathway, blocking miRNAs
necessary for survival, and off-target effects, where the siRNA binds
nonspecifically and knocks down expression of an unintended gene (Kim and
Rossi, 2009). Since each cellular miRNA can potentially regulate the expression
of hundreds of genes, even minor alterations in or saturation of the RNAi
pathway can have profound consequences (Castanotto and Rossi, 2009). These
various factors are all expected to contribute to cell death and likely caused an
underestimation in the cell count data.
This data underestimation due to cell death was most apparent following
knockdown of Importin β. It was visually readily apparent that cultures transfected
with Imp β showed a more whole-cell distribution of TRα, but it was also apparent
that a high percentage of cells had died. Yet only healthy cells, as determined by
nuclear morphology, were counted for scoring purposes. Many cells exhibiting a
whole-cell distribution of TRα were not counted because they showed a lack of
nuclear membrane integrity, yet this is an extremely likely consequence of
effective Imp β knockdown given its importance to both importin α-dependent
73 and importin α-independent pathways. This strong selection against Imp β knockdown cells following Imp β down-regulation has been documented in the literature. For example, Quensel et al. noted that knockdown of Imp β caused the strongest reduction in the number of living cells, and that a small reduction in Imp
β expression is more harmful to living cells than the reduction of any importin α isoform (Quensel et al., 2004). Therefore, in this thesis research, it is likely that
Imp β plays more of a role in TRα import than the data suggest.
Imp α5 deserves a closer investigation based on the data collected. Imp
α5 has not been mentioned as a possible import factor for TRα, because the cell count data showed that of the importin α isoforms, it caused only the third most significant shift in TRα distribution. However, the qPCR data show only 30% knockdown of Imp α5, in comparison to the 70% or greater knockdown of other importins. It is interesting to consider what the effects would have been had Imp
α5 been reduced in expression to the same level as the other importins. It is possible that if Imp α5 had shown ~70% knockdown, that there would be more evidence for its role in TRα import in the RNAi import assays.
Support for multiple import pathways of TRα
The findings that Imp β and Imp α1 play a large role in TRα nuclear import is supported by past work on TRα. It has been shown that TRα is localized to the nucleus in permeabilized HeLa cells reconstituted with only Imp α1 and Imp β as import factors (Roggero, 2008). This shows that TRα nuclear import can be mediated in vitro by importin α1/ β, but the nuclear import pathway of TRα had
74 yet to be studied in vivo. This thesis research used import assays to examine the
effect of RNAi-mediated importin knockdown on TRα localization, and represents
in vivo study of TRα nuclear import. This in vivo research supports the original in
vitro findings, as knockdown of Imp α1 and Imp β were found to cause a
significant shift in TRα localization from nuclear to more cytoplasmic.
It was important that the study of TRα nuclear import be continued using in
vivo methodology. In vitro and in vivo results can differ since the controlled
conditions present in an in vitro system often differ significantly from those present in vivo, and can lead to misleading results. Often, proteins can bind in vitro to several different importins, but show a preference for one or two particular importins in vivo (Fried and Kutay, 2003). For example, in the study of the nuclear import of the transcription factor STAT3, in vitro binding assays showed that STAT3 interacted with Imp α3 and Imp α6. However, in vivo studies demonstrated that transient transfections of Imp α3 siRNA produced dramatic effects on the localization of STAT3, while Imp α6 was unlikely to play more than a small role (Liu et al., 2005). However, in vivo findings often agree with and confirm in vitro results; for example, in vivo immunoprecipitation experiments confirmed the in vitro findings that NF-κB binds Imp α3 and Imp α4 (Fagerlund et al., 2005). It is standard practice in the literature to use both in vitro and in vivo methods to examine the nuclear import pathways of specific proteins (Liu et al.,
2005; Miki et al., 2009; Nakahara et al., 2006; Neimanis et al., 2007; Pan et al.,
2008).
75 The results of this thesis research suggest the possibility that more than
one import pathway is followed for complete nuclear import of TRα, the concept
of which is supported by past work as well. Although importin α1/β was shown to mediate TRα import, it was not as efficient as import in the presence of complete cytosol replacement which would contain the full suite of importins found in a cell
(Roggero, 2008). Furthermore, import efficiency was highly variable across replicates. From these results, it was suggested that importins in addition to the importin α1/β complex may be necessary for complete nuclear import of TRα.
This thesis research confirms that the Imp α1/β complex does appear to play a role in import. However, this research also suggests that Ipo 7 might actually be the primary importin used by TRα to mediate import, since depletion of Ipo 7 in the cells caused the most significant shift in TRα localization. In addition, the Imp
α3/β complex could act as an alternative, more minor import pathway. It is possible that these three import mechanisms are all necessary for complete, efficient TRα import.
Further support for the possibility of two import pathways comes from the fact that TR contains two NLSs (M.S. Mavinakere and L.A. Allison, manuscript in preparation). It is not uncommon for nuclear receptors to contain more than one
NLS; the glucocorticoid receptor, androgen receptor, estrogen receptor, and progesterone receptor all contain more than one sequence thought to act as an
NLS (Freedman and Yamamoto, 2004; Kaku et al., 2008; Savory et al., 1999;
Shank and Paschal, 2005; Ylikomi et al., 1992). The discovery of two or more
NLSs in a nuclear receptor, as is the case for TRα, would suggest the use of
76 multiple import pathways for the overall complete and efficient import of TRα. It is
possible that two NLSs exist because each binds a specific importin, a
suggestion which is valid even if the importin binding partners of the NLS are as
of yet unknown.
The suggestion that a key import pathway for TRα involves Imp β acting with Imp α1 (and possibly with Imp α3) would fit the finding that the NLS in the hinge region of TRα is a classical NLS (Lee and Mahdavi, 1993). Import from a cNLS would require the use of classical importins α and β acting together. The second NLS in the transactivation domain of TRα is a non-classical NLS, so the use of the classical Imp α/ β import pathway is not required (M.S. Mavinakere and L.A. Allison, unpublished observations). It is possible that this second, non- classical NLS follows an alternative pathway at a different time or in a cooperative fashion with the classical NLS to enable complete TRα import. This thesis research attempted to determine which importins were the binding partners of one or both of these NLSs, and future work should investigate the physical binding interactions of these importins with each NLS. Until all of TRα’s
NLS sites and the importins which bind those sites to mediate import are known, it is difficult to definitively say whether multiple signal-mediated pathways exist.
The use of multiple import pathways is not uncommon; for example, NF- kB, the adenovirus core protein pVII, the herpesvirus ORF 57 protein, the guanine-nucleotide exchange factor RCCI, the transcription factor c-Jun, the HIV
Rev protein, and DNA topoisomerase II are all imported by more than one importin (Table 4) (Arnold et al., 2006a; Arnold et al., 2006b; Fagerlund et al.,
77 Table 4. Proteins with Multiple Import Pathways
Protein Importins
GR Imp 7, Imp 8, Imp α/β, Imp 13
AR Imp α/β, nonclassical
HIV Rev Impβ, Imp 5, Imp 7, Transportin
NF-κB Imp α3, Imp α4
STAT 3 Imp α5, Imp α7
DNA Topoisomerase II Imp α1, Imp α3, Imp α5
herpesvirus ORF 57 Imp α1, Imp α5
RCC1 Imp α3, nonclassical
Adenovirus Core Protein pVII Imp α, Imp β, Imp 7, Transportin
c-Jun Imp β, transportin, Imp 7, Imp 9
ribosomal proteins Imp β, transportin, Imp 5, Imp 7
78 2005; Goodwin and Whitehouse, 2001; Mirski et al., 2007; Nemergut and
Macara, 2000; Waldmann et al., 2007; Wodrich et al., 2006). There is also
evidence that nuclear hormone receptors comprising the same superfamily as
TRα are commonly imported via multiple pathways. The glucocorticoid receptor
(GR) is an example of a nuclear receptor that utilizes multiple importins to gain entry into the nucleus. GR contains two NLSs and complete nuclear import appears to be facilitated by two import pathways, one using Ipo 7 and the other using Imp α/β (Freedman and Yamamoto, 2004; Sebastian et al., 2004). In addition, recent work has demonstrated that Importin 13 regulates the nuclear import of GR in airway epithelial cells (Tao et al., 2006). Therefore, the complete nuclear import of GR is likely accomplished by multiple importins, and individual importins may mediate GR import after activation by cell-specific signals. The androgen receptor (AR) is another nuclear receptor that contains two NLS and whose import is mediated via two pathways: one that is dependent on Imp α/β, and one that is Imp α/β-independent (Cutress et al., 2008; Kaku et al., 2008)
Substrate specificity of importins and the importin α family
Results from this thesis show that Ipo 7 was the most effective of all the importins tested at mediating TRα’s nucear import. The α1/β complex was shown to play a significant role in import, as well as the α3/β1 complex to a slightly lesser extent. This relates to the idea that all importins, whether in the Imp α or the Imp β-like family, demonstrate some level of substrate specificity. Importins
79 are only able to recognize certain cargo proteins, and cargo proteins can use
only certain importins for import.
The idea of substrate specificity is particularly well studied for the classical
importins. The existence of at least six distinct mammalian importin α isoforms implies that each isoform could recognize distinct target cargoes. While the alpha importins show cell and tissue-specific expression patterns, they are ubiquitously expressed among the various tissues, with the exception of importin α6, which is limited to the testis (Kohler et al., 1999; Sorokin et al., 2007). Although the six isoforms share significant homology and are all composed mainly of ARM repeats that bind basic NLSs, it is becoming increasingly clear that they exhibit distinct substrate specificities (Fontes et al., 2003; Fontes et al., 2000; Friedrich et al., 2006; Jans et al., 2000; Kohler et al., 1999; Miyamoto et al., 2002;
Miyamoto et al., 1997; Quensel et al., 2004). It is likely that the entire NLS binding groove of the importin α isoforms contribute to the specificity of cargo binding, and not just the two sites that interact directly with the NLS (Fagerlund et al., 2005). The importin’s NLS binding groove interacts with the structural features of the cargo to mediate high selectivity (Friedrich et al., 2006). It has also been suggested that the specificity of importin α subtypes is likely to be a cause of the diversity of NLSs found in cargo proteins (Kosugi et al., 2009).
Many substrate proteins co-exist in a living cell and compete for import into the nucleus by a particular importin α isoform. In studies where substrates are made to simultaneously compete for one importin, the importin will demonstrate selectivity in the cargo it imports (Friedrich et al., 2006). Therefore,
80 out of the host of importins present in a cell, each will differ in their functional
relevance for any particular protein’s nuclear import pathway. Studies of the
nuclear import pathways of different proteins shows that proteins are selectively
imported by specific importins (Refer to Tables 1 and 2 in the Introduction)
(Arnold et al., 2006b; Christ et al., 2008; Fries et al., 2007; Liu et al., 2005;
McBride et al., 2002; Miki et al., 2009; Nakahara et al., 2006; Pan et al., 2008;
Saijou et al., 2007; Talcott and Moore, 2000; Welch et al., 1999).
Appropriateness of the Livak method for qPCR analysis
Thesis results suggest that Importins 7, α 1/β, and α3/β play a role in mediating TRα import due to the changes in localization that occurred when RNA interference reduced their levels in the cell. The significance of these results, therefore, is entirely dependent on confirmation of knockdown. Real-time PCR allows for simultaneous amplification and detection of a cDNA template to measure the relative change in levels of mRNA expression. Real-time PCR is considered the gold standard for RNAi validation, and is preferable to methods that analyze knockdown at the protein level as well as other RNA-based assays
(Derveaux et al., 2010; Provenzano and Mocellin, 2007; Schmittgen and Livak,
2008).
There are multiple advantages associated with qPCR that make it the method of choice. RT-qPCR can amplify trace amounts of cDNA with high sensitivity, high specificity, good reproducibility, and has a wide dynamic quantification range (Derveaux et al., 2010; Valasek and Repa, 2005). Most
81 important for RNAi validation is qPCR’s extreme sensitivity. Since just 20 cycles
of PCR will amplify the initial cDNA amount over a million-fold, even small
changes in gene expression can be detected and analyzed. This high sensitivity
was required for this thesis research because the long half-life of importins can
mask the effect of RNAi at the protein level, particularly in transient transfections.
Therefore, a more direct measurement at the level of mRNA is needed to ensure
transcript levels are actually decreasing.
This thesis research used the Livak method for qPCR data analysis, which
relies on two assumptions which have to be examined prior to use. First, it
assumes that the reactions are all occurring with similar and near ideal (near
100%) efficiency. This was assumed to be the case for this research, since the
computer algorithm used to generate the RT2 qPCR primers ensures that the
amplification efficiency of all the primers is at least 90%, achieving efficiency
levels appropriate for the Livak method (Wang et al., 2010). Visual inspection of
amplification plots of housekeeping genes can also be used to estimate amplification efficiency. The GAPDH-primed samples all have the same shape to their amplification plots, showing that their amplification efficiencies are similar.
The second assumption is that the housekeeping gene used (GAPDH) is
expressed at a constant level between the treated and control samples. GAPDH
is a standard housekeeping gene that has been shown to be resistant to
experimental treatment in the literature, and is the housekeeping gene
recommended for use with these shRNA plasmids. The samples run using
GAPDH primers all have similar threshold cycles, demonstrating that their
82 expression is not affected by RNAi treatment. This is important in order for the
housekeeping gene to effectively normalize transcript levels of test genes before
comparing samples.
For the Livak method to be valid, the amplification efficiencies of the gene
of interest and reference gene must be approximately equal, and must be close to 100% (Schmittgen and Livak, 2008). The exponent conversion raising 2 to the negative of the ΔΔCt value is due to fact that the reaction ideally doubles the
amount of product per cycle. Yet amplification efficiency can differ depending on
the primer or sample, and even between individual wells (Schefe et al., 2006).
Efficiency strongly influences the measured Ct value, and due to the exponential
nature of qPCR kinetics even small variations in Ct values can have large effects
on calculated gene expression ratios. Therefore, for accurate determination of
initial gene levels, any differences in amplification efficiency must be taken into
account.
Since the inception of the Livak method, critics have pointed out that its
accuracy is limited by the assumption of ideal efficiency, and have published new
methods in an attempt to increase accuracy. For example, Pfaffl refined the
original Livak method of relative quantification to include the amplification
efficiency of the target and reference genes as calculated from cDNA standard
curves (Pfaffl, 2001). An alternative method determines amplification efficiencies
directly from the slope of the amplification plots (Liu and Saint, 2002). The
concept of amplification efficiency is just one of the many biological and technical
issues involved in qPCR data collection and analysis that can affect accuracy of
83 results. Over the past decade there has been a flood of research on qPCR
analysis and validity because of its wide applicability in multiple fields of
research. For many of its applications, especially in the field of medicine, there is
a great need for extreme accuracy in results (Burns et al., 2005).
Studies over the past decade have continued to examine the statistical
design qPCR experiments, calculation of normalized gene expression, and
statistical analysis of subsequent data (Cikos et al., 2007; Rebrikov and
Trofimov, 2006; VanGuilder et al., 2008). Multiple authors have published
approaches for enhanced, more accurate qPCR analysis, and many of these
focus on different methods of determining amplification efficiency and different
strategies to use these data once they are found (Alvarez et al., 2007; Batsch et
al., 2008; Burns et al., 2005; Bustin and Nolan, 2004; Gallup and Ackermann,
2008; Kitchen et al., 2010; Peirson et al., 2003; Pfaffl, 2010; Rutledge and Cote,
2003; Rutledge and Stewart, 2008; Schefe et al., 2006; Tichopad et al., 2010;
Yuan et al., 2008). Currently, there are a large number of statistical tools and programs that can be applied to analyze the results, including but not limited to
DART-PCR, LinRegPCR, REST, Q-gene, SAS, GenStat, geNorm, qBase, qCalculator, qPCR-DAMS, and SoFar (Dorak, 2008; (Lefever et al., 2009). In fact, as a result of the influx of multiple strategies and analyses, guidelines were recently published that detail the essential information that authors should provide whenever publishing a study (Bustin et al., 2009). Additionally, the XML- based Real-Time PCR Data Markup Language (RDML) has been developed to
84 enable straightforward, universal exchange of qPCR data and related information
(Lefever et al., 2009).
Today there are a number of quantification strategies available, and there
is no universal consensus on which is the best or most accurate method (Cikos
et al., 2007; Rebrikov and Trofimov, 2006). The Livak method is still the most commonly used technique for relative quantification due to its ease of use and
reliability of results (Cikos et al., 2007; VanGuilder et al., 2008). It is widely
accepted as providing a good approximation of gene expression ratios, and is
appropriate for qPCR data analysis for this thesis research for several reasons. It
is recommended by the RT2 Primer Assay system; since the primers are guaranteed to have consistently high levels of amplification efficiency, the Livak method is recommended for use. While critics are correct in pointing out the flaws in the Livak method, for our purposes, any published corrections to the
Livak method or entirely new methods would not serve to increase the accuracy of our results by any significant amount. Real-time PCR was used in this thesis research as part of a simple experiment to determine if knockdown occurred. So
while it is very true that Livak method does not give exact gene expression ratios,
this is not a problem for our study since the goal is just to verify that mRNA was
in fact knocked down to some level. The Livak method provides a very good
approximation of fold change in gene expression; at worst, it gives a close
estimate of the true relative quantification. The newer methods are useful for those experiments requiring very precise calculations of fold change, or those
with complex, high throughput designs, but are not generally necessary for
85 straightforward validation of RNAi knockdown (Derveaux et al., 2010; VanGuilder
et al., 2008).
Future Directions
The research presented in this thesis prompts several questions and
directions for future research. For example, while qPCR data were collected for
Imp α6 that confirmed knockdown, no cell count data were ever collected. Since
Imp α6 is limited to the testis, it is unlikely to play a role in mediating TRα’s nuclear import in HeLa cells; but still, it is an importin that we have the capability to test and analysis should be completed. In addition, due to time constraints, importin 5 (Ipo 5) was not examined although we have the shRNA plasmid and primers specific for Ipo 5. Given the significant role that Ipo 7 appears to play in
TRα’s nuclear import, it is important to test Ipo 5 and other β-like importins for their role in TRα import.
Another possible future direction would be to determine which shRNA plasmids, out of the 4 provided for each importin, are most effective at inducing knockdown. This would require testing the four plasmids individually using qPCR to determine which of the four produced the greatest knockdown. This could be done for Ipo 7, Imp β, Imp α1, and Imp α3, and the most effective plasmid(s) for each could then be transfected into HeLa cells to see if a greater shift in TRα localization could be observed. This could be of particular importance for Imp α5,
since the combination of 4 plasmids tested in this thesis research produced only
30% knockdown. It is possible that determining the one or two most effective
86 shRNA plasmids would produce effective knockdown at the levels seen for the
other importins.
Finally, given that Ipo 7, Imp α1/β, and Imp α3/ β seemed to play the greatest role in TRα’s import mechanism, future work could observe the effect on
TRα localization after knockdown of combinations of these importins to see if a greater change in distribution could be seen. For example, expression of Ipo 7 and Imp α1/β could be suppressed simultaneously since they appear to play the largest role in TRα import, or Imp α1, Imp α3, and Imp β could all be knocked down to test the role of the classical importins. In the same line as this thesis research, further importins could be tested. In addition, testing of different cell lines would enable the investigation of any cell-specific function of importins.
A number of in vitro binding assays could be performed in order to demonstrate a direct protein-protein interaction between the NLSs of TRα and the importins proposed in this thesis to mediate TRα import, such as Ipo 7, Imp
α1/β, and α3/β. GST pulldown assays and mammalian two-hybrid assays can both be used for this purpose. In addition, nuclear import assays using permeabilized cells could be used to investigate the ability of specific purified recombinant importins to reconstitute nuclear import in vitro. This has already been completed for Imp α1 and Imp β, but has yet to be done for Ipo 7, Imp α3, or any of the other importins tested in this research.
87 Significance and Conclusions
The role of TRα as a DNA-binding transcription factor depends on its
ability to gain entry into the nucleus. Nucleocytoplasmic transport is a critical
cellular process that provides the cell with an additional level of gene regulation.
By controlling nuclear import and export, the distribution of transcription factors and other important molecules can be regulated in response to external stimuli or
the specific needs of the cell (Bonamy et al., 2005; Kohler et al., 2002).
Nucleocytoplasmic transport therefore has important effects on signal
transduction pathways, major cellular processes, and cell cycle progression.
Regulation of these processes is essential for basic survival, as well as
development, differentiation and transformation (McLane and Corbett, 2009;
Poon and Jans, 2005; Yoneda, 2000). The study of nuclear transport, including
both import and export, is therefore necessary in order to understand these major
cellular processes. It is also critical in order to understand how the disturbance of
this delicately balanced process can lead to pathological cell conditions (Chahine
and Pierce, 2009; Davis et al., 2007; McLane and Corbett, 2009). In addition,
targeting nuclear transport of transcription factors has arisen in the past few
years as a new strategy to develop cancer drugs and other medical therapies
(Chahine and Pierce, 2009; Cheng, 2005b; Cheng et al., 2010; Henderson, 2003;
Kau and Silver, 2003; Vennstrom et al., 2008).
As is evidenced here, the study of nuclear import and export is in general
a vital topic of research with multiple applications. Nucleocytoplasmic transport is
of particular importance to TRα, since it is a shuttling protein that must enter the
88 nucleus to bind and regulate T3-responsive genes. Determining the mechanism by which TRα enters the nucleus is critical for a full understanding of the cellular response to T3. It also adds another level to our knowledge of TRα’s regulatory
function, which is particularly important given the physiological and medical
significance of its interaction with T3 (Cheng, 2005b; Cheng et al., 2010;
Vennstrom et al., 2008). This thesis research, along with future work relating to the nuclear import of TRα, will help shed light on the ways TRα regulates gene expression, and as to how mislocalization of TRα could contribute to pathological cell conditions.
89 Appendix
90 Detailed Methods for qPCR Validation of RNAi
Transfection prior to RNA extraction
Day One
1. Make 100 mm plates at 6 x 105 cells
Make one 100 mm plate per target RNA
• Aspirate media, 10 ml wash DPBS, aspirate; 2 ml trypsin for 2 minutes, aspirate, place in incubator for 2 minutes; add HeLa media to flask and resuspend so that there are 60 cells on hemocytometer grid • To each 100 mm plate, add 1 ml of the cells just counted and 9 ml of fresh media, so each has a total of 10 ml HeLa media • Incubate overnight
Day Two
2. Prepare transfection solutions:
Solution A: Add 10 µg plasmid DNA (plasmids diluted to 1 µg/µl = 10 µl) to 1.25 ml Opti-Mem to appropriately labeled 2 ml Eppendorf tubes
Solution B: Add 20 µl LPF to 1.25 ml Opti-Mem. Prepare one tube (15 ml Falcon tube, each labeled per plasmid) for each plasmid to be transfected
Incubate both solutions at room temperature for 5 min
3. Add Solution A to Solution B, mix, and let incubate at room temp for 20 min 4. Add entire 2.5 ml mixture to appropriately labeled 100 mm plate 5. Incubate and change media (10 ml fresh HeLa media) at 8 hrs after transfection
91
RNA Purification
Prior to first use (new kit):
• Add 500 µl Beta-mercaptoethanol (14.2 M) to the lysis solution for a final concentration of 1% • Make 95 - 100% ethanol: Add 80 ml (4 volumes) of 95-100% ethanol to the low stringency wash solution (provided as 5x concentrate) • Make 10 mM Tris, pH 7.5: Reconstitute DNase I (powder) by adding 250 µl 10 mM/pH 7.5 Tris, resuspending to mix; Store reconstituted DNase I in 10 µl aliquots stored at -20°C
Before each use :
• Heat elution solution to 70°C in a water bath • Find 1.5 ml microcentrifuge tubes • Make fresh 70% ethanol • Clean everything with 95% EtOH - and during as well
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
After plating, transfection, and media change:
Day Three - Procedure (Spin Format) for Cultured HeLa Cells
Purification to be carried out at normal fixing time after transfection (26 hrs post- transfection) All centrifugations at room temp; at 120,000 rpms. USE SOLUTIONS AS MADE ABOVE
1) Adherent cell culture seeded at 6 x 105 in 100 mm plate:
Rinse plate with DPBS twice (to remove all media), leaving about 1 ml of DPBS after the second wash Release cells from plate using cell scraper (scrape thoroughly; DPBS should look cloudy), transfer into 2 ml capped and labeled microcentrifuge tube (provided) Centrifuge tube for 2 min Decant supernatant, blot inside of tube with paper towel 92
2) Add 350 µl lysis solution to each tube, pipet up and down at least 12x to lyse cells
3) Add 350 µl of 70% EtOH to each tube, pipet up and down to mix well Make sure no bilayer is present, and that viscosity is substantially less than before
4) Insert labeled RNA binding column into 2 ml capless wash tube (both provided) Pipet the homogenized lysate into the RNA binding column Centrifuge 30 seconds Remove RNA binding column from wash tube, discard filtrate from wash tube, replace column into same wash tube
5) Add 700 µl low stringency wash solution to RNA binding column Centrifuge 30 sec Discard low stringency wash solution from wash tube, replace column into the same wash tube
6) For each column being prepared: Dilute 5 µl DNase I with 75 µl DNase dilution solution in 1.5 ml microcentrifuge tube (not provided) Add this 80 µl of diluted DNase I to the membrane stack at the bottom of each column
Allow digest to incubate at room temp for 30 min (increased from 15 minutes given in instructions) (cover with Kimwipe to prevent contamination) Centrifuge the columns for 30 sec Discard digest buffer from wash tube and replace column into same wash tube
7) Add 700 µl of high stringency wash solution to the RNA binding column Centrifuge 30 sec Discard high stringency wash solution from the wash tube and replace column into the same wash tube
8) Add 700 µl of low stringency wash solution to the RNA binding column Centrifuge 1 min 93
Discard low stringency wash solution from the wash tube and replace column into the same wash tube
9) Centrifuge additional 2 min to remove residual wash solution
10) Transfer the RNA binding column to a labeled 1.5 ml capped microcentrifuge tube (provided) Pipette 80 µl of the warmed (70°C) elution solution onto the membrane stack at the bottom of the RNA binding column Allow 1 min for solution to saturate the membranes Centrifuge 3:30 minutes to elute total RNA
11) Aliquot RNA into labeled 0.5 ml tubes (aliquots of ten µl). Store the eluted RNA samples in the -80°C; and save a small aliquot of each to run on the NanoDrop.
94
RNA Quality Control Following RNA purification: NanoDrop Evaluate RNA concentration and purity on the NanoDrop’s RNA setting. All RNA samples should demonstrate consistent quality according to the following criteria: a) Concentration greater than 4µg/ml total RNA ( = ng/µl)
b) A260:A280 ratio greater than 2.0
c) A260:A230 ratio greater than 1.7 If the extracted RNA samples do not meet these criteria…do them again.
Agilent 2100 BioAnalyzer RNA samples of good quality and concentration as determined by Nanodrop are then tested on the Agilent Bioanalyzer using an RNA 6000 Pico LabChip. Refer to Reagent Kit Guide for more information. Allow all reagents to equilibrate to room temperature for 30 minutes before use. Heat denature all RNA samples before use (70°C, 2 min) Protect dye and gel-dye mix from light. Always pipet into the bottom of the well to avoid bubbles Vortex Mixer set to 2400 rpm
Set up the Chip Priming Station 1. Remove the old syringe, insert the new syringe into the clip, slide it into the hole of the luer lock adapter and screw it tight. 2. Adjust the base-plate: Open the Chip Priming Station, unscrew and lift the base plate and insert it in position C. Retighten the screw. 3. Adjust the syringe clip to the topmost position
Set up the BioAnalyzer
95
1. Open the lid of the BioAnalyzer and make sure the electrode cartridge is inserted. 2. Adjust the chip selector to position 1. There should be no chip currently in place. Clean electrodes before running assays 1. Fill one of the well of an electrode cleaner chip with 350 ul RNase-free water 2. Place the electrode cleaner in the BioAnalyzer, close lid, wait 5 minutes
3. Open lid, remove and keep cleaner, wait 30 seconds for the water on the electrodes to evaporate and then close lid.
Protocol Prepare the Assay: 1) Prepare the diluted Ladder a) Place 5 µl of RNA 6000 ladder in a 1.5 ml microfuge tube; heat at 70°C for 2 minutes. Spin down the tube and keep on ice. b) Add 745 µl of RNase-free water; shortly vortex and spin down tube c) Make 10 µl aliquots of diluted RNA ladder and store at -80°C 2) Prepare the Gel a) When all reagents have equilibrated to room temperature for 30 minutes, place 550 µl of RNA 6000 Pico gel matrix into the top receptable of a spin filter (provided with kit) b) Place the spin filter in a microcentrifuge, spin for 10 minutes at 1500 g c) Aliquot 65 µl filtered gel into 0.5 ml RNase-free microfuge tubes from kit; store aliquots at 4°C 3) Prepare the Gel-Dye Mix – Careful, kit components contain DMSO a) Allow all reagents to come to room temperature for 30 minutes before use, protecting the dye concentrate from light during this time
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b) Vortex RNA 6000 Pico dye concentrate for 10 seconds and spin down c) Add 1 µl of RNA 6000 Pico dye concentrate to a 65 µl aliquot of filtered gel (prepared in step 2). Cap tube, vortex thoroughly and look to see that gel and dye are mixed. Store the dye concentrate at 4°C in the dark again. d) Spin tube for 10 minutes at room temperature at 13000g
Load the Chip: 1) Load Gel-Dye Mix: Refer to picture for location of wells a) Place a new RNA Pico chip on the Chip Priming Station b) Carefully pipette 9 µl of the gel-dye mix into the bottom of the well marked G (white G on black background, far right column, second well from bottom) c) Set the timer for 30 seconds. Make sure the plunger is at 1 ml, then close the Chip Priming Station until the latch lock clicks. Press the plunger, wait for 30 seconds and release the plunger with the clip release mechanism. Wait for 5 seconds, slowly pull back the plunger to the 1 ml position d) Open the Chip Priming Station, pipette 9 µl of the gel-dye mix in each of the wells marked G (black G on light background, top two wells in far right column. 2) Load RNA 6000 Pico Conditioning Solution and Marker a) Pipette 9 µl of the RNA 6000 Pico Conditioning Solution into the well marked CS (bottom well of far right column) b) Pipette 5 µl of the RNA 6000 Pico marker into the well marked with a ladder and each of the 11 sample wells (the first three columns from left). Do not leave any wells empty – add 6 µl of the RNA 6000 Pico Marker to each unused sample well 3) Load the Diluted Ladder and Samples a) Pipette 1 µl of the diluted RNA 6000 ladder (prepared above) into the well marked with the ladder (bottom well of third column from left). b) Heat denature RNA samples for 2 min at 70°C
97
c) Pipette 1 µl of each sample into each of the 11 sample wells (only one type of RNA per well) d) Place the Chip in the adaptor of the Vortex mixer; vortex for 1 min at 2400 rpm. Remove any liquid spill on the top of the chip with a tissue. **Make sure the run is started within 5 minutes from this point** Run the Assay: 1) Insert chip a) Open BioAnalyzer lid, make sure that the electrode cartridge is inserted properly and that the chip selector is in position 1. Place the Chip in the receptacle and close the lid – the electrodes located in the cartridge fit into the wells of the chip. b) The 2100 software screen shows that you have inserted a chip and closed the lid by displaying the chip icon at the top left of the instrument context. 2) Start Chip Run a) In the Instrument context, select Eukaryote Total RNA Pico assay from the Assay menu: Assays →Electrophoresis→RNA→ Eukaryote Total RNA Pico b) Modify/accept file prefix and file location (accept it) c) Click the start button to begin the run d) Enter sample information (names) by going to Data and Assay context and selecting the Chip Summary tab. Complete sample table and press apply. e) Data is presented as it comes up f) When done, remove the chip and clean electrodes.
Check Results (Electropherogram) 1) Successful Ladder run: (see Reagent Kit Guide for figure) 1 initial marker peak, 6 RNA peaks, all 7 peaks well resolved
2) Successful total RNA run (see Reagent Kit Guide for figure)
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2 ribosomal peaks (18S and 28S); 1 marker peak, all 3 peaks well- resolved Data can also be displayed in a Gel format
RT-qPCR
RNA/cDNA from cells transfected with importin specific-shRNA are just called the name of that importin For example: “β” = RNA/cDNA from cells transfected with shRNA coding for importin β “shRNA” or “sh” = RNA/cDNA from cells transfected with control shRNA
Only use RNA that has passed the quality control measures
1. Genomic DNA Elimination Mixtures:
Need two PCR tubes (little colored tubes) for each RNA sample. Assign one color tube to each RNA sample Label one tube + (positive; RT) and one tube – (negative; NRT)
For each RNA sample, combine in each PCR tube: 0.735 ug RNA + 2 ul GE + RNfH2O to total 10 ul and mix gently with pippetor
Each tube gets 0.735 ug RNA – this means the amount (ul) of RNA put into the tube will differ depending on the concentration of each RNA sample For example, you will use 7.35 ul of RNA that has a concentration of 100 ng/ul
At the end of this step, there are two identical tubes (+ and -) for each RNA sample
Brief centrifugation Incubate 42°C for 5 min Ice immediately for at least 1 min
2. Make real (RT #1) and NRT control (RT #2) RT cocktails: 99
Need 1 RT and 1 NRT cocktail for each RNA sample (one per GE tube made above). Make the RT/NRT controls in batches, making enough cocktail for one more sample than you actually have so there is sure to be enough. Use two tubes (one for RT, one for NRT)
Per ONE reaction/sample:
RT Cocktail (+) NRT cocktail ( - ) BC3 4 ul BC3 4 ul P2 1 ul P2 1 ul RE3 2 ul RNfH2O 3 ul RNfH2O 5 ul Total 10 ul Total: 10 ul
So, for example, if you’re preparing 5 samples of RNA/cDNA, prepare enough for 6 RT cocktails and 6 NRT cocktails
EX: for 5 samples = 6 reaction’s worth
RT Cocktail (+) NRT cocktail ( - ) BC3 24 µl BC3 24 µl P2 6 µl P2 6 µl RE3 12 µl RNfH2O 18 µl RNfH2O 30 µl
Total 60 ul Total: 60 ul
3. Make cDNA:
Add 10 µl of appropriate RT cocktail to each GE+RNA mix (10 µl tube from Step 1), mix with pippetor
Each RNA sample from Step 1 has 2 identical GE tubes. One receives the RT cocktail; the other receives the NRT cocktail
This uses all GE mix tubes made
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Brief centrifugation Incubate 42°C for 15 min Heat at 95°C for 5 min (degrades RNA, inactivates RT) ICE
At this point can hold cDNA on ice til PCR or overnight at -20°C
**So for each RNA sample, have an RT and an NRT cDNA tube**
4. Real-Time PCR setup: Each reaction is 25 ul total For each sample tested, there is an experimental control, and a matching NRT control There is one no template (cDNA) control per plate In each PCR well:
12.5 µl RT2 qPCR Master Mix 10.5 µl ddH20 1.5 µl cDNA template (for NTC, cDNA template is omitted) 1 µl RT2 qPCR primer
**For each importin-knockdown sample tested, there must be 6 wells** (given in format “cDNA + primer”):
For example, if testing Importin β knockdown, the wells are:
EXPERIMENTAL (RT) NRT
β + β NRT β + β β + HKG NRT β + HKG shRNA + β NRT shRNA + β
Also must be 2 wells for: shRNA + HKG NRT shRNA + HKG
Finally, one well for a NTC where ddH20 replaces the cDNA template
Explanation of the controls:
NTC – detects genomic contamination
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NRTs – detect contamination introduced during reaction setup. There is one NRT control for every sample run
There should be no fluorescence for these controls (i.e. no Ct or a very high Ct)
Cover wells carefully with optical tape Brief centrifugation to settle reactions to bottom of plate and get rid of air bubbles** 1-3 min, 1300 rpm
Run cycles (ABI StepOne)
1 cycle 95°C 10 min to activate DNA pol
40 cycles 95°C 15 sec detects and records SYBR 55°C 15 sec Green fluorescence from 72°C 30 sec every well during the annealing step
Produces amplification plot and gives Ct values for each well Look at Melting Curve, should be 1 peak per primer
Click on Gene Expression analysis – RQ graph RQ should be less than 1.0 and ideally less than 0.30 for cultures experiencing 70% knockdown
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1 2 3 4 5 6 7 8 U shRNA ctrl A GAPDH
U U U U shRNA ctrl shRNA ctrl shRNA ctrl shRNA ctrl B Imp β Imp α1 Imp α3 Ipo 7
U U U U NRT control set: Exact copy but NRT (N) not RT (U) Imp β Imp α1 Imp α3 Ipo 7 C Imp β Imp α1 Imp α3 Ipo 7
U U U U Imp β Imp α1 Imp α3 Ipo 7 D GAPDH GAPDH GAPDH GAPDH 103
E
U No cDNA F GAPDH
Include 4 components sample cDNA for ∆∆Ct formula Control cDNA (”sample”) GOI cDNA (”sample”) = - A- HKG primers(target)* - C- HKG primers(target) target primer - B- GOI primers(target) - D- GOI primers(target) * Only needed once ComparisonFile2 EukaryoteTotal RNA Pico.xac
Assay Class: EukaryoteTotal RNA Pico Comparison Path: D:\...rison for laura\ComparisonFile2 EukaryoteTotal RNA Pico.xac
Electrophoresis File Run Summary
Sh (7/29) Beta (8/4) Ladder
1 (8/1) 2 (8/1) 3 (8/4)
4 (8/4 alpha 5 alpha6
ipo7 Ladder
104 ComparisonFile2 EukaryoteTotal RNA Pico.xac
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Electrophoresis File Run Summary (Comparison Summary)
Chip Comments :
105 ComparisonFile2 EukaryoteTotal RNA Pico.xac
Assay Class: EukaryoteTotal RNA Pico Comparison Path: D:\...rison for laura\ComparisonFile2 EukaryoteTotal RNA Pico.xac
Electropherogram Summary
Sh (7/29)
106 ComparisonFile2 EukaryoteTotal RNA Pico.xac
Assay Class: EukaryoteTotal RNA Pico Comparison Path: D:\...rison for laura\ComparisonFile2 EukaryoteTotal RNA Pico.xac
Electropherogram Summary Cont ...
107 ComparisonFile2 EukaryoteTotal RNA Pico.xac
Assay Class: EukaryoteTotal RNA Pico Comparison Path: D:\...rison for laura\ComparisonFile2 EukaryoteTotal RNA Pico.xac
Electropherogram Summary Cont ...
Beta (8/4)
108 ComparisonFile2 EukaryoteTotal RNA Pico.xac
Assay Class: EukaryoteTotal RNA Pico Comparison Path: D:\...rison for laura\ComparisonFile2 EukaryoteTotal RNA Pico.xac
Electropherogram Summary Cont ...
109 ComparisonFile2 EukaryoteTotal RNA Pico.xac
Assay Class: EukaryoteTotal RNA Pico Comparison Path: D:\...rison for laura\ComparisonFile2 EukaryoteTotal RNA Pico.xac
Electropherogram Summary Cont ...
Ladder
110 ComparisonFile2 EukaryoteTotal RNA Pico.xac
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Electropherogram Summary Cont ...
111 ComparisonFile2 EukaryoteTotal RNA Pico.xac
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Electropherogram Summary Cont ...
1 (8/1)
112 ComparisonFile2 EukaryoteTotal RNA Pico.xac
Assay Class: EukaryoteTotal RNA Pico Comparison Path: D:\...rison for laura\ComparisonFile2 EukaryoteTotal RNA Pico.xac
Electropherogram Summary Cont ...
113 ComparisonFile2 EukaryoteTotal RNA Pico.xac
Assay Class: EukaryoteTotal RNA Pico Comparison Path: D:\...rison for laura\ComparisonFile2 EukaryoteTotal RNA Pico.xac
Electropherogram Summary Cont ...
2 (8/1)
114 ComparisonFile2 EukaryoteTotal RNA Pico.xac
Assay Class: EukaryoteTotal RNA Pico Comparison Path: D:\...rison for laura\ComparisonFile2 EukaryoteTotal RNA Pico.xac
Electropherogram Summary Cont ...
115 ComparisonFile2 EukaryoteTotal RNA Pico.xac
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Electropherogram Summary Cont ...
3 (8/4)
116 ComparisonFile2 EukaryoteTotal RNA Pico.xac
Assay Class: EukaryoteTotal RNA Pico Comparison Path: D:\...rison for laura\ComparisonFile2 EukaryoteTotal RNA Pico.xac
Electropherogram Summary Cont ...
117 ComparisonFile2 EukaryoteTotal RNA Pico.xac
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Electropherogram Summary Cont ...
4 (8/4
118 ComparisonFile2 EukaryoteTotal RNA Pico.xac
Assay Class: EukaryoteTotal RNA Pico Comparison Path: D:\...rison for laura\ComparisonFile2 EukaryoteTotal RNA Pico.xac
Electropherogram Summary Cont ...
119 ComparisonFile2 EukaryoteTotal RNA Pico.xac
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Electropherogram Summary Cont ...
alpha 5
120 ComparisonFile2 EukaryoteTotal RNA Pico.xac
Assay Class: EukaryoteTotal RNA Pico Comparison Path: D:\...rison for laura\ComparisonFile2 EukaryoteTotal RNA Pico.xac
Electropherogram Summary Cont ...
121 ComparisonFile2 EukaryoteTotal RNA Pico.xac
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Electropherogram Summary Cont ...
alpha6
122 ComparisonFile2 EukaryoteTotal RNA Pico.xac
Assay Class: EukaryoteTotal RNA Pico Comparison Path: D:\...rison for laura\ComparisonFile2 EukaryoteTotal RNA Pico.xac
Electropherogram Summary Cont ...
123 ComparisonFile2 EukaryoteTotal RNA Pico.xac
Assay Class: EukaryoteTotal RNA Pico Comparison Path: D:\...rison for laura\ComparisonFile2 EukaryoteTotal RNA Pico.xac
Electropherogram Summary Cont ...
ipo7
124 ComparisonFile2 EukaryoteTotal RNA Pico.xac
Assay Class: EukaryoteTotal RNA Pico Comparison Path: D:\...rison for laura\ComparisonFile2 EukaryoteTotal RNA Pico.xac
Electropherogram Summary Cont ...
125 ComparisonFile2 EukaryoteTotal RNA Pico.xac
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Ladder
126 ComparisonFile2 EukaryoteTotal RNA Pico.xac
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Electropherogram Summary Cont ...
127 KPNA1 Replicates Treatments 1 2 3 Mean Stdev* SEM** Var n 76 86 84 82.0 5.29 3.06 28.00 n+c 16 10 14 13.3 3.06 1.76 9.33 wc 8 4 2 4.7 3.06 1.76 9.33
KPNA2 Replicates Treatments 1 2 3 Mean Stdev* SEM** Var n 75 75 82 77.3 4.04 2.33 16.33 n+c 17 23 16 18.7 3.79 2.19 14.33 wc 8 2 2 4.0 3.46 2.00 12.00
KPNA3 Replicates Treatments 1 2 3 Mean Stdev* SEM** Var n 89 89 87 88.3 1.15 0.67 1.33 n+c 11 10 11 10.7 0.58 0.33 0.33 wc 0 1 2 1.0 1.00 0.58 1.00
KPNA4 Replicates Treatments 1 2 3 Mean Stdev* SEM** Var n 83.5 79 81 81.2 2.25 1.30 5.08 n+c 12 15 13 13.3 1.53 0.88 2.33 wc 4.5 6 6 5.5 0.87 0.50 0.75
KPNA6 Replicates Treatments 1 2 3 Mean Stdev* SEM** Var n 88.5 75.5 89 84.3 7.65 4.42 58.58 n+c 5.5 21 8 11.5 8.32 4.80 69.25 wc 6 3.5 3 4.2 1.61 0.93 2.58
KPNB1 Replicates Treatments 1 2 3 Mean Stdev* SEM** var n 61 79 75 71.7 9.45 5.46 89.33 n+c 25 17 20 20.7 4.04 2.33 16.33 wc 14 4 5 7.7 5.51 3.18 30.33
Ipo7 Replicates Treatments 1 2 3 Mean Stdev* SEM** n 74 57 67.5 66.2 8.58 4.95 73.58 n+c 11.5 15 20 15.5 4.27 2.47 18.25 wc 14.5 28 12.5 18.3 8.43 4.87 71.08
128 Tra cont Treatments 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Mean Stdev* SEM** n 89.0 90.0 90.0 78.0 81.0 93.5 91.0 80.5 91.5 84.0 82.0 84.0 91.5 82.0 77.0 85.7 5.4859 1.416 n+c 9.0 9.0 9.0 13.0 14.5 3.0 8.0 13.5 6.5 14.5 14.5 10.0 5.0 12.0 13.5 10.3 3.6823 0.951 129 wc 2.0 1.0 1.0 9.0 4.5 3.5 1.0 6.0 2.0 1.5 3.5 6.0 3.5 6.0 9.5 4.0 2.7967 0.722
shRNA cont Treatments 1 2 3 4 5 6 7 8 9 10 11 12 13 Mean Stdev* SEM** n 85 90 85 90.5 89 75.5 86.5 80.5 86.5 90 82 76 87.5 84.9 5.06 1.404 n+c 10 10 9.5 3.5 11 17 11.5 14.5 9.5 6.5 11 16.5 9 10.7 3.71 1.028 wc 5 0 5.5 6 0 7.5 2 5 4 3.5 7 7.5 3.5 4.3 2.53 0.701 TRa Control Statistics
Descriptives Descriptive Statistics
N Range Minimum Maximum Mean Statistic Statistic Statistic Statistic Statistic Std. Error NTR 15 16 77 94 85.67 1.416 NCTR 15 12 3 14 10.33 .951 WCTR 15 8 1 10 4.00 .722 Valid N (listwise) 15
Descriptive Statistics
Std. Deviation Variance Skewness Statistic Statistic Statistic Std. Error NTR 5.486 30.095 -.110 .580 NCTR 3.682 13.560 -.525 .580 WCTR 2.797 7.821 .763 .580 Valid N (listwise)
Descriptive Statistics
Kurtosis Statistic Std. Error NTR -1.539 1.121 NCTR -.709 1.121 WCTR -.352 1.121 Valid N (listwise)
130 shRNA Control Statistics
Descriptives
Descriptive Statistics
N Range Minimum Maximum Mean Statistic Statistic Statistic Statistic Statistic Std. Error N 13 15 76 90 84.92 1.404 N+C 13 14 4 17 10.73 1.028 WC 13 8 0 8 4.35 .701 Valid N (listwise) 13
Descriptive Statistics
Std. Deviation Variance Skewness Statistic Statistic Statistic Std. Error N 5.061 25.619 -.859 .616 N+C 3.706 13.734 .056 .616 WC 2.528 6.391 -.550 .616 Valid N (listwise)
Descriptive Statistics
Kurtosis Statistic Std. Error N -.291 1.191 N+C .402 1.191 WC -.545 1.191 Valid N (listwise)
131 References
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