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Ch-Ch-Ch-Changes! Measuring the Differential Expression of Small Noncoding Rna in Influenza A H1n1 Infected Epithelial C...

Permanent Link: http://ncf.sobek.ufl.edu/NCFE004667/00001

Material Information

Title: Ch-Ch-Ch-Changes! Measuring the Differential Expression of Small Noncoding Rna in Influenza A H1n1 Infected Epithelial Cells Using Q-Pcr
Physical Description: Book
Language: English
Creator: Schuster, Andrew
Publisher: New College of Florida
Place of Publication: Sarasota, Fla.
Creation Date: 2012
Publication Date: 2012

Subjects

Subjects / Keywords: Influenza
PCR
Molecular Biology
sncRNA
mRNA
Swine Flu
Biochemistry
Micro RNA
Small Nucleolar RNA
Genre: bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Micro and small nucleolar RNA (miRNA and snoRNA) represent two classes of functional small-noncoding RNA (sncRNA) whose expression is closely regulated within the cell (Holley and Topkara, 2011). Viral infection has been shown to influence the expression levels of these sncRNA. One such virus, influenza A H1N1, poses a threat to the current and future public health (Peng et al, 2011). Prior to this project, deep sequencing of the small RNA (<200 nt) extracted from normal human bronchial epithelial (NHBE) cells that were infected with influenza A H1N1 (2009 strain) was performed. By measuring sncRNA expression in a similar set of influenza infected cells using quantitative polymerase chain reaction (qPCR), this project sought to confirm previous observations of differentially expressed sncRNA. The small RNA extracted from uninfected and infected NHBE cells was converted to cDNA and quantified via qPCR. Primers were designed to target highly expressed fragments of genes that were shown to be differentially expressed in the deep sequencing results. A comparison of fold-changes measured by each technique yielded a Pearsons' correlation coefficient of 0.12, suggesting inherent differences between techniques and experimental error. Despite these issues, this project identified several sncRNA that are differentially expressed in response to influenza infection.
Statement of Responsibility: by Andrew Schuster
Thesis: Thesis (B.A.) -- New College of Florida, 2012
Electronic Access: RESTRICTED TO NCF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE
Bibliography: Includes bibliographical references.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The New College of Florida, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Local: Faculty Sponsor: Walstrom, Katherine

Record Information

Source Institution: New College of Florida
Holding Location: New College of Florida
Rights Management: Applicable rights reserved.
Classification: local - S.T. 2012 S39
System ID: NCFE004667:00001

Permanent Link: http://ncf.sobek.ufl.edu/NCFE004667/00001

Material Information

Title: Ch-Ch-Ch-Changes! Measuring the Differential Expression of Small Noncoding Rna in Influenza A H1n1 Infected Epithelial Cells Using Q-Pcr
Physical Description: Book
Language: English
Creator: Schuster, Andrew
Publisher: New College of Florida
Place of Publication: Sarasota, Fla.
Creation Date: 2012
Publication Date: 2012

Subjects

Subjects / Keywords: Influenza
PCR
Molecular Biology
sncRNA
mRNA
Swine Flu
Biochemistry
Micro RNA
Small Nucleolar RNA
Genre: bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Micro and small nucleolar RNA (miRNA and snoRNA) represent two classes of functional small-noncoding RNA (sncRNA) whose expression is closely regulated within the cell (Holley and Topkara, 2011). Viral infection has been shown to influence the expression levels of these sncRNA. One such virus, influenza A H1N1, poses a threat to the current and future public health (Peng et al, 2011). Prior to this project, deep sequencing of the small RNA (<200 nt) extracted from normal human bronchial epithelial (NHBE) cells that were infected with influenza A H1N1 (2009 strain) was performed. By measuring sncRNA expression in a similar set of influenza infected cells using quantitative polymerase chain reaction (qPCR), this project sought to confirm previous observations of differentially expressed sncRNA. The small RNA extracted from uninfected and infected NHBE cells was converted to cDNA and quantified via qPCR. Primers were designed to target highly expressed fragments of genes that were shown to be differentially expressed in the deep sequencing results. A comparison of fold-changes measured by each technique yielded a Pearsons' correlation coefficient of 0.12, suggesting inherent differences between techniques and experimental error. Despite these issues, this project identified several sncRNA that are differentially expressed in response to influenza infection.
Statement of Responsibility: by Andrew Schuster
Thesis: Thesis (B.A.) -- New College of Florida, 2012
Electronic Access: RESTRICTED TO NCF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE
Bibliography: Includes bibliographical references.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The New College of Florida, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Local: Faculty Sponsor: Walstrom, Katherine

Record Information

Source Institution: New College of Florida
Holding Location: New College of Florida
Rights Management: Applicable rights reserved.
Classification: local - S.T. 2012 S39
System ID: NCFE004667:00001


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CH CH CH CHANGES! MEASURING THE DIFFERENTIAL EXPRESSION OF SMALL NONCODING RNA IN INFLUENZA A H1N1 INFECTED EPITHELIAL CELLS USING Q PCR BY ANDREW SCHUSTER A Thesis Submitted to the Division of Natural Sciences New College of Florida In partial ful fillment of the requirement for the degree Bachelor of Arts Under the Sponsorship of Dr. Katherine Walstrom Sarasota, Florida May, 2012

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ii Acknowledgements This thesis would not exist if not for the contributions of my friends, family, academic mento rs, and everything in between. I would like to thank my friends for putting up with my sporadic absence from the social sphere. I would also like to thank my family for always being emotionally supportive, as well as accepting cuses. I am very grateful to have had the financial and academic support of both Lovelace Respiratory Research Institute and New College of Florida. As is true with many things, certain individuals have played a larger role than others. Without the help and solidarity of Justin, Sarah, and Chris, this entire process would have been far more excruciating. I would like to thank Elizabeth for babysitting me in the lab and being a constant source of support, no matter what. I would also like to thank the fac ulty and staff I have interacted with over the years. Dr. Chris topher vital. Without Colleen, I would not have been able to acquire the materials necessary for this project. And of cours e, I would like to thank Dr. Steven Shipman for taking the time to be on my committee. Last, but certainly not least, I would like to thank Dr. Katherine Walstrom. Her guidance during this project and over the years has been an invaluable resource in my a cademic development. I would not be where I am now without her advice and unwillingness to let me slack off.

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iii Table of Contents Acknowledgements ii List of Tables vii List of Figures viii Abstract ix Chapter 1: Introduction 1 1.1 Influenza 2 1.1.1 Infection 3 1.1.2 Replication 4 1.2 Micro RNA 6 1.2.1 Micro RNA basics 7 1.2.2 miRNA biogenesis 8 1.2.3 Enter: RISC 10 1.2.4 Gene silencing 12 1.3 Small nucleolar RNA 13 1.3. 1 snoRNA basics 14 1.3.2 C/D box snoRNA 15 1.3.3 H/ACA box snoRNA 18 1.3.4 snoRNA or miRNA? 19 1.4 Small noncoding RNA and disease 21 1.4.1 miRNA and viruses 21 1.4.2 miRNA and influenza 23 1.4.3 snoRNA and viruses 26

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iv 1.4.4 snoRNA and influenza 27 1.5 Project Outline 29 Chapter 2: Methods 30 2.1 Cell culture and infection 30 2.2 RNA extraction 31 2.3 cDNA pool creation 33 2.4 qPCR 35 2.4.1 qPCR the basics 35 2 .4.2 Primer selection 38 2.4.3 qPCR procedure 39 2.5 Deep sequencing 40 Chapter 3: Results and Discussion 45 3.1 Turning C t 46 3.2 General comparison of qPCR and deep sequencing results 52 3.3 SNORD15B 5 4 3.4 SNORD26 59 3.5 hsa miR 200a 65 3.6 hsa miR 34c 69 Chapter 4: Conclusion 73 4.1 Future improvement s 73 4.2 Future directions 74 4.3 Differential expression a poten tial basis for anti viral therapeutics? 76 4.3.1 Expression signatures as biomarkers of disease 76 4.3.2 RNA based therapeutics 78

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v Appendix A Primers 81 A.1.1 Forward primers 8 1 Appendix B qPCR data 82 A.2.1 SNORD15B L 82 A.2.2 SNORD15B R 83 A.2.3 SNORD26 L (trial 1) 84 A.2.4 SNORD26 L (trial 2) 85 A.2.5 SNORD26 R (trial 1) 86 A.2.6 SNORD26 R (trial 2) 87 A.2.7 hsa miR 200a 5p ( L ) 88 A.2.8 hsa miR 200a 3p ( R ) (trial 1) 89 A.2.9 hsa miR 200a 3p ( R ) (trial 2) 90 A.2.10 hsa miR 34c 3p ( R ) 91 Appen dix C Dissociation curves 92 A.3.1 SNORD15B L 92 A.3.2 SNORD15B R 93 A.3.3 SNORD26 L (trial 1) 94 A.3.4 SNORD26 L (trial 2) 95 A.3.5 SNORD26 R (trial 1) 96 A.3.6 SNORD26 R (trial 2) 97 A.3.7 hsa miR 200a 5p ( L ) 98 A.3.8 hsa miR 200a 3p ( R ) (trial 1) 99 A.3.9 hsa miR 200a 3p ( R ) (trial 2) 100 A.3.10 hsa miR 34c 3p ( R ) 101

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vi Appendix D Deep s equencing data 102 A.4.1 hsa miR 200a deep sequencing results 102 References 103

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vii List of Tables 2.1 t 39 2.2 Cycler conditions for dissociation curve 40 3.1 Overview of deep sequencing data 45 3.2 hsa miR 34c L C t data 48 3.3 Overview of qPCR results and analogous deep sequencing read count ratios 52 3.4 Comparison of SNORD15B qPCR and deep sequencing results 54 3.5 Average SNO RD15B C t values 54 3.6 Comparison of SNORD26 qPCR and deep sequencing results 59 3.7 Average SNORD26 C t values 59 3.8 Averages of SNORD26 C t standard errors 60 3.9 Comparison of hsa miR 200a qPCR and deep sequencing results 65 3.10 Average hsa miR 200a C t values 65 3.11 Comparison of hsa miR 34c qPCR and deep sequencing results 69 3.12 Average hsa miR 34c C t values 69 3.13 Averages of hsa miR 34c C t standard errors 70

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viii List of Figures 1.1 Influenza 3 1.2 miRNA biogenesis and function 8 1.3 C/D box snoRNA and associated proteins 16 1.4 snoRNP asse mbly 17 1.5 H/ACA box snoRNA and associated proteins 18 2.1 Flowchart of cDNA pool creation 34 2.2 Polymerase chain reaction 37 2.3 Overview of GA II deep sequencing 41 2.4 hsa miR 200a R sequencing data 43 2.5 44 3.1 hsa miR 34c L amplification curve 47 3.2 hsa miR 34c L dissociation curve 51 3.3 SNORD15B deep sequencing results 55 3.4 SNORD15B L deep sequencing results 56 3.5 SNORD15B R deep sequencing results 56 3.6 Comparison of human and mouse SNORD26 61 3.7 Patterns of snoRNA expression in the deep sequencing data 63 3.8 hsa miR 34c deep sequencing results 71

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ix Abstract Micro and small nucleolar RNA (miRNA and snoRNA) represent two classes of functional small noncoding RNA (sncRNA) whose expression is closely regulated within the cell (Holl ey and Topkara, 2011). Viral infection has been shown to influence the expression levels of these sncRNA. One such virus, influenza A H1N1, poses a threat to the current and future public health (Peng et al 2011). Prior to this project, deep sequencing of the small RNA (<200 nt) extracted from normal human bronchial epithelial (NHBE) cells that were infected with influenza A H1N1 (2009 strain) was performed. By measuring sncRNA expression in a similar set of influenza infected cells using quantitative poly merase chain reaction (qPCR), this project sought to confirm previous observations of differentially expressed sncRNA. The small RNA extracted from uninfected and infected NHBE cells was converted to cDNA and quantified via qPCR. Primers were designed to t arget highly expressed fragments of genes that were shown to be differentially expressed in the deep sequencing results. A comparison of fold changes measured by each technique 2 suggesting inherent differ ences between techniques and experimental error. Despite these issues, this project identified several sncRNA that are differentially expressed in r esponse to influenza infection. Dr. Katherine Walstrom Division of Natural Sciences

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1 1. Introduction Beginn ing with the discovery of lin 4 in 1993, small non coding RNA C. elegans to being recognized as an important regulator of cellular processes (Lee et al 1993). Another class of small non c oding RNA, known as small nucleolar RNA (snoRNA), has been shown to possess an increasingly diverse role in the cell (Bachellerie et al 2002). The importance of miRNA and snoRNA in the cell is matched only by their relevancy to disease. One disease in par ticular, influenza, currently poses a threat to the public health and has the potential to pose an even greater one in the future. Seasonal epidemics of influenza are responsible for the loss of 250,000 to 500,000 lives every year and pandemics have in the past taken the lives of millions, such as Spanish Flu which had a death toll of over 40 million people in 1918 (WHO, 2009; Davis et al 2000). The interaction between influenza and miRNA, as well as snoRNA, has been described numerous times in past litera ture. In this thesis, the differential expression of miRNA and snoRNA in normal human bronchial epithelial cells (NHBE) infected with the 2009 H1N1 strain of influenza was measured using quantitative polymerase chain reaction (qPCR) and compared to previou sly acquired deep sequencing data. Verification of this data will provide future targets for studies on the role non coding RNA play in influenza infection, which could ultimately result in the development of miRNA or snoRNA based treatments of influenza i nfection.

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2 1.1 Influenza Influenza, a member of the Orthomyxoviridae family of viruses, possesses a genome of negative sense single stranded segments of RNA. The virus is separated into three genuses influenza A, B, and C (Umbach et al, 2010). Influenza viruses consist of a protein spiked viral envelope that surrounds a core of viral proteins and genetic material (Fig. 1.1 A) (Nayak et al 2004). Influenza A (referred to as the genus that is home to Swine Flu, the strain utilized in the research described in this thesis. In addition to being sorted into the A, B, and C genera, influenza viruses are placed into subtypes based on their viral envelope proteins, hemagglutinin (HA) and neuraminidase (NA). Based on the antigenic properties of these two proteins, virus strains are designated as H_N_, with the appropriate number filling in the blanks. In birds, 16 HA and 9 NA subtypes have been observed, while only two subtypes (H1 N1 and H3N2) are known to currently circulate in humans (Medina et al 2011). Strains are named in the following format: genera/geographical origin/strain number/year of isolation and (H_N_). Therefore, the fourth strain of H1N1 influenza A virus isolated in California in 2009 would be A/Cal/04/09 (H1N1) (WHO, 1980).

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3 Figure 1.1 Influenza A. General overview of influenza structure and genome. Eight negative sense strands of viral RNA compose the influenza genome, encoding polymerase basic protein 1 (PB1) polymerase basic protein 2 (PB2), polymerase acidic protein (PA), hemaglutinin (HA), nucleoprotein (NP), neuraminidase (NA), matrix proteins 1 and 2 (encoded by M), and non structural proteins 1 and 2 (encoded by NS). NA, M1, HA, and M2 are expressed on the surface of the viral envelope. B. Infection overview. 1) Influenza binds to host sialic acid receptors via HA. 2) Virus enters the host via endocytosis. 3) The low pH of the endosome leads to viral envelope endosmal fusion, and releases the viral ribon ucleoproteins (vRNP) which localizes in the nucleus. 4) The viral genome is transcribed into mRNA, positive sense RNA (which is transcribed back into negative sense RNA), or negative sense small viral RNA. 5) Viral envelope proteins are transported to the outer plasma membrane, allowing vRNP (exported via nuclear export proteins, NEP, and M1) to bud off of the cell. 6) After budding, the new virion goes on to infect other cells. C. Diagram of the vRNP complex. Viral RNA is wrapped around NP and NEP (not sho wn). The three polymerase proteins (PB1, PB2, and PA) form the RNA dependent RNA polymerase (RdRp) at the end of the complex (From Medina et al 2011). 1.1.1 Infection In order to replicate, influenza must infect a host, as shown in Fig. 1.1 B (1 6). In order to accomplish this task, influenza must bind to the surface of host

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4 epithelial cells via its own surface proteins. The viral protein HA binds to host cell sialic acid receptors, granting the virus access. In humans, HA proteins primarily recognize s ialic acids bound to galactose in 2, 6 linkages, while the HA of avian 2, 3 linkages (Gamblin et al 2004). After binding to host receptors (1), HA must be cleaved by endogenous proteases to permit the virus access to the cell, via endocytosis (2 ) (Medina et al 2011). Once the virus is within the endosome, the relatively acidic environment (pH = 5 6) induces conformational changes in the HA proteins leading to the fusion of the viral and endosomal membranes. The increase in acidity also induc es the opening of the viruses M2 ion channel, a type III transmembrane tetramer protein. M2 allows viral ribonucleoproteins (vRNP). The vRNP then enters the nucleus via nuclear localization signals, which allow it to hijack endogenous nuclear import proteins (3) (Samji, 2009). 1.1.2 Replication The vRNP consists of a complex of proteins and viral genetic material. Negative sense singled strands of vRNA wrap around the nucleoprotein (NP) and nuclear export protein (NEP). At the end of this complex are the three viral polymerase proteins polymerase basic protein 1 (PB1), polymerase basic protein 2 (PB2), and polymerase acidic protein (PA) making up the RNA dependent RNA polymerase, or RdRp (Fig. 1.1 C) (Samji, 2009). RdRp produces three types of RNA. In order to produce copies of its genome, the RdRp transcribes complementary

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5 positive sense strand RNA, which are later transcribed into complementary negative sense RNA the reby copying the viral genome (Medina et al, 2011). The RdRp also produces negative sense small viral RNAs, which regulate the transition between viral transcription and translation (Umbach et al 2010). In order to replicate itself, the virus must also g enerate additional viral proteins via viral mRNA (4). Endogenous RNA polymerase II, along with the RdRp, cap fr om endogenous mRNA (Samji, 2009). The viral mRNA must also be polyadenylated at of cleava ge at a polyadenylation signal followed by the addition of the poly(A) tail. Viral template RNA contains a series of 5 7 uracil residues approximately 17 towards the end of transcription, producing a long adenosine tail (Poon et al 1999). This allows the viral non structural 1 (NS1) protein to inhibit proteins involved in cellular mRNA poly(A) tail formation, preventing the exportation of cellular mRNA, thereby favoring viral mRNA translation (Nemeroff et al 1998). Viral mRNA are then exported from the nucleus to the cytoplasm of their hosts, where they are translated into viral proteins via host ribosomal machinery (Medina et al 2011). For infection to propagate, viru ses must release several copies of themselves from each cell they infect. The new copies of negative sense vRNP are exported to

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6 the cytoplasm, through the activity of the viral protein M1. Once in the cytoplasm, the vRNP uses the plasma membrane of its hos t to form a new virion. The surface proteins of influenza (HA, NA, and M2) are transported to the outer surface of the plasma membrane, creating a new viral envelope as the vRNP buds off of the cell (5) (Samji, 2009). After budding from its previous host, the virus goes on to infect other cells in the organism, repeating the cycle of infection (6). 1.2 Micro RNA Since the initial discovery of micro RNA in 1993, our understanding of these short non coding RNA sequences has increased dramatically (Lee et al 1993). Up until the early 2000s, miRNA were referred to as small temporal RNAs due to their discovered role in the timing of C. elegans development and were not identified in humans until 2000 (Pasquinelli et al 2000). Not long after this, Calin et al i dentified a correlation between the loss of miR 15 and mi R 16 and the presence of B cell leukemia providing a distinct link between miRNA expression and human disease (Calin et al 2002). Since then, miRNA have been associated with various cancers, genetic disorders, and infectious diseases (van Rooji, 2011; Kloosterman and Plasterk, 2006). The focus of this section will be to answer the following questions: What are miRNA? What is their function in the cell, and how do they perform it? Subsequent sections will discuss the role miRNA plays in disease, specifically influenza.

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7 1.2.1 Micro RNA basics Simply put, miRNAs are short (~22 nucleotide) single stranded RNA sequences which associate with a complex of proteins called the RNA induced silencing complex (RISC), permitting the regulation of countless cellular pathways and diseases (van Rooji, 2011). As stated earlier, hundreds of miRNA have been identified in a wide variety of organisms. The naming convention for miRNA takes the diversity and breadth of t hese RNA into consideration. There are four parts to a miR 200a 3p). The first three letter prefix describes the specific organism from which the miRNA is isolated, which in this case is H omo sa piens. The next three lette r prefix indicates whether a precursor (mir) or mature (miR) miRNA is being described. The differences between these two types of miRNA will be elaborated on in the next section. MiRNA are numbered based on when they were reported, meaning hsa miR 200a 3p is the 200 th mature miRNA discovered in Homo sapiens another member of the miR 200 family (e.g. hsa miR 200b 3p). As will be discussed later, mature miRNA are derived from a double stranded RNA sequen ce. The respectively, is being described. It should be noted that in older literature, a dichotomy between guide (functional) and star (non functional) strands of miRNA was made In this system, the guide strand has no label after the miRNA number and miR 200a*). This naming convention has been largely abandoned, due to the discovery of miRNA

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8 species which possess t wo functional duplex strands (miRBase, 2011). In order to glean a more complete understanding of what miRNA are, it is important to understand where they come from. 1.2.2 miRNA biogenesis While miRNAs actively perform their role as 22 nucleotide long s ingle strands of RNA, they begin as a part of a much larger transcript. The genes encoding these primary transcripts, or pri miRNA, can be found at intronic, exonic, or intergenic genomic locations. Each miRNA gene may contain several different miRNA, form ing polycistronic transcripts (Fig. 1.2., 1) (Holley and Topkara, 2011). Figure 1.2 miRNA biogenesis and function 1) miRNA are transcribed from intergenic, intronic, or polycistronic sources. 2) Drosha and DGCR8 (not shown) cleave the pri miRNA, forming the pre miRNA. 3) Exportin 5, coupled with Ran GTP (not shown) transports the pre miRNA from the nucleus to the cytoplasm. 4) Dicer, enhanced by PACT and TRBP (not shown), cleaves the pre miRNA forming the miRNA:miRNA* duplex. 5) The miRNA:miRNA* duplex consists of a guide (miRNA) and star (miRNA*) strand. 6) One of the duplex strands (miRNA) is loaded into the RISC, which consists of Argonaute, Dicer and TRBP. RISC is guided by the associated miRNA towards target mRNAs. 7) Transla tional repression can occur once UTR. 8) mRNA degradati on can occur in cases of complete (top) or incomplete (bottom) binding to UTR. Which of the two forms of gene silencing occurs is widely debated, as discussed in Section 1.2.4 (From Holley and Topkara, 2011).

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9 These genes are transcribed by either RNA polymerase II or III, depending on the gene (Borchert et al 2006). The resulting RNA strand then folds into itself, forming a hair pin stru cture within the transcript. Inside the stem, which is roughly 33 nucleotides long, is the mature miRNA. In order to obtain the mature miRNA from the pri miRNA, the transcript must be trimmed down in two enzymatic reactions (Carthew and Sontheimer, 2009). While the pri miRNA is still contained within the nucleus, it interacts with a complex consisting of a Drosha and DGCR8 protein. Drosha, a RNase III enzyme, is responsible for the cleavage of the RNA sequences flanking the miRNA containing hair pin. DGCR8 (DiGeorge critical region 8 or Pasha in C. elegans and D. melanogaster ) possesses RNA binding domains, allowing it to directly interact with the flanks of the pri miRNA. The RNA binding domains of DGCR8 keep Drosha both stable and in the appropriate locati on during cleavage, making it vital to pri miRNA processing (2) (Winter et al 2009). After Drosha DGCR8 processing, a 70 100 nucleotide long hairpin structure is all that remains of the pri miRNA (Holley and Topkara, 2011). This pre cursor miRNA hairpin called pre miRNA, exits the nucleus via Exportin 5, a member of the karyopherin family of nucleocytoplasmic transport factors, coupled with the presence of Ran GTP (3) (Yi et al 2003). Upon entering the cytoplasm, the pre miRNA is met by Dicer, another member of the RNase III family. Dicer contains two RNase III domains and a PAZ domain. The cleaving abilities of the RNase III domains are guided by the PAZ domain, which determines the cleavage site (Carthew and Sontheimer, 2009). Cleavage of the pre miRN A results in a 21 25

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10 nucleotide long overhangs at both ends, called the miRNA duplex (Winter et al 2009). The RNase III domains and PAZ are roughly the length of 25 base pairs (65 Angstroms) apart, imparting Dicer with the ability to cleave out the appropriate length of RNA from the pre miRNA (MacRae et al 2006). Dicer activity, while not dependent on their presence, is enhanced by the protein activator of PKR (PACT) and Tar RNA binding protein (TRB P) which helps stabilize Dicer (4) (Winter et al 2009). 1.2.3 Enter: RISC After its formation, only one of the strands of the miRNA duplex is utilized (Holley and Topkara, 2011). As stated earlier, only one strand within the duplex possesses function wi thin the cell at a time (5). In the past, the functioning and respectively. Due to recent discoveries of functional star strands, miRNAs are now nding on their position within the duplex (miRBase, 2011). However, for the sake of simplicity, the guide / star dichotomy will be employed in this section unless otherwise noted. In order for the guide strand to serve its purpose, it must first be load ed into the RNA induced silencing complex (RISC). A functional RISC must contain three proteins: Dicer, Ago2, and TRBP. Ago2 is one of the four members of the human Argonaute (Ago) protein family and is responsible for binding to the guide miRNA, facilitat ing its entry into the RISC (MacRae et al 2008). Ago proteins across all organisms possess a PAZ, PIWI, and MID domain. The PAZ domain allows the

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11 (Cenik and Zamore, 2011). By binding to the guide miRNA, the unwinding of the duplex is facilitated (possibly by some unidentified helicase), releasing the star strand (Khvorova et al 2003; Carthew and Sontheimer, 2009). In some cases, the PIWI endonuclease domain of the Ago prote in (in this case, Ago2) cleaves the star strand (Shin, 2008; Cenik and Zamore, 2011). The factors that determine which strand of the miRNA duplex is selected as the guide strand are not entirely understood. When the sequences of 16 D. melanogaster 96 C. elegans and 87 H. sapiens miRNA were analyzed, a bias end were observed as guide strands more often than their counterparts (Khvorova et al 2003). Stability at end is not always the determi nant behind strand selection. It has been observed that, across a range of different M. musculus (mouse) tissue samples, miRNA guide and star strands are differentially expressed and in some instances, the ended strand is predominant (Ro et al 2007). Analysis of D. melanogaster and H. sapiens miRNA sequences has also shown that the presence of star strands, respectively, in human miRNA (Hu et al 2009). Th e apparent complexity behind strand selection is one of the many ways a single miRNA species is able to regulate numerous pathways in several different cell lines. Once the appropriate strand has been selected and the star strand has been discarded, the m iRNA bound Ago protein coupled with Dicer and TRBP forms the

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12 mature RISC. As its name would suggest, the guide strand is responsible for guiding RISC to its intended mRNA target (6) (Gregory et al 2005). By leading the RISC to the mRNA, the miRNA is able to serve its function in the cell as a regulator of gene expression. 1.2.4 Gene Silencing Unlike their cousins, the small interfering RNA (siRNA), miRNA do not always bind to their mRNA targets with perfect complementarity. The main determinant of which miRNA mRNA pairs will form is a six to eight nucleotide importance in target specificity, the seed region of a miRNA is conserved across organisms (Friedman et al 2009). The ma UTR), although miRNAs have been recently shown to be capable of targeting sites within the protein coding region of a mRNA (Tay et al 2008). Target sites could potentially lie wi untranslated region of mRNA as well (Lytle et al 2007). The small size of the seed region (6 8 nucleotides) allows one miRNA family, or a group of miRNA sharing the same seed region, to target 300 to 400 mRNAs on average (Bartel, 2009). T he genes encoded within mRNA can be silenced by miRNA through either translational repression (7), mRNA degradation (8), or a combination of the two (Holley and Topkara, 2011). Translational repression is thought to be accomplished by either inhibition of translation initiation or increased ribosomal dissociation during elongation (Humphreys et al 2005; Peterson et al 2005). While translational

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13 repression was once thought to be responsible for the majority of miRNA gene silencing, recent findings indicate that at least 84% of silencing may be attributed to miRNA induced mRNA destabilization. The destabilization of mRNA is thought to most often occur through miRNA mediated mRNA deadenylation, removing the transcripts protective poly(A) tail. Whether or not translational repression or mRNA decay are coupled is still under debate (Guo et al 2010). While some studies show that mRNA decay occurs independent of the repression of translation, others support the notion that mRNA decay and translational inhibition are coupled processes (Eulalio et al 2009; Iken and Maquat, 2007). However, r ecent work by Bazzini et al has shown that translational repression occur s independent of deadenylation. When miR s determined that translational repression occurred despite the disruption of mRNA deadenylation. This finding suggests that miRNA mediated gene silencing can be accomplished by reducing the rate of translation initiation (Bazzini et al 2012). Regardles s of the mechanism, miRNA mRNA binding prevents the translation of the encoded gene, are able to serve an important and far reaching role in biological systems.

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14 1.3 S mall nucleolar RNA RNA is undoubtedly one of the most important chemicals in biology, playing an extremely diverse role in all living things. In order for a gene in DNA to be expressed, it must first be converted to RNA (mRNA). Those sequences of RNA are then translated into amino acid sequences (proteins) by the ribosomes, which contain RNA (rRNA) at their cores. The amino acid chain is only formed if tRNAs containing the appropriate amino acid are present during translation. Translation itself, as discus sed in the previous section, is regulated by the activity of other RNAs, such as miRNA. For translation to occur at all, rRNA must first be modified by another type of RNA the small nucleolar RNA. Guiding the modification of rRNA is one of several key fun ctions small nucleolar RNAs (snoRNA) perform in the cell (Kiss, 2002). 1.3.1 snoRNA basics Small nucleolar RNAs (snoRNA) are one of several members of the non coding RNA family, consisting of the RNAs that are not translated into proteins (i.e. any RNA th at is not a messenger RNA). As their name suggests, snoRNA are relatively short RNA sequences which range from 60 to 220 nucleotides in length and reside in a particular domain of the nucleus called the nucleolus (Holley and Topkara, 2011). The nucleolus s erves several key functions within the cell, such as acting as the site of ribosome synthesis (Thiry and Lafontaine, 2005).

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15 The role of snoRNA within the cell is similarly diverse. Although snoRNAs serve multiple roles, their predominant function in the c ell is to guide the chemical modification of other RNAs. By associating with various proteins, snoRNAs are able to target other RNAs, resulting in enzymatic reactions between the associated proteins and the target RNA. The majority of these targeted RNAs a re ribosomal RNAs (rRNA), which reside in the nucleolus as well (Matera et al 2007). Apart from facilitating rRNA processing, snoRNAs have been implicated in mRNA splicing as well as functioning as miRNA precursors (Holley and Topkara, 2011). Based on t heir function and sequence motifs, snoRNAs are separated into two families, called C/D and H/ACA box snoRNAs. Over 200 snoRNAs have been identified in eukaryotes, comprising a processing network that is responsible for facilitating roughly 200 nucleotide m odifications on a single mature rRNA (Matera et al 2007; Holley and Topkara, 2011). Along with the aforementioned roles in mRNA splicing and miRNA biogenesis, snoRNA hold an undoubtedly important place in post transcriptional regulatory networks. 1.3.2 C/ D box snoRNA As stated before, snoRNAs are classified into their respective families based on their function and sequence. For a snoRNA to be placed in the C/D box family, it must contain two sequence motifs called the C (RUGAUGA; R represents a purine) a C/D box snoRNA also possess a stem loop secondary structure. As the C and D

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16 structure brings t he two boxes close to one another. C/D box snoRNA will also et al 2007). Figure 1.3 C/D box snoRNA and associated protei ns General overview of a member of the C/D snoRNA family. structure, possessing C box and D box (along with less box). 15.5 K protein binds close to stem loop, followed by NOP58 and fi brillarin binding. After transcription, Bcd1 is exchanged for a NOP56 and second boxes (From Holley and Topkara, 2011) The majority of C/D and H/ACA snoRNAs are transcribe d from intronic sources. During their transcription by RNA polymerase II, C/D box snoRNA associate with several proteins forming the pre ribonucleoprotein (pre RNP) complex (Hirose and Steitz, 2001; Hirose et al 2006). Assembly of the pre RNP begins with the association of the 15.5 KDa protein (15.5 K) with the C/D box motif et al 2007). The binding of 15.5K leads NOP56 proteins to proteins then recruits a methyl transferase named fibrillarin (Holley and Topkara, 2011). It is thought that the pre RNP consists of 15.5K, NOP58, fibrillarin, and an exchange factor protein, Bcd1 ( Fig. 1.4, 1). The pre RNP is transported to the

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17 Figure 1.4 snoRNP assembly 1) Core RNP (ribonucleoprotein) proteins (red) and an exchange fact or (yellow) interact with RNA polyermase terminal domain (CTD) and assemble at nascent snoRNA, forming pre RNP. 2) The exchange factor (Bcd1for C/D pre RNP; Naf1 for H/ACA pre RNP) is traded for the fourth core RNP protein (NOP56 for C/D sn oRNP; Gar1 for H/ACA snoRNP) upon transportation via Cajal bodies. 3) The mature RNP localizes in the nucleolus, a Cajal body, or a telomere (not discussed) (From Matera et al 2007). nucleolus via Cajal bodies, wherein Bcd1 is replaced with NOP56 and a second fibrillarin (2), forming the mature C/D ribonucleoprotein, or C/D RNP (3) (Matera et al 2007). Upon maturation, the C/D RNP as sociates with another RNA, called the target RNA. The target RNA is bound by an anti boxes, positioning the fibrillarins for targeted methylation, the main function of C/D RNPs. Fibrillarin perform O methylation on the target RNA base five nucleotides from each D box. This methylation is an important part of rRNA processing, accounting for 101 chemical modifications on each rRNA molecule. While their exact utility in rRNA has yet to be elucida O methylations may confer some nuclease resistance on rRNA or help facilitate ribosomal assembly (Holley and Topkara, 2011).

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18 1.3.3 H/ACA box snoRNA H/ACA box snoRNA also possess two sequence motifs, in this case the H (ANANN A; N is any nucleotide) and ACA (ACA) boxes (Holley and Topkara, 2011). Unlike C/D box snoRNAs, H/ACA box snoRNAs have a secondary structure containing two stem loops, making them slightly longer than their counterparts. The H and ACA boxes lay between the respectively (Fig. 1.5 A) (Kiss, 2002). Similar to C/D RNP formation, H/ACA RNP formation begins during the transcription of the H/ACA snoRNA. The pre RNP consists of three of the four core components of th e H/ACA RNP dyskerin, NOP10, and NHP2 (Fig 1.4, 1). The missing component, Gar1, is incorporated after the departure of Naf1 (Fig 1.4, 2), an exchange protein, in a pathway similar to the aforementioned maturation of C/D Figure 1.5 H/ACA box snoRN A and associated proteins A. General overview of a member of the H/ACA box snoRNA family. In humans, H/ACA box snoRNA consist of two stem loops, with an H box between the stems and an ACA box lank. Each stem loop possesses guide sequences in the bulges of the stems, also mature Archaeal H/ACA RNP. Eukaryotic proteins homologous to the Archaeal proteins shown are dyskerin (Cbf5), NHP2 (L7Ae), GAR1 (Gar1), and N OP10 (Nop10). Dyskerin and NHP2bind to the snoRNA directly, whereas GAR1 and NOP10 bind indirectly via protein protein interactions (From Matera et al 2007).

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19 RNPs via Cajal bodies (Matera et a l 2007). Assembly of the pre RNP is initiated through the binding of dyskerin to the ACA box. NHP2 also binds to the snoRNA directly. The other two proteins, NOP10 and GAR1, then bind to dyskerin forming a complete H/ACA RNP (Fig 1.4, 3) (Kiss et al 2010 ). The mature H/ACA RNPs main function is to convert uridine residues on the target RNA to pseudouridine residues through the enzymatic properties of dyskerin. Target RNAs are bound by guide sequences contained in bulges of the H/ACA snoRNA stem loops ca occurs 14 16 nucleotides upstream of the H and ACA boxes, with one conversion per stem loop (Fig. 1.5 B). H/ACA RNPs perform 91 of these conversions for each rRNA, making them similarly important to ribosome synthesis (Holley and Topkara, 2011). 1.3.4 snoRNA or miRNA? In addition to facilitating the chemical modifications of other RNA, such as rRNA, some snoRNAs have been identified as potential sources of miRNA. The first snoRNA shown to have miRNA like functions was a small Cajal body specific RNA, or scaRNA. Unlike canonical snoRNA, which localize in the nucleolus, scaRNA accumulate specifically in the Cajal bodies (the same organelle used to transport pre RNP to the nucleolus). Both C/D and H/ACA box scaRNA have been identified (Darzacq et al 2002). In 2008, Ender et al demonstrated that scaRNA15, also known as ACA45, is processed by the miRNA machinery. This scaRNA possesses

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20 designation). Apart from its pseudouridylation capabilities, ACA45 serves as the source of a small RNAs that hold post transcriptional regulatory capabilities similar to miRNA. This finding was initially met with skepticism by the authors, as it was possib le that ACA45 was mislabeled as a snoRNA and was actually just a miRNA et al ., who validated that ACA45 binds with Gar1, a sign of H/ACA RNP formation. The researchers also demonstrated that a fragment of ACA45, around 22 to 23 nucleotides in length, was derived in a Dicer dependent manner and associated with Ago1 and Ago2. In addition to interacting with cytoplasmic miRNA associated proteins, the ACA45 small RNA fragment was shown to reg ulate a predicted mRNA target (Ender et al 2008). Two years after Ender et al ., several other snoRNAs with miRNA like functions (termed sno miRNA) were discovered. Brameir et al were able to indentify 11 C/D box snoRNAs that also functioned as sno miRN A, and were therefore capable of effectively silencing genes complementary to their miRNA like snoRNA fragment (Brameir et al 2011). Conversely, selections of miRNA precursors that possess sequence and structural similarities to H/ACA box snoRNAs have bee n shown to associate with dyskerin. The continually growing list of known and suspected sno miRNA suggests an evolutionary relationship between snoRNA and miRNA (Scott et al 2009).

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21 1.4 Small n on coding RNA and disease Considering their diverse and wid e reaching role in cellular regulatory networks, it is not surprising that small non coding RNA (i.e. miRNA and snoRNA) are active participants in a similarly wide range of human diseases. As these RNAs are capable of both aiding and hindering the progress ion of a number of diseases, their continued study is absolutely necessary in establishing a thorough understanding of human disease. Unfortunately, the role of miRNA and snoRNA in disease is too diverse to give a comprehensive overview of their involveme nt in all disease. Therefore, in keeping with t he subject and aims of this thesis, this section will focus on the role miRNA and snoRNA possess in viral infections, specifically influenza. 1.4.1 miRNA and viruses As described in section 1.2.4, miRNA are post transcriptional regulators of gene expression, allowing them to affect great changes in a biological system. Understandably, this overarching role in the cell brings miRNA into contact with invading viruses. The nature of this contact, unfortunately, is not always of cellular defense. Viruses are capable of encoding their own miRNA, or even using host miRNA to promote themselves (Cullen, 2010). The Herpesviridae family of viruses possesses the majority of known virally encoded miRNA. Among this fami ly is the Epstein Barr virus (EBV), which is currently known to encode 25 pre miRNA in its double stranded DNA (dsDNA)

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22 genome (Skalsky and Cullen, 2010). EBV, best known for its suspected role in infectious mononucleosis, is also associated with several ty pes of cancers. In order to evade immune responses, EBV is able to prevent apoptosis of its host through the use of its miRNA (Boss and Renne, 2010). One example is the activity of miR BART 16, miR BART 17 5p, and miR BART1 5p, miRNAs encoded within the vi UTR of the LMP1 gene, reducing the levels of the encoded LMP1 protein in the host. Low levels of LMP1 aides the virus in two ways. The decrease in LMP1 expression stimulates the nuclear fac light chain enhancer of activated B cells (NF promoting cell proliferation. Over expression of LMP1 has been shown to reduce the level of NF LMP1 levels in its host ( Lo et al 2007). Decreases in LMP1 expression also contributes to the prevention of the apoptotic effects associated with high levels of LMP1 expression. The expression of LMP1 is associated with lower levels of Bcl 2 expression, an anti apoptotic protein (Liu et al 2002). The three aforementioned known miRNA, which all possess similarly important roles in the EBV life cycle (Skalsky and Cullen, 2010). Viruses are also able to suppress the expression of host mi RNAs, allowing them to circumvent the anti viral capabilities of many host proteins. An example of this viral defense is the suppression of miR 17 5p and miR 20a by the notorious human immunodeficiency virus type 1 (HIV UTR of the PCAF gene, suppressing the expression of the endogenous p300/CBP associated

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23 factor (PCAF). PCAF, a histone acetylase, is an important cofactor of the HIV 1 protein, Tat, which itself is associated with enhanced viral gene expression. By suppressing ho st miR 17 5p and miR 20a levels, through some unknown mechanism, HIV network (Triboulet et al 2007). Fortunately for humans and other organisms, cellular miRNAs are also capable of participating in host anti viral defenses. The replication of primate foamy virus type 1 (PFV 1), a retrovirus that is related to the aforementioned HIV 1, is inhibited by the presence of miR 32 in the host. Using human cells, researchers demonstrated t hat miR 32 is capable of binding and suppressing the expression of PFV et al 2005). The amount of research on miRNAs that are known to be beneficial or harmful to hosts during viral infection con tinues to grow and will no doubt lead to a better understanding of host virus interactions and potential targets for therapeutics. 1.4.2 miRNA and influenza Among the myriad of viruses which are targeted by or utilize miRNA is influenza. Unlike the Herpes viridae viruses, influenza has not yet been demonstrated to encode miRNA. Considering the single stranded RNA genome of influenza, it would be advantageous for the virus to avoid the miRNA processing enzymes Drosha and Dicer. Ensuring the protection of its genome from these host RNase enzymes would therefore increase the difficulty for influenza to successfully express its own miRNA (Boss and Renne, 2010). Regardless of its lack of miRNA

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24 ownership, several instances of miRNA mediated host influenza interac tions have been described in the literature. In 2010, Song et al demonstrated that three human miRNA (miR 323, 491, and 654) are able to inhibit the replication of H1N1 Influenza A virus (A/WSN/33). The researchers initially identified potential binding sites for the aforementioned split into fragments that were inserted into a luciferase reporter. The reporter was placed in MDCK cells (a canine epithelial cell line), which overexpressed miR 323, 491, and 654 artificially. This assay and subsequent sequencing of the targeted PB1 fragment (PB1 5) revealed a conserved region of PB1 which all three miRNA target. Repeating the experiment in different cell lines revealed variab le expression of miR 323 and 491, and relatively high expression levels of miR 654 in MDCK cells alone. This suggests that these miRNA serve as a defense against influenza in MDCK cells, and potentially in other cell lines (Song et al 2010). Focusing o n another H1N1 virus, a reconstructed 1918 influenza virus (r1918), Li et al demonstrate a correlation between lethality and miRNA expression. By measuring the levels of 567 miRNA in mouse lung tissue infected with either the r1918 or non lethal A/Texas/36 /91 (Tx/91) strain of influenza, the researchers were able to identify eighteen miRNAs which were differentially expressed. Among these miRNA is miR 200a, one of the miRNAs focused on in this thesis. In the study, miR 200a was shown to be downregulated dur ing r1918 infection and below the cutoff (1.5 fold change) in Tx/91 infected cells, suggesting a link to lethality. The

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25 loss of miR 200a in r1918 infected cells was shown to be correlated to the upregulation of several key immune responses (Li et al 2010a ). Among the mRNAs targeted by miR 200a are those encoding signal transduction and transcription proteins 2 and 4 (STAT2 and STAT4), which are responsible for regulating the expression of several other interferon (IFN) stimulated genes. IFNs members of t he cytokine protein family, hold an important role in the anti viral immune response. proteins necessary to mount a cellular defense via various cell signaling pathways ( Fensterl and Sen, 2009). Atypical immune response has been suggested as a factor of miR 200a expression and high lethality (Kobasa et al 2006; Li et al 2010a). Usi ng the same set of lethal and seasonal strains, Peng et al identified another set of miRNA related to pathogenicity. It should be noted that the only miRNAs that appear in both Li et al and Peng et al are miR 223 and miR 21, which are consistently upregula ted (Peng et al 2011; Li et al 2010a). This may indicate different experimental conditions and may also suggest that factors other than pathogenicity may be influencing the differential expression of the miRNAs described in each study. Influenza is une quivocally influenced by, and an affecter of, the miRNA present in host cells. In a demonstration of the general importance of RNA interference (RNAi), Matskevich and Moelling knocked out Dicer in Vero cells (kidney epithelial cells which lack a completely functioning interferon system). This

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26 led to a sharp increase in susceptibility, indicating the importance of miRNA and other members of the RNAi network to host defenses. The researchers also observed that H7N7 influenza A infection in A549 (a human epith elial cell line) cells possessing a functional IFN led to a decrease in the levels of Dicer mRNA and protein, suggesting influenza targets Dicer by some unknown mechanism (Matskevich and Moelling, 2007). We can therefore assume that miRNA pose some type of threat a gainst influenza infection, there by necessitating strains evolve to evade host defenses. 1.4.3 snoRNA and viruses When compared to miRNA, the literature has far less to say about the relationship between snoRNAs and viruses during infection. The articles that do discuss snoRNAs in the context of viral infection show that these RNA are undoubtedly affected by viruses, and in some cases, influence infection. As stated before, snoRNA are small non coding RNA sequences which localize in the nucleolu s of the cell, the site of ribosome assembly (Kiss, 2002). Along with snoRNA, the nucleolus has been shown to house viral proteins. The list of nucleolus localized viral proteins is quite diverse, including proteins from RNA, retro, and DNA viruses (Hiscox 2002). Among these proteins are Tat and Rev, a pair of HIV 1 regulatory proteins. Research conducted by Michienzi et al suggests that HIV 1 and the nucleolus are connected to an even higher degree. The researchers designed ribozymes that would target and cleave a conserved sequence long terminal repeat of HIV 1 RNA. When the HIV specific ribozymes

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27 (localized in the nucleolus) were added to cells infected with HIV 1, viral replication rates dropped significantly. This suggests that the nucleo lus plays a role in HIV 1 RNA processing and may even be directly involved. It is even possible that the virus utilizes endogenous snoRNP complexes for modifying its own RNA (Michienzi et al 2000). Based on this suspected localization and the effectivenes s of the ribozyme (a hammerhead ribozyme was incorporated into U16, a snoRNA), therapeutics based on the interaction between the nucleolus and HIV 1 may one day be developed to combat infection (Hiscox, 2007). In 2009, a snoRNA of viral origin was discov ered. Hutzinger et al identified a snoRNA, dubbed v snoRNA1, encoded within the genome of Epstein Barr virus (EBV). Like human C/D box snoRNA, v snoRNA1 possesses a C and D box motif. The researchers also demonstrated that v snoRNA1 associates with fibrill arin, Nop56, Nop58, and the 15.5 K protein. Unlike most human C/D box snoRNA, v snoRNA1 does not seem to target any rRNA or small nuclear RNA. When EBV mutants that lacked v snoRNA1 were studied, no phenotypic changes were observed, leaving the function of v snoRNA1 unsolved. Hutzinger et al also identified a v lending further evidence to the functionality of the RNA (Hutzinger et al 2009). 1.4.4 snoRNA and influenza As mentioned in the previous section, viral proteins have been shown to structural 1 (NS1) and nucleoprotein (NP), are known to localize within the nucleolus (Volmer et

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28 al 2010; Davey and Dimm ock, 1985). The function of NS1 nucleolar localization is not currently known, but differences between infected human and avian cells have been observed. When Volmer et al infected various cell cultures with influenza A, NS1was shown to localize in the nuc leolus of all avian cells but only a few mammalian cells. While influenza A was shown to alter the morphology of infected cells, it is not known whether this is dependent on NS1 localization or simply virally induced intracellular stress (Volmer et al 201 localization to the nucleolus is also unknown. However, research by Ozawa et al has demonstrated that nucleolar localization of this protein, while not strictly essential, is important to influenza replication (Ozawa et al 20 07). What does the presence of these viral proteins mean for snoRNA expression and function? In their 2011 study, Peng et al observed differential expression of snoRNA, in addition to miRNA, in infected mouse lung cells. The changes in expression varied across cell lines and snoRNA species (Peng et al 2011). It is known that, upon nucleolar localization, viral proteins can alter the distribution of nucleolar protein such as fibrillarin (Hiscox, 2002). Cells completely lacking fibrillarin are known to und ergo apoptosis, making the enzyme essential to both the life of the organism, as well as any invading virus. However, low levels of fibrillarin can be tolerated, as shown in a study conducted by Newton et al The researchers demonstrated that decreases in fibrillarin levels cause a similar decrease in C/D box snoRNA (specifically U76) levels (Newton et al 2003). Without the protection of the snoRNP complex proteins, the C/D box snoRNA may be more susceptible to

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29 endogenous RNase enzymes. If proteins affilia ted with the H/ACA snoRNP are also affected by nucleolar localization of influenza proteins, a similar effect on H/ACA box snoRNA would be expected. 1.5 Project Outline In this thesis, the expression levels of several small nucleolar and micro RNA will be measured using quantitative polymerase chain reaction (which will be described in more detail in Section 2.5). The changes in expression levels will then be compared to previously acquired deep sequencing data (detailed in Section 2.1). The small non co ding RNA that will be investigated are SNORD15B (a C/D box snoRNA), SNORA26 (an H/ACA box snoRNA), hsa miR 200a, and hsa miR 34c. If both methods yield similar levels of expression, this project will identify a small group of non coding RNAs that are affec ted by influenza. This will provide a better understanding of the effects of influenza infection and possibly targets for small non coding RNA based therapeutics.

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30 2. Methods In this thesis, the small non coding RNA from influenza infected cells wa s measured via quantitative polymerase chain reaction (qPCR). This required a series of steps in which the cells were cultured, infected, and lysed. The RNA from this lysis solution was extracted. The small (<200 nucleotides) RNA were extracted from the t otal RNA and were then reverse transcribed and amplified, yielding a complementary DNA (cDNA) pool. In order to measure the levels of small nucleolar and microRNA expression, qPCR was utilized. These results were then analyzed t method and c ompared against previously acquired deep sequencing results. 2.1 Cell culture and infection Normal human bronchial epithelial (NHBE) cells were utilized as the hosts in this project, as they are readily infected by influenza virus (Zeng et al 2011). The NHBE cells were cultured on an air liquid interface in 250 mL BEBM (Cambrex), 250 mL DMEM H (Sigma), altered BEGM Singlequots(Cambrex), 500 L retinoic acid (5 x 10 5 M stock), and BSA (1.5 mg/mL stock). The following alterations were made to the BEGM Sin glequots for each culture 10 L of epidermal growth factor (as opposed to 500 L) were used and both gentamicin and amphotericin were removed. Cultures were kept at 37 C / 5% CO 2 Half of the NHBE cells were infected at one multiplicity of infection (1 MOI). As the average cell count for the cultures was 3 x 10 5 cells well 1 3 x 10 5 plaque

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31 forming units (PFU) for each well were necessitated. Using a sample of virus (A/Cal/04/2009, a strain of influenza A H1N1 virus) with a viral concentration of 2 x 10 7 PFU mL 1 0.015 L of concentrated virus was added to each of the infected (24 hr) wells in the form of a diluted solution of virus and growth media (85 L growth media and 15 L virus solution per well). Growth media consisted of Opti MEM 1 reduced seru m media (Invitrogen), 1X Antibiotic Antimycotic (Invitrogen), and 10% fetal bovine serum (Invitrogen). The uninfected (0 hr) wells were treated with 100 L of growth media. In total, there were six uninfected and six infected wells. Both sets of cells were incubated for 70 minutes at 37 C / 5% CO 2 After the incubation period, the inoculin (diluted viral solution) was aspirated off the wells. The uninfected wells were treated with 100 L of QIAzol (Qiagen) twice. These QIAzol washes were transferred int o separate Eppendorf tubes, with 2 wells per tube (i.e. 400 L of lysed cells per tube). The infected wells were washed with 100 L of 7.2 pH 1X PBS (Invitrogen) three times, filled with 600 L of fresh growth media, and incubated at 37 C / 5% CO 2 for 24 hours. After the incubation period, the 24 hr wells underwent the same QIAzol treatment described above. 2.2 RNA extraction The total RNA was extracted from each lysis solution (0 hr A, B, C and24 hr A, B, and C) using the Tri Reagent protocol (Molecular Research Center, Inc). The first step of the protocol, the addition of QIAzol (which was substituted for Tri Reagent), was described in the previous section. Next, 400 L of bromochloropropane was added to each sample. The samples were mixed vigorously,

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32 pl aced at room temperature (25 C) for 10 minutes, and were then centrifuged at 12,000 g for 15 minutes at 4 C. After this, the aqueous phase of each sample was transferred to a fresh tube and combined with 2 L of blue glycol and 200 L of isopropanol. The mixture was inverted gently approximately 30 times, stored at room temperature for 10 minutes, and centrifuged at 12,000 g for 10 minutes at 4 C. The supernatant of the resulting solution was decanted, leaving an RNA pellet. The pellet was washed with 1 m L of 75% ethanol and centrifuged at 12,000 g for 5 minutes at 4 C. After this, the ethanol was decanted and the pellet was air dried. Air dried pellets were reconstituted in 50 L of nanopure water. The RNA concentration of each sample was measured via a Nano Drop 8000 Spectrophotometer (ThermoScientific). In order to obtain small (< 200 nucleotides) RNA segments, the previously extracted total RNA (via the Tri Reagent protocol) was treated with the mir Vana miRNA isolation kit (Ambion). The total RNA was mixed with 5 volumes of Lysis/Binding Buffer (i.e. 250 L for 50 L of total RNA solution). A tenth (1/10) volume of miRNA Homogenate Additive was then added, and the mixture was placed on ice for 10 minutes. After the incubation on ice, a third (1/3) vol ume of 100% ethanol was added and the solution was vortexed thoroughly. This mixture was then passed through a filter cartridge into a fresh tube via 1 minute of centrifugation at 5,000 g. After passing all of the solution through this filter, the flow thr ough contained RNAs less than 200 nucleotides in length. The larger (200+ nucleotides) RNAs were saved by passing nanopure water through the filter via a

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33 similar centrifugation (two nanopure water washes of 100 L and 50 L and 1 minute of centrifugation a t 10,000 g). The filtrate was mixed with two thirds volume of ethanol and vortexed thoroughly. This solution was then passed through a second filter cartridge in the same manner as described above. This time, the filter contained the small RNA fraction. Th e filter was washed with 700 L of miRNA Wash Solution 1 via centrifugation (1 min at 5,000 g). After discarding the flow through, the filter was washed twice with 500 L of Wash Solution 2/3 via centrifugation (1 min at 5,000 g). The small RNA fraction wa s removed from the filter using two 50 L washes of hot (95 C) Elution Solution. Each wash was spun down at 10,000 g for 1 minute, providing 100 L of small RNA fraction. The presence of RNA was confirmed using a Nano Drop 8000 Spectrophotometer (ThermoSc ientific). Samples were stored at 80 C. 2.3 cDNA pool creation Before qPCR could be utilized, the small RNAs obtained were converted to their DNA equivalents and elongated. This serves two purposes. As PCR (described in greater detail in the next secti on) utilizes a thermostable DNA polymerase ( Taq polymerase, from Thermus aquaticus bacteria), it is only able to read DNA templates. The short length of these sequences also makes designing primers difficult, as miRNAs and primers are a similar length. In order to avoid both issues, the QuantiMir TM RT Reaction (System Biosciences) was used. This kit elongates the small RNA through the addition of a poly(A) tail by a PolyA polymerase and converts them to DNA via a reverse transcriptase. Figure 2.1 outlines t his process

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34 First, 5 L of total RNA sample set of NHBE cells was mixed with 2 L 5X PolyA Buffer, 1 L 25 mM MnCl 2 1.5 L 5 mM ATP, and 0.5 L PolyA Polymerase. The tubes were then incubated at 37 C for 30 minutes. Next, 0.5 L of Oligo dT Adaptor so lution was added to each tube. Samples were then incubated at 60 C for 5 minutes, followed by a cooling period. Once cooled to room temperature, the samples were mixed with 4 L 5X RT Buffer, 2 L dNTP mix, 1.5 L 0.1M DTT, 1.5 L RNase free H 2 O, and 1 L Reverse Transcriptase. The mixture was then incubated at 42 C for 60 minutes, followed by 10 minutes of heating at 95 C. The resulting 20.5 L solutions contained the cDNA that was utilized in the qPCR reactions. As the average amount of small RNA used in the experiment was between 100 ng 1 specifications. Samples were stored at 80 C. Figure 2.1 Flow chart of cDNA pool creation. From QuantiMir RT Kit Small RNA Quantitation System Use r Manual (System Biosciences).

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35 2.4 qPCR Quantitative polymerase chain reaction (qPCR) was used to measure the levels of particular small nucleolar and micro RNA (i.e. SNORD15B, SNORD26, hsa miR 200a, and hsa miR 34c). Before the experimental procedure th at was used in this project is described, it would be useful to cover the theory behind qPCR. 2.4.1 qPCR the basics Since its development in the 1980s, the use of polymerase chain reactions (PCR) has revolutionized the field of biology (Bartlett and Ste rling, 2003). Through a series of simple steps, PCR is able to amplify o ne sequence of DNA into over a b nucleic acids to a high temperature, usually around 94 95 C, separating the double stranded DNA (dsDNA) (Fig. 2.2, Step 1). When the sample is brought down to a lower temperature (i.e. 50 60 C), shorter single stranded DNA molecules, called primers, anneal to particular regions of the DNA strands (Fig. 2.2, step 2). This a llows researchers to select only certain sequences of DNA for amplification, as the length of the primers (+20 nucleotides) allows for a high degree of specificity. The m ), whic C allowing a thermostable DNA polymerase to bind to the primer template duplex, and replicate the targeted sequences of DNA in a proc ess called elongation (Fig. 2.2, step 3). The most common DNA polymerase utilized is Taq polymerase, which is obtained from the

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36 hyperthermophilic Thermus aquaticus bacteria. Running the sample through these steps several times (cycles) will produce a sizea ble number of copies of one DNA template. In theory, 30 cycles would produce 2 30 or 1 073 ,741, 824 copies of just two original copies of dsDNA (Miller and Tanner, 2008). qPCR builds on this process by measuring the increasing amount of DNA in the reactio n through the inclusion of a reporter and detector. Two types of reporters are commonly used; (1) dyes that fluoresce in the presence of dsDNA and (2) sequence specific probes that fluoresce when they bind to their targets (Skeidsvoll and Ueland, 1995; Hei d et al 1996). Both methods require a detector. The detector takes the form of a real time PCR cycler that is capable of maintaining and changing the temperatures of the samples, as w ell as measuring the fluorescence of each sample (at a certain wavelengt h). This allows the instrument to measure the increase in PCR product for every cycle, thereby quantifying the amount of dsDNA present in each sample over tim e. Both types of reporters have advantages and disadvantages. Dyes (such as SYBR Green I, a cyanin e dye, which is utilized in this project) are relatively inexpensive, but are subject to false positives as they will fluoresce in the presence of any dsDNA, not just the targeted sequence. Probes (such as the TaqMan probes) are relatively expensive, but a re sequence specific, thereby decreasing the risk of fluorescence from contaminating or unintentionally amplified dsDNA being reported erroneously.

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37 Figure 2.2 Polymerase chain reaction C). 2) After cooling C), primers anneal to the single strands of DNA template. 3) Taq DNA polymerase (blue oval) binds to primer template dsDNA and produces two new dsDNA templates.

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38 2.4.2 Primer selection Primers were designed to target SNORD15B, SNORD26, hsa mi R 200a, and hsa miR 34c. Two primers were used for each RNA, as the deep sequencing data showed two conserved areas on both snoRNAs. As described in Section 1.2, a species of miRNA consists of two opposing strands (derived from the miRNA duplex) in the cel l. While one strand is typically more prevalent than the other, the deep sequencing data showed differences in expression for both strands. For each set L sequencing data is being targeted. As the cDNA produced possesses the DNA equivalent of the complementary strand for each target RNA sequence, primers were designed as the DNA equivalent of the RNA being targeted. For example, the left peak of SNORD15B had the sequence CUUCAGUGAUGACACGAUGACG, so the primer targeting that sequence has the sequence CTTCAGTGATGACACGATGACG. The cDNA also which was targeted by a universal re verse primer provided by System Biosciences. All forward primers were obtained from Integrated DNA Technologies. As a negative control, an anti sense primer was also used for each targeted RNA sequence. In order to ensure the effectiveness of both the qPCR experiments and the steps that lead up to it, hsa miR 16 (a highly expressed miRNA) was used as a positive control.

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39 2.4.3 qPCR procedure qPCR reactions took place in a 7500 Real Time PCR System (Applied Biosystems). For this project, 20 L reactions were employed. This volume size was the reasonable compromise between accuracy (larger volumes, such as 50 L, reactions are the standard) and cost effectiveness. Each sample (0 hr A, B, C and 24 hr A, B, C) was tested in quadruplicate. Due to the space co nstraints of the 96 well plates, only one primer set (sense, anti sense, and miR 16) was run at a time. Each reaction consisted of 10 L of 2X QuantiTect SYBR Green PCR Master Mix (Qiagen), 4 L of nanopure water, 2.66 L of diluted cDNA, 0.66 L of 10 M Universal Reverse Primer solution (System Biosciences), and 2.67 L of 2.5 M forward primer (Integrated DNA Technologies) solution. The real time cycler conditions are described in Table 2.1 Step Cycles Time (min:sec) C) PCR initial activa tion step 1 15:00 95 Denaturation 40 45 0:15 94 Annealing 40 45 0:30 T m of forward primer 5 Extension 40 45 0:32 72 Table 2.1 t After each real time program had run its course, the plates were run under a st andard dissociation curve program (Table 2.2). Accounting for the susceptibility of SYBR Green I assays to false positives, the dissociation curve shows whether or not more than one sequence of DNA was being amplified in each sample.

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40 Cycles Time (min:sec) Temperature ( C) 1 0:15 95 1 1:00 60 1 0:15 95 Table 2.2 C as the change in fluorescence was measured. t programs were given in the form of cycle thresholds or C t hr), uninfected (0 hr), and positive control (hsa miR 16) C t t (described in Section 3.1.1) gave the relative quantification of the target RNA. 2.5 Deep sequencing The results (i.e., the relative quantification of the target RNA) from the qPCR experiments were compared to results of the deep sequencing of the small RNA fraction (<200 nucleotides) extracted from NHBE cells infected with the same virus. While this sequencing data was obtained prior to the start of this project, a basic un derstanding of how this data was obtained would still be valuable. Deep sequencing was performed by the Genome Analyzer II (GA II), a next generation sequencing platform (Illumina). In short, GA II elucidates that sequence of sample DNA by replicating th e sample DNA and observing the addition of each individual deoxyribonucleotide ( dNTP) via fluorescence Light is given off by fluorescently labeled terminators which are bound to each dNTP and dissociate after the dNTP is incorporated into the growing stra nd of DNA, giving off light at a certain wavelength. Since each dNTP carries a label unique to its base, the sequencer

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41 is able to convert fluorescence emissions into nucleotides, creating the sequence. Figure 2.3 divides the process into three main stages library preparation (1), cluster generation (2), and sequencing (3). Before the DNA can be sequenced, it must first be fragmented into smaller pieces (Step A). If a sample of small RNA is being sequenced (such as the small RNA fragment studied in this afo rementioned deep sequencing experiment), it must first be converted into cDNA before fragmentation can occur. After fragmentation, the ends of the DNA fragments are adenylated (Step B) and ligated to adapter oligonucleotides (Step C). The fragments are the n size selected and purified (Step D). Figure 2.3 Overview of GA II deep sequencing (From Illumina, 2010).

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42 igo adapters whose sequences are complementary to those bound to the DNA fragments. This causes the DNA to attach to the surface of the flow cell at one end (Step E) and then DNA (Step G). The reverse strands of each bound DNA fragment are then removed, allowing its exposed end to be capped. Sequence primers then anneal to the capped ends (S tep H). After the sequencing primers hybridize to the ends of the bound DNA fragments, a new complementary DNA strand is synthesized using the previously mentioned fluorescently tagged dNTPs. When the dNTP binds to the DNA, its fluorescent tag dissociates and emits light at a particular wavelength. The dissociation also allows the next dNTP (as decided by the template strand of bound DNA) to be incorporated into the growing strand (Step I). This process is repeated until each DNA fragment is replicated (Ste p J). The emissions measured from each the genome (Step K) (Illumina, 2012). A supplementary video describing the sequencing process can be viewed by going to: http://www.illumina.com/media/flash_player.ilmn?dirname=systems&swfname=GA _workflow_vid&width=780&height=485&iframe The Intergrated G enome Viewer software (Broad Institute) was utilized to visualize the deep sequencing data. Sequences of the cDNA fragments were aligned

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43 to the hg18 assembly of the human genome, thereby matching the reads to their respective genes (UCSC, 2006) Figure 2 .4 hsa miR 200a 3p ( R ) sequencing data Shown as a bar graph on IGV 2.0 (using hg18). Read count ranges (Readcount0hr: 0 12504 RC; Readcount24hr: 0 25764) are displayed at the top left corner of the main window. Sequence is shown on the bottom window. The fragments of cDNA (created from the small RNA fragment) were aligned to the genome, and a chart was made showing the number of times a specific sequence was observed in the sequencing data. These charts contained plateaus consisted of t he most prevalent sequence fragments of a gene observed in the data set One example, shown in Figure 2.4, is the miRNA hsa miR 200a. Since mature miRNA are roughly 22 nucleotides long, and since few immature miRNAs are observed in the small RNA sample, th e peak of hsa miR 200a sequence data is about 21 22 nt long However, a small number of incompletely processed miRNA s

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44 may still be present, so longer sequences will occasionally be observed in the data The alignment of the reads for the hsa miR 200a gen e will therefore consist mostly of cDNA with the sequence of the mature miRNA, and a few sequences that contain nucleotides that have not yet been cleaved by Dicer. This creates the mesa like structure seen in Figure 2.4, the base and peak of which are com posed of the longer reads (immature miRNA sequences) and shorter reads (mature miRNA) respectively. Figure 2.5 Read counts composing of the hsa miR 200a 3p ( R ) peak are shown (Fig. 2.4). The longer, less prevalent sequences of composed primarily of the mature miRNA strands (not to scale). Primers were designed to amplify the sequences in the peaks, as they would be the most prevalent sequences of the target RNA gene. The increase or decrease in deep sequencing read counts from the uninfected and infected samples for each peak provide a measure of whether the small RNAs hav e increased or decreased expression after influenza infection For example, Figure 2.4 shows that the right peak of hsa miR 200a ( ) has 12504 read counts in the uninfected and 25764 read counts in the infected NHBE cells. T his shows a 2.06 fold change in expression (i.e. hsa miR 200a 3p is upregulated during infection). The relative abundance of the RNAs identified using the deep sequencing data were compared to the relative quantification (via qPCR) of the target RNA obtain ed for this project.

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45 3. Results and Discussion After reviewing several small non coding RNA genes with differential expression in the deep sequencing results, the following four were selected as the focus of this project: SNORD15B, SNORD26, hsa miR 200a, and hsa miR 34c. The equivalent to the fold change in gene expression) is provided in Table 3.1. As discussed in Section 2.5, the different sequences of each gene detected during deep indicated differential expression in the two samples. Table 3.1 lists the read count re spectively). The forward sense primers used in the qPCR experiments were designed to match the sequences of these peaks (discussed in Section 2.4.2). By using these forward sense primers, only the gene fragment corresponding to the llowing for a comparison of differential expression levels as measured by qPCR and deep sequencing. Gene Read count ratio SNORD15B L 0.630 SNORD15B R 0.220 SNORD26 L 0.346 SNORD26 R 1.182 hsa miR 34c 5p ( L ) 1.101 hsa miR 34c 3p ( R ) 3.169 hsa miR 200 a 5p ( L ) 1.869 hsa miR 200a 3p (R) 2.060 Table 3.1 Overview of deep sequencing data. The read count ratios (read count 24 hr / read count 0 hr coding RNA gene were determined.

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46 3.1 Turning C t expression before (0 hr) and after (24 hr) infection. While determining the expression changes in the deep sequencing data was rel atively straight forward, converting data from the qPCR experiments into fold changes required the utilization of the 2 method. As the qPCR progresses, cDNA is amplified (if the necessary conditions are met). Initially, the level of cDNA is too low for the reporter dye (SYBR Green I, Qiagen) to be appreciably detected. There is, however, a detectable level of backgr ound fluorescence present. This period of the reaction is used to calculate a subtracted from the Rn, or the fluorescence of the reporter dye (SYBR Green I) divided by the fluorescence of the passive reference dye (ROX) detected on a different channel n values for each reaction of the qPCR experiment.

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47 Figure 3.1 hsa miR 34c 5p (L) amplification curve. The a m plification curves for miR 34c 5p sense (green), anti sense (yellow), and miR 16 (green) primers are shown Baseline and threshold s hown are not those used in calculations. The graph plots the log 2 Once cDNA levels begin to rise to greater numbers, the level of f luorescence n ) begins to increase exponentially in what is aptly termed the exponential phase of the PCR. During the data analysis a line, called the threshold, is drawn through approximately the middle of the exponential phase of each reaction. The cyc le at which a reaction passes through the threshold is called the threshold cycle, or C t (Applied Biosystems, 2008). Figure 3.1 shows the amplification curves, threshold, and baseline for the miR 34c L series of primers.

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48 Table 3.2 hsa miR 34c 5p ( L ) C t da ta Sample miR 34c LS (C t ) miR 16 (C t ) Sample miR 34c LS (C t ) miR 16 (C t ) 0 hr A 32.41 31.97 24 hr A 29.35 28.52 0 hr A 31.52 31.04 24 hr A 29.14 27.93 0 hr A 31.13 30.66 24 hr A 29.03 28.00 0 hr A 30.69 30.22 24 hr A 29.25 28.00 0 hr B 30.21 32.02 24 hr B 30.01 28.54 0 hr B 29.90 30.64 24 hr B 30.16 29.20 0 hr B 30.02 29.06 24 hr B 30.49 29.38 0 hr B 29.39 28.10 24 hr B 31.19 29.62 0 hr C 31.32 28.96 24 hr C 28.19 27.24 0 hr C 31.14 28.58 24 hr C 28.64 28.27 0 hr C 32.52 28.52 24 hr C 29.10 28.59 0 hr C 31.20 28.71 24 hr C 29.22 29.80 A) C t values for sense (miR 34c LS) and endogenous control (miR 16) primers. Anti sense primer C t values (not displayed) indicated only a low level of contamination or primer dimer formation in those reactions. Val ues highlighted in yellow were deemed outliers (defined as one standard deviation away from the mean) Sample Average (miR 34c LS) C t Average (miR 34c LS) C t w/o outliers Average (miR 16) C t Average (miR 16) C t w/o outliers 0 hr A 31.4 0. 4 31. 3 0.1 3 1.0 0. 4 30. 9 0. 2 0 hr B 29.9 0. 2 30.04 0.08 30.0 0. 9 29.9 0.3 0 hr C 31.6 0.3 31.22 0.05 28. 7 0.1 28.6 0.1 24 hr A 29.19 0.07 29.20 0.07 28.1 0.1 27.98 0.02 24 hr B 30.5 0. 3 30.2 0.1 29. 2 0.2 29.4 0.1 24 hr C 28.8 0.2 28.99 0.04 28. 5 0.5 28.4 0.1 B) Average C t values and corresponding standard errors ( n ) for miR 34c LS and miR 16, with and without the outliers (described in A) included.

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49 Once the C t values were obtained and outliers removed (Table 3.2), t t for the uninfected (0 hr) and infected (24 hr) samples were determined using Equations 1 and 2 respectively. t uninfected = average uninfected target RNA C t average miR 16 C t (1) t infected = average infected target RNA C t average miR 16 C t (2) In order to account for any differences in cDNA content between the samples (0 hr A C, 24 hr A C), the average C t value of each uninfected and infected sample was compared to the average C t F or example, the average miR 34c LS 0 hr A C t value was 31.33 and the average miR 16 0 hr A C t value was 30.85: t uninfected (0 hr A) = 31.33 30.85 = 0.48 Likewise, the average miR 34c LS 24 hr A C t value was 29.20 and the average miR 16 24 hr A C t value was 27.98: t infected (24 hr A) = 29.20 27.98 = 1.22 The average of the uninfected and infected samples ( A t values (1.20 t was determined using Equation 3. t t infected t uninfected (3) t = 0.88 1.20 = 0.32 In order to determine the rel ative quantification of the target gene (miR 34c L), Equation 4 was utilized, producing a value of 1.17 (indicating a 1.17 fold

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50 increase in hsa miR 34c 5p over the course of 24 hours of infection) (Tuzmen et al 2007). Relative quantification of target ge ne = 2 (4) Relative quantification of target gene = 2 ( 0.32) = 1.17 After the initial relative quantification program (Table 2.1) had reached completion, the standard dissociation curve program was run (Table 2.2). Figure 3.2 show s that only one sequence of cDNA was amplified in the sense and endogenous control reactions (as only one T m is observed), indicating a successful PCR experiment. This data analysis approach was applied to the results from each qPCR experiment. The relativ e quantifications of each target gene are shown in Table 3.3.

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51 Figure 3.2 hsa miR 34c 5p dissociation curve. The d issociation curves for A. miR 34 LS (maximum derivative at 0.08 on the y axis), B. miR 16 (maximum derivative at 0.10 on the y axis), and C. miR 34 LA (maximum derivative at 0.08 on the y axis) primers are displayed

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52 3.2 General comparison of qPCR and deep sequencing results Target RNA 2 (Trial 1) 2 (Trial 2) Deep Sequencing (RC ratio) SNORD15B L 0. 3 (0.2 0.7) n/a 0.630 SNORD15B R 1.0 (0.5 2.1) n/a 0.220 SNORD26 L 0.7 (0.5 1.1) 0 .7 (0.5 1.0) 0.346 SNORD26 R 3.8 (1.5 9.6) 0.8 (0.2 2.5) 1.182 hsa miR 34c 5p ( L ) 1.6 (0.9 2.9) n/a 1.101 hsa miR 34c 3p ( R ) 1.2 ( 0.4 3.8) n/a 3.169 hsa miR 200a 5p ( L ) 1.8 (1. 4 2.4) n/a 1.869 hsa miR 200a 3p ( R ) 0.7 (0.4 1.2) 0.6 (0.5 0.8) 2.060 Table 3.3 Overview of qPCR results and analogous deep sequencing read count (RC) ratios. Second trials of three of the target RNA were performed. 2 Ct values (equivalent to fold method (detailed in Section 3.1). Data ranges are provided by each fold change (low to high range) (Livak and Schmittgen, 2001). The results of the qPCR experi ments performed during this project showed a certain degree of concordance with the previously acquired deep sequencing data (Table 3.1). While both qPCR and deep sequencing measure gene expression, they do so through different mechanisms and technology. I n the aforementioned study by Peng et al deep sequencing and qPCR measurements of the same miRNA and r ) of 0.72 (Peng et al 2011). The r value equals 1 if the values in two data sets correlate exactly, 1 if the values are inversely correlated, and 0 if there is no correlation (Rodgers and Nicewander, 1988). A second, cross platform analysis of identical miRNA targets resulted in a similar agreement ( r an Real II deep sequencer (the same technology 2010). If all the data from this project is considered, an r of 0.1 2 is obtained, indicating very little correlation.

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53 While the inherent differences between qPCR and deep sequencing are expected to contribute to a less than perfect correlation between the results obtained t analysi s approach assumes that each PCR amplification is 100% efficient (i.e. cDNA amounts are doubled by each cycle), or that efficiency is at least identical between reactions (Yuan et al 2006; Karlen et al 2007). Efficiency estimations, or experiments that compare amplification relative to a range of sample cDNA concentrations, were not performed in this project. Therefore, it is possible that the qPCR results were skewed by uneven efficiencies during DNA amplification Experimental errors, such as imprecise pipetting and poor sample handling, may have also lead to erroneous qPCR results. The combination of technical differences between qPCR and deep sequencing, the absence of any correction for differing PCR efficiencies, and occasional poor experimental tec hnique undoubtedly lead to high standard deviations for each 2 value (shown in Table 3.4). In addition, the small number of RNAs that could be analyzed due to time and budget constraints did not provide enough data points to obtain a representative correlation coefficient ( r ) value. Results for each small non co ding RNA investigated are discussed in greater detail in the following sections.

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54 3. 3 SNORD15B Target RNA 2 Deep Sequencing (RC ratio) SNORD15B L 0.3 (0.2 0.7) 0.630 SNORD15B R 1.0 (0.5 2.1) 0.220 Table 3.4 Comparison of SNORD15B qPCR (2 ) and deep sequencing (RC ratio) results. The relative expression of the infected samples compared to the uninfected samples is shown. Standard deviations of fold change are also given for each 2 value. Target RNA (primer) Av. Ct (0 hr A) Av. Ct (0 hr B) Av. Ct (0 hr C) Av. Ct (24 hr A) Av. Ct (24 hr B) Av. Ct (24 hr C) SNORD15B L (sense) 25.81 0.06 26. 17 0.05 26. 46 0.0 9 27.8 0.1 27.18 0.04 27.41 0.04 SNORD15B L (miR 16) 28.0 0.2 26.8 0.6 26.93 0.09 26.32 0.0 6 27.6 0. 2 27.08 0.06 SNORD15B R (sense) 30 .8 1.2 28.8 0. 3 29.4 0. 4 28.70 0.0 3 30.8 0. 2 31.4 0. 4 SNORD15B R (miR 16) 27.3 0. 2 25.1 0. 2 25.8 0.08 25.9 0.1 27.6 0. 2 26.6 0. 2 Table 3.5 Average SNORD15B C t values. Average Ct values (after the removal of outliers) for sense and endogenous control (miR 16) primers with corresponding standard errors are given for each qPCR experiment Average Ct values before outlier removal are provided in A.2.1B and A.2.2B for SNORD15B L and SNORD15B R primers respectively. The qPCR experiments utilizing the SNORD15B primer sets (Table 3.4) yielded mixed results. Both deep sequencing RC ratios did not fall in the range of either analogous 2 values, indicating poor agreement. Initially low SNORD15B expression levels may have influenced this outcome. In the deep sequencing data, the left (Fig. 3.4) and right (Fig. 3.5) peaks (i.e. the highly expressed fragments of the SNORD15B gene) of SN ORD15B had maximum read counts of 108 and 757 (at 0 hr) respectively. Compared to the read counts of other small non coding RNA under

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55 investigati on (which range from 474 to 210 021 read counts), these are low numbers. Similarly, levels of read counts corre sponding to the less prevalent portions of SNORD15B (i.e. the non Figure 3.4 and 3.5 ) contain only 31 read counts in the uninfected samples and 3 in prevalent portions, which have approximately 600 and 160 read counts corresponding to the uninfected and infected samples respectively, SNORD15B was present in relatively low levels prior to infection. Therefore, any slight errors in the qPCR or deep sequ encing work up would have a far more drastic effect on SNORD15B levels than they would with another, more highly expressed RNA target. Figure 3.3 SNORD15B deep sequencing results. Both the left (SNORD15B L) and right (SNORD15B R) peaks are shown. Read cou nts(RC) for each peak are scaled from 0 757 RC. RC ave of the left peak is 108 in uninfected (0 hr) samples and 68 in infected (24 hr) samples. RC ave of the right peak is 757 in uninfected (0 hr) samples and 163 in the infected (24 hr) samples. The less e xpressed portion of the gene, seen in the middle, has an RC ave of 31 in the uninfected (0 hr) samples and 3 in the infected (24 hr) samples.

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56 Figure 3.4 SNORD15B L deep sequencing results 108 RC. The primer target sequence (black) and C box (red) are identified at the bottom of the figure. Figure 3.5 SNORD15B R deep sequencing results 757 RC. The primer target sequence (black), snoRNA guide sequence (blue), and D box (green) are identified at the bottom of the figure.

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57 During the analysis of the SNORD15B qPCR data, 16 of the 48 (33.3%) C t values for SNORD15B L were removed as outliers. Likewise, 15 of the 48 (31 .3 %) C t values for SNORD15B R were removed as outliers. Even after the removal of these outliers average C t standard errors for some reactions, such as those using 0 hr A cDNA and SNORD15B R sense primers (Table 3.5), remained egregiously large. The relat ively high C t values for the SNORD15B qPCR are consistent with the low read counts for this RNA, and it is common to obtain large errors in qPCR when the target has low abundance (K. Walstrom, personal communication). Although both qPCR experiments were wr ought with error, results from both methods indicated a downregulation of the left peak of SNORD15B. This was not the case with SNORD15B R, which was shown to be unchanged (fold change of 1.0 ) in the qPCR data and downregulated (fold change of 0.220) in th e deep sequencing data. On average, SNORD15B R primers experienced higher standard errors. The high level of variation in the qPCR results suggests error in the preparation experiment, but potentially more so in the SNORD15B R experiment. These errors were likely related to poor pipetting or mixing, which would have had a sizeable effect on the results due to the aforementioned low abundance of SNORD15B cDNA template in each reaction. Apart from the likely erroneous 2 values (in this case, SNORD15B R ), SNORD15B seems to be downregulated during influenza infection. As was mentioned in Section 1.4.4, influenza is known to localize in the nucleolus of its host. This localization may alter the distribution of the C/D box snoRNA associated

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58 proteins, such a s fibrillarin. Without the presence of these proteins, the snoRNA are more susceptible to endogenous RNases. Therefore, it is reasonable to assume that levels of C/D box snoRNA, such as SNORD15B, may decrease after influenza infection. O methylations on 28S rRNA) as a C/D box snoRNA represents its only known role in the cell (Lim et al 2002). While there is a lack of research indicating any unorthodox functions the differential expression of this snoRNA has be en observed in a variety of cell types. The gene encoding SNORD15B is located on a section of the genome (chromosomal region 11q13) that is commonly amplified in breast cancer and mantle cell lymphoma (Holm et al 2011; Vater et al, 2008). SNORD15B was als o shown to be upregulated in multiple mylenoma, a cancer in which malignant plasma cells accumulate in the bone marrow (Zismanov et al 2012). In addition to these cancer tissues, SNORD15B was shown to experience a 2.16 fold change in breast tissue undergo ing cross sex hormone therapy, used in sex reassignment surgeries for female to male transsexuals (Bentz et al 2010). Although SNORD15B is known to be differentially expressed in certain tissue types and disease states, the reason behind this differential expression is unknown. Future research into the cause of these expression changes in cancerous tissues, as well as cells infected with influenza, will hopefully yield results of significance. The distinct differential expression of SNORD15B in some diseas e states may also be useful as biomarkers, as will be discussed in Section 4. 3.1

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59 3. 4 SNORD26 Target RNA 2 (Trial 1) 2 (Trial 2) Deep Sequencing (RC ratio) SNORD26 L 0.7 (0.5 1.1) 0. 7 (0.5 1.0) 0.346 SNORD26 R 3.8 (1.5 9.6) 0. 8 (0.2 2.5) 1.182 Table 3.6 Comparison of SNORD26 qPCR (2 ) and deep sequencing (RC ratio) results. The relativ e expression of the infected samples compared to the uninfected samples is shown. Standard deviations of fold change are also given for each 2 value. Target RNA (primer) Av. Ct (0 hr A) Av. Ct (0 hr B) Av. Ct (0 hr C) Av. Ct (24 hr A) Av. Ct (2 4 hr B) Av. Ct (24 hr C) SNORD26 L trial 1 (sense) 26.97 0.03 27.19 0.05 26.78 0.03 27.46 0.01 28.01 0.08 27.07 0.03 SNORD26 L trial 1 (miR 16) 29.4 0.1 27.6 0. 4 27.64 0.03 27.5 5 0.02 29.2 0.3 28.2 0.2 SNORD26 L trial 2 (sense) 26.53 0.07 26.90 0.07 26.46 0.03 27.194 0.006 27.7 0.1 26.74 0.05 SNORD26 L trial 2 (miR 16) 29.04 0.09 27.2 0.3 27.30 0.02 27.2 3 0.0 3 28.8 0.3 27.6 0.3 SNORD26 R trial 1 (sense) 34.7 0. 4 35.79 0.0 8 30.7 0.2 30.3 0. 3 32.0 0. 7 32.6 1.0 SNORD26 R trial 1 (miR 16) 28.18 0.0 3 25.9 0.6 26.32 0.07 25.84 0.0 3 2 8.00 0.0 8 26.1 0.1 SNORD26 R trial 2 (sense) 31. 67 0.08 32.5 0. 2 29.1 0. 2 29.40 0.09 35.5 0.4 30.9 0. 8 SNORD26 R trial 2 (miR 16) 28. 3 0. 2 26.2 0. 4 25.83 0.01 26.46 0.02 29.13 0.06 26.09 0.09 Table 3.7 Average SNORD26 C t values. Average Ct values (after the removal of outliers) for sense and endogenous control (miR 16) primers with corresponding standard errors are given for each q PCR experiment Average Ct values before outlier removal are provided in Appendix B, A.2.3B A.2.6B. The SNORD26 qPCR experiments also yielded mixed results (Table 3.6) The average 2 value of SNORD26 L (0.7) showed a 0.3 54 fold change

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60 difference b etween the qPCR and deep sequencing experiments. SNORD26 L qPCR experiments showed no significant difference in fold changes. The two SNORD26 R qPCR experiments differed by a significant margin (a 2.618 fold change difference from one another). A commonali ty between the two 2 values was their abhorrently large range of values. In the second trial of the SNORD26 R qPCR experiment the standard deviation was larger than the fold change value itself (the first trial was close to also achieving this), indicating highly variabl e C t measurements between the samples. Based on C t values obtained for the SNORD26 R qPCR experiments, this RNA has very low abundance, so it is expected that the errors would be high. This poor agreement between C t values in each trial, and the between t he trials themselves suggests a systemic error in the SNORD26 R qPCR the more abundant miR 16 C t values for both the SNORD26 L and R experiments shows a higher average error for the SNOR D26 R experiments (Table 3. 8 ). Target RNA Sense primer (average C t standard error) miR 16 primer (average C t standard error) SNORD26 L (trial 1) 0.04 0.2 SNORD26 L (trial 2) 0.06 0. 2 SNORD26 R (trial 1) 0.4 0.2 SNORD26 R (trial 2) 0.3 0.1 Table 3 .8 Averages of SNORD26 C t standard errors. Table 3. 8 also shows that the C t values obtained using the SNORD26 R sense primers were consistently more erroneous relative to the C t values obtained using the

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61 miR 16 primers. This suggests that the SNORD26 R s ense primers themselves may have influenced the poor results. The GC% of these primers was 33.3%, below the desired range of 50 55% (Applied Biosystems, 2009). This would impact the t ana lysis (Dieffenbach et al 1993). Errors that occurred during the preparation of the experiment, such as poor pipetting or failure to generate solutions with appropriate homogeneity and proportions of the reagents may have also contributed to the variable r esults in both the SNORD26 R and L experiments. SNORD26 was one of the 30 murine snoRNA identified in Peng et al study of differential small non coding expression during infection. Peng et al infected four types of mouse lung cell (129, WSB, PWK, and CAST) with a 1934 strain of H1N1 influenza A (A/Pr/8/34). The sequence homology between human and mouse SNORD26 is shown in Figure 3.6. Across the four lung cell cultures, SNORD26 was downregulated in one lung cell type (129) and upregulated in the o ther lung samples (WSB, PWK, and CAST). The respective down and up regulation of SNORD26 in these samples was low compared to some of the other small non coding RNA investigated (Peng et al 2011). Figure 3.6 Comparison of human ( H. sapiens ) and mouse ( M. muscles ) SNORD26. C (red) and D (green) boxes, as well as guide sequences (blue) are displayed. Primers are outlined in black. (Figure self made; sequences obtained from snoRNA Orthological Gene Database, 2008). The fluctuations between the different l ung cell types in Peng et al suggests that changes in the expression of SNORD26 may not hold a conserved role

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62 in viral offense or defense, and may just represent an indirect effect of infection ion). Whether or not that is true is ambiguous, as Table 3. 6 a disproportionate manner. In fact, SNORD26 R seems to increase during infection in the deep sequencing data, whereas SNORD26 L decreases significa ntly (4250 to 1476 read counts) As both peaks represent fragments of the same snoRNA gene, if the snoRNA is being degraded, it would be expected that both fragments would be degraded at similar rates. It is possible that while one fragment (SNORD26 L) is degraded by endogenous RNases, the other may proliferate during infection by associating with proteins as a small snoRNA derived fragment (sdRNA), such as those discussed in Section 1.3.4. The existence of a SNORD26 sdRNA, while not experimentally investig ated in this project, is suggested by both the differing levels of SNORD26 fragments (and similarly, SNORD15B) as well as the conservation of the snoRNA fragment length displayed in the deep sequencing data shown in Figure 3.7. These patterns of expression more so for the right peak of SNORD26 than the left, are reminiscent of those seen for miRNA, such as hsa miR 200a (Fig 2.4). Degradation of the snoRNA would create random fragments, instead of the long fragments seen in Figure 3.7 (Scott et al 2011). I n the uninfected NHBE cells (Readcount0hr in Fig. 3.7 B), the right peak has over twice as many read counts as the left, and over seven times as many in the infected (Readcount24hr in Fig. 3.7 B), indicating a potentially functional sdRNA originating from sdRNA could possibly associate with a process in the cell that increases in activity during infection, leading to a similar increase in the sdRNA (SNORD26 R). The

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63 same may be true for the peaks in SNORD15B L, but is less likely in SN ORD15B R sequencing read count patterns with these features typically indicate random degradation by RNases, not conserved processing (Scott et al 2011). Further study of SNORD2 6 and SNORD15B as the hosts of functional sdRNA is no doubt warranted. Figure 3. 7 Patterns of snoRNA expression in deep sequencing data A. SNORD15B (located at Chr11:74,793,085 74,793,267 on hg18). The range of read counts displayed in each window are 0 757 and 0 163 for 0hrReadCount and 24hrReadCount respectively. B. SNORD26 (located at Chr11:62,379,321 62,379,420 on hg18). The range of read counts displayed in each window are 0 8939 and 0 10561 for 0hrReadCount and 24hrReadCount respectively In both A. and B., the black lines indicate the ends of the sequences that each L and R sense primers were designed to target.

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64 Less is known about SNORD26 than SNORD15B. Other than being a suspected site of a functional sdRNA or sno miRNA, SNORD26 is on ly known to perform canonical snoRNA functions. As its name suggests, SNORD26 is a C/D O methylation of) 28S rRNA (Kiss Laszlo et al 1996). It is possible that, like SNORD15B, SNORD26 may be diffe rentially expressed in other disease states. This would make it a potential biomarker of disease, which could be utilized in new diagnostic approaches.

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65 3. 5 hsa miR 200a Target RNA 2 (Trial 1) 2 (Trial 2) Deep Sequencing (RC ratio) hs a miR 200a 5p ( L ) 1.8 (1.4 2.4) n/a 1.869 hsa miR 200a 3p ( R ) 0.7 (0.4 1.2) 0.6 (0.5 0.8) 2.060 Table 3.9 Comparison of hsa miR 200a qPCR (2 ) and deep sequencing (RC ratio) results. The relative expression of the infected samples compared to t he uninfected samples is shown. Standard deviations of fold change are also given for each 2 value. Target RNA (primer) Av. Ct (0 hr A) Av. Ct (0 hr B) Av. Ct (0 hr C) Av. Ct (24 hr A) Av. Ct (24 hr B) Av. Ct (24 hr C) hsa miR 200a 5p ( L ) (sens e) 35.53 0.0 6 35.0 0. 3 36.0 0.5 34.32 0. 10 35.62 0.0 9 34.5 0.3 hsa miR 200a 5p ( L ) (miR 16) 29.7 0.2 28.6 0. 5 27.64 0.04 27.90 0.01 29.88 0.0 9 28.7 0. 3 hsa miR 200a 3p ( R ) trial 1 (sense) 32.03 0.07 30.2 0. 3 31.9 0.6 31.22 0.05 32.605 0.005 30.4 0.4 hsa miR 200a 3p ( R ) trial 1 (miR 16) 29.968 0.004 29. 0.6 27.61 0.09 27.83 0.0 5 29.9 0. 2 28.4 0. 4 hsa miR 200a 3p ( R ) trial 2 (sense) 30.1 0.2 28.89 0.09 30.3 0.3 30.34 0.0 8 30.6 0.1 32.4 0. 4 hsa miR 200a 3p ( R ) trial 2 (miR 16) 28.9 0.2 27.4 0. 4 26.90 0.0 8 27.430 0.009 28.25 0.06 29.5 0. 3 Table 3.10 Average hsa miR 200a C t values. Average Ct values (after the removal of outliers) for sense and endogenous control (miR 16) primers with corresponding standard errors are given for each qPCR experiment Average Ct values before outlier removal are provided in A.2.7B, A.2.8B, and A.2.9B for hsa miR 200a 5p ( L ) hsa miR 200a 3p ( R ) (trial 1), and hsa miR 200a 3p (R) (trial 2) primers respe ctively. The miR 200a qPCR experiments were partially consistent with the deep sequencing data (Table 3. 9 ). Results from the experiments using the miR 200a L primer set were successful, showing a similar upregulation of hsa miR 200a 5p (the

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66 miRNA containe functional or star strand of the mir 200a duplex (and therefore, the high C t values for the sense primers, shown on Table 3.1 0 are expected for a less abundant RNA ). A.3.7 suggests the presence of random cDNA amp lification (i.e. dissociation curves that are separate from the majority of the other curves), an error indicative of a low abundance target (personal communication, K. Walstrom). The results in Table 3. 9 indicate that the functio nal strand, hsa miR 200a 3p (R) increased in expression based on the deep sequencing data but decreased based on the qPCR data As mentioned earlier in Section 1.4.2, miR 200a was shown to be downregulated in cells infected with the reconstituted strains of the highly virulent 191 8 H1N1 influenza virus (r1918) and to a lesser degree in cells infected with a seasonal variety of influenza. Since the strain of influenza virus used to infect the NHBE cells for this project (A/Cal/04/09) differs very slightly from the strain used in the deep sequencing experiment (A/Cal/07/09), a similar effect was suspected. Using the sequence variation analysis tool on the Influenza Research Database website showed a difference of only three nucleotides quires et al 2012). It is therefore unlikely that such a stark difference in miRNA expression would be cause by any variation between influenza strains. Another possible explanation for t he qPCR deviation from the deep sequencing data is that the primer s targeting hsa miR 200a 3p differed from the miR 16 primers in their efficiencies. The sense primers for hsa miR 200a 3p had a GC% of 43%, which as discussed in Section 3.3, can adversely affect efficiencies. If

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67 these effects were not accounted for in the calculations, the 2 analysis becomes less reliable. Furthermore, the high C t values obtained indicate that the hsa miR 200a 3p small RNA has low abundance in the sample. Other, less obvious factors may have affected the results of the hsa miR 200a 3 p qPCR experiments. A slight difference in the culturing and infection of the NHBE cells, or issues in the cDNA pool creation experiments may have affected the levels of hsa miR 200a 3p in the samples. Basic experimental errors during the qPCR plate prepar ation, such as poor pipetting, may have also played a role in the inconsistencies. It should also be noted that when miR 200a was studied by Li et al no change in expression was observed during the first day of infection (i.e. any changes were below the 1 .5 fold change cutoff set by the researchers). Since the epithelial cell model used by Li et al (mouse lung) differs from those used in this project, it is uncertain whether this chronology would apply to the cultures studied here (Li et al 2010). If the pattern of expression were conserved across species and cell cultures, it would mean that any differences in miR 200a expression between uninfected and infected cells would be subtle and therefore more susceptible to slight experiment errors or variations. In addition, a change in expression may be more apparent at times longer than 24 hours after infection. The up or down regulation of hsa miR 200a could have a variety of effects on the cell Among the targets of miR 200a 3p are mRNA that carry genes enco ding proteins associated with viral gene replication, as well as the Jak STAT signaling pathway. The Jak STAT signaling pathway is composed of Jak kinases, which

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68 phosphorylate STAT proteins when activated (by IFN receptor interactions), inducing DNA transc ription of, among other things, inflammation associated genes (Darnell et al 1994; Kim et al 2002). If the miRNA were downregulated, fewer (mRNAs containing) genes that promote viral replication and inflammatory responses to infection would be silenced. This is the case with r1918, known for both its virulence and pathogenicity (Li et al 2010). Upregulation of hsa miR 200a 3p would indicate a less severe viral infection, as the abundance of miRNA during infection would lead to reduced viral replication a nd over active immune responses. It is also possible, that like some other miRNA, hsa miR 200a is capable of targeting miR 200a yields hundreds of H1N1 strains that are potential ly targeted by the miRNA (the 2009 H1N1 strains are not included in the database currently) (Hsu et al 2007). Further investigation into endogenous and viral targets of hsa miR 200a will fluenza infection.

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69 3. 6 hsa miR 34c Target RNA 2 Deep Sequencing (RC ratio) hsa miR 34c 5p ( L ) 1. 6 (0.9 2.9) 1.101 hsa miR 34c 3p ( R ) 1.3 (0.4 3.8) 3.169 Table 3.11 Comparison of hsa miR 34c qPCR (2 ) and deep sequencing (RC ratio ) results. The relative expression of the infected samples compared to the uninfected samples is shown. Standard deviations of fold change are also given for each 2 value. Target RNA (primer) Av. Ct (0 hr A) Av. Ct (0 hr B) Av. Ct (0 hr C) Av. Ct (24 hr A) Av. Ct (24 hr B) Av. Ct (24 hr C) hsa miR 34c 5p ( L ) (sense) 31 .3 0. 2 30.05 0.09 31.22 0.0 6 29.19 0.07 30.2 0.1 29.16 0.0 6 hsa miR 34c 5p ( L ) (miR 16) 30.4 0.2 28.6 0. 5 28.69 0. 10 27.98 0.02 29.4 0.1 28.4 0. 2 hsa miR 34c 3p ( R ) (sense) 34.7 0.1 34.73 0.08 3 4.9 0.4 34.7 0. 4 38.2 0.1 33.54 0.06 hsa miR 34c 3p ( R ) (miR 16) 29.3 0. 2 27.6 0. 3 27.75 0.06 28.13 0.0 4 29.28 0.05 27.7 0.1 Table 3.12 Average hsa miR 34c C t values. Average Ct values (afte r the removal of outliers) for sense and endogenous control (miR 16) primers with corresponding standard errors are given for each qPCR experiment Average Ct values before outlier removal are provided in A.2.10B and A.2.11B for hsa miR 34c 5p ( L ) and hsa miR 34c 3p ( R ) primers respectively. Just as in the previous section, only one of hsa miR 34c primer sets seemed to agree with the deep sequencing data. The differences in the measured expression of hsa miR 34c 5p (which corresponds to hsa miR 34c L) were no doubt due to minor experimental error (i.e. poor pipetting) and inherent differences in the two methods of quantification (discussed in Section 3. 2 ). Again, this small RNA has relatively low expression based on the high C t values obtained. This is appa rent in Table 3.1 2 which shows a large range of standard erro rs associated with hsa miR 34c C t values.

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70 Target RNA Sense primer (average C t standard error) miR 16 primer (average C t standard error) hsa miR 34c 5p 0.1 0.2 hsa miR 34c 3p 0.2 0.1 Table 3 .13 Averages of hsa miR 34c C t standard errors. The expression changes in hsa miR 34c 3p (which corresponds to hsa miR 34c R) differ significantly between the qPCR and deep sequencing data. It is likely that experimental errors and technique differences af fe cted the results of the miR 34c 3p qPCR experiments, but to a higher degree than the other experiments. The average C t standard errors were similar between hsa miR 34c 5p and 3p primers (Table 3.1 3 ), but the range change (Table 3.11 ) was much larger, suggesting a variation between the three biological replicates or unequal efficiencies of the sense and endogenous control primers. In the hsa miR strand (3p) is often the non functional strand. This appears to hold tr ue in the NHBE cells under investigation. The functional (5p) strand has 190745 read counts in uninfected cells, whereas 3p has only 687. The level of hsa miR 34c 3p increases to 2177 read counts in the infected cells, yielding an RC ratio of 3.169 (Fig. 3 .8). The low abundance of hsa miR 34c 3p is consistent with the high C t values obtained in the qPCR experiments and poor dissociation curves shown in A.3.10 Similar to SNORD15B (Section 3.3), the low levels of hsa miR 34c mean that errors in the qPCR and deep sequencing experiments will have a greater effect on the results than they would if the miRNA were present in higher numbers. A previous study by Loveday et al shows that miR 34c 3p was upregulated almost 5 fold in MDCK cells

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71 infected with a different 2009 H1N1 strain (A/Mex/InDRE4487/2009), suggesting that it was more likely that some error was made before or during the qPCR experiments, rather than there being an issue with the deep sequencing data (Loveday et al 2012). Figure 3.8 hsa miR 34c (loca ted at Chr11:110889374 110889450 on hg18) deep sequencing results Read counts (RC) are scaled from 0 213021 RC. Maximum RC for left peak (5p) : 190745 RC in uninfected (0 hr) and 210021 RC in infected (24 hr) samples. Maximum RC for right peak (3p) : 687 in uninfected (0 hr) and 2177 in infected (24 hr) samples. Forward primers / mature miRNAs are outlined in black. Differential expression of miR 34c has been noted in other previous literature. Wang et al demonstrated that miR 34c was upregulated in chicke n tracheae, but not lung, during infection with avian influenza virus. Targets of miR 34c include the genes for B cell CLL pymphoma 2 and 11, proteins associated with B cell differentiation as well as leukemia (Wang et al 2010). The miRNA was also shown

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72 t o be upregulated in macaque lung tissue that were infected with a highly pathogenic H5N1 strain of influenza, as well as in mouse tissue infected with r1918 virus (Li et al 2011; Li et al 2010) Another target of hsa miR 34c 5p is c Myc which encodes c Myc, a transcription factor. Following damage to endogenous DNA, miR 34c is upregulated, allowing it to inhibit the translation of c Myc. Overexpression of c Myc is associated with apoptosis (Cannell et al 2010). Hsa miR 34c 5p has also been shown to targ et Bcl 2 modifying factor (which is pro apoptotic), demonstrating the et al 2012). Knowing that influenza has been shown to induce apoptosis in its hosts, it is likely that the differential expression of hsa miR 34c is directly connected to infection, and therefore may serve as either a cellular defense mechanism, or pro viral response (Brydon et al 2003). Like hsa miR 200a, hsa miR 34c also shows a wide range of potential target sites on different H1N1 Influ enza A viral genomes on the ViTa database (Hsu et al 2007). The continuation of research on the role of miR 34c in influenza infection is therefore undoubtedly warranted.

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73 4. Conclusion In this project, the expression of certain small non coding R NA (sncRNA) in normal human bronchial epithelial cells that were, and were not, infected with a 2009 strain of H1N1 influenza A (Swine flu) was measured through the use of quantitative polymerase chain reaction (qPCR). These measurements were compared to t hose obtained from the deep sequencing of a similar set of cells. Results from each method had low correlation due to various errors and differences between quantification approaches. Despite these issues, the comparison of the qPCR and deep sequencing dat a indicates that these RNA molecules are differentially expressed in response to influenza infection. 4.1 Future Improvements Some obvious improvements could be made during projects using similar approaches (i.e. quantifying micro and small nucleolar RNA through the creation of a cDNA pool and qPCR). First and foremost, experimenters should take great care while transferring qPCR solutions onto the reaction plate. Although the majority of pipette related errors occurred towards the beginning of the projec t, reactions were not always homogenous in each well of the plate due to the presence of bubbles or solution on the sides of the wells. This type of error, while still present in some degree, was largely avoided by taking a less hurried approach to pipetti ng. Samples should also be stored in the appropriate temperatures at all time, to prevent unnecessary degradation of samples and reagents. Multiple freeze thaw cycles of solutions should also be avoided, as gradual thawing and freezing can alter the

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74 confor mation of DNA and proteins (Anchordoquy et al 1998). This could be avoided in the future through the use of multiple smaller aliquots of each reagent. Future projects quantifying small non coding RNA could also make use of alternative approaches to cDNA pool creation and different reporters during qPCR. Applied Biosystems (ABI) markets the TaqMan MicroRNA Reverse Transcription kit, which serves as an alternative to the QuantiMir RT kit used in this project (Systems Biosciences). Instead of attaching a po ly(A) tail to extracted small RNA, the ABI kit utilizes stem loop primers to amplify miRNAs of interest. This, combined with TaqMan probes (instead of SYBR Green) which are also designed to a specific target RNA, affords a higher degree of specificity dur ing quantification (Applied Biosytems, 2011). These techniques could be used alongside the approach utilized in this project, as a technical replicate. 4.2 Future Directions While this project has lent evidence towards the existence of conserved differen tial expression of certain sncRNA (i.e. SNORD15B, SNORD26, hsa miR 200a, and hsa miR 34c), there are hundreds of additional miRNA and snoRNA whose expression patterns have not been investigated in the context of influenza infection. Future projects focused on studying other miRNA and snoRNA (as well as other sncRNA, such as siRNA) could utilize the methods and analysis described in this thesis. In particular, RNA molecules with higher abundance could be studied more reliably using the qPCR method.

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75 As discu ssed in Section 3.4, some of the snoRNA described by the deep sequencing data possess sequence fragments (i.e. the gene fragments with the highest read counts) that, taken as separate RNA molecules, are analogous to other sncRNA such as miRNA. It is possib le that these snoRNA gene fragments may possess functions separate from the snoRNAs from which they were derived. These types of sncRNA have been d escribed in previous literature (as sno miRNAs, discussed in detail in Section 1.3.4) making their presence in the deep sequencing data plausible. During future experiments, in addition to quantifying the presence of particular snoRNA with fragment like deep sequencing read count patterns, attempts to establish the functionality (and in turn, the existence of) these sdRNA could be made. Brameier et al method of establishing the functionality of several sno miRNA through the use of luciferase assays (which contained putative sno miRNA targets) could be used as a frame work for these studies (Brameier et al 2011) Future projects could also explore other epithelial cell lines (such as MDCK or A549 cells) to determine whether the differential expression of particular miRNA or snoRNA is conserved across cell lines. Use of different influenza A strains may also reve al strain specific expression patterns, which could serve as biomarkers of disease (discussed in the next section). Functional assays of investigated miRNA could also be performed. These studies could focus on the changes in expression of mRNA which are ta rgeted by the miRNAs of interest. The same miRNA could be silenced themselves (through methods described in the next section), which would allow the researcher to determine whether the expression of that miRNA enhances of

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76 inhibits influenza infection. Thi s would provide a more direct measure of the functional consequences of differential miRNA expression and therefore serve as a better identifier of miRNA whose expression is important in the infection process. 4.3 Differential expression a potential basis for anti viral therapeutics? While this project only investigated four sncRNA, the finding that these RNA are (ignoring some aforementioned errors) differentially expressed in two sets of cells infected by a practically identical virus lends strong evide nce to the existence of a conserved sncRNA response to infection. Knowing which snoRNA or miRNA are affected by infection, as well as the basis for their up or downregulation, provides researchers with additional subjects to consider while developing new d iagnostic techniques or RNA based therapeutics. 4.3.1 Expression signatures as biomarkers of disease Before any disease can be treated, it must firs t be identified. While this is relatively straight forward in some diseases, diagnosis can be very difficu lt and time consuming for others. The development of more accurate and efficient diagnosis techniques are therefore of understandably high level of medical importance. Researchers have studied the differential expression of certain miRNA in a wide range of cancer tissues, many of which may be useful as points of study during diagnosis or prognosis (Calin and Croce, 2006). Although less research has been conducted on the subject, there are several articles that demonstrate that miRNA expression is altered du ring viral infection. Li et al were able to identify 13 miRNA

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77 whose expression distinguished samples infected with hepatitis B from those infected with hepatitis C. Particular patterns of expression for these miRNA (called miRNA signatures) were also conse rved enough to allow the researchers to accurately classify uninfected tissue from infected (Li et al 2010b). Similar findings by Houzet et al identified miRNA whose expression signatures could differentiate uninfected from HIV 1 infected samples. These m iRNA signatures were also capable of sorting samples based on their respective viral load and CD4+ T cell count (Houzet et al 2008). Influenza has also been shown to create strain specific miRNA signatures. Several miRNA have been associated with highly pathogenic strains of influenza A virus, such as H5N1 avian and 1918 pandemic H1N1 (Li et al 2011). These miRNA are differentially expressed in less pathogenic strains of influenza, such as A/Texas/36/91, a seasonal strain of H1N1 virus (Li et al 2010b) Research by Loveday et al has also identified strain specific miRNA signatures, associated with swine origin H1N1 infections and avian origin H7N7 infections (Loveday et al 2012). These findings contribute to our understanding of the causes behind high pathogenicity, as well as establish which miRNAs could be used to help doctors determine which strain of influenza a patient possesses. This project shows, in addition to miRNA, snoRNA expression is also affected by viral infection. As discussed in Secti on 3. 3 differential (in this case, SNORD15B) expression of snoRNA has been observed across various cancer tissues. Liao et al were able to identify six snoRNA that are overexpressed in non small cell

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78 lung cancer (NSCLC) tissue, relative to normal tissue. Using these snoRNA as biomarkers, the researchers were able to differentiate NSCLC from normal tissue with 81.1% sensitivity and 95.8% specificity (Liao et al 2010). Peng et al also observed differential expression of 30 snoRNA during influenza and SARS i nfection across mouse lung cell types (Peng et al 2011). Additional studies of the effects of influenza infection on snoRNA expression may one day result in strain specific snoRNA signatures. 4.3.2 RNA based therapeutics Unique miRNA and snoRNA expressio n signatures are useful as more than just diagnostic tools. As these signatures indicate which sncRNA are affected by viral infection, they also indicate which sncRNA may serve a role in either cellular defenses or pro viral responses. Once you have determ ined that a miRNA or snoRNA is significant in either viral promotion or inhibition, how do you utilize it therapeutically? There are a few approaches to hindering the effects of miRNA whose expression abets viral infection. One way to prevent a miRNA fro m silencing its targets is through the use of antagomirs. Antagomirs are single strands of RNA that are complementary in sequence to their miRNA targets. The antagomirs are also chemically modified and conjugated to cholesterol groups, allowing the modifie d RNA to stably bind to its miRNA target (Krutzfeldt et al 2005). Locked nucleic acid (LNA) modified oligonucleotides have also been shown to be efficacious in downregulating miRNA (Lanford et al

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79 oxygen of carbon (Kurreck et al 2002). The silencing capabilities of LNA modified oligonucleotides were also successfully demonstrated on snoRNA (Ploner et al, 2009). Another way to prevent miRNA mRNA binding is through the use of a competitive inhibitor. One example of this are transcripts containing multiple tandem miRNA binding sites, called into cells, and expressed in large number s. As both target mRNA and the miRNA sponges contain the miRNA target sites, the abundance of miRNA sponge transcripts causes a competitive inhibition of miRNA mediated silencing of the particular gene (Ebert et al 2007). In some cases, miRNA (and poss ibly snoRNA) expression is associated with cellular defense. As Song et al observed in cells infected with H1N1 influenza A virus, some miRNA are capable of inhibiting viral replication by silencing viral mRNA (Song et al 2010). Hammerhead ribozymes, inco rporated into snoRNAs such as U16, have also been shown to be effective in targeting viral RNA (Michienzi et al 2000). Several miRNAs have also been shown to be downregulated in cells infected with highly pathogenic influenza strains (Li et al 2010a; Li et al 2011; Peng et al 2011). One potential treatment option would be to increase the amount of these miRNA whose expression counters the interests of the virus. Kota et al were able to replenish the amount of miR 26a in hepatocellular carcinoma cells up to levels observed in healthy tissue through the use of adenovirus associated vector (AAV) delivery systems. By using AAV to deliver the missing miRNA, the

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80 researchers inhibited the proliferation of the cancer, induced tumor specific apoptosis, and all wi th no apparent toxicity to healthy tissue (Kota et al 2009). The use of AAV based miRNA replacement therapies could potentially increase the number of miRNA and snoRNA whose lowered expression is associated with highly pathogenic influenza viruses. AAVs c ould also be harnessed to deliver miRNA or modified snoRNA capable of targeting viral genomes or mRNA. As research on sncRNA expression during infection continues, we become aware of more and more potential therapeutic targets. Coupled with the developmen t of new or improved approaches to RNA based therapy, it is likely just a matter of time before anti viral drugs based on miRNA or snoRNA enter the world of medicine.

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81 Appendix Appendix A Primers

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82 Appendix B qP CR data A.2.1 SNORD15B L Sample SNORD15B LS (C t ) miR 16 (C t ) Sample SNORD15B LS (C t ) miR 16 (C t ) 0 hr A 26.1 30.1 24 hr A 27.5 27.0 0 hr A 25.9 28.3 24 hr A 27.9 26.4 0 hr A 25.7 28.0 24 hr A 27.9 26.4 0 hr A 25.6 27.5 24 hr A 27.4 26.2 0 hr B 26.32 28.1 24 hr B 26.95 undetermined 0 hr B 26.13 27.4 24 hr B 27.35 27.4 0 hr B 26.12 26.2 24 hr B 27.23 27.8 0 hr B 26.26 25.7 24 hr B 27.14 28 .4 0 hr C 26.3 27.12 24 hr C 27.07 26.3 0 hr C 26.6 26.77 24 hr C 27.49 27.0 0 hr C 28.0 26.77 24 hr C 27.37 27.1 0 hr C 26.5 27.06 24 hr C 27.36 28.7 A) C t values for sense (SNORD15B LS) and endogenous control (miR 16) primers. Anti sense primer C t values (not displayed) indicated only a low level of contamination or primer dimer formation in those reactions. Values highlighted in yellow were deemed outliers (defined as one standard deviation away from the mean). Sample Average (SNORD15B LS) C t Average (SNORD15B LS) C t w/o outliers Average (miR 16) C t Average (miR 16) C t w/o outliers 0 hr A 25.8 0.1 25.81 0.06 28.5 0.6 28.0 0.2 0 hr B 26.21 0.05 26.17 0.05 26.9 0.6 26.8 0.6 0 hr C 26.8 0.4 26.46 0.09 26.93 0.09 26.93 0.09 24 hr A 27.7 0.2 27.8 0.1 26.5 0.2 26.32 0.06 24 hr B 27.17 0.08 27.18 0.04 27.9 0.3 27.6 0.2 24 hr C 27.32 0.09 27.41 0.04 27.3 0.5 27.08 0.06 B) Average C t values for SNORD15BLS and miR 16, with and without the outliers (described in A) included. Corresponding standard errors for each average are also provided.

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83 A.2.2 SNORD15B R Sample SNORD15B RS (C t ) miR 16 (C t ) Sample SNORD15B RS (C t ) miR 16 (C t ) 0 hr A 32.03 28.38 24 hr A 28.65 26.02 0 hr A 32.97 27.59 24 hr A 28.75 25.59 0 hr A 29.61 27.29 24 hr A 29.50 25.70 0 hr A 28.70 27.02 24 hr A 28.69 26.08 0 hr B 30.08 26.31 24 hr B 2 9.32 26.32 0 hr B 29.10 25.30 24 hr B 31.01 27.41 0 hr B 28.60 24.96 24 hr B 30.46 27.38 0 hr B 28.11 24.21 24 hr B 31.08 27.96 0 hr C 30.15 25.98 24 hr C 29.90 26.28 0 hr C 29.01 25.75 24 hr C 30.66 26.66 0 hr C 32.40 25.72 24 hr C 31.64 26.88 0 hr C 29.12 26.09 24 hr C 31.78 29.42 A) C t values for sense (SNORD15B RS) and endogenous control (miR 16) primers. Anti sense primer C t values (not displayed) indicated only a low level of contamination or primer dimer formation in those reactions. Values h ighlighted in yellow were deemed outliers (values one standard deviation away from the mean). Sample Average (SNORD15B RS) C t Average (SNORD15B RS) C t w/o outliers Average (miR 16) C t Average (miR 16) C t w/o outliers 0 hr A 31 1 30.8 1.2 27.6 0.3 27. 0 0.2 0 hr B 29.0 0.4 28.9 0.3 25.2 0.4 25.1 0.2 0 hr C 30.1 0.8 29.4 0.4 25.89 0.09 25.82 0.08 24 hr A 28.9 0.2 28.70 0.03 25.9 0.1 25.9 0.1 24 hr B 30.5 0.4 30.9 0.2 27 .3 0.4 27.6 0.2 24 hr C 31.0 0.4 31.4 0 .4 27.3 0.7 26.6 0.2 B) Average C t values for SNORD15B R S and miR 16, with and without the outliers (described in A) included. Corresponding standard errors for each average are also provided.

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84 A.2.3 SNORD26 R (trial 1) Sample SNORD26 RS (C t ) miR 16 (C t ) Sample SNORD26 RS (C t ) miR 16 (C t ) 0 hr A 37 29.2 24 hr A 30.7 25.93 0 hr A 35 28.2 24 hr A 29.9 25.82 0 hr A 34 28.1 24 hr A 30.1 25.79 0 hr A 32 27. 5 24 hr A 30.6 25.87 0 hr B 36 27.2 24 hr B 31 26.7 0 hr B 36 26.5 24 hr B 32 27.8 0 hr B 44 25. 3 24 hr B 33 28.1 0 hr B 31 24.8 24 hr B 38 28.1 0 hr C 31.1 26.3 24 hr C 31 25.2 0 hr C 30.5 26.2 24 hr C 34 26.2 0 hr C 30.2 27.3 24 hr C 33 26.3 0 hr C 30.9 26.4 24 hr C 18 25.8 A) C t values for sense (SNORD26 RS ) and endogenous control (miR 16) p rimers. Anti sense primer C t values (not displayed) indicated only a low level of contamination or primer dimer formation in those reactions. Values highlighted in yellow were deemed outliers (defined as one standard deviation from the mean). Sample Averag e (SNORD26 RS ) C t Average (SNORD26 RS ) C t w/o outliers Average (miR 16) C t Average (miR 16) C t w/o outliers 0 hr A 35 1 34.7 0.4 28.3 0.4 28.18 0.03 0 hr B 37 3 35.79 0.08 25.9 0.6 25.9 0.6 0 hr C 30.7 0.2 30.7 0.2 26.6 0.3 26.32 0.07 24 hr A 30.3 0.2 30.4 0.3 25.85 0.03 25.84 0.03 24 hr B 33 2 32.0 0.7 27.7 0.4 28.00 0.08 24 hr C 29 4 33 1 25.9 0.2 26.1 0.1 B) Average C t values for SNORD26 RS and miR 16, with and without the outliers (described in A) included. Corresponding standard errors for each average are also provided.

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85 A.2.4 SNORD26 R (trial 2) Sample SNORD26 RS (C t ) miR 16 (C t ) Sample SNORD26 RS (C t ) miR 16 (C t ) 0 hr A 31.8 28.5 24 hr A 29.8 26.69 0 hr A 31.6 28.4 24 hr A 29.3 26.4 4 0 hr A 31.6 27.9 24 hr A 29.3 26.51 0 hr A 30.3 27.6 24 hr A 29.6 26.43 0 hr B 32.7 27.1 24 hr B 33.1 28.2 0 hr B 32.3 26.6 24 hr B 34.7 29.0 0 hr B 33.8 25.8 24 hr B 36.1 29.1 0 hr B 30.4 25.3 24 hr B 35.6 29.2 0 hr C 29.2 25.82 24 hr C 30.2 25.9 0 hr C 29.2 25.68 24 hr C 30.1 26.1 0 hr C 28.7 25.81 24 hr C 32.5 26.2 0 hr C 28.5 25.85 24 hr C 33.1 27.1 A) C t values for sense (SNORD26 RS ) and endogenous control (miR 16) primers. Anti sense primer C t values (not displayed) indicated only a low level of contamination or primer dimer formation in those reactions. Values highlighted in yell ow were deemed outliers ( one standard deviation from the mean). Sample Average (SNORD26 RS ) C t Average (SNORD26 RS ) C t w/o outliers Average (miR 16) C t Average (miR 16) C t w/o outliers 0 hr A 31.3 0.3 31.67 0.08 28.1 0.2 28.3 0.2 0 hr B 32.3 0.7 32.5 0.2 26.2 0.4 26.2 0.4 0 hr C 28.9 0.2 29.1 0.2 25.79 0.04 25.83 0.01 24 hr A 29.5 0.1 29.40 0.09 26.52 0.06 26.46 0.02 24 hr B 34.9 0.7 35.5 0.4 28.9 0.3 29.13 0.06 24 hr C 31.5 0.8 30.9 0.8 26.3 0.3 26.09 0.09 B) Average C t values for SNORD26 RS and miR 16, with and without the outliers (described in A) included. Corresponding standard errors for each average are also provided.

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86 A.2.5 SNORD26 L (trial 1) Sample SNORD26 LS (C t ) miR 16 (C t ) Sample SNORD26 LS (C t ) miR 16 (C t ) 0 hr A 27.3 30.7 24 hr A 27.68 28.2 0 hr A 26.9 29.6 24 hr A 27.45 27.6 0 hr A 27.0 29.4 24 hr A 27.30 27.5 0 hr A 26.7 29.3 24 hr A 27.47 27.5 0 hr B 27.3 29.0 24 hr B 27.9 28.7 0 hr B 27.2 28.0 24 hr B 2 8.0 29.2 0 hr B 28.1 27.3 24 hr B 28.2 29.6 0 hr B 27.1 26.6 24 hr B 28.4 30.8 0 hr C 26.80 27.87 24 hr C 27.1 27.4 0 hr C 26.72 27.59 24 hr C 27.1 28.1 0 hr C 26.61 27.64 24 hr C 27.0 28.4 0 hr C 26.83 27.70 24 hr C 27.5 29.8 A) C t values for sense (SNORD26 LS ) and endogenous control (miR 16) primers. Anti sense primer C t values (not displayed) indicated only a low level of contamination or primer dimer formation in those reactions. Value s highlighted in yell ow were deemed outliers ( one standard deviation from the mean). Sample Average (SNORD26 LS ) C t Average (SNORD26 LS ) C t w/o outliers Average (miR 16) C t Average (miR 16) C t w/o outliers 0 hr A 2 7 .0 0.1 26.97 0.03 29.7 0.3 29.4 0.1 0 hr B 27.4 0.2 27.19 0.05 27.7 0.5 27.6 0.4 0 hr C 26.74 0.05 26.78 0.03 27.70 0.06 27.64 0.03 24 hr A 27.48 0.08 27.46 0.01 27.7 0.2 27.55 0.02 24 hr B 28.1 0.1 28.01 0.08 29.6 0.5 29.2 0.3 24 hr C 27.2 0.1 27. 07 0.03 28.4 0.5 28.3 0.2 B) Average C t values for SNORD 26 LS and miR 16, with and without the outliers (described in A) included. Corresponding standard errors for each average are also provided.

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87 A.2.6 SNORD26 L (trial 2) Sample SNORD26 LS (C t ) mi R 16 (C t ) Sample SNORD26 LS (C t ) miR 16 (C t ) 0 hr A 27. 0 30.2 24 hr A 27.38 27.8 0 hr A 26.6 29.2 24 hr A 27.19 27.3 0 hr A 26.6 29.0 24 hr A 27.05 27.2 0 hr A 26.4 28.9 24 hr A 27.20 27.2 0 hr B 27.0 28.4 24 hr B 27.6 28.3 0 hr B 26.9 27.5 24 hr B 2 7.6 28.8 0 hr B 27.9 26.9 24 hr B 27.9 29.2 0 hr B 26.8 26.3 24 hr B 28.1 30.3 0 hr C 26.47 27.50 24 hr C 26.8 27.1 0 hr C 26.39 27.26 24 hr C 26.7 27.7 0 hr C 26.30 27.29 24 hr C 26.7 28.0 0 hr C 26.51 27.34 24 hr C 27.2 29.4 A) C t values for sense (SNORD26 LS ) and endogenous control (miR 16) primers. Anti sense primer C t values (not displayed) indicated only a low level of contamination or primer dimer formation in those reactions. Values highlighted in yellow were deemed outliers ( one standard dev iation away from the mean). Sample Average (SNORD26 LS ) C t Average (SNORD26 LS ) C t w/o outliers Average (miR 16) C t Average (miR 16) C t w/o outliers 0 hr A 26.7 0.1 26.53 0.07 29.3 0.3 29.04 0.09 0 hr B 27.2 0.3 26.90 0.06 27.3 0.5 27.2 0.3 0 hr C 26.42 0.05 26.46 0.03 27.35 0.05 27.30 0.02 24 hr A 27.20 0.07 27.19 0.01 27.4 0.1 27.23 0.03 24 hr B 27. 8 0.1 27.7 0.1 29.1 0.5 28.8 0.3 24 hr C 26.9 0.1 26.74 0.05 28.1 0.5 27.6 0.3 B) Average C t values for SNORD 26 RA and miR 16, with and without the outliers (described in A) included. Corresponding standard errors for each average are also provided.

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88 A.2.7 hsa miR 200a 5p (L) Sample hsa miR 200a LS (C t ) miR 16 (C t ) Sample hsa miR 200a LS (C t ) miR 16 (C t ) 0 hr A 35.5 30.7 24 hr A 33.9 28.4 0 hr A 35.7 29.9 24 hr A 34.5 27.9 0 hr A 35.5 29.5 24 hr A 34.1 27.9 0 hr A 34.9 28.7 24 hr A 34.4 2 7.9 0 hr B 35.3 30.2 24 hr B 35.6 28.9 0 hr B 35.2 29.1 24 hr B 35.8 29.7 0 hr B 34.3 28.2 24 hr B 35.5 29.9 0 hr B 34.5 27.4 24 hr B 34.6 30.0 0 hr C 35.0 28.0 24 hr C 34.2 27.5 0 hr C 36.1 27.6 24 hr C 33.9 28.4 0 hr C 36. 8 27.7 24 hr C 34.9 29.0 0 hr C 33.4 27.6 24 hr C 35.3 30.1 A) C t values for sense (hsa miR 200a LS) and endogenous control (miR 16) primers Anti sense primer C t values (not displayed) indicated only a low level of contamination or primer dimer formation in those reactions. Values highlighted in yel low were deemed outliers ( one standard deviation away from the mean). Sample Average (hsa miR 2 00a LS) C t Average (hsa miR 200a LS) C t w/o outliers Average (miR 16) C t Average (miR 16) C t w/o outliers 0 hr A 35.4 0.2 35.53 0.06 29.7 0.4 29. 7 0.2 0 hr B 34.8 0.3 35.0 0.3 28.7 0.6 28.6 0.5 0 hr C 35.3 0.7 36.0 0.5 27.7 0.1 2 7.64 0.04 24 hr A 34.2 0.1 34.3 0.1 28.0 0.1 27.90 0.01 24 hr B 35.4 0.3 35.62 0.09 29.6 0.3 29.88 0.09 24 hr C 34.6 0.3 34.5 0.3 28.8 0.5 28.7 0.3 B) Average C t values for hsa miR 200a LS and miR 16, with and without the outl iers (described in A) included. Corresponding standard errors for each average are also provided.

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89 A.2.8 hsa miR 200a 3p (R) (trial 1) Sample hsa miR 200a RS (C t ) miR 16 (C t ) Sample hsa miR 200a RS (C t ) miR 16 (C t ) 0 hr A 3 3.0 31.5 24 hr A 31.6 28. 1 0 hr A 32.0 30.0 24 hr A 31.0 27.6 0 hr A 31.1 30.0 24 hr A 31.2 27.8 0 hr A 32.1 28.7 24 hr A 31.3 27.9 0 hr B 30.5 30.5 24 hr B 31.5 28.6 0 hr B 30.5 30.6 24 hr B 32.6 29.6 0 hr B 29.7 28.7 24 hr B 32.6 30.01 0 hr B 29.4 27.2 24 hr B 34.0 30.2 0 hr C 32 27.8 24 hr C 29.8 27.04 0 hr C 33 27.5 24 hr C 30.1 28.1 0 hr C 38 27.4 24 hr C 31.2 28.8 0 hr C 31 27.7 24 hr C 31.9 30.0 A) C t values for sense (hsa miR 200a RS) and endogenous control (miR 16) primers. Anti sense primer C t values (not displayed) indicated only a low level of contamination or primer dimer formation in those reactions. Values highlighted in yell ow were deemed outliers ( one standard deviation away from the mean). Sample Average (hsa miR 200a RS) C t Average (hsa miR 200a RS) C t w/o ou tliers Average (miR 16) C t Average (miR 16) C t w/o outliers 0 hr A 32.0 0.4 32.03 0.07 30.0 0.6 29.968 0.004 0 hr B 30.0 0.3 30. 3 0.3 29.3 0.8 29.9 0.6 0 hr C 34 2 31. 9 0.6 27.6 0.1 27.61 0.09 24 hr A 31 .3 0.1 31.22 0.05 27 .8 0.1 27.83 0.05 24 hr B 32.7 0.5 32.60 0.01 29.6 0.4 29.9 0.2 24 hr C 30.8 0.5 30.4 0.4 28.5 0.6 28.4 0.4 B) Average C t values for hsa miR 200a RS and miR 16, with and without the outliers (described in A) included. Corresponding s tandard errors for each average are also provided.

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90 A.2.9 hsa miR 200a 3p (R) (trial 2) Sample hsa miR 200a RS (C t ) miR 16 (C t ) Sample hsa miR 200a RS (C t ) miR 16 (C t ) 0 hr A 31.3 29.4 24 hr A 30.7 27. 5 0 hr A 30.5 29.1 24 hr A 30.2 27.4 0 hr A 30.0 28. 7 24 hr A 30.5 27.4 0 hr A 29.7 28.5 24 hr A 30.3 27.8 0 hr B 29.2 28.9 24 hr B 29.7 28.2 0 hr B 29.0 27.7 24 hr B 30.5 28.2 0 hr B 28.8 27.0 24 hr B 30.5 28.4 0 hr B 28.5 26.3 24 hr B 30.9 28.7 0 hr C 30.6 27.04 24 hr C 31.3 28.4 0 hr C 30.7 26.79 24 hr C 32.1 29.2 0 hr C 32. 5 26.74 24 hr C 32.8 29.8 0 hr C 29.7 27.04 24 hr C 33.5 31.0 A) C t values for sense (hsa miR 200a RS) and endogenous control (miR 16) primers. Anti sense primer C t values (not displayed) indicated only a low level of contami nation or primer dimer formation in those reactions. Values highlighted in yellow were deemed outliers (one standard deviation away from the mean). Sample Average (hsa miR 200a RS) C t Average (hsa miR 200a RS) C t w/o outliers Average (miR 16) C t Average ( miR 16) C t w/o outliers 0 hr A 30.4 0.3 30.1 0.2 28.9 0.2 28.9 0.3 0 hr B 28.9 0.2 28.89 0.09 27.5 0.5 27.4 0.4 0 hr C 30.9 0.6 30.3 0.3 26.90 0.08 26.90 0.08 24 hr A 30.4 0.1 30.34 0.08 27.5 0.1 27.43 0.01 24 hr B 30. 4 0.3 30.6 0.1 28.4 0.1 28.25 0.06 24 hr C 32.4 0.5 32.4 0.4 29.6 0.5 29.5 0.3 B) Average C t values for hsa miR 200a RS and miR 16, with and without the outliers (described in A) included. Corresponding standard errors for each average ar e also provided.

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91 A.2.10 hsa miR 34c 3p (R) Sample hsa miR 34c RS (C t ) miR 16 (C t ) Sample hsa miR 34c RS (C t ) miR 16 (C t ) 0 hr A 36.6 30.0 24 hr A 35 28.23 0 hr A 34.6 29.6 24 hr A 34 28.17 0 hr A 34.6 29.3 24 hr A 35 28.10 0 hr A 35.0 29.1 24 hr A 39 28.03 0 hr B 34.2 28.4 24 hr B 35 28.6 0 hr B 34.6 27.9 24 hr B 38 29.2 0 hr B 34.9 27.3 24 hr B 41 29.4 0 hr B 34.8 26.7 24 hr B 38 29.3 0 hr C 36.3 27.8 24 hr C 32.6 27.0 0 hr C 34.4 27.6 24 hr C 33.5 27.5 0 hr C 35.8 28.1 24 hr C 33.7 27.8 0 hr C 34.6 27.7 24 hr C 33.5 28.3 A) C t values for sense (hsa miR 34c RS) and endogenous control (miR 16) primers. Anti sense primer C t values (not displayed) indicated only a low level of contamination or primer dimer formation in those reactions. Values hig hlighted in yellow were deemed outliers (one standard deviation away from the mean) Sample Average (hsa miR 34c RS) C t Average (hsa miR 34c RS) C t w/o outliers Average (miR 16) C t Average (miR 16) C t w/o outliers 0 hr A 35.2 0.5 34.7 0.1 29.5 0.2 29.3 0.2 0 hr B 34.6 0.1 34.73 0.08 27.6 0.4 27.6 0.3 0 hr C 35.3 0.5 34.9 0.4 27.8 0.1 27.75 0.06 24 hr A 36 1 34.7 0.4 28.13 0.04 28.13 0.04 24 hr B 38 1 38.2 0.1 29.1 0.2 29.28 0.05 24 hr C 33.3 0.2 33.54 0.06 27.7 0.3 27.7 0.1 B) Average C t values for hsa miR 34c RS and miR 16, with and without the outliers (described in A) included. Corresponding standard errors for each average are also provided.

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92 Appendix C Dissociation curves A.3.1 SNORD15B L d issociation curves. A. SNORD15B LS (max. derivative at 0.06). B. miR 16 (max. derivative at 0.06). C. SNORD15B LA (max. derivative at 0.05).

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93 A.3.2 SNORD15B R dissociation curves. A. SNORD15B RS. B. miR 16. C. SNORD15B RA. Maximum derivate at 0.06 for A C.

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94 A.3.3 SNORD26 R dissociatio n curves (trial 1). A. SNORD26 RA primer (max. derivative at 0.07). B. miR 16 (max. derivative at 0.06). C. SNORD26 RS (max. derivative at 0.06).

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95 A.3.4 SNORD26 R dissociation curves (trial 2). A. SNORD26 RA primers (max. derivative at 0.12). B. miR 16 (max. derivative at 0.10). C. SNORD26 RS (max. derivative at 0.10).

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96 A.3.5 SNORD26 L dissociation curves (trial 1). A. SNORD26 LA (max. derivative at 0.06). B. miR 16 (max. derivative at 0.06). C. SNORD26 LS (max. derivative at 0.07).

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97 A.3.6 SNORD26 L dissociation curves (trial 2). A. SNORD26 LA (max. derivative at 0.10). B. miR 16 (max. derivative at 0.10). C. SNORD26 LS (max. derivative at 0.12).

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98 A.3.7 hsa miR 200a 5p ( L ) dissociation curves. A. hsa mi R 200a LS (max. derivative at 0.06). B. miR 16 (max. derivative at 0.07). C. hsa miR 200a LA (max. derivative at 0.04).

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99 A.3.8 hsa miR 200a 3p ( R ) dissociation curve (trial 1). A. hsa miR 200a RS (max. derivative at 0.04). B. miR 16 (max. derivative a t 0.06). C. hsa miR 200a RA (max. derivative at 0.05).

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100 A.3.9 hsa miR 200a 3p ( R ) dissociation curves (trial 2). A. hsa miR 200a RS (max. derivative at 0.08). B. miR 16 (max. derivative at 0.12). C. hsa miR 200a RA (max. derivative at 0.10).

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101 A.3.1 0 hsa miR 34c 3p ( R ) dissociation curves. A. hsa miR 34c RS. B. miR 16. C. hsa miR 34c RA. Maximum derivative at 0.10 for A C.

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102 Appendix D Deep Sequencing Data A.4.1 Deep sequencing data for hsa miR 200a. The hsa miR 200a gene (located at Chr1: 1093 106 1093195 on hg18) and corresponding read counts (RC). RC range is 0 25764 RC. Maximum RC for left peak ( 5p) : 474 RC in uninfected (0 hr) and 886 RC in infected (24 hr) samples. Maximum RC for right peak ( 3p) : 12504 in uninfected (0 hr) and 25764 in infected (24 hr) samples. Forward primers / mature miRNA are outlined in black at the bottom of the figure. Deep sequencing data for the other investigated sncRNA can be found at the following locations SNORD15B: Figure 3.3, 3.4, 3.5, and 3.7, pages 55, 56, 56, and 64 respectively. SNORD26: Figure 3.7B, page 64 hsa miR 34c: Figure 3.8, page 71

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