University of Cincinnati

UNIVERSITY OF CINCINNATI

Date: 23-Jul-2010

I, Michael C Tranter , hereby submit this original work as part of the requirements for the degree of: Doctor of Philosophy in Molecular, Cellular & Biochemical Pharmacology It is entitled: Investigation of NF-kappaB-Dependent Transcriptional and

Post-Transcriptional Regulatory Networks in Late Ischemic Preconditioning

Student Signature: Michael C Tranter

This work and its defense approved by: Committee Chair: Walter Keith Jones, PhD Walter Keith Jones, PhD

8/18/2010 927 Investigation of NF-κB-Dependent Transcriptional and Post- Transcriptional Regulatory Networks in Late Ischemic Preconditioning

A dissertation submitted to the Graduate School of the University of Cincinnati

In partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

In the Department of Pharmacology and Cell Biophysics of the College of Medicine

2010

Michael Tranter B.S. Rose-Hulman Institute of Technology, 2004

Dr. W. Keith Jones, Committee Chairperson

Abstract

Ischemic preconditioning (IPC) is a cardioprotective phenomenon initiated in response to short bouts of sublethal ischemia and reperfusion that serves as an endogenous defense mechanism to protect the mammalian heart against a subsequent prolonged ischemia/reperfusion (I/R) injury. The late phase of IPC provides protection for approximately 12-72 hours after the IPC stimulus and requires de novo gene expression. Activation of the transcription factor NF-κB has been shown to be necessary for the development of late phase IPC-induced cardioprotection. However, the specific mechanisms and downstream gene expression profiles that underlie the cardioprotection of NF-κB in late IPC remain unknown.

To investigate NF-κB-dependent gene expression in IPC, we conducted a directed microarray analysis to compare the gene expression patterns of wild-type and NF-κB dominant negative mice after an IPC stimulus. Microarray results indicated that NF-κB- dependent gene expression changes after late IPC were enriched for genes involved in angiogenesis, programmed cell death, and the heat shock response. We further demonstrated that the expression of two inducible heat shock protein 70 genes, Hsp70.1 and Hsp70.3, are induced by NF-κB after late IPC. However, results of infarct studies show that only the Hsp70.3 gene contributes to cardioprotection in the late phase of IPC, while the Hsp70.1 gene, encoding a nearly identical protein, does not contribute to IPC- induced cardioprotection and is pro-cell death after ischemia/reperfusion injury.

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Interestingly, inhibition of NF-κB in late IPC only partially (50%) reduced IPC- induced expression of Hsp70.3 mRNA, while it completely blocked the increase of

Hsp70.3 protein. Upon further investigation of the Hsp70.3 3’-untranslated region (3’-

UTR) of the mRNA transcript, we found evidence for miRNA and alternative polyadenylation mediated post-transcriptional regulation of Hsp70.3. To assess potential miRNA regulation of Hsp70.3, comparative miRNA expression arrays were performed in wild-type and NF-κB dominant-negative mice after IPC. Two Hsp70.3 targeting miRNAs, miR-378* and miR-711, were found to be downregulated by IPC, coinciding with increased Hsp70 protein expression. However, only miR-711 was suppressed in an

NF-κB-dependent manner after late IPC. Our results demonstrate that both of these miRNAs exert a 3’-UTR-mediated post-transciptional suppression on Hsp70.3 expression.

In addition to regulation by miRNAs, it was found that four distinct size populations of Hsp70.3 transcripts exist as a result of alternative polyadenylation (polyA) within the 3’-UTR. Two predominant populations of alternatively polyadenylated

Hsp70.3 transcripts exist that represent the result of polyadenylation at one of two sites on either side of the binding site for miR-378*. Quantitative analysis of polyA site utilization after late IPC using QRT-PCR revealed a significant increase in Hsp70.3 mRNA transcripts with shortened 3’-UTRs leading to the removal of the miR-378* binding site. These results indicate for the first time that alternative polyadenylation plays a role in post-transcriptional regulation of cardioprotective gene programs following a late IPC stimulus. In addition, it appears that these processes may be coordinated with miRNA-mediated post-transcriptional regulation.

Analysis of predicted transcription factor binding sites within the Hsp70.3 depicted binding sites for the IPC-dependent transcription factors HSF-1, STAT3, and

AP-1, in addition to NF-κB. Hsp70.3-luciferase reporter constructs were used to assess the role of these transcription factors on the regulation of Hsp70.3 gene expression in

H9c2 cells and murine embryonic fibroblasts (MEFs) after stimulus by either heat shock

(HS) or simulated I/R. Results indicate that HSF-1 is absolutely obligatory to Hsp70.3 expression following both HS and simulated I/R. Inhibition of either NF-κB, STAT3, or

AP-1 blunted Hsp70.3 reporter expression following HS and simulated I/R. This work demonstrates that mRNA expression of Hsp70.3 is transcriptionally co-regulated through the obligatory coordinated actions of NF-κB, STAT3, AP-1 and HSF-1.

As a future means to acutely inhibit activation of multiple transcription factors in

IPC and examine the resulting effects on gene expression, we sought to demonstrate the efficacious application of non-viral PGAA glycopolymers (T4) for the functional delivery of transcription factor decoys and transcriptional blockade in the in vivo heart. Results show that T4-mediated pericardial delivery of NF-κB decoys resulted in efficient transfection of the myocardium and functional blockade of NF-κB as evidenced by inhibition of NF-κB induced COX-2 gene expression and a decrease in infarct size after

I/R injury. Successful application of this system for the blockade of NF-κB, STAT-3, or

AP-1 transcriptional activation in late IPC demonstrated that the activation of these factors are all critically important to late phase IPC cardioprotection. This is in agreement with our in vitro results indicating coordinated gene expression by these three factors.

The use of PGAA glycopolymers makes it possible to specifically inhibit transcription

factor activation in vivo for the semi-high-throughput study of transcriptional networks in myocardial pathophysiology.

The results presented herein delineate the immediate NF-κB-dependent transcriptome after late IPC and contribute to the global understanding of the cardioprotective mechanisms of late IPC. In addition, the regulation of cardioprotective

Hsp70.3 requires coordinated regulation of transcription as well as post-transcriptional modulation by polyadeylation site selection and miRNA binding/translational regulation.

Our studies of the pre- and post-transcriptional regulation of Hsp70.3 expression elucidate a complex multi-factorial regulation of Hsp70.3 and contribute to the understanding of the regulatory networks controlling gene expression in late IPC.

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Acknowledgements

I would like to extend my sincere appreciation and thanks to the many people who have provided both the personal and professional guidance that have made possible my completion of this dissertation.

Special thanks to my committee members, Dr. Keith Jones, Dr. Theresa Reineke,

Dr. Scott Belcher, Dr. JoEl Schultz, and Dr. Hong-Sheng Wang, for their time spent discussing and reviewing my research and the invaluable expertise and feedback they have provided throughout my graduate career. I am very grateful for the genuine interest that each has shown in not only my current research, but also in my future success and development as a scientist. I respect and appreciate the advice and mentoring I have received from each.

I owe much of my development as a scientist to Dr. Keith Jones, who gave me the opportunity to perform my graduate work in his laboratory. Keith has been an excellent mentor and friend, and his dedication and passion for science is contagious and evident throughout the lab. I feel very luck to have had the privilege of performing my graduate research and training in Keith’s lab. I considered it a great compliment when Keith once said that I reminded him of a younger scientific version of himself. It is my hope and goal that I may someday achieve the same level of professional success in my career as Dr.

Keith Jones.

I would also like to thank Dr. Theresa Reineke for the opportunity for me to perform a large chapter of my graduate work as a collaborative effort with her lab. I am

viii

grateful for her continuing collaboration and support of my work even following her move from the University of Cincinnati to Virginia Tech.

I would like to thank all the members of the Jones lab, both past and present, for their help, teaching, and camaraderie during my graduate career. Dr. Maria Brown and

Dr. Suiwen He both deserve credit for providing additional teaching and mentoring during my early time in the lab. I am very indebted to Jackie Belew for her time and effort in maintaining the Jones lab mouse colonies, which were very critical to my research. I am also thankful for the friendship of my lab mates, whose conversational company helped make coming to the lab everyday an enjoyable experience. Many great scientific ideas and experimental plans were also born out of casual conversations with labmates.

I would also like to thank other teachers and mentors who have helped in my scientific development through the years. Rose-Hulman Institute of Technology did an outstanding job of preparing me academically for the rigors of graduate school. Dr.

Richard Anthony and Dr. Christine Buckley, along with the rest of the RHIT department of Applied Biology and Biomedical Engineering, deserve special thanks for their time and advice. Dr. Leland Sudlow, and Dr. Ron Millard were patient and kind enough to mentor me as an NSF REU summer student when I just beginning to learn experimental procedures. Thanks to Dr. Ron Millard for the continued and often needed support and advice from the time I joined his lab as a summer student in 2003 throughout my graduate career.

Thanks to the Department of Pharmacology & Cell Biophysics faculty and staff for their hard work to support a successful graduate program. Thanks also to the faculty, staff, and fellow students of the NSF IGERT program that supported three years of my graduate education.

Finally, I would like to thank my family for their understanding and support of my need to remain a student of science for the time needed to obtain a PhD. My parents and grandparents deserve great credit for the encouragement and nurturing of my scientific curiosity that I have seemingly had since a very young age. I also owe them a great deal of thanks for the financial support allowing me to gain an excellent undergraduate education, without which any further accomplishments may not have been possible. My accomplishments also would have been possible without the overwhelming support of my former college homework partner and now wife, Brandi. I am very grateful for her patience and understanding of my decision to forego or delay a more financially lucrative position for the pursuit of scientific knowledge.

I feel extremely lucky and grateful for my current accomplishments and potential future endeavors. However, by no means have I accomplished this alone. Each of the people mentioned above, and likely many more that I am unable to list for the sake of time and space, have made significant contributions to my development as an individual and as a scientist.

Table of Contents

Abstract iii

Acknowledgments viii

Table of Contents 1

Figures and Tables 6

Chapter I Introduction

I.1 Ischemia/Reperfusion Injury and Ischemic Preconditioning 9

I.1.1 Ischemia/Reperfusion Injury 9

I.1.2 Ischemic Preconditioning 15

I.1.3 Cardioprotective Signaling Mediators of Late IPC 18

I.1.4 Heat Shock Proteins as Mediators of Late IPC 39

I.1.5 The Role of NF-κB in Late Ischemic Preconditioning 45

I.2 PGAA-Mediated Delivery of Transcription Factor Decoys 51

I.3 Summary of Goals and Hypotheses 57

Chapter II Materials and Methods

II.1 Animals 59

II.2 Ischemia/Reperfusion and Ischemic Preconditioning 60

II.3 RNA Isolation 61

II.4 Gene Expression Microarray Analysis 62

II.5 Bioinformatics Analysis of Gene Microarray Results 64

II.6 Limitations of Gene Microarray Results 64

II.7 miRNA Expression Array 65

II.8 cDNA Synthesis and Quantitative Real-Time RT-PCR 66

II.9 Transcription Factor Decoys and PGAA Polymers 68

II.10 Cell Culture and Transfections 71

II.11 Simulated Ischemia/Reperfusion 72

II.12 Primary Neonatal Rat Ventricular Myocytes 73

II.13 Pericardial Delivery of PGAA/Decoy Polyplexes 74

II.14 Nuclear/Cytoplasmic Protein Extraction 75

II.15 Whole Cell Protein Extraction 76

II.16 Electrophoretic Mobility Shift Assay (EMSA) 76

II.17 Western Blotting 77

II.18 Generation of Hsp70.3 Luciferase Reporter Constructs 78

II.19 Luciferase Reporter Assay 82

II.20 MTS Assay 82

II.21 Rapid Amplification of cDNA Ends (RACE) 83

II.22 Statistical Analysis 84

Chapter III Results

III.1 The Role of NF-κB-dependent Gene Expression in Late IPC 85

III.1.1 Identification of the NF-κB-dependent Gene Program 85 In Late Ischemic Preconditioning

III.1.2 NF-κB Regulation of the Heat Shock Response 114 After Late IPC

III.1.3 Hsp70.3 is an NF-κB-dependent Mediator of Late 117 IPC Cardioptrotection

III.2 Transcriptional Co-Regulation of Hsp70.3 Gene Expression 123 in Late Ischemic Preconditioning

III.2.1 Evidence for Cooperative Regulation of Hsp70.3 Gene 123 Expression in Late IPC

III.2.2 Regulation of Hsp70.3-Luciferase Reporters 126

III.3 Post-Transcriptional Regulation of Hsp70.3 Gene 138 Expression in Late Ischemic Preconditioning

III.3.1 Evidence for Post-Transcriptional Regulation of the 138 Hsp70.3 Transcript in Late IPC

III.3.2 Post-Transcriptional Regulation of Hsp70.3 is Mediated 142 By the 3’-Untranslated Region

III.3.3 IPC Induced Expression Changes of Hsp70.3 Targeting 149 MicroRNAs

III.3.4 MiRNA-378* and miRNA 711 Mediate Post- 158 Transcriptional Suppression of Hsp70.3 Expression via the 3’-UTR

III.3.5 Alternative Polyadenylation of the Hsp70.3 Transcript 169

III.4 Poly(glycoamidoamine)-Mediated Delivery of 182 Transcription Factor Decoys

III.4.1 In Vitro Characterization of PGAA-NF-κB Decoy 182 Polyplexes

III.4.2 Inhibition of In Vivo NF-κB Activation via PGAA- 195 Mediated Delivery of NF-κΒ Decoys

Chapter IV Discussion

IV.1 Summary of Hypotheses and Results 206

IV.2 NF-κB-dependent Gene Expression in Late IPC 209

IV.2.1 Antithetical Nature of NF-κB in Cardiac 209 Pathophysiology

IV.2.2 Functional Role of NF-κB Mediated Gene 215 Expression Products in Late IPC

IV.3 The Role of Hsp70.1/3 in Late IPC Cardioprotection 227

IV.4 Regulation of Hsp70.3 Gene Expression in Late IPC 232

IV.4.1 Transcriptional Regulation of Hsp70.3 Gene 234 Expression

IV.4.2 Post-transcriptional Regulation of Hsp70.3 Protein 236 Expression

IV.5 Poly(glycoamidoamine)-mediated Delivery of 244 Transcription Factor Decoys

IV.6 Conclusions 248

IV.7 Future Directions 250

References 255

Appendix 284

Figures and Tables

Figures:

1. Chemical structures of PGAA polymers 70

2. Hsp70.3-luciferase reporter vectors 80

3. NF-κB activation is necessary for late phase IPC cardioprotection 86

4. Experimental design for hypothesis driven microarrays to 91 determine NF-κB-dependent gene expression in late IPC

5. Functional ontology clustering of genes regulated by NF-κB in late IPC 107

6. Individual genes contained within NF-κB regulated gene ontology 109 clusterings

7. QRT-PCR mRNA expression validation of select NF-κB-dependent 112 genes in late IPC

8. NF-κB-dependent expression of Hsp70.1 and Hsp70.3 mRNA in late IPC 115

9. Effect of Hsp70.1 KO and Hsp70.1/70.3 KO on late IPC cardioprotection 118

10. Effect of Hsp70.1 KO and Hsp70.1/70.3 KO on I/R injury 121

11. Promoter analysis of Hsp70.3 and Hsp70.1 transcription factor 124 binding sites

12. In vitro Hsp70.3-luciferase reporter expression 128

13. In vitro simulated I/R in H9c2 cells 130

14. Decoy inhibition of HS induced Hsp70.3 reporter expression 132 in H9c2 Cells

15. Decoy inhibition of simulated I/R induced Hsp70.3 reporter expression 134 and exacerbation of cell death

16. HSF-1 KO MEFs cannot induced Hsp70.3 expression in response 136 to HS or simulated I/R

17. Hsp70 protein expression in late IPC 140

18. Hsp70.3 specific 3’-UTR mediated suppression of reporter expression 143

19. Hsp70.3 and Hsp70.1 3’-UTR sequence homology and predicted 147 miRNA binding sites

20. IPC induced expression changes in Hsp70.3 targeting miRNAs 156

21. MiRNA mediated regulation of Hsp70.3 3’-UTR luciferase reporter 160

22. Hsp70 expression following heat shock in MEFs 164

23. MiRNA regulation of endogenous Hsp70.3 protein in Hsp70.1 KO MEFs 167

24. Prediction of polyadenylation sites within the Hsp70.3 transcript 172

25. Alternative polyadenylation of the Hsp70.3 transcript 174

26. Hsp70.3 polyadenylation products in WT MEFs cells following HS 176

27. Alternative polyadenylation of Hsp70.3 following IPC 178

28. Quantitative assessment of Hsp70.3 polyadenylation products 180

29. PGAA-decoy mediated inhibition of NF-κB in H9c2 cells 184

30. Nuclear localization of decoys delivered via T4 PGAA 187

31. In vitro transfection efficiency in NRVMs 191

32. Cytotoxicity of transfection vectors in NRVMs 193

33. Transfection of T4-NF-κB decoy polyplexes to the in vivo murine heart 198

34. Functional blockade of NF-κB in vivo via T4-decoy polyplex delivery 200

35. Decoy mediated blockade of NF-κB, AP-1, or STAT3 inhibits late IPC 204 induced cardioprotection

Tables:

1. A brief list of publications reporting functional delivery of transcription 53 factor decoys in animal models

2. QRT-PCR parameters and primer sequences 67

3. Transcription factor decoy sequences 69

4. Genes ontology categorization of genes regulated by late IPC 93

5. Genes significantly regulated by NF-κB in late IPC 96

6. Gene ontology categorization of genes regulated by NF-κB in late IPC 104

7. IPC-induced changes in miRNA expression in WT and 2M mice 152

8. IPC-induced expression changes in Hsp70.3 targeting miRNA 154

Chapter I

Introduction

Section 1. Ischemia/Reperfusion Injury and Ischemic Preconditioning

I.1.1 Ischemia/Reperfusion Injury

On a very basic level, myocardial ischemia is defined as the impairment of coronary blood flow and is often the result of coronary heart disease. Ischemic heart disease is the largest single killer of American males and females; it was responsible for approximately 20% of all deaths in the United States in 2005.1 It is estimated that this year 1.5 million Americans will suffer a heart attack with the total 2009 economic impact being around $165 billion.1

In addition to the myocardial damage that can result from ischemia, it is now accepted that reperfusion, the resumption of blood flow, can also independently contribute to myocardial damage. However, the cumulative resulting damage to the heart is usually taken together as ischemia/reperfusion (I/R) injury. Myocardial I/R leads to acute injuries such as contractile dysfunction, arrhythmogenesis, and cell death within the region of affected myocardium and may also lead to congestive heart failure if the myocardium is sufficiently damaged such that contractile function of the heart is permanently compromised.

Mechanisms of Ischemia/Reperfusion Injury

Myocardial ischemia and reperfusion initiates intracellular processes and signaling pathways that give rise to several factors that can lead to cellular necrosis or apoptosis. Rapidly after the onset of ischemia, the loss of available oxygen and metabolic substrates leads to disruption of mitochondrial oxidative phosphorylation. This compels the affected cardiomyocytes to reduce mitochondrial fatty acid metabolism and increased anaerobic glycolysis as a compensatory mechanism of adenosine triphosphate (ATP) production. However, this compensatory cellular metabolic state is not sufficient to supply contracting myocytes with the amount of ATP necessary to maintain efficient excitation-contraction (E-C) coupling. The initation of anaerobic metabolism and subsequent depletion of cellular ATP levels leads to the manifestation of multiple damaging factors within the cardiomyocyte that together lead to cell necrosis. First, anaerobic metabolism, especially in the absence of blood flow to clear metabolites, results in an increase in the intracellular concentration of H+ ions, lactate, and inorganic phosphates. The drop in intracellular pH resulting from an increased concentration of H+ ions initiates activation of the sodium-hydrogen exchanger (NHE). The resulting buildup of intracellular Na+ results in osmotic swelling of the cell and is exacerbated by the reduced function of the ATP-dependent sodium-potassium pump (Na+/K+-ATPase) due to the decline in available ATP.

Meanwhile, the decline in ATP production also impairs calcium uptake to the sarcoplasmic reticulum (SR) by the sarco/endoplasmic reticulum Ca2+-ATPase (SERCA) and result in increased cytoplasmic calcium concentrations. Large increases in cytosolic

calcium concentration has been shown to induce mitochondrial uncoupling and trigger cell death.2 Protease activation can lead to non-specific ultrastructural changes within the cardiomyocytes which leads to impairment and decreased sensitivity of contractile proteins.

In addition to the cellular damage that can occur resulting from ischemia, there is mounting evidence that the rapid restoration of blood flow in the form of reperfusion can also elicit further damage to the affected tissue.3 The re-introduction of available oxygen to the cell reinitiates mitochondrial oxidative phosphorylation and ATP production.

Following the oxygen and substrate deprived state of ischemia, this burst of mitochondrial oxygen consumption upon reperfusion results in the production of reactive oxygen (ROS) and nitrogen (RNS) species resulting from multiple sources including mitochondrial electron transport chain dysfunction, NO production, NADPH oxidase, or cyclooxygenase.4 Both ROS and RNS can cause oxidative and nitrosative damage to cellular proteins leading to further impairment of contractile proteins and SR calcium cycling. Reperfusion also further exacerbates the condition of cytosolic calcium overload.

One particularly critical step that appears to be mediated by the combined conditions of calcium overload, ROS/RNS, and intracellular pH upon reperfusion is the opening of the mitochondrial permeability transition pore (mPTP).5, 6 Most evidence suggests that mPTP opening induces cell death by necrosis7, though there is also evidence supporting its’ involvement in the induction of apoptosis.5 Regulation of mPTP opening is just one example of the integration of cell death signals at the level of the mitochondria that takes place during I/R injury. In fact, throughout the processes of ischemia and reperfusion, the mitochondrial milieu of a cell serves complex and critical roles in

determining cell death or survival.8, 9 Further reperfusion damage can also be induced by neutrophil infiltration and inflammatory signaling.10, 11

Other damaging factors associated with myocardial ischemia/reperfusion injury include damage to intracellular membranes resulting from abnormal phospholipase activity and accumulation free fatty acids.12-14 Depletion of cellular ATP stores also leads to the accumulation of unfolded proteins and the activation of the ER stress response.15

While activation of ER stress response pathways may be initially beneficial, they can lead to the initiation of pro-apoptotic signals if the ischemic stress is not quickly resolved.15 It should be noted that the accepted time threshold for inducing irreversible damage from ischemia is around 20-40 minutes for cardiomyocytes in the subendocardium, and the resulting infarct is usually fully developed within 3-4 hours of the onset of ischemia.11, 16

In addition to the generally non-specific processes outlined above that take place within a cell subjected I/R injury, specific cellular signaling pathways may be perturbed as a result of these seemingly “non-specific” processes. Many of the cell signaling pathways that have been investigated in I/R injury are initiated as an adaptive or compensatory response to the change in intracellular environment during I/R (i.e. hypoxia, ROS/RNS, unfolded protein accumulation) and play a direct role in cell death pathways. For example, it is well known that hypoxia can initiate the activation of kinase signaling cascades as well as transcriptional regulators. It has also been shown that ROS and RNS can act as specific cell signaling mediators.17, 18 Activation of the heat shock and unfolded protein response would be another such example of a specific cellular process initiated by I/R.19 One could also include the autophagic response as an adaptive response to I/R induced cell damage, though its role in determining cell death or survival

is less defined.20 For signaling pathways such as ROS, RNS, autophagy, and the unfolded protein response, it still not completely clear what their role is in myocardial ischemia in regard to contributing to or protecting I/R injury.19, 20

As a whole, all of the aforementioned I/R-induced changes to the internal cellular environment and signaling pathways must be integrated to determine cell survival. It is commonly recognized that I/R injury results in both necrotic and apoptotic cell death. 6, 21,

22 Apoptosis is recognized as the more organized specific of the two types of cell death.

The programmed cell death response of apoptosis is characterized by changes in membrane organization and blebbing, cell shrinkage, chromatin condensation, nuclear fragmentation.23 It has been suggested that as many as 30% of the cardiomyocytes within the ischemic zone undergo cell death within the first 16 hours of an ischemic insult, but it remains controversial which forms of cell death is predominantly responsible for the loss of cardiac myocytes in I/R injury.21, 23

It is well accepted that the human heart lacks the ability to replace cardiomyocytes lost from cell death to the extent that would be needed to regenerate functional myocardium after an I/R injury. Thus, there exist two primary foci that have been examined in both isolation and combination for the development of clinical therapeutics for the treatment of I/R injury. One approach is to exploit the regenerative capabilities of stem cells for the repair or regeneration of damaged myocardium post-I/R injury.24, 25 The second approach is to manipulate signaling in the indigenous cardiac cell population to diminish the amount of cell death and damage resulting from the affliction of I/R. The work presented in this dissertation is aligned with the second therapeutic approach and seeks to further the understanding of relevant endogenous cell signaling

pathways that can lead to a cardioprotected state against prolonged ischemia/reperfusion injury.

I.1.2 Ischemic Preconditioning

Ischemic preconditioning (IPC) was first described in 1986 by Murry et. al.26 as an endogenous signaling mechanism that induces a state of protection against a prolonged ischemia/reperfusion injury. Even before Murry’s groundbreaking paper demonstrating the protective effects of IPC, reports had been published showing that reperfusion dispersed between intermittent ischemic events did not lead to cumulative ischemic damage to the myocardium.27, 28 In fact, it was found that although a large amount of

ATP is lost from ischemic myocardium within the first 10 minutes of ischemia, reperfusion preceding additional 10 minute periods of ischemia were able to prevent further ATP depletion in the subsequent ischemic periods.29 In the twenty-three years since the first true description of IPC, it has been commonly accepted that the short repetitive bouts of ischemia and reperfusion that comprise IPC trigger intracellular signaling pathways that afford the heart a state of protection against subsequent prolonged ischemic insults. In addition, the protective effects of preconditioning are very reproducible and have been shown in all mammalian species tested thus far including mice30, rats31, dogs26, rabbits32, and pigs33. Though difficult to prove experimentally, correlative data also supports IPC to be a real phenomena in the human myocardium.34-36

The initial work with IPC-mediated cardioprotection was done using the IPC stimulus to protect against an immediately subsequent ischemic injury. This acute or early window of IPC has become to be recognized as “classic” ischemic preconditioning. It wasn’t until seven years after the initial discovery of the IPC phenomenon that a later window of cardioprotection was described.37, 38 It is now accepted that there are two

distinct phases of cardioprotection that result from an IPC stimulus: an early phase that begins immediately following the IPC stimulus and lasting 3-4 hours and a late phase that initiates approximately 12 hours following the stimulus that affords protection for up to a few days.39

It should be noted that in the time since the discovery of IPC, other conditioning phenomena that protect against I/R injury have also been described including remote preconditioning40, 41, post-conditioning42, and preconditioning via hypoxia43, rapid cardiac pacing44, heat stress45, or addition of various pharmacological agents

(pharmacological preconditioning). In contrast to preconditioning, which is applied prior to a prolonged ischemia, an ischemic postconditioning stimulus is applied after the prolonged ischemia, from just before to shortly after (30 min) initiation of terminal reperfusion.42 The ability to initiate cardioprotection after the completion of a prolonged ischemia emphasizes the importance of reperfusion injury on the development of a myocardial infarction. Remote preconditioning can provide cardioprotection following the application of ischemic preconditioning on remote sites such as the gut, kidney, or lower limbs and has been shown to have both an early and a late phase much like traditional myocardial IPC.46 Pharmacological preconditioning is achieved through exogenous treatment with various agents, such as an adenosine receptor agonist or nitric oxide donor, that are capable of activating the endogenous IPC signaling pathways. In addition to the conditioning stimuli mentioned above, recent work in the Jones lab has also established remote preconditioning of cardiac trauma (RPCT)47, neurogenic stimulation via topical capsaicin48, and electroaccupuncture (APC) (unpublished data) as novel cardiac conditioning stimuli.

Although the initial triggers of cardiac preconditioning against I/R injury may differ, one finds that they generally converge on the activation of common cardioprotective signaling pathways and end effectors. One major difference, however, between the signaling pathways of early and late phase cardioprotection (regardless of the activation stimulus employed) is that the early phase is dependent on post-translational modifications of pre-existing proteins; whereas, the late phase is dependent on transcriptional regulation of genes and de novo protein expression as well as post- translational modifications. It remains to be seen whether or not the genomic and proteomic changes associated with late phase conditioning provide cardioprotection through a “priming” mechanism that allows the myocardium to readily initiate kinase cascades similar to those activated by early phase preconditioning. The work in this dissertation is focused on the late phase of ischemic preconditioning-mediated protection of the myocardium against cell death induced by a prolonged ischemia/reperfusion injury.

I.1.3 Cardioprotective Signaling Mediators of Late Ischemic Preconditioning

Ischemic preconditioning elicits two distinct windows of cardioprotection that is each initiated and executed by different signaling pathways. This section will discuss and examine all the potential end effectors of late IPC cardioprotection that have been thus far established. When applicable, it will be pointed out which of these potential end effectors have also been implicated in other means of cardioprotection since, as previously mentioned, there are some pathways that appear to be commonly shared among the different initiators of cardioprotection. However, in accordance with the work to be presented in this dissertation, the focus will remain on the molecular mechanisms of late

IPC-induced cardioprotection.

When discussing the mechanisms that drive late phase cardioprotection, it is easiest to take the approach of breaking the signaling components into “triggers”,

“mediators”, and “end effectors” as suggested in a review by Bolli in 2000.39 Signaling triggers can be defined as molecular species generated in the initial steps of the preconditioning that subsequently trigger a perturbation in the activation state of the mediators. The signaling mediators are then the pathways involved in transduction of the preconditioning stimulus into a cardioprotective one, through modification of activation or expression of the end effectors. Though the molecular triggers and mediators are necessary to achieve cardioprotection after IPC, it is ultimately the end effectors that bestow and execute the state of cardioprotection in the myocardium.

Triggers of Late Phase Preconditioning

Triggers of late IPC can be defined as the molecular agents generated at the initiation of the preconditioning stimulus that subsequently set in motion the signaling necessary to achieve the late phase of cardioprotection. In addition to this, one could also define all pharmacological stimulators of late preconditioning as triggers of late preconditioning (PC), though in this case they are not triggers of ischemic PC per se.

Many of these pharmacological agents that induce a late phase of cardioprotection are also found to associate with endogenous triggering of late ischemic PC. Both the triggers and the intermediate signaling mediators can be thought of being transient agents within pathways, but it still remains that only the presence of the end effectors can lead to a cardioprotected against I/R.

Adenosine. Adenosine is one of the most studied mediators of preconditioning; a critical role for adenosine has been well established in the case of both classic and late preconditioning. In fact, adenosine was a known mediator of classic preconditioning prior to the description of late preconditioning and it took only one year from the discovery of late IPC to the first study showing an obligatory role for adenosine signaling in the phenomenon.49 Two early studies by the same group showed that not only did pharmacological blockade of adenosine receptors abolish late IPC cardioprotection49, but a selective agonist to the A1 receptors was able to mimic a late phase of protection 24-72 hours after administration.50

Like all triggers of late IPC cardioprotection, adenosine, through activation of the adenosine A1 or A3 receptors, leads to activation of a kinase cascade, transcriptional

activation, and gene expression of downstream effectors. Activation of one or more PKC isozymes directly downstream of the adenosine receptors is obligatory for adenosine mediated triggering of late IPC.51 In addition to PKC, activation of p38 MAPK has also been demonstrated following adenosine treatment.51, 52 In this manner, adenosine may mediate cardioprotection through effectors such as Hsp27, a protective small heat shock protein that has been reported to be a phosphorylation target of p38.53

To date, very little work has been done to investigate adenosine-dependent transcriptional regulation in late IPC. However, the Kukreja group has shown that NF-κB activation occurs downstream of adenosine A3 receptor stimulation and p38 activation to induce iNOS mRNA expression and cardioprotection.54, 55 The importance of iNOS signaling in the transduction of protection triggered by adenosine is controversial as studies using iNOS knockout mice were inconclusive in showing a definitive role for iNOS in adenosine-mediated PC,56, 57 while more recent studies tend to support a role for iNOS in adenosine triggered PC.58 Interstingly though, COX-2 does not appear to play a role in this protection.59 An increase in expression of manganese-superoxide dismutase

(Mn-SOD) has also been demonstrated 24 hours following adenosine receptor stimulation in the rabbit myocardium.60

In addition to the already mentioned Mn-SOD and Hsp27, the KATP channels have been suggested as key end effectors acting downstream of adenosine mediated PC. The

61 KATP channels are critical mediators of IPC cardioprotection and display enhanced function in the presence of exogenous adenosine.62, 63 Some evidence suggests that the

KATP channels may be targets of PKC or p38 MAPK activation downstream of adenosine

53 signaling. Further implicating the KATP channels as downstream effectors of adenosine

triggered preconditioining is data showing that blockade of the KATP channels with either glibenclamide or 5-HD abolished the late phase of adenosine-induced protection.64, 65

Opioids. The opioid receptors are G-protein-coupled receptors consisting of four major subtypes: the delta, kappa, mu, and nociceptin receptors.66 Multiple studies have confirmed that the δ and κ receptors as well as the endogenous opioid peptide agonists are expressed in the adult myocardium. 67, 68 The first evidence for a role of opioids in

IPC was shown in 1995 by Schultz et. al.69 and was thereafter confirmed by their lab and others that this effect was indeed the result of opioid receptors located in the myocardium

70, 71 and not the CNS. Stimulation of both the δ1 and κ opioid receptors were subsequently shown to elicit a delayed phase of cardioprotection that was dependent on

72-74 KATP channels and PKC. Opioid receptor activation has also been associated with the additional known signaling mediators of late IPC including adenosine75, NF-κB76,

JAK/STAT77, COX-278, iNOS58, 78, p38 MAPK79, and PI3 kinase/Akt77, 80, 81 rendering them potential candidates for mediating opioid-induced cardioprotection.

Bradykinin. Late phase IPC-induced cardioprotection is dependent on endogenous activation of the bradykinin B2 receptors and can be triggered through exogenous addition of bradykinin.82, 83 It was also shown that inhibition of NO synthase abrogated the delayed cardioprotection of exogenous bradykinin.83 However, almost no follow up studies have further addressed the direct mechanisms of bradykinin-triggered late IPC. Much like triggering of late PC through adenosine A1 receptors and δ-opioid receptors coupling to Gi proteins, bradykinin triggering of late PC is thought to be achieved through activation of the bradykinin B2 receptor coupled Gi protein. In fact,

Yellon and Downey propose a model in which activation of any Gi coupled receptor can

trigger late PC and such receptors act in parallel to trigger late IPC, likely through the ultimate activation of PKC, tyrosine kinases, and PI3K.84 Bradykinin is also a known trigger of classic preconditioning as well as RPCT mediated preconditioning.48, 84

Nitric Oxide/Nitric Oxide Synthase. Just a few years after the first description of delayed IPC, nitric oxide was hypothesized to be a very critical factor to both the triggering and execution stages of late IPC.85, 86 This has since been confirmed through the discovery of biphasic response of NO synthase activation following IPC in the rabbit myocardium.87

Nitric oxide was first implicated as a trigger for late IPC when Bolli’s group showed that treatment of rabbits with a nitric oxide synthase inhibitor prior to preconditioning blocked the protective effects of the late phase against I/R injury.88 An obligatory role for iNOS in late IPC was also demonstrated by Guo et. al. who showed that iNOS expression is increased following IPC, and knockout mice lacking the iNOS gene failed to display a late phase of cardioprotection in response to IPC.89 It comes as no surprise then that a late phase of PC can also be induced through the overexpression of either iNOS or eNOS and the administration of a variety of NO-releasing agents suggesting that NO may act as a trigger of late IPC.86, 88, 90, 91 It is now recognized that

NO-induced cardioprotection is dependent on transcriptional activation of NF-κB.92 As will be further discussed later, NF-κB activation (along with STAT-1/3) leads to a transcriptional upregulation of iNOS and COX-2 mRNA important for the execution of cardioprotection during the late phase of IPC.93

Reactive Oxygen Species. Generation of reactive oxygen species (ROS) has been associated with IPC and is generally now considered to be an essential element of the

trigger mechanism for late phase cardioprotection.94-96 Other forms of preconditioning such as heat shock or exercise preconditioning have also been associated with ROS production in the heart.97 ROS is believed to play a role in triggering late PC through the modulation of redox sensitive transcription factors such as NF-κB and AP-1.98-100

Activation of these transcriptional mediators would then induce gene expression changes in the end effectors of late IPC including antioxidant enzymes such as MnSOD.97

Additional gene targets of these factors that may play a role in late IPC will be discussed in more detail later.

Exercise. Many different epidemiological studies have indicated not only a lower prevalence of cardiovascular events correlating with regular physical exercise, but also decreased risk of death following I/R injury.101 Many studies employing animal models also corroborate these findings. 102 Numerous cardioprotective mechanisms have been proposed to explain this effect including activation of KATP channels and increased expression of heat shock proteins, COX-2, and antioxidant enzymes.102 Exercise induced preconditioning is intriguing since it appears, to date, to be the only practical and sustainable non-pharmacological method of triggering endogenous cardioprotective signaling pathways.

Cytokines. Even before the formal description of delayed ischemic preconditioning, pretreatment with the cytokine IL-1 was demonstrated to produce a delayed phase of cardioprotection.103 Shortly after it was shown that pretreatment with

TNF-α also produced cardioprotective effects, although these authors did not investigate a delayed phase of TNF-α induced protection.104 In both instances, the authors attributed the mechanism of cardioprotection to be related to the induced changes in oxidative state

of the heart following cytokine treatment.103, 104 Subsequent evidence has indicated an obligatory role for endogenous cytokines in the development of late IPC.105

It is now accepted that the major mechanistic underpinning of cytokine-induced delayed PC, much like for ROS triggered PC, is a result of their direct activation of transcriptional mediators such as NF-κB, AP-1, or STAT-1/3 and their subsequent regulation on end effector expression. Pro-inflammatory cytokines such as IL-1 and TNF-

α are known activators of NF-κB and AP-1106, 107, and JAK/STAT signaling has also been shown to be sensitive to cytokine stimulation.108, 109 In fact, both TNF-α and IL-6 have been shown to necessary for IPC-induced delayed cardioprotection via activation and regulation of NF-κB and JAK/STAT pathway dependent genes, respectively.110, 111

Heat Shock. The response to heat shock-induced stress has been shown to have a general cardioprotective effect that can induce a preconditioned or cardioprotected state.38, 45, 112 Heat shock-triggered cardioprotection is generally attributed to the increased expression of heat shock proteins, Hsp70 in particular113, 114, but heat shock can also trigger additional mediators and end effectors of delayed PC.97, 115 For this reason, heat shock can be considered a trigger, but as will be discussed in more detail later, the resulting increases in expression of certain heat shock proteins are primarily considered to be end effectors of cardioprotection.

ATP Dependent Potassium Channels (KATP). The KATP channels are ATP dependent, calcium-activated potassium channels located on both the sarcolemmal and

116, 117 inner mitochondrial membranes. Although it is well accepted that the KATP channels have a prominent role in IPC, it is slightly controversial whether they can actually act as triggers of preconditioning.6 While there is some data to suggest they may also act as

118 triggers of delayed IPC , the prevailing thought is that KATP channels usually serve as end effectors of late IPC. The notion of KATP channels as end effectors of late IPC will be discussed in more detail later.

Signaling Mediators of Late IPC

The signaling mediators of late IPC are encompassed by a complex network of signaling kinases and transcriptional regulators that are much more poorly defined than are the triggers or the end effectors. This is likely attributed to the transient nature of the mediators and the fact that identification of an agent that triggers or mimics cardioprotection is generally much easier to do than it is to delineate the specific signaling mechanism through which the cardioprotection is mediated. This is especially true for late IPC where there is a large window of time over which the mediation of cardioprotective signaling occurs. One thing that is known about the signaling of late IPC is that it requires regulatory changes in the level of gene and protein expression for execution of the protective program. Thus, signaling mediators include any regulatory protein involved in the transmission of the trigger signal to the pertinent regulator of transcription or translation as well as all co-factors involved. Surely, we have just begun to scratch the surface with our knowledge of all the players involved in these processes, but the few that we do have an understanding of are discussed herein.

Protein Kinase C (PKC). A role for PKC in mediating late IPC has been established through multiple studies demonstrating that genetic or pharmacological inhibition of PKC abolishes late IPC cardioprotection.51, 85, 119 Many different studies have highlighted roles for multiple isoforms of PKC in mediating the cardioprotective

signal of late IPC, but much of this attention has been focused on PKCε as it would appear to be the most critical of the PKC isozymes in late IPC.85, 120, 121 The mechanism of PKC signaling in IPC is quite complex given the number of putative phosphorylation targets and directly interacting proteins present in the myocardium.

Receptors for activated C kinases (RACKs) are important for the localization of activated PKC to subcellular compartments as they bind active PKC and lead to its compartmentalization within the cell. This compartmentalization of activated PKC is thought to be important for bringing the active kinase in proximity with its desired substrates. A few studies have associated PKC/RACK interaction with cardioprotection.122, 123 Korzick et. al. showed that local delivery of PKCε activating ψε-

RACK protein protected against global ischemia in an isolated heart model.124 Cellular translocation and compartmentalization of PKC mediated by interaction with RACKs may be very critical to its role in late IPC signaling. Multiple studies have shown that localization of PKCε to the mitochondria is important to its ability to induce opening of

125-127 the mitochondrial KATP channels and inhibit MPTP formation. Glycogen synthase kinase-3β (GSK-3β) has been suggested a key target of PKCε in mediating MPTP inhibition.125-127 Activation of NF-κB has also been shown to be a downstream effector of

PKC signaling.128

Protein Tyrosine Kinases (PTKs). Tyrosine kinases were first indicated as signaling mediators of preconditioning in an isolated heart model in 1996 when Maulik et. al. showed that pretreatment with genistein, a PTK inhibitor, abolished the preconditioning effect.129 This was followed by studies showing that adminstration of

PTK inhibitors was sufficient to block the late phase of preconditioning in in vivo animal

models.130, 131 Due to the lack of specificity of the PTK inhibitors used in these studies, there is some question as to the identity of the specific tyrosine kinases responsible for mediating late IPC. This has been addressed to some extent by Ping et. al. who found that only the Src and Lck PTKs were active in preconditioning and through demonstration that late IPC is abrogated in lck knockout mice.132, 133 The cardioprotective signaling of the Lck tyrosine kinase is thought to occur via its interaction with PKCε.133

Mitogen-Activated Protein Kinases (MAPKs). Extracellular signal-related kinases (ERK), p38 MAPK, and c-Jun N-terminal kinases (JNK) comprise the three major classes of stress responsive MAPKs in the myocardium.134 Activation of both ERK and JNK have been shown to be PKC-dependent following ischemic stimuli in the myocardium.135, 136 A role for the p38 MAPK in the cardioprotection of late IPC was mentioned previously with regards to its activation downstream of adenosine. It has been shown that p38 kinase activity is active 24 hrs following adenosine treatment, making p38 readily available in the active form during the late window of protection.51 It is still unclear what the full range of MAPK phosphorylation targets may be in IPC, but activation p38 has been associated with increased phosphorylation of the small heat shock protein 27 (Hsp27).51

PI3K/Akt. The PI3K/Akt axis of signaling is well recognized for its anti-apoptotic properties.137, 138 For this reason, it should come as no surprise to find these signaling pathways implicated in preserving cardiomyocytes as part of late IPC protection. PI3K may be cardioprotective through its activation of PKCε as shown by Tong et. al.139

Transcriptional activation of NF-κB and HIF-1 has also been shown to be a downstream effector pathway of PI3K/Akt signaling.140

Similar to the other kinases implicated in late IPC though, demonstrating an effect is much easier than delineating mechanism of activation or full array of phosphorylation targets. Thus, much as for the case of PKC, PTKs, or MAPKs, the specific aspects of these signaling pathways remain largely unknown.

Reperfusion Injury Salvage Kinase (RISK) Pathway. While most of the kinases activated in association with late IPC-induced protection have been discussed individually as mediators of late IPC, above, it is worth mentioning that the activation of these prosurvival kinases by protective conditioning stimuli has become known generally as the RISK pathway. The RISK pathway includes a group of pro-survival kinases that confer cardioprotection upon activation and is stimulated by IPC as well as a number of additional pharmacological preconditioning agents.141 The kinases most commonly associated with the RISK pathway are Akt and Erk1/2, but other pro-survival kinases such as PKC-ε, PKG, GSK-3β, and p38 have also been associated with the RISK pathway.141

Nuclear Factor-kappaB (NF-κB). NF-κB was the first transcriptional regulator to be shown necessary for late IPC.98 Many conclusive studies, including work presented herein (Fig. 3), have corroborated this original finding of the importance of NF-κB to late

IPC. While a few of the NF-κB transcriptional targets of late IPC are known, the full complexity of the NF-κB regulated transcriptome enabling the late phase of cardioprotection is still incompletely understood. The delineation of these NF-κB- dependent genes is a main goal of this work. Thus, given the importance of NF-κB, section I.1.5 will focus on the role of NF-κB in mediating late IPC in greater detail.

JAK/STAT. Janus kinases (JAKs) have tyrosine kinase activity that is activated in response to cytokine signaling such as IFN-γ or IL-6.142 JAKs then phosphorylate protein members of the signal transducer and activator of transcription (STAT) family which dimerize and translocate to the nucleus upon activation (phosphorylation) where they mediate transcriptional regulation of target genes.142 The JAK/STAT pathway was first described in 1992 and first shown to be activated in the heart in response to I/R and permanent occlusion in 2000 and 2001, respectively.143-145

The JAK/STAT pathway was first shown to play a role in late IPC in 2001 and appears to signal in late IPC in direct response to MAPKs, PKC, and/or nitric oxide stimulation.146 A very comprehensive study by Xuan et. al. identified two members of each of the JAK and STAT families (JAK1/2 and STAT1/3) as the key players of

JAK/STAT signaling in late IPC.147 Their results also suggested that the dependence of late IPC cardioprotection on JAK/STAT signaling is due, at least in part, to STAT1/3- mediated upregulation of iNOS gene expression.147 Subsequent studies by the same group also implicated STAT-mediated transcriptional activation as being critical for the upregulation of COX-2 gene expression in late IPC.148 These findings would suggest that the JAK/STAT pathway may act in parallel or in direct cooperation with NF-κB in the regulation of late IPC gene expression.146 In addition to iNOS and COX-2, STAT transcription factors have also been shown to regulate the expression of cardioprotective heat shock proteins Hsp70 and Hsp90, though this has not been directly demonstrated in the setting of ischemic preconditioning or cardioprotection.149-151

Activating Protein-1 (AP-1). AP-1 is a transcription factor composed of dimers of the c-Fos, c-Jun, and ATF families and is activated in response to a variety of stimuli

including cytokines, growth factors, and oxidative stress. Activation of AP-1 in the myocardium is known to occur in response to ischemic stimuli, including IPC.100, 110, 152

Many of the studies suggesting AP-1 as a cardioprotective have only correlated its activation with cardioprotection, and often, activation of other known protective factors such as NF-κB or HIF-1 are also activated under similar circumstances. For example, work by Li et. al. established both NF-κB and AP-1, via c-Jun N-terminal kinase (JNK), as downstream targets of cardioprotective PKCε signaling in cardiomyocytes. Given the importance, of PKCε to late IPC, this work established an important link between known cardioprotective mediators and AP-1 activation.128 Subsequently, Xi et. al. showed that treatment of adult mice with cobalt chloride could mimic a late phase of protection against subsequent global I/R in an isolated heart model that was associated with increased AP-1 DNA binding.153 However, HIF-1 activation was also observed in this model and could just as easily account for the observed cardioprotection. Unfortunatley, due primarily to the lack of genetic mouse models available, a conclusive role for AP-1 activation in late IPC has not been shown.

Hypoxia Inducing Factor-1 (HIF-1). HIF-1 is an oxygen sensitive transcription factor that is a heterodimer composed of a constitutive subunit and the HIF-1α subunit that becomes stabilized under conditions of low oxygen tension.154 Many studies have concluded a cardioprotective role for HIF-1 activation against myocardial I/R injury.155-

158 However, despite the accepted ideology that HIF-1 activation plays a critical role in the adaptive gene regulation response to hypoxic stimuli and the role that these processes are likely to play in the adaptive responses of IPC, a definitive role for HIF-1 in mediating late IPC has been difficult to establish. Gregg Semenza’s group demonstrated

in 2003 that mice exposed to intermittent hypoxia displayed a late phase of protection against I/R in an isolated heart model that was dependent on HIF-1α.157 This protective effect could also be mimicked through administration of erythropoietin, a HIF-1α dependent gene product.157 As previously discussed, treatment with cobalt chloride also produced a late phase of protection that was associate with HIF-1 DNA binding.153 More recent work has suggested an obligatory role for HIF-1α in early IPC cardioprotection as well.156, 159

The potential mechanisms of HIF-1 mediated late PC are largely unknown, but would presume to be attributed to the transcriptional regulation ability of HIF-1. Though, given the potential role of HIF-1 in acute IPC156, this may not be entirely true. A few

HIF-1 dependent gene products that have been implicated as playing a role in HIF-1 dependent cardioprotection include erythropoietin157, TGF-β160, HO-1161, vascular endothelial growth factor (VEGF)155, and Hsp70.155, 162

Some evidence exists suggesting that HIF-1 may act in concert with or be dependent upon NF-κB activation in certain scenarios. For instance, HIF-1 activity, much like NF-κB, may be regulated by IL-1β and TNF-α as well as other cytokines.163, 164 In addition, BelAiba et. al. suggested that HIF-1α mRNA expression in pulmonary artery smooth muscle cells in response to hypoxia is dependent upon the PI3K/Akt pathway and

NF-κB activation.140 Additional work by the same group indicated that NF-κB also plays an important role in ROS-mediated upregulation of HIF-1α gene expression.165 Our work also demonstrates for the first time that HIF-1α mRNA expression in the cardiomyocyte in vivo in response to ischemic preconditioning is dependent on NF-κB (Fig. 3).

End Effectors of Late IPC

The end effectors of late IPC can be defined as the molecular entities that bestow and execute the cardioprotective phenotype in the myocardium. Some of the end effectors for late IPC, such as the KATP channels may also share end effector duties in early IPC.

Others however, such as the heat shock proteins, must be expressed de novo and are unique to late IPC. This is to be expected given the delay in the onset of late phase cardioprotection following the early phase. There is a window lasting approximately 4-12 hours following IPC where the heart is in an unprotected phase between the early and late cardioprotective windows. This is likely to be the time where, for reasons of consumption, degradation, or desensitization, the end effectors of early IPC are no longer actionable, but the end effectors of late IPC have not yet become active. The end effectors must be in place upon subsequent prolonged I/R in order for the myocardium to bear a benefit from any previous preconditioning stimulus.

Inhibition of Mitochondrial Permeability Transition Pore (MPTP) Opening. It seems fairly well established that conditions arising in the cardiomyocyte as a result of

I/R injury result in mitochondrial damage manifest by increased permeability of the inner mitochondrial membrane to any molecule less than ~1.5 kD in size.166 This permeability transition of the mitochondria is associated with the formation of a pore on the inner membrane termed the mitochondrial permeability transition pore (MTPT). Opening of the MTPT is associated with increased cell death during reperfusion and can be inhibited by cardioprotective stimuli such as IPC.2, 167 While the exact mechanisms regulating the opening and inhibition of the MTPT opening remains unclear, it seems that MTPT

inhibition is a common theme of cardioprotection and a critical end effector of late

IPC.166

Nitric Oxide/Nitric Oxide Synthase. In the years since the description of late IPC,

Roberto Bolli’s research group has been at the forefront of establishing the critical importance of the NF-κB-iNOS-COX-2 axis of signaling in late IPC. As previously discussed, a role for NOS/NO as a trigger of late PC was established using nitric oxide donor agents to mimic late PC cardioprotection.90, 91 However, additional findings of iNOS-mediated effects of additional late PC triggers would suggest a role for NO also as a mediator or end effector of late PC. As an end effector, iNOS was the first gene identified whose expression increase in response to IPC was mandatory for late phase cardioprotection, and is thought to be increased in an NF-κB-dependent manner in

168-170 IPC. Guo et. al. also found that late PC triggered in mice by either adenosine A1 receptor agonists or δ1-opioid receptor agonists were abolished by pharmacological or genetic blockade of iNOS.58

One of the primary mechanisms of iNOS-mediated action in late PC is thought to be activation of COX-2 mediated through production of nitric oxide.171 It has also been

172 suggested that NO can directly induce the opening of cardioprotective KATP channels.

Additional plausible mechanisms for the cardioprotective role attributed to nitric oxide include prevention of Ca2+ overload, antioxidant properties, antagonism of β-adrenergic receptors, and reduction in oxygen demand of the myocardium.93, 173

Cyclooxygenase-2 (COX-2). COX-2 is an inducible form of cycloxygenase that is critical for the synthesis of prostaglandins from arachidonic acid.174 COX-2 was first shown to play a role in late phase IPC by Bolli’s group in 2002 in two near simultaneous

publications showing it to be necessary for late IPC in both the rabbit and mouse heart.175,

176 Using a conscious rabbit model, they showed an IPC-induced increase in both COX-2 mRNA and protein and an obligatory role for COX-2, but not COX-1, activity in late IPC cardioprotection. In addition, they posited that the prostaglandins PGE2 and PGI2 as the likely end effectors of COX-2 mediated protection.175 This corroborates a large number of studies that concluded prostaglandins to exert protective effects against myocardial I/R injury.171, 177-179 The proposed mechanisms of prostaglandin-mediated cardioprotection include inhibition of Ca2+ influx171, 180, adenylyl cyclase antagonism171, 177, induction of

171, 179, 181 171, 182 KATP channel opening , and reduction of neutrophil infiltration . The COX-

2 prostaglandin products PGF2α and 6-keto-PGF1α have also been demonstrated to be increased in the late IPC window of protection and may play a role in the cardioprotective phenotype.171

IPC-induced upregulation of COX-2 mRNA is thought to be co-induced with iNOS gene expression and dependent on activation of the transcription factors STAT1/3 and NF-κB.171 Expression of Cox-2 in late IPC is also dependent on activity of PKC and the protein tyrosine kinases Src and Lck.132, 183 Once expressed, COX-2 activity and the resulting prostaglandin production are then dependent on iNOS-mediated NO production.171 Many of the proposed cardioprotective mechanisms of COX-2 are similar to those suggested for iNOS/NO further indicating that iNOS/NO and COX-2 are likely to act together or in a direct linear pathway of each other to mediate preconditioning.93, 171

Additional work by Bolli’s group demonstrated that induction of iNOS and COX-2 mRNA and protein are independent of each other following IPC but both are required for cardioprotection since iNOS is needed to induce COX-2 activity.148 Thus, COX-2 is

directly downstream of NO production as an end effector of late IPC, although NO is thought to have additional independent cardioprotective effects as well.171

Antioxidant Enzymes. Increases in antioxidant enzymes such as SODs and catalase have been shown concomitant with the late window of IPC cardioprotection and may protect against I/R injury through prevention of ROS-mediated damage.60, 97, 184, 185

However, ROS generated from the initial preconditioning stimulus may be a crucial initiator of antioxidant enzyme upregulation during delayed PC. Manganese superoxide dismutase (Mn-SOD), a presumed NF-κB-dependent gene product in late IPC186, 187, has been reported to display a biphasic increase in expression following IPC in a canine model suggesting a potential role in both early and late IPC.188 Other reports show

MnSOD expression to increase gradually following IPC with a peak expression approximately 24 hours after the stimulus.97 The use of antisense oligonucleotides to block MnSOD expression and activity abolished ischemic, heat shock, exercise and adenosine-induced delayed cardioprotection.60, 105, 189, 190 Catalase has been clearly demonstrated to be an end effector of cardioprotection through multiple studies demonstrating an inhibition of the protective effects of preconditioning by the inhibition of catalase just prior to the ischemia/reperfusion injury.191-193 Gene transfer of extracellular superoxide dismutase (ecSOD) in mice was shown to confer a cardioprotetive phenotype in mice similar to what is observed in IPC.93, 194

The primary cardioprotective mechanism of protection for these enzymes is, of course, thought to be via mitochondrial protection from oxidative stress. However, these enzymes may act through additional mechanisms in such a way to provide synergy with other late IPC mediators as well. For example, ecSOD is a secreted enzyme that prevents

ONOO- formation, thus preventing NO inactivation and increasing the bioavailabilty of nitric oxide.93

Heme Oxygenase (HO-1). Heme oxygenase-1 is a stress inducible enzyme involved in the catabolism of heme to form biliverden (ultimately bilirubin) and carbon monoxide. HO-1 expression has been shown to be increased following I/R injury in the heart and has long been thought to play a role in late IPC protection against I/R injury.195,

196 A recent study by Li et. al. implicated HO-1 as an end effector of iNOS-triggered late

PC.92 This same study implicated NF-κB activity in the transcriptional upregulation of

HO-1 in late IPC.92

Aldose Reductase. Aldose reductase (AR) was first identified to be upregulated in the heart following IPC and necessary for the late phase of cardioprotection in 2002 by

Shinmura et. al.197 AR is a member of the aldo-keto reductase family that could lend cardioprotective properties through its ability to metabolize cytotoxic aldehydes resulting from lipid peroxidation, thus protecting against their potential oxidative injury.197 AR induction following IPC appears to dependent on common IPC mediators such as NO,

PKC, ROS, and HIF-1.197-199

ATP Dependent Potassium Channels (KATP). The KATP channels were mentioned previously as potential triggers of late IPC, but are primarily implicated as being end effectors of late IPC cardioprotection. It is also well documented that opening of KATP

6, 118 channels plays a critical in classic early IPC. Involvement of the KATP channels in preconditioning was first noted in 1992 and appears to be a near universal mechanism to all triggers of cardioprotection as blockade of these channels inhibits protection conferred from IPC, heat shock, adenosine, nitric oxide donors, and opioid stimulation.118, 200

There also exists some controversy over the exact intracellular location

(mitochondrial vs. sarcolemmal) of the KATP channels responsible for mediating the cardioprotection of late IPC.201 The existence of these channels were first described in the sarcolemmal membrane in 1983 and subsequently found to also be located in the inner mitochondrial membrane.116, 117 Unfortunately, while the structural composition of the sarcolemmal KATP channels have been determined, the precise subunit composition of the mitochondrial KATP channels have not yet been worked out. Due to the lack of structural knowledge and availability specific inhibitors, it is very difficult to differentiate the potential cardioprotective properties of the two channels.118 Many studies have indicated

201, 202 an importance of primarily mitochondrial KATP channels , while others have suggested the sarcolemmal KATP are primarily responsible for late IPC cardioprotection.203-205 There is also evidence to support a distinct temporally separated role for the both the sarcolemmal and mitochondrial KATP channels in protection of the heart against I/R injury in response to IPC.206, 207

Though some debate may still exist surrounding the relative importance of sarcolemmal versus mitochondrial KATP channels, there is little doubt to the importance of KATP channels in late IPC. However, the upstream mechanisms leading to their opening in late IPC are not completely understood. Direct modulation of KATP channel activation has been observed in response to late IPC triggers and signaling mediators many adenosine208, PKC 207-209, MAPK210, and nitric oxide.211, 212 Additional evidence suggests that opening of the KATP channels is dependent on both PKG and PKCε acivation.213

Heat Shock Proteins. Heat shock proteins are well recognized as mediators of cardioprotection and have emerged as primary therapeutic targets for cardioprotective drug development.214 Heat shock proteins and their potential role in mediating the protective effects of late IPC will be discussed in detail in the following section.

I.1.4 Heat Shock Proteins as Mediators of Late IPC

Heat shock proteins (HSPs) were first described in Drosophila melanogaster in

1962 as a group of proteins whose expression increased following heat shock or other cellular stressors.215 It is now known that HSPs are well-conserved proteins that are ubiquitously found in all organsisms from yeast to higher mammals. Various HSPs can be found in many subcellular locales and many heat shock proteins are expressed under basal cellular conditions as well. Heat shock proteins are annotated and grouped into families by molecular weight. The most basic grouping of heat shock proteins consists of the Hsp27, Hsp60, Hsp70, Hsp90, and Hsp110/104 families.216 Many different molecular functions have been assigned to each of these HSP families, but a full discussion of these functions in various cell types is beyond the scope of this dissertation. Instead, focus will be lent to the role of heat shock proteins in the heart, particularly the role of the Hsp70 family in the cardioprotection of late IPC.

A potential cardioprotective role for heat shock (HS) was suggested prior to the discovery of late phase preconditioning by Currie et. al. in 1988.217 This was confirmed in 1992 by Donnelly et. al. who showed that whole body hyperthermia 24 hours prior to ischemia was associated with an increase in Hsp70 protein expression and cardioprotection.218 Shortly thereafter, heat shock proteins were reported as the original proposed end effectors of late IPC, and heat stress or induction of the heat shock response was shown to be induced in association with the first descriptions of late phase IPC in

1993.38, 45 Since this initial finding, it has been shown that the protective effects of heat

shock can be assigned to the induced expression of multiple families of HSPs.219 With much of the cytoprotective properties of heat shock having been ascribed to the Hsp70 protein family, a large body of work exists demonstrating cardioprotective properties of the Hsp70 family of heat shock proteins, which will be discussed in more detail below.

In addition to Hsp70, members of the other heat shock protein families have been implicated in late IPC as well. For example, overexpression of Hsp27 in isolated adult rat cardiomyocytes was protective against simulated I/R injury.220 Many other studies have associated Hsp27 expression cardioprotection and delayed IPC.220-224 As previously mentioned, Hsp27-mediated cardioprotection in late IPC is thought to be mediated by a

PKC-p38 MAPK dependent pathway.51 Additionaly, simultaneous overexpression of

Hsp10 and Hsp60 was shown to protect rat cardiomyocytes from simulated ischemic injury in vitro.225

The cardioprotective properties of the small heat shock protein Hsp20 (or HspB6) has been extensively investigated by the Kranias lab. Although characterized as a heat shock protein, there is some debate as to whether or not this protein is inducible in a temperature sensitive manner.226, 227 However, it is has been shown that after a stressor such as heat or ischemic insult, Hsp20 translocates within the cell from a predominately cytoplasmic localization to the nuclei and myofibrils in cardiac myocytes.228 Evidence suggesting Hsp20 translocation to myofibrils and interaction with actin in a phosphorylation state and stress specific manner has led to the hypothesis that Hsp20 may provide anti-apoptotic effects in ischemic stress through cytoskeletal stabilization.228

Hsp20 may also exert anti-apoptotic effects through a physical interaction with Bax, a proapoptotic Bcl-2 protein, preventing its translocation to the mitochondria.229 The

cardioprotective effects of Hsp20 and the potential mechanisms and regulation underlying these effects have been nicely reviewed by Fan et. al.228

Much like the other pathways of late preconditioning, the cardioprotection elicited through heat shock appears to be a complex signaling network involving many of the common mediators of late PC previously discussed. For example, heat shock as a trigger of delayed protection has been demonstrated to lead to a rapid production of of NO in the heart and administration of an iNOS inhibitor abolishes heat shock-induced late PC.230-232

However, the causal relationship between heat shock and nitric oxide as they relate to cardioprotection is uncertain as it was also shown that nitric oxide donor compounds can induce the expression of Hsp70 in the heart.233 Nitric oxide production can also lead to the formation of reactive oxygen species or activation of COX-2.234 In addition to iNOS and NO production, heat shock-triggered late PC has been demonstrated to be dependent upon both COX-2 activity and the production of reactive oxygen species.221, 235-237

Yamashita et. al. also showed that inhibition of the cytokines TNF-α and IL-1β via neutralizing antibodies given prior to whole body hyperthermia in rats abolished the late phase of HS induced cardioprotection.189 Follow up work of these results could directly link heat shock-mediated PC with known IPC mediating transcription factors such as NF-

κB and subsequent effectors, such as COX-2, whose activity can be induced by TNF-α and IL-1β.238

To further substantiate the relationship between heat shock proteins and other known mediators of late IPC, heme oxygenase-1 (HO-1), a known mediator of late IPC, is also regarded as a heat shock protein, Hsp32, whose expression can be induced following hyperthermia.196, 239, 240 Furthermore, it has been established that heme

oxygenase activity is necessary for HS-induced cardioprotection.196 Interestingly, this pathway has also been linked with the cardioprotection afforded by the neurotransmitter calcitonin gene-related peptide (CGRP). CGRP has shown to be produced in response to heat stress and mediates HS-induced delayed cardioprotection.241 More recent studies have suggested CGRP expression and cardioprotection to be dependent on HO-1 activity.242 Heat shock-induced cardioprotection has also been associated with other signaling triggers and mediators of late IPC including opioids243, PKC244, and p38

MAPK245.

Heat shock preconditioning has been shown to induce and be dependent on the

246, 247 opening of KATP channels as potential end effectors of this cardioprotection. Thus, through the aforementioned heat shock-induced activation of PKC, NO, and COX-2, all known inducers of KATP channel activity, it is possible to circumstantially link many players in heat shock induced preconditioning from trigger to potential end effector of cardioprotection. Increased expression of antioxidant enzymes such as catalase and

MnSOD have also been postulated as potential end effectors of heat shock mediated late

PC.193, 248

In summary, heat shock has been demonstrated to have general protective effects on the myocardium including antiarrhythmic193, 249, prevention of cell death/necrosis217,

250, preservation of coronary endothelial function251, 252, and maintenance of metabolic/high energy phosphate levels253, 254 and calcium homeostasis.255, 256 However, despite many studies demonstrating their cardioprotective effects, a well-defined mechanism for exactly how the heat shock response conveys late phase cardioprotection has not been determined.

Heat Shock Protein 70

The Hsp70 family of heat shock proteins is perhaps the most complex the HSP families consisting of multiple subfamilies including the inducible Hsp70 proteins

(Hsp70 or Hsp72), the constitutively expressed Hsp70s (Hsp73 or Hsc70), mitochondrial expressed Hsp70s (Hsp75 or mHsp70), and the Hsp78/GRP78 family of Hsp70s.216

Proteins of the Hsp70 group consist of an ATPase domain and a substrate binding domain that can be further divided into a β-sandwich region and an α-helical region.257 Cellular functions of Hsp70 proteins are dependent on their ability to act as molecular chaperones via the binding of various protein substrates in an ATP-dependent manner. Hsp70s have been shown to assist in many molecular chaperone processes including the folding and membrane translocation of newly synthesized proteins, refolding of misfolded proteins, removal of aggregated proteins, as well as the binding and modulation of regulatory proteins.257 Hsp70 proteins are the most conserved and the inducible Hsp70 proteins are the most stress responsive of any of the HSPs.216

The first correlation of increased Hsp70 in response to heat stress and delayed cardioprotection was made by Donnelly et. al. in 1992.218 An increase in expression of inducible Hsp70 in the myocardium was associated with the first descriptions of delayed preconditioning in 1993.38 Two years later, Marber et. al. showed that overexpression of inducible Hsp70 was sufficient to provide the heart with resistance to ischemic injury.114

Since these initial findings, an anti-apoptotic role for Hsp70 has been demonstrated and many additional reports have demonstrated that expression of inducible Hsp70 in the heart is protective against I/R injury.114, 258

Though the data is often not presented as such, there are two distinct genes that compose stress inducible Hsp70 in the mammalian myocardium. Unfortunately, the published literature concerning gene expression and cardioprotection, has not made distinction between the two inducible Hsp70 genes. Hsp70.1 (encoded by the hspa1b in mice) and Hsp70.3 (hspa1a) are paralogous genes closely situated together in a head-to- tail alignment on the murine chromosome 17.259 Although it has been shown using a genetic knockout of both inducible genes that Hsp70 expression is absolutely needed for late IPC cardioprotection260, there are still many unknowns regarding the inducible

Hsp70.1 and Hsp70.3 in the myocardium. It is unknown what the specific mechanism of cardioprotection of these two proteins may be. In mice, these genes share near 100% homology in their amino acid sequence, with the only difference being an addition of a proline residue near the C-terminus of Hsp70.1. Since this proline insertion occurs near the substrate binding domain, it is plausible that this may result in differential substrate selectivity between the two Hsp70 proteins. The regulatory mechanisms that govern the expression of Hsp70.1 and Hsp70.3 within the myocardium are also incompletely understood.

I.1.5 The Role of NF-κB in Late Ischemic Preconditioning

As was mentioned when discussing signaling mediators of late IPC, transcriptional regulation is of critical importance to cardioprotection. This was first indicated by evidence that the late phase of IPC is dependent on de novo protein synthesis261 that is presumably the result of transcriptional regulation of gene expression involved in cardioprotective signaling. Nuclear factor-kappaB (NF-κB) was the first transcriptional regulator to be implicated as playing a role in directing gene expression necessary for the late phase of IPC cardioprotection.98 NF-κB has since become the most well studied transcription factor in late IPC, though the full extent to which NF-κB- dependent genes contribute specifically to executing the cardioprotective phenotype of late IPC is still not well understood.

Control of NF-κB Transcriptional Regulation

The number of genes whose transcriptional control is regulated at least in part by

NF-κB has grown continuously since the first description of NF-κB as a potential regulator of gene expression.262 It is now recognized that NF-κB can regulate the expression of over 500 known genes in many different tissues and is thought to be a major integration point for many cellular pathophysiological responses determining inflammation, programmed cell death, and cell survival.263 It is commonly accepted that

NF-κB can be activated by a plethora of factors including viral or bacterial products, metabolic or oxidative stress, micro-gravity, as well as specialized signaling molecules such as chemokines and cytokines.264-266

There exists a complex signaling cascade and feedback mechanism that regulates the activation and translocation of NF-κB dimers to the nucleus and its subsequent effects on transcriptional regulation. The canonical NF-κB activation pathway involves three primary signaling players: 1) the IκB kinase (IKK) that phosphorylates 2) an inhibitor of

NF-κB (IκB) protein leading to its degradation and release of a free 3) NF-κB dimer. The

IκB proteins serve as the gatekeepers that prevent NF-κB translocation to the nucleus by sequestering it in the cytoplasm through binding and masking of the nuclear localization sequence. Although it is now understood to be a dynamic system of constant nuclear/cytoplasmic shuttling, NF-κB is primarily present in the cytosol in an inactive

IκBα bound form at the basal state.

Upon activation of IKK (IκB kinase) via NF-κB-inducing kinase (NIK), IκBα is specifically phosphorylated at Ser32 and Ser36 by IKK leading to ubiquitination and degradation of the IκBα by the 26S proteosome.267, 268 The removal of IκBα from the protein complexes releases the NF-κB dimers allowing for nuclear translocation.269 NF-

κB transcriptional activation is terminated as NF-κB-dependent gene products accumulate and provide negative feedback on the signaling cascade. Among the most common of these is IκBα which then acts as a negative feedback modulator to bind and remove NF-κB from nucleus.270

In reality, the signaling pathways regulating the activation and translocation of

NF-κB to the nucleus are, of course, much more complex than this. The NF-κB dimers themselves can be composed of different combinations of the five subunits p50, p52,

RelA (p65), c-Rel, or RelB. The NF-κB subunits all have in common a Rel homology domain that functions in DNA binding as well as dimer formation and IκBα binding. The

predominant form of NF-κB found in cardiomyocytes are dimers consisting of p50 and p65 subunits.271 These NF-κB dimers are then bound by different IκB isoforms (IκBα,

IκBβ, IκBγ, IκBε, IκBζ, and Bcl-3) which can be phosphorylated by the IKK complex consisting of two different kinases (IKKα, IKKβ) and a regulatory subunit

(NEMO/IKKγ).269, 271

The composition of activated NF-κB subunits often depends on the pathways of upstream activation. The canonical activation pathway results in NF-κB activation downstream of TNF, IL-1, or toll-like receptor signaling and primarily results in nuclear translocation of p50/p65 heterodimers.265, 266 In the non-canonical activation pathway, the

IKK complex (lacking the IKKγ regulatory subunit) directly phosphorylates the C- terminal region of p100 leading to its ubiquitination and processing to p52, which then translocates to the nucleus primarily as a heterodimeric complex with RelB.266

In addition to NIK, the IKK complex may be phosphorylated and activated by a host of other kinase signaling pathways including MAPKs and PKC.272 There are also many diverse signaling pathways that can directly and indirectly influence NF-κB transcriptional activity following its release from IκBα and nuclear translocation. It has also been shown that direct phosphorylation of the NF-κB dimers themselves can play a critical role in transcriptional regulation of the activated NF-κB. For example, the individual NF-κB subunits can be phosphorylated by a large host of kinases including

MAPKs (all three major branches), PKA, Akt, PKC, NIK/IKK, and CaMK.272 Some phosphorylation sites on the subunits have been shown to enhance, or even be necessary for, transcriptional activation.272 Additional post-translational modifications, such as acetylation by p300, CBP, or PCAF, have been shown to alter DNA binding properties of

NF-κB dimers.273-275 It remains to be seen as to how many post-translational modifications the NF-κB subunits may be subject to or how they may contribute to transcriptional activity or management of nuclear binding partners.

Specific Actions of NF-κB in Late IPC

To date, NF-κB signaling has been implicated in a diverse collection of pathological states including ischemic injury, cancer, inflammation, atherosclerosis, asthma, diabetes, and stroke.271 Much like the diverse set of pathologies that its activation is thought to be a critical aspect of, the sets of downstream genes that NF-κB regulates can be quite diverse under different cellular conditions or pathologies. NF-κB is known to regulate gene expression promoting both cell death and survival depending on the activating stimuli and the cellular environment in which the activation occurs. An example of the differential function of NF-κB-dependent gene expression under different cellular states is the implication of NF-κB action in the pro-injurious development of infarction after myocardial ischemia/reperfusion.276, 277 Thus, while NF-κB activation is critical and necessary for the development of cardioprotection in the late phase of IPC, its activation is detrimental to the myocardium in face of an ischemia/reperfusion injury.

It is very well established that NF-κB transcriptional activation plays a critical role in the development of late phase cardioprotection following IPC as well as other forms of preconditioning. Two major questions still exist surrounding the role of NF-κB in late IPC. First, how is NF-κB activation achieved in late IPC? Secondly, what are the identities of the specific cardioprotective gene products whose expression is controlled by

NF-κB-dependent mechanisms following late IPC?

NF-κB has been shown to be activated through very diverse mechanisms in the heart including cytokines, G-protein-coupled agonists, and oxidative stress.278-280 As mentioned in Section I.1.3, many of the triggers and mediators of late IPC such as adenosine, NO, ROS, and PKC have been suggested to act at least in part through induction of NF-κB activation.54, 55, 92, 98, 128, 281

The work in this dissertation is focused on the second question relating to the identification of NF-κB-dependent gene products in late IPC. To date, only a small handful of genes regulated by NF-κB following ischemic stimuli in the heart have been identfied. Many of the signaling mediators and effectors of late IPC whose gene expression levels change in response to IPC have been shown to be under the transcriptional control of NF-κB. Potential cardioprotective genes whose expression is thought to be controlled by NF-κB include iNOS, COX-2, HO-1, Mn-SOD, and HIF-1α

(Fig 7).92, 168, 176, 187 It is likely that additional end effectors of late IPC, both known and yet undiscovered, are also transcriptionally controlled by NF-κB following late IPC. This speaks to the central role that NF-κB is believed to play in the development of late IPC cardioprotection. A recent review by Jones et. al. compiled a detailed list of NF-κB- dependent genes believed to play a role in processes in the heart including preconditioning and I/R injury.271

Although a few NF-κB-dependent have been identified as key players in late IPC protection, it is likely that NF-κB regulates the expression of far more genes than just the ones mentioned above after an IPC stimulus. However, the identity of these genes that comprise the complete NF-κB regulated transcriptome after IPC is unknown. Thus, one of the primary goals of this work is to identify the complete NF-κB-regulated

transcriptome of late IPC that is likely to underlie the NF-κB-dependent cardioprotection of late IPC. This was achieved through the use of hypothesis driven gene microarray analyses comparing changes in gene expression following IPC in both wild-type and cardiac myocyte specific NF-κB dominant-negative (2M) mice. These results will contribute to the understanding of the NF-κB-dependent gene expression network following IPC and how these gene networks regulate cell death and survival following in different pathophysiological states.

Section 2. PGAA-Mediated Delivery of Transcription Factor Decoys

There exists no easy way to coordinately study the role of multiple transcription factors in the same in vivo system. Achieving specific blockade of multiple factors simultaneously or any combination thereof would likely require investing years of effort and many tens of thousands of dollars into conditional transgenic mouse models.

Pharmacological inhibitors do exist to inhibit the activity of certain transcription factors, but the vast majority of these can present problems in the interpretation of resulting data since they are usually not specific for the factors they are intended to inhibit and can exhibit “off-target” effects. For example, many of the current pharmacological agents that inhibit NF-κB activation are designed to prevent the phosphorylation or degradation of IκBα. A large fraction of these agents, such as

NSAIDs, omega-3 fatty acids, and statins inhibit IKK activity. However, IKK is a kinase that has many phosphorylation targets in addition to IκBα.282 Thus, inhibition of IKK has the potential to display consequences on cell signaling in addition to NF-κB blockade.

Many of the pharmacological agents that have been used to block NF-κB are either not entirely specific or inhibit members of the pathway that may have known NF-κB- independent effects, such as the IKK kinase282, and thus may result in “off-target” effects in addition to NF-κB blockade. Such off-target activity of available inhibitors complicates research studies and results in side effects in clinical usage.

Transcription factor decoys are a promising approach that have been engineered to specifically and acutely block transcription factor activation. Transcription factor decoys, designed to specifically bind and sequester transcription factors, are small (~20-

30 base pair) sequences of DNA containing the consensus binding site for the transcription factor it is designed to inhibit. Decoys inhibit transcription factor activation through competitive binding with the genomic promoter sites for the targeted factor.

Thus, the targeted factor binds the “decoy” consensus site upon activation instead of a genomic promoter consensus site and thus cannot exert a regulatory effect on the genomic promoter elements. In addition to their specificity, decoys are an attractive approach to block transcription factors because they are rather simple to manufacture and, unlike many pharmacological agents, do not require knowledge of the molecular structure of the target for design.283 Through the use of chemical modifications such as locked nucleic chemistry (LNA) or phosphorothioation, the short oligonucleotide acid decoys can be made more resistant to degradation allowing for more long-term applications.284 For these reasons, decoys have become an attractive to specifically activation of select transcription factors in in vitro cell culture systems. Many studies have also already been published demonstrating the application and efficacy of transcription factor decoys to block transcription factor activation in animal models in vivo (Table 1).

Targeted Reference Comments Factor NF-κB 277 Coronary infusion in rats using HVJ-liposome vectors to reduce infarct size after I/R injury NF-κB 285 Catheter based coronary infusion of decoys to reduce neointimal hyperplasia NF-κB 286 Intrathecal injection of naked decoy reduced nerve injury in a model of rat lumbar disc herniation AP-1 287, 288 Catheter based coronary infusion of decoys to reduce neointimal hyperplasia in a rats and minipigs STAT-1 289 Ex vivo coronary perfusion inhibited acute rejection of mouse heart transplants E2F 290 Catheter based coronary infusion of decoys to reduce neointimal hyperplasia in a rat model

Table 1. A brief list of publications reporting functional delivery of transcription factor decoys in animal models. The limited number of publications employing in vivo application of decoys is the result of limited options of delivery vectors for non-toxic in vivo transfection. Some of the studies listed above employed ex vivo transfection of tissue followed by in vivo transplantation of the transfected tissue.

In addition to their research use in animal models, there have also been a few human clinical trials initiated using transcription factor decoys. One such multicenter trial began in 2005 to evaluate the safety and preliminary efficacy of topical administration of an NF-κB decoy for the treatment of atopic dermatitis (ClinicalTrials.gov Identifier:

NCT00125333). Although this study utilized decoys targeted to NF-κB, it involved only topical application of the decoys (in the absence of a transfection vector). A more recent trial initiated in 2009 by the University of Pittsburgh will investigate the biological activity of a single in vivo intratumoral injection of decoys targeting STAT-3 in head and neck tumors (ClinicalTrials.gov Identifier: NCT00696176). In addition, the PREVENT

IV trial investigated the efficacy of E2F decoys for the treatment of neointimal hyperplasia in autogenous vein graft failure.291 Unfortunately the results concluded that the application of the E2F decoys were no more successful than placebo at preventing death or restenosis following bypass graft surgery.292 The PREVENT IV trial also did not involve in vivo administration of the decoys; they were applied to the vein grafts ex vivo prior to the grafting surgery.291

It is well recognized that development of a suitable delivery vector is needed to enhance the in vivo transfection efficiency of the therapeutic DNA cargo. Unfortunately, an ideal non-viral delivery vector suitable for human use does not exist and none of the above trials employed any sort of transfection vector. This brings to light the major limiting problem with the in vivo application of transcription factor decoys of achieving non-toxic and efficient delivery that functionally blocks the targeted factor without perturbing other physiological factors. Many commercially available transfection agents have been developed to aid in the transfection of in vitro cell culture systems, but safe

and efficacious transfection of in vivo systems utilizing these reagents remains a major challenge. Functional delivery of transcription factor decoys to the in vivo heart has only previously been achieved with very limited success.277 A key to successful in vivo transfection is the use of a delivery vector that does not display toxicity or elicit any effects independent of the cargo it is intended to deliver. Such independent effects would include triggering systemic or acute inflammatory signaling. This is especially critical when attempting to use foreign vectors to deliver decoys to block NF-κB signaling since

NF-κB activation is so central to the intracellular inflammation signaling pathways.

The most successful in vivo transfection technologies applied to date have been viral derived delivery vectors. Naturally evolved over millions of years, viruses are readily capable of achieving internalization into mammalian cells and unloading their genetic cargo into the nucleus. Genetically modified viruses began being utilized in gene therapy clinical trials in 1993, but serious safety concerns over their clinical use arose with the first reported death associated with viral-mediated gene delivery in 1999.293 This was followed up in 2002 with the development of a leukemia-like syndrome in two young children being treated for X-linked severe combined immunodeficiency (SCID-

X1) using viral derived gene delivery vectors.294 Strong safety concerns regarding the clinical application of viral-mediated gene delivery vectors remain today.

In response to the safety concerns surrounding the use of viral-derived vectors, nonviral systems, including lipid carriers, nanoparticles, and cationic polymers, are being intensively investigated to facilitate the efficient and nontoxic delivery of nucleic acid therapeutics.295-298 Ideal nonviral systems must be safe and free of cytotoxicity,

inexpensive to prepare at a clinical scale, evade triggering immune and inflammatory responses, and capable of tissue penetration and stable intracellular trafficking.298, 299

Unfortunately, there has been limited success using either viral or non-viral vector delivery of nucleic acids, but each currently available delivery vector has drawbacks that limit its potential therapeutic use in vivo.298 Oligonucleotide decoys have shown a high potential for specific transcriptional inhibition for therapeutic and research use, but their success is largely dependent on the development of suitable delivery vectors that will allow for the safe and efficacious use of these agents in vivo. A primary goal of this work is to demonstrate the successful in vivo application of a novel non-viral polyglycoamidoamine transfection technology for delivery of transcription factor decoys to the murine heart.

Section 3. Summary of Goals and Hypotheses

Given the large impact that myocardial ischemia/reperfusion injury has on human health and its economic burden, it is of utmost importance that research efforts be directed at understanding the cardioprotective mechanisms that underlie ischemic preconditioning with the ultimate long-term goal of translating the endogenous mechanism of IPC cardioprotection into an applicable therapeutic against myocardial infarction. As a whole, the work presented in this dissertation consists of three interconnected goals as a means to contribute to the achievement of this global objective.

Goal 1: To delineate the NF-κB-dependent transcriptome following ischemic preconditioning. Since we know that NF-κB activation is necessary for late IPC cardioprotection, this goal is driven by the hypothesis that changes in NF-κB-dependent gene expression underlie the cardioprotection of late phase IPC.

Goal 2: To delineate additional pre- and post-transcriptional regulatory elements controlling Hsp70.3 protein expression in late IPC. As a result of the successful completion of goal 1, it was found that Hsp70.3 is a cardioprotective protein whose mRNA expression is regulated by NF-κB following an IPC stimulus. However, additional transcription factors obligatory to late IPC may also mediate Hsp70.3 expression. Hsp70.3 protein expression also appears to be subject to post-transciptional regulation following late IPC. Thus, the experimental work of goal 2 was driven by the hypothesis that both pre-and post-transcriptional regulation of the Hsp70.3 transcript is critical to its expression in late IPC.

Goal 3: As a future means to acutely inhibit activation of multiple transcription factors in IPC and examine the resulting effects on gene expression, it was necessary to demonstrate the efficacious application of a non-viral transfection vector (PGAA glycopolymers) for the functional delivery of transcription factor decoys to the in vivo heart. This work was driven by the hypothesis that T4 glycopolymer mediated devlivery of transcription factor decoys will result in efficient and functional transcriptional blockade in the myocardium.

Chapter II

Materials and Methods

II.1 Animals

All mice were maintained in accordance with institutional guidelines and the

Guide for the Care and Use of Laboratory Animals (NIH, revised 1996), and all procedures were approved by the University of Cincinnati Institutional Animal Care and

Use Committee. IκBα dominant-negative mice (2M) (cardiomyocyte-specific mutant

IκBαS32A,S36A cDNA) were previously characterized and demonstrated to completely block NF-κB activation following acute ischemic insults 276, 300. These mice are on the

C57Bl/6J strain (Jackson labs, strain # 000664). Hsp70.1 KO mice, also on the C57Bl/6J strain, were previously characterized 301 and were obtained from Macrogen (South

Korea). The Hsp70.1/3 double knockout mice were a kind gift from Dr. Hector R. Wong

(Cincinnati Children’s Medical Center) and are on the B6129SF2/J strain 302. All studies were controlled by same-strain mice matched for age (10-16 weeks) and gender. Groups were of mixed gender and post-hoc statistical analysis was used to determine gender effect; there were none as we previously reported 276. Control groups were done with strain matching wild type mice of the C57Bl6/J strain (Jackson Lab, strain # 000664) and

B6129SF2/J (Jackson Lab, strain # 101045).

II.2 Ischemia/Reperfusion and Ischemic Preconditioning

Ischemia and reperfusion were achieved as previously described 47, 303. Mice were anesthetized with sodium pentobarbital (90mg/kg I.P.), intubated with polyethylene-90 tubing, and ventilated using a mouse miniventilator (Harvard Apparatus) (respiratory rate

100-105 cycles/min). pO2, pCO2, blood pH, and body temperature were maintained within normal limits as described 89. The heart was accessed via lateral thoracotomy, and an 8-0 nylon suture and occluder (0.64mm ID, 1.19mm OD silicon tubing) was placed around the left anterior descending (LAD) coronary artery 2-4mm from the tip of the left auricle. Ischemia was then achieved by tightening the suture to press the silicon tubing against the coronary artery. Ischemia was confirmed by visual observation (i.e. by cyanosis) and by ECG monitoring (widening of the QRS complex, T wave inversion, and

ST segment changes). Mice undergoing sham surgery were subjected to the same procedure without tightening of the suture (i.e. no occlusion).

To induce an ischemic preconditioning stimulus, mice were exposed to 6 cycles of 4 min ischemia followed by 4 min of reperfusion 47 on day 1 of the protocol. On day 2

(24 hrs subsequent to the IPC procedure), mice were then subjected to 30 min coronary occlusion (ischemia) followed by 24 hrs of reperfusion employing surgical procedures similar to those outlined above.

Following 24 hrs of reperfusion, mice were euthanized and the hearts were cannulated with polyethylene-10 tubing and perfused through the aortic root with 1% triphenyltetrazolium chloride (TTC) at 60mmHg.276 The LAD was then re-occluded

(original suture was left in place during the 24 hrs of reperfusion) and the heart perfused

with a 5% solution of phthalo blue dye (Heucotech) to delineate the nonischemic region

276, 303. Hearts were then removed, frozen, sectioned, and imaged for infarct analysis 47,

303. Infarct measurements were calculated by the accepted method of Fishbein et al. 304 and are presented as a percent of the region-at-risk.

II.3 RNA Isolation

Mice were euthanized 3.5 hours following the initiation of the IPC or sham protocol and hearts were removed and quickly rinsed in RNase-free PBS on ice. The ischemic was dissected out (10-20 mg of tissue), quickly flash frozen in liquid nitrogen and frozen at -80oC for future use. Tissues were pulverized at liquid nitrogen temperature and total RNA was extracted from the isolated ischemic zone using an RNeasy Mini Kit

(Qiagen) following the protocol in Appendix C (Isolation of Total RNA from Heart,

Muscle, and Skin Tissue) in the RNeasy Mini Handbook (3rd edition; June 2001).

Columns were treated with RNase-free DNase (Qiagen) to assure no nuclear DNA contamination. Total RNA quantity and quality was assessed by optical density at 260 nm and 260/280 nm and 260/230 nm ratios, respectively. RNA isolated in this manner was used for RT-PCR expression analysis of all non-miRNA genes and RACE.

For isolation of mRNA containing small RNA populations such as miRNAs, tissues were isolated and treated as described above. Total RNA (including small RNAs) were then isolated through an acid phenol:chloroform technique per manufacturer’s instructions (miRVana miRNA Isolation Kit; Ambion). RNA isolated in this manner was

used for RT-PCR expression analysis of miRNAs and for application on RT-PCR based

SA Bioscience miRNA expression arrays.

II.4 Gene Expression Microarray Analysis

RNA samples (0.1 µg/µl) were submitted to the University of Cincinnati

Microarray Core for Agilent Bioanalyzer/Nanodrop analysis (Agilent 2100 Bioanalyzer).

RNA samples were amplified by the University of Cincinnati Microarray Core using

Amino Allyl MessageAMP kit (catalog #1753) from Ambion based on a modified

Eberwine procedure.305 cDNA synthesis and indirect amino-allyl labeling was performed by the University of Cincinnati Microarray Core using a modified version of the Brown

Lab (Stanford University) protocol.306 Microarray hybridization and wash conditions were performed as previously described.307, 308 Competitive hybridization to microarrays of labeled cDNA targets generated from 4 separate samples per group was performed, 2 dye flips performed per group.

RNA samples with an RNA integrity number (RIN) greater then 7 and OD

260/280 ratios ≥ 1.7 were used for microarrays. Imaging and data generation were carried out using a GenePix 4000A and GenePix 4000B (Axon Instruments; Union City, CA) and associated software from Axon Instruments, Inc. (Foster City, CA). The microarray slides were scanned with dual lasers with wavelength frequencies to excite Cy3 and Cy5 fluorescence emittance. Images were captured in JPEG and TIFF files, and DNA spots captured by the adaptive circle segmentation method. Information extraction for a given spot is based on the median value for the signal pixels and the median value for the

background pixels to produce a gene set data file for all the DNA spots. The Cy3 and

Cy5 fluorescence signal intensities were normalized.

Statistical analysis for microarrays was performed using R statistical software and the limma Bioconductor package.309 Data normalization was performed in two steps for each microarray. First, background adjusted intensities were log-transformed and the differences (M) and averages (A) of log-transformed values were calculated as M = log2(X1) - log2(X2) and A = [log2(X1) + log2(X2)]/2, where X1 and X2 denote the Cy5 and Cy3 intensities, respectively. Second, normalization was performed by fitting the array-specific local regression model of M as a function of A. Normalized log-intensities for the two channels were then calculated by adding half of the normalized ratio to A for the Cy5 channel and subtracting half of the normalized ratio from A for the Cy3 channel.

The statistical analysis was performed for each gene separately by fitting the following

Analysis of Variance model: Yijk = µ + Ai + Sj + Ck+ εijk, where Yijk corresponds to the normalized log-intensity on the ith array, with the jth treatment, and labeled with the kth dye (k = 1 for Cy5, and 2 for Cy3). µ is the overall mean log-intensity, Ai is the effect of

th th th the i array, Sj is the effect of the j treatment and Ck is the gene-specific effect of the k dye. Estimated fold changes were calculated from the ANOVA models, and resulting t- statistics from each comparison were modified using an intensity-based empirical Bayes method (IBMT).308

II.5 Bioinformatics Analysis of Gene Microarray Results

Once gene microarray expression results were statistically filtered for significant gene expression changes as described above, the resulting genes were categorized based according to gene ontology of biological process using the DAVID Bioinformatics

Resources Database (http://david.abcc.ncifcrf.gov/).310, 311 Resulting gene lists were uploaded to DAVID’s functional annotation tool and searched by official gene symbol.

All GO filtering in DAVID was done using threshold values of an EASE score/P-value cutoff of 0.01 with a minimum of 5 genes per group.

II.6 Limitations of Gene Microarray Results

It is a recognized limitation in our microarray analyses in that only one timepoint

(3.5 hours following IPC) was chosen to represent NF-κB-dependent gene expression after IPC. We believe this is the proper timepoint to capture direct NF-κB-dependent gene expression events following IPC. It has been shown that NF-κB is activated rapidly following IPC98, thus assessing gene expression changes 3.5 hours post-IPC is long enough to allow mRNA accumulation but still quick enough to capture gene expression primarily targeted directly by NF-κB activation. Time must also be allowed for protein synthesis from IPC induced changes in mRNA expression to occur, thus gene expression must be assessed sufficiently prior to the point at which cardioprotection is observed (24 hours post-IPC). We believe 3.5 hours is a near optimal time to allow for transcriptional activation and mRNA accumulation and assess the resulting primary gene expression changes.

It is also recognized that there are certain limitations to hybridization based microarray techniques. Strict statistical filtering of massively parallel data may miss genes whose expression is regulated to only low fold-changes in mRNA. This was exemplified by the exclusion of Cox-2 and iNOS (whose genes have been previously shown to involved in NF-κB-dependent cardioprotection) despite the fact their expression patterns did trend toward the expression changes one would have expected. It is due this recognized limitation that we utilized QRT-PCR to validate the expression changes of genes critical to the interpretation of our results. In doing so, we found only one gene

(fadd) out of five validated (hif1a, fadd, hsp90aa1, hspa1a, hspa1b) whose expression pattern change delineated via QRT-PCR differed than that predicted by the microarrays.

Even so, both methods found a decrease in fadd expression after IPC in wild-type mice and only differed in the NF-κB-dependency of the downregulation.

II.7 miRNA Expression Array

Whole genome mouse miRNA RT^2 miRNA PCR array plates (MAM-100A; SA

Biosciences) were used according to manufacturer’s instructions. As described in Section

II.3 total RNA was isolated in a way such that it was suitable for miRNA applications. cDNA was then synthesized from 4.0 µg total RNA using an RT2 First Strand cDNA

Synthesis kit (SABiosciences). Total RNA from 4 independent samples from each group

(WT sham, WT IPC, and 2M IPC) was pooled such that the resulting array data is representative of an average expression value from 4 different samples for each group.

Since the data generated from these arrays is considered as preliminary, microRNAs with a positive or negative fold change of ≥ 2.0 were considered significant.

Since our whole genome microarray results had previously determined there to be very few changes in basal gene expression between WT and 2M mice (discussed in section III.1.1), an miRNA expression comparison was not done between WT and 2M sham as a cost saving measure.

II.8 cDNA Synthesis and Quantitative Real-Time RT-PCR

Synthesis of cDNA from isolated total RNA was performed using an RNA-to- cDNA kit (Applied Biosystems) according to manufacturer’s instructions. A starting amount of 1.0 µg of total RNA was used for cDNA synthesis and optical density was used to determine quantity and quality of product (as described above for isolated RNA).

Quantitative real-time RT-PCR (QRT-PCR) was done in 20 µl total reaction volume using a Stratagene MX3000P machine using a SYBR Green 2X RT-PCR master mix

(Applied Biosystems). For all genes, the thermocycling parameters were 90o C for 10 mins followed by 40 cycles of 90o C for 15s and 60oC for 60s (with data collection at the end of the 60oC step at each cycle). All reactions were performed in triplicate on each plate with a minimum of 3 independent experimental replicates. Gene expression values were calculated using the difference in target gene expression relative to 18S mRNA using the delta-delta Ct method.312 See Table 2 for detailed information on the amount of cDNA per reaction and primer sequences used for each gene.

Gene cDNA/Reaction Primer Sequences 18S 60 ng 5’-AGTCCCTGCCCTTTGTACACA-3’ 5’-CCGAGGGCCTCACTAAACC-3’ Cox-2 100 ng 5’-CAACACCTGAGCGGTTACCAC-3’ 5’-CAGAGGCAATGCGGTTCTG-3’ Hsp70.1 100 ng 5’-GAAGACATATAGTCTAGCTGCCCAGT-3’ 5’-CCAAGACGTTTGTTTAAGACACTTT-3’ Hsp70.3 100 ng 5’-GGCCAGGGCTGGATTACT-3’ 5’-GCAACCACCATGCAAGATTA-3’ HIF-1α 100 ng Quantitect Primer Assay (proprietary) Hsp90 100 ng Quantitect Primer Assay (proprietary) FADD 120 ng Quantitect Primer Assay (proprietary)

Table 2. Amount of cDNA per reaction and primer sequences used for gene expression analysis via QRT-PCR. Primer sequences for 18S, Cox-2, Hsp70.3, Hsp70.1 were ordered from Integrated DNA Technologies and were used at a concentration of

1.25 mM. Primers for HIF-1α, Hsp90, and FADD were obtained from Qiagen

(Quantitect Primer Assays) and used at 50% of supplier recommended concentration.

II.9 Transcription Factor Decoys and PGAA Polymers

Transcription factor decoys were synthesized as individual single-stranded sense and antisense oligonucleotides with sequences as denoted in Table 3. Decoys were hydrated to a stock concentration of 5.0 µg/µl. Annealing was done by adding 11.0 µl of each decoy stock to 23 µl water and 5 µl of 10X annealing buffer (100 mM Tris-HCl pH

7.5, 0.5 M NaCl, 10 mM EDTA pH 8.0) and heating at 95oC for 10 mins followed by a slow cooling back to room temperature. The resulting working stock (2.2 µg/µl) was stored at -20oC and subjected to the heating and cooling cycle prior to each use to assure a primary population of properly annealed double-stranded decoys.

PGAAs D4, G4, M4, and T4 (Fig. 1) were synthesized by copolymerization of esterified D-glucaric acid (D), dimethyl-meso-galactarate (G), D-mannaro-1,4:6,3- dilactone (M), and dimethyl L-tartarate (T) with pentaethylenehexamine in methanol, as previously described 313, 314. JetPEI (Avanti Polar Lipids, Alabaster, AL) and

Lipofectamine 2000 (Invitrogen, Carlsbad, CA) were used as transfection controls at the manufacturer recommended doses [JetPEI: N/P = 5; lipofectamine 2000: lipofectamine

2000 (µL) to DNA (µg) ratio of 2.5].

Targeted Factor Sequence NF-κB Sense: 5’-CCT TGA AGG GAT TTC CCT CC-3’ Antisense: 5’-GGA GGG AAA TCC CTT CAA GG-3’ AP-1 Sense: 5’-CGC TTG ATG AGT CAG CCG GAA-3’ Antisense: 5’-TTC CGG CTG ACT CAT CAA GCG-3’ STAT-3 Sense: 5’-CGT CAC ATT TCC TGG AAA TGC TTG-3’ Antisense: 5’-CAA GCA TTT CCA GGA AAT GTG ACG-3’ None (scrambled Sense: 5’-TTG ACT GCA CTA TTC GAG CC-3’ control decoy) Antisense: 5’-GGC TCG AAT AGT GCA GTC AA-3’

Table 3. Sequences of oligodeoxynucleotide decoys used for specific targeting and blockade of transcription factors.

Figure 1. Chemical structures of PGAA polymers

II.10 Cell Culture and Transfections

H9c2(2-1) cells were cultured according to ATCC specifications in Dulbecco's

Modified Eagle Medium (DMEM, supplemented with 10% fetbal bovine serum (FBS),

100 units/mg penicillin, 100 µg/mL streptomycin, and 0.25 µg/mL amphotericin) in 5%

CO2 at 37 ºC.

Primary murine embryonic fibroblasts (MEFs) from wild-type (C57/Bl6),

Hsp70.1 KO, and Hsp70.1/3 double KO mice were isolated as previously described

(Gene Targeting: A Practical Approach; D. Rickwood and B.D. Hames; Section 2.3

Production of fibroblast feeder layers; University of Toronto, Toronto, Canada).315

Briefly, embryos were removed between embryonic days 13-18 and heads and internal organs were removed. The remaining tissues were minced in sterile PBS and passed through a 100 µM cell filter while adding trypsin and DNaseI as necessary to allow to easy pass through the filter. Isolated cells were then grown in DMEM with 10% FBS

-/- under conditions of 5% CO2 at 37°C. MEFs from HSF1 KO mice were obtained from

Dr. Hector Wong (Cincinnati Children’s Hospital) and have been previously described.316

Prior to transfection, cells were seeded and transfected at the following conditions according to culture plate and growth area:

Culture Plate Growth Area Cells/Well Transfection Volume 10 cm dish 7.85 cm2 7.5 × 105 4.0 ml 24 well plate 1.88 cm2 2.0 × 104 0.5 ml 96 well plate 0.32 cm2 6.0 × 103 0.1 ml

All cells were incubated overnight in complete DMEM after plating prior to transfection. JetPEI (N/P = 5, Avanti Polar Lipids, Alabaster, AL) and Lipofectamine

2000 (µL to µg DNA ratio of 2.5, Invitrogen, Carlsbad, CA) were used according to manufacturers instructions. D4, G4, M4, and T4 were incubated with DNA at an N/P ratio of 30 and incubated at room temperature for 30 mins in ultrapure DNase free water

(Gibco/Invitrogen) to allow for polyplex formation prior to transfection. Media was aspirated and cells were rinsed in sterile PBS. Transfection solutions were then added to the cells in serum-free media (Opti-MEM, pH 7.2) (Gibco/Invitrogen) at the volumes indicated in the above table. After 4 h of transfection, equal volume DMEM with 20%

FBS (to achieve a final concentration of FBS in the media of 10%) was added and cells were incubated overnight (12-16 hrs) to allow for efficient transfection prior to experimental use.

For heat shock treatment, cells were placed at 42°C (with all other conditions held constant) for 1hr and allowed to recover at 37°C for the indicated experimental times.

Cells were treated with TNF-α (25 ng/mL, Peprotech, Rocky Hill, NJ) in Opti-MEM at

37 ºC for 30 min to activate NF-κB.

II.11 Simulated Ischemia/Reperfusion

Simulated ischemia was achieved through subjecting cells to growth conditions of nutrient deprivation, acidosis, and hypoxia for 3 hours as previously described.317

Ischemic media (125 mM NaCl, 8 mM KCl, 1.2 mM KH2PO4, 1.25 mM MgSO4, 1.2 mM

CaCl2, 6.25 mM NaHCO3, 20 mM 2-deoxyglucose, 5 mM Na-lactate, 20 mM HEPES,

pH 6.6) was added to cells just prior to placement in a hypoxia chamber (O2 purged to ≤

0.5% using N2, 5% CO2). After 3 hours of simulated ischemia, ischemic media was aspirated, reperfusion buffer (Krebs-Henseleit buffer: 110 mM NaCl, 4.7 mM KCl, 1.2 mM KH2PO4, 1.25 mM MgSO4, 1.2 mM CaCl2, 25 mM NaHCO3, 15 mM glucose, 20 mM HEPES, pH 7.4) was added, and cells were incubated at normal growth conditions

o (37 C, 95% air, 5% CO2) for 3 hours.

II.12 Primary Neonatal Rat Ventricular Myocytes

Neonatal rat ventricular myocytes (NRVMs) were isolated from one day old

Wistar rats (Charles River Laboratories). Cardiac myocytes were dispersed from the ventricles by digestion with type IV collagenase (Sigma), 0.1% trypsin (Life

Technologies, Inc.), and 15 µg/ml DNase I (Sigma). Cells were applied on a discontinuous Percoll gradient (1.060/1.086 g/ml) prepared in Ads buffer [116 mM NaCl,

20 mM HEPES, 1 mM NaH2PO4, 5.5 mM glucose, 5.4 mM KCl, 0.8 mM MgSO4, pH

7.35] and centrifuged at 250 × g for 3 min. Cells were grown in DMEM supplemented with 10% fetal bovine serum, 100 units/mg penicillin, 100 µg/ml streptomycin, and 0.25

µg/ml amphotericin. Cultures were determined to be more than 95% enriched for cardiac myocytes based on assessment of immunofluorescent staining with a sarcomeric myosin monoclonal antibody.

Toxicity effects of transfection vectors on NRVMs were analyzed via the release of adenylate kinase from damaged cells into the culture media using the ToxiLight assay system (Cambrex, East Rutherford, NJ) according to manufacturer’s specifications.

II.13 Pericardial Delivery of PGAA/Decoy Polyplexes

Animals were anesthetized, intubated, and hearts were accessed via lateral thoracotomy as described in section II.2. Double stranded decoys were polyplexed with

T4 PGAA at an N/P ratio of 30 in a total volume of 45 µl for 30-60 minutes to allow for complex formation. 5.0 µl of 10X PBS (0.2 µm filtered) was added immediately prior to injection resulting in a total volume of 50 µl per mouse was injected directly into the pericardial sac using a microsyringe (Hamilton Co.). For imaging analysis of transfection efficiency, mice were sacrificed 24 hrs post-injection and hearts and livers were removed, rinsed in ice cold PBS, fixed in 4% paraformaldehyde and razor sectioned for imaging.

The hearts were imaged using a Zeiss Axioplan Imaging 2 microscope and using a

Hamamatsu ER CCD-camera, and control hearts (no Alexafluor488 on decoys) were set at zero (to average out background fluorescence) and any signal above zero was treated as positive for transfection for purposes of estimating (using Image J) the area of the ventricle positive for fluorescence (Fig 33). Secondly, an unbiased measure of pixel density was used in Image J to calculate the fluorescent signal intensity of the entire ventricular sections (minus chambers; Fig 33).

To assess functional blockade of NF-κB dependent gene expression Cox-2 expression, mice were administered a cytomix (0.008 µg/g IL-1β, 0.1 µg/g TNF-α, 0.2

µg/IFN-γ)276 24 hrs post-injection of decoys and sacrificed 3 hours following cytomix.

RNA was then isolated for assessment of Cox-2 gene expession via QRT-PCR as described in sections II.3 and II.8, respectively.

II.14 Nuclear/Cytoplasmic Protein Extraction

For preparation of in vitro nuclear extracts cells trypsinized from culture plates and pelleted by centrifugation at 200 × g for 5 min. Cell pellets were then resuspended in ice-cold solution A (300 µl for an 80% confluent 10 cm plate of cells) [10 mM HEPES, pH 7.9, 1.5 mM MgCl2, 10 mM KCl, 0.5 mM DTT, 25 µg/ml leupeptin, 0.2 mM sodium orthovanadate, 0.1% (v/v) Triton X] and incubated on ice for 20 min (with vortexing every 10 min). Samples were centrifuged at 5,000 × g for 10 min. The supernatant was collected as the cytoplasmic protein fraction when appropriate. The pellets were resuspended in ice-cold solution C (50% volume of solution A starting volume) [20 mM

HEPES, pH 7.9, 25% (v/v) glycerol, 0.6 M KCl ad 1.5 mM MgCl2, 0.2 mM EDTA, 0.5 mM PMSF, 0.5 mM DTT, 25 µg/mL leupeptin and 0.2 mM sodium orthovanadate] and incubated on ice for 40 minutes (with vortexing every 10 min). Samples were then centrifuged at 10,000 × g for 15 min and the supernatants were collected and flash frozen in liquid nitrogen as the nuclear protein fraction.

For in vivo preparation of nuclear protein extracts, tissues were removed, rinsed in ice-cold PBS, and blotted dry before flash freezing in liquid nitrogen. Ventricular tissues were pulverized at liquid N2 temperature, tissue yields were weighed, and ice-cold solution A was added at a ratio of 10 µl solution A per 1.0 mg of tissue. Tissues were homogenized on ice for 10 sec using a rotor/stator tissue homogenizer (6 mm blade diameter) at 4000 rpm. Samples were then further prepared as described above for in vitro preparations with a solution C ratio of 5 µl for every 1.0 mg of tissue. All protein

concentrations were determined using a Bio-Rad protein assay with bovine serum albumin as a standard.

II.15 Whole Cell Protein Extraction

Total cellular protein was was extracted using RIPA lysis buffer [50 mM Tris,

150 mM NaCl, 1% Nonidet P-40, 0.5% sodium deoxycholate, 0.1% SDS] (Santa Cruz

Biotechnology) supplemented with 0.5 mM DTT, 0.2 mM sodium orthovanadate, and a protease inhibitor cocktail tablet (Complete Mini; Roche). Media was aspirated and cells were washed with PBS prior to addition of lysis buffer for 3 minutes at room temperature. Cells were then scraped from the culture dishes and transferred to a microcentrifuge tube and centrifuged 5 minutes at 1,000 × g. Supernatant was flash frozen in liquid nitrogen and stored at -80°C as total cellular protein extract. Protein concentrations were determined using a Bio-Rad protein assay with bovine serum albumin as a standard.

II.16 Electrophoretic Mobility Shift Assay (EMSA)

A double-stranded oligonucleotide probe for NF-κB (5’-

AGTTGAGGGGACTTTCCCAGGC-3’, Promega, Madison, WI) containing an NF-κB- binding site (underlined) was end-labeled with [γ-32P]-ATP usin T4 polynucleotide kinase (Promega). Enrichment for the labeled probe was done using G-25 sephadex columns (Pharmacia Biotech, Piscataway, NJ). Binding reactions were performed in a

total volume of 15 µL consisting of 10 µg of nuclear protein, 10 mM Tris-HCl (pH 7.5),

50 mM NaCl, 1 mM MgCl2, 0.5 mM EDTA, 0.5 mM DTT, 4% glycerol (v/v), and 1 µg poly(dI-dC). After incubation for 10 min at RT, the labeled NF-κB probe (~1 x 105 cpm/reaction) was added to the reaction and further incubated for 20 min at room temperature. The total reaction volume containing the DNA-protein complexes were then separated using a 6% non-denaturing polyacrylamide gel in 1X TBE gel running buffer.

The gels were vacuum-dried and exposed to X-ray film using intensifying screen cassettes.

II.17 Western Blotting

Protein extracts were run on 10% SDS-polyacrylamide gels and transferred onto a nitrocellulose membrane, 45-micron pore size (Hybond-C Extra Amersham Biosciences).

The membranes were then blocked for 1 hr at room temp using 5% milk in Tris-buffered saline [10mM Tris-HCl (ph 7.50) and 0.15M NaCl] with 0.1% Tween-20. Primary antibodies (Hsp70: Santa Cruz sc-24, 1:1,000; pan-actin: Sigma A2066, 1:10,000) were added overnight at 4oC. After 3 washes of 3 mins each with 1X TTBS [1X TBS + 0.1%

Tween-20], HRP conjugated secondary antibodies (anti-mouse: GE Health NA931V,

1:5,000; anti-rabbit: Santa Cruz sc-2054, 1:10,000) were added in 5% milk in TTBS for

1-2 hrs at room temp. Membranes were again washed 3 times with 1X TTBS for minutes each. Signal detection was done using Perkin-Elmer Western Lighting

Chemiluminescence’s Reagent Plus (NEL-104) for 60 seconds at room temp followed by exposure to Kodak BioMax Light Film. Densitometry was performed using Image J software to quantify protein levels.

II.18 Generation of Hsp70.3 Luciferase Reporter Constructs

The endogenous Hsp70.3 promoter including the 5’-untranslated region (3974 bp ending at the start codon of the protein coding sequence) was PCR purified from C57/Bl6 mice genomic DNA preparations using the following primer sequences: 5’-CACTGAGC

GTCTCCCAT GGCTTCTCAAAGG-3’; 5’-CGCCGTGTCGTCTCCCATGGCGCCGC

GCTCTGCTTC TG-3’. Genomic DNA and PCR products were purified using a kit according to manufacturer’s instructions (Qiagen). We chose to clone the promoter fragment into a Promega pGL4.10 vector using an NcoI restriction site provided within the MCS just prior to the luciferase coding sequence in the plasmid. Since NcoI sites were also found within the promoter sequence, the primers used for PCR purification were designed to introduce BsmBI sites to the ends of the promoter insert. Restriction digestion using BsmBI gives single-strand overhangs compatible with NcoI digestion performed on the plasmid. Figure 2 represents a schematic of the Hsp70.3 luciferase reporter construct. The cytomegalovirus (CMV) promoter was restriction purified from the Promega pGL4.75 plasmid and cloned into our pGL4 plasmid using NcoI sites (Fig.

2).

For analysis of post-transcriptional regulation, the Hsp70.3 3’-untranslated region

(1253 bp beginning from the end of the protein coding sequence incorporating the predicted poly-adenylation signals) was also PCR purified from C57/Bl6 mice genomic

DNA using the following primer sequences: 5’-GGTGGATTAGAGGCCTCTGCTGGC

TCTCCCGGTGCT-3’; 5’-GGTGGTACCCGGCCGGCCTGTTGTCAGTTCTCACCT-

3’. The 3’-UTR and polyA signal of Hsp70.3 was cloned into the pGL4 luciferase

reporter plasmids utilizing FseI and StuI restriction sites at the end of the luciferase coding sequence (Fig. 2).

All resulting constructs were transformed into e. coli and amplified under the selection of ampicillin at 100 µg/ml. Plasmid DNA was purfied from saturated overnight cultures using Qiagen plasmid DNA isolation kits according manufacturer’s instructions.

All clones were sequence verified prior to use.

Figure 2. Hsp70.3-luciferase reporter vectors

Luciferase reporter plasmids were constructed to allow for high-throughput in vitro assessment of the regulatory role of the Hsp70.3 promoter and 3’-UTR. The plasmid as shown is a modified version of the Promega pGL4.10 luciferase gene expression cassette under regulatory control of the Hsp70.3 promoter and 3’-UTR. We were also able to utilize this reporter with a constitutively active CMV promoter upstream of the luciferase gene to isolate the regulatory role of the Hsp70.3 3’-UTR.

II.19 Luciferase Reporter Assay

Transfections were done as described in section II.7. All luciferase reporter constructs were used at 300 ng/well (24 well plate layout) unless otherwise noted.

Luciferase assays were performed according to manufacturer’s instructions utilizing the

Luciferase Assay System kit (Promega). Briefly, cells were rinsed and lysed (150 µl Cell

Culture Lysis Reagent/well; Promega) for 5 mins at room temp with brief vortexing of the cell plate every 60 seconds. 40 µl of cell lysate solution was then combined with 100

µl of luciferase assay reagent in a flat bottom white opaque 96 well plate (Falcon) and emitted luminescence was read immediately.

II.20 MTS Assay

A colorimetric MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-

2-(4-sulfophenyl)-2H-tetrazolium) assay (CellTiter 96® AQueous One Solution Cell

Proliferation Assay; Promega) was used to assess cell death following simulated I/R.

Cells were treated in a 96-well plate and 20 µl of assay reagent was added directly to 100

µl of complete DMEM culture medium. Cell plates were then incubated at 37oC for 1-4 hours and absorbance at 490 nm was recorded. Background absorbance from 20 µl assay reagent + 100 µl complete DMEM (in the absence of cells) was subtracted from each sample to allow for accurate representation of results as a ratio of untreated control cells.

II.21 Rapid Amplification of cDNA Ends (RACE)

Rapid Amplification of cDNA Ends (RACE) was used to assess the population of poly-adenylation signals within the 3’-UTR of the Hsp70.3 transcript. Total RNA was isolated and cDNA was synthesized as described above (Section II.3 and II.6, respectively). Amplification of the Hsp70.3 3’-UTR populations via RACE was done via

PCR reaction using an Hsp70.3 3’-UTR sequence specific primer (5’-GGTGGATTA

GAGGCCTCTGCTGGCTCTCCCGGTGCT-3’) and a modified oligo-dT18 primer (5’-

NT18-3’), with “N” representing a base of either T, G, or C to allow for more specific binding at the beginning of the polyA signal. PCR conditions used for the reaction were:

94oC x 1 min and 40 cycles of (94oC x 30s, 58oC x 30s, 72oC x 80s), followed by 72oC x

2 min. Products were then electrophoresed on a 1.0% agaraose gel for observation of 3’-

UTR product populations.

QRT-PCR was performed as described in Section 11.6 to specifically amplify polyadenylation products 2 and 3 (Fig. 28) to determine relative ratios. The following primers were used:

PolyA product 2: S: 3’-GGCCAGGGCTGGATTACT-5’

AS: 3’-GCAACCACCATGCAAGATTA-5’

PolyA product 3: S: 3’-GGTCAGGAGTTGCTGTGTATGACAGTTTC-5’

AS: 3’-CTCACACAAGCAGATCACAATGCAATG-5’

II.22 Statistical Analysis

Results are reported as means ± standard error of the mean (SE). Unpaired student t-test were performed and P-values reported with differences between groups considered significant at P ≤ 0.05. Power analysis was used to ensure proper sample size needed to determine significance. Statistical analysis of gene microarrays was done as previously described (Section II.4).

Chapter III

Results

Section 1. The Role of NF-κB-dependent Gene Expression in Late IPC

III.1.1 Identification of the NF-κB-dependent Gene Program In Late Ischemic Preconditioning

NF-κB activation is necessary for late phase IPC cardioprotection

Although the requirement of NF-κB activation for the late phase of IPC had been previously shown98, it was important to also demonstrate this result with our experimental protocols prior to their use in the investigation of NF-κB-dependent gene expression in late IPC. In wild-type mice, ischemic preconditioning 24 hours prior to a 30 minute I/R injury led to a significant reduction in infarct size (40.6% of risk region in sham compared to 21.4% following IPC; P ≤ 0.05) (Fig. 3A). As predicted, the cardioprotection of late IPC was not observed in NF-κB dominant negative mice (2M)

(32.8% infarct/risk in sham group compared to 35.3% infarct/risk following IPC; P value

N.S.) (Fig. 3A). Assessment of region at risk indicated there were no significant differences between any of the groups (Fig. 3B).

Figure 3. NF-κB activation is necessary for late phase IPC cardioprotection

(A) IPC 24 hrs preceding a 30 min ischemia/reperfusion (I/R) reduces infarct size in WT mice from 40.6% to 21.4% as compared to sham IPC. Late IPC cardioprotection is lost in the 2M NF-κB DN mice (35.3% infarct) compared to sham (32.8% infarct). Infarct sizes are expressed as a percent of region at risk. (B) Examination of the sizes of the regions at risk found no significant differences among the groups. Data is expressed as percent of the LV that was within the ischemic risk region. N = 6-10 per group. *P ≤ 0.05 vs. Sham.

Identification of ischemic preconditioning induced gene expression changes

A comparison of gene expression between wild-type IPC and sham treated mice was done via microarray analysis to identify genes whose expression is regulated by IPC

(Fig. 4A). The microarrays used contained 34,473 oligonucleotide probes enabling the relative expression measurement of 21,928 distinct individual genes. It was found that

617 genes (represented by 744 oligonucleotide probes) were significantly up or down regulated by IPC (P ≤ 0.01 or a fold change ≥ 2.0) (Fig. 4B). See Appendix A for a full list of these 617 genes along with their fold change and associated P-value for the WT

IPC vs. sham expression comparison. Functional annotation of these 617 genes using the

DAVID gene ontology (GO) database310, 311 according to biological process resulted in a significant enrichment for GO terms associated with heat shock response, angiogenesis, programmed cell death, and metabolic processes (Table 4). All GO filtering in DAVID was done using threshold values of an EASE score/P-value cutoff of 0.01 with a minimum of 5 genes per group.

Identification of NF-κB-dependent gene expression changes after IPC

In addition to a gene expression comparison between WT IPC and sham, a comparison of gene expression was also done between WT and 2M mice following an

IPC stimulus (Fig. 4A). These results yielded 1,136 dysregulated (significantly up or down-regulated) genes (Fig. 4B). However, since a gene must also be regulated by IPC in the WT mice (i.e. dysregulated in the WT IPC vs. sham comparison) in order to play a potential role in IPC-mediated cardioprotection, further analysis was focused only on the

dysregulated genes falling into the overlap between the two groups. Specifically, the 238 genes represented by the overlap of the two comparisons are the genes whose expression is regulated by NF-κB after IPC (Fig. 4B). Thus, genes within this group of 238 are the most likely candidates to underlie the NF-κB dependent cardioprotection after late IPC

(Table 5).

The DAVID bioinformatics database was again used to functionally annotate these 238 genes according to gene ontology by biological process (Table 6). The resulting gene ontologies were enriched for many of the same biological processes as were found for the 617 IPC regulated genes. When these gene ontologies were clustered based on similar processes (done via DAVID using a filter of P ≤ 0.01 and an ontology similarity score of ≥ 0.75), angiogenesis, programmed cell death, and response to heat shock were the three predominant categories of biological processes that appeared to regulated by

NF-κB after IPC (Fig. 5). Figure 6 provides a look at the individual genes comprising each of the three gene ontology categories of angiogenesis, programmed cell death, and response to heat shock. Since each of these biological processes has been previously implicated in late IPC cardioprotection, it is likely that one or all of these may provide the foundation for the cardioprotective effects mediated by NF-κB-dependent gene expression changes following IPC.

To confirm the validity of the general gene expression patterns detected by the microarray analysis, quantitative real-time RT-PCR (QRT-PCR) was used to assess relative mRNA expression of a few select genes from each ontology grouping. QRT-PCR confirmed the microarray detected NF-κB-dependent increase in gene expression after

IPC for hif1a and hsp90aa1 (Fig. 7). QRT-PCR also confirmed the IPC-mediated down-

regulation of fadd expression, but was inconclusive on the NF-κB-dependency of this down-regulation (Fig. 7). We also confirmed the NF-κB-dependent expression of COX-

2, a known mediator of late IPC, in our model of late ischemic preconditioning (Fig. 7).

Basal gene expression changes in 2M transgenic mouse model

We were able to assess basal gene expression changes in our transgenic model by virtue of a microarray comparison of gene expression changes between wild-type and 2M sham groups. We found that there are 125 genes (0.57% of the total genes examined) whose expression is significantly different between the wild-type and 2M sham groups. Only 8 of these 125 genes are among the 238 genes found to be NF-κB-dependent following

IPC. Additionally, these 8 genes include hsp90aa1 and hspa1b, whose transcript levels we determined via QRT-PCR to be not changed at basal levels in the sham groups (Fig. 7 and 8, respectively).

Figure 4. Experimental design for hypothesis driven microarrays to determine NF- κB-dependent gene expression in late IPC

Figure 4. Experimental design for hypothesis driven microarrays to determine NF-κB- dependent gene expression in late IPC

Schematic of experimental design for hypothesis driven microarrays to compare gene expression changes in WT and NF-κB dominant negative (2M) mice after IPC (A). Three independent microarray comparisons of gene expression were performed: 1) WT IPC vs.

WT sham (represented by the green line), 2) 2M IPC vs. WT IPC (red line), and 3) 2M sham vs. WT sham (black line). Venn diagram representing results of microarray expression comparisons as the number of genes whose expression was significantly up or down regulated between WT IPC and WT sham (green circle) and 2M and WT IPC (red circle) (B). The 238 genes represented in the overlap of the two groups are the genes whose expression was regulated by NF-κB after IPC. The statistical cutoff filter used to determine significantly regulated genes was P ≤ 0.01 or a fold change ≥ 2.0. N = 3 per group for microarray comparisons.

Table 4. Gene ontology categorization of genes regulated by late IPC

Fold Gene Ontology Term Count P-Value Enrichment GO:0009408~response to heat 7 0.00029 7.45 GO:0006479~protein amino acid methylation 6 0.00255 6.19 GO:0008213~protein amino acid alkylation 6 0.00255 6.19 GO:0045765~regulation of angiogenesis 6 0.00377 5.67 GO:0009266~response to temperature stimulus 8 0.00046 5.67 GO:0006986~response to unfolded protein 10 0.00016 5.01 GO:0051789~response to protein stimulus 10 0.00016 5.01 GO:0001525~angiogenesis 18 0.00000 4.03 GO:0001558~regulation of cell growth 11 0.00094 3.60 GO:0048646~anatomical structure formation 21 0.00000 3.49 GO:0048514~blood vessel morphogenesis 19 0.00002 3.33 GO:0001568~blood vessel development 22 0.00000 3.33 GO:0001944~vasculature development 22 0.00000 3.28 GO:0006631~fatty acid metabolic process 15 0.00046 3.02 GO:0016049~cell growth 11 0.00374 3.00 GO:0008361~regulation of cell size 12 0.00248 2.96 GO:0006457~protein folding 18 0.00016 2.90 GO:0032787~monocarboxylic acid metabolic process 19 0.00014 2.84 GO:0040008~regulation of growth 14 0.00218 2.69 GO:0009628~response to abiotic stimulus 14 0.00252 2.65 GO:0016477~cell migration 23 0.00009 2.59 GO:0043549~regulation of kinase activity 13 0.00503 2.56 GO:0051338~regulation of transferase activity 13 0.00657 2.47 GO:0045859~regulation of protein kinase activity 12 0.00983 2.46 GO:0007169~transmembrane receptor protein tyrosine 13 0.00880 2.38 kinase signaling pathway GO:0043066~negative regulation of apoptosis 13 0.00993 2.34 GO:0006928~cell motility 25 0.00019 2.34 GO:0051674~localization of cell 25 0.00019 2.34 GO:0030029~actin filament-based process 14 0.00814 2.30 GO:0050790~regulation of catalytic activity 22 0.00222 2.08 GO:0007167~enzyme linked receptor protein signaling 18 0.00700 2.06 pathway GO:0065009~regulation of a molecular function 25 0.00124 2.05 GO:0040007~growth 17 0.00945 2.04 GO:0009893~positive regulation of metabolic process 26 0.00182 1.96 GO:0006915~apoptosis 39 0.00013 1.93 GO:0009887~organ morphogenesis 30 0.00093 1.93 GO:0007264~small GTPase mediated signal transduction 23 0.00431 1.93

GO:0012501~programmed cell death 39 0.00019 1.90 GO:0008219~cell death 40 0.00019 1.88 GO:0016265~death 40 0.00020 1.87 GO:0007242~intracellular signaling cascade 64 0.00000 1.80 GO:0000902~cell morphogenesis 28 0.00397 1.79 GO:0032989~cellular structure morphogenesis 28 0.00397 1.79 GO:0006950~response to stress 44 0.00026 1.79 GO:0048522~positive regulation of cellular process 46 0.00029 1.75 GO:0048523~negative regulation of cellular process 49 0.00021 1.73 GO:0048518~positive regulation of biological process 52 0.00014 1.73 GO:0048519~negative regulation of biological process 52 0.00016 1.72 GO:0007010~cytoskeleton organization and biogenesis 27 0.00856 1.71 GO:0009653~anatomical structure morphogenesis 58 0.00016 1.66 GO:0048468~cell development 56 0.00112 1.55 GO:0030154~cell differentiation 79 0.00051 1.46 GO:0048869~cellular developmental process 79 0.00051 1.46 GO:0043687~post-translational protein modification 60 0.00481 1.42 GO:0048856~anatomical structure development 84 0.00134 1.39 GO:0006464~protein modification process 67 0.00525 1.38 GO:0050794~regulation of cellular process 154 0.00001 1.37 GO:0043412~biopolymer modification 69 0.00593 1.37 GO:0032502~developmental process 121 0.00017 1.36 GO:0050789~regulation of biological process 164 0.00002 1.33 GO:0019222~regulation of metabolic process 104 0.00145 1.33 GO:0044260~cellular macromolecule metabolic process 121 0.00101 1.30 GO:0044267~cellular protein metabolic process 118 0.00186 1.29 GO:0016070~RNA metabolic process 101 0.00500 1.28 GO:0010467~gene expression 121 0.00184 1.28 GO:0006350~transcription 90 0.00949 1.28 GO:0065007~biological regulation 172 0.00013 1.27 GO:0019538~protein metabolic process 122 0.00232 1.27 GO:0044238~primary metabolic process 264 0.00000 1.25 GO:0044237~cellular metabolic process 263 0.00000 1.24 GO:0006139~nucleobase, nucleoside, nucleotide and nucleic 126 0.00703 1.22 acid metabolic process GO:0008152~metabolic process 288 0.00000 1.22 GO:0043283~biopolymer metabolic process 169 0.00180 1.21 GO:0043170~macromolecule metabolic process 224 0.00011 1.21 GO:0009987~cellular process 392 0.00075 1.06

Table 4. Gene ontology categorization of genes regulated by late IPC

Functional annotation of the 617 IPC-dependent genes resulted in a significant enrichment for GO terms associated with heat shock response, angiogenesis, programmed cell death, and metabolic processes. Gene ontology groups are listed in order of descending fold enrichment.

Table 5. Genes significantly regulated by NF-κB in late IPC

Fold Changes P-Values WT 2M 2M WT 2M 2M IPC IPC Sham IPC IPC Sham v v v v v v WT WT WT WT WT WT Accession Gene name Symbol Sham IPC Sham Sham IPC Sham NM_010478 heat shock protein 1B Hspa1b 8.53 -4 8.11 ≤0.001 NA 0.0015 NM_010516 cysteine rich protein 61 Cyr61 3.91 -2.34 1.39 ≤0.001 0.0051 0.1969 NM_010849 myelocytomatosis oncogene Myc 3.55 -1.71 2.44 NA 0.0048 0.0042 cell division cycle 42 homolog (S. NM_009861 cerevisiae) Cdc42 3.17 -5.18 1.63 ≤0.001 ≤0.001 0.3104 heat shock protein 90kDa alpha (cytosolic), Hsp90a NM_010480 class A member 1 a1 3.08 -2.07 2.24 ≤0.001 0.0290 0.0022 heterogeneous nuclear ribonucleoprotein NM_146130 A3 Hnrpa3 3.06 -3.79 1.65 ≤0.001 ≤0.001 0.3995 NADH dehydrogenase (ubiquinone) 1, NM_025523 subcomplex unknown, 1 Ndufc1 3.05 -5.87 1.63 ≤0.001 ≤0.001 0.4934 NM_144799 LIM and cysteine-rich domains 1 Lmcd1 3.03 -2.27 -1.46 ≤0.001 ≤0.001 0.0132 NM_175121 solute carrier family 38, member 2 Slc38a2 2.91 -2.81 1.62 ≤0.001 0.0014 0.1455 DnaJ (Hsp40) homolog, subfamily B, NM_025926 member 4 Dnajb4 2.72 -2.83 1.42 ≤0.001 ≤0.001 0.1419 NM_011327 sterol carrier protein 2, liver Scp2 2.72 -4.07 1.77 ≤0.001 ≤0.001 0.2086 B-cell translocation gene 2, anti- NM_007570 proliferative Btg2 2.67 -3.62 1.77 ≤0.001 NA 0.0794 NM_013506 eukaryotic translation initiation factor 4A2 Eif4a2 2.66 -4.89 1.73 NA ≤0.001 0.2536 polymerase (RNA) II (DNA directed) NM_153798 polypeptide B Polr2b 2.6 -3.59 1.36 ≤0.001 ≤0.001 0.4890 NM_018819 brain protein 44-like Brp44l 2.56 -6.21 1.79 ≤0.001 NA 0.3090 heterogeneous nuclear ribonucleoprotein NM_010447 A1 Hnrpa1 2.54 -2.55 1.65 NA ≤0.001 0.2830 DnaJ (Hsp40) homolog, subfamily A, NM_008298 member 1 Dnaja1 2.48 -2.06 1.72 ≤0.001 0.0065 0.0118 1200015 RIKEN cDNA 1200015M12 gene M12Rik 2.36 -2.37 1.4 ≤0.001 ≤0.001 0.2214 NM_011225 RAB18, member RAS oncogene family Rab18 2.35 -3.65 2.21 ≤0.001 ≤0.001 0.0062 coiled-coil-helix-coiled-coil-helix domain NM_024166 containing 2 Chchd2 2.34 -3.48 1.66 ≤0.001 ≤0.001 0.2110 NM_175606 homeobox only domain Hod 2.33 -3.4 1.13 ≤0.001 ≤0.001 0.8137 NM_024221 pyruvate dehydrogenase (lipoamide) beta Pdhb 2.33 -4.01 1.77 ≤0.001 ≤0.001 0.2175 transmembrane emp24 protein transport NM_028876 domain containing 5 Tmed5 2.31 -1.76 1.93 ≤0.001 ≤0.001 NA serine (or cysteine) peptidase inhibitor, NM_008871 clade E, member 1 Serpine1 2.29 -2.08 1.54 ≤0.001 0.0135 0.2930 heterogeneous nuclear ribonucleoprotein Hnrpa2 NM_182650 A2/B1 b1 2.24 -3.24 1.55 ≤0.001 ≤0.001 0.3537 XM_131355 proline-rich nuclear receptor coactivator 1 Pnrc1 2.23 -2.87 1.61 ≤0.001 ≤0.001 0.2468 NM_133816 SH3-domain binding protein 4 Sh3bp4 2.22 -1.89 1.55 ≤0.001 ≤0.001 0.2443

XM_484289 elongation factor RNA polymerase II 2 Ell2 2.2 -2.07 1.41 ≤0.001 0.0297 0.0748 BB1464 NM_178908 expressed sequence BB146404 04 2.19 -1.57 1.11 ≤0.001 0.0095 0.4426 NM_008748 dual specificity phosphatase 8 Dusp8 2.18 -2 1.43 NA NA 0.0619 phosphatidylinositol glycan anchor NM_019543 biosynthesis, class P Pigp 2.15 -3.28 1.68 ≤0.001 ≤0.001 0.3341 NM_008710 nicotinamide nucleotide transhydrogenase Nnt 2.13 -1.88 1.34 ≤0.001 0.0012 0.4799 XM_126808 tetratricopeptide repeat domain 32 Ttc32 2.13 -2.81 1.93 ≤0.001 ≤0.001 0.1830 NM_011767 zinc finger RNA binding protein Zfr 2.12 -1.94 1.25 NA ≤0.001 0.3394 231000 RIKEN cDNA 2310002J21 gene 2J21Rik 2.12 -3.06 1.46 0.00001 0.00005 0.42712

NM_009735 beta-2 microglobulin B2m 2.11 -4.23 1.45 ≤0.001 ≤0.001 0.4627 NM_145507 aspartyl-tRNA synthetase Dars 2.08 -2.17 -1.39 ≤0.001 ≤0.001 0.6308 NM_008410 integral membrane protein 2B Itm2b 2.07 -4.22 1.62 ≤0.001 ≤0.001 0.3182 XM_135172 leucine rich repeat containing 2 Lrrc2 2.06 -2.83 1.66 ≤0.001 ≤0.001 0.2875

NM_025703 transcription elongation factor A (SII)-like 8 Tceal8 2.05 -2.66 1.72 ≤0.001 ≤0.001 0.1197 NM_008448 kinesin family member 5B Kif5b 2.05 -3.52 1.63 ≤0.001 ≤0.001 0.1246 2210403 RIKEN cDNA 2210403K04 gene K04Rik 2.04 -2.44 1.21 NA ≤0.001 0.6156 NM_009609 actin, gamma, cytoplasmic 1 Actg1 2.04 -2.91 1.31 ≤0.001 ≤0.001 0.4585 NM_054098 STEAP family member 4 Steap4 2.03 -2.18 1.32 ≤0.001 0.0012 0.2460 NM_026879 chromatin modifying protein 2B Chmp2b 2.03 -2.62 1.68 ≤0.001 ≤0.001 0.1421 leukocyte immunoglobulin-like receptor, NM_013532 subfamily B, member 4 Lilrb4 2.02 -1.71 1.45 ≤0.001 0.0023 0.3306 NM_011896 sprouty homolog 1 (Drosophila) Spry1 2.02 -2.16 1.43 ≤0.001 ≤0.001 0.4244 NM_008618 malate dehydrogenase 1, NAD (soluble) Mdh1 2.02 -2.57 1.93 ≤0.001 ≤0.001 0.1497 1600012 NM_025904 RIKEN cDNA 1600012F09 gene F09Rik 2 -3.25 1.61 ≤0.001 ≤0.001 0.3631 NM_013493 cellular nucleic acid binding protein Cnbp 1.99 -4.56 1.06 ≤0.001 ≤0.001 0.8977 succinate dehydrogenase complex, subunit A, flavoprotein (Fp) Sdha 1.96 -3.04 1.67 ≤0.001 ≤0.001 0.1927 NM_007840 DEAD (Asp-Glu-Ala-Asp) box polypeptide 5 Ddx5 1.95 -2.27 1.44 ≤0.001 ≤0.001 0.3816 NM_010902 nuclear factor, erythroid derived 2, like 2 Nfe2l2 1.94 -2.66 1.43 ≤0.001 ≤0.001 0.1978 NM_010726 phytanoyl-CoA hydroxylase Phyh 1.94 -2.72 1.34 ≤0.001 ≤0.001 0.3541 SMT3 suppressor of mif two 3 homolog 2 NM_133354 (yeast) Sumo2 1.93 -3.1 1.86 ≤0.001 ≤0.001 0.1317 NM_024255 hydroxysteroid dehydrogenase like 2 Hsdl2 1.93 -3.89 1.36 ≤0.001 ≤0.001 0.4969

NM_007840 DEAD (Asp-Glu-Ala-Asp) box polypeptide 5 Ddx5 1.9 -2.24 1.56 ≤0.001 ≤0.001 0.3035 NM_013468 ankyrin repeat domain 1 (cardiac muscle) Ankrd1 1.89 -1.55 -1.04 ≤0.001 0.0095 0.7531 NM_013525 growth arrest specific 5 Gas5 1.89 -1.88 1.75 ≤0.001 ≤0.001 0.1635 NM_134079 adenosine kinase Adk 1.89 -3.54 1.59 ≤0.001 ≤0.001 0.3032 NM_013614 ornithine decarboxylase, structural 1 Odc1 1.88 -2.15 1.37 ≤0.001 0.0011 0.2767 6430527 NM_145836 RIKEN cDNA 6430527G18 gene G18Rik 1.85 -2.36 1.34 ≤0.001 ≤0.001 0.3235 NM_008831 prohibitin Phb 1.85 -2.9 1.75 ≤0.001 ≤0.001 0.2073 2310057 NM_026655 RIKEN cDNA 2310057M21 gene M21Rik 1.81 -1.66 1.25 ≤0.001 0.0058 0.2942 hydroxyacyl-Coenzyme A dehydrogenase/3-ketoacyl-Coenzyme A thiolase/enoyl-Coenzyme A hydratase NM_145558 (trifunctional protein), beta subunit Hadhb 1.81 -3.03 -1.04 ≤0.001 ≤0.001 0.8512 Mitochondrial COXII NA 1.8 -1.82 1.04 ≤0.001 0.0012 0.8894 COP9 (constitutive photomorphogenic) NM_009939 homolog, subunit 2 (Arabidopsis thaliana) Cops2 1.8 -2.26 1.02 ≤0.001 NA 0.9676 NM_023270 ring finger protein 128 Rnf128 1.8 -3.54 1.8 ≤0.001 ≤0.001 0.1791 NM_007917 eukaryotic translation initiation factor 4E Eif4e 1.78 -2.47 1.43 ≤0.001 ≤0.001 0.1146 XM_132983 leiomodin 2 (cardiac) Lmod2 1.77 -2.27 1.13 ≤0.001 0.0019 0.6529 061001 NM_133771 RIKEN cDNA 0610016J10 gene 6J10Rik 1.77 -2.36 1.61 ≤0.001 ≤0.001 0.2173 NM_008866 lysophospholipase 1 Lypla1 1.77 -2.73 1.53 ≤0.001 ≤0.001 0.2413 2810001 RIKEN cDNA 2810001A02 gene A02Rik 1.76 -1.6 1.7 ≤0.001 0.0012 0.2298 NM_025279 heterogeneous nuclear ribonucleoprotein K Hnrpk 1.76 -2.13 1.46 ≤0.001 ≤0.001 0.2179 NM_172648 interferon activated gene 205 Ifi205 1.75 -1.6 1.01 ≤0.001 0.0068 0.9526 NM_010477 heat shock protein 1 (chaperonin) Hspd1 1.75 -2.13 -1.08 ≤0.001 0.0011 0.6253 NM_024406 fatty acid binding protein 4, adipocyte Fabp4 1.75 -3.34 1.37 ≤0.001 ≤0.001 0.4516 NM_020560 mitochondrial ribosomal protein S31 Mrps31 1.74 -1.39 1.29 ≤0.001 0.0093 0.3976 polymerase (RNA) II (DNA directed) NM_009090 polypeptide C Polr2c 1.74 -2.05 1.55 ≤0.001 NA 0.3053

translocase of outer mitochondrial NM_024214 membrane 20 homolog (yeast) Tomm20 1.74 -2.2 1.6 ≤0.001 ≤0.001 0.1287 potassium voltage-gated channel, Isk- NM_008424 related subfamily, member 1 Kcne1 1.73 1.44 1.66 ≤0.001 0.0070 0.0078 XM_129647 glutamyl-prolyl-tRNA synthetase Eprs 1.73 -2.19 1.62 ≤0.001 0.0012 0.2550 NM_028803 glucan (1,4-alpha-), branching enzyme 1 Gbe1 1.73 -2.27 1.49 ≤0.001 ≤0.001 0.3267 Tmem5 NM_030018 transmembrane protein 50B 0b 1.73 -2.29 1.45 ≤0.001 ≤0.001 0.0832 NM_174990 GTPase, IMAP family member 4 Gimap4 1.73 -2.41 1.2 ≤0.001 ≤0.001 0.6239 NM_016807 syndecan binding protein Sdcbp 1.72 -2.49 -1.16 ≤0.001 ≤0.001 0.3761 NM_009888 complement component factor h Cfh 1.71 -2.33 1.5 ≤0.001 ≤0.001 0.2878 9430083 NM_026565 RIKEN cDNA 9430083G14 gene G14Rik 1.71 -2.48 1.63 ≤0.001 ≤0.001 0.2132 NM_172648 interferon activated gene 205 Ifi205 1.7 -1.72 1.03 ≤0.001 0.0022 0.8800 transmembrane BAX inhibitor motif NM_027154 containing 1 Tmbim1 1.7 -1.86 1.11 ≤0.001 0.0031 0.6281 NM_009694 apolipoprotein B editing complex 2 Apobec2 1.7 -2.12 1.93 ≤0.001 ≤0.001 0.0975 B23033 RIKEN cDNA B230339E18 gene 9E18Rik 1.69 -1.97 1.51 ≤0.001 ≤0.001 0.0813 NM_008692 nuclear transcription factor-Y gamma Nfyc 1.69 -2.03 1.31 ≤0.001 ≤0.001 0.3959

Mus musculus RIKEN cDNA 2610016F04 2610016 NM_025847 gene (2610016F04Rik), mRNA. F04Rik 1.69 -2.29 1.19 ≤0.001 ≤0.001 0.5870 cell division cycle 42 homolog (S. NM_009861 cerevisiae) Cdc42 1.68 -1.83 1.35 ≤0.001 ≤0.001 0.3588 NM_019744 nuclear receptor coactivator 4 Ncoa4 1.68 -2.61 1.65 ≤0.001 ≤0.001 0.1859 NM_021278 thymosin, beta 4, X chromosome Tmsb4x 1.67 -1.73 1.04 ≤0.001 ≤0.001 0.9009 ATPase, H+ transporting, lysosomal V1 NM_133826 subunit H Atp6v1h 1.67 -1.88 1.38 ≤0.001 0.0015 0.3469 NM_028058 FUN14 domain containing 1 Fundc1 1.67 -1.89 1.45 ≤0.001 ≤0.001 NA NM_145541 RAS-related protein-1a Rap1a 1.67 -1.97 1.52 ≤0.001 0.0011 0.1371 NM_013902 FK506 binding protein 3 Fkbp3 1.67 -2.27 1.63 ≤0.001 ≤0.001 0.2240 NM_019840 phosphodiesterase 4B, cAMP specific Pde4b 1.66 -1.51 -1.03 0.0011 0.0082 0.8036 NM_133781 calcium binding protein 39 Cab39 1.66 -2.2 -1.01 ≤0.001 ≤0.001 0.9399 NM_011883 ring finger protein 13 Rnf13 1.66 -2.7 1.47 0.0017 ≤0.001 0.3180 NM_008650 methylmalonyl-Coenzyme A mutase Mut 1.65 -2.17 1.55 ≤0.001 ≤0.001 0.1754 2410127 NM_026120 RIKEN cDNA 2410127L17 gene L17Rik 1.65 -2.4 1.79 ≤0.001 ≤0.001 0.1369 ATPase, H+ transporting, lysosomal V0 NM_009729 subunit C Atp6v0c 1.64 -2.13 1.25 0.0012 ≤0.001 0.3174 NM_010891 septin 2 sep2 1.64 -2.26 1.13 ≤0.001 ≤0.001 0.6653 NM_008249 transcription factor B2, mitochondrial Tfb2m 1.64 -2.34 1.43 ≤0.001 ≤0.001 0.3532 NM_009716 activating transcription factor 4 Atf4 1.63 -1.8 1.13 ≤0.001 0.0019 0.3561 NM_010431 hypoxia inducible factor 1, alpha subunit Hif1a 1.63 -1.94 1.37 0.0051 0.0061 0.2760 9430072 RIKEN cDNA 9430072K23 gene K23Rik 1.63 -2.03 -1.13 ≤0.001 ≤0.001 0.7172 9430010 RIKEN cDNA 9430010M06 gene M06Rik 1.63 -2.45 1.43 0.0015 ≤0.001 0.3909 NM_025369 mitochondrial ribosomal protein S36 Mrps36 1.63 -2.96 1.3 ≤0.001 ≤0.001 0.6083 NM_025887 RAB5A, member RAS oncogene family Rab5a 1.62 -2.49 1.03 0.0011 ≤0.001 0.9090 NM_023211 upregulated during skeletal muscle growth 5 Usmg5 1.62 -2.62 1.36 0.0036 ≤0.001 0.3057 A63008 RIKEN cDNA A630089N07 gene 9N07Rik 1.62 -4.01 1.29 0.0033 ≤0.001 0.5862 NM_134071 ankyrin repeat domain 32 Ankrd32 1.61 -1.8 1.53 ≤0.001 ≤0.001 0.2731 NM_008822 peroxisome biogenesis factor 7 Pex7 1.61 -2.02 1.24 0.0016 ≤0.001 0.3489 NM_019648 methionine aminopeptidase 2 Metap2 1.61 -2.29 1.21 ≤0.001 ≤0.001 0.3865 NM_029344 acylphosphatase 2, muscle type Acyp2 1.61 -2.6 1.51 0.0020 ≤0.001 0.2348 0610040 NM_023153 RIKEN cDNA 0610040D20 gene D20Rik 1.6 -1.64 1.19 ≤0.001 ≤0.001 0.6403 1700009 XM_131323 RIKEN cDNA 1700009N14 gene N14Rik 1.6 -1.65 1.24 0.0011 ≤0.001 0.3654 NM_194053 reticulon 4 Rtn4 1.6 -1.7 -1.58 0.0033 0.0053 0.0146

NM_009975 casein kinase 2, beta polypeptide Csnk2b 1.6 -1.7 1.46 0.0014 ≤0.001 0.2480 NM_016807 syndecan binding protein Sdcbp 1.6 -1.91 1.19 0.0020 0.0031 0.3450 XM_486002 trinucleotide repeat containing 6a Tnrc6a 1.59 -1.62 1.55 0.0038 0.0039 0.0400 Mitochondrial ATP8 NA 1.59 -1.79 1.07 0.0063 0.0018 0.7947 REX2, RNA exonuclease 2 homolog (S. NM_024233 cerevisiae) Rexo2 1.59 -2.09 1.53 0.0021 ≤0.001 0.2262 NM_019771 destrin Dstn 1.59 -2.11 1.27 0.0024 ≤0.001 0.3001 NM_011545 transcription factor 21 Tcf21 1.58 -1.63 1.22 ≤0.001 ≤0.001 0.5950 deoxynucleotidyltransferase, terminal, NM_153806 interacting protein 2 Dnttip2 1.58 -1.85 1.49 0.0034 0.0036 0.2309 NM_011464 serine protease inhibitor, Kunitz type 2 Spint2 1.58 -1.96 1.45 0.0016 ≤0.001 0.2983 NM_008300 heat shock protein 4 Hspa4 1.58 -2.05 1.46 0.0028 ≤0.001 0.2005 Tmem3 NM_133718 transmembrane protein 30A 0a 1.58 -2.61 1.59 0.0035 ≤0.001 0.2271 NM_016900 caveolin 2 Cav2 1.58 -3.1 1.23 0.0023 NA 0.7031 NM_009905 CDC-like kinase 1 Clk1 1.57 -2.03 2.02 0.0040 0.0024 0.0763

NM_028871 heterogeneous nuclear ribonucleoprotein R Hnrpr 1.57 -2.07 1.46 0.0027 ≤0.001 0.1605 capping protein (actin filament) muscle Z- NM_007604 line, alpha 2 Capza2 1.57 -2.34 1.19 0.0020 ≤0.001 0.3038 XM_483889 triadin Trdn 1.56 -1.67 1.16 0.0023 0.0013 0.6596 2410129 NM_175245 RIKEN cDNA 2410129H14 gene H14Rik 1.56 -1.95 1.34 0.0023 ≤0.001 0.1272 NM_023229 Fas-activated serine/threonine kinase Fastk 1.56 -2.27 -1.26 0.0036 ≤0.001 0.1367 A23004 XM_283480 RIKEN cDNA A230048G03 gene 8G03Rik 1.56 -2.52 1.32 0.0037 ≤0.001 0.3154 tumor necrosis factor (ligand) superfamily, NM_011614 member 12 Tnfsf12 1.55 -1.44 -1.24 ≤0.001 0.0015 0.1501 NM_009743 Bcl2-like 1 Bcl2l1 1.55 -1.57 -1.53 0.0026 0.0095 0.0085 NM_009011 RAD23b homolog (S. cerevisiae) Rad23b 1.55 -1.65 1.32 0.0022 0.0011 0.1113 NM_008543 MAD homolog 7 (Drosophila) Smad7 1.55 -1.66 1.16 0.0037 0.0030 0.5226 capping protein (actin filament) muscle Z- NM_007604 line, alpha 2 Capza2 1.55 -2.02 1.5 0.0019 ≤0.001 0.2951 XM_128924 RNA binding motif protein 27 Rbm27 1.55 -2.14 1.33 0.0025 0.0013 0.3545 NM_007483 ras homolog gene family, member B Rhob 1.55 -2.45 -1.08 0.0036 ≤0.001 0.8105 cysteine rich transmembrane BMP regulator XM_128751 1 (chordin like) Crim1 1.55 -2.61 1.46 0.0029 ≤0.001 0.2351 NM_172648 interferon activated gene 205 Ifi205 1.54 -1.62 1.51 0.0042 0.0039 0.3280 endothelial differentiation sphingolipid G- NM_007901 protein-coupled receptor 1 Edg1 1.54 -1.94 1.03 0.0074 0.0021 0.9142 NM_009817 calpastatin Cast 1.54 -2.05 1.2 0.0033 0.0022 0.2309 E430007 RIKEN cDNA E430007M08 gene M08Rik 1.54 -2.32 1.5 0.0027 ≤0.001 0.2121 neurobeachin like 1 Nbeal1 1.53 -1.57 1.3 0.0025 0.0049 0.4095 COP9 (constitutive photomorphogenic) NM_012001 homolog, subunit 4 (Arabidopsis thaliana) Cops4 1.53 -1.59 -1.02 0.0033 0.0061 0.8923 NM_008903 phosphatidic acid phosphatase 2a Ppap2a 1.53 -1.7 -1.08 0.0035 0.0029 0.6145 NM_019936 cysteine-rich PDZ-binding protein Cript 1.53 -1.93 1.57 0.0035 ≤0.001 0.0736 proteasome (prosome, macropain) subunit, NM_011970 beta type 2 Psmb2 1.53 -2.12 1.11 0.0050 ≤0.001 0.5556 NM_025527 signal recognition particle 19 Srp19 1.53 -2.44 1.66 0.0062 ≤0.001 0.2232 BC033468 dystonin Dst 1.52 -1.52 -1.04 0.0067 0.0047 0.7532 C13000 hypothetical protein C130006E23 6E23 1.52 -1.61 1.27 0.0030 0.0016 0.1517 061001 NM_133771 RIKEN cDNA 0610016J10 gene 6J10Rik 1.52 -1.63 -1.1 0.0064 0.0067 0.5267 acidic nuclear phosphoprotein 32 family, NM_130889 member B Anp32b 1.52 -1.73 1.33 0.0015 ≤0.001 0.3942 proteasome (prosome, macropain) 26S NM_178616 subunit, non-ATPase, 11 Psmd11 1.52 -1.88 1 0.0037 ≤0.001 0.9914 NM_026218 FGFR1 oncogene partner 2 Fgfr1op2 1.52 -2.57 1.33 0.0026 ≤0.001 0.4302 NM_016807 syndecan binding protein Sdcbp 1.51 -1.53 -1.03 0.0045 0.0051 0.8173

SMT3 suppressor of mif two 3 homolog 3 NM_019929 (yeast) Sumo3 1.51 -1.54 -1.06 0.0039 0.0066 0.6977 MAP kinase-interacting serine/threonine NM_021462 kinase 2 Mknk2 1.51 -2.01 -1.24 0.0046 ≤0.001 0.1535 DnaJ (Hsp40) homolog, subfamily C, NM_026332 member 19 Dnajc19 1.51 -2.12 1.49 0.0043 ≤0.001 0.3226 mitogen-activated protein kinase kinase 1 Map2k1 NM_019920 interacting protein 1 ip1 1.51 -2.62 1.43 0.0037 ≤0.001 0.3189 protein tyrosine phosphatase, non-receptor NM_008977 type 2 Ptpn2 1.5 -1.59 1.28 0.0072 0.0058 0.2303 BC0226 NM_177632 cDNA sequence BC022623 23 1.5 -1.66 1.44 0.0043 0.0016 0.1535 XM_488538 SET binding factor 2 Sbf2 1.5 -1.74 1.34 0.0052 ≤0.001 0.2463 ATPase, H+ transporting, lysosomal V1 NM_133699 subunit C2 Atp6v1c2 1.5 -1.8 1.02 0.0062 0.0019 0.8944 TAF7 RNA polymerase II, TATA box NM_011901 binding protein (TBP)-associated factor Taf7 1.5 -1.83 1.11 0.0080 0.0042 0.3797 NHP2 non-histone chromosome protein 2- NM_011482 like 1 (S. cerevisiae) Nhp2l1 1.5 -1.87 1.63 0.0046 0.0013 0.0679 transmembrane emp24 protein transport NM_026211 domain containing 9 Tmed9 1.5 -2.04 -1.16 0.0059 ≤0.001 0.2525 NM_025517 RNA terminal phosphate cyclase domain 1 Rtcd1 1.5 -2.1 1.6 0.0063 ≤0.001 0.1710 3100002 XM_355388 RIKEN cDNA 3100002L24 gene L24Rik 1.5 -2.15 1.54 0.0049 ≤0.001 0.2352 6230416 XM_127325 RIKEN cDNA 6230416A05 gene A05Rik 1.5 -2.15 1.31 0.0045 ≤0.001 0.2884 proteasome (prosome, macropain) subunit, NM_011965 alpha type 1 Psma1 1.5 -2.64 1.43 0.0053 ≤0.001 0.3351 cell division cycle 42 homolog (S. NM_009861 cerevisiae) Cdc42 1.5 -2.85 -1.02 0.0048 ≤0.001 0.9376 NM_011545 transcription factor 21 Tcf21 1.49 -1.6 1.4 0.0033 ≤0.001 0.1605 heat shock protein 90kDa alpha (cytosolic), Hsp90a NM_008302 class B member 1 b1 1.49 -1.74 -1.11 0.0075 0.0026 0.3611 NM_145131 pitrilysin metallepetidase 1 Pitrm1 1.49 -1.81 1.31 0.0062 0.0074 0.3470 NM_007592 carbonic anhydrase 8 Car8 1.49 -1.91 1.14 0.0035 ≤0.001 0.7204 NM_134079 NM_007411 adenosine kinase Adk 1.49 -2 -1.25 0.0084 ≤0.001 0.1825 5230400 NM_029409 RIKEN cDNA 5230400G24 gene G24Rik 1.49 -2.76 1.59 0.0068 ≤0.001 0.3121

XM_136364 dual specificity phosphatase 27 (putative) Dusp27 1.48 -1.4 -1.49 0.0014 0.0024 0.0209 NM_023311 Yip1 domain family, member 5 Yipf5 1.48 -1.72 1.49 0.0072 0.0047 0.0465 NM_020050 TMEM9 domain family, member B Tmem9b 1.48 -1.73 -1.05 0.0063 0.0013 0.8197 NM_018798 ubiquilin 2 Ubqln2 1.48 -2.01 1.32 0.0025 ≤0.001 0.4711 NM_011992 reticulocalbin 2 Rcn2 1.48 -2.04 1.25 0.0070 ≤0.001 0.1908 NM_013697 transthyretin Ttr 1.47 1.37 1.42 0.0029 0.0055 0.0597 NM_008722 nucleophosmin 1 Npm1 1.47 -1.66 1.48 0.0098 0.0047 0.1248 NM_025942 GTP-binding protein 9 (putative) Gtpbp9 1.47 -1.7 1.32 0.0027 ≤0.001 0.4982 potassium voltage-gated channel, Isk- NM_008424 related subfamily, member 1 Kcne1 1.46 1.57 1.37 0.0069 0.0025 0.0290 arsA (bacterial) arsenite transporter, ATP- NM_019652 binding, homolog 1 Asna1 1.46 -1.56 1.1 0.0085 0.0086 0.3846 guanosine diphosphate (GDP) dissociation NM_008112 inhibitor 2 Gdi2 1.46 -1.56 -1.04 0.0051 0.0011 0.7539 NM_009005 RAB7, member RAS oncogene family Rab7 1.46 -1.61 -1.03 0.0070 0.0020 0.8069 protein-L-isoaspartate (D-aspartate) O- NM_008786 methyltransferase 1 Pcmt1 1.46 -1.63 -1.03 0.0095 0.0052 0.8242 NM_021884 tumor susceptibility gene 101 Tsg101 1.46 -1.77 1.2 0.0087 ≤0.001 0.3222 NM_146062 periphilin 1 Pphln1 1.46 -1.88 1.13 0.0042 ≤0.001 0.6760 platelet-activating factor acetylhydrolase, Pafah1 NM_013625 isoform 1b, beta1 subunit b1 1.46 -1.93 1 0.0080 ≤0.001 0.9859 NM_013590 P lysozyme structural Lzp-s 1.46 -1.95 1.25 0.0065 ≤0.001 0.5779 NM_007664 cadherin 2 Cdh2 1.46 -2.07 1.14 0.0092 0.0014 0.5367 1110001 RIKEN cDNA 1110001A16 gene A16Rik 1.46 -2.2 1.3 0.0075 ≤0.001 0.4039

100

NM_172677 YTH domain family 3 Ythdf3 1.46 -2.85 1.21 0.0069 ≤0.001 0.5070 AK081365 A kinase (PRKA) anchor protein 2 Akap2 1.45 -1.51 -1.04 0.0071 0.0084 0.8092 BC0038 NM_198609 cDNA sequence BC003885 85 1.45 -1.54 -1.1 0.0076 0.0067 0.5070 NM_008188 THUMP domain containing 3 Thumpd3 1.45 -1.89 1.27 0.0045 ≤0.001 0.4689 NM_025673 golgi phosphoprotein 3 Golph3 1.45 -2.42 1.12 0.0065 ≤0.001 0.6272 1300007 NM_175119 RIKEN cDNA 1300007C21 gene C21Rik 1.44 -1.51 NA 0.0035 0.0019 NA nuclear receptor subfamily 4, group A, NM_010444 member 1 Nr4a1 1.44 -1.8 1.07 0.0089 0.0018 0.7457 NM_029271 mitochondrial ribosomal protein L32 Mrpl32 1.44 -2.27 1.37 0.0073 ≤0.001 0.3907 AK019477 dystrobrevin alpha Dtna 1.43 -1.31 1.08 0.0022 0.0082 0.6607 NM_144948 RNA binding motif protein 7 Rbm7 1.43 -1.67 1.47 0.0088 0.0035 0.1537 NM_198303 eukaryotic translation initiation factor 5B Eif5b 1.43 -2 1.12 0.0082 ≤0.001 0.4639 proteasome (prosome, macropain) 26S NM_025959 subunit, ATPase, 6 Psmc6 1.43 -2.22 1.51 0.0098 ≤0.001 0.1834 9530046 RIKEN cDNA 9530046B11 gene B11Rik 1.42 -1.39 1.13 0.0091 0.0088 0.6779 NM_012006 acyl-CoA thioesterase 1 Acot1 1.42 -1.5 1.23 0.0042 ≤0.001 0.3851 B-cell translocation gene 1, anti- NM_007569 proliferative Btg1 1.42 -1.69 1.02 0.0092 0.0034 0.9205 type 1 tumor necrosis factor receptor NM_030711 shedding aminopeptidase regulator Arts1 1.42 -1.97 1.53 0.0076 ≤0.001 0.2798 tumor necrosis factor receptor superfamily, Tnfrsf1 NM_011609 member 1a a 1.4 -1.79 -1.14 0.0085 ≤0.001 0.2362 NM_009272 spermidine synthase Srm 1.39 -1.33 -1.2 0.0063 0.0072 0.1685 PRP38 pre-mRNA processing factor 38 NM_172697 (yeast) domain containing A Prpf38a 1.37 -1.65 1.03 0.0088 ≤0.001 0.8438 1700022 NM_029602 RIKEN cDNA 1700022C21 gene C21Rik -1.4 1.57 -1.11 0.0092 ≤0.001 0.5755 NM_012017 zinc finger protein 346 Zfp346 -1.4 1.44 1.09 0.0074 0.0012 0.4289 NM_008702 nemo like kinase Nlk -1.41 1.49 1 0.0095 0.0032 0.9699 solute carrier family 16 (monocarboxylic NM_009197 acid transporters), member 2 Slc16a2 -1.41 1.49 -1.26 0.0047 0.0011 0.0863 NM_145512 SFT2 domain containing 2 Sft2d2 -1.42 1.42 -1.11 0.0098 0.0062 0.5243 NM_020496 T-box 20 Tbx20 -1.43 1.41 -1.04 0.0053 0.0090 0.8232 533043 NM_175407 RIKEN cDNA 5330439J01 gene 9J01Rik -1.43 1.33 -1.24 0.0075 0.0088 0.1464 NM_031874 RAB3D, member RAS oncogene family Rab3d -1.44 1.51 1.17 0.0094 0.0090 0.1720 sema domain, immunoglobulin domain (Ig), NM_009153 short basic domain, secreted, (semaphorin) 3B Sema3b -1.44 1.38 -1.12 0.0031 0.0031 0.3670 NM_173364 zinc finger protein 445 Zfp445 -1.45 1.95 1.01 0.0057 ≤0.001 0.9459 XM_483975 oxysterol binding protein 2 Osbp2 -1.46 1.83 1.31 0.0087 ≤0.001 0.0618 B23031 NM_172740 RIKEN cDNA B230312I18 gene 2I18Rik -1.46 1.4 -1.18 0.0028 0.0021 0.1575 NM_025339 transmembrane protein 42 Tmem42 -1.47 1.52 -1.01 0.0061 0.0026 0.9287 NM_013761 serine racemase Srr -1.48 1.74 -1.05 0.0063 0.0020 0.7470 NM_174874 autophagy-related 4B (yeast) Atg4b -1.48 1.39 -1 0.0018 0.0032 0.9907 NM_153580 coiled-coil domain containing 95 Ccdc95 -1.49 1.72 -1.07 0.0045 ≤0.001 0.5905 NM_016745 ATPase, Ca++ transporting, ubiquitous Atp2a3 -1.49 1.4 -1.32 0.0040 0.0076 0.0591 NM_173745 dual specificity phosphatase 18 Dusp18 -1.5 1.54 -1.02 0.0028 0.0017 0.9062 XM_132501 PDGFA associated protein 1 Pdap1 -1.51 2.46 1.08 0.0044 ≤0.001 0.6280 B93000 NM_175446 RIKEN cDNA B930008K04 gene 8K04Rik -1.51 1.42 -1.22 0.0022 0.0017 0.0780 tensin 1 Tns1 -1.52 1.91 1.12 0.0056 ≤0.001 0.3766 1600010 NM_198016 RIKEN cDNA 1600010O03 gene O03Rik -1.52 1.78 1.09 0.0081 0.0021 0.5863 NM_181411 aftiphilin Aftph -1.52 1.56 -1 0.0038 0.0098 0.9876 WW domain containing transcription NM_133784 regulator 1 Wwtr1 -1.53 1.98 1.59 0.0062 0.0022 0.0092 AP2 associated kinase 1 Aak1 -1.53 1.92 1.01 0.0045 ≤0.001 0.9505

101

Mus musculus 7 days neonate cerebellum cDNA, RIKEN full-length enriched library, clone:A730032N19 product:unknown EST, AK042872 full insert sequence. NA -1.53 1.54 -1.18 0.0028 0.0029 0.1496 NM_010518 insulin-like growth factor binding protein 5 Igfbp5 -1.54 1.8 1.29 0.0033 0.0023 0.0663 NM_009974 casein kinase 2, alpha prime polypeptide Csnk2a2 -1.54 1.59 -1.17 0.0012 0.0011 0.2866 NM_019467 allograft inflammatory factor 1 Aif1 -1.55 1.47 -1.21 0.0018 0.0025 0.1201 1810014 RIKEN cDNA 1810014B01 gene B01Rik -1.55 1.3 1.02 0.0011 0.0090 0.9010 4930521 NM_026260 RIKEN cDNA 4930521E07 gene E07Rik -1.56 1.33 -1.42 ≤0.001 0.0047 0.0375 2700060 NM_026528 RIKEN cDNA 2700060E02 gene E02Rik -1.57 1.77 -1.23 0.0035 0.0021 0.1263 NM_027903 dihydrodiol dehydrogenase (dimeric) Dhdh -1.57 1.51 -1.05 0.0021 0.0075 0.7494

immunoglobulin heavy chain 1a (serum XM_484178 IgG2a) Igh-1a -1.58 1.85 1.45 0.0023 ≤0.001 0.0268 BCL2/adenovirus E1B interacting protein NM_009761 3-like Bnip3l -1.59 1.58 -1.17 0.0030 0.0028 0.1777 2010305 NM_027250 RIKEN cDNA 2010305A19 gene A19Rik -1.59 1.52 1.06 0.0018 0.0012 0.6171 NM_144903 aldolase 2, B isoform Aldob -1.6 2.09 1.03 0.0013 ≤0.001 0.9168 Fas (TNFRSF6)-associated via death NM_010175 domain Fadd -1.6 1.54 -1.25 0.0011 0.0030 0.1684 XM_129145 tetratricopeptide repeat domain 9C Ttc9c -1.6 1.48 -1.08 0.0016 0.0084 0.5511 NM_007841 DEAD (Asp-Glu-Ala-Asp) box polypeptide 6 Ddx6 -1.61 2.25 1.07 0.0014 ≤0.001 0.5305 NM_019832 G kinase anchoring protein 1 Gkap1 -1.61 1.72 -1.08 0.0013 0.0030 0.5136 NM_007754 carboxypeptidase D Cpd -1.62 1.74 -1.2 0.0021 ≤0.001 0.1326 CDC42 effector protein (Rho GTPase Cdc42e NM_026514 binding) 3 p3 -1.62 1.55 1.08 0.0011 0.0065 0.5600 adenylate cyclase activating polypeptide 1 Adcyap NM_007407 receptor 1 1r1 -1.62 1.41 -1.16 ≤0.001 0.0034 0.2106 NM_024196 TBC1 domain family, member 20 Tbc1d20 -1.63 1.57 -1.02 0.0020 0.0067 0.8889 protein phosphatase 1, regulatory (inhibitor) Ppp1r1 subunit 12B 2b -1.65 1.55 -1.1 ≤0.001 0.0042 0.4268 NM_178051 MTERF domain containing 2 Mterfd2 -1.69 1.48 -1.09 ≤0.001 0.0094 0.4815 PRP19/PSO4 pre-mRNA processing factor NM_134129 19 homolog (S. cerevisiae) Prpf19 -1.7 1.6 -1.01 ≤0.001 0.0083 0.9420 NM_011675 uridine-cytidine kinase 1 Uck1 -1.7 1.55 -1.14 ≤0.001 0.0076 0.3038

NM_017397 DEAD (Asp-Glu-Ala-Asp) box polypeptide 20 Ddx20 -1.71 1.37 -1.27 ≤0.001 0.0095 0.0936 NM_013768 protein arginine N-methyltransferase 5 Prmt5 -1.73 2.09 1.11 ≤0.001 ≤0.001 0.4631 2900054 RIKEN cDNA 2900054D09 gene D09Rik -1.73 1.48 -1.21 ≤0.001 0.0077 0.2656 NM_001004 293 adhesion molecule with Ig like domain 1 Amigo1 -1.74 1.4 -1.05 ≤0.001 0.0097 0.6281 phosphatidylinositol 3-kinase, regulatory NM_011085 subunit, polypeptide 1 (p85 alpha) Pik3r1 -1.76 1.72 -1.16 ≤0.001 0.0046 0.3380 NM_008720 Niemann Pick type C1 Npc1 -2.03 1.81 -1.43 ≤0.001 ≤0.001 0.0423 6330407 RIKEN cDNA 6330407A06 gene A06Rik -2.07 1.55 -1.13 ≤0.001 ≤0.001 0.3888

NM_183023 regulating synaptic membrane exocytosis 4 Rims4 -2.09 2.51 -1.03 ≤0.001 ≤0.001 0.8500

102

Table 5. Genes significantly regulated by NF-κB in late IPC

Genes significantly up or down-regulated in both WT IPC vs. Sham and WT vs. 2M IPC represent genes whose expression is altered in an NF-κB-dependent manner in late IPC.

This table provides a detailed list of the 238 unique genes identified as meeting this criteria. Genes are listed in descending order of fold change in WT IPC vs. Sham.

103

Table 6. Gene ontology categorization of genes regulated by NF-κB in late IPC

Gene Ontology Term Count P-Value Fold Enrichment GO:0009408~response to heat 6 0.00004 15.35 GO:0009266~response to temperature stimulus 6 0.00028 10.23 GO:0051789~response to protein stimulus 6 0.00141 7.22 GO:0006986~response to unfolded protein 6 0.00141 7.22 GO:0001525~angiogenesis 9 0.00054 4.85 GO:0048514~blood vessel morphogenesis 10 0.00062 4.22 GO:0048646~anatomical structure formation 10 0.00091 3.99 GO:0009628~response to abiotic stimulus 8 0.00643 3.64 GO:0001568~blood vessel development 10 0.00174 3.64 GO:0032787~monocarboxylic acid metabolic process 10 0.00191 3.59 GO:0001944~vasculature development 10 0.00191 3.59 GO:0006457~protein folding 9 0.00430 3.49 GO:0006928~cell motility 12 0.00499 2.70 GO:0051674~localization of cell 12 0.00499 2.70 GO:0042981~regulation of apoptosis 14 0.00420 2.49 GO:0043067~regulation of programmed cell death 14 0.00468 2.45 GO:0009893~positive regulation of metabolic process 13 0.00929 2.35 GO:0015031~protein transport 20 0.00101 2.31 GO:0019752~carboxylic acid metabolic process 14 0.00895 2.26 GO:0006915~apoptosis 19 0.00179 2.26 GO:0006082~organic acid metabolic process 14 0.00909 2.26 GO:0012501~programmed cell death 19 0.00216 2.22 GO:0045184~establishment of protein localization 20 0.00197 2.18 GO:0046907~intracellular transport 19 0.00315 2.15 GO:0008219~cell death 19 0.00320 2.14 GO:0016265~death 19 0.00329 2.14 GO:0048523~negative regulation of cellular process 25 0.00063 2.12 GO:0008104~protein localization 21 0.00205 2.12 GO:0048519~negative regulation of biological process 26 0.00070 2.07 GO:0033036~macromolecule localization 21 0.00320 2.04 GO:0048522~positive regulation of cellular process 22 0.00292 2.01 GO:0048518~positive regulation of biological process 25 0.00146 2.00 GO:0051649~establishment of cellular localization 21 0.00613 1.92 GO:0051641~cellular localization 21 0.00767 1.88 GO:0048468~cell development 26 0.00777 1.73 GO:0051179~localization 57 0.00043 1.54 GO:0016043~cellular component organization and 50 0.00162 1.52 biogenesis GO:0006810~transport 47 0.00475 1.47

104

GO:0051234~establishment of localization 47 0.00790 1.43 GO:0019538~protein metabolic process 56 0.00487 1.40 GO:0050794~regulation of cellular process 63 0.00581 1.35 GO:0050789~regulation of biological process 68 0.00616 1.32 GO:0044238~primary metabolic process 114 0.00009 1.30 GO:0008152~metabolic process 123 0.00015 1.25 GO:0044237~cellular metabolic process 110 0.00087 1.25

105

Table 6. Gene ontology categorization of genes regulated by NF-κB in late IPC

Functional annotation of the 238 NF-κB-dependent genes in IPC resulted in a significant enrichment for GO terms associated with heat shock response, angiogenesis, programmed cell death. Gene ontology groups are listed in order of descending fold enrichment.

106

Figure 5. Functional ontology clustering of genes regulated by NF-κB in late IPC

107

Figure 5. Functional ontology clustering of genes regulated by NF-κB in late IPC

Functional annotation of the 238 NF-κB regulated genes post-IPC resulted in a significant enrichment (P ≤ 0.01) for ontology terms associated with angiogenesis, programmed cell death, and heat shock response. A gene ontology similarity score of ≥ 0.75 and a P value

≤ 0.01 were used as the criteria for ontology clustering into the associated groupings of angiogenesis, programmed cell death, and heat shock response.

108

Figure 6. Individual genes contained within NF-κB regulated gene ontology clusterings

Genes contained within angiogenesis associated GO Terms Fold Expression P-value WT IPC 2M v. WT IPC 2M v. Symbol Gene Name v. Sham WT IPC v. Sham WT IPC hif1a hypoxia inducible factor 1, alpha subunit 1.63 -1.94 0.00507 0.00616 btg1 b-cell translocation gene 1, anti-proliferative 1.42 -1.69 0.00924 0.00343 serine (or cysteine) peptidase inhibitor, clade e, serpine1 member 1 2.29 -2.08 0.00001 0.01353 rhob ras homolog gene family, member b 1.55 -2.45 0.00363 0.00009 cyr61 cysteine rich protein 61 3.91 -2.34 < 0.00001 0.00518 rtn4 reticulon 4 1.60 -1.70 0.00331 0.00533 tbx20 t-box 20 -1.43 1.41 0.00532 0.00907 tumor necrosis factor (ligand) superfamily, tnfsf12 member 12 1.55 -1.44 0.00073 0.00159 endothelial differentiation sphingolipid g- edg1 protein-coupled receptor 1 1.54 -1.94 0.00749 0.00213 cdh2 cadherin 2 1.46 -2.07 0.0092 0.00145 platelet-activating factor acetylhydrolase, pafah1b1 isoform 1b, beta1 subunit 1.46 -1.93 0.00807 0.00025

Genes contained within programmed cell death associated GO Terms Fold Expression P-value WT IPC 2M v. WT IPC 2M v. Symbol Gene Name v. Sham WT IPC v. Sham WT IPC fadd fas (tnfrsf6)-associated via death domain -1.60 1.54 0.00118 0.00304 hspa1b heat shock protein 1b 8.53 -4.00 < 0.00001 NA bnip3l bcl2/adenovirus e1b interacting protein 3-like -1.59 1.58 0.00309 0.00283 hif1a hypoxia inducible factor 1, alpha subunit 1.63 -1.94 0.00507 0.00616 btg1 b-cell translocation gene 1, anti-proliferative 1.42 -1.69 0.00924 0.00343 bcl2l1 bcl2-like 1 1.55 -1.57 0.00262 0.00958 itm2b integral membrane protein 2b 2.07 -4.22 0.00002 0.00001 zfp346 zinc finger protein 346 -1.40 1.44 0.00743 0.0012 rhob ras homolog gene family, member b 1.55 -2.45 0.00363 0.00009 igh-1a immunoglobulin heavy chain 1a (serum igg2a) -1.58 1.85 0.00231 0.00015 btg2 b-cell translocation gene 2, anti-proliferative 2.67 -3.62 0.00003 NA rtn4 reticulon 4 1.60 -1.70 0.00331 0.00533 myc myelocytomatosis oncogene 3.55 -1.71 NA 0.00484 tumor necrosis factor (ligand) superfamily, tnfsf12 member 12 1.55 -1.44 0.00073 0.00159 nuclear receptor subfamily 4, group a, member nr4a1 1 1.44 -1.8 0.00891 0.00188 tumor necrosis factor receptor superfamily, tnfrsf1a member 1a 1.4 -1.79 0.00856 0.00008 phosphatidylinositol 3-kinase, regulatory pik3r1 subunit, polypeptide 1 (p85 alpha) -1.76 1.72 0.00079 0.00461 fastk fas-activated serine/threonine kinase 1.56 -2.27 0.0036 0.00011 ddx20 dead (asp-glu-ala-asp) box polypeptide 20 -1.71 1.37 0.0003 0.00951 platelet-activating factor acetylhydrolase, pafah1b1 isoform 1b, beta1 subunit 1.46 -1.93 0.00807 0.00025

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actg1 actin, gamma, cytoplasmic 1 2.04 -2.91 0.00013 0.00029 amigo1 adhesion molecule with ig like domain 1 -1.74 1.4 0.00022 0.0097 potassium voltage-gated channel, isk-related kcne1 subfamily, member 1 1.46 1.57 0.0069 0.00255 cav2 caveolin 2 1.58 -3.1 0.00239 NA prp19/pso4 pre-mrna processing factor 19 prpf19 homolog (s. cerevisiae) -1.7 1.6 0.00095 0.00837 pex7 peroxisome biogenesis factor 7 1.61 -2.02 0.00165 0.00028

Genes contained within heat shock response associated GO Terms Fold Expression P-value WT IPC 2M v. WT IPC 2M v. Symbol Gene Name v. Sham WT IPC v. Sham WT IPC hspa4 heat shock protein, 110 kda 1.58 -2.05 0.00286 0.00014 hspa1b heat shock protein 1b 8.53 -4 < 0.00001 NA hsp90ab1 heat shock protein 1, beta 1.49 -1.74 0.00755 0.00263 dnaja1 dnaj (hsp40) homolog, subfamily a, member 1 2.48 -2.06 < 0.00001 0.00657 hspd1 heat shock protein 1 (chaperonin) 1.75 -2.13 0.0004 0.00118 hsp90aa1 heat shock protein 1, alpha 3.08 -2.07 < 0.00001 0.02907 nfe2l2 nuclear factor, erythroid derived 2, like 2 1.94 -2.66 0.00011 0.0004 ttc9c tetratricopeptide repeat domain 9c -1.6 1.48 0.00164 0.00846 fkbp3 fk506 binding protein 3 1.67 -2.27 0.00076 0.00013 dnajb4 dnaj (hsp40) homolog, subfamily b, member 4 2.72 -2.83 < 0.00001 0.00038 bcl2l1 bcl2-like 1 1.55 -1.57 0.00262 0.00958 myc myelocytomatosis oncogene 3.55 -1.71 NA 0.00484

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Figure 6. Individual genes contained within NF-κB regulated gene ontology clusterings

A list of all the individual genes comprising the three predominant NF-κB regulated gene ontology groupings of angiogenesis, programmed cell death, and response to heat shock.

Genes in blue are found in both angiogenesis and programmed cell death ontology categories. Genes in red are found in both programmed cell death and response to heat shock ontology categories. No individual gene was present in all three of the ontology groupings.

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Figure 7. QRT-PCR mRNA expression validation of select NF-κB-dependent genes in late IPC

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Figure 7. QRT-PCR mRNA expression validation of select NF-κB-dependent genes in late IPC

QRT-PCR expression analysis (red bars) was used to confirm the microarray detected gene expression patterns (blue bars) after IPC in WT and 2M mice for HIF-1α, Hsp90,

Fadd, and COX-2. mRNA expression levels are displayed as fold change relative to the

WT sham treated group (horizontal line corresponding to fold change of 1.0). * P ≤ 0.05 vs. WT Sham. N = 6-8 per group. # P ≤ 0.05 vs. WT IPC.

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III.1.2 NF-κB Regulation of the Heat Shock Response After Late IPC

Analysis of the NF-κB-dependent genes and gene ontologies following IPC indicated that NF-κB may be a central regulator of the heat shock response after IPC. The most significantly enriched gene ontology among the 238 genes regulated by NF-κB after

IPC was response to heat (Fold enrichment ~ 15.3, P ≤ 0.0001) (Fig. 5). Additionally, the hspa1b gene (encoding for the Hsp70.1 protein) was the most strongly induced gene after

IPC (8.53 fold increase in WT IPC vs. sham, P ≤ 0.001) and showed a strong NF-κB- dependent effect in its expression (decreased 4.0 fold in 2M vs. WT IPC, P-value not available due to a loss of quantifiable data from one or more microarray chips). Hspa1b was also one of three genes whose function overlapped between both the cell death and heat shock response related gene ontologies (Fig. 6).

QRT-PCR was used to confirm an NF-κB-dependent increase in mRNA expression in Hsp70.1 following IPC (Fig. 8). Since Hsp70.1 (hspa1b) is only one of two stress-inducible Hsp70 genes expressed in the myocardium, the expression change of

Hsp70.3 (hspa1a) following IPC was also examined. Although the expression change of

Hsp70.3 after IPC narrowly missed being significant according to the statistical cutoff applied to the microarrays (1.99 fold increase in WT IPC vs. sham, P-value NA), QRT-

PCR also confirmed a large NF-κB-dependent upregulation of the expression of this gene as well following IPC (Fig. 8).

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Figure 8. NF-κB-dependent expression of Hsp70.1 and Hsp70.3 mRNA in late IPC

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Figure 8. NF-κB-dependent expression of Hsp70.1 and Hsp70.3 mRNA in late IPC

QRT-PCR analysis of Hsp70.1 and Hsp70.3 expression in changes in WT and 2M mice following IPC confirmed that mRNA expression of both genes was strongly induced follwing IPC. Inhibition of NF-κB activation in the 2M mouse model significantly blunted the increase in expression of both genes indicating NF-κB-dependent expression.

*P ≤ 0.05 vs. WT Sham. N = 6-8 per group. #P ≤ 0.05 vs. WT IPC.

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III.1.3 Hsp70.3 is an NF-κB-dependent Mediator of Late IPC Cardioprotection

Given the substantial increase in expression of the two Hsp70 genes following

IPC (44.8 and 26.5 fold increase in WT IPC vs. sham for 70.1 and 70.3, respectively) and the NF-κB-dependency of the expression of these two genes, we sought to delineate the role that each of these two stress inducible genes may play in the cardioprotection of late

IPC. Mice lacking both the Hsp70.1 and 70.3 genes were completely lacking of late phase IPC protection (45.5% infarct in double-knockout after IPC; 38.8% infarct in sham group; P = 0.29) (Fig. 9A). This result is in accordance with previously published work showing that knockout of both the Hsp70 stress inducible genes abrogates the late phase of IPC cardioprotection.260 However, late phase IPC cardioprotection appears to be fully intact in Hsp70.1 single knockout mice (24.9% infarct in Hsp70.1 KO; 41.8% infarct in sham group; P ≤ 0.05) (Fig. 9B). This result indicates that Hsp70.1 does not play a role in the cardioprotection provided by late phase IPC. Thus, Hsp70.3 is the stress inducible gene of Hsp70 that plays a critical role in the development of the cardioprotective effects afforded by late IPC. There were no detectable differences between the ischemic risk regions in any of the groups (Fig. 9C). The wild-type mice used for this study were background matched for the Hsp70.1/3 KO and Hsp70.1 KO (B6/129 and C57, respectively).

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Figure 9. Effect of Hsp70.1 KO and Hsp70.1/70.3 KO on late IPC cardioprotection

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Figure 9. Effect of Hsp70.1 KO and Hsp70.1/70.3 KO on late IPC cardioprotection

Infarct analysis confirmed that a double-knockout of both Hsp70.1 and Hsp70.3 completely abrogates late IPC cardioprotection (A). Late phase IPC cardioprotection is maintained when only the Hsp70.1 gene is knocked out. There were no significant differences detected between the wild-type groups of the two different mouse strains (B).

There were also no significant differences in the sizes of the regions at risk between the groups (C). N = 6-11 per group. *P ≤ 0.05 vs. corresponding WT sham group.

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Hsp70.3 and Hsp70.1 are not functionally redundant in the myocardium

To investigate the possibility that cardioprotection is maintained after late IPC in the Hsp70.1 single knockout animals due to functional redundance of the two Hsp70 genes, we also assessed the role of these two genes in I/R injury in the absence of IPC.

Knockout of both Hsp70.1 and Hsp70.3 genes led to an increase in infarct size following

I/R injury (30 min ischemia followed by 24h reperfusion; 40.8% infarct in KO vs. 15.5% infarct in WT; P ≤ 0.05) (Fig. 10). This confirmed that at least one of the inducible Hsp70 genes serves a cardioprotective role. Surprisingly, knockout of only Hsp70.1 reduced infarct size following I/R injury (30 min ischemia followed by 24h reperfusion; 4.4% infarct in KO vs. 20.5% infarct in WT; P ≤ 0.05) (Fig. 10). This result indicates that

Hsp70.1 promotes injury following I/R injury. Further, this result corroborates the conclusion that the two Hsp70 genes serve different roles in cardiac ischemia. Hsp70.3, but not Hsp70.1, contributes to cardioprotection after the late phase of IPC (as well as after I/R) while Hsp70.1 is not necessary for the development of late IPC cardioprotection and actually contributes to cardiac injury following I/R.

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Figure 10. Effect of Hsp70.1 KO and Hsp70.1/70.3 KO on I/R injury

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Figure 10. Effect of Hsp70.1 KO and Hsp70.1/70.3 KO on I/R injury

Knockout of both Hsp70.1 and 70.3 increased infarct size after I/R injury (4th bar); whereas, knockout of only Hsp70.1 decreased infarct size following I/R (2nd bar). C57 and B6/129 groups serve as wild-type control groups for Hsp70.1/3 double KO and

Hsp70.1 KO, respectively. There were no significant differences detected between the wild-type groups of the two different strains. N = 6-11 per group. *P ≤ 0.05 vs. corresponding WT sham group.

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Section 2. Transcriptional Co-Regulation of Hsp70.3 Gene Expression

in Late Ischemic Preconditioning

III.2.1 Evidence for Cooperative Regulation of Hsp70.3 Gene Expression in Late IPC

Our results show that Hsp70.3 gene expression is controlled by NF-κB following late IPC (Fig. 8). However, inhibition of NF-κB activation only resulted in a ~50% decrease in Hsp70.3 mRNA expression indicating that NF-κB is not the only regulator of

Hsp70.3 gene expression following late IPC. Therefore, our results indicate potential co- regulation of Hsp70.3 gene expression by additional transcriptional mediators in late IPC.

Obvious candidates for the co-regulation of Hsp70.3 gene expression in late IPC are the transcription factors HSF-1, STAT3, and AP-1 since these three factors have already been shown to be involved in both regulation of Hsp70 gene expression and cardioprotective processes.

Analysis of predicted transcriptional binding motifs within the Hsp70.3 promoter identified binding sites for NF-kB as well as HSF-1, AP-1, and STAT3 (Fig. 11). Each of these factors has been previously implicated in late IPC cardioprotection, but their direct role on Hsp70.3 gene expression in the heart has not yet been demonstrated. Thus, the rationale of the work in this section is that Hsp70.3 gene expression is cooperatively regulated by NF-κB, HSF-1, AP-1, and STAT3.

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Figure 11. Promoter analysis of Hsp70.3 and Hsp70.1 transcription factor binding sites

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Figure 11. Promoter analysis of Hsp70.3 and Hsp70.1 transcription factor binding sites

The first 1000 bases upstream of the transcriptional start for the Hsp70.1 and Hsp70.3 genes were analyzed with regard to predicted transcriptional binding sites for HSF-1, NF-

κB, STAT3, and AP-1 using the MatInspector algorithm of transcriptional matrices.318

The two genes share sequence homology and predicted transcriptional binding sites for the first 250 bases upstream of their transcriptional start sites (represented by the red box).

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III.2.2 Regulation of Hsp70.3-Luciferase Reporters

To study the regulation of Hsp70.3 expression in a high-throughput manner in vitro, we developed an expression reporter construct using the 3.94 kb of the endogenous

Hsp70.3 promoter sequence to drive expression of luciferase (Section II.18, Fig. 2). As expected, these reporters resulted in a dose-dependent expression of luciferase in primary murine embryonic fibroblast (MEF) cells with an expression increase of between 10 and

100 fold following HS (Fig. 12). Since Hsp70.3 expression is also observed following I/R in an NF-κB-dependent manner (Michael Wilhide; unpublished data), we utilized an in vitro model of simulated I/R to examine Hsp70.3 reporter expression in H9c2 cardiac myoblast cells (Section II.11). Results show that simulated I/R resulted in 50% cell death in H9c2 cells (Fig. 13A). Simulated I/R also induced expression of the Hsp70.3 reporter that was accompanied by an increase in NF-κB, AP-1, and STAT3 activity (Fig. 13B-C).

We examined the role of NF-κB, AP-1, and STAT3 activation on Hsp70.3 reporter expression through the use of transcription factor decoys to specifically block transcriptional activation of each factor individually during both heat shock and simulated I/R. Inhibition of NF-κB, AP-1, or STAT3 during HS resulted in a dose- dependent decrease in Hsp70.3 reporter expression (Fig. 14). However, decoy-mediated inhibition of the three transcription factors resulted in a different maximal efficacy of

Hsp70.3 reporter inhibition following HS. Inhibition of NF-κB resulted in a maximal reporter inhibiton of approximately 50% occurring at a decoy dose of 50 nM. Inhibition of AP-1 and STAT3 resulted in a more efficacious reduction of Hsp70.3 expression with

AP-1 decoys achieving a 75% suppression of reporter expression at a 100 nM dose and

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STAT3 decoys achieving a ~90% suppression at 100-300 nM (Fig. 14). A 150 nM dose of the decoys showed similar suppressive effects on Hsp70.3 reporter expression after simulated I/R (Fig. 15A). Inhibition of Hsp70.3 expression through decoy blockade of

NF-κB, AP-1, or STAT3 also led to a decrease in H9c2 cell survival following simulated

I/R (Fig. 15B). The degree of increased cell death from decoy-mediated transcriptional blockade correlated with the degree of suppressed Hsp70.3 reporter expression corroborating our finding that Hsp70.3 is cardioprotective.

HSF-1 KO MEFs were also used to assess the role of HSF-1 on Hsp70.3 expression. Results showed no induction of Hsp70.3 luciferase reporter expression following HS or simulated I/R in cells lacking HSF-1 (Fig. 16).

In summary, these results demonstrate that HSF-1 is critically important for

Hsp70.3 expression in both HS and simulated I/R. However, NF-κB, AP-1, and STAT3 activation also contribute to Hsp70.3 transcriptional regulation after heat shock and simulated I/R in H9c2 cardiac myoblast cells.

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Figure 12. In vitro Hsp70.3-luciferase reporter expression

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Figure 12. In vitro Hsp70.3-luciferase reporter expression

We depeloped an Hsp70.3-luciferase reporter vector resulted in a heat shock inducible, dose-dependent expression pattern of the reporter in MEF cells. A promoterless reporter vector (pGL4) did not express a signal significantly different than untreated cells. CMV promoter driven luciferase expression gave a robust signal that was not sensitive to HS.

N = 3 per group. * P ≤ 0.05 vs. corresponding 37 Deg Ctrl.

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Figure 13. In vitro simulated I/R in H9c2 cells

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Figure 13. In vitro simulated I/R in H9c2 cells

H9c2 cells were subjected to 3 hrs of simulated I/R consisting of hypoxia (<0.5% O2) accompanied by nutrient deprivation and acidosis (pH 6.4) followed by 3 hrs of recovery under normal growth conditions. Simulated I/R induced cell death (as measured by MTS assay) (A) and increased expression of Hsp70.3-luciferase reporter (B). Simulated I/R also induced transcriptional activation of NF-kB, AP-1, and STAT3 (as measured by luciferase reporter assays) (C). N = 6 per group. *P ≤ 0.05 vs. untreated control cells.

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Figure 14. Decoy inhibition of HS induced Hsp70.3 reporter expression in H9c2 cells

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Figure 14. Decoy inhibition of HS induced Hsp70.3 reporter expression in H9c2 cells

H9c2 cells were transfected with Hsp70.3-Luciferase reporter and increasing doses of transcription factor decoys targeting NF-κB, AP-1, STAT3, or a non-targeting control

(scrambled). Cells were then subjected to a 1 hr HS (42oC) to stimulate transcriptional expression of the Hsp70.3 reporter and allowed to recover at normal growth conditions for 5 hrs to allow for luciferase accumulation. Treatment with decoys suppressed reporter expression in a dose-dependent manner. Suppression results are expressed relative to fold expression change from transfection of an equal dose non-targeting negative control

(scrambled) decoy (represented by the horizontal line at fold = 1.0). * P ≤ 0.05 vs.

Hsp70.3-Luc expression with equal dose scrambled decoy. N = 6 per group. # P ≤ 0.05 vs. Hsp70.3-Luc expression with equal dose NF-κB decoy.

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Figure 15. Decoy inhibition of simulated I/R-induced Hsp70.3 reporter expression and exacerbation of cell death

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Figure 15. Decoy inhibition of simulated I/R-induced Hsp70.3 reporter expression and exacerbation of cell death

H9c2 cells were transfected with Hsp70.3-Luciferase reporter and increasing doses of transcription factor decoys targeting NF-κB, AP-1, STAT3, or a non-targeting control

(scrambled) prior to simulated I/R. Treatment with decoys suppressed reporter expression

(A) and resulted in an increase in simulated I/R-induced cell death (B). Results are expressed relative to fold change from treatment with an equal dose of non-targeting negative control (scrambled) decoy. N = 6 per group. * P ≤ 0.05 vs. scrambled decoy control.

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Figure 16. HSF-1 KO MEFs cannot induce Hsp70.3 expression in response to HS or simulated I/R

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Figure 16. HSF-1 KO MEFs cannot induce Hsp70.3 expression in response to HS or simulated I/R

HSF-1 KO MEFs were transfected with Hsp70.3 luciferase reporter (with and without the

Hsp70.3 3’-UTR) prior to stimulation by either HS or simulated I/R. No induction of

Hsp70.3-Luc expression was observed from either reporter plasmid in these cells in response to either HS or simulated I/R. Results are expressed relative to fold expression change from untreated control cells. N = 8 per group. * P ≤ 0.05 vs. untreated control cells.

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Section 3. Post-Transcriptional Regulation of Hsp70.3 Gene Expression

in Late Ischemic Preconditioning

III.3.1 Evidence for Post-Transcriptional Regulation of the Hsp70.3 Transcript in Late IPC

Having shown that Hsp70.3 is an end effector of late IPC cardioprotection (Fig.

9), protein expression was examined 24 hours following the IPC stimulus, a time point corresponding to our observed late window of cardioprotection. Due to the lack of availability of an antibody specific to Hsp70.3 as a result of protein similarity between the two genes, total inducible Hsp70 (Hsp70.1 and Hsp70.3) protein expression was measured and found to be robustly increased in wild-type mice 24 hours following an

IPC stimulus (Fig. 17). Increased Hsp70 expression is apparent in both the cytoplasmic

(~8 fold increase over sham treatment; Fig. 17A) and nuclear protein fractions (~14 fold increase over sham; Fig. 17B). The ratio of nuclear/cytoplasmic total Hsp70 is also increased 24 hours following IPC, indicating a nuclear translocation of Hsp70 after IPC

(Fig. 11C).

As shown in Fig. 8, transgenic suppression of NF-κB significantly reduces, but does not completely inhibit mRNA expression of Hsp70.1 and Hsp70.3 following IPC.

Upon examination of total Hsp70 protein levels following IPC, it was that found that there was no significant increase in Hsp70 expression in either the cytoplasmic or nuclear fraction following IPC in 2M mice (Fig. 17A,B). Also, in contrast to wild-type mice, there was no observed change in the nuclear/cytoplasmic ratio of Hsp70 in following IPC

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in 2M mice (Fig. 17C). No significant differences were found in any comparisons between the wild-type and 2M sham groups. An increase in total Hsp70 protein following

IPC is still observed in Hsp70.1 knockout mice, confirming increased expression of

Hsp70.3 in late IPC (Fig. 17D).

The lack of Hsp70 protein induction in 2M mice in late IPC comes in spite of the

8.6 and 7.2 fold increase in Hsp70.1 and 70.3 mRNA expression relative to sham, respectively (Fig. 8) and would suggest that Hsp70 expression may be subject to post- transcriptional regulation following ischemic preconditioning. Thus, it is plausible that the loss of Hsp70.3 expression in the 2M mice due to decreased mRNA expression and post-transcriptional suppression of protein expression both contribute to the lack of cardioprotection following IPC.

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Figure 17. Hsp70 protein expression in late IPC

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Figure 17. Hsp70 protein expression in late IPC

Western blots for total Hsp70 protein in following IPC revealed a large increase in expression of Hsp70 in both the cytoplasmic (7.7 fold increase in IPC vs. sham) (A) and nuclear fraction (14.3 fold increase in IPC vs. sham) (B) of wild-type mice. There was also an observed increase in the ratio of total Hsp70 protein in the nucleus vs. cytoplasm after IPC (0.14 nuclear/cytoplasmic ratio in sham vs. 0.61 nuclear/cytoplasmic ratio after

IPC) (C). In contrast to WT mice, western blotting revealed no significant increase in cytoplasmic (A) or nuclear (B) expression of total Hsp70 following IPC in 2M mice.

There was also no observed increase in the ratio of total Hsp70 protein in the nucleus vs. cytoplasm after IPC in 2M mice (C). An IPC-dependent increase in both cytoplasmic and nuclear Hsp70 protein was maintained in Hsp70.1 KO mice (D). For all groups, total

Hsp70 protein is reported due to the lack of a commercially available antibody able to distinguish between Hsp70.1 and Hsp70.3. Total cytoplasmic and nuclear Hsp70 is expressed relative to WT IPC. N = 4-10 for panels A-C. N = 3-8 per group for panel D.

*P ≤ 0.05 vs. corresponding sham.

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III.3.2 Post-Transcriptional Regulation of Hsp70.3 is Mediated by the 3’-Untranslated Region

3’-UTR mediated regulation of Hsp70.3-Luciferase reporter expression

It is well established that post-transcriptional regulation is often mediated by the

3’-UTR sequence of a transcript.319 Thus, we modified our Hsp70.3 luciferase reporters such that, in addition to the Hsp70.3 promoter, the Hsp70.3 3’-UTR was included in the proper location relative to the promoter and luc-coding sequenc (Section II.18). Upon inclusion of the 3’-UTR coding sequence of Hsp70.3, the luciferase reporter expression is reduced at basal levels as well as after HS in MEF cells (Fig. 18A). To confirm that this suppressive effect on luciferase expression is specific to the 3’-UTR and independent of promoter regulation, a luciferase reporter plasmid driven by a CMV promoter was created with the Hsp70.3 3-UTR. Presence of the Hsp70.3 3’-UTR also suppressed CMV promoter controlled luciferase expression in both MEF and H9c2 cells confirming that

3’-UTR mediated suppression is independent of promoter regulation (Fig. 18B-C).

These results corroborate our finding that Hsp70.3 gene expression is subject post-transcriptional regulation following IPC in vivo and support that a similar post- transcriptional regulation that we observed in in vitro models of endogenous Hsp70.3 expression is mediated by the 3’-untranslated region of the Hsp70.3 mRNA transcript.

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Figure 18. Hsp70.3 specific 3’-UTR mediated suppression of reporter expression

143

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Figure 18. Hsp70.3 specific 3’-UTR mediated suppression of reporter expression

Luciferase reporter expression is reduced by the addition of the Hsp70.3 specific 3’-UTR region to the Hsp70.3 promoter-luciferase reporter construct. This inhibition occurs both at basal levels of expression as well as after heat shock induction (A). Addition of the

70.3 3’-UTR also suppresses CMV promoter-luciferase expression in both MEFs (B) and

H9c2 cells (C). Data is displayed as luciferase expression relative to expression at 37 degrees or following heat shock from reporter plasmid lacking the Hsp70.3 3’-UTR sequence (represented as an expression level of 1.0 on each graph). N = 6-8 per group. *

P ≤ 0.05 vs. corresponding luciferase reporter expression without the Hsp70.3 3’-UTR.

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Hsp70.3 and Hsp70.1 mRNA sequence homology

After establishing that post-transcriptional regulation of Hsp70.3 gene expression is mediated through the 3’-UTR, we examined the mRNA sequence Hsp70.3 gene for likely markers of post-transcriptional regulation. It is interesting to note that the mRNA sequence homology of Hsp70.3 and Hsp70.1 is greater than 99% from the transcriptional start site to the beginning of the 3’-untranslated region (3’-UTR). The proximal promoter regions (the first ~250 bp upstream of transcriptional start site) of the two genes also share >99% homology. However, the homology between the 3’-UTRs of the two transcripts is much less (< 50%), suggesting a difference in post-transcriptional regulation between the two genes (Fig. 19). It has been recently established that 3’-UTR mediated post-transcriptional regulation is likely to be mediated by microRNAs (miRNAs).319 With very little sequence homology between the 3’-UTRs of the two genes, the predicted miRNA binding sites (as determined via the Sanger miRBase Target Database)320 in the

3’-UTRs displayed no overlap between Hsp70.1 and Hsp70.3 (Fig. 19). Based on our finding that Hsp70.3 is a cardioprotective mediator of late IPC cardioprotection, we focused on potential miRNA mediated post-transcriptional regulation of Hsp70.3 gene expression.

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Figure 19. Hsp70.3 and Hsp70.1 3’-UTR sequence homology and predicted miRNA binding sites

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Figure 19. Hsp70.3 and Hsp70.1 3’-UTR sequence homology and predicted miRNA binding sites

Sequence alignment of the 3’-UTRs of Hsp70.1 and 70.3 reveals very little homology between the two genes. Putative miRNA binding sites (as predicted by the Sanger miRBase Target Database5) are depicted for 70.1 (red) and 70.3 (blue). A completely distinct set of miRNAs is predicted to bind the 3’-UTRs of the two genes, indicating a potential difference in regulation of the two transcripts.

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III.3.3 IPC-Induced Expression Changes of Hsp70.3 Targeting miRNAs

The results presented thus far have shown that Hsp70.3 expression is required for the development of NF-κB-dependent cardioprotection against MI in late IPC.

Transcriptional activation of NF-κB after IPC leads to an increased expression of

Hsp70.3 mRNA. Interestingly, blockade of NF-κB only partially (~50%) reduces IPC- induced expression of Hsp70.3 mRNA, while it completely blocks the increase of

Hsp70.3 protein. It was then demonstrated using in vitro reporter assays that Hsp70.3 is post-transcriptionally suppressed via signals specific to the 3’-UTR of the mRNA transcript.

We therefore hypothesized that IPC also regulates expression changes of miRNAs that in turn impose post-transcriptional regulation on Hsp70.3 expression via its 3’-UTR.

Since the post-transcriptional regulation of Hsp70.3 is most prominently manifested in

2M transgenic mice following IPC in vivo, it is likely that this process is regulated by

NF-κB. Thus, we further hypothesize that the candidate miRNA(s) playing a role in the post-transcriptional regulation of Hsp70.3 may itself be regulated in expression by NF-

κB in late IPC. To test this hypothesis, comparative miRNA expression arrays were perfomed in wild-type and NF-κB dominant-negative mice after IPC, with the goal of delineating miRNAs that meet two criteria, they are; 1) predicted to target Hsp70.3, and

2) downregulated in an NF-κB-dependent manner post-IPC. MicroRNAs that meet the first criterion but appear to be down-regulated post-IPC independent of NF-κB will also

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be considered as they may still play a role in post-transcriptional regulation of Hsp70.3 in late IPC.

As depicted in Fig. 19, there are 12 miRNAs predicted by the Sanger miRBase miRNA target database320 to target the 3’-UTR of Hsp70.3. Whole genome mouse miRNA QRT-PCR arrays (SA Biosciences) were used to determine expression levels of

356 murine miRNAs in wild-type and 2M mice 3.5 hours after a sham or IPC stimulus.

This is the same time point that was used for the gene expression arrays presented in

Section III.1.1. Of the 356 murine miRNAs examined, 24 miRNAs were found to be up or down regulated ≥ 2 fold following IPC (Table 7). Of these 24 IPC regulated miRNAs,

6 were found to be regulated by NF-κB in late IPC (up- or down-regulated ≥ 2 fold following IPC in WT and 2M mice) (Table 4; bold red font). However, none of the 12 miRNAs predicted to target Hsp70.3 were among the 24 miRNAs determined to be dysregulated by IPC (Table 8). Yet, as noted within Table 8, four of the twelve Hsp70.3 targeting miRNAs were not present on the miRNA expression arrays: miR-485*, miR-

711, miR-7a*, and miR-378*. Therefore, the expression changes of these four miRNAs in WT and 2M mice after IPC were assessed on an individual basis using QRT-PCR.

No significant changes in expression were detected for either miR-485* or miR-

7a* (Fig. 20A). Though miR-485* expression does trend to increase post IPC in both WT and 2M (1.95-fold increase, P = 0.32 in WT; 2.51-fold increase, P = 0.27 in 2M), the detected change was not statistically significant. However, it was found that both miR-

378* and miR-711 were significantly down-regulated following IPC (Fig. 20B). The expression pattern detected for miR-711 indicate that it is down-regulated in an NF-κB- dependent manner following IPC, and thus fits both criteria as outlined above. However,

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miR-378* down-regulation post-IPC does not appear to be NF-κB-dependent. These results support the hypothesis that IPC-induced down-regulation of miR-378* and/or miR-711 may contribute to the post-IPC upregulation of Hsp70.3.

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Table 7. IPC-induced changes in miRNA expression in WT and 2M mice

# miRNA ID WT IPC vs. Sham 2M vs. WT IPC 2M IPC vs. WT Sham 1 mmu-miR-196a 2.14 2.23 4.76 2 mmu-miR-370 -3.40 2.39 -1.42 3 mmu-miR-876-3p -2.05 4.41 2.15 4 mmu-miR-881* -3.64 3.39 -1.07 5 mmu-miR-675-5p -2.42 -2.95 -7.14 6 mmu-miR-679 -2.09 -2.1 -4.39 7 mmu-miR-291b-3p -2.13 1.91 -1.11 8 mmu-miR-205 -3.32 1.7 -1.95 9 mmu-miR-770-3p -2.21 1.62 -1.37 10 mmu-miR-871 -2.17 1.57 -1.38 11 mmu-miR-429 -2.07 1.46 -1.42 12 mmu-miR-299 2.31 -1.3 1.78 13 mmu-miR-302c* -2.13 1.26 -1.69 14 mmu-miR-137 -2.29 1.25 -1.83 15 mmu-miR-144 2.06 1.06 2.19 16 mmu-miR-361 -2.10 -1.1 -2.31 17 mmu-miR-883a-3p -2.46 -1.06 -2.61 18 mmu-miR-216b -2.32 1.16 -2.00 19 mmu-miR-338-5p -2.08 -1.01 -2.10 20 mmu-miR-448 -2.37 1.16 -2.04 21 mmu-miR-449b -2.42 1.09 -2.22 22 mmu-miR-509-5p -2.02 1.09 -1.85 23 mmu-miR-449c -2.61 -1.03 -2.69 24 mmu-miR-686 -5.37 -1.12 -6.01

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Table 7. IPC induced changes in miRNA expression in WT and 2M mice

Results from a QRT-PCR based miRNA expression array show an IPC-dependent expression change (≥ 2 fold up- or down-regulation post-IPC) in 24 miRNAs in WT mice. 21 of the 24 IPC-regulated miRNAs were decreased in expression following IPC

(WT IPC vs. Sham; blue font); whereas, only 3 of the 24 were increased following IPC

(WT IPC vs. Sham; red font). Six of these miRNAs exhibited NF-κB-dependent regulation in IPC (≥ 2 fold up- or down-regulation post-IPC in WT and 2M mice; red font). Three independent samples were pooled and used as an N = 1 for each group. A fold change ≥ 2 was used as a threshold to determine significance of regulation (Section

II.7).

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Table 8. IPC-induced expression changes in Hsp70.3 targeting miRNA

WT IPC vs. 2M vs. WT 2M IPC vs. WT # miRNA ID Sham IPC Sham

1 mmu-miR-485* 2 mmu-miR-224 -1.03 -1.45 -1.49 3 mmu-miR-711 4 mmu-miR-7a* 5 mmu-miR-139-5p -1.16 -1.63 -1.89 6 mmu-miR-378* 7 mmu-miR-342-3p -1.2 -1.36 -1.62 8 mmu-miR-28 -1.09 -1.07 -1.16 9 mmu-miR-301b 1.04 1.06 1.1 10 mmu-miR-212 -1.25 -1.24 -1.55 11 mmu-miR-490 -1.11 -1.44 -1.6 12 mmu-miR-301a 1.01 -1.16 -1.16 Indicates not present on miRNA expression array

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Table 8. IPC induced expression changes in Hsp70.3 targeting miRNA

The 12 miRNAs depicted in this table are all the miRNAs predicted to target the Hsp70.3

3’-UTR by the Sanger miRBase miRNA target database. The expression changes of 8 of these 12 were determined via a QRT-PCR based miRNA expression array to not be significantly up- or down-regulated in WT or 2M mice following IPC. The remaining 4

(bold red font; no expression values in the table) were not available on the miRNA expression array. Three independent samples were pooled and used as an N = 1 for each group. A fold change ≥ 2 was used as a threshold to determine significance of regulation

(Section II.7).

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Figure 20. IPC-induced expression changes in Hsp70.3 targeting miRNAs

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Figure 20. IPC-induced expression changes in Hsp70.3 targeting miRNAs

Four of the 12 Hsp70.3 targeting miRNAs depicted in Table 8 were not represented in the miRNA expression arrays and were individually assessed via QRT-PCR for post-IPC expression changes in WT and 2M mice. QRT-PCR analysis indicated no significant expression changes in miR-485* or miR-7a following IPC in either WT or 2M mice (A).

However, both miR-378* and miR-711 expression analysis revealed a significant IPC- dependent decrease in expression in WT mice (B). IPC-induced repression of miR-711 was not observed in 2M mice indicating NF-κB-dependent suppression of miR-711 in late IPC. N = 4-8 per group. * P ≤ 0.05 vs. WT Sham. # P ≤ 0.05 vs. WT IPC.

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III.3.4 MiRNA-378* and miRNA-711 Mediate Post-Transcriptional Suppression of Hsp70.3 Expression via the 3’-UTR

In the previous section, it was demonstrated that Hsp70.3 is regulated post- transcriptionally via the 3’-UTR. Additionally, miR-711 and miR-378* are the only miRNAs predicted to target the Hsp70.3 3’-UTR whose expression is down-regulated after IPC. To address the hypothesis that miR-711 and miR-378* act to suppress Hsp70.3 protein expression via sequence specific regulatory actions at the 3’-UTR, the ability of these miRNAs to modulate expression of the Hsp70.3 3’-UTR luciferase reporter was examined in the H9c2 rat cardiomyocyte-like cell line. The CMV promoter-luciferase-

70.3 3’-UTR reporter construct was used to give constitutive gene expression of luciferase and isolate the regulatory actions of the Hsp70.3 3’-UTR (through absence of the Hsp70.3 promoter).

To test miR-378*- and miR-711-mediated regulation of the Hsp70.3 3’-UTR,

H9c2 cells were transfected with CMV-Luciferase-70.3 3’-UTR reporter (CMV-Luc-U3) and increasing doses of miR-378*, miR-711, a non-targeting negative control RNAi, or an Hsp70.3 targeting siRNA. Results show that treatment with both miR-378* and miR-

711, or an Hsp70.3 targeting siRNA, led to a dose-dependent reduction in CMV-Luc-U3 expression compared to treatment with an equal dose of non-targeting negative control siRNA (Fig. 21A). The miR-378* and miR-711 mediated suppression of the CMV-Luc-

U3 reporter is sequence specific to the Hsp70.3 3’-UTR as evidenced by the inability of the miRNAs to reduce expression of a similar reporter lacking the Hsp70.3 3’-UTR sequence (Fig. 21B).

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These results confirm that Hsp70.3 is subject to post-transcriptional repression of protein regulation due, at least in part, by sequence specific targeting of miR-711 and miR-378* to the 3’-UTR of the Hsp70.3 mRNA transcript.

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Figure 21. miRNA mediated regulation of Hsp70.3 3’-UTR luciferase reporter

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Figure 21. miRNA-mediated regulation of 70.3 3’-UTR luciferase reporter

H9c2 cells were transfected with CMV-Luciferase-70.3 3’-UTR reporter (CMV-Luc-U3)

(A) or CMV-Luciferase reporter without the Hsp70.3 3’-UTR (CMV-Luc) (B) and increasing doses of a non-targeting negative control RNAi, miR-378*, miR-711, or an

Hsp70.3 targeting siRNA. Treatment with miR-378*, miR-711, or an Hsp70.3 targeting siRNA suppressed reporter expression in a dose-dependent manner with near complete suppression of CMV-Luc-U3 expression occurring at a dose of 100 nM (A). The suppression induced by miR-378*, miR-711, and the Hsp70.3 siRNA was shown to be sequence specific to the Hsp70.3 3’-UTR as evidenced by the lack of suppression of a

CMV-Luc reporter (B). Suppression results are expressed relative to fold expression change from transfection of an equal dose non-targeting negative control RNAi

(represented by the horizontal line at fold = 1.0 in both graphs). N = 4-6 per group. * P ≤

0.01 vs. CMV-Luc-U3 expression with equal dose negative control siRNA.

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Heat shock as an in vitro model of Hsp70.3 expression regulation

Results using the luciferase reporter system clearly showed that miR-378* and miR-711 have an inhibitory effect on protein expression mediated through the Hsp70.3

3’-UTR. However, we next sought to demonstrate a physiologically relevant role for these miRNAs in regulation of endogenous Hsp70.3 protein expression. In order to investigate these mechanisms on the regulation of endogenous Hsp70 protein expression, an in vitro model of isolated murine embryonic fibroblasts (MEFs) was employed. MEFs were chosen as a model of Hsp70 regulation since it is known they robustly induce

Hsp70 in response to a heat shock (HS) stimulus.321 They also provide the advantage of being a system in which inducible Hsp70.3 expression can be isolated through the use of

MEFs originating from Hsp70.1 KO mice.

o In response to a one hour heat shock stimulus (42 C, 5% CO2, 95% ambient air) followed by recovery under normal growth conditions at 37oC, total Hsp70 expression increased with a peak expression occurring about 3-4 hours post-HS in MEFs isolated from C57 wild-type mice (Fig. 22A-B). Examination of Hsp70.1 and 70.3 mRNA levels

1.5 hrs post-HS revealed that the expression of both genes was induced following the HS stimulus in WT MEFs (Fig. 22C).

To further confirm that the Hsp70.3 gene is contributing to the increase in total

Hsp70 protein expression following HS in wild-type MEFs, the timecourse of Hsp70 protein expression post-HS was also assessed in Hsp70.1 KO MEFs. Similar to the results obtained with WT MEFs, it was found that Hsp70 protein expression peaked at 3 hours of recovery at 37 degrees following a one hour long HS stimulus (Fig. 22B). Hsp70

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protein expression in the 70.1 KO MEFs peaked at about a 3-fold increase over basal levels. Time course of protein expression exhibited similar kinetics in the wild-type and

70.1 KO MEFs, but the fold increase in expression in Hsp70 protein in Hsp70.1 KO

MEFs was generally lower than that observed in WT MEFs following HS. As expected, no mRNA expression of Hsp70.1 was observed in the 70.1 KO MEFs, but a large increase in Hsp70.3 mRNA expression was still observed post-HS in these cells (Fig.

22C).

These results confirm that Hsp70.3 is increased in response to heat shock in primary MEF cells in vitro and provide a model for the study of Hsp70.3 protein expression.

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Figure 22. Hsp70 expression following heat shock in MEFs

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Figure 22. Hsp70 expression following heat shock in MEFs

Hsp70 protein and mRNA expression is increased in WT and Hsp70.1 KO MEFs following a one-hour HS stimulus at 42oC. Panel A is a representative western blot showing total Hsp70 expression in WT MEFs after HS and the indicated recovery time at

37 degrees. Quantitative analysis indicated that the total Hsp70 protein levels peaked approximately 3 hours post-HS in both WT and Hsp70.1 KO MEFs (B). Hsp70 expression was normalized to the pan-actin signal as a control for protein loading and is expressed relative to control cells maintained at 37 degrees. Examination of mRNA levels of the two inducible Hsp70 genes indicated an increase in both Hsp70.3 and 70.1

1.5 hrs following HS in WT MEFs (C). As expected, only Hsp70.3 gene expression was increased following HS in Hsp70.1 KO MEFs (C). mRNA expression was normalized to

18S RNA and is expressed relative to control cells maintained at 37 degrees. N = 3 per group for panel B. N = 6 per group for panel C. * P ≤ 0.05 vs. non-HS control.

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miRNA-mediated regulation of endogenous Hsp70.3 expression in vitro

Next, we sought to establish a role for miR-378* and miR-711 in the regulation of endogenous Hsp70.3 protein expression in primary MEFs in vitro. MEFs isolated from

Hsp70.1 knockout mice were transfected with miR-378*, miR-711, a non-targeting negative control RNAi, or an Hsp70.3 targeting siRNA. The following day, transfected cells were subjected to 1 hr of HS and allowed to recover at 37 degrees for 3 hrs (a timepoint shown in Fig. 17 to give near peak Hsp70 expression) prior to whole cell protein collection for analysis of endogenous Hsp70 levels. Treatment with a non- targeting negative control RNAi had no effect on post-HS induction of Hsp70.3 protein

(Fig. 23, 1st and 2nd lanes). Representative western blots show that treatment with either an Hsp70.3 specific siRNA, miR-378*, or miR-711 resulted in an apparent reduction of

Hsp70.3 protein induction post-HS (Fig. 23, 3rd-5th lanes).

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Figure 23. MiRNA regulation of endogenous Hsp70.3 protein in Hsp70.1 KO MEFs

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Figure 23. MiRNA regulation of endogenous Hsp70.3 protein in Hsp70.1 KO MEFs

Hsp70.1 knockout MEFs were transfected with 100 nM of either a non-targeting negative control RNAi, an Hsp70.3 targeting siRNA, miR-378*, or miR-711. Cells were stimulated with a one hour HS and allowed to recover at 37oC for 3 hours. Representative western blots show a reduced induction of Hsp70.3 following HS with treatment of miR-

378*, miR-711, or an Hsp70.3 targeting siRNA.

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III.3.5 Alternative Polyadenylation of the Hsp70.3 Transcript

It is clear from the data presented in the previous section that miR-378* and miR-

711 play a suppressive regulatory role on the 3’-UTR of Hsp70.3. However, alternative selection of polyadenylation sites is another way in which the 3’-UTR sequence of a gene may exert post-transcriptional regulation.322

Upon investigation of the 3’-UTR of the Hsp70.3 mRNA sequence, it was found that multiple polyadenylation sites are predicted to exist within this region (Fig. 24).

Polyadenylation sites were predicted based on the known strong polyadenylation sequences of AAUAAA and AUUAAA, which account for the majority (>75%) of all polyadenylation signals.323, 324 Interestingly, predicted polyadenylation signals appear on both sides of the predicted miR-378* binding site. Thus, if polyadenylation were to occur at predicted PolyA site #1 or 2, then no miR-378*-dependent post-transcriptional suppression would be expected to be observed due to lack of an available binding site

(Fig. 24). Only polyadenylation products produced from sites #3 and 4 would be subject to miR-378* suppression.

To assess whether multiple endogenous Hsp70.3 transcript sizes exist as a result of alternative polyadenylation, rapid amplification of cDNA ends (RACE) was utilized.

Using oligodT PCR primers in conjunction with primers specific to the end of the

Hsp70.3 protein coding sequence resulted in amplification of DNA fragments corresponding to the population of Hsp70.3 3’-UTR sizes. Results show that four distinct populations of 3’-UTR length were found with approximate sizes of 250, 650, 840, and

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1240 bases within WT MEF cells (Fig. 25). The four polyadenylation products depicted by RACE in Fig. 20 match very closely with what would be expected based on the predicted product lengths. Although there does appear to be a smaller band present in the

“A” lane of Fig. 25, it is unclear what this band is at the present time. It may be an imprecise copy of product from site #1, but only appears ~150 bp in length and is thus too small to represent a product from a predicted a polyA site.

It was next investigated whether alternative polyadenylation of the Hsp70.3 transcript is a static phenonema or subject to stimulus-specific regulation. RACE was again used to examine changes in the distribution 3’-UTR lengths of Hsp70.3 in MEFs following a HS stimulus as well as in cardiac tissue in vivo following IPC. The pattern of polyadenylation in MEFs appears to shift in favor of larger amounts of the smaller variants after HS (Fig. 26). This is most striking for the appearance for polyA site 2 product after HS that is not visible in the 37 degree control groups (Fig. 26, lanes 3G and

3,G vs. 1,G and 2,G; red box). The largest polyA variant product from site 4 (~1250 bp) was not detectable in these groups, but only very faintly detected previously (Fig. 25B).

Upon assessment of the population of 3’-UTR transcript sizes in the myocardium of WT mice following IPC, an increase in production of the smaller polyadenylation products was also very pominent after IPC compared to sham (Fig. 27A). The smaller product appears to be produced more in the 2M mice after IPC as well, but to a lesser extent than is present in WT (Fig. 27B), suggesting this may be an NF-κB-dependent process.

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To quantitatively assess the relative amounts of the polyadenylation products resulting from sites 2 and 3, QRT-PCR was performed using primers designed to specifically recognized the Hsp70.3 3’-UTR within these regions. Polyadenylation products resulting from sites 2 and 3 were selectively investigated due to the importance of these two site products to miR-378* binding and implication on the regulation of

Hsp70.3 expression. Assessment of the ratio of polyA product 3 relative to the amount of polyA product 2 indicates a decrease in the presence of product 3 after a HS stimulus in both WT and Hsp70.1 KO MEFs in vitro (Fig. 28A). However, H9c2 cells did not appear to undergo the same shift polyadenylation products following HS (Fig. 28B), but a shortening of the 3’-UTR to favor polyadenylation product 2 was observed after an in vivo IPC stimulus in the myocardium of WT mice (Fig. 28C).

These results suggest regulation of Hsp70.3 polyadenylation following both HS and IPC to be a cell-type and/or stimulus-specific process. Polyadenylation at site 3 resulting in a larger, and miR-378* accessible, 3’-UTR of Hsp70.3 is much more prevalent under basal conditions in both MEFs and myocardial tissue. Also, though qualitative data in Fig. 22 suggested this alternative polyadenylation following IPC to be an NF-κB-dependent process, quantitative assessment of the signal did not confirm this to be the case (Fig. 28C).

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Figure 24. Prediction of polyadenylation sites within the Hsp70.3 transcript

1 GGTGGATTAG AGGCCTCTGC TGGCTCTCCC GGTGCTGGCT AGGAGACAGA TATGTGGCCT TGAGGACTGT

71 CATTATTTCA AGTTTAGTAC TTCACTCCTT AGTTTGTCCT GCAATCAAGT CCTAGACTTA GGGAAACTAA

141 ACTGTCTTTC AGTTACTTTG TGTATTGCAC GTGGGCTTTA TCTTCCCTGT TAATTAACAC TGCAAGTGTG PolyA Site #1 211 TCTTTGTAAA TATAAATAAA TAAGTATATA TATTCTTCAA TTCAGCACTG CCCCGCTGAT GTGATTTGTT

281 TTGCAGGACA GCCAAAGCTA TGTAGAGAGA TATTCTGTAT CAGAATACAC AAAGAGACAG AGATATGTTA

351 TGAAAACATC AGGAGACTGT TGAGTTCTTT GTGTTTGGAC TCTCCCCTGG GCCACATTGT TGATACATGC miR-711 Binding Site 421 TTGTGTCGGG TCCTTCAGAG GCCAGGGCTG GATTACTGAC AGCGGAGACT CTGCTGCTTC TCCTTGCGTT

491 TATAATCTTG CATGGTGGTT GCACTGTAGG ACTTGTTTCC AGGTTGGTGA ACTTGGAGGT GAAGTGACAG PolyA Site #2 561 CACCAGCATG TGTTCAGTTT TTACACAACC ATCCTGAACT CGGGTCAATT TTTACCGGTC ATTTGAAAAT miR-378* Binding Site 631 AAACTTCAAA ATCACTTGCC AGGTCTTGTG CCTGTTGTGT TTGGAGGTCA GGAGTTGCTG TGTATGACAG

701 TTTCGGCTAC GCTTGACTAA CATTTTATCT GAAGACACAG ACACGGGTGT GGAGCACTCA CAGGGACATT PolyA Site #3 771 GCATTGTGAT CTGCTTGTGT GAGAAAAATT AAAGTCAGAG CAAATAAAAA CCCTGCCCGG GGCTGAGGTC

841 CAGCTTAGTG AAGAGGGTCT GGAGTAAATT CTCGTGGAAG TGGGGGAGAG CTTGAGCATG GGGGTCCAGA

911 GAATCAGGCC CCTGGCAGGG TGGCAGGGAT GCAAGTGACT TAAAACTGAC TTCAGAAAAG GCGGTAGGGA

981 GCCAGGCATG GTGGCGCATG CCTTTGATCC CAGCACTCGG GAGGCAGAGG CAGGCAGATT TCTGAGTTCA

1051 AGGTCAGCCT GTTCTACAAA GTGAGTTCCA GGACAGCCAG GGCTATACAG AGAAACCCTG TCTCGAAAAA

1121 CAAAAAACAA ACAAACAAAC AAAAAGGTAG AGAGCTCACT TAGTGTACAC ACTACTGTAG CAAAACCAGG PolyA Site #4 1191 TGGCGTCAGT GAAATAAAGT CAGAAAGGAG GCTGCAGGTG ATGTTCTTTA CAAGGTGAGA ACTGACAACA

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Figure 24. Prediction of polyadenylation sites within the Hsp70.3 transcript

Polyadenylation sites were predicted from the first 1260 bases of the 3’-UTR of Hsp70.3 based on the accepted strong polyadenylation sequences of AAUAAA and AUUAAA.

Four different polyadenylation sites are predicted giving resulting 3’-UTR lengths of approximately 250, 650, 840, and 1240 base pairs. The predicted binding site for miR-

378* is located between the predicted PolyA sites #3 and #4.

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Figure 25. Alternative polyadenylation of the Hsp70.3 transcript

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Figure 25. Alternative polyadenylation of the Hsp70.3 transcript

(A) Schematic of potential polyadenylation products and primer design for Hsp70 3’-

UTR RACE. Oligo dT primers were used for PCR priming of the polyA end of the transcript, but the proximal base was altered between A, C, or G to ensure consistent PCR priming at the proximal end of the polyA tail. Primers specific to Hsp70.3 were designed at the beginning of the 3’-UTR region where little homology exists between the two

Hsp70 genes. Depending on which site polyadenylation occurred at, any one of four 3’-

UTR products could be produced. Predicted binding sites for miR-711 and miR-378* are shown here for reference. (B) RACE identified at least four distinct populations 3’-UTR transcript size (product arrows 1-4) for the Hsp70.3 mRNA transcript in WT MEFs.

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Figure 26. Hsp70.3 polyadenylation products in WT MEF cells following HS

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Figure 26. Hsp70.3 polyadenylation products in WT MEF cells following HS

RACE was used to assess changes in the distribution of Hsp70.3 3’-UTR polyA products in MEFs following a HS stimulus. Wild-type MEFs were maintained at 37 degrees

(samples 1 and 2) or stimulated by an hour long HS (samples 3 and 4) and RACE was performed using oligo dT primers with either an A, C, or G base on the proximal end.

The labels above the lanes refer to sample number and the proximal end nucleotide of the oligo dT primer. The red boxes indicate a slightly increased presence of the smaller polyA variant after HS.

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Figure 27. Alternative polyadenylation of Hsp70.3 following IPC

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Figure 27. Alternative polyadenylation of Hsp70.3 following IPC

RACE was used to assess changes in the distribution of Hsp70.3 3’-UTR polyA products in the myocardium of wild-type (A) or 2M (B) mice following sham or IPC treatment.

Samples 1-4 represent WT mice given either sham (1 and 2) or IPC (3 and 4) surgery, and samples 5-8 represent 2M mice given either sham (5 and 6) or IPC (7 and 8) surgery.

As in Fig. 26, the labels above the lanes refer to sample number and the proximal end nucleotide of the oligo dT primer. The red boxes indicate an increased presence of the smaller polyA variant after IPC in WT and, to a much lesser exent, 2M mice.

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Figure 28. Quantitative assessment of Hsp70.3 polyadenylation products

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Figure 28. Quantitative assessment of Hsp70.3 polyadenylation products

QRT-PCR assessment of the relative ratios of polyadenylation products 2 and 3 were done using primers specific to each of these polyadenylation products. The amount of polyadenylation product 3 relative to product 2 is significantly decreased after HS in WT and Hsp70.1 KO MEFs (A), but not in H9c2 cells (B). The amount of polyadenylation product 3 relative to product 2 is significantly decreased after IPC in both WT and 2M mice (C). The relative amount of polyadenylation product 3 is also decreased under basal

(sham) conditions in 2M mice compared to WT (C). N = 6 per group for panel A, 4 per group for panel B, and 6-8 per group for panel C. * P ≤ 0.05 vs. corresponding control or sham group. # P ≤ 0.05 vs. WT sham.

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Section 4. Poly(glycoamidoamine)-Mediated Delivery of Transcription

Factor Decoys

III.4.1 In Vitro Characterization of PGAA-NF-κB Decoy Polyplexes

In vitro efficacy of PGAA-NF-κB decoy polyplexes in H9c2 cells

To achieve efficient and non-cytotoxic in vivo transfection of transcription factor decoys, we sought to utilize synthetic polyglycoamidoamine polymers designed and characterized by the Reineke lab.313, 314, 325, 326 These polymers have also been demonstrated to be suitable for the delivery and expression of luciferase reporter pDNA.325

Since preliminary studies done by the Reineke group demonstrated PGAA- mediated delivery of reporter plasmids in H9c2 cells325, we chose delivery of NF-κB targeting decoys to H9c2 cells as an initial model to examine the efficacy of the PGAA polymers in conjunction with trascription factor decoys. All decoys were polyplexed with

PGAA polymers at the previously optimized nitrogen/phosphate (N/P) ratios.325 NF-κB decoys delivered via PGAA polymers were much more efficient at inhibiting TNF-α- induced NF-κB DNA binding than were decoys delivered in the absence of a transfection vector (Fig. 29). NF-κB inhibition via T4-mediated transfection of decoys was the most efficient compared to the other three PGAAs tested as determined by calculation of the

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IC50 doses for NF-κB blockade of the PGAAs (Fig. 29B). The IC50 for decoy delivered via T4 was 2.8 µg/10 cm plate, compared to an IC50 of ~20 µg for decoys delivered in the absence of a delivery vector (Fig. 29B). Importantly, the calculated IC50 dose for T4 is not significantly different than the determined IC50 doses for either Lipofectamine 2000 or JetPEI, two commonly used and commercially available transfection reagents (Fig.

29B). Treatment with the PGAA polymers only did not alter the DNA binding activity of

NF-κB (Fig. 29D, second lane). PGAA-mediated delivery of a scrambled sequence decoy lacking the NF-κB consensus binding sequence had no effect on TNF-α-induced NF-κB activation (Fig. 29E).

In addition, T4-mediated transfection of the NF-κB decoys facilitates delivery of the decoys to the nucleus of the cells. Visual tracking of fluorescently labeled decoys indicated that more than 80% of cells were positive for decoy in their nucleus 22 hours after transfection, while almost no nuclear localization was observed in the absence of a transfection vector (Fig. 30). This finding is important since the nucleus is the site of competition with genomic DNA for transcription factor binding.

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Figure 29. PGAA-decoy mediated inhibition of NF-κB in H9c2 cells

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Figure 29. PGAA/decoy-mediated inhibition of NF-κB in H9c2 cells

H9c2 cells were grown in 100 mm diameter cell culture dishes to ~75% confluency and transfected with NF-κB decoys with or without a delivery vector 24 hrs prior to stimulation with 20 ng/µl TNF-α to induce NF-κB activation. Assessment of NF-κB

DNA binding was done using EMSA analysis. (A) All four of the tested PGAAs greatly increased the efficacy of NF-κB blockade via decoy relative to the delivery of decoy without a transfection vector. (B) The T4 PGAA was found to have the lowest IC50 of the four PGAAs and was comparible in potency to Lipofectamine 2000 or JetPEI. (C) A representative EMSA of NF-κB inhibition by delivery of decoy alone reveals residual

NF-κB activity still present after treatment with 25 µg of decoy in a 100 mm cell plate.

(D) NF-κB activation (as measured by DNA binding) was not detected after delivery of

7-10 µg of decoy via T4 PGAA. Treatment with T4 polymer only had no effect on NF-

κB activation (2nd and 4th lanes). (E) Treatment with a scrambled negative control decoy had no effect on TNF-α-induced NF-κB activation. N = 3 per group. * P ≤ 0.05 vs. M4,

G4, D4, or naked decoy.

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Figure 30. Nuclear localization of decoys delivered via T4 PGAA

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Figure 30. Nuclear localization of decoys delivered via T4 PGAA

Fluorescently labeled decoys were transfected into H9c2 cells via T4 PGAA and the percent of fluorescently positive nuclei were counted visually at the indicated times. In the absence of a delivery vector (ODN only), no significant presence of the decoy in the nucleus was detected. T4-mediated delivery resulted in greater than 80% positive nuclei by 22 hrs after transfection. N = 8 per group. * P ≤ 0.05 vs. ODN only at the same timepoint.

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PGAA-mediated delivery of NF-κB decoys to NRVMs

After demonstrating efficient delivery of NF-κB decoy in H9c2 cells, we sought to demonstrate efficient transfection of rat neonatal ventricular myocytes (NRVMs), a model more relevant to the in vivo cardiac system. Decoys were again fluorescently labeled to allow for intracellular tracking via flow cytometry and confocal microscopy.

Confocal microscopy indicated almost no presence of the decoy within the NRVMs after

24 hrs when transfected in the absence of delivery vector (Fig. 31A). Transfection of decoys via the PGAAs appeared equal or superior to the transfection efficiency achieved using Lipofectamine 2000 or JetPEI (Fig. 31A). Similar to prior results observed in H9c2 cells, decoy delivery to NRVMs using the PGAAs resulted in a strong nuclear localization of the decoy (Fig. 31A).

Quantitative analysis of the transfection efficiency using flow cytometry revealed a more robust difference in the transfection efficiencies between the PGAAs and commercially available Lipofectamine 2000 and JetPEI. With the exception of D4, the other three PGAAs (G4, M4, and T4) were substantially more efficient in the transfection than Lipofectamine 2000 or JetPEI (Fig. 31B). T4 was the most efficient of all the delivery vectors tested, resulting in the highest mean fluorescence in the cell poplulation and nearly 90% of the cells transfected (Fig. 31B).

To assess the cytotoxicity of these transfection vectors on NRVMs, the release of adenylate kinase, a biomarker for cell death327, was measured every 2 hours over the first

8 hours of transfection. JetPEI, G4, and to a lesser extent M4 showed considerably more toxicity, as measured by adenylate kinase release, than the other transfection vectors (Fig.

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32). D4 showed very little toxicity (Fig. 32), but also very low transfection efficiency

(Fig. 31), relative to the other delivery vectors. The toxicity profile of T4 and

Lipofectamine 2000 in NRVMs appears very similar (Fig. 32). However, when transfection efficiency and cytotoxicity are taken together, these results indicate that the

T4 PGAA is the most efficacious and least cytotoxic delivery vector for use in NRVMs.

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Figure 31. In vitro transfection efficiency in NRVMs

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Figure 31. In vitro transfection efficiency in NRVMs

Fluorescently labeled decoys were transfected into NRVMs using the indicated delivery vector and transfection efficiency was assessed 24 hrs later. (A) Uptake of a fluorescently labeled decoy (FITC; green) was assessed visually via confocal microscopy. Nuclei of all cells were stained using TO-PRO-3 (blue) and cardiomyocytes were identified using a troponin I specific antibody (red). (B) Quantitative assessment of transfection was done using flow cytometry to track cellular internalization of fluorescence.

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Figure 32. Cytotoxicity of transfection vectors in NRVMs

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Figure 32. Cytotoxicity of transfection vectors in NRVMs

Adenylate kinase release into the cell culture media was assessed as a measure of cytotoxicity. Cells were transfected using the indicated vector and adenylate kinase release was measured at 2, 4, 6, and 8 hours after transfection.

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III.4.2 Inhibition of In Vivo NF-κB Activation via PGAA-Mediated Delivery of NF-κB Decoys

Transfection of T4-NF-κB decoy polyplexes to the in vivo murine heart

In the previous section, T4 was shown to be the PGAA that displayed the most overall desirable characteristics with regard to transfection efficiency and cytotoxity in

H9c2 cells and primary NRVMs. For this reason, T4 was the PGAA of choice for transfection applications in the in vivo heart.

In order to achieve physiologically relevant transcriptional blockade of a factor through delivery of decoys, transfection of the myocardium must be as homogenous and complete as possible. To assess the extent and efficacy of transfection, 10 µg of fluorescently labeled decoys were polyplexed with T4 PGAA and delivered to the myocardium via pericardial injection. After allowing 24 hours for penetration and transfection into the myocardium, localization of the fluorescently labeled decoy was assessed via microscopy. T4-mediated pericardial delivery of the labeled decoys resulted in a strong presence of the fluorescent signal nearly throughout the myocardium, whereas pericardial delivery of the decoy alone resulted in only minimal and non-homogenous uptake to the myocardium (Fig. 33A-B). Quantitative assessment of the average transfected area of each myocardial section shows that T4-mediated delivery resulted in more than 87% of the myocardium transfected compared to only 40% for the decoy alone

(Fig. 33C). Within the transfected regions of the myocardium, T4-mediated transfection

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was also accompanied by a 350% increase in the intensity of the fluorescent signal compared to the decoy alone (Fig. 33D).

To assess desired localization of the transfected decoy to the myocardium, examination of fluorescent signal in the liver was also done 24 hours following pericardial injection. Pericardial injection of T4-decoy polyplex resulted in less off-target transfection of the liver than injection of decoy alone (Fig. 33E-F). This result is consistent with the increased efficiency of T4-decoy polyplex particles to enter and transfect the myocardium, thus making them less accessible to systemic circulation or transfection of off-target sites.

T4-mediated delivery of decoys to achieve functional NF-κB inhibition in vivo

Next, we sought to show a physiologically relevant blockade of NF-κB activation through pericardial delivery of T4-decoy polyplexes in the in vivo heart. Since NF-κB is a transcription factor and exerts its effects through gene expression, the expression change of Cox-2, a well established NF-κB-dependent gene in the heart 328, was used as a physiological marker of NF-κB activity levels. An intraperitoneal injection of a cytokine cocktail (cytomix; 0.1 µg/g TNF-α, 0.001 µg/g IL-1β, and 0.2 µg/g IFN-γ)276 was used to activate NF-κB and resulted in a 16-fold increase in Cox-2 mRNA expression in the myocardium (Fig. 34). This cytomix-induced expression of Cox-2 was inhibited in a dose-dependent manner with pericardial delivery of T4-NF-κB decoy 24 hours prior to cytomix injection (Fig. 34A). This result demonstrates that pericardial delivery of NF-κB targeting decoys via T4 is effective in silencing NF-κB-dependent gene expression.

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In addition, it is known that inhibition of NF-κB-dependent gene expression protects the myocardium against I/R injury.276 Therefore, the ability of T4-decoy polyplexes delivered to the myocardium to inhibit NF-κB and reduce infarct size following 45 minutes of coronary occlusion and 24 hours of reperfusion was also examined. Neither NF-κB decoys nor T4 PGAA delivered alone had a significant effect on infarct size (Fig. 34B). The lack of observable effect by delivery of decoy alone indicates that, although it was able to transfect 40% of the myocardium (Fig. 34D), the amount of decoy taken by the global population of cardiomyocytes was below the threshold needed to achieve a physiologicl effect. However, an equivalent amount of NF-

κB delivered polyplexed with T4 PGAA reduced infarct size by more than 40% compared to a PBS vehicle control injection (Fig. 34B). This reduction in infarct size is comparable to what is observed through genetic blockade of NF-κB (Fig. 34B, 2nd bar).

Taken together with the above results demonstrating reduction in Cox-2 gene expression, these results demonstrate the ability T4-decoy polyplexes to achieve a functional blockade of NF-κB and its downstream gene expression providing a therapeutic effect against myocardial I/R injury in vivo.

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Figure 33. Transfection of T4-NF-κB decoy polyplexes to the in vivo murine heart

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Figure 33. Transfection of T4-NF-κB decoy polyplexes to the in vivo murine heart

Alexa488 labeled NF-κB decoy alone (A) or decoy polyplexed with T4 (B) were delivered to the heart in vivo by pericardial injection. Hearts were removed and sectioned for microscopy 24 hours after injection. Quantitative analysis of Alexa488 fluorescence presence in the myocardium showed a 3.5-fold increase in mean pixel density when labeled decoy was delivered with T4 PGAA (C). T4-mediated delivery also resulted in transfection of 87% of the myocardium compared to ony 40% with decoy alone (D).

Representative sections from liver taken 24 hrs after pericardial injection show less fluorescent signal in the T4-decoy polyplex delivery group (E) relative to the decoy alone

(F). N = 5 and 6 for decoy only and T4/decoy groups, respectively, in panels C and D. *

P ≤ 0.05 vs. decoy only.

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Figure 34. Functional blockade of NF-κB in vivo via T4-decoy polyplex delivery

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Figure 34. Functional blockade of NF-κB in vivo via T4-decoy polyplex delivery

(A) T4-NF-κB decoy polyplexes were delivered via pericardial injections 24 hrs prior to an intraperioneal injection of cytomix injection to stimulate NF-κB activation and subsequent expression of Cox-2 mRNA. Cox-2 mRNA levels were assessed by QRT-

PCR 3 hrs following cytokine injection. *P ≤ 0.001 vs. 0 µg NF-κB decoy/T4. (B) 10 µg of T4-NF-κB decoy polyplexes were delivered via pericardial injections 24 hrs prior to an I/R injury (45 min coronary occlusion followed by 24 hrs reperfusion). Delivery of

T4-decoy reduced infarct size compared to untreated C57 wt mice or a vehicle control group (B, 4th bar vs. 1st and 3rd). This reduction in infarct size is also comparable to what is achieved through genetic dominant negative blockade of NF-κB (3M Tg). N = 3-5 per group for panel A and 6-10 per group for panel B. * P ≤ 0.01 vs. C57 wt mice and PBS only vehicle control group.

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Application of decoy delivery technology to ischemic preconditioning

Upon establishing T4-mediated delivery of transcription factor decoys as a feasible method to efficiently block transcription factor activation in the in vivo myocardium, we sought to utilize this system to investigate the dependency of transcription factor activation on the cardioprotection of late IPC. Decoys designed to inhibit the activation of either NF-κB, AP-1, STAT-3, or a non-targeting “scrambled” sequence decoy were delivered pericardially to the myocardium 24 hours preceding ischemic preconditioning. Mice were then subjected to a 30 minute LAD occlusion in the late phase of IPC protection (24 hours subsequent to the IPC). Analysis of the resulting infarcts indicated that T4-mediated delivery of NF-κB decoys ablated the cardioprotection afforded by late phase IPC (Fig. 35, 4th bar). Similar to results obtained for I/R injury alone as in Fig. 29, results obtained with NF-κB decoys delivered via the

T4 glycopolymer are similar to those obtained using NF-κB dominant-negative transgenic mice (Fig. 35, 3rd bar).

STAT-3 and AP-1 are transcription factors that have been shown to be active following ischemic stress in the myocardium and implicated in late IPC cardioprotection.

Inhibition of STAT3, through both pharmacological and genetic blockade, has shown a role for this transcription factor in late IPC.146, 329 However, AP-1 has yet to be conclusively shown through specific inhibition to play a role in the development of late phase IPC. Transcription factor were designed to contain the consensus binding sites for these two factors and delivered to the myocardium prior to IPC in the same manner as the

NF-κB decoys. Results show that pericardial delivery of transcription factor decoys

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designed to inhibit STAT-3 or AP-1, but not a scrambled non-targeting decoy, inhibited the late phase of IPC cardioprotection (Fig. 35, 5th-7th bars).

These results demonstrate the application of a novel glycopolymer transfection system that is non-toxic and suitable for in vivo transfection of the myocardium. The application of this transfection system allowed us to acutely and specifically inhibit NF-

κB, STAT3, and AP-1 activation in the myocardium during late IPC and show conclusively for the first time that transcriptional activation of AP-1 is critical for the development of late IPC cardioprotection.

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Figure 35. Decoy mediated blockade of NF-κB, AP-1, or STAT3 inhibits late IPC induced cardioprotection

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Figure 35. Decoy-mediated blockade of NF-κB, AP-1, or STAT3 inhibits late IPC induced cardioprotection

Delivery of oligodeoxynucleotide decoys targeting the transcription factor NF-κB blocked late IPC-induced cardioprotection to the same extent as seen in IκBα dominant- negative transgenic mice (4th vs. 3rd bar). Blockade of either STAT-3 or AP-1 by transcription factor decoys also inhibits late IPC cardioprotection (5th and 6th bar).

Delivery of a scrambled decoy lacking any transcription factor targeting had no effect on late IPC cardioprotection (7th bar). N = 3-10 per group. * P ≤ 0.05 vs. WT Sham.

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Chapter IV

Discussion

Section 1. Summary of Hypotheses and Results

In summary, the work presented herein addressed the following central hypotheses and has contributed the following novel results to the understanding of the cardioprotective mechanisms that manifest the late phase of ischemic preconditioning.

Central Hypothesis 1: NF-κB-dependent gene expression changes underlie the cardioprotection of late phase IPC. The results presented in this work support this hypothesis through the demonstration of the following key results:

1. The NF-κB-regulated transcriptome, associated with late phase IPC, is

enriched for genes involved in the biological processes of angiogenesis,

programmed cell death, and the heat shock response.

2. NF-κB-dependent regulation of transcription is responsible for the down-

regulation of pro-cell death genes (Nix, Fadd) as well as the upregulation of

cell survival genes (Hsp90, Hif-1α, Hsp70.3) following late IPC.

3. Hsp70.3 is an NF-κB regulated gene whose expression is critical to late IPC

cardioprotection. In contrast, Hsp70.1 does not play a role in late IPC and is

instead be injurious following I/R injury.

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a. This result also demonstrates differential roles for Hsp70.3 and Hsp70.1 in

response to myocardial ischemia and emphasizes the need to differentiate

between these two different inducible Hsp70 genes.

Central Hypothesis 2: Both pre-and post-transcriptional regulation of the

Hsp70.3 transcript is critical to its cardioprotective expression in late IPC. This hypothesis is supported by the following key results:

1. In addition to NF-κB, the Hsp70.3 promoter contains predicted binding sites

for HSF-1, AP-1, and STAT3, all of which are transcription factors also

shown be involved in late IPC.

2. Inhibition of HSF-1, NF-κB, AP-1, or STAT3 inhibits Hsp70.3 expression in

response to heat shock and simulated I/R. In addition, the reduced Hsp70.3

expression correlated with increased cell death following simulated I/R in

H9c2 cells.

3. Hsp70.3 protein expression is subject to post-transcriptional regulation

following late IPC.

4. Two Hsp70.3 targeting miRNAs, miR-378* and miR-711, are down-regulated

following late IPC. Both of these miRNAs were shown to have a suppressive

role in post-transcriptional regulation of the Hsp70.3 mRNA transcript.

5. The Hsp70.3 mRNA transcript is alternatively polyadenylated in a manner

that may contribute to post-transcriptional regulation of protein expression

following IPC.

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Central Hypothesis 3: T4 glycopolymer-mediated delivery of transcription factor decoys will result in efficient and functional transcriptional blockade in the myocardium. This hypothesis was supported through successful transfection of the in vivo murine myocardium and utilization of this transfection technology to achieve the following key results:

1. The T4-mediated pericardial delivery of NF-κB decoys resulted in efficient

transfection of the myocardium and functional blockade of NF-κB.

2. Application of this system for the blockade of NF-κB, STAT-3, or AP-1

transcriptional activation in late IPC demonstrated the critical importance of

these factors to late phase IPC cardioprotection in the murine heart.

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Section 2. NF-κB-dependent Gene Expression in Late IPC

IV.2.1 Antithetical Nature of NF-κB in Cardiac Pathophysiology

Work by our group and others conclusively shows that NF-κB plays antithetical roles in the heart depending on the stimulus that induces its activation. Activation of NF-

κB has been shown to injurious in I/R injury such that inhibition of its activation results in reduction of infarct size following I/R injury.271 Conversely, NF-κB appears to play a protective role against infarction after the ischemic stimuli of permanent occlusion (PO) and late IPC; inhibition of NF-κB exacerbates the injury of PO271 and negates the cardioprotective effects of late IPC.98

The antithetical nature of NF-κB in the heart suggests that IPC-induced NF-κB activation (protective) leads to transcriptional regulation of a different set of effector genes than I/R-induced NF-κB activation (injurious). One of the major underyling questions is how this differential set of transcriptional products is selected for and regulated under different conditions or stimuli of NF-κB activation (i.e., IPC vs. I/R).

Understanding this aspect of differential NF-κB gene regulation is a critical step to understanding how NF-κB-dependent gene products may be injurious following certain stimuli (I/R), but protective after others (IPC, PO). Given that the translocation of free and “active” NF-κB complexes to the nucleus is common to both stimuli, there are a few primary explanations that could, individually or taken together, account for the observed antithetical roles of NF-κB in cardiovascular pathophysiology.

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The stimulatory signals responsible for NF-κB activation could lead to differential post-translational modifications of the active NF-κB complexes altering their DNA binding properties or interactions with transcriptional cofactors. There are many known post-translational modifications possible for the p65 subunit of NF-κB that have been shown to affect its transcriptional activation capacity.265 At least seven different phosphorylation sites exist on p65 and modulate transactivation potential, histone acetylase/cofactor binding, and interactions with IκB proteins.265 Phosphorylation of p50 by PKA has also been shown to be important for its DNA binding emphasizing the importance of post-translational modifications for other NF-κB subunits as well.330 In addition to phosphorylation, multiple NF-κB subunits have also been to be subject to acetylation, S-nitrosylation, nitration, and ubiquitination that may also modulate parameters of NF-κB activity and play active physiological roles.265 Thus, many different post-translational modifications have been identified that have the potential to either directly alter NF-κB DNA binding/transcriptional activation or the association of NF-κB with transcriptional cofactors.

The antithetical roles of NF-κB activation may also be a result of differential subunit composition of the activated NF-κB complexes resulting in differential gene expression output. A parallel story of antithetical roles in cardioprotection has also been noted for the p38 MAP kinase with studies indicating that p38 activation is protective in

IPC, but injurious in I/R injury. The full story of the antithetical nature of p38 in the myocardium is summarized nicely in a recent review by Bassi et. al.331 The nature of p38 signaling with regard to cardioprotection in the face of ischemic injury may have implications on the nature of NF-κB signaling in ischemia. It has been suggested that

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p38α and p38β may play opposite roles in the regulation of cardiomyocyte apoptosis.332

Thus, it may not truly be an antithetical role for p38 per se, but rather antithetical roles for two different isoforms of the same kinase. Of the NF-κB subunits, only p65, RelB, and c-Rel contain the necessary transcriptional activation domains (TAD) to achieve positive regulation of target genes.266 So while p50 and p52 subunits may act as transcriptional activators when paired with a TAD containing subunit, homodimers of p50 or p52 may act as transcriptional repressors through competition for promoter binding sites.265 It is now recognized that NF-κB transcriptional activation within the cardiomyocyte occurs via p65/p50 dimers; whereas, p50/p50 homodimers are primarily responsible for NF-κB mediated transcriptional repression.265 The existence of opposite transcriptional roles for p65/p50 and p50/p50 dimers of NF-κB on transcriptional activation make it very possible that a similar scenario regarding subunit composition will emerge to explain the apparent antithetical roles of NF-κB.

It is generally accepted that both I/R and IPC in the myocardium lead to activation of predominately NF-κB consisting of p65/p50 dimers.98, 271 Interestingly, it has been shown in renal model of late IPC that the preconditioning stimulus results in an increased population of p50/p50 homodimers among the activated NF-κB in subsequent I/R.333

Unfortunately, parallel studies have not yet been done for preconditioning in the myocardium, but this finding, taken together with the suppressive nature of p50/p50 homodimers, corroborates the notion of suppressed NF-κB gene expression in subsequent

I/R contributing to late IPC cardioprotection. In contrast to data from our lab suggesting that global blockade of NF-κB following permanent coronary occlusion exacerbated infarct size, Timmers et al. concluded that knockout of only the p50 subunit of NF-κB

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had no effect on initial infarct size following permanent occlusion.271, 334 The precise composition of activated NF-κB complexes and the roles of each of the subunits in NF-

κB-dependent gene programs in cardiac pathologies remains to be elucidated.

It is also possible that the functioning proteome within the cell at the time of initiation of NF-κB stimulatory signals may be sufficiently different such that the activation kinetics of NF-κB may be different under different cellular conditions (I/R vs.

IPC) even if the activation signals and post-translational modifications of NF-κB are identical. This could be considered similar to the previous explanation with regard to possible differential availability of cofactors. However, this scenario could also arise from altered effector function of NF-κB-dependent gene products depending on the current proteomic state of the cell. For example, it is thought that apoptotic cell death is highly dependent on the ratio and balance of pro- and anti-apoptotic cell death proteins.22,

335 Thus, depending on the immediate level of a specific apoptotic regulatory protein, an

NF-κB-dependent expression change of another apoptotic regulator may or may not result in a change of the apoptotic fate of a cell. Different cellular proteomic states could also give rise to direct modulation of NF-κB activation resulting in different patterns of gene expression changes from the same stimulatory signal.

Due to the nature of feedback signaling within the NF-κB-dependent gene expression network, it is widely recognized that activation of NF-κB is oscillatory in nature. This oscillatory activation model of NF-κB has been very nicely modeled336, 337 and has profound implications for the kinetics of NF-κB activation. For example, chronic

TNF-α stimulation induces long-term oscillatory activation in single cells, but is unable to do so in a population of cells due to the asynchronous nature of the activation between

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cells of the entire population.338 In contrast to chronic stimulation, acute TNF-α stimulation induces only a single peak of activation.338 Thus, these findings of differential activation kinetics paved the way for more recent work showing a role for NF-κB oscillations in differential gene expression.339 Interestingly, NF-κB has been suggested to display a biphasic activation pattern in the myocardium following I/R injury with the two activational peaks occurring at 0.5-3 hours and 6-16 hours post-reperfusion.340

Unfortunately, the second activational window would be outside of the initial 4 hour window in which the resulting infarct develops and is likely to thus not play a role in modulating initial infarct size, determined by cell death following I/R injury. It has not yet been fully investigated, but it is plausible that differences in NF-κB activation kinetics are responsible for the paradoxical actions of NF-κB signaling following the different ischemic conditions.

It is plausible that IPC-induced activation of NF-κB may result in altered NF-κB activitation kinetics following subsequent I/R compared to naive NF-κB activation after

I/R in the absence of a preceding IPC stimulus. This potential inhibition of NF-κB activation during the late window of protection could be the result of negative feedback from NF-κB-dependent gene expression specific to IPC-induced activation or a negative feedback inherent to the oscillatory mode of NF-κB activation. It is known that inhibitors of NF-κB such as A20 and IκBα are themselves NF-κB-dependent genes that act as negative feedback inhibitors to NF-κB activation.270, 338, 341, 342 If such post-I/R inhibition of NF-κB does occur, it could also be the result of an NF-κB-dependent gene more specific to IPC. One such possibility for this negative feedback on I/R-induced NF-κB activation is Hsp70.3 and will be discussed in more detail later.

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In summary, since activation of NF-κB is common to both the injurious and protective stimuli, the difference in resulting gene expression products is likely to be due to differential transcription factor/cofactor binding partners, NF-κB complex subunit composition, post-translational modifications of the activated NF-κB subunits, or the specific cellular proteome composition at the time of NF-κB activation. However, regardless of the activating stimulus, the difference in the NF-κB-dependent transcriptomes following IPC and I/R provide the mechanistic basis for the observed antithetical actions of NF-κB in IPC and I/R. The results and analyses of this work are focused not on the upstream mechanisms or conditions of NF-κB activation, but rather on the resulting NF-κB-dependent gene products following an ischemic preconditioning stimuli and how they contribute to the cardioprotection of late IPC against MI. While a small handful of NF-κB-dependent genes following late IPC have been identified, this work is the first to investigate and functionally characterize the full NF-κB-dependent transcriptome following late IPC.

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IV.2.2 Functional Role of NF-κB-Mediated Gene Expression Products in Late IPC

This work demonstrates that the NF-κB-dependent transcriptome following late

IPC consists of more than 200 genes and is enriched for genes involved in the cellular processes of angiogenesis, programmed cell death, and response to heat (Figs. 5-6). The fact that these same gene ontologies were enriched for similar biological processes when the analysis was done with all IPC regulated genes as well as with only genes regulated by NF-κB in IPC indicates that NF-κB plays a global or central role in gene regulation after late IPC. NF-κB does not appear to simply regulate genes involved in a few specialized processes or pathways after IPC, but rather regulates genes that play a role in nearly all IPC-associated processes.

NF-κB-dependent gene expression products that have a functional role in IPC would be considered end effectors of IPC cardioprotection based on our previously laid out definitions. While this is likely to be the role for these genes, it is also important to keep in mind that there is another possible role for NF-κB-dependent genes. Newly synthesized gene products may prime crucial signaling cascades such that a protective signaling response can be initiated with the onset of a subsequent ischemia or reperfusion. While this role for NF-κB-dependent gene products has not been directly shown, it is possible, for instance, that some of these gene products may prime pathways such as the protective RISK pathway allowing for protective rather than injurious signaling to ensue at the initiation of subsequent I/R.141 This potential aspect of NF-κB- dependent gene expression following late IPC would be much more difficult to study due

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to the likelihood that the action of these signaling agents would be transient in nature compared to the end effector molecules; thus, analysis of many timepoints and careful downstream functional analyses would be necessary to identify such mechanisms. Our analysis and interpretation of the resulting gene expression patterns, is focused on the identification of likely end effectors of cardioprotection. In the following sections, the three major gene ontology groupings of NF-κB-dependent genes in late IPC

(programmed cell death, angiogenesis, and heat shock response) will be discussed with regard to the role of the genes as a group, as well the individual genes within the group, in the development of late phase IPC cardioprotection.

NF-κB-dependent regulation of programmed cell death in late IPC

Given the cardioprotective properties of late IPC against I/R-induced cell death and the critical importance of NF-κB activation to this effect, it is no surprise that NF-κB would play a key role in regulating genes involved in the programmed cell death response. It is already well accepted that NF-κB plays a role in cell death modulation following I/R injury in the heart through regulation of many pro-apoptotic and proinflammatory gene products such as FasL, Bax, Bad, TNF-α, and IL-1β.271 The role of necrotic vs. apoptotic cell death following I/R injury has been previously discussed, and since it is accepted that apoptosis is more heavily regulated by specific cell signaling pathways and gene expression changes, our results will be discussed with regard to their potential role in the signaling of apoptosis. In addition, since the gene products of interest are being considered as end effectors of late IPC for protection against I/R induced

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apoptosis, they must exert their effects on the cell death occurring after the subsequent

I/R injury.

Previous work has already indicated that ischemic preconditioning reduces apoptosis following subsequent I/R by altering the expression of genes involved in apoptosis.343 Our results found that NF-κB regulates the expression of 26 genes associated with modulation of programmed cell death (as determined by DAVID gene ontology classification described in section II.5) after late IPC. Our findings indicate that

NF-κB may not only be inducing the expression of pro-survival genes, but also decreasing the expression of pro-apoptotic genes following late IPC. As expected, some of these 26 genes have already been suggested to play a role in late IPC. For example,

Bnip3l (Nix), is a Bcl-2-like family member previously shown to be pro-apoptotic in the heart344 whose expression is down-regulated in an NF-κB-dependent manner in late IPC

(Fig. 6). In contrast, hif1a (Hif-1α) expression has been shown to be protective against

I/R induced injury in the heart158, 345 and is induced by NF-κB following IPC (Fig. 6).

HIF-1α is also thought to be a critical factor for preconditioning stimuli in the heart156,

159, but our results are the first to show an NF-κB-dependent expression of HIF-1α in the heart in response to preconditioning. Bcl2l1 (Bcl-xL) is also a well known anti-apoptotic gene previously shown to be under transcriptional control of NF-κB thought to be involved in preconditioning and protection against I/R injury.343, 346 Myc (c-Myc), an apoptotic regulator and oncogene that serves a positive regulational role on protein translation, is another example of an NF-κB-dependent gene previously shown to be induced in the myocardium in response to IPC.347-350

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Regulation of additional apoptotis associated genes may also be critical for late

IPC cardioprotection. For example, it has been shown in an isolated rat heart model of early IPC that upregulation of the NF-κB-dependent anti-apoptotic gene bcl2 correlates with cardioprotection.351, 352 The bcl2 gene narrowly missed the statististical significance needed from our array analysis to be listed in Table 6 as being an NF-κB-dependently regulated gene in IPC as determined by our results, but was found to trend toward NF-

κB-dependent upregulation in late IPC (1.23 fold increase in WT IPC vs. WT Sham, P =

0.07; 1.35 fold decrease in 2M vs. wt IPC, P = 0.07). The non-inclusion of bcl2 in the data set of significant genes regulated by NF-κB following late IPC is the result of a recognized shortcoming of such strict statistical filtering of such massive data sets (for a further explanation of limitations see Section II.6).

The results presented herein have also identified genes whose regulation may be novel NF-κB-dependent mediators of cell death or survival in late IPC. Fadd is a well known stimulator of apoptosis through the Fas death receptor and subsequent caspase activation353, 354, but has not yet been demonstrated to be directly regulated by NF-κB or to play a role in late IPC in the heart. Fadd is one of the genes whose microarray predicted expression was validated using QRT-PCR. Microarray results indicated that fadd is down-regulated by IPC in an NF-κB-dependent manner (Fig. 6). However, QRT-

PCR only confirmed an IPC-dependent down-regulation of fadd that appeared independent of NF-κB (Fig. 7). Although Fadd (in addition to COX-2) displayed a QRT-

PCR determined expression change inconsistent with a microarray determined expression change, QRT-PCR generally confirmed the expression change patterns depicted by the microarray results. QRT-PCR confirmed microarray expression results with 14 of the 18

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tested expression changes being statististically similar between the two assays (and 100% similar in trend of expression change) (Figs. 7 and 8). Btg1 and rhob, genes whose expression is induced in late IPC via NF-κB (Fig. 6), are also intriguing possibilities as novel cardioprotective mediators in late IPC as they have both been previously suggested to inhibit NF-κB activation.355, 356 As previously discussed, upregulation of NF-κB inhibitory gene products such as Btg1 and RhoB may play a role in late IPC cardioprotection through inhibition of NF-κB activation during subsequent prolonged I/R

(thus inhibiting the known pro-injury activity of NF-κB in ischemia/reperfusion).

Additional genes from Figure 6 have been loosely associated with preconditioning phenomena, but have not yet been shown to be transcriptionally regulated by NF-κB. For example, btg2 was identified by microarray analysis to be upregulated via IPC in the rat spinal cord.357 Rtn4 expression in the myocardium has been reported to increase with exercise-induced gene expression changes.358 Yet other genes that our results have determined to be NF-κB regulated following late IPC have neither been previously associated with NF-κB-dependent regulation nor late IPC cardioprotection (itm2b, zfp346, igh-1a, tnfsf12, nr4a1, tnfrsf1a, pik3r1, fastk, ddx20, pafah1b1, actg1, amigo1, kcne1, cav2, prpf19, pex7).

NF-κB-dependent regulation of the angiogenic response in late IPC

Our analysis of the NF-κB-dependent gene regulation following IPC also indicates that NF-κB is altering the expression of many gene products involved in angiogenic processes (Figs. 5 and 6). Ischemic or hypoxic preconditioning was shown earlier in this decade to be a trigger of angiogenic processes in the heart.359-361

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Interestingly, it was shown very early in studies of angiogenesis in response to hypoxic preconditioning that NF-κB may play a role in these processes in the heart.359 Our findings support a strong role for NF-κB in the regulation of angiogenesis in the myocardium and specifically identifies 11 genes associated with angiogenesis whose regulation is controlled by NF-κB following late IPC (Fig. 6).

Among the most prominent and well-studied of the gene products in the angiogenesis GO grouping is hif1a, the oxygen sensitive HIF-1α subunit of the cardioprotective HIF-1 transcription factor. Though NF-κB has been shown to regulate

HIF-1α gene expression in vitro in cells such as fibroblasts362, HEKs363, and lung carcinoma cells364, we believe these are the first results to demonstrate that HIF-1α gene expression in the myocardium is transcriptionally controlled by NF-κB activation. Using

QRT-PCR to confirm the microarray results, we conclusively show that HIF-1α is transcriptionally controlled by NF-κB following late IPC (Fig. 7).

As previously discussed, the cardioprotective properties of HIF-1α in early

IPC156, 159 and against I/R injury345, 365 have been well established, but a conclusive role for this factor in late IPC has not. Our finding that NF-κB may directly upregulate HIF-

1α mRNA following an IPC stimulus is an important direct link between NF-κB and

HIF-1 signaling and places HIF-1 signaling downstream of NF-κB in late IPC. Findings by Kukreja’s group suggested that cobalt chloride (CoCl2) treatment is able to induce a late phase of cardioprotection through upregulation of HIF-1α that is independent of NF-

κB.153 Taken together with findings that induction of HIF-1α activity is sufficient to reduce I/R injury345, 365, these results would suggest that HIF-1 activation downstream of

NF-κB contributes to the late phase of IPC cardioprotection.

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The precise mechanism by which HIF-1 activation may be contributing to late

IPC cardioprotection or what specific subset of genes HIF-1 may be regulating in late

IPC remains largely unanswered. Genes shown to be regulated by HIF-1 transcriptional activation include vascular endothelial growth factor (VEGF), bcl-2, erythropoietin

(EPO), Hsp70, and many other genes involved in processes such as metabolic adaptation, angiogenesis, and apoptosis.155,366, 367 In addition to the anti-apoptotic role of Bcl-2 in cardioprotection already discussed, Iervolino et al. showed that Bcl-2 expression may also enhance HIF-1 transcriptional activity and subsequent VEGF expression.368

Treatment with EPO has been shown to be sufficient to protect the heart against I/R injury, presumably through activation of protective signaling kinases such as Akt, PKC, and ERK1/2.369 VEGF has also been shown to be upregulated by preconditioning stimuli and is also now recognized as having cardioprotective properties.370-372

Given a primary finding of this work that Hsp70.3 is induced by an NF-κB transcriptional network following late IPC and is critical to the resulting cardioprotection, it is interesting to note the potential of HIF-1 to contribute to the induction of Hsp70 expression. While direct transcriptional regulation of HIF-1 on the Hsp70.1 or Hsp70.3 promoters has yet to be shown, Hsp70 is considered to be regulated, at least in part, by

HIF-1 and increased Hsp70 expression in association with HIF-1 activity in cardiocytes has been observed. Date et al. showed that exogenous expression of HIF-1 protected neonatal cardiomyocytes against simulated I/R injury in vitro and resulted in increased

Hsp70 expression.155 It was also very recently shown through selective pharmacological blockade that HIF-1α may be regulating Hsp70 expression following hypoxic preconditioning in the kidney.162 Unfortunately, neither of these works distinguished

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between the two inducible Hsp70 genes and were thus unable to conclude which Hsp70 gene was responsible for the observed HIF-1-mediated increase in Hsp70 expression. The direct regulation of HIF-1 on cardioprotective Hsp70.3 expression has not been well studied in the context of preconditioning, but, as will be more thoroughly discussed later,

HIF-1 appears to be a prime candidate transcription factor for co-regulation of Hsp70.3 gene expression following late IPC.

Many of the genes from the angiogenesis-associated GO terms, including hif1a, btg1, rhob, rtn4, tnfsf12, and pafah1b1 are also categorized in the GO terms associated with programmed cell death (Fig. 6; denoted by blue font). A potential role for btg1, rhob, and rtn4 in late IPC was mentioned briefly in the discussion of NF-κB-regulated cell death genes, but very little is known about their role in myocardial angiogenesis.

However, a 2006 study by Skuli et al. suggests a role for rhob upregulation in the cardioprotection of late IPC by showing that activation of Rhob leads to stablization and enhanced activation of HIF-1α.373 It still remains to be seen for many of these angiogenesis-associated gene products how or if they contribute to late IPC and whether their roles are through direct modulation of the cell death limiting effects of IPC or via more long-term adaptive responses geared toward post-MI remodeling of the myocardium.

NF-κB-dependent regulation of the heat shock response in late IPC

Our results also indicated that NF-κB is a central regulator of the heat shock response following IPC (Figs. 5 and 6). This result is not overly surprising given the known cytoprotective properties of heat shock proteins and the importance of heat shock

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proteins (especially Hsp70) to late IPC.38, 112, 260, 374, 375 Unfortunately, there is only a small body of work indicating NF-κB-dependent regulation for a few individual heat shock proteins, but not much known about the importance of NF-κB activation in the overall process of a large scale heat shock response.374, 376 Much of the information regarding the interplay of NF-κB and heat shock proteins suggest a role for heat shock proteins in the inhibition of NF-κB activation. Our results suggests a role for NF-κB- dependent transcriptional regulation of more than a dozen heat shock response-associated genes after late IPC, including the well studied heat shock proteins Hsp110 (hspa4),

Hsp90α/β, and Hsp70.1/3 (Fig. 6).

Hsp70 is the most well studied of the heat shock proteins in cardiac preconditioning and is widely recognized as being an obligatory induced protein for late

IPC and is generally thought to be cytoprotective in response to cellular stress.216, 257, 260

Previous work has not, however, distinguished between the roles of the two different inducible Hsp70 genes, Hsp70.1 and Hsp70.3, with regard to cardioprotection. Our work is the first to demonstrate differing roles for these gene products in the myocardium and show that only Hsp70.3 contributes to late IPC cardioprotection (Figs. 9 and 10).

Potential mechanisms for Hsp70 cytoprotection against I/R injury following late IPC will be discussed in greater detail in the next section.

In addition to Hsp70 serving a known role in programmed cell death, Bcl-xL

(bcl2l1) and myc are also found in both programmed cell death and heat shock response- associated GO terms (Fig. 6; red font). Although not represented as such, many proteins involved in the heat shock response could be said to play role in programmed cell death pathways by virtue of the cytoprotective properties of the heat shock response in

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myocardial preconditioning. Hsp90, in particular, has been directly linked to inhibition of cell death pathways through direct interactions with apoptosis signaling proteins such as

Apaf-1377 and survivin378. As a result of its anti-apoptotic effects, Hsp90 inhibitors are of interest as antitumor agents to prevent evasion of apoptotic signaling and oncogenesis.379,

380 Hsp90 has been shown to play a role in classical (early) preconditioning and Jiao et al. associated Hsp90 with KATP channel-mediated protection, which is known to be important for both early and late phases of preconditioning.381,382, 383,384 Hsp90 inhibitors have subsequently been shown to inhibit anesthetic- and ischemia-induced early preconditioning.385 Increased Hsp90 has been suggested to be involved in delayed heat shock preconditioning of cultured myocytes in vitro, but very little is known about the role of Hsp90 in delayed preconditioning in the in vivo myocardium.386

In summary, results from our microarray analysis demonstrate that NF-κB is a key regulator of the heat shock response following late IPC (Figs. 5 and 6). QRT-PCR expression analysis confirms that Hsp70.1, Hsp70.3, and Hsp90 are all subject to NF-κB- dependent transcriptional regulation following late IPC (Figs. 7 and 8). There is also strong evidence put forth by ourselves and others to support the role of these heat shock proteins in late IPC. The observed overlap of gene products between the gene ontology groups further reinforces the notion that NF-κB regulates gene products centrally critical to the cardioprotection of late IPC.

Summary of NF-κB-dependent gene regulation in late IPC

In addition to the aforementioned genes whose expression is regulated by NF-κB in late IPC, our results were also assessed with regard to other known mediators of NF-

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κB-dependent late IPC such as iNOS168, COX-2176, and HO-192. While none of these genes were determined by our analysis to be of statistical significance with regard to NF-

κB regulation in late IPC, their expression changes as depicted by our microarray results did trend toward previously published results. Given its generally accepted critical importance to NF-κB-dependent late IPC cardioprotection, QRT-PCR was used to confirm an upregulation of COX-2 gene expression in our model of late IPC (Fig. 7).

COX-2 expression following IPC has been presumed to be NF-κB-dependent171, but our results are the first to conclusively show NF-kB-dependence of post-IPC COX-2 expression using a transgenic mouse model to specifically and completely inhibit NF-κB activity. We believe the statistical exclusion of these previously published late IPC genes from our final data analysis is a short-coming of high-throughput analysis of large data sets such as whole genome gene microarray analysis (see Section II.6 for a discussion of limitations). Thus, the results depicted from large microarray analyses are often (as we have done here) validated through select QRT-PCR expression analysis of key genes.

Though a small handful of key genes may have been missed, such as COX-2, QRT-PCR confirmation of gene microarray predicted expression changes (Figs. 7 and 8) indicate a high degree of accuracy within our microarray results.

In summary, it is now well-established and generally accepted that NF-κB- dependent transcriptional activation is necessary for the development of late IPC cardioprotection. However, only a handful of NF-κB-dependent gene products following late IPC had been previously identified. Our results contribute to the understanding of

NF-κB-dependent cardioprotection in late IPC through the demonstration that NF-κB is a key regulator of genes involved in multiple processes in late IPC including programmed

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cell death, angiogenesis, and the heat shock response. Our findings not only corroborate previously published findings regarding the regulation of cell death genes by late IPC, but also provide novel contributions to the field through either being unknown members of the NF-κB-dependent transcriptome or novel mediators of late IPC cardioprotection. One key novel contribution of this work is the demonstration that, while both Hsp70.1 and

Hsp70.3 are subject to NF-κB-dependent transcriptional regulation following late IPC, only Hsp70.3 contributes to late IPC cardioprotection. The following sections will further explore the role of Hsp70.3 in late IPC as well as additional mechanisms by which its expression following late IPC is modulated.

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Section 3. The Role of Hsp70.1/3 in Late IPC Cardioprotection

One of the pioneering studies of heat shock proteins in cardioprotection investigated the hypothesis that IPC would lead to the expression of the cytoprotective inducible Hsp70 gene.38 This initial study was successful in showing a correlation between Hsp70 expression and cardioprotection 24 hours following the initial IPC stimulus.38 Since then, it has been demonstrated that genetic knockout of both inducible genes of Hsp70 (70.1 and 70.3) abolishes the cardioprotection of late IPC.260 A key finding of the work presented in this dissertation is that only the Hsp70.3 gene contributes to cardioprotection in late IPC, while Hsp70.1 appears to play an injurious role in I/R injury (Figs. 9 and 10). Unfortunately, very little distinction between the

Hsp70.1 and Hsp70.3 has been made in the published literature. These two distinct inducible genes of Hsp70 have been primarily considered as one and the same throughout the published literature and taken together as simply “Hsp70”. Our results put forth strong evidence for a distinct and different role for these two genes indicating that they should no longer be considered as simply inducible “Hsp70”, but must be considered as two separate genes, Hsp70.1 and Hsp70.3, in regard to both expression and function.

Though there exist multiple possibilities as to what the mechanism(s) of Hsp70.3-induced cardioprotection may be, the specific processes by which Hsp70.3 induction leads to cardioprotection remains unknown.

Due to the fact that inducible Hsp70 proteins serve many functions as a molecular chaperone in response to cell stress, it is likely to have many protein binding partners

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rendering the determination of a precise mechanism of its cardioprotection very difficult.

However, since we have shown the two inducible Hsp70 proteins to function differently in the myocardium in response to ischemic stimuli, the examination of the differential binding partners of the two may provide valuable insight to the mechanism of action of the two proteins. Unfortunately, the previous lack of knowledge concerning their distinct functions along with the lack of available antibodies able to distinguish between the two gene products has contributed to the indiscriminate consideration of Hsp70.1 and

Hsp70.3 as simply “Hsp70” in most of the available literature. There are no available antibodies to distinguish between the two proteins since they vary by only a single amino acid change. The lack of a differentiating antibody has also made it very difficult to design studies on the protein level addressing the molecular functions of the individual gene products.

One possible cardioprotective mechanism of Hsp70.3 is related to the classical function of Hsp70 as a protein folding chaperone.257 It is plausible that the chaperone function of Hsp70.3 is required for the increased protein synthesis that the late phase of preconditioning is dependent upon.261 There is some evidence to suggest that an increase in expression of Hsp70 proteins may not play a direct role in the cardioprotection, but rather an auxiliary role as a chaperone to folding and/or transport of newly synthesized proteins to subcellular organelles such as the mitochondria. Hoshida et. al. concluded that

Hsp70 induction could not account for cardioprotection resulting from whole body hyperthermia in rats, but rather suggested that it may be necessary for the processing and transport of cardioprotective proteins such as MnSOD to the mitochondria.97 This conclusion was based on their observation that Hsp70 expression peaked ~30 hours prior

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to observed cardioprotection but is contradictory to the finding that overexpression of

Hsp70 is sufficient to induce protection against I/R injury.114 However, our findings of opposing functions of the two inducible Hsp70 proteins in ischemic injury raise questions of concern with such conclusions based on an ambiguous expression increase of

“Hsp70”. It is very possible that conflicting reports on the role or functionality of

“Hsp70” are due to the differential expression and function of Hsp70.1 and Hsp70.3, but such a possibility requires further investigation.

In a similar manner, Hsp70.3 could also provide protection through its role in the unfolded protein response (UPR) in protection against endoplasmic reticulum (ER) stress.

Hsp70 has been shown to act as part of the beneficial UPR in response to ER stress and aids in the clearance of misfolded proteins from the ER.387 It is well established that ER stress is triggered in the ischemic myocardium and can lead to apoptosis if not quickly remediated (for a review of ER stress in the heart see Toth et. al.15).

Hsp70 has also been linked directly to the inhibition of apoptotic pathways. Two different studies by Beere et al. and Saleh et al. demonstrated Hsp70 inhibition of Apaf-

1-mediated apoptosome formation through inhibition of procaspase-9 recruitment.388, 389

Hsp70 may also act as an inhibitor to pro-apoptotic c-Jun N-terminal kinase (JNK) signaling.390, 391 Other studies have even suggested that Hsp70 expression may prevent apoptosis in caspase-independent manner or even by acting downstream of caspase activation.392 Additional anti-apoptotic mechanisms for Hsp70 such as stabilization of anti-apoptotic Bcl-xL or inhibition of pro-apoptotic Smac, AIF, or cytochrome c release have also been put forth, but a clear definition of how and where Hsp70 interacts with the apoptotic signaling cascade remains elusive.393, 394

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One very intriguing potential mechanism for Hsp70.3-induced cardioprotection is via modulation of NF-κB activity during subsequent ischemia in the late window of IPC- induced protection. It is known that NF-κB inhibition is protective against prolonged I/R injury.271 It has been previously suggested that Hsp70 may inhibit NF-κB activation in neuronal cells.395, 396 The concept that Hsp70.3 may inhibit nuclear translocation of NF-

κB is an intriguing possibility since it has previously been shown that preventing NF-κB activation after I/R is protective against I/R injury.271 Thus, elevated protein levels of

Hsp70.3 during the late phase of IPC could provide cardioprotection through inhibition of

NF-κB activation during subsequent ischemia/reperfusion (I/R). In addition to NF-κB,

Hsp70 has been suggested to also modulate the transcriptional activity of AP-1, another transcription factor suspected to play a role in late IPC.100, 397 Preliminary results such as these could lead to new hypotheses concerning a role for Hsp70.3, but not Hsp70.1, in modulation of NF-κB, AP-1, or other transcriptional processes.

In addition to potential Hsp70 regulation of NF-κB activity, many studies have suggested that heat shock induces a general inhibitory effect on NF-κB activity. This is especially true for the case of heat shock factor 1 (HSF-1), a master transcriptional regulator of the heat shock response, that is believed to display an inhibitory relationship with NF-κB.398 This may appear to be in contrast with our results indicating an importance of NF-κB in regulating heat shock response genes in the myocardium.

However, feedback regulation of heat shock and NF-κB activation have yet to be examined together in the case of late IPC and subsequent I/R in the myocardium.

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It is also very interesting to note that the inducibility and functionality of Hsp70 appears to be an age-dependent process.399, 400 It is unclear what the specific mechanisms behind this may be, but a decline in Hsp70 expression with age appears to play a role in cell senescence and renders the heart uncapable of mounting a sufficient HS-induced PC protection against I/R injury.401, 402 It has not been directly investigated whether a decrease in Hsp70.3 expression is directly responsible for the diminished preconditioning ability of the aged myocardium.

The findings presented herein would suggest a potential clinical benefit to cardioprotection from the increased expression of Hsp70.3 expression. Bimoclomol and arimoclomol are both synthetic hydroxylamine derivatives that act as coinducers of heat shock proteins, including Hsp70, and may have therapeutic potential for the treatment of pathologies that may benefit from an increased heat shock response.403 The development and testing of these compounds is still ongoing, but it appears they act through stabilization of HSF-1 DNA binding in the absence of stress inducers and have shown promise as neuroprotective agents in the treatment of amyotrophic lateral sclerosis

(ALS).403, 404 Although investigation into the use of compounds to enhance the expression of Hsp70 for the hope of clinical benefit is ongoing, the molecular mechanisms underlying Hsp70 cytoprotection, including Hsp70.3 cardioprotection, remain unknown.

Indeed, several or all of the mechanisms delineated above may be occurring simultaneously and together underlie the observed Hsp70.3-mediated protective effects.

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Section 4. Regulation of Hsp70.3 Gene Expression in Late IPC

The mechanisms by which Hsp70 expression is controlled are also an important aspect that is not completely understood. Our novel results showing opposing functions with regard to cardioprotection for Hsp70.1 and Hsp70.3 make the understanding of this regulation all the more important. Two gene products whose molecular functions are opposing are also likely to be regulated differently such that they may be expressed within the cell at different times. Although Hsp70.1 and Hsp70.3 only differ by one amino acid in their protein sequence, these two genes share much less homology in their regulatory regions. The promoter regions of the two genes are homologous only through the first 250 base pairs and subsequently diverge in sequence and predicted transcription factor binding motifs past that point. Additionaly, the two genes display almost no homology between their 3’-UTRs, a region recognized to be important for post-transcriptional regulatory activity. Since we have shown differential function of these two gene products in the heart as they relate to IPC and I/R injury, it is likely that the expression of these two genes would also be subject to differential regulation.

Our results demonstrate that expression of Hsp70.3 following late IPC is subject to both transcriptional and post-transcriptional regulation. At the transcriptional level, inhibition of NF-κB significantly reduces the induction of Hsp70.3 mRNA following late IPC, indicating transcriptional regulation by NF-κB-dependent processes

(Fig. 8). However, even in the absence of NF-κB activation, a significant induction of

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Hsp70.3 mRNA is observed following IPC. This result indicates transcriptional regulation of Hsp70.3 by other IPC-dependent transcriptional processes in addition to

NF-κB. However, upon examination of Hsp70 protein levels (comprised of both Hsp70.1 and 70.3 due to the lack of a discriminating antibody), we found no significant increase in

Hsp70 protein expression following IPC in 2M mice despite a 13.8-fold increase in mRNA expression (Fig. 11). Thus, the expression of Hsp70.3 is also subject to NF-κB- dependent alleviation of post-transcriptional silencing following late IPC.

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IV.4.1 Transcriptional Regulation of Hsp70.3 Gene Expression

HSF-1 is recognized as being a master transcriptional regulator of the heat shock response.405 Despite the vast amount of work undertaken investigating the cardioprotective properties of the heat shock response and the role of heat shock proteins in preconditioning, very little work has investigated the role of HSF-1 in these processes.

Overexpression of HSF-1 in the heart was sufficient to protect against cardiomyocyte cell death and protect the heart from I/R injury.406 Kukreja’s group also showed that siRNA knock-down of HSF1 expression decreases heat shock protein expression and abrogated heat shock-induced delayed preconditioning.407 In both cases, the effects of HSF-1 could be expected to be, at least in part, through induction of Hsp70 expression.

As discussed in Section I.1.3 and demonstrated by our results (Fig. 31), both

STAT3 and AP-1 are implicated in late IPC cardioprotection. STAT3 and other members of the STAT family of transcription factors are already well known regulators of Hsp70 expression.149 The case for AP-1 controlled transcriptional regulation of Hsp70 genes is not as strong, but there is published work supporting a transcriptional role for AP-1 as well.408, 409

Analysis of the gene promoter sequences of Hsp70.1 and Hsp70.3 reveals a strong homology (~99%) between the two promoter sequences for approximately the first 250 bases upstream of the transcriptional start site (Fig. 32; homology indicated by red box).

Prediction of transcription factor binding sites using MatInspector318 revealed that consensus binding sites for NF-κB, HSF-1, STAT3, and AP-1 are all found within this

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region (Fig. 11). However, beyond the first 250 bases, the promoters of the two genes share very little homology, indicating that transcriptional regulation may represent a node of differential regulation for the two genes. However, this hypothesis is not consistent with the observation of a very similar IPC and NF-κB-dependent pattern of expression depicted by our results (Fig. 8). The predicted transcription factor binding sites within the non-homologous regions of the two promoters are of interest though given our findings of a protective role for Hsp70.3 and an injurious role for Hsp70.1. Curiously, the distal regions of the Hsp70.1 promoter contain two additional predicted binding sites for AP-1, a transcription factor whose activation occurs downstream of JNK and is considered to be pro-apoptotic in many circumstances.410-412 In contrast, the distal Hsp70.3 promoter contains additional binding sites for NF-κB and STAT3, two factors commonly associated with avoidance of apoptosis and cell survival.413, 414

Using T4-mediated delivery of transcription factor targeting decoys, we showed that, in addition to NF-κB, activation of STAT-3 and AP-1 are also necessary for late IPC protection (Fig. 31). Taken together with previously published results, our results support a role for STAT3 and AP-1 in transcriptional regulation of Hsp70.3 after late IPC. A more detailed understanding of coordinated transcriptional regulation of Hsp70.3 in late

IPC would beneficial to the understanding of how IPC induces a cardioprotective gene program giving rise to a late phase of cardioprotection.

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IV.4.2 Post-Transcriptional Regulation of Hsp70.3 Protein Expression

Our results first suggested Hsp70 protein expression to be subject to post- transcriptional regulation by the observation of an inconsistency between the induced mRNA and protein levels in 2M mice following IPC. There has been evidence for the post-transcriptional regulation of Hsp70 mRNA transcripts dating as far back as 1986 when it was observed that heat shock appeared to have a stabilizing effect on induced avian Hsp70 transcripts that were quickly degraded upon return to normal growth temperatures.415 Post-transcriptional regulation of Hsp70 genes was also observed in simpler eukaryotes such as trypanosoma and leishmania, and this effect was actually linked to the 3’-untranslated regions (3’-UTR) of the genes.416, 417 More recently, the 3’-

UTR of Hsp70 genes have been shown to be important regulatory regions in porcine and murine animal models.418, 419

Post-transcriptional modulation of gene expression mediated at the 3’-UTR has been cast into a new light recently with the description of microRNAs (miRNAs) as small non-coding RNAs that exert regulatory modulation on gene expression through sequence specific interactions at the 3’-UTR of the target gene.420 Work is now beginning to demonstrate miRNA targeting and regulation of the Hsp70 genes in cardiomyocytes.421,

422 Yin et al. even demonstrated a recapitulated cardioprotective state through exogenous treatment with a set of miRNAs found to be upregulated following late IPC.422 This miRNA treatment was associated with an increase in protein expression of eNOS, HSF-1, and Hsp70. The understanding of the miRNA regulatory network is rapidly expanding

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and miRNAs are likely to be attributed larger roles in the process of ischemic preconditioning as miRNA regulation of several additional mediators of IPC including

NF-κB, COX-2, and HIF-1α have already been described under other circumstances.423-

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In addition to post-transcriptional regulation by miRNA-mediated processes, mRNA transcripts are susceptible to modulation of their stability and degradation through alternative polyadenylation.322 Although as many as 50% of all mouse and human contain multiple polyadenylation signals within their 3’-UTR, it is not completely clear what the regulatory purpose of alternative polyadenylation of these gene products may be.324 Upon examination of the 3’-UTR sequence of the Hsp70.3 gene, four different predicted polyadenylation sites were identified, making Hsp70.3 a candidate for regulation via alternative mRNA polyadenylation (Fig. 24).

The results presented in this work suggest the existence of at least two different means of post-transcriptional regulation that appear to act in concert to modulate Hsp70.3 expression in late IPC. First, Hsp70.3 expression appears to be modulated by two different miRNAs, miR-378* and miR-711, whose expression is down-regulated by IPC.

In addition, the Hsp70.3 is subject to alternative polyadenylation following late IPC in such a way that would appear to make it not susceptible to targeting by miR-378*.

Post-transcriptional regulation of Hsp70.3 protein expression by miRNAs

To begin to examine the miRNA regulatory network of late IPC and the influence of NF-κB activation on miRNA expression levels, we performed a preliminary QRT-

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PCR based array to examine the expression changes of nearly 400 murine miRNAs following late IPC. Our results depicted 24 miRNAs whose fold expression was changed

≤ 2-fold following late IPC (Table 7). It was beyond the scope of this work to determine the potential role of these miRNA expression changes. This would prove to be an especially difficult task given that each miRNA is putatively predicted to target upwards of hundreds of individual gene products. However, it is very interesting to note that of the

21 of the 24 miRNAs whose fold expression was changed ≥ 2-fold are represented by a decreased expression following late IPC. When considering the tendency of late IPC to induce positive changes in gene expression (≥ 60% of all genes significantly changed in

WT IPC vs. Sham were upregulated; Appendix A), it is logical that the majority of expression changes observed in suppressive miRNAs would be negative.

Given the post-transcriptional suppression of Hsp70.3 observed in the 2M mice following late IPC, we hypothesized that NF-κB-dependent suppression of an Hsp70.3 targeting miRNA may be contributing to the increase in Hsp70.3 protein expression post-

IPC. Of the twelve miRNAs predicted to target Hsp70.3, only miR-378* and miR-711 were significantly downregulated following late IPC (Table 8, Fig. 20). Furthermore, only miR-711 was downregulated post-IPC in an NF-κB-dependent manner. Since nothing was currently known about the ability of either of these two miRNAs to regulate

Hsp70.3 or their role in late IPC, we examined the regulatory role of both on endogenous

Hsp70 expression as well as an Hsp70.3 3’-UTR luciferase reporter. We chose to use heat shock as an in vitro model of Hsp70.3 induction since it resulted in a similar level of induction as in vivo IPC. Whether or not miR-378* and miR-711 are similarly regulated by IPC and heat shock was not of critical concern since miRNA-mediated repression was

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tested through the addition of exogenous miRNAs. Our results show that both miR-378* and miR-711 repress Hsp70.3 induction of both endogenous protein and luciferase reporter in response to heat shock in a dose-dependent and sequence specific manner.

An IPC-dependent decrease in the levels of miR-378* and miR-711 would argue for their importance in the regulation of basal Hsp70.3 expression. Therefore, Hsp70.3 is regulated by both transcriptional and post-transcriptional means, both of which are dependent on the stimulus of IPC and activation of NF-κB. Upon a stressor such as IPC, the cell not only ramps up production of the transcript (through NF-κB and other transcriptional mediators), but also represses production of silencing miRNAs (miR-378* and miR-711). In the particular case of Hsp70.3 expression, it would appear that NF-κB serves a direct role through transcriptional activation of the Hsp70.3 gene as well as an indirect role through repression of miR-711.

The concept of stimulus dependent post-transcriptional regulation might be a sensible evolutionary choice for stress response genes such as Hsp70.3, where increased expression of the protein is desired as quickly as possible in response to a potentially damaging stimulus. Thus, the Hsp70.3 expression system represents a basal (but inducible) level of mRNA expression that provides a buffer of “translation ready” mRNA transcript that is post-transcriptionally silenced until needed. This model of regulation is supported by previous work on avian Hsp70 expression demonstrating a stabilization of mRNA transcript in response to a heat shock stimulus that quickly dissipated upon return to normal temperature.415

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Post-transcriptional regulation of Hsp70.3 protein expression by alternative polyadenylation

The second post-transcriptional regulatory mechanism suggested by our data is alternative polyadenylation of the Hsp70.3 3’-UTR. Alternative polyadenylation has been a known phenomenon for quite some time and has gained interest lately as an important mechanism by which gene expression may be controlled.322 Multiple polyadenylation sites have been predicted to exist in roughly 50% of all mouse and human gene sequences.322, 324 In fact, the Hsp70.3 3’-UTR was found to contain four different polyadenylation sites, all of which were expressed to some degree in primary MEF cells

(Fig. 24-25). This process, by way of removal of specific miRNA binding sites, may provide another means by which expression of Hsp70.3 protein is controlled.

Given the prevalence of miRNA targeting to the 3’-UTR of target genes to regulate post-transcriptional gene expression, it seems likely that alternative polyadenylation is another means by which cells can fine tune protein production from transcribed mRNAs. A very elegant study in 2009 by Mayr and Bartel illustrated the widespread nature of alternative polyadenylation products among oncogenes and demonstrated that shortened 3’-UTRs resulting from alternative polyadenylation led to an increased protein expression.426 The increased expression from a shortened 3’-UTR was presumably the result of a loss of regulatory elements (i.e. miRNA binding sites) within the 3’-UTR. This example illustrates the ability of post-transcriptional regulatory processes to modulate protein expression in the absence of transcriptional alterations and makes clear the critical nature that alternative polyadenylation may play in regulating gene expression.

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Our results showed that under basal conditions in the in vivo myocardium as well as in H9c2 and MEF cells in vitro, the mRNA product from polyadenylation at site 3 was the most prevalent. Interestingly, the binding site for miR-378* is located between PolyA site 2 and site 3. Our results indicate a shortening of the Hsp70.3 3’-UTR, or a decreased ratio of polyadenylation product #3 to product #2, following late IPC (Fig. 28C).

Therefore, an alternative polyadenylation at site 2 instead of site 3 in response to a stimulus such as IPC or heat shock would be consistent with an increase in protein expression due to the loss of regulatory elements within the 3’-UTR. This process, along with transcriptional upregulation of gene expression and decreased expression of two

Hsp70.3 targeting miRNAs, is likely to assist in the robust and timely increase in

Hsp70.3 protein expression required for late IPC induced cardioprotection.

Interestingly, a similar alteration of polyadenylation sites in response to a heat shock stimulus in vitro was found to occur in MEFs, but not H9c2 cells. Thus, one may expect to observe a differential regulation of Hsp70.3 protein expression by miRNAs post-HS in MEFs versus H9c2 in light of the results indicating that MEFs, but not H9c2 cells, undergo shortening of their 3’-UTR sequence following HS (Fig. 28). The heat shock-induced shift to a higher ratio of polyadenylation at more proximal to the end of the coding sequence (PolyA site 2) leads to a smaller population of transcripts harboring the predicted miR-378* binding site presumably making the total population less susceptible to miR-378* regulation. This effect is not observed when using the Hsp70.3

3’-UTR luciferase reporter since the cells do not need to be subjected to heat shock stimulus due to the constitutive nature of the CMV promoter driven expression of the reporter. The increased potency of the siRNA compared to the miRNAs would also be

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expected as the siRNA targets within the first 100 bases of the 3’-UTR such that it would silence all populations of Hsp70.3 mRNA transcripts regardless of polyadenylation site.

The removal of the miR-378* binding site is likely to contribute to increased expression from the shortened 3’-UTR version of Hsp70.3 mRNA following late IPC.

However, it is unclear at this time what other implications this alternative polyadenylation may have on Hsp70.3 expression. It is also unknown to what extent polyadenylation products 1 and 4 are expressed in the myocardium or if there is a physiological role for these other polyadenylation products. Another unknown is that of mechanisms involved in regulating the site selection and polyadenylation of the Hsp70.3 transcript. These mechanisms must be stimulus specific as the polyadenylation site of the

Hsp70.3 transcript is altered in response to IPC and a heat shock stimulus. However, the lack of a heat shock specific polyadenylation in H9c2 cells also indicates to this to be a cell-type specific process. Qualitative DNA gels presented in Fig. 27 hinted at a possible

NF-κB-dependent regulation of this process, but quantitative data regarding the ratio of polyadenylation products from the 2nd and 3rd predicted polyadenylation sites (Fig. 28) indicate this to be an NF-κB-independent process.

In summary, our data indicates that increased Hsp70.3 protein expression following IPC is likely to be supported by post-transcriptional processes leading to the stabilization of the Hsp70.3 mRNA transcript. IPC induces the downregulation of the miRNAs miR-378* and miR-711 which are predicted to target the Hsp70.3 3’-UTR and were demonstrated in in vitro cell culture systems as exerting suppressive post- transcriptional effects on Hsp70.3 protein expression. In addition, the Hsp70.3 3’-UTR is alternatively polyadenylated following IPC, removing the binding site for the suppressive

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miR-378*. NF-κB appears to be central player in the modulation of post-IPC Hsp70.3 expression by directing an increase in gene expression of Hsp70.3 and a parallel decrease in the expression of the Hsp70.3 repressive miR-711.

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Section 5. Poly(glycoamidoamine)-Mediated Delivery of Transcription

Factor Decoys

This work was undertaken based on the rationale that there exists no efficient manner to acutely and specifically inhibit transcription factor activation in the in vivo heart. To demonstrate the successful application of a non-viral gene delivery system to address this need, we investigated the hypothesis that T4 glycopolymer-mediated delivery of transcription factor decoys will result in efficient and functional transcriptional blockade in the myocardium.

We have demonstrated efficient PGAA-mediated delivery of NF-κB decoys and their subsequent competitive inhibition of transcription factor-DNA binding, activation of

NF-κB-dependent gene expression, and NF-κB-dependent biological effects in vivo. The

PGAA polymers were developed in Theresa Reineke’s lab at both the University of

Cincinnati and Virginia Tech University, and their chemical properties and ability to compact and deliver plasmid DNA to various in vitro cell culture models have been previously described.313, 314, 325, 326 NF-κB was chosen as a primary target for the application of decoys with this system due to the relevance of NF-κB to cardiovascular disease and our lab’s ongoing interest in the role of this transcription factor in both late

IPC and I/R injury.271, 272, 276, 427 We also possess an IκBα dominant-negative mouse model of NF-κB inhibition that allows us to benchmark PGAA/decoy-mediated blockade of NF-κB with an established model of transgenic NF-κB blockade.276, 300

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Results demonstrate efficient T4 PGAA-mediated delivery of NF-κB decoys for in vitro inhibition of NF-κB transcriptional activation. PGAA-mediated delivery of NF-

κB decoys to H9c2 cells or NRVMs displays an enhanced efficacy of delivery and decreased cellular toxicity compared to the commonly used delivery vehicles

Lipofectamine 2000 and JetPEI (Figs. 29-32). PGAA-mediated delivery also led to a high degree of nuclear localization of the decoys (Figs. 30-31). Since this effect was observed in the absence of an NF-κB activating simulant, it is likely that the nuclear localization is inherent to the PGAA delivery and not a result of the decoy binding NF-κB in the cytoplasm and translocating with NF-κB upon activation. This is an important result as localizing the decoys to the nucleus is likely to enhance their efficiency for transcription factor blockade.

T4-mediated delivery of NF-κB decoys to the in vivo murine heart resulted in deeper penetration and more homogenous distribution of the decoys in the myocardium compared to delivery of the decoys without PGAA (Fig. 33). T4-mediated delivery of the

ODN decoys was sufficient to inhibit NF-κB activation in vivo as evidenced by not only the dose-dependent decrease in the gene expression of Cox-2, but also by the decreased infarct size following myocardial I/R injury (Fig. 34). Importantly, these results demonstrate T4-mediated delivery of NF-κB decoys is able to functionally block the transcriptional activation of NF-κB and provide a therapeutic benefit against myocardial infarction in vivo.

We also utilized this system to demonstrate that blockade of NF-κB, STAT3, or

AP-1 by PGAA delivered decoys inhibits the late phase of IPC cardioprotection (Fig. 35).

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The NF-κB-dependent cardioprotection of late IPC is in accordance with our results as well as published observations (Fig. 3).98 This important demonstration of applicability of

PGAA-mediated delivery of decoys for transcriptional blockade in late IPC makes it now possible to acutely block these factors both individually and in combination to assess their role in regulating gene expression changes after late IPC.

The ability to specifically block transcriptional activation in the in vivo myocardium using transcription factor decoys opens the door to many potential research and clinical applications. The further development of this system would allow for the inhibition of multiple transcription factors through specific decoy competition and aid in the investigation of their function in preconditioning stimuli as well as acute myocardial pathologies. In addition, specific and acute transcriptional inhibition achieved by decoys delivered in a non-cytotoxic manner would alleviate concerns surrounding compensatory effects potentially found in chronic transgenic and knockout animal models. Ideally, this system would be able to be applied in a semi-high-throughput manner in which its timely execution and flexibility with regard to target(s) and degree of blockade would make it a preferred choice over the pursuit of conditional transgenic mouse models.

The flexibility with regard to target(s) and degree of blockade is another major advantage of this system. As expected, and shown by our results (Section III.4), the effect of decoy inhibiton is titrateable much like other pharmacological drug entities. However, the decoys have a major advantage in that their precise sequence can be altered to be made more or less specific as desired and can also be made specific for distinct transcriptional subunits or isoforms, which often cannot be achieved with other pharmacological inhibitors. For example, decoys can be designed to be selective for NF-

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κB dimers harboring the p50 subunit-based selectiveness of DNA binding sequence; whereas, alternative pharmacological inhibitors such as Bay-11 or SN-50 tend to non- selectively inhibit all NF-κB activation. This modulation of DNA binding selectiveness can, in theory, be achieved for any transcription factor composed of multiple subunits or isoforms that have variable DNA binding sequences. Decoys may also be designed as longer sequences to contain binding sites for multiple transcription factors. PGAA- mediated decoy inhibition of transcription factors is also completely reversible as both the polymers and decoys are naturally biodegradable.

The glycopolymers used herein, or other similar polymers already under development, could also be applied for the delivery of RNA interference agents such as siRNA or miRNA. The ability to deliver siRNA or miRNA to the heart would greatly extend the applicability of this system and make it amenable to the study of the ever expanding miRNA regulatory network of the myocardium or allow for the acute and specific silencing (through siRNA) of nearly any gene expression product. Thus, while we have demonstrated the efficacy of this system for the inhibition of in vivo transcription factor activation through the delivery of transcription factor decoys, the potential of the in vivo transfection capability of the PGAA polymers may extend much further. Due to the specificity and flexibility that can be achieved through nucleic acid applications, this technology holds potential for a wide range of therapeutic and research applications.

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Section 6. Conclusion

In conclusion, while we may know many of the individual players involved in initiating and executing the processes that lead to the cardioprotected phenotype observed in late IPC, it is clear that our overall understanding of how these processes interact with each other to achieve cardioprotection is still in its infancy. Even in the cases where many of the effectors of cardioprotection have plausible or seemingly known mechanisms of action, we are left with the conundrum that they all seem equally and critically important to the protective phenotype. Thus, there has yet to be shown any additive effects of cardioprotection from the mediators of late IPC as all the pathways appear to have an all or none effect on the cardioprotection. It is likely that these end effectors are acting in a very coordinated scale-free network-like manner to manifest a cardioprotected state and we have not yet identified all the nodes and interactions possible within the network as they relate to cardioprotection.

The regulation of expression of these end effectors of late IPC is also not well understood. The roles of only a few transcription factors and their co-factors are partially understood in how they orchestrate the gene expression programs necessary to elicit the late phase of IPC. In addition, as demonstrated by the work presented herein, the emerging importance of post-transcriptional regulation posited by miRNAs on the 3’-

UTRs of mRNA sequence is just beginning to come into focus. The work presented in this dissertation aspires to make significant contributions to understanding the molecular mechanisms driving late IPC-induced cardioprotection with the goal of aiding in the

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development of future therapeutics for the treatment of ischemic heart disease. Though the knowledge in the field has advanced greatly in the twenty-plus years since the first description of ischemic preconditioning, it appears likely that a grand unifying theory of cardioprotection describing the complexity and coordination of all regulatory networks and factors involved will require a great deal more work.

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Section 7. Future Directions

The role of IPC induced changes in miRNA expression

Although data was gathered on the NF-κB-dependent expression changes of 356 conserved miRNAs, the work presented herein focused only on Hsp70.3 targeting miRNAs. Our results here determined IPC-induced expression changes in 25 different miRNAs; only one of which we determined to play a role in the post-transcriptional regulation of the IPC regulated, cardioprotective Hsp70.3 gene. It remains to be seen what other functional roles the remainder of the IPC regulated miRNAs may be playing in the development of the cardioprotection of late phase IPC. In addition, it remains unknown as to what extent the six NF-κB-regulated miRNAs play in the overall effects of NF-κB-dependent cardioprotection post-IPC.

Mechanism of Hsp70.3-mediated cardioprotection

The next logical step after demonstration of Hsp70.3 as a cardioprotective mediator of late IPC is to investigate the potential mechanism of this cardioprotection.

One intriguing possibility that has been previously discussed is that Hsp70.3 induction may be modulating subsequent NF-κB activity. Experiments are currently ongoing in WT and Hsp70.1 KO MEFs utilizing NF-κB-luciferase reporters in vitro to investigate the role of heat shock and Hsp70.3 on NF-κB activity. NF-κB activity following I/R subsequent to late IPC will also be assessed in vivo in WT, Hsp70.1, and Hsp70.1/3 KO

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mice to investigate the correlation between NF-κB activity and the presence of Hsp70.3 at the onset of I/R.

Additionaly, since we have shown the two proteins to function differently in both

IPC and I/R with Hsp70.3 being cardioprotective and Hsp70.1 mediating injurious effects, it is likely that these two stress inducible chaperone proteins are mediating differential effects through differential binding partners. We will utilize immunoprecipitation and proteomic techniques in WT, Hsp70.1, and Hsp70.1/3 KO mice to investigate the binding partners of these two proteins that may be responsible for mediating differential effects observed after cardiac ischemic stimuli.

Alternative polyadenylation of Hsp70.3 mRNA

We have determined that the mRNA transcript of Hsp70.3 has multiple sites at which addition of a polyadenylation signal can occur (Figs. 25-27). It is very likely that this has profound implications on miRNA targeting and regulation. For example, miR-

378* is predicted to bind between polyadenylation sites 2 and 3, making its predicted binding site only present in only a portion of the mRNA populations. The contribution and susceptibility of each polyadenylated transcript population to post-transcriptional regulation of Hsp70.3 will be addressed through site specific mutagenesis of the Hsp70.3 luciferase reporter plasmids to express 3’-UTRs containing only one polyadenylation site per reporter plasmid. The reporter constructs employed herein express the endogenous full-length Hsp70.3 3’-UTR and are thus subject to alternative polyadenylation much like the endogenous 70.3 transcript.

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Future studies will also need to address the mechanisms regulating the polyadenylation site selection following different stimuli. Binding analyses using the

Hsp70.3 3’-UTR sequence as a capture molecule could be performed to identify candidate proteins potentially involved in the regulation of polyadenylation. The expression, regulation, and Hsp70.3 3’-UTR binding of these candidates could then be examined following stimuli such as IPC to assess whether they may be playing a role in the IPC-mediated alternative polyadenylation of the transcript. Knockout or gene silencing models could then be employed to investigate the physiological relevance of the candidate polyadenylation regulating proteins to Hsp70.3 polyadenylation and cardioprotection following late IPC.

Role of miRNA on Hsp70.3 regulation in vivo following IPC

Our work herein has shown the ability of both miR-378* and miR-711 to suppress the expression of Hsp70.3 protein in vitro in multiple cell types. In addition, our results in an in vivo model of late IPC indicate that these regulatory mechanisms may play a role in the regulation of Hsp70.3 protein expression in late IPC. Future work will be undertaken to directly determine a functional role for miRNAs in the post-transcriptional regulation of Hsp70.3 in the myocardium.

Future work should also be done to investigate the coordinated regulation of

Hsp70.3 expression by miRNAs and alternative polyadenylation. Our results suggest that regulation of miRNA expression may act in concert with selection of alternative polyadenylation sites to post-transcriptionally modulate the expression of the Hsp70.3

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protein. Given the apparent coordinated nature of miRNA regulation and polyadenylation, it is possible that the miRNAs themselves may be involved in the regulatory selection of polyadenylation sites.

Transcriptional co-regulation of gene expression in late IPC

Future studies should be undertaken to inhibit AP-1 and STAT-3 activation in IPC through the in vivo application of transcription factor decoys for the purpose of examining the resulting global gene expression changes in much the same way we employed microarrays to assess NF-κB-dependent gene expression changes. ChIP should also be performed to determine direct binding of NF-κB, AP-1, and STAT-3 to the

Hsp70.3 promoter (as well as any other potentially co-regulated genes) in vivo following

IPC. These factors may all show the ability to directly bind the promoter region and regulate transcription, or they may act at the promoter in a coordinated manner such that they are tethered to the promoter through other DNA binding partners as part of a large transcriptional regulatory complex.

Effect of IPC on the NF-κB-dependent gene program following subsequent I/R

Not only is it well established that NF-κB activation is necessary for late IPC, but it is also well established that NF-κB is detrimental following I/R injury.271 In light of this, there has been very little investigation into the hypothesis that the end effectors of

IPC exert their cardioprotective effects, at least in part, through the inhibition of NF-κB

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activation during a subsequent I/R injury. As exemplified by the discussion of the potential cardioprotective mechanisms of Hsp70.3 (Section IV.3), there is a limited amout of existing data suggesting this to be a possibility. Future work should be undertaken to investigate expression changes in the NF-κB-dependent gene network following both I/R and IPC (in contrast to IPC or I/R alone) and what role specific end effectors of late IPC may play in modulating this gene expression.

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283 Appendix A. Genes significantly regulated by IPC relative to sham in wild-type mice.

Fold Changes P-Values Accession Gene name Symbol wtIPC v Sham 2M v wt IPC 2M v wt Sham wtIPC v Sham 2M v wt IPC 2M v wt Sham heat shock protein 1B Hspa1b 8.53 -4 8.11 0 NA 0.00154 NM_019664 potassium inwardly-rectifying channel, subfamily J, member 15 Kcnj15 5.42 NA NA NA NA NA XM_125745 integrin beta 1 binding protein 3 Itgb1bp3 4.17 -1.01 1.09 0 0.96282 0.38377 NM_010516 cysteine rich protein 61 Cyr61 3.91 -2.34 1.39 0 0.00518 0.19695 RIKEN cDNA 5430416G10 gene 5430416G10Rik 3.77 NA NA NA NA NA XM_130015 OTU domain containing 1 Otud1 3.71 -1.61 1.23 0 0.18966 0.32431 NM_010849 myelocytomatosis oncogene Myc 3.55 -1.71 2.44 NA 0.00484 0.00423 NM_010217 connective tissue growth factor Ctgf 3.41 -1.18 1.03 0 0.31614 0.81879 NM_007498 activating transcription factor 3 Atf3 3.37 -1.31 1.31 0 0.16056 0.04839 NM_009861 cell division cycle 42 homolog (S. cerevisiae) Cdc42 3.17 -5.18 1.63 0 0 0.31044 NM_010480 heat shock protein 90kDa alpha (cytosolic), class A member 1 Hsp90aa1 3.08 -2.07 2.24 0 0.02907 0.00222 NM_146130 heterogeneous nuclear ribonucleoprotein A3 Hnrpa3 3.06 -3.79 1.65 0 0.00011 0.39954 NM_025523 NADH dehydrogenase (ubiquinone) 1, subcomplex unknown, 1 Ndufc1 3.05 -5.87 1.63 0 0 0.49343 NM_144799 LIM and cysteine-rich domains 1 Lmcd1 3.03 -2.27 -1.46 0 0.00028 0.01329 AK009352 integrin beta 1 binding protein 3 Itgb1bp3 3.03 -1.34 1.01 0 0.09201 0.90181 NM_010275 glial cell line derived neurotrophic factor Gdnf 2.96 -1.27 -1.28 NA 0.19577 0.09763 NM_175456 actin-binding Rho activating protein Abra 2.95 -1.27 1.4 0 0.37964 0.02089 NM_175121 solute carrier family 38, member 2 Slc38a2 2.91 -2.81 1.62 0 0.00141 0.14557 NM_144799 LIM and cysteine-rich domains 1 Lmcd1 2.85 -1.65 -1.06 0 0.0236 0.56307 NM_030724 uridine-cytidine kinase 2 Uck2 2.84 -1.56 1.23 0 0.02033 0.26685 NM_009621 a disintegrin-like and metallopeptidase (reprolysin type) with thrombospondinAdamts1 type 1 motif, 1 2.83 NA NA 0.00003 NA NA NM_177603 frequently rearranged in advanced T-cell lymphomas 2 Frat2 2.78 -1.58 -1.03 0.00017 0.13845 0.86216 NM_019466 Down syndrome critical region homolog 1 (human) Dscr1 2.77 -1.86 1.06 0 0.03351 0.69308 NM_030724 uridine-cytidine kinase 2 Uck2 2.73 -1.26 1.15 0.00001 0.24234 0.41974 NM_025926 DnaJ (Hsp40) homolog, subfamily B, member 4 Dnajb4 2.72 -2.83 1.42 0 0.00038 0.14198 NM_011327 sterol carrier protein 2, liver Scp2 2.72 -4.07 1.77 0 0.00003 0.20864 NM_007570 B-cell translocation gene 2, anti-proliferative Btg2 2.67 -3.62 1.77 0.00003 NA 0.07942 NM_013506 eukaryotic translation initiation factor 4A2 Eif4a2 2.66 -4.89 1.73 NA 0.00001 0.25368 NM_175121 solute carrier family 38, member 2 Slc38a2 2.65 1.26 1.2 0 0.19636 0.34823 NM_175541 melanoma associated antigen (mutated) 1-like 1 Mum1l1 2.65 -1.26 1.44 0 NA 0.12083 NM_144515 zinc finger protein 52 Zfp52 2.63 -1.83 1.25 0 0.01216 0.33215 NM_013560 heat shock protein 1 Hspb1 2.61 -1.18 2.19 0.00001 0.36838 0.0002 NM_011451 sphingosine kinase 1 Sphk1 2.61 1.1 -1.03 0.00009 0.64512 0.81075 NM_153798 polymerase (RNA) II (DNA directed) polypeptide B Polr2b 2.6 -3.59 1.36 0 0.00003 0.4896 NM_011724 cardiomyopathy associated 1 Cmya1 2.6 -1.16 1.2 NA 0.52015 0.11557 NM_148928 general transcription factor IIIC, polypeptide 5 Gtf3c5 2.58 -1.47 1.77 NA NA 0.1058 NM_018819 brain protein 44-like Brp44l 2.56 -6.21 1.79 0 NA 0.3093 NM_010415 heparin-binding EGF-like growth factor Hbegf 2.56 -1.15 1.22 0.00001 0.43018 0.11109 NM_010447 heterogeneous nuclear ribonucleoprotein A1 Hnrpa1 2.54 -2.55 1.65 NA 0.0006 0.2833 NM_175456 actin-binding Rho activating protein Abra 2.53 -1.25 1.14 0.00002 0.25478 0.37876 NM_010480 heat shock protein 90kDa alpha (cytosolic), class A member 1 Hsp90aa1 2.51 -1.25 2.01 0.00008 0.48303 0.00262 NM_008298 DnaJ (Hsp40) homolog, subfamily A, member 1 Dnaja1 2.48 -2.06 1.72 0 0.00657 0.01188 RIKEN cDNA 1200015M12 gene 1200015M12Rik 2.36 -2.37 1.4 0.00002 0.0005 0.22143 NM_008987 pentraxin related gene Ptx3 2.36 1.19 1.86 0.00031 0.33324 0.00084 XM_284686 RIKEN cDNA 1700023I07 gene 1700023I07Rik 2.36 NA NA NA NA NA NM_011225 RAB18, member RAS oncogene family Rab18 2.35 -3.65 2.21 0 0 0.00627 XM_355606 RAS-like, family 11, member B Rasl11b 2.35 1.01 1.38 0 0.95087 0.01946 NM_024166 coiled-coil-helix-coiled-coil-helix domain containing 2 Chchd2 2.34 -3.48 1.66 0 0.00002 0.21102 NM_175606 homeobox only domain Hod 2.33 -3.4 1.13 0 0.00004 0.81378 NM_024221 pyruvate dehydrogenase (lipoamide) beta Pdhb 2.33 -4.01 1.77 0 0.00001 0.21759 NM_013562 interferon-related developmental regulator 1 Ifrd1 2.33 -1.58 1.32 0.00035 0.06482 0.03767 NM_028876 transmembrane emp24 protein transport domain containing 5 Tmed5 2.31 -1.76 1.93 0.00001 0.00022 NA NM_145431 notchless homolog 1 (Drosophila) Nle1 2.31 -1.08 1.46 0.00003 0.72938 0.01581 NM_177020 RIKEN cDNA E030011O05 gene E030011O05Rik 2.31 NA NA NA NA NA NM_008871 serine (or cysteine) peptidase inhibitor, clade E, member 1 Serpine1 2.29 -2.08 1.54 0.00001 0.01353 0.293 NM_008013 fibrinogen-like protein 2 Fgl2 2.26 1.15 1.24 0.00108 0.63191 0.08966 NM_182650 heterogeneous nuclear ribonucleoprotein A2/B1 Hnrpa2b1 2.24 -3.24 1.55 0 0.00001 0.35376 NM_013749 tumor necrosis factor receptor superfamily, member 12a Tnfrsf12a 2.24 -1.09 1.26 0.00001 0.55866 0.0755 XM_131355 proline-rich nuclear receptor coactivator 1 Pnrc1 2.23 -2.87 1.61 0.00001 0.00024 0.24684 NM_176860 RIKEN cDNA 2810457I06 gene 2810457I06Rik 2.22 -1.24 1.39 0.00001 0.31991 0.11818 NM_133816 SH3-domain binding protein 4 Sh3bp4 2.22 -1.89 1.55 0.00002 0.00106 0.24439 NM_011851 5' nucleotidase, ecto Nt5e 2.21 1.21 -1.02 0.0001 0.17274 0.8606 XM_484289 elongation factor RNA polymerase II 2 Ell2 2.2 -2.07 1.41 0.00012 0.02973 0.07481 NM_178908 expressed sequence BB146404 BB146404 2.19 -1.57 1.11 0.00001 0.00953 0.44267 NM_008748 dual specificity phosphatase 8 Dusp8 2.18 -2 1.43 NA NA 0.06192 NM_019543 phosphatidylinositol glycan anchor biosynthesis, class P Pigp 2.15 -3.28 1.68 0.00001 0.00001 0.33418 NM_010577 integrin alpha 5 (fibronectin receptor alpha) Itga5 2.15 -1.06 NA NA NA NA NM_008710 nicotinamide nucleotide transhydrogenase Nnt 2.13 -1.88 1.34 0.00001 0.00122 0.47998 XM_126808 tetratricopeptide repeat domain 32 Ttc32 2.13 -2.81 1.93 0.00001 0.00003 0.18301 NM_010234 FBJ osteosarcoma oncogene Fos 2.13 -1.11 2.24 0.00394 0.71698 0.02997 RIKEN cDNA 2310002J21 gene 2310002J21Rik 2.12 -3.06 1.46 0.00001 0.00005 0.42712 NM_025926 DnaJ (Hsp40) homolog, subfamily B, member 4 Dnajb4 2.12 -1.21 1.32 0.00014 0.37148 0.084 NM_010135 enabled homolog (Drosophila) Enah 2.12 -1.2 1.01 0.00056 0.53488 0.94199 NM_011767 zinc finger RNA binding protein Zfr 2.12 -1.94 1.25 NA 0.00002 0.33941 NM_009735 beta-2 microglobulin B2m 2.11 -4.23 1.45 0.00002 0.00003 0.4627 XM_131399 src homology 2 domain-containing transforming protein B Shb 2.1 -1.17 1.14 0.0001 0.46331 0.31761 NM_183029 insulin-like growth factor 2 mRNA binding protein 2 Igf2bp2 2.1 1.43 1.2 0.00024 0.13065 0.14348 NM_018808 DnaJ (Hsp40) homolog, subfamily B, member 1 Dnajb1 2.1 -1.69 3.07 0.00127 0.11027 0.0064 NM_145507 aspartyl-tRNA synthetase Dars 2.08 -2.17 -1.39 0.00001 0.00006 0.63084 expressed sequence AI481316 AI481316 2.08 1.22 -1.03 0.00011 0.26026 0.78393 NM_008410 integral membrane protein 2B Itm2b 2.07 -4.22 1.62 0.00002 0.00001 0.31825 RIKEN cDNA C330006P03 gene C330006P03Rik 2.07 -1.06 1.09 0.00007 0.79162 0.48298 XM_135172 leucine rich repeat containing 2 Lrrc2 2.06 -2.83 1.66 0.00002 0.00014 0.28756 NM_011878 T-cell lymphoma invasion and metastasis 2 Tiam2 2.06 -1.2 NA NA 0.18132 NA NM_008448 kinesin family member 5B Kif5b 2.05 -3.52 1.63 0.00002 0.00001 0.12465 NM_025703 transcription elongation factor A (SII)-like 8 Tceal8 2.05 -2.66 1.72 0.00002 0.00003 0.11977 NM_009609 actin, gamma, cytoplasmic 1 Actg1 2.04 -2.91 1.31 0.00013 0.00029 0.45853 RIKEN cDNA 2210403K04 gene 2210403K04Rik 2.04 -2.44 1.21 NA 0.00001 0.61564 NM_026879 chromatin modifying protein 2B Chmp2b 2.03 -2.62 1.68 0.00003 0.00005 0.1421 NM_007669 cyclin-dependent kinase inhibitor 1A (P21) Cdkn1a 2.03 -1.13 1.11 0.00003 0.40114 0.37387 NM_054098 STEAP family member 4 Steap4 2.03 -2.18 1.32 0.00005 0.00127 0.24603 AK019472 RIKEN cDNA 4631422O05 gene 4631422O05Rik 2.03 -1.54 1.59 NA 0.05328 0.01518 NM_011896 sprouty homolog 1 (Drosophila) Spry1 2.02 -2.16 1.43 0.00002 0.00007 0.42449 XM_147132 kelch repeat and BTB (POZ) domain containing 5 Kbtbd5 2.02 -1.7 1.08 0.00003 0.04893 0.57812 NM_013532 leukocyte immunoglobulin-like receptor, subfamily B, member 4 Lilrb4 2.02 -1.71 1.45 0.00008 0.00239 0.33064 NM_008618 malate dehydrogenase 1, NAD (soluble) Mdh1 2.02 -2.57 1.93 0.00008 0.0002 0.14975 NM_175132 synaptopodin 2-like Synpo2l 2.01 1.05 -1.1 0.00022 0.85342 0.55531 RIKEN cDNA 8030402F09 gene 8030402F09Rik 2.01 NA 1.32 NA NA NA NM_025904 RIKEN cDNA 1600012F09 gene 1600012F09Rik 2 -3.25 1.61 0.00002 0.00002 0.36313 NM_172053 a disintegrin-like and metallopetidase (reprolysin type) with thrombospondinAdamts16 type 1 motif, 16 2 -1.38 1.67 0.00024 0.13552 0.13752 NM_013493 cellular nucleic acid binding protein Cnbp 1.99 -4.56 1.06 0.00004 0 0.89778 NM_010275 glial cell line derived neurotrophic factor Gdnf 1.98 -1.13 1.26 0.00006 0.39808 0.06474 NM_008726 natriuretic peptide precursor type B Nppb 1.98 1.12 1.88 0.00043 0.56693 0.01013 XM_135174 CLIP associating protein 2 Clasp2 1.96 -1.28 1.9 0.00008 0.2785 0.06722 succinate dehydrogenase complex, subunit A, flavoprotein (Fp) Sdha 1.96 -3.04 1.67 0.00011 0.00005 0.19272 NM_010500 immediate early response 5 Ier5 1.96 -1.48 1.51 0.00181 0.18176 0.0583 NM_007840 DEAD (Asp-Glu-Ala-Asp) box polypeptide 5 Ddx5 1.95 -2.27 1.44 0.00005 0.00015 0.38163 NM_010902 nuclear factor, erythroid derived 2, like 2 Nfe2l2 1.94 -2.66 1.43 0.00011 0.0004 0.19789 NM_010726 phytanoyl-CoA hydroxylase Phyh 1.94 -2.72 1.34 0.00014 0.00008 0.35419 NM_026406 zinc finger protein 364 Zfp364 1.94 -1.16 -1.04 0.00041 0.61699 0.7382 NM_133354 SMT3 suppressor of mif two 3 homolog 2 (yeast) Sumo2 1.93 -3.1 1.86 0.00006 0.00004 0.13175 NM_024255 hydroxysteroid dehydrogenase like 2 Hsdl2 1.93 -3.89 1.36 0.0001 0.00001 0.49697 NM_178080 RIKEN cDNA 9430063L05 gene 9430063L05Rik 1.92 -1.06 1.17 0.00009 0.70976 0.26358 NM_011636 phospholipid scramblase 1 Plscr1 1.91 -1.75 1.01 0.00004 NA 0.89519 RIKEN cDNA 4930564I24 gene 4930564I24Rik 1.91 1.12 1.08 0.00036 0.48048 0.44208 NM_026473 tubulin, beta 6 Tubb6 1.91 1.09 1.31 0.00156 0.66919 0.03952 NM_018764 protocadherin 7 Pcdh7 1.9 -1.05 -1.07 0.00008 0.80124 0.65757 NM_007840 DEAD (Asp-Glu-Ala-Asp) box polypeptide 5 Ddx5 1.9 -2.24 1.56 0.00013 0.00026 0.30353 NM_013525 growth arrest specific 5 Gas5 1.89 -1.88 1.75 0.00004 0.00061 0.16354 NM_134079 adenosine kinase Adk 1.89 -3.54 1.59 0.00011 0.00001 0.30324 NM_013468 ankyrin repeat domain 1 (cardiac muscle) Ankrd1 1.89 -1.55 -1.04 0.00062 0.00953 0.75318 NM_013614 ornithine decarboxylase, structural 1 Odc1 1.88 -2.15 1.37 0.00013 0.00109 0.27674 NM_153574 RIKEN cDNA D430015B01 gene D430015B01Rik 1.88 -1.31 -1.16 0.00061 0.28415 0.21168 RIKEN cDNA 2300006M17 gene 2300006M17Rik 1.87 -1.27 1.37 0.00055 0.1963 0.02456 NM_145836 RIKEN cDNA 6430527G18 gene 6430527G18Rik 1.85 -2.36 1.34 0.0001 0.00053 0.32353 NM_008831 prohibitin Phb 1.85 -2.9 1.75 0.00017 0.00003 0.20739 NM_021297 toll-like receptor 4 Tlr4 1.84 -1.16 1.04 0.00003 0.18825 0.7181 RIKEN cDNA 1810012N18 gene 1810012N18Rik 1.84 -1.77 1.5 0.00068 0.03068 0.05152 NM_026655 RIKEN cDNA 2310057M21 gene 2310057M21Rik 1.81 -1.66 1.25 0.00013 0.00589 0.29429 NM_007669 cyclin-dependent kinase inhibitor 1A (P21) Cdkn1a 1.81 1.29 1.1 0.00033 0.09078 0.42556 NM_145558 hydroxyacyl-Coenzyme A dehydrogenase/3-ketoacyl-Coenzyme AHadhb thiolase/enoyl-Coenzyme A1.81 hydratase (trifunctional-3.03 protein), -1.04beta subunit 0.00067 0.00001 0.85121 XM_125706 breakpoint cluster region homolog Bcr 1.81 1.35 1.79 0.00139 0.08192 0.00434 NM_023270 ring finger protein 128 Rnf128 1.8 -3.54 1.8 0.00015 0 0.17917 NM_009939 COP9 (constitutive photomorphogenic) homolog, subunit 2 (ArabidopsisCops2 thaliana) 1.8 -2.26 1.02 0.00028 NA 0.9676 Mitochondrial COXII NA 1.8 -1.82 1.04 0.00087 0.00122 0.88947 NM_133753 ERBB receptor feedback inhibitor 1 Errfi1 1.79 -1.39 -1.05 0.00086 0.04155 0.73114 NM_009366 TSC22 domain family, member 1 Tsc22d1 1.78 -1.33 -1.22 0.00022 0.04255 0.16499 XM_284175 filamin C, gamma (actin binding protein 280) Flnc 1.78 -1.38 1.11 0.00034 0.10591 0.34251 NM_007917 eukaryotic translation initiation factor 4E Eif4e 1.78 -2.47 1.43 0.00044 0.00011 0.11466 NM_009825 serine (or cysteine) peptidase inhibitor, clade H, member 1 Serpinh1 1.78 -1.43 1.37 0.00051 0.19182 0.03648 NM_010135 enabled homolog (Drosophila) Enah 1.78 -1.35 -1.02 0.00051 0.15859 0.8615 NM_019466 Down syndrome critical region homolog 1 (human) Dscr1 1.78 -1.02 1.59 0.00375 0.93285 0.00236 NM_133771 RIKEN cDNA 0610016J10 gene 0610016J10Rik 1.77 -2.36 1.61 0.00031 0.00016 0.21733 XM_132983 leiomodin 2 (cardiac) Lmod2 1.77 -2.27 1.13 0.00061 0.00199 0.65294 NM_008866 lysophospholipase 1 Lypla1 1.77 -2.73 1.53 0.00064 0.00038 0.2413 NM_010442 heme oxygenase (decycling) 1 Hmox1 1.77 -1.06 1.29 0.00109 NA 0.04179 NM_183426 strawberry notch homolog 2 (Drosophila) Sbno2 1.77 -1.07 1.03 0.00167 0.62145 0.79108 RIKEN cDNA 2810001A02 gene 2810001A02Rik 1.76 -1.6 1.7 0.00011 0.00123 0.2298 NM_025279 heterogeneous nuclear ribonucleoprotein K Hnrpk 1.76 -2.13 1.46 0.00045 0.00088 0.21795 NM_009344 pleckstrin homology-like domain, family A, member 1 Phlda1 1.76 -1.13 2.14 0.00357 0.50047 0.00933 NM_010477 heat shock protein 1 (chaperonin) Hspd1 1.75 -2.13 -1.08 0.0004 0.00118 0.62535 NM_172648 interferon activated gene 205 Ifi205 1.75 -1.6 1.01 0.00043 0.00686 0.95267 NM_024406 fatty acid binding protein 4, adipocyte Fabp4 1.75 -3.34 1.37 0.0006 0.00001 0.4516 NM_175132 synaptopodin 2-like Synpo2l 1.75 -1.22 -1.18 0.00115 0.19417 0.25095 NM_153287 AXIN1 up-regulated 1 Axud1 1.75 -1.31 1.09 0.00258 0.18865 0.59613 NM_020560 mitochondrial ribosomal protein S31 Mrps31 1.74 -1.39 1.29 0.00008 0.0093 0.39764 NM_139292 receptor accessory protein 6 Reep6 1.74 -1.39 2.17 0.00012 NA NA NM_009090 polymerase (RNA) II (DNA directed) polypeptide C Polr2c 1.74 -2.05 1.55 0.00022 NA 0.30539 NM_133971 ankyrin repeat domain 10 Ankrd10 1.74 -1.2 1.04 0.00041 0.28862 0.73153 NM_024214 translocase of outer mitochondrial membrane 20 homolog (yeast) Tomm20 1.74 -2.2 1.6 0.00045 0.00044 0.12871 NM_009621 a disintegrin-like and metallopeptidase (reprolysin type) with thrombospondinAdamts1 type 1 motif, 1 1.73 -1.29 -1.29 0.00004 NA NA NM_174990 GTPase, IMAP family member 4 Gimap4 1.73 -2.41 1.2 0.00019 0 0.62395 NM_030018 transmembrane protein 50B Tmem50b 1.73 -2.29 1.45 0.00019 0.00001 0.08328 NM_028803 glucan (1,4-alpha-), branching enzyme 1 Gbe1 1.73 -2.27 1.49 0.0002 0.00008 0.32671 NM_008424 potassium voltage-gated channel, Isk-related subfamily, member 1Kcne1 1.73 1.44 1.66 0.00045 0.00701 0.00782 XM_129647 glutamyl-prolyl-tRNA synthetase Eprs 1.73 -2.19 1.62 0.00055 0.00123 0.25502 NM_016807 syndecan binding protein Sdcbp 1.72 -2.49 -1.16 0.00038 0.00009 0.37613 XM_355243 proteoglycan 4 (megakaryocyte stimulating factor, articular superficialPrg4 zone protein) 1.72 -1.24 -1.14 0.00137 0.14195 0.22942 NM_026565 RIKEN cDNA 9430083G14 gene 9430083G14Rik 1.71 -2.48 1.63 0.00047 0.00009 0.21321 NM_009888 complement component factor h Cfh 1.71 -2.33 1.5 0.00062 0.00029 0.28781 NM_007481 ADP-ribosylation factor 6 Arf6 1.71 1.03 1.03 0.00103 0.89721 0.82853 NM_007497 activating transcription factor 1 Atf1 1.7 NA 1.53 0.0001 NA 0.36379 hypothetical LOC73317 LOC73317 1.7 -1.26 1.47 0.00012 0.18148 0.21034 NM_020581 angiopoietin-like 4 Angptl4 1.7 -1.2 1.27 0.00042 0.1303 0.12667 NM_172648 interferon activated gene 205 Ifi205 1.7 -1.72 1.03 0.00049 0.00225 0.88003 NM_027154 transmembrane BAX inhibitor motif containing 1 Tmbim1 1.7 -1.86 1.11 0.00055 0.0031 0.62818 NM_029688 sulfiredoxin 1 homolog (S. cerevisiae) Srxn1 1.7 -1.24 1.19 0.00091 0.14148 0.22696 NM_009694 apolipoprotein B editing complex 2 Apobec2 1.7 -2.12 1.93 0.00095 0.00037 0.0975 RIKEN cDNA B230339E18 gene B230339E18Rik 1.69 -1.97 1.51 0.0001 0.00014 0.08135 NM_025847 Mus musculus RIKEN cDNA 2610016F04 gene (2610016F04Rik),2610016F04Rik mRNA. 1.69 -2.29 1.19 0.00029 0.00001 0.58706 NM_008692 nuclear transcription factor-Y gamma Nfyc 1.69 -2.03 1.31 0.00035 0.00007 0.39592 NM_026153 RIKEN cDNA 5730557B15 gene 5730557B15Rik 1.69 -1.45 1.52 0.00064 0.03443 0.04631 RIKEN cDNA 4933430A20 gene 4933430A20Rik 1.68 -1.36 1.49 0.00034 0.02184 0.08505 NM_009861 cell division cycle 42 homolog (S. cerevisiae) Cdc42 1.68 -1.83 1.35 0.00037 0.00055 0.3588 NM_019744 nuclear receptor coactivator 4 Ncoa4 1.68 -2.61 1.65 0.00043 0.00004 0.18591 NM_028058 FUN14 domain containing 1 Fundc1 1.67 -1.89 1.45 0.00021 0.00002 NA RIKEN cDNA 5830407P18 gene 5830407P18Rik 1.67 1.25 -1.21 0.00046 0.13877 0.33458 NM_021278 thymosin, beta 4, X chromosome Tmsb4x 1.67 -1.73 1.04 0.00065 0.00038 0.90091 NM_133826 ATPase, H+ transporting, lysosomal V1 subunit H Atp6v1h 1.67 -1.88 1.38 0.00072 0.00151 0.34691 NM_013902 FK506 binding protein 3 Fkbp3 1.67 -2.27 1.63 0.00076 0.00013 0.224 NC_001499 Abelson murine leukemia virus, complete genome. AbMLVgp1 1.67 1.15 1.12 0.00092 0.32962 0.33203 NM_133838 EH-domain containing 4 Ehd4 1.67 -1.11 1.03 0.00094 0.48751 0.82111 NM_145541 RAS-related protein-1a Rap1a 1.67 -1.97 1.52 0.00104 0.00113 0.13715 NM_133781 calcium binding protein 39 Cab39 1.66 -2.2 -1.01 0.00054 0.00021 0.93992 NM_019840 phosphodiesterase 4B, cAMP specific Pde4b 1.66 -1.51 -1.03 0.0011 0.00829 0.80369 NM_011883 ring finger protein 13 Rnf13 1.66 -2.7 1.47 0.00176 0.00023 0.31809 NM_019550 polypyrimidine tract binding protein 2 Ptbp2 1.66 -1.6 1.52 0.0021 0.02724 0.01799 NM_025846 related RAS viral (r-ras) oncogene homolog 2 Rras2 1.66 -1.44 1.28 0.00481 0.11373 0.0644 XM_485240 TSC22 domain family 2 Tsc22d2 1.66 -1.02 1.16 0.00835 0.91992 0.25161 NM_010757 v-maf musculoaponeurotic fibrosarcoma oncogene family, proteinMafk K (avian) 1.65 -1.62 1.06 0.00057 0.02913 0.58927 NM_026120 RIKEN cDNA 2410127L17 gene 2410127L17Rik 1.65 -2.4 1.79 0.00078 0.00006 0.13691 NM_008650 methylmalonyl-Coenzyme A mutase Mut 1.65 -2.17 1.55 0.00083 0.00036 0.17543 NM_028121 ADP-dependent glucokinase Adpgk 1.64 -1.3 NA 0.00024 NA NA NM_008249 transcription factor B2, mitochondrial Tfb2m 1.64 -2.34 1.43 0.00025 0.00002 0.35328 NM_010891 septin 2 sept2 1.64 -2.26 1.13 0.00051 0.00004 0.66535 NM_020581 angiopoietin-like 4 Angptl4 1.64 1.16 1.33 0.00121 0.3219 0.04784 NM_009729 ATPase, H+ transporting, lysosomal V0 subunit C Atp6v0c 1.64 -2.13 1.25 0.00128 0.00032 0.31747 NM_011451 sphingosine kinase 1 Sphk1 1.63 -1.19 NA 0.00024 0.07375 NA NM_025369 mitochondrial ribosomal protein S36 Mrps36 1.63 -2.96 1.3 0.00098 0.00005 0.60839 RIKEN cDNA 9430072K23 gene 9430072K23Rik 1.63 -2.03 -1.13 0.00102 0.00012 0.71724 NM_009716 activating transcription factor 4 Atf4 1.63 -1.8 1.13 0.00103 0.00193 0.35612 RIKEN cDNA 9430010M06 gene 9430010M06Rik 1.63 -2.45 1.43 0.00158 0.00072 0.39092 NM_010431 hypoxia inducible factor 1, alpha subunit Hif1a 1.63 -1.94 1.37 0.00507 0.00616 0.27601 NM_011113 plasminogen activator, urokinase receptor Plaur 1.63 1.02 1.37 0.00979 0.90729 0.01604 NM_025887 RAB5A, member RAS oncogene family Rab5a 1.62 -2.49 1.03 0.00108 0.00004 0.90907 RIKEN cDNA A630089N07 gene A630089N07Rik 1.62 -4.01 1.29 0.00334 0 0.58626 NM_023211 upregulated during skeletal muscle growth 5 Usmg5 1.62 -2.62 1.36 0.00365 0.00004 0.3057 NM_009851 CD44 antigen Cd44 1.62 -1.03 1.31 0.00373 0.82486 0.14282 NM_018811 abhydrolase domain containing 2 Abhd2 1.62 -1.26 1.1 0.0069 0.1561 0.38316 NM_019648 methionine aminopeptidase 2 Metap2 1.61 -2.29 1.21 0.00089 0.00002 0.38652 NM_134071 ankyrin repeat domain 32 Ankrd32 1.61 -1.8 1.53 0.00089 0.00012 0.27311 NM_008822 peroxisome biogenesis factor 7 Pex7 1.61 -2.02 1.24 0.00165 0.00028 0.34892 NM_029344 acylphosphatase 2, muscle type Acyp2 1.61 -2.6 1.51 0.00204 0.00012 0.23484 NM_023153 RIKEN cDNA 0610040D20 gene 0610040D20Rik 1.6 -1.64 1.19 0.00077 0.00029 0.64037 XM_131323 RIKEN cDNA 1700009N14 gene 1700009N14Rik 1.6 -1.65 1.24 0.00115 0.00098 0.36548 NM_009975 casein kinase 2, beta polypeptide Csnk2b 1.6 -1.7 1.46 0.00147 0.00092 0.24803 NM_016807 syndecan binding protein Sdcbp 1.6 -1.91 1.19 0.00206 0.00312 0.34502 NM_022431 membrane-spanning 4-domains, subfamily A, member 11 Ms4a11 1.6 -1.03 -1.06 0.00238 0.832 0.5997 NM_183274 RIKEN cDNA 0610041G09 gene 0610041G09Rik 1.6 -1.7 -1.01 0.00272 0.01597 0.96687 NM_194053 reticulon 4 Rtn4 1.6 -1.7 -1.58 0.00331 0.00533 0.01467 NM_054087 solute carrier family 19 (thiamine transporter), member 2 Slc19a2 1.6 -1.25 1.21 0.00466 0.20985 0.09836 RIKEN cDNA 2310003D02 gene 2310003D02Rik 1.6 -1.33 -1.07 0.00575 0.18939 0.58433 NM_024233 REX2, RNA exonuclease 2 homolog (S. cerevisiae) Rexo2 1.59 -2.09 1.53 0.00219 0.00015 0.2262 NM_008882 plexin A2 Plxna2 1.59 1.08 1.19 0.00235 0.70555 0.15561 NM_019771 destrin Dstn 1.59 -2.11 1.27 0.00242 0.00035 0.3001 NM_011582 thrombospondin 4 Thbs4 1.59 -1.33 1.52 0.00378 0.05865 0.17957 XM_486002 trinucleotide repeat containing 6a Tnrc6a 1.59 -1.62 1.55 0.00388 0.0039 0.04002 Mitochondrial ATP8 NA 1.59 -1.79 1.07 0.00631 0.00181 0.79477 X69025 S47546PCTAIRE-motif protein kinase 1 Pctk1 1.58 -1.34 1.34 0.0005 0.02995 0.18289 NM_011545 transcription factor 21 Tcf21 1.58 -1.63 1.22 0.00065 0.00016 0.59509 NM_009769 Kruppel-like factor 5 Klf5 1.58 -1.03 NA 0.00092 NA NA NM_011464 serine protease inhibitor, Kunitz type 2 Spint2 1.58 -1.96 1.45 0.0016 0.00046 0.29839 NM_016900 caveolin 2 Cav2 1.58 -3.1 1.23 0.00239 NA 0.70314 XM_128893 Nedd4 family interacting protein 1 Ndfip1 1.58 -1.93 -1.06 0.00266 NA NA NM_008300 heat shock protein 4 Hspa4 1.58 -2.05 1.46 0.00286 0.00014 0.20059 NM_153806 deoxynucleotidyltransferase, terminal, interacting protein 2 Dnttip2 1.58 -1.85 1.49 0.00344 0.00366 0.2309 NM_133718 transmembrane protein 30A Tmem30a 1.58 -2.61 1.59 0.00352 0.00069 0.22715 NC_001363 Murine sarcoma virus, complete genome. Msvgp4 1.58 1.24 1.02 0.00359 0.15366 0.81853 NM_024435 neurotensin Nts 1.58 -1.12 1.62 0.00837 0.53689 0.01403 NM_178601 IMP4, U3 small nucleolar ribonucleoprotein, homolog (yeast) Imp4 1.57 -1.32 1.06 0.00083 0.01429 0.62375 NM_007604 capping protein (actin filament) muscle Z-line, alpha 2 Capza2 1.57 -2.34 1.19 0.00208 0.00014 0.3038 NM_178616 proteasome (prosome, macropain) 26S subunit, non-ATPase, 11 Psmd11 1.57 -1.36 1.09 0.0025 0.04912 0.50023 NM_010220 FK506 binding protein 5 Fkbp5 1.57 -1.26 -1.05 0.00254 0.12927 0.72794 NM_028871 heterogeneous nuclear ribonucleoprotein R Hnrpr 1.57 -2.07 1.46 0.00271 0.0003 0.16054 NC_001502 Moloney murine sarcoma virus, complete genome. MoMSVgp1 1.57 1.12 1.08 0.00384 0.43181 0.45527 NM_009905 CDC-like kinase 1 Clk1 1.57 -2.03 2.02 0.00403 0.00243 0.07633 XM_125808 cytoskeleton-associated protein 4 Ckap4 1.57 -1.03 1.42 0.00593 0.85945 0.0731 NM_146207 cullin 4A Cul4a 1.56 NA 1.38 0.00056 NA 0.35017 NM_010828 Cbp/p300-interacting transactivator, with Glu/Asp-rich carboxy-terminalCited2 domain, 2 1.56 -1.26 1.1 0.00144 0.05377 0.37429 NM_021322 WD repeat domain 4 Wdr4 1.56 -1.48 NA 0.0018 NA NA NM_175245 RIKEN cDNA 2410129H14 gene 2410129H14Rik 1.56 -1.95 1.34 0.0023 0.00044 0.12721 NM_008880 phospholipid scramblase 2 Plscr2 1.56 1.13 1.11 0.00238 0.34749 0.45884 XM_483889 triadin Trdn 1.56 -1.67 1.16 0.00239 0.0013 0.65964 NM_011445 SRY-box containing gene 6 Sox6 1.56 NA -1.05 0.00245 NA 0.6865 CC799012 RRJ565 BayGenomics Gene Trap Library pGT0Lxf Mus musculusNA cDNA, genomic survey sequence.1.56 -1.57 -1.21 0.00288 0.01009 0.30886 NM_026835 membrane-spanning 4-domains, subfamily A, member 6D Ms4a6d 1.56 -1.38 1.07 0.00325 0.02582 0.57113 NM_023229 Fas-activated serine/threonine kinase Fastk 1.56 -2.27 -1.26 0.0036 0.00011 0.13675 XM_283480 RIKEN cDNA A230048G03 gene A230048G03Rik 1.56 -2.52 1.32 0.0037 0.00013 0.31549 NM_173414 LanC lantibiotic synthetase component C-like 3 (bacterial) Lancl3 1.56 1 1.15 0.00395 0.98635 0.22331 NM_011614 tumor necrosis factor (ligand) superfamily, member 12 Tnfsf12 1.55 -1.44 -1.24 0.00073 0.00159 0.15014 NM_007604 capping protein (actin filament) muscle Z-line, alpha 2 Capza2 1.55 -2.02 1.5 0.00192 0.00102 0.29513 NM_009011 RAD23b homolog (S. cerevisiae) Rad23b 1.55 -1.65 1.32 0.00223 0.00115 0.11133 XM_128924 RNA binding motif protein 27 Rbm27 1.55 -2.14 1.33 0.00251 0.00135 0.35459 NM_009743 Bcl2-like 1 Bcl2l1 1.55 -1.57 -1.53 0.00262 0.00958 0.00854 XM_128751 cysteine rich transmembrane BMP regulator 1 (chordin like) Crim1 1.55 -2.61 1.46 0.00299 0.00015 0.23516 NM_007483 ras homolog gene family, member B Rhob 1.55 -2.45 -1.08 0.00363 0.00009 0.81052 NM_008543 MAD homolog 7 (Drosophila) Smad7 1.55 -1.66 1.16 0.00371 0.00306 0.52268 NC_001502 Moloney murine sarcoma virus, complete genome. MoMSVgp1 1.55 1.17 -1.01 0.00378 0.27617 0.94227 NM_009649 A kinase (PRKA) anchor protein 2 Akap2 1.55 1.09 1.08 0.00543 0.52498 0.58582 NM_153502 ankyrin repeat domain 23 Ankrd23 1.55 -1.34 1.31 0.00569 0.06792 0.04556 NM_199322 DOT1-like, histone H3 methyltransferase (S. cerevisiae) Dot1l 1.54 -1.17 NA 0.00092 0.11007 NA RIKEN cDNA E430007M08 gene E430007M08Rik 1.54 -2.32 1.5 0.0027 0.00047 0.21213 NM_009817 calpastatin Cast 1.54 -2.05 1.2 0.00336 0.00222 0.23092 NM_172648 interferon activated gene 205 Ifi205 1.54 -1.62 1.51 0.00428 0.00392 0.32804 RIKEN cDNA 6720435I21 gene 6720435I21Rik 1.54 -1.3 1.17 0.00465 0.06651 0.21743 NC_001501 Murine leukemia virus, complete genome. MLVgp1 1.54 1.31 1.07 0.00519 0.08086 0.57724 NM_175482 ubiquitin specific peptidase 28 Usp28 1.54 -1.62 1.05 0.0063 0.01938 0.81073 NM_007901 endothelial differentiation sphingolipid G-protein-coupled receptorEdg1 1 1.54 -1.94 1.03 0.00749 0.00213 0.91423 NM_145610 peter pan homolog (Drosophila) Ppan 1.54 1.11 1.13 0.00997 0.36326 0.30287 NM_133703 RIKEN cDNA 2810453I06 gene 2810453I06Rik 1.53 -1.5 1.48 0.00133 0.01117 0.24658 NM_007516 heterogeneous nuclear ribonucleoprotein D Hnrpd 1.53 -1.38 1.3 0.00248 0.01764 0.15345 neurobeachin like 1 Nbeal1 1.53 -1.57 1.3 0.00257 0.00492 0.40952 NM_012001 COP9 (constitutive photomorphogenic) homolog, subunit 4 (ArabidopsisCops4 thaliana) 1.53 -1.59 -1.02 0.00338 0.00617 0.89239 NM_008903 phosphatidic acid phosphatase 2a Ppap2a 1.53 -1.7 -1.08 0.00351 0.00292 0.6145 NM_019936 cysteine-rich PDZ-binding protein Cript 1.53 -1.93 1.57 0.00354 0.00072 0.07369 NM_029688 sulfiredoxin 1 homolog (S. cerevisiae) Srxn1 1.53 -1.17 -1.06 0.00394 0.29896 0.57293 NM_011970 proteasome (prosome, macropain) subunit, beta type 2 Psmb2 1.53 -2.12 1.11 0.005 0.00034 0.5556 NM_025527 signal recognition particle 19 Srp19 1.53 -2.44 1.66 0.00627 0.00005 0.22325 NM_130889 acidic nuclear phosphoprotein 32 family, member B Anp32b 1.52 -1.73 1.33 0.00154 0.00083 0.39427 NM_026218 FGFR1 oncogene partner 2 Fgfr1op2 1.52 -2.57 1.33 0.00262 0.00005 0.4302 hypothetical protein C130006E23 C130006E23 1.52 -1.61 1.27 0.00305 0.0016 0.15172 NM_146035 mannoside acetylglucosaminyltransferase 2 Mgat2 1.52 -1.35 1.4 0.00366 0.04864 0.20664 NM_178616 proteasome (prosome, macropain) 26S subunit, non-ATPase, 11 Psmd11 1.52 -1.88 1 0.00374 0.00016 0.99146 NC_001819 Rauscher murine leukemia virus, complete genome. Rmlvgp3 1.52 1.26 1.01 0.00454 0.11425 0.92884 NM_028127 FERM domain containing 6 Frmd6 1.52 -1.28 1.09 0.00457 0.13491 0.53429 NM_025757 RIKEN cDNA 4933439F18 gene 4933439F18Rik 1.52 -1.89 1.52 0.00457 NA 0.25769 NM_020590 gamma-aminobutyric acid (GABA(A)) receptor-associated protein-likeGabarapl1 1 1.52 -1.14 1.12 0.00516 0.34154 0.28793 NC_001702 Murine type C retrovirus, complete genome. MtCrVgp3 1.52 -1 1.01 0.00631 0.97589 0.96136 NM_133771 RIKEN cDNA 0610016J10 gene 0610016J10Rik 1.52 -1.63 -1.1 0.00647 0.0067 0.52672 BC033468 dystonin Dst 1.52 -1.52 -1.04 0.00673 0.00475 0.75321 NC_001506 Murine osteosarcoma virus, complete genome. Movgp1 1.52 1.28 1.03 0.0071 0.09903 0.77345 NM_019920 mitogen-activated protein kinase kinase 1 interacting protein 1 Map2k1ip1 1.51 -2.62 1.43 0.00376 0.00003 0.31899 NM_019929 SMT3 suppressor of mif two 3 homolog 3 (yeast) Sumo3 1.51 -1.54 -1.06 0.00394 0.0066 0.69772 NM_026332 DnaJ (Hsp40) homolog, subfamily C, member 19 Dnajc19 1.51 -2.12 1.49 0.00434 0.00035 0.3226 NM_011175 legumain Lgmn 1.51 -1.43 -1.22 0.00441 0.0329 0.19654 NM_016807 syndecan binding protein Sdcbp 1.51 -1.53 -1.03 0.00451 0.00517 0.81738 NM_021462 MAP kinase-interacting serine/threonine kinase 2 Mknk2 1.51 -2.01 -1.24 0.0046 0.00058 0.15355 NC_001501 Murine leukemia virus, complete genome. MLVgp1 1.51 1.25 1 0.00672 0.1227 0.9975 NM_029688 sulfiredoxin 1 homolog (S. cerevisiae) Srxn1 1.51 -1.07 1.14 0.00729 0.68259 0.37516 NM_019662 Ras-related associated with diabetes Rrad 1.51 -1.45 1.18 0.00793 0.02495 0.23593 NM_146142 tudor domain containing 7 Tdrd7 1.5 -1.25 1.41 0.00128 0.0412 0.3234 NC_001819 Rauscher murine leukemia virus, complete genome. Rmlvgp3 1.5 1.24 1.16 0.0042 0.09675 0.18694 NM_177632 cDNA sequence BC022623 BC022623 1.5 -1.66 1.44 0.00437 0.00167 0.15352 XM_127325 RIKEN cDNA 6230416A05 gene 6230416A05Rik 1.5 -2.15 1.31 0.00456 0.00009 0.28847 NM_011482 NHP2 non-histone chromosome protein 2-like 1 (S. cerevisiae) Nhp2l1 1.5 -1.87 1.63 0.00466 0.00135 0.06792 NM_009861 cell division cycle 42 homolog (S. cerevisiae) Cdc42 1.5 -2.85 -1.02 0.00481 0.00005 0.93764 XM_355388 RIKEN cDNA 3100002L24 gene 3100002L24Rik 1.5 -2.15 1.54 0.00495 0.00017 0.23523 NM_009082 ribosomal protein L29 Rpl29 1.5 -1.12 1.2 0.00521 0.43817 0.13446 XM_488538 SET binding factor 2 Sbf2 1.5 -1.74 1.34 0.00522 0.00073 0.24635 NM_011965 proteasome (prosome, macropain) subunit, alpha type 1 Psma1 1.5 -2.64 1.43 0.00531 0.00007 0.33519 NM_026211 transmembrane emp24 protein transport domain containing 9 Tmed9 1.5 -2.04 -1.16 0.00592 0.00066 0.25254 AF304451 sarcolemma associated protein Slmap 1.5 -1.53 1.1 0.00598 0.01585 0.41386 NM_027193 DPH5 homolog (S. cerevisiae) Dph5 1.5 1.35 1.03 0.0061 0.09274 0.84099 NM_133699 ATPase, H+ transporting, lysosomal V1 subunit C2 Atp6v1c2 1.5 -1.8 1.02 0.00629 0.00191 0.89446 NM_025517 RNA terminal phosphate cyclase domain 1 Rtcd1 1.5 -2.1 1.6 0.00633 0.00053 0.171 NM_008977 protein tyrosine phosphatase, non-receptor type 2 Ptpn2 1.5 -1.59 1.28 0.00729 0.00584 0.23034 NM_011901 TAF7 RNA polymerase II, TATA box binding protein (TBP)-associatedTaf7 factor 1.5 -1.83 1.11 0.00801 0.00421 0.37977 NC_001362 Friend murine leukemia virus, complete genome. FrMLVgp3 1.5 1.19 -1.02 0.00824 0.22131 0.88362 NM_008147 glycoprotein 49 A Gp49a 1.49 -1.2 1.12 0.00156 0.06466 0.54195 NM_134188 acyl-CoA thioesterase 2 Acot2 1.49 -1.34 1.24 0.00326 0.01137 0.53294 NM_011545 transcription factor 21 Tcf21 1.49 -1.6 1.4 0.0033 0.00079 0.16059 NM_007592 carbonic anhydrase 8 Car8 1.49 -1.91 1.14 0.00354 0.00005 0.72044 NM_019685 RuvB-like protein 1 Ruvbl1 1.49 1.12 -1.03 0.00422 0.24788 0.7941 NM_013689 cytoplasmic tyrosine kinase, Dscr28C related (Drosophila) Tec 1.49 -1.01 1.13 0.00425 0.94247 0.2732 NM_018764 protocadherin 7 Pcdh7 1.49 -1.13 -1.01 0.00518 0.38456 0.92524 NC_001501 Murine leukemia virus, complete genome. MLVgp1 1.49 1.16 1.15 0.00527 0.25523 0.23678 NM_010495 inhibitor of DNA binding 1 Id1 1.49 -1.15 -1.04 0.00574 0.33047 0.87997 NM_145131 pitrilysin metallepetidase 1 Pitrm1 1.49 -1.81 1.31 0.00624 0.0074 0.34704 NC_001506 Murine osteosarcoma virus, complete genome. Movgp1 1.49 1.14 1.05 0.00659 0.32694 0.67453 NM_008491 lipocalin 2 Lcn2 1.49 -1.23 1.12 0.00676 0.20057 0.5427 NM_029409 RIKEN cDNA 5230400G24 gene 5230400G24Rik 1.49 -2.76 1.59 0.00683 0.00007 0.31217 NM_138599 translocase of outer mitochondrial membrane 70 homolog A (yeast)Tomm70a 1.49 1.03 -1.03 0.00734 0.86509 0.7779 NM_008302 heat shock protein 90kDa alpha (cytosolic), class B member 1 Hsp90ab1 1.49 -1.74 -1.11 0.00755 0.00263 0.36112 NM_134079 NM_007411adenosine kinase Adk 1.49 -2 -1.25 0.00842 0.00074 0.18259 XM_147036 RIKEN cDNA 1190002N15 gene 1190002N15Rik 1.49 -1.62 1.19 0.00906 0.05781 0.31606 NM_027604 ubiquitin specific peptidase 15 Usp15 1.49 -1.16 1.15 0.0095 0.31958 0.40086 NC_001362 Friend murine leukemia virus, complete genome. FrMLVgp3 1.49 1.27 1.06 0.00972 0.10845 0.64786 XM_136364 dual specificity phosphatase 27 (putative) Dusp27 1.48 -1.4 -1.49 0.00149 0.00244 0.0209 NM_018798 ubiquilin 2 Ubqln2 1.48 -2.01 1.32 0.00257 0.00008 0.47115 NM_011680 upstream transcription factor 2 Usf2 1.48 NA 1.64 0.00277 NA 0.29048 NM_172545 euchromatic histone methyltransferase 1 Ehmt1 1.48 -1.35 1.43 0.00547 0.02277 0.17027 NM_020050 TMEM9 domain family, member B Tmem9b 1.48 -1.73 -1.05 0.00631 0.0013 0.81974 NM_181404 ankyrin repeat domain 15 Ankrd15 1.48 -1.23 -1 0.00699 0.17517 0.99327 NM_011992 reticulocalbin 2 Rcn2 1.48 -2.04 1.25 0.00709 0.0004 0.19083 NM_023311 Yip1 domain family, member 5 Yipf5 1.48 -1.72 1.49 0.00722 0.00472 0.04657 NC_001819 Rauscher murine leukemia virus, complete genome. Rmlvgp3 1.48 1.17 1.05 0.00747 0.24637 0.66803 NM_028491 RIKEN cDNA 1700040L02 gene 1700040L02Rik 1.48 -1.4 1.32 0.00871 0.01256 0.19639 NM_026424 coenzyme Q10 homolog B (S. cerevisiae) Coq10b 1.47 -1.09 -1.17 0.00183 0.28405 0.15071 NM_025942 GTP-binding protein 9 (putative) Gtpbp9 1.47 -1.7 1.32 0.00271 0.00014 0.49821 NM_013697 transthyretin Ttr 1.47 1.37 1.42 0.0029 0.00551 0.0597 NM_027769 copine III Cpne3 1.47 -1.19 1.18 0.00538 0.10451 0.49614 NM_181728 ADP-ribosyltransferase 3 Art3 1.47 -1.27 1.04 0.00883 0.10745 0.8037 NM_016737 stress-induced phosphoprotein 1 Stip1 1.47 -1.23 1.12 0.00896 0.21569 0.30705 NM_008722 nucleophosmin 1 Npm1 1.47 -1.66 1.48 0.00984 0.00478 0.12488 XM_136364 dual specificity phosphatase 27 (putative) Dusp27 1.46 -1.08 -1.09 0.00217 0.36436 0.54222 NM_177710 slingshot homolog 2 (Drosophila) Ssh2 1.46 1.06 NA 0.00343 0.60455 NA NM_146062 periphilin 1 Pphln1 1.46 -1.88 1.13 0.00429 0.0001 0.67601 NM_008112 guanosine diphosphate (GDP) dissociation inhibitor 2 Gdi2 1.46 -1.56 -1.04 0.00517 0.00119 0.75392 NM_025629 ADAMTS-like 5 Adamtsl5 1.46 1.14 1.2 0.00582 0.27323 0.1004 NM_009186 splicing factor, arginine/serine-rich 10 (transformer 2 homolog, Drosophila)Sfrs10 1.46 -1.39 -1.08 0.00594 0.01229 0.54549 NM_013590 P lysozyme structural Lzp-s 1.46 -1.95 1.25 0.00652 0.00013 0.57793 NM_008424 potassium voltage-gated channel, Isk-related subfamily, member 1Kcne1 1.46 1.57 1.37 0.0069 0.00255 0.02909 NM_172677 YTH domain family 3 Ythdf3 1.46 -2.85 1.21 0.00695 0.00003 0.50708 NM_009005 RAB7, member RAS oncogene family Rab7 1.46 -1.61 -1.03 0.00702 0.00208 0.80696 NC_001502 Moloney murine sarcoma virus, complete genome. MoMSVgp1 1.46 1.08 1.15 0.0071 0.51772 0.26163 NM_019666 synaptotagmin binding, cytoplasmic RNA interacting protein Syncrip 1.46 -1.12 1.07 0.00748 0.38206 0.58976 NC_001501 Murine leukemia virus, complete genome. MLVgp1 1.46 1.21 1.07 0.00756 0.14204 0.53715 RIKEN cDNA 1110001A16 gene 1110001A16Rik 1.46 -2.2 1.3 0.00758 0.00015 0.40391 NM_013625 platelet-activating factor acetylhydrolase, isoform 1b, beta1 subunitPafah1b1 1.46 -1.93 1 0.00807 0.00025 0.98597 NM_019652 arsA (bacterial) arsenite transporter, ATP-binding, homolog 1 Asna1 1.46 -1.56 1.1 0.00857 0.00865 0.38467 NM_021884 tumor susceptibility gene 101 Tsg101 1.46 -1.77 1.2 0.0087 0.0008 0.32222 NM_026517 ribosomal protein L22 like 1 Rpl22l1 1.46 -1.36 1.08 0.00887 0.05071 0.70969 NM_007664 cadherin 2 Cdh2 1.46 -2.07 1.14 0.0092 0.00145 0.53671 NM_008786 protein-L-isoaspartate (D-aspartate) O-methyltransferase 1 Pcmt1 1.46 -1.63 -1.03 0.0095 0.00522 0.82429 NM_010555 interleukin 1 receptor, type II Il1r2 1.46 1.24 1.07 0.00997 0.12025 0.80655 NM_008188 THUMP domain containing 3 Thumpd3 1.45 -1.89 1.27 0.00458 0.00009 0.46894 hypothetical protein 6330400D04 6330400D04 1.45 -1.02 -1.04 0.00641 0.84358 0.79305 NM_025673 golgi phosphoprotein 3 Golph3 1.45 -2.42 1.12 0.00656 0.00005 0.6272 AK081365 A kinase (PRKA) anchor protein 2 Akap2 1.45 -1.51 -1.04 0.00715 0.00843 0.80927 NM_198609 cDNA sequence BC003885 BC003885 1.45 -1.54 -1.1 0.00767 0.00672 0.50704 NM_023785 chemokine (C-X-C motif) ligand 7 Cxcl7 1.45 -1.28 1.23 0.0087 0.05367 0.1993 NM_018744 sema domain, transmembrane domain (TM), and cytoplasmic domain,Sema6a (semaphorin) 6A 1.45 -1.08 1.25 0.00907 0.59786 0.24794 NC_001362 Friend murine leukemia virus, complete genome. FrMLVgp3 1.45 1.23 1.08 0.00953 0.11351 0.51474 NM_022022 ubiquitination factor E4B, UFD2 homolog (S. cerevisiae) Ube4b 1.44 1.34 -1.25 0.00322 NA NA NM_175119 RIKEN cDNA 1300007C21 gene 1300007C21Rik 1.44 -1.51 NA 0.00356 0.00196 NA microtubule associated monoxygenase, calponin and LIM domain Micall1containing -like 1 1.44 1.45 NA 0.00481 NA NA NM_029271 mitochondrial ribosomal protein L32 Mrpl32 1.44 -2.27 1.37 0.00731 0.00003 0.39072 NM_007484 ras homolog gene family, member C Rhoc 1.44 -1.27 1 0.00814 0.08379 0.98359 NM_010444 nuclear receptor subfamily 4, group A, member 1 Nr4a1 1.44 -1.8 1.07 0.00891 0.00188 0.74574 NM_022314 NM_011628tropomyosin 3, gamma Tpm3 1.44 -1.3 -1.06 0.00957 0.05164 0.60893 AK019477 dystrobrevin alpha Dtna 1.43 -1.31 1.08 0.00221 0.00822 0.66073 NM_011241 RAN GTPase activating protein 1 Rangap1 1.43 1.17 1.09 0.00387 0.11714 0.4069 NM_023270 ring finger protein 128 Rnf128 1.43 NA -1.38 0.0045 NA 0.06319 NM_146107 ARP1 actin-related protein 1 homolog B (yeast) Actr1b 1.43 1.09 -1.07 0.0067 0.3377 0.64008 NM_172546 Cnksr family member 3 Cnksr3 1.43 -1.27 1.28 0.00767 0.10082 0.08015 NM_198303 eukaryotic translation initiation factor 5B Eif5b 1.43 -2 1.12 0.00821 0.00008 0.46399 NM_144948 RNA binding motif protein 7 Rbm7 1.43 -1.67 1.47 0.00889 0.00355 0.15378 NM_025959 proteasome (prosome, macropain) 26S subunit, ATPase, 6 Psmc6 1.43 -2.22 1.51 0.00987 0.00068 0.18345 NM_010197 fibroblast growth factor 1 Fgf1 1.42 1.03 -1.33 0.00287 0.7558 0.06044 NM_012006 acyl-CoA thioesterase 1 Acot1 1.42 -1.5 1.23 0.00426 0.0007 0.38513 NM_080471 ankyrin repeat domain 6 Ankrd6 1.42 1.35 1.13 0.00466 NA NA NM_030711 type 1 tumor necrosis factor receptor shedding aminopeptidase regulatorArts1 1.42 -1.97 1.53 0.00769 0.00034 0.27981 NM_153550 disrupted in renal carcinoma 2 (human) Dirc2 1.42 -1.05 1.51 0.00784 0.68817 0.12684 XM_484925 RIKEN cDNA 3110040M04 gene 3110040M04Rik 1.42 -1.04 1.18 0.0081 0.69699 0.27667 RIKEN cDNA 9530046B11 gene 9530046B11Rik 1.42 -1.39 1.13 0.00917 0.00882 0.67793 NM_007569 B-cell translocation gene 1, anti-proliferative Btg1 1.42 -1.69 1.02 0.00924 0.00343 0.92055 NM_181549 mannose receptor-like precursor Mrcl 1.41 -1.19 1.1 0.00267 0.06608 0.54511 NM_177616 RIKEN cDNA 4930562D19 gene 4930562D19Rik 1.41 NA NA 0.00522 NA NA NM_016798 PDZ and LIM domain 3 Pdlim3 1.41 -1.04 1 0.00688 0.73432 0.96894 NM_182997 protein kinase, AMP-activated, beta 2 non-catalytic subunit Prkab2 1.4 -1.29 1.07 0.00558 0.02212 0.53435 NM_025700 phosphoglucomutase 1 Pgm1 1.4 -1.42 1.1 0.00622 0.01068 0.6102 NM_011609 tumor necrosis factor receptor superfamily, member 1a Tnfrsf1a 1.4 -1.79 -1.14 0.00856 0.00008 0.23626 NM_025802 patatin-like phospholipase domain containing 2 Pnpla2 1.39 1.2 -1.1 0.00478 NA 0.49781 RIKEN cDNA C430014H23 gene C430014H23Rik 1.39 NA 1.44 0.00609 NA NA NM_009272 spermidine synthase Srm 1.39 -1.33 -1.2 0.00632 0.0072 0.16854 NM_009278 Sjogren syndrome antigen B Ssb 1.39 -1.12 1.42 0.00637 0.24173 NA NM_146222 cDNA sequence BC024479 BC024479 1.39 -1.26 1.12 0.00811 0.01914 0.42709 AF390178 brain-specific angiogenesis inhibitor 1-associated protein 2 Baiap2 1.38 NA NA 0.00551 NA NA NM_175164 Rho GTPase activating protein 26 Arhgap26 1.38 1.16 -1.08 0.00631 0.08167 0.5495 XM_132006 Wolf-Hirschhorn syndrome candidate 1 (human) Whsc1 1.38 -1.1 NA 0.0083 NA NA NM_183269 RIKEN cDNA 4930511N19 gene 4930511N19Rik 1.38 -1.3 1.31 0.00911 0.01356 0.40598 NM_172697 PRP38 pre-mRNA processing factor 38 (yeast) domain containingPrpf38a A 1.37 -1.65 1.03 0.00889 0.0001 0.84383 NM_013888 DnaJ (Hsp40) homolog, subfamily C, member 12 Dnajc12 1.36 -1.06 1.45 0.00475 0.50114 0.1722 myosin IA Myo1a 1.36 -1.12 NA 0.00556 0.26549 NA NM_026949 CCR4-NOT transcription complex, subunit 8 Cnot8 1.36 -1.36 1 0.00966 NA NA XM_111790 p21 (CDKN1A)-activated kinase 6 Pak6 1.36 1.16 -1.04 0.00992 0.15605 0.78185 NM_009892 chitinase 3-like 3 Chi3l3 1.35 -1.1 -1.24 0.00706 0.28097 0.14067 NM_022019 dual specificity phosphatase 10 Dusp10 1.33 -1.22 -1.06 0.00987 0.1141 0.66418 XM_489187 hypothetical protein 4932443D20 4932443D20 -1.33 1.1 1.14 0.00872 0.44616 0.2306 NM_134175 vomeronasal 1 receptor, C20 V1rc20 -1.37 -1.15 -1.02 0.00492 0.19078 0.89563 NM_144911 expressed sequence AW060207 AW060207 -1.37 1.3 -1.15 0.00796 NA 0.33008 NM_011758 zinc finger protein 39 Zfp39 -1.38 1.07 -1.15 0.00777 0.53983 0.22594 XM_356161 myosin IXa Myo9a -1.38 1.24 -1.04 0.00784 0.02228 0.67957 NM_026576 Ewing's tumor-associated antigen 1 Etaa1 -1.38 -1.02 -1.09 0.00829 0.82452 0.43105 XM_134736 zinc finger protein 26 Zfp26 -1.38 1.14 1 0.0086 0.20348 0.98357 NM_019576 thrombospondin, type I, domain 1 Thsd1 -1.38 1.02 -1.03 0.00977 0.78298 0.81993 NM_010417 hephaestin Heph -1.39 -1.17 -1.24 0.00781 NA 0.3123 NM_024245 kinesin family member 23 Kif23 -1.39 1.05 -1.09 0.00789 0.53886 0.45109 NM_007894 eosinophil-associated, ribonuclease A family, member 1 Ear1 -1.4 1.14 -1.3 0.00526 0.14943 0.09094 NM_144905 RIKEN cDNA 6330416G13 gene 6330416G13Rik -1.4 1.14 -1.05 0.00557 0.13892 0.65448 ankyrin repeat and KH domain containing 1 Ankhd1 -1.4 1.28 -1.37 0.00604 0.01288 0.01459 NM_011202 protein tyrosine phosphatase, non-receptor type 11 Ptpn11 -1.4 1.26 -1.54 0.00611 NA 0.00528 XM_484353 RIKEN cDNA 1700112E06 gene 1700112E06Rik -1.4 NA -1.02 0.0066 NA 0.89856 NM_012017 zinc finger protein 346 Zfp346 -1.4 1.44 1.09 0.00743 0.0012 0.42894 RIKEN cDNA 2010313D22 gene 2010313D22Rik -1.4 1.94 -1.08 0.00888 NA 0.63118 NM_029602 RIKEN cDNA 1700022C21 gene 1700022C21Rik -1.4 1.57 -1.11 0.00925 0.00051 0.57553 NM_009197 solute carrier family 16 (monocarboxylic acid transporters), memberSlc16a2 2 -1.41 1.49 -1.26 0.00476 0.00115 0.08632 RIKEN cDNA 4632413I24 gene 4632413I24Rik -1.41 -1.08 1.15 0.00569 NA 0.30933 NM_177789 V-set and immunoglobulin domain containing 4 Vsig4 -1.41 1.07 -1.12 0.00689 0.43601 0.36419 NM_203491 cholinergic receptor, muscarinic 2, cardiac Chrm2 -1.41 1.65 -1.07 0.00724 NA 0.56038 NM_008702 nemo like kinase Nlk -1.41 1.49 1 0.00958 0.00323 0.96992 NM_028390 anillin, actin binding protein (scraps homolog, Drosophila) Anln -1.42 1.14 -1.06 0.00579 0.11739 0.59438 NM_177907 expressed sequence AI593442 AI593442 -1.42 1.57 1.2 0.0062 NA 0.45522 NM_029850 B-cell CLL/lymphoma 7A Bcl7a -1.42 1.19 -1.28 0.00961 0.15021 0.08484 NM_175530 F-box protein 46 Fbxo46 -1.42 1.2 -1.3 0.00974 0.08867 0.09327 NM_145512 SFT2 domain containing 2 Sft2d2 -1.42 1.42 -1.11 0.0098 0.00625 0.5243 RIKEN cDNA 9430099M06 gene 9430099M06Rik -1.43 -1.07 1.23 0.002 NA 0.09515 NM_026346 F-box protein 32 Fbxo32 -1.43 NA -1.07 0.00335 NA 0.79383 NM_198886 zinc finger and BTB domain containing 12 Zbtb12 -1.43 NA -1.11 0.00386 NA 0.37031 NM_020496 T-box 20 Tbx20 -1.43 1.41 -1.04 0.00532 0.00907 0.82328 NM_175407 RIKEN cDNA 5330439J01 gene 5330439J01Rik -1.43 1.33 -1.24 0.00751 0.00883 0.14648 NM_172381 expressed sequence AI314180 AI314180 -1.43 1.03 -1.06 0.00815 0.81026 0.58258 XM_130930 Smg-5 homolog, nonsense mediated mRNA decay factor (C. elegans)Smg5 -1.43 1.03 -1.04 0.00888 0.74906 0.71458 NM_144558 basic, immunoglobulin-like variable motif containing Bivm -1.43 1.05 1.13 0.00994 0.61395 0.26401 NM_009153 sema domain, immunoglobulin domain (Ig), short basic domain, secreted,Sema3b (semaphorin) 3B -1.44 1.38 -1.12 0.00315 0.00318 0.367 NM_134189 UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-acetylgalactosaminyltransferaseGalnt10 10 -1.44 1.59 -1.28 0.0045 NA 0.08893 NM_028874 sorting nexin 19 Snx19 -1.44 1.36 -1.19 0.00718 NA 0.14566 NM_029282 RIKEN cDNA 2610036D13 gene 2610036D13Rik -1.44 1.21 -1.14 0.00755 0.10233 0.23479 NM_021896 guanylate cyclase 1, soluble, alpha 3 Gucy1a3 -1.44 1.4 -1.13 0.00849 0.01137 0.29696 NM_031874 RAB3D, member RAS oncogene family Rab3d -1.44 1.51 1.17 0.00947 0.009 0.172 NM_198103 exocyst complex component 8 Exoc8 -1.44 1.19 -1.27 0.0095 0.2015 0.08713 NM_177870 solute carrier family 5 (sodium-dependent vitamin transporter), memberSlc5a6 6 -1.44 1.24 -1.12 0.0098 0.10291 0.31718 NM_020584 telomeric repeat binding factor 2, interacting protein Terf2ip -1.44 1.18 -1.01 0.00985 0.22289 0.93818 NM_011160 protein kinase, cGMP-dependent, type I Prkg1 -1.44 1.04 -1.03 0.00989 0.7569 0.80376 RIKEN cDNA 2900060P06 gene 2900060P06Rik -1.45 1.1 -1.04 0.00243 0.28648 0.71435 XM_129976 solute carrier organic anion transporter family, member 6c1 Slco6c1 -1.45 NA 1.13 0.00304 NA 0.28421 NM_153514 Rho-related BTB domain containing 2 Rhobtb2 -1.45 NA -1.15 0.0032 NA 0.33256 NM_011784 angiotensin receptor-like 1 Agtrl1 -1.45 -1.05 -1 0.00344 0.53329 0.97266 NM_173364 zinc finger protein 445 Zfp445 -1.45 1.95 1.01 0.00578 0.00008 0.94591 NM_175465 SEC14 and spectrin domains 1 Sestd1 -1.45 1.33 -1.15 0.0072 0.02286 0.2577 RIKEN cDNA 2810470K03 gene 2810470K03Rik -1.45 1.19 -1.01 0.00867 0.18895 0.94124 NM_145476 TBC1 domain family, member 22a Tbc1d22a -1.45 1.2 -1.18 0.00874 0.2133 0.19698 XM_484809 RIKEN cDNA 6030443O07 gene 6030443O07Rik -1.45 1.09 1.08 0.00884 0.51306 0.58481 NM_080444 ankyrin repeat and SOCS box-containing protein 10 Asb10 -1.45 1.65 -1.07 0.0089 NA 0.59219 RIKEN cDNA C030002B11 gene C030002B11Rik -1.45 1 -1.05 0.0092 0.96981 0.66091 NM_013640 proteasome (prosome, macropain) subunit, beta type 10 Psmb10 -1.45 1.44 -1.17 0.0095 0.02694 0.1946 NM_145534 BTB (POZ) domain containing 3 Btbd3 -1.46 -1.02 -1.06 0.00196 0.85645 0.62503 hypothetical protein 9330117B14 9330117B14 -1.46 1.05 -1.11 0.0022 0.52092 0.30824 NM_172740 RIKEN cDNA B230312I18 gene B230312I18Rik -1.46 1.4 -1.18 0.00286 0.00216 0.1575 NM_007377 apoptosis-associated tyrosine kinase Aatk -1.46 1.09 -1.26 0.00325 0.30273 0.14328 NM_133969 cytochrome P450, family 4, subfamily v, polypeptide 3 Cyp4v3 -1.46 1.35 -1.22 0.00498 0.01915 0.10309 NM_025747 RIKEN cDNA 4933411K20 gene 4933411K20Rik -1.46 1.1 -1.14 0.00565 0.36412 0.23863 NM_023913 endoplasmic reticulum (ER) to nucleus signalling 1 Ern1 -1.46 1.21 1.06 0.00705 0.11324 0.62158 NM_178784 asparagine-linked glycosylation 6 homolog (yeast, alpha-1,3,-glucosyltransferase)Alg6 -1.46 1.1 1.07 0.0072 0.38489 0.50413 NM_009572 zinc fingers and homeoboxes protein 1 Zhx1 -1.46 1.07 -1.07 0.00734 0.53325 0.52481 NM_029895 cDNA sequence BC026657 BC026657 -1.46 1.27 -1.26 0.00822 0.13353 0.09728 NM_177684 zinc finger protein 637 Zfp637 -1.46 1.45 -1.05 0.00861 0.016 0.65899 NM_144551 tribbles homolog 2 (Drosophila) Trib2 -1.46 1.29 -1.08 0.00875 0.05404 0.49789 XM_483975 oxysterol binding protein 2 Osbp2 -1.46 1.83 1.31 0.00876 0.0005 0.06186 NM_023184 Kruppel-like factor 15 Klf15 -1.46 1.34 -1.57 0.00963 0.16614 0.03148 NM_173374 splicing factor, arginine/serine-rich 1 (ASF/SF2) Sfrs1 -1.46 1.27 -1.16 0.00998 0.11578 0.33193 NM_025780 THAP domain containing, apoptosis associated protein 2 Thap2 -1.47 1.04 -1.09 0.00212 0.60309 0.58048 NM_175446 RIKEN cDNA B930008K04 gene B930008K04Rik -1.47 1.2 -1.21 0.00244 0.14358 0.11441 NM_009453 zinc finger (CCCH type), RNA binding motif and serine/arginine richZrsr2 2 -1.47 1.01 1 0.00506 0.90413 0.98431 XM_203601 phosphofurin acidic cluster sorting protein 2 Pacs2 -1.47 1.22 -1.21 0.00524 NA 0.14086 NM_025339 transmembrane protein 42 Tmem42 -1.47 1.52 -1.01 0.00615 0.00266 0.92871 NM_009053 radical fringe gene homolog (Drosophila) Rfng -1.47 1.33 -1.15 0.00625 0.0264 0.24729 NM_134034 SMEK homolog 2, suppressor of mek1 (Dictyostelium) Smek2 -1.47 1.19 1.04 0.00625 0.1655 0.73352 NM_177768 cDNA sequence BC038156 BC038156 -1.47 1.17 -1.13 0.00689 0.26981 0.32828 NM_007936 Eph receptor A4 Epha4 -1.47 1.1 1.15 0.00694 0.3968 0.28104 NM_133818 expressed sequence AI597479 AI597479 -1.47 -1.1 -1.02 0.00871 0.49215 0.86998 NM_174874 autophagy-related 4B (yeast) Atg4b -1.48 1.39 -1 0.00182 0.00321 0.99074 NM_023596 solute carrier family 29 (nucleoside transporters), member 3 Slc29a3 -1.48 1.2 -1.37 0.00295 0.08635 0.04589 NM_172563 hepatic leukemia factor Hlf -1.48 1.06 -1.09 0.00437 0.56202 0.47941 XM_127501 RIKEN cDNA G630013P12 gene G630013P12Rik -1.48 1.34 -1.25 0.00542 0.04114 0.18611 NM_146094 fatty acid desaturase 1 Fads1 -1.48 1.28 -1.18 0.00589 0.09926 0.21056 NM_021356 growth factor receptor bound protein 2-associated protein 1 Gab1 -1.48 1.09 -1.05 0.00595 0.52982 0.68472 NM_013761 serine racemase Srr -1.48 1.74 -1.05 0.00633 0.00201 0.74707 RIKEN cDNA 5830454D03 gene 5830454D03Rik -1.48 -1.05 -1.01 0.00649 0.70002 0.89673 NM_198300 cytoplasmic polyadenylation element binding protein 3 Cpeb3 -1.48 1.27 1.03 0.00776 0.12128 0.80569 NM_177545 vang-like 1 (van gogh, Drosophila) Vangl1 -1.48 1.21 -1.28 0.00847 0.13783 0.05545 NM_197985 adiponectin receptor 2 Adipor2 -1.48 1.2 -1.09 0.00847 0.22139 0.46309 XM_147946 zinc finger protein 251 Zfp251 -1.48 1.21 -1.01 0.00858 0.12936 0.92107 NM_009822 runt-related transcription factor 1; translocated to, 1 (cyclin D-related)Runx1t1 -1.48 1.36 1.04 0.00943 0.05139 0.76627 NM_028223 transmembrane protein 175 Tmem175 -1.49 1.29 -1.14 0.00257 0.01244 0.2309 NM_016745 ATPase, Ca++ transporting, ubiquitous Atp2a3 -1.49 1.4 -1.32 0.00404 0.00769 0.05915 AK015096 Mus musculus adult male testis cDNA, RIKEN full-length enrichedNA library, clone:4930405F09-1.49 product:telomerase1.1 associated protein-1.35 1, full insert0.00412 sequence. 0.35179 0.06053 NM_173734 transmembrane protein 87A Tmem87a -1.49 1.32 -1.15 0.00443 0.02624 0.21581 NM_153580 coiled-coil domain containing 95 Ccdc95 -1.49 1.72 -1.07 0.00457 0.00088 0.59052 NM_015829 solute carrier family 25 (mitochondrial carrier, adenine nucleotide Slc25a13translocator), member 13 -1.49 1.37 -1.14 0.00472 0.02894 0.26522 NM_173181 RIKEN cDNA 3110050N22 gene 3110050N22Rik -1.49 1.28 -1.33 0.00478 0.0531 0.05071 NM_173866 glutamic pyruvate transaminase (alanine aminotransferase) 2 Gpt2 -1.49 1.21 -1.18 0.005 0.15655 0.20937 NM_010880 nucleolin Ncl -1.49 1.46 -1.08 0.00517 0.01224 0.53029 NM_007943 epidermal growth factor receptor pathway substrate 15 Eps15 -1.49 1.12 -1.12 0.00559 0.42066 0.43872 NM_176920 leucine-rich repeats and transmembrane domains 1 Lrtm1 -1.49 1.27 1.1 0.0056 0.11067 0.4317 NM_134029 5',3'-nucleotidase, mitochondrial Nt5m -1.49 1.29 1.03 0.00623 0.09834 0.78382 NM_139063 muted Muted -1.49 1.34 -1.09 0.00685 0.05592 0.45726 NM_145611 ankyrin repeat domain 25 Ankrd25 -1.49 1.13 -1.07 0.00699 0.41355 0.55049 NM_138579 TRIO and F-actin binding protein Triobp -1.49 1.39 -1.18 0.00721 0.04017 0.26339 NM_010148 epsin 2 Epn2 -1.49 1.55 1.3 0.00723 0.01024 0.06986 NM_146184 UDP-GlcNAc:betaGal beta-1,3-N-acetylglucosaminyltransferase 8B3gnt8 -1.49 1.29 -1.2 0.00753 0.0591 0.28361 NM_026859 MAF1 homolog (S. cerevisiae) Maf1 -1.49 1.35 -1.01 0.00792 0.04934 0.92093 NM_172768 GRAM domain containing 1B Gramd1b -1.49 1.25 -1.38 0.00987 0.03481 0.05147 RIKEN cDNA 2010205O06 gene 2010205O06Rik -1.5 1.69 -1.08 0.00105 NA 0.57712 NM_009675 amine oxidase, copper containing 3 Aoc3 -1.5 1.3 -1.14 0.00202 0.01271 0.22968 NM_173745 dual specificity phosphatase 18 Dusp18 -1.5 1.54 -1.02 0.00283 0.00173 0.90629 NM_027865 transmembrane protein 25 Tmem25 -1.5 1.26 -1.57 0.00359 0.06558 0.00771 NM_023485 syncoilin Sync -1.5 NA -1.19 0.00383 NA 0.21234 NM_172819 expressed sequence AI317237 AI317237 -1.5 1.33 -1.11 0.00412 0.02958 0.3919 NM_139063 muted Muted -1.5 1.27 -1.1 0.00461 0.07833 0.4074 NM_007610 caspase 2 Casp2 -1.5 1.47 -1.02 0.0047 0.01098 0.90085 NM_025590 acyl-CoA thioesterase 11 Acot11 -1.5 1.1 -1.1 0.00496 0.49214 0.4393 NM_001001735Mus musculus Wolf-Hirschhorn syndrome candidate 1-like 1 (human)Whsc1l1 (Whsc1l1), mRNA. -1.5 1.26 -1.09 0.00497 0.08597 0.45489 XM_135158 expressed sequence AW107703 AW107703 -1.5 1.52 -1.17 0.00632 0.01354 0.28946 NM_009202 solute carrier family 22 (organic cation transporter), member 1 Slc22a1 -1.5 1.33 -1.01 0.00654 0.02576 0.93139 NM_023055 solute carrier family 9 (sodium/hydrogen exchanger), isoform 3 regulatorSlc9a3r2 2 -1.5 1.22 -1.22 0.00691 0.18619 0.1894 NM_178795 histidine acid phosphatase domain containing 2A Hisppd2a -1.5 1.23 -1.21 0.0073 0.15115 0.17058 XM_134954 RIKEN cDNA 5430433E21 gene 5430433E21Rik -1.51 1.29 -1.17 0.00153 0.02868 0.31474 NM_175446 RIKEN cDNA B930008K04 gene B930008K04Rik -1.51 1.42 -1.22 0.00225 0.00174 0.07806 NM_008057 frizzled homolog 7 (Drosophila) Fzd7 -1.51 1.19 -1.01 0.00285 0.08793 0.94133 NM_007631 cyclin D1 Ccnd1 -1.51 1.15 -1.3 0.00405 0.19124 0.051 XM_132501 PDGFA associated protein 1 Pdap1 -1.51 2.46 1.08 0.00441 0.00007 0.62804 NM_013723 podocalyxin-like Podxl -1.51 1.23 -1.29 0.00828 0.15107 0.07297 NM_145987 cDNA sequence BC025833 BC025833 -1.52 1.01 -1.22 0.00103 NA 0.47135 NM_173181 RIKEN cDNA 3110050N22 gene 3110050N22Rik -1.52 1.2 -1.15 0.00288 0.13392 0.25171 XM_489508 RIKEN cDNA 1810013L24 gene 1810013L24Rik -1.52 1.34 -1.1 0.00299 0.0288 0.48429 NM_147220 ATP-binding cassette transporter sub-family A member 9 Abca9 -1.52 -1.13 -1.13 0.00333 0.29444 0.34683 NM_181411 aftiphilin Aftph -1.52 1.56 -1 0.00385 0.0098 0.98764 NM_019444 receptor (calcitonin) activity modifying protein 2 Ramp2 -1.52 1.34 -1.13 0.00529 0.14542 0.27361 tensin 1 Tns1 -1.52 1.91 1.12 0.00563 0.00087 0.37666 RIKEN cDNA 1110030E23 gene 1110030E23Rik -1.52 1.26 -1.24 0.00618 0.0843 0.17386 NM_198016 RIKEN cDNA 1600010O03 gene 1600010O03Rik -1.52 1.78 1.09 0.00813 0.00217 0.58637 NM_172153 ligand dependent nuclear receptor corepressor-like Lcorl -1.53 1.12 1.35 0.00231 0.22182 0.02082 XM_355939 RIKEN cDNA D430042O09 gene D430042O09Rik -1.53 1.14 -1.07 0.00236 0.22192 0.53 NM_015769 excision repair cross-complementing rodent repair deficiency, complementationErcc4 group 4 -1.53 1.31 -1.16 0.00252 0.0213 0.2417 AK042872 Mus musculus 7 days neonate cerebellum cDNA, RIKEN full-lengthNA enriched library, clone:A730032N19-1.53 product:unknown1.54 EST,-1.18 full insert sequence.0.00281 0.00291 0.14961 NM_024244 RIKEN cDNA 1200015N20 gene 1200015N20Rik -1.53 1.16 -1.04 0.00327 0.20775 0.71401 NM_028134 LysM, putative peptidoglycan-binding, domain containing 1 Lysmd1 -1.53 1.33 -1.14 0.00354 0.04424 0.34384 NM_146105 RIKEN cDNA 9630058J23 gene 9630058J23Rik -1.53 -1.37 -1.24 0.00373 0.02422 0.10147 NM_178914 spermatogenesis associated 7 Spata7 -1.53 -1.07 -1.09 0.00424 0.45542 0.49337 NM_027995 progestin and adipoQ receptor family member VII Paqr7 -1.53 -1.16 -1.39 0.00436 0.21886 0.03237 AP2 associated kinase 1 Aak1 -1.53 1.92 1.01 0.00451 0.00109 0.95056 NM_007908 eukaryotic elongation factor-2 kinase Eef2k -1.53 1.19 -1.14 0.00464 0.14592 0.28245 NM_016712 tropomodulin 4 Tmod4 -1.53 1.01 -1.31 0.00502 0.92147 0.02975 NM_011653 tubulin, alpha 1 Tuba1 -1.53 1 -1.29 0.00574 0.98435 0.04904 NM_133784 WW domain containing transcription regulator 1 Wwtr1 -1.53 1.98 1.59 0.00625 0.00222 0.00928 NM_146878 olfactory receptor 30 Olfr30 -1.54 NA 1.08 0.00077 NA 0.57681 NM_009974 casein kinase 2, alpha prime polypeptide Csnk2a2 -1.54 1.59 -1.17 0.0012 0.00118 0.28663 RIKEN cDNA C030033M12 gene C030033M12Rik -1.54 1.16 -1.05 0.0016 0.1078 0.64431 XM_134869 RIKEN cDNA 3300001A09 gene 3300001A09Rik -1.54 1.08 -1.08 0.00212 0.3428 0.66104 RIKEN cDNA 1110029I05 gene 1110029I05Rik -1.54 1.17 -1.01 0.00253 0.17809 0.94132 NM_026313 RIKEN cDNA 3300001P08 gene 3300001P08Rik -1.54 1.36 1.15 0.00316 0.04688 0.31449 NM_010518 insulin-like growth factor binding protein 5 Igfbp5 -1.54 1.8 1.29 0.00336 0.00238 0.06632 NM_011050 programmed cell death 4 Pdcd4 -1.54 1.07 -1.27 0.00383 0.62137 0.06782 RIKEN cDNA 1810014B01 gene 1810014B01Rik -1.55 1.3 1.02 0.00114 0.00908 0.90102 NM_019467 allograft inflammatory factor 1 Aif1 -1.55 1.47 -1.21 0.0018 0.00254 0.12012 RIKEN cDNA 6330407I18 gene 6330407I18Rik -1.55 1.38 -1.15 0.00192 0.01583 0.22159 NM_172605 tudor domain containing 3 Tdrd3 -1.55 1.29 -1.13 0.00194 0.03595 0.27848 NM_008688 nuclear factor I/C Nfic -1.55 1.31 -1.29 0.00222 0.02377 0.07373 NM_153779 apoptosis-inducing factor (AIF)-like mitochondrion-associated inducerAmid of death -1.55 1.07 -1.17 0.00255 0.64798 0.2016 NM_178062 superkiller viralicidic activity 2-like (S. cerevisiae ) Skiv2l -1.55 NA -1.42 0.00255 NA 0.03547 XM_355579 myeloid/lymphoid or mixed-lineage leukemia 3 Mll3 -1.55 1.29 1.09 0.0042 0.09011 0.41326 NM_023049 ankyrin repeat and SOCS box-containing protein 2 Asb2 -1.55 1.07 1.05 0.00476 0.65602 0.66245 NM_026260 RIKEN cDNA 4930521E07 gene 4930521E07Rik -1.56 1.33 -1.42 0.00093 0.00476 0.03754 RIKEN cDNA 4832420A03 gene 4832420A03Rik -1.56 -1 1.02 0.00149 0.97658 0.86241 NM_172591 FCH domain only 2 Fcho2 -1.56 1.12 -1.25 0.00244 0.43129 0.07518 NM_009955 dihydropyrimidinase-like 2 Dpysl2 -1.56 -1.02 -1.15 0.00248 0.89389 0.25141 NM_022022 ubiquitination factor E4B, UFD2 homolog (S. cerevisiae) Ube4b -1.56 1.14 -1.27 0.00278 0.31859 0.07614 NM_007706 suppressor of cytokine signaling 2 Socs2 -1.56 1.28 -1.55 0.00281 0.13103 0.00321 NM_172417 RIKEN cDNA 2310042D19 gene 2310042D19Rik -1.56 1.11 -1.02 0.00296 0.48219 0.87212 NM_172904 fibronectin type III and SPRY domain containing 2 Fsd2 -1.56 1.63 -1.18 0.00318 NA 0.36437 XM_128550 RIKEN cDNA 1700065O13 gene 1700065O13Rik -1.56 1.08 -1.24 0.00428 0.44892 0.10918 NM_178765 RIKEN cDNA 5730410E15 gene 5730410E15Rik -1.56 1.04 -1.07 0.00601 0.77396 0.50848 NM_009644 aryl-hydrocarbon receptor repressor Ahrr -1.57 NA 1.18 0.0005 NA 0.2276 NM_027903 dihydrodiol dehydrogenase (dimeric) Dhdh -1.57 1.51 -1.05 0.00211 0.00751 0.74944 XM_128308 prickle like 1 (Drosophila) Prickle1 -1.57 1.17 -1.13 0.00231 0.28573 0.36089 NM_026528 RIKEN cDNA 2700060E02 gene 2700060E02Rik -1.57 1.77 -1.23 0.00356 0.00214 0.12639 XM_485570 myeloid/lymphoid or mixed-lineage leukemia 5 Mll5 -1.58 NA -1.27 0.00143 NA 0.05751 NM_178404 zinc finger CCCH type containing 6 Zc3h6 -1.58 1.19 1.08 0.00144 0.12404 0.55426 XM_144310 RIKEN cDNA 6820424L24 gene 6820424L24Rik -1.58 1.1 -1.15 0.00203 NA 0.20573 tetratricopeptide repeat domain 17 Ttc17 -1.58 1.17 -1.13 0.00203 0.27174 0.29934 transmembrane protein 181 Tmem181 -1.58 1.4 -1.13 0.0023 0.01534 0.3202 XM_484178 immunoglobulin heavy chain 1a (serum IgG2a) Igh-1a -1.58 1.85 1.45 0.00231 0.00015 0.02683 NM_010512 insulin-like growth factor 1 Igf1 -1.59 -1.16 1.03 0.0015 0.13929 0.73794 NM_009672 acidic (leucine-rich) nuclear phosphoprotein 32 family, member AAnp32a -1.59 1.13 -1.41 0.00178 0.41576 0.0764 NM_016769 MAD homolog 3 (Drosophila) Smad3 -1.59 1.3 -1.24 0.00182 0.06821 0.07241 NM_027250 RIKEN cDNA 2010305A19 gene 2010305A19Rik -1.59 1.52 1.06 0.00185 0.00123 0.61719 NM_011119 proliferation-associated 2G4 Pa2g4 -1.59 1.45 -1.24 0.00219 0.01274 0.12964 XM_126551 trinucleotide repeat containing 6C Tnrc6c -1.59 1.22 -1.18 0.00264 0.15566 0.3125 NM_009761 BCL2/adenovirus E1B interacting protein 3-like Bnip3l -1.59 1.58 -1.17 0.00309 0.00283 0.17779 AK081844 Mus musculus 16 days embryo head cDNA, RIKEN full-length enrichedNA library, clone:C130081G24-1.59 product:hypothetical1.13 protein,-1.32 full insert sequence.0.00315 0.33727 0.04337 NM_010175 Fas (TNFRSF6)-associated via death domain Fadd -1.6 1.54 -1.25 0.00118 0.00304 0.16848 NM_144903 aldolase 2, B isoform Aldob -1.6 2.09 1.03 0.00131 0.00002 0.91689 XM_129145 tetratricopeptide repeat domain 9C Ttc9c -1.6 1.48 -1.08 0.00164 0.00846 0.55114 NM_013896 translocase of inner mitochondrial membrane 9 homolog (yeast) Timm9 -1.6 1.36 -1.01 0.00197 0.05089 0.9007 NM_019832 G kinase anchoring protein 1 Gkap1 -1.61 1.72 -1.08 0.00137 0.00304 0.51367 NM_007841 DEAD (Asp-Glu-Ala-Asp) box polypeptide 6 Ddx6 -1.61 2.25 1.07 0.00141 0.00008 0.53053 NM_008772 purinergic receptor P2Y, G-protein coupled 1 P2ry1 -1.61 1.1 -1.09 0.00192 0.43617 0.47945 NM_133196 cleavage stimulation factor, 3' pre-RNA subunit 2 Cstf2 -1.61 1.35 -1.2 0.00225 0.03374 0.11818 NM_007407 adenylate cyclase activating polypeptide 1 receptor 1 Adcyap1r1 -1.62 1.41 -1.16 0.00043 0.00342 0.21064 NM_009669 amylase 2, pancreatic Amy2 -1.62 1.38 -1.25 0.0008 0.0146 0.13825 NM_011749 zinc finger protein 148 Zfp148 -1.62 1.26 -1.11 0.00084 0.04728 0.35682 NM_173432 protein serine kinase H1 Pskh1 -1.62 1.38 -1.38 0.00094 0.01721 0.05857 NM_053246 docking protein 4 Dok4 -1.62 1.16 -1.11 0.00097 0.20016 0.3402 NM_146033 ankyrin repeat and MYND domain containing 2 Ankmy2 -1.62 1.4 -1.15 0.00108 0.01766 0.25446 NM_026514 CDC42 effector protein (Rho GTPase binding) 3 Cdc42ep3 -1.62 1.55 1.08 0.0011 0.00652 0.56001 NM_133792 lysophospholipase 3 Lypla3 -1.62 1.14 -1.16 0.00131 0.28232 0.27327 NM_138664 open reading frame 28 ORF28 -1.62 1.4 -1.14 0.00139 0.03599 0.33414 NM_007754 carboxypeptidase D Cpd -1.62 1.74 -1.2 0.0021 0.00052 0.1326 XM_290115 Bcl3 binding protein B3bp -1.63 1.21 -1.09 0.0008 0.10187 0.54335 NM_027897 rhophilin, Rho GTPase binding protein 2 Rhpn2 -1.63 1.12 1.1 0.0009 0.1989 0.50746 NM_007764 v-crk sarcoma virus CT10 oncogene homolog (avian)-like Crkl -1.63 1.49 -1.07 0.00109 0.01389 0.58322 NM_025304 leucine carboxyl methyltransferase 1 Lcmt1 -1.63 1.53 1.11 0.00117 0.01196 0.35263 XM_109657 RIKEN cDNA A730024A03 gene A730024A03Rik -1.63 1.12 -1.08 0.00174 0.43979 0.52723 NM_024196 TBC1 domain family, member 20 Tbc1d20 -1.63 1.57 -1.02 0.00207 0.00674 0.88892 NM_020265 dickkopf homolog 2 (Xenopus laevis) Dkk2 -1.64 -1.02 1.18 0.0006 0.82911 0.19236 XM_484765 N-acetyltransferase 11 Nat11 -1.64 1.25 -1.1 0.00103 0.08802 0.48586 NM_172303 PHD finger protein 17 Phf17 -1.64 1.22 1.02 0.00133 0.17601 0.84836 protein phosphatase 1, regulatory (inhibitor) subunit 12B Ppp1r12b -1.65 1.55 -1.1 0.00072 0.00421 0.42685 NM_027992 transmembrane protein 106B Tmem106b -1.65 -1.05 -1.17 0.00151 0.71877 0.18436 NM_010476 hydroxysteroid (17-beta) dehydrogenase 7 Hsd17b7 -1.66 1.34 -1.22 0.00081 0.06235 0.10175 NM_022887 tuberous sclerosis 1 Tsc1 -1.66 1.27 1 0.00126 0.08532 0.99057 NM_053273 tweety homolog 2 (Drosophila) Ttyh2 -1.67 1.07 -1.35 0.00027 NA 0.02241 NM_027230 protein kinase C binding protein 1 Prkcbp1 -1.67 1.32 -1.32 0.00081 0.04955 0.04223 NM_030206 cytoglobin Cygb -1.67 1.31 -1.05 0.00095 0.1352 0.65031 RIKEN cDNA 5033403D15 gene 5033403D15Rik -1.67 -1.02 -1.11 0.00139 0.85462 0.40363 NM_145587 SH3-binding kinase 1 Sbk1 -1.67 1.13 -1.41 0.00257 0.39888 0.03477 NM_011331 chemokine (C-C motif) ligand 12 Ccl12 -1.68 -1.22 1.3 0.00054 0.16403 0.31324 NM_029554 RIKEN cDNA 0610040J01 gene 0610040J01Rik -1.68 1.18 -1.14 0.0006 0.16867 0.23186 NM_178051 MTERF domain containing 2 Mterfd2 -1.69 1.48 -1.09 0.00082 0.00946 0.48158 NM_172581 RIKEN cDNA 9830169C18 gene 9830169C18Rik -1.7 1.27 -1.12 0.00043 0.04386 0.32777 NM_026497 nudix (nucleoside diphosphate linked moiety X)-type motif 12 Nudt12 -1.7 1.24 -1.14 0.00049 0.08681 0.26373 XM_127279 ras responsive element binding protein 1 Rreb1 -1.7 -1.05 -1.14 0.0005 0.72756 0.3943 NM_011675 uridine-cytidine kinase 1 Uck1 -1.7 1.55 -1.14 0.0005 0.00767 0.30386 NM_134129 PRP19/PSO4 pre-mRNA processing factor 19 homolog (S. cerevisiae)Prpf19 -1.7 1.6 -1.01 0.00095 0.00837 0.94206 NM_146099 DNA segment, Chr 19, Wayne State University 162, expressed D19Wsu162e -1.7 1.24 -1.39 0.00105 0.15526 0.0227 NM_017397 DEAD (Asp-Glu-Ala-Asp) box polypeptide 20 Ddx20 -1.71 1.37 -1.27 0.0003 0.00951 0.09366 NM_172652 RIKEN cDNA 4632411B12 gene 4632411B12Rik -1.71 1.46 -1.42 0.00041 0.01674 0.05802 NM_010882 necdin Ndn -1.71 1.4 -1.08 0.0005 0.02651 0.50143 RIKEN cDNA 2900054D09 gene 2900054D09Rik -1.73 1.48 -1.21 0.0004 0.00771 0.26563 NM_013768 protein arginine N-methyltransferase 5 Prmt5 -1.73 2.09 1.11 0.00047 0.00039 0.46315 NM_026929 ChaC, cation transport regulator-like 1 (E. coli) Chac1 -1.73 1.33 -1.37 0.00086 0.09208 0.12135 NM_145433 mitochondrial rRNA methyltransferase 1 homolog (S. cerevisiae) Mrm1 -1.73 1.33 -1.11 0.00118 0.03296 0.41437 NM_153410 G-protein signalling modulator 1 (AGS3-like, C. elegans) Gpsm1 -1.73 1.26 -1.27 0.00216 0.12904 0.21177 NM_001004293adhesion molecule with Ig like domain 1 Amigo1 -1.74 1.4 -1.05 0.00022 0.0097 0.62815 RIKEN cDNA 2210011G09 gene 2210011G09Rik -1.75 1.16 -1.02 0.00029 0.23593 0.86332 NM_015733 caspase 9 Casp9 -1.75 1.17 -1.21 0.0004 0.2164 0.14495 NM_199007 shugoshin-like 2 (S. pombe) Sgol2 -1.76 1.17 -1.12 0.00012 0.10367 0.47584 NM_008340 insulin-like growth factor binding protein, acid labile subunit Igfals -1.76 1.1 -1.42 0.00019 0.31934 0.03177 NM_013799 arginine-tRNA-protein transferase 1 Ate1 -1.76 1.41 -1.12 0.00027 0.02507 0.3598 NM_175097 LIM domain only 6 Lmo6 -1.76 1.18 -1.19 0.00038 0.22584 0.12718 NM_011085 phosphatidylinositol 3-kinase, regulatory subunit, polypeptide 1 (p85Pik3r1 alpha) -1.76 1.72 -1.16 0.00079 0.00461 0.33808 NM_007647 ectonucleoside triphosphate diphosphohydrolase 5 Entpd5 -1.77 1.27 -1.04 0.0004 0.10948 0.71693 NM_177242 PTC7 protein phosphatase homolog (S. cerevisiae) Pptc7 -1.77 1.4 -1.04 0.00049 0.03192 0.7236 XM_485010 RIKEN cDNA 2510042P03 gene 2510042P03Rik -1.78 1.08 -1.36 0.00173 0.60068 0.08501 NM_009895 cytokine inducible SH2-containing protein Cish -1.8 1.24 -1.31 0.00072 0.24276 0.07917 RIKEN cDNA 2310002D06 gene 2310002D06Rik -1.8 1.03 1.01 0.00202 0.72817 0.91261 NM_133986 T-cell leukemia translocation altered gene Tcta -1.83 1.26 -1.08 0.00017 0.16092 0.53409 NM_009642 angiotensin II, type I receptor-associated protein Agtrap -1.83 1.36 -1.08 0.0002 0.03662 0.63165 XM_125637 Rho-related BTB domain containing 1 Rhobtb1 -1.83 1.14 -1.49 0.0003 0.39043 0.05233 XM_131633 protein kinase, AMP-activated, alpha 2 catalytic subunit Prkaa2 -1.85 NA -1.28 0.00045 NA 0.24254 NM_178671 UBX domain containing 3 Ubxd3 -1.86 1.24 1.27 0.00002 NA 0.18665 NM_009778 complement component 3 C3 -1.86 1.31 -1.58 0.00023 0.07822 0.02802 NM_009349 indolethylamine N-methyltransferase Inmt -1.88 -1.05 -1.21 0.00077 0.76588 0.41198 NM_017376 thyrotroph embryonic factor Tef -1.89 1.25 -1.36 0.00009 0.21791 0.04429 NM_026436 transmembrane protein 86A Tmem86a -1.89 1.03 1.03 0.00012 0.77219 0.75906 NM_175475 cytochrome P450, family 26, subfamily b, polypeptide 1 Cyp26b1 -1.94 1.59 -1.37 0.00005 0.02651 0.04153 XM_130388 RIKEN cDNA 1110034G24 gene 1110034G24Rik -1.95 -1.27 -1.19 0.00008 0.12177 0.17456 NM_008825 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 2 Pfkfb2 -1.97 -1.21 -1.25 0.00003 0.11436 0.07761 NM_026689 RIKEN cDNA 0610009K11 gene 0610009K11Rik -1.97 1.35 -1.44 0.00005 0.06222 0.01665 NM_133994 glutathione S-transferase, theta 3 Gstt3 -1.97 1.29 -1.17 0.00007 0.15014 0.21559 NM_028521 phosphatase, orphan 2 Phospho2 -2.02 1.32 -1.01 0.00002 0.03911 0.94111 NM_008720 Niemann Pick type C1 Npc1 -2.03 1.81 -1.43 0.00002 0.0006 0.04235 NM_028582 influenza virus NS1A binding protein Ivns1abp -2.05 NA -1.01 0.00001 NA 0.95655 NM_024291 kyphoscoliosis peptidase Ky -2.06 1.08 -1.54 0.00002 0.39375 0.02348 RIKEN cDNA 6330407A06 gene 6330407A06Rik -2.07 1.55 -1.13 0.00001 0.00091 0.38881 NM_183023 regulating synaptic membrane exocytosis 4 Rims4 -2.09 2.51 -1.03 0.00002 0.00005 0.85001 NM_138741 serum deprivation response Sdpr -2.12 1.46 -1.15 0.00001 0.02777 0.22622 NM_139309 Fukuyama type congenital muscular dystrophy homolog (human) Fcmd -2.14 1.25 -1.19 0.00001 0.07471 0.16535 NM_011540 titin-cap Tcap -2.14 1.54 -1.24 0.00006 0.03902 0.17064 NM_028582 influenza virus NS1A binding protein Ivns1abp -2.17 -1.01 1.05 0.00001 0.92608 0.66073 RIKEN cDNA A530047J11 gene A530047J11Rik -2.21 -1.18 1.13 0.02733 0.23707 0.36959 expressed sequence AW120700 AW120700 -2.34 1.77 1.35 0 NA 0.05418 RIKEN cDNA 4833418N17 gene 4833418N17Rik -2.36 NA NA NA NA NA