University of Cincinnati

U UNIVERSITY OF CINCINNATI

Date:

I, , hereby submit this original work as part of the requirements for the degree of:

It is entitled:

Student Signature:

This work and its defense approved by:

Committee Chair:

Approval of the electronic document:

I have reviewed the Thesis/Dissertation in its final electronic format and certify that it is an accurate copy of the document reviewed and approved by the committee.

Committee Chair signature: Immunobiology of IFRD1, a Novel Genetic Modifier of Cystic Fibrosis Lung Disease

A dissertation submitted to the

Division of Research and Advanced Studies

of the University of Cincinnati

in partial fulfillment of the requirements for the degree of

DOCTORATE OF PHILOSOPHY (Ph.D.)

In the Graduate Program of Molecular and Developmental Biology

of the College of Medicine

2009

by Yuanyuan Gu

M.D.-M.S. Nanjing University, China, 2005

Committee Chair: Christopher L. Karp, M.D. Marie-Dominique Filippi, Ph.D. H. Leighton Grimes, Ph.D. David A. Hildeman, Ph.D. Jeffrey A. Whitsett, M.D. Abstract

Cystic fibrosis is the most common, lethal autosomal recessive disorder in the United

States. Lung disease is the major cause of morbidity and mortality in CF. In the CF lung, chronic infection and dysregulated neutrophilic inflammation lead to progressive airway destruction. Despite the molecular insights afforded by identification of disease-causing gene, CFTR, a clear understanding of the pathogenesis of lung disease in CF remains elusive. There is a poor correlation of genotype with phenotype in lung disease in CF, which strongly suggests that the expression of lung disease in CF is influenced by environmental exposures and/or modifier genes. To search for genes modifying CF lung disease, the Karp lab performed a genome-wide association study in collaboration with

GMSG cohort, validating top candidates in collaboration with the CFTSS cohort. Using this approach, genetic variation in IFRD1 was identified and verified as a modifier of lung disease severity in CF. IFRD1 is a HDAC-dependent transcriptional co-activator or co-repressor whose expression is particularly enriched in neutrophils.The goal of my dissertation studies was mechanistic insight into the modulation of CF lung disease by

IFRD1. This dissertation research provides evidence in favor of the hypothesis that

IFRD1 modulates the course of airway disease in CF through regulation of neutrophil effector function. This study also strongly suggests a mechanism by which IFRD1 modulates neutrophil function in a HDAC-dependent manner to co-suppress the expression of ATF3, a transcriptional repressor of NF-B activity in neutrophils. Finally, this research emphasizes the translational implications for therapeutic targeting of neutrophils in CF. This study suggests that the IFRD1/HDAC axis may provide a tractable therapeutic target in CF, and the plethora of other diseases in which neutrophils play an important pathogenic role.

iii

Acknowledgements

I would like to thank my mentor, Dr. Christopher Karp, for his guidance and support during last four years. His passion for science has motivated me throughout my research training. His sense of humor has made the lab a pleasant place to work in. I would also like to thank him for his help in pursuing my career as a surgeon-scientist.

I am lucky to have Dr. Jeffrey Whitsett, Marie-Dominique Filippi, Lee Grimes, and David

Hildeman on my committee. They have challenged me and, more importantly, have provided valuable insight in my work.

I am very thankful to all of my lab members for their help not only in research and but also in my life in the US. Leah Flick, Senad Divanovic, Rajat Madan, and Aurelien Trompette are all great teachers. Jessica Allen and Isaac Harley are the sweetest persons I have ever met.

I would also like to thank Dr. Bruce Aronow and Isaac Harley for their help in bioinformatic analysis. I appreciate help from many other fellow graduate students, faculty and staff in the division of Molecular Immunology, Immunobiology and Developmental Biology,

Finally, I would like to thank my family for their belief in education, braveness to let me sail in this ―big world‖, and unconditional love all the time.

Abbreviation

ATF3: activating transcription factor 3

CF: Cystic Fibrosis

CFTR: CF transmembrane conductance regulator

CFTSS: Cystic Fibrosis Twin and Sibling Study

ChIP: chromatin immunopreciptation

ER: endoplasmic reticulum fMLP: formyl-methionyl-leucyl-phenylalanine

GMSG: Modifier Study Group

GWAS: genome-wide association studies

HDAC: histone deacetylase

ICZ: indolo[3,2-b]carbazole

IFN-Interferon 

IFRD1: interferon-related developmental regulator

IL-10: interleukin 10

IL-8: interleukin 8

LPS: lipopolysaccharides

LTB4: leukotriene B4

MBL2: mannose-binding lectin 2

TAP-MS: Tandem Affinity Purification-Mass spectrometry

TGF1: Transforming growth factor beta 1

TLR4: Toll-like receptor 4

TNF-: tumour necrosis factor

Table of Contents

Chapter I Introduction ...... 1 1. Cystic Fibrosis and CFTR ...... 2 2. Neutrophil biology ...... 8 3. CF modifier genetics ...... 10 4. IFRD1 ...... 13 5. Hypothesis ...... 15 Table 1. Classification of CFTR mutations ...... 16 References ...... 17 Chapter II IFRD1 modifies CF lung disease by regulating neutrophil functions ...... 27 Identification of IFRD1 as a modifier gene for cystic fibrosis lung disease ...... 28 Methods Summary ...... 37 Figure 1. IFRD1-deficient neutrophils exhibit decreases in specific effector functions...... 41 Figure 2. Genetic deficiency of IFRD1 is associated with delayed bacterial clearance, but decreased neutrophilic inflammation and ameliorated disease, after airway challenge with mucoid P. aeruginosa...... 43 Figure 3. Association of IFRD1 polymorphisms with variation in human neutrophil effector function...... 44 Table 1 Transmission analysis of IFRD1 SNPs ...... 45 Methods ...... 46 Supplementary Information ...... 54 Supplementary Table 1. Association of SNPs at the IFRD1 locus with lung disease severity: GMSG cohort genome-wide SNP scan with pooled DNA; Affymetrix 100K array...... 54 Supplementary Table 2. Individual genotyping in 779 Caucasian CFTR ΔF508 homozygotes in the GMSG cohort; IFRD1 locus; Illumina SNP beadarray genotyping...... 56 Supplementary Table 3. Comparison of pooled estimates of allele frequencies with allele frequencies obtained via individual genotyping in 320 CFTR ΔF508 homozygotes GMSG cohort; IFRD1 locus; Affymetrix 100K pooled genotyping versus Illumina SNP beadarray genotyping. 58 Supplementary Table 4...... 61 Supplementary Table 5. Association of SNPs at the CEBPA/CEBPG locus with lung disease severity: GMSG cohort genome-wide SNP scan with pooled DNA; Affymetrix 100K array...... 62 Supplementary Table 6. Individual genotyping in 779 Caucasian CFTR ΔF508 homozygotes the GMSG cohort; CEBPA/CEBPG locus; Illumina SNP beadarray genotyping...... 63 Supplementary Table 7. Comparison of estimated allele frequencies with allele frequencies obtained via individual genotyping in 320 Caucasian CFTR ΔF508 homozygotes GMSG cohort; CEBPA/CEBPG locus; Affymetrix 100K pooled genotyping versus Illumina SNP bead array genotyping...... 64 Supplementary Table 8. Association of CEBP SNP haplotype rs7253865 G and rs1423062 A with CF lung function derived from family based association testing ...... 66 Supplementary Figure 1. Comparison of genome-wide SNP allele frequencies in the CF cohort vi

(GMSG) with those from a genome-wide SNP scan in asthma patients and controls (Isle of Wight birth cohort study)...... 67 Supplementary Figure 1. (b) Graphical representation of the P values of the 34 chromosome 7 SNPs (among the top 38 SNPs) as a function of chromosomal location...... 69 Supplementary Figure 2. Cluster analysis...... 72 Supplementary Figure 4. IFRD1 expression in human primary cells and cell lines...... 76 Supplementary Figure 5. IFRD1 expression is upregulated during terminal differentiation of neutrophils. (top panels) ...... 77 Supplementary Figure 6. Inhibition of IFRD1 expression in HL-60 cells leads to inhibition of oxidative burst capacity...... 79 Supplementary Figure 7. Macrophages from IFRD1-deficient mice are not impaired in oxidative burst capacity or TNF- production...... 81 Supplementary Figure 8. The reconstitution efficiency of bone marrow from IFRD1-knockout mice is similar to that of wild type bone marrow...... 82 Supplementary Figure 9. Lack of hematopoietic cell expression of IFRD1 is associated with delayed bacterial clearance, but decreased neutrophilic inflammation and ameliorated disease, after airway challenge with P. aeruginosa...... 84 Supplementary Figure 10. Lack of non-hematopoietic cell expression of IFRD1 is not associated with delayed bacterial clearance, decreased neutrophilic inflammation or ameliorated disease, after airway challenge with P. aeruginosa...... 85 Supplementary Figure 11. Role of histone deacetylases in the regulation of inflammation by IFRD1...... 86 Supplementary Figure 12. IFRD1 modulates neutrophilic NF-B activity...... 88 Chapter III IFRD1 is a co-suppressor of the transcriptional repressor ATF3 ...... 93 Introduction ...... 94 Results ...... 97 1. Ifrd1-/- neutrophils have increased levels of ATF3 expression ...... 97 2. Atf3-/- neutrophils produce increased level of inflammatory cytokines ...... 98 3. IFRD1 nuclear translocation and dynamics of IFRD1 at the promoter of ATF3 ...... 99 4. Ifrd1 inhibits ATF3 promoter-driven luciferase expression...... 100 5. The interactome of IFRD1 ...... 101 Discussion ...... 102 Materials and Methods ...... 105 Figure 1. Ifrd1-/- neutrophils have increased ATF3 mRNA...... 109 Figure 2. Atf3-/- neutrophils produce increased levels of inflammatory cytokines in response to LPS stimulation...... 110 Figure 3. Correlation of IFRD1 nuclear translocation and presence at the Atf3 promoter, with ATF3 expression...... 111 Figure 4. Ifrd1 inhibits Atf3 promoter-driven luciferase expression...... 112 Figure 5. Proposed transcriptional network model in neutrophils...... 114 Table 1. Top candidate transcriptional factors identified in coactivator trap screen ...... 115 vii

References ...... 117 CHAPTER IV Summary and Perspective ...... 121

viii

Chapter I Introduction

1. Cystic Fibrosis and CFTR

1.1 CF: clinical manifestations and epidemiology

Cystic Fibrosis (CF), the most common, life-shortening, autosomal recessive disease in the

United States, is caused by mutations in the gene encoding the CF transmembrane conductance regulator (CFTR)1. The earliest, likely reports of CF can be traced back to the 17th century in the form of European folklore that foretold that "the child will soon die whose forehead tastes salty when kissed"2 — salty-tasting skin being a typical manifestation of CF. However, it was not until

1938 that Dorothy Andersen, a pathologist in New York, clearly defined CF as a specific clinical entity3. Clinical manifestations of CF, which vary from patient to patient in timing and severity, include elevated sweat chloride concentrations, recurrent sinopulmonary infections leading to bronchiectasis, exocrine and endocrine pancreatic insufficiency, meconium ileus in newborns, obstructive biliary tract disease, hepatic fibrosis, and azospermia1. Among these various clinical manifestations, lung disease is the leading cause of morbidity and mortality1.

CF affects more than 70,000 individuals worldwide and is most common in populations with

European origins4. The disease occurs in roughly 1 in 3,000 European Americans,

1 in 15,000 African Americans, and 1 in 32,000 Asian Americans. Carriers of one defective CFTR gene are widespread, occurring about one in every 31 European Americans5.

1.2 CFTR

CFTR, the causal gene of CF, was cloned in 19896-8. Since then, there has been dramatic progress in our understanding of the genetics of CF. More than 1500 mutations in CFTR have been identified. The classification of these mutations is based on their functional alterations

(Table 1)1. The most common mutation worldwide is F508, which occurs in 70% of CF patients in the US (www.cff.org). This deletion of phenylalanine at position 508 of CFTR results in a misfolded CFTR protein, which is retained in the endoplasmic reticulum (ER) and degraded by cytoplasmic proteasomes9.

CFTR is expressed in the apical plasma membrane of epithelial cells of affected tissues such as the airways, pancreas, and sweat glands. Recent studies have suggested functional expression of CFTR in neutrophils10 and macrophages11 as well. CFTR functions as an exporter of chloride across the epithelial membrane in a cyclic AMP-dependent manner9. However, recent studies indicate that the roles of CFTR go well beyond being a chloride channel in epithelial cells. It has been shown that CFTR can inhibit the epithelial sodium channel12, regulate the outwardly-rectifying chloride channel13 and inhibit endogenous calcium-activated chloride channels14. CFTR has also been reported to be a transporter of ATP15 and glutathione16. More broadly, CFTR has been proposed, directly or indirectly, to modulate vesicle trafficking17, organelle acidification11, Pseudomonas binding to epithelial cells18 and NF-B-mediated innate immune response19. These multifunctional roles of CFTR are underlined by its interaction with

multiple protein complexes in recent proteomic studies20. CFTR expression and/or function is modified by a variety of factors, such as inflammatory mediators {e.g., Interferon IFN-}, hormones (e.g., androgens), signaling pathways (e.g., cAMP pathways), extracellular conditions

(e.g., osmolarity), and pharmacological agents (e.g., long-acting 2-adrenergic agonist like salmeterol)21, 22. Studies on the genetics, function, and expression of CFTR have led to novel therapeutic approaches in CF, such as inhaled hypertonic saline to improve mucociliary clearance 23and mutation-specific correctors (e.g.,PTC124, in phase II trials, which allows read-through of premature stop codons in nonsense mutations restoring CFTR expression24).

1.3 CF lung disease

Despite remarkable progress in the therapy of CF in last twenty years, CF remains a lethal disease—with a median survival in 2008 of around 37.4 years (www.cff.org). Lung failure continues to be the primary cause of death in patients with CF. The exact timing of the initiation of CF lung disease is poorly defined. The CF lung was previously believed to be normal at birth, but this has been challenged by a recent study showing reduced lung function in a 72% of infants at the time of diagnosis by sweat test and/or by genetic screen 25. Soon after birth, usually within the first year or two, airways in CF patients quickly become colonized and/or infected, initially with Staphylococcus aureus, and/or Haemophilus influenzae26. Usually by the age of ten,

Pseudomonas aeruginosa becomes a predominant and persistent pathogen in CF airway27.

Infection in the CF airway is associated with excessive neutrophilic inflammation28, which, in turn,

damages the airway, favoring chronic infection. Over time, this vicious cycle results in progressively bronchiectatic destruction of the lung. The molecular mechanisms underlying chronic bacterial colonization and exuberant inflammation in CF are not known. Several hypotheses have been proposed, including alterations in mucociliary clearance due to low airway surface fluid volume and viscid mucus29, increased bacterial adherence and decreased bacterial phagocytosis by airway epithelial cells30, compromise of innate immune defenses31, and dysregulated inflammatory responses32. Specific properties of colonizing/infecting bacteria have also been implicated. For example, the P. aeruginosa toxin, pyocyanin, has been shown to inhibit oxidase-based antimicrobial system in airway epithelial cells and inhibit phagocytosis of apoptotic neutrophils by macrophages33, 34, which may facilitate persistent airway infection and inflammation. All these specific hypotheses can only partially explain the phenotype of CF lung disease, however. It is likely that a complex host-environment interaction in the abnormal CF airway environment contributes to the pathogenesis of CF lung disease.

Airway inflammation in CF is dysregulated and a principle cause of lung damage in CF. There are increased levels of NFB- dependent proinflammatory mediators such as interleukin 8 (IL-8),

35-37 tumor necrosis factor  (TNF-), and leukotriene B4 (LTB4) in the CF airway . In contrast,

levels of anti-inflammatory molecules such as interleukin 10 (IL-10) and lipoxin A4 are decreased36, 38. This imbalance between proinflammatory and anti-inflammatory mediators leads to persistent and excessive inflammation. Remarkably, airway inflammation in CF is

predominantly neutrophilic. While neutrophils are, in general, important in antimicrobial defense, neutrophils are thought to play a major role in generating lung damage in CF39. Neutrophils are targets of proinflammatory mediators in the CF airway as well as provide an important source of these same mediators. Neutrophils perpetuate the inflammatory response by releasing IL-8 and

40 LTB4, recruiting more neutrophils, and delaying the apoptosis of neutrophils . Neutrophil-derived proteases and oxidants can impose great damage to the airway wall41. DNA released from apoptotic neutrophils increases the viscosity of secretions and impairs mucociliary clearance42.

There is increased evidence indicating that neutrophilic inflammation in CF can be intrinsic, occurring even in the absence of overt infection. This is supported by increased neutrophils and

IL-8 for given bacterial load and LPS load: (a) in the airways of children with CF compared to inflammatory controls28; and (b) in human fetal CF airway grafts (compared with wild type fetal airway grafts) transplanted into the skin of severe combined immunodeficient mice 43. This notion is also supported by reports of elevated inflammatory mediator production by cultured CF airway epithelial cells and cell lines36, although this remains controversial44. What underlies this aberrant inflammatory response remains unclear. Possibilities with some evidence in the literature include:

(a) retention of mis-folded CFTR F508 in the ER of airway epithelial cells leading to the induction of ER stress pathways45, 46; (b) aberrant cholesterol metabolism in such cells47; (c) inhibition of airway epithelial transport of glutathione, a major antioxidant in cells48, 49; and (d) decreased lipoxin production by airway epithelia and neutrophils38. None of these putative

mechanisms are fully satisfying. The recent reports of myeloid cell expression of CFTR suggest that part of the underlying mechanism may involve intrinsic abnormalities in innate immune cell function. Against this, abnormal neutrophilic inflammation only seems to be a problem in the airway in CF, not systemically.

Since inflammation and infection are key contributors to the decline of lung function in CF, anti-inflammatory medications along with aggressive elimination of infection continue to be the mainstay of therapy for CF. Corticosteroids, ibuprofen and azithromycin are the three anti-inflammatory medications that have been widely tested in clinical trials50. Currently, corticosteroids are not recommended for routine use in CF, because of a lack of efficacy and unacceptable side effects, such as growth inhibition50. The only exception is that short-term intravenous corticoids have been found to be beneficial in acute exacerbations when combined with antibiotics51. High-dose ibuprofen has been shown to be beneficial to in younger CF patients

(5–13 years) but not in those over 13 years of age52. It seems that ibuprofen should be started early before severe inflammation and permanent damage in CF lung are established. The occasional severe gastro-intestinal bleeding seen with high dose ibuprofen therapy, along with the need for individualized pharmacokinetic studies, has led to under-utilization of ibuprofen53.

Azithromycin has demonstrated effectiveness, both as an antibiotic and an anti-inflammatory medication. The largest clinical trial addressing the chronic use of azithromycin in CF showed improvement in lung function and a reduction in pulmonary exacerbations54. However, bacterial

resistance induced by chronic use of antibiotics is worrisome. There is thus still a pressing need to identify new therapeutic targets for neutrophilic inflammation in CF.

2. Neutrophil biology

Neutrophils act as double-edged swords in CF. Neutrophils are a critical part of the host defense against microorganisms. However, excessive neutrophilic inflammation can lead to remarkable tissue damage in CF. It used to be thought that neutrophils have highly condensed polymorphous nuclei and, accordingly, are unable to induce gene transcription. However, recent studies have indicated that neutrophils have unexpectedly versatile synthetic capacity and play a bi-directional role in linking innate and adaptive immunity 55. Neutrophils are the first cells to be recruited to, and predominate at, sites of bacterial infection. Neutrophils fight microorganisms in multiple ways, including phagocytosis, releasing of three classes of granules (containing proteases), producing superoxide anions, and eventually extruding their chromatin, which forms extracellular nets decorated with granule contents, trapping and killing microorganisms55, 56. At the resolution phase of inflammation, neutrophils undergo apoptosis, which triggers their recognition and phagocytosis by macrophages57. If neutrophil apoptosis is delayed or the production of neutrophil-derived mediators is dysregulated, neutrophil-derived products (e.g. reactive oxidative species, protease and cytokines) can cause significant tissue damage.

Functional genomics and proteomics have shown that neutrophils can actively synthesize and release a surprisingly wide variety of cytokines58, 59. These cytokines include proinflammatory cytokines and neutrophil chemoattractants such as TNF- and IL-8 which trigger a positive auto-feedback loop, amplifying and perpetuating neutrophilic inflammation60. Moreover, neutrophils can produce immunoregulatory cytokines such as IL-10, an anti-inflammatory mediator acting at multiple levels of innate and adaptive immunity61. Neutrophils can secrete

TNF-related ligand B-lymphocyte stimulator62 and IFN-, which drives maturation of B cells and differentiation of T helper 1 cells, respectively. Thus, the role of neutrophils is not limited to being effector cells in innate immunity. Neutrophils are also important in linking innate immunity and adaptive immunity.

Functional genomics has also suggested that the expression of neutrophil-derived mediators is incredibly fast and tightly controlled58, 59. Neutrophils are capable of rapidly altering transcription, within 10 minutes of exposure to bacteria-derived stimuli such as lipopolysaccharides (LPS) and formyl-methionyl-leucyl-phenylalanine (fMLP)58. Interestingly, besides genes involved in signaling pathways within the cell and between cells (i.e., cytokines), a large group of these early responsive genes fall into the category of genes important in regulating gene expression at multiple levels, including transcription factors, chromatin remodeling and modifying proteins,

DNA helicases, and regulators of protein synthesis or stability58. Thus, studying the mechanism of fine control of the expression of neutrophil-derived mediators offers unique opportunities for

novel therapeutic approaches to diseases in which neutrophil-derived products play an important pathogenic role.

3. CF modifier genetics

The manifestations of CF lung disease can be very different between patients, even with the same CFTR genotype (e.g., ΔF508 homozygotes)64. This variability can be attributed to genetic modifiers other than CFTR, environmental factors, and/or interactions among and between these two sources.

3.1 Environmental factors

Studies of environmental factors have shown that passive cigarette smoking, ambient air pollution, poor nutritional status, pulmonary colonization with P.aeruginosa and Burkholderia cepacia and low socioeconomic status are associated with reduced pulmonary function and higher mortality rate in CF patients65. However, not all studies have generated consistent results.

This inconsistency is probably due to the multiple environmental components involved, the lack of optimal measurements, different genetic backgrounds and numbers of patients studied. The

US Cystic Fibrosis Twin and Sibling Study (CFTSS), which controls CF genotype and environment66, may serve to better distinguish environmental factors from genetic factors.

3.2 Modifier genes

A recent twin and sibling study from the CFTSS cohort has shown that monozygous twins have higher similarity of lung function than dizygous twins, with heritability estimates ranging from 0.54 to 1.067. This strongly suggests a substantial genetic contribution to variation in CF lung disease severity, independent of CFTR genotype.

Up to date, most studies in searching CF modifier genes have used a ―candidate gene‖ approach.

More than 30 candidate modifiers have been examined, based on the known biological functions of these genes68. However, the results of these studies are usually conflicting and very few of them have been validated in a second independent population. Transforming growth factor beta

1 (TGF1) is modifier gene of CF lung disease which has been validated in multiple studies68. It has been confirmed that TGF1 −509 TT and codon 10 CC genotype is associated with decreased lung function69. However, SNPs in TGF1 alone have not been associated with acquisition of infection or survival of CF patients70, 71. This could be due to unknown interactions with other genetic modifiers and/or environmental factors. Notably, a recent study has shown that the high-level TGF1 genotype amplifies the effect of earlier onset infection imposed by the low-level mannose-binding lectin 2 (MBL2) genotype72.

Candidate gene association studies have the advantage of being less expensive and having direct therapeutic implications if there is a known therapy targeting the candidate gene of interest.

However, the candidate gene approach may be biased towards false positive results and is unable to discover novel pathways involved in the pathogenesis of the disease of interest68. With advances in genotyping techniques and the availability of the human HapMap, genome-wide association studies (GWAS) have become possible. GWAS employs representative SNP markers throughout the human genome and identify loci with statistically different allele frequencies between cases and controls, pointing to their association with certain diseases.

Superior in some ways to candidate gene approaches, GWAS enables a comprehensive survey of the genome in an unbiased fashion, allowing the identification of novel disease-associated genes. Although GWAS is a powerful approach, it also has its limitations. Because GWAS relies on representative SNPs to predict genetic variance at adjacent loci, GWAS only finds loci, not genes, or causal polymorphisms73. Moreover, GWAS can give rise to both false-positive and false-negative associations68. Key components to reduce false-positive associations include using sufficient sample sizes, avoiding or correcting population stratification, rigorous phenotyping, comprehensive mapping of SNPs, improving accuracy of high-throughput genotyping technologies, rigorous data analysis (e.g. accepting a much lower p value), replication in independently collected populations, and, most importantly, careful examination of biological implications74.

GWAS has successfully identified susceptibility genes in many common diseases, such as systemic lupus erythematosus, Crohn‘s disease, rheumatoid arthritis, multiple sclerosis, type 1

diabetes and etc75. No genome-wide association study in CF had been published prior to the study that led to my Ph.D. work. To identify genetic modifiers of CF lung disease that could be therapeutic targets for CF, the Karp lab performed a genome-wide association study in collaboration with the Gene Modifier Study Group (GMSG), validating top candidates in collaboration with the CFTSS cohort. In this way, interferon-related developmental regulator

(IFRD1) was identified and validated as a novel genetic modifier of CF lung disease76.

4. IFRD1

IFRD1 got its name from similarity between the carboxyl-terminal half of IFRD1 protein and the

IFN- protein77. However, IFRD1 is not secreted from cells and lacks antiviral activity78. IFRD1 is devoid of any known DNA-binding motifs. IFRD1 is thought to be a histone deacetylase

(HDAC)-dependent transcriptional co-regulator. A yeast two-hybrid screen and coimmunoprecipitation analysis by Ilja Vietor‘s group has characterized IFRD1 as a novel transcriptional co-regulator that interacts with multiple components of HDAC complex79. IFRD1 can be a transcriptional co-activator or co-repressor, depending on cell type and stimulus. IFRD1 appears to act as a co-activator during myogenesis by displacing (and removing the inhibitory effect on transcription of) HDAC4 from transcription factor complexes80. In contrast, reporter assays in muscle cells have revealed that IFRD1 inhibits the transcriptional activity of

β-catenin/TCF-4, LEF-1 81and C/EBP-Sp182.

The biological function of IFRD1 has been somewhat obscure. Ifrd1-/- mice have defects in muscle regeneration in response to crash injury83. In addition, mouse Ifrd1 mRNA expression is up-regulated in the remnant small bowel following surgical resection84. Specific overexpression of IFRD1 in the intestine of mice increases intestinal triglyceride absorption and adiposity, suggesting that IFRD1 is involved in adaptive response following gut resection84.

IFRD1 mRNA is expressed in a wide variety of human tissues at different levels, highest in blood and skeletal muscle; in contrast, almost undetectable in kidney77. In situ hybridization has revealed that mouse IFRD1 is highly expressed at mid-gestation in more specifically differentiated structures77. This expression pattern is also supported by a comprehensive microarray analysis of the transcriptional program of neutrophil terminal differentiation85. This study contained data indicating that IFRD1 is up-regulated in terminally-differentiated neutrophils.

IFRD2 is a related molecule, regarded to be the second member of IFRD family, with 47% sequence identity to IFRD1 protein77. However, IFRD2 exhibits a different developmental expression pattern from IFRD1. IFRD2 expression appears soon after gastrulation in the early hematopoietic site, the hepatic primordium77. Moreover, IFRD2 is down-regulated during neutrophil differentiation77. These expression data suggested a possible role for IFRD1 in neutrophil terminal differentiation and function, and for IFRD2 in early hematopoiesis. No role for

IFRD1 in neutrophils or the immune systems had ever been defined, however.

5. Hypothesis

CF lung disease is marked by chronic infection and persistent neutrophilic inflammation.

Whether neutrophils in CF patients are intrinsically abnormal or conditioned by infection or neighboring cells remains to be defined. In terms of how polymophisms in IFRD1 might alter the pathogenesis of CF lung disease, IFRD1 had not previously been implicated in the lung homeostasis or the immune system. However, following on expression data indicating that

IFRD1 was heavily expressed in human peripheral blood cells, and our discovery that IFRD1 expression is particularly enriched in neutrophils, being up-regulated with terminal differentiation, we hypothesized that: polymorphisms in IFRD1 modulate the pathogenesis of CF lung disease through regulation of neutrophil effector function

During my thesis research, I thus aimed to:

1) Define the role of IFRD1 in modulation of neutrophil function and airway inflammation.

2) Determine the molecular mechanisms underlying IFRD1-mediated regulation of neutrophil

function.

Table 1. Classification of CFTR mutations

Functional Effect on CFTR CFTR present Sample mutations Stop codons (designation ending in X; eg, Trp1282X); splicing defects with no protein Class I Lack of protein production No production (eg, 711+1G→T) Protein trafficking defect with ubiquitination and degradation in No/substantially Class II endoplasmic reticulum/golgi body reduced eg, Phe508del non-functioning Defective regulation; CFTR not activated CFTR present in Class III by ATP or cyclic AMP apical membrane eg, Gly551Asp Reduced chloride transport through Class IV CFTR at the apical membrane Yes eg, Ala455Glu Splicing defect with reduced production Class V of normal CFTR Yes eg,3849+10kb C→T

References

1. Brian, P.O., et al. Cystic fibrosis. Lancet 373, 1891-1904 (2009).

2. Busch, R. The history of cystic fibrosis. Acta Univ. Carol Med 36, 13-15 (1990).

3. Andersen, D.H. Cystic fibrosis of the pancreas and its relation to celiac disease: a

clinical and pathological study Am J Dis Child, 344-399 (1938).

4. Storey, S., et al. Novel agents in cystic fibrosis. Nature Reviews Drug Discovery 7,

555-6 ( 2008).

5. in NIH Consens Statement Online 1-37 (1997).

6. Kerem, B., et al. Identification of the cystic fibrosis gene: genetic analysis.

Science 245, 1073-80 (1989).

7. Riordan, J.R., et al. Identification of the cystic fibrosis gene: cloning and

characterization of complementary DNA. Science 245, 1066-73 (1989).

8. Rommens, J.M., et al. Identification of the cystic fibrosis gene: chromosome

walking and jumping. Science 245, 1059-65 (1989).

9. Nissim-Rafi nia, M., et al. in Cystic fibrosis (ed. Hodson M, G.D., Bush A) 54-55

(London: Edward Arnold, 2007).

10. Painter, R.G., et al. CFTR Expression in human neutrophils and the

phagolysosomal chlorination defect in cystic fibrosis. Biochemistry 45, 10260-9

(2006).

11. Di, A., et al. CFTR regulates phagosome acidification in macrophages and alters

bactericidal activity. Nat Cell Biol 8, 933-44 (2006).

12. Stutts, M.J., et al. CFTR as a cAMP-dependent regulator of sodium channels.

Science 269, 847-50 (1995).

13. Schwiebert, E.M., et al. CFTR regulates outwardly rectifying chloride channels

through an autocrine mechanism involving ATP. Cell 81, 1063-73 (1995).

14. Perez-Cornejo, P., et al. Regulation of Ca2+-activated chloride channels by cAMP

and CFTR in parotid acinar cells Biochemical and Biophysical Research

Communications 316, 612-7 (2004).

15. Reisin, I.L., et al The cystic fibrosis transmembrane conductance regulator is a

dual ATP and chloride channel. J Biol Chem 269, 20584-91 (1994).

16. Kogan, I., et al. CFTR directly mediates nucleotide-regulated glutathione flux.

EMBO J 22, 1981-1989 (2003).

17. Bradbury, N.A. Intracellular CFTR: localization and function. Physiol Rev 79,

S175-191 (1999).

18. Imundo, L., et al. Cystic fibrosis epithelial cells have a receptor for pathogenic

bacteria on their apical surface. Proc Natl Acad Sci USA 92, 3019-23 (1995).

19. Neeraj, V., et al. CFTR Is a Negative Regulator of NFkB Mediated Innate Immune

Response. PLoS ONE 4, e4664 (2009).

20. Wang, X., et al. Hsp90 cochaperone Aha1 downregulation rescues misfolding of

CFTR in cystic fibrosis. Cell 127, 803-15 (2006).

21. Victoria, A., et al. The CFTR Gene and Regulation of Its Expression. Pediatric

Pulmonology 40, 1-8 (2005).

22. Delavoie, F., et al. Salmeterol Restores Secretory Functions in Cystic Fibrosis

Airway Submucosal Gland Serous Cells. Am J Respir Cell Mol Biol Vol 40, 388-97

(2009).

23. Elkins, M.R., et al. A controlled trial of long-term inhaled hypertonic saline in

patients with cystic fi brosis. N Engl J Med 354, 229-40 (2006).

24. Kerem, E., et al. Effectiveness of PTC124 treatment of cystic fibrosis caused by

nonsense mutations: a prospective phase II trial. Lancet 372, 719-27 (2008).

25. Ranganathan, S.C., et al. The evolution of airway function in early childhood

following clinical diagnosis of cystic fibrosis. Am J Respir Crit Care Med 169,

928-33 (2004).

26. Dakin, C.J., et al. Inflammation, infection, and pulmonary function in infants and

young children with cystic fibrosis. . Am J Respir Crit Care Med 165, 904-10

(2002).

27. Rosenfeld, M., et al. Pseudomonas acquisition in young patients with cystic fi

brosis: pathophysiology, diagnosis, and management. Curr Opin Pulmonol Med 9,

492-97 (2003).

28. Muhlebach, M.S., et al. Quantitation of inflammatory responses to bacteria in

young cystic fibrosis and control patients. Am J Respir Crit Care Med 160, 186-91

(1999).

29. Matsui, H., et al. Evidence for periciliary liquid layer depletion, not abnormal ion

composition, in the pathogenesis of cystic fi brosis airways disease. Cell 95,

1005-15 (1998).

30. Campodonico, V.L., et al. Airway epithelial control of Pseudomonas aeruginosa

infection in cystic fibrosis. Trends Mol Med 14, 120-33 (2008).

31. Goldman, M.J., et al. Human beta-defensin-1 is a salt-sensitive antibiotic in lung

that is inactivated in cystic fibrosis. Cell 88, 553-60 (1997).

32. Rottner, M., et al. Mechanisms of the noxious inflammatory cycle in cystic fibrosis.

Respiratory Research 10, 23-32 (2009).

33. Bianchi, S.M., et al. Impairment of apoptotic cell engulfment by pyocyanin, a toxic

metabolite of Pseudomonas aeruginosa. Am J Respir Crit Care Med 177, 35-43

(2008).

34. Rada, B., et al. The pseudomonas toxin pyocyanin inhibits the dual

oxidase-based antimicrobial system as it imposes oxidative stress on airway

epithelial cells. J Immunol 181, 4883-4893 (2008).

35. Zaman, M.M., et al. Interleukin 8 secretion from monocytes of subjects

heterozygous for the deltaF508 cystic fibrosis transmembrane conductance

regulator gene mutation is altered. Clin Diagn Lab Immunol 11, 819-24 (2004).

36. Bonfield, T.L., et al. Altered respiratory epithelial cell cytokine production in cystic

fi brosis. J Allergy Clin Immunol 104, 72-78 (1999).

37. Freedman, S.D., et al. Association of cystic fibrosis with abnormalities in fatty acid

metabolism. N Engl J Med 350, 560-69 (2004).

38. Karp, C.L., et al. Defective lipoxin-mediated anti-infl ammatory activity in the cystic

fi brosis airway. Nat Immunol 5, 388-92 (2004).

39. Chmiel,J.F., et al. The Role of Inflammation in the Pathophysiology of CF Lung

Disease. Clinical Reviews in Allergy and Immunology 23, 5-27 (2002).

40. Witko-Sarsat, V., et al. Neutrophils: Molecules, Functions and Pathophysiological

Aspects. Laboratory Investigation 80, 617-53 (2000).

41. Konstan,M.W., et al. Bronchoalveolar lavage findings in cystic fibrosis patients

with stable, clinically mild lung disease suggest ongoing infection and

inflammation Am. J. Respir. Crit. Care Med 150, 448-54 (1994).

42. Chernick, W.W., et al. Composition of tracheobronchial secretions in cystic

fibrosis of the pancreas and bronchiectasis. Pediatrics 24, 739-45 (1959).

43. Tirouvanziam, R., et al. Inflammation and infection in naive human cystic fibrosis

airway grafts Am J Respir Cell Mol Biol Vol 23, 121-7 (2000).

44. Machen, T.E., et al. Innate immune response in CF airway epithelia:

hyperinflammatory? Am J Physiol Cell Physiol 291, C218-C230 (2006).

45. Wang, X., et al. Hsp90 cochaperone Aha1 downregulation rescues misfolding of

CFTR in cystic fibrosis. Cell 127, 803-15 (2006).

46. Rottner, M., et al. Mechanisms of the noxious inflammatory cycle in cystic fibrosis.

Respiratory Research 10, 23-32 (2009).

47. White, N.M., et al. Altered cholesterol homeostasis in cultured and in vivo models

of cystic fibrosis. Am J Physiol Lung Cell Mol Physiol 292, L476-L486 (2007).

48. Hudson, V.M. New insights into the pathogenesis of cystic fibrosis: pivotal role of

glutathione system dysfunction and implications for therapy. Treat Respir Med 3,

353-63 (2004).

49. Gao, L., et al. Abnormal glutathione transport in cystic fibrosis airway epithelia.

Am J Physiol 277, L113-118 (1999).

50. Brian, P. O., et al. Cystic fibrosis. Lancet 373, 1891-1904 (2009).

51. Conway, S., et al. Intravenous colistin sulphomethate in acute respiratory

exacerbations in adult patients with cystic fi brosis. Thorax 52, 987-93. (1997).

52. Konstan, M.W., et al. Eff ect of high-dose ibuprofen in patients with cystic fibrosis. .

N Engl J Med 332, 848-54 (1995).

53. Lands, L.C., et al. High-dose ibuprofen in cystic fi brosis: Canadian safety and eff

ectiveness trial. J Pediatr 151, 249-54. (2007).

54. Saiman, L., et al. Azithromycin in patients with cystic fi brosis chronically infected

with Pseudomonas aeruginosa: a randomized controlled trial. . JAMA 290,

1749-56 (2003).

55. Nathan, C. Neutrophils and immunity: challenges and opportunities. Nature

Reviews Immunology 6, 173-182 (2006).

56. Brinkmann, V., et al. Neutrophil extracellular traps kill bacteria. Science 303,

1532-1535 (2004).

57. Greenberg, M.E., et al. Oxidized phosphatidylserine-CD36 interactions play an

essential role in macrophage-dependent phagocytosis of apoptotic cells. J Exp

Med 203, 2613-625 (2000).

58. Zhang, X, et al. Gene expression in mature neutrophils: early responses to

inflammatory stimuli. Journal of Leukocyte Biology 75, 358-372 (2004).

59. Theilgaard-Monch,K., et al Systems biology of neutrophil differentiation and

immune response. Current Opinion in Immunology 18, 54-60 (2006).

60. Witko-Sarsat, V., et al. Neutrophils: Molecules, Functions and Pathophysiological

Aspects. Laboratory Investigation 80, 617-53 (2000).

61. Romani, L., et al. Neutrophil production of IL-12 and IL-10 in candidiasis and

efficacy of IL-12 therapy in neutropenic mice J Immunol 158, 5349-5356 (1997).

62. Scapini, P., et al. Proinflammatory mediators elicit secretion of the intracellular

B-lymphocyte stimulator pool (BLyS) that is stored in activated neutrophils:

implications for inflammatory diseases. Blood 105, 830-7 (2005).

63. Ethuin, F., et al. Human neutrophils produce interferon gamma upon stimulation

by interleukin-12. Laboratory Investigation 84, 1363-1371 (2004).

64. Kerem, E., et al. The relation between genotype and phenotype in cystic fibrosis -

analysis of the most common mutation (deltaF508). N Engl J Med 323, 1517-1522

(1990).

65. Cutting, G.R. Modifier Genetics: Cystic Fibrosis. Annu. Rev. Genomics Hum.

Genet 6, 237-60 (2005).

66. Collaco,J.M., et al. Interactions Between Secondhand Smoke and Genes That

Affect Cystic Fibrosis Lung Disease. JAMA 299, 417-24 (2008).

67. Vanscoy, L.L., et al. Heritability of Lung Disease Severity in Cystic Fibrosis. Am J

Respir Crit Care Med 175, 1036-43 (2007).

68. Collaco, J.M., et al. Update on gene modifiers in cystic fibrosis. Current Opinion in

Pulmonary Medicine 14, 559-66 (2008).

69. Drumm, M.L., et al. Genetic Modifiers of Lung Disease in Cystic Fibrosis. NEJM

353, 1443-53 (2005).

70. Brazova J, et al. Polymorphisms of TGF-beta1 in cystic fibrosis patients. Clin

Immunol 121, 350-7 (2006).

71. Arkwright, P.D., et al. TGF-beta(1) genotype and accelerated decline in lung

function of patients with cystic fibrosis. Thorax 55, 459-62 (2000).

72. Dorfman, R., et al. Complex two-gene modulation of lung disease severity in

children with cystic fibrosis. J Clin Invest 118, 1040-1049 (2008).

73. Hardy, J., et al. Genomewide Association Studies and Human Disease. NEJM

360, 1759-68 (2009).

74. Hirschhorn, J. N., et al. Genome-wide association studies for common diseases

and complex traits. Nature Reviews Genetics 6, 95-108 (2005).

75. Xavier, R.J., et al. Genome-wide association studies: a new window into

immune-mediated diseases. Nature Reviews Immunology 8, 631-643 (2008).

76. Gu, Y., et al. Identification of IFRD1 as a modifier gene for cystic fibrosis lung

disease. Nature 458, 1039- 42 (2009).

77. Pasquale, B., et al. Cloning of the Human Interferon-Related Developmental

Regulator (IFRD1) Gene Coding for the PC4 Protein, a Member of a Novel Family

of Developmentally Regulated Genes. Genomics 51, 233-242 (1998).

78. Varnum, B.C., et al. Synthesis, degradation, and subcellular localization of

proteins encoded by the primary response genes TIS7/PC4 and TIS21/PC3. J

Cell Physiol 158, 205-13 (1994).

79. Vietor,I., et al. TIS7 interacts with the mammalian SIN3 histone deacetylase

complex in epithelial cells EMBO J 21, 4621-31 (2002).

80. Micheli, L., et al. PC4 coactivates MyoD by relieving the histone deacetylase

4-mediated inhibition of myocyte enhancer factor 2C. Mol Cell Biol 25, 2242-59

(2005).

81. Vietor, I., et al. TIS7 Regulation of the b-Catenin/Tcf-4 Target Gene Osteopontin

(OPN) Is Histone Deacetylase-dependent. The Journal of Biological Chemistry

280, 39795-801 (2005).

82. Wick, N., et al. Inhibitory Effect of TIS7 on Sp1-C/EBPa Transcription Factor

Module Activity. J. Mol. Biol 336, 589-95 (2004).

83. Santhosh, K.V., et al. Muscle Regeneration and Myogenic Differentiation Defects

in Mice Lacking TIS7 Molecular and Cellular Biology 24, 3514-25 (2004).

84. Yuan, W., et al. Targeted Intestinal Overexpression of the Immediate Early Gene

tis7 in Transgenic Mice Increases Triglyceride Absorption and Adiposity. The

Journal of Biological Chemistry 280, 34764-75 (2005).

85. Kim, T.M., et al. The transcriptional program of terminal granulocytic differentiation.

Blood 105, 1785-96 (2005).

Chapter II IFRD1 modifies CF lung disease by regulating neutrophil functions

Identification of IFRD1 as a modifier gene for cystic fibrosis lung disease

YuanYuan Gu1, Isaac T. W. Harley1, Lindsay B. Henderson5, Bruce J. Aronow2, Ilja Vietor7, Lukas

A. Huber7, John B. Harley8, Jeffrey R. Kilpatrick8, Carl D. Langefeld9, Adrienne H. Williams9, Anil G.

Jegga2, Jing Chen2, Marsha Wills-Karp3, S. Hasan Arshad10, Susan L. Ewart11, Chloe L. Thio6,

Leah M. Flick1, Marie-Dominique Filippi4, H. Leighton Grimes3, Mitchell L. Drumm12, Garry R.

Cutting5, Michael R. Knowles13 & Christopher L. Karp1

Divisions of 1Molecular Immunology, 2Biomedical Informatics, 3Immunobiology and

4Experimental Hematology & Cancer Biology, Cincinnati Children's Hospital

Research Foundation, and the University of Cincinnati College of Medicine,

Cincinnati, Ohio 45229, USA; 5McKusick-Nathans Institute of Genetic Medicine, and

6Division of Infectious Diseases, Department of Medicine, Johns Hopkins University

School of Medicine, Baltimore, MD 21205, USA; 7Biocenter, Division of Cell Biology,

Innsbruck Medical University, Innsbruck A-6020, Austria; 8Arthritis & Immunology

Program, Oklahoma Medical Research Foundation, and JK Autoimmunity Inc.,

Oklahoma City, Oklahoma 73104, USA; 9Wake Forest University Health Sciences,

Winston-Salem, North Carolina 27157, USA; 10The David Hide Asthma and Allergy

Research Centre, Newport, Isle of Wight, UK; 11Department of Large Animal Clinical

Sciences, College of Veterinary Medicine, Michigan State University, East Lansing,

MI, USA; 12Departments of Pediatrics and Genetics, Case Western Reserve

University, Cleveland, OH 44106, USA; 13Cystic Fibrosis–Pulmonary Research and

Treatment Center, University of North Carolina, Chapel Hill, NC 27599, USA

Published in Nature 458, 1039-1042 (23 April 2009)

Lung disease is the major cause of morbidity and mortality in cystic fibrosis

(CF), an autosomal recessive disease caused by mutations in CFTR. In CF, chronic infection and dysregulated neutrophilic inflammation lead to progressive airway destruction. The severity of CF lung disease has significant heritability, independent of CFTR genotype1. To identify genetic modifiers, we performed a genome-wide single nucleotide polymorphism (SNP) scan in one cohort of CF patients, replicating top candidates in an independent cohort. This approach identified IFRD1 as a modifier of CF lung disease severity. IFRD1 is a histone deacetylase (HDAC)-dependent transcriptional co-regulator expressed during terminal neutrophil differentiation. Neutrophils, but not macrophages, from Ifrd1-deficient mice exhibited blunted effector function, associated with decreased NF-B p65 transactivation. In vivo, IFRD1 deficiency caused delayed bacterial clearance from the airway, but also less inflammation and disease—a phenotype primarily dependent on hematopoietic cell expression, or lack of expression, of IFRD1. In humans, IFRD1 polymorphisms were significantly associated with variation in neutrophil effector function. These data suggest that IFRD1 modulates the pathogenesis of CF lung disease through regulation of neutrophil effector function. Polymorphisms in the intergenic region between CEBPA and CEBPG, genes implicated in neutrophil differentiation, also showed significant association with CF lung disease severity. Together, these data suggest therapeutic utility for targeting neutrophils in CF, and suggest that IFRD1 may provide a tractable therapeutic target in CF and other diseases in which neutrophilic inflammation plays an important pathogenetic role.

Attention to the role of CFTR in regulating epithelial ion transport has failed to illuminate the path from gene to pathogenesis in CF lung disease. In CF, colonization and infection

(paradigmatically with P. aeruginosa) is associated with neutrophilic inflammation, the end result being progressive airway destruction2. This inflammatory response is out of proportion to inciting infectious stimuli3, which may well be due to compromise of lipid mediator pathways driving resolution of neutrophilic inflammation4.

To identify genetic modifiers of CF lung disease severity, we performed a genome-wide

SNP scan in the Genetic Modifier Study Group (GMSG) cohort, followed by validation of top candidates in the US CF Twin and Sib Study (CFTSS) cohort. The former enrolled

F508-CFTR homozygotes with extremes of lung function for age, for case-control association approaches to modifier gene identification5. The latter enrolled CF-affected twins and siblings with any CFTR genotype, and their parents, for transmission-based approaches1.

Severity status was quantified using lung function measures highly correlated with survival in

CF6.

Genome-wide SNP analysis was performed using Affymetrix 100K microarrays in 320 patients from the GMSG cohort5: 160 with severe and 160 with mild lung disease, with DNA from 20 subjects pooled per microarray. To assess the robustness of the pooling approach, we first compared gene chip estimates of allele frequencies in pooled samples with individually genotyped frequencies in a subset of 93 SNPs. A high degree of correlation (r2 =

0.88) was found. Second, comparison of genome-wide allele frequencies in these CF patients with those from a similar pooled genome-wide scan in asthma patients and controls

(from an Isle of Wight birth cohort study7) unambiguously identified CFTR as the disease-causing locus in CF. Of the top-ranked polymorphisms distinguishing the two cohorts, in terms of statistically significant differences in allele frequency, 34 out of 38 were clustered on chromosome 7, centered around CFTR, with a median uncorrected P value of 3 x 10-8

(Suppl. Fig. 1). Several of these SNPs would not pass Bonferroni correction for multiple testing, given the >100K SNPs on the microarrays—despite the known biological significance

of CFTR. Thus, in addition to generating false positive results, correction for multiple testing in genome-wide SNP scans can generate false negative results as well.

To differentiate true from false association (or false lack of association), we prioritized follow-up efforts according to hierarchical criteria, focusing, firstly, on regional clusters of

SNPs exhibiting different allele frequencies (Suppl. Fig. 2) and, secondly, on regions containing genes with biological coherence: regulators of transcription, genes with known function in the immune system or in lung biology, and genes implicated in biological functions abnormal in the CF airway (e.g., ion channels). This criterion was unlikely to facilitate identification of genes with unknown function or of unexpected pathogenetic pathways.

However, we aimed to identify true modifiers amongst false positives, not to identify all modifiers. This heuristic approach was used to select 6 regions/genes for follow-up study:

IFRD1, CEBPA/CEBPG, CHI3L2, C6, SLC4A3, and ABCA1.

The top-ranked locus in terms of clustering was IFRD1, 5 Mb away from CFTR, the 3‘ region of which contained a cluster of 9 SNPs (Suppl. Table 1; Suppl. Fig. 2b) with significantly different allele frequencies between patient groups. Biology is addressed below.

To confirm the genotyping in the pooled scan, as well as define which SNPs to pick for replication purposes, SNPs in IFRD1 reaching significance in the pooling experiment, along with tagging SNPs throughout and flanking the region of the effect, were individually genotyped in the wider GMSG cohort. While no association signal was observed for

IFRD1-flanking markers, retention of association signal for a cluster of SNPs on the haplotype block containing the 3‘ IFRD1 exons (Suppl. Tables 2 and 3, Suppl. Fig. 3) led us to pursue replication in a separate population.

Three IFRD1 SNPs (rs7817, rs3807213, rs6968084) that linkage disequilibrium analysis suggested captured the bulk of the variation observed at this locus were genotyped in patients in the CFTSS cohort (Suppl. Table 4). Notably, the family-based association test demonstrated significant association between the rs7817 polymorphism and both cross-sectional and longitudinal measures of lung function (Table 1a). The other two SNPs

showed trends toward significance. A second, complementary method (quantitative transmission disequilibrium test) verified the result derived for rs7817 (Table 1b). Intriguingly, both methods revealed that the heterozygote genotype (―CT‖) was associated with lower lung function than either homozygote (―CC‖ or ―TT‖; data not shown). However, other genotype models (additive, recessive and dominant) could not be conclusively excluded. It was important to exclude linkage with CFTR alleles as the cause of the observed association between IFRD1 SNPs and CF lung function. No correlation was detected between genotypes composed of IFRD1 SNPs and the presence of 0, 1 or 2 copies of the common mutation

F508, or when CFTR mutations were grouped according to their association with exocrine pancreatic status. Furthermore, there was no evidence of linkage between the SNPs and the pulmonary phenotypes8, important for validating the association model used (a test of association in the absence, as opposed to the presence, of linkage). These data indicate that

IFRD1 polymorphisms contribute to lung function variation in CF independent of CFTR.

IFRD1 acts in an HDAC-dependent manner to mediate transcriptional co-repression and co-activation9. Expression and genetic deletion studies have implicated IFRD1 in cell differentiation and stress responses10. Available databases suggested highest expression in human blood cells11. Flow cytometric analysis of such cells revealed greatest expression in neutrophils (Suppl. Fig. 4a). Similarly, quantitative RT-PCR analysis of cells relevant to the

CF airway indicated particular enrichment of expression in neutrophils (Suppl. Fig. 4b).

Terminal differentiation of human and mouse neutrophils was associated with robust upregulation of IFRD1 expression (Suppl. Fig. 5), something mirrored in expression databases12. Neutrophilic differentiation of HL-60 cells also led to upregulation of IFRD1 expression (Suppl. Fig. 6a), and siRNA-mediated knockdown of IFRD1 in such cells blunted oxidative burst capacity (Suppl. Fig. 6b and 6c) without altering visual morphology or surface expression of CD11b (data not shown).

No alteration in peripheral blood neutrophil count, morphology, or CD11b and Gr-1 expression were observed in Ifrd1-/- mice13 (data not shown). However, neutrophils from

Ifrd1-/- mice exhibited significant impairment of specific effector functions, including oxidative burst, bacterial killing, TNF-, KC and LTB4 production—but not chemotaxis— compared with wild type and/or heterozygote littermate controls (Fig. 1). In vivo stimulation led to similar results: after intratracheal LPS stimulation, Ifrd1-/- mice exhibited significantly less TNF- production on a per cell basis in neutrophils, but not macrophages, compared with wild type controls (data not shown). No differences in airway neutrophil numbers or apoptosis were seen in these studies (data not shown). These functional effects exhibited specificity among myeloid cells; peritoneal macrophages from Ifrd1-/- mice exhibited normal oxidative burst capacity and LPS-driven TNF- production (Suppl. Fig. 7). Thus, IFRD1 plays an important role in regulating neutrophil effector function.

We subsequently analyzed the role of IFRD1 in modulating airway infection with P. aeruginosa. Genetic deficiency of IFRD1 was associated with significantly slower bacterial clearance (Fig. 2a). Notably, however, Ifrd1-/- mice also had significantly ameliorated disease, with less weight loss, and less airway and systemic inflammation (Fig. 2b-f). To define whether this was due to hematopoietic cell IFRD1 expression, C57BL/6 (CD45.1) mice were lethally irradiated and reconstituted with bone marrow cells from CD45.2-expressing wild type or Ifrd1-/- mice. No differences in bone marrow reconstitution efficiency were observed

(Supplementary Figure 8a). Indeed, competitive reconstitution assays formally demonstrated the lack of a role for IFRD1 in early neutrophil development (Suppl. Fig 8b). Wild type mice reconstituted with IFRD1-deficient bone marrow cells mirrored the phenotype of Ifrd1-/- mice during P. aeruginosa infection—with less efficient bacterial clearance, but less inflammation and disease (Suppl. Fig. 9). When reciprocal bone marrow transfers were performed

(reconstituting lethally-irradiated CD45.2-expressing wild type and Ifrd1-/- mice with bone marrow cells from wild type C57BL/6 (CD45.1) mice), no such differences in bacterial burden, inflammation or disease course were seen (Suppl Fig. 10). Thus, IFRD1 modulation of the airway response to P. aeruginosa infection is largely dependent on hematopoietic cell expression of IFRD1.

Airway challenge with LPS was similarly associated with increased TNF- and KC in BAL fluid from wild type, compared with Ifrd1-/-, mice (Suppl. Fig. 11a-b). In vivo HDAC inhibition blunted LPS-driven airway TNF- and KC production, specifically in wild type, not in Ifrd1-/-, mice (Suppl. Fig. 11a-b). Furthermore, bone marrow transfer experiments revealed that the effects of HDAC inhibition on LPS-driven airway TNF- production—in BAL and by airway neutrophils (Suppl. Fig. 11c-d); but not by airway macrophages (data not shown)—was dependent on hematopoietic cell IFRD1 expression.

The effector functions blunted in the absence of IFRD1 are dependent on NF-Bp6514,15.

Ifrd1-/- mice exhibited significantly decreased LPS-stimulated neutrophil NF-Bp65 transactivation, compared with littermate controls (Suppl. Fig. 12a). As IFRD1, NF-Bp65 and

HDAC1 were co-immunoprecipitable in neutrophil nuclear extracts (Suppl. Fig. 12b), it appears likely that IFRD1 mediates its effects on neutrophils, at least in part, by direct interactions with NF-B. While our data are compatible with IFRD1 being a co-activator of transcriptional activity or a co-repressor of an inhibitor of transcriptional activity, the HDAC inhibition experiments suggest the latter. Co-immunoprecipitation analysis suggests the possibility of HDAC-mediated co-repression of an NF-B-driven transcriptional inhibitor of

NF-B transactivation.

While we may have not identified the causal variant(s), cogent hypotheses exist for how the identified SNPs may alter IFRD1 expression and/or function (Suppl. Information). In order to directly test the association of IFRD1 polymorphisms with neutrophil effector function, we studied neutrophils from healthy subjects. Notably, analysis of human peripheral blood neutrophils revealed significant association of IFRD1 polymorphisms with quantitative measures of neutrophil effector function (Fig. 3). Taken together, these data suggest that

IFRD1 modulates the course of CF airway disease through regulation of neutrophil effector function.

Biology is rarely simple, however. It remains possible that neutrophils are not the only cells influenced by IFRD1 polymorphisms in a fashion relevant to cystic fibrosis. There may, 34

for example, be ways by which respiratory epithelia and neutrophils interact through IFRD1 polymorphisms to modulate cystic fibrosis lung disease. On the whole, Cftr knockout and mutant mouse models have been somewhat disappointing; despite recapitulation of gut pathology, pulmonary phenotypes have been subtle. Whether this relates to different lung architecture in mice and humans, the influence of other genes in the mouse strains used, or stronger baseline immune counter-regulation in the mouse lung remains unclear. There is thus an essential problem with using such models to define whether the absence of Ifrd1 (or the presence of mutant Ifrd1 alleles) ameliorates cystic fibrosis lung disease, in the absence of a robust phenotype to ameliorate. In this light, the recent report of pigs with targeted disruption of CFTR16 may point the way to informative models.

Of the other top-ranked genes/regions from the initial scan, C6, SLC4A3 and ABCA1 did not survive genotyping in the wider GMSG cohort; CHI3L2 failed replication in the CFTSS cohort (data not shown). However, after refinement of the association signal from the pooled scan in the CEBPA/CEBPG locus (Suppl. Table 5) via individual genotyping in the wider

GMSG cohort (Suppl. Tables 6 and 7), replication was pursued in the CFTSS cohort. As shown in Supplementary Table 8, polymorphisms in the 40 kb intergenic region between

CEBPA and CEBPG (genes oriented 5‘ to each other) were significantly associated with variation in CF lung function. While there are clearly other possible ways in which these transcription factors may affect CF lung disease severity17,18, CEBP- is essential for neutrophil development19, a pathway that CEBP- has been implicated in as well20.

Despite considerable progress in CF therapy over recent decades, the norm is still an inexorable decline in pulmonary function. Identification of genes modifying CF lung disease, and delineation of the pathogenetic pathways that they influence, holds promise for the development of novel therapeutic strategies. The current data suggest likely utility for therapeutic targeting of neutrophils in this devastating disease. These data also suggest that the IFRD1/HDAC axis may provide a tractable therapeutic target in CF, and other diseases in which neutrophils play an important pathogenetic role.

Methods Summary

Genetic analysis.

CF Patients were from the GMSG5 and CFTSS1 cohorts. Healthy controls were recruited at

CCHMC. Studies were approved by the relevant Institutional Review Boards. Genome-wide analysis was performed using Affymetrix GeneChip 100K Human Mapping microarrays.

Other genotyping was performed using Taqman PCR, AcycloPrime-FP SNP PCR, Illumina

610 Quad chips and llumina SNP beadarray genotyping. Follow-up association analysis

(GMSG cohort) was performed using SNPGWA21. Association and transmission analysis

(CFTSS cohort) was done using PEDSTATS v.0.6.6

(http://www.sph.umich.edu/csg/abecasis/Pedstats)22, Intercooled Stata 8, QTDT v.2.5.0

(http://www.sph.umich.edu/csg/abecasis/QTDT)23, Golden Helix software

(http://www.goldenhelix.com). Quantitative Trait Analysis (Healthy Donor cohort) was performed using PLINK version 1.03 (http://pngu.mgh.harvard.edu/~purcell/plink/).

Cellular assays.

Surface and intracellular FACS staining was performed as described24. Quantification of mRNA was performed by quantitative RT-PCR, as described24. Oxidative burst capacity was quantified by flow cytometry using the dihydrorhodamine 123 assay25. TNF- production was

26 quantified by ELISA (BDPharmingen) or by intracellular staining, as described . LTB4 production was quantified by ELISA (Neogen). Bacterial killing was quantified as described27.

Neutrophil chemotaxis was quantified as described28. Nuclear NF-B p65 DNA-binding activity was quantified using the EZ-Detect Transcription Factor ELISA (Pierce).

Co-Immunoprecipitation of nuclear proteins was performed using the Nuclear Complex Co-IP kit from Active Motif (Carlsbad, CA).

Mouse models.

Mice were non-traumatically challenged intratracheally with P. aeruginosa (FRD1 strain). 48 h later, mice were sacrificed. BAL cell analysis, lung bacterial burden and myeloperoxidase activity were quantified as described4. BAL and serum cytokines were quantified by ELISA

(BDPharmingen, R&D) or by the CCA ELISA29. Standard bone marrow cell transfer techniques were employed. SAHA, from Cayman, was administered intraperitoneally. Mice were non-traumatically challenged intratracheally with P. aeruginosa LPS (Sigma). Animal care was provided in accordance with National Institutes of Health guidelines. Studies were approved by the CCHMC Institutional Animal Care and Use Committee.

1 Vanscoy, L. L. et al. Heritability of lung disease severity in cystic fibrosis. Am. J. Respir.

Crit. Care Med. 175, 1036-1043 (2007).

2 Welsh, M. J. et al., in Metabolic and Molecular Basis of Inherited Disease, edited by

C.R Scriver, A.L. Beaudet, W.S. Sly et al. (McGraw-Hill, New York, 2001), pp.

5121-5188.

3 Muhlebach, M. S. and Noah, T. L. Endotoxin activity and inflammatory markers in the

airways of young patients with cystic fibrosis. Am. J. Respir. Crit. Care Med. 165,

911-915. (2002).

4 Karp, C. L. et al. Defective lipoxin-mediated anti-inflammatory activity in the cystic

fibrosis airway. Nature Immunol. 5, 388-392 (2004).

5 Drumm, M. L. et al. Genetic modifiers of lung disease in cystic fibrosis. N. Engl. J.

Med. 353, 1443-1453 (2005).

6 Schluchter, M. D. et al. Classifying severity of cystic fibrosis lung disease using

longitudinal pulmonary function data. Am. J. Respir. Crit. Care Med. 174, 780-786

(2006).

7 Kurukulaaratchy, R. J. et al. Characterisation of atopic and non-atopic wheeze in 10

year old children. Thorax 59, 563-568 (2004).

8 Abecasis, G. R. et al. Merlin--rapid analysis of dense genetic maps using sparse gene

flow trees. Nature Genet. 30, 97-101 (2002).

9 Vietor, I. et al. TIS7 interacts with the mammalian SIN3 histone deacetylase complex

in epithelial cells. EMBO J. 21, 4621-4631 (2002).

10 Vietor, I. & Huber, L. A. Role of TIS7 family of transcriptional regulators in

differentiation and regeneration. Differentiation 75, 891-897 (2007).

11 http://symatlas.gnf.org/SymAtlas. (2008).

12 Theilgaard-Monch, K. et al. The transcriptional program of terminal granulocytic

differentiation. Blood 105, 1785-1796 (2005).

13 Vadivelu, S. K. et al. Muscle regeneration and myogenic differentiation defects in mice

lacking TIS7. Mol. Cell. Biol. 24, 3514-3525 (2004).

14 Anrather, J. et al. NF-kappaB regulates phagocytic NADPH oxidase by inducing the

expression of gp91phox. J. Biol. Chem. 281, 5657-5667 (2006).

15 Riedemann, N. C. et al. Regulation by C5a of neutrophil activation during sepsis

Immunity 19, 193-202 (2003).

16 Rogers, C. S. et al. Disruption of the CFTR gene produces a model of cystic fibrosis

in newborn pigs. Science 321, 1837-1841 (2008).

17 Martis, P. C. et al. C/EBPalpha is required for lung maturation at birth. Development

133, 1155-1164 (2006).

18 Mullins, D. N. et al. CEBPG transcription factor correlates with antioxidant and DNA

repair genes in normal bronchial epithelial cells but not in individuals with

bronchogenic carcinoma. BMC Cancer 5, 141 (2005).

19 Zhang, D. E. et al. Absence of granulocyte colony-stimulating factor signaling and

neutrophil development in CCAAT enhancer binding protein alpha-deficient mice.

Proc. Natl. Acad. Sci. USA 94, 569-574 (1997).

20 Bjerregaard, M. D. et al. The in vivo profile of transcription factors during neutrophil

differentiation in human bone marrow. Blood 101, 4322-4332 (2003).

21 http://www.phs.wfubmc.edu/public_bios/sec_gene/home.cfm.

22 Wigginton, J. E. & Abecasis, G. R. PEDSTATS: descriptive statistics, graphics and

quality assessment for gene mapping data. Bioinformatics 21, 3445-3447 (2005).

23 Abecasis, G. R. et al. A general test of association for quantitative traits in nuclear

families. Am. J. Hum. Genet. 66, 279-292 (2000).

24 Divanovic, S. et al. Negative regulation of Toll-like receptor 4 signaling by the Toll-like

receptor homolog RP105. Nature Immunol. 6, 571-578 (2005).

25 Richardson, M. P. et al. A simple flow cytometry assay using dihydrorhodamine for the

measurement of the neutrophil respiratory burst in whole blood: comparison with the

quantitative nitrobluetetrazolium test. J. Immunol. Methods 219, 187-193 (1998).

26 Atabani, S. F. et al. Natural measles causes prolonged suppression of interleukin-12

production. J. Infect. Dis. 184, 1-9 (2001).

27 Ellson, C. D. et al. Neutrophils from p40phox-/- mice exhibit severe defects in NADPH

oxidase regulation and oxidant-dependent bacterial killing. J. Exp. Med. 203,

1927-1937 (2006).

28 Filippi, M. D. et al. Localization of Rac2 via the C terminus and aspartic acid 150

specifies superoxide generation, actin polarity and chemotaxis in neutrophils. Nature

Immunol. 5, 744-751 (2004).

29 Finkelman, F. D. & Morris, S. C. Development of an assay to measure in vivo cytokine

production in the mouse. Int. Immunol. 11, 1811-1818 (1999).

Supplementary Information is linked to the online version of the paper at www.nature.com/nature.

Acknowledgements This work was funded by grants from the National Cystic Fibrosis

Foundation (C.L.K., M.L.D., G.R.C., M.R.K.), the National Heart Lung and Blood Institute

(G.R.C., M.R.K., C.L.K.), the Wake Forest University Health Sciences Center for Public

Health Genomics (C.D.L., A.H.W.) and the Austrian Science Fund (I.V., L.A.H). We thank R.

Pace and J. Yeatts for technical assistance, and D. Hassett for P. aeruginosa FRD1 strain. 40

Author Information: Reprints and permissions information is available at www.nature.com/reprints. The authors declare competing financial interests: J.B.H. and

J.R.K. hold equity interest in and receive consulting income from JK Autoimmunity Inc.

Correspondence and requests for materials should be addressed to C.L.K.

([email protected]).

Figure 1. IFRD1-deficient neutrophils exhibit decreases in specific effector functions.

(a) Oxidative burst, quantified by the dihydrorhodamine 123 assay in peripheral blood neutrophils from Ifrd1-/- mice and heterozygote littermate controls. (b) Bacterial Killing. Killing of P. aeruginosa by neutrophils purified from bone marrow, calculated as [(control cfu–experimental cfu)/control cfu] x 100. Means ± S.E.M. are shown, of 6 different mice/genotype, tested in duplicate. Similar results were seen with whole blood neutrophil killing assays in 3 separate experiments. (c) TNF- production. Intracellular TNF- expression by neutrophils, quantified by flow cytometry in LPS-stimulated whole blood

isolated from Ifrd1-/- mice and wild type controls. (d) KC production. KC mRNA expression, quantified by qRT-PCR in bone marrow neutrophils from wild type or Ifrd1-/- mice. Means ±

S.E.M. are shown, of 6 different mice/genotype, tested in duplicate. (e) LTB4 production. LTB4, quantified by ELISA in supernatants from bone marrow neutrophils from wild type or Ifrd1-/- mice incubated with arachidonic acid, and stimulated with LPS in the presence or absence of

GM-CSF. (f) Chemotaxis. Microscopic quantification of chemotaxis by bone marrow neutrophils from wild type or Ifrd1-/- mice in response to fMLP (1 M ) or IL-8 (1 g/ml). Means

± SE are shown, representative of 3 separate experiments for a, c and e. *P < 0.05, **P <

0.01. Arachidonic acid (AA); NS, mock stimulation; PMA, phorbol-12-myristate-13-acetate.

Figure 2. Genetic deficiency of IFRD1 is associated with delayed bacterial clearance, but decreased neutrophilic inflammation and ameliorated disease, after airway challenge with mucoid P. aeruginosa.

Wild type and Ifrd1-/- mice were challenged intratracheally with P. aeruginosa (FRD1 strain), and analyzed 48 h later. (a) Lung bacterial burden. (b) Weight loss. Solid line, Ifrd1-/-; dashed line, WT. (c) Serum TNF-. (d) BAL: total cells. (e) BAL: neutrophils. (f) BAL

TNF-. (g) BAL KC. No differences in parenchymal accumulation of neutrophils (quantified by analysis of myeloperoxidase activity in blanched lungs) were observed. Means ± S.E.M. of

6 mice/group are shown; data are representative of 3 separate experiments. *P < 0.05.

rs6968084 CCC Haplotype 170 170 145 145 120 120 95 95 70 70 Oxidative Index Oxidative 45

CC CT TT Index Oxidative 45 Genotype 0 1 2 # of copies rs3807213 170 CCC Haplotype 4 145

120 3

95 2

(ng/ml) 

70 - 1

Oxidative Index Oxidative 45 CC CA AA TNF 0 Genotype 0 1 2 # of copies rs7817 170

145

120

Oxidative Index Oxidative 45 CC CT TT Genotype

Figure 3. Association of IFRD1 polymorphisms with variation in human neutrophil effector function.

Oxidative burst capacity following PMA stimulation, and LPS-driven TNF- secretion, was quantified in neutrophils from healthy donors of self-reported European descent (n = 36). (a)

Oxidative index, SNP rs7817. C/C (N = 13); T/C (N = 13); T/T (N = 10). P =0.005 (Wald test).

(b) Oxidative index, SNP rs3807213. C/C (N = 12); A/C (N = 11); A/A (N = 13). P = 0.007. (c)

Oxidative index, SNP rs6968084. C/C (N = 19); T/C (N = 15); T/T (N = 2). P = 0.06. (d)

Oxidative index, 3-Marker Haplotype (rs7817, rs3807213, rs6968084). 0 (N = 13), 1 (N =

11) or 2 (N = 12) copies of CCC haplotype. P = 0.007. (e) TNF-, 3-Marker Haplotype

(rs7817, rs3807213, rs6968084). CCC haplotype copy N as in (d). P = 0.03. Data represent means ± S.E.M.

Table 1 Transmission analysis of IFRD1 SNPs

Table 1a. Transmission analysis of IFRD1 SNPs in CF patients using family based association testing (PBAT module, Golden Helix®, Bozeman, MT)

SNP Allele Genetic Model n P value Phenotype Effect a rs7817 C/T Het. Distortion 248 0.004 Cross-sectional lung function - b C/T Het. Distortion 186 0.016 Longitudinal lung function - rs3807213 C Dominant 141 0.080 Longitudinal lung functionb + A Recessive 141 0.080 Longitudinal lung functionb - rs6968084 C/T Het. Distortion 142 0.082 Cross-sectional lung functiona + Table 1b. Transmission analysis of IFRD1 rs7817 in CF patients using Quantitative Transmission Disequilibrium Testing (QTDT23)

SNP F N P value Phenotype rs7817 4.10 467 0.0168* Cross-sectional lung functiona 4.00 314 0.0187* Longitudinal lung functionb Het., heterozygous; n, number of informative families; N, number of informative individuals; F, QTDT test statistic23; Effect, phenotypic effect associated with the over-transmitted allele (+, better function). a 1 BayesFEV1%Pred@20yrs, estimated FEV1%-predicted at age 20 years as described in . b MaxFEV1CF%, maximum CF-specific percentile for FEV1 in patient’s most recent year of available data, as described in1. *P < 0.05 after Bonferroni correction for two tests. Minor allele frequencies: rs7817, 0.48 C; rs3807213, 0.40 C; rs6968084, 0.14 T

Methods

Cohorts. The GMSG cohort consists of CF patients homozygous for F508 CFTR whose longitudinal FEV1 measurements were in the highest or lowest quartile for age among F508 homozygotes. Enrolment criteria, data collection and genotyping have been described5. The study was approved by the institutional review boards (IRB) of all participating institutions.

Patients and parents of minors provided written informed consent.

CF twins and siblings (N =1,118) and their parents from 619 families were recruited by the CFTSS as previously described1. Twenty-one dizygous and 49 monozygous (MZ) twin pairs were included. Raw pulmonary function test data, CFTR genotypes, and height and weight measurements were obtained from medical records. In some cases in which genotypes were unavailable, CFTR exons were sequenced to identify mutations. Written informed consent or assent was obtained from all subjects. FEV1 was used to derive cross-sectional (MaxFEV1CF%) and longitudinal (AvgFEV1CF% and EstFEV1%Pred) measures, as previously described1. To include as many subjects as possible and to avoid randomly excluding one member of each pair, lung function measures were averaged for MZ twin pairs and included in analyses only if the twins‘ values were within ten percentiles of each other (or ten percent-predicted), as not to double-count genetically identical individuals.

For MZ twin pairs in which only one of the twins had pulmonary data, that twin‘s data was included.

Healthy controls (inclusion and exclusion criteria: standard for routine blood donation, plus exclusion for use of immunosuppressive medications or non-steroidal anti-inflammatory drugs in the 2 wk prior to blood donation for functional assays) were recruited at CCHMC.

Blood for neutrophil function studies was obtained from 46 participants, 37 of whom self-reported European ancestry. Due to the small numbers of non-Europeans and the possibility of confounding due to stratification, analysis was restricted to these 37. Blood samples were blinded to haplotype and genotype status prior to functional analysis. All

participants gave written informed consent; the study was approved by the CCHMC IRB.

Genotyping. Genome-wide analysis, using Affymetrix GeneChip 100K Human Mapping microarrays, was performed in 320 CF patients from the GMSG Study5: 160 with severe lung disease (lowest quartile of FEV1 for age; 160 with mild lung disease (highest quartile of FEV1 for age); 308 of whom self-reported European ancestry. Each group of 160 was comprised of

80 males and 80 females. Each such group of 80 was divided into groups of 20 (from across the relevant quartile) for pooling purposes. Equimolar amounts of DNA, with an A260/A280 ratio between 1.65-2 and an A260/A230 ratio between 1.0-2.2, as quantified by NanoDrop spectrophotometry (Wilmington, DE, USA), were combined into pools containing 250 ng DNA.

DNA pools were digested with XbaI or HindIII, adapter-ligated, and PCR-amplified. Samples were separated on 4% agarose gels to ensure DNA fragmentation in the 100-300 bp range.

PCR yields were compared between microarray chips to ensure uniformity (>1200 ng/ul accepted), and PCR products were separated on 2% agarose gels to ensure the proper range of amplified product. GeneChip Genotyping software (v.4.0, Affymetrix, Inc.) was used for relative quality control assessment, detection rates, and allele distributions. Hybridization intensity comparisons of the case and control pools were used to identify significant allele frequency differences for each SNP. A set of 100,198 (out of 111,664) SNPs provided data of sufficient quality on all microarrays.

Taqman PCR genotyping, using assays from ABI, was performed: (a) for assessment of the robustness of pooled estimates of allele frequency, by individual sample genotyping of the initial 320 patients in the GMSG cohort; (b) for individual genotyping of samples from 2194 subjects in the CFTSS cohort (see below); (c) for genotyping 91 normal healthy controls.

AcycloPrime-FP SNP PCR assays (Perkin Elmer) were used (d) to genotype 100 healthy controls. Autoclustering algorithms were used ([a] SDS Version 1, [b, and c] SDS Version 2.3, both from ABI; and [d] FP Caller, from Johns Hopkins) to call SNPs. The call rates were (a)

99.9%, (b) 97.1%, (c) 98.6% and (d) 99.6% respectively.

Individual sample genotyping of an expanded sample set of patients in the GMSG cohort, comprised of the 320 samples from the pooling experiment, plus an additional 485 samples, for a total of 805 samples (261 severe, 541 mild), was performed. To minimize possible issues of population stratification, genetic analysis at this stage was confined to individuals self-reporting European ancestry (779 out of 805, including 241 and 538 patients with severe and mild lung disease, respectively). SNPs that reached significance in the pooling experiment, along with tagging SNPs30 throughout the region of the effect, were selected for follow-up. Tag SNPs were chosen based on HapMap data using Tagger

(http://www.broad.mit.edu/mpg/tagger/) (minor allele frequency threshold = 0.05; pairwise R2 threshold = 0.8). Additionally, a reported non-synonymous polymorphism in IFRD1, rs11542463, was assayed; it was monomorphic in the GMSG cohort. A final list of tag SNPs was chosen based on predicted assay design scores for the SNP beadarray. Genotyping was done by custom Illumina GoldenGate assays. An autoclustering algorithm was used on all

SNPs. Clusters of SNPs were manually inspected when they had a low call rate (<98.5) or a low clustering score (<0.6). 3 individuals (out of an initial 808) with DNA quality or gender reporting problems were excluded. The genotype success rate for each SNP was >99.6%; the overall call rate was 99.95%. 8 samples assayed in duplicate as technical replicates had >99.93% concordance.

CFTSS subjects were genotyped by two methods, TaqMan (Applied Biosystems, Foster

City, CA) and the Illumina 610 Quad chips. In total, approximately 2200 individuals were typed for three IFRD1 SNPs (rs6968084, rs3807213, and rs7817). Of these, 76% were typed by both methods, 20% were typed by TaqMan only, and 4% were typed by Illumina only. The discrepancy rates between the two methods were 0.42%, 0.06%, and 0.49% for rs6968084, rs3807213, and rs7817, respectively. No Mendelian errors were detected in families typed using the Illumina platform, while five Mendelian errors were detected in families typed by

TaqMan. Because the former method appeared to be more reliable, Illumina genotypes were used in cases where calls made by the two methods were different.

Genetic association data analysis. Allele frequencies for Affymetrix data were determined using adjustment factors for pooled samples31. Z2 P values were used to rank all SNPs. A cluster analysis of Z2 statistics was performed. While a previous report has found evidence for minimal stratification in the GMSG cohort32, the possibility of confounding due to population substructure in the pooling step was investigated by applying the genomic control (GC) method to the Z2 statistics for pooled DNA33. Direct application of GC to pooled data assumes the variance due to pooling has properties delineated by Devlin, et al.35 While the pooling experiment did not contain the technical replicates necessary to definitively satisfy these assumptions, GC was directly applied to the Z2 statistics. Using this approach, the inflation factor  = 1.04 when estimated using the mean—again suggesting that stratification is minimal in this population33.

Follow-up association analysis in the GMSG cohort was performed using SNPGWA21.

Each SNP was tested for departures from Hardy-Weinberg equilibrium expectations. The additive genetic model test of association was the primary inference. Imputation analysis was performed using the gwas software, impute v0.4.2 and snptest v.1.1.5

(http://www.stats.ox.ac.uk/~marchini/software/gwas/gwas.html)34.

For association and transmission analysis in the CFTSS cohort, genotype distributions were tested for Hardy-Weinberg equilibrium using the ‗--unrelatedsOnly‘ option in PEDSTATS v.0.6.6 (http://www.sph.umich.edu/csg/abecasis/Pedstats)22, which performs an exact test in a subset of unrelated individuals, so as to avoid bias from correlated genotypes within families. Because correlation among sibling marker genotypes may invalidate the results of family-based tests of association in the presence of linkage, linkage between SNPs and pulmonary phenotypes was evaluated using Merlin software (MERLIN v. 1.1.2; http://www.sph.umich.edu/csg/abecasis/Merlin/).

Association between three IFRD1 SNPs (rs6968084, rs3807213, and rs7817) and the three pulmonary phenotypes was analyzed using the PBAT module implemented within

Golden Helix software (Golden Helix, Inc. Bozeman, MT, USA. Golden Helix PBAT Software http://www.goldenhelix.com). Four genetic models were tested: additive, dominant, recessive, and heterozygote distortion. The best associated SNP and phenotypes resulting from PBAT analysis were tested for transmission disequilibrium by a second method, QTDT

(http://www.sph.umich.edu/csg/abecasis/QTDT)23, using the orthogonal model of association and assuming dominance. Complete and incomplete trios were utilized in both analyses.

General statistics were performed in Intercooled Stata 8 (StataCorp, College Station, TX).

Analysis of data on neutrophil oxidative index and TNF production in healthy donors was performed using PLINK version 1.03 (http://pngu.mgh.harvard.edu/~purcell/plink/) standard quantitative trait association options for genotypes (--assoc) and haplotypes

(--hap-assoc) as indicated. Estimated Haplotypes were imputed using the

Expectation-Maximization algorithm as implemented in PLINK using the (--hap-phase) option.

Cellular phenotypic and functional assays. Human neutrophils and mononuclear cells were isolated by Ficoll-Hypaque sedimentation, monocytes by leukapheresis and counter-current elutriation. CD34+ cells (CCHMC Normal Donor Repository) were differentiated in vitro with rG-CSF (50 ng/ml) plus rSCF (50 ng/ml, both from Peprotech) for 8 d, followed by rG-CSF alone for 8 d. Primary tracheobronchial cells were harvested from bronchial brushings from normal subjects (UCCOM Bronchoscopy Core). THP-1, BEAS-2B and HL-60 cells were from ATCC. HL-60 cells were differentiated with 1.5% DMSO or with retinoic acid (1 g/ml). Mouse peripheral blood leukocytes were isolated from whole blood after lysis of erythrocytes with ACK buffer. Neutrophils were isolated immunomagnetically from mouse bone marrow using Gr-1 beads (Miltenyi), a purification strategy yielding a highly purified population of mature neutrophils, as demonstrated by stained cytospins (data not shown). Mouse peritoneal exudate macrophages were isolated after thioglycollate elicitation24.

Mouse hematopoietic progenitor (Lin-c-kit+sca-1+) cells were purified by flow-cytometric

sorting28, and differentiated in vitro for 11 d with rSCF (100 ng/ml), rMGDF (100 ng/ml) and rG-CSF (100 ng/ml; all from Amgen).

Surface and intracellular FACS staining was performed as described24, using antibodies from Sigma (IFRD1), Molecular Probes (mouse IgG2a), eBioscience (CD11b, Gr-1, CD16,

CD3, CD4, CD8, CD19), an LSRII flow cytometer and FACSDiVa Software (BDPharmingen).

Fc-receptor blockade was performed with human AB serum Gemini Bio; human cells) or blocking Ab to CD16 and CD32 (Fc block, eBioscience; mouse cells). Quantification of mRNA was performed by qRT-PCR24, using a LightCycler (Roche) and the following primers: IFRD1

5‘ TGCAGCGTTAGCATCTGTTC, IFRD1 3‘ ACCAAAGCAAGTTGCACAAG; IFRD2 5‘

TGTTTTCAGCCGGTTCTATGG, IFRD2 3‘ TGCCTGTCAAGGATGTGGC; ubiquitin 5‘

CACTTGGTCCTGCGCTTGA, ubiquitin 3‘ CAATTGGGAATGCAACAACTTTAT. KC 5‘

ACCCAAACCGAAGTCATAGC, KC 3‘ TCTCCGTTACTTGGGGACAC. Oxidative burst capacity was quantified by flow cytometry in mouse cells treated with PMA (Sigma), using the dihydrorhodamine 123 assay25. HL-60 cells were mock transfected, or transfected by

Nucleofection (Amaxa) with 90 pmol (45nM) synthetic siRNA against IFRD1, or negative control siRNA, and incubated for 48h, during differentiation to a neutrophil phenotype with

DMSO. IFRD1 siRNA sense/antisense: r(GGU GAG UUC UGA UUA UUA A)dTdT/ r(UUA

AUA AUC AGA ACU CAC C)dAdG; Control (non-silencing) siRNA sense/antisense: r(UUC

UCC GAA CGU GUC ACG U) dTdT/ r(ACG UGA CAC GUU CGG AGA A) dTdT. Oxidative burst was quantified by flow cytometry in human neutrophils by the dihydrorhodamine 123 assay25. Fluorescence was quantified in neutrophils (CD11b+CD15+ cells; antibodies from

Biolegend; within the granulocyte gate set based on forward and side scatter characteristics) using an LSRII flow cytometer. TNF- production by cells or in airways was quantified by

ELISA (BDPharmingen) or by intracellular staining26 (anti-TNF- from eBioscience). KC was quantified by ELISA (R&D) or qRT-PCR. LTB4 was quantified by ELISA (Neogen) after stimulation of neutrophils with P. aeruginosa LPS or GM-CSF, followed by incubation with arachidonic acid35. Killing of P. aeruginosa (FRD1 strain) was quantified as described27.

Neutrophil chemotaxis was quantified as described28. Nuclear NF-B p65 DNA-binding activity was quantified using the EZ-Detect Transcription Factor ELISA (Pierce).

Co-Immunoprecipitation of nuclear proteins was performed using the Nuclear Complex Co-IP kit from Active Motif (Carlsbad, CA). Immunoprecipitating and immunoblotting antibodies were from Santa Cruz. Immunoreactive proteins were visualized by ECL (Amersham).

Mouse model. Six to 8 wk-old Ifrd-/- mice13 on a C57BL/6 background (> 10 generations) and wild type controls, were challenged intratracheally (non-traumatically, as described4) with 6 x106 CFU of P. aeruginosa (FRD1 strain). 48 h after challenge, mice were sacrificed, bronchoalveolar lavage (BAL) was performed, and serum and lungs were harvested. Lung bacterial burden and myeloperoxidase activity were quantified by standard techniques4. BAL and serum cytokines were quantified by ELISAs (BDPharmingen, TNF-; R&D, KC).

CD45.1+ congenic C57BL/6 (B6.SJL-PtprcaPep3b/BoyJ) mice were lethally irradiated, and rescued with 2 x 106 bone marrow cells from wild type or IFRD1-deficient C57BL/6 (CD45.2+) mice. In these experiments, TNF- was measured by the CCA ELISA29. Similarly, wild type and Ifrd1-/- C57BL/6 (CD45.2+) mice were lethally irradiated, and rescued with 2 x 106 bone marrow cells from wild type (CD45.1+) mice. Reconstitution was monitored by flow cytometric analysis of peripheral blood cell populations, using mAb to CD45.2, CD11b, Gr-1, TCR, B220, and NK1.1 (eBioscience). Reconstituted mice were challenged with P. aeruginosa ≥ 2 months after transplantation. To formally test the ability the relative reconstitution ability of bone marrow cells from Ifrd1-/- and wild type mice, bone marrow cells from Ifrd1-/- or wild type mice

(both CD45.2+) were transplanted into lethally irradiated wild type (CD45.1+) recipient mice, along with an equal number (1x106) of competitor bone marrow cells (CD45.1+). Mice were treated with SAHA (10 mg/kg; Cayman) i.p., followed 1 h later by intratracheal challenge with

P. aeruginosa LPS (2 mg/kg; Sigma). Animal care was provided in accordance with National

Institutes of Health guidelines. Studies were approved by the CCHMC Institutional Animal

Care and Use Committee.

30 de Bakker, P. I. et al. Transferability of tag SNPs in genetic association studies in

multiple populations. Nature Genet. 38, 1298-1303 (2006).

31 Yang, H. C. et al. New adjustment factors and sample size calculation in a

DNA-pooling experiment with preferential amplification. Genetics 169, 399-410

(2005).

32 Hillian, A. D. et al. Modulation of cystic fibrosis lung disease by variants in

interleukin-8. Genes. Immun. 9, 501-508 (2008).

33 Devlin, B. et al. Unbiased methods for population-based association studies. Genet.

Epidemiol. 21, 273-284 (2001).

34 Marchini, J. et al. A new multipoint method for genome-wide association studies by

imputation of genotypes. Nature Genet. 39, 906-913 (2007)

35 Gronert, K. et al., in Eicosanoid Protocols, edited by E.A. Lianos (Humana Press,

Totowa, 1999)

Supplementary Information

Supplementary Table 1. Association of SNPs at the IFRD1 locus with lung disease severity: GMSG cohort genome-wide SNP scan with pooled DNA;

Affymetrix 100K array.

Rank SNP Position A Mild Severe P value ORL ORU 44630 rs2249151 111774039 T 0.19 0.21 0.453418 0.68 2.04 85085 rs2188535 111834463 T 0.76 0.76 0.856725 0.62 1.73 39540 rs720639 111854865 C 0.29 0.26 0.402541 0.52 1.39 73279 rs728273 111855337 G 0.54 0.53 0.740025 0.61 1.47 185 rs6966318 111880093 T 0.21 0.34 0.000546 1.18 3.22 16856 rs10500007 111880180 G 0.24 0.19 0.167844 0.44 1.28 597 rs3109112 111882858 C 0.24 0.36 0.002979 1.07 2.84 1065 rs6967593 111884028 C 0.37 0.49 0.006470 1.02 2.48 1842 rs6968084 111884357 T 0.18 0.10 0.012602 0.27 1.00 24228 rs4730549 111888006 A 0.79 0.83 0.244570 0.74 2.27 9 rs2529588 111890291 G 0.23 0.41 0.000004 1.44 3.79 81701 rs10487291 111890873 G 0.04 0.04 0.823565 0.28 2.74 1071 rs7817 111902894 C 0.51 0.62 0.006533 1.02 2.48 96 rs1981610 111954200 A 0.55 0.70 0.000226 1.20 3.01 291 rs3111451 111981309 A 0.57 0.70 0.001044 1.12 2.82 125 rs3111449 111981969 A 0.58 0.72 0.000299 1.19 3.04

Rank: numeric rank (by Z2 score) out of all SNPs passing quality control for pooling (100,198). rsid: reference SNP identifier from the dbSNP Build 129. SNPs within IFRD1 are bolded.

Position: SNP position on chromosome 7 from NCBI dbSNP build 129. A: RefSNP allele, from NCBI dbSNP build 129 Mild/Severe: average estimated allele frequency (RefSNP allele) in pools from patients with mild/severe disease. P value: P value of the Z2 value. The

significance of the difference between two pools is calculated according to the 2 test with one

2 2 2 degree of freedom using the modified  statistic, Z = (p1-p2) /(V1-V2), where p1 and p2 are

the estimated allele frequencies and V1 and V2 are the variance of the estimated allele frequencies in the two pools.1,2 ORL/ORU are the lower and upper limits, respectively, of the

95% confidence interval for a recessive genetic model odds ratio based on estimated allele frequencies. Supplementary Table 3 compares estimates of IFRD1 SNP allele frequencies from the pooled DNA scan with direct measurement of allele frequencies by individual genotyping.

Supplementary Table 2. Individual genotyping in 779 Caucasian CFTR

ΔF508 homozygotes in the GMSG cohort; IFRD1 locus; Illumina SNP beadarray genotyping.

SNP A Mild Severe Add P Add LOF P OR CIL CIU rs2520475 T 0.17 0.17 0.765 0.494 1.05 0.78 1.4 rs2708597 C 0.14 0.14 0.801 0.254 1.04 0.76 1.43 rs720639 C 0.23 0.22 0.558 0.838 0.93 0.72 1.19 rs3109105 C 0.25 0.24 0.559 0.720 0.93 0.72 1.19 rs2396540 A 0.19 0.21 0.589 0.768 1.07 0.83 1.39 rs6966318 T 0.25 0.28 0.125 0.610 1.21 0.95 1.54 rs10500007 G 0.15 0.11 0.072 0.940 0.75 0.54 1.03 rs3109112 C 0.25 0.28 0.114 0.584 1.22 0.95 1.55 rs6967593 C 0.42 0.47 0.043 0.788 1.25 1.01 1.54 rs6968084 T 0.15 0.11 0.056 0.999 0.73 0.53 1.01 rs2529587 A 0.17 0.19 0.402 0.550 1.12 0.85 1.48 rs2529588 G 0.25 0.28 0.135 0.634 1.20 0.94 1.53 rs10487291 G 0.07 0.05 0.245 0.797 0.76 0.48 1.21 rs3807213 C 0.42 0.47 0.043 0.788 1.25 1.01 1.54 rs7817 C 0.48 0.55 0.026 0.199 1.28 1.03 1.59 rs1362 T 0.37 0.34 0.371 0.251 0.90 0.72 1.13 rs3128384 A 0.40 0.42 0.412 0.692 1.09 0.88 1.36 rs2041066 A 0.43 0.39 0.170 0.264 0.86 0.69 1.07 rs10246851 G 0.12 0.10 0.135 0.924 0.76 0.54 1.09 rs1006536 A 0.37 0.35 0.352 0.178 0.90 0.72 1.12 rs2906789 G 0.33 0.31 0.333 0.424 0.89 0.71 1.12 rs2190589 C 0.24 0.26 0.571 0.983 1.07 0.84 1.37

SNP: reference SNP identifier from the dbSNP Build 129. SNPs within IFRD1 are bolded. A:

RefSNP allele, from NCBI dbSNP build 129. Mild/Severe: Frequency of the minor allele in the

population of patients with mild/severe lung disease. Add P: P value of the additive genetic model, which has been found to account for the majority of genetic variation contributing to complex traits 3. Add LOF P: P value of lack of fit to an additive model. No markers show significant deviation from additivity, consequently, other models were not applied. OR: Odds

Ratio of the additive model CIL: Lower limit of the 95% confidence interval of the additive OR.

CIU: Upper limit of the 95% confidence interval of the additive OR. Imputation analysis was subsequently performed. No imputed marker on chromosome 7 in this interval displayed a marked increase in significance relative to the directly typed markers.

Supplementary Table 3. Comparison of pooled estimates of allele frequencies with allele frequencies obtained via individual genotyping in

320 CFTR ΔF508 homozygotes GMSG cohort; IFRD1 locus; Affymetrix 100K pooled genotyping versus Illumina SNP beadarray genotyping.

Pool SNP 1 2 3 4 5 6 7 8 9 10 11 12 Pool r FM1 Pool 0.77 0.73 0.80 0.73 0.79 0.19 0.85 0.72 0.96 0.65 0.54 0.53 0.96 Ind 0.80 0.68 0.83 0.83 0.83 0.33 0.83 0.83 0.98 0.65 0.53 0.53 FM2 Pool 0.75 0.77 0.89 0.73 0.75 0.27 0.82 0.78 0.91 0.53 0.53 0.59 0.97 Ind 0.78 0.80 0.85 0.83 0.85 0.30 0.83 0.85 0.90 0.63 0.63 0.63 FM3 Pool 0.68 0.90 0.78 0.69 0.71 0.38 0.80 0.74 0.98 0.58 0.53 0.63 0.96 Ind 0.75 0.90 0.80 0.78 0.80 0.38 0.78 0.80 0.95 0.60 0.60 0.63 FM4 Pool 0.67 0.64 0.78 0.81 0.84 0.30 0.87 0.83 1.00 0.55 0.58 0.50 0.98 Ind 0.65 0.70 0.85 0.88 0.85 0.35 0.88 0.85 0.98 0.55 0.63 0.63 MM1 Pool 0.83 0.64 0.76 0.74 0.79 0.40 0.69 0.82 0.96 0.49 0.38 0.48 0.83 Ind 0.85 0.65 0.68 0.83 0.68 0.50 0.83 0.68 0.93 0.48 0.58 0.58 MM2 Pool 0.73 0.68 0.80 0.82 0.72 0.50 0.84 0.73 0.97 0.44 0.64 0.66 0.90 Ind 0.70 0.80 0.83 0.88 0.83 0.43 0.88 0.83 0.90 0.50 0.63 0.60 MM3 Pool 0.86 0.64 0.75 0.74 0.71 0.59 0.78 0.73 0.96 0.33 0.67 0.68 0.95 Ind 0.80 0.73 0.73 0.80 0.73 0.55 0.80 0.73 0.93 0.43 0.65 0.63 MM4 Pool 0.77 0.67 0.76 0.84 0.76 0.38 0.90 0.81 0.95 0.37 0.49 0.55 0.98 Ind 0.78 0.75 0.78 0.88 0.78 0.40 0.88 0.78 0.95 0.50 0.55 0.55 FS1 Pool 0.75 0.75 0.71 0.75 0.76 0.45 0.87 0.68 0.94 0.44 0.77 0.78 0.93 Ind 0.75 0.75 0.70 0.90 0.70 0.45 0.90 0.70 0.95 0.43 0.70 0.70 FS2 Pool 0.75 0.85 0.70 0.81 0.66 0.33 0.87 0.60 1.00 0.62 0.82 0.77 0.92 Ind 0.73 0.83 0.70 0.88 0.70 0.40 0.88 0.70 1.00 0.53 0.70 0.70 FS3 Pool 0.76 0.73 0.60 0.85 0.61 0.61 0.91 0.45 0.98 0.34 0.79 0.75 0.89 Ind 0.75 0.70 0.68 0.90 0.68 0.53 0.90 0.68 0.95 0.38 0.70 0.70 FS4 Pool 0.71 0.68 0.59 0.88 0.60 0.53 0.95 0.54 0.97 0.32 0.55 0.62 0.88 Ind 0.63 0.75 0.70 0.90 0.70 0.50 0.90 0.70 0.93 0.43 0.50 0.50 MS1 Pool 0.76 0.74 0.73 0.73 0.67 0.42 0.81 0.64 0.94 0.43 0.57 0.66 0.94 Ind 0.80 0.78 0.78 0.80 0.78 0.43 0.80 0.78 0.93 0.55 0.65 0.68 MS2 Pool 0.83 0.71 0.65 0.82 0.68 0.54 0.92 0.63 0.97 0.27 0.70 0.77 0.98 Ind 0.84 0.74 0.71 0.92 0.71 0.61 0.92 0.74 0.95 0.29 0.76 0.76 58

MS3 Pool 0.80 0.79 0.69 0.77 0.61 0.50 0.87 0.61 0.94 0.31 0.64 0.67 0.97 Ind 0.85 0.78 0.68 0.85 0.68 0.55 0.85 0.68 0.95 0.35 0.63 0.63 MS4 Pool 0.73 0.69 0.61 0.87 0.57 0.52 0.98 0.56 0.96 0.32 0.74 0.78 0.97 Ind 0.73 0.68 0.55 0.95 0.53 0.60 0.95 0.53 1.00 0.35 0.78 0.78 SNP r 0.79 0.77 0.81 0.86 0.70 0.86 0.81 0.62 0.55 0.87 0.77 0.79 0.93

The rows contain first the estimated allele frequency for each indicated pool corresponding to the SNP indicated in the column in white and the allele frequency obtained via individual genotyping of the pool in grey. (All allele frequencies refer to the frequency of the ―A‖ allele, as designated by the Affymetrix 100K mapping set annotation) Ind – Allele frequency obtained via individual genotyping. Pool – Allele frequency obtained via the pooled GWA study. SNPs in the columns are coded as: SNP1 - rs2188535, SNP2 - rs720639, SNP3 - rs6966318, SNP4

- rs10500007, SNP5 - rs3109112, SNP6 - rs6967593, SNP7 - rs6968084, SNP8 - rs2529588,

SNP9 - rs10487291, SNP10 - rs7817, SNP11 - rs1981610, SNP12 - rs3111449. Pools in the rows are coded as FM1 – Female Mild Pool 1, FM2 – Female Mild Pool 2, FM3 Female Mild

Pool 3, FM4, Female Mild Pool 4, MM1 – Male Mild Pool 1, MM2 – Male Mild Pool 2, MM3 –

Male Mild Pool 3, MM4 – Male Mild Pool 4, FS1 – Female Severe Pool 1, FS2 – Female

Severe Pool 2, FS3 – Female Severe Pool 3, FS4 – Female Severe Pool 4, MS1 – Male

Severe Pool 1, MS2 – Male Severe Pool 2, MS3 – Male Severe Pool 3, MS4 – Male Severe

Pool 4. SNP r – Pearson product moment correlation coefficient, r, comparing the allele frequencies obtained via individual genotyping to the estimated allele frequencies from the pooled GWA study pool by pool across each SNP. Pool r - Pearson product moment correlation coefficient, r, comparing the allele frequencies obtained via individual genotyping

to the estimated allele frequencies from the pooled GWA study SNP by SNP across each pool.

One of the samples in the pool MS2 was unavailable for individual genotyping. Consequently, the individual allele frequencies for this pool, Ind, reflect estimates with this individual omitted.

One of the genotypes for a single individual at SNP2 failed to pass quality control measures.

Consequently, the individual allele frequency for this pool at this SNP, reflect the estimate with this individual omitted. Otherwise, each pool contains 20 individuals.

Supplementary Table 4.

Demographics of CFTSS cohort

Total number of patients with a) rs7817 genotypes b) MaxFEV1CF%, 1118 (619) AvgFEV1CF%, and/or EstFEV1%Pred (number of families) Sex (% male) 52.6% Homozygosity for ΔF508 mutation 47.5% Race (% Caucasian) 95.9 %

Mean MaxFEV1CF%  standard deviation (median) 0.70  0.26 (0.78)

Mean AvgFEV1CF%  standard deviation (median) 0.61  0.23 (0.66)

Mean EstFEV1%Pred  standard deviation (median) 84  23 (87) Number of MZ twins included (phenotypes averaged if twin-pair values within 10 percentile (MaxFEV1CF%, AvgFEV1CF%) or 10 %-predicted 49 (4.4%)

(EstFEV1CF%Pred) of each other

Supplementary Table 5. Association of SNPs at the CEBPA/CEBPG locus with lung disease severity: GMSG cohort genome-wide SNP scan with pooled DNA; Affymetrix 100K array.

Rank SNP Position A Mild Severe P value ORL ORU 32011 rs10500264 38442154 G 0.76 0.80 0.32587 0.72 2.09 263 rs10518275 38542681 G 0.18 0.30 0.00089 1.16 3.33 512 rs4805877 38568234 G 0.34 0.47 0.00231 1.08 2.65 72149 rs3898492 38755894 A 0.41 0.43 0.72928 0.68 1.65

The columns are the same as for Supplementary Table 1, except that Position indicates position on chromosome 19. Supplementary Table 6 compares estimates of CEBP SNP allele frequencies from the pooled DNA scan with direct measurement of allele frequencies by individual genotyping.

Supplementary Table 6. Individual genotyping in 779 Caucasian CFTR

ΔF508 homozygotes the GMSG cohort; CEBPA/CEBPG locus; Illumina SNP beadarray genotyping.

SNP A Mild Severe Add P Add LOF P OR CIL CIU rs877567 A 0.08 0.07 0.4479 0.19 0.86 0.58 1.27 rs7251505 A 0.08 0.11 0.0550 0.23 1.41 0.99 2.00 rs9646647 C 0.05 0.06 0.3194 0.34 1.25 0.80 1.94 rs12608723 G 0.13 0.14 0.7349 0.85 1.06 0.77 1.44 rs7253865 G 0.26 0.32 0.0156 0.82 1.34 1.06 1.69 rs7247962 C 0.21 0.23 0.2779 0.73 1.15 0.89 1.49 rs16967971 T 0.04 0.04 0.9929 0.84 1.00 0.59 1.70 rs17529824 C 0.04 0.04 0.8955 0.70 0.97 0.59 1.60 rs12610763 A 0.06 0.07 0.3661 0.24 1.21 0.80 1.82 rs1423062 A 0.60 0.52 0.0020 0.47 0.71 0.57 0.88 rs4805872 C 0.27 0.33 0.0183 0.43 1.31 1.05 1.64 rs10518275 G 0.17 0.22 0.0259 0.68 1.34 1.03 1.72 rs999503 A 0.23 0.25 0.2787 0.87 1.14 0.90 1.46 rs10425856 C 0.09 0.10 0.5184 0.72 1.12 0.79 1.60 rs17530508 T 0.17 0.17 0.9467 0.08 0.99 0.75 1.31 rs3745968 G 0.12 0.10 0.2339 0.50 0.81 0.58 1.14 rs4805877 G 0.38 0.46 0.0019 0.86 1.41 1.13 1.76 rs2241382 A 0.51 0.45 0.0175 0.71 0.77 0.62 0.96

The columns are the same as Supplementary Table 2.

Supplementary Table 7. Comparison of estimated allele frequencies with allele frequencies obtained via individual genotyping in 320 Caucasian

CFTR ΔF508 homozygotes GMSG cohort; CEBPA/CEBPG locus; Affymetrix

100K pooled genotyping versus Illumina SNP bead array genotyping.

Pool SNP rs10518275 rs4805877 FM1 Pool 0.77 0.65 Ind 0.83 0.68 FM2 Pool 0.85 0.78 Ind 0.90 0.70 FM3 Pool 0.83 0.72 Ind 0.93 0.65 FM4 Pool 0.83 0.70 Ind 0.88 0.74 MM1 Pool 0.80 0.52 Ind 0.93 0.65 MM2 Pool 0.83 0.54 Ind 0.85 0.58 MM3 Pool 0.83 0.71 Ind 0.83 0.73 MM4 Pool 0.83 0.63 Ind 0.80 0.73 FS1 Pool 0.65 0.54 Ind 0.73 0.48 FS2 Pool 0.71 0.55 Ind 0.83 0.58 FS3 Pool 0.74 0.55 Ind 0.73 0.48 FS4 Pool 0.81 0.63 Ind 0.85 0.60 MS1 Pool 0.66 0.40 64

Ind 0.78 0.55 MS2 Pool 0.56 0.52 Ind 0.61 0.53 MS3 Pool 0.75 0.55 Ind 0.85 0.63 MS4 Pool 0.71 0.50 Ind 0.75 0.53 SNP r 0.80 0.71

The details of this table are the same as Supplementary Table 3. Note that pool MS2 contains the estimate using the 19 individuals with available individual genotyping data.

Supplementary Table 8. Association of CEBP SNP haplotype rs7253865 G

and rs1423062 A with CF lung function derived from family based

association testing

CFTR genotype Trait # Famsd Z Pe All Longitudinal lung functiona 58.8 -2.588 .0097

Non-ΔF508 homozygotes Cross-sectional lung functionb 35.2 -2.318 .0205

Longitudinal lung functiona 29.4 -2.708 .0068

Longitudinal lung functionc 27.3 -2.175 .0297 a 4 AvgFEV1CF% as described in b 4 MaxFEV1CF% as described in c 4 BayesFEV1%Pred@20yrs as described in d Number of families informative in transmission analysis; partially informative families (i.e., one parent informative) generates the fractional values e uncorrected P values

Supplementary Figure 1. Comparison of genome-wide SNP allele frequencies in the CF cohort (GMSG) with those from a genome-wide SNP scan in asthma patients and controls (Isle of Wight birth cohort study).

(a) Heat map, generated using hierarchical clustering employing an average linkage algorithm with a standard correlation similarity metric (as implemented in GeneSpring), representing allele frequencies (in each DNA pool) of top 38 SNPs, as defined by ANOVA with a Benjamini Hochberg false discovery rate cut-off of P < 0.005 and an assumption of equal variance. The relative allele frequency is depicted on a blue to red scale, where red indicates a higher frequency (and blue, lower frequency) of the first allele, relative to the

median frequency for that allele across the entire data set, in a given DNA pool (see frequency scale inset). Rows are labeled with SNP ID number (chromosome). Right column: chromosomal location. 34 of the 38 top SNPs were on chromosome 7.

Supplementary Figure 1. (b) Graphical representation of the P values of the

34 chromosome 7 SNPs (among the top 38 SNPs) as a function of chromosomal location.

The figure indicates SNP position relative to the genome assembly (USCSC Genome

Browser, Version May 2004) on the x axis; the negative log of the P value of the SNP on the y axis. The 34 SNPs were centered around CFTR, with a median P value of 3 x 10-8.

Supplementary Figure 2. Cluster analysis.

(a) Heat map representation of relative allele frequencies in DNA pools from patients with mild versus severe CF lung disease. The map, generated using a hierarchical clustering metric as in Supplementary Figure 1, represents relative SNP allele frequencies in each DNA pool for the 2000 top-ranked SNPs as defined by Z2 scores. The relative allele frequency is depicted on a blue to red scale, as in Supplementary Figure 1 (see frequency scale inset). (b) Graphical representation of SNP clustering. Frequency distribution of

2000 top-ranked SNPs, defined by Z2 scores, as a function of chromosomal distance. X axis:

SNP rank (of the SNPs involved in clusters of the relevant size); Y axis: inter-SNP distance

(bp). The different lines represent clusters of 2-9 SNPs (dyads-nonads), respectively. Clusters, weighted for SNP distance and SNP cluster number, were selected for further consideration of biological relevance. Triangles mark the location of SNP clusters in IFRD1; arrows point to 72

the location of overlapping clusters in IFRD1. (c) Schematic illustration of distance definition for SNP clusters.

Supplementary Figure 3. Relationship of polymorphisms in IFRD1 to linkage disequilibrium structure.

Linkage disequilibrium structure in the region of IFRD1 on chromosome 7, based on individual genotyping in the region of IFRD1 of 779 CFTR F-508 homozygous subjects of

European descent (GMSG cohort). Created in Haploview, the color scheme is according to the GOLD Heatmap option (D‘ < 0.2 - Dark Blue; 0.2 <= D‘ < 0.4 – Light Blue; 0.4 <= D‘ < 0.6 –

Green; 0.6 <= D‘ < 0.8 – Yellow; 0.8 <= D‘ < 1; Orange/Red); the LD value displayed is r2; the blocks are defined according to the confidence interval rule and the positions are from NCBI

Build 36, dbSNP build1285,6. The cluster of SNPs suggestive of association with lung function in the 779 Caucasian patients in the GMSG cohort are labeled in green: rs6967593 (Padditive 74

= 0.043), rs6968084 (0.056), rs3807213 (0.043), rs7817 (0.026).

Supplementary Figure 4. IFRD1 expression in human primary cells and cell lines.

(a) IFRD1 protein expression is enriched in neutrophils. Peripheral blood mononuclear cells (PBMC) and neutrophils were purified by density gradient purification, followed by staining for the indicated surface markers (PBMC) and intracellular staining for IFRD1 (PBMC and neutrophils). Data shown represent means ± S.E.M. of 3 separate donors. *P < 0.05. (b)

IFRD1 mRNA expression. Quantitative RT-PCR was used to analyze IFRD1 mRNA expression by myeloid and airway epithelial cells and cell lines. Data were normalized by ubiquitin mRNA expression. Means ± S.E.M. are shown. BEAS-2B, airway epithelial cell line;

THP-1, myeloid cell line; DC, primary dendritic cells; HTBC, primary human tracheobronchial epithelial cells.

0 d 7 d 12 d 16 d

Human: counts CD34+ peripheral

blood cells IFRD1-FITC counts

Isotype control

5 d 9 d 11 d counts Mouse: lin-c-kit+sca-1+ IFRD1-FITC bone marrow

cells counts

Isotype control

Supplementary Figure 5. IFRD1 expression is upregulated during terminal differentiation of neutrophils. (top panels)

Human peripheral blood progenitor (CD34+) cells were differentiated in vitro with G-CSF and

SCF. Intracellular IFRD1 expression was quantified by flow cytometry at the indicated times by flow cytometry. The percentage of cells that were CD11b+CD16+ increased from 0.5% at d

0 to 70.5% at d 16. (bottom panels) Mouse bone marrow progenitor (Lin-c-kit+sca-1+) cells were differentiated in vitro with G-CSF, MDGF and SCF. Intracellular IFRD1 expression was quantified at the indicated times by flow cytometry. The percentage of cells that were Gr-1+

reached 96.6% by d 11.

Supplementary Figure 6. Inhibition of IFRD1 expression in HL-60 cells leads to inhibition of oxidative burst capacity.

(a) Differentiation of HL-60 cells to a neutrophil phenotype is associated with upregulation of IFRD1, and downregulation of IFRD2, expression. HL-60 cells were differentiated with 1.5% DMSO. IFRD1 and IFRD2 mRNA expression were quantified by qRT-PCR, with normalization based on ubiquitin mRNA expression. (representative of 3 separate experiments). Similar results were seen with retinoic acid-induced differentiation. (b)

IFRD1-specific siRNA efficiently knocks down IFRD1 expression. HL-60 cells were mock transfected, or transfected by Nucleofection (Amaxa) with 90 pmol synthetic siRNA against

IFRD1, or negative control siRNA, and incubated for 48h. IFRD1 mRNA expression was quantified by qRT-PCR. (c) siRNA-mediated knockdown of IFRD1 expression in differentiating HL-60 cells leads to inhibition of oxidative burst capacity. HL-60 cells were transfected with siRNA against IFRD1, or negative control siRNA, and subsequently treated with 1.5% DMSO for 3d. Oxidative burst capacity was quantified by flow cytometry,

using the dihydrorhodamine 123 assay, in PMA-treated cells. Oxidative index = mean fluorescence in PMA-stimulated cells/mean fluorescence in unstimulated cells. Data represent means ± S.E.M. of 3 culture replicates/condition; representative of 3 separate experiments. *P < 0.05.

a b

3.5 Ifrd1+/- 7500 Ifrd1-/- Ifrd1+/- 3.0 Ifrd1-/-

2.5 5000 (pg/ml)

2.0 1.5 

1.0 2500 Oxidative Index Oxidative 0.5 TNF- 0.0 0 NS LPS

Supplementary Figure 7. Macrophages from IFRD1-deficient mice are not impaired in oxidative burst capacity or TNF- production.

(a) Oxidative burst capacity. Oxidative burst capacity was quantified by flow cytometry using the dihydrorhodamine 123 assay in thioglycollate-elicited peritoneal macrophages from

Ifrd1+/- and Ifrd1-deficient mice. (b) LPS-stimulated TNF- production. Macrophages from

Ifrd1+/- and Ifrd1-deficient mice were stimulated (or mock stimulated) with P. aeruginosa LPS.

TNF- was quantified in supernatants by ELISA. Means ± S.E.M. of 3 mice/group are shown.

NS, mock stimulation.

Supplementary Figure 8. The reconstitution efficiency of bone marrow from

IFRD1 -knockout mice is similar to that of wild type bone marrow.

(a) C57BL/6 mice expressing a congenic marker (CD45.1) were lethally irradiated, and reconstituted with bone marrow from CD45.2-expressing wild type or Ifrd1-/- mice. Eight weeks later, flow cytometric techniques were used to analyze bone marrow reconstitution efficiency. (b) Bone marrow cells from Ifrd1-/- or wild type mice (both CD45.2+) were transplanted into lethally irradiated wild type (CD45.1+) recipient mice, along with an equal number (1x106) of competitor bone marrow cells (CD45.1+). Flow cytometric techniques were used to analyze bone marrow reconstitution efficiency. Data represent means ± S.E.M. of 6 mice/group.

Supplementary Figure 9. Lack of hematopoietic cell expression of IFRD1 is associated with delayed bacterial clearance, but decreased neutrophilic inflammation and ameliorated disease, after airway challenge with P. aeruginosa.

Lethally irradiated CD45.1+wild type mice were reconstituted with CD45.2+ wild type or Ifrd1-/- bone marrow cells, as indicated (6 mice/group). Mice were subsequently challenged with 6 x

106 CFU of the FRD1 strain of P. aeruginosa, and analyzed as in Figure 3. (a) Lung bacterial burden. (b) Weight loss. Solid line, Ifrd1-/-; dashed line, WT. (c) Serum TNF-. (d) BAL: total cells. (e) BAL: neutrophils. (f) BAL TNF-. (g) BAL KC. TNF- was measured using the CCA ELISA29 in these experiments. Means ± S.E.M. of 6 mice/group are shown. *P <

0.05.

a b c d

) 6 6 0 2.0 NS NS 500 NS NS 5 400 1.5

-5 (pg/ml) 4  300 1.0 -10 200 3 0.5

100 Weight change (%) 2 -15 0 BAL:Total cells (x 10 0.0

0 20 40 60 Serum TNF- Bacterial burden (log10) WT Ifrd1-/- Time (h) WT Ifrd1-/- WT Ifrd1-/-

e f g

1.5 NS ) 800 6 1000 NS NS 800 600 (x 10 1.0

(pg/ml) 600  400

PMNs PMNs 0.5 400

200 200

BAL KC (pg/ml)KC BAL BAL: BAL:

0.0 TNF- BAL 0 0 WT Ifrd1-/- WT Ifrd1-/- WT Ifrd1-/-

Supplementary Figure 10. Lack of non-hematopoietic cell expression of

IFRD1 is not associated with delayed bacterial clearance, decreased neutrophilic inflammation or ameliorated disease, after airway challenge with P. aeruginosa.

Lethally irradiated CD45.2+ wild type mice or CD45.2+ Ifrd1-/- mice were reconstituted with

CD45.1+ wild type bone marrow cells, as indicated (6 mice/group). Mice were subsequently challenged with 6 x 106 CFU of the FRD1 strain of P. aeruginosa, and analyzed as in Figure 3.

(a) Lung bacterial burden. (b) Weight loss. Solid line, Ifrd1-/-; dashed line, WT. (c) Serum

TNF-. (d) BAL: total cells. (e) BAL: neutrophils. (f) BAL TNF-. (g) BAL KC. Means ±

S.E.M. of 6 mice/group are shown.

a b WT Ifrd1-/- 60 WT Ifrd1-/- * 10 *

40 (ng/ml)



20 5

BAL TNF- BAL

0 (ng/ml) KC BAL 0 LPS LPS LPS LPS LPS LPS LPS LPS SAHA SAHA SAHA SAHA c d WT Ifrd1-/- WT Ifrd1-/- 50  * 2000 40 * 1500

(ng/ml) 30 

20 1000 (MFI)

10 500

BAL TNF- BAL 0 BAL neutrophil BAL TNF- 0 LPS LPS LPS LPS SAHA SAHA LPS LPS LPS LPS SAHA SAHA

Supplementary Figure 11. Role of histone deacetylases in the regulation of inflammation by IFRD1.

(a, b) HDAC inhibition leads to blunting of LPS-driven airway TNF- and KC production in wild type, not IFRD1-KO, mice. Mice were treated with SAHA (10 mg/kg) i.p., followed 1 h later by intratracheal (i.t.) challenge with P. aeruginosa LPS (2 mg/kg). 4 h later, BAL was performed. TNF- (a) and KC (b) were quantified in BAL fluid by ELISA. Data represent means + S.E.M. of 3 mice/group. *P < 0.05 (ANOVA, Dunnett). (c, d) HDAC inhibitor-mediated suppression of LPS-driven airway TNF- production is dependent on hematopoietic cell expression of IFRD1. Lethally irradiated CD45.1+wild type mice were reconstituted with CD45.2+ wild type or Ifrd1-/- bone marrow, as indicated. 10 weeks later, mice were treated with SAHA (10 mg/kg) i.p., followed 1 h later by i.t. challenge with E. coli

LPS (2 mg/kg). 4 h later, BAL was performed. (c) TNF- was quantified in BAL fluid by ELISA.

(d) Intracellular expression of TNF- was quantified in BAL neutrophils by flow cytometry. No significant differences were seen in airway macrophage TNF- expression, or in BAL neutrophil numbers (data not shown). Data represent means ± S.E.M. of 4-5 mice/group. *P <

0.05.

Supplementary Figure 12. IFRD1 modulates neutrophilic NF-B activity.

(a) Nuclear NF-B p65 binding activity is attenuated in neutrophils from Ifrd1-deficient mice. Neutrophils were purified from the bone marrow of WT and Ifrd1-/- mice, and either mock stimulated or stimulated with LPS (P. aeruginosa 1 g/ml) for 1 hr. Subsequently, nuclei were extracted, and nuclear NF-B p65 binding activity was quantified in nuclear extracts by

ELISA. Data shown represent means ± S.E.M. of 3 mice/group. *P < 0.05. Similar results were seen with E. coli LPS. (b) IFRD1 interacts with a complex containing HDAC1 and

NF-B p65. Neutrophils were purified from wild type mice, and mock stimulated or stimulated with LPS (E. coli 1 g/ml) for the indicated period of time. Nuclei were extracted and nuclear

lysates were immunprecipitated (IP) with antibodies to IFRD1, HDAC1 or NF-B p65 (each, from a separate experiment, given limiting neutrophil numbers), followed by western blotting

(WB) with antibodies to these proteins, as indicated. IgG, isotype control IgG.

Supplementary Discussion

The transition at SNP rs7817 creates a predicted target site for microRNA-577

(expressed in neutrophils; data not shown), as well as a predicted splice enhancer site.

As for SNP rs6968084, the transition in this intronic SNP is predicted to lead to a non-synonymous change in an alternatively spliced IFRD1 isoform observed in other species. Finally, SNP rs3807213 is in a primate-specific endogenous retroviral transposed element that contains human cis regulatory motifs7,8.

Of interest, polymorphisms in TGFB1, previously been reported to be associated with lung disease severity in CF9,10, did not emerge as compelling candidates from the genome-wide SNP scan in this study—the result of sparse coverage of the TGFB1 locus region by the Affymetrix 100K mapping microarray. The closest markers are 50 kB away in one direction (on another haplotype block with very little LD (r2)) from the TGFB1 markers reported by Drumm, et al.10 and Bremer, et al.9, and > 200 kB away in the other direction.

Supplementary References

1 Sham, P. et al. DNA Pooling: a tool for large-scale association studies. Nature Rev.

Genetics 3, 862-871 (2002).

2 Simpson, C. L. et al. A central resource for accurate allele frequency estimation

from pooled DNA genotyped on DNA microarrays. Nucleic Acids Res. 33, e25

(2005).

3 Hill, W. G. et al. Data and theory point to mainly additive genetic variance for

complex traits. PLoS Genetics 4, e1000008 (2008).

4 Vanscoy, L. L. et al. Heritability of lung disease severity in cystic fibrosis. Am. J.

Respir. Crit. Care Med. 175, 1036-1043 (2007).

5 Barrett, J. C. et al. Haploview: analysis and visualization of LD and haplotype

maps. Bioinformatics 21, 263-265 (2005).

6 Gabriel, S. B. et al. The structure of haplotype blocks in the human genome.

Science 296, 2225-2229 (2002).

7 Wang, T. et al. Species-specific endogenous retroviruses shape the

transcriptional network of the human tumor suppressor protein p53. Proc. Natl.

Acad. Sci. USA 104, 18613-18618 (2007).

8 Jegga, A. G. et al. Functional evolution of the p53 regulatory network through its

target response elements. Proc. Natl. Acad. Sci. USA 105, 944-949 (2008).

9 Bremer, L. A. et al. Interaction between a novel TGFB1 haplotype and CFTR

genotype is associated with improved lung function in cystic fibrosis. Hum. Mol.

Genetics 17, 2228-2237 (2008).

10 Drumm, M. L. et al. Genetic modifiers of lung disease in cystic fibrosis. N. Engl. J.

Med. 353, 1443-1453 (2005).

Chapter III IFRD1 is a co-suppressor of the transcriptional repressor ATF3

Introduction

Neutrophils act as double-swords in the pathogenesis of CF. Neutrophils are a critical part of host defense against microorganisms. However, neutrophilic inflammation in response to infection in

CF is excessive. Neutrophil-derived products, such as oxidants, proinflammatory cytokines and proteases contribute to lung injury in CF1. The complicated role of neutrophils in the pathogenesis of CF lung disease is also compatible with the bi- or multi-phasic nature of the disease2. Anti-inflammatory therapy with high dose ibuprofen showed clinical benefit in those under, but not over, 13 years of age in CF3. Thus, robust neutrophil effector function may be more harmful to the CF host early on (damaging the airway and favoring establishment of the pernicious cycle of infection and inflammation), but more helpful to the CF host with increasing disease progression (better for controlling increasingly problematic airway infection). This is also supported indirectly by our genetic data on IFRD1 (Chapter 2). The best genetic model for IFRD1 polymorphisms in CF is heterozygote distortion4. In particular, heterozygotes for IFRD1 SNP rs7817 had significantly lower lung function than either homozygote4. Heterozygotes may follow an intermediate path, disadvantageous in both major phases, in which, early on, the greater vigor of their neutrophil effector responses (than those of one homozygote) is more likely to cause lung damage; and in which, in later phases of disease, the lesser vigor of their effector responses

(than those of the other homozygote) are less able to clear ever more complicated infection.

Neutrophils are a major source of proinflammatory mediators (e.g., IL-8 and TNF-) in the CF

airway5, 6. Functional genomics has shown that neutrophils are capable of rapidly altering transcription— within 10 minutes of exposure to bacteria-derived stimuli such as LPS and f-MLP7.

Interestingly, besides genes involved in signaling pathways within the cell and between cells (i.e., cytokines), a large group of these early responsive genes fall into the category of genes important in regulating gene expression at multiple levels, including transcription factors, chromatin remodeling and modifying proteins, DNA helicases, and regulators of protein synthesis or stability7. Transcription in neutrophils is rapid and tightly controlled7, 8. Studying the mechanism of fine control of neutrophil transcription offers unique opportunities for novel therapeutic approaches to diseases in which neutrophil-derived products play a pathogenic role.

We have identified IFRD1 as a novel genetic modifier of CF lung disease and have generated compelling data suggesting that IFRD1 modifies the expression of CF lung disease by regulating neutrophil effector functions (Chapter 2). Ifrd1-/- neutrophils have blunted NF-B-driven effector functions, suggesting that IFRD1 acts as a co-activator of transcriptional activator or as a co-suppressor of repressor in regulating transcription of NF-B-dependent neutrophil effector proteins. HDAC inhibition led to suppression of LPS-driven inflammatory cytokine production by airway neutrophils, specifically in wild type, not in IFRD1-deficient, mice4. Furthermore co-immunoprecipitation data revealed that IFRD1 interacts directly with NF-B/p654. Taken together, these data suggest the hypothesis that IFRD1 acts as a co-repressor of a transcriptional suppressor of NF-B signaling in neutrophils. In order to identify gene targets of

IFRD1, We performed microarray analysis to compare mRNA expression in WT and Ifrd1-/- neutrophils, with and without LPS stimulation. Activating transcription factor 3 (ATF3) was the top-ranked gene whose expression was increased at baseline in Ifrd1-/- neutrophils, compared with wild type neutrophils, with increased (and continued differential) expression after LPS treatment (data not shown). Subsequent qRT-PCR analysis confirmed increased expression of

ATF3, both at baseline and after LPS stimulation by Ifrd1-/- neutrophils (see below).

ATF3 belongs to the ATF/CREB family of basic leucine zipper transcription factors, binding to consensus ATF/CREB sites with different affinities9. ATF3 homodimers act as transcriptional repressors, while ATF3/c-Jun heterodimers function as transcriptional activators9, 10. ATF3 has been implicated in stress responses, tumorgenicity and apoptosis, although the mechanism of its function in these systems is not fully understood10-13. Recently, a systems biology approach found that ATF3 acts as a transcriptional repressor in the immune system14. Specifically, Alan

Aderem‘s group showed that ATF3 is an LPS-driven negative regulator of Toll-like receptor 4

(TLR4) signaling14. ATF3 also regulates the anti-MCMV response by suppressing IFN- expression in natural killer cells15. Furthermore, ATF3 dampens inflammatory responses in mouse model of human asthma by inhibiting Th2 cytokine production16. In the case of

TLR4/NF-B signaling, ATF3 mRNA is immediately and transiently induced by LPS (peaking at 1 h). ATF3, in turn, recruits HDACs to CREB/ATF sites proximal to NF-B sites, leading to closure of chromatin and suppression of transcription14. Moreover, analysis of the ATF3 promoter across

species has revealed the presence of conserved ATF/CREB and NF-B binding sites, suggesting that ATF3 expression is finely regulated by NF-B and ATF3 itself17, 18.

Given the decreased expression of NF-B-driven genes in Ifrd1-/- neutrophils, the increased expression of ATF3 in such neutrophils, and the reported negative regulation of NF-B signaling by ATF3, we hypothesized that ATF3 plays an important mechanistic role in IFRD1-mediated downstream transcriptional regulation of NF-B activity in neutrophils. Notably, our preliminary data indicate that: (a) Ifrd1-/- neutrophils have increased ATF3 mRNA expression both at baseline and after LPS stimulation; (b) Atf3-/- neutrophils produce increased levels of TNF- and KC in response to LPS stimulation; (c) IFRD1 is present at the ATF3 promoter, but rapidly (and transiently) removed from the promoter and transported out of the neutrophil nucleus after LPS stimulation; and (d) Ectopic IFRD1 expression suppresses expression of ATF3 promoter-driven luciferase expression at baseline and after LPS stimulation. Together, these data strongly suggest that IFRD1 regulates neutrophil transcription by suppressing the expression of ATF3, a transcriptional repressor of NF-B activity in neutrophils.

Results

1. Ifrd1-/- neutrophils have increased levels of ATF3 expression

In order to identify gene targets of IFRD1, we performed Affymetrix microarray analysis

(GeneChip Mouse Gene 1.0 ST Array) to compare mRNA expression in WT and Ifrd1-/-

neutrophils, with and without LPS stimulation. ATF3 was the top-ranked gene whose expression was increased at baseline in Ifrd1-/- neutrophils, compared with WT neutrophils, with increased

(and continued differential) expression after LPS treatment (data not shown). These expression array data were subsequently validated by qRT-PCR analysis (Fig 1). ATF3 mRNA is rapidly induced by LPS in both WT and Ifrd1-/- neutrophils. However, Ifrd1-/- neutrophils have increased

ATF3 mRNA both at baseline and after LPS stimulation (Fig 1a). Because of the important role of

ATF3 in regulating NF-B-driven transcription in macrophages14, we also quantified ATF3 mRNA in Ifrd1-/- and WT macrophages. Interestingly, Ifrd1-/- macrophages also have increased ATF3 mRNA after LPS stimulation, but not at baseline (Fig 1b)—despite the fact that our data indicate relative specificity for the functional effects of genetic deletion of IFRD1 in neutrophils vs. macrophages (Chapter 2).

2. Atf3-/- neutrophils produce increased level of inflammatory cytokines

ATF3 is a negative regulator of NF-B-driven transcription14. Atf3-/- macrophages produce more

IL-6, IL-12 and TNF- compared to wild typemacrophages, in response to LPS stimulation14.

Whether ATF3 plays a similar role in regulating neutrophil transcription was not known. Atf3-/- mice were recently generated and published by Tsonwin Hai‘s group13. We gained collaborative access to these mice in order to study their neutrophil function. TNF- and KC are two major inflammatory cytokines, and as well as neutrophil chemoattractants, produced by neutrophils—and under-produced by neutrophils from mice lacking IFRD1 (Chapter 2). As

hypothesized, we found that Atf3-/- neutrophils express more TNF- and KC mRNA (Fig 2. a, b) and protein (Fig 2. c, d) after LPS stimulation, than do WT neutrophils. These data support the hypothesis that ATF3 is a transcriptional repressor of NF-B activity that is downstream of IFRD1 in neutrophils.

3. IFRD1 nuclear translocation and dynamics of IFRD1 at the promoter of ATF3

We subsequently performed a series of complementary experiments to obtain evidence about whether IFRD1 directly regulates the expression of ATF3 in neutrophils. First, we quantified nuclear translocation of IFRD1 using an imaging flow cytometer (ImageStream, Amnis Corp.,

Seattle WA). Most IFRD1 is in the nucleus of unstimulated neutrophils (Fig. 3b). After LPS stimulation, IFRD1 translocates rapidly out of the nucleus of neutrophils (Fig. 3b). This outward translocation is most significant 1 hour after LPS stimulation, when ATF3 mRNA expression reaches its peak (Fig. 3a). 2 hours after LPS stimulation, IFRD1 begins to translocate back into the nucleus (Fig. 3a), at a time when ATF3 mRNA expression starts to be turned off. (Fig. 3a).

These data suggest, indirectly, that IFRD1 is an inhibitor of ATF3 expression in neutrophils.

We have previously shown that IFRD1 interacts directly with NF-B/p65 in neutrophils (Chapter

2). We thus hypothesized that IFRD1 inhibits ATF3 expression by binding to the NF-B site of

ATF3 promoter. To determine whether IFRD1 can interact directly with the ATF3 promoter,

chromatin immunopreciptation (ChIP) experiments were performed using an IFRD1-specific antibody. ChIP analysis revealed maximal IFRD1 binding to the NF-B site of ATF3 promoter in unstimulated neutrophils, which was rapidly decreased after LPS stimulation, becoming minimal

1 h after LPS stimulation (Fig. 3c). This dynamic binding pattern suggests that IFRD1 represses

ATF3 expression in neutrophils.

4. Ifrd1 inhibits ATF3 promoter-driven luciferase expression

To test whether the binding of IFRD1 to the ATF3 promoter is functional, we analyzed ATF3 promoter-driven luciferase expression using lentivirus pFWATF3luc19. We utilized H4.14 cells

(HEK293 cells stably expressing TLR4 and CD14), because these cells don‘t express endogenous IFRD1 and can respond to LPS after being transfected with MD-220. Notably, the expression of IFRD1 as a transgene suppressed ATF3 promoter-driven luciferase expression, both at baseline and after LPS stimulation (Fig. 4a). To test the specificity of the inhibiting effect of IFRD1 on the ATF3 promoter, we analyzed the expression of XRE-luc (AHR specific xenobiotic response element- driven luciferase)21. Unexpectedly, ectopic IFRD1 expression enhanced XRE promoter-driven luciferase expression at baseline and after AHR agonist indolo[3,2-b]carbazole (ICZ) stimulation (Fig. 4b). This is in accordance with previous data that

IFRD1 can be a transcriptional co-repressor or co-activator9. While we clearly need to examine other promoter constructs to define specificity, our data indicate that the IFRD1 functionally suppresses the expression of ATF3, possibly via the NF-B site in the ATF3 promoter.

100

5. The interactome of IFRD1

To see the big picture of the mechanism of IFRD1 in regulating transcription, a coactivator trap experiment was performed in collaboration with a Core at the Scripps Research Institute (Jupiter,

FL) in order to identify transcription factors besides NF-B that IFRD1 functionally interacts with.

The coactivator trap screen is a high-through mammalian two-hybrid performed in HEK293T cells aimed at identifying functional interactions between transcription factors and coactivators22.

Preliminary data suggest that the transcription factors that IFRD1 interacts with identified in this screen function in one of the three categories: (1) regulators of the transcription of inflammatory cytokines (ZNF68823, ELF324, NFkb225); (2) regulators of myeloid development (CITED26, ATF427,

STAT328); and (3) regulators of mitochondria enzyme expression and apoptosis (Gabpa29,

E2F830) (Table 1). These data are potentially interesting. However, they need to be validated in specific cell types with specific stimuli. Currently we are validating these data with two complementary methods: Tandem Affinity Purification-Mass spectrometry (TAP-MS)31 and co-immunoprecipitation analysis.

101

Discussion

Neutrophils are double-edged swords, essential for host defense against microorganisms, however also contributing significantly to tissue damage—as they do in CF. In the airways of CF patients, there are increased levels of TNF- and IL-8. Functional genomics has shown that neutrophils can actively synthesize a surprisingly wide variety of proinflammatory cytokines.

Furthermore, the number of neutrophils in the CF airway is over 20 times that of monocytes.

Neutrophils are present from the beginning of CF lung disease, and remain present throughout the course of disease. Once in the airway, neutrophils become targets of proinflammatory cytokines, which recruit more neutrophils and amplify production of proinflammatory cytokines from neutrophils. These data support the idea that neutrophils are a major source of various proinflammatory cytokines in the CF airway. Thus, studying the mechanism of regulation of transcription of proinflammatory cytokines in neutrophils may offer unique therapeutic targets for controlling inflammation in CF.

Ifrd1-/- neutrophils have decreased production of inflammatory cytokines, including TNF- and

KC (Chapter 2). Preliminary data presented here indicate that Ifrd1-/- neutrophils exhibit increased ATF3 mRNA expression both at baseline and after LPS stimulation and that Atf3-/- neutrophils produce increased levels of TNF- and KC in response to LPS stimulation. These data suggest that IFRD1 regulates neutrophil transcription by suppressing the expression of

ATF3, a transcriptional repressor of NF-B activity in neutrophils. This is further supported by our

102

data indicating that IFRD1 is rapidly removed from the promoter of ATF3 after LPS stimulation, and transported out of the nucleus of neutrophils. Ectopic IFRD1 expression suppresses Atf3 promoter-driven luciferase expression at baseline and after LPS stimulation. Together, these data suggest a hierarchical regulation of transcription in neutrophils, with ATF3 being a transcriptional repressor of NF-B activity downstream of IFRD1 (Fig. 5).

Despite increased ATF3 expression in LPS-stimulated Ifrd1-/- macrophages, and the important role of ATF3 as a negative regulator of TLR4 signaling in macrophages14, we have not seen differences in oxidative burst capacity or LPS-driven TNF- production between wild type and

Ifrd1-/- macrophages (Chapter 2). This could be due to the very low expression of IFRD1 in macrophages, which may make the effect of IFRD1 less significant. Moreover, microarray analysis of transcription factor expression has revealed clearly different neutrophil transcription factor clusters from those of monocytes/macrophages, both in the resting state and after E. coli stimulation, suggesting different mechanisms of transcription regulation between these two types of cells7. It is also possible that, in macrophages, ATF3 may mainly regulate late-onset cytokines, such as IL-6, IL-12 and IFN-. Thus, we need to comprehensively examine the expression of these cytokines before we can exclude functional effects of IFRD1 in macrophages.

We have previously shown that IFRD1 interacts with HDAC1 in neutrophils, and acts in a

HDAC-dependent manner (Chapter 2). The substrates of HDACs can be histones or non-histone proteins, such as NF-B/p65. Thus, to further determine the mechanism of action of IFRD1 in

103

regulating ATF3 expression, it will be interesting to define histone acetylation at the promoter of

ATF3 and acetylation of NFB/p65. We have also shown that IFRD1 binding at the ATF3 promoter, and nuclear localization of IFRD1, is rapidly decreased after LPS stimulation. This could be due to decreased expression of IFRD1, increased degradation of IFRD1 protein or mRNA, or export out of the nucleus. We hypothesize the latter. These possibilities can be addressed by quantifying the regulation of IFRD1 transcription, stability of IFRD1 mRNA and protein, as well as the interactome of IFRD1 protein.

Structural analysis of the IFRD1 molecule reveals a nuclear localization motif, a co-repressor nuclear box, and an Armadillo repeat region (not shown). The latter motif, resembling -catenin, creates a surface for protein–protein interactions32. Thus IFRD1 may be like -catenin, having different binding partners depending on the cellular compartment, cell type and stimulus. We have obtained some interesting data from co-activator trap experiments, finding that IFRD1 interacts functionally with three, biologically-relevant functional categories of transcription factors.

All of the candidate transcription factors thereby implicated provide potential insight into the molecular mechanism of action of IFRD1 in regulating neutrophil function. However, these data need to be validated in traditional biochemical assays (e.g., TAP-MS, coimmunoprecipitatin) as well as functional assays (e.g., loss of function assays with siRNA). Of note TAP-MS experiments are currently ongoing, but too preliminary to report here (see Chapter 4). In addition to further clarifying the molecular mechanism of action of IFRD1 in neutrophils, the knowledge gained from understanding the interactome of IFRD1 in neutrophils may be of translational

104

significance, e.g., to identify novel therapeutic targets for the treatment of CF and other neutrophilic inflammation-associated diseases.

Materials and Methods

Mice. C57BL/6 (The Jackson Laboratory), Ifrd1-/- (C57BL/6 background; Huber, L., Medical

University Innsbruck, Austria), and Atf3-/- (C57BL/6 background; Hai, T., Ohio State University,

Columbus), mice were housed in a pathogen-free environment. Animal care was provided in accordance with National Institutes of Health guidelines. Studies were approved by the CCHMC

Institutional Animal Care and Use Committee.

Cell preparation and stimulation. Neutrophils were isolated immunomagnetically from mouse bone marrow using Gr-1 beads (Miltenyi), a purification strategy optimized to yield a highly purified population of mature neutrophils, as demonstrated by stained cytospins (data not shown).

Mouse bone-marrow-derived macrophages were differentiated with 50 ng ml-1 rmuM-CSF

(peprotech) for 7 d. Neutrophils or macrophages were stimulated with TLR4-specific LPS

(InvivoGen) or P. aeruginosa-derived LPS (Sigma) at indicated dose for the indicated time.

105

qRT-PCR, ELISA

Quantification of mRNA was performed by qRT-PCR, using a LightCycler (Roche) and the following primers: mouse ATF3 5‘: CTCTGGCCGTTCTCTGGA, 3':

GTGAGAGGCAGGGGACAAT. KC 5‘: ACCCAAACCGAAGTCATAGC, 3‘:

TCTCCGTTACTTGGGGACAC. Mouse TNF- 5‘: CCACCACGCTCTTCTGTCTAC, 3‘:

AGGGTCTGGGCCATAGAACT. Mouse -actin (Actb) : 5‘: GGCCCAGAGCAAGAGAGGTA, 3' :

GGTTGGCCTTAGGGTTCAGG. TNF- (BD Pharmingen) and KC protein (R&D) were quantified by ELISA according to manufacturer‘s instruction.

Imaging flow cytometry

Nuclear translocation of IFRD1 in neutrophils was quantified with an imaging flow cytometer

(ImageStream, Amnis Corp., Seattle WA). Briefly, Whole blood from wild type mice was treated with LPS 1ug/ml for indicated time. Red blood cells were lysed with BD Pharm LyseTM lysing solution. The remaining white blood cells were fixed and stained following the protocol of Foxp3

Staining Buffer Set (ebioscience) with rabbit anti-ATF3 (Santa Cruz), Alexa Fluor 488 goat anti-rabbit IgG (Invitrogen), mouse anti-IFRD1 (Sigma), Alexa Fluor 610/PE goat anti-mouse IgG

(Invitrogen), and pacific blue anti-Gr-1 (ebioscience). Nuclei were stained with DRQ5. 5,000 single and focused cells were acquired per sample on an Imagestream imaging flow cytometer.

Nuclear location of IFRD1 in Gr-1+ neutrophils was analyzed with IDEASTM 3.0 software

(Amnis).

106

Chromatin immunoprecipitation (ChIP) assay

Purified wild type bone marrow neutrophils were stimulated with LPS 1 g/ml for indicated time.

After stimulation, neutrophils were fixed in 1% formaldehyde and chromatin fragment was prepared with EZ-Zyme™ Chromatin Prep Kit (Upstate Biotech). ChIP assay was performed using the EZ-ChIP™Chromatin Immunoprecipitation Kit (Upstate Biotech) according to the manufacturer‘s instructions. Antibodies used included IFRD1 (M-20) antibody and goat IgG

(Santa Cruz Biotech). Immunoprecipitated DNA was quantified by qRT-PCR using a LightCycler

(Roche) with primers specific for the indicated regions. The relative abundance of each region was plotted relative to input DNA before immunopreciptation (%Input). Primers used in the ChIP assay include: Mouse Atf3 promoter NF-B site forward: CGACACGCCTGGGGTTTACC, reverse: CGGGATTACAGCAGCATCGC; Mouse Atf3 exon 2 forward:

AGGATTTTGCTAACCTGACACCC, reverse: TGTTGACGGTAACTGACTCCAGC; -actin forward: GGCCCAGAGCAAGAGAGGTA, reverse: GGTTGGCCTTAGGGTTCAGG.

Luciferase reporter assay

H4.14 cells (HEK293 cells stably expressing TLR4 and CD14) were transduced with lentivirus pFWATF3luc (VSV-G) in the presence of 8 g/ml polybrene. 24 h later, cells were transfected with pMD-2-HA, huIFRD1-pCMVSPORT6 (Open Biosystems) or empty vector pCMVSPORT6, and pRL-CMV (Promega) using Polyfect (Qiagen). 48 h later, cells were stimulated with 1 g/ml

LPS for 18 h. Luciferase activities were analyzed according to the manufacturer‘s instructions using Dual-Luciferase® Reporter Assay System (promega). Luciferase activity was normalized

107

to the total protein concentration (Bradford assay, Biorad) and Renilla luciferase activity. Similarly,

H4.14 cells were transfected with XRE-Luc, huIFRD1-pCMVSPORT6 (or empty vector pCMVSPORT6) and pRL-CMV. 48 hrs later, cells are stimulated with 10 nM ICZ for 18h and analyzed using Dual-Luciferase® Reporter Assay System.

Coactivator trap

Coactivator trap assay was performed by our collaborator at the Scripps Research Institute, FL.

Briefly, 293T cells were transfected in 384-well plate with test cDNA (cDNAs encoding human

IFRD1 or mouse Ifrd1), the reporter GAL4: luciferase, and the arrayed transcription factor library collection containing transcription factor CM-GAL4 fusion cDNA. Cells were cultured for 24 h and luciferase luminescence was measured with an Acquest plate reader (LJL Biosystems). The data was analyzed as follows. (1) Each plate was normalized to the median of the pBIND controls on that plate (FC pBIND). (2) Each sample well (transfected with mIFRD1 or hIFRD1) was normalized to the average of that well transfected with pSPORT6. (FC pSPORT). (3) The overall average was the average of the fold change (FC pSPORT) for each well. (4) The pBIND controls on the entire screen were averaged and the standard deviation of those controls calculated to find the values for +3, +5 and +10 S.D. The wells with averages that were +3,

+5,and +10 S.D. higher than the controls were evaluated as hits. At least 2 replicates had to be above each threshold to be called a hit.

108

a b 1000 800 * Wt * 800 Ifrd1-/- 600 600 400 400

* ATF3 mRNA (a.u.) mRNA ATF3 200 (a.u.) mRNA ATF3 200

0 0 NS TLR4LPS NS TLR4LPS

Figure 1. Ifrd1-/- neutrophils have increased ATF3 mRNA.

WT or Ifrd1-/- (a) purified bone marrow neutrophils and (b) bone-marrow-derived macrophages were stimulated with PBS (NS) or TLR4-specific LPS (1 g/ml) for 1h. ATF3 mRNA were quantified by qRT–PCR. Means and s.e.m. are shown, of six different mice per genotype, representing 2 experiments. a.u., arbitrary units. *p <0.05 (unpaired two-tailed Student‘s t test)

109

a b 8000 wt 1000 Atf3-/- * 6000 * 800

600 4000 400

2000 (a.u.) mRNA

200



- KC mRNA KC (a.u.) 0 0 TNF NS LPS NS LPS c d 150

100 * *

(pg/ml)

 -

50 KC (pg/ml)KC

TNF 5

0 0 Wt Atf3-/- Wt Atf3-/-

Figure 2. Atf3-/- neutrophils produce increased levels of inflammatory cytokines in response to LPS stimulation.

(a) TNF-and (b) KC mRNA were quantified by qRT–PCR in WT or Atf3-/- neutrophils stimulated with PBS (NS) or P. aeruginosa LPS 1 g/ml for 1h. (c) TNF- and (d) KC protein were quantified by ELISA in WT or Ifrd1-/- stimulated with P. aeruginosa LPS 1g/ml for 12h. Means and s.e.m. are shown, of six different mice per genotype, in a single experiment. a.u., arbitrary units. *p

<0.05 (unpaired two-tailed Student‘s t test)

110

a b 300 60

55 200 50

100 45

ATF3 mRNA (a.u.) mRNA ATF3 40

0 35 0 1 2 3 4 5 0 1 2 3 4 5 Time (h) IFRD1nuclear translocation% Time (h) c d 10 IFRD1 ChIP 15

8 10

6 -actin Input% Input% 4 5 2 IgG control

0 0 0 1 2 3 4 5 0 1 2 3 4 5 time (h) time (h) ATF3 exon 2

Figure 3. Correlation of IFRD1 nuclear translocation and presence at the Atf3 promoter, with ATF3 expression.

(a) ATF3 mRNA , quantified by qRT–PCR in WT neutrophils stimulated with P. aeruginosa LPS

100 ng/ml for the indicated times. (b) Nuclear translocation of IFRD1 and ATF3 were visualized and quantified using the Amnis ImageStream system. (c) Dynamics of IFRD1 binding to ATF3 promoter, quantified by ChIP, using an IFRD1 antibody (solid line) or IgG control (dash line). (d)

Negative control for ChIP experiments: IFRD1 ChIP at Atf3 exon 2 (solid line) or the Actb promoter (dashed line). The relative abundance of each region being precipitated was plotted relative to input DNA before immunoprecipitation (%Input). Representative of 2 experiments.

111

a b * 6000 100 80 * 4000 * 60 * 40 2000 20

Luciferase activity (a.u.) Luciferaseactivity 0 0 vector IFRD1 vector IFRD1 (a.u.) Luciferaseactivity vector IFRD1 vector IFRD1 NS LPS NS ICZ

Figure 4. Ifrd1 inhibits ATF3 promoter-driven luciferase expression.

(a) ATF3 promoter-driven luciferase expression. H4.14 cells were transduced with lentivirus pFWATF3luc. 24 h later, cells were transfected with pMD2-HA, huIFRD1-pCMVSPORT6 (or empty pCMVSPORT6) and pRL-CMV. 48 h later, cells were stimulated with 1 g/ml LPS for 18 h. A single experiment, representative of 3 experiments, is shown. (b) XRE-driven luciferase expression. H4.14 cells were transfected with XRE-Luc, huIFRD1-pCMVSPORT6 (or empty pCMVSPORT6) and pRL-CMV. 48 h later, cells were stimulated with 10nM AHR agonist ICZ for

18h. Luciferase activities were analyzed with Dual-Luciferase® Reporter Assay System

(promega). Luciferase activity was normalized to the total protein concentration and Renilla luciferase activity. (the data are preliminary, representing a single experiment) *p <0.05

112

(unpaired two-tailed Student‘s t test, comparing empty vector with vector expressing human

IFRD1)

113

TLR4 IFRD1

NF-B

ATF3

TNF- KC

Figure 5. Proposed transcriptional network model in neutrophils.

In this model, TLR4 stimulates nuclear translocation of NF-B, which in turn activates transcription of inflammatory mediators (e.g., TNF- and KC). Concomitantly, NF-B induces expressions of its negative regulators (e.g., ATF3), preventing excessive transcription of inflammatory mediators. In addition, IFRD1 suppresses the transcription of ATF3, allowing adequate transcription of inflammatory mediators to combat infection. The mechanisms underlying regulation of the expression of IFRD1 remains to be defined.

114

Table 1. Top candidate transcriptional factors identified in coactivator trap screen

Average

Symbol score * Function

ZNF688 6.0 regulating transcription of inflammatory cytokines

ELF3 5.8 regulating transcription of inflammatory cytokines

Gabpa 4.1 regulating mitochondrial enzyme expression

E2F8 3.8 control apoptosis

CITED2 3.6 regulating myeloid development

NFYB 3.4

CREM 3.4

ATF4 3.3 regulating myeloid development

FHL3 3.3

STAT3 3.3 regulating myeloid development

Sgk 3.2

TCFL5 3.2

FBLIM1 3.1

NFkb2 3.0 regulating transcription of inflammatory cytokines

115

* The average scores are derived from luminescence of wells transfected with mIFRD1 or hIFRD1 normalized to the average of the well transfected with control vector pSPORT6. The wells with averages that are +3 (yellow), +5 (orange) and +10 (red) S.D. higher than the controls were evaluated as hits. The candidate transcription factor shown are hits in both mouse and human screen (detailed description is in Materials and Methods).

116

References

1. Brian, P.O., et al. Cystic fibrosis. Lancet 373, 1891-1904 (2009).

2. Strausbaugh, S.D., et al. Cystic Fibrosis: A Review of Epidemiology and

Pathobiology. Clin Chest Med 28, 279-88 (2007).

3. Konstan, M.W., et al. Effect of high-dose ibuprofen in patients with cystic fibrosis.

N Engl J Med 332, 848-54 (1995).

4. Gu, Y., et al. Identification of IFRD1 as a modifier gene for cystic fibrosis lung

disease. Nature 458, 1039- 42 (2009).

5. Chmiel, J.F., et al. The Role of Inflammation in the Pathophysiology of CF Lung

Disease. Clinical Reviews in Allergy and Immunology 23, 5-27 (2002).

6. Witko-Sarsat, V., et al. Neutrophils: Molecules, Functions and Pathophysiological

Aspects. Laboratory Investigation 80, 617-53 (2000).

7. Zhang, X., et.al. Gene expression in mature neutrophils: early responses to

inflammatory stimuli. Journal of Leukocyte Biology 75, 358-372 (2004).

8. Cloutier, A., et al. Inflammatory Cytokine Production by Human Neutrophils

Involves C/EBP Transcription Factor. The Journal of Immunology 182, 563-71

(2009).

9. Hai, T., et al. The molecular biology and nomenclature of the activating

transcription factor/cAMP responsive element binding family of transcription

117

factors: activating transcription factor proteins and homeostasis. Gene 273, 1-11

(2001).

10. Hai, T., et al. Cross-family dimerization of transcription factors Fos/Jun and

ATF/CREB alters DNA binding specificity. Proc. Natl. Acad. Sci. USA 88, 3720-24

(1991).

11. Yan, C., et al. Activating transcription factor 3, a stress sensor, activates p53 by

blocking its ubiquitination. EMBO J 24, 2425--35 (2005).

12. Ishiguro, T., et al. Inhibitory effect of ATF3 antisense oligonucleotide on ectopic

growth of HT29 human colon cancer cells. J. Cancer Res 91, 833--36 (2000).

13. Hartman, M.G., et al. Role for activating transcription factor 3 in stress-induced

beta-cell apoptosis. Mol. Cell. Biol 24, 5721--32 (2004).

14. Gilchrist, M., et al. Systems biology approaches identify ATF3 as a negative

regulator of Toll-like receptor 4. Nature 441, 173-8 (2006).

15. Rosenberger, C.M., et al. ATF3 regulates MCMV infection in mice by modulating

IFN-g expression in natural killer cells. Proc Natl Acad Sci U S A 105, 2544-9

(2008).

16. Gilchrist, M., et al. Activating transcription factor 3 is a negative regulator of

allergic pulmonary inflammation J Exp Med 205, 2349-57 (2008).

17. Wolfgang, C.D., et al Transcriptional autorepression of the stress-inducible gene

ATF3. J. Biol. Chem. 275, 16865-70 (2000).

118

18. Liang, G., et al. ATF3 gene. Genomic organization, promoter, and regulation. J

Biol Chem 271, 1695-701 (1996).

19. Sabine, I.M., et al. Expression of the Transcriptional Repressor ATF3 in

Gonadotrophs Is Regulated by Egr-1, CREB, and ATF2 after

Gonadotropin-Releasing Hormone Receptor Stimulation. Endocrinology 149,

126311-325 (2008).

20. Divanovic, S., et al. Negative regulation of Toll-like receptor 4 signaling by the

Toll-like receptor homolog RP105. Nat Immunol 6, 571-8 (2005).

21. Emmons, R.B., et al. The Spineless-Aristapedia and Tango bHLH-PAS proteins

interact to control antennal and tarsal development in Drosophila. Development

126, 3937-45 (1999).

22. Amelio, A.L., et al. A coactivator trap identifies NONO (p54nrb) as a component of

the cAMP-signaling pathway. Proc Natl Acad Sci U S A 104, 20314-9 (2007).

23. Liang, J., et al. Genome-wide survey and expression profiling of CCCH-zinc finger

family reveals a functional module in macrophage activation. PLoS ONE 3, e2880

(2008).

24. Rudders, S., et al. ESE-1 is a novel transcriptional mediator of inflammation that

interacts with NF-kappa B to regulate the inducible nitric-oxide synthase gene. J

Biol Chem 276, 3302-9 (2001).

119

25. Duckett, C.S., et al. Dimerization of NF-KB2 with RelA(p65) regulates DNA

binding, transcriptional activation, and inhibition by an I kappa B-alpha (MAD-3).

Mol Cell Biol 13, 1315-22 (1993).

26. Chen, Y., et al. Cited2 is required for normal hematopoiesis in the murine fetal

liver. Blood 110, 2889-98 (2007).

27. Gombart, A.F., et al. ATF4 differentially regulates transcriptional activation of

myeloid-specific genes by C/EBPepsilon and C/EBPalpha. J Leukoc Biol 81,

1535-47 (2007).

28. Panopoulos, A.D., et al. STAT3 governs distinct pathways in emergency

granulopoiesis and mature neutrophils. Blood 108, 3682-90 (2006).

29. Carter, R.S., et al. Cooperative binding of GA-binding protein transcription factors

to duplicated transcription initiation region repeats of the cytochrome c oxidase

subunit IV gene. J Biol Chem 269, 4381-7 (1994).

30. Li, J., et al. Synergistic function of E2F7 and E2F8 is essential for cell survival and

embryonic development. Dev Cell 14, 62-75 (2008).

31. Collins, M.O., et al. Mapping multiprotein complexes by affinity purification and

mass spectrometry. Current Opinion in Biotechnology 19, 1-7 (2008).

32. Städeli, R., et al. Transcription under the control of nuclear Arm/beta-catenin. Curr

Biol 16, R378-85 (2006).

120

CHAPTER IV Summary and Perspective

121

Lung disease is the principle cause of morbidity and mortality in CF patients. Chronic infection and dysregulated neutrophilic inflammation are the hallmarks of CF lung disease. The manifestations of CF lung disease can be very different between patients, even twins with the same CFTR genotype. This variability can be attributed to genetic modifiers other than CFTR, environmental factors, and interactions between the two. We have identified IFRD1 as a genetic modifier of lung disease severity in CF via a genome-wide association study. In my thesis study, I set out to determine the immunobiological functions of IFRD1 relevant to CF lung disease, and the molecular mechanisms of action of IFRD1.

The published studies in Chapter 2 have shown that:

(1) among peripheral blood cells, IFRD1 expression is highest in neutrophils;

(2) neutrophil differentiation in mice and humans is associated with up-regulation of IFRD1

expression;

(3) neutrophils (but not macrophages) from Ifrd1-/- mice have blunted NF-B-associated

effector functions, associated with decreased NF-B/p65 activation;

(4) genetic deficiency of IFRD1 is associated with a significant delay in P. aeruginosa

clearance from the airway, but also with significantly ameliorated disease: less weight

loss, along with less airway and systemic inflammation;

(5) this in vivo phenotype is dependent upon hematopoietic cell expression (or, lack of

expression) of IFRD1; and

122

(6) IFRD1 polymorphisms are significantly associated with variation in neutrophil effector

function (e.g., oxidative burst and LPS-induced TNF- production).

Together, these data suggest IFRD1 modulates the pathogenesis of airway disease in CF through regulation of neutrophil effector function.

Furthermore, mechanistic studies in chapter 2 and chapter 3 have shown that:

(1) IFRD1 interacts directly with NF-B/p65 and HDAC1;

(2) Ifrd1-/- neutrophils have decreased NF-B/p65 nuclear translocation;

(3) In vivo HDAC inhibition blunts LPS-driven airway TNF- production, specifically in WT (not in

Ifrd1-/-) mice; bone marrow transfer techniques and intracellular analysis of cytokine

production localized these effects to neutrophils;

(4) microarray analysis and qRT-PCR revealed increased expression of ATF3, a negative

regulator of NF-B signaling, in Ifrd1-/- neutrophils;

(5) Atf3-/- neutrophils produce increased level of TNF- and KC in response to LPS stimulation;

(6) imaging flow cytometry and ChIP assay revealed LPS-driven dynamic changes nuclear

IFRD1 localization and IFRD1 binding to the Atf3 promoter, that are inversely correlated with

ATF3 mRNA expresion;

(7) reporter assays revealed suppression of ATF3 promoter-driven luciferase expression by

ectopic IFRD1 expression.

While the latter studies remain somewhat preliminary, taken together, these studies strongly

123

suggest a mechanism by which IFRD1 modulates neutrophil function in a HDAC-dependent manner to co-suppress the expression of ATF3, a transcriptional repressor of NF-B activity in neutrophils. (Fig. 1)

This work represents the first implication of IFRD1 in immune system functioning. More importantly, this work suggests that pathogenesis of CF lung disease goes beyond epithelial cells, in the sense that intrinsic neutrophil differences can contribute to the variability seen in CF lung disease severity. Furthermore, this study not only underscores the likely role of neutrophils in driving the pathogenesis of CF lung disease, but also represents a powerful alternative approach for discovering novel therapeutic targets, e.g. the identified IFRD1-HDAC1 axis may be a promising target for anti-inflammatory therapy in CF. It is also possible that the modifier genes identified from this study of a rare monogenic disease may contribute to the development and/or progression of more common diseases or conditions, such as rheumatoid arthritis and ischemia-reperfusion Injury. The effects of modifier genes may be less penetrant in healthy people, however the altered homeostasis in patients may expose the effect of these genetic modifiers. Thus, the achievement from the genetic modifier study could benefit people beyond those suffering from monogenic conditions.

124

Obviously, many mechanistic questions remain to be defined, including:

1. Are neutrophils the only type of cells important for IFRD1 modification of the severity of CF

lung disease?

Some aberrant behavior of neutrophils from CF patients has been observed. Pulmonary neutrophils in CF, but not peripheral blood neutrophils, were reported to have decreased iC3b-mediated phagocytic capacity, which could contribute to the compromised airway defense in CF1. Blood neutrophils of both CF homozygotes patients and heterozygote carriers display increased myeloperoxidase-dependent oxidant activity, suggesting the role of a genetic component in altering neutrophil function in CF2. Likewise, it has been reported the shedding of

L-selectin in stimulated CF neutrophils is decreased, indicating that neutrophils in CF have a dysregulated migration control and maintain a "acute-type" inflammatory response to infection3.

Notably, this decreased shedding of L-selection was not observed in non-CF bronchiectasis patients, suggesting the specific role of dysregulated neutrophil behaviors in the pathogenesis of

CF lung disease3. Several studies have revealed that neutrophils from CF patients produce excessive pro-inflammatory mediators out of the proportion of infectious stimuli. Remarkably, spontaneous release of IL-8 in the absence of stimuli by airway neutrophils from children with CF is significantly higher than that from children with dyskinetic cilia syndrome, suggesting a specific genetic factor in CF that control neutrophil behaviors4. Moreover, inflammatory neutrophils in the

CF airways present profound functional and signaling differences from blood neutrophils5. All of these data are potentially interesting. However, neutrophil behaviors are exquisitely susceptible to the influence of infection and inflammation, and CF patients have dynamic infection and

125

inflammation. Thus, these findings remain to be validated in studies with strict control on patients‘ genotype, disease state and origin of neutrophils (e.g.,lung- or blood-derived). It is more likely that combinatory effects of CFTR deficiency in airway epithelial cells and intrinsic neutrophil abnormalities, together lead to the neutrophilic airway inflammation in characteristic CF.

We have shown that expression of IFRD1 is particularly enriched in neutrophils. Ifrd1-/- neutrophils have impaired effector function contributing to delayed clearance of P. aeruginosa, but ameliorated disease course in a mouse model of CF lung disease induced by airway challenge of P. aeruginosa. However, the mouse model we used represents acute lung infection, with rapid clearance of most of the bacteria by the end of 48 hours after intratracheal challenge of

P. aeruginosa6. This model also stresses the effect of rapid-response cells such as neutrophils, not the possible effects of late-response cells (e.g., macrophages) as well as the role of respiratory movement and nutritional status in chronic infection. Indeed, studies with Ifrd1-/- mice have indicated role for IFRD1 in muscle regeneration7 and axon growth8. In addition, a recent linkage analysis study in humans identified IFRD1 as a disease-causing candidate gene for autosomal-dominant sensory/motor neuropathy with ataxia9. These studies suggest a possible role for IFRD1 in chronic pulmonary infection through controlling respiratory movement, which is orchestrated by neurons and respiratory muscle. Furthermore, Specific overexpression of IFRD1 in the intestine of mice increases intestinal triglyceride absorption and adiposity10, suggesting a potential modulating role for IFRD1 in nutritional status in CF patients, which has been shown to be associated with pulmonary function and outcome of CF patients11. A more chronic mouse

126

model of CF lung disease (infection and inflammation) may help address these possibilities.

There are, however, no perfect mouse models of CF-related chronic infection and inflammation.

Intratracheal infection of P. aeruginosa with immobilizing media such as agarose beads and alginate, or a special strain of P. aeruginosa that expresses a stable mucoid phenotype (due to a deletion in mucA), while often used in CF research, only give rise to subacute infection6. With the appearance of the CFTR-null pig12, knockout pig models may be more informative.

2. What is the interactome of IFRD1?

We have shown that IFRD1 interacts directly with HDAC1 and NF-B/p65 in by immunoprecipitation analysis. And IFRD1 modulates neutrophil function in a HDAC-dependent manner to co-suppress the expression of ATF3, a transcriptional repressor of NF-B activity in neutrophils. Structural analysis of IFRD1 molecule reveals an armadillo repeat region, which resembles the one in -catenin creating a surface for protein–protein interactions13. Thus IFRD1 may be like -catenin in having different binding partners depending on the cellular compartment, cell type and stimulus. Thus, definition of the protein interactome of IFRD1 in neutrophils will be essential to better define the molecular mechanism of action of IFRD1 in neutrophils. Moreover, definition of the IFRD1 interactome in neutrophil may provide information that can be translated into therapeutic applications. For example, HDAC inhibitors have anti-inflammatory effects in various mouse models, including ulcerative colitis, asthma and systemic lupus erythematosus14.

Multiple HDAC inhibitors have entered clinical trials, and SAHA (Vorinostat) has been approved for treatment of cutaneous T-cell lymphoma15. However, given the wide involvement of HDAC in

127

development and homeostasis, systemic HDAC inhibition with compounds may well give rise to nonspecific effects, which makes them unlikely to be safe, chronic therapeutic approach to CF lung disease. Structural and functional knowledge derived from the interactome of IFRD1 may allow for specific targeting of IFRD1/HDAC complex interactions.

We are currently using two complementary methods to define the interactome of IFRD1: Tandem

Affinity Purification-Mass spectrometry (TAP-MS), and a coactivator trap screen. The latter is discussed in Chapter 3. TAP-MS combines purification of a protein complex of interest, using affinity purification tags, with subsequent mass spectrometry identification of specifically co-purified protein complex components16. The critical feature of this method is the use of two different affinity purification tags that are fused to the protein interest by genetic methods.

Performing two consecutive purification steps using affinity purification tags that have gentle washing and elution conditions allows for isolation without disruption of the targeted complex.

Thus, TAP allows for sensitive and specific purification of physiological multi-protein complexes even without good antibodies for protein of interest16. With the availability of retroviral gene transfer techniques, TAP-MS has been successfully applied in primary cells, e.g., to map the specific interactome and pathway of TNF-/NF-B signaling17. Furthermore, a TAP-knock-in mouse line has been generated to characterize interactome of proteins in vivo 16.

I added streptavidin-binding peptide and calmodulin binding peptide tandem tags (Stratagene) to

128

the N-terminal of IFRD1. To analyze the binding partners of IFRD1 in primary mouse neutrophils,

TAP-tagged mouse IFRD1 was transduced into Ifrd1-/- bone marrow with pRetroX-IRES-ZsGreen1 retroviral transduction vector (containing an GFP marker; Clontech).

Positively transduced cells were FACS-sorted based on the presence of GFP expression, and differentiated in vivo via standard bone marrow ablation and transfer techniques. Empty TAP vector/GFP+ construct retrovirus-transduced cells were used as controls. Since the addition of a

TAP tag to a protein may affect its folding, activity and/or physiological interactions, I first examined whether TAP-tagged mouse Ifrd1 could rescue the function of Ifrd1-/- neutrophils.

Indeed, low expression of tagged Ifrd1 partially rescued LPS-induced TNF- production by Ifrd1-/- blood neutrophils (Fig 2a, 2b). Moreover, TAP-tagged IFRD1 could successfully bind to HDAC1, as tested in Western blot assays (Fig. 3). The next planned steps will be purification of

IFRD1-containing protein complexes will be TAP-purified using the InterPlay TAP purification kit

(Stratagene). Isolated complexes will undergo SDS-PAGE and silver staining. Bands consistently present in experimental isolates, but not present in control TAP purifications, will be cut out and taken to LC/MS/MS at the CCHMC/UCCOM Proteomics Core for identification of IFRD1 partners.

For verification that proteins identified by TAP interact physiologically with IFRD1, we will perform co-immunoprecipitation analysis in mouse neutrophils. Functional studies will involve quantitative analysis of neutrophil function after specific knockdown of IFRD1 partners by siRNA techniques in HL-60 cells.

3. How is the expression of IFRD1 regulated?

129

Our study in healthy donors has indicated that IFRD1 polymorphisms are associated with quantitative measure of neutrophil effector function. The information derived from this study may provide some insight in predicting phenotype and/or prognosis from the genotype of IFRD1 in CF patients. This information will be more precise if the mechanism in regulation of expression of

IFRD1 is clearly defined. Furthermore, mechanistic study of the regulation of expression of

IFRD1 will provide additional knowledge on manipulating IFRD1 as an alternative therapeutic approach in CF lung disease. Promoter analysis of IFRD1 gene has uncovered paired

NFB-binding and CREB/ATF-binding sites (Fig. 4), which are conserved between mice and humans. This suggests potential reciprocal regulation among IFRD1, ATF3 and NF-B. Whether these sites are functional in neutrophils remains to be tested. Besides transcriptional modulation, regulation of IFRD1 mRNA stability also influences the expression pattern of IFRD1 in response to TNF- stimulation18. This is possibly due to the abundant AU-rich elements in the

3‘-untranslated region of IFRD1 mRNA. Together, these indicate the necessity of a comprehensive study of the regulation of IFRD1 expression.

4. What are the roles of IFRD1 in other diseases in which neutrophils play important pathogenic

roles?

Neutrophils combat invading microorganisms by phagocytosis, releasing prepacked enzymes, producing superoxide anions and forming extracellular nets19. However, these responses are nonspecific and can cause damage to normal tissue in addition to combating infection. The nonspecific response and the powerful weapons of neutrophils make neutrophils common in the

130

pathogenesis of many other diseases such as ischemia-reperfusion injury, gout, glomerulonephritis, rheumatoid arthritis, and hereditary periodic fever syndromes20. We have shown that IFRD1 modulates neutrophil effector function. Thus, determination of roles of IFRD1 in animal models of these diseases, as well as studies analyzing the role of IFRD1 polymorphisms as a gene modifying these diseases, may provide additional preventative and therapeutic strategies for these diseases.

Overall, this dissertation research provides evidence in favor of the hypothesis that IFRD1 modulates the course of airway disease in CF through regulation of neutrophil effector function.

This study also strongly suggests a mechanism by which IFRD1 modulates neutrophil function in a HDAC-dependent manner to co-suppress the expression of ATF3, a transcriptional repressor of NF-B activity in neutrophils. Finally, this research emphasizes the translational implications for therapeutic targeting of neutrophils in CF. This study also suggests that the IFRD1/HDAC axis may provide a tractable therapeutic target in CF, and the plethora of other diseases in which neutrophils play an important pathogenic role.

131

TLR4 IFRD1

NF-B

ATF3

TNF- KC

Figure 1. Proposed transcriptional network model in neutrophils. In this model, TLR4 stimulates nuclear translocation of NF-B, which in turn activates transcription of inflammatory mediators (e.g., TNF- and KC). Concomitantly, NF-B induces expressions of its negative regulators (e.g., ATF3), preventing excessive transcription of inflammatory mediators. In addition,

IFRD1 suppresses the transcription of ATF3, allowing adequate transcription of inflammatory mediators to combat infection. The mechanisms underlying regulation of the expression of

IFRD1 remains to be defined.

132

a b

4000 25000 Wt Ifrd1ko+NTAP 20000 3000 *

Ifrd1ko+NTAPmIfrd1 MFI)

* ( 15000 

2000 -

10000

mIfrd1 (MFI)mIfrd1 TNF 1000 5000

0 0 NS LPS

Figure 2. Low expression of N-terminal TAP-tagged mouse IFRD1 protein partially rescues

LPS-driven TNF- production in Ifrd1-/- neutrophils. (a) Rescue of mouse IFRD1 protein expression with retrovirus encoding N-terminal TAP-tagged mouse IFRD1. (b) Rescue of

LPS-driven TNF- production in blood neutrophils. TAP-tagged mouse IFRD1 was transduced into Ifrd1-/- bone marrow progenitors with pRetroX-IRES-ZsGreen1 retroviral transduction vector.

Low GFP-expressing bone marrow cells were FACS-sorted and differentiated in vivo via standard bone marrow reconstitution. Empty TAP vector/GFP+ construct retrovirus-transduced cells were used as controls. After 8-week‘s in vivo differentiation, IFRD1 protein expression and

LPS-induced TNF- production by neutrophils was quantified by intracellular staining of whole blood in flow cytometry. N=3 mice per group, representing one experiment. *P < 0.05 (ANOVA,

Dunnett).

133

Empty IFRD1 vector TAP

IFRD1

HDAC1

Figure 3. Interaction of TAP-tagged IFRD1 with HDAC1. HEK293 cells are transduced with pRetroX-IRES-ZsGreen1 retrovirus expression TAP-tagged IFRD1 or empty TAP vector. TAP was performed using the InterPlay TAP purification kit (Stratagene). Isolated complexes underwent SDS-PAGE and immunoblot on IFRD1 and HDAC1. Representative of single experiment.

134

Human IFRD1 promoter -160 -60

Mouse Ifrd1 promoter

ATF/CREB NF-B binding site binding site

Figure 4. Paired CREB/ATF and NF-B sites at the promoter of human and mouse IFRD1.

Promoter analysis was performed using 3 scanning methods: MotifLocator, MotifScanner and

Clover (http://xerad.systemsbiology.net/MotifMogulServer/).

135

References

1. Morris, M.R., et al. Reduced iC3b-mediated phagocytotic capacity of pulmonary

neutrophils in cystic fibrosis. Clin Exp Immunol 142, 68-75 (2005).

2. Witko-Sarsat, V., et al. Disturbed myeloperoxidasedependent activity of

neutrophils in cystic fibrosis homozygotes and heterozygotes, and its correction

by amiloride. The Journal of Immunology 157, 2728-35 (1996).

3. Russell, K.J., et al. Neutrophil Adhesion Molecule Surface Expression and

Responsiveness in Cystic Fibrosis. Am J Respir Crit Care Med 157, 756-61

(1998).

4. Corvol, H., et al Distinct cytokine production by lung and blood neutrophils from

children with cystic fibrosis. Am J Physiol Lung Cell Mol Physiol 284, L997-L1003

(2003).

5. Tirouvanziam, R., et al. Profound functional and signaling changes in viable

inflammatory neutrophils homing to cystic fibrosis airways. Proc Natl Acad Sci U S

A 105, 4335-9. (2008).

6. Guilbault, C., et al. Cystic Fibrosis Mouse Models. Am J Respir Cell Mol Biol 36,

1-7 (2007).

7. Santhosh, K.V., et al. Muscle Regeneration and Myogenic Differentiation Defects

in Mice Lacking TIS7 Molecular and Cellular Biology 24, 3514-25 (2004).

8. Dieplinger, B., et al. The transcriptional corepressor TPA-inducible sequence 7

regulates adult axon growth through cellular retinoic acid binding protein II

136

expression. Eur J Neurosci 26, 3358-67 (2007).

9. Brkanac, Z., et al. IFRD1 is a candidate gene for SMNA on chromosome

7q22-q23. Am J Hum Genet. 84, 692-7 (2009).

10. Yuan, W., et al. Targeted Intestinal Overexpression of the Immediate Early Gene

tis7 in Transgenic Mice Increases Triglyceride Absorption and Adiposity. The

Journal of Biological Chemistry 280, 34764-75 (2005).

11. Zemel , B.S. Longitudinal relationship among growth, nutritional status, and

pulmonary function in children with cystic fibrosis: analysis of the cystic fibrosis

foundation national CF patient registry. J. Pediatr 137, 374-80 (2000).

12. Rogers, C.S., et al. Disruption of the CFTR Gene Produces a Model of Cystic

Fibrosis in Newborn Pigs. Science 321, 1837 (2008).

13. Städeli, R., et al. Transcription under the control of nuclear Arm/beta-catenin. Curr

Biol 16, R378-85 (2006).

14. Haberland, M., et al. The many roles of histone deacetylases in development and

physiology: implications for disease and therapy. Nat Rev Genet 10, 32-42 (2009).

15. Duvic, M., et al. Phase 2 trial of oral vorinostat (suberoylanilide hydroxamic acid,

SAHA) for refractory cutaneous T-cell lymphoma (CTCL). Blood 109, 31-9 (2007).

16. Collins, M.O., et al. Mapping multiprotein complexes by affinity purification and

mass spectrometry. Current Opinion in Biotechnology 19, 1-7 (2008).

17. Bouwmeester, T., et al. A physical and functional map of the human TNF-/NF-B

signal transduction pathway. Nature Cell Biology 6, 97-105 (2004).

137

18. Hao, S., et al. The stability of mRNA influences the temporal order of the induction

of genes encoding inflammatory molecules. Nature Immunology 10, 281 - 88

(2009).

19. Nathan, C. Neutrophils and immunity: challenges and opportunities. Nature

Reviews Immunology 6, 173-182 (2006).

20. Witko-Sarsat, V., et al. Neutrophils: Molecules, Functions and Pathophysiological

Aspects. Laboratory Investigation 80, 617-53 (2000).

138