Loss of Sbds in Shwachman-Diamond Syndrome Murine Model Leads to Reduction of 80S Ribosomes and Altered Transcript Binding

Hongrui Liu

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Department of Molecular Genetics University of Toronto

Hongrui Liu

Doctor of Philosophy

Department of Molecular Genetics University of Toronto

2016 Abstract

Shwachman-Diamond syndrome (SDS) is an autosomal recessive disease characterized

by growth retardation, exocrine pancreatic dysfunction, skeletal dysplasia, cognitive

impairment and bone marrow failure. SDS is caused by mutations in SBDS (Shwachman-

Bodian-Diamond syndrome). A recent model proposes that SBDS/Sbds functions

together with EFL1/Efl1 to release EIF6/Eif6 from the pre-60S complex, enabling

ribosomal subunit joining for translation initiation. To assess the protein synthesis

deficiency that has been detected in SDS, I examined ribosomal profiles of murine fetal

organs with the SDS-associated missense mutation, R126T (SbdsR126T/R126T). The SDS

organ extracts revealed reduced 80S monosomes and preserved polysomes, with no ribosomal subunit imbalance compared to matched controls. Further, Eif6 was found to bind to both the 60S and 80S ribosome complexes in mutants in contrast to only 60S complexes in controls. To investigate these changes and to learn how the SDS translatome is affected, total and polysomal mRNAs of mutant and control samples were studied using cDNA microarray analyses. By comparing individual polysomal transcripts

ii to respective total transcript levels, I found 799 transcripts (of 18,936 analyzed) with

altered polysome loading in mutant fetal livers, with 634 being increased. Changes in

polysome loading did not correspond with steady state protein levels, as indicated by

proteome analysis using label-free mass spectrometry. Rather, these changes correlated

with physical features of the transcripts, including 5' untranslated region composition as

well as the lengths and nucleotide contents of the open reading frame and the 3'

untranslated regions. Together, I conclude that the untimely release of Eif6 due to Sbds

deficiency results in ribosomes with compromised translation initiation and that SDS

disease phenotypes reflect protein synthesis insufficiency and the formation of sub-

populations of ‘SDS ribosomes’ with non- or poor- translating capability.

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Acknowledgments

I would like to express my deep appreciation and gratitude for my supervisor, Dr. Johanna M. Rommens’ patient guidance and mentorship over the years, from the first day I started as a rotation student in her lab just admitted into the graduate program to the completion of this degree. From her I learned not just the enthusiasm for academic pursuits, rigorous attitude for science, but also her genuinely good nature and infectious kindheartedness toward everyone around her. I am truly fortunate to have had the opportunity to work with her.

I would like to thank my thesis committee members, Drs. Barbara Funnell and Craig Smibert, for their friendly guidance and thought-provoking suggestions that together nourished my intellectual growth over the years.

I would also like to extend my thanks to staff from The Centre for Applied Genomics of The Hospital for Sick Children: Dr. Chao Lu and Xiaolin Wang for assistance with microarray preparation and analyses, to Jeff MacDonald for providing the transcript length and GC content data, and to Dr. Andrew Paterson and Dr. Pingzhao Hu for assistance and critical discussion of the statistical analyses of my data. Thank you to Paul Taylor and Dr. Jiefei Tong from the SPARC Biocentre of The Hospital for Sick Children for advice and support for mass spectrometry. I also thank the staff at the Toronto Centre for Phenogenomics for outstanding technical assistance.

I am grateful to all members of the Rommens lab (past and present) for their technical expertise, stimulating discussions and above all, moral support during my studies. Special thanks to Dr. Marina Tourlakis, Rikesh Gandhi, and Fan Lin, who enriched my graduate study experience and made ‘Rommens lab’ have a special meaning to my life.

Lastly, I would like to acknowledge my family for their ongoing support. Particularly to my husband Huapu Zhao; for his computer technical rescues during my many panicky moments, in addition to the hot meals awaiting me after long days of experiment.

Table of Contents

Acknowledgements……………………………………………………...………………..iv

Table of Contents…………………………………………………………….……………v

List of Tables…………………………………………………………………..………….x

List of Figures…………………………………………………………………………..xi

List of Appendices………………………………………………….…………………xiii

List of Abbreviations……………………………………………………………………xiv

Chapter 1 Shwachman-Diamond Syndrome: a Ribosomopathy Due to Loss of SBDS……………………………………………….……………………………………. 1

1 Shwachman-Diamond Syndrome: a Ribosomopathy Due to Loss of SBDS…………2 1.1 Shwachman-Diamond syndrome and natural history……………………...……2 1.1.1 Clinical features of Shwachman-Diamond syndrome………………….…2 1.1.1.1 Neutropenia and other hematological abnormalities…………….….2 1.1.1.2 Exocrine pancreatic dysfunction…………………………….………3 1.1.1.3 Skeletal abnormalities…………………………………………….…4 1.1.1.4 Neuro-developmental issues………………………………...………4 1.1.1.5 Liver features………………………………………………..………5 1.1.1.6 Other features………………………………………………………..5 1.1.1.7 Diagnosis and management of SDS……………………………..…..6 1.1.2 Molecular basis of SDS…………………………………………...………6 1.1.2.1 Identification of SBDS………………………………………………6 1.1.2.2 Structure and function of SBDS and relation to EIF6………………7 1.1.2.3 Models of SDS……………………………………………………...9 1.2 Ribosomes and translation…………………………………………………..…13 1.2.1 Ribosomopathies…………………………………………………….…...13 1.2.2 Translation overview…………………………………………….………18

1.2.3 Transcription and processing of rRNA……………………………..……22 1.2.4 Building ribosomes and ribosome structure…………………………..…23 1.2.4.1 Ribosomal proteins…………………………………………...……23 1.2.4.2 Regulation of RPs and extra-ribosomal functions…………………24 1.2.4.3 Ribosome structure…………………………………………...……26 1.2.5 The translation of mRNAs………………………………………….……28 1.2.5.1 Translation initiation………………………………………….……28 1.2.5.2 Translation elongation……………………………………..………33 1.2.5.3 Translation termination and recycling…………………………..…35 1.2.6 Contribution of mRNAs to translational control…………………...……36 1.2.6.1 5' Untranslated regions (5' UTRs) ………….………………...……36 1.2.6.2 Open reading frames (ORFs) ………………………………...……39 1.2.6.3 3' Untranslated regions (3' UTRs) ……….……………….….…….40 1.3 Thesis objectives……………………………………………………….………40

Chapter 2 Loss of Sbds Function Results in Abnormal Polysome Profiles with 80S Reduction and Abnormal Eif6 Binding……..……………………………………43

2 Loss of Sbds Function Results in Abnormal Polysome Profiles with 80S Reduction and Abnormal Eif6 Binding …………………………………………………………44 2.1 Summary…………………………………………………………………………44 2.2 Background………………………………………………………………………45 2.3 Materials and methods……………………………………………………...……46 2.3.1 Mice………………………………………………………...……………46 2.3.2 Polysome profiling and peak quantification…………………….….……46 2.3.3 Ribosome run-off profiling and quantification analyses...………………47 2.3.4 Western immunoblottings and quantification analyses………….………49 2.3.4.1 Protein precipitation from polysome profile fractions…….………49 2.3.4.2 Protein preparation from frozen tissues……………………………49 2.3.4.3 Western immunoblotting from prepared protein extracts…………49 2.3.5 Statistical analyses ………………………………………………………50 2.4 Results……………………………………………………………………………52

2.4.1 Loss of Sbds leads to reduced 80S and persistent polysomes in multiple murine fetal organs………………………………………………………52 2.4.2 No ribosome subunit imbalance observed with Sbds deficiency in vivo……………………………………………………………………….52 2.4.3 Persistent high molecular complexes observed in SDS mutant polysome profiles represent polysomes…………………………………….…….…57 2.4.4 Aberrant polysome profiles observed in SbdsR126T/- fetal livers……….…57 2.4.5 Loss of Sbds function leads to aberrant association of Eif6 with 80S..…58 2.5 Discussion……………………………………………………………………..…67

Chapter 3 The Preserved Polysomes Associated with Sbds Deficiency Result From Altered mRNA Association………...……………………………………………70

3 The Preserved Polysomes Associated with Sbds Deficiency Result From Altered mRNA Association…………………………………………………………………..71 3.1 Summary…………………………………………………………………………71 3.2 Background………………………………………………………………………72 3.3 Materials and methods……………………………………………………...……73 3.3.1 Mice…………………………………………………………...…………73 3.3.2 Polysome profiles and RNA extraction…………………………………73 3.3.3 cDNA microarray and analyses…………………………………………74 3.3.4 Quantitative real-time RT-qPCR…………………………………...……75 3.3.5 Western immunoblottings and quantification analyses…………………78 3.3.6 Functional classification of transcripts and proteins……………………78 3.3.7 Physical characterization of transcripts with altered polysome loading……………………………………………………………………78 3.3.7.1 Transcript length and GC content analyses……………………..…78 3.3.7.2 uORF, TOP and IRES analyses UTRs……………………….……80 3.3.8 Label-free mass spectrometry and analyses………………………...……80 3.3.9 Compilation of protein synthesis and related genes……………..………81 3.3.10 Statistical analyses……………………………………………….………81 3.4 Results……………………………………………………………………………82

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3.4.1 Altered steady state total mRNA and polysome loading……………..…82 3.4.1.1 General work flow of microarray gene expression analyses………82 3.4.1.2 Microarray data processing, normalization and quality control…...83 3.4.1.3 Validation of microarray analyses with real time RT-qPCR..….…87 3.4.1.4 Modest steady state mRNA level changes in SDS fetal liver..….…91 3.4.1.5 Transcript and polysome analyses reveal altered ribosome loading for a subset of mRNAs in Sbds deficient fetal livers………………96 3.4.2 The Sbds-deficient proteome including translation-related components does not exhibit major changes……………………………………..……96 3.4.3 Steady state cytoplasmic protein levels do not correspond to changes in the polysome loading levels………………………………………..….…99 3.4.4 Transcript features determine differences in polysome loading…..……..99 3.5 Discussion………………………………………………………………………115

Chapter 4 Conclusions and Future Directions…………………………………..118

4 Conclusions and Future Directions…………………………………………………119 4.1 Summary………………………………………………………………..………119 4.2 Main findings………………………………………………………………...…121 4.2.1 Loss of Sbds function is associated with loss of 80S monoribosomes and reduced translation………………………………………………...……121 4.2.2 Preserved polysomes in SDS polysome profiles indicate sub-populations of non-functional or poorly functioning ribosomes………….…………122 4.2.3 Physical characteristics determine the level of ribosome retention on transcripts with Sbds deficiency…………………………………..……123 4.2.4 Aberrant association of Eif6 with 80S ribosomes in SDS polysome profiles provides clues to Sbds deficiency………………….…….…….126 4.2.5 Proposed model of Sbds deficiency, translation impairment and implication of SDS ribosome complexes……………………….………128 4.3 Shwachman-Diamond syndrome and ribosomopathies………………...………134 4.3.1 Tissue specific phenotypes in SDS and ribosomopathies………………134 4.3.2 Translation insufficiency and cancer predisposition……………………135

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4.4 Concluding remarks…………………………………………………………….141

References…………………………………………………………………………...….143

Appendices……………………………………….……………………………………..159

List of Tables

Table 1.1 In vivo models of SDS………………………………………..……..…..15

Table 1.2 Ribosomopathies…………………………………………………..…….19

Table 2.1 Genotyping primers…………………………………………………..….48

Table 2.2 Antibodies used in western immunoblottings……………………………51

Table 3.1 List of primers used in real time RT-qPCR for transcript analyses……………………………...………………………………...…76

Table 3.2 Antibodies used in western immunoblottings……………………………79 Table 3.3 Comparison of directional changes in transcript expression levels in mutant versus control fetal livers ……….……………………………….88 Table 3.4 Validation of selected genes by real time RT-qPCR…………….………89

Table 3.5 Functional analyses of genes with altered steady state mRNA levels in Sbds deficient fetal livers……………………………………………..….94

Table 3.6 Expression levels of factors involved in translation…………..………..100

Table 3.7 Transcript feature analyses of 25 genes that showed the greatest changes in polysome loading levels with Sbds deficiency………………………113

List of Figures

Figure 1.1 Crystal structure of archaeal SBDS orthologue (A. fulgidus, AF0491)….10

Figure 1.2 Typical polysome profile of wild type fetal liver extract………………..12

Figure 1.3 Structure of human 80S ribosome……………..…………………………29

Figure 1.4 Translation initiation in eukaryotes…………………………………..….34

Figure 1.5 Translation elongation in eukaryotes…………………………………….37

Figure 1.6 Translation termination and ribosome recycling in eukaryotes……….…38

Figure 2.1 SDS mutant organs exhibit reduced 80S monosomes and persistent polysomes…………………………………………………………….….53

Figure 2.2 Composition of ribosomal components of SDS mutant organs showed loss of 80S…………………………………………………………………….55

Figure 2.3 Ribosome subunit levels are balanced in SDS model organs……………56

Figure 2.4 E18.5 lung and liver of SDS mice show reduced 80S and persistent polysomes…………………………………………………………….….60

Figure 2.5 Polysome profiles of SbdsR126T/- fetal livers show aberrant profiles with increased free 40S and 60S subunit proportions …...……………………62

Figure 2.6 Eif6 is aberrantly associated with ribosomal peaks with loss of Sbds function…………………………………………………………………..64

Figure 3.1 Flow chart of cDNA microarray analyses of steady state total mRNA levels and transcript polysome loading levels of fetal livers………….…84

Figure 3.2 Quality of RNA extracts used for cDNA synthesis and microarray determined by automatic electropherosis system ………………...…..…85

Figure 3.3 Few genes exhibit steady state total mRNA level changes in SDS mutant fetal livers …………………………..………………………………..….92 Figure 3.4 Cell stress response factors p53 and p21Cip1 are not consistently altered in mutant livers………………………………………………………..…….93

Figure 3.5 Summary of changes in probe set intensities in transcript and polysome loading levels between SDS mutant fetal livers and controls……………97

Figure 3.7 SDS mutant proteome is comparable to controls regardless of polysome loading……………………………………………………………….….106

Figure 3.8 Genes with altered polysome loadings do not have corresponding changes in steady state protein levels …………………………..……………….108

Figure 3.9 Transcripts with increased polysome loadings are long with lower GC content…………………………………………………………….…….111

Figure 4.1 Proposed model of SDS ribosome complexes with altered transcript binding……………………………………………………………….…131

Figure 4.2 Less severe loss of 80S monoribosomal peaks in SDS mutant fetal livers deficient for p53……………………………………………………..….139

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List of Appendices

Appendix 2.1 Raw data of polysome profile quantification

Appendix 2.2 Raw data of ribosome run-off profile quantification

Appendix 2.3 Liver cell size count in SDS mutants and controls

Appendix 3.1 Length and GC content of RefSeq genes, grouped by the levels of polysome loading in mutants compared to controls

Appendix 3.2 Expression levels of fetal liver transcripts, raw microarray data

Appendix 3.3 Expression analyses and polysome loading of mutant and control fetal liver RefSeq transcripts (subset of Appendix 3.2 with analyses)

Appendix 3.4 RefSeq transcripts with altered transcription levels (subset of Appendix 3.3)

Appendix 3.5 RefSeq transcripts with altered polysome loading (subset of Appendix 3.3)

Appendix 3.6 Proteins and comparisons identified in mutant and control fetal livers by mass spectrometry

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List of Abbreviations

A site Acceptor site

AML Acute myeloid leukemia

AUC Area under the curve cM Centimorgan

CP Central protuberance

Cryo-EM Cryo-electron microscopy

Database for Annotation, Visualization and Integrated Discovery DAVID (https://david.ncifcrf.gov/)

DBA Diamond Blackfan Anemia del Deletion

E site Exit site eEF, EEF, Eef Eukaryotic elongation factor eIF, EIF, Eif Eukaryotic initiation factor

FC Fold change

FCPL Fold change of polysome loading

FDR False discovery rate

FYSH Fungal, Yhr087wp, Shwachman

GAIT IFN-gamma-activated inhibitor of translation

GO Gene ontology

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HUGO Human Genome Organization i Isochromosome

IRE Iron response element

IRES Internal ribosome entry site kb Kilobase

LC-MS/MS Liquid chromatography–tandem mass spectrometry limma Linear Models for Microarray and RNA-Seq Data

Mb Megabase

MEF Mouse embryonic fibroblast

MRI Magnetic resonance imaging mRNA Messenger RNA mRNP Messenger ribonucleoprotein mTOR Mammalian target of rapamycin nt Nucleotide

ORF Open reading frame

P site Peptidyl-tRNA site

PABP Poly (A)-binding-protein

Pol I RNA polymerase I

Pol II RNA polymerase II

Pol III RNA polymerase III

PTC Peptidyl transferase centre

RMA Robust multichip average

ROX ROX™ Passive Reference Dye

RP Ribosomal protein

RPL Large ribosomal protein

RPS Small ribosomal protein rRNA Ribosomal RNA

S Svedberg unit

SBDS Shwachman-Bodian-Diamond syndrome (Human)

Sbds Shwachman-Bodian-Diamond syndrome (Mouse)

SBDSP Shwachman-Bodian-Diamond syndrome pseudogene

SDS Shwachman-Diamond syndrome snoRNA Small nucleolar RNA

TOP Terminal Oligopyrimidine Sequence uORF Upstream open reading frame

UTR Untranslated region

UTRdb UTR database (http://utrdb.ba.itb.cnr.it/)

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Chapter 1 Shwachman-Diamond Syndrome: a Ribosomopathy Due to Loss of SBDS

Chapter 1 Shwachman-Diamond Syndrome: a Ribosomopathy Due to Loss of SBDS 1 Shwachman-Diamond Syndrome: a Ribosomopathy Due to Loss of SBDS 1.1 Shwachman-Diamond syndrome and natural history 1.1.1 Clinical features of Shwachman-Diamond syndrome

Shwachman-Diamond syndrome (SDS) was first described in 1964 (Bodian et al. 1964; Shwachman et al. 1964). Common features of SDS are overall failure to thrive with notable hematological abnormalities including bone marrow failure and cytopenia, and exocrine pancreatic dysfunction. These are the fundamental elements of disease that must be fulfilled to meet the diagnosis of SDS. Other aspects, including skeletal abnormalities, hepatomegaly and biochemical alterations in liver at young age, behavioral and learning difficulties are also often observed in patients (Aggett et al. 1980; Ginzberg et al. 1999). The spectrum of the disorder is broad and severity varies even between siblings. Life span of SDS patients also varies, with median expectancy to be around 36 years (Dror et al. 2011). Bone marrow failure and elevated risk of leukemia is the most life threatening complication of this disease. The prevalence has been estimated to be about 1/76,000 live births (Ginzberg et al. 1999).

1.1.1.1 Neutropenia and other hematological abnormalities

SDS is an inherited bone marrow failure syndrome. In SDS, bone marrow hypoplasia is typically found with fat infiltration, along with decreased granulopoiesis and decreased megakaryocytes (Dror et al. 2011).

As a consequence of bone marrow failure, SDS patients may experience multiple hematological complications. Neutropenia is the most common hematological abnormality in SDS, with almost all patients showing persistent or intermittent neutropenia, leading to high susceptibility to frequent infections that can be life 3

threatening, especially in children (Cipolli et al. 1999; Dror and Freedman 2002; Grinspan and Pikora 2005; Orelio and Kuijpers 2009). Interestingly, the level of neutropenia does not always correlate with the variation of the degree of severity of bone marrow hypoplasia (Dror and Freedman 2002). It has also been reported that the neutrophils also have compromised function, including defects in chemotaxis and migration (Cipolli et al. 1999). Cytopenia of any or combination of the myeloid blood lineages can occur, for example thrombocytopenia, and/or pancytopenia, although the deficiencies in cell numbers may be mild to moderate compared to neutropenia (Ginzberg et al. 1999; Dror et al. 2011). Red blood cell deficiency with anemia can also occur. In a subset of patients, severe aplastic anemia may develop and leading to transfusion- dependency and eventual stem cell transplant.

SDS patients are at increased lifelong risk for leukemia, typically acute myeloid leukemia (AML), with projections that about 30% of patients will develop AML by 30 years of age (Donadieu et al. 2005). Patients with SDS-associated AML typically have poor prognosis especially with chemotherapy alone, although advances in the hematopoietic stem cell transplant have shown improved outcome (Cupit et al. 2016). Chromosome instability in SDS bone marrows have been reported, and some are thought to be associated with reduced risk of transformation. The most common clonal cytogenetic abnormalities are i(7q) and del(20q) (Parikh et al. 2012; Pressato et al. 2012).

1.1.1.2 Exocrine pancreatic dysfunction

Before the identification of the gene associated with SDS, pancreatic dysfunction was one of the most important clinical markers for this disease, with the exclusion of other causes (Aggett et al. 1980; Ginzberg et al. 1999; Dror et al. 2011). Exocrine pancreatic insufficiency is present early and is characterized by the loss of acinar tissue, the exocrine component of pancreas. There is replacement of tissue with fat, but endocrine and ductal architecture and function appear preserved. Patients frequently present low serum digestive enzyme levels, malabsorption of nutrients and steatorrhea that can lead to malnutrition without digestive enzyme supplementation (Mack et al. 1996; Ip et al. 2002; Dror et al. 2011). Symptoms related to pancreas function including growth failure with feeding difficulties often manifested in infancy, which are typically diagnosed within the 4 first six months of life. Low serum digestive enzymes of pancreatic trypsinogen and isoamylase are often used as indicators for pancreatic insufficiency (Mack et al. 1996). Pancreatic trypsinogen production in SDS is low in most patients at young age and may show improvement with age. Thus, the serum trypsinogen measure is most useful as a diagnostic test in younger children (Ip et al. 2002). Serum amylase activity is generally low in both patients and healthy infants due to the slow rate of maturation of starch digestion function of the postnatal pancreas. In contrast to healthy children, the amylase level remains low in SDS patients as they become older and is a specific and sensitive marker in the diagnosis of children over three years old (Ip et al. 2002). Food digestion will improve with age, and some patients (50%) eventually become independent of digestive enzyme supplementation (Ip et al. 2002; Dror et al. 2011).

1.1.1.3 Skeletal abnormalities

Skeletal defects appear in SDS patients with variable degree of manifestation. Sub- clinical findings may be observed in some patients. The defects typically involve delayed appearance of secondary ossification centres in comparison to age matched controls (Makitie et al. 2004) and increased osteoporosis with low turnover (Toiviainen-Salo et al. 2007). Metaphyseal widening and irregularity, progressive thinning of growth cartilage, and generalized osteopenia are all associated with the disease (Rommens and Durie 2008; Dror et al. 2011; Dall'oca et al. 2012). In addition, joint deformities usually are found at the hip or the knees. Abnormalities of long bones, including shortened ribs with flared ends and narrow rib cage may lead to thoracic dystrophy and respiratory failure during the newborn period (Danks et al. 1976; Dror and Freedman 2002; Makitie et al. 2004).

1.1.1.4 Neuro-developmental issues

Variable cognitive impairment was suggested in earlier reports of SDS patients (Bodian et al. 1964; Aggett et al. 1980; Kent et al. 1990; Cipolli et al. 1999). A study by a Finnish group studied the brain by magnetic resonance imaging (MRI) in nine patients. Their data showed overall small head circumference, total brain volume, global grey and white matters, delayed myelination and majority of brain structures are smaller after age and head-size adjustment compared to control group (Toiviainen-Salo et al. 2008; Booij et al. 5

2013). Further, greater cortical thickness in both sides of the brain and diffuse white matter connectivity was found in SDS patients, and this negatively correlated with cognitive performance scores (Perobelli et al. 2015). Despite the small sample size and variability between patients, findings from both studies showed structural basis for the observed neuro-developmental deficits in SDS.

Comprehensive analyses of cognitive, behavioral and adaptive functioning of a group of 32 diagnosed SDS patients were reported (Kerr et al. 2010). Cognitive and intellectual assessment, including language skills, perceptual skills, memory, attention and aspects of academic achievement in SDS group all showed significant weakness compared to the general population or a cystic fibrosis (CF) control group (Kerr et al. 2010). Behavioral problems, such as socialization skills, communication skills and repetitive behaviors, were documented in 6% of SDS patients, more prevalent than in the general population (1/165) (Fombonne et al. 2006; Kerr et al. 2010). These deficits appear to be independent of age, sex, pancreas function, infection history, nutritional status, family situation or other genetic factors; thus are direct consequences of SDS.

1.1.1.5 Liver features

Hepatomegaly is common in young SDS patients. Elevated liver aminotransferase enzymes are observed in up to 75% of infants and children. These liver disease features tend to resolve by age five and generally require minimal clinical attention (Aggett et al. 1980; Ginzberg et al. 1999; Ritchie et al. 2002). Later age effects for the liver remain somewhat uncertain, although liver dysfunction was noted in a case upon onset of symptoms for pancreatoduodenal carcinoma (Nakaya et al. 2014).

1.1.1.6 Other features

Delayed dentition of permanent teeth, dental dysplasia, increased risk of dental caries and periodontal diseases have also been reported and may contribute to malnutrition (Ho et al. 2007). Involvement of other organs, such as kidney malformation, skin rashes, diabetes- related concerns of endocrine pancreas, failing eyesight, testicular fibrosis, and defects in heart development and cranial facial structures have been reported (Dror et al. 2011). 6

1.1.1.7 Diagnosis and management of SDS

Most SDS patients are diagnosed at very young age typically due to feeding difficulties, failure to thrive, and recurrent infections. In a few cases, diagnoses have been reported in older children or even adults. Currently, the clinical criteria requires evidences of both exocrine pancreatic dysfunction and hematological cytopenia of any myeloid lineage with exclusion of other diseases with overlapping morbidities including CF, Johanson Blizzard syndrome, Diamond-Blackfan anemia (DBA), Fanconi anemia or Dyskeratosis congenita. Other symptoms, as described above, are considered supporting but are not required for diagnosis. Molecular diagnosis with biallelic SBDS mutations is possible since the discovery of the gene in 2003 and is now recommended for suspected SDS cases (Boocock et al. 2003; Dror et al. 2011).

After diagnosis, all possible aspects of organ functions should be evaluated or tested as suggested by the consensus guidelines for SDS (Dror et al. 2011). At follow up, complete blood counts should be tested every three to six months, and bone marrow evaluation should be performed every 1 to 3 years, or as clinically indicated. Further, monitoring of nutrition, growth and dental care should be ongoing. The variations of clinical phenotypes and degree of severity, as well as the ongoing risk related to bone marrow failure, such as AML or aplastic anemia, highlight the importance of consistent follow-up care. Since most patients are diagnosed at young age, patient care and compliance often depend on parents or guardians. Thus, communication, understanding and support between the clinical team and patient families are crucial for the optimal health of SDS patients.

1.1.2 Molecular basis of SDS

1.1.2.1 Identification of SBDS

The segregation analysis of 70 families with 84 SDS patients established an autosomal recessive mode of inheritance as was suspected based on family reports (Ginzberg et al. 2000). Using these resources, linkage analysis revealed an interval of 2.7cM on chromosome 7 spanning the centromere that was strongly associated with the disease. Greater than 50 genes or transcription units were mapped to this region but none of the 7

subset of genes with defined biological functions appeared as obvious candidates (Goobie et al. 2001). Recombination and haplotype mapping was then employed to further refine the disease locus to 1.9cM on the long arm of chromosome 7, providing the basis for the identification of the causal gene. Thirteen Expressed Sequence Tags and three known genes mapped within this 1.9cM interval. Of these, only TPST1 encoded a characterized functional product but was excluded as a likely SDS candidate gene based on mutational analysis (Popovic et al. 2002).

Eventually, a previously uncharacterized gene was identified to be associated with SDS from 18 candidate transcription units in the refined 7q11 interval. The transcript of SBDS is 1.6kb long with five exons spanning 7.9kb in a locally duplicated region on chromosome 7. The encoded protein predicts a molecular mass of 28.8 kDa of 250 amino acids and pI of 8.9. The gene is highly conserved with orthologs in archaea and all eukaryotes (Boocock et al. 2003; Boocock et al. 2006).

An unprocessed pseudogene (SBDSP) located 5.8Mb distally is 97% identical to SBDS with sequence changes including deletions and point mutations that could lead to disruption of the protein coding potential. The two most common mutations found in 89% SDS patients include c.183-184TA→CT (also named c.183_184delinCT) leading to early polypeptide truncation (p.Lys62Ter or p.Lys62*) and c.258+2T→C leading to markedly impaired splicing with inclusion of intron 2 predicting early peptide truncation (p.Cys84Tyrfs*4). This latter mutation does permit some splicing of intron 2 leading to a very low level of normal SBDS mRNA. Other less common mutations found in SDS patients are due to point mutations or frame shift mutations in other coding regions of SBDS. The identification of SBDS led to advancement in the development of model systems as well as mutational, structural and functional studies of SBDS.

1.1.2.2 Structure and function of SBDS and relation to EIF6

Together with the highly conserved nature, the absence of any patient with two null alleles (despite the occurrence of the common p.Lys62* disease causing mutation) indicates that SBDS is essential for life with a fundamental role in cellular processes. The first hints of the function of SBDS came from ortholog studies. Alignments of putative 8

SBDS orthologs showed marked conservation in all eukaryotic and archael species but not in eubacteria. In archea, SBDS orthologs were located in operons involved in RNA metabolism and, in yeast, the orthologs clustered with RNA processing genes.

X-ray crystallography studies with the archael ortholog from A. fuldigus revealed a three domain structure with an N-terminal FYSH (Fungal, Yhr087wp, Shwachman) domain with mixed α helices and β sheets, a central helical domain and a C-terminal ferredoxin- like fold arranged in a V-shape (Figure. 1.1, Savchenko et al. 2005; Shammas et al. 2005; Finch et al. 2011). The FYSH domain, corresponding to Met1- Val95 in human SBDS, is a unique fold that is necessary but not sufficient for SBDS function as determined by yeast growth. It is the most conserved domain in SBDS and can functionally complement amongst closely related species. NMR analysis of human SBDS showed that this domain had high degree of dynamic flexibility with the potential to propagate structural and positional changes to other domains (Finch et al. 2011). The central domain together with this FYSH domain is sufficient for SBDS function and confers species specificity indicated by genetic studies in yeast. The C-terminal ferredoxin-like structure is a common RNA-binding fold and may be an RNA recognition motif based on structural homology (Savchenko et al. 2005). This latter domain is the least conserved domain in SBDS and is dispensable for survival in yeast (Boocock et al. 2006). The apparent Sbds orthologs of some species in flowering plants and chromalveolates have extended C- terminal regions with C2H2 zinc finger domains. The functional consequences of this extra domain are still unclear, although it has been proposed to be consistent with the RNA binding features.

The involvement of SBDS in translation was supported in mammalian systems. Transient depletion of SBDS in HEK293 cells as well as mouse embryonic fibroblasts (MEFs) derived from mouse models with homozygous disease associated SbdsR126T mutations showed reduced total protein synthesis in [35-S] Methionine incorporation assays (Ball et al. 2009). Genetic studies using yeast and mammalian model systems provided insight toward the role of SBDS in protein synthesis and has linked the function of SBDS to translation initiation factor EIF6 (TIF6 in yeast, Menne et al. 2007; Finch et al. 2011). eIF6 is a eukaryotic translation initiation factor with a five-fold pseudo-symmetry and 9

two large flat surfaces (Klinge et al. 2011). It cycles between the nucleolus and cytoplasm for pre-60S ribosome subunit synthesis and export, and binds to the 60S subunit at the subunit joining interface near Rpl23 and Rpl24 and the sarcine-ricine loop. The binding to the 60S subunit prevents the joining of the 40S subunit and any premature formation of the 80S monoribosomes, thus eIF6 acts as a type of anti-association factor in mammalian cells that must be released for translation initiation (Figure 1.3C, Senger et al. 2001; Ceci et al. 2003; Gandin et al. 2008; Gartmann et al. 2010; Klinge et al. 2011). Later studies suggested that SBDS (Sdo1 in yeast) cooperates with the GTPase elongation-like factor 1(Efl1 in yeast, EFL1 in human) in the release of EIF6/TIF6 from pre-60S in a 60S- dependent manner (Finch et al. 2011). That the translation ancillary factor GTPase Efl1directly catalyzes the removal of Eif6 from pre-60S subunits with dependence on GTP binding and hydrolysis has been shown in an ex vivo Eif6 release assay (discussed later in Section 4.2.4, Finch et al. 2011).

1.1.2.3 Models of SDS

Model systems developed for studies of SDS and SBDS include yeast, slime mold, zebrafish, mouse, as well as human cell culture systems. Examples of these models are discussed here. Polysome profiles were used in these studies to get a first glimpse of the influence of loss of Sbds on translation. Such profiles are obtained by separating protein and protein-RNA complexes in cytoplasmic lysates of sample tissues by size using sucrose density gradient centrifugation. By measuring the absorbance at 254 nm as the gradients are fractionated, peaks are observed corresponding to ribosomal complexes that can be verified by immunoblotting with representative biomarkers. In a typical polysome profile, moving from lighter to heavier sucrose, the peaks correspond to the 40S small ribosomal subunits, 60S large ribosomal subunits, 80S monoribosomes, and polysomes; where the latter are multiple ribosomes on mRNA transcripts (Figure 1.2). The polysomes, particularly heavy polysomes (typically with more than three ribosomes, Gandin et al. 2014), reflect ongoing active translation (Mikulits et al. 2000; Mathews et al. 2007). Variations of the polysome profiles are useful diagnostic indicators of disturbance of the ribosome biogenesis or protein synthesis. For example, the occurrence of abnormal peak proportions or split peaks (known as halfmers, see below) would 10

Figure 1.1 Crystal structure of archaeal SBDS orthologue (A. fulgidus, AF0491). A, a ribbon representation of AfSBDS, red=α helices in domain I (FYSH domain), orange=β strands in domain I, yellow= α helices in domain II, blue = α helices in domain III, cyan = β strands in domain III. B, electrostatic surface potential of AfSBDS, blue = positively charged areas, red = negatively charged areas. The R126 residue targeted in the mouse models used in this study is indicated. The panels in this figure are reproduced with permission (Shammas et al. 2005). 11 suggest problems in the biogenesis of ribosomes or their subunits (Helser et al. 1981; Wong et al. 2011).

Yeast models of SDS were achieved by deleting Sdo1 or inducing missense mutations in Sdo1. Overall severe growth deficiency was observed in these strains, along with reduction of mature 60S ribosomal subunits in the cytoplasm and increased sensitivity to translation inhibitors such as aminoglycosides, neomycin, paromomycin, and hygromycin (Menne et al. 2007; Moore et al. 2010). Polysome profiles by different groups yielded inconsistent results. In one study, deletion of Sdo1 led to substantial decrease in the number of 80S monosomes and polysomes in yeast profiles with overall reduced 60S ribosomal subunit peaks and reduced 60S to 40S subunit ratios (Menne et al. 2007). In another study, Sdo1 deletion led to presence of halfmers and reduced polysomes with increased free 60S ribosomal subunits (Moore et al. 2010). The reasons behind the discrepancies in these studies were unclear as it was understood that the same yeast deletion strain was used in both; nevertheless, links to involvement in ribosome biogenesis and function were apparent. Other models soon followed, see Table 1.1.

A constructed temperature-sensitive Sbds-deficient strain of Dictyostelium discoideum was observed to lead to growth failure at the restrictive temperature. Analysis of the polysome profiles of total cell extract from the conditional mutant D. discoideum showed increased free 40S and 60S ribosomal subunit peaks as well as reduced monosome and polysome peaks with distinctive halfmer features. The halfmers reflect 43S initiation complexes bound to mRNAs that are stalled at the AUG start codon without 60S ribosomal subunits (Helser et al. 1981; Wong et al. 2011).

A zebrafish model of SDS with the morpholino knockdown of the Sbds ortholog led to a number of developmental defects, including neutrophil loss, bone deformity and hypoplasia in the pancreas (Provost et al. 2012). Polysome analysis of cellular lysates from embryos with loss of Sbds displayed increased level of free ribosome subunits 40S and 60S, and mildly reduced polysome peaks (Provost et al. 2012).

SDS was first modeled in mammalian systems in mice. Complete deletion of Sbds resulted in early embryonic lethality so conditional Sbds alleles were needed to study the 12

Figure 1.2 Typical polysome profile of wild type fetal liver extract. Ribosomal peaks representing 40S small ribosomal subunits, 60S large ribosomal subunit, 80S monoribosomes and polysomes are indicated with cartoon model of each complex below the profile. 13

effect of complete loss of Sbds in various cell lineages and organs. Sbds null neutrophils showed impaired oxidative burst response to infections (Zhang 2009). And ablation of Sbds in monocytes led to defects in osteoclast formation due to both migration and fusion impairments (Leung et al. 2011). A complete Sbds-deficient liver exhibited profound degenerative appearance in adult mice. Polysome profiles from extracts of these adult mouse livers showed decreased 80S monoribosomes with accumulation of free 40S and 60S subunits and presence of 43S halfmers. No significant changes in the ratio of total 60S to 40S ribosomal subunit peaks were seen (Finch et al. 2011).

Our lab previously generated SDS mouse models with conditional and disease-associated missense alleles. The SDS model with SbdsR126T/R126T showed severe growth deficiency that became apparent only near mid-gestation at ~E14. Although these mice failed to survive birth, they did exhibit features that closely paralleled SDS (Tourlakis et al. 2015). Foremost, the bone marrow of the SDS embryo was hypocellular and defective in hematopoiesis, with decreased levels of all myeloid lineage progenitors. Reduced ossification in the metacarpals in late gestation and rib cage anomalies were observed. Apoptosis and reduced proliferation in the developing brain was evident as early as E14 in SbdsR126T/R126T embryos. The postnatal pancreas with conditional and SbdsR126T allele combinations showed pancreatic growth impairment, fat infiltration and reduced digestive enzyme secretion (Tourlakis et al. 2012). Polysome profiles of pancreas lysates revealed reduced 80S monosome peaks with sustained polysomes comparable to their corresponding control littermates (Tourlakis et al. 2012).

1.2 Ribosomes and translation 1.2.1 Ribosomopathies

Ribosomopathies are a class of disorders caused by defects in ribosome biogenesis, including deficiencies in the ribosomal components, ancillary translation factors with roles in ribosome maturation, pre-rRNA transcription, processing and modification. A number of diseases have been categorized as ribosomopathies and are given in Table 1.2, including Diamond-Blackfan Anemia (DBA), Treacher Collins syndrome, acquired 5q- syndrome, isolated congenital asplenia, Bowen-Conradi syndrome as well as SDS (Freed 14

et al. 2010). As a common process is affected in all these disorders, they undoubtedly share some characteristics; most often growth retardation, and predisposition to cancer. However, ribosomopathies are also highly heterogeneous, and each disorder has distinct clinical manifestations. Even for conditions with bone marrow failure as the common feature, affected blood cell lineages show differing specificities (Yelick and Trainor 2015). For example, erythroid hypoplasia is typically associated with DBA and 5q- syndrome, but neutropenia is the most common hematological concern with SDS. Specific organs or tissues are also affected more prominently in different ribosomopathies, including the exocrine pancreas in SDS, the spleen in isolated congenital asplenia, and craniofacial developmental abnormalities in Treacher Collins syndrome (Yelick and Trainor 2015). In fact, tissue specificity remains as an interesting conundrum in ribosomopathies.

Possible mechanisms of the tissue specificities in ribosomopathies, amongst different diseases or for a particular syndrome, have been postulated. The most obvious explanation is the variation of expression levels of factors involved in translation in different tissues; such that specific factors are rate limiting (Yelick and Trainor 2015). Alternatively, some of the ribosomal components may have extra-ribosomal functions. However, it remains difficult to segregate the effects of such a complex and fundamental process such as translation from other cellular pathways or processes. Another explanation is that tissues have different sensitivity to the loss of translation capacity. Mutations in ribosomal proteins (RPs) or ancillary factors lead to reduced fully functional ribosome or overall translation capacity, although minimum requirements for cellular survival may be supported. Cells or cell types that need to divide rapidly or have high protein synthesis demand during development may have higher risks of being affected, due to direct loss of protein output, leading to adverse organ development, or the triggering of cellular stress signaling pathways (Tourlakis et al. 2015).

It may be that disturbances in ribosome proteins or ribosome subunits may involve or lead to the some selective targeting of sets of mRNAs, through characteristics of the mRNAs (for example, IRES mRNAs, TOP mRNAs), or by changes in interaction with other binding factors (Kondrashov et al. 2011; Nakhoul et al. 2014). Another mechanism 15

Table 1.1 In vivo models of SDS

Mutation Organ phenotypes Ribosome profile References Saccharomyces cerevisiae Sdo1Δ Severe slow growth, impaired Decreased (Menne et al. 2007) mitochondrial function monoribosome and Hypersensitivity to translation polysome peaks, reduced inhibitors ratio of 60S to 40S Defective nucleolar cycling of subunits; halfmers not Tif6, rescued by Tif6 mutants apparent with gain of function

Sdo1Δ Not studied Increased free 60S (Moore et al. 2010) ribosomal subunits, reduced 80S and reduced polysome peaks, presence of halfmers

Dictyostelium discoideum Temperature Growth failure at restrictive Increased free 40S and (Wong et al. 2011) sensitive self-splicing temperature, no impairment of 60S ribosomal subunits, inteins that leads to chemotaxis with one hour pre- reduced 80S, presence of non-functional or incubation at restrictive halfmers degraded Sbds temperature Rescued with Eif6 mutants possessing reduced affinity for 60S subunit

Ectopic integrated Localization of fusion protein Not studied (Wessels et al. 2006) and non-integrated accumulated in the migration in-frame Sbds-GFP edge of pseudopods with fusion protein treatment of chemoattractant (cAMP)

Zebrafish Morpholino Altered spatial relationship Not studied (Venkatasubramani knockdown between endocrine and and Mayer 2008) (translation blocking exocrine pancreas, but and splice blocking) apparently normal acinar cell development Granulopoiesis defect

Morpholino Early lethality by 7dpf, defects Altered ratio of free 40S (Provost et al. 2012) knockdown in neutrophil lineages, and 60S ribosomal (translation blocking cartilage and bone subunit peaks compared and splice blocking) deformities, pancreatic to 80S monoribosomal hypoplasia with defective peak, reduced polysomes proliferation of pancreatic progenitors p53 independent organogenesis defects

Mouse Sbds-/-, early Early embryonic lethal (E6.5) Not studied (Zhang et al. 2006; truncating null with Zhang 2009) neomycin knock in cassette in exon 1 Sbdslox/- : LysM-Cre Impaired oxidative burst in Not studied (Zhang 2009) (neutrophils) response to infection

Sbdslox/- : LysM-Cre Impaired osteoclast formation Not studied (Leung et al. 2011) (monocytes) with defective monocyte migration and fusion; severely reduced Rac2 (Rho-GTPase) level Sbdslox/- : Mx1-Cre Disordered liver architecture, Accumulation of free (Finch et al. 2011) (liver) necrosis, apoptosis, acute 40S and 60S subunits, inflammation reaction presence of halfmers, normal 40S:60S ratio SbdsR126T/R126T, Perinatal lethal, small embryo Reduced 80S (Tourlakis et al. SbdsR126T/- size, apoptosis in brain monoribosome peak, and 2015) (E11.5), skeletal and lung remained low with p53 defects, defective ablation (fetal liver) hematopoiesis in myeloid lineage Improvement of organ phenotypes with ablation of p53 Sbdslox/R126T : Ptf1a- Small pancreas, acinar cell Moderate reduction of (Tourlakis et al. Cre (pancreas) hypoplasia, fat infiltration, 80S monoribosome peak, 2012) loss of zymogen granules which normalized with p53 ablation

includes the possibilities of tissue specific aspects of ribosome or translation processes whereby the composition of ribosomes is not exactly the same in all tissues, and leads to variation in the clinical features across certain tissues with mutations in ribosomal components (Nakhoul et al. 2014; Yelick and Trainor 2015). Clear and definitive mechanisms for tissue specificity in the described ribosomopathies or even just one disorder has not been developed; however, it is important to keep in mind these possible explanations are not mutually exclusive and a full explanation of disease understanding may require a combination of them.

Disease phenotypes of ribosomopathies result from not just inadequate protein synthesis but also the secondary effects of cellular signaling pathways in response to the stresses of reduced translation. Signaling pathways, especially those that coordinate cellular growth and division, such as mTOR and Myc, have been indicated in the manifestation of ribosomopathies, although specific contribution have not been investigated extensively. The p53 pathway has been studied and is implicated for most of these diseases (discussed in detail in 1.2.4.2). Deregulated rRNA transcription, processing and unincorporated RPs are all effectors of nucleolar stress, which in turn results in stabilization of p53 and cell cycle arrest or apoptosis (Zhang and Lu 2009; Danilova and Gazda 2015). Elevated level of p53 has been observed in patients with DBA, SDS and Treacher Collins syndrome. Further, depletion of p53 was shown to rescue some of the phenotypes in these diseases, at least partially (Panic et al. 2006; Chakraborty et al. 2009; Danilova et al. 2011). Different tissues may have specific response to p53 activation, including apoptosis or senescence, as seen in the murine model studies of SDS (Tourlakis et al. 2015), and provide a possible explanation for some of the tissue specificity in clinical manifestation of ribosomopathies.

Beyond these specific features, another dilemma in the field of ribosomopathy is the understanding how the overall reduced growth due to translation deficiency relates to the predisposition to cancer where there is uncontrolled cell growth. A possible scenario is that the reduced protein synthesis capacity and the potential cell cycle response with the defective ribosome biogenesis may ultimately stress cells which lead to hypoproliferation of affected cells. This puts the cell populations, especially those with high proliferation or 18 protein synthesis demand, under pressure for selecting cells with secondary mutations to bypass the limitations at the cost of translation with compromised fidelity. Amongst all the other growth deficient cells, the few cells which successfully re-gain any growth potential will have a competitive advantage over their neighbours; thus, priming early steps of transformation.

1.2.2 Translation overview

Translation is a highly conserved and indispensible cellular process that enables cells to direct protein synthesis from mRNA transcripts. As a critical step in the gene expression pathway, protein synthesis maintains cellular functions and allows living organisms to respond to intrinsic and extrinsic signals, to stress, to nutrient supplies and stressors, all to support homeostasis. The importance of translation and translational regulation is illustrated by the high proportion of energy and resources devoted to make protein synthesis machineries, the ribosomes. In rapidly growing yeast, 60% of transcriptional activity is used in transcribing rRNAs, one third of the bulk cellular mRNA encode RPs and together, the translation can require up to half of the cells’ energy consumption (Mathews et al. 2007).

The assembly and availability of the ribosomes, as determined by the transcription and processing of the rRNAs, production of RPs and other ancillary components affect protein synthesis globally. Beyond the aspects of machinery biogenesis, the translation process can be divided into initiation, elongation, termination and recycling steps. Regulation of translation can occur at multiple steps. In general, factors affecting the efficiency of protein synthesis can be broadly divided into two categories: the efficiencies of the translation machineries and the features of mRNAs to be translated (Mathews et al. 2007). During translation, the initiation step is a common determinant of regulation, as early steps are ultimately more resource efficient compared to interruptions midway or toward the end of a process, although elongation and termination do play roles under specific circumstances (Mathews et al. 2007). Intrinsic properties of messages, including abundance and stability of transcripts, as well as the strength of the ‘translatability’ of the 5' and 3' regions are all factors that modulate the overall protein expression level 19

Table 1.2 Ribosomopathies Disease OMIM ID Gene Molecular Clinical Inheritance References1 function manifestation Ribosomal proteins Diamond-Blackfan RPS7, 40S and 60S Anemia, bone marrow Autosomal (Danilova anemia RPS10, components, rRNA failure, craniofacial dominant, and Gazda 105650 RPS17, processing abnormalities, cardiac 1 in 100,000 2015) RPS19, defects, limb and to 200,000 RPS24, urogenital live births RPS26, abnormalities, cancer RPS27, predisposition RPS28, RPS29, RPL5, RPL11, RPL15, RPL26, RPL27, RPL31, RPL35A

5q- syndrome RPS14 40S component, Severe macrocytic acquired 153550 pre-18S rRNA anemia, processing myelodysplasia, cancer predisposition

Isolated congenital RPSA 40S component, Absence of spleen at Autosomal (Bolze et al. asplenia pre-18S rRNA birth, severe bacterial dominant, 2013) 271400 processing infections, no other <100 cases developmental defect reported

Hereditary RPL21 60S component Non-syndromic hair Autosomal (Zhou et al. hypotrichosis loss dominant 2011) simplex 615885

Ribosome biogenesis and ancillary proteins Treacher Collins TCOF1 rDNA transcription Craniofacial Autosomal syndrome and methylation abnormalities dominant, (Teng et al. 606847 1 in 10,000 to 2013) 1 in 50,000 Treacher Collins POLR1D syndrome-2 613717

Treacher Collins POLR1C syndrome-3 248390

Native American CIRH1A Maturation of 18S Neonatal jaundice, Autosomal Indian childhood rRNA of the 40S biliary cirrhosis, lethal recessive, cirrhosis ribosomal subunit by adolescence < 100 cases 604901 without liver reported transplant 20

Bowen-Conradi EMG1 Maturation of the Growth retardation, Autosomal syndrome 40S ribosomal psychomotor delay, recessive, 211180 subunit, skeletal abnormalities, 1 in 355 live methyltransferase lethal by early births in the for 18S rRNA childhood Hutterite methylation population

ANE syndrome RBM28 Maturation of the Growth retardation, Autosomal (Alopecia, 60S ribosomal loss of motor ability, recessive neurologic defects, subunit mental retardation, endocrinopathy) skeletal and skin 612079 abnormalities, hair loss, central adrenal insufficiency

Richieri Costa EIF4A3 Exon junction Craniofacial Autosomal (Favaro et al. Pereira syndrome complex abnormalities, severe recessive, 2014) 268305 constituent, DEAD limb defects with founder effect box RNA helicase language and learning in Brazilian difficulties population

Shwachman- SBDS Translation Growth retardation, Autosomal Diamond syndrome initiation and 60S exocrine pancreatic recessive, 260400 maturation dysfunction, skeletal 1 in 76,000 abnormalities, neuro- live births developmental concerns, neutropenia and hematological abnormalities with risk of AML

Small nucleolar ribonucleoproteins2 Cartilage hair RMRP RNA component of Short-limbed Autosomal hypoplasia RNase MRP dwarfism, hypoplastic recessive, 250250 complex, precursor hair, defective <200,000 rRNA cleavage, erythropoiesis and cases in the maturation of 5.8S immunity USA, higher rRNA and 60S in Finnish ribosomal subunit and Amish populations X-linked DKC1 Pseudouridine Bone marrow failure, X-linked dyskeratosis synthase, cancer predisposition, recessive, congenita component of mucocutaneous 1 in 305000 telomerase abnormalities 1,000,000 live births Hoyeraal- DKC1 Pseudouridine Severe DKC X-linked Hreidarsson synthase, phenotypes with recessive, syndrome component of immunodeficiency, very rare 305000 telomerase microcephaly and cerebellar hypoplasia Dyskeratosis NOP10 Protein component Bone marrow failure, Autosomal congenita NHP2 of box H/ACA cancer predisposition, recessive, 305000 RNPs, component mucocutaneous 1 in of telomerase abnormalities 1,000,000 live births 21

1Information adapted from Freed, 2010, with additional data from Online Mendelian Inheritance in Man, OMIM®. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University (Baltimore, MD), December 1, 2015 (World Wide Web URL: http://omim.org/) and medical literature as cited.

2Genes involved in rRNA and other nuclear RNA processing; disease manifestations extend beyond ribosome biogenesis.

(Mathews et al. 2007). For examples, in the 5' untranslated region (5' UTR) of eukaryotic transcripts, the length and secondary structure in the 5' UTR, the presence of uORFs or 5' terminal oligopyrimidine tract (5' TOP) sequences and other sequence elements that can be recognized by trans-acting translation factors, and all influence the initiation efficiency. On the other hand, the 3' UTR is generally believed to harbor cis-acting elements that regulate the stability and localization of a message (Mathews et al. 2007).

Regulation of gene expression at the translational level has many critical facets. It allows rapid and prompt adjustment to internal or external signaling cues. Controlling at the translational level also allows fine tuning and reversibility, as the range of changes in translation rate are generally much narrower than transcription (Mathews et al. 2007). Specific groups of mRNAs can be co-regulated through trans-acting translation ancillary factors or common sequence/ structural elements. Further, translation of mRNAs with specific localization as a means of spatial control is important for development and particularly in some organs, such as the brain, to achieve synaptic plasticity (Mathews et al. 2007).

1.2.3 Transcription and processing of rRNA

Ribosomal RNAs (rRNAs) are the RNA component of the ribosome. Four rRNAs are included in the eukaryotic ribosome: 18S in the small ribosomal subunit, 40S; 28S, 5.8S and 5S in the large ribosomal subunit, 60S. rRNA accounts for roughly 60% of the ribosome by weight and is encoded by rDNAs clustered at a few sites interspersed in mammalian genomes including humans (Turowski and Tollervey 2015). The human genome contains 300-400 copies of rDNA, with about half of them being active. The accessibility of the chromatin state by RNA polymerase around the rDNA is dynamic and regulated by the growth conditions and developmental demands for ribosomes. 5S rRNA is transcribed by RNA Polymerase III (Pol III) and the other three rRNAs are transcribed by RNA Pol I as a single pre-rRNA 47S transcript (Turowski and Tollervey 2015). Differences in the density of Pol I on rDNAs suggest regulation of elongation rate to accommodate co-transcriptional processing of the transcript and assembly of pre- ribosomal complexes according to cellular requirements (Turowski and Tollervey 2015). 23

The maturation of the 47S pre-rRNA involves a multiple step pathway of digestion with endonuclease and exonuclease events (Fatica and Tollervey 2002). Transcription of the pre-rRNA is coupled with processing, cleavage and modification of the nascent transcript that ultimately leads to the production of 18S for 40S, and 28S and 5.8S for 60S (Fatica and Tollervey 2002; Turowski and Tollervey 2015). The co-transcription maturation process takes place in the form of ribonucleoprotein particles (RNP) with transient and dynamic composition involving over 150 non-ribosomal proteins and over 75 small nucleolar RNAs (snoRNAs) (Turowski and Tollervey 2015). As the rRNAs are being processed, folding of the rRNAs and assembly with RPs imported from cytoplasm into pre-40S and 60S complexes also occur (Turowski and Tollervey 2015).

The production of rRNA, particularly by RNA Pol I is generally viewed as a rate limiting step in ribosome biogenesis; and thus is carefully regulated to maintain equimolar production of all ribosomal components according to cell growth and proliferation requirements mediated by environmental conditions (Laferte et al. 2006; Mayer and Grummt 2006). One conserved pathway in the regulation of the ribosomal components and ribosome biogenesis is the Target of Rapamycin (TOR) kinase signaling cascade (Mayer and Grummt 2006). Rapamycin-dependent concomitant repression of the expression of all ribosomal components has been observed. In this homeostasis regulation, Pol I is a key determinant involved in cross-talk with the Pol II processes which transcribe mRNAs (Laferte et al. 2006). This coordination, however, is limited to the mRNAs encoding RPs and excludes the ancillary proteins involved in the processing and assembly of ribosomes or other ancillary factors. The level of 5S rRNA is also concomitantly co-regulated by the level of 47S rRNA, although links between Pol I and Pol III have been less studied (Laferte et al. 2006; Mayer and Grummt 2006).

1.2.4 Building ribosomes and ribosome structure

1.2.4.1 Ribosomal proteins

Ribosome biogenesis requires the production, processing and assembly of four rRNAs, 33 small RPs and 47 large RPs. These RPs are translated in the cytoplasm like other proteins and are then transported into the nucleus for ribosome subunit assembly with 24

nascent rRNAs as they are processed. Genes encoding the RPs are highly conserved across eukaryotic species although the chromosomal locations vary. In human, RP genes are interspersed in the nuclear genome and most have only one copy (Uechi et al. 2001; Gupta and Warner 2014). RPS4 has one copy on the X chromosome (RPS4X) and two copies on the Y chromosome (RPS4Y1 and 2) (Uechi et al. 2001). RPS17 has two copies on chromosome 15 due to an ancient duplication event. The extra copies for these two genes exist in human but are not found in the mouse genome. Further, RPS27, RPL3, RPL22 and RPL26 have their corresponding duplicated ‘like’ genes (e.g. RPS27L). Some of these ‘like’ genes may have tissue specific expression or appear to be only involved in the assembly of the ribosome rather than being final constitutive components in contrast to their respective original genes. These ‘RP like’ genes exist in both human and mouse except for RPL26L that does not occur in mouse. In addition, all RPs have multiple copies of pseudogenes, likely derived from processed RP mRNAs and have almost identical sequence to their authentic mRNA copies. Three of these appeared to have been retained as active, RPL10, RPL36 and RPL39, but have lost their original introns and are under control of alternate promoters (Gupta and Warner 2014).

1.2.4.2 Regulation of RPs and extra-ribosomal functions

Keeping balance of ribosomal gene transcription and translation, and of RP mRNA stability or RP turnover, would appear to be critical tasks for cells. Ribosome biogenesis requires strictly equimolar incorporation of rRNAs and all RPs (Gupta and Warner 2014). RPs are relatively small and highly charged, with the ability to bind nucleic acids. Therefore, unincorporated RPs or imbalanced production may be a serious threat to the stability of cells. Imbalance has been shown to trigger a p53 response and may result in apoptosis (see below, in next paragraph), (Golomb et al. 2014; James et al. 2014). In mammalian systems, regulation of ribosomal protein amounts is thought to occur at both the transcription and translation levels. Transcriptional regulation of RNA polymerase I, II and III is achieved by crosstalk as mentioned in Section 1.2.3. It has been shown in yeast, that there are about five fold differences in mRNA levels across the RPs, which highlights a need for translational control of the RP levels (Gupta and Warner 2014). The major regulation of the production of RPs by translation is achieved by the TOR pathway 25

that is conserved across eukaryotic species. It regulates an array of anabolic processes in response to hormones, growth factors and stress. All RP mRNAs have signature oligopyrimidine sequences at their 5' terminus, called the 5' TOP, with a cytidine residue at the cap site instead of a more common adenine residue, followed by an uninterrupted sequence of 7 to 13 pyrimidine nucleotides (Levy et al. 1991; Avni et al. 1997). In activation situations, the 5' TOP mRNAs are translated at close to maximum efficiency and are found in the polysomal fractions only. In growth repressed cells, a large proportion of the 5' TOP mRNAs are sequestered as mRNP (messenger ribonucleoprotein) particles and are not translated. Thus, the growth dependent translation of the RPs can exhibit an ‘all or none’ response (Wang and Proud 2006). It remains to be elucidated precisely how the translation of the 5' TOP mRNAs is controlled by the TOR pathway, or whether other constraints also have influence. The activity of Rps6 Kinase 1 (S6K1) and the phosphorylation of Rps6 appear to act as ‘gatekeepers’ in some studies, but exactly how these work together has been debated (Wang and Proud 2006; Ma and Blenis 2009).

An obvious aspect that may explain why disruption of one player in a complex process with overall coordinated regulation may lead to variation and specific disease phenotypes would be to recognize that some components of the process may have extra-functional roles. Examples of more authentic extra-ribosomal functions came from RPL13A and RPS3. RPL13A can inhibit translation initiation of specific mRNAs by interacting with the initiation factor EIF4G as part of a gamma interferon inhibitor of translation (GAIT) complex upon phosphorylation of RPL13A (Warner and McIntosh 2009). RPS3 can nick DNA at abasic sites upon genomic damage, exhibiting endonuclease activities (Warner and McIntosh 2009). In most cases, however, the so-called extra-ribosomal functions of the RPs can include roles in the biogenesis or function of ribosomes, aside from being purely constituents of ribosomes. In human, RPS13 can bind to the first intron of its own transcript to inhibit splicing (Warner and McIntosh, 2009). In vertebrates, some RPs are involved in transducing nucleolar stress when defects in ribosome assembly occur or there is accumulation of free RPs, leading to cell cycle arrest or apoptosis as a self- regulation and protective mechanism. RPL5 and RPL11 typically form a complex with 5S rRNA during 60S subunit biogenesis. Components of this 5S subcomplex can 26 individually or synergistically associate with MDM2, releasing p53 and leading to cell cycle arrest or apoptosis due to p53 activation (Warner and McIntosh 2009; Zhou et al. 2015). In addition, knockdown of many RPs in zebrafish embryos often leads to p53- dependent apoptosis and major developmental defects (Warner and McIntosh 2009). Overexpression or dysregulation of subsets of RPs have been observed in different types of tumors (Warner and McIntosh 2009; Bhavsar et al. 2010). Further, in various systems (Drosophila, human or mice), haploinsufficiency of RPs leads to a spectrum of phenotypes including lethality, growth failure, developmental defects, bone marrow failure, tumor susceptibility as well as no apparent phenotypes in some cases (Warner and McIntosh 2009). Variable responses to RP imbalance, in addition to translation insufficiency, may contribute to differential tissue and organ disease features across specific ribosomopathies (Warner and McIntosh 2009).

For SBDS, extra-ribosome/translation roles have not been excluded and a number of extra-ribosomal functions have been alluded to. SBDS was found to co-localize with mitotic spindle in the cytoplasm with the suggestion that this could maintain mitotic spindle stability. It was further suggested that deficiency in SBDS leads to accumulation of mitotic abnormalities and chromosome segregation errors, and thus contribute to bone marrow failures and AML susceptibility (Austin et al. 2008). A mass spectrometry study of proteins interacting with SBDS identified RPs of the 60S subunit as well as factors involved in DNA-damage response (RPA70 and DNA-PK) (Ball et al. 2009). Cells deficient in SBDS also appear to be hypersensitive to DNA damage, further suggesting roles of SBDS in cellular stress pathways and genome stability (Ball et al. 2009). However, as translation is such a fundamental process, it remains difficult to separate the direct consequences of SBDS loss from more indirect effects due to the translation deficiency imposed by the loss of SBDS for any observed phenotypes.

1.2.4.3 Ribosome structure

Knowledge of the structure of ribosomes allows for detailed understanding of the mechanisms of translation and provides insight for ribosomopathies. Crystallographic structures of intact ribosomes or ribosomal subunits from an archaeal organism were first 27 available in 2000 (Ben-Shem et al. 2010). More recently, cryo-electron microscropy (Cryo-EM) has provided additional structural data which enabled the construction of eukaryotic ribosomes at close to atomic level with location and folding information for most of the RNA and protein components of the ribosomes. This was first performed with yeast model systems in 2011(Figure 1.3A), and data from more complex organisms have rapidly become available (Rabl et al. 2011; Anger et al. 2013; Khatter et al. 2015). While eukaryotic ribosomes are significantly larger and more complex than prokaryotic ones, the ribosomes from metazoans are also larger than those of single celled eukaryotes such as S. cerevisiae. In humans, a mature ribosome is approximately 4.3 MDa, with 18S rRNA and 33 proteins (RPS) in the 40S small subunit and 28S, 5S, 5.8S rRNA and 47 proteins (RPL) in the 60S large subunit (Khatter et al. 2015). Human ribosomes were first analyzed with 9Å resolution from peripheral mononuclear blood cells and later at 3Å for ribosomes of HeLa cells (Figure 1.3B), (Khatter et al. 2015). The 40S small subunit has structural landmark features including the head and the body, with a beak like structure on the head and left and right feet on the body (Dube et al. 1998a; Dube et al. 1998b). The active site of the ribosome—the peptidyl transferase centre (PTC) resides in the 60S large subunit. 60S landmark features include the central protuberance (CP), with P and L1 stalks along the sides of the CP (Figure 1.3A and B) (Dube et al. 1998a; Dube et al. 1998b). EIF6, the translation initiation factor that relates to the function of Sbds, binds to the pre-60S subunits in the cytoplasm at the subunit joining interface near RPL23 and RPL24, away from the CP (Ban et al. 2014; Khatter et al. 2015). Two take- home messages can be made from the observations of the human ribosome structure. First, the constituents of the eukaryotic ribosome align well with the constituents of the bacterial prokaryotic 70S ribosome and the core elements are maintained, indicating the basic mechanism for peptide bond formation is fundamental and conserved across all living systems. Due to the additional ribosomal protein extensions and rRNA expansion- segments, the human (mammalian) ribosome exhibits a substantial increase in size and higher interaction complexity compared to prokaryotic ribosomes. Second, the 40S ribosome subunit is flexible with considerable conformational change upon shifting between pre- and post- translocation states during each peptide bond formation. These changes involve rearrangements of the inter-subunit bridges and interfaces during each 28 peptide bond formation and transition to the subsequent codon of an mRNA (Khatter et al. 2015). A number of diseases have been discussed (see Section 1.2.1), but it is possible that more diseases may be categorized as ribosomopathies as we come to better understand the processes of ribosome biogenesis and protein synthesis. Such additional diseases could readily involve defects in ancillary interaction factors beyond the core ribosomal constituents (Khatter et al. 2015).

1.2.5 The translation of mRNAs

Translation of mRNAs can be divided into initiation, elongation, termination and recycling steps with initiation being the most regulated phase of translation due to the need to engage the ribosomal machinery onto the mRNA.

1.2.5.1 Translation initiation

Figure 1.4 illustrates the general steps in the process of translation initiation (reviewed in Mathews et al. 2007; Hinnebusch and Lorsch 2012; Voigts-Hoffmann et al. 2012). In canonical cap-dependent translation, the first step in initiation is the formation of a stable ternary complex (TC) consisting of formylmethionyl-tRNAi (delivered to the peptidyl- tRNA site (P site) of the ribosome for decoding the AUG start codon), eIF2 and GTP (that is bound to eIF2; Figure 1.4, Steps 1 and 2). Specific affinity between the activated eIF2 with the methionine moiety of the Met-tRNAi prevents binding of eIF2 with other tRNAs. The TC then binds to the 40S ribosomal subunit to form a 43S pre-initiation complex (PIC), which is stimulated by other initiation factors eIF1, eIF1A, eIF3 complex and eIF5 (Figure 1.4, Step 2). [Upon successful translation initiation, the GTP will be hydrolyzed to GDP and must be released from eIF2 to facilitate entry for a new GTP for the next round of TC assembly. Another initiation factor, eIF2B acts as the GDP- exchange factor for eIF2 recycling.]

The third step in initiation is the binding of the PIC to mRNA at the 5' cap of the mRNA (Figure 1.4, Steps 2 and 3). This process requires the cooperative activities of eIF3 complex, the poly (A)-binding-protein (PABP) and eIF4B, eIF4H and 4F complexes. The 29

Figure 1.3 Structure of human 80S ribosome. (To be continued on the next page)

Figure 1.3 Structure of human 80S ribosome.

Figure 1.3 Structure of human 80S ribosome. A, view from the interface (upper panels) and the solvent side (lower panels) of ribosomal subunits of the yeast ribosome, showing the decoding center (DC), head, body, platform, beak, and shoulder in the 40S small subunit and the central protuberance (CP), peptidyl transferase center (PTC), L1 stalk, and P stalk in the 60S large subunit. Global views of the human 80S ribosome from B, the solvent side of the 40S (left panel) and 60S (right panel) subunits and from C, the inter-subunit interfaces with RPs indicated on atomic models and structural landmarks as labeled. Models of the 40S and 60S are not to scale. The RPs in this figure are labeled with a new naming system which is not yet been wildly used (Ban et al. 2014). The panels in this figure are reproduced with permission (Yusupova and Yusupov 2014, Khatter et al. 2015). The superimposed orange-dashed circle in panel C approximates the region on the 60S interface in the P site where Sbds interact with Rpl10 (or uL16 in the new naming scheme) (Weis et al. 2015). The dashed red circle also in panel C highlights the region on the 60S interface where eIF6 would interact with Rpl23 and Rpl24 (uL14 and eL24 in the new naming scheme, respectively) (Klinge et al. 2011).

eIF4F complex is comprised of eIF4G (scaffold protein), eIF4E (cap-binding protein) and eIF4A (helicase). The binding of eIF4E, eIF4G and PABP allow independent and indirect interactions with the cap, the mRNA sequence and the poly (A) tail of mRNAs, respectively, leading to a stable ‘closed-loop’ circular mRNP (Hinnebusch and Lorsch 2012). Interaction between eIF4G and eIF3 complex enables the attachment of PIC to mRNA (48S) to scan the 5' leader sequence of the message for the AUG start codon (Figure 1.4, Steps 3 and 4). It is generally assumed that the ‘closed-loop’ conformation increases the efficiency of PIC recruitment to the mRNA, although the involvement varies in different cell types and may depend on the growth parameters. In the PIC- mRNA loading step, eIF4A unwinds any secondary structure of the mRNA near the 5' end to allow easier access for the 43S PIC. The helicase activity of eIF4A is stimulated by eIF4B through a mechanism that remains unclear (Hinnebusch and Lorsch 2012). Once the PIC is loaded on the mRNA, it adopts a conformation that allows its movement along the single stranded mRNA at a speed 6-8 bases/sec on average (mammalian system) until it reaches the first AUG start codon in a suitable context with the anticodon

of the Met-tRNAi. In this process, eIF1 and eIF1A stabilize the 43S into an ‘open’ conformation along the 5' leader sequence until the P site of the small ribosomal subunit is occupied by the AUG. Codon recognition triggers GTP hydrolysis on eIF2 to set up the release of eIF1 (Figure 1.4, Steps 3 and 4).

Ejection of eIF1 leads to two events: first, switching of the PIC into a ‘closed’ conformation essential for downstream subunit joining and polypeptide synthesis (Figure 1.4, Step 4); and second, the release of the inorganic phosphate from the hydrolyzed GTP of eIF2 (Figure 1.4, Steps 4 and 5). The GDP-bound eIF2 is then dissociated from the PIC by the binding of GTP-eIF5B, another GTPase, and possibly the 60S subunit (Figure 1.4, Steps 4 and 5). eIF5B promotes the joining of 60S to the PIC and hydrolyzes its GTP in doing so, which in turn triggers its release from the 80S initiation complex. Release of eIF1A follows the dissociation of eIF5B, and marks the completion of translation initiation (Figure 1.4, Steps 5 and 6).

Before subunit joining, the free 60S large ribosome subunits are bound by eIF6, the initiation factor with anti-association activity to prevent premature formation of 80S 33

before the small subunit is ready or to prevent re-association of available recycled ribosomal subunits (Figure 1.4, Steps 7 and 8), (Brina et al. 2015). While this mechanism is essential for efficient translation, it must be tightly regulated by cellular factors to avoid translation inhibition. A proposed model for this regulation involves the cooperative activity of SBDS-Efl1. As described in Section 1.1.2.2, SBDS promotes the release of eIF6 from the 60S inter-subunit bridge by facilitating Efl1 in displacing eIF6 through a conformational switch (Weis et al. 2015). Hydrolysis of GTP triggers the release of Efl1∙GDP, and subsequently SBDS from the 60S, making the ribosomal subunit available for 80S monosome formation (Figure 1.4, Step 8).

1.2.5.2 Translation elongation

With a successful translation initiation set up, the Met-tRNAi occupies the P site of the 80S ribosome with the anti-codon base-paired with the start codon. This positions the next codon of the mRNA to be decoded by the appropriate aminoacyl-tRNA in the acceptor site (A site) of the ribosome (Figure 1.4, Steps 6, 7 and 8; Figure 1.5, Step 1, Dever and Green 2012). The charged aminoacyl-tRNA is brought to the A site by eEF1A in a GTP-dependent manner (Figure 1.5, Steps 1, 2 and 3). Prior to peptide bond formation, the GDP bound eEF1A is released from the ribosome and recycled to GTP bound active form with the guanine exchange factor eEF1B (Figure 1.5, Step 2).

Peptide bond formation between the aminoacyl moiety of the tRNAs in the P site and A site occurs rapidly with the catalytic function of the conserved ribozyme activity of the PTC of the 60S subunit (Figure 1.5, Steps 3 and 4). Following peptide bond formation, a conformational change of the 40S subunit triggers the movement of tRNAs with their acceptor ends in the P and A sites shifting respectively to the exit site (E site) and P site with their anti-codon ends held in the P and A sites (Figure 1.5, Step 4). This transition to a P/E and A/P hybrid state is called the ‘ratcheting’ of the ribosome. The GTPase activity of eEF2 is required for the tRNAs to enter the full E and P sites (Figure 1.5, Steps 4 and 5). The hydrolysis of GTP-eEF2 and phosphate release triggers the tRNA switch to the post-translocation state, leaving the deacylated tRNA in the E site and the tRNA with the

Figure 1.4 Translation initiation in eukaryotes. A summary description of translation initiation including the steps depicted is given in Section 1.2.5.1. Participating factors are indicated. The 40S and 60S ribosomal subunits are shown as the large pink and pale blue structures, respectively. The subunits are joined to form a monoribosome in step 5. eIF6 and SBDS are the focus of my studies and are indicated in steps 7 and 8 shown. These factors are involved with the 60S subunit and contribute to monoribosome formation. The relative position of the ‘E’, ‘P’ and ‘A’ sites representing the polypeptide exit, peptidyl-tRNA binding, and the amino acyl-tRNA acceptor sites of the ribosome are shown drawn on 60S subunit in step 8. This figure is reproduced with permission (Voigts-Hoffmann et al. 2012).

peptide bond in the P site (Figure 1.5, Step 5). The A site is then available for the next tRNA to be brought to the ribosome by eEF1A (Figure 1.5, Step 1).

The efficiency of elongation is affected by additional factors such as eIF5A (Dever and Green 2012). eIF5A was first classified as an initiation factor based on its in vitro involvement in the formation of peptide bond between the first methionine with the aminoacyl-tRNA analog puromycin, but has subsequently been suggested to be involved in elongation. Specifically, it has been shown that an unusual modification of a lysine residue of eIF5A to hypusin stimulates elongation by decreasing ribosome transit time on the mRNA (Saini et al. 2009; Dever and Green 2012). Furthermore, physical features of mRNAs also influence the elongation rate. Strong secondary structures in the coding region of the mRNA must be unfolded by the ribosome before being translated, a process that can slow down or even temporarily halt ongoing protein synthesis (Chen et al. 2013; Pop et al. 2014).

1.2.5.3 Translation termination and recycling

Translation termination occurs when the ribosome reaches the stop codon (UAA, UGA, and UAG) at the end of the coding sequence of the mRNA, leading to the release of the newly synthesized polypeptide. In eukaryotes, this process is achieved largely by two factors eRF1 and eRF3 (reviewed in Dever and Green 2012). eRF1 is a protein factor that mimics the structure of tRNA, as its amino-terminal domain can interact with the stop codon similar to codon-anticodon interactions. Its middle domain is analogous to the acceptor stem of the tRNA in structure, and is thought to facilitate nascent polypeptide release. Release is stimulated by the GTPase activity of eRF3 through an interaction with the carboxyl-terminal segment of eRF1 directly on the ribosome. The completion of termination is marked by nascent polypeptide release and dissociation of the GDP-bound eRF3. Following termination, recycling of a ribosome can take place, with eRF1 remaining bound, leading to the dissociation or partial dissociation of a ribosome. Binding and ATP hydrolysis by a eukaryotic factor ABCE1 is believed to promote the dissociation of ribosomal subunits and permit eRF1 release as summarized in Figure 1.6. Further, both eRF1 and eRF3, and the Dom34:Hbs1complex also function cooperatively 36

to promote subunit separation. In addition, Ligatin (also called eIF2D) and MCT- 1/DENR may function in stabilizing the separated subunits.

1.2.6 Contribution of mRNAs to translational control

1.2.6.1 5' Untranslated regions (5' UTRs)

All elements of mRNAs contribute to the efficiency of translation. The 5' UTR plays a fundamental role in regulating translation initiation, and thus, can determine the overall rate of translation (Meijer and Thomas 2002). In general, short 5' UTRs with few guanine (G) and cytosine (C) base residues facilitate 43S subunit complex scanning and initiation, consistent with the absence of interference of these steps due to obstructive secondary structure (Ross 1995; Qu et al. 2011). For examples, highly structured 5' UTRs of growth factor genes TGFβ5 and IGF2 have been noted, and found to be pertinent in regulation of these genes (Meijer and Thomas 2002). The typical 5' UTR lengths for human mRNAs are within 90 to 210 nucleotides (Chatterjee and Pal 2009). The entry of 43S PIC and stable binding with the mRNA does require a minimum 5' UTR length, such that an extremely short 5' UTR reduces the accessibility of the start codon by the PIC. On the other hand, a very long 5' UTR may lead to increased time required for translation, as the leader sequence is typically scanned at relatively constant speed (6 to 8 nucleotides in mammalian systems). However, at least in yeast, it has been shown that multiple PICs can load along the long 5' UTRs to compensate for the necessary scanning step. The absence of upstream open reading frames (uORFs) further facilitates 43S subunit scanning for efficient initiation. The secondary structure complexity and the presence of uORFs both impede translation initiation by decreasing the accessibility of the encoded start codon of the mRNA.

Sequence specific features in the 5' UTR also contribute to the control of translation. Examples of such elements include the iron response element (IRE) pertinent to the maintenance of iron homeostasis (Meijer and Thomas 2002). Another example is the cap- independent internal ribosome entry (IRES) (Meijer and Thomas 2002; Jackson 2013). These non-canonical ribosome binding sites do not use PIC scanning but involve specific 37

Figure 1.5 Translation elongation in eukaryotes.

A summary description of translation elongation including the steps depicted is given in Section 1.2.5.2. Participating factors are indicated. ‘EPA’ represents the exit, peptidyl- tRNA, and the acceptor sites of the ribosome. Green and red filled small circles represent GTP and GDP, respectively. This figure is reproduced with permission (Voigts- Hoffmann et al. 2012). 38

Figure 1.6 Translation termination and ribosome recycling in eukaryotes.

A summary description of translation termination including the steps depicted is given in Section 1.2.5.3. Participating factors are indicated. ‘EPA’ represents the exit, peptidyl- tRNA, and the acceptor sites of the ribosome. This figure is reproduced with permission (Dever and Green 2012).

secondary and tertiary structures of the mRNA sequences to promote the landing of the ribosome close to a nearby AUG start codon of the mRNA. Thus, IRES translation does not require the cap-binding protein eIF4E, although almost all other translation factors involved in canonical translation are still necessary (Meijer and Thomas 2002; Barrett et al. 2012). The use of IRES sequences for translation is common in a number of viruses (e.g. picornavirus, poliovirus). Cellular (non-viral) IRES mRNAs have been identified, including c-myc, FGF-2, VEGF, amongst others. Currently there are approximately 100 putative celluar IRESs known (Jackson 2013). Proteins from mRNAs with these elements typically are involved in the process of growth control, apoptosis, G2/M cell cycle check point, and responses to cellular stress, including situations where general protein synthesis is inhibited or is unfavorable (Jackson 2013).

Another sequence specific translational control feature known in the 5' UTR are the 5' TOP sequences that occur in mRNAs that encode RPs or other translation factors, as described in Section 1.2.4.2. The translation of this set of mRNAs may be coordinately stimulated in a variety of situations when specific mitotic signals, growth factors, various nutrients or oxygen levels are present or other types of growth favorable environments are attained. These responses are controlled by the TOR signaling pathway and the downstream PI3K-Akt pathway (Levy et al. 1991; Avni et al. 1997; Wang and Proud 2006).

1.2.6.2 Open reading frames (ORFs)

The average GC content of human ORF sequences is 49% (Wan et al. 2014), an aspect that does not often play a role in determining the translation efficiency, although the presence of strong secondary structures in ORFs may impede elongation (Chen et al. 2013; Pop et al. 2014).

Another factor in ORFs that influence the translation rate is the recognition of the AUG start codon which depends on the sequence context around it (reviewed in (Meijer and Thomas 2002). The Kozak sequence, the ideal consensus sequence in eukaryotes optimal for translation initiation is CC(A/G)CCAUGG, where the underlined AUG is the start codon, and the A is designated as the +1 position. The presence of an A or G at the -3 40

position and a G at the +4 position are both preferred factors influencing the strength of the start codon, where at least one of these features are considered adequate for start codon recognition (Meijer and Thomas 2002). In the vast majority of mRNAs (95 to 97%), the main ORF possess an initiation sequence context that is at least adequate, whereas the uORFs generally have poorer consensus with fewer (43 to 63%) being adequate and are generally unfavorable for translation initiation (Meijer and Thomas 2002).

1.2.6.3 3' Untranslated regions (3' UTRs)

The 3' untranslated region (3' UTR) of mRNAs play roles in the control of translation, localization and stability (mRNA turnover) (Mathews et al. 2007; Jia et al. 2013). In human, the average length of 3' UTR is about 1000 nt, with a much wider size range compared to the 5' UTR. The GC content of 3' UTRs is typically low, about 45%, partially due to the polyadenylation sequences downstream of the stop codon (Wan et al. 2014). Translational control through the 3' UTR is often mediated by the recognition and binding of trans-acting protein or protein complexes to specific sequence or secondary structures in the 3' UTR (Jia et al. 2013). For example, the poly(A) tail bound to PABPs promote translation initiation by interacting with eIF4E and eIF4G to form a ‘closed- loop’ conformation (translation book 1, chapter 1). In general, a longer poly(A) tail is associated with more efficient translation. Deadenylation can lead to polysome disassembly and translational silencing. Other aspects of mRNA specific regulation also rely heavily on the 3' UTR, including signals for intracellular localization or sequestration for translational silencing (e.g. P bodies or stress granules). Alternatively, mechanisms such as microRNA repression also affect translation via a process that involves degradation of target mRNAs where complementary microRNA binding sequences often occur in the 3' UTR of the target mRNAs (Jia et al. 2013).

1.3 Thesis objectives

Complete knockout of Sbds in mice results in early embryonic arrest before E6.5 (Zhang et al. 2006) and although mice with the R126T missense allele (SbdsR126T/R126T) do not 41

survive birth, they do show 100% penetrant phenotypes of early disease features (Tourlakis et al. 2015). The corresponding late gestation mutant embryos are small, consistent with classic hallmarks of defects in translation (Marygold et al. 2007; Freed et al. 2010). This model also highlights tissue and developmental stage specific phenotypes. The early brain (E14.5) exhibits rampant apoptosis in differentiating neurons (E14.5) with notable necrosis by late gestation (E18.5). Developing metacarpels show decreased ossification and the bone marrow at E18.5 is hypocellular. Impairment of myeloid progenitors is evident, but the fetal liver shows negligible pathology based on histology with modest reduction of granulocytes in portal areas. Finally, the pancreas appears normal at late gestation but was seen to parallel human disease following birth with the use of a conditional knockout model system (Tourlakis et al. 2012). Mouse embryonic fibroblast cells deficient in Sbds function and HEK293 cells treated with SBDS siRNA exhibit decreased global translation as measured by [35-S] Methionine incorporation assays (Ball et al. 2009).

My thesis work focused on the translation defect of SDS and use of our constitutive missense SbdsR126T/R126T murine model (referred to as the SDS mouse in subsequent chapters) to characterize and investigate the molecular consequences of translation deficiency in multiple organs in vivo. I propose that despite the variations in organ phenotypes, protein synthesis in all systems are affected, which should be reflected on abnormal polysome profiles compared to corresponding control organs. I will combine the polysome profile studies with transcriptome, translatome and proteome studies to understand how the global gene expression changes in response to Sbds deficiency, and to gain insight into how the loss of Sbds affects the function of ribosomes. Given the functional interactions between Sbds and Eif6, I propose a model of translation- incompetent ribosomes in cells deficient in Sbds due to inefficient release of Eif6 from the large 60S subunit.

In my project, I am interested in the following questions using our missense SDS murine model: 1. How does Sbds deficiency affect translation in vivo, in organs with and without apparent pathologies? 42

2. Given the proposed interaction of Sbds and Eif6 in subunit joining and translation initiation, does the loss of Sbds function affect the association of Eif6 with ribosomal components? 3. Does Sbds deficiency affect global translation or only the expression of specific sets of proteins, which may contribute to organ responses in SDS? 4. If there are specific targets; what are they, what characteristics do they have, and are they functionally involved in one or a few cellular pathways or process that could explain the tissue specific phenotypes?

To investigate the last three questions, I focused my study on fetal liver as this organ appears only modestly affected in morphology, and thus should reflect nominal confounding from secondary disease responses. Ultimately, my long term goal is to provide a better understanding of SDS as well as other ribosomopathies, and hopefully provide other perspectives in terms of treatment options and preventative measures in patient management.

Chapter 2 Loss of Sbds Function Results in Abnormal Polysome Profiles with 80S Reduction and Abnormal Eif6 Binding

Contributions:

I planned and coordinated the investigations in this chapter. I performed all experiments, carried out the data analyses and prepared all the figures.

2 Loss of Sbds Function Results in Abnormal Polysome Profiles with 80S Reduction and Abnormal Eif6 Binding

2.1 Summary

To investigate the role of Sbds in protein synthesis in vivo I characterized polysome profiles of multiple SDS mouse (SbdsR126T/R126T) fetal organs. Aberrant polysome profiles with reduced 80S monoribosome peaks were observed in fetal liver, lung, brain, kidney, pancreas and skeletal muscle organs. Sustained polysome peaks were also apparent. Ribosome run-off profiles were used to determine the ratio of 60S to 40S subunits. Both fetal liver and lung organs showed 60S:40S ratios that were indistinguishable from the controls, suggesting that synthesis and assembly of ribosomal components were not impaired.

A proposed function of Sbds is to enable the release of Eif6 from 60S prior to subunit joining to ensure the proper formation of ribosomes for translation initiation. The liver extract profiles showed the most severe loss of 80S, and were selected for further characterization and studies. I tested the association of Eif6 with ribosomal components in fetal livers with deficient and wild type Sbds function. The severe reduction of the 80S in the mutant polysome profiles was accompanied with strong association of Eif6 with the 80S monoribosome. I conclude that the abnormal binding of Eif6 resulting from the loss of Sbds function leads to perturbed subunit joining and functionally defective ribosomes. In the mutant condition, I did observe that Eif6 was eventually released, as Eif6 was not present in the mutant polysome peaks. Together, the reduced pool of translation-competent ribosomes leads to translation deficiency in Shwachman-Diamond syndrome.

2.2 Background

The role of Sbds in ribosome maturation and function, and how translation is affected with Sbds mutations are still unclear. SBDS/Sbds was proposed to be a ribosome ancillary factor involved in ribosome biogenesis and protein synthesis, contributing to initiation of translation (Boocock et al. 2006; Menne et al. 2007; Finch et al. 2011). Reduced protein synthesis with loss of function mutations in Sbds has been evident in model system studies. Complete knockout of Sbds in mice results in early embryonic arrest before E6.5, supporting that Sbds is essential for development (Zhang et al. 2006). Mouse models with the disease associated R126T missense allele (SbdsR126T/R126T), called SDS mouse/embryos, show100% penetrant phenotypes, and recapitulate disease features but do not survive birth (Tourlakis et al. 2015). Late gestation SDS embryos are very small, consistent with poor growth and defects in translation.

Disturbance of core ribosomal constituents or ancillary factors can lead to perturbed ribosome/polysome profiles with altered ribosomal subunits or 80S monosomes, occurrence of halfmers, or to absent or decreased polysomes (Mathews et al. 2007; Robledo et al. 2008). Polysome profiles of systems deficient for SBDS or its orthologs have been examined in a number of studies, with 80S reduction being a common feature (Menne et al. 2007; Finch et al. 2011; Wong et al. 2011; Burwick et al. 2012; Provost et al. 2012; Tourlakis et al. 2012; Sezgin et al. 2013; Tourlakis et al. 2015). However, the consequences of loss of Sbds function have not been extensively studied in vivo. Furthermore, the extents of translation defects have not been studied in organs or tissues despite the occurrence of pathological phenotypes in various tissues. My primary aim was to characterize translation in fetal organs of SDS mice using polysome profiles.

Toward the understanding of the impact on translation with Sbds deficiency, I also studied the ribosome run-off profiles of organs from the SDS mouse models given the proposed role of Sbds in 60S maturation. In addition, a current model links the function of Sbds to the dissociation of Eif6 from 60S (Menne et al. 2007; Finch et al. 2011), a critical step required for ribosomal subunit joining (Senger et al. 2001; Ceci et al. 2003; Gartmann et al. 2010). I further investigated to see if a defect in the dissociation of Eif6 46

from the 60S was evident in vivo in the SDS mouse which may suggest the existence of translation incompetent ribosomes.

2.3 Materials and Methods

2.3.1 Mice

Null (Sbds-) and missense (SbdsR126T; c.377G>C) disease associated Sbds alleles were generated using knock-in gene targeting methodology to achieve disease models for SDS (Zhang 2009; Tourlakis et al. 2012). Both alleles were maintained in the C57BL/J6 mouse strain background. Heterozygous carriers of either Sbds R126T or Sbds- alleles were indistinguishable from wild type mice with respect to appearance, overall health and fertility.

Mouse models were analyzed at late embryonic stage (E18.5). The morning that a vaginal plug was found was counted as embryonic day 0.5 (E0.5). The pregnant female was sacrificed the morning of embryo harvest by cervical dislocation. The late stage embryos (E18.5) were sacrificed by asphyxiation before the organs were harvested. Genotyping of adult or embryonic mice was performed from tail DNA samples using AccuStart™ II Mouse Genotyping Kit (Quanta Biosciences). A list of oligonucleotide primers is given in Table 2.1.

2.3.2 Polysome profiling and peak quantification

Flash frozen mouse organ specimens (10-15 mg) were homogenized in 500-750 µl

polysome lysis buffer (100 mM KCl, 5 mM MgCl2, 10 mM Tris-HCl, pH9, 1% Triton X- 100, 1% sodium deoxycholate). Extracts were clarified by centrifugation at 2,500 ×g for 15 min at 4°C and cycloheximide and heparin were added to 0.1 µg/ml and 1 µg/ml,

respectively. Consistent loading amount was achieved by A260 UV measurement of total RNA. Cytoplasmic lysates with equal RNA content for control and mutant extracts from 47

each organ were adjusted to be 500 µl with polysome lysis buffer and then loaded onto 13 ml of 10-50% sucrose gradient solution in a polyallomer ultracentrifuge tube. Following centrifugation at 151,000 ×g at 4°C for 2 hr in a SW41Ti (Beckman) rotor. The fractions were monitored by UV absorbance at 254 nm by ISCO UA-6 UV detector (Teledyne Isco) and every 15 drops of the sample was collected as one fraction using the fractionation system (Brandel) for subsequent analysis.

Area under the curve (AUC) was measured using the Magic wand function in Adobe Photoshop CS4 as described using a baseline drawn across the two lowest peak valleys of the profile (Masek et al. 2011; Tourlakis et al. 2012). The area representing each individual ribosomal peak was expressed relative to the total AUC of the profile.

2.3.3 Ribosome run-off profiling and quantification analyses

The ribosome run-off lysis buffer was prepared with 600 mM KCl, 10 mM Tris-HCl, pH9.0, 1% Triton X-100, 1% Sodium Deoxycholate with 1 tablet of protease inhibitor

(Roche) per 10 ml of buffer prepared in DEPC (Diethylpyrocarbonate) treated H2O. 1 µl of RNaseIN (Invitrogen) was added to each 650 µl of run-off buffer. Flash frozen mouse organs (about 10 mg) were homogenized with 650 µl prepared run-off buffer and the extract was clarified by centrifugation at 2,500 ×g for 15 min at 4°C. Cycloheximide and heparin was added to the supernatant with final concentration of 0.1 µg/ml and 1 µg/ml respectively. EDTA was added to the supernatant to final concentration of 1 mM.

Consistent loading was achieved by A260 UV measurement of total RNA with adjustment to total volume of 500 µl using run-off lysis buffer before loading onto 10-40% sucrose gradients. The gradients with lysates loaded were subjected to ultracentrifugation and fractionation, as well as subsequent peak quantification analyses as described above in Section 2.3.2. Monosome and polysome ribosomes do not appear on ‘run off’ profiles as magnesium was not included in the lysis buffer.

Table 2.1 Genotyping primers. Allele of Primer Sequence (5'- 3') Product size (bp) interest name A CCAGGGTCACGTTAATACAAACC Targeted allele 450 R126T1 B TGAGTTTCAATCCTCAGCATCC Wildtype allele 329 C CGAATCAAGCTGATCCGGAACCC Targeted allele 184 KO2 D CAGGCGTGGTTGCTTTCTTAT Wildtype allele 354 E CTGGGCACAGGATTACTCACAC

1The R126T allele was generated by site directed mutagenesis through the introduction of 377G>C nucleotide change in the exon 3 region of Sbds, along with a neomycin selection cassette flanked by FRT sequences in the intron 2 region. After homologous recombination and removal of the neomycin cassette by FLP-recombinase-mediated excision, a FRT sequence and a loxP sequence remain in intron 2, resulting in an increased PCR product size compared to wild type allele using with primers correspond to sequences in intron 2 region that flanks the insertion positions.

2 The KO allele was generated using a knock-in LacZ cassette that included a neomycin selection marker in the first exon of Sbds to disrupt its coding sequence. The KO and wild type alleles could be detected individually or simultaneously by amplification with primers C, D and E. For targeted alleles (KO), primers C and E generate PCR products of 184 base pairs, and for wild type alleles, primers D and E generate PCR products of 354 base pairs.

2.3.4 Western immunoblottings and quantification analyses

2.3.4.1 Protein precipitation from polysome profile fractions

For investigations requiring protein analysis of collected fractions, ~500 µl aliquots were incubated with 0.02% sodium deoxycholate for 30 min at room temperature and then precipitated by TCA (Trichloroacetic acid, final concentration 10%) at 4ºC, overnight. The precipitate was then centrifuged at 13,200 ×g for 15 min at 4°C followed by two washes with ice-cold acetone. The pellet was air dried and resuspended in Laemmli buffer. 20 µl was typically used for application to polyacrylamide gels and immunoblotting.

2.3.4.2 Protein preparation from frozen tissues

Tissue was homogenized in ice-cold RIPA buffer (150 mM NaCl, 1% NP-40, 0.5% sodium deoxycholate, 0.1% sodium dodecyl sulfate, 50 mM Tris-HCl, pH 7.5) using polytron (Caframo Ultra Torque Stirrer, BDC1850) at 1,000 rpm. Insoluble components were pelleted at 17,000 ×g at 4°C for 30 min. Protein concentration of the soluble cytoplasmic components were measured by Lowry assay (BioRad) and equal amounts of total protein was aliquoted and diluted with RIPA buffer and 4× Laemmli buffer for polyacrylamide gel electrophoresis.

2.3.4.3 Western immunoblotting from prepared protein extracts

Equal protein aliquots (25-40 µg) were separated on 12% Tris-glycine-SDS acrylamide gels and transferred onto membrane using a Trans-Blot® TurboTM Transfer System (BioRad). The supported nitrocellulose membranes provided in the transfer kit (BioRad) were rinsed using phosphate buffered saline with 1% Tween-20 (PBST) solution before blocking with 5% powdered skim milk in PBST for 1 hr, incubated with primary antibodies overnight at 4°C, washed with PBST, and then incubated with a horseradish peroxidase conjugated species specific secondary antibody at room temperature for 2 hr. The membranes were then washed in PBST before visualization with AmerhsamTM ECLTM Prime Western Blotting Detection Reagent (GE Healthcare Life Sciences) using a 50

ChemiDocTM MP Imaging System (BioRad). Protein abundance was quantified using the Image-LabTM 4.1 Software (BioRad) relative to control proteins. Primary and secondary antibodies used are listed in Table 2.2.

2.3.5 Statistical analyses

All statistical analyses were carried out using the R statistical software (® R Foundation, accessed from http://www.r-project.org/). Comparisons between groups, including polysome profile analysis and western immunoblotting quantification were achieved using unpaired 2-tailed T tests, with P-values <0.05 declared as significant changes. Raw P-values are reported.

Table 2.2 Antibodies used in western immunoblottings. Antigen Species Source Dilution Sbds Mouse In house 1:1000 Actb Mouse Abcam (Ab6276) 1:5000 Gapdh Rabbit Santa Cruz (sc-25778) 1:200 Rpl4 Mouse Abnova (H00006124-M01) 1:1000 Rps6 Rabbit NEB (2217S) 1:5000 Rplp0 Rabbit Abcam (Ab101279) 1:1000 Rps11 Rabbit Abcam (Ab175213) 1:1000 Eif6 Mouse BD Biosciences (611120) 1:3000

Secondary antibodies Goat anti-rabbit Bio-Rad (172-1019) 1:5000 Goat anti-mouse Santa Cruz (sc-2005) 1:5000

2.4 Results

2.4.1 Loss of Sbds leads to reduced 80S and persistent polysomes in multiple murine fetal organs

To determine the consequences of loss of function mutations in Sbds on translation, polysome profiles were characterized across several fetal tissues of SbdsR126T/R126T embryos (E18.5; SDS embryos): liver, lung, brain, kidney, skeletal muscle and pancreas, compared to age-matched littermate controls. Reduced 80S monosome peaks were apparent across all SDS tissues tested (Figure 2.1). Quantification of the relative proportion of each peak in the profiles using the AUC measurement also revealed reduced 80S in fetal liver, lung, brain, and kidney from SDS embryos compared to Sbds+/R126T or Sbds+/+ controls (Figure 2.2, Appendix 2.1). The reduction of 80S peak levels of skeletal muscle did not reach significance. Mutant pancreas profiles generally displayed less distinctive profiles, but reduced 80S peak levels were readily detected in the postnatal period when disease phenotypes are pronounced as previously reported by our laboratory (Tourlakis et al. 2012).

2.4.2 No ribosome subunit imbalance observed with Sbds deficiency in vivo

In the context of reduced protein synthesis in SDS and the suggested role of Sbds in 60S subunit maturation (Menne et al. 2007), a decrease of 80S could be indicating reduction in available 60S ribosomal subunits. We studied the proportions of the 60S and 40S subunits using ribosome run-off profiles. The ratio of 60S to 40S subunits of the E18.5 mouse fetal lung with Sbds+/R126T, as measured by AUC, was 2.82:1 (N=3), indistinguishable from those of mutants (SbdsR126T/R126T, ratio= 2.89:1, N=6, P-value = 0.81, Figure 2.3A, Appendix 2.2). Fetal liver samples yielded comparable findings, with the 60S to 40S ratio being 3.05 and 3.06 in controls and mutants, respectively (P-value = 0.96, N=5 each, Figure 2.3B, Appendix 2.2). From these findings, the reduced 80S monoribosome peak observed in vivo with SDS-associated mutation does not appear to be a result of ribosome subunit imbalance. 53

Figure 2.1 SDS mutant organs exhibit reduced 80S monosomes and persistent polysomes. 54

Figure 2.1 SDS mutant organs exhibit reduced 80S monosomes and persistent polysomes. Representative polysome profiles of organ extracts are shown of matching littermate wild type Sbds+/+ (top panel), heterozygous control Sbds+/R126T (middle panel) and mutant SbdsR126T/R126T (bottom panel) embryos. Arrow indicates 80S peak in each profile. Mutant profiles reveal reduction of 80S monoribosome peaks and persistent polysomes. Profiles for liver (150µg RNA), lung (125µg RNA), brain (100µg RNA), kidney (50µg RNA), skeletal muscle (100µg RNA), and pancreas (50µg RNA) are shown. Sensitivities of UV absorbance (254nm) measurement for liver fractions was set at 1/5 that of the other organs.

Figure 2.2 Composition of ribosomal components of SDS mutant organs showed loss of 80S. Proportions of 40S, 60S, 80S and polysomes of each profile were calculated with the AUC measurement. The calculated proportion of profile peaks for E18.5 Sbds+/+, Sbds+/R126T and SbdsR126T/R126T profiles (N=6 for skeletal muscle, N=5 for liver, N=4 for brain, N=3 for lung, kidney and pancreas) are shown. The 40S peaks of brain profiles of all genotypes were not readily detectable; see Figure 2.1, upper right panels. P-values were calculated using T-test.

Figure 2.3 Ribosome subunit levels are balanced in SDS model organs.

Representative ribosome run-off profiles of E18.5 lung (A) and liver (B) extracts are shown with 40S (left peaks) and the 60S (right peaks) ribosome subunits. Scatter plots with calculated ratios of 60S:40S peaks are shown in right panels; horizontal lines in red indicate average ratios of N=6 for Sbds+/R126T lungs, N=3 for SbdsR126T/R126T lungs and N=5 for Sbds+/R126T and SbdsR126T/R126T livers. P-values of the comparison of average ratios of the two genotype groups =0.81 and 0.96 (as determined by T-test) for lung and liver, respectively.

2.4.3 Persistent high molecular complexes observed in SDS mutant polysome profiles represent polysomes

In addition to the obvious 80S monosome reduction, the profiles of mutant liver, lung, brain and kidney also showed altered amplitude and increased number of polysome peaks that would correspond to very high molecular complexes, compared to the matched control profiles, with this pattern being most striking in fetal liver (Figure 2.1, upper left panels). To confirm that the high-molecular weight fractions did indeed correspond to ribosomes, I assessed the fetal lung profile fractions by immunoblotting with antibodies of representative constitutive proteins of the large (RplP0, Rpl4) and small (Rps6, Rps11) ribosomal subunits (Figure 2.4A). Distinct fractions corresponding to 40S and 60S subunits were resolved; likewise, 80S peaks corresponded to fractions with both small and large ribosomal proteins. Control fetal lung extracts showed a typical profile fraction pattern with 40S and 60S ribosomal components, 80S monosomes and fading heavy polysomes. Ribosomal proteins were notably increased in fractions of the higher molecular weight peaks of mutant samples, consistent with increased absorbance and heavy polysomes. Similar to lung, altered levels of ribosomal proteins were also detected for mutant fetal liver extracts (Figure 2.4B) with an even more notable shift in immuno- reactivity toward the later profile fractions. Together, these findings confirmed that loss of Sbds in liver and lung tissues did not result in ribosomal subunit imbalance, but did lead to the accumulation of high molecular weight, or heavy polysomes.

2.4.4 Aberrant polysome profiles observed in SbdsR126T/- fetal livers

From SDS population analyses, many patients carry one null Sbds allele. The disease associated SbdsR126T allele occurs with null alleles in two families studied in our laboratory. I anticipated that the presence a null allele together with a SbdsR126T allele could lead to more severe phenotypes. Indeed even smaller embryos were observed with SbdsR126T/- compared to SbdsR126T/R126T genotypes versus heterozygous Sbds+/- or Sbds+/R126T control genotypes, consistent with further loss of Sbds function. 58

I then asked how the polysome profiles were affected. Compared with littermate controls (Sbds+/- or Sbds+/R126T), the heterozygous null mutants (SbdsR126T/-) showed the typical SDS polysome profiles similar to the homozygous missense mutants (SbdsR126T/R126T), with loss of 80S peaks and persistent polysomal peaks as evident by quantification. However, in addition, the free 40S and 60S subunit peaks were both significantly increased in the SbdsR126T/- mutant profiles compared to control profiles (Figure 2.5, Appendix 2.1 and 2.2).

2.4.5 Loss of Sbds function leads to aberrant association of Eif6 with 80S

The normal ratio of ribosomal subunits and the aberrant polysome profiles, particularly the reduced 80S support involvement of translation initiation in the defect in protein synthesis as a result of deficiency in Sbds. Given the current model that Sbds is required for the dissociation of Eif6 from 60S (Menne et al. 2007; Finch et al. 2011) and that this represents a bottleneck for translation initiation (Senger et al. 2001; Ceci et al. 2003; Gartmann et al. 2010), we hypothesized that the observed aberrant profiles may reflect inefficient dissociation of Eif6 and altered ribosomal complexes.

I first established that Eif6 steady state protein levels were not altered by Sbds deficiency in fetal liver extracts by immunoblotting (Figure 2.6A). This finding was corroborated by subsequent analyses of the transcriptome and proteome using mass spectroscopy (see Chapter 3).

I then sought to examine where Sbds and Eif6 occur with respect to the polysome profile fractions. The SbdsR126T allele is readily expressed, and Sbds was found in the flow- through and lower molecular weight fractions of mutant and control fetal liver extracts (Figure 2.6B, C, D). Eif6 was also observed in the flow-through and early fractions, but not in the polysome fractions of either mutant or control samples (Figure 2.6B, C, D). However, close examination of the association of Eif6 revealed differences in control versus mutant organ fractions. In control profiles, prominent levels of Eif6 were detected in the 60S fractions (positive for 60S ribosomal biomarker) with notably decreased 59 intensity in the 80S fractions (with both 40S and 60Sribosomal biomarkers, see red box, Figure 2.6B, C, D left panels). In contrast, and recognizing the notable reduction in the 80S peak, prominent levels of Eif6 were associated with both the 60S and 80S fractions of mutant profiles. To assess how stable the association of Eif6 with 80S is, I used polysome extraction buffers with various KCl concentrations (50, 100 and 150mM). The differences in salt concentration result in changes in ionic strength, which could affect the electrostatic interaction between proteins and/or RNAs. This persistent association of Eif6 with the 80S or 80S-like ribosomes in mutants was observed across different ionic strengths, including sufficiently high salt levels that led to overall dampened profiles. These findings, with the marked reduction in 80S monosome level, support that loss of Sbds leads to disturbed Eif6 association.

Figure 2.4 E18.5 lung and liver of SDS mice show reduced 80S and persistent polysomes.

Preparative traces of polysome profiles of (A) fetal lung and (B) liver extracts from Sbds+/R126T (upper left panels) and SbdsR126T/R126T (upper right panels) embryos are shown. Arrows indicate 80S peaks. Aliquots of sucrose gradient fractions were precipitated, resuspended in loading buffer and subjected to SDS-PAGE for immunoblotting. The protein blots are not directly aligned with traces shown. Antibodies to constituent ribosomal proteins RplP0, Rpl4, Rps6, and Rps11 were used to detect large (60S) and small (40S) ribosomal subunits and characterize profile peaks (lower panels). 62

Figure 2.5 Polysome profiles of SbdsR126T/- fetal livers show aberrant profiles with increased free 40S and 60S subunit proportions.

A, polysome profiles of fetal liver samples of Sbds+/- (control, N=6) and SbdsR126T/- (mutant, N=5) showed aberrant profiles in SDS mutants. Representative profiles illustrate the reduction of 80S monoribosome peaks and the persistent peaks of representing polysomes. B, proportions of 40S, 60S, 80S and polysomes of controls and mutants were calculated with the AUC measurement. P-values were calculated using T-test. C, ribosome run-off profiles of E18.5 liver samples indicated no 60S:40S subunit imbalance with the loss of Sbds function. D, scatter plot of ratios of 60S:40S for ribosome run-off profiles from Sbds+/- and SbdsR126T/- fetal livers are shown. N=5 and 4 for Sbds+/- and SbdsR126T/- fetal livers, respectively. P-value of the comparison of average ratios (indicated by the red lines) of the two groups =0.77.

Figure 2.6 Eif6 is aberrantly associated with ribosomal peaks with loss of Sbds function. (To be continued on the next page)

Figure 2.6 Eif6 is aberrantly associated with ribosomal peaks with loss of Sbds function. 66

Figure 2.6 Eif6 is aberrantly associated with ribosomal peaks with loss of Sbds function.

A, Total cellular steady state Eif6 levels were maintained in SbdsR126T/R126T livers as determined by immunoblotting using cellular extracts from four independent control and mutant fetal livers (upper panel). Eif6 was measured as compared to Actβ loading control; the scatter plot of densitometry analysis is shown (lower panel). P-value = 0.10 (as determined by T-test). Co-sedimentation of Eif6 with profile fractions of fetal liver polysome extracts (150 μg RNA each) with buffer containing (B) 50 mM, (C) 100 mM and (D) 150 mM KCl. Traces of polysome profiles from E18.5 control Sbds+/R126T (left) and mutant SbdsR126T/R126T (right) livers loaded with 150 µg of RNA are shown in the top panels. Equal volumes of collected fractions were precipitated, suspended in loading buffer and subjected to SDS-PAGE for western blotting. Small and large ribosomal subunit proteins, Rps6, RplP0 and Rpl4, were used as indicators of ribosomal components as shown in figures. The protein blots are not directly aligned with traces shown. Eif6 was predominantly enriched in 60S and did not persist in polysome fractions in both control and mutant samples. However, taking into account the marked reduction of 80S monosomes in mutants, Eif6 remains prominent in mutant 80S fractions (compare aligned fractions with red outlining). This binding was maintained at 150 mM KCl where some dissociation of ribosome complexes (reduced overall peaks) was already evident, see panel D.

2.5 Discussion

In this study, I used the disease associated missense mutation SbdsR126T to model SDS. This hypomorphic allele is anticipated to retain residual function of Sbds and exert a chronic effect in this constitutive mouse model of SDS. The loss of 80S observed in vivo is consistent with earlier studies. The variations and the different patterns of polysome profiles in control fetal organs reflect the differences in the level of ribosome activities, as prominent polysomes have been assumed to be associated with actively translating ribosomes (Mathews et al. 2007). The different organs studied showed variation in the response to loss of Sbds function, aside from the common feature of reduced monoribosomal peaks; indicating that the range of severity of the 80S reduction is wide between the different organs, and may reflect the differences in sensitivities to Sbds deficiency independent of disease pathology. Notably, I did not observe ‘halfmers’, as have been reported in SdoI deficient yeast studies (Finch et al. 2011; Moore et al. 2010) that reflect a lingering of 40S subunit binding on mRNA when 60S joining is deficient. This may reflect a difference in the function of SdoI versus Sbds in that, the yeast homolog appears to have a more prominent role in 60S biogenesis leading also to the noted imbalance in the ribosomal subunit levels (see Section 1.1.2.3).

Further, I observed that the loss of Sbds function is also associated with preserved polysomes. These preserved polysome peaks were associated with increased size of the polysomes (the number of polysomal peaks) as well as the increased amplitude of the peaks containing heavier polysomes. This indicated an overall trend of increased binding of the ribosomes to mRNAs in the SDS mutants. Thus beyond the concerns for translation initiation (reflected by the lost 80S monosome peaks) in SDS, there may also be delayed elongation, or issues with termination or ribosome recycling.

I noticed that the reduction of 80S proportions in the SDS mutant profiles may not be fully explained by the polysomal peaks. In fetal livers, where the loss of 80S peaks are most severe, a trending increase of 40S and 60S subunit peaks could be observed, consistent with problems in subunit joining. I suspected that with further loss of Sbds function, the subunit joining defect may be more severe. SDS embryos with SbdsR126T/- 68

genotypes have only one copy of the hypomorphic R126T allele, thus their pathological phenotypes are more severely affected than the homozygous SbdsR126T/R126T mutants. Indeed, increased levels of free 40S and 60S subunits were indicated in their ribosomal extracts (Figure 2.5B), consistent with the current model of Sbds function, to permit ribosome subunit joining and translation initiation through interactions with Eif6.

The aberrant polysome profiles were not due to subunit imbalances given the comparable ratios of total 60S to 40S between mutants and controls. This differs from findings in yeast where loss of SdoI leads to reduced levels of 60S relative to 40S, a finding that was interpreted to indicate a role for SdoI in the biogenesis of 60S large ribosomal subunit (Menne et al. 2007). In my mammalian in vivo model, Sbds deficiency did not affect the relative level of ribosome subunit biogenesis, and thus defects in polysome profiling and translation appear to be due to issues of the joining of the 60S and 40S subunit and/or the function of the assembled ribosomes. Further, Sbds deficiency does not affect the expression levels of ribosomal proteins (discussed in Chapter 3, Section 3.4.2), providing additional support that the relative levels of 60S and 40S in fetal organs of Sbds murine models are unaltered.

Toward understanding of the defect in polysome profiles and the role of Sbds in translation, I investigated the level of cellular Eif6 and its association with ribosomal components considering the relation between Eif6 and Sbds in a number of earlier genetic and in vitro functional studies. Eif6 is a crucial translation initiation factor that is found in both the nucleolus and cytoplasm (Gandin et al. 2008). It is thought that it regulates the availability of 60S by binding to the 60S subunit to prevent 40S from aberrantly forming the 80S monomer, and in this context would limit translation initiation in mammalian cells. Eif6 mutations can suppress and rescue phenotypes in yeast models of SDS (Menne et al. 2007). Later, it was shown by an in vitro study that Sbds coupled the 60S dependent GTPase activity of Efl1 with the release of Eif6 from the 60S, a step which has been deemed crucial for translation initiation (Finch et al. 2011).

In this study, I found that whereas the total steady state level of Eif6 was not affected, deficiency in Sbds did influence association of Eif6 with ribosomal fractions containing 69

60S subunits and even 80S ribosomes. This persistent association of Eif6 with the 80S or 80S-like ribosomes was observed with different ionic strengths in SDS extracts, including salt levels that led to overall dampened profiles, and eventually diminished in the later fractions. My observations 1) are consistent with the loss of Sbds function leading to defect in the release of Eif6 from the pre-60S/60S; and 2) suggest that an atypical 40S- 60S-Eif6 (80S-like) complex may occur. The binding is transient however and does not persist, as Eif6 is not present in late profile fractions. The unaltered total cellular Eif6, and the aberrant association of Eif6 with the ribosome suggested that with Sbds deficiency, ribosome subunit joining was perturbed. The 80S formed with Eif6 bound may be structurally malformed and non-functional or may only translate sub-optimally. It is tempting to speculate that the lingering of Eif6 on 80S like-ribosome structures may indicate a shifting of the ribosome or of the 60S subunit on the ribosome, enabling only sub-optimal or non-translating conformation with the binding of transcripts. Presumably, these Eif6-bound complexes compromise translation capacity and may even indirectly precipitate the formation of the preserved polysomes. How this abnormal lingering of Eif6 on ribosomes affects translation and what the preserved polysomes in SDS mean for protein synthesis will be further investigated in the next chapter.

Overall, I showed that all organs in SDS mouse embryos tested exhibit aberrant polysome profiles with loss of 80S and retained polysome peaks. To better understand how these findings relate to translation defects of SDS mice, late stage fetal liver was chosen for further study in Chapter 3. Normalized to body weight, liver size of SbdsR126T/R126T E18.5 embryos was comparable to controls (Zhang 2009). No difference in cell size was observed as measured by counting the number of cells in a fixed area of fetal liver (Appendix 2.3). Further, no apparent apoptosis, or other forms of cell death were observed in SDS fetal livers. Finally, of all the organs studied, fetal liver showed the most dramatic effects in polysome profiles, making it an interesting organ to study the primary effects on translation due to the loss of Sbds.

Chapter 3 The Preserved Polysomes Associated with Sbds Deficiency Result From Altered mRNA Association

Contributions:

I planned and coordinated the investigations in this chapter. I performed all experiments, carried out the data analyses and prepared all the figures.

I received technical assistance for the following: cDNA microarray (C. Lu and X. Wang at The Centre for Applied Genomics, The Hospital for Sick Children), raw data of transcript length and GC content (J. MacDonald at The Centre for Applied Genomics, The Hospital for Sick Children) and label-free mass spectrometry (P. Taylor and J. Tong at the SPARC Biocentre, The Hospital for Sick Children).

3 The Preserved Polysomes Associated with Sbds Deficiency Result From Altered mRNA Association 3.1 Summary

To understand the maintained/preserved polysomes seen with Sbds deficiency as well as to investigate how the polysome loading level for each transcript is affected in SDS, total and polysomal mRNAs of mutant and control fetal liver samples were studied using cDNA microarray and proteome analyses. Overall the transcript expression profiles of Sbds deficient and control fetal livers were not dramatically altered. Functional consideration of the genes with altered transcript levels did not suggest common pathways/processes were targeted by Sbds mutations. By comparing polysome-bound transcript levels to respective total transcript levels, we could establish which genes were being differentially retained by the polysomes with loss of Sbds function. With normalization, 165 transcripts showed reduced association with polysomes, while 634 showed increased association. However, the changes of polysome association did not correspond to respective steady state protein levels, as shown by western immunoblotting and label-free mass spectrometry. The proteome studies revealed that relative levels of ribosomal proteins and translation factors are unaltered in SDS fetal livers. Analysis of transcripts showed that features of 5' UTRs, transcript lengths and GC content influenced the altered polysome association. Further, the observation of prominent polysomes and the large number of transcripts with high polysome association in absence of corresponding increase in the protein levels indicated that loss of Sbds function leads to the formation of ribosomes that are not fully functional. Together, with consideration of my previous observations of abnormal Eif6 binding these ‘SDS ribosome complexes’ highlight how Sbds deficiency may result in translation insufficiency in SDS.

3.2 Background

Sbds deficiency is associated with the loss of 80S and preserved polysomes. Whereas the reduced 80S peak is consistent with Sbds mutations leading to ribosome subunit joining defects, little attention has been paid to the polysome peaks. The prominent polysomes are of particular interest as it leads to a conundrum regarding what we know about the function of Sbds. Prominent polysomes, or higher association of ribosomes with transcripts, are indications of active global and/or transcript specific translation. However, we know from other studies that loss of Sbds leads to deficiency in translation and reduced protein synthesis; although it remains unclear if the impact on translation is global or impacted specific target gene/gene sets was unclear. Two main questions can be raised: 1) what are the polysome associated messages and 2) are they being actively translated? To address these, I sought to identify all genes with expression changes at the level of protein, transcript or polysome associations, and then establish whether and which pathways or cellular processes were targeted by Sbds deficiency. I first examined gene expression by analyzing protein levels by label-free mass spectrometry and transcript levels by cDNA microarray using mutant and control fetal liver samples. As was shown in the Chapter 2, mutant fetal liver displayed prominent preservation of polysomes, and neither Sbds nor Eif6 was present in the fractions containing these polysomes. I was particularly interested in genes involved in translation as it is generally known that the expression of factors involved in translation, especially the core ribosomal components are synchronized and tightly regulated. Disturbance in one player generally leads to altered cellular protein level in groups of translation factors (Warner 1999; Rudra and Warner 2004). To see if the loss of Sbds would lead to imbalanced expression of RPs and other translation related proteins such as translation initiation and elongation factors, I compared the expression of these factors at the transcript and protein levels.

I then sought to correlate the changes of polysome association levels with the steady state cytoplasmic protein levels to see if the polysomes observed in the mutant profiles correspond to actively translating ribosomes. I further investigated the sequence or structural features of the transcripts that were enriched in the groups of genes with altered polysome association in SDS samples. Together, these investigations allowed me to 73

identify a possible mechanism of ribosome retention with Sbds deficiency that would likely contribute to translation insufficiency in SDS.

3.3 Materials and Methods

3.3.1 Mice

All animal experiments were carried out under the guidelines of the Canadian Council on Animal Care, with approval of procedures by The Animal Care Committee of the Toronto Centre for Phenogenomics, Toronto, AUP #0093. Colonies of mice with the SbdsR126T allele were maintained by breeding as described in Section 2.3.1 in the previous chapter. Harvesting and genotyping of adult or embryonic mice were achieved using AccuStart™ II Mouse Genotyping Kit (Quanta Biosciences) with tail DNA samples, also as outlined in Section 2.3.1.

3.3.2 Polysome profiles and RNA extraction

The polysome profiles were generated as in Section 2.3.2 in the previous chapter.

Total RNA for microarray analyses was isolated from Trizol (Invitrogen) treated cytoplasmic lysates (500 µl:250 µl sample) prepared as for polysome profile studies using PureLink RNA Mini kit (Ambion) according to manufacturer’s protocol. Corresponding matched set polysomal RNAs were extracted from Trizol treated (500 µl/1 ml sample) collected fractions containing the respective polysome peaks using PureLink RNA mini kit (Ambion) according to manufacturer’s protocol.

Typically, fractions 11 to 18 of the total of ~20 fractions were included. Quality control of the extracted RNA was assessed using the Experion System (BioRad, Hercules, CA).

RNA samples with A260/A280 >1.8 and 28S/18S >0.4 were used for analysis. cDNA was generated using the Ambion®WT Expression kit (Life Technologies); 5.5 µg cDNA from fetal liver samples of paired total and polysome RNA extracts were used for hybridization with AffymetrixGeneChip® Gene 1.0 ST Array for Mouse microarrays.

3.3.3 cDNA microarray and analyses

The level of expression of each array probe set was provided as log2 of the average intensity raw fluorescence value measured. The raw data was first normalized using Robust Multichip Average (RMA) algorithm (Irizarry et al. 2003) for two batches of samples. Batch effect was corrected using ParTek software (www.partek.com: version: 6.11.0613).

To identify genes that were differentially expressed at the total transcript level, limma (linear models for microarray data) was used (Smyth 2004). Essentially, a linear model was fitted for each gene in the data set; the empirical Bayes method was then used to moderate the standard errors for each gene, which shrunk the standard errors toward a common value. The corresponding P-value for the comparison of the average total transcript level or average polysome associated transcript level for each probe set between mutant and control groups was adjusted using the multiple testing procedure developed by Benjamini and Hochberg (Benjamini and Hochberg 1995). Only RefSeq genes (MM10, December 2011, most updated version at time of study) that were on the arrays (total 18,936 genes) were used for subsequent analysis.

Transcripts with over two fold change between the average probe set intensity of mutant versus control group total RNA and a corresponding false discovery rate (FDR, Benjamini and Hochberg 1995) P-value ≤0.05 were called as showing significant change (increased or decreased expression) at the transcription level.

I calculated the polysome loading levels for each sample by comparing the probe set intensity levels in the polysome associated RNA sample to that of its paired corresponding total RNA sample:

Polysome associated RNA Polysome loading = Total RNA

To identify transcripts that have altered polysome loading between mutants and controls, the polysome loading changes were calculated by comparing the average of the polysome loading levels. Transcripts that showed over two fold changes at polysome loading level 75

between the average of mutant and control groups with a corresponding FDR P-value ≤0.05 were called as showing significant changes at the polysome loading level. Transcripts with multiple probe sets were called as significant if 1) at least one probe set had ≥3 fold change with an adjusted P-value ≤0.05; or 2) at least one probe set had ≥2 fold change and over 50% probe sets had at least trending changes with consistent direction (fold change ≥1.5 with an adjusted P-value ≤0.05).

3.3.4 Quantitative real-time RT-qPCR

RNA for RT-qPCR analysis was isolated as for the total RNA used for microarray analyses, see Section 3.3.2. RNA quality control and RT-qPCR were performed as previously described (Tourlakis et al. 2012). Expression levels are presented as relative to control genes as indicated. N=5 for both mutant and control groups. Samples with

A260/A280 ≈2 and 28S/18S >2 were used. Complementary DNA was synthesized from the extracted RNA samples using iScript cDNA Synthesis kit with random priming (Bio- Rad). RT-qPCR was performed using SsoFast EvaGreen Supermix with low ROX (Bio- Rad) with the Stratagene Mx3005P platform (Agilent). Each biological sample was assayed separately in triplicates. Amplification primers for genes analyzed are listed in Table 3.1.

Table 3.1 List of primers used in real time RT-qPCR for transcript analyses.

Gene of interest Sequence (5'-3') CTGCATAGTCTCTTTCTACCTGA Cyp4a10 ACACTGGGAACTTTATTGCAGA CTGGGATCACAGAAGCCAAG Serpina3k GCCTTACGAATGCCACCAAT GGCAGGGCTTTCATTTCAAC Onecut1 CCTATCCTGGTCCCTTAGCA CAAGGAACTGCATTGGGAAAC Cyp4a14 CTTAGCTTCTTGAGACACAGGT CCTCCTAGGGGGTAACACAA Serpina4-ps1 GCCAGCTTCATTCACGTCTA GAAGCCACAAGATCCCCTTC Prok1 TTGGCATTCTTCAAGTCCCG TGCCAGTGGAGTAGAACAGA Myo9a CCCAAGTACAGTGCCTGTTT ACCAGACACCTCACTAGACC Ces1g TTTGCCATGAACGGTGTGTA TGCCAGTGTCCTACCTACTC Tm6sf2 GTATCAGAGCACTCGTCAGC CTCATAGCTGAACTGCTCCAA Ugt2b36 AGTCCTCCACTGTATTTTTCAAGA CCTTGTGAAGAATCCAGGGT Sult2a1 TGCTCAAACCATGATCCGAA GGGTCACCCAGAATATCCCT Akr1c19 GAGCTGTGCGTAGAAGTCAT TTCGAGTGTCAATGAACCCC Ctse TGGATGAACCGGTGTCAAAG CCCATCCATTCCCCACAAAG Hist2h3c2 ATGTGCGGACAAAGCTCTAA 77

CACAGGTGAGACCATTACGG Tnnc1 AGCATCTACTCCACACCCTT AGTGCCAAAGCACAACTGAC Rfc4 TCCACACACTTTGGACGGTA TCAAGATCCGCGGGCGTAAAC Trp53 CAAGGCTTGGAAGGCTCTAGGC GCGGTGTCAGAGTCTAGGGGAA Cdkn1a (p21Cip1) GCGGAACAGGTCGGACATCAC GAGTGCAAGAACTCACCCAT Eif4ebp3 TTCAAACTGTTCGTCATCGGTT TCATCACTAGCAAAGAGGACC Eif3g GACACAATCTTCTGGCCCTT GGGCTTTGGATCCCTACAAG mTOR CAGGTTTCCCATGTTGACCA ACTCCCTGTTCAGTGCTTTG Eif4e CAACCGGGGTCTCATACAAG AAGCCAAGCCAAGTACCATT Eif6 AATGAGCCAAAGTCCAGAGG ACAATTCCTGGCGTTACCTT Tgfb TGGAGTTTGTTATCTTTGCTGTCA TGGCCCATTACTGTTTTGCT Nf1 GAAACCCGCTACAGGAAGAG TTTTCCCGAGTGTGAGTTGG Rictor ACTAGTGCAGACTCCTGTGT CTGCTCTGGCTCCTAGCACCA Actb CAGCTCAGTAACAGTCCGCCTAGAA TCACCACCATGGAGAAGGC Gapdh GCTAAGCAGTTCGTGGTGCA

3.3.5 Western immunoblottings and quantification analyses

Protein extract preparation from frozen tissues and western immunoblottings were performed as detailed in Section 2.3.4.2 and 2.3.4.3. Primary and secondary antibodies used are listed in Table 3.2.

3.3.6 Functional classification of transcripts and proteins

Functional classification of genes with significant changes in transcript expression levels or steady state protein levels were analyzed using the online tool Database for Annotation, Visualization and Integrated Discovery (DAVID, https://david.ncifcrf.gov/home.jsp) (Huang da et al. 2009a; Huang da et al. 2009b). The enrichment of GO term categories of molecular functions, biological processes and cellular components were considered. All Refseq genes on the microarray chip were used as the background comparison group for transcript expression and all proteins detected by mass spectrometry were used as the background comparison group for protein level analysis.

3.3.7 Physical characterization of transcripts with altered polysome loading

3.3.7.1 Transcript length and GC content analyses

As for expression level analysis, only RefSeq genes (MM10, December 2011, most updated version at time of study) that were on the microarray (total 18,936 genes) were used for transcript characterization. Genes with increased and decreased polysome association levels (634 and 165 respectively) as well as the remaining RefSeq mRNAs with no change were analyzed for their lengths and the percentage of GC content of 5' UTR, 3' UTR and ORF segments; using values obtained from UCSC Genome Bioinformatics (http://genome.ucsc.edu). The transcript parameters from the decreased and increased polysome loading groups were compared to the group with unaltered polysome loading levels. Median lengths and GC content of each group were used since non-normal distributions of measurements were observed in all groups (See Appendix 3.1 for raw data). Comparisons of groups was performed using Wilcoxon Rank Sum test (significance was defined as P-value <0.05). 79

Table 3.2 Antibodies used in western immunoblottings. Antigen Species Source Dilution Sbds Mouse In house 1:1000 Actb Mouse Abcam (Ab6276) 1:5000 Gapdh Rabbit Santa Cruz (sc-25778) 1:200 Rpl4 Mouse Abnova (H00006124-M01) 1:1000 Rps6 Rabbit NEB (2217S) 1:5000 Rplp0 Rabbit Abcam (Ab101279) 1:1000 Rps11 Rabbit Abcam (Ab175213) 1:1000 Eif6 Mouse BD Biosciences (611120) 1:3000 Eif5a Rabbit Abcam (Ab32443) 1:5000 Mcm3 Rabbit NEB 4012S 1:1000 Uev2 Rabbit Thermo scientific PA5-30994 1:1000

Secondary antibodies Goat anti-rabbit Bio-Rad (172-1019) 1:5000 Goat anti-mouse Santa Cruz (sc-2005) 1:5000

3.3.7.2 uORF, TOP and IRES analyses in UTRs

Genes with increased (total 634) and decreased (total 165) polysome loading levels, as well as a random sample of not-significantly changed genes (1 in every 50 genes of the non-changed group, for a total of 368 genes) were analyzed for presence of uORFs, TOP sequences and IRESs in their corresponding 5' UTR regions. This information was collected by manual counting from the UTRdb database (http://utrdb.ba.itb.cnr.it/) using the stated definitions (Grillo et al. 2010). The frequency of uORFs of each of the increased, decreased or unchanged polysome loading level groups was then calculated as the total number of uORFs per 100 nt of the 5' UTR length of the respective group.

3.3.8 Label-free mass spectrometry and analyses

12 mg of E18.5 liver tissue samples were lysed by sonication in 1.2 ml lysate buffer (20 mM HEPES pH8.0, 9 M urea) until the lysis solution became clear. The lysates were centrifuged at 20,000 ×g for 15 min. Supernatants were transferred and reduced by treatment with DTT (Sigma) to 4.5 mM. The samples were then alkylated with 10 mM iodoacetamide (Sigma, freshly prepared before use). The treated samples were diluted in 20 mM HEPES (pH8.0) to final urea concentration of 2 M and digested with mass spectrometry grade trypsin (Pierce, final concentration 10 µg/ml) overnight at room temperature.

30µg of digested protein was purified and concentrated using C18 Spin Columns (Thermo Scientific Pierce) following the manufacturer’s protocol. The proteins were eluted with 60 µl elution buffer, 30 µl was lyophilized (SpeedVac) and used for mass spec analysis.

The tryptic peptides were loaded onto a 50 cm x 75 μm ID column with RSLC 2 μm C18 packing material (EASY-Spray, Thermo-Fisher, Odense, Denmark) with an integrated emitter. The peptides were eluted into a Q-Exactive hybrid mass spectrometer (Thermo- Fisher, San Jose, CA) using an Easy-Spray nLC 1000 chromatography system (Thermo- Fisher, Odense Denmark) with a 4 hr. gradient from 0% to 35% acetonitrile in 0.1% formic acid. The mass spectrometer was operated in a data dependent mode with 1 MS followed by 10 MS/MS spectra. The MS was acquired with a resolution of 70,000 81

FWHM, a target of 1×106 ions and a maximum scan time of 120 ms. The MS/MS scans were acquired with a resolution of 17,500 FWHM, a target of 1×106 ions and a maximum scan time of 120 msec using a relative collision energy of 27%. A dynamic exclusion time of 15 sec was used for the MS/MS scans. The raw data files were acquired with XCalibur 2.2 (Thermo-Fisher Scientific) and processed with the PEAKS7 search engine (Bioinformatics Solutions) using the UniProt rodent database (Uniprot_2011_04, www.uniprot.org). Peptides were identified if the PEAKS score exceeded the 0.1% FDR as determined by searching against the reversed UniProt rodent database. Proteins were considered identified with at least one unique detected peptide. Label-free relative quantification was performed using the Quantification module of the PEAKS7 software suite (Ma et al. 2003; Zhang et al. 2012). Only proteins that were identified in all eight samples were considered. Proteins with greater than two fold change between the mutant and control group with significance score greater than 20 (i.e. P-value <0.01, significance score= -10log10(P-value)) were considered as showing significant changes at the steady state protein level.

3.3.9 Compilation of protein synthesis and related genes

A manually compiled list of eukaryotic genes including 47 Rpl genes, 31 Rps genes, 57 initiation genes (Eif) and 20 genes involved in elongation and termination was generated based on information from databases (using search terms "Rpl", "Rps", and "Eif", HUGO Gene Nomenclature Committee at the European Bioinformatics Institute, http://www.genenames.org/) published reviews and literature reports (Mathews et al. 2007; Scherer and Cold Spring Harbor Laboratory. Press. 2010; Hershey et al. 2012).

3.3.10 Statistical analyses

Statistical methods used in the analyses of microarray cDNA expression studies were detailed in Section 3.3.3.

All other statistical analyses were carried out using the R statistical software (® R Foundation, accessed from http://www.r-project.org/). Comparisons between groups, including polysome profile analysis and western immunoblotting quantification were 82

achieved using unpaired 2-tailed T tests, with P-values <0.01 declared as significant changes. Raw P-values are reported.

Other analyses were carried out as indicated in their respective sections.

3.4 Results

3.4.1 Altered steady state total mRNA levels and polysome loading

3.4.1.1 General work flow of microarray gene expression analyses

Matched total RNA and pooled polysome RNA fractions from fetal livers of six embryos with SbdsR126T/R126T genotypes were compared to that of six littermate control embryos with Sbds+/R126T genotypes. In this scheme, total RNA transcripts can first be compared between mutant and control samples, to investigate whether there are differences in steady state total mRNA levels. Subsequently, comparison of transcript levels in total and matched polysome RNA samples will provide indication of the level of engagement of transcripts in polysomes in mutant versus control samples (Figure 3.1).

The profiles of an example of total and polysomal RNA samples for one control and one mutant liver are shown in Figure 3.2. A critical step for the scheme as proposed, Figure 3.1, was that the isolated RNA was adequate for the microarray analysis and that the sucrose gradient centrifugation and subsequent fractionation did not compromise RNA integrity. The concentration and the quality of the prepared RNA samples were assessed using the Experion Automated Electrophoresis System and Experion Software Version 3.1 (BioRad). All 24 samples (12 total RNA, including six mutant and six control samples with 12 matched pooled polysome RNA samples) showed adequate quality and quantity to meet requirements for cDNA synthesis with fluorescent labeling for microarray hybridization. The microarray was carried out in two batches with three controls and three mutants in each.

3.4.1.2 Microarray data processing, normalization and quality control

Statistical analyses and evaluation of microarray data was kindly provided by Drs. A. Paterson and P. Hu of the statistical facility of The Centre for Applied Genomics (The Hospital for Sick Children). Briefly, the raw data from all the 24 microarray experiments were first normalized using the RMA algorithm (Irizarry et al. 2003). The distributions of probe intensities for all the total and polysomal RNA samples from both controls and mutants were found to be normal as performed by Dr. Hu, indicating that the data between all the samples were comparable. A batch effect was observed, which was corrected using ParTek software (www.partek.com: version: 6.11.0613). A linear model for microarray data analysis method (Smyth 2004) was used to identify genes whose mRNA levels or polysome loading levels were statistically significantly altered in the mutants compared to the controls.

As a first measure of the consistency of the microarray results between the mutants and controls, data from the total RNA samples were compared by a subgroup analysis by Dr. Hu. The six pairs of controls and mutants were separated into two groups, with three pairs of controls and mutants in each subgroup and the number of overlapping differentially and similarly expressed probe sets were counted (Table 3.3A). Any intensity changes that reflected a ≥2 fold of change (FC, of mutant to control) in expression level (increased or decreased) with an adjusted P-value ≤0.05 were considered as significant. At the total transcript level, the majority of probe sets showed no difference in expression, while 227 genes were commonly differentially expressed across the two subgroups. No probe sets exhibited contradicting differential expression suggesting that the results are biologically and technically consistent. A comparable conclusion was apparent for the polysomal RNA data (Table 3.3B). The raw microarray data indicating the RNA levels of all probe sets/genes assessed are given in Appendix 3.2.

Figure 3.1 Flow chart of cDNA microarray analyses of steady state total mRNA levels and transcript polysome loading levels of fetal livers.

Figure 3.2 Quality of RNA extracts used for cDNA synthesis and microarray determined by automatic electropherosis system.

Figure 3.2 Quality of RNA extracts used for cDNA synthesis and microarray determined by automatic electropherosis system. A, Electropherogram views of matched total (left panels) and polysomal RNA (right panels) extracted from a single control and a single mutant fetal liver samples. The two major peaks on each electropherogram indicate the 18S (left peak) and 28S (right peak) rRNAs. Degradation of RNA samples is evident by the smaller peaks, more degradation was apparent in control sample shown. 100 ng of RNA was loaded for each total extract sample and 20 – 50 ng of RNA was loaded for each polysomal RNA extract. B, Virtual gel image of the four RNA samples based on densitometric bands converted from the electropherogram in A. The red arrow indicated the lowest band (50 bp) of the RNA marker that was used for the alignment of all samples. 87

3.4.1.3 Validation of microarray analyses with real time RT-qPCR

As the first step towards the analyses of microarray gene expression studies and the identification of transcripts targeted by Sbds deficiency, a series of probe sets/genes that exhibited a range of changes in expression was selected for RT-qPCR analyses to validate the microarray comparisons. Total RNA from five additional mutants and five additional control livers were prepared and used for RT-qPCR. β-actin and Gapdh were used to enable direct comparison of mutant and control gene expression levels. A total of 26 genes with changes in transcript levels as determined by microarray were examined similarly, the FCs calculated by microarray and RT-qPCR analyses are listed in Table 3.4 for comparison. The RT-qPCR results agree with the microarray in terms of increased or decreased expression for 20 of the 26 genes. All seven genes tested that showed no significant difference in expression by microarray also showed no difference in expression by RT-qPCR. For the genes that appear down regulated in mutants, the changes appear consistently larger when measured by RT-qPCR. Six genes (Myo9a, Ces1g, Hist2h3c2, Rfc4, p21cip1, and Eif3g) with over two fold changes in total transcript levels determined by microarray did not show significant changes by RT-qPCR. Of these six genes, the changes of transcript expression of p21cip1, and Eif3g by microarray were just over two fold (2.02 and 2.07, respectively).

Analysis of sequences of the probe sets and oligonucleotide primers for genes with discordant transcript level measurements showed that the sequences used in the microarray probe sets typically cover the entire transcripts whereas the primer sequences used in RT-qPCR were designed to be close to the 5' end of the transcripts. From this aspect, it appears that the measurement by microarray is more convincing than by RT- qPCR.

Table 3.3 Comparison of directional changes in transcript expression levels in mutant versus control fetal livers. Average probe set intensity differences in mutant versus control fetal livers are compared across sets (phases) for A, total and B, polysomal transcripts.

A Total RNA Phase 11

Increased Unchanged Decreased Increased 111 177 0 Phase 21 Unchanged 28 25,222 168 Decreased 0 92 116

B Polysomal RNA Phase 11

Increased Unchanged Decreased Increased 438 432 0 Phase 21 Unchanged 385 23,958 405 Decreased 0 88 208

1Phase 1 included the first set of three control and mutant fetal liver samples; and Phase 2 included the second set of three control and mutant samples.

Table 3.4 Validation of selected genes by real time RT-qPCR. Comparison of gene expression levels between mutant (SbdsR126T/R126T) and controls (Sbds+/ R126T) by microarray and RT-qPCR. RT-qPCR measures relied on normalization to Actβ and Gadph as house-keeping genes.

RT-qPCR Microarray Gene Actβ Gapdh Fold FDR Fold T-test Fold T-test change P-value change P-value change P-value Cyp4a10 -3.19 8.96E-7 -16.67 2.87E-3 -12.05 2.00E-2 Serpina3k -4.92 7.63E-9 -111.11 4.45E-3 -76.92 5.20E-3 Onecut1-2 -4.551 5.53E-7 -12.50 4.20E-6 -5.88 4.87E-4 Reduced transcript Cyp4a14 -8.10 5.82E-8 -14.29 1.08E-2 -11.11 1.29E-2 levels Serpina4-ps1 -8.04 1.03E-8 -16.67 1.78E-6 -8.77 3.54E-3 in mutants Prok -4.64 1.01E-6 -11.11 1.51E-3 -6.85 3.69E-2 Eif4ebp3 -2.04 1.19E-5 -3.53 3.43E-2 -2.45 8.64E-2 2Myo9a -5.393 8.34E-6 1.21 1.34E-1 2.13 1.90E-1 2Ces1g 3.94 8.13E-6 3.01 6.54E-2 5.23 1.00E-1 Tm6sf2 3.23 5.99E-7 3.77 2.17E-2 5.48 9.08E-3 Ugt2b36 3.75 1.25E-6 4.83 2.56E-6 8.62 3.50E-2 Sult2a1 3.43 4.54E-6 32.01 4.21E-2 48.18 1.29E-2 Increased Akr1c19 4.09 1.21E-8 2.70 3.32E-2 5.57 1.25E-1 transcript CtsE 5.11 1.01E-7 2.35 6.97E-3 4.30 9.92E-2 levels in mutants 2Hist2h3c2 4.42 1.87E-7 1.39 1.12E-1 2.53 1.90E-1 Tnnc1 3.43 1.03E-6 7.40 2.16E-2 16.52 9.58E-2 2Rfc4 4.84 8.02E-6 1.24 3.90E-1 2.43 2.00E-1 2p21Cip1 2.02 1.21E-4 1.29 4.65E-1 1.88 1.27E-1 2Eif3g 2.07 4.10E-6 1.03 7.11E-1 1.73 3.46E-1 mTOR -1.16 0.14 -1.11 7.29E-1 1.12 5.84E-1 Eif4e 1.00 9.86E-1 1.19 4.14E-1 2.25 2.21E-1 No change Eif6 1.79 8.86E-6 1.23 1.65E-1 2.27 2.04E-1 in transcript Tgfb 1.06 6.64E-1 -1.14 1.22E-1 1.64 3.08E-1 levels in mutants Nf1 -1.96 8.43E-6 -1.08 7.80E-1 1.23 4.55E-1 Rictor -1.43 6.26E-3 -1.34 1.05E-1 1.34 5.67E-1 Trp53 1.80 8.86E-6 1.00 9.69E-1 1.77 2.48E-1 90

1Onecut1 was represented by two probe sets, with measured fold differences of -5.25 and -3.85; the average is listed.

2Transcript expression changes with discrepancies between the two analysis methods.

3Myo9a was represented by 11 probe sets, with measured fold differences that ranged from -2.22 to -5.39; the average is listed.

3.4.1.4 Modest steady state mRNA level changes in SDS fetal liver

The microarray platform contained a total 25,914 distinct probe sets that include 18,936 RefSeq genes. Comparison of the total mRNA expression profiles of SDS mutant livers to controls (transcriptome) showed that overall the transcript expression profiles were not dramatically altered (Figure 3.3A, Appendix 3.2 and 3.3). When comparing mutants to controls, only 170 probe sets indicated increased mRNA levels and 248 probe sets indicated decreased mRNA levels with ≥ 2 fold change (FDR adjusted P-value ≤0.05; Appendix 3.4). Of these, the majority had less than three fold changes (Figure 3.3B). Using the functional classification tool DAVID, the group of 368 genes that showed altered mRNA levels indicated some enrichment for recognized liver pathways, including heme and iron binding and also glutathione transferase activities (Table 3.5A). Examination of individual genes within the clustered GO groups (Table 3.5B) revealed down regulation of a series of Cyp4a genes, amongst other more modest Cyp gene changes possibly reflecting a developmental disturbance of metabolism. The genes in the glutathione transferase activity pathways revealed modest but consistent increases in transcript levels suggesting some activation of glutathione utilization or xenobiotic response (Townsend et al. 2003) in SDS mutants.

Loss of Sbds has been shown to be associated with cell cycle arrest pathways, with apoptosis in the early brain and senescence in the postnatal pancreas (Tourlakis et al. 2015). However, expression of factors implicated in these processes did not show consistent changes, including Trp53, p15Ink4b, Tgfβ and its receptors. p21Cip1 did show 2.02 fold increase in the mutant fetal livers, but the significance of this modest change is unclear as this difference was not supported in independent samples by RT-qPCR analysis (Figure 3.4).

Figure 3.3 Few genes exhibit steady state total mRNA level changes in SDS mutant fetal livers. A, probe sets with changes in mRNA level are shown summarized by Volcano plot. Normalized probe intensities with changes greater than ± 2 fold with FDR P-values ≤0.05 were considered significant, and are depicted as black dots. B, the number of probe sets with significant fold changes between SbdsR126T/R126T and Sbds+/R126T are indicated.

Avg log2(probe set intensity) Gene Fold Change P-value Sbds+/R126T SbdsR126T/R126T Trp53 (p53) 10.66 11.51 1.80 8.86E-06 Cdkn1a (p21Cip1) 10.16 11.17 2.02 1.21E-04

Figure 3.4 Cell stress response factors p53 and p21Cip1 are not consistently altered in mutant livers. Steady state mRNA levels for p53 and p21Cip1 in SbdsR126T/R126T and Sbds+/R126T (fold change) fetal livers measured by microarray are shown in upper panel. Relative levels of p53 and p21Cip1 transcripts measured by RT-qPCR with normalization to the house- keeping gene Actβ in independent samples, are shown in lower panel (P-values for T-test are 0.97 and 0.46, respectively).

Table 3.5 Functional analyses of genes with altered steady state mRNA levels in Sbds deficient fetal livers. A Top 10 GO terms obtained from the molecular function ontology (GO_MF_FAT)1 identified for genes with altered transcript levels using DAVID

Fold Term Count % P-Value Bonferroni Benjamini2 FDR Enrichment GO:0046906~tetrapyrrole 1 13 3.58 6.37E-05 4.18 2.82E-02 2.82E-02 0.09 binding GO:0004364~glutathione 2 6 1.65 1.22E-04 11.83 5.33E-02 2.70E-02 0.17 transferase activity 3 GO:0020037~heme binding 12 3.31 1.80E-04 4.06 7.78E-02 2.66E-02 0.26 GO:0005506~iron ion 4 19 5.23 2.03E-04 2.74 8.73E-02 2.26E-02 0.29 binding GO:0016712~oxidoreducta se activity, acting on paired donors, with incorporation or reduction of molecular 5 7 1.93 2.97E-04 7.53 1.25E-01 2.63E-02 0.42 oxygen, reduced flavin or flavoprotein as one donor, and incorporation of one atom of oxygen GO:0016765~transferase activity, transferring alkyl 6 7 1.93 6.01E-04 6.62 2.37E-01 4.40E-02 0.85 or aryl (other than methyl) groups GO:0070330~aromatase 7 6 1.65 6.72E-04 8.35 2.61E-01 4.22E-02 0.95 activity GO:0009055~electron 8 13 3.58 7.97E-04 3.19 3.01E-01 4.38E-02 1.12 carrier activity GO:0046914~transition 9 72 19.83 2.35E-03 1.38 6.52E-01 1.11E-01 3.28 metal ion binding GO:0004091~carboxylester 10 8 2.20 3.05E-03 4.16 7.46E-01 1.28E-01 4.23 ase activity 1Enrichment by the other two alternative ontologies (biological processes; GO_BP_FAT and cellular components; GO_CC_FAT) yielded GO: 0055114 (oxidation reduction) group (Benjamini P-values =1.5E-02) and GO:0019898 (extrinsic to membrane) group (Benjamini P- values=6.3E-04), respectively. No other groups were enriched by these two classifications.

2Benjamini P-values ≤5E-02 was used as indication of significant enrichment. Groups 9 and 10 were not considered significant with this criterion.

B Genes with altered steady state mRNA levels found to be associated with two or more GO terms3. Gene GO Fold polysome GO clusters RefSeq ID P-value P-value name terms change loading NM_145949 Ido2 1, 3, 4 2.89 6.26E-06 -1.69 7.03E-02 1, 3, 4, NM_206537 Cyp2c54 -2.65 1.65E-06 1.22 4.05E-01 5, 7, 8 1, 3, 4, NM_177406 Cyp4a12a -18.07 9.65E-08 -1.19 9.04E-01 5, 7, 8 NM_001100181 Cyp4a32 1, 3, 4, 8 -2.33 7.16E-05 -1.12 8.29E-01 1, 3, 4, NM_007813 Cyp2b13 2.92 1.32E-06 1.05 9.52E-01 5, 8 NM_007822 Cyp4a14 1, 3, 4, 8 -8.10 5.82E-08 -1.08 9.10E-01 1, 3, 4, NM_133657 Cyp2a12 2.06 1.05E-04 -1.73 1.23E-02 5, 7, 8 Cluster of 1, 3, 4, NM_010001 Cyp2c37 -2.47 4.85E-04 -1.16 6.98E-01 heme/ iron 5, 7, 8 binding NM_010011 Cyp4a10 1, 3, 4, 8 -3.19 8.96E-07 1.23 4.00E-01 NM_007809 Cyp17a1 1, 3, 4, 8 2.33 8.96E-07 -1.34 1.22E-01 NM_172306 Cyp4a12b 1, 3, 4, 8 -12.85 3.20E-08 1.29 4.09E-01 1, 3, 4, NM_010000 Cyp2b9 2.38 8.64E-04 1.60 1.41E-01 5, 7, 8 1, 3, 4, NM_007818 Cyp3a11 2.17 1.41E-05 -1.50 6.36E-02 5, 7, 8 1, 3, 4, NM_001101467 Cyp2a22 2.36 4.72E-04 1.04 9.46E-01 5, 7, 8 NM_144870 Ndufs8 4, 8 2.70 4.87E-08 -1.17 3.98E-01 NM_007996 Fdx1 4, 8 -2.07 9.43E-05 -1.22 2.97E-01 4NM_008181 Gsta1 2, 6 3.06 2.54E-05 -1.45 2.26E-01 NM_008181 Gsta1 2, 6 2.65 4.26E-05 -1.35 2.98E-01 4 Cluster of NM_010359 Gstm3 2, 6 2.30 1.37E-06 -1.32 1.35E-01 glutathione NM_010359 Gstm3 2, 6 2.55 1.07E-07 -1.18 3.16E-01 transferase NM_008184 Gstm6 2, 6 3.02 1.38E-06 -1.41 1.40E-01 activities NM_029555 Gstk1 2, 6 2.40 1.14E-03 -1.79 5.06E-02 NM_010363 Gstz1 2, 6 2.14 2.21E-07 -1.25 1.63E-01 NM_026672 Gstm7 2, 6 2.29 7.91E-08 -1.06 7.31E-01 3Based on enrichment listed above in A.

4Transcripts with two probe sets on the microarray.

3.4.1.5 Transcript and polysome analyses reveal altered ribosome loading for a subset of mRNAs in Sbds deficient fetal livers

As mentioned, polysome loading is generally interpreted to reflect the ongoing translation of mRNAs. The ribosome bound level of each transcript defined as ‘polysome loading’ was calculated as the polysome associated mRNA per corresponding total message level for each gene. Calculated polysome loading levels of each transcript were then compared between mutants and controls to investigate if loss of Sbds function resulted in a change in the level of bound transcript. Comparison of polysome loading levels for all of the transcripts in the microarray showed 817 probe sets with increased polysome loading and 272 probe sets with decreased polysome loading in mutants (FC ≥2; FDR adjusted P- value ≤0.05), corresponding to 634 and 165 genes, respectively (Appendix 3.5). The majority showed no change in total RNA expression levels between mutants and controls, see Figure 3.5. A total of 127 probe sets (122+5) showed changes in both steady state mRNA levels and polysome loading levels, but all of these changes were in the opposing direction. The observation of the transcripts with increased polysome loading in mutants appears consistent with the abundant heavy polysomes in these samples, but is intriguing from the current proposed function of Sbds, where loss of Sbds should result in decreased protein translation as measured by total radiotracer incorporation (Ball et al. 2009).

3.4.2 The Sbds-deficient proteome including translation-related components does not exhibit major changes

To resolve the conundrum of prominent polysome peaks in the context of translation insufficiency, I assessed whether the proteome was altered in response to the loss of Sbds function. I performed label-free liquid chromatography- tandem mass spectrometry (LC- MS/MS) of E18.5 liver extracts from four Sbds+/R126T and four SbdsR126T/R126T embryos. Annotation of peptides using PEAKS7 software by database search (Uniprot_2011_04) identified a total of 4,109 distinct proteins from the eight samples. No individual protein was found exclusively in only the mutant or control sets and 3,188 proteins were found in each of the eight samples where 3,123 correspond to proteins with RefSeq annotations (Appendix 3.6). 97

Figure 3.5 Summary of changes in probe set intensities in transcript and polysome loading levels between SDS mutant fetal livers and controls. The number of probe sets with changes in transcript and polysome loading levels in SbdsR126T/R126T compared to Sbds+/R126T fetal livers are shown summarized by Venn diagram.

The expressions of translation-related genes, particularly those encoding ribosome constitutive proteins, have been proposed to be coordinately regulated (Warner 1999; Rudra and Warner 2004). To see if the loss of Sbds leads to unsynchronized expression of ribosome proteins and other protein synthesis-related proteins, I compared the steady state cellular levels of manually compiled lists of 46 Rpl proteins (46 detected by the mass spectrometry analysis), 31 Rps proteins (30 detected), 57 eukaryotic translation initiation factors (37 detected), and 20 proteins involved in translation elongation/termination (14 detected) (Table 3.6). None of the detected factors, including Eif6, were changed at the cellular level (with criteria of ≥ two fold change and P-value ≤ 0.01).

Correspondingly, the microarray analysis above did not show general changes of total transcript expression or polysome loading levels for these protein synthesis-related genes (Appendix 3.3). Only three initiation factors showed some change in transcript level; where for one of these, Eif3g, no change in protein level, was measured by mass spectrometry (Table 3.6). I further examined a selected group of factors: RplP0, Rpl4, Rps6, Eif6 and Eif5a in liver, lung, brain, kidney, pancreas and skeletal muscle tissues from the E18.5 mouse embryos and did not observe gross differences in their steady state levels between mutants and controls based on immunoblotting (Figure 3.6).

Examination of proteins beyond protein synthesis factors also did not exhibit substantial changes. With normalization to the total peptide intensity counts (total area under all peptide peaks), only 26 (0.9%) proteins showed differences in steady state protein levels (Figure 3.7, Appendix 3.6, ≥ two fold with P-value ≤0.01). Of these, only four exceeded three fold differences: Hsd3b1, Asgr1, Hacl1 and Pnliprp1. Hsd3b1 and Hacl1 participate in liver metabolism, but these proteins appear otherwise unrelated. The remaining 3097 (99.1%) proteins exhibited comparable levels between mutants and controls (Figure 3.7), indicating an overall relatively unaltered proteome with Sbds deficiency in fetal livers. Correlating with the microarray analyses, only five of these 26 proteins showed consistent directional changes of transcript expression (≥ two fold with P-value ≤0.05). In addition, I performed bioinformatic analyses of the 26 genes that appeared to be differentially expressed at the RNA level, but no enrichment of specific cellular 99

components, molecular functions or biological processes in GO terms were indicated using DAVID. Overall, it appeared that protein synthesis in the Sbds deficient environment reflected a general effect on translation and did not pinpoint specific process disturbances based on this en masse analysis of abundantly expressed proteins.

3.4.3 Steady state cytoplasmic protein levels do not correspond to changes in the polysome loading levels

I then specifically considered the genes with altered polysome loading levels and compared the steady state cellular levels of their encoded proteins. From the 3,123 RefSeq proteins identified by LC-MS/MS, I was able to detect 57 out of 634 proteins encoded by transcripts that showed increased polysome loading, and of these, only one protein (butyrylcholinesterase [Bche]) showed an increase in steady state protein level, as measured by mass spectrometry (2.12 fold change with P-value =8.57×10-4, Figure 3.8A). 120 out of the 165 proteins encoded by transcripts with decreased polysome loading (Figure 3.8A) were identified, but none of these showed differences in protein levels. Two genes with strikingly altered polysome association and sensitive antibodies (MCM3, 3.3 fold reduced and Uev2, 4.8 fold increased in mutants; Appendix 3.3) were also examined in independent samples. The steady state levels of the corresponding proteins again did not reflect the changes in polysome loading for these genes (Figure 3.8B and Appendix 3.6). Together, it was evident that the vast majority of transcripts with altered polysome loading in the SDS mutants did not correspond to changes at the protein level.

3.4.4 Transcript features determine differences in polysome loading

As changes in polysome loading did not reflect expression, I then asked if differentially associated transcripts had distinguishing sequence or structural features. A comparison of the median length of all the transcripts of RefSeq coding genes with increased or decreased polysome loading to those with no change revealed overall increased lengths

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Table 3.6 Expression levels of factors involved in translation. Comparison of expression levels of translation related factors in SbdsR126T/R126T and Sbds+/R126T fetal livers. A, 46 large ribosomal subunit genes (Rpl); B, 31 small ribosomal subunit genes (Rps); C, 57 eukaryotic initiation factor genes (Eif); and D, 20 elongation, termination/recycling factor genes were studied. Relative fold change (FC) in total transcript level, polysome loading level and steady state protein levels of mutant to control, with respective P-values are shown. Genes with FC ≥2 or ≤-2 with FDR adjusted P-value ≤0.05 (transcript level and polysome loading level) or P-value ≤0.01 (protein level) are indicated in red font. Components/factors that were not detected by microarray or mass spectrometry analyses are left blank.

A. Large ribosomal subunit genes (Rpl) Transcript level Polysome loading level Protein level Gene FC Adjusted P-value FC Adjusted P-value FC Adjusted P-value Rpl3 1.08 3.78E-01 -1.27 4.78E-02 -1.06 2.12E-01 Rpl4 -1.27 2.67E-02 -1.01 9.39E-01 1.18 9.66E-02 Rpl5 -1.54 2.40E-04 -1.01 9.65E-01 -1.12 1.85E-01 Rpl6 1.09 2.36E-01 -1.20 5.23E-02 1.12 2.20E-01 Rpl7 -1.02 9.02E-01 -1.30 1.54E-01 1.21 1.06E-01 Rpl7a 1.17 3.77E-02 -1.16 1.47E-01 1.11 1.66E-01 Rpl8 1.19 6.52E-02 -1.27 6.04E-02 -1.09 3.20E-01 Rpl9 1.13 3.38E-01 -1.11 4.78E-01 -1.10 1.25E-01 Rpl10 1.12 3.16E-02 1.09 2.55E-01 -1.06 2.31E-01 Rpl10a 1.22 3.79E-03 -1.09 3.84E-01 -1.09 4.33E-01 Rpl11 -1.37 2.99E-02 1.16 4.30E-01 -1.25 6.84E-02 Rpl12 -1.01 9.39E-01 1.01 9.14E-01 -1.56 3.96E-03 Rpl13 -1.09 4.77E-01 -1.03 7.99E-01 -1.08 2.02E-01 Rpl13a 1.08 5.12E-01 -1.05 7.73E-01 -1.08 3.16E-01 Rpl14 -1.28 7.83E-02 -1.25 2.19E-01 1.08 1.65E-01 Rpl15 1.07 5.63E-01 1.17 2.03E-01 1.02 1.46E-01 Rpl17 1.33 4.74E-03 1.09 5.90E-01 -1.06 2.58E-01 Rpl18 1.35 1.91E-03 1.17 1.87E-01 1.31 9.46E-02 Rpl18a 1.18 2.13E-03 1.05 4.84E-01 1.09 1.86E-01 Rpl19 1.10 2.85E-01 -1.12 2.88E-01 -1.01 1.75E-01 Rpl21 1.25 8.65E-03 1.38 7.64E-03 -1.03 2.23E-01 Rpl22 -1.27 4.85E-02 1.01 9.98E-01 1.01 2.64E-01 Rpl23 -1.67 6.19E-03 1.62 8.63E-02 -1.14 2.34E-01 Rpl23a 1.01 9.66E-01 1.01 8.95E-01 -1.08 5.06E-01 101

Rpl24 -1.32 4.91E-02 -1.04 9.03E-01 1.05 1.87E-01 Rpl26 1.35 3.31E-02 -1.04 8.75E-01 -1.11 1.45E-01 Rpl27 1.00 9.83E-01 -1.22 1.32E-01 -1.02 2.02E-01 Rpl27a 1.09 4.15E-01 -1.10 4.43E-01 -1.03 4.74E-01 Rpl28 1.07 3.14E-01 1.01 8.43E-01 1.02 1.61E-01 Rpl29 1.45 5.55E-05 1.18 1.42E-01 1.06 1.75E-01 Rpl30 -1.54 5.90E-03 1.36 1.93E-01 -1.18 1.35E-01 Rpl31 0.98 9.23E-01 1.21 2.50E-01 -1.05 4.32E-01 Rpl32 1.25 2.54E-03 1.08 4.21E-01 1.05 5.83E-01 Rpl34 1.40 3.57E-03 -1.01 9.77E-01 1.31 9.46E-02 Rpl35 0.95 6.21E-01 -1.02 8.71E-01 1.21 1.86E-01 Rpl35a 1.18 1.02E-01 -1.10 5.22E-01 -1.12 1.68E-01 Rpl36 1.19 9.44E-02 -1.04 8.26E-01 1.12 7.46E-02 Rpl36a 1.00 9.91E-01 -1.14 5.47E-01 -1.09 1.70E-01 Rpl37 -1.15 1.60E-01 -1.08 6.16E-01 -1.11 3.58E-01 Rpl37a -1.45 4.95E-04 1.09 5.51E-01 1.06 2.19E-01 Rpl38 1.05 6.52E-01 1.02 9.06E-01 -1.11 1.92E-01 Rpl39 1.04 6.42E-01 1.03 7.91E-01 -1.14 1.35E-01 Rpl41 1.05 6.12E-01 -1.20 7.93E-02 Rplp0 1.16 1.80E-02 1.12 1.87E-01 -1.19 1.89E-01 Rplp1 -1.33 7.02E-02 -1.02 8.21E-01 -1.18 8.79E-02 Rplp2 1.22 1.92E-01 -1.20 3.40E-01 -1.23 8.91E-02

B. Small ribosomal subunit genes (Rps) Transcript level Polysome loading level Protein level Gene FC Adjusted P-value FC Adjusted P-value FC Adjusted P-value Rps2 -1.11 1.33E-01 1.08 4.33E-01 1.12 3.78E-01 Rps3 -1.22 1.91E-01 -1.09 6.79E-01 -1.18 8.11E-02 Rps3a -1.14 5.14E-01 -1.89 1.23E-02 -1.03 5.22E-01 Rps4x 1.01 9.48E-01 -1.20 1.58E-01 -1.14 8.79E-02 Rps5 1.11 1.06E-01 1.00 9.78E-01 -1.15 1.84E-01 Rps6 1.01 8.80E-01 -1.01 9.18E-01 -1.10 1.46E-01 Rps7 -1.11 4.43E-01 -1.04 8.54E-01 -1.32 4.36E-02 Rps8 1.02 9.02E-01 -1.20 2.94E-01 1.01 2.55E-01 Rps9 -1.35 1.71E-02 1.10 6.40E-01 1.14 8.22E-02 Rps10 -1.03 5.70E-01 1.07 3.54E-01 -1.49 1.74E-02 Rps11 1.33 1.01E-03 -1.23 7.83E-02 -1.15 7.73E-02 Rps12 -1.52 1.30E-02 1.11 6.16E-01 -1.15 9.59E-02 Rps13 1.43 2.22E-03 -1.22 1.87E-01 -1.11 1.76E-01 102

Rps14 -1.49 3.27E-02 1.00 9.96E-01 -1.11 3.23E-01 Rps15 1.36 1.71E-02 1.23 2.72E-01 -1.15 4.43E-01 Rps15a 1.02 9.22E-01 1.33 1.05E-01 1.13 1.28E-01 Rps16 -1.20 4.81E-02 1.04 8.39E-01 -1.06 2.33E-01 Rps17 1.27 1.10E-02 -1.01 9.49E-01 -1.18 2.02E-01 Rps18 1.20 3.81E-02 -1.20 1.40E-01 -1.16 2.06E-01 Rps19 1.48 1.33E-03 -1.20 2.49E-01 -1.14 1.26E-01 Rps20 -1.06 6.79E-01 1.05 7.76E-01 -1.09 1.23E-01 Rps21 -1.27 1.02E-01 -1.33 1.38E-01 -1.11 1.30E-01 Rps23 -1.16 3.33E-01 1.10 6.38E-01 -1.09 1.13E-01 Rps24 1.02 9.26E-01 1.03 9.35E-01 1.03 2.22E-01 Rps25 1.03 8.68E-01 -1.12 4.22E-01 -1.04 1.58E-01 Rps26 1.04 6.28E-01 1.23 2.90E-02 1.03 2.15E-01 Rps27 -1.23 3.35E-01 1.21 5.74E-01 -1.01 2.36E-01 Rps27a -1.12 4.63E-01 1.00 9.46E-01 -1.20 6.22E-02 Rps28 -1.41 2.40E-03 -1.03 8.52E-01 -1.15 1.28E-01 Rps29 -1.82 1.03E-03 1.00 9.90E-01 -1.06 3.22E-01 Rps4y2 1.24 1.64E-01 -1.54 2.26E-02

C. Eukaryotic initiation factor genes (Eif) Transcript level Polysome loading level Protein level Gene FC Adjusted P-value FC Adjusted P-value FC Adjusted P-value Eif1 1.17 1.70E-01 -1.06 7.67E-01 1.07 4.15E-01 Eif1a -1.43 1.08E-02 1.90 2.03E-03 Eif1ad 1.37 2.47E-04 1.29 2.66E-02 Eif1ax -1.09 6.42E-01 1.04 8.90E-01 Eif1ay 1.01 1.97E -01

Eif1b -1.12 2.61E-01 -1.01 9.37E-01 1.01 4.94E-01 Eif2a -1.12 3.57E-01 -1.10 5.26E-01 -1.09 8.59E-01 Eif2ak1 1.19 2.57E-01 1.30 2.65E-01 1Eif2ak2 -2.00 4.99E-03 1.38 4.33E-01 Eif2ak3 -1.18 2.10E-01 1.00 9.64E-01 Eif2ak4 -1.27 4.77E-03 1.35 1.37E-02 1.05 4.70E -01 Eif2b1 1.18 1.83E-01 -1.14 3.79E-01 Eif2b2 1.52 7.23E-03 -1.22 2.48E-01 -1.14 2.92E -01 Eif2b3 1.41 4.38E-03 -1.61 4.82E-03 Eif2b4 1.24 4.36E-02 -1.89 2.05E-04 -1.06 2.77E -01 Eif2b5 1.1 5.60E-01 -1.79 4.73E-03 Eif2c1 -1.15 1.92E-01 1.22 1.98E-01

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Eif2c2 -1.09 5.53E-01 1.55 1.41E-02 -1.06 2.74E-01 2Eif2c3 -1.75 1.52E-05 2.66 3.98E-06 Eif2c4 -1.27 2.51E-03 1.17 1.46E-01 Eif2s1 -1.11 6.89E-01 1.40 2.89E-01 -1.11 3.66E -01 Eif2s2 -1.27 5.10E-01 1.17 5.98E-01 1.02 3.45E-01 Eif2s3x 1.23 3.53E-01 -1.04 8.86E-01 1.17 2.08E-01 Eif2s3y -1.75 6.05E-01 -1.12 7.61E-01 Eif3a -1.45 1.83E-02 -1.54 4.80E-02 1.06 2.64E -01 2Eif3b 1.37 5.14E-03 -2.08 5.17E-05 1.08 2.88E-01 2Eif3c 1.19 1.23E-01 -2.22 1.97E-05 -1.18 1.40E-01 2Eif3d 1.43 3.53E-03 -2.04 2.23E-04 1.02 2.81E-01 Eif3e -1.15 5.10E-01 -1.18 4.90E-01 1.11 3.50E-01 Eif3f 1.51 1.09E-04 -1.18 2.15E-01 1.06 2.13E-01 1Eif3g 2.07 4.10E-06 -1.69 4.02E-03 1.02 2.93E-01 Eif3h 1.28 6.05E-03 -1.49 2.47E-03 1.14 2.81E-01 Eif3i 1.23 2.55E-01 -1.45 1.32E-01 1.11 1.74E-01 Eif3j 1.11 8.19E-01 1.17 7.49E-01 1.17 2.28E-01 Eif3k 1.17 2.60E-01 -1.43 3.37E-02 -1.04 3.14E-01 Eif3l 1.25 1.56E-02 -1.18 2.17E-01 -1.04 3.39E-01 Eif3m 1.03 9.31E-01 -1.56 1.78E-01 1.08 3.24E-01 Eif4a1 1.11 1.66E-01 -1.20 5.23E-02 1.10 2.62E-01 Eif4a2 -1.96 4.83E-06 1.42 5.15E-02 0.80 2.04E-01 Eif4a3 1.31 8.09E-02 -1.89 2.45E-03 1.12 3.33E-01 Eif4b 1.12 3.41E-01 -1.43 1.99E-02 -1.12 2.92E-01 Eif4e 1.09 2.92E-01 -1.14 2.18E-01 1.00 3.33E-01 Eif4e1b 1.12 2.54E-01 -1.27 6.32E-02 Eif4e2 -1.09 2.82E-01 1.31 1.22E-02 -1.67 2.45E -01 Eif4e3 -1.33 1.09E-03 1.23 8.18E-02 Eif4ebp1 -1.04 7.61E-01 1.18 1.96E-01 Eif4ebp2 -1.25 3.37E-02 1.54 4.22E-03 1Eif4ebp3 -2.04 1.19E-05 1.29 2.06E-01 Eif4enif1 1.02 8.91E-01 1.35 3.48E-02 Eif4g1 -1.16 1.41E-02 -1.52 5.95E-05 -1.10 4.55E -01 Eif4g2 -1.33 7.45E-03 1.87 2.75E-04 1.10 2.98E-01 Eif4g3 -1.39 4.00E-04 -1.06 5.97E-01 Eif4h -1.03 8.46E-01 -1.04 6.95E-01 -1.19 2.20E -01 Eif5 -1.27 1.58E-01 1.76 1.69E-02 -1.02 5.14E-01 Eif5a2 1.05 7.93E-01 1.60 1.05E-02 -1.22 1.34E-01 Eif5b -1.41 5.03E-02 -1.12 6.40E-01 -1.06 8.75E-01 Eif6 1.79 8.86E-06 -1.54 7.73E-03 1.28 5.53E-02 104

1Transcripts for Eif2ak2, Eif4ebp3, and Eif3g indicated two fold changes in mutants; only Eif3g was identified by mass spectrometry, but no change in protein level was detected.

2Transcripts for Eif2c3, Eif3b, Eif3c and Eif3d indicated over two fold changes in polysome loading; however Eif3b, Eif3c, and Eif3d showed no change in transcript or protein levels.

D. Elongation, termination and recycling factor genes Transcript level Polysome loading level Protein level Gene FC Adjusted P-value FC Adjusted P-value FC Adjusted P-value Eef1a1 -1.08 1.90E-01

Eef1a2 -1.09 1.43E-01

Eef1b -1.01 1.74E-01

Eef1b2 -1.02 8.71E-01 1.04 7.37E-01 Eef1d 1.11 2.75E-01 -1.61 6.02E-04 1.00 7.55E -01 Eef1e1 1.21 1.33E-01 1.04 8.57E-01 1.17 2.83E-01 Eef1g -1.05 7.60E-01 -1.32 1.25E-01 -1.16 1.13E-01 Eef2 -1.05 4.81E-01 -1.19 4.91E-02 -1.09 4.19E-01 Eef2k -1.12 2.09E-01 -1.03 8.72E-01 Eef2k 1.17 5.82E-01 1.43 3.00E-01 Eefsec 1.88 6.70E-06 -1.89 3.73E-04 1.23 1.58E -01 Eif5a 1.16 1.14E-02 1.05 5.33E-01 -1.22 1.34E-01 Etf1 -1.25 4.37E-01 1.37 3.72E-01 -1.02 5.31E-01 Gspt1 1.00 9.95E-01 1.36 4.43E-01 -1.04 4.39E-01 Gspt2 -1.20 2.75E-02 -1.20 1.14E-01 -1.06 1.48E-01 Abce1 -1.30 8.21E-02 1.19 4.42E-01 -1.08 3.61E-01 Pelo 1.13 2.51E-01 -1.14 2.98E-01 1.29 1.28E-01 Hbs1l -1.59 5.88E-05 1.38 3.11E-02 Lgtn 1.31 8.27E-05 1.21 2.80E-02 Denr -1.67 2.14E-03 1.09 6.81E-01

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Figure 3.6 Steady state protein levels of selected translation related factors in multiple organs of mutant (SbdsR126T/R126T) and control (Sbds+/R126T) embryos are comparable. Representative immunoblot of total cellular extracts from E18.5 kidney, lung, brain, pancreas, liver and skeletal muscle were probed with Rps6, Rpl4, Rplp0, Rps11, Eif6 and Eif5a. House-keeping proteins Actβ and Gapdh were used as controls, but they did not align well with each other such that consistent quantification of the candidate test proteins on these immunoblots could not be achieved.

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Figure 3.7 SDS mutant proteome is comparable to controls regardless of polysome loading. 99.1% of proteins showed no change in steady state levels between mutants and controls using label-free LC-MS/MS, as indicated by volcano plot. Each dot represents an identified protein; those with ≥ 2 fold change in steady state levels and associated P-value ≤0.01 were defined as significant (0.9%), and are depicted in black.

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for both comparisons (Figure 3.9A). Further, the length of the increased polysome loading group is greater than the decreased group (P-value <2.2×10-16).

I then considered the individual transcript segments including the 5' UTR, ORF and 3' UTR (Figure 3.9A). All these three components contributed to the longer length of transcripts with increased polysome loading. The group with decreased loading also had longer ORFs, but shorter 3' UTR components. The opposing differences in the 3' UTR lengths paralleled the differences in ribosome retention, indicating that 3' UTR length may be influencing binding.

Given the known participation of the 5' UTR in the regulation of translation initiation and that the transcripts with increased loading had notably long 5' UTRs (Figure 3.9A), I also examined individual features of this component. These included the occurrence of 5' TOP sequences, IRES that are thought to facilitate specific ribosome-mRNA interactions (Mathews et al. 2007) as well as uORFs that are generally associated with translation repression (Kawaguchi and Bailey-Serres 2005). These features were counted directly from UTRdb (Grillo et al. 2010). The 5' TOP feature is associated with transcripts of genes involved in growth control, including many that correspond to components of the translation machinery (Avni et al. 1997; Yamashita et al. 2008). Four and 17 transcripts with putative TOP sequences appeared in the decreased and increased polysome loading groups (2.42% and 2.68%, respectively) of mutant liver transcripts indicating that the TOP sequence did not determine binding changes. The three groups of transcripts with increased, decreased and unchanged polysome loading had comparable occurrences of putative IRES sequences (24%, 28% and 21%, respectively; Figure 3.9B). On the other hand, I observed enrichment of genes with uORFs in the group with higher polysome loading; 43% of these transcripts had uORF elements (and of these, 90/273 had more than one), compared to genes with unaltered polysome loading (22%). In contrast, only 7% of genes with decreased polysome loading had a uORF (Figure 3.9B; and none had more than one). Moreover, with normalization for the length of the 5' UTR, the transcripts with increased polysome loading had the highest occurrences of uORFs and those with decreased polysome loading the lowest (Figure 3.9B). It appears that, in 108

Figure 3.8 Genes with altered polysome loadings do not have corresponding changes in steady state protein levels.

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Figure 3.8 Genes with altered polysome loadings do not have corresponding changes in steady state protein levels. A, Strategy of microarray and mass spectrometry studies to identify targets of altered translation with Sbds deficiency. Of the total of 799 transcripts that showed increased or decreased polysome loading levels in mutants, proteins products of 177 were detected by mass spectrometry, but only one (of 177) had corresponding changes in steady state protein levels (fold change ≥ 2, P-value ≤ 0.01). The remaining 176 proteins were comparable between mutants and controls. B, Immunoblotting of independent control and mutant cytoplasmic lysates (N=4) showed constant levels of Mcm3 and Uev2 in E18.5 fetal livers despite marked changes in transcript polysome loading. Scatter plots show comparisons of steady state levels of Mcm3 (left) and Uev2 (right) normalized to Actβ or Gapdh.

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addition to transcript lengths, especially the lengths of the 3' UTRs, occurrence of uORFs may also be contributing to the retention of ribosomes on transcripts in mutant fetal liver. I then examined the GC content of the three groups of transcripts as a measure of potential secondary structure, which can impact translation (Ross 1995; Qu et al. 2011). Typically, individual transcript components themselves have differing GC richness; the 5' UTRs are GC rich, ORFs have intermediate richness reflecting coding constraints, and 3' UTRs are GC poor (Wan et al. 2014). Transcripts with decreased polysome loading exhibited modestly increased GC content compared to the group of transcripts with no loading change. In contrast, transcripts with increased polysome loading had low GC content, specifically in their ORFs and 3' UTRs (Figure 3.9C).

The contribution of multiple features to altered ribosome binding is evident upon examination of the 25 transcripts showing the most increased, and the 25 transcripts showing the most decreased ribosome binding in SDS fetal liver extracts (Table 3.7). All 25 transcripts with increased binding have low GC content (with all being lower than the average of the transcripts with no change; Figure 3.9A), with contribution from the ORF and/or the 3' UTR. Eleven have 3' UTRs that are longer than their respective ORFs. Further, while their 5' UTR lengths are quite variable, 10 of these 25 transcripts have uORFs. In contrast, all 25 with the most reduced binding have GC content higher than the median of the 25 transcripts with the most increased binding. These 25 have ORFs that are close in size or longer than their respective 3' UTRs, and only four have a uORF.

My findings indicate that the GC content of the ORF and 3' UTR, as well as the 3' UTR length and occurrence of uORFs contribute to transcript polysome loading in the mutant fetal liver (Figure 3.9D).

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Figure 3.9 Transcripts with increased polysome loadings are long with lower GC content. 112

Figure 3.9 Transcripts with increased polysome loadings are long with lower GC content. A, Boxplots show distribution of length of transcript with decreased, increased, and unchanged polysome loading. Comparisons that showed changes (using Wilcoxon Rank Sum test; significance defined as P-value <0.05) are marked by *. Median lengths of transcript groups and P-values are tabulated in lower panel. B, The occurrence of putative IRES and uORFs are tabulated for the transcript groups. C, Boxplots show distribution of GC content of each transcript group. Comparisons that showed significant changes are indicated as in A. Median GC content of transcript groups and P-values are tabulated in lower panel. D, Multiple features determine polysome loading with loss of Sbds. The occurrence of uORFs, increased length and reduced GC content are associated with increased polysome loading.

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Table 3.7 Transcript feature analyses of 25 genes that showed the greatest changes in polysome loading levels with Sbds deficiency. A. Genes with increased polysome loading

2FC mRNA 5'UTR ORF 3'UTR ORF+3'UTR Gene PL Lth (nt) GC% Lth (nt) GC% uORF IRES Lth (nt) GC% Lth (nt) GC% Lth (nt) GC% Fam63b 10.84 8081 42.31 128 73.44 0 0 1806 55.04 6147 37.92 7953 41.81 Ikzf5 10.04 4390 40.09 142 52.82 0 0 1260 49.68 2988 35.44 4248 39.67 Cox20 8.27 448 42.41 17 70.59 0 0 354 43.22 77 32.47 431 41.30 Stxbp4 7.9 6008 44.87 227 51.10 1 YES 1674 46.83 4107 43.73 5781 44.63 Lpp 7.35 15649 43.16 550 58.36 1 0 1842 54.83 13257 40.91 15099 42.61 9930021 6.59 8026 43.31 135 79.26 0 YES 6285 44.79 1606 34.50 7891 42.69 J03Rik Phc3 6.59 11051 43.74 28 46.43 0 0 2982 52.45 8041 40.50 11023 43.74 Nfat5 6.14 13443 42.83 700 60.86 1 0 4377 46.74 8366 39.28 12743 41.84 Trpm7 5.78 7145 40.27 285 68.42 0 0 5592 39.43 1268 37.62 6860 39.10 Myo9a 5.73 11974 41.60 243 63.79 0 YES 7896 41.92 3835 39.53 11731 41.14 Rc3h2 5.70 8965 38.85 239 60.67 1 0 3564 44.75 5162 33.77 8726 38.25 Pkhd1l1 5.69 12739 43.46 0 NA NA NA 12739 43.46 0 NA 12739 43.46 C430048 5.56 3426 41.13 297 56.57 1 0 2505 42.12 624 29.81 3129 39.66 L16Rik Prlr 5.47 10328 39.65 748 49.33 2 0 1827 46.80 7753 37.03 9580 38.89 Jhdm1d 5.41 9553 39.55 139 73.38 0 YES 2823 45.20 6591 36.41 9414 39.05 Atp13a3 5.37 7309 39.66 279 69.53 1 0 3750 41.71 3280 34.79 7030 38.48 Cd46 5.35 1213 40.81 23 43.48 0 0 1098 41.17 92 35.87 1190 40.76 Qser1 5.24 8975 41.28 206 63.59 0 0 5097 41.97 3672 39.08 8769 40.76 Mll5 5.23 7258 46.16 469 62.90 1 YES 5607 47.53 1182 32.99 6789 45.00 Nbeal1 5.23 12108 38.19 328 46.95 1 0 8067 40.50 3713 32.40 11780 37.95 Olfr1033 5.10 3743 33.42 251 45.02 1 YES 933 42.44 2559 29.00 3492 32.59 5830418 5.08 7371 44.13 45 66.67 0 0 7143 44.42 183 27.32 7326 43.99 K08Rik Zfp738 5.07 4504 38.72 74 40.54 NA NA 2382 39.55 2048 37.70 4430 38.69 Pik3c2g 5.07 4408 43.99 0 NA NA 3068 46.87 1340 37.39 4408 43.99

Ttpa 5.03 3053 38.13 12 83.33 0 0 837 48.98 2204 33.76 3041 37.95 1Uev2 4.76 5892 36.39 43 65.12 0 0 438 40.18 5411 35.85 5849 36.18 Median 5.56 7371 42.28 206 60.86 NA NA 2982 44.75 2988 36.14 7326 40.76 Average 6.19 7647 41.27 222.6 60.30 NA NA 3820 45.30 3604 35.80 7424 40.72 1 Uev2 ranked 37 in the genes that showed increase in transcript polysome loading levels with Sbds deficiency and was reported in this table since the protein level was studied by immunoblotting (Fig. 5C). The parameters of Uev2 were excluded from the calculation of group medians or averages. 2 FCPL indicates fold change of average polysome loading level of mutants to controls 114

B. Genes with decreased polysome loading

2FC mRNA 5'UTR ORF 3'UTR ORF+3'UTR Gene PL Lth (nt) GC% Lth (nt) GC% uORF IRES Lth (nt) GC% Lth (nt) GC% Lth (nt) GC% Mcm3 -3.31 2886 52.74 5 80 0 0 2439 53.55 442 47.96 2881 52.69 Ftsj3 -3.04 2865 50.30 68 52.94 0 0 2517 50.70 280 46.07 2797 50.23 Msh2 -3.01 3056 47.51 39 58.97 0 0 2808 47.19 209 49.76 3017 47.36 Renbp -2.96 1418 54.23 107 57.94 0 YES 1293 53.98 18 50 1311 53.93 Hist2h3c2 -2.93 1020 60.20 186 70.97 1 YES 411 69.10 423 46.81 834 57.79 Nup93 -2.89 2928 48.63 97 55.67 0 0 2460 50.08 371 37.2 2831 48.39 Pfkl -2.72 3717 56.98 113 75.22 0 0 2343 57.53 1261 54.32 3604 56.41 Hmcn1 -2.68 18314 47.99 384 61.72 1 0 16905 48.71 1025 31.02 17930 47.70 Ttc27 -2.65 2820 47.98 162 53.09 1 0 2544 47.96 114 41.23 2658 47.67 Lgals3bp -2.65 2168 56.04 168 58.93 1 YES 1734 56.81 266 49.25 2000 55.80 Hsd17b4 -2.63 2685 49.16 151 66.89 0 0 2208 49.77 326 36.81 2534 48.11 Cand1 -2.63 8209 44.23 347 75.22 0 0 3693 46.17 4169 39.94 7862 42.86 Aldh1l1 -2.62 3084 55.80 146 60.27 0 0 2709 55.63 229 55.02 2938 55.58 Cyp2a4 -2.61 1715 50.44 26 57.69 0 0 1485 52.05 204 37.75 1689 50.33 Mthfd1 -2.61 3324 52.77 272 64.71 0 YES 2808 52.53 244 42.21 3052 51.70 Elp2 -2.6 2789 48.23 34 64.71 0 0 2496 48.76 259 40.93 2755 48.02 Sec23b -2.59 2810 48.33 128 61.72 0 YES 2304 49.52 378 36.51 2682 47.69 Parp1 -2.57 3827 52.52 59 72.88 0 0 3045 53.50 723 46.75 3768 52.20 Sdha -2.57 2859 48.20 32 65.63 0 0 1995 51.23 832 40.26 2827 48.00 Prmt10 -2.54 2781 47.93 151 66.89 0 YES 2541 47.26 89 34.83 2630 46.84 Pygb -2.53 3838 54.30 72 65.28 0 0 2532 54.98 1234 52.27 3766 54.09 Snrnp200 -2.52 6740 51.04 90 61.11 0 0 6411 51.15 239 44.35 6650 50.90 Glg1 -2.51 3870 51.73 6 66.67 0 0 3528 51.13 336 57.74 3864 51.71 Smu1 -2.5 3273 43.60 83 73.49 0 YES 1542 46.43 1648 39.44 3190 42.82 Aldh1b1 -2.5 2302 55.52 157 63.06 0 0 1560 57.24 585 48.89 2145 54.97 Median -2.62 2886 50.44 107 64.71 NA NA 2496 51.15 336 44.35 2831 50.32 Average -2.69 3812 51.06 123 64.47 NA NA 3052 52.12 636 44.29 3689 50.55

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3.5 Discussion

Polysome profiling has been assessed in different SDS model systems with the common observation of reduced 80S monosome peaks (Menne et al. 2007; Finch et al. 2011; Wong et al. 2011; Burwick et al. 2012; Provost et al. 2012; Tourlakis et al. 2012; Sezgin et al. 2013; Tourlakis et al. 2015). Our SbdsR126T/R126T model extends this observation of the 80S monosome reduction in two aspects. First, the range of severity of the 80S monosome reduction is remarkably wide between different organs. Further, the loss of Sbds function is also clearly associated with preserved polysomes that display both increased number as well as increased amplitude of the peaks containing heavier polysomes (Chapter 2). Whereas the reduced 80S monosomes would be consistent with ribosome subunit joining defects (Finch et al. 2011), a concomitant increase in free 40S and 60S subunits could be expected, but this has not been apparent even in the fetal liver where the 80S monosome was so dramatically reduced. However, a shifting of ribosomes to high molecular weight complexes, or the heavy polysomes as are evident in the profiles, would explain the loss of the 80S peaks. Prominent polysomes, or high polysome loading levels of mRNAs, indicate higher association of ribosomes along the transcripts, or at least a subset of mRNAs, and ostensibly suggest more protein synthesis.

Despite the anomalies in the polysome profiles, the proteins identified by the mass spectrometry analysis as well as their relative levels were comparable between SDS and control livers, and there was no obvious targeting of major pathways, at least not for those with abundantly expressed genes. Particularly, we studied the players involved in translation, including the large and small ribosomal constituent proteins and translation initiation, elongation, termination factors. None of these robustly expressed genes showed significant differences at transcript and/or protein levels, consistent with the ribosome run-off investigation indicating no ribosomal subunit imbalance in vivo (Chapter 2). Further, the mild pathological phenotypes in the fetal liver and the absence of any major cell cycle arrest responses are consistent with the absence of any profound adverse effect with constitutive loss of Sbds in this organ.

Correlating protein and transcript expression analyses provided compelling evidence that the ribosomes in the polysomal fractions of SDS mutant extracts may not be fully 116

functional. Notably, genes with altered polysome loading levels did not have corresponding changes in the steady state protein levels, indicating that the different polysome loading levels observed in SDS fetal livers did not parallel actively translating ribosomes.

Given that the genes that did indicate altered total transcript levels or polysome loading levels did not share functional characteristics and did not show changes at the protein levels, I looked for structural similarities in transcripts that were particularly favoured or disfavoured for ribosome loading (as measured by the proportion of transcript associated with polysomes). Overall, the length and GC content of transcripts appear to influence the changes in the level of polysome loading with Sbds deficiency.

I found that transcripts with increased polysome loadings had longer 5' UTRs. There was also positive correlation between the occurrences of uORF in the 5' UTR and polysome loading. The 5' UTR has strong influence on the efficiency of translation initiation. Presence of uORFs generally impedes translation by retaining ribosomes prior to the start codon of the main ORF. In SDS cells, transcripts with long 5' UTR and high occurrences of uORFs may both lead to increased numbers of ribosomes along the 5' UTR, which could result in increased polysome loading. How this occurs in context of loss of Sbds and compromised subunit joining is not clear, beyond speculation that the uORFs act as attractant to ribosome or ribosome subunit complexes. Longer transcripts and reduced GC content also contribute to increased ribosome binding.

Further, the study of groups of transcripts with altered polysome loadings together with the analysis of subsets of 25 genes with the highest increase or decrease in polysome loading levels highlight that multiple features of transcripts influences the level of ribosome binding in SDS cells. Of these, length and GC% including the 3' UTRs are the most distinguishing features. Transcripts with increased polysome loadings had significantly longer 3' UTR and lower GC content than the average RefSeq transcript. On the contrary, the group with decreased polysome loadings had shorter 3' UTR. The GC content was also comparable to the group with no change in polysome loadings, possibly reflecting a limit on the nucleotide composition of the genome. 117

While longer transcripts would allow more ribosomes to be retained based solely on size; the observation that lower GC content is associated with increased polysome association is an intriguing clue to this binding. Lower GC content, an indicator of potentially less secondary folding, suggests ribosomes are preferentially associated with transcripts that are less obstructed by structure. The ribosomes in SDS cells may be less competent at overcoming secondary structures of the transcripts compared to non-SDS cells and thus more easily dissociated with the encounter of any secondary structures. These findings suggest that the retention of ribosomes on the mRNAs with loss of Sbds is based on the physical characteristics of the transcripts and favours those that are longer and free of secondary structures, i.e., those that would appear to be less inhibited for binding.

That both features of longer length and reduced GC content are also evident in the 3' UTRs as well as the ORF with increased polysome loading argues that this region may be contributing to polysome loading, in a manner similar to the ORF region. Keeping in mind that translating ribosomes should dissociate after encounter with a stop codon with nascent polypeptide release in a typical process, these 3' UTR findings give a tantalizing clue to the nature of the ribosomes bound to these messages. I propose that these SDS ribosome complexes are not translating, or at least are not translating very efficiently. I further propose a model whereby the delayed release of Eif6 (Chapter 2) contributes to this special type of mRNA bound ribosome. This model will be described in detail in the next chapter, providing some explanation for the translation insufficiency in SDS.

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Chapter 4 Conclusions and Future Directions

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4 Conclusions and Future Directions 4.1 Summary

Consistent with the recessive inheritance pattern of SDS, mouse embryos with only one copy of the wild type allele (Sbds+/R126T) are indistinguishable from the wild type littermates (Sbds+/+) phenotypically including at the polysome profile level. Overall, despite a range of severity of organ pathology, I found all mutant (SbdsR126T/R126T) organs tested showed polysome profiles with reduced 80S monoribosome peaks. The range of the 80S monosome reduction is remarkably wide between different organs. Interestingly, the level of 80S reduction was not further reduced with the decreased Sbds level achieved in embryos with just one copy of SbdsR126T (SbdsR126T/-) versus two (SbdsR126T/R126T), at least not in fetal livers.

Most notably, the loss of Sbds function is also clearly associated with preserved heavy polysomes, with profiles that display both increased number as well as increased amplitude of peaks, possibly suggesting increased ribosomal binding to a special group of mRNAs, even in organs with mild or modestly affected histological phenotypes. Of all the organs tested, fetal liver showed the most dramatic alteration in polysome profiles. Further, I found that the ratio of 60S to 40S levels was unchanged in SDS mutants, indicating that the synthesis of large and small ribosomal subunits was not affected in my in vivo model.

The proteins identified by the mass spectrometry analyses as well as their relative levels, were generally comparable between mutant and control livers including the known factors involved in translation processes. Further, no gross changes of expression were found at the transcription level by cDNA microarray. Only 368 genes (of 18,936 RefSeq genes) showed over two-fold changes in transcript levels. This indicated that despite the profound defect in polysome profiles, the transcriptome and proteome of SDS fetal livers are comparable to controls. These findings indicate that development and function of fetal liver is resilient to the loss of Sbds function and is consistent with the modest pathological phenotype seen in this organ. 120

I have also identified a subset of transcripts whose polysome loading levels were changed in SDS livers. Analysis of these transcripts revealed that the retention of ribosomes on messages correlates with physical features of the mRNAs. Overall, high occurrence of uORF in the 5' UTR, long length of transcript and lower GC content are favored for ribosome binding in SDS. Together, it was evident that polysomes are readily present with Sbds deficiency but ribosomes were retained on transcripts based on their physical characteristics as opposed to their specific functional process involvement or modulation of signaling pathways in response to disease. Moreover, I found a lack of coherence between the level of polysome loading and respective relative protein levels. This indicated the possibility that ribosomes in SDS livers were retained on transcripts as heavy polysomes but may not be working correctly, and are not involved in typical translation. The preserved polysomes are also not inconsistent with problems in elongation or termination steps possibly leading to increased binding with reduced ribosome movement. Regardless, the attached SDS ribosomes appear not be ‘translating’ in a classical manner.

My immunoblotting study of Eif6 with ribosomal components suggested additional clues to these poorly working ribosomes. Current models posit that Eif6 occupies a position at the 60S inter-subunit interface and is typically released from mature 60S before subunit joining (Senger et al. 2001; Ceci et al. 2003; Gandin et al. 2008; Gartmann et al. 2010). In SDS mutant fetal liver extracts, Eif6 appeared in fractions containing 80S monoribosomes and did not persist in heavier polysomes. These aberrantly bound Eif6- ribosomes likely possess atypical conformation(s). Given the most up-to-date structural information placing Eif6 at the subunit interface (Weis et al. 2015), an untimely release of Eif6 and the imposed conformation limits may still permit binding of transcript but compromise translation capacity. I propose that the aberrantly formed ribosomes stay bound to mRNAs yielding the preserved polysomes, but could remain in a type of translation-limited conformation even after the release of Eif6. Such ribosomes may only be ‘reset’ after dissociation of the subunits from transcripts. The 80S monoribosome loss together with the presence of the subpopulation of compromised ribosomes would indicate fewer fully functional ribosomes even in absence of altered cell processes, thus contributing to the translation insufficiency in SDS. 121

4.2 Main findings 4.2.1 Loss of Sbds function is associated with loss of 80S monoribosomes and reduced translation

Reduced 80S monoribosomal peaks in polysome profiles of samples with Sbds deficiency was described in previous studies of SDS model systems, and is consistent with the role of Sbds in ribosomal subunit joining and translation initiation (Menne et al. 2007; Finch et al. 2011; Wong et al. 2011; Burwick et al. 2012; Provost et al. 2012; Tourlakis et al. 2012, 2015; Sezgin et al. 2013). Consistent with the loss of 80S and the subunit joining concern, was my observation of an untimely release of Eif6, to occur only after 80S formation for at least some ribosomes. I further extend the observations of the 80S monosome reduction by showing that the range of severity of the 80S monosome reduction is remarkably wide between different organs in the SbdsR126T/R126T mouse model. The differences in 80S reduction across organs in our model with the same constitutive mutation probably reflect both aspects of organ specific subunit pool reservoirs and different levels of tolerance to the loss of Sbds; thus, the severity of impact on polysome profiles with Sbds deficiency is organ specific.

The 80S peak in a typical polysome profile consists of a mixed population of monoribosomes with and without transcripts engaged in translation (Mathews et al. 2007). In wild type and the heterozygous control embryonic livers, strong 80S peaks are observed and a consistent low polysome/monosome ratio (P/M) was observed compared to the SDS mutant liver profiles. This suggest that fetal livers have a large repertoire of ribosomal components and a large portion of these ribosomes are not absolutely required to engage in active protein synthesis despite a relatively high translation demand (Mathews et al. 2007). As the E18.5 SDS mutant livers display only minimal histology and pathology compared to organs such as brain, I suspect that sufficient pools of ribosome subunits may help the liver be resilient to modest or moderate deficiency in protein synthesis. On the other hand, organs that are more affected by the disease, such as brain, may not have the luxury of extra-translational capacities; and thus fail early while attempting to cope with the stress associated with translation insufficiency due to loss of Sbds. The measure of the proportion of active ribosomes in different organ systems may 122

be a marker to quantify the capacity of translation potential and a predictive parameter by which organs prone to failure can be determined. To test this directly, partial ribonuclease digestion studies of ribosome extracts can distinguish ribosomes bound to mRNAs (active ribosomes) versus those that are not, and so can be used to determine the proportion of active ribosomes across organs or development stages (Martin 1973).

4.2.2 Preserved polysomes in SDS polysome profiles indicate sub-populations of non-functional or poorly functioning ribosomes

The loss of Sbds function is also associated with preserved polysomes. Upon careful examination, some changes in polysomes were evident in the work of others (Provost et al. 2012; Sezgin et al. 2013), but they were not commented upon, nor were further investigations carried out. In my study, the preserved polysome peaks were associated with increased size of the polysomes (increased numbers of polysomal peaks) as well as the increased amplitude of the peaks. Both indicated a stronger binding of the ribosomes to mRNAs in the SDS mutants, contrary to the idea of translation dysfunction and ribosomopathy. I proposed two possible explanations for the preserved polysomal peaks: 1) increased translation of certain, or sub-classes of, mRNAs as a compensatory response to Sbds deficiency with overall translation reduction; or 2) ribosomes are being retained by transcripts, or at least subsets of transcripts, with atypical attachment and/or translation.

Examples of the first possible scenario with altered transcript subsets due to mutation of factors involved in the translation process leading to tissue specific phenotypes have been described. One such example is Rpl38. Loss of one copy of murine Rpl38, which resulted in the loss of Rpl38 steady state protein level by approximately 50%, led to deregulation with loss of translation of a subset of Hox mRNAs, whereas the global protein synthesis, including other members of the Hox mRNAs were not affected, leading to overall disturbed homeotic transformation and defects in tissue patterning (Kondrashov et al. 2011). In the case of SDS, however, subsets of genes appear to exhibit increased polysome loading levels above the general background that were not obviously associated with a response or signaling pathway and were not generally associated with 123 increased steady state protein levels. This indicated that products of these genes were not consistently affected by a disease-directed response, arguing against my first explanation.

As polysomes represent the association of ribosomes with mRNAs for translation, the prominent polysomes in the mutant profiles ostensibly suggest increased protein synthesis. However, translation deficiency is a feature of SDS, leading to marked growth limitations in both patients and disease models (Dror et al. 2011; Ruggero and Shimamura 2014). Our mutant mouse embryos show increasing discrepancy in size with littermates in later gestation periods (Tourlakis et al. 2015), which is not compatible with any suggestion of increased protein synthesis. The proteins identified by the mass spectrometry analyses as well as their relative levels, were generally comparable between SDS and control livers including the known factors involved in translation processes. These analyses, although limited to relative quantifications, further argue against increased global translation or sub-sets of proteins as explanation of the preserved polysomes. Moreover, as transcripts with altered polysome loading levels did not reflect corresponding changes in their encoded steady state protein levels, these findings indicate that the different polysome loading levels did not parallel ribosome translation activities, and provide compelling evidence for my second explanation that sub-populations of ribosomes in the polysomal fractions are not undergoing typical translation.

4.2.3 Physical characteristics determine the level of ribosome retention on transcripts with Sbds deficiency

As the genes whose transcripts had altered polysome loading levels with Sbds deficiency in fetal livers did not reveal insight that would be directly aligned with SDS phenotypes, I considered the physical features of the groups of transcripts with increased or decreased polysome loadings. As described in Section 3.4.4 and summarized in Figure 3.9, the group analyses of transcripts with differential changes in polysome loading levels showed that multiple features, including the length and GC content of transcripts, as well as the occurrence of uORFs were all associated with altered ribosome binding of SDS cell mRNAs. 124

While both transcript groups with increased and decreased polysome loading were longer than the remaining group with unaltered polysome loading, there were differences between the increased versus decreased binding groups. One distinguishing feature is the length of the 3' UTR, with the median length of the decreased group being only 58% of the unchanged group (378 nt vs. 649 nt), and the median length of the increased group being 260% of the unchanged group (1694 nt vs. 649 nt). This is intriguing given that translating ribosomes should fall off a transcript upon encountering the termination codon, and it is not expected that the 3' UTRs of mRNAs should necessarily participate in the retention of ribosomes. The suggestion of involvement of the 3' UTRs could imply that the ribosomes are not released upon the encounter of stop codons and that the corresponding termination signals are not recognized as in typical translation.

The increased polysome loading group also had longer 5' UTR than the control group, and this was associated with increased occurrence of uORFs, both by total uORF counts and by the number of uORF per unit of 5' UTR length. Having uORFs in the 5' UTR is generally thought to impede translation as they tend to hold and delay the 43S pre- initiation complex (PIC) in reaching the main ATG start codon. They also may be able to undergo some initiation, although in context of SDS, how to relate frequent occurrence of uORFs and longer 5' UTRs is not straight forward; beyond suggesting that they lead to prolonged ribosome lingering, and favoured retention.

If the 5' UTR and uORF occurrence have significant contribution to the retention of ribosomes, and the ribosome components are engaging as typical components, I would expect to observe 43S PIC rather than full 80S monosomes. These 43S ribosomal complexes should lead to the presence of halfmers which would be represented as shoulder peaks associated with 80S or polysomal peaks in the polysome profiles (Helser et al. 1981; Wong et al. 2011). However, halfmer peaks were not observed in my mutant polysome profiles. One possible explanation is that the occurrence of halfmer peaks from a few hundred genes with high occurrence of uORFs and long lengths is overwhelmed by the genes in the unchanged group and decreased polysome loading groups and thus just not evident. Another possibility is that the ribosomes associated with the highlighted 5' 125

UTR features actually do assemble 80S monoribosomes that are bound to transcripts but are not working.

The analysis of the GC content of transcripts showed that those with increased polysome loading had reduced GC content, with contributions from both the ORF and 3' UTR. As low GC content is generally associated with less secondary structure, it appears that SDS ribosome complexes prefer to reside on single strand regions with no self-hybridizing sections in either the coding region or 3' UTR. The involvement of GC content of 3' UTR in the ribosome retention, like the 3' UTR length is compatible with the absence of stop codon recognition.

The features of transcripts, particularly those of 5' UTR and 3' UTR regions that are associated with the binding of ribosomes, could be tested using cell culture models to establish individual effects on ribosome binding as well as protein synthesis. After characterizing the polysome profiles of these cell culture models, the identified 5' UTR and 3' UTR features with various lengths or GC content could be cloned into plasmids with a reporter system such as GFP or luciferase, and then transfected into cell lines possessing wild type or mutant Sbds genetic background. Monitoring the levels of total engineered reporter transcripts and transcripts associated with the polysomal fractions in control and mutant cell lines by RT-qPCR can be used to reveal the corresponding changes in ribosome binding due to the specific UTR feature being tested. Combinations of 5' and 3' UTR features can also be tested in this way. Further, assaying the fluorescence (with GFP reporters) or the luminescence levels (with luciferase reporters) will allow the study of the relationships between changes in ribosome binding level vs. changes in protein synthesis (measured by functional assay of the reporter system). The results of these investigations will not only reveal the roles of the lengths or GC contents of UTRs in determining the level of ribosome association, but also confirm that the changes in ribosome loading do not correspond to changes in translation, further establishing evidence for my model of defective ribosomes with the loss of Sbds function.

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4.2.4 Aberrant association of Eif6 with 80S ribosomes in SDS polysome profiles provides clues to Sbds deficiency

The aberrant association of Eif6 to an atypical complex that includes both 40S and 60S components may provide a clue to the non-translating ribosomes, in that aberrantly bound Eif6-ribosomes could involve altered conformation. Typical translation initiation requires the removal of Eif6 from the pre-mature 60S ribosomal subunit, a process coordinated by the function of Sbds and a GTPase elongation-like factor Efl1 (Senger et al. 2001; Gandin et al. 2008; Gartmann 2010; Ceci 2003; Menne et al. 2007; Finch et al. 2011). An ex vivo Eif6 release assay showed that the 60S dependent removal of Eif6 requires functional Sbds, Efl1, and hydrolysis of GTP by Efl1. Sbds mutants, including the missense SbdsR126T, did not support Eif6 release in this assay (Finch et al. 2011). The very recent cryo-EM study provided structural support to the proposed functions of Sbds, Eif6 and Efl1 (Weis et al. 2015). SBDS was found to be bound to the P site of the 60S subunit, interacting with the peptidyl transferase centre, and the entrance to polypeptide- exit tunnel (see Figure 1.3). The authors further suggest that prior to subunit joining, Efl1 also occupies a site near the inter-subunit bridge of the 60S, and partially overlaps with the binding site for Sbds. The competition for binding site between Efl1 and Sbds leads to a conformational change with a displacement of domain III of SBDS away from the P site on 60S. Conformational changes of both Efl1 and Sbds then promote the removal of Eif6 from the 60S. In the final step of subunit joining, stable contact of Efl1 with the 60S induces GTP hydrolysis, shifting the equilibrium of Efl1 binding to low-affinity binding state, and promote the release of both Sbds and Efl1 to make binding sites available for the incoming 40S (Weis et al. 2015).

My studies of the association of Eif6 with ribosomal components in control polysome profiles indicated that predominant amounts of Eif6 are associated with ribosomal fractions containing free 60S subunits. Immuno-reactive Eif6 was absent in the ribosomal fractions containing 80S monoribosomes, consistent with the current understanding of the role of Eif6 in subunit joining and translation initiation. The majority of Sbds appears in the flow-through fractions which do not contain ribosomal complexes and with diminishing amounts of Sbds in the later fractions containing 40S, 60S, or 127 monoribosomes. This suggests that the binding of Sbds to ribosome subunit complexes may be dynamic in vivo, and/or does not withstand the polysome extraction conditions and processes.

In the Sbds mutant polysome profiles, association of Eif6 with 60S fractions is also evident. However, this association persists in the fractions containing 80S monoribosomes, especially considering the markedly reduced 80S levels, consistent with SbdsR126T allele leading to reduced Eif6 release activity in vitro (Finch et al. 2011). This functional defect could lead to untimely and improper subunit joining, leading to aberrant translation initiation and possibly other defects in later translation steps. I observed that even though Eif6 is retained in the 80S-containing fractions, it does not persist in later polysome fractions, suggesting that the simultaneous binding of Eif6 and 40S small ribosomal subunit to 60S subunits is not persistently compatible and that Eif6 is eventually freed from being bound to ribosomal complexes. The removal of Eif6 from 60S subunits does not appear to require strong and stable binding of Sbds to ribosome subunit complexes as Sbds does not appear to be associated with polysome fractions. As discussed in the previous section, the ribosomal complexes formed this way (with the atypical and transient 40S-60S-Eif6 complex followed by the removal of Eif6) do not appear to be fully translation-competent.

Very recently, insight into the binding of Sbds onto the 60S ribosomal subunit in D. discoideum cryo-EM has been achieved, highlighting the proximity and partial overlaps in binding of Efl1•GTP and Sbds (Weis et al. 2015). From this, it is clear that it would be quite interesting to know where Efl1 is in relation to ribosomal complexes in mutant and control polysome profiles. Detection of Efl1 by immunoblotting (commercial antibodies have not been promising) or other methods such as mass spectrometry would reveal how Efl1 and Sbds or Eif6 co-exist as suggested, and how this is perturbed in SDS.

Despite the current understanding of the links of Sbds and Eif6 to translation, the interactions with the 60S subunit has not been investigated extensively. Using an ex vivo Eif6 release assay, titration of Sbds and Sbds mutants, including disease associated mutants such as SbdsR126T, could be performed to assess detailed kinetics of the release of 128

Eif6 from 60S ribosomal subunit by Sbds. In this assay, Eif6-loaded pre-60S subunit complexes isolated from cell culture or tissue samples were reconstituted with recombinant Sbds and Efl1, and the released (free) Eif6 was quantified by immuno- detection. A similar study was used earlier to confirm the role of Sbds in removing Eif6 from pre-60S complex (Finch et al. 2011). In this proposed Eif6 release assay with Sbds titrations, Sbds missense mutations will not favour release of Eif6 from 60S ribosomal subunits compared to the wild type Sbds. Further, mutations in Eif6 could be examined for suppression of the Sbds mutation using the Eif6 release assay. Earlier yeast studies of Eif6/Tif6 suppressor mutants of strains deficient of Sbds/Sdo1(Menne et al. 2007) could be used as the starting point for designing Eif6 mutants. These Eif6 mutants could be tested for their binding to 60S in the release assay with various concentrations of wild type or mutant Sbds. In addition, these in vitro tested Eif6 mutants could be studied in cell culture systems with Sbds mutations, such as the mouse embryonic fibroblast cells for their various effects, including growth and global protein synthesis (with radioactive [35S]-Methionine incorporation assays). An in vivo mouse model with mutation in both Sbds and Eif6 genes could be further studied and characterized for phenotypic and functional improvement for comparison to mice with Sbds mutations alone. When tested with the Sbds mutants, Eif6 mutants with reduced binding to 60S compared to wild type Eif6 could provide suppressor activity as have been observed in yeast models, and it is know that mice with only one intact Eif6 allele are viable (Gandin et al. 2008). Chemicals or small molecules that interfere with the sequence or structure around these suppressor mutation sites in Eif6 could be subsequently developed as therapeutic targets for SDS treatment.

4.2.5 Proposed model of Sbds deficiency, translation impairment and implication of SDS ribosome complexes

Based on previous and my studies, I propose a model highlighting consequences of loss of Sbds and provide implications for what I will call ‘SDS ribosome complexes’ (Figure 4.1).

Eif6 typically occupies the subunit interface on the 60S ribosomal subunit prior to 40S and mRNA joining. Binding of Sbds and Efl1 on the 60S subunit compete for space with 129

Eif6 and the GTP-bound Efl1 appears to have high affinity for the binding space on the subunit interface. Conformational changes in both Efl1 and Sbds enable Eif6 to be removed from the 60S subunit. Hydrolysis of GTP subsequently triggers the auto-release of Efl1 and Sbds, making the subunit inter-subunit bridge now free for binding with mRNA-bound 40S subunits (Figure 4.1, upper panel) (Finch et al. 2011; Weis et al. 2015). When Sbds is deficient, translation is affected by inhibiting subunit joining as evident by reduced monoribosome formation. The transcriptome and proteome studies indicated that ribosome constituent levels are not altered in the mutant state per organ mass. However, despite the dramatic loss of 80S monoribosome in SDS organs, increased free 40S and 60S subunit levels were not readily observed. Together, these evidences provide clues to the origin of the very heavy polysomes. Loss of function mutants such as SbdsR126T are ineffective at Eif6 removal, and may lead to the lingering of Eif6 on 60S subunit. I propose that ribosomes in absence of Sbds function may be forced to adapt into a unique conformation with the 60S subunit interface accommodating both the 40S subunit with its bound mRNA and Eif6 (Figure 4.1, lower panel). This suggestion is supported by the observation that Eif6 was found in mutant polysome fractions containing both large and small ribosomal subunits. These atypical 40S-60S-Eif6 complexes, however, appear transient and do not persist beyond monoribosomes (Figure 4.1, lower panel). I propose that even when Eif6 subsequently leaves, in the mutant state, the SDS monoribosome complex with the 40S, bound mRNA and 60S cannot attain a (fully) working translation state, although the SDS ribosome can remain bound and move along the transcript. The late departure of Eif6 may have imposed constraints on the monoribosome with the consequence of disabled translation. This would also include inhibition or blockage of the complex sequence of events or their timing requirements to permit initiation and the engaged elongation process. In this model, the prominent polysomes reflect the bound SDS ribosome complexes. Together, as consistent amounts of total extracts were compared between mutants and controls for all organs, the 40S and 60S subunit levels would then not directly reflect the 80S monoribosome changes. The subunits are present in the SDS ribosome complexes that are being held or sequestered on a subpopulation of transcripts in the mutant cell. 130

It would be very interesting to further validate these mal-formed ribosomes in SDS cells (SDS ribosome complexes) and to decipher their properties. As discussed in Section 4.2.2, the steady state protein levels do not parallel the increased level of polysome loading for corresponding coding transcripts, suggesting the bound SDS ribosome complexes simply are not translating, or translate only sub-optimally with elongation and/or termination issues. The 40S-60S-mRNA complexes (bound SDS ribosome complexes) that eventually become free of Eif6 may not have translation potential. Thus, although the fundamental cause of translation failure in SDS is the ineffective release of Eif6 from 60S prior to subunit joining, there are additional consequences with the SDS ribosomes.

The consequences of sustained heavy polysomes and the involvement of transcript components as discussed in Section 4.2.3, with length and nucleotide content features hint that these SDS ribosome complexes would be able to move along the transcripts. These SDS ribosome complexes may not be as stable as normal ribosomes, possibly due to the aberrant initial subunit joining, as presence of secondary folding in bound mRNA as indicated by the GC content is not favoured. The transcripts with SDS ribosome complexes bound may provide a type of ‘ribosome sponge’, providing a reservoir for ribosome subunits that do not engage in typical translation. By doing so, these SDS ribosome complexes and their subunits are not lost to the cell. At the same time, being bound by ribosomes may protect mRNAs from being degraded, and restoration of protein synthesis may then occur without any delay if the limitation of the Sbds deficiency is released by any means.

With this model, it is presumed that SDS ribosome complexes can dissociate from mRNAs for subsequent attempts, with eventual or at least occasional fruitful protein synthesis. In this way, the residual function of Sbds and/or other alternative mechanisms of Eif6 removal, can keep the population of functional ribosomes versus non-translating SDS ribosome complexes in equilibrium without progressive accumulation of bad ribosomes over time in at least some cell conditions. The set points of such equilibrium may in fact contribute to disease phenotypes. Further, as the SDS ribosome complexes 131

Figure 4.1 Proposed model of SDS ribosome complexes with altered transcript binding.

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Figure 4.1 Proposed model of SDS ribosome complexes with altered transcript binding.

Sbds, together with GTP-bound Efl1 transiently binds to pre-initiation 60S to release Eif6 from the interface of the 60S large ribosomal subunit. The timely removal of Eif6 is thought to be critical for translation initiation and normal protein synthesis. This is consistent with the proposed role of Sbds (Finch et al., 2011). I extend this model based on my observations. Sbds mutations cause defective removal of Eif6, which leads to reduced translation and prolonged or disturbed association of Eif6 with 60S. With this disturbance, it may be that the steric hindrance imposed by the delayed Eif6 departure still permits mRNA binding, but causes aberrant subunit joining and poor or no translation initiation. The abnormal ribosomes formed are not capable of translation (called ‘SDS ribosome complexes’), and together with the ineffective 80S monoribosome formation lead to the translation insufficiency in SDS. These SDS ribosome complexes appear to have interesting properties. They appear to be able to move along the transcripts, but may be prone to dissociate with the encounter of secondary structures of mRNAs. Because they are not actually functioning in context of the reading frame they may no longer be able to recognize and so do not dissociate upon the encounter of stop codons. The occurrence of such SDS ribosome complexes explains the occurrence of the preserved polysomes and why 40S and 60S subunit levels are not altered in SDS organ profiles.

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can transit and dissociate from the mRNAs, normal working ribosomes would not be blocked, permitting residual translation in SDS cells and cell viability.

An important aspect in considering my model would be how SDS ribosome complexes are bound, transit and distribute along the transcripts, as well as how they differ from normally translating ribosomes. To study these questions, ribosome profiling with deep sequencing of ribosome protected mRNA fragments using fetal liver samples of SDS mouse could be considered. Such investigation would be done in parallel with standard deep sequencing of total cytoplasmic mRNA and require four steps: 1) preparation of cell extracts where translation is abruptly blocked to prevent the degradation of RNAs, 2) partial nuclease digestion of ribosome bound mRNAs followed by recovery of ribosome- protected message fragments, 3) library generation and deep sequencing of cDNAs reverse transcribed from the recovered RNAs, and 4) finally, alignment and comparison of recovered sequences to total mRNA sequences. The identified ribosome protected mRNA sequences will provide snapshots of ribosome positions along all transcripts in the control and mutant samples, and would thus show how Sbds deficiency alters the distribution of ribosomes (Ingolia et al. 2011; Ingolia 2014; Brar and Weissman 2015). Further, the density of the ribosome footprints relative to total transcript level should be proportional to the number of ribosomes on each region of an mRNA. For example, the features that associated with altered binding should contribute to the ribosome profile of mutant samples. These would include altered densities for transcripts with low GC content. Examination of the 3' UTRs would be very interesting. Typical ribosome profiling study results in very low recovery of 3' UTRs. However, given my model, the protection of 3' UTRs, especially those with low GC content would be anticipated.

In addition, the ribosome elongation rate could also be investigated with similar experimental setup using cultured cells. Harringtonine is a translation blocker that inhibits the first round of peptide formation. For these studies, ribosome profiling following harringtonine treatments at multiple time points in control and mutant samples will enable the determination of numbers of bound ribosomes over time. Tracing the run- off elongation by deep sequencing will reveal the progressive depletion of ribosomes from the 5' to 3' direction on transcripts and yielded information of the ribosome 134 movement on individual mRNA (Ingolia et al. 2011). Comparing the rate of elongation, or in the case of SDS cells, the rate of ribosome movement on transcripts will indicate the differences in the rate of ribosome transit with Sbds deficiency. Collectively, these studies will also provide evidence for my model of SDS ribosome complexes, their transit along mRNAs, as well as their presence on the 3' UTRs.

4.3 Shwachman-Diamond syndrome and ribosomopathies 4.3.1 Tissue specific phenotypes in SDS and ribosomopathies

It is evident that ribosomopathies often lead to disease characteristic tissue specific phenotypes despite the expression of ribosome-related genes in all cell types and the ubiquitous requirement of protein synthesis (Warner and McIntosh 2009; Freed et al. 2010). Obviously the nucleotide sequence variations of mutations in a particular affected gene may lead to differences in the levels of residual function of the resulting gene product. Further, variations in the genetic background of individuals, including the function of modifier genes and where they are expressed also play roles in the penetrance and severity of disease.

Tissue specificity in ribosomopathies may depend on the expression levels or on the net balance of specific translation demand and translation output during particular developmental or maintenance stages. In the study of SDS, variations in the timing and severity of organ phenotypes as well as responses to the loss of Sbds function have been observed (Tourlakis et al. 2015). Despite the range of effects across different organs, many organs in SDS mouse embryos exhibited aberrant polysome profiles indicating potential for cellular stress associated with translation insufficiency. It appeared that the severity of alteration on the polysome profiles, particularly the 80S monosome reduction, did not correlate with the severity of the organ disease phenotypes, suggesting that monosome level was not the best indicator and that other aspects or thresholds may determine outcome. These may involve the repertoire of the translation machineries themselves, the net level of protein synthesis or demands for specific protein levels at specific times in a specific organ or cell type. Certainly, capacities and thresholds would not always be limiting under all cell conditions. Ultimately, some proteins would show 135

limitations, triggering response pathologies, cell cycle arrest and cell death, as have been observed (Tourlakis et al. 2015).

These responses also contribute to the tissue specific targeting of clinical phenotypes. For example, how each tissue system responds to the activation of p53 pathway due to cellular stress leading to cell cycle arrest and apoptosis or other pathways as a result of translation insufficiency may explain the tissue specific targeting of ribosomopathies. As seen in SDS mouse models, p53 activation leads to different cellular responses in different tissue systems, for example apoptosis in developing brain may provide explanation for learning and behavioural features of SDS or senescence in mature exocrine pancreas may contribute to digestive enzyme dysfunction in patients. A number of aspects of disease phenotypes in SDS have been found to be mediated through the activation of p53 checkpoint and subsequent cellular responses using model systems (Provost et al. 2012; Tourlakis et al. 2015).

4.3.2 Translation insufficiency and cancer predisposition

As discussed in Section 1.2.1, many studies link the ribosomopathies to aberrant cellular growth and predisposition to cancer. It has been suggested that disruption of translational control results in increased expression of transcripts that may allow cells to adapt to stressful conditions, which are frequently genes involved in stress response pathways and players in malignant transformation (Montanaro et al. 2008). Cancer predisposition in ribosomopathies remains a conundrum as cancer is characterized by dysregulated growth requiring at least maintained or increased ribosome biogenesis and translation output.

Ribosomopathies that involve alterations to the synthesis of ribosomal components, such as rRNAs or ribosomal proteins, are typically linked to increased cancer risk, for example Diamond-Blackfan anemia (leukemia, osteosarcoma), and acquired 5q- syndrome (leukemia) and are typically associated with the activation of p53 pathway due to ribosomal stress. p53 is known to play a fundamental role in surveillance of protein translation, being able to sense the presence of free ribosomal proteins in the nucleoplasm. Presence of these ribosomal proteins often reflects translation dysfunction, and leads to the binding of MDM2 and inhibition of MDM2 activity, which consequently 136

permits the accumulation of p53 (Warner and McIntosh 2009; Zhou et al. 2015). Consequences of p53 activation include cell cycle arrest pathways, leading to growth inhibition. Any cell that escapes the imposed growth inhibition through additional mutations may have a competitive advantage over their neighbours, a key step in transformation. Zebrafish and mouse models of DBA or other ribosomal model systems support this p53-mediated ribosomal stress hypothesis (Chakraborty et al. 2009; Danilova et al. 2011), and ablation of p53 alleviates some of the disease phenotypes. My studies indicate that Sbds deficiency in mouse does not appear to affect the synthesis of the core ribosome components. In the fetal liver, despite the abnormalities in polysome profiles, ribosomal subunit imbalance was not detected. Further, the relative levels of ribosomal protein and translation-associated factors were not altered. Therefore, in SDS, unlike DBA or 5q- syndrome, nucleolar stress due to imbalance of ribosomal proteins is not expected.

Activation of p53 with tissue dependent cellular responses in SDS mice, however, is detected in multiple organs with dramatic phenotypes, presumably due to cellular stress triggered by pervasive poor growth limitations due to insufficient protein synthesis. Although deletion/loss of p53 does not resolve the severe growth retardation of SDS embryos or the lack of survival beyond birth, it does alleviate apoptosis in the very early SDS mouse brain to non-detectable levels and restores some populations of myeloid blood progenitors from early SDS fetal livers (E14-16). A p53-dependent senescence is also evident in the mature mouse pancreas with loss of Sbds (Tourlakis et al. 2015). Despite indications that ribosomes are not efficiently translating in SDS, it appears that they are poised for action as their relative levels were unaltered. In addition to AML (Donadieu et al. 2012), risk of solid tissue cancers (breast and pancreas cancers) in SDS has also become evident in recent reports (Singh et al. 2012; Dhanraj et al. 2013; Nakaya et al. 2014). It could be expected that slight alteration of the circumstances in a subpopulation of cells may be sufficient to overcome any hurdle of cell cycle arrest. For example, the loss of a copy of the Eif6 gene (20q deletion) in the bone marrow has been reported to be beneficial for patients (Minelli et al. 2009; Nacci et al. 2014). Other changes, including cellular stresses triggered p53 activation, may have more insidious 137

consequences, with regained translation capacities that have potential to out-compete slow growing cells and divert growth programs toward cancer.

The role of p53 activation in all individual SDS disease phenotypes is not clear. In SDS, p53 has been observed to be elevated in patient bone marrow (Dror 2002; Elghetany and Alter 2002). I carried out some preliminary studies to examine the effect of Sbds/p53 double mutants on polysome profiles in fetal liver samples. Mutations of both Trp53 and Sbds, did lead to less severely reduced 80S peaks compared to Sbds deficient fetal livers (Figure 4.2A lower panels, SbdsR126T/R126T; Trp53-/- versus SbdsR126T/R126T; Trp53+/-). However, the 80S monosome peaks remained much lower than that of control samples (Figure 4.2A right panels, SbdsR126T/R126T; Trp53-/- versus Sbds+/R126T; Trp53-/- and Figure 4.2B). A recovery effect in the 80S monosomes was also seen in our pancreas SDS model when Trp53 was deleted (Tourlakis et al. 2015). The role of p53 in polysome profiles in the SDS fetal livers is intriguing as I did not find any evidence to support the overt activation of p53 signaling in my cDNA microarray and label-free tandem mass spectrometry investigations.

The activation status of p53, or more likely the complete absence of p53 activation, remains unconfirmed in SDS fetal livers. Early indications of stress should result in post- translational modification of these sensors. It should also be kept in mind that the fetal liver is comprised of many different cell types that may not respond equally to cellular stresses. Although neither Trp53 nor Cdkn2b transcripts showed expression increases, fetal liver lysates from control and SDS mutant samples, as well as fetal liver tissue sections should be used to test for the presence of activated p53 and its regulator MDM2. The phosphorylation status of these two factors, as well as the level of cell cycle regulator p21cip should be examined using western immunoblotting and immuno-histochemistry staining. As we have shown the differences in p53 activation in various tissues, the activation of p53 could be potentially a molecular indication for the need of clinical attention in SDS and perhaps in other ribosomal diseases, and be developed as a biomarker in the diagnosis and management of patients. 138

The exact function of p53 in relation to the polysome profiles and translation is also an interesting area for additional investigations. First, one may consider why is there an improvement of the 80S monoribosome peak with the complete loss of p53? Second, and more importantly, does this improvement reflect increased translation in these cells? I suggest that there may exist quality checking mechanisms on translation that depend on the basal cellular p53 activities. With the removal of p53 from the cellular environment in face of problems in ribosome subunit joining, it may be that there is less stringent or altered control on ribosome pools or there are shifts as to which genes are being expressed. Alternatively, the apparent improvement of 80S in the polysome profiling may just be a reflection of the increase of the monoribosome population rather than improvement of protein synthesis. Primary cell cultures derived from mouse embryos deficient in both Sbds and p53, or established mouse embryonic fibroblast cell lines depleted of Sbds and p53 may be used to investigate the changes in polysome profiles, and to see if these changes parallel those observed in the in vivo mouse embryos. To determine if translation is actually affected, global protein synthesis could be measured using SDS cells by the radioactive [35S]-Methionine incorporation assay to compare translation in the presence and absence of p53. A mass spectrometry investigation could then be used to see which proteins are being translated.

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Figure 4.2 Less severe loss of 80S monoribosomal peaks in SDS mutant fetal livers deficient for p53. (To be continued on the next page)

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B Genotype sample ID %-40S %-60S %-80S %-polysome M3772-7 3.05 3.05 44.75 49.15 Sbds+/R126T; Trp53+/- M4274-5 3.96 3.60 36.33 56.12 Average 3.50 3.32 40.54 52.63 M3443-5 0.57 3.44 41.49 54.49 M3772-2 2.96 4.28 35.53 57.24 M3827-1 1.56 2.26 39.41 56.77 Sbds+/R126T; Trp53-/- M3998-5 2.62 1.84 87.14 8.40 M4276-6 1.69 2.82 55.65 39.83 Average 1.88 2.93 51.84 43.35 M3442-1 13.99 18.88 13.29 53.85 M3443-2 8.13 15.45 17.07 59.35 Sbds R126T /R126T; Trp53+/- M3998-1 7.87 16.85 12.36 62.92 M4009-2 10.53 18.42 11.40 59.65 Average 10.13 17.40 13.53 58.94 M3443-1 4.93 8.10 26.76 60.21 M4009-1 5.09 11.11 24.07 59.72 Sbds R126T /R126T; Trp53-/- M4009-5 7.82 11.93 27.16 53.09 M4009-3 7.80 10.64 21.28 60.28 Average 6.41 10.45 24.82 58.33

Figure 4.2 Less severe loss of 80S monoribosomal peaks in SDS mutant fetal livers deficient for p53.

A, polysome profiles of extracts of Sbds deficient embryos are shown in lower panels; corresponding profiles of embryo controls are shown in the upper panels. Representative polysome traces illustrate that the deletion of Trp53 (p53) in addition to loss of Sbds leads to less severe loss of 80S peaks in fetal liver extracts (compare lower right panel to lower left panel). B, quantitation of ribosomal peaks in the polysome profiles studied.

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4.4 Concluding remarks

In conclusion, the study of polysome profiles in multiple fetal organs using our disease- associated SDS mouse model gives insights about the consequences of Sbds deficiency. I provide a model of how Sbds participates in translation with other known interacting factors including Eif6, and how loss of Sbds function results in what may be non- translating ribosomes. These findings shed light on the understanding of Sbds in protein synthesis and the manifestation of disease phenotypes due to translation insufficiency in SDS, and potentially other ribosomopathies. Foremost with Sbds deficiency, it is evident the ribosome synthesis and the subunit repertoire remain largely intact, and the SDS cells retain potential for translation beyond the critical early steps of requiring Sbds to remove Eif6 from 60S.

As a fundamental and ‘house-keeping’ cellular process, any loss or disturbance of the components in the process of translation can be potentially lethal or lead to serious consequences. Ribosomopathies are a collection of syndromes associated with failure at any step during the production and functioning of ribosomes. Considering the large number of genes involved in ribosome biogenesis and the translation process, the number of currently described and identified ribosomopathies would appear to be remarkably small. While this may reflect the severity of the consequences when mutations related to factors involved in translation occur, it would also suggest the need for better understanding of the processes of ribosome biogenesis and mRNA translation, as well as the cellular consequences associated with translation insufficiency. My research has played an important role in helping to understand how Shwachman-Diamond syndrome occurs when Sbds is mutated. The long term goal to envision better treatment requires this information. Further, I am hopeful that insight into SDS will also benefit the understanding of other ribosomopathies, and their future treatment.

Over the years, I have attended many seminars, conferences or webinars. These events not only direct the transfer of knowledge and information, but also capture the emotions and hopes of patients and parents. From researchers and clinicians, we would like to extend to them a clear message that they are not alone. Nothing can be more encouraging or inspiring than hearing success and happy stories. Further, with clear mission and 142 responsibility coupled with steady progress in research, we are more likely to engage society and governments into the care for rare diseases, including SDS.

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Appendices 2.1- 2.3

160

Appendix 2.1 Raw data of polysome profile quantification

Organ Genotype sample ID %-40S %-60S %-80S %-poly M4531-8 3.26 5.70 51.25 39.78 M4683-9 4.44 6.63 75.73 13.20 M4639-1 5.90 16.58 48.64 28.88 M4415-6 5.19 6.01 73.30 15.51 Sbds+/+ M4538-3 4.54 4.34 36.21 54.91 M4639-4 3.96 6.79 47.88 41.37 M4639-5 3.98 7.92 55.12 32.98 Average 4.47 7.71 55.45 32.38 M4683-1 4.32 10.39 61.99 23.30 M4639-2 4.29 7.92 46.03 41.76 M4415-3 4.61 5.61 54.48 35.30 Liver Sbds+/R126T M4538-2 4.20 6.36 58.87 30.57 M4521-3 4.67 2.39 28.70 64.24 M4686-2 2.72 3.54 68.79 24.95 Average 4.14 6.03 53.14 36.69 M4683-2 8.42 15.38 14.87 61.34 M4639-3 3.31 6.10 5.49 85.11 M4415-1 19.45 18.08 11.47 51.01 SbdsR126T/R126T M4538-1 4.04 5.55 4.81 85.59 M4521-1 14.96 15.12 10.06 59.86 M4686-1 10.04 12.05 9.34 68.58 Average 7.01 5.80 4.21 15.81

M4415-6 7.47 7.29 45.82 39.42 M4531-8 21.11 21.23 40.12 17.54 Sbds+/+ M4639-4 17.35 18.24 42.48 21.93 Average 15.31 15.59 42.81 26.30 M4415-3 6.30 5.51 53.42 34.78 M4686-2 16.97 20.65 41.82 20.56 Sbds+/R126T lung M4639-2 20.04 24.69 38.43 16.85 Average 14.43 16.95 44.55 24.06 M4415-1 12.27 10.66 28.07 49.00 M4531-3 18.80 19.80 28.26 33.14 SbdsR126T/R126T M4686-1 18.99 16.19 31.19 33.63 M4639-3 20.80 19.68 32.41 27.12 Average 17.71 16.58 29.98 35.72

M4531-8 18.65 24.45 56.91 brain Sbds+/+ M4531-9 4.38 32.17 63.45

161

M4683-9 17.67 28.80 53.53

M4639-1 17.37 39.05 43.58

Average 14.52 31.12 54.37

M5370-3 17.61 33.29 49.11

M5370-4 23.92 44.88 31.21

M4531-4 17.97 22.66 59.37

Sbds+/R126T M4531-5 12.65 29.31 58.04

M4683-1 9.90 27.78 62.32

M4639-2 20.00 39.40 40.59

Average 17.01 32.89 50.11

M5370-1 27.39 27.25 45.36

M5370-2 29.30 27.80 42.90

M4531-2 8.90 27.49 63.61

SbdsR126T/R126T M4531-3 15.61 16.91 67.47

M4639-3 18.92 21.37 59.71

M4683-6 23.55 24.60 51.85

Average 20.61 24.24 55.15

M4415-6 13.29 7.72 41.84 37.16 M4531-8 19.80 18.21 32.13 29.86 Sbds+/+ M4531-9 24.67 24.63 34.62 16.08 M4639-1 18.01 18.50 47.12 16.36 Average 18.94 17.26 38.93 24.86 M4415-3 9.48 8.52 37.52 44.48 M4490-3 10.37 7.67 42.67 39.29 M4531-4 17.72 21.14 40.33 20.81 kidney M4531-5 22.85 22.82 35.67 18.65 Sbds+/R126T M4538-2 31.82 21.11 23.62 23.45 M4639-2 21.62 20.44 41.93 16.00 M4683-1 17.16 20.09 49.86 12.90 Average 18.72 17.40 38.80 25.08 M4415-1 20.77 15.35 25.93 37.95 M4531-2 34.91 25.87 19.05 20.17 SbdsR126T/R126T M4531-3 27.78 21.52 23.08 27.62 Average 27.82 20.91 22.69 28.58

M4531-9 10.61 8.51 33.41 47.46 M4639-1 33.28 27.08 33.07 6.57 skeletal Sbds+/+ M4683-9 14.17 21.66 32.97 31.20 muscle Average 19.36 19.08 33.15 28.41 Sbds+/R126T M4531-4 17.24 18.19 37.28 27.29 162

M4531-5 12.10 8.63 23.42 55.85 M4531-6 9.56 10.01 33.99 46.44 M4639-2 19.53 24.74 34.37 21.35 M4531-2 24.72 10.58 11.31 53.39 M4531-3 16.74 14.52 20.64 48.10 SbdsR126T/R126T M4639-3 20.93 22.47 39.92 16.69 M4683-2 18.53 23.08 28.88 29.51 Average 20.23 17.66 25.19 36.92

M4415-6 3.94 6.92 89.14 0.00 M4490-1 4.09 5.78 90.14 0.00 Sbds+/+ M4531-8 3.65 7.27 87.12 1.97 M4531-9 2.84 5.48 90.32 1.35 Average 3.63 6.36 89.18 0.83 M4415-3 5.76 5.80 88.01 0.43 M4490-3 7.04 13.90 70.61 8.45 pancreas Sbds+/R126T M4531-5 3.09 6.31 86.29 4.31 Average 5.30 8.67 81.64 4.39 M4415-1 8.59 7.74 83.67 0.00 M4490-7 0.00 8.15 91.85 0.00 SbdsR126T/R126T M4531-2 5.20 9.92 84.89 0.00 M4531-3 7.99 9.54 82.47 0.00 Average 5.45 8.84 85.72 0.00

M3399-3 10.00 6.25 25.25 58.50 P2219-8 11.62 7.68 23.92 56.77 P3125-7 3.01 3.42 68.44 25.13 Sbds+/- P3126-4 2.85 6.71 52.23 38.21 P3126-7 3.19 3.67 56.46 36.68 P3126-8 3.41 4.55 70.64 21.40 Liver Average 5.68 5.38 49.49 39.45 M3399-1 15.88 15.45 11.51 57.16 M2219-2 17.36 17.76 13.10 51.79 P3125-1 9.60 11.18 16.67 62.54 SbdsR126T/- P3126-2 18.56 19.89 16.08 45.47 P3126-3 10.83 13.16 17.45 58.57 Average 14.44 15.49 14.96 55.11

163

Appendix 2.2 Raw data of ribosome run-off profile quantification

Organ Genotype sample ID %-40S %-60S Ratio M3211-1 23.98 76.02 3.17 M3219-9 22.35 77.65 3.47 M3219-10 24.56 75.44 3.07 Sbds+/R126T M3273-2 24.85 75.15 3.02 M3273-5 28.39 71.61 2.52 Average 3.56 Liver M3211-2 25.81 74.19 2.87 M3273-4 18.72 81.28 4.34 M3273-8 26.40 73.60 2.79 SbdsR126T/R126T M3423-1 27.78 72.22 2.60 M3554-8 26.84 73.16 2.73 Average 3.07

M5141-1 26.43 73.57 2.78 M5141-2 26.49 73.51 2.78 M5141-3 24.79 75.21 3.03 Sbds+/R126T M5370-3 24.60 75.40 3.07 M5384-5 26.59 73.41 2.76 Lung M5520-2 28.51 71.49 2.51 Average 2.82

M5370-1 21.79 78.21 3.59 M5384-6 26.72 73.28 2.74 SbdsR126T/R126T M5520-1 30.01 69.99 2.33 Average 2.89

P3088-5 17.37 82.63 4.76 P3088-6 18.45 81.55 4.42 P3088-9 20.69 79.31 3.83 Sbds+/- P3125-7 28.60 71.40 2.50 P3126-8 22.22 77.78 3.50 Liver Average 3.80

P3088-2 19.68 80.32 4.08 P3088-3 18.75 81.25 4.33 SbdsR126T/- P3125-1 22.29 77.71 3.49 P3126-3 27.60 72.40 2.62 Average 3.63

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Appendix 2.3 Liver cell size is comparable between mutant and littermate controls.

Count (number of nuclei/10,000 µm2) Genotype Sample ID A B C Average M3702-1 204 141 139 161 M3702-2 160 167 195 174 Sbds+/+ M3702-3 158 184 191 178 M3702-8 157 151 152 153 M3702-6 198 220 184 201 Sbds+/R126T M3702-7 145 162 174 160 M3702-4 180 184 183 182 SbdsR126T/R126T M3702-5 169 163 199 177 M3702-9 155 160 172 162

Number of nuclei per 10,000 µm2 fetal liver area in mutants (SbdsR126T/R126T) were comparable to matched controls (all embryos were from one litter). In the upper panel, each dot represents the mean measurement of three non-overlapping areas in one organ (nuclei counts from each area are indicated in the lower panel); red bars represent the mean measurement of each group. P-value by T-test for each comparison is indicated.

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Appendices 3.1 – 3.6, see Supplemental files

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