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Applicability of Multidimensional Fractionation to Affinity Purification Mass Spectrometry Samples and Protein Phosphatase 4 Substrate Identification

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Applicability of Multidimensional Fractionation to
Affinity Purification Mass Spectrometry Samples and
Protein Phosphatase 4 Substrate Identification

by
Wade Hampton Dunham
A thesis submitted in conformity with the requirements for the degree of Master of Science

Department of Molecular Genetics
University of Toronto

© Copyright by Wade Dunham 2012

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Applicability of Multidimensional Fractionation to Affinity Purification Mass Spectrometry Samples and Protein
Phosphatase 4 Substrate Identification

Wade Dunham Master of Science
Department of Molecular Genetics
University of Toronto

2012

Abstract

Affinity-purification coupled to mass spectrometry (AP-MS) is gaining widespread use for the identification of protein-protein interactions. It is unclear however, whether typical AP sample complexity is limiting for the identification of all protein components using standard one-dimensional LC-MS/MS. Multidimensional sample separation is a useful for reducing sample complexity prior to MS analysis, and increases peptide and protein coverage of complex samples, yet the applicability of this approach to AP-MS samples remains unknown. Here I present work to show that multidimensional separation of AP-MS samples is not a cost-effective method for identifying increased peptide or protein coverage in these sample types. As such this approach was not adapted for the identification of putative Phosphoprotein Phosphatase 4 (PP4c) substrates. Instead, affinity purification coupled to onedimensional LC-MS/MS was used to identify putative PP4c substrates, and semiquantitative methods applied to identify possible PP4c targeted phosphosites in PP2A subfamily phosphatase inhibited (okadaic acid treated) cells.

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Acknowledgments

I would like to acknowledge and thank everyone in the Gingras lab, in particular my supervisor Anne-Claude, for allowing me to take on complex projects and to be an integral part in the pursuit of high impact publications. I would also like to thank our lab manager Marilyn, for patiently letting me know where I can find my much needed reagents, although initially, I likely asked her the same question at least once a month, and Brett for his initial training and work, which helped me to secure my first publication. I must also thank both of my committee members; Drs. Ben Blencowe and Thomas Kislinger for their helpful insight and guidance over the course of my projects. The past two and a half years have definitely been a great learning experience. Thanks to everybody!

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Table of Contents

Chapter 1: Introduction…………………………………………………………..……..1

1.1 1.2 1.3 1.4 1.5

General Introduction and thesis overview……..………………………………….1 Identification of Proteins by Mass Spectrometry………………………………....2

Affinity-purification Coupled to Mass Spectrometry (AP-MS)………………......6

Affinity-purification using epitope tags…………………………………………...9

Background contaminants in AP-MS……………………………………………13 1.5.1 Strategies to remove the contaminants from the sample before mass

spectrometry……………………………………………………………...14

1.5.2 Strategies to remove the contaminants during or after MS analysis:

Label-free approaches……….…………………………………………..15
Fractionation of mass spectrometry samples………………………….…………17

Utilizing MS for identification of protein phosphorylation……………………...19 1.7.1 Phosphopeptide enrichment approaches and identification……..……….20 1.7.2 Label free phosphopeptide quantification and phosphosite
1.6 1.7

localization…………………………………………………………..…...24

PP2A subfamily phosphatases…………………………………………..……….31

1.8.1 PP4c biology and regulation………...…………….…………………......31 1.8.2 PP4c interactions and substrate identification……..…………………….39 1.8.3 PP4c regulation of mRNA transcription and splicing……..……...……...49 Thesis objectives…………………………………………………………………52

1.8 1.9

Chapter 2: A cost-benefit analysis of multidimensional fractionation of affinity purification-mass spectrometry samples……………………………..….54

2.1

Methods………………………………………………………………………….55

2.1.1 Generation and culture of stably transfected Flp-In
T-REx 293 cell lines……………………………………………………..55
2.1.2 Affinity purification……………………………………………………...56 2.1.3 One dimensional (1D) LC-MS/MS analysis……………………………..57 2.1.4 Multidimensional LC-MS/MS analysis……………………………….…57

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2.1.4.1 MudPIT…………………………………………………………..58 2.1.4.2 RP/RP…………………………………………………………….58 2.1.4.3 GeLC……………………………………………………………..59

2.1.5 Data Analysis………………………………………………….……..…..59

Results……………………………………………………………………………60

2.2.1 Reproducibility of protein identifications made by AP-MS……………..64 2.2.2 Effect of fractionating affinity purified samples on spectral count, unique

peptide, and protein identification…..…………………………………...69

2.2.3 Effect of fractionating affinity purified samples on protein complex component identification.………………………………………………..75
2.2.4 Applying Significance Analysis of INTeractome (SAINT) to fractionated affinity purified samples…………………………………………………82

Discussion………………………………………………………………………..84

2.3.1 Multidimensional fractionation of AP-MS samples appears to allow for a better depth of coverage of low level background proteins and not core
2.2 2.3 protein complex components....……………………………………...…..84
2.3.2 Primary benefits and disadvantages of multidimensional fractionation of

AP-MS samples…..……..……………………………………………….85

2.3.3 Applicability of multidimensional fractionation of AP-MS samples to the

expansion of the PP4c network and substrate identification………….....86
2.3.4 Conclusions………………………………………………………………87

Chapter 3: PP4c interactor/subunit phosphosite identification…………………......88

3.1

Methods………………………………………………………………………….88

3.1.1 Generation and culture of stably transfected Flp-In
T-REx 293 cell lines………………………………………………..........88
3.1.2 Affinity purification……………………………………………………...91 3.1.3 Enrichment of phosphopeptides…………………………………….....…91 3.1.4 LC-MS/MS analysis…………………………………………………..…92 3.1.5 Data Analysis……………………………………………………….....…93

Results……………………………………………………………………………94

3.2

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3.2.1 PP4c interactor/subunit phosphosite identification………………...…….94 3.2.2 Phosphosite detection reproducibility……………….……………….....104 3.2.3 Changes in PP4c interactor phosphorylation upon PP2A subfamily phosphatase inhibition…………...………………….……………….…114
3.2.4 Reproducibility of phosphosite quantification across biological

replicates…………….…………...………………….……………….…136
Discussion……………………………………………………………………....141

3.3.1 Discerning whether PP4c interactors are possible substrates for the

enzyme……..…………………………………………………………...141

3.3

Chapter 4: Thesis Summary and Future Directions…………………………….….144

4.1 4.2

Thesis Summary……………………………………………………………...…144 Future Directions…………………………………………………………….....145

4.2.1 PP4c substrate identification……...……………...…………………......145

Conclusions………………………………………………………………….….150

4.3

References……………………………………………..…………………………….…152

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List of Tables

Table 1-1. Select peptide, protein, and dual affinity tags successfully used for purification of recombinant proteins in AP-MS studies………………………………………………10

Table 1-2. Classification of human protein phosphatases……………………………….32 Table 1-3. Gene and protein identifiers for PP4c, PP4c regulatory subunits, and interacting proteins investigated and discussed in detail in this thesis…………..………38

Table 2-1. Summary of the mass spectrometry data for this project.…....………..……..63 Table 2-2. Spectral counts, unique peptides, and non-redundant protein identification (A) for all proteins identified in COPS5 samples after background contaminant removal, (B) for the COPS5 interactors reported in BioGRID and detected in our samples, or (C) for all proteins prior to background contaminant removal..…………………………………65

Table 2-3. Spectral counts, unique peptides, and non-redundant protein identification for two biological replicate analyses of EIF4A2 and RAF1 (A) for all proteins after background contaminant removal, (B) for the interaction partners reported in BioGRID, or (C) for all protein hits prior to background contaminant removal..………………..…70

Table 2-4. Spectral counts, unique peptides, and non-redundant protein identifications for MEPCE samples (A) for all proteins identified after background contaminant removal, (B) for the interactors reported in BioGRID, and (C) for all protein hits prior to background contaminant removal………………………………………………………..72

Table 2-5. Paired t-test analysis comparing enrichment of spectra, unique peptides and protein identification by RP-RP analysis of FLAG-eIF4A2, RAF1, and MEPCE samples, after background removal (A), or for BioGRID annotated interactors only (B)………...74

Table 2-6. Proteins identified in "Core" interaction network of FLAG-COPS5 purifications, that are not annotated as COPS5 interactors in BioGRID…………...……76

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Table 2-7. Fold increase in spectral counts (A), or unique peptides (B) for BioGRID- annotated COPS5 interactors..……………………………………………………...……78

Table 2-8. A) Fold increase in spectral counts or unique peptides shown as the ratio of 2D/1D for BioGRID-annotated EIF4A2 interactors for each of the biological replicates analyzed (replicates annotated 1 and 2). B) Fold increase in spectral counts or unique peptides shown as the ratio of 2D/1D for BioGRID-annotated RAF1 interactors for each of the biological replicates analyzed (replicates annotated 1 and 2)...…….…………….80

Table 2-9. Fold increase in spectral counts or unique peptides shown as the ratio of 2D/1D for BioGRID-annotated MEPCE interactors………………………………..…...81

Table 3-1. FLAG tagged constructs and stable cell lines generated for identification of puataive PP4c substrates………………………………………………………………....90

Table 3-2. Summary table listing phosphopeptides identified for PP4c interacting proteins

or regulatory subunits..…………………………………………….………………..…...98

Table 3-3. Quantification of phosphopeptides identified in biological replicate analysis of

FLAG-DHX38…………………………………………………….……………………115

Table 3-4. Quantification of phosphopeptides identified in biological replicate analysis of

FLAG-HTASF1..………………………………………………….……………………119

Table 3-5. Quantification of SUPT5H phosphopeptides identified in biological replicate

analysis of FLAG-SUPT4H……………………………………….……………………124

Table 3-6. Fold change in DHX38 phosphosite abundance upon okadaic acid

treatment………………………………………………….……….…………………....129

Table 3-7. Fold change in HTATSF1 phosphosite abundance upon okadaic acid

treatment………………….……………………………………….……………………131

Table 3-8. Fold change in SUPT5H phosphosite abundance upon okadaic acid

treatment………………….……………………………………….……………………134

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Table 3-9. Reproducibility of DHX38 phosphopeptide quantification across biological

replicates………………….……………………………………….……………………137

Table 3-10. Reproducibility of HTATSF1 phosphopeptide quantification across biological replicates……....……………………………………….……………………138

Table 3-11. Reproducibility of SUPT5H phosphopeptide quantification across biological

replicates………………….……………………………………….……………………139

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List of Figures

Figure 1-1. Schematic overview of protein identification by mass spectrometry………...4 Figure 1-2. Affinity purification coupled to mass spectrometry……………………….…8 Figure 1-3. Peptide fragmentation induced by collision-induced dissociation (CID)...…22 Figure 1-4. Proteome Discoverer PhosphoRS algorithm phosphosite localization and

scoring……………………………………..………..……………………………………29

Figure 1-5. Phylogenetic tree of PPP family of phosphatases…………………………...34

Figure 1-6. PP4c regulatory subunits………..…………………………...………………37

Figure 1-7. PP4c Network highlighting selective high-confidence interactions

(SAINT > 0.8)..…………………………………………………………………………..40

Figure 1-8. Network highlighting the interactions of SUPT5H-SUPT4H-RNGTT and PP4c-PP4R2-PP4R3A complex………………………………………………………….42

Figure 1-9. Network highlighting the interactions of DHX38-PRP19-U5 snRNP and PP4c-PP4R2-PP4R3A complex……………………………………………...…………..43

Figure 1-10. Network highlighting the interactions of SLC4A1AP-HTATSF1-U2 snRNP and PP4c-PP4R2-PP4R3A complex……………………………………………………..45

Figure 1-11. PP4R3A targeting of transcription and splicing factors occurs through its

EVH1 domain……………………………………………………………………………47

Figure 1-12. Real-time PCR analysis of FOSB, JUNB mRNA expression.....…...……...51 Figure 2-1. Sample preparation…………………………………………………………..61 Figure 2-2. Venn diagrams showing protein identification overlap for COPS5…………68

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Figure 2-3. Venn diagram of SAINT result overlap in (A) EIF4A2 and (B) RAF1

sample analysis…………………………………………………………………………..83

Figure 3-1. Experimental workflow for PP4c interacting protein or regulatory subunit phosphopeptide identification…………………………………………………………...95

Figure 3-2. Venn diagrams showing phosphosite identification overlap for DHX38,

HTATSF1, and SUPT5H………………………...……………………………………..105

Figure 3-3. Venn diagrams showing PhosphoSitePlus phosphosite identification overlap for DHX38, HTATSF1, and SUPT5H………………………………………………….107

Figure 3-4. DHX38 protein diagram illustrating phosphosites identified in biological replicate analysis of FLAG-DHX38………………………...………………………….108

Figure 3-5. HTATSF1 protein diagram illustrating phosphosites identified in biological replicate analysis of FLAG-HTATSF1…………...…………………………………….110

Figure 3-6. SUPT5H protein diagram illustrating phosphosites identified in biological replicate analysis of FLAG-SUPT4H………………...………………………………...112

Figure 3-7. Quantification of phosphopeptides reproducibly identified in biological replicate analysis of FLAG-DHX38. …………...……………………………………...117

Figure 3-8. Quantification of phosphopeptides reproducibly identified in biological replicate analysis of FLAG-HTATSF1……………..………………………………......122

Figure 3-9. Quantification of SUPT5H phosphopeptides reproducibly identified in biological replicate analysis of FLAG-SUPT4H...……………………...……………...126

Figure 3-10. Gel shift assay for identifying putative PP4c substrates by monitoring protein de-phosphorylation in the absence of PP4c…...………………………………..148

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List of Appendices

Appendix 1. Effect of siRNA directed depletion of PP4c on gene expression and RNA splicing………………………………………………………………………..….175

A1.1 Materials and Methods………………………………………………………….176
A1.1.1 Cell culture and siRNA directed PP4c depletion………….……………176 A1.1.2 Western blotting…………………………...……………………………177 A1.1.3 RNA isolation, reverse transcription, PCR, and qRT-PCR.……………177

A1.1.4 RNAseq analysis of alternate exon inclusion…………...………….…..180

A1.1.5 Splicing Assay….………………………………………………………180

A1.1.6 Data Analysis……..……………………………..…………………...…181
A1.2 Results……………………………………………………………………….…181

A1.2.1 Effect of PP4c depletion on gene expression……..……...……………..181 A1.2.2 Effect of PP4c depletion on RNA splicing……...…..…………………195

A1.3 Discussion…………………………………………………………………...….205
A1.3.1 siRNA off target effects and proper siRNA controls…..………………205

A1.4 Conclusions and Future Directions…………………………………………..…209
A1.4.1 PP4c regulation of Transcription and Splicing………..…..……………209

Appendix 1. List of Tables

Table A1-1. PCR primers used for gene expression analyses by qRT-PCR.....……..…179 Table A1-2. RNAseq genes demonstrated to be differentially expressed upon siPP4c ± EGF treatment selected for validation by qRT-PCR………..…..…………………...…190

Table A1-3. qRT-PCR validation of genes observed to be differentially expressed upon PP4c depletion (by RNAseq) in the presence or absence of epidermal growth factor

(EGF) treatment..………..…..……………………………………………….…………191

Table A1-4. Raw data from qRT-PCR validation of genes observed to be differentially expressed upon PP4c depletion (by RNAseq) in the presence or absence of epidermal growth factor (EGF) treatment..………..…..………………………………………...…193

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Table A1-5. RNAseq genes demonstrated to be differentially spliced upon siPP4c ± EGF selected for validation by RT-PCR assay…………..…..………………………………197

Appendix 1. List of Figures

Figure A1-1. PP4c protein and mRNA levels after siRNA and EGF treatment..…..…..183 Figure A1-2. FOSB expression upon siRNA and epidermal growth factor (EGF)

treatment..………..…..…………………………………………………………………186

Figure A1-3. JUNB expression upon siRNA and epidermal growth factor (EGF)

treatment..………..………………………………………………………..……………187

Figure A1-4. NUMA1 and WBSCR1 alternate exon inclusion after siRNA and EGF

treatment..……………………………………………………………..…..……………198

Figure A1-5. NUMA1 alternate exon inclusion after siRNA and EGF treatment..…….200 Figure A1-6. WBSCR1 alternate exon inclusion after siRNA and EGF treatment……..202

Appendix 2. A cost-benefit analysis of multidimensional fractionation of affinity purification-mass spectrometry samples……………………………………….……212

A2.1 Supplementary Tables
Table S2-1. A) List of the proteins removed because they are listed as "frequent fliers" in our internal FLAG AP-MS HEK293 cell database (contains >1000 AP-MS analyses). B) Proteins removed from subsequent analysis because they were detected in FLAG alone negative Proteins identified in FLAG-COPS5 purifications after

background removal…………….............…………………………212

Table S2-2. Proteins identified in FLAG-COPS5 purifications after background removal…………….....………………………………217
Table S2-3. Proteins identified in FLAG-EIF4A2 purifications after background removal….…...………………….……………………228
Table S2-4. Proteins identified in FLAG-RAF1 purifications after background removal……………………….………………………233

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Table S2-5. Proteins identified in FLAG-MEPCE purifications after background removal…….…………………………………………238
Table S2-6. SAINT analysis of proteins identified in FLAG-EIF4A2 or
FLAG-RAF1 purifications………………..……….……..……......240

1

Chapter 1 Introduction

1.1 General Introduction and Thesis Overview

In 2005, the Aebersold lab, where Dr. Gingras was a post doctoral fellow, discovered a novel mammalian trimeric complex containing protein phosphatase 4 (PP4c), a serine-threonine phosphatase conserved throughout eukaryotic evolution and involved in resistance to cisplatin, one of the oldest anticancer drugs [1]. This trimeric complex was demonstrated to consist of PP4c and the PP4c regulatory subunit PP4R2, in addition to one of two novel proteins PP4R3A or PP4R3B, now known to function in PP4c substrate targeting. Furthermore, Gingras et al. [1] went on to demonstrate that a similar complex is functional in yeast (comprised of the PP4c ortholog Pph3, the PP4R2 ortholog Ybl046w and the PP4R3A ortholog Psy2), deletion of which reduces cell viability following cisplatin induced DNA damage. In addition, they demonstrated that mammalian PP4R3A was able to revert the cisplatin hypersensitivity of a psy2∆ yeast strain indicating the human and yeast proteins are functionally equivalent and that a reduction in PP4R3A activity in Drosophila (flfl encodes the fly homolog of the PP4R3A and Psy2 proteins) renders them hypersensitive to cisplatin, indicating that this PP4c containing trimeric complex may function in a conserved role in DNA damage repair from yeast to higher eukaryotes, in addition to facilitating cisplatin resistance in mammalian cells. Hereafter this PP4c trimeric complex will be referred to as PP4cPP4R2-PP4R3A.

To begin to understand how the PP4c-PP4R2-PP4R3A complex outlined above is linked to the cisplatin resistance phenotype and to uncover its physiological role in mammalian cells, a graduate student of the Gingras lab, Ginny Chen, extensively characterized the cellular context in which the components of this complex reside using affinity purification coupled to mass spectrometry (AP-MS). What Ginny discovered was that the components of the PP4c-PP4R2-PP4R3A trimeric complex associate with components of the splicing and transcription elongation machineries, especially PP4R3A, which was observed to localize to nuclear speckles (thought to be storage sites for the transcription and splicing machineries) in a transcription dependant manner.

2

Additionally, she demonstrated a positive role for PP4c in the regulation of mRNA transcription following epidermal growth factor (EGF) stimulation, in addition to a role for PP4c in the regulation of RNA splicing (both explained in more detail in section 1.8). Based on these discoveries, I postulated that in concert with its associated partners PP4R2 and PP4R3A, PP4c may serve as a master controller in the processes of splicing and transcription.

I begin this thesis with a general overview of protein identification by mass spectrometry and AP-MS (most of which is part of a review in Proteomics; in press), as these methods were used to generate the data presented in chapter 2 and 3, and by Ginny Chen to generate the PP4c interaction network presented in section 1.8. Next I move into the applicability of multidimensional fractionation methods (demonstrated to increase peptide and protein coverage of complex MS samples), to the identification of additional components of protein complexes in AP-MS samples (presented in chapter 2 and published in Proteomics [2]). At the onset of my studies, I was interested in testing whether fractionation of AP-MS samples could uncover new components of protein complexes. If this were the case, I would have re-interrogated the PP4c interactome in an attempt to expand the PP4c interaction network generated by Ginny Chen and further our understanding of PP4c regulation of transcription and splicing. Next I discuss methods for identifying protein phosphosites using mass spectrometry, and for label free quantification of changes in protein phosphorylation, techniques I used as an initial step towards determine the enzyme-substrate relationship between PP4c and its interaction partners (presented in chapter 3). Lastly, I end this introduction with background on PP4c and a summary of the work done by Ginny Chen, setting the stage for my thesis rational and a presentation of my thesis objectives. My thesis summary and future directions are presented in chapter 4. The role of PP4c in regulating mRNA transcription and splicing is further investigated using PCR-based assays and presented in appendix 1.

1.2 Identification of Proteins by Mass Spectrometry

Mass spectrometry (MS) has become the analytical technique of choice for the identification of proteins in biological samples. In general terms, mass spectrometers

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    bioRxiv preprint doi: https://doi.org/10.1101/2021.06.01.446243; this version posted June 1, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. Genome-wide identification and analysis of prognostic features in human cancers Joan C. Smith1,2 and Jason M. Sheltzer1* 1. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724. 2. Google, Inc., New York, NY 10011. * Lead contact; to whom correspondence may be addressed. E-mail: [email protected]. bioRxiv preprint doi: https://doi.org/10.1101/2021.06.01.446243; this version posted June 1, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. Abstract Clinical decisions in cancer rely on precisely assessing patient risk. To improve our ability to accurately identify the most aggressive malignancies, we constructed genome-wide survival models using gene expression, copy number, methylation, and mutation data from 10,884 patients with known clinical outcomes. We identified more than 100,000 significant prognostic biomarkers and demonstrate that these genomic features can predict patient outcomes in clinically-ambiguous situations. While adverse biomarkers are commonly believed to represent cancer driver genes and promising therapeutic targets, we show that cancer features associated with shorter survival times are not enriched for either oncogenes or for successful drug targets.
  • Downloaded from [266]

    Downloaded from [266]

    Patterns of DNA methylation on the human X chromosome and use in analyzing X-chromosome inactivation by Allison Marie Cotton B.Sc., The University of Guelph, 2005 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in The Faculty of Graduate Studies (Medical Genetics) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) January 2012 © Allison Marie Cotton, 2012 Abstract The process of X-chromosome inactivation achieves dosage compensation between mammalian males and females. In females one X chromosome is transcriptionally silenced through a variety of epigenetic modifications including DNA methylation. Most X-linked genes are subject to X-chromosome inactivation and only expressed from the active X chromosome. On the inactive X chromosome, the CpG island promoters of genes subject to X-chromosome inactivation are methylated in their promoter regions, while genes which escape from X- chromosome inactivation have unmethylated CpG island promoters on both the active and inactive X chromosomes. The first objective of this thesis was to determine if the DNA methylation of CpG island promoters could be used to accurately predict X chromosome inactivation status. The second objective was to use DNA methylation to predict X-chromosome inactivation status in a variety of tissues. A comparison of blood, muscle, kidney and neural tissues revealed tissue-specific X-chromosome inactivation, in which 12% of genes escaped from X-chromosome inactivation in some, but not all, tissues. X-linked DNA methylation analysis of placental tissues predicted four times higher escape from X-chromosome inactivation than in any other tissue. Despite the hypomethylation of repetitive elements on both the X chromosome and the autosomes, no changes were detected in the frequency or intensity of placental Cot-1 holes.
  • Mouse Cdk9 Antibody (C-Term) Affinity Purified Rabbit Polyclonal Antibody (Pab) Catalog # Ap16162b

    Mouse Cdk9 Antibody (C-Term) Affinity Purified Rabbit Polyclonal Antibody (Pab) Catalog # Ap16162b

    10320 Camino Santa Fe, Suite G San Diego, CA 92121 Tel: 858.875.1900 Fax: 858.622.0609 Mouse Cdk9 Antibody (C-term) Affinity Purified Rabbit Polyclonal Antibody (Pab) Catalog # AP16162b Specification Mouse Cdk9 Antibody (C-term) - Product Information Application WB,E Primary Accession Q99J95 Other Accession Q641Z4, P50750, Q5EAB2, NP_570930.1 Reactivity Human, Mouse Predicted Bovine, Rat Host Rabbit Clonality Polyclonal Isotype Rabbit Ig Calculated MW 42762 Antigen Region 251-278 Mouse Cdk9 Antibody (C-term) - Additional Information Western blot analysis of lysates from 293, Gene ID 107951 mouse NIH/3T3, rat C6 cell line and mouse kidney tissue lysate(from left to right), using Other Names Mouse Cdk9 Antibody (C-term)(Cat. Cyclin-dependent kinase 9, Cell division #AP16162b). AP16162b was diluted at protein kinase 9, Cdk9 1:1000 at each lane. A goat anti-rabbit IgG H&L(HRP) at 1:5000 dilution was used as the Target/Specificity secondary antibody. Lysates at 35ug per This Mouse Cdk9 antibody is generated lane. from rabbits immunized with a KLH conjugated synthetic peptide between 251-278 amino acids from the C-terminal Mouse Cdk9 Antibody (C-term) - region of mouse Cdk9. Background Dilution Member of the cyclin-dependent kinase pair WB~~1:1000 (CDK9/cyclin-T) complex, also called positive transcription elongation factor b (P-TEFb), Format which facilitates the transition from abortive to Purified polyclonal antibody supplied in PBS production elongation by phosphorylating the with 0.09% (W/V) sodium azide. This CTD (C-terminal domain) of the large subunit antibody is purified through a protein A of RNA polymerase II (RNAP II), SUPT5H and column, followed by peptide affinity RDBP.
  • WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT

    WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT

    (12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization I International Bureau (10) International Publication Number (43) International Publication Date WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT (51) International Patent Classification: CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, C12Q 1/68 (2018.01) A61P 31/18 (2006.01) DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, C12Q 1/70 (2006.01) HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, (21) International Application Number: MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, PCT/US2018/056167 OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, (22) International Filing Date: SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, 16 October 2018 (16. 10.2018) TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (25) Filing Language: English (84) Designated States (unless otherwise indicated, for every kind of regional protection available): ARIPO (BW, GH, (26) Publication Language: English GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, (30) Priority Data: UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ, 62/573,025 16 October 2017 (16. 10.2017) US TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, ΓΕ , IS, IT, LT, LU, LV, (71) Applicant: MASSACHUSETTS INSTITUTE OF MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TECHNOLOGY [US/US]; 77 Massachusetts Avenue, TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, Cambridge, Massachusetts 02139 (US).
  • RNA Editing at Baseline and Following Endoplasmic Reticulum Stress

    RNA Editing at Baseline and Following Endoplasmic Reticulum Stress

    RNA Editing at Baseline and Following Endoplasmic Reticulum Stress By Allison Leigh Richards A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Human Genetics) in The University of Michigan 2015 Doctoral Committee: Professor Vivian G. Cheung, Chair Assistant Professor Santhi K. Ganesh Professor David Ginsburg Professor Daniel J. Klionsky Dedication To my father, mother, and Matt without whom I would never have made it ii Acknowledgements Thank you first and foremost to my dissertation mentor, Dr. Vivian Cheung. I have learned so much from you over the past several years including presentation skills such as never sighing and never saying “as you can see…” You have taught me how to think outside the box and how to create and explain my story to others. I would not be where I am today without your help and guidance. Thank you to the members of my dissertation committee (Drs. Santhi Ganesh, David Ginsburg and Daniel Klionsky) for all of your advice and support. I would also like to thank the entire Human Genetics Program, and especially JoAnn Sekiguchi and Karen Grahl, for welcoming me to the University of Michigan and making my transition so much easier. Thank you to Michael Boehnke and the Genome Science Training Program for supporting my work. A very special thank you to all of the members of the Cheung lab, past and present. Thank you to Xiaorong Wang for all of your help from the bench to advice on my career. Thank you to Zhengwei Zhu who has helped me immensely throughout my thesis even through my panic.
  • Ribosome and Translational Control in Stem Cells

    Ribosome and Translational Control in Stem Cells

    cells Review Ribosome and Translational Control in Stem Cells Mathieu Gabut 1,2 , Fleur Bourdelais 1,2 and Sébastien Durand 1,2,* 1 Equipe ‘Transcriptome Diversity in Stem Cells’, Cancer Cell Plasticity Department, INSERM 1052, CNRS 5286, Cancer Research Center of Lyon, Centre Léon Bérard, 69008 Lyon, France; [email protected] (M.G.); fl[email protected] (F.B.) 2 Université Claude Bernard Lyon 1, 69100 Villeurbanne, France * Correspondence: [email protected]; Tel.: +33-469-856-092 Received: 15 January 2020; Accepted: 17 February 2020; Published: 21 February 2020 Abstract: Embryonic stem cells (ESCs) and adult stem cells (ASCs) possess the remarkable capacity to self-renew while remaining poised to differentiate into multiple progenies in the context of a rapidly developing embryo or in steady-state tissues, respectively. This ability is controlled by complex genetic programs, which are dynamically orchestrated at different steps of gene expression, including chromatin remodeling, mRNA transcription, processing, and stability. In addition to maintaining stem cell homeostasis, these molecular processes need to be rapidly rewired to coordinate complex physiological modifications required to redirect cell fate in response to environmental clues, such as differentiation signals or tissue injuries. Although chromatin remodeling and mRNA expression have been extensively studied in stem cells, accumulating evidence suggests that stem cell transcriptomes and proteomes are poorly correlated and that stem cell properties require finely tuned protein synthesis. In addition, many studies have shown that the biogenesis of the translation machinery, the ribosome, is decisive for sustaining ESC and ASC properties. Therefore, these observations emphasize the importance of translational control in stem cell homeostasis and fate decisions.
  • A High Resolution Physical and RH Map of Pig Chromosome 6Q1.2 And

    A High Resolution Physical and RH Map of Pig Chromosome 6Q1.2 And

    A high resolution physical and RH map of pig chromosome 6q1.2 and comparative analysis with human chromosome 19q13.1 Flávia Martins-Wess, Denis Milan, Cord Drögemüller, Rodja Voβ-Nemitz, Bertram Brenig, Annie Robic, Martine Yerle, Tosso Leeb To cite this version: Flávia Martins-Wess, Denis Milan, Cord Drögemüller, Rodja Voβ-Nemitz, Bertram Brenig, et al.. A high resolution physical and RH map of pig chromosome 6q1.2 and comparative analysis with human chromosome 19q13.1. BMC Genomics, BioMed Central, 2003, 4, pp.435-444. 10.1186/1471-2164-4- 20. hal-02680244 HAL Id: hal-02680244 https://hal.inrae.fr/hal-02680244 Submitted on 31 May 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. BMC Genomics BioMed Central Research article Open Access A high resolution physical and RH map of pig chromosome 6q1.2 and comparative analysis with human chromosome 19q13.1 Flávia Martins-Wess1, Denis Milan*2, Cord Drögemüller1, Rodja Voβ- Nemitz1, Bertram Brenig3, Annie Robic2, Martine Yerle2 and Tosso Leeb*1 Address: 1Institute of Animal Breeding and Genetics, School of Veterinary Medicine Hannover, Bünteweg 17p, 30559 Hannover, Germany, 2Institut National de la Recherche Agronomique (INRA), Laboratoire de Génétique Cellulaire, BP27, 31326 Castanet Tolosan Cedex, France and 3Institute of Veterinary Medicine, University of Göttingen, Groner Landstr.
  • Bioinformatics Approach to Probe Protein-Protein Interactions

    Bioinformatics Approach to Probe Protein-Protein Interactions

    Virginia Commonwealth University VCU Scholars Compass Theses and Dissertations Graduate School 2013 Bioinformatics Approach to Probe Protein-Protein Interactions: Understanding the Role of Interfacial Solvent in the Binding Sites of Protein-Protein Complexes;Network Based Predictions and Analysis of Human Proteins that Play Critical Roles in HIV Pathogenesis. Mesay Habtemariam Virginia Commonwealth University Follow this and additional works at: https://scholarscompass.vcu.edu/etd Part of the Bioinformatics Commons © The Author Downloaded from https://scholarscompass.vcu.edu/etd/2997 This Thesis is brought to you for free and open access by the Graduate School at VCU Scholars Compass. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of VCU Scholars Compass. For more information, please contact [email protected]. ©Mesay A. Habtemariam 2013 All Rights Reserved Bioinformatics Approach to Probe Protein-Protein Interactions: Understanding the Role of Interfacial Solvent in the Binding Sites of Protein-Protein Complexes; Network Based Predictions and Analysis of Human Proteins that Play Critical Roles in HIV Pathogenesis. A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University. By Mesay Habtemariam B.Sc. Arbaminch University, Arbaminch, Ethiopia 2005 Advisors: Glen Eugene Kellogg, Ph.D. Associate Professor, Department of Medicinal Chemistry & Institute For Structural Biology And Drug Discovery Danail Bonchev, Ph.D., D.SC. Professor, Department of Mathematics and Applied Mathematics, Director of Research in Bioinformatics, Networks and Pathways at the School of Life Sciences Center for the Study of Biological Complexity. Virginia Commonwealth University Richmond, Virginia May 2013 ኃይልን በሚሰጠኝ በክርስቶስ ሁሉን እችላለሁ:: ፊልጵስዩስ 4:13 I can do all this through God who gives me strength.