Genome-wide Investigation of Cellular Functions for tRNA Nucleus-
Cytoplasm Trafficking in the Yeast Saccharomyces cerevisiae
DISSERTATION
Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University
By
Hui-Yi Chu
Graduate Program in Molecular, Cellular and Developmental Biology
The Ohio State University
2012
Dissertation Committee:
Anita K. Hopper, Advisor
Stephen Osmani
Kurt Fredrick
Jane Jackman
Copyright by
Hui-Yi Chu
2012
Abstract
In eukaryotic cells tRNAs are transcribed in the nucleus and exported to the cytoplasm for their essential role in protein synthesis. This export event was thought to be unidirectional. Surprisingly, several lines of evidence showed that mature cytoplasmic tRNAs shuttle between nucleus and cytoplasm and their distribution is nutrient-dependent.
This newly discovered tRNA retrograde process is conserved from yeast to vertebrates.
Although how exactly the tRNA nuclear-cytoplasmic trafficking is regulated is still under investigation, previous studies identified several transporters involved in tRNA subcellular dynamics. At least three members of the β-importin family function in tRNA nuclear-cytoplasmic intracellular movement: (1) Los1 functions in both the tRNA primary export and re-export processes; (2) Mtr10, directly or indirectly, is responsible for the constitutive retrograde import of cytoplasmic tRNA to the nucleus; (3) Msn5 functions solely in the re-export process. In this thesis I focus on the physiological role(s) of the tRNA nuclear retrograde pathway. One possibility is that nuclear accumulation of cytoplasmic tRNA serves to modulate translation of particular transcripts. To test this hypothesis, I compared expression profiles from non-translating mRNAs and polyribosome-bound translating mRNAs collected from msn5Δ and mtr10Δ mutants and wild-type cells, in fed or acute amino acid starvation conditions. Microarray data revealed that several amino acid biosynthetic pathways, including the sulfur assimilation, arginine ii
biosynthesis, and leucine biosynthesis pathways, are primary targets of the tRNA
trafficking processes. I confirmed the microarray data by both Northern and Western blot
analyses. Levels of all tested target proteins involved in such amino acid biogenesis pathways are down-regulated when the tRNA nuclear import or re-export is disrupted.
The steady state levels of target total RNAs are similar between wild-type cells and tRNA trafficking defective mutants. The data suggest that the reduction of target proteins most likely results from translation defects of the target mRNAs. This study provides information that tRNA nuclear-cytoplasmic dynamics is connected to amino acid biosynthesis via control at the level of translation.
iii
This thesis is dedicated to my family and the memory of my dear mother.
iv
Acknowledgements
First and foremost I want to thank my advisor Anita K. Hopper, for her warm encouragement and advice. From her I have learned how to enjoy the research life and open my mind for every possibility. She is always patient, optimistic, and enthusiastic,
even during difficult moments. I appreciate all her supports and contributions of time,
ideas, and funding in my Ph.D pursuits. When I lose my confidence, she always believed me more than myself. I thank her wide knowledge and logical way of thinking, which made this thesis possible. I am grateful for the excellent role model which she provided as a woman scientist. It is my honor to be one of her students.
I am acknowledged my thesis committee, Professor Stephen Osmani, Professor
Kurt Fredrick, and Professor Jane Jackman, for their interests, professional comments, and personal encouragements. I am also grateful that my previous advisor in the
Nationwide Children Hospital, Dr. Akihira Otoshi, for his giving me the chance coming to the United States. I want to thank the director of MCDB graduate program, Professor
David Bisaro, for his understanding and decision of accepting me, which made me get the opportunity to join in this wonderful lab.
Being part of the Hopper lab would definitely become a beautiful memory in my life. It was my pleasure to work with such nice lab members. I thank our post-docs
Rebecca, for her good ideas and always willing to help us whenever we have questions, v
and Emily, for her nice communication skills. Thanks to Greetchen, Nripesh, Tsung-Po,
Hsiao-Yun (Ivy), Jingyan, and Marina. When I became too serious or too panic, they
provided laughs and supports. When I had successful experiments, they shared the joyful
moments.
My life in the OSU was fulfilling and the learning experience was most
comprehensive in my study journey. As a hearing impaired foreigner, I sincerely thank
Cindy Knecht and Andrea Crago from the disability service that was provided by the
OSU and the Professional Reporters, Inc. Their amazing real-time transcribing work truly helped me overcome the double barriers of disability and language. Thank to OSU provided such a friendly campus. I thank people in the department of Molecular Genetics, in particular Professor Jim Hopper, Professor Paul Herman, and their lab members.
In these years of joys and frustrations, thanks for all the supports my friends in
Columbus. Thanks to Rosa, Keith and their dog Bailey, for their generosity and warm supports. Thanks to I-Ju, Liang-Chun, and Yuh-Ying, for banding together over food and
life. I also thank Kuo-Fang and Shu-Hao, studying together with them was a different and
great experience. Also thanks to many old friends in Taiwan, they keep caring about me
no matter the distance.
Finally, and most importantly, thanks to my parents for their nurture and
education. Their unconditional love made me brave to face the challenges and was the
motivation of my pursuits. Thanks to my sister and brother, and their families, for their
understanding and supports. And last, but not least, to Yuan-Sheng, thank you for the
love and consistent being there for me.
vi
Vita
March 1978 ...... Born−Taipei, Taiwan
2000...... B.S. Biology, National Taiwan Normal
University, Taiwan
2003...... M.S. Genetics, National Yang-Ming
University, Taiwan
2006 to present ...... Graduate Research Associate, Molecular,
Cellular, and Developmental Biology
Program, The Ohio State University
Publication
Lai, Y.-S., Murali, S., Ju, H. Y., Wu, M. F., Guo, I. C., Chen, S. C., Fang, K. and Chang, C. Y.
Two iridovirus-susceptible cell lines established for kidney and liver of grouper,
Epinephelus awoara, and partial characterization of grouper iridovirus. Journal of fish
disease 23, 379-338, 2000.
vii
Lai, Y.-S., Murali, S., Chiu, H. C., Ju, H. Y., Lin, Y. S., Chen, S. C., Guo, I. C., Fang, K. and
Chang, C. Y. Preparation of yellow grouper nervous necrosis virus in a new nodavirus-
susceptible cell lines from yellow grouper, Epinephelus awoara brain tissue. Journal of fish
disease 24,299-309, 2001.
Chu, H. Y. Phenotypic Analysis of the Aurora-A Transgenic Mice. Master Thesis. Institute of
Genetics, National Yang Ming University, July, 2003.
Chu, H. Y. and Otoshi, A. Cloning and functional analysis of hypothalamic homeobox gene
Bsx1a and its isoform, Bsx1b., Mol Cell Biol. 27(10), 3743-9, 2007.
Li, C.C., Chu, H.Y., Yang, C.W., Chou, C.K. and Tsai, T.F. Aurora-A overexpression in mouse
liver causes p53-dependent premitotic arrest during liver regeneration. Mol Cancer Res.
7(5), 678-88, 2009.
Chu, H. Y. and Hopper, A. K. Genome-wide investigation of the cellular functions for tRNA
nucleus-cytoplasm trafficking in S. cerevisiae. (in prep.)
Fields of Study
Major Field: Molecular, Cellular and Developmental Biology
viii
Table of Contents
Abstract ...... ii
Acknowledgements ...... v
Vita ...... vii
Fields of Study ...... viii
Table of Contents ...... ix
List of Tables ...... xiv
List of Figures ...... xvi
Chapter 1 Introduction...... 1
1.1 Nucleocytoplasmic transport ...... 1
1.2 tRNA biology in yeast ...... 5
1.2.1 The life of tRNA: biogenesis, maturation, and turnover ...... 5
1.2.2 tRNA subcellular dynamics ...... 10
1.3 Amino acid stress responses ...... 12
1.4 Microarray analysis ...... 14
1.4.1 Microarray general applications and advanced usage for translational
profiling ...... 14
1.4.2 Data analysis ...... 16
1.5 Aim of this study ...... 21 ix
Chapter 2 Materials and Methods...... 22
2.1 Yeast growth conditions and strain construction ...... 22
2.1.1 Media and growth conditions ...... 22
2.1.2 Yeast strain construction ...... 23
2.1.3 Growth assay ...... 25
2.2 Polysome profiles and RNA isolation ...... 25
2.2.1 Polysome profiles ...... 25
2.2.2 Quantification of polysome profiles ...... 27
2.2.3 RNA extraction ...... 28
2.3 Microarray data analysis ...... 28
2.3.1 Pre-processing of raw data ...... 29
2.3.2 Identification of significant differentially-expressed genes ...... 29
2.3.3 Hierarchical clustering of significantly changed target transcripts ...... 30
2.4 Northern blot analysis ...... 30
2.4.1 Preparation of DNA probes ...... 31
2.5 Western blot analysis ...... 31
2.5.1 Protein extraction ...... 31
2.5.2 Western blot and quantification ...... 32
2.6 Cis-regulatory element analyses of targets ...... 33
2.6.1 Codon bias and amino acid usage ...... 33
Chapter 3 Genome-wide investigations of cellular functions for tRNA nuclear- cytoplasmic trafficking in regulation of translation ...... 43
x
3.1 Introduction ...... 43
3.2 Results ...... 48
3.2.1 Polysome profiles analysis ...... 48
3.2.2 Microarray data pre-processing ...... 49
3.2.3 Identification of significant differentially-expressed genes (DEGs) ...... 57
3.2.4 Transcriptional control and RNA stability of target mRNAs are not
affected ...... 60
3.2.5 Verification of microarray data by western blot analyses ...... 61
3.2.6 tRNA subcellular trafficking affects translation regulation of amino acid
biosyntheses transcripts ...... 77
3.2.7 Cis-regulatory elements analysis of tRNA trafficking targets ...... 85
3.3 Discussion ...... 86
Chapter 4 Transcriptional analysis of mtr10Δ cells ...... 96
4.1 Introduction ...... 96
4.2 Results ...... 97
4.2.1 Microarray analysis of transcription profiles in msn5∆ cells ...... 98
4.2.2 Microarray analysis of transcription profiles in mtr10∆ cells ...... 98
4.2.3 Confirmations of selected target mRNA expressions by Northern blot
analysis ...... 104
4.2.4 Phenotypic analyses by growth assays ...... 105
4.3 Discussion ...... 112
Chapter 5 Summary and future directions ...... 115
xi
APPENDIX A: Tested possibilities for mechanism of down-regulated translation of the
target mRNAs involved in the Met, Arg, and Leu biosynthetic pathways ...... 117
APPENDIX B: Transcripts with decreased translation activity index in fed mtr10Δ cells
...... 121
APPENDIX C: Transcripts with increased translation activity index in fed mtr10Δ cells
...... 129
APPENDIX D: Transcripts with changed translation activity index in mtr10Δ cells in
amino acid starvation condition ...... 136
APPENDIX E: Transcripts with decreased translation activity index in fed msn5Δ cells
...... 138
APPENDIX F: Summary tables of functional catalogue analysis for affected transcripts
in mtr10Δ cells in fed or amino acid starvation conditions...... 140
APPENDIX G: Down-regulated genes in mtr10Δ cells in fed condition ...... 145
APPENDIX H: Up-regulated genes in mtr10Δ cells in fed condition ...... 157
APPENDIX I: Down-regulated genes in mtr10Δ cells in amino acid starvation condition
...... 178
APPENDIX J: Up-regulated genes in mtr10Δ cells in amino acid starved condition ... 212
APPENDIX K: Summary tables of functional catalogue analysis for affected transcripts in msn5Δ cells in fed or amino acid starvation conditions ...... 251
APPENDIX L: Down-regulated genes in msn5Δ cells in fed condition...... 256
APPENDIX M: Up-regulated genes in msn5Δ cells in fed condition ...... 264
xii
APPENDIX N: Down-regulated genes in msn5Δ cells in amino acid starvation condition
...... 275
APPENDIX O: Up-regulated genes in msn5Δ cells in amino acid starvation condition290
APPENDIX P: R codes for microarray data analysis ...... 302
References ...... 326
xiii
List of Tables
Table 2. 1. Yeast strains used in this study ...... 34
Table 2. 2. Oligonucleotides used in this study ...... 38
Table 3. 1. Summary tables of numbers of differentially expressed genes in mtr10Δ or msn5Δ cells in fed or starved conditions...... 67
Table 4. 1. Numbers of significant differentially transcriptionally expressed genes from
total mRNA samples of msn5Δ or mtr10Δ cells in fed or amino acid starvation conditions...... 103
Table 4. 2. GO term enrichment analysis and FunCat analysis of genes with significant
changed in mtr10Δ cells in fed condition...... 107
Table 4. 3. GO term enrichment analysis and FunCat analysis of genes with significant
changed in mtr10Δ cells in acute amino acid starved condition...... 108
Table F. 1. FunCat analysis of down-regulated transcripts in mtr10Δ cells in fed condition...... 140
Table F. 2. FunCat analysis of up-regulated transcripts in mtr10Δ cells in fed condition.
...... 141 xiv
Table F. 3. FunCat analysis of down-regulated transcripts in mtr10Δ cells in amino acid starved condition...... 142
Table F. 4. FunCat analysis of up-regulated transcripts in mtr10Δ cells in amino acid
starved condition...... 143
Table K. 1. FunCat analysis of down-regulated transcripts in msn5Δ cells in fed condition...... 251
Table K. 2. Up-regulated transcripts in msn5Δ cells in fed condition...... 252
Table K. 3. FunCat analysis of down-regulated transcripts in msn5Δ cells in amino acid starvation condition...... 254
Table K. 4. FunCat analysis of up-regulated transcripts in msn5Δ cells in amino acid starvation condition...... 255
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List of Figures
Figure 1. 1. Ran Cycle and Ran-dependent nuclear export and import...... 4
Figure 1. 2. Summary of tRNA processing pathways...... 8
Figure 1. 3. Probe and probesets...... 19
Figure 2. 1. Illustration of quantification method...... 27
Figure 3. 1. Current model of tRNA subcellular movement in yeast...... 46
Figure 3. 2. Flowchart of experimental designs...... 47
Figure 3. 3. Polysome profiles of wild-type, mtr10Δ, and msn5Δ cells collected from fed or 30-min amino acid starved conditions...... 51
Figure 3. 4. Polysome profiles of wild-type, los1Δ, and los1Δ msn5Δ cells collected from fed or 30-min amino acid starved condition...... 52
Figure 3. 5. Quantification of polysome profiles of tRNA trafficking mutants in fed (A-
C) or amino acid starved (D-F) conditions...... 54
Figure 3. 6. Summary of microarray experiments...... 55
Figure 3. 7. Global assessment of microarray raw data set from fractionated RNA
samples by using principle component analysis (PCA)...... 56
Figure 3. 8. Box plot of raw microarray data...... 64 xvi
Figure 3. 9. Box plot of RMA normalized microarray data...... 65
Figure 3. 10. Volcano plot showing differential expressed genes selected by P/NP index
compared from mtr10Δ to wild-type cells...... 66
Figure 3. 11. Heat map of hierarchical clustering results of gene expression profiles from translational DEGs in mtr10Δ cells versus wild-type cells in fed condition...... 68
Figure 3. 12. Heat map of hierarchical clustering results of translational DEGs from
msn5Δ cells compared to wild-type cells in fed condition...... 69
Figure 3. 13. Venn diagram of DEGs which were decreased in translation activity index
in both mtr10Δ and msn5Δ cells...... 70
Figure 3. 14. Amino acid biosynthesis pathways affected in tRNA trafficking mutants.
...... 72
Figure 3. 15. Expression profile of MET3 transcript among all microarray samples. .... 74
Figure 3. 16. Northern blot analysis of total RNA collected from wild-type and tRNA
trafficking mutants in fed condition...... 75
Figure 3. 17. Growth assay of tRNA trafficking mutants on SC, SC-arginine and SC-
methionine plates...... 76
Figure 3. 18. Growth assays of cells with Met22-3HA fusion protein...... 79
Figure 3. 19. Western blot analysis of endogenously 3HA- or GFP- tagged target
proteins...... 81
Figure 3. 20. Western blot analysis of protein levels in tRNA trafficking mutants
harboring recovery plasmids...... 82
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Figure 3. 21. Immunoblot analyses of Met3-, Arg3-, and Leu4-3HA fusion proteins in
wild-type cells and dhh1Δ pat1Δ cells...... 83
Figure 3. 22. Heat map of hierarchical clustering of codon occurrence frequency for
DEGs with decreased translation activity index in mtr10Δ cells...... 84
Figure 4. 1. PCA analysis of transcription profiles of total mRNAs from wild-type,
msn5Δ, and mtr10Δ cells in fed or acute amino acid starvation condition...... 101
Figure 4. 2. Volcano plots showing differential expressed genes at transcriptional level in mtr10Δ cells versus wild-type cells...... 102
Figure 4. 3. Venn diagram of differentially expressed genes from mtr10Δ cells relative to wild-type cells in fed and amino acid starved conditions...... 109
Figure 4. 4. Northern blot analysis of total mRNAs collected from wild-type, mtr10Δ,
and msn5Δ cells grown in fed condition...... 110
Figure 4. 5. Growth assay to assess the function of Mtr10 in DNA damage response. 111
Figure A. 1. Growth assay of mutants defective in tRNA export and modifications.. . 118
Figure A. 2. Growth assay of mutants defective in tRNA import and modifications.. . 119
Figure A. 3. Western blot analysis of Arg3-3HA in tRNA modification mutants...... 120
xviii
CHAPTER 1
Introduction
Eukaryotic cells contain a variety of lipid bilayer membrane-surrounded organelles. One of these organelles, the nucleus, is the genetic information center that controls biological activities. An important issue in eukaryotic cell biology concerns the coordination of cellular process via spatial and temporal regulation. The nucleus communicates with the cytoplasm via nuclear pores through which macromolecules flow. DNA contains the genetic information which is transcribed into RNAs and translated into proteins. Transfer
RNA (tRNA) is one of three main forms of RNAs. tRNAs function by delivering amino acids to the protein synthesis machinery in the cytoplasm. Nuclear-cytoplasmic cross-talk involving tRNAs is of particular interest to our laboratory. To investigate such questions, we employ the powerful genetic tool, the budding yeast Saccharomyces cerevisiae, which has been broadly used for biological research, as our model system.
1.1 Nucleocytoplasmic transport
The traffic between nucleus and cytoplasm occurs through nuclear pores. Nuclear pores are composed of nuclear pore complexes (NPCs) which are organized into channel structures that cross the inner and outer nuclear membranes. NPCs allow diffusion of 1
small molecules (< 40 kDa). In contrast, macromolecules (large proteins or
ribonucleoproteins [RNPs]) contain specific sequences (nuclear export sequences, NES;
or nuclear localization sequences, NLS) that are recognized and actively translocated in
and out of the NPCs by transport receptors in an energy dependent manner (for review,
see Aitchison and Rout, 2012).
In the S. cerevisiae genome, there are 14 nuclear transport receptors that are
members of karyopherin β family (Stewart, 2003). According to the direction in which they move their cargos, the receptors are referred to as exportins (nucleus to cytoplasm) or importins (cytoplasm to nucleus). The process of nuclear transport is controlled by the small GTPase, Ran. There are two forms of Ran, the GTP-bound form, which is primarily located in the nucleus, or the GDP-bound form, which is primarily located in the cytoplasm (Kuersten et al., 2001). This asymmetric distribution results from the distinct subcellular localizations of the Ran cycle regulators, the cytoplasmic GTPase-activating protein (RanGAP, encoded by RNA1) and the nuclear guanine nucleotide exchange factor
(RanGEF, encoded by RCC1), which are responsible for the conversion of Ran-GTP and
Ran-GDP states (top panel of Figure 1. 1) (for reviews, see Nakielny and Dreyfuss, 1999;
Rodriguez et al., 2004). The exportin receptors bind Ran-GTP and the cargo in the nucleus, and the complex moves via the nuclear pore to the cytoplasm where the complex disassociates upon Ran-GTP hydrolysis to Ran-GDP by RanGAP (middle panel of Figure
1. 1). Ran-GDP returns to the nucleus by the aid of NTF2 (Ribbeck et al., 1998), and is converted to Ran-GTP form again by RanGEF. The importin receptors bind the cargo
(directly or adaptor-mediated) in the cytoplasm in a Ran-GTP independent manner. The
2 importin-cargo complex translocates to the nucleus where importin encounters Ran-GTP, and this leads to the release of cargo and importin returns to the cytoplasm for next cycle
(bottom panel of Figure 1. 1) (for review, see Chook and Süel, 2011).
As the molecular size of tRNA is ~25 kDa which is theoretically able to leave the nucleus by diffusion, its export requires the Ran pathway and the functions of karyopherin.
3
Figure 1. 1. Ran Cycle and Ran-dependent nuclear export and import.
4
1.2 tRNA biology in yeast
1.2.1 The life of tRNA: biogenesis, maturation, and turnover
tRNA biogenesis
tRNA is an essential adaptor molecule composed 73 to 93 nucleotides that fold into L-
shaped tertiary structures. tRNAs function to translate genetic information encoded in
mRNAs to proteins by delivering the appropriate amino acids during translation at the
ribosome. In yeast tRNA transcription occurs in the nucleolus, a sub-compartment in the nucleus (Thompson et al., 2003). tRNA genes (tDNAs) are transcribed by RNA polymerase III (Pol III), which recognizes the promoter sequences within tRNA genes
(Dieci et al., 2007). Transcriptional control of tRNAs is regulated by Pol III via the master regulator, Maf1 (for reviews, see Ciesla and Boguta, 2008; Willis and Moir, 2007).
tRNA processing
Since the initial tRNA transcripts contain extra nucleotides at 5’and 3’ ends and some of tRNA precursors have intervening sequences (IVS, or introns) between exons, the tRNA transcripts need to be processed to mature sized molecules. tRNAs are also highly modified in post-transcriptional steps. The processing events, including removal of
5’ and 3’ termini, CCA addition, and intron splicing, generally occur in a specific order.
The leader sequence is cleaved from pre-tRNAs to generate a mature 5’ end by the ribonucleoprotein endonuclease (RNase P). RNase P is composed of a RNA molecule and protein subunits (for reviews, see Esakova and Krasilnikov, 2010; Walker and
Engelke, 2006). The 3’ end of pre-tRNA is processed by removal of the trailer sequence 5
and the subsequent addition of CCA nucleotides after the residue N73. The former
reaction is catalyzed by the yeast endoribonuclease tRNase Z (Trz1) and exonuclease
Rex1, which is balanced by the presence of Lhp1 (yeast La protein, which is a chaperon required for tRNA maturation) (for reviews, see Nakanishi and Nureki, 2005; Phizicky and Hopper, 2010). Addition of nucleotides C74, C75, and A76 is required for subsequent
amino acid attachment. This reaction is catalyzed by the enzyme tRNA
nucleotidyltransferase, encoded by CCA1 in yeast. CCA1 is an essential gene, but there is
a temperature-sensitive mutant of this gene, cca1-1. Cells with this mutation cca1-1 have
a phenotype that is associated with incompletely-processed tRNAs. Interestingly, yeast
Cca1 is an isozyme which has three ATGs. The multiple transcripts result in the
production of three proteins that are differentially distributed to nucleus, cytoplasm, and
mitochondria (Chen et al., 1992b; Wolfe et al., 1994).
Among the total of 286 nuclear tRNA genes in yeast, 59 encode introns. Introns
are located between the 3’ end of the anticodon after nucleotide 37 and the loop 1. Intron
lengths vary from 14 to 60 nucleotides. The intron-containing tRNAs belong to 10
different tRNA families (for reviews, see Hopper and Phizicky, 2003; Abelson et al.,
1998; Hani and Feldmann, 1998). tRNA intron removal is catalyzed by the
heterotetrameric splicing endonuclease complex (SEN). Although the SEN complex is
essential, a previous study demonstrated that the intron of one tRNA, tRNATrp(CCA), is
unnecessary in translation and cell growth (Mori et al., 2011). In yeast, the pre-tRNA
splicing reaction includes three steps. First, the intron-containing tRNAs are cleaved at
both ends of the intron by SEN. Second, the subsequently generated two halves are
6 ligated by tRNA ligase Trl1 and 2’-phosphate is formed at the splice junction. Removal of the 2’ phosphate at the splice junction is catalyzed by the 2’ phosphotransferase Tpt1
(for review, see Hopper and Phizicky, 2003). SEN is functionally conserved from yeast to vertebrates; however, its subcellular localization is not. The work from Yoshihisa showed that the yeast SEN is located at mitochondria surface (Yoshihisa et al., 2003), but vertebrate SEN is in the nucleus (Paushkin et al., 2004). A recent study reveals that SEN possesses a novel function unrelated to splicing that requires all four subunits to be located on the mitochondria (Dhungel and Hopper, 2012).
tRNA modification
To date, more than 100 post-transcriptional modified nucleotides have been described in tRNAs (Cantara et al., 2011). Although the biological function(s) of many tRNA modifications are not completely understood, some of them have been shown to play a role in stabilizing tRNA tertiary structure, facilitating tRNA folding, and preventing translation frameshift to increase the accuracy of protein synthesis (for review, see
Gustilo et al., 2008). tRNA modification enzymes locate at different subcellular compartments, including nucleolus, nucleoplasm, the inner nuclear membrane, the cytoplasm, and mitochondria (for review, see Hopper and Phizicky, 2003).
7
Figure 1. 2. Summary of tRNA processing pathways. tRNA modification pathways are not included.
8
tRNA aminoacylation
Before binding to ribosomes, tRNAs are aminoacylated, or charged, at their 3’ end of the
CCA sequence. Charging is catalyzed by aminoacyl tRNA synthetases (aaRS). There are
20 aaRSs, each corresponding to a single amino acid. Since there are multiple tRNAs for
given amino acids, one enzyme generally recognize more than one tRNA substrate.
Although the classical view was that tRNA aminoacylation solely occurs in the
cytoplasm, several aminoacyl tRNA synthetases are located in the nucleus in vertebrates
and yeast, and tRNA charging has been shown to occur in the nucleus (Lund and
Dahlberg, 1998; Sarkar and Hopper, 1998).
tRNA turnover/degradation
As tRNAs are long-lived relative to mRNAs and are recycled for multiple rounds of
tRNA charging, it is not surprising that there are at least two quality control pathways
that function in repairing or degrading damaged tRNAs (for reviews, see Hopper et al.,
2010; Phizicky and Hopper, 2010; Thompson and Parker, 2009). The yeast TRAMP complex, that includes Trf4/5, Air1/Air2, and Mtr4, was the first discovered mechanism to monitor the integrity of pre-tRNA during tRNA biogenesis (Kadaba et al., 2004;
Kadaba et al., 2006). This pathway recognizes hypomodified nuclear tRNAs, such as
Met 1 tRNAi at m A58, caused by mutation of Trm6. Substrates of the TRAMP pathway are
poly-adenylated and subjected to degradation from their 3’ end by the nuclear exosome.
Other studies demonstrated that additional tRNA substrates, for example, pre-tRNA
transcript (LaCava et al., 2005), tRNAAla with a structural defect (Vanacova et al., 2005),
9
U6 small nuclear RNA, or 3’ truncated 5S rRNA (Kadaba et al., 2006), are also checked
by this pathway.
In contrast to the TRAMP pathway which is restricted to the nucleus, the second
surveillance mechanism, the rapid tRNA decay (RTD) pathway, is located in both the
nucleus and the cytoplasm. The RTD pathway acts on mature tRNAs from the 5’ end (for
review, see Phizicky and Hopper, 2010). Cells with defective Trm8 and Trm4, which
7 5 normally modify tRNAs at m G46 and m C49, respectively, exhibit elevated temperature- sensitive growth, caused by the rapid degradation of hypomodified tRNAVal(AAC) by the
RTD pathway (Alexandrov et al., 2006). Furthermore, tRNA substrates with different modification defects are also RTD targets (Chernyakov et al., 2008). The same study reported a similar rate of tRNA substrate degradation after addition of transcription inhibitor thiolutin, showing that the degradation targets mature tRNAs, rather than pre- tRNAs (Chernyakov et al., 2008). Two 5’−3’ exonucleases were identified to function in the RTD pathway by suppressor analysis, Rat1, which locates in the nucleus, and Xrn1, which locates in the cytoplasm. Both of them require Met22 activity since a MET22-null mutant leads to accumulation of the substrate pAp that subsequently inhibits the activities of Xrn1 and Rat1 (Chernyakov et al., 2008; Dichtl et al., 1997).
1.2.2 tRNA subcellular dynamics
Transfer RNAs must exit the nucleus to the cytoplasm to function in protein synthesis;
therefore, it has been thought for a long time that tRNAs move one-way into the
cytoplasm. However, surprisingly, it was subsequently learned that cytoplasmic mature
10 tRNAs re-visit the nucleus, a process referred to as retrograde tRNA nuclear import
(Shaheen and Hopper, 2005; Takano et al., 2005). Imported cytoplasmic tRNAs return back to the cytoplasm via a process termed tRNA re-export (Shaheen and Hopper, 2005;
Takano et al., 2005; Whitney et al., 2007). This retrograde process is conserved from yeast to vertebrates (Barhoom et al., 2011; Shaheen et al., 2007; Zaitseva et al., 2006).
In vertebrate cells, tRNA nuclear export is mediated by Exportin-t (Exp-t), a member of the Ran-binding karyopherins (Arts et al., 1998a; Kutay et al., 1998). The orthologue of Exp-t in fungi, Xpo-t or Los1, is so far the only known tRNA exporter able to bind to end-processed intron-containing tRNAs in a Ran-GTP dependent manner. The crystal structure showed that Xpo-t from S. pombe binds tRNAs directly by contacting the acceptor arm, TψC and D loops (Cook et al., 2009). The data support the early observations that Exp-t prefers tRNAs with 5’ and 3’ matured ends but has no preference for intron-containing or intron-less tRNAs. (Arts et al., 1998b; Lipowsky et al., 1999;
Lund and Dahlberg, 1998).
In yeast, los1Δ cells are viable, suggesting that there are tRNA export pathway(s) in addition to Los1 (Hurt et al., 1987). Exp-5 (Msn5 in yeast) was identified as a tRNA exporter and functions in parallel with Los1. However, there must exist additional export pathway because los1Δ msn5Δ mutant is still viable (Takano et al., 2005).
Exportin-5 (Exp-5), or yeast orthologue Msn5, functions in exporting double strand miRNA in mammalian cells (Bohnsack et al., 2004; Gwizdek et al., 2003; Lund et al., 2004; Yi et al., 2003) and plants (Bollman et al., 2003). In yeast, Msn5 exports phosphorylated nuclear proteins to cytoplasm such as transcription factors Pho4 and Far1
11
(Blondel et al., 1999; Kaffman et al., 1998). The role of Msn5 in tRNA subcellular dynamics is solely in re-exporting back to the cytoplasm, as msn5Δ cells accumulate mature tRNAs but not precursor tRNAs in the nucleus (Murthi et al., 2010). Since it has been shown that the nuclear import process is constitutive, but tRNA nuclear accumulation is dependent on nutrient availability, it has been proposed that tRNA
nuclear-cytoplasmic trafficking is most likely regulated at the re-export process in response to nutrient availability (Murthi et al., 2010).
Spliced tRNAs are found to accumulate in the nucleus upon nutrient starvation
(Hurto et al., 2007; Shaheen and Hopper, 2005; Takano et al., 2005; Whitney et al., 2007).
One β-importin member, Mtr10 (vertebrate TRN-SR2), is responsible for tRNA nuclear accumulation upon nutrient deprivation and for the constitutive retrograde nuclear transport, directly or indirectly (Murthi et al., 2010). Relative to other members, there is limited understanding of the mechanism by which Mtr10 functions in tRNA nucleocytoplasmic dynamics. It is not known if Mtr10 binds tRNAs directly or adaptor mediated, and it is conceivable that Mtr10 functions in tethering tRNAs in the nucleus, therefore further studies on Mtr10 are necessary.
1.3 Amino acid stress responses
Yeast cells rapidly respond to changing environments by reprogramming gene expression.
Several studies have described the changes in global gene expression when the
availability of nutrient, such as amino acids, carbon source, or phosphate, are limited
(Castelli et al., 2011; Gasch et al., 2000; Kuhn et al., 2001; Natarajan et al., 2001; Ogawa
12 et al., 2000). Amino acid starvation is of particular interest in this work. It is known that amino acid deprivation results in global translation repression, which has been thought to inhibit general protein synthesis by which cells can save energy. Moreover, amino acid stress also causes induced translation of the central positive transcription regulator Gcn4.
Gcn4 is a transcription activator that stimulates the transcription of several amino acid biosynthesis pathways upon amino acid deprivation (for review, see Hinnebusch, 2005).
Signaling pathways involved in translation regulation for amino acid-starved cells are well-studied (for reviews, see Gasch and Werner-Washburne, 2002; Wilson and
Roach, 2002). Uncharged tRNAs increase when cells are deprived for amino acids which subsequently activates the protein kinase Gcn2. Gcn2 phosphorylates the translation initiation factor eIF2-α, which prevents the regulatory circuit of eIF2 and initiation complex formation. Phosphorylation of eIF2α subsequent leads to general translation inhibition. Interestingly, although the global translation is inhibited, starvation of amino acids, purine, or glucose, activates the translation of Gcn4 mRNA (for review, see
Hinnebusch, 2005). Gcn4 is regulated at multiple levels. There are four small uORFs in the upstream sequence of the Gcn4 transcript. They serve to negatively regulate translation of Gcn4. Under nutrient starvation, the ribosome bypasses the uORFs and thus derepresses the translation of Gcn4 in a Gcn2-dependent manner (Hinnebusch, 1997).
Moreover, there are also several reports demonstrating that the derepression of Gcn4 can also be regulated in a Gcn2-independent fashion (for review, see Hinnebusch, 2005).
Finally, Gcn4 can be regulated at the level of protein stability. The half-life of Gcn4 is 2 minutes when cells are grown in rich media, however, in cells deprived of amino acids
13
the half-life of Gcn4 increased to 10 minutes. Phosphorylation of Gcn4 by cyclin
dependent kinase Pho85 is required before it is degraded by Rad6 and Cdc34 (Kornitzer et al., 1994; Shemer et al., 2002).
1.4 Microarray analysis
1.4.1 Microarray general applications and advanced usage for
translational profiling
Microarray technology is a very powerful approach to examine gene expression levels on large or genome-wide scale. Arrays are created by thousands of probes robotically spotted and immobilized on microscope glass slides or silicon chips (Affymetrix). The probes can be DNA, cDNA, or oligonucleotides, and they are designed to hybridize to specific fluorophore-labeled targets in samples. Thus the abundance of targets can be detected and quantified based on fluorescence signal levels.
There are two basic types of arrays for expression analysis, dual channel (two- color) and single channel (one-color). Two-color microarray (also called cDNA or spotted array) was first developed by the Patrick Brown laboratory (Schena et al., 1995); these arrays are generated by spotting cDNA probes on to the solid supports. The experiment design typically compares two biological samples, for example, reference
(control) versus drug-treatment, and is thus measuring the relative abundance of targets between samples. The two samples are usually labeled with two different dyes, such as
Cy3 (green) and Cy5 (red), and then mixed and hybridized to one array. During image analysis, the ratio of the two dye intensities is calculated for each spot and thus represents 14
the relative amount of each target. The advantages of this technology are that the probes
can be customized for only a set of genes of interest (for example, immune-response
related genes, Kim et al., 2005) and the cost is relatively inexpensive.
Single-channel arrays, or high-density arrays, contain short-oligonucleotides (for
example, in Affymetrix GeneChip®, probes are 25 nucleotides in length), these are
designed to match part of each single dye-labeled target and thus are referred as
“probeset”. These oligonucleotides are produced directly on silicon chips by
photolithography technique (Fodor et al., 1991). A large number of different probesets
can be synthesized on a single chip, (for example, the Affymetrix Human Genome U133
Plus 2.0 Array is comprised of more than 54,000 probesets, http://www.affymetrix.com/) and thus it is also called high-density array. This array generally provides “absolute” intensity of each labeled target, so two chips are needed to compare the expression level of a gene from two biological conditions.
To date, many microarray experiments are utilized for various applications.
Different experimental designs provide different kinds of information. In the last few years, a growing number of studies combined technology of translation status and microarray analysis to analyze translation profiles in a genome-wide scale under many different conditions, such as environmental stress (Arava et al., 2003; Blais et al., 2004;
Kuhn et al., 2001; Shenton et al., 2006; Smirnova et al., 2005). The sucrose gradient sedimentation of mRNA is affected by its ribosome occupancy and that is an indicator of translational status as ribosome binding (initiation), moving along the coding region
(elongation), and falling off (termination). Thus, the distribution pattern of ribosome
15
becomes an important indicator for analyzing global translation profiles or translational status of genes of interest. Different organization of ribosome association on an mRNA can reflect its translation efficiency; for example, high occupancy on polysomes of a particular transcript usually correlates with high efficiency of translation and may
generate high levels of protein products (Tuller et al., 2007).
In this study, Affymetrix GeneChip arrays (Yeast Genome 2.0 arrays) are used,
so the experiment and data analysis are specific to these arrays. RNA was extracted from
the biological samples, converted to cDNA, and then labeled with Biotin. The labeled
cDNAs were hybridized to the surface of the arrays, followed by washing and processing.
The arrays were scanned by a laser to generate the digital image files and the raw
intensity data were obtained.
1.4.2 Data analysis
After scanning the hybridized array an image file is created (.dat). Software provided by
Affymetrix, GeneChip Operating System (GCOS), computes the signal intensity from
image file and subsequently creates .cel files. The files need further processing before
being subjected to statistic analysis and selection of differentially expressed genes.
Although commercial software from microarray companies is costly, there are other
open-source programs in various platforms that are freely available. These programs
include Significance Analysis of Microarrays (SAM, which can be executed in Excel,
Tusher et al., 2001), Bioconductor (Gentleman et al., 2004,
http://www.bioconductor.org/), and GenePattern (Reich et al., 2006,
16
http://www.broadinstitute.org/cancer/software/genepattern/). In this project all of the
microarray data analyses were executed by employing the Bioconductor in R
environment or the GenePattern web-interface platform. Bioconductor uses the R
statistical programming language and provides various packages not only for microarray
analysis, but also for sequence data and other high-throughput data assays (554 software
packages while writing the thesis). There are also several diagnostic plots that can be utilized to visualize the data set and to assess data quality for each step of data analysis.
For example, box plots and principle component analysis (PCA) can be applied as a general assessment of microarray data. Scatter plots and volcano plots provide comparison results of two samples. GenePattern is another useful platform for genomic analysis, which offers many tools to analyze large-scale data in web-based interface or
the user’s computer.
There is no standard procedure for analyzing microarray data. Therefore, each
step of analyzing decisions leads to non-identical results. Generally, the pipeline of
microarray data analysis includes pre-processing of raw data, identification of significant
differential genes, visualization, and interpretation. Each of the steps is briefly described below and the details are written with R language in Appendix P.
Pre-processing
On Affymetrix GeneChips, there are many probes (short oligonucleotides), referred as a
probeset, designed for targeting a single gene (Figure 1. 3). The .cel file contains signal
intensity data of each probe. Therefore, it is necessary to process the intensities of those
17
probesets into gene expression values for each target. This step is called normalization. In
addition, there are other factors that must be considered such as technical artifacts. Thus
the purpose of normalization is not only to process expression values, but also to remove
these artificial effects and to correct backgrounds among chips. In this procedure, the cdf
(chip description file) file, which is provided from Affymetrix, is required because it
offers the identity of the probe information. There are several methods to perform
normalization. One of them, Robust Multiarray Average (RMA), available in a package
called “affy” from Bioconductor, is currently most commonly used for microarray
normalization (Bolstad et al., 2003; Irizarry et al., 2003). RMA is an algorithm and the
assumption of RMA is that usually only a small percentage of genes are altered in
different conditions. Thus RMA uses quantile normalization and median polish (an
alternative way to get median value) to calculate the expression values by using all
probesets (the log2-converted perfect-match intensities) across all chips. In this way it
provides sensitive detection of differential expressed genes with statistical tests.
Identification of differential expressed genes
The main purpose of microarray analysis is to identify the genes which are differentially expressed in different conditions or treatments. Numerous statistical strategies are designed and developed for selecting those expression-changed genes; most of them use variants of t-test to take sample size and variations into account. Since the traditional multiple tests is not appropriate for the genome-wide scale data, several alternative
methods for permutation of p-value are developed for more accurate selection of genes of
18
interest, for example, the Bonferroni correction or false discovery rate (FDR). In this
project, the package of “Linear Models for Microarray data analysis” (limma) in
Bioconductor was used to select significant differentially expressed genes (DEGs).
Employing limma analysis, DEGs can be ranked by both fold-change and Bonferroni-
adjusted p-value (Smyth, 2004). Once the list of significant differentially expressed genes
is created, the data can be used for a variety of following analyses, such as hierarchical
clustering and pattern recognition.
Figure 1. 3. Probe and probesets.
Hierarchical clustering
When significant differentially expressed genes are selected, the next challenge is how to focus on real genes of interest. One approach is clustering, which is widely used in microarray analysis. Based on various algorithms, the produced clusters can have 19
differences in properties. For example, hierarchical, k-means, and self-organizing map
(SOM), etc., are common methods for analyzing microarray data (Sherlock, 2000). The
hierarchical clustering model is generated based on distance connectivity. Briefly, the
algorithm calculates the distance of each gene expression value and returns groups of
genes or samples with similar expression profiles. Additionally, there are various ways to
measure distances and to cluster groups. Moreover, the input files can be normalized expression values from genome-wide array without statistical analysis or only the
expression profiles from differential expression genes. Therefore, each choice in
measurement and in clustering would give different results. In this project, the
hierarchical analysis and the visualization of the results were generated from GenePattern.
GO term enrichment analysis and FunCat
Other approaches to focus on genes of interest are by employing the Gene Ontology (GO) enrichment analysis (http://amigo.geneontology.org/cgi-
bin/amigo/term_enrichment?session_id=) or MIPS FunCat analysis (Functional
Catalogue, http://mips.helmholtz-muenchen.de/proj/funcatDB/search_main_frame.html)
(Ruepp et al., 2004). GO is a biological database that provides information of gene
products within defined categories (for review, see Rhee et al., 2008). FunCat provides
such information in a different way. Using similar concepts like clustering, GO term
enrichment analysis and FunCat group genes associated with similar phenotypes and
compare the occurrence frequency with genome frequency by statistical analysis. In this
work both tools are performed to gain more insight of the gene list.
20
1.5 Aim of this study
The major goal of this study is to investigate the potential role(s) of tRNA nuclear-
cytoplasmic trafficking in translation regulation, globally and specifically, by analyzing
the polysome profiles and by employing microarrays of mRNAs that are or are not
associated with polysomes. I compared the translation profiles by microarray analysis of
mRNAs isolated from wild-type cells to mRNAs isolated from cells defective in tRNA
nuclear import (mtr10Δ) or re-export (msn5Δ) grown in fed condition or acutely starved by removal of all amino acids (Chapter 3). In addition, since little is known about the importer Mtr10, a minor aim of this work was to analyze transcriptional profiles of mtr10Δ cells, which could provide insights into its cellular function(s) (Chapter 4).
21
CHAPTER 2
Materials and Methods
2.1 Yeast growth conditions and strain construction
Most Saccharomyces cerevisiae strains employed in this study were derived from haploid
BY4742 (MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0, from Open Biosystems (Winzeler et al.,
1999) or are otherwise indicated. All of the strains are listed in Table 2. 1.
2.1.1 Media and growth conditions
Yeast cells were grown in either YEPD (yeast extract/peptone medium with 2 %
glucose/dextrose) or SC medium (Synthetic Complete defined medium; Difco yeast
nitrogen base without amino acids, supplemented with amino acids, adenine [0.04 g/L],
uracil [0.04 g/L], and 2 % glucose as carbon source) at 23°C or 30°C in an air shaker
with speed of 220 rpm. Amino acids in SC medium included L-arginine (0.02 g/L), L-
aspartic acid (0.1 g/L), L-glutamic acid (0.1 g/L), L-histidine (0.02 g/L), L-isoleucine
(0.06 g/L), L-leucine (0.06 g/L), L-lysine (0.03 g/L), L-methionine (0.02 g/L), L-
phenylalanine (0.05 g/L), L-serine (0.375 g/L), L-threonine (0.2 g/L), L-tryptophan (0.02
22 g/L), L-tyrosine (0.05 g/L), L-valine (0.15 g/L). For amino acid starvation experiments, pre-warmed SC medium without addition of amino acids was used.
2.1.2 Yeast strain construction
Oligonucleotides
DNA oligonucleotides were synthesized by SIGMA-ALDRICH. The sequences of all employed oligonucleotides are in Table 2. 2.
High efficiency yeast transformation
Double deletion and genomic C-terminal 3HA- or GFP-tag strains were generated by introducing PCR fragments with selection marker using a Lithium acetate (LiOAc) transformation method (Chen et al., 1992a; Longtine et al., 1998). Yeast cells were grown overnight in 50 ml of rich YEPD media until OD600 reached 1-3. Cells were harvested, washed twice with 1X TE/LiOAc solution (1M LiOAc and TE buffer containing 10 mM
Tris-HCl, pH7.4, and 1mM of EDTA, pH8.0, served as 10X stocks), and then resuspended in 0.7-1 ml of 1X TE/LiOAc solution. For each reaction, 100 µl of yeast competent cells were then mixed with 10 µl of PCR products of replacement cassettes after ethanol-precipitated or column-purified, and 10 µl of denatured salmon-sperm DNA, followed with addition of 650 µl transformation solution (ratio of 60% PEG: LiOAc: TE:
H2O as 7:1:1:1). The mixtures were incubated at 23°C or 30°C in an air shaker for 1 hr, followed by heat-shock in a 42°C water bath for 15 min. Cells were then collected by centrifugation for 10 seconds and then plated onto selective solid media and incubated for
23
2 or more days. Colonies were then picked and grown on new selective solid media for a
second round of selection. For drug-selective media, such as Hygromycin B, cells were
grown in YEPD media for 6 hr or overnight prior to plating.
Strain construction
To select deletion strains, yeast cells were mixed with an appropriate DNA replacement
cassette, natMX4, hphMX4 (Goldstein and McCusker, 1999), or Kleuyveromyces lactis
LEU2 (Gueldener et al., 2002), followed by high efficiency transformation, and grown on
solid media containing the appropriate drugs: YEPD + clonNAT (100 mg/L; Werner
BioAgents, Jena, Germany), YEPD + hygromycin B (300 mg/L, Calbiochem, La Jolla,
CA), or SC-Leu, respectively. For construction of genomic C-terminal 3HA- or GFP-tag
strains, yeast cells were incubated with knock-in cassette (pFA6a-3HA-His3 or pFA6a-
GFP-His3) and were grown and selected on the SC-his solid media.
Preparation of yeast genomic DNA
For deletion strains, yeast cells were confirmed by PCR analysis of genomic DNA with
primers complementary with an upstream region of the target gene and with selection
marker. To confirm C-terminal tagging, primers complementary with middle of the
particular coding region and with epitope were used. To extract genomic DNA, one
colony (or more than one) with size of approximately 1 mm was picked from plates,
transferred into a microfuge tube containing 100 µl of zymolyase digestion solution, and
incubated at 37°C for 30 min. 1/10 volume of 10% SDS and 2 volumes of
24
phenol/chloroform were added followed by vigorously shaking. After centrifugation,
supernatants were transferred and the nucleic acids were precipitated with 2 volumes of
ethanol and 1/10 volume of 3M sodium acetate in -80°C. DNA pellets were then
collected, washed by 70 % ethanol, and dissolved in 20 µl of sterile H2O.
2.1.3 Growth assay
Yeast cells were cultured overnight in YEPD medium prior to the growth assay. Cells
were adjusted to same starting OD600 and then 10-fold serial dilutions in sterile H2O were prepared. 5 µl aliquots of each dilution was spotted on the indicated solid media, such as
SC, SC-met, or SC-arg, followed by incubated at indicated temperatures for 2 or more days and then photographed.
2.2 Polysome profiles and RNA isolation
For microarray analysis, wild-type (BY4742), msn5Δ, and mtr10Δ cells were used; for polysome profile analysis, additional tRNA retrograde mutants, los1Δ and los1Δ msn5Δ,
were used.
2.2.1 Polysome profiles
One liter of cell culture was grown in synthetic complete media at 30°C in an air shaker
to early logarithmic phase (A600 around 0.35). The culture was divided into two bottles;
after centrifuging (~ 2,700 g, room temperature, 5 min) the cultures were resuspended
with 500 ml of 30°C pre-warmed SC and SC-aa (SC medium lacks all amino acids) 25
medium, respectively, and the two cultures were returned to the shaker for 30 min.
Cycloheximide was added to each culture to final concentration 100 µg/ml for two
minutes before cultures were harvested. Cultures were cooled on ice less than 5 min, and
cells were harvested and washed with 20 ml of cold freshly-prepared lysis buffer (20 mM
Tris-HCl with pH 8.0, 140 mM KCl, 1.5 mM MgCl2, 0.5 mM dithiothreitol, 100 µg/ml
cycloheximide, 1 mg/ml heparin). After the culture was washed, the cells were
transferred to 15 ml conical tubes and then resuspended in 750 µl of lysis buffer. ~350 µl
of cold acid-washed glass beads (0.4-0.6 mm diameter, Thomas Scientific) were added
and then cells were disrupted by vortexing in cold room with 10 cycles of 30 seconds burst and 30 seconds ice. The clear crude lysate was obtained after centrifugation at ~
12,600 g for 5 min at 4°C. 5 µl of lysate was transferred into 995 µl of water for A260 measurement to estimate the RNA concentration.
Twenty A260 units of cell extract with the addition of Triton X-100 to final
concentration 1 % were loaded onto an 11ml gradient (Polyallomer tube, Beckman
Coulter) composed with five layers of 10%, 20%, 30%, 40%, and 50% sucrose (w/v) in
gradient buffer (20 mM Tris-HCl with pH 8.0, 140 mM KCl, 5 mM MgCl2, 0.5 mM dithiothreitol, 100 µg/ml cycloheximide, 0.5 mg/ml heparin). Gradients were centrifuged in SW41 Ti rotor (Beckman Coulter) at 39,000 rpm for 2.5 hr in 4°C. Gradient was fractionated into 14 tubes (~0.9 ml) using ISCO UA-6 collection system (Brandel,
Gaithersburg, MD) with UV254 recorder. During the process of fractionation polysome
profiles were monitored and traced continuously.
26
2.2.2 Quantification of polysome profiles
The scanned polysome profiles were analyzed using an area measurement tool in Adobe
Acrobat 8 Professional version. Statistical significant analyses were conducted in
Microsoft Excel with two tail t test. Figure 2. 1 shows how each peak was selected in the
polysome profiles in fed or starved conditions. In fed condition, the areas of 40S and 80S
peaks are measured separately. This sum of 40S and 80S area represented non-translating
pool. In amino acid starved cells, there was a large increase in the non-translating region
and usually above the range of UV254 detector. Therefore, the area of 40S + 80S was
estimated by projecting (the shading region of right panel in Figure 2. 1). Profiles shown
in Figure 2. 1 are representatives from more than three repeated experiments.
Fed Starved
Figure 2. 1. Illustration of quantification method.
27
2.2.3 RNA extraction
For microarray experiments, polysomal RNA isolation procedures were similar as
previously described (Arava et al., 2003; Coller and Parker, 2005). Briefly, fractions 5 to
8 (non-polysomal RNA samples) and 10 to 14 (polysomal RNA samples) were pooled
together in 30 ml glass tubes. Fractions were then mixed with 2-fold volumes (2V) of 8M
Guanidine-HCl; the RNAs were then precipitated in 100% ethanol. The nucleic acid
pellets were obtained by centrifugation using JA-17 rotor (~13,800 g, for 20 min at
4°C). The pellets were washed once with 85% ethanol, dissolved in 400 µl of TE buffer, and then transferred into 2 ml microfuge tube following by phenol/chloroform extraction.
After centrifugation (~ 12,600 g for 10 min at 4°C), the supernatant was transferred to a new 1.5 ml microfuge tube and 200 µl of 7.5 M LiCl (to final concentration 2.5M) was added to precipitate the RNAs after stored at -20°C overnight. RNA samples were collected at ~ 12,600 g for 20 min at 4°C and they were washed with 85% ethanol followed with dissolving in sterile H2O. To remove LiCl, RNA samples were precipitated
again by ethanol/NaOAc and resuspended in 7 to 15 µl of nuclease-free water.
Unfractionated RNA samples were directly extracted from the lysates for “total RNA”.
Concentrations of RNA samples were measured by A260.
2.3 Microarray data analysis
The quality of total, polysomal, or non-polysomal RNAs from BY4742, msn5Δ, and
mtr10Δ cells grown in fed or amino acid starvation conditions were estimated using an
Agilent Bioanalyzer. Microarray experiments were then performed using Affymetrix 28
GeneChip Yeast Genome 2.0 arrays according to manufacturer’s instructions by the OSU
microarray shared resource (http://www.osuccc.osu.edu/microarray/). Data pre-
processing and further statistical analysis were conducted using the free open-source
software R (version 2.7.2 or later, R development Core Team, 2011) (http://www.r-
project.org) and Bioconductor suite (Gentleman et al., 2004) supplemented with packages
of Affy (Irizarry et al., 2003), Limma (linear models for microarray data) (Smyth, 2004),
and others, as described in the programming codes in Appendix P. Microarray raw and
normalized data set will be deposited in the GEO database.
2.3.1 Pre-processing of raw data
In this project, the procedures of pre-processing included raw intensity data (.cel files) imported into R, removal of S. pombe probes, Robust Multiarray Average (RMA) normalization, and filtering outliers. After RMA normalization, expression intensities of each target were simultaneously transformed to a single log2 value, which was then
utilized for calculation of the translational activity index (P/NP) using the equation log2 P
– log2 NP. Distinct processed dataset from each mutant under fed or starvation were then
generated (mtr10Δ versus wild-type in fed or in starvation conditions; msn5Δ versus
wild-type in fed or in starvation conditions). Codes are in Appendix P.
2.3.2 Identification of significant differentially-expressed genes
Each dataset was subjected to fit the linear model using the limma program according to
instructions. Significant differentially expressed genes were selected by setting cutoff 29
conditions, such as log2 (fold-change) and adjusted p-value, and were highlighted in
volcano plots. Codes are in Appendix P. These selected genes were then annotated and
exported for further applications. In this study, hierarchical clustering, gene ontology
enrichment analysis, functional categories were used.
2.3.3 Hierarchical clustering of significantly changed target transcripts
When differentially-expressed targets were selected, their expression profiles from P, NP,
and T, were extracted for heatmap creation. Hierarchical clustering analysis and
generation of heatmaps were conducted using web-based tool in the GenePattern
platform (by using the modules of HierarchicalClustering and
HierarchicalClusteringViewer) (Reich et al., 2006) with the parameters of pearson-
correlation in row distance, no column clustering, and pair-wise complete linkage.
(http://www.broadinstitute.org/cancer/software/genepattern/).
2.4 Northern blot analysis
Total RNA (12 µg in 6 µl) samples were denatured (for each sample, denaturing buffer contained 3 µl of 10X MOPS buffer, 5.3 µl of 37 % formaldehyde, 15 µl of de-ionized formamide, 1 µl of 1mg/ml ethidium bromide) at 65°C for 15 min, following by chilling on ice and addition of loading dye (75 % glycerol and 0.2 % bromophenol blue in RNA denature buffer). Denatured RNA samples were separated by 1.2 % agarose formaldehyde gels in 1X MOPS buffer subjected to electrophoresis at 50-80 V for
approximately 5-6 hr. RNAs were then capillary transferred to Hybond N membrane (GE
30
Healthcare) using 20X SSC, following by UV cross-linking to the membranes. The blot was hybridized at 42°C with purified radiolabeled DNA probes. Blots were washed with
wash buffer (2X SSC, 0.05 % SDS). The blots were then analyzed using Typhoon Trio
variable Mode Imager (GE Healthcare) and ImageQuant 5.0 (Molecular Dynamics).
2.4.1 Preparation of DNA probes
DNA probes (length between 300 to 800 base pair) used for Northern blots were generated by PCR with genomic DNA from wild-type cells or with plasmids from the
yeast genomic tiling collection (Jones et al., 2008, OpenBiosystems). DNA probes were randomly radiolabeled with random primers (Invitrogen), α–32P-dCTP (PerkinElmer,
Massachusetts, USA), dATP, dTTP, and dGTP, by DNA polymerase Klenow (Fermentas,
Maryland, USA). Labeled probes were purified by eluting from G-50 column (GE
Healthcare). The purified probes were then denatured in boiled water for 5 min and added
into hybridization buffer. All of the oligonucleotides used in this study are listed in
Table 2. 2.
2.5 Western blot analysis
2.5.1 Protein extraction
Whole cell protein extracts were prepared from 15 ml cell cultures grown at 30°C to a
density of 0.35-0.45 OD600. Cells were washed once with cold water and disrupted in 100
µl lysis buffer (50 mM Tris-HCl with pH7.4, 150 mM NaCl, 25 mM EDTA, 1 % Triton
X-100, 0.5 % SDS, 10 mM PMSF, 1X protease inhibitor [Calbiochem, cocktail set IV]) 31
with approximately 50-70 µl of acid-washed glass beads. Cleared cell extracts were obtained after centrifugation at ~ 12,600 g for 5 min at 4°C. Protein concentrations were determined from 1 µl of extracts using the Bradford method (Bio-Rad). Protein extracts were then denatured in 4X SDS sample loading buffer (0.2 M Tris-HCl with pH6.8, 8 % SDS, 0.4 % BPB, 40 % Glycerol, 57.2 mM 2-ME) in boiling water for 5 minutes.
2.5.2 Western blot and quantification
Depending on the individual endogenous target protein intracellular levels, total amounts
of 5 to 20 µg of protein extracts from each strain were resolved on 10% SDS-PAGE with running buffer (0.025 M Tris, 0.192 M glycine, 0.1 % SDS). Immunoblotting was employed using a semi-dry transfer apparatus (BioRad Trans-Blot) at 15 V for 1 hr in transfer buffer (30 mM glycine, 0.037 % SDS, 20 % methanol, 48 mM Tris-HCl, pH 8.0).
The blots were probed with primary antibodies in 5 % non-fat milk in 1X TBST buffer
(20 mM Tris-HCl, pH 7.4, 120 mM NaCl, 0.1 % Tween-20). 3HA or GFP fusion proteins,
Pgk1, or Kar2, were detected using horseradish peroxidase (HRP)-conjugated
electrochemiluminescence (ECL) technology (Pierce, Rockford, IL, USA) using primary
antibodies: anti-HA, (Roche, Rat monoclone, 3F10, 1:1,500), anti-GFP (Roche, mouse
polyclone, 1:1,500), anti-Pgk1 (a kind gift from Paul Herman lab; rabbit polyclone, 1:
15,000), anti-Kar2 (Santa Cruz Biotechnology, rabbit polyclone, y-115, sc-33630, 1:
5,000). Quantification of protein signals were performed by ImageJ open source software
(http://rsbweb.nih.gov/ij/).
32
2.6 Cis-regulatory element analyses of targets
Sequences of upstream and downstream of target genes were retrieved from web-based
resource (Regulatory Sequence Analysis Tools, RSAT, http://rsat.ulb.ac.be/rsat/)
(Thomas-Chollier et al., 2011; Thomas-Chollier et al., 2008; van Helden, 2003). The output text file was later utilized for further analyses of codon bias usage and amino acid usage.
2.6.1 Codon bias and amino acid usage
For investigating the codon bias and amino acid usage, the open reading frame sequences
of translational differentially expressed genes (DEGs) in fed mtr10Δ cells were extracted
from the table supplemented with the frequencies of each codon in each gene by the
generous help of Dr. Thomas Begley (State University of New York in Albany) to
compile the algorithm. Z score for each transcript, calculated as described previously
(Begley et al., 2007). The data were imported into GenePattern for generation of
hierarchical heat map.
33
Table 2. 1. Yeast strains used in this study
Strain Genotype Source BY4742 MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0, wild-type Winzeler et al., 1999
los1Δ MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0 los1::kanMX4 Winzeler et al., 1999
msn5Δ MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0 msn5::kanMX4 Winzeler et al., 1999 los1Δmsn5Δ MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0 los1::kanMX4 msn5::hphMX4 This study mtr10Δ MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0 mtr10::natMX4 This study
BY4741 MATa his3Δ0 leu2Δ0 met15Δ0 ura3Δ0, wild-type Winzeler et al., 1999
dhh1ko1 MATa his3Δ0 leu2Δ0 met15Δ0 ura3Δ0 dhh1::hphMX4 Hurto and Hopper, 2011
pat1ko8 MATa his3Δ0 leu2Δ0 met15Δ0 ura3Δ0 pat1::natMX4 Hurto and Hopper, 2011
pat1dhh1-11 MATa his3Δ0 leu2Δ0 met15Δ0 ura3Δ0 pat1::natMX4 dhh1::hphMX4 Hurto and Hopper, 2011 34
mtr10Δ MATa his3Δ0 leu2Δ0 met15Δ0 ura3Δ0 mtr10::natMX4 Murthi et al., 2010 HYC163 MATα BY4742 MET22-3HA::His3MX6 This study HYC171 MATα los1Δ MET22-3HA::His3MX6 This study HYC179 MATα msn5Δ MET22-3HA::His3MX6 This study HYC186 MATα los1Δ msn5Δ MET22-3HA::His3MX6 This study HYC192 MATα mtr10Δ MET22-3HA::His3MX6 This study HYC261 MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0 MET22::natMX4 This study HYC296 MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0 trm8::Kl.leu2 trm4::natMX4 This study HYC348 MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0 trm8::Kl.leu2 trm4::kanMX4 met22:natMX4 This study HYC337 MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0 msn5::kanMX4 trm8::Kl.leu2 trm4::natMX4 This study HYC335 MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0 los1::kanMX4 trm8::Kl.leu2 trm4::natMX4 This study HYC341 MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0 los1::kanMX4 msn5::hphMX4 trm8::Kl.leu2 This study trm4::natMX4 (continued.) 34
Table 2. 1. (continued.) Strain Genotype Source HYC375 MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0 mtr10:: natMX4 trm8::Kl.leu2 This study trm4::kanMX4 HYC354 MATα BY4742 MET2-3HA::His3MX6 This study HYC364 MATα los1Δ MET2-3HA::His3MX6 This study HYC359 MATα msn5Δ MET2-3HA::His3MX6 This study HYC367 MATα los1Δ msn5Δ MET2-3HA::His3MX6 This study HYC370 MATα mtr10Δ MET2-3HA::His3MX6 This study HYC479 MATα BY4742 MET3-3HA::His3MX6 This study HYC483 MATα los1Δ MET3-3HA::His3MX6 This study HYC486 MATα msn5Δ MET3-3HA::His3MX6 This study HYC489 MATα los1Δ msn5Δ MET3-3HA::His3MX6 This study HYC492 MATα mtr10Δ MET3-3HA::His3MX6 This study
35 HYC495 MATα BY4742 MET14-3HA::His3MX6 This study HYC500 MATα los1Δ MET14-3HA::His3MX6 This study HYC504 MATα msn5Δ MET14-3HA::His3MX6 This study HYC508 MATα los1Δ msn5Δ MET14-3HA::His3MX6 This study HYC514 MATα mtr10Δ MET14-3HA::His3MX6 This study HYC560 MATα BY4742 CYS4-3HA::His3MX6 This study HYC565 MATα los1Δ CYS4-3HA::His3MX6 This study HYC570 MATα msn5Δ CYS4-3HA::His3MX6 This study HYC574 MATα los1Δ msn5Δ CYS4-3HA::His3MX6 This study HYC578 MATα mtr10Δ CYS4-3HA::His3MX6 This study HYC1162 MATα BY4742 ARG3-3HA::His3MX6 This study (continued.) 35
Table 2. 1. (continued.) Strain Genotype Source HYC619 MATα los1ΔARG3-3HA::His3MX6 This study HYC1165 MATα msn5Δ ARG3-3HA::His3MX6 This study HYC630 MATα los1Δ msn5Δ ARG3-3HA::His3MX6 This study HYC1167 MATα mtr10Δ ARG3-3HA::His3MX6 This study HYC1171 MATα BY4742 ARG5,6-GFP(S65T)::His3MX6 This study HYC636 MATα los1Δ ARG5,6-GFP(S65T)::His3MX6 This study HYC1174 MATα msn5Δ ARG5,6-GFP(S65T)::His3MX6 This study HYC638 MATα los1Δ msn5Δ ARG5,6-GFP(S65T)::His3MX6 This study HYC1175 MATα mtr10Δ ARG5,6-GFP(S65T)::His3MX6 This study HYC1012 MATa his3Δ0 leu2Δ0 met15Δ0 ura3Δ0 pat1ko8 ARG3-3HA::His3MX6 This study
36 HYC1017 MATa his3Δ0 leu2Δ0 met15Δ0 ura3Δ0 pat1ko8 MET3-3HA::His3MX6 This study HYC1041 MATa his3Δ0 leu2Δ0 met15Δ0 ura3Δ0 mtr10::natMX4 MET3-3HA::His3MX6 This study HYC1057 MATa his3Δ0 leu2Δ0 met15Δ0 ura3Δ0 mtr10::natMX4 ARG3-3HA::His3MX6 This study HYC1078, BY4741 MATa his3Δ0 leu2Δ0 met15Δ0 ura3Δ0 ARG3-3HA::His3MX6 This study HYC1082, BY4741 MATa his3Δ0 leu2Δ0 met15Δ0 ura3Δ0 MET3-3HA::His3MX6 This study HYC1086, dhh1 MATa his3Δ0 leu2Δ0 met15Δ0 ura3Δ0 ARG3-3HA::His3MX6 This study HYC1090, dhh1 MATa his3Δ0 leu2Δ0 met15Δ0 ura3Δ0 MET3-3HA::His3MX6 This study HYC1094, dhh1pat1 MATa his3Δ0 leu2Δ0 met15Δ0 ura3Δ0 ARG3-3HA::His3MX6 This study HYC1098, dhh1pat1 MATa his3Δ0 leu2Δ0 met15Δ0 ura3Δ0 MET3-3HA::His3MX6 This study HYC1185, BY4742 (HYC1171 derivative) ARG5,6-GFP(S65T)::His3MX6 [pRS426 URA3] This study HYC1186, BY4742 (HYC1171 derivative) ARG5,6-GFP(S65T)::His3MX6 [Trz1-MORF URA3] This study HYC1195, tyw1 MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0 tyw1::kanMX4 ARG3-3HA::His3MX6 This study HYC1201, BY4742 MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0 ARG3-3HA::His3MX TRM5-GFP::kanMX4 This study
36 (continued.)
Table 2. 1. (continued) Strain Genotype Source HYC1210, BY4742 MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0 ARG3-3HA::His3MX TRM5-NES- This study GFP::kanMX4 HYC1231, mtr10 MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0 mtr10::natMX4 ARG3-3HA::His3MX This study TRM5-GFP::kanMX4 HYC1232, mtr10 MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0 mtr10::natMX4 ARG3-3HA::His3MX This study TRM5-NES-GFP::kanMX4 HYC1360, BY4742 MATα BY4742 LEU4-3HA::His3MX6 This study HYC1360, los1msn5 MATα los1Δ msn5Δ LEU4-3HA::His3MX6 This study HYC1374, mtr10 MATα mtr10Δ LEU4-3HA::His3MX6 This study HYC1381, BY4741 MATa BY4741 LEU4-3HA::His3MX6 This study HYC1378, dhh1pat1 MATa dhh1Δ pat1Δ LEU4-3HA::His3MX6 This study HYC1380, dhh1 MATa dhh1Δ LEU4-3HA::His3MX6 This study HYC1383, mtr10 MATa mtr10Δ LEU4-3HA::His3MX6 This study 37
37
Table 2. 2. Oligonucleotides used in this study
Name Purpose Sequence (5’→3’) Source mtr10 deletion F TGAGGATATACTAAGGATTAACTTGCGTTATGGACAACTTACAG Shaheen and Hopper, WHIT37
(::nat or ::hph) GTATCTGCAGGTCGACGGATCCCCGG 2005 mtr10 deletion R CCATCCAACAAACGCCCTAATCCTTCCTTCCCTCACCTTTCTGTA Shaheen and WHIT38
(nat or hph) ATCCCTTGAGTGGATCTGATATCGA Hopper, 2005 Shaheen and WHIT47 mtr10 upstream CATCGGCACTATTCACAGGAA
Hopper, 2005 Shaheen and WHIT48 mtr10 downstream GGCATATCCTGATGTACTCAG
Hopper, 2005 msn5 deletion F CGTTGATTGGAAGAAAAGTAATGGATTCCACAGGCGCTTCTCAG WHIT59 Michael Whitney (::nat or ::hph) ATTGTTACAGCTGAAGCTTCGTACGC msn5 deletion R CAGACCCACATTAAAACGCTTGATTATATGCATATTTACCGGCT WHIT60 Michael Whitney (::nat or ::hph) GCCGACTGTGGATCTGATATCATCGA
38 HY01 F msn5 upstream CCATTGAACAGAGGTGCTGGAG This study HY02 R msn5 downstream GGTGTATGCACGTACCTCTTAC This study HY06 F mtr10 Midstream TTGGTAGATATACCGAATGGACAG This study HY07 R mtr10 Midstream ACTCAATATCAATCGAACTCTGCAC This study HY25 F met22 DNA probe generation TCCTATTGACGGAACCAAGG This study HY26 R met22 DNA probe generation CCTCCAGCTTCATGGACAAT This study HY33 F met3 DNA probe generation TAGAGCCCACAGGGAGTTGACTG This study HY34 R met3 DNA probe generation GGTCTTGGTGGGTTGGATTCTC This study HY37 F met2 DNA probe generation CTAGTGCTTGAGTCTGGCGTGGT This study HY38 R met2 DNA probe generation GACATACGTGCAGCCGATAGTCC This study HY43 F met14 DNA probe generation CGCAAGGCATTGAGAAAACAGG This study HY45 F Met28 DNA probe generation CATTGCTGCTGGTTAGCAGTGG This study (continued.) 38
Table 2. 2. continued. Name Purpose Sequence (5’→3’) Source HY46 R Met28 DNA probe generation CTACCTGCCCATGTTCCGTCTCT This study GTTATAGCGTCAAGTGGCCCACGCGAGTTACATGACTTGGTGGT HY73 F Met22 C-term tag GTCTACATCATGCGATGTCATTCAGTCAAGAAACGCCcggatccccgg This study gttaattaa CACATACACACATATATATATGTACTCATATATTTATGTCTATCA HY74 R Met22 C-term tag This study ATAAAGTAAAATATATGTTATgaattcgagctcgtttaaac HY75 R confirm 3HA tag GCGCCTCAGCACTGAGCAGCG This study TPL010 R confirm GFP tagg TTGTGACCATTAACATCACCATCT Tsung-Po Lai HY83 F Trm8 upstream GCTAAGTGGGTTGGGTTTGGTTCGTACG This study HY84 R Trm8 downstream CGGAAGAGCTTGCGGCTCAAATAACCTGG This study HY85 F Trm4 deletion (::natMX4 or CACTTCCTTTTATCTACACTGTAATCCGAAGAATACACTATAAG 39 This study ::kanMX4) GCTGGCTAGAAGccaggtcgacggatccccgg GCCTTTTAATAATATACATTTACTTTACAGTGGAGGGGATAAGA Trm4 deletion (::natMX4 or HY86 R AACATGATAACTATCAATTAGCAGCGCcgtggatctgatatcatcgatgaattcga This study ::kanMX4) gc HY87 F Trm4 upstream GCGGTGCTGAAGTAACGAAACCGCGCGC This study HY88 R Trm4 downstream CCGCAGGTCTTTCGCAATTTATACCTTGGGTC This study HY89 F Trm8 midstream GGCTGTGGATTCGGTGGGTTGATGATAG This study HY90 F Trm4 midstream GTCGAAGATAGCACTGAGGCGGC This study CAGCAGTTCCCATAGGATAAAATTTTCAAGCGTTTATTGTTAAG HY91 F Trm8 deletion (::Kl.leu2) This study CTGAAAGCCAAGCCcgccagctgaagcttcgtac GTATATGTGGTAAATTGTTCTAGTTATACATCTATGTTACAATAT HY92 R Trm8 deletion (::Kl.leu2) This study GGCTGGCGccttcacttgcatctatccgttc GTTAGTAAGTAAGAAGTTTAAAGACAACTCAGAAGACATCAGC Met22 deletion (::natMX4 or HY93 F ACTTTACTCTGGCATTGGAAAGAGAATTATTGGccaggtcgacggatccc This study ::hphMX4) cgg HY94 R Met22 deletion (::natMX4 or GTACTCATATATTTATGTCTATCAATAAAGTAAAATATATGTTAT This study 39 (continued.)
Name Purpose Sequence (5’→3’) Source ::hphMX4) TTAGGCGTTTCTTGACTGAATGACATCGCcgtggatctgatatcatcgatgaatt cgagc HY95 F Met22 upstream CAAGAGGCTCCTTGAGGGCATTCAAG This study HY96 R Met22 downstream CGGTCAGAATCCTGCTGTATTTTCTTGTCTCAC This study HY97 F Met22 midstream CCCTTGGTTCCGTCAATAGGATCCAAACACC This study GGTAACGATGAAACGAAGACGTCTGTCTTTGGTGAGGCCGAAG HY106 F Met2 C-term tagging This study AAGTTACCAACTGGcggatccccgggttaattaa GCTGTTTGTCTATATATAAATATAGATATAGATATACATGTACTG HY107 R Met2 C-term tagging This study GTTTATCTATGTTATGCCgaattcgagctcgtttaaac HY110 F Met2 upstream CCGCTTCGTTGTACAACCTACCTG This study HY111 F Arg3 DNA probe generation GGTGCCCAACCGATGTTTTTAGG This study HY112 R Arg3 DNA probe generation CGCACCGTTTCTCTCAGCAAC This study 40 CCTATTTCACATATTGTACAAAAAGTTGTCCTATTCTTGGAAGAC HY117 F Met3 C-term tagging This study AATGGCTTTTTTGTATTTcggatccccgggttaattaa GCAAATCTATTTATTTTGCGCGGTCGATCATGAATTTTGCCCTAC HY118 R Met3 C-term tagging This study TTTTGAGATGGGgaattcgagctcgtttaaac CGGTTGAAGAATGTGCTACCATTATTTATGAGTACTTAATCAGT HY119 F Met14 C-term tagging This study GAAAAAATCATCCGTAAGCATTTGcggatccccgggttaattaa CCTCGAATAAATATGTTCTATATTATATATATACATCTTTTATAT HY120 R Met14 C-term tagging This study ATCATTAAATGTACAGgaattcgagctcgtttaaac GCAGCTATGTCTGCCATTGATATCTTTGTTAATAATAAAGGTAAT HY133 F Arg3 C-term tagging This study TTCAAGGACTTGAAAcggatccccgggttaattaa GCAAAATAATCAATGTATATCATTATTCATGCATCTATATCTGTA HY134 R Arg3 C-term tagging This study TTTATATATTAGTTATTAAGgaattcgagctcgtttaaac HY135 F Arg5,6 DNA probe generation CGCGCTTATTGGTGCTAGAGGT This study HY136 R Arg5,6 DNA probe generation CCTTGAAACCACTGCCCAACAT This study HY137 F Arg5,6 C-term tagging GCAAAATATTAATCTTGCTATGGGTTATGGAGAGTATGCTGGTA This study 40 (continued.)
Table 2. 2. (continued.) Name Purpose Sequence (5’→3’) Source TCCCTGAAAATAAAATTATTGGTGTCcggatccccgggttaattaa CGTTATTTAATGGATATATATATATTATATATTTATATACGTTAA HY138 R Arg5,6 C-term tagging This study TGTCTCATGTGACTGAGCTGCAGgaattcgagctcgtttaaac GCCGTTATCACTGATGGCTTGAAACCAATCCATATCGTTACTAA HY144 F Cys4 C-term KI This study GATGGATTTACTGAGCTACTTAGCAcggatccccgggttaattaa GAGAACGGTGCAATTGAATAGGAAAGGAATGACGGATTTTGCT HY145 R Cys4 C-term KI This study TCTATGTTTGCTTTTATTTGAAGCgaattcgagctcgtttaaac HY146 F Cys4 midstream conf. GACTCTTCAAAGCTGGAGGCTTCGACG This study CWC15_DNA probe CAGACCACAGTTAGAAGCAAGAAGCGG HY203 F This study generation CWC15_DNA probe HY204 R GTCATATCGTTAATGTATCCGGACGCTG This study generation HY205 F LCD1_DNA probe generation CAGATCTGAAATCCTTGATAAGCGGTAGTTGGTGG This study 41 HY206 R LCD1_DNA probe generation CCAAGTTCGGGGAGTCTTGTCCCACGTACC This study HY207 F RAD9_DNA probe generation GGGGATGCTGTTACCTTTGATGGAAATGAGTACG This study HY208 R RAD9_DNA probe generation CCAGTGTAGTGTAGGCCACCCCAACGCC This study GTO3_DNA probe HY213 F CTCCTGTGGCTAACATACCCAATAACGCGC This study generation GTO3_DNA probe HY214 R GACCCGTGGCTGCGAGCGAGTATATCCGAG This study generation HY219 F ACT1 DNA probe generation CCGGTGATGGTGTTACTCACGTCGTTCC This study HY220 F ACT1 DNA probe generation GACCTTCATGGAAGATGGAGCCAAAGCGGTG This study HY252 F MTH1 DNA probe generation CCCCCTCTACTGTGCACACGCAACTAAC This study HY253 R MTH1 DNA probe generation GGTATTATGCTTGGTGGGGGCATGTCCGGTC This study HY267 F Trm5 midstream GGCGCCACTGATACCATCCAAATGCCATGGGTGC This study HY275 R Trm5 C-term KI CATCTCGTTTCTTTTTCTAATGGTCGCCTTTTTGCCTTTTTCCTCC Ohira and Suzuki, 41 (continued.)
Table 2. 2. continued. Name Purpose Sequence (5’→3’) Source
CACGGCgaattcgagctcgtttaaacTTTTCGACACTGGATGGCGGCGTT 2011 AGTA gcacccaccaaaccgatgtattgcgcgagtttccaacttccagctaacgtactaccaccgcttgagag Ohira and Suzuki, HY276 F Trm5 C-term KI (NES-GFP)
acttactcttgatggtcgacggatccccggg 2011 Ohira and Suzuki, HY277 F Trm5 C-term KI (GFP) ccgatgtattgcgcgagtttccaacttccagctaacgtaggtcgacggatccccggg
2011 HY308 F Sps2 DNA probe generation CTGATACTGCATTAACCTCCATCG This study HY309 R Sps2 DNA probe generation CGCCTCTTATGATCGAACGACCGG This study CTGGTGATGTGTCCATTCCATCTTTGGCCGAGGTCGAAGGTAAG HY312 F LEU4 C-term KI This study AATGCTGCGGCATCTGGCTCTGCAcggatccccgggttaattaa CCGTGCTTCTAGTAATTATATGGTTAAAAAAAAAGGAAAGGAA HY313 R LEU4 C-term KI GTAAATAAATAAGTATAGAAATAAATAGAAGCGgaattcgagctcgtttaa This study ac HY314 F LEU4 midstream GCGTGCTCAAGGTGAAACTCAATGGAGAATCCC This study 42 HY325 R LEU4 DNA probe generation TCGGAGACACCTACACCCCATTTGTAGGCC This study
42
CHAPTER 3
Genome-wide investigations of cellular functions for tRNA nuclear-cytoplasmic trafficking in regulation of translation
3.1 Introduction
In eukaryotic cells, tRNAs are transcribed in the nucleus but function in protein synthesis in the cytoplasm. For decades, tRNAs were thought to travel only unidirectionally, nucleus to cytoplasm. However, it is now known that tRNAs actively shuttle between the nucleus and the cytoplasm (Shaheen and Hopper, 2005; Takano et al., 2005; Whitney et al., 2007), and this bi-directional movement is conserved in vertebrate cells (Barhoom et al., 2011; Shaheen et al., 2007; Zaitseva et al., 2006). Cytoplasmic mature tRNAs rapidly accumulate in the nucleus when cells are nutrient-deprived as observed by fluorescence in situ hybridization. Upon re-feeding, the nuclear previously-cytoplasmic tRNAs return to the cytoplasm to participate in translation (Hurto et al., 2007; Shaheen and Hopper,
2005; Whitney et al., 2007).
43
In the current working model, at least three nuclear transporters (all belong to β-
importin family) function in tRNA nucleus-cytoplasm subcellular dynamics: Los1, Mtr10,
and Msn5 (Figure 3. 1). Los1 functions in the initial export of intron-containing and
intron-less tRNAs, and also in the re-export of previously cytoplasmic mature tRNAs
back to the cytoplasm. Mtr10, directly or indirectly, is responsible for the retrograde
movement of cytoplasmic tRNAs to the nucleus. Little is known about the biological
roles of Mtr10 and one goal of this study is also to investigate the transcriptional
expression profiles of mtr10Δ cells, which will be addressed in Chapter 4. Msn5 can
export intron-less tRNAs and appears to function solely in tRNA re-export (Hopper et al.,
2010; Murthi et al., 2010).
There are still many questions regarding the tRNA trafficking process. For
example, not all the exporters/importers have been identified (for reviews, see Hopper,
2006; Hopper et al., 2010; Hopper and Shaheen, 2008). Although it appears that tRNA re-export is regulated by nutrient status of the cell and the regulation is coordinated with
P-body formation (Hurto and Hopper, 2011), none of the details of the molecular regulation have been uncovered. Whitney et al. demonstrated that the PKA pathway is required for the tRNA nuclear accumulation and this is independent of Gcn2 (Whitney et al., 2007). Perhaps the most important question “Why does cytoplasmic tRNA travel back to the nucleus” remains completely unresolved. Previous studies showed that tRNA accumulate in the nucleus when cells are nutrient stressed or when tRNA is damaged, implicating the potential roles of tRNA retrograde pathway in response to stress or in tRNA quality control (Feng and Hopper, 2002; Sarkar et al., 1999).
44
Based on the essential role that tRNAs serve as adaptors to deliver amino acids for polypeptides synthesis during translation in the cytoplasm, in this chapter we focus on the impacts of tRNA nuclear-cytoplasmic dynamics on translation regulation in the yeast,
Saccharomyces cerevisiae. In this work, we demonstrate that tRNA nuclear-cytoplasmic movement plays a role in regulating translation of mRNAs encoding proteins involved in several amino acid biosynthetic pathways, including sulfur assimilation (methionine), arginine, and leucine. Thus, tRNA nuclear-cytoplasm trafficking is involved in cellular metabolism/physiology.
45
Figure 3. 1. Current model of tRNA subcellular movement in yeast. The proposed model of tRNA nuclear-cytoplasmic trafficking includes three steps. 1. Primary export. The primary tRNA transcripts are end-processed and exported to the cytoplasm by the tRNA exportin Los1. 2. Constitutive retrograde import. After maturation processes (including intron-splicing on the mitochondria, base modifications, and aminoacylation), Mtr10 is responsible for the import of the cytoplasmic mature tRNAs to the nucleus. 3. Re-export. The previously-cytoplasmic tRNAs are re-exported from the nucleus to the cytoplasm by the functions of Los1 and Msn5.
46
Figure 3. 2. Flowchart of experimental designs. For polysome profiles, more than three repeats were performed. For microarray samples, two biological repeats were performed and RNAs were extracted from indicated pools and hybridized to two Affymetrix chips.
47
3.2 Results
This section includes results of polysome profiles analyses, microarray data of
translational activity, followed by verification and sequence analyses. Figure 3. 2 illustrates the steps of experiments for polysome profile analysis and microarray sample collection.
3.2.1 Polysome profiles analysis
Previous studies showed that tRNAs accumulate in the nucleus when cells are nutrient
starved (Hurto et al., 2007; Shaheen and Hopper, 2005; Whitney et al., 2007). Amino
acid or glucose deprivation also causes translation inhibition (Ashe et al., 2000; Holmes et al., 2004). Therefore, we proposed that cells regulate the translation machinery by
controlling tRNA availability via its subcellular distribution. If it is true, we expected that
the global translation should be inhibited in cells with defects in tRNA export pathways,
such as in msn5Δ, los1Δ, and los1Δ msn5Δ mutants in which tRNAs constitutively
accumulated in the nucleus. Furthermore, in cells which fail to accumulate tRNAs in the
nucleus the global translation in nutrient starvation, such as mtr10Δ cells, translation
should not be inhibited. To test the hypothesis, we analyzed the polysome profiles of wild
type, mtr10Δ, msn5Δ, los1Δ, and los1Δ msn5Δ cells grown in fed or acute amino acid
deprivation conditions (Figure 3. 3 and Figure 3. 4).
As anticipated, in wild-type cells there was a major shift from polysomes to the
non-translating region when cells were nutrient deprived (Figure 3. 3). Surprisingly, in
general, the polysome profiles from cells with defects in the tRNA trafficking pathways 48
were similar in fed or amino acid starvation condition, but three differences were
demonstrated. First, quantifications of polysome profiles from fed msn5Δ and los1Δ msn5Δ cells revealed that there were statistically significant increases in the ratio of P/NP relative to that in wild-type cells (NP, non-polysomal region, represented the region of
40S+80S, Figure 3. 5). Since the polysome region did not exhibit large changes, the decreases of NP region accounted for the increases in P/NP ratio. Second, the polysome region of fed mtr10Δ cells was smaller, suggesting a translation defect. Third, in starved mtr10Δ cells the NP region was smaller and polysomal region was larger, suggesting the rate of ribosome runoff in mtr10Δ cells is slower. This suggested that mtr10Δ cells were defective in repressing translation when responding to nutrient deprivation (Figure 3. 5).
Possible explanations are addressed in the Discussion section. In summary, the similar polysome profiles of tRNA export mutants suggested that the global translation is not affected when tRNAs are accumulated in the nucleus, and this led us to employ microarray experiments to identify the specific targets.
3.2.2 Microarray data pre-processing
To investigate whether the tRNA trafficking process regulates translation of particular
mRNAs, we performed microarray analysis on the distribution of polysome-bound (P,
translating) and non-polysome bound (NP, not-translating) mRNAs from wild-type,
msn5Δ, and mtr10Δ cells grown in fed or acute amino acid starvation conditions
(summary of the microarray experiments is cartooned in Figure 3. 6). After microarray
experiments, all of the raw intensity data files (*.cel files) from fractioned (P or NP) and
49
unfractionated (total, T) RNA samples were generated. These files were imported together into the platform R with Bioconductor (Gentleman et al., 2004; R development
Core Team, 2011). A diagnostic plot, principle component analysis (PCA) plot was
utilized to roughly assess the similarity of microarray data. Figure 3. 7 exhibited that
expression profiles of msn5Δ cells are similar to those from wild-type cells whereas
mtr10Δ profiles displayed larger differences. Prior to the normalization, an optional
procedure of masking S. pombe probes was performed because Affymetrix Yeast 2.0
GeneChips, contain two yeast species genomes, S. cerevisiae and S. pombe. In addition,
this step can decrease background noise.
As mentioned in Introduction, it is required to process the intensity data from
probesets into gene expression values by normalization. We used the function of Robust
Multiarray Average (RMA) (Irizarry et al., 2003) in the affy package. It is an algorithm that has been commonly used for microarray normalization (Ashe et al., 2000; Shenton et al., 2006; Smirnova et al., 2005; Vyas et al., 2009). Box plots of microarray data before and after RMA normalization were shown in Figure 3. 8 and Figure 3. 9. After a step of filtering low signals, a log2-value matrix with 36 columns (RNA samples) and 5684 rows
(genes) was used for calculation of P/NP ratio and subsequently subject to statistical
analysis to select differential expressed genes.
50
Figure 3. 3. Polysome profiles of wild-type, mtr10Δ, and msn5Δ cells collected from fed or 30-min amino acid starved conditions. Twenty A260 units of cell lysate were loaded
and separated on 10% to 50% sucrose gradient. Polysome traces were recorded by UV254 detector during fractionation and are shown as representatives from more than five experiments. Cartoons represent the phenotypes of tRNA distribution.
51
Figure 3. 4. Polysome profiles of wild-type, los1Δ, and los1Δ msn5Δ cells collected from fed or 30-min amino acid starved condition. Twenty A260 units of cell lysate were loaded and separated on 10% to 50% sucrose gradient. Polysome traces were recorded by
UV254 detector during fractionation and are shown as representatives from more than five experiments. Cartoons represent the phenotypes of tRNA distribution.
52
A. Fed D. Starved 1.2 1.4 1 * * * 1.2
P/T 1 0.8 P/T 0.8 0.6 0.6 0.4 0.4 0.2 0.2 n=8 n=8 n=11 n=5 n=11 n=5 n=8 n=8 0 n=17 0 n=18 WT mtr10∆ msn5∆ los1∆ los1∆ WT mtr10∆ msn5∆ los1∆ los1∆ msn5∆ msn5∆
B. Fed E. Starved 1.2 1.4 1 * * 1.2 1 0.8 * NP/T 0.8 0.6 NP/T 0.6 0.4 0.4 0.2 0.2 0 0 WT mtr10∆ msn5∆ los1∆ los1∆ WT mtr10∆ msn5∆ los1∆ los1∆ msn5∆ msn5∆
C. Fed F. Starved
2 2.5
* * 2 * 1.5 1.5 1 P/NP
P/NP 1 0.5 0.5
0 0 WT mtr10∆ msn5∆ los1∆ los1∆ WT mtr10∆ msn5∆ los1∆ los1∆ msn5∆ msn5∆
53
Figure 3. 5. Quantification of polysome profiles of tRNA trafficking mutants in fed (A- C) or amino acid starved (D-F) conditions. NP represents the region of 40S and 80S in the polysome profile; P represents the region of disome and polysome in the profile; T represents the area of whole profile. *: p-value 0.01
54
55
Figure 3. 6. Summary of microarray experiments. One RNA sample was hybridized to one Affymetrix yeast 2.0 GeneChip.
55
Figure 3. 7. Three dimensional view of result from principle component analysis (PCA). Global assessment of microarray raw data set from fractionated RNA samples was evaluated by using principle component analysis (Courtesy of microarray center of OSU). WT_fed_mono represents the samples that were collected from non-polysomal bound fractions of wild-type cells grown in fed condition. One block represents one microarray experiment. Same color represents biological repeats of same condition.
56
3.2.3 Identification of significant differentially-expressed genes (DEGs)
In order to understand the impact on translational status of specific mRNAs when cells are defective in tRNA nuclear import or re-export pathway, we analyzed the translation activity index of each mRNA, which was obtained from the ratio of P/NP (Smirnova et al., 2005; Vyas et al., 2009). There are several indexes which have served as indicators of translation status. Generally, higher translational index correlates with higher protein level (Greenbaum et al., 2002; Tuller et al., 2007). The tRNA retrograde process was proposed to function in regulation of translation of a subset of transcripts (Shaheen and
Hopper, 2005; Whitney et al., 2007); therefore, we expected that aberrant tRNA nuclear- cytoplasmic traffic could result in a re-distribution between P- and NP- bound population for such target mRNAs. Moreover, considering that Msn5 and Mtr10 also transport other protein cargos, we proposed that the transcripts that are affected commonly in mtr10Δ cells and msn5Δ cells are more likely the primary targets of tRNA trafficking. Thus, we paid more attention on the transcripts with changes in translational activity index affected in both msn5Δ and mtr10Δ cells.
To identify such tRNA trafficking targets, a three-step approach was executed.
First, the ratio of P/NP of each mRNA from the pre-processed matrix was calculated.
Second, the translationally differentially expressed genes (DEGs) were selected separately from mtr10Δ cells or from msn5Δ cells versus wild-type cells in fed or starved condition by using the statistic method of linear model for microarray data (Limma)
(Smyth, 2004). In this work, translationally DEGs were selected when adjusted p-values were smaller than 0.05 and 1.5 fold change (log2 FC = 0.6) from each mutant relative
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to wild-type under each condition. The numbers of statistically DEGs are summarized in
Table 3. 1. Detail lists are in Appendix B to Appendix E. Expression profiles from these
genes were selected and imported to GenePattern website for generation of hierarchical
heat map (Figure 3. 11 from fed mtr10Δ and Figure 3. 12 from fed msn5Δ). We also performed the same statistical analyses of “total” RNA samples; with this information we can check transcriptional effects by selecting potential targets with changes in P/NP ratio and no or little changes in their “total” expression levels. For details of transcriptional
analyses, see Chapter 4.
The translation profiling is unchanged in msn5-depleted cells under fed or amino
acid starvation condition
Because in msn5Δ cells tRNAs constitutively accumulate in the nucleus, we expected a
large change in its translation profile. However, the numbers of translationally
differentially expressed genes in msn5Δ cells were few or none in fed or starved
condition (see summary table of number of translationally differentially expressed genes
in Table 3. 1). This result was consistent with the data from polysome profiles (Figure 3.
5), which showed that there was no large difference between msn5Δ and wild-type cells.
The unchanged translational profiles of msn5Δ cells in fed or starved condition might be due to two, or more, possibilities. First, tRNA nuclear accumulation is uncoupled with translation. Second and more likely, the Los1 and unknown tRNA re-export pathways are sufficient to compensate or maintain translation. Further interpretations are in the
Discussion.
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Normal responses to amino acid deprivation at translation level in tRNA import
mutant
Considering that in mtr10-null cells tRNAs fail to accumulate in the nucleus upon
nutrient stress (Hurto et al., 2007; Shaheen and Hopper, 2005) and that a defect of
translation repression was demonstrated (Figure 3. 3), we anticipated a subset of
transcripts were translationally changed in starved mtr10Δ cells. However, very few
transcripts that were translationally changed in starved mtr10Δ cells (Table 3. 1), which
suggested that tRNA nuclear retrograde transport process is not regulating translation in response to nutrient availability. Furthermore, this correlates to the observation that tRNA nuclear import is a constitutive process (Murthi et al., 2010).
A group of transcripts is translationally down-regulated in both mtr10Δ cells and msn5Δ cells
On the basis of the opposite phenotypes in tRNA subcellular distribution between msn5Δ
and mtr10Δ cells, we attempted to identify transcripts which display opposite translation
expression patterns (i.e., up-regulated in mtr10Δ cells and down-regulated in msn5Δ cells, or vise versa, when compared to wild-type cells); however, such transcripts were not identified. More surprisingly, we identified a group of transcripts that was down- regulated in translation (with decreased P/NP ratio) in both msn5Δ and mtr10Δ cells
under fed condition. Furthermore, by employing the web-based tool which provides a
survey of the functional distribution of genes of interests, MIPS functional catalogue
(Ruepp et al., 2004), we found that the group of the affected transcripts was functionally
59
overrepresented in methionine/sulfur and arginine amino acid biosynthesis (Figure 3. 13).
In addition, the translational indexes of transcripts encoding other amino acid
biosynthesis pathways, such as LEU4, SER33, and LYS5, were also decreased in mtr10Δ
cells (Figure 3. 11). Our data indicated that tRNA subcellular dynamics has a unique role
in regulating translation of a subset of mRNAs encoding enzymes involved in several amino acid biogenesis pathways, including methionine, arginine, leucine, serine, and lysine (methionine, arginine, and leucine biosynthetic pathways are shown in Figure 3.
14).
3.2.4 Transcriptional control and RNA stability of target mRNAs are
not affected
According to the microarray expression profiles, mRNAs encoding proteins in the
methionine and arginine biosynthetic pathways were translationally down-regulated in fed msn5Δ and mtr10Δ cells. The total expression levels of these targets in tRNA trafficking mutants were similar to that in wild-type cells from microarray data (for example, MET3 in (Figure 3. 15). To confirm the microarray results, we employed
Northern blot analysis. The total RNAs were extracted from wild-type and tRNA trafficking mutant cells (msn5Δ, mtr10Δ, los1Δ, los1Δ msn5Δ) grown in SC media (fed condition). Since Los1 functions in tRNA primary export and re-export pathways, we anticipated that blocking nuclear export pathways by deleting both Los1 and Msn5 would further enhance the phenotypes of the potential targets, which would be shown in the next section. The Northern blots were hybridized to the DNA probes for target mRNAs, such
60
as ARG5,6, MET3, and LEU4 (Figure 3. 16). The result showed that the steady state
levels of target mRNAs were not largely changed in all tested strains. In addition, another line of evidence supported that the transcriptional control of these target transcripts were not affected. When cells were amino acid deprived, translation of the master regulator
Gcn4 is induced and it subsequently activates the transcription of amino acid biogenesis- related genes. If the transcriptional control is defective, cells cannot grow in amino acid deprived conditions. Given this consideration, we performed a growth assay of tRNA nuclear-cytoplasmic shuttling mutants on SC solid media without methionine or arginine.
Interestingly, those mutants were able to grow on SC-methionine or SC-arginine plates
(Figure 3. 17), suggesting that the general amino acid transcriptional control is not affected.
3.2.5 Verification of microarray data by western blot analyses
The Northern blot analysis and growth assays indicated that the steady state levels of
mRNAs and transcriptional control are not affected in the mutants that are defective in
tRNA subcellular trafficking. To confirm the results of the microarray analysis which
indicated that these transcripts are changed at the translation level, we employed western
analysis to determine the endogenous levels of protein targets. We introduced the 3HA or
GFP epitope tags at the C-terminal of the endogenous locus to generate in-frame fusion
of the Met, Arg, or Leu protein targets in wild-type cells and each tRNA trafficking
mutant cells, including mtr10Δ, msn5Δ, los1Δ, and los1Δ msn5Δ. All of the fusion proteins were full length and functional by assessing cell growth on methionine (a
61
representative result is shown in Figure 3. 18) or arginine (not shown) depletion plates.
We then examined the steady state levels of endogenous protein targets by immunoblots
of whole-cell extracts obtained from normal SC media cultures.
Correlated to their decreased translation activity indexes from microarray data,
the expression levels of all tested proteins (Met2-3HA, Met3-3HA, Met14-3HA, Met22-
3HA, Arg3-3HA, Arg5,6-GFP, and Leu4-3HA) were remarkably reduced or even not
apparent in mtr10Δ cells (Figure 3. 19) while these target proteins had basal level
expression in wild-type cells. In msn5Δ or los1Δ cells these protein levels were not
largely changed. As mentioned earlier, it has been shown that Los1 and Msn5 function in
parallel in the tRNA nuclear re-export process. Blocking tRNA re-export pathway only
by deleting Msn5 would not be sufficient to cause significant result; this led to the prediction that further blocking tRNA re-export by deleting both Los1 and Msn5 would
lead to significant decrease in target protein levels. In agreement with this expectation,
the levels of Met, Arg, and Leu biosynthetic pathway proteins in los1Δ msn5Δ cells were
synergistically decreased when compared to that in single deletion strains (Figure 3. 19).
Importantly, this result also demonstrated that the growth defect of mtr10Δ cells was not
accounted by the reduced level of target proteins because los1Δ msn5Δ cells grow as well
as wild-type cells. Furthermore, most of tested proteins exhibited higher levels in los1Δ
cells (Figure 3. 19). We reasoned that it was likely caused by up-regulated translation of
Gcn4 mRNA, because it has been shown that translation of Gcn4 is up-regulated when
unspliced tRNA accumulates in the nucleus in los1Δ cells (Qiu et al., 2000).
62
To further validate microarray data, Cys4-3HA fusion proteins were also tested as unchanged candidate in all tested cells (Figure 3. 19). Moreover, when we introduced the functional Los1, Msn5, or Mtr10 proteins back into individual mutants, the levels of
Met3-3HA protein expression in tRNA trafficking mutants were mostly or partially recovered to basal level as in wild-type cells (Figure 3. 20).
Collectively, the results from western blot analysis confirmed the microarray data that target mRNAs with lower translation activity indexed were poorly-translated.
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Raw data log2 intensity log2 6 8 10 12 14 X26_Wt.fed.total.CEL X01_WT.fed.poly.CEL X02_WT.fed.poly.CEL X03_WT.fed.mono.CEL X04_WT.fed.mono.CEL X29_msn5.fed.total.CEL X30_msn5.fed.total.CEL X09_msn5.fed.poly.CEL X10_msn5.fed.poly.CEL X33_mtr10.fed.total.CEL X34_mtr10.fed.total.CEL X17_mtr10.fed.poly.CEL X18_mtr10.fed.poly.CEL X25_Wt.fed_total_c.CEL X11_msn5.fed.mono.CEL X12_msn5.fed.mono.CEL X28_Wt.starved.total.CEL X19_mtr10.fed.mono.CEL X20_mtr10.fed.mono.CEL X05_WT.starved.poly.CEL X06_WT.starved.poly.CEL X07_WT.starved.mono.CEL X08_WT.starved.mono.CEL X31_msn5.starved.total.CEL X32_msn5.starved.total.CEL X13_msn5.starved.poly.CEL X14_msn5.starved.poly.CEL X35_mtr10.starved.total.CEL X36_mtr10.starved.total.CEL X21_mtr10.starved.poly.CEL X22_mtr10.starved.poly.CEL X27_Wt.Starved_total_c.CEL X15_msn5.starved.mono.CEL X16_msn5.starved.mono.CEL X23_mtr10.starved.mono.CEL X24_mtr10.starved.mono.CEL
Figure 3. 8. Box plot of raw microarray data.
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After Remove probes and RMA normalized
14
12
10
8 log2 intensity log2
6
4 X26_Wt.fed.total.CEL X01_WT.fed.poly.CEL X02_WT.fed.poly.CEL X03_WT.fed.mono.CEL X04_WT.fed.mono.CEL X29_msn5.fed.total.CEL X30_msn5.fed.total.CEL X09_msn5.fed.poly.CEL X10_msn5.fed.poly.CEL X33_mtr10.fed.total.CEL X34_mtr10.fed.total.CEL X17_mtr10.fed.poly.CEL X18_mtr10.fed.poly.CEL X25_Wt.fed_total_c.CEL X11_msn5.fed.mono.CEL X12_msn5.fed.mono.CEL X28_Wt.starved.total.CEL X19_mtr10.fed.mono.CEL X20_mtr10.fed.mono.CEL X05_WT.starved.poly.CEL X06_WT.starved.poly.CEL X07_WT.starved.mono.CEL X08_WT.starved.mono.CEL X31_msn5.starved.total.CEL X32_msn5.starved.total.CEL X13_msn5.starved.poly.CEL X14_msn5.starved.poly.CEL X35_mtr10.starved.total.CEL X36_mtr10.starved.total.CEL X21_mtr10.starved.poly.CEL X22_mtr10.starved.poly.CEL X27_Wt.Starved_total_c.CEL X15_msn5.starved.mono.CEL X16_msn5.starved.mono.CEL X23_mtr10.starved.mono.CEL X24_mtr10.starved.mono.CEL
Figure 3. 9. Box plot of RMA normalized microarray data.
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Volcano plot of limma -- P/NP_fed_mtr10 vs wt -log10 p value p -log10 0123456
-3 -2 -1 0 1 2 3 4
diff(logFC)
Volcano plot of limma -- P/NP_starved_mtr10 vs wt -log10 p -log10 value 012345
-2-101234
diff(logFC)
Figure 3. 10. Volcano plot showing differential expressed genes selected by P/NP index compared from mtr10Δ to wild-type cells. Upper, fed condition; bottom, starved condition.
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mtr10∆ vs. wild-type P/NP T P/T
Fed a Starved a Fed b Starved b Fed c Starved c Up 93 2 335 605 0 102 Down 131 5 198 560 1 143
msn5∆ vs. wild-type P/NP T P/T
Fed d Starved d Fed e Starved e Fed f Starved f Up 0 0 177 193 0 0 Down 12 0 119 258 0 0
Table 3. 1. Summary tables of numbers of differentially expressed genes in mtr10Δ or msn5Δ cells in fed or starved conditions. top, mtr10Δ cells vs. wild-type cells; bottom, msn5Δ cells vs. wild-type cells. P/NP, translation activity index, obtained from log2 P- log2 NP. T, total RNA, obtained from unfrationated RNA sample. P/T, index for analysis of ribosome occupancy, obtained from log2 P- log2 T. a: cutoff condition: adjusted p-
value < 0.05 with log2 FC 0.6 (Up-regulated) or − 0.6 (Down-regulated); b: cutoff condition: adjusted p-value < 0.001 with log2 FC 0.6 or − 0.6; c: adjusted p-value <
0.01 with log2 FC 0.6 or < − 0.6; d: adjusted p-value < 0.05 with log2 FC 0.6 or −
0.6; e: adjusted p-value < 0.05 with log2 FC 0.6 or − 0.6; f: adjusted p-value < 0.05 with log2 FC 0.6 or − 0.6.
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Figure 3. 11. Heat map of hierarchical clustering results of gene expression profiles from translational DEGs in mtr10Δ cells versus wild-type cells in fed condition.
68
Figure 3. 12. Heat map of hierarchical clustering results of translational DEGs from msn5Δ cells compared to wild-type cells in fed condition.
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Figure 3. 13. Venn diagram of DEGs which were decreased in translation activity index in both mtr10Δ and msn5Δ cells.
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71
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Figure 3. 14. Amino acid biosynthesis pathways affected in tRNA trafficking mutants. A. Sulfur assimilation pathway. B. Arginine 72 biosynthesis pathway. C. Leucine biosynthesis pathway. Transcripts with decreased translation activity index were marked with green shadow. Targets confirmed by Western blot analyses are indicated with orange box.
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73
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Figure 3. 15. Expression profile of MET3 transcript among all microarray samples. The expression values were log2 and centered to the median of all values from all samples. WT_fed_poly represents RNA sample was collected from polysome-bound pool from wild-type cells in fed condition. WT_fed_mono represents RNA samples was collected from non-polysome bound pool 74 from wild-type cells in fed condition. WT_starved_poly represents RNA sample was collected from polysome-bound pool from wild-type cells in amino acid starvation condition.
74
Figure 3. 16. Northern blot analysis of total RNA collected from wild-type and tRNA trafficking mutants in fed condition. The blot was hybridized to α-32P-dCTP-labeled ARG5,6, MET3, or LEU4 DNA probes. ACT1 served as an internal loading control. 25S and 18S rRNAs were indicated in the bottom ethidium bromide-staining formaldehyde agarose gel. Each ratio was normalized to the expression values of ACT1 and compared to WT.
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Figure 3. 17. Growth assay of tRNA trafficking mutants on SC, SC-arginine and SC- methionine plates. All tested cells were grown in rich liquid media overnight and adjusted to similar density before spotted on indicated solid plates and incubated at 30°C for two days. arg3Δ and met22Δ cells served as controls for SC-arginine and SC-methionine, respectively.
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3.2.6 tRNA subcellular trafficking affects translation regulation of
amino acid biosyntheses transcripts
We attempted to study the roles of Mtr10 and Msn5, by analyzing transcripts
translationally affected by both of Mtr10 and Msn5. However, it still could be argued that
the reduced levels of proteins involved in the Met, Arg, and Leu biosynthetic pathways
resulted from defects in protein transport, rather than tRNA subcellular dynamics. We
sought to alter tRNA nuclear/cytoplasmic dynamics in an independent way. Dhh1 and
Pat1 are not β-importin members and it has been shown that in dhh1Δ pat1Δ cells
translation is repressed (Coller and Parker, 2005) and tRNAs fail to accumulate in the
nucleus upon amino acid starvation (Hurto and Hopper, 2011). Both of these phenotypes
exist for mtr10Δ cells and thus the tRNA subcellular trafficking is also aberrant in dhh1Δ
pat1Δ cells. Therefore, we disrupted tRNA nuclear-cytoplasmic trafficking and assessed the levels of target proteins in dhh1Δ pat1Δ cells.
As predicted, the levels of endogenous fusion proteins in Met, Arg, and Leu biosynthetic pathways were largely decreased in dhh1Δ pat1Δ cells (Figure 3. 21).
Moreover, it appeared that Dhh1 played a more important role in regulating translation of the Met, Arg, and Leu biosynthetic pathway transcripts because the reduction was also observed in dhh1Δ cells, but not in pat1Δ cells. It is also worthy to notice that BY4741 and its isogenic mtr10Δ cells were utilized as isogenic controls for dhh1Δ pat1Δ cells, and their protein expression patterns were similar as which shown in BY4742 derivative strains. This result further provides the evidence that the reduced levels of enzymes in
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Met, Arg, and Leu biosynthetic pathways can be reproduced in different mating type/background.
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Figure 3. 18. Growth assays of cells with Met22-3HA fusion protein. WT, wild-type cells, as the parent strain for Met22-3HA knock-in, #6 and #12 represent candidate numbers. All of the parental strains and their derivatives were grown in liquid YEPD media overnight at 30°C. Cells were adjusted to similar density and followed by 10X serial dilution before spotting on the indicated solid media and incubated at 30°C or 37°C for two days.
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80
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Figure 3. 19. Western blot analysis of endogenously 3HA- or GFP- tagged target proteins. A. targets with decreased translation 81 activity index were poorly-translated in tRNA trafficking mutants. B. target with no change in translation index was also not changed in protein expression. Pgk1 and Nsp1 served as internal loading controls. Each ratio was obtained from normalizing to internal control and then comparing to WT.
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Figure 3. 20. Western blot analysis of protein levels in tRNA trafficking mutants harboring recovery plasmids. Cells with Met3-3HA fusion proteins were transformed with pRS416 vector or MORF plasmids containing individual functional karyopherin and its promoter (courtesy from Ivy Huang). Cells harboring plasmids were then cultured in SC-uracil liquid media to early log phase. Met3-3HA proteins were determined by immunoblotting with α-HA antibody. Pgk1 served as internal loading control.
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Figure 3. 21. Immunoblot analyses of Met3-, Arg3-, and Leu4-3HA fusion proteins in wild-type cells and dhh1Δ pat1Δ cells. Pgk1 and Kar2 served as internal loading controls. Each ratio was obtained from normalizing to internal control and then comparing to WT.
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Figure 3. 22. Heat map of hierarchical clustering of codon occurrence frequency for DEGs with decreased translation activity index in mtr10Δ cells. 84
Taken together, our investigation of translational expression profiles by
microarray analysis of the tRNA subcellular trafficking mutants showed that the translation of proteins responsible for methionine, arginine, and leucine biosynthesis
pathways were defective, suggesting that tRNA trafficking process plays a role in
regulating cell metabolism by influencing the translation of a subset of mRNAs. This is
the first and novel connection between tRNA subcellular dynamics and cell physiological
process.
3.2.7 Cis-regulatory elements analysis of tRNA trafficking targets
Protein translation is generally regulated at the steps of translation initiation or the rate of
elongation (Day and Tuite, 1998; McCarthy, 1998). Translation initiation is the rate-
limiting step and can be regulated by the 5’ sequences or length of upstream region of the
ORF, or by the secondary structure of the 5’ leader (Hinnebusch, 2005; Lawless et al.,
2009; Tuller et al., 2009). We were unable to identify a common cis-acting motif from upstream and downstream sequences, except for Gcn4-responsive, methionine-, arginine- response elements, among these primary target genes (data not shown, for reviews see
Hinnebusch, 1988; Thomas and Surdin-Kerjan, 1997).
Translation elongation can be affected by the number of rare codons (Letzring et al., 2010). We proposed that the target transcripts (mRNAs encode enzymes in Met, Arg, and Leu biosynthetic pathways) might have rarely used codons or amino acids, which can lead to inefficient translation and cause the reduced level of protein products. We analyzed the codon usage as well as amino acid usage for each target by comparing the
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appearance frequencies of each codon or amino acid in targets open reading frames
versus genome average occurrence. However, we were unable to document rare codon
bias (Figure 3. 22) or particular amino acid usage patterns (not shown). The possible
mechanisms for translation regulation will be further described in Discussion.
3.3 Discussion
In this work we demonstrate that constitutive tRNA nuclear accumulation in cells with
defective tRNA re-export (los1Δ, msn5Δ, and los1Δ msn5Δ) does not cause large global
translation repression. There are several possible explanations. First, it is possible that the
remaining cytoplasmic pool of tRNAs is sufficient to maintain the translation machinery.
Second, and a non-exclusive, possibility is that the additional tRNA export pathway(s) is
sufficient to transport tRNAs back to the cytoplasm, and that these pathways contribute to
the generally normal translation. Finally, although we cannot rule out the impacts from
additional tRNA export pathway, perhaps global translation is not coupled with tRNA
subcellular distribution and, if this is the case, the mechanisms of translation inhibition
and tRNA nuclear accumulation upon nutrient stress must be distinct. Thus, the
translation repression upon nutrient stress is likely not resulted from nuclear
accumulation of tRNA. Although there is no dramatic change, we still found a significant
reduction in non-polysome region in the polysome profiles from msn5Δ and los1Δ msn5Δ
cells. This result might be attributed to the combination of small translation defects of affected mRNAs (e. g. 10 % decrease of each mRNA), which are not selected in the microarray data analysis.
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In contrast, polysomal region is decreased in mtr10Δ cells which are defective in tRNA nuclear import, suggesting a translation defect. This observation might be contributed from several factors. The mtr10Δ strain exhibits growth defect and this is likely due to impaired import of the essential mRNA export factor, Npl3, to the nucleus
(Pemberton et al., 1997; Senger et al., 1998). Furthermore, Npl3 has been shown to associate with polysomes and act as a negative translation regulator, and Mtr10 is involved in the disassociation of Npl3 from polysome-bound mRNAs (Windgassen et al.,
2004). Therefore, the translation defect might result from the cytoplasmic pool of Npl3 that are associated with polysomes. Taken together, the translation defect in mtr10Δ strain could be an ultimate outcome of several factors: growth defect, impaired nuclear import of Npl3, impaired tRNA nuclear import, and also likely the aberrant transport of unidentified cargos.
Upon amino acid deprivation, mtr10Δ cells, which fail to accumulate tRNAs in the nucleus upon nutrient stress, are also defective in translation repression. This result is consistent with the model proposed previously (Hurto and Hopper, 2011). In this model, tRNA nuclear accumulation is a downstream event of the global translation repression in response to nutrient stress. If this is the case, the mutant with failure to repress translation upon nutrient stress should also not able to accumulate tRNA in the nucleus. In agreement with this prediction, in dhh1Δ pat1Δ cells, mutations which have been shown to block general translation repression in amino acid or glucose withdrawal condition
(Coller and Parker, 2005; Holmes et al., 2004), tRNAs also fail to accumulate in the nucleus in amino acid or glucose starvation condition (Hurto and Hopper, 2011).
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A defect of translation repression in response to amino acid starvation is observed in mtr10Δ cells, but microarray analysis of translational profiles from starved mtr10Δ cells does not correlate such change. We reason that the changes in translation of affected mRNAs are slight, so they are not selected by the cutoff conditions with large fold- change while compared to wild-type cells in the microarray analysis, but each slight change can be additive and ultimately affects the polysome profile.
Surprisingly, we discover that the basal level of translation of a group of target mRNAs is reduced in cells defective in tRNA trafficking by mutations in tRNA retrograde import or re-export or independently by deleting Dhh1 and Pat1 proteins. The affected mRNAs encode enzymes involved in amino acid biosynthesis pathways, including sulfur assimilation (methionine), arginine, and leucine. In addition, Ser33 and
Lys5 are also potential targets; however, the endogenous abundance of Ser33 and Lys5 proteins are too low to be assessed by western blot. According to previous results with reduced levels of enzymes in Met, Arg, and Leu biosynthetic pathways, we anticipate the protein levels of Ser33 and Lys5 should also decrease in tRNA trafficking mutants. If so, there are five amino acid biosynthesis pathways affected. Taken together, these results support that tRNA subcellular dynamics serves as a novel mechanism to regulate the translation of mRNAs involved in amino acid biosynthesis pathways. And this role also further correlates with the constitutive tRNA import process (Murthi et al., 2010).
The mechanism(s) for how the affected enzymes are reduced is not clear. It appears that the reduction of these proteins is defective at translation regulation of the target mRNAs based on three observations from this work. First, the transcriptional
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control and the steady state levels of the mRNAs involved in are generally not changed.
Second, the levels of Met, Arg, and Leu biosynthetic enzymes are also decreased in
dhh1Δ pat1Δ cells, in which cells mRNAs are stabilized. Third, the expression profiles of
most target mRNAs showed that their decreased translation activity indexes (P/NP) are
due to increasing in the NP values, suggesting a translation initiation defect of such
mRNAs (Figure 3. 15). Although we do not exclude the possibility for enhanced protein
turnover, it would be important to investigate whether and how the translation of the
target mRNAs is affected.
There is still much to learn about how signaling pathway(s) regulate tRNA
nuclear-cytoplasmic dynamics, which may also provide a possible mechanism for
regulating translation of target mRNAs. So far no evidence indicates that Dhh1 and Pat1
are tRNA transporters, and it was proposed that both Dhh1 and Pat1 are involved in a
signal transduction pathway for tRNA subcellular trafficking (Hurto and Hopper, 2011).
Considering the tRNA import process is constitutive and its impairment causes decreased
basal levels of translation of mRNAs that encode Met, Arg, and Leu biosynthetic
pathways, the signaling pathway must be active in normal condition. Moreover, given the
nuclear accumulation of tRNAs in nutrient starvation stress, the signaling pathway must
be responsive to nutrient availability and transmit the signal through Dhh1, Pat1, and
Mtr10 into the nucleus. In agreement with this hypothesis, the amino acid responsive
Gcn2-dependent signaling pathway is not involved in the tRNA subcellular movement because Gcn2-depleted cells do not prevent the nuclear accumulation of tRNAs (Whitney
et al., 2007). The PKA pathway has been shown to be involved in the tRNA trafficking
89
(Whitney et al., 2007), but given the complexity of this pathway, as well as TOR and glucose derepression pathways, how exactly the signaling pathway(s) plays a role in tRNA nuclear-cytoplasmic trafficking needs further investigation (Whitney et al., 2007).
It is unknown what the consequence(s) of reduced enzyme levels in the Met, Arg, and Leu biogenesis pathways is. One possibility is that reduction of such enzymes leads to decrease amino acid products. However, it is less likely according to several lines of evidence. First, no tRNA charging defects are observed in tRNA trafficking mutants
(Whitney et al., 2007, and personal communication with Dr. Rebecca Hurto), suggesting that aminoacylation status of bulk tRNAs must be not affected in tRNA trafficking mutants. Second, uncharged tRNAs can activate the kinase Gcn2 and subsequently enhance translation of GCN4, the general regulator of amino acid biosynthesis, which activates the transcription of Gcn4-responsive genes. Evidence from transcription expression profiles of fed mtr10Δ and msn5Δ cells showed that Gcn4-responsive genes are not induced (data not shown). This result correlates to previous observation that aminoacylation status of tRNAs are not affected when tRNA nuclear-cytoplasmic trafficking is defective (Whitney et al., 2007). We still cannot completely rule out this possibility that the productions of amino acids, including Met, Arg, Leu, or more, production are reduced in the tRNA trafficking mutants and this reduction is complemented by amino acids from media.
It is also likely that tRNA nuclear-cytoplasmic trafficking functions in maintaining the basal levels of these target enzymes to regulate intermediates of the Met,
Arg, and Leu biosynthetic pathways, not to regulate the amino acid production per se. If
90 it is true, we can predict the intracellular levels of specific substrates of these target enzymes may be accumulated and/or enzyme products decreased in the Met, Arg, and
Leu biosynthetic pathways in tRNA trafficking mutants.
Since several amino acid biogenesis-related enzymes can be feedback regulated by their products (Cherest et al., 1973, for reviews, see Hinnebusch, 1988; Thomas and
Surdin-Kerjan, 1997), it seems also possible that tRNA trafficking defects lead to changes in metabolic flux and that subsequently causes accumulation of amino acids. The excess amino acid products further inhibit the rate-limiting enzyme activities without affecting aminoacylation levels of tRNAs. If it is true, it will also be interesting to learn why sulfur assimilation and arginine biosynthesis pathways are repressed by regulating multiple proteins of the pathways, whereas the affected Leu, Ser, and Lys biosynthetic pathway proteins function at first step of each pathway (leucine biosynthesis pathway see
Figure 3. 14, serine and lysine pathways are not shown). Therefore, extensive analysis of amino acid intermediates and metabolome in tRNA trafficking mutants may shed light on understanding the physiological functions of tRNA subcellular dynamics.
How these specific mRNA targets are commonly regulated at translation level by tRNA nuclear-cytoplasmic trafficking? It is not clear. We are unable to identify common cis-regulatory elements among the ORFs within these down-regulated translating mRNAs by analyzing codon bias or amino acid usage bias. Moreover, other than Gcn4- responsive elements, methionine-responsive element, and arginine-responsive element, we are not able to find other common regulatory motifs among the 5’ UTR and 3’ UTR regions from all of the target mRNAs. These results led us to propose that perhaps these
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targets are regulated by secondary structure close to AUG or by uORF, which serves as
negative translation regulator. Importantly, a previous study characterizing MET2
transcript provides a hint in which MET2 is implicated to be regulated at post-
transcriptional level and there are two possible stem-loop-stem structures from upstream
region to open reading frame (-9 to +61 and -80 to -44, Baroni et al., 1986; Forlani et al.,
1991). Whether and how MET2 transcript is regulated via the secondary structure is still unknown. A recent publication has been shown that proteins synthesis can be initiated by non-canonical uORF in mammalian cells (Starck et al., 2012). Thus, investigation of secondary structure near start codon as well as identification of possible uORF (canonical and non-canonical) among the upstream sequences of these targets might provide possible regulatory mechanisms.
Although we discovered a novel function for tRNA subcellular trafficking in cellular metabolism, its other potential functions are not excluded. For example, tRNA nuclear retrograde import might function in transporting damaged or hypomodified tRNAs back to the nucleus for quality control/proofreading. It has been shown that two tRNA surveillance systems exist in yeast cells, the TRAMP pathway, which locates in the nucleus, and the RTD pathway, which locates in the nucleus and the cytoplasm.
(Alexandrov et al., 2006; Chernyakov et al., 2008; Kadaba et al., 2006). Since tRNA nuclear import is constitutive, transporting damaged cytoplasmic tRNAs for repair or turnover further supports this observation. Moreover, in trm8Δ trm4Δ cells tRNAVal(AAC) is hypomodified and degraded by the RTD pathway, which was proposed to cause the
temperature sensitive growth at 37°C (Chernyakov et al., 2008). When we introduced
92 los1Δ into trm8Δ trm4Δ cells, we found this triple mutant is even more sensitive when grown at 30°C (Appendix A, Figure A. 1). We reason that it is likely because tRNA substrates are retained in the nucleus in cells lacking Los1, and this subsequently leads to increasing tRNA substrates degraded by endonucleases.
A recent study showed that the tRNA nuclear-cytoplasmic trafficking is required for wybutosine (yW) base modification of tRNAPhe at position 37 (Ohira and Suzuki,
Phe 2011). G37 of tRNA must be imported to the nucleus and modified by nuclear Trm5,
1 Phe and the product m G37 of tRNA needs to return cytoplasm for next modification to form yW. The yW formation is catalyzed by the four cytoplasmic enzymes, Tyw1-4, and is important to prevent translation frameshift (Kalhor et al., 2005; Noma et al., 2006).
Therefore, interrupting tRNA nuclear import or re-export would generate the hypomodified tRNAPhe , which subsequently increase translation frameshift, and that might be the reason for reduced levels of proteins involved in Met, Arg, and Leu biosynthetic pathways in cells defective in tRNA nuclear import or export. And if this is the case, mislocation of the nuclear Trm5 to the cytoplasm may prevent the reduction.
Furthermore, in tyw1Δ cells the levels of proteins responsible for Met, Arg, and Leu biosynthetic pathways should also decrease. However, our result revealed that mislocation of the nuclear Trm5 to the cytoplasm in mtr10Δ cells does not rescue the reduced protein levels (Appendix A, Figure A. 3). Also, Arg3-3HA (Appendix A, Figure
A. 3) or Met3-3HA (data not shown) protein levels are unchanged in tyw1Δ cells.
Therefore, hypomodified tRNAPhe is not the reason for down-regulation of translating amino acid biosynthetic mRNAs. However, there are other modifications whose
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pathways are not yet discovered. This might also follow the tRNA nuclear-cytoplasmic
trafficking and contribute to the regulation on the translation of the target mRNAs.
It is also possible that the tRNA nuclear import functions for nuclear translation.
Whether nuclear translation does occur in the eukaryotic cells has been argued for a long time. Recently it has been reported that nuclear translation occurs in the mammalian cells
(David et al., 2012; Reid and Nicchitta, 2012). Others argued that their data do not support the nuclear translation (Dahlberg and Lund, 2012). Even if nuclear translation occurs in the mammalian cells, it may or may not occur in yeast. Thus, tRNA import may still leave a novel function in the nucleus.
We seek to provide addition information about the cellular function of tRNA nucleocytoplasmic trafficking. For example, we would like to extend the analysis of cis- regulatory elements by identifying possible uORFs or secondary structures in the upstream region of target mRNAs. Moreover, the mRNAs encode Met, Arg, and Leu biosynthetic pathways should be associated with the non-polysomal region in the
Northern blot analysis of samples collected from tRNA trafficking mutants. Furthermore, since there is no large change in microarray analysis of msn5Δ cells and Los1 also functions in tRNA re-export, therefore, we expect a larger change in los1Δ msn5Δ cells by microarray analysis.
There are more questions remaining unanswered. How do cells sense tRNA intracellular distribution? Are nuclear accumulated tRNAs functional? If so, what is the physiological role(s) of previously-cytoplasmic nuclear tRNA during nutrient starvation?
The roles of tRNA nuclear-cytoplasmic trafficking in quality control and the newly
94 discovered regulation/maintenance of amino acid biosynthesis are still not able to explain why and how tRNAs are retained in the nucleus upon nutrient stress? More studies on how exporters are regulated during nutrient stress and which signaling pathway(s) is involved will be important to answer these questions.
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CHAPTER 4
Transcriptional analysis of mtr10Δ cells
4.1 Introduction
Mtr10 was first discovered in a genome-wide screen for polyadenylated mRNAs accumulation in the nucleus at 37°C. Thus, Mtr10 was suggested to function in mRNA nuclear export (Kadowaki et al., 1994). Later, it was reported to be a member of the karyopherin family that functions in nuclear import of an essential mRNA binding protein, Npl3. Npl3 functions in nuclear export of mRNAs and large ribosomal subunits
(Hackmann et al., 2011; Lee et al., 1996; Stage-Zimmermann et al., 2000; Windgassen et al., 2004). Npl3 immunoprecipitates with Mtr10 and the complex only disassociates in the presence of Ran-GTP. Npl3 shuttles between the nucleus and the cytoplasm, and thus in the absence of Mtr10 as expected for a β-importin, Npl3 mislocalizes to the cytoplasm
(Pemberton et al., 1997; Senger et al., 1998).
Mtr10 is also required for the nuclear import of the RNA component of the yeast telomerase, TLC1 (Ferrezuelo et al., 2002; Gallardo et al., 2008). The nucleo-cytoplasmic trafficking of TLC1 RNA is required for telomerase biogenesis (Gallardo et al., 2008;
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Teixeira et al., 2002). In wild-type cells TLC1 RNA is predominantly in the nucleus and
it is mislocalized to the entire cell in mtr10Δ cells (Ferrezuelo et al., 2002).
To date, the understanding of the physiological roles of Mtr10 is limited. It was
proposed that there must be more unexplored cargos which depend on Mtr10 (Senger et
al., 1998). In this study, we employed a genome-wide approach to determine the direct and indirect targets of Mtr10 by comparing the gene expression level of each transcript in mtr10Δ to wild-type cells in fed or amino acid starvation condition. We demonstrated that
in addition to its previously known role to import Npl3, Mtr10 also likely functions in
sporulation, DNA damage response, glutathione homeostasis, metal ion homeostasis, and
lipid metabolism, directly or indirectly.
4.2 Results
This section includes data analysis of transcription profiles from total mRNAs of msn5Δ
(section 4.2.1) and mtr10Δ (section 4.2.2 and thereafter) cells compared to wild-type cells
in fed or amino acid starvation conditions. Because the transcription profiles of msn5Δ
cells were as expected from the large numbers of studies of this karyopherin (Bakhrat et
al., 2008; Blondel et al., 1999; Bollman et al., 2003; DeVit and Johnston, 1999; Kaffman
et al., 1998; Shimada et al., 2000; Willis and Moir, 2007), the results described in this
section predominately focus on the transcriptional analyses of mtr10Δ cells. The data
include the microarray analyses of the transcriptional profiles, the validations of selected
transcripts, and functional analyses.
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4.2.1 Microarray analysis of transcription profiles in msn5∆ cells
Since Msn5 is known to export several nuclear phosphorylated transcription factors, such
as Far1 and Pho4, to the cytoplasm in fed conditions (Blondel et al., 1999; Kaffman et al.,
1998), we expected that msn5Δ cells would accumulate these cargos in the nucleus and
therefore their target genes would have induced transcription. After employing statistical
analysis of the transcription profiles from msn5Δ cells to the profiles from wild-type cells,
we used the cutoff conditions with adjusted p-value 0.05 and log2 FC 0.6 or −0.6 to select differentially expressed genes. In msn5Δ cells, 177 genes were up-regulated and
119 genes were down-regulated in the fed condition; and 193 genes were up-regulated and 258 genes were down-regulated in acute amino acid starved condition (see
Table 4. 1). All of the genes with statistically significant changes in expression are listed in Appendix L to Appendix O (msn5Δ vs. wild-type in fed or amino acid starved conditions). Most of the affected mRNAs are consistent with previous studies (Blondel et al., 1999; Springer et al., 2003). For example, the transcription of several Pho4- responsive genes, such as PHO84, PHO89, PHO11, and PHO5, were activated in msn5Δ
cells (Appendix M). Hence, in the following sections we focus on the transcriptional
profiles from mtr10Δ cells.
4.2.2 Microarray analysis of transcription profiles in mtr10∆ cells
Current understanding of the cellular roles of the importin Mtr10 is largely unclear. Since the results from analysis of the transcriptional profiles of msn5Δ cells confirmed its known roles, we proposed that analysis of transcriptional profiles of mtr10Δ cells could 98
provide further insights into the cellular functions of Mtr10. In general, the transcription
profiles from mtr10Δ cells exhibited a relatively larger change as roughly assessed by
PCA analysis (Figure 4. 1). We next performed statistical analysis of the microarray
transcriptional profiles from total RNA samples of mtr10Δ cells compared to total RNA samples isolated from wild-type cells that were propagated in fed or acute amino acid starvation condition. Because ~10% of genome was affected in mtr10Δ cells (~900 genes affected in fed and ~1400 genes affected in starved condition) when using adjusted p-
value 0.05, we used a more stringent condition to select significant differentially expressed genes to narrow down the range of genes of interests (Table 4. 1). Total numbers of 533 genes from fed mtr10Δ cells and 1165 genes from starved mtr10Δ cells were selected using the cutoff condition as adjusted p-value 0.001 and log2 FC 0.6 or
−0.6 (Figure 4. 2 and Table 4. 1). More details are described below. All of the genes with
statistically significant changed in expression are listed in Appendix G to Appendix J
(mtr10Δ vs. wild-type).
Genes with expression changed in mtr10Δ cells under fed condition
There are 335 genes that were up-regulated and 198 genes that were down-regulated in
cells lacking Mtr10 (Table 4. 1). To understand the functions of the differential expressed
genes, we employed GO term-enrichment analysis and FunCat analysis (complete result
of FunCat analysis is in Appendix F). Among the 335 up-regulated genes, the top one
over-represented function of each analysis was sexual reproduction (from GO term
enrichment analysis, n=28) and zygospore development (from FunCat, n=21),
99 respectively (Table 4. 2). The results suggested that Mtr10, directly or indirectly, is involved in the regulation of zygote or spore formation. Among 198 down-regulated genes, 11 encoded proteins that function in metal ion homeostasis (top one from GO term enrichment analysis) and 22 encoded proteins that function in lipid metabolism (top one from FunCat analysis) (Table 4. 2). Taken together, the transcriptional profiling of fed mtr10Δ cells revealed several potential functions of Mtr10.
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Figure 4. 1. PCA analysis of transcription profiles of total mRNAs from wild-type, msn5Δ, and mtr10Δ cells in fed or acute amino acid starvation condition. PCA analysis provided a general view of microarray data before statistical analysis. One ball represents one microarray experiment.
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Volcano plot of limma -- Tf_mtr10 vs wt -log10 p value -log10 02468
-6 -4 -2 0 2 4 Diff (logFC)
Volcano plot of limma -- Ts_mtr10 vs wt -log10 p -log10 value 02468
-6 -4 -2 0 2 Diff (logFC)
Figure 4. 2. Volcano plots showing differential expressed genes at transcriptional level in mtr10Δ cells versus wild-type cells. Upper, in fed condition; bottom, in acute amino acid starved condition. Red dots represent up-regulated genes; blue dots represent down-
regulated genes. Cutoff values of adjusted p-value < 0.001 with log2 FC 0.6 or − 0.6 are shown in dashed lines in the plots. Y axis represents log10 of original p-value.
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msn5Δ vs. WT mtr10Δ vs. WT
Total.Fed a Total.Starved a Total.Fed b Total.Starved b Up 177 193 335 605 Down 119 258 198 560
Table 4. 1. Numbers of significant differentially transcriptionally expressed genes from total mRNA samples of msn5Δ or mtr10Δ cells in fed or amino acid starvation conditions. a: adjusted p-value < 0.05 with log2 FC 0.6 or − 0.6; b: adjusted p-value < 0.001
with log2 FC 0.6 or − 0.6.
Genes changed in mtr10-deletion cells under starved condition
By identifying genes with expression changes in amino acid starved mtr10Δ cells
compared to amino acid starved wild-type cells, we may be able to discover “Mtr10-
dependent amino acid-deprived responsive” genes. If so, the data could provide
information about the possible role of Mtr10 in response to environmental stress. In the
transcriptional profiles from starved mtr10Δ cells, there were 605 genes that were up-
regulated and 560 genes that were down-regulated (Table 4. 1). Some of the affected
genes were attributed to loss of Mtr10 per se, and thus appeared in both profiles from fed
and starved conditions (the common affected genes in fed and starved conditions in
Figure 4. 3). We identified numerous genes with affected expression only in starved condition. The functions of up-regulated genes included cell cycle, DNA repair, nuclear mRNA splicing, and protein folding (Table 4. 3). Interestingly, among starvation-only
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down-regulated genes via identified a large numbers of genes encoded transporters. This
result suggested that Mtr10 is likely involved in regulation of transporters in response to
amino acid starvation.
4.2.3 Confirmations of selected target mRNA expressions by Northern
blot analysis
Genome-wide studies usually provide valuable information, but often produce errors or
false-positive results. Hence, it is necessary to validate every gene of interest selected
from microarray expression profiles by different methods, such as Northern blot analysis
or real-time RT-PCR. To confirm the microarray data analysis of the transcriptional
profile from mtr10Δ cells grown in fed condition, I performed the Northern analyses. The
total mRNAs were collected from wild-type, mtr10Δ, and msn5Δ cells grown in SC media. In this experiment, msn5Δ cells served as an internal negative control. At least one target gene from each functional category was chose to be validated.
Most of the targets from up-regulated expression profiles in mtr10Δ cells were confirmed: MTH1 (encodes a protein involved in negative regulator of glucose signaling transduction pathway), CWC15 (encodes a protein involved in mRNA splicing), RAD9
(encodes DNA-damage checkpoint protein), and LCD1 (encodes a protein essential in
DNA integrity pathway) (Figure 4. 4) were all up-regulated. The expression profiles of
SNF3 (encodes membrane glucose sensor) and DAL80 (encodes negative regulator of nitrogen degradation pathway) were tested but no signals were detected, likely due to their low expressions (data not shown). SPS2, SPR3, and SPR28, encode proteins
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expressed during sporulation, were also tested. The expression level of SPS2 gene was
not significantly increased in mtr10Δ cells (Figure 4. 4). In addition, the signals of SPR3
and SPR28 were too low to be detected by Northern analysis (data not shown).
We also examined the expression levels of two candidate genes, GTO3 and TPO4,
which were down-regulated by microarray analyses. GTO3 encodes a glutathione
transferase and TPO4 encodes a polyamine transporter. The results of Northern analysis showed that the expression level of GTO3 was dramatically reduced in mtr10Δ cells
relative to wild-type or msn5Δ cells and thus confirmed the microarray data (Figure 4. 4).
In contrast, the expression level of TPO4 was unchanged in mtr10Δ cells when compared
to wild-type cells (data not shown).
In conclusion, by employing Northern blot analysis, the expression profiles of
most tested targets from microarray analyses in fed mtr10Δ cells were confirmed. Mtr10
not only imports Npl3 but also is possibly involved in regulation of glucose signaling
pathway, DNA damage checkpoint pathways, and glutathione homeostasis. Although
further investigations are required, the microarray analysis of transcription profiles from mtr10Δ cells provides valuable information and hints of the physiological roles of Mtr10.
4.2.4 Phenotypic analyses by growth assays
Since it appeared that the DNA damage-related genes were up-regulated in mtr10Δ cells
(Figure 4. 4), we proposed that the DNA damage pathway was activated in mtr10Δ cells.
If so, mtr10Δ cells might be more resistant to DNA damage than wild-type cells. To test
this hypothesis, we performed growth assay of wild-type, mtr10Δ, msn5Δ, los1Δ, and
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los1Δ msn5Δ cells, on solid media containing the mutagen 5-fluorouracil (5-FU) which
induces DNA base excision repair pathway (Seiple et al., 2006), or methyl
methanesulfonate (MMS) which causes DNA double strand breaks (Lundin et al., 2005)
(Figure 4. 5). Since it was previously shown that los1Δ cells are sensitive to 5-FU
(Gustavsson and Ronne, 2008), we utilized los1Δ strain as a positive control. We also
examined other tRNA nucleocytoplasmic trafficking mutants, such as msn5Δ and los1Δ
msn5Δ, to test their growth in the presence of these mutagens.
Contrary to the expectation, the mtr10Δ strain was not resistant to either DNA
damage agent. Conversely, the mtr10Δ cells were more sensitive (Figure 4. 5). These results indicated that both DNA repair pathways were defective in the absence of Mtr10
even through RAD9 and LCD1 mRNA levels were induced (Figure 4. 4). As expected, los1Δ cells were not viable in the presence of 5-FU, msn5Δ cells grew similarly as wild- type cells. These results suggest that Los1 and Msn5 do not function in parallel in response to DNA damage. Since 5-FU activates DNA base-excision pathway (Seiple et al., 2006) and los1Δ cells are sensitive to 5-FU (Gustavsson and Ronne, 2008), we hypothesized that Los1 functions in the DNA damage response. If this is the case, los1Δ cells would be also sensitive when DNA damage response alternatively induced by MMS.
However, wild-type, msn5Δ, los1Δ, and los1Δ msn5Δ strains had similar growth on the
MMS-containing solid media. The data suggest that these tRNA exportin pathways function independently for the DNA double-strand damage response.
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Table 4. 2. GO term enrichment analysis and FunCat analysis of genes with significant changed in mtr10Δ cells in fed condition. Significant up-regulated or down-regulated expressed genes were analyzed and grouped with their GO terms or functional categories by using web-interface tools in GO or FunCat, respectively. Bold words represent the over-represented GO term or functional categories. Numbers of genes in each term or category are noted in parentheses. The p-value was obtained by comparing the occurrence frequency to background frequency.
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Table 4. 3. GO term enrichment analysis and FunCat analysis of genes with significant changed in mtr10Δ cells in acute amino acid starved condition. Significant up-regulated or down-regulated expressed genes were analyzed and grouped with their GO terms or functional categories by using web-interface tools in GO or FunCat, respectively. Bold words represent the over- represented GO term or functional categories. Numbers of genes in each term or category are noted in parentheses. The p-value was obtained by comparing the occurrence frequency to background frequency.
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Figure 4. 3. Venn diagram of differentially expressed genes from mtr10Δ cells relative to wild-type cells in fed and amino acid starved conditions.
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Figure 4. 4. Northern blot analysis of total mRNAs collected from wild-type, mtr10Δ, and msn5Δ cells grown in fed condition. ACT1 served as internal loading control. 25S and 18S rRNAs were also serving as internal loading controls, the bottom pictures were ethidium bromide-stained agarose gels. Each ratio was obtained from normalizing to ACT1 expression value and then compared to WT.
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Figure 4. 5. Growth assay to assess the function of Mtr10 in DNA damage response. All of the strains were grown in liquid YEPD media overnight at 30°C and were adjusted to similar density followed with 10X serial dilution before spotting on the indicated solid media. 5-FU, 5-fluorouracil, a pyrimidine analog. MMS, an agent causes DNA double strand breaks. The concentrations of 5-FU and MMS were 15 µg/ml and 0.02 %, respectively, in SC media. The cells were incubated at indicated temperatures for 2 days.
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4.3 Discussion
Here we report that in mtr10Δ cells a large number of genes (about ~10% of the genome
when we used the stringent conditions with adjusted p-value < 0.001 and log2 FC 0.6 or − 0.6) have affected expression from microarray analysis. Among the up-regulated genes, those encoding zygote development and sporulation are over-represented. In
Northern analysis the expression level of one of these genes, SPS2, is not significantly increased. It is likely because its expression is too low to be detected. Therefore, the potential function of Mtr10, and/or its unknown cargo(s), in the regulation of sporulation may not be exclusive.
Other than sporulation-related genes, we also confirmed the enhanced expression of DNA damage check point-related genes, RAD9 and LCD1, in fed mtr10Δ cells.
However, the results of growth assay indicated that the mtr10Δ cells are highly sensitive to 5-FU and MMS and do not grow in the presence of these agents. It is unknown whether up-regulated expression of RAD9 and LCD1 are direct or indirect due to loss of
Mtr10, but this misregulation may contribute to the defect of DNA damage response in mtr10Δ cells. Since the studies about Rad9 and Lcd1 mainly focus on the regulation of their phosphorylation states during DNA damage signaling pathway (Andrew, 1998;
Rouse and Jackson, 2000), our findings provide a potentially different level of regulation.
More interestingly, previous work showed that intron-containing tRNAs are retained in the nucleus after UV-induced DNA damage due to the translocation of Los1 from the nucleus to cytoplasm (Ghavidel et al., 2007). Although the regulation of this translocation and its biological significance are unclear, this observation leads to the idea that Mtr10 112
might play a role in the DNA damage signaling transduction pathway. If so, it might be directly regulated by the translocation of Mtr10 upon DNA damage (like Los1) or
through Mtr10’s function in shuttling the unknown cargo(s) that function in the DNA
damage signaling pathways.
Northern analysis also confirmed that induction of expression levels of MTH1,
which encodes a negative regulator in the glucose signaling pathway (for review, see
Santangelo, 2006). When cells are grown in glucose-containing media, MTH1 expression
is repressed by the Snf1-Mig1 glucose repression pathway (Kim et al., 2006). Strikingly,
MTH1 not only de-represses in mtr10Δ cells but also in msn5Δ cells. This observation
indicates that the Snf1-Mig1 glucose repression pathway is not fully functional in cells
with defective in tRNA nuclear-cytoplasmic traffic. It is unknown whether the
accumulating MTH1 transcripts leads to increased Mth1 protein and more phosphorylated
Mth1. Because it has been shown that the regulation of tRNA subcellular distribution is
glucose-dependent/specific (Whitney et al., 2007), our data provides supporting evidence
that there might be a connection between the regulation of glucose signaling transduction
pathway and tRNA subcellular movement.
For down-regulated genes, we confirmed GTO3 reduced transcript levels. GTO3
encodes a glutathione transferase. This result provides another potential function for
Mtr10 in glutathione homeostasis. In addition, Mtr10 may participate in two other
cellular processes: metal ion homeostasis and lipid metabolism, according to the
microarray analysis. It is particularly interesting to examine the expression levels of
genes related to the lipid metabolism in mtr10Δ cells, because lipid homeostasis is
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important for maintaining cell membrane and cell shape, and previous observations shows that mtr10Δ cells have abnormal long and shield-like shape (Murthi et al., 2010;
Whitney et al., 2007). Therefore, although further investigations are needed, the transcriptional profiles provide explanations for the abnormal shape of mtr10Δ mutant cells.
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CHAPTER 5
Summary and future directions
In this study the data provide no evidence for the possibility that tRNA nuclear retrograde process functions as a stress signal upon nutrient deprivation from the results of polysome profiles and microarray data of starved tRNA trafficking mutants. But we discovered a novel connection between tRNA nuclear-cytoplasmic trafficking and translation of the Met, Arg, and Leu amino acid biosynthetic mRNAs. In cells with defects of tRNA trafficking, several enzymes involved in the Met, Arg, and Leu amino acid biosynthesis pathways are poorly produced, suggesting a role of tRNA nuclear- cytoplasmic trafficking in maintaining the basal levels of translation of mRNAs encoding
Met, Arg, and Leu biosynthetic pathways.
Future direction
It is unknown what are the consequence(s) caused by the reduced levels of the
Met, Arg, and Leu biosynthetic enzymes. A high-throughput analysis of amino acid intermediates in Met, Arg, and Leu biosynthesis pathways by LC/MS might provide more insights in the ultimate outcomes in amino acid metabolism when tRNA nuclear- cytoplasmic traffic is defective. Additionally, it is also interesting to study by which 115 mechanism that tRNA trafficking specifically regulates the translations of mRNAs involved in Met, Arg, and Leu biosynthetic pathways.
On the other hand, more work is required to confirm the microarray data for the mtr10Δ mutant. In addition, further investigations on the consequences of the affected genes will contribute a greater understanding of the biological functions of Mtr10. In particular, what is the role of Mtr10 in DNA damage response pathways? Whether or not the glucose signaling pathways are misregulated in the mutants with defects of tRNA nuclear-cytoplasmic trafficking? If so, how these two pathways are coupled?
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APPENDIX A: Tested possibilities for mechanism of down-regulated translation of the target mRNAs involved in the Met, Arg, and Leu biosynthetic pathways
This section includes two results. First, in Figure A. 1 and Figure A. 2, I employed the growth assay to determine the temperature sensitive growth of tRNA modification mutants and its derivative strain with tRNA trafficking mutants. Second, we tested the hypothesis that mislocate the nuclear Trm5 to the cytoplasm might rescue the reduced levels of Arg3-3HA in cells with defective of tRNA import process (mtr10Δ) (Figure A.
3). In addition, we assessed Arg3-3HA protein levels in tRNA yW modification mutant
(tyw1Δ) (Figure A. 3). The result showed that Arg3-3HA protein level was not rescued when mislocating Trm5 into the cytoplasm in mtr10Δ cells and Arg3-3HA protein was unchanged in tyw1Δ cells. Moreover, nuclear retention tRNAs by overexpressing Trz1-
MORF does not lead to reduced levels of Arg5,6-GFP. All together, several lines of evidence indicated that the reduced levels of target amino acid biosynthesis pathways do not result from tRNAPhe modification defect.
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Figure A. 1. Growth assay of mutants defective in tRNA export and modifications. trm8Δ trm4Δ mutants combined with defective tRNA export processes (los1Δ, msn5Δ, los1Δ msn5Δ). trm8Δ trm4Δ-derived defective tRNA export mutants. All mutants were grown in liquid media overnight and density-adjusted cells were serial-diluted on SC solid media and incubated two or more days at indicated temperature. #1, #2, and #3 represented three different candidates.
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Figure A. 2. Growth assay of mutants defective in tRNA import and modifications. All mutants were grown in liquid media overnight and density-adjusted cells were serial-diluted on SC solid media and incubated two or more days at indicated temperature. #1, #2, and #3 represented three different candidates. A. trm8Δ trm4Δ mutants combined with defective tRNA export processes (los1Δ, msn5Δ, los1Δ msn5Δ). B. trm8Δ trm4Δ mutants combined with defective of tRNA import process mtr10Δ.
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Figure A. 3. Western blot analysis of Arg3-3HA in tRNA modification mutants. A. Endogenously GFP or NES-GFP tagged Trm5 was created in Wild-type or mtr10Δ cells that harboring Arg3-3HA fusion protein. The whole cell extracts were collected from early log phase of cell culture in SC media. Kar2 is internal loading control. B. Assessment of Arg3-3HA protein levels in tyw1Δ cells. Wild-type or mtr10Δ cells served as positive and negative controls. C. Assessment of Arg5,6-GFP in cells expressing vector or Trz1-MORF plasmid before and after galactose 3-hr induction.
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APPENDIX B: Transcripts with decreased translation activity index in fed mtr10Δ cells
The transcripts with decreased P/NP ratio (cutoff condition: log2 FC 0.6 and adjusted p-value 0.05) in mtr10Δ cells while compared to wild-type cells in fed condition are listed below and ordered according to log2 (fold-change).
Transcripts with decreased P/NP ratio in fed mtr10Δ cells.
log2 (mtr10[P Symbols ORF Description f/NP.f]/ adj.p.val WT[Pf/N P.f]) SUL1 YBR294W High affinity sulfate permease; sulfate uptake is mediated by specific ‐2.8202 0.0026 sulfate transporters Sul1p and Sul2p, which control the concentration of endogenous activated sulfate intermediates STR3 YGL184C Peroxisomal cystathionine beta‐lyase, converts cystathionine into ‐2.4652 0.0024 homocysteine; may be redox regulated by Gto1p BDH2 YAL061W Putative medium‐chain alcohol dehydrogenase with similarity to ‐2.2714 0.0037 BDH1; transcription induced by constitutively active PDR1 and PDR3 PHM6 YDR281C Protein of unknown function, expression is regulated by phosphate ‐2.0514 0.0171 levels MET2 YNL277W L‐homoserine‐O‐acetyltransferase, catalyzes the conversion of ‐1.8123 0.0041 homoserine to O‐acetyl homoserine which is the first step of the methionine biosynthetic pathway MET28 YIR017C Basic leucine zipper (bZIP) transcriptional activator in the Cbf1p‐ ‐1.7081 0.0050 Met4p‐Met28p complex, participates in the regulation of sulfur metabolism NA YGR226C Dubious open reading frame, unlikely to encode a protein; not ‐1.6090 0.0408 conserved in closely related Saccharomyces species; overlaps significantly with a verified ORF, AMA1/YGR225W AAD14 YNL331C Putative aryl‐alcohol dehydrogenase with similarity to P. ‐1.5827 0.0127 chrysosporium aryl‐alcohol dehydrogenase; mutational analysis has not yet revealed a physiological role TPK1 YJL164C cAMP‐dependent protein kinase catalytic subunit; promotes ‐1.5650 0.0060 vegetative growth in response to nutrients via the Ras‐cAMP signaling pathway; inhibited by regulatory subunit Bcy1p in the absence of cAMP; partially redundant with Tpk2p and Tpk3p MET3 YJR010W ATP sulfurylase, catalyzes the primary step of intracellular sulfate ‐1.4839 0.0060 activation, essential for assimilatory reduction of sulfate to sulfide, involved in methionine metabolism 121
log2 (mtr10[P Symbols ORF Description f/NP.f]/ adj.p.val WT[Pf/N P.f]) MMP1 YLL061W High‐affinity S‐methylmethionine permease, required for utilization ‐1.4724 0.0050 of S‐methylmethionine as a sulfur source; has similarity to S‐ adenosylmethionine permease Sam3p NA YHR140W Putative integral membrane protein of unknown function ‐1.4717 0.0288 FRE5 YOR384W Putative ferric reductase with similarity to Fre2p; expression ‐1.4654 0.0205 induced by low iron levels; the authentic, non‐tagged protein is detected in highly purified mitochondria in high‐throughput studies NA YBR184W Putative protein of unknown function; YBR184W is not an essential ‐1.4519 0.0060 gene MET32 YDR253C Zinc‐finger DNA‐binding protein, involved in transcriptional ‐1.4489 0.0096 regulation of the methionine biosynthetic genes, similar to Met31p NA YFR032C‐B Putative protein of unknown function; identified by gene‐trapping, ‐1.4458 0.0171 microarray‐based expression analysis, and genome‐wide homology searching QDR2 YIL121W Multidrug transporter of the major facilitator superfamily, required ‐1.4424 0.0301 for resistance to quinidine, barban, cisplatin, and bleomycin; may have a role in potassium uptake COX17 YLL009C Copper metallochaperone that transfers copper to Sco1p and ‐1.3702 0.0459 Cox11p for eventual delivery to cytochrome c oxidase; contains twin cysteine‐x9‐cysteine motifs MET14 YKL001C Adenylylsulfate kinase, required for sulfate assimilation and involved ‐1.3027 0.0171 in methionine metabolism ARG3 YJL088W Ornithine carbamoyltransferase (carbamoylphosphate:L‐ornithine ‐1.2977 0.0060 carbamoyltransferase), catalyzes the sixth step in the biosynthesis of the arginine precursor ornithine SAP4 YGL229C Protein required for function of the Sit4p protein phosphatase, ‐1.2958 0.0216 member of a family of similar proteins that form complexes with Sit4p, including Sap155p, Sap185p, and Sap190p MHT1 YLL062C S‐methylmethionine‐homocysteine methyltransferase, functions ‐1.2937 0.0108 along with Sam4p in the conversion of S‐adenosylmethionine (AdoMet) to methionine to control the methionine/AdoMet ratio MET16 YPR167C 3'‐phosphoadenylsulfate reductase, reduces 3'‐phosphoadenylyl ‐1.2880 0.0112 sulfate to adenosine‐3',5'‐bisphosphate and free sulfite using reduced thioredoxin as cosubstrate, involved in sulfate assimilation and methionine metabolism NA YDR366C Putative protein of unknown function ‐1.2866 0.0204 AMS1 YGL156W Vacuolar alpha mannosidase, involved in free oligosaccharide (fOS) ‐1.2697 0.0112 degradation; delivered to the vacuole in a novel pathway separate from the secretory pathway NA YKL069W Methionine‐R‐sulfoxide reductase, reduces the R enantiomer of free ‐1.2514 0.0366 Met‐SO, in contrast to Ycl033Cp which reduces Met‐R‐SO in a peptide linkage; has a role in protection against oxidative stress SEO1 YAL067C Putative permease, member of the allantoate transporter subfamily ‐1.2394 0.0293 of the major facilitator superfamily; mutation confers resistance to ethionine sulfoxide EMI2 YDR516C Non‐essential protein of unknown function required for ‐1.2189 0.0416 transcriptional induction of the early meiotic‐specific transcription factor IME1; required for sporulation; expression is regulated by glucose‐repression transcription factors Mig1/2p NA YBL008W‐A Putative protein of unknown function; identified by fungal homology ‐1.2071 0.0459 and RT‐PCR NA YLR361C‐A Putative protein of unknown function ‐1.1860 0.0429 SUL2 YLR092W High affinity sulfate permease; sulfate uptake is mediated by specific ‐1.1638 0.0252 sulfate transporters Sul1p and Sul2p, which control the concentration of endogenous activated sulfate intermediates 122
log2 (mtr10[P Symbols ORF Description f/NP.f]/ adj.p.val WT[Pf/N P.f]) SPC3 YLR066W Subunit of signal peptidase complex (Spc1p, Spc2p, Spc3p, Sec11p), ‐1.1282 0.0431 which catalyzes cleavage of N‐terminal signal sequences of proteins targeted to the secretory pathway; homologous to mammalian SPC22/23 MET1 YKR069W S‐adenosyl‐L‐methionine uroporphyrinogen III transmethylase, ‐1.1043 0.0205 involved in the biosynthesis of siroheme, a prosthetic group used by sulfite reductase; required for sulfate assimilation and methionine biosynthesis ARG7 YMR062C Mitochondrial ornithine acetyltransferase, catalyzes the fifth step in ‐1.1018 0.0205 arginine biosynthesis; also possesses acetylglutamate synthase activity, regenerates acetylglutamate while forming ornithine HXT9 YJL219W Putative hexose transporter that is nearly identical to Hxt11p, has ‐1.0957 0.0454 similarity to major facilitator superfamily (MFS) transporters, expression of HXT9 is regulated by transcription factors Pdr1p and Pdr3p RGI1 YER067W Protein of unknown function involved in energy metabolism under ‐1.0825 0.0308 respiratory conditions; protein abundance is increased upon intracellular iron depletion PAU17 YLL025W Protein of unknown function, member of the seripauperin ‐1.0771 0.0171 multigene family encoded mainly in subtelomeric regions; YLL025W is not an essential gene NAT5 YOR253W Subunit of the N‐terminal acetyltransferase NatA (Nat1p, Ard1p, ‐1.0728 0.0454 Nat5p); N‐terminally acetylates many proteins, which influences multiple processes such as the cell cycle, heat‐shock resistance, mating, sporulation, and telomeric silencing NA YDL114W Putative protein of unknown function with similarity to acyl‐carrier‐ ‐1.0609 0.0108 protein reductases; YDL114W is not an essential gene SET4 YJL105W Protein of unknown function, contains a SET domain ‐1.0592 0.0330 NA YJL218W Putative protein of unknown function, similar to bacterial ‐1.0499 0.0108 galactoside O‐acetyltransferases; induced by oleate in an OAF1/PIP2‐dependent manner; promoter contains an oleate response element consensus sequence; non‐essential gene NA YOR387C Putative protein of unknown function; regulated by the metal‐ ‐1.0169 0.0293 responsive Aft1p transcription factor; highly inducible in zinc‐ depleted conditions; localizes to the soluble fraction NA YIL046W‐A Putative protein of unknown function; identified by expression ‐1.0125 0.0293 profiling and mass spectrometry BNA4 YBL098W Kynurenine 3‐mono oxygenase, required for the de novo ‐1.0108 0.0498 biosynthesis of NAD from tryptophan via kynurenine; expression regulated by Hst1p; putative therapeutic target for Huntington disease RTT102 YGR275W Component of both the SWI/SNF and RSC chromatin remodeling ‐1.0074 0.0171 complexes, suggested role in chromosome maintenance; possible weak regulator of Ty1 transposition IRC15 YPL017C Microtubule associated protein; regulates microtubule dynamics; ‐0.9902 0.0301 required for accurate meiotic chromosome segregation; null mutant displays large budded cells due to delayed mitotic progression, increased levels of spontaneous Rad52 foci NA YJL163C Putative protein of unknown function ‐0.9795 0.0330 GID7 YCL039W Protein of unknown function, involved in proteasome‐dependent ‐0.9741 0.0424 catabolite inactivation of fructose‐1,6‐bisphosphatase; contains six WD40 repeats; computational analysis suggests that Gid7p and Moh1p have similar functions PAU5 YFL020C Member of the seripauperin multigene family encoded mainly in ‐0.9672 0.0301 subtelomeric regions; induced during alcoholic fermentation; 123
log2 (mtr10[P Symbols ORF Description f/NP.f]/ adj.p.val WT[Pf/N P.f]) induced by low temperature and also by anaerobic conditions; negatively regulated by oxygen and repressed by heme NA YMR105W‐ Putative protein of unknown function ‐0.9640 0.0207 A AIM19 YIL087C Putative protein of unknown function; the authentic, non‐tagged ‐0.9508 0.0365 protein is detected in purified mitochondria in high‐throughput studies; null mutant displays reduced respiratory growth MER1 YNL210W Protein with RNA‐binding motifs required for meiosis‐specific mRNA ‐0.9436 0.0301 splicing; required for chromosome pairing and meiotic recombination MRPL49 YJL096W Mitochondrial ribosomal protein of the large subunit ‐0.9428 0.0288 AVT6 YER119C Vacuolar aspartate and glutamate exporter; member of a family of ‐0.9385 0.0408 seven genes (AVT1‐7) related to vesicular GABA‐glycine transporters; involved in compartmentalizing acidic amino acids in response to nitrogen starvation DOG2 YHR043C 2‐deoxyglucose‐6‐phosphate phosphatase, member of a family of ‐0.9243 0.0205 low molecular weight phosphatases, similar to Dog1p, induced by oxidative and osmotic stress, confers 2‐deoxyglucose resistance when overexpressed AGE2 YIL044C ADP‐ribosylation factor (ARF) GTPase activating protein (GAP) ‐0.9228 0.0366 effector, involved in Trans‐Golgi‐Network (TGN) transport; contains C2C2H2 cysteine/histidine motif YHC3 YJL059W Vacuolar membrane protein involved in the ATP‐dependent ‐0.8925 0.0293 transport of arginine into the vacuole and possibly in balancing ion homeostasis; homolog of human CLN3 involved in Batten disease (juvenile onset neuronal ceroid lipofuscinosis) NA YOL162W Putative protein of unknown function; member of the Dal5p ‐0.8915 0.0205 subfamily of the major facilitator family MET10 YFR030W Subunit alpha of assimilatory sulfite reductase, which converts ‐0.8869 0.0301 sulfite into sulfide MET22 YOL064C Bisphosphate‐3'‐nucleotidase, involved in salt tolerance and ‐0.8845 0.0215 methionine biogenesis; dephosphorylates 3'‐phosphoadenosine‐5'‐ phosphate and 3'‐phosphoadenosine‐5'‐phosphosulfate, intermediates of the sulfate assimilation pathway NSE4 YDL105W Nuclear protein that plays a role in the function of the Smc5p‐ ‐0.8792 0.0205 Rhc18p complex HUL4 YJR036C Protein with similarity to hect domain E3 ubiquitin‐protein ligases, ‐0.8737 0.0293 not essential for viability IRC24 YIR036C Putative benzil reductase;(GFP)‐fusion protein localizes to the ‐0.8731 0.0301 cytoplasm and is induced by the DNA‐damaging agent MMS; sequence similarity with short‐chain dehydrogenase/reductases; null mutant has increased spontaneous Rad52p foci NA YGR204C‐A Putative protein of unknown function; identified by gene‐trapping, ‐0.8700 0.0288 microarray‐based expression analysis, and genome‐wide homology searching GSY1 YFR015C Glycogen synthase with similarity to Gsy2p, the more highly ‐0.8694 0.0252 expressed yeast homolog; expression induced by glucose limitation, nitrogen starvation, environmental stress, and entry into stationary phase MSW1 YDR268W Mitochondrial tryptophanyl‐tRNA synthetase ‐0.8661 0.0288 BUR6 YER159C Subunit of a heterodimeric NC2 transcription regulator complex ‐0.8641 0.0207 with Ncb2p; complex binds to TBP and can repress transcription by preventing preinitiation complex assembly or stimulate activated transcription; homologous to human NC2alpha NA YOR238W Putative protein of unknown function; green fluorescent protein ‐0.8601 0.0171 124
log2 (mtr10[P Symbols ORF Description f/NP.f]/ adj.p.val WT[Pf/N P.f]) (GFP)‐fusion protein localizes to the cytoplasm MET17 YLR303W Methionine and cysteine synthase (O‐acetyl homoserine‐O‐acetyl ‐0.8522 0.0205 serine sulfhydrylase), required for sulfur amino acid synthesis ECM11 YDR446W Non‐essential protein apparently involved in meiosis, GFP fusion ‐0.8511 0.0293 protein is present in discrete clusters in the nucleus throughout mitosis; may be involved in maintaining chromatin structure PHO80 YOL001W Cyclin, interacts with cyclin‐dependent kinase Pho85p; regulates the ‐0.8458 0.0439 response to nutrient levels and environmental conditions, including the response to phosphate limitation and stress‐dependent calcium signaling FMN1 YDR236C Riboflavin kinase, phosphorylates riboflavin to form riboflavin ‐0.8445 0.0281 monophosphate (FMN), which is a necessary cofactor for many enzymes; localizes to microsomes and to the mitochondrial inner membrane CIR2 YOR356W Putative ortholog of human electron transfer flavoprotein ‐0.8429 0.0216 dehydrogenase (ETF‐dH); found in a large supramolecular complex with other mitochondrial dehydrogenases; may have a role in oxidative stress response HOR2 YER062C One of two redundant DL‐glycerol‐3‐phosphatases (RHR2/GPP1 ‐0.8370 0.0248 encodes the other) involved in glycerol biosynthesis; induced in response to hyperosmotic stress and oxidative stress, and during the diauxic transition MET5 YJR137C Sulfite reductase beta subunit, involved in amino acid biosynthesis, ‐0.8342 0.0295 transcription repressed by methionine PIG1 YLR273C Putative targeting subunit for the type‐1 protein phosphatase Glc7p ‐0.8249 0.0248 that tethers it to the Gsy2p glycogen synthase NA YFR017C Putative protein of unknown function; green fluorescent protein ‐0.8211 0.0416 (GFP)‐fusion protein localizes to the cytoplasm and is induced in response to the DNA‐damaging agent MMS; YFR017C is not an essential gene RRF1 YHR038W Mitochondrial ribosome recycling factor, essential for mitochondrial ‐0.8157 0.0366 protein synthesis and for the maintenance of the respiratory function of mitochondria RDL2 YOR286W Protein with rhodanese activity; contains a rhodanese‐like domain ‐0.8092 0.0354 similar to Rdl1p, Uba4p, Tum1p, and Ych1p; overexpression causes a cell cycle delay; null mutant displays elevated frequency of mitochondrial genome loss BNA3 YJL060W Kynurenine aminotransferase, catalyzes formation of kynurenic acid ‐0.8022 0.0301 from kynurenine; potential Cdc28p substrate XYL2 YLR070C Xylitol dehydrogenase, converts xylitol to D‐xylulose; expression ‐0.7997 0.0437 induced by xylose, even though this pentose sugar is not well utilized by S. cerevisiae; null mutant has cell wall defect NA YDL218W Putative protein of unknown function; YDL218W transcription is ‐0.7966 0.0366 regulated by Azf1p and induced by starvation and aerobic conditions; expression also induced in cells treated with the mycotoxin patulin CWC15 YDR163W Non‐essential protein involved in pre‐mRNA splicing, component of ‐0.7961 0.0301 a complex containing Cef1p; has similarity to S. pombe Cwf15p PAU18 YLL064C Protein of unknown function, member of the seripauperin ‐0.7936 0.0408 multigene family encoded mainly in subtelomeric regions; identical to Pau6p LYS5 YGL154C Phosphopantetheinyl transferase involved in lysine biosynthesis; ‐0.7917 0.0293 converts inactive apo‐form of Lys2p (alpha‐aminoadipate reductase) into catalytically active holo‐form by posttranslational addition of phosphopantetheine
125
log2 (mtr10[P Symbols ORF Description f/NP.f]/ adj.p.val WT[Pf/N P.f]) HBN1 YCL026C‐B Putative protein of unknown function; similar to bacterial ‐0.7903 0.0454 nitroreductases; green fluorescent protein (GFP)‐fusion protein localizes to the cytoplasm and nucleus; protein becomes insoluble upon intracellular iron depletion APQ12 YIL040W Protein required for nuclear envelope morphology, nuclear pore ‐0.7753 0.0293 complex localization, mRNA export from the nucleus; exhibits synthetic lethal genetic interactions with genes involved in lipid metabolism OSH6 YKR003W Member of an oxysterol‐binding protein family with overlapping, ‐0.7748 0.0301 redundant functions in sterol metabolism and which collectively perform a function essential for viability; GFP‐fusion protein localizes to the cell periphery NA YGL117W Putative protein of unknown function ‐0.7739 0.0256 MSK1 YNL073W Mitochondrial lysine‐tRNA synthetase, required for import of both ‐0.7704 0.0301 aminoacylated and deacylated forms of tRNA(Lys) into mitochondria and for aminoacylation of mitochondrially encoded tRNA(Lys) YSW1 YBR148W Protein required for normal prospore membrane formation; ‐0.7665 0.0301 interacts with Gip1p, which is the meiosis‐specific regulatory subunit of the Glc7p protein phosphatase; expressed specifically in spores and localizes to the prospore membrane RSM18 YER050C Mitochondrial ribosomal protein of the small subunit, has similarity ‐0.7607 0.0478 to E. coli S18 ribosomal protein ODC2 YOR222W Mitochondrial inner membrane transporter, exports 2‐oxoadipate ‐0.7406 0.0293 and 2‐oxoglutarate from the mitochondrial matrix to the cytosol for use in lysine and glutamate biosynthesis and in lysine catabolism PYK2 YOR347C Pyruvate kinase that appears to be modulated by phosphorylation; ‐0.7392 0.0408 PYK2 transcription is repressed by glucose, and Pyk2p may be active under low glycolytic flux MGT1 YDL200C DNA repair methyltransferase (6‐O‐methylguanine‐DNA methylase) ‐0.7329 0.0301 involved in protection against DNA alkylation damage NA YGL185C Putative protein with sequence similarity to hydroxyacid ‐0.7248 0.0400 dehydrogenases; green fluorescent protein (GFP)‐fusion protein localizes to the cytoplasm SHC1 YER096W Sporulation‐specific activator of Chs3p (chitin synthase III), required ‐0.7160 0.0365 for the synthesis of the chitosan layer of ascospores; has similarity to Skt5p, which activates Chs3p during vegetative growth; transcriptionally induced at alkaline pH MAM33 YIL070C Acidic protein of the mitochondrial matrix involved in oxidative ‐0.7148 0.0301 phosphorylation; related to the human complement receptor gC1q‐ R RTR2 YDR066C Protein of unknown function with high similarity to Rtr1p; exhibits ‐0.6954 0.0437 genetic interactions with Rtr1p; green fluorescent protein (GFP)‐ fusion protein localizes to the cytoplasm; YDR066C is not an essential gene AIM45 YPR004C Putative ortholog of mammalian electron transfer flavoprotein ‐0.6932 0.0293 complex subunit ETF‐alpha; interacts with frataxin, Yfh1p; null mutant displays elevated frequency of mitochondrial genome loss; may have a role in oxidative stress response SAM37 YMR060C Component of the Sorting and Assembly Machinery (SAM or TOB ‐0.6908 0.0478 complex) of the mitochondrial outer membrane, which binds precursors of beta‐barrel proteins and facilitates their outer membrane insertion; contributes to SAM complex stability VPS30 YPL120W Subunit of phosphatidylinositol (PtdIns) 3‐kinase complexes I and II; ‐0.6876 0.0293 Complex I is essential in autophagy and Complex II is required for vacuolar protein sorting; ortholog of the higher eukaryotic gene Beclin 1 126
log2 (mtr10[P Symbols ORF Description f/NP.f]/ adj.p.val WT[Pf/N P.f]) NA YLR031W Putative protein of unknown function ‐0.6863 0.0326 YSC84 YHR016C Actin‐binding protein involved in bundling of actin filaments and ‐0.6811 0.0324 endocytosis of actin cortical patches; activity stimulated by Las17p; contains SH3 domain similar to Rvs167p BDH1 YAL060W NAD‐dependent (R,R)‐butanediol dehydrogenase, catalyzes ‐0.6753 0.0431 oxidation of (R,R)‐2,3‐butanediol to (3R)‐acetoin, oxidation of meso‐ butanediol to (3S)‐acetoin, and reduction of acetoin; enhances use of 2,3‐butanediol as an aerobic carbon source IME1 YJR094C Master regulator of meiosis that is active only during meiotic events, ‐0.6740 0.0431 activates transcription of early meiotic genes through interaction with Ume6p, degraded by the 26S proteasome following phosphorylation by Ime2p JEN1 YKL217W Lactate transporter, required for uptake of lactate and pyruvate; ‐0.6735 0.0449 phosphorylated; expression is derepressed by transcriptional activator Cat8p during respiratory growth, and repressed in the presence of glucose, fructose, and mannose NA YDR248C Putative protein of unknown function; sequence similarity to ‐0.6716 0.0416 bacterial and human gluconokinase; green fluorescent protein (GFP)‐fusion protein localizes to the cytoplasm; upregulated by deletion of the RNAP‐II associated factor, PAF1 LEU4 YNL104C Alpha‐isopropylmalate synthase (2‐isopropylmalate synthase); the ‐0.6661 0.0330 main isozyme responsible for the first step in the leucine biosynthesis pathway CLD1 YGR110W Mitochondrial cardiolipin‐specific phospholipase; functions ‐0.6656 0.0482 upstream of Taz1p to generate monolyso‐cardiolipin; transcription increases upon genotoxic stress; involved in restricting Ty1 transposition; has homology to mammalian CGI‐58 RSB1 YOR049C Suppressor of sphingoid long chain base (LCB) sensitivity of an LCB‐ ‐0.6587 0.0454 lyase mutation; putative integral membrane transporter or flippase that may transport LCBs from the cytoplasmic side toward the extracytoplasmic side of the membrane TMT1 YER175C Trans‐aconitate methyltransferase, cytosolic enzyme that catalyzes ‐0.6578 0.0358 the methyl esterification of 3‐isopropylmalate, an intermediate of the leucine biosynthetic pathway, and trans‐aconitate, which inhibits the citric acid cycle COQ4 YDR204W Protein with a role in ubiquinone (Coenzyme Q) biosynthesis, ‐0.6484 0.0416 possibly functioning in stabilization of Coq7p; located on the matrix face of the mitochondrial inner membrane; component of a mitochondrial ubiquinone‐synthesizing complex SPS19 YNL202W Peroxisomal 2,4‐dienoyl‐CoA reductase, auxiliary enzyme of fatty ‐0.6481 0.0425 acid beta‐oxidation; homodimeric enzyme required for growth and sporulation on petroselineate medium; expression induced during late sporulation and in the presence of oleate NA YDR336W Putative protein of unknown function; sumoylated under stress ‐0.6439 0.0301 conditions in a genome wide study; YDR336W is not an essential gene TUM1 YOR251C Rhodanese domain sulfur transferase, accepts persulfite from Nfs1p ‐0.6330 0.0366 and transfers it to Uba4p in the pathway for 2‐thiolation of the wobble uridine base of tRNAs; also stimulates sulfur transfer by Nfs1p; may be mitochondrially localized NA YHR112C Putative protein of unknown function; green fluorescent protein ‐0.6309 0.0416 (GFP)‐fusion protein localizes to the cytoplasm AIM20 YIL158W Putative protein of unknown function; overexpression causes a cell ‐0.6267 0.0408 cycle delay or arrest; green fluorescent protein (GFP)‐fusion protein localizes to the vacuole; null mutant displays elevated frequency of mitochondrial genome loss 127
log2 (mtr10[P Symbols ORF Description f/NP.f]/ adj.p.val WT[Pf/N P.f]) SWD3 YBR175W Essential subunit of the COMPASS (Set1C) complex, which ‐0.6238 0.0366 methylates histone H3 on lysine 4 and is required in transcriptional silencing near telomeres; WD40 beta propeller superfamily member and ortholog of mammalian WDR5 SNX41 YDR425W Sorting nexin, involved in the retrieval of late‐Golgi SNAREs from the ‐0.6154 0.0393 post‐Golgi endosome to the trans‐Golgi network; interacts with Snx4p STB2 YMR053C Protein that interacts with Sin3p in a two‐hybrid assay and is part of ‐0.6085 0.0419 a large protein complex with Sin3p and Stb1p NA YMR315W Protein with NADP(H) oxidoreductase activity; transcription is ‐0.6063 0.0424 regulated by Stb5p in response to NADPH depletion induced by diamide; promoter contains a putative Stb5p binding site DCG1 YIR030C Protein of unknown function, expression is sensitive to nitrogen ‐0.6057 0.0449 catabolite repression and regulated by Dal80p; contains transmembrane domain ARG5,6 YER069W Protein that is processed in the mitochondrion to yield ‐0.5991 0.0424 acetylglutamate kinase and N‐acetyl‐gamma‐glutamyl‐phosphate reductase, which catalyze the 2nd and 3rd steps in arginine biosynthesis; enzymes form a complex with Arg2p HAP3 YBL021C Subunit of the heme‐activated, glucose‐repressed Hap2p/3p/4p/5p ‐0.5909 0.0459 CCAAT‐binding complex, a transcriptional activator and global regulator of respiratory gene expression; contains sequences contributing to both complex assembly and DNA binding YCT1 YLL055W High‐affinity cysteine‐specific transporter with similarity to the ‐0.5853 0.0400 Dal5p family of transporters; green fluorescent protein (GFP)‐fusion protein localizes to the endoplasmic reticulum; YCT1 is not an essential gene NA YLR352W Putative protein of unknown function with similarity to F‐box ‐0.5850 0.0493 proteins; interacts with Skp1p and Cdc53p; YLR352W is not an essential gene NA YNL193W Putative protein of unknown function; exhibits a two‐hybrid ‐0.5735 0.0493 interaction with Yhr151cp in a large‐scale analysis MET13 YGL125W Major isozyme of methylenetetrahydrofolate reductase, catalyzes ‐0.5689 0.0459 the reduction of 5,10‐methylenetetrahydrofolate to 5‐ methyltetrahydrofolate in the methionine biosynthesis pathway ARG1 YOL058W Arginosuccinate synthetase, catalyzes the formation of L‐ ‐0.5550 0.0493 argininosuccinate from citrulline and L‐aspartate in the arginine biosynthesis pathway; potential Cdc28p substrate TNA1 YGR260W High affinity nicotinic acid plasma membrane permease, responsible ‐0.5346 0.0460 for uptake of low levels of nicotinic acid; expression of the gene increases in the absence of extracellular nicotinic acid or para‐ aminobenzoate (PABA)
128
APPENDIX C: Transcripts with increased translation activity index in fed mtr10Δ cells
The transcripts with decreased P/NP ratio (cutoff condition: log2 FC 0.6 and adjusted p-value 0.05) in mtr10Δ cells while compared to wild-type cells in fed condition are listed below and ordered according to log2 (fold-change).
Transcripts with increased P/NP ratio in fed mtr10Δ cells.
log2 (mtr10[Pf/N adj.p.v Symbols ORF Description P.f]/WT[Pf/ al NP.f]) AFFX‐ NA NA 2.9412 0.0267 18srRnac_a t AFFX‐r2‐Sc‐ NA NA 2.9310 0.0454 18SrRNA‐ 3_at NA YHL012W Putative protein of unknown function, has some homology to 1.6485 0.0171 Ugp1p, which encodes UDP‐glucose pyrophosphorylase NA YBR238C Mitochondrial membrane protein with similarity to Rmd9p; not 1.3591 0.0403 required for respiratory growth but causes a synthetic respiratory defect in combination with rmd9 mutations; transcriptionally up‐ regulated by TOR; deletion increases life span YAP7 YOL028C Putative basic leucine zipper (bZIP) transcription factor 1.2656 0.0276 KAP123 YER110C Karyopherin beta, mediates nuclear import of ribosomal proteins 1.2417 0.0293 prior to assembly into ribosomes and import of histones H3 and H4; localizes to the nuclear pore, nucleus, and cytoplasm; exhibits genetic interactions with RAI1 KAP95 YLR347C Karyopherin beta, forms a complex with Srp1p/Kap60p; interacts 1.2363 0.0346 with nucleoporins to mediate nuclear import of NLS‐containing cargo proteins via the nuclear pore complex; regulates PC biosynthesis; GDP‐to‐GTP exchange factor for Gsp1p ACC1 YNR016C Acetyl‐CoA carboxylase, biotin containing enzyme that catalyzes 1.2323 0.0171 the carboxylation of acetyl‐CoA to form malonyl‐CoA; required for de novo biosynthesis of long‐chain fatty acids
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log2 (mtr10[Pf/N adj.p.v Symbols ORF Description P.f]/WT[Pf/ al NP.f]) SSA3 YBL075C ATPase involved in protein folding and the response to stress; plays 1.1830 0.0431 a role in SRP‐dependent cotranslational protein‐membrane targeting and translocation; member of the heat shock protein 70 (HSP70) family; localized to the cytoplasm CDC34 YDR054C Ubiquitin‐conjugating enzyme (E2) and catalytic subunit of SCF 1.1211 0.0293 ubiquitin‐protein ligase complex (together with Skp1p, Rbx1p, Cdc53p, and an F‐box protein) that regulates cell cycle progression by targeting key substrates for degradation RNR1 YER070W Major isoform of the large subunit of ribonucleotide‐diphosphate 1.1207 0.0205 reductase; the RNR complex catalyzes rate‐limiting step in dNTP synthesis, regulated by DNA replication and DNA damage checkpoint pathways via localization of small subunits AFFX‐r2‐Sc‐ NA NA 1.1078 0.0302 SRB4‐5_at NA YMR230W‐ Putative protein of unknown function 1.0989 0.0171 A NUP192 YJL039C Essential structural subunit of the nuclear pore complex (NPC), 1.0915 0.0319 localizes to the nuclear periphery of nuclear pores, homologous to human p205 DRS2 YAL026C Aminophospholipid translocase (flippase) that maintains 1.0772 0.0416 membrane lipid asymmetry in post‐Golgi secretory vesicles; contributes to clathrin‐coated vesicle formation and endocytosis; mutations in human homolog ATP8B1 result in liver disease TIF4632 YGL049C Translation initiation factor eIF4G, subunit of the mRNA cap‐ 1.0711 0.0293 binding protein complex (eIF4F) that also contains eIF4E (Cdc33p); associates with the poly(A)‐binding protein Pab1p, also interacts with eIF4A (Tif1p); homologous to Tif4631p GSC2 YGR032W Catalytic subunit of 1,3‐beta‐glucan synthase, involved in 1.0557 0.0496 formation of the inner layer of the spore wall; activity positively regulated by Rho1p and negatively by Smk1p; has similarity to an alternate catalytic subunit, Fks1p (Gsc1p) MPT5 YGL178W Member of the Puf family of RNA‐binding proteins; binds to mRNAs 1.0514 0.0301 encoding chromatin modifiers and spindle pole body components; involved in longevity, maintenance of cell wall integrity, and sensitivity to and recovery from pheromone arrest NA YNL247W Cysteinyl‐tRNA synthetase; may interact with ribosomes, based on 1.0468 0.0293 co‐purification experiments SRP40 YKR092C Nucleolar, serine‐rich protein with a role in preribosome assembly 1.0348 0.0288 or transport; may function as a chaperone of small nucleolar ribonucleoprotein particles (snoRNPs); immunologically and structurally to rat Nopp140 CDC60 YPL160W Cytosolic leucyl tRNA synthetase, ligates leucine to the appropriate 1.0205 0.0301 tRNA CHC1 YGL206C Clathrin heavy chain, subunit of the major coat protein involved in 1.0150 0.0403 intracellular protein transport and endocytosis; two heavy chains form the clathrin triskelion structural component; the light chain (CLC1) is thought to regulate function RPB2 YOR151C RNA polymerase II second largest subunit B150, part of central 1.0088 0.0301 core; similar to bacterial beta subunit
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log2 (mtr10[Pf/N adj.p.v Symbols ORF Description P.f]/WT[Pf/ al NP.f]) TCB3 YML072C Lipid‐binding protein, localized to the bud via specific mRNA 1.0075 0.0416 transport; non‐tagged protein detected in a phosphorylated state in mitochondria; GFP‐fusion protein localizes to the cell periphery; C‐termini of Tcb1p, Tcb2p and Tcb3p interact NA YGR054W Eukaryotic initiation factor (eIF) 2A; associates specifically with 0.9970 0.0326 both 40S subunits and 80 S ribosomes, and interacts genetically with both eIF5b and eIF4E; homologous to mammalian eIF2A PMA1 YGL008C Plasma membrane H+‐ATPase, pumps protons out of the cell; 0.9908 0.0377 major regulator of cytoplasmic pH and plasma membrane potential; part of the P2 subgroup of cation‐transporting ATPases; Hsp30p plays a role in Pma1p regulation SFC1 YJR095W Mitochondrial succinate‐fumarate transporter, transports 0.9668 0.0293 succinate into and fumarate out of the mitochondrion; required for ethanol and acetate utilization DED1 YOR204W ATP‐dependent DEAD (Asp‐Glu‐Ala‐Asp)‐box RNA helicase, 0.9665 0.0410 required for translation initiation of all yeast mRNAs; mutations in human DEAD‐box DBY are a frequent cause of male infertility NA YBR056W‐A Dubious open reading frame unlikely to encode a protein, based on 0.9631 0.0330 available experimental and comparative sequence data; partially overlaps the dubious ORF YBR056C‐B NA YHR210C Putative protein of unknown function; non‐essential gene; highly 0.9419 0.0171 expressed under anaeorbic conditions; sequence similarity to aldose 1‐epimerases such as GAL10 COS7 YDL248W Protein of unknown function, member of the DUP380 subfamily of 0.9406 0.0459 conserved, often subtelomerically‐encoded proteins; the authentic, non‐tagged protein is detected in highly purified mitochondria in high‐throughput studies PIR3 YKL163W O‐glycosylated covalently‐bound cell wall protein required for cell 0.9395 0.0248 wall stability; expression is cell cycle regulated, peaking in M/G1 and also subject to regulation by the cell integrity pathway JLP1 YLL057C Fe(II)‐dependent sulfonate/alpha‐ketoglutarate dioxygenase, 0.9375 0.0205 involved in sulfonate catabolism for use as a sulfur source; contains sequence that resembles a J domain (typified by the E. coli DnaJ protein); induced by sulphur starvation FAA4 YMR246W Long chain fatty acyl‐CoA synthetase, activates imported fatty acids 0.9120 0.0459 with a preference for C12:0‐C16:0 chain lengths; functions in long chain fatty acid import; important for survival during stationary phase; localized to lipid particles NOT3 YIL038C Subunit of the CCR4‐NOT complex, which is a global transcriptional 0.9069 0.0366 regulator with roles in transcription initiation and elongation and in mRNA degradation CRM1 YGR218W Major karyopherin, involved in export of proteins, RNAs, and 0.8817 0.0464 ribosomal subunits from the nucleus; exportin NA YBL095W Putative protein of unknown function; the authentic, non‐tagged 0.8798 0.0295 protein is detected in highly purified mitochondria in high‐ throughput studies FLC3 YGL139W Putative FAD transporter, similar to Flc1p and Flc2p; localized to 0.8766 0.0215 the ER TRA1 YHR099W Subunit of SAGA and NuA4 histone acetyltransferase complexes; 0.8766 0.0488 interacts with acidic activators (e.g., Gal4p) which leads to transcription activation; similar to human TRRAP, which is a cofactor for c‐Myc mediated oncogenic transformation
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log2 (mtr10[Pf/N adj.p.v Symbols ORF Description P.f]/WT[Pf/ al NP.f]) NUM1 YDR150W Protein required for nuclear migration, localizes to the mother cell 0.8647 0.0454 cortex and the bud tip; may mediate interactions of dynein and cytoplasmic microtubules with the cell cortex NA YBR196C‐B Putative protein of unknown function; identified by expression 0.8608 0.0293 profiling and mass spectrometry AAD6 YFL056C Putative aryl‐alcohol dehydrogenase with similarity to P. 0.8545 0.0312 chrysosporium aryl‐alcohol dehydrogenase, involved in the oxidative stress response; expression induced in cells treated with the mycotoxin patulin DCP2 YNL118C Catalytic subunit of the Dcp1p‐Dcp2p decapping enzyme complex, 0.8334 0.0459 which removes the 5' cap structure from mRNAs prior to their degradation; member of the Nudix hydrolase family PRP16 YKR086W RNA helicase in the DEAH‐box family involved in the second 0.8202 0.0308 catalytic step of splicing, exhibits ATP‐dependent RNA unwinding activity NA YMR102C Protein of unknown function; transcription is activated by 0.8193 0.0408 paralogous transcription factors Yrm1p and Yrr1p along with genes involved in multidrug resistance; mutant shows increased resistance to azoles; YMR102C is not an essential gene PDH1 YPR002W Mitochondrial protein that participates in respiration, induced by 0.8185 0.0426 diauxic shift; homologous to E. coli PrpD, may take part in the conversion of 2‐methylcitrate to 2‐methylisocitrate AFFX‐ NA NA 0.8087 0.0288 YER022w5_ at MCM7 YBR202W Component of the hexameric MCM complex, which is important 0.8077 0.0366 for priming origins of DNA replication in G1 and becomes an active ATP‐dependent helicase that promotes DNA melting and elongation when activated by Cdc7p‐Dbf4p in S‐phase SAC1 YKL212W Phosphatidylinositol phosphate (PtdInsP) phosphatase involved in 0.8050 0.0291 hydrolysis of PtdIns[4]P; transmembrane protein localizes to ER and Golgi; involved in protein trafficking and processing, secretion, and cell wall maintenance VIP1 YLR410W Inositol hexakisphosphate (IP6) and inositol heptakisphosphate 0.7931 0.0366 (IP7) kinase; IP7 production is important for phosphate signaling; involved in cortical actin cytoskeleton function, and invasive pseudohyphal growth analogous to S. pombe asp1 IRC7 YFR055W Putative cystathionine beta‐lyase; involved in copper ion 0.7880 0.0437 homeostasis and sulfur metabolism; null mutant displays increased levels of spontaneous Rad52p foci; expression induced by nitrogen limitation in a GLN3, GAT1‐dependent manner IRR1 YIL026C Subunit of the cohesin complex, which is required for sister 0.7801 0.0288 chromatid cohesion during mitosis and meiosis and interacts with centromeres and chromosome arms, essential for viability
RTS1 YOR014W B‐type regulatory subunit of protein phosphatase 2A (PP2A); 0.7654 0.0301 homolog of the mammalian B' subunit of PP2A MCM1 YMR043W Transcription factor involved in cell‐type‐specific transcription and 0.7526 0.0488 pheromone response; plays a central role in the formation of both repressor and activator complexes RPN2 YIL075C Subunit of the 26S proteasome, substrate of the N‐ 0.7460 0.0349 acetyltransferase Nat1p
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log2 (mtr10[Pf/N adj.p.v Symbols ORF Description P.f]/WT[Pf/ al NP.f]) SMI1 YGR229C Protein involved in the regulation of cell wall synthesis; proposed 0.7433 0.0301 to be involved in coordinating cell cycle progression with cell wall integrity DIA1 YMR316W Protein of unknown function, involved in invasive and 0.7421 0.0454 pseudohyphal growth; green fluorescent protein (GFP)‐fusion protein localizes to the cytoplasm in a punctate pattern GFA1 YKL104C Glutamine‐fructose‐6‐phosphate amidotransferase, catalyzes the 0.7396 0.0254 formation of glucosamine‐6‐P and glutamate from fructose‐6‐P and glutamine in the first step of chitin biosynthesis
TCB1 YOR086C Lipid‐binding protein containing three calcium and lipid binding 0.7338 0.0293 domains; non‐tagged protein localizes to mitochondria and GFP‐ fusion protein localizes to the cell periphery; C‐termini of Tcb1p, Tcb2p and Tcb3p interact UFD2 YDL190C Ubiquitin chain assembly factor (E4) that cooperates with a 0.7338 0.0293 ubiquitin‐activating enzyme (E1), a ubiquitin‐conjugating enzyme (E2), and a ubiquitin protein ligase (E3) to conjugate ubiquitin to substrates; also functions as an E3 NDD1 YOR372C Transcriptional activator essential for nuclear division; localized to 0.7332 0.0252 the nucleus; essential component of the mechanism that activates the expression of a set of late‐S‐phase‐specific genes
SIC1 YLR079W Inhibitor of Cdc28‐Clb kinase complexes that controls G1/S phase 0.7241 0.0459 transition, preventing premature S phase and ensuring genomic integrity; phosphorylation targets Sic1p for SCF(CDC4)‐dependent turnover; functional homolog of mammalian Kip1 SEC27 YGL137W Essential beta'‐coat protein of the COPI coatomer, involved in ER‐ 0.7130 0.0437 to‐Golgi and Golgi‐to‐ER transport; contains WD40 domains that mediate cargo selective interactions; 45% sequence identity to mammalian beta'‐COP SIS2 YKR072C Negative regulatory subunit of protein phosphatase 1 Ppz1p and 0.7129 0.0326 also a subunit of the phosphopantothenoylcysteine decarboxylase (PPCDC; Cab3p, Sis2p, Vhs3p) complex, which catalyzes the third step of coenzyme A biosynthesis RAD51 YER095W Strand exchange protein, forms a helical filament with DNA that 0.7101 0.0301 searches for homology; involved in the recombinational repair of double‐strand breaks in DNA during vegetative growth and meiosis; homolog of Dmc1p and bacterial RecA protein LHS1 YKL073W Molecular chaperone of the endoplasmic reticulum lumen, 0.7092 0.0288 involved in polypeptide translocation and folding; nucleotide exchange factor for the ER lumenal Hsp70 chaperone Kar2p; regulated by the unfolded protein response pathway MSN4 YKL062W Transcriptional activator related to Msn2p; activated in stress 0.7027 0.0326 conditions, which results in translocation from the cytoplasm to the nucleus; binds DNA at stress response elements of responsive genes, inducing gene expression ADE2 YOR128C Phosphoribosylaminoimidazole carboxylase, catalyzes a step in the 0.7018 0.0454 'de novo' purine nucleotide biosynthetic pathway; red pigment accumulates in mutant cells deprived of adenine
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log2 (mtr10[Pf/N adj.p.v Symbols ORF Description P.f]/WT[Pf/ al NP.f]) STP1 YDR463W Transcription factor, undergoes proteolytic processing by SPS 0.7012 0.0454 (Ssy1p‐Ptr3p‐Ssy5p)‐sensor component Ssy5p in response to extracellular amino acids; activates transcription of amino acid permease genes and may have a role in tRNA processing ACS2 YLR153C Acetyl‐coA synthetase isoform which, along with Acs1p, is the 0.6811 0.0368 nuclear source of acetyl‐coA for histone acetylation; mutants affect global transcription; required for growth on glucose; expressed under anaerobic conditions NUP133 YKR082W Subunit of the Nup84p subcomplex of the nuclear pore complex 0.6787 0.0464 (NPC), localizes to both sides of the NPC, required to establish a normal nucleocytoplasmic concentration gradient of the GTPase Gsp1p MNN5 YJL186W Alpha‐1,2‐mannosyltransferase, responsible for addition of the 0.6765 0.0366 second alpha‐1,2‐linked mannose of the branches on the mannan backbone of oligosaccharides, localizes to an early Golgi compartment PSD2 YGR170W Phosphatidylserine decarboxylase of the Golgi and vacuolar 0.6765 0.0384 membranes, converts phosphatidylserine to phosphatidylethanolamine JJJ2 YJL162C Protein of unknown function, contains a J‐domain, which is a 0.6749 0.0431 region with homology to the E. coli DnaJ protein RIO2 YNL207W Essential serine kinase involved in the processing of the 20S pre‐ 0.6679 0.0408 rRNA into mature 18S rRNA; has similarity to Rio1p MUD1 YBR119W U1 snRNP A protein, homolog of human U1‐A; involved in nuclear 0.6666 0.0301 mRNA splicing CDC20 YGL116W Cell‐cycle regulated activator of anaphase‐promoting 0.6666 0.0301 complex/cyclosome (APC/C), which is required for metaphase/anaphase transition; directs ubiquitination of mitotic cyclins, Pds1p, and other anaphase inhibitors; potential Cdc28p substrate RFC4 YOL094C Subunit of heteropentameric Replication factor C (RF‐C), which is a 0.6648 0.0301 DNA binding protein and ATPase that acts as a clamp loader of the proliferating cell nuclear antigen (PCNA) processivity factor for DNA polymerases delta and epsilon TFA1 YKL028W TFIIE large subunit, involved in recruitment of RNA polymerase II to 0.6596 0.0459 the promoter, activation of TFIIH, and promoter opening
YPT32 YGL210W Rab family GTPase, very similar to Ypt31p; involved in the exocytic 0.6574 0.0301 pathway; mediates intra‐Golgi traffic or the budding of post‐Golgi vesicles from the trans‐Golgi NA YGL140C Putative protein of unknown function; non‐essential gene; contains 0.6567 0.0454 multiple predicted transmembrane domains ARO9 YHR137W Aromatic aminotransferase II, catalyzes the first step of 0.6498 0.0431 tryptophan, phenylalanine, and tyrosine catabolism PFK27 YOL136C 6‐phosphofructo‐2‐kinase, catalyzes synthesis of fructose‐2,6‐ 0.6479 0.0330 bisphosphate; inhibited by phosphoenolpyruvate and sn‐glycerol 3‐ phosphate, expression induced by glucose and sucrose, transcriptional regulation involves protein kinase A TPD3 YAL016W Regulatory subunit A of the heterotrimeric protein phosphatase 2A 0.6442 0.0400 (PP2A), which also contains regulatory subunit Cdc55p and either catalytic subunit Pph21p or Pph22p; required for cell morphogenesis and transcription by RNA polymerase III
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log2 (mtr10[Pf/N adj.p.v Symbols ORF Description P.f]/WT[Pf/ al NP.f]) REB1 YBR049C RNA polymerase I enhancer binding protein; DNA binding protein 0.6403 0.0431 which binds to genes transcribed by both RNA polymerase I and RNA polymerase II; required for termination of RNA polymerase I transcription SMP1 YBR182C Putative transcription factor involved in regulating the response to 0.6394 0.0485 osmotic stress; member of the MADS‐box family of transcription factors NUP145 YGL092W Essential nucleoporin, catalyzes its own cleavage in vivo to 0.6392 0.0431 generate a C‐terminal fragment that assembles into the Nup84p subcomplex of the nuclear pore complex, and an N‐terminal fragment of unknown function that is homologous to Nup100p KES1 YPL145C Member of the oxysterol binding protein family, which includes 0.6191 0.0478 seven yeast homologs; involved in negative regulation of Sec14p‐ dependent Golgi complex secretory functions, peripheral membrane protein that localizes to the Golgi complex MNN2 YBR015C Alpha‐1,2‐mannosyltransferase, responsible for addition of the first 0.5941 0.0459 alpha‐1,2‐linked mannose to form the branches on the mannan backbone of oligosaccharides, localizes to an early Golgi compartment NA YOL098C Putative metalloprotease 0.5910 0.0459 CDC55 YGL190C Non‐essential regulatory subunit B of protein phosphatase 2A 0.5698 0.0459 (PP2A), which has multiple roles in mitosis and protein biosynthesis; involved in regulation of mitotic exit; found in the nucleus of most cells, also at bud neck and bud tip CDC3 YLR314C Component of the septin ring of the mother‐bud neck that is 0.5571 0.0458 required for cytokinesis; septins recruit proteins to the neck and can act as a barrier to diffusion at the membrane, and they comprise the 10nm filaments seen with EM ARO7 YPR060C Chorismate mutase, catalyzes the conversion of chorismate to 0.5515 0.0454 prephenate to initiate the tyrosine/phenylalanine‐specific branch of aromatic amino acid biosynthesis
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APPENDIX D: Transcripts with changed translation activity index in mtr10Δ cells in amino acid starvation condition
This section includes the transcripts with decreased P/NP ratio (cutoff condition: log2 FC