Embed Size (px)
Citation preview
Identification and Validation of the Regulators of Riboneogenesis
by
Yoomi Oh
A thesis submitted in conformity with the requirements for the degree of Masters in Science
Graduate Department of Molecular Genetics
University of Toronto
© Copyright by Yoomi Oh 2017
ii
Identification and Validation of the Regulators of Riboneogenesis
Yoomi Oh
Master of Science
Molecular Genetics
University of Toronto
2017
Abstract
The riboneogenesis pathway provides yeast with an alternate route for ribose production. Ribose
is an essential precursor for nucleotide synthesis. Riboneogenesis connects glycolysis to the
non-oxidative pentose phosphate pathway by the action of the key enzyme, Shb17, which
converts sedoheptulose-1,7,-bisphosphate into sedoheptulose-7-phosphate. To understand why
this pathway evolved and to gain insight into ribose metabolism in yeast, I used fluorescence
based assays to identify genetic regulators of Shb17. Using reporter fluorescence, I probed the
levels of Shb17 across the budding yeast prototrophic deletion collection using both a Typhoon
fluorescent imager as well as flow cytometry. My screens identified genes involved in ribosome
biogenesis as potential regulators of Shb17, which were validated by immunoblotting and
further characterized by polysome profiling. My results raise the hypothesis that the relative
level of the 60S to the 40S ribosomal subunit influence the regulation of Shb17.
iii
Acknowledgements
I would like to thank my family for their unconditional support throughout my degree. Their
understanding and patience has set the foundation for me to be motivated and to further develop
personal and intellectual growth during my term in graduate school. I cannot thank them
enough. I am also appreciative of my good friends for their valuable friendship, which was
instrumental for my sense of well-being in graduate school.
I greatly appreciate the supportive guidance and help from my supervisor, Dr. Amy Caudy. She
has been an amazing source of ideas and her love and passion for science has been inspirational.
I am also grateful for her help in teaching me several steps in the robot procedure for my screens
and in carrying out some of the steps in the polysome profiling experiments, from making the
sucrose gradients to operating the fractionation system at the SickKids facilities.
I would like to thank Dr. Barbara Funnell for her kind support and the opportunity to teach the
laboratory course, MGY314, as a teaching assistant. I appreciate her thoughts and I have learned
a great deal about teaching and helping students from this valuable experience.
My committee members, Dr. Brenda Andrews, and Dr. Leah Cowen, have been supportive of
my thoughts and I am thankful for their insights and expertise in many topics. Dr. Brenda
Andrews has been especially caring in helping me write a better thesis, which I highly
appreciate for her support and her time.
I am also grateful to Dr. Johanna Rommens and Dr. Holly Liu for sharing their fractionator,
centrifuge, and other machines at SickKids which made my polysome profiles possible.
I am grateful to Dr. Adam Rosebrock for his help in running the flow cytometer and the FACS
machine. I thank him for showing me some of the steps in the flow cytometry analyses and the
GenePix software for my analyses on my Typhoon scanned data.
I am thankful to the help I received from my fellow lab members, past and present – in
particular, Julia Hanchard, Olga Zaslaver, Dr. Soumaya Zlitni, Dr. Mike Cook, and my
undergraduate students who helped me learn valuable teaching skills, Jie Gao and Melinda Fan.
I would like to acknowledge and thank my funding sources, NSERC CGS-M and U of T
fellowship award, for supporting this research.
iv
Table of Contents
Contents
1 Introduction ............................................................................................................................ 1
1.1 The riboneogenesis pathway and Shb17 ......................................................................... 1
1.2 SHB17 and sedoheptulose metabolism in other organisms ............................................ 2
1.3 Rationale ......................................................................................................................... 3
2 Methods .................................................................................................................................. 7
2.1 Strains used ..................................................................................................................... 7
2.2 Construction of ZsGreen reporter ................................................................................... 8
2.3 Flow cytometry and Typhoon image of follow-up sets .................................................. 9
2.4 Typhoon fluorescence imaging ..................................................................................... 10
2.5 FlowJo method .............................................................................................................. 11
2.6 Statistical analysis methods .......................................................................................... 12
2.6.1 Normalization of Typhoon data ............................................................................. 12
2.6.2 Benjamini-Hochberg FDR and Z-score calculation for flow cytometry data ....... 12
2.7 Regular flow cytometry method ................................................................................... 13
2.8 Nutrient conditions on flow cytometry ......................................................................... 14
2.9 Fluorescence activated cell sorting ............................................................................... 15
2.10 Polysome profiling ........................................................................................................ 15
2.11 Immunoblotting ............................................................................................................. 16
2.12 Pseudonative gels .......................................................................................................... 18
2.13 Metabolite extraction .................................................................................................... 18
3 Results .................................................................................................................................. 20
3.1 Construction of a customized deletion collection expressing an SHB17 reporter ........ 20
3.2 Confirmation of T2A cleavage ..................................................................................... 24
3.3 Shb17 activity correlates with Shb17 protein levels ..................................................... 27
v
3.4 Typhoon fluorescent scanning for initial screen of potential regulators of SHB17 ...... 30
3.5 Comparison of normalization methods ......................................................................... 33
3.6 Complementary fluorescence measurement with flow cytometry ................................ 40
3.7 False discovery rate analysis ......................................................................................... 53
3.8 FACS screening of deletion collection ......................................................................... 61
3.9 Direction into ribose as further validations ................................................................... 66
3.10 Polysome Profiles for probing ribosome side of the story ............................................ 68
3.11 Immunoblotting for validation of SHB17 regulators .................................................... 82
4 Summary of significance and future directions .................................................................... 84
5 References ............................................................................................................................... I
vi
List of Tables
Table 1. List of strains used. ....................................................................................................... 15
vii
List of Figures
Figure 1. Riboneogenesis connects glycolysis and the pentose phosphate pathway in yeast ....... 5
Figure 2. Flux through Riboneogenesis is affected by cellular redox state ................................... 6
Figure 3. Protein fusion reporter construct .................................................................................. 22
Figure 4. Immunoblot of SHB17-GFP and SHB17-T2A-ZsGreen fusion proteins ..................... 23
Figure 5. Confirmation of T2A cleavage of the ZsGreen protein from Shb17 protein using a
pseudonative gel .......................................................................................................................... 25
Figure 6. Tests for enzymatic activity of the SHB17-T2A-ZsGreen and Shb17-T2A proteins. . 26
Figure 7. Flow cytometry measurement of ZsGreen reporter for SHB17 expression across
nutrient conditions. ...................................................................................................................... 29
Figure 8. Identifying regulators of SHB17 through Typhoon readout. ....................................... 31
Figure 9. Z-scores of the entire deletion collection scanned on the Typhoon and visualization of
the cut offs for downstream analysis ........................................................................................... 32
Figure 10. Variability of fluorescence of red and green fluorescent proteins. ............................ 35
Figure 11. Green normalization method has a low correlation with Green/Red method from the
Typhoon scans. ............................................................................................................................ 36
Figure 12. Green normalization method has a low correlation with Green/Red method from the
Typhoon scans. ............................................................................................................................ 38
Figure 13. Venn diagram of significant Z-scores that overlap between media and normalization
methods. ....................................................................................................................................... 39
Figure 14. Validation of the putative hits from high-throughput screening by flow cytometry. 41
Figure 15. In-well control cells increases reproducibility ........................................................... 43
Figure 16. Flow cytometry solid growth results correlate with Typhoon Green normalization . 44
Figure 17. Flow cytometry liquid growth results correlate with Typhoon Green normalization. 46
Figure 18. Flow cytometry solid correlates with liquid growth. ................................................. 47
Figure 19. FlowJo gating example. ............................................................................................. 49
Figure 19. Reproducibility and dynamic range for Typhoon increases with the time of growth
from pinning and to scanning ...................................................................................................... 51
Figure 20. Flow cytometry data compared with mRNA levels from large scale screen. ............ 55
Figure 21. Raw flow cytometry plots for a set of putative repressors. ........................................ 56
Figure 22. Raw flow cytometry plots for a set of putative activators.......................................... 58
viii
Figure 23. Flow cytometry measurement of reporter expression correlates with whole colony
imaging ........................................................................................................................................ 59
Figure 24. The putative regulators from the screens and validation screens ............................... 60
Figure 25. Schematic of fluorescence activated cell sorting procedure and validation of sorted
cells. ............................................................................................................................................. 62
Figure 26. Reporter expression changes are stable following sorting. ........................................ 64
Figure 27. SHB17 transcription coincides with ribosomal proteins ............................................ 67
Figure 28. Polysome profile of wild-type cells ........................................................................... 74
Figure 29. Polysome profile of shb17 deletion strain. ................................................................. 75
Figure 34. Location and orientation of YPL080C ........................................................................ 80
Figure 35. Immunoblot of polysome profiles. ............................................................................. 81
Figure 36. Immunoblots of candidate activators and repressors ................................................. 83
ix
List of Abbreviations
DHAP dihydroxyacetone-phosphate
E4P erythrose-4-phosphate
F6P fructose-6-phosphate
FACS fluorescence activated cell sorting
G6P glucose-6-phosphate
GAP glyceraldehyde-3-phosphate
GO gene ontology
NADPH nicotinamide adenine dinucleotide phosphate
OD optical density
ORF open reading frame
PPP pentose phosphate pathway
R5P ribose-5-phosphate
RiBi ribosome biogenesis
RT room temperature
S7P sedoheptulose-7-phosphate
SBP sedoheptulose-1,7-bisphosphate
WT wild-type
YPD or YEPD yeast extract peptone dextrose
YNB yeast nitrogen base
1
1 Introduction
1.1 The riboneogenesis pathway and Shb17
The riboneogenesis pathway provides the budding yeast, Saccharomyces cerevisiae, among
other fungi, with a new pathway for the synthesis of ribose-5-phosphate (R5P) (Clasquin et al,
2011). For cell growth and division, R5P is required as an essential precursor for DNA and
RNA synthesis. As illustrated in Figure 1, R5P can be produced through both the oxidative and
non-oxidative branches of the pentose phosphate pathway (PPP). In the oxidative branch of the
PPP, glucose-6-phosphate (G6P) is converted to R5P. In the non-oxidative PPP, the glycolytic
intermediates fructose-6-phosphate (F6P) and glyceraldehyde-3-phosphate (GAP) are converted
to R5P by the enzyme transketolase and transaldolase. The oxidative branch produces two redox
co-factor NADPH molecules per G6P while making ribose. NADPH is involved in the
maintenance of intracellular reactive oxygen species levels, by converting oxidized glutathione
to its reduced form, and replenishing the cellular antioxidants that combat oxidative damage to
cellular components such as DNA, proteins, and lipids (Grant et al, 1996). NADPH is also
required in anabolic reactions for production of cellular components such as fatty acids, amino
acids, and nucleotides (Clasquin et al, 2011).
The riboneogenesis pathway connects glycolysis to the non-oxidative PPP, and provides
a strong thermodynamically favorable reaction towards synthesis of R5P, without producing
NADPH (Clasquin et al, 2011). Thus, this pathway allows cells to produce ribose when cells
have high demand for ribose but low demand for NADPH. The key enzyme in riboneogenesis in
budding yeast, named Shb17, converts the precursor metabolite, SBP (sedoheptulose-1,7-
bisphosphate) to the product, S7P (sedoheptulose-7-phosphate). S7P is then converted to R5P
by transketolase (Clasquin et al, 2011). The enzyme Shb17 was discovered by Dr. Amy Caudy’s
group through metabolite profiling (Clasquin et al, 2011). In this metabolomics study, it was
discovered that deletion of SHB17, which was an uncharacterized open reading frame at the
time, resulted in the accumulation of a bisphosphorylated seven carbon sugar compound, SBP.
The open reading frame was found to encode a phosphatase, which was named Shb17
(sedoheptulose-1,7-bisphosphatase) and had enzymatic activity for dephosphorylating SBP into
a monophosphorylated seven carbon compound, S7P. The Shb17 activity of converting SBP to
S7P is the committed step defining riboneogenesis. By partial labeling of the glucose source, it
2
was confirmed that the source of SBP in the cell was from four-carbon and three-carbon
compounds, which were identified as dihydroxyacetone phosphate (DHAP) and erythrose-4-
phosphate (E4P) (Clasquin et al, 2011). As DHAP derives from glycolysis, and E4P can be
made from GAP and F6P, both of which are produced during glycolysis, SBP is synthesized
from glycolysis intermediates. Thus, Shb17 activity in the riboneogenesis pathway allows a
connection from glycolysis to the non-oxidative PPP for production of R5P and nucleotides.
Unlike non-oxidative PPP where the reactions are thought to be fully reversible, the
riboneogenesis pathway provides an essentially non-reversible thermodynamically favorable
reaction towards the production of S7P (Clasquin et al, 2011).
1.2 SHB17 and sedoheptulose metabolism in other organisms
The riboneogenesis pathway is conserved in some bacteria and other fungal organisms including
fungal pathogens such as Candida albicans (Kim et al, unpublished). The Shb17 protein seems
to be conserved (unpublished communication from Dr. Marie Durand) but full riboneogenesis
pathway has not been established in organisms other than S. cerevisiae. Studies by our
collaborators, Jigar Desai and Dr. Aaron Mitchell, showed that deletion of SHB17 orthologs in
C. albicans caused smaller hyphae width in biofilm cells but not for planktonic cells, as
observed by Calcofluor white staining of the cell wall. This defect was rescued by
complementation with wild-type (WT) SHB17 gene of C. albicans. Importantly, growth of the
shb17 ortholog deletion mutant on ribose rescued the morphology defect (Desai, J.,
unpublished). Hence, it seems likely that riboneogenesis provides cells in the biofilm with a
considerable amount of ribose, enabling S7P production from the C. albicans Shb17, which is
used for making R5P and nucleotides. The smaller width of hyphae is probably due to a growth
defect, which is supported by the observation that shb17 mutants had a significant reduction in
biofilm dry weight than WT (Kim et al, unpublished). In addition, in the yeast metabolic cycle
(Tu et al, 2005), SHB17 transcript level is co-regulated with transketolase TKL1 and anti-
correlated with transaldolase TAL1 of the pentose phosphate pathway, leading to the observation
that S7P produced from Shb17 may be directed towards producing R5P through Tkl1 rather than
towards producing F6P and E4P through Tal1. The riboneogenesis pathway may be also active
at distinct phases of the yeast metabolic cycle, as glucose-6-phosphate dehydrogenase (ZWF1),
which carries out the first step in the oxidative pentose phosphate pathway, is anti-correlated
with SHB17 expression (Tu et al, 2005).
3
Sedoheptulose is an interesting metabolite in mammals as well as in fungi. In humans,
the enzyme sedoheptulokinase (SHPK, first discovered and named as carbohydrate kinase-like
(CARKL)) converts sedoheptulose to sedoheptulose-7-phosphate (Kardon et al, 2008;
Wamelink et al, 2008). This was first found in cystinosis patients who had abnormally high
levels of sedoheptulose and erythritol in their urine; this was found to be due to heterozygous
deletion in a genomic region containing the CARKL gene (Kardon et al, 2008; Wamelink et al,
2008). CARKL has since then been shown to be involved with immune function in addition to
bridging glycolysis to the PPP; CARKL was shown be one of the novel regulators of
macrophage activation, and regulation of CARKL is also important for defining the polarity of
the macrophage (Haschemi et al, 2012).
SHB17 is a member of the histidine phosphatase (HP) superfamily of diverse enzymes,
specifically the phosphoglycerate mutase branch that also includes fructose-1,6-bisphosphatase
(Clasquin et al, 2011). Shb17 is able to hydrolyze fructrose-1,6-bisphosphate (FBP) which is a
homologous substrate to SBP due to structural similarity (Kuznetsova et al, 2010; Clasquin et al,
2011); however, deletion in SHB17 does not alter FBP levels but accumulates SBP levels, and
Shb17 has a higher affinity and activity for SBP compared to FBP (Clasquin et al, 2011). As
with all HP enzymes, Shb17 has a highly conserved RHG motif (Kuznetsova et al, 2010) but
contain motifs specific to Shb17 (Kim et al, unpublished). This unique motif is found in many
fungal species (for example, Basidiomycota, Schizosaccharomyces, Pezizomycotina, Candida,
and Saccharomyces) and some bacterial species (for example, Rhizobia and Frankia), of which
most are nitrogen fixing plant symbionts; the motif is not thought to occur in plants or animals
(Kim et al, unpublished). As fungal SHB17-like sequences are more similar to bacterial SHB17-
like sequences than to other fungal histidine phosphatases, the origin of SHB17 is thought to be
from a fungal species that acquired the ancestral SHB17 gene from bacteria through horizontal
transfer (Kim et al, unpublished).
1.3 Rationale
Riboneogenesis may be most important and active in cells with a reduced need for NADPH
production, such as when there is less demand for fatty acids, amino acids, and nucleotides.
Previous work by Dr. Caudy investigated the influence of supplementation of WT yeast with
these nutrients, and measured the flow of SBP to S7P through Shb17 by a carbon labeling
experiment. As shown in Figure 2, nutrient supplementation by the addition of fatty acids
4
(ergosterol, palmitate and tween), amino acids (18 amino acids), and some nucleotides (adenine
and uracil) decreased the requirement of NADPH production. The oxidative PPP is the primary
source of the NADPH redox cofactor in the cell (Grabowska, D., and Chelstwoska, A, 2003),
and thus these supplements reduced the use of the oxidative PPP, and show a corresponding
increase in the metabolic flux through Shb17. These results suggest that the activity of Shb17 is
modulated according to conditions such as the redox state of the cell. This also suggests that
there are regulators that modulate Shb17, at the transcriptional and/or post-transcriptional levels.
The goal of my thesis work was to discover genetic regulators of SHB17 in order to help to
better understand how the riboneogenesis pathway is controlled, and how cells modulate ribose
production.
5
Figure 1. Riboneogenesis connects glycolysis and the pentose phosphate pathway in yeast.
Riboneogenesis is the conversion of sedoheptulose-1,7-bisphosphate (SBP) to sedoheptulose-7-
phosphate (S7P) through the key enzyme Shb17. The product, S7P, is converted to ribose-5-
phosphate (R5P) for nucleic acid synthesis. The structure of SBP and S7P are shown below the
highlighted riboneogenesis pathway.
6
Figure 2. Flux through Riboneogenesis is affected by cellular redox state. Wild-type yeast
(RCY308) was grown in various media conditions and the y-axis indicates the flux through
Shb17 expressed as the proportion of double-labeled SBP present in the cell. The plus signs
indicate the list of supplements that were added to YNB (yeast nitrogen base without amino acid
plus 2% glucose). Glucose-6-phosphate that was labeled on its 6th
carbon with 13
C was fed to the
yeast as carbon source for 90 minutes. Sedoheptulose-1,7-bisphosphate (SBP) is made from a 3-
carbon dihydroxyacetone-phosphate (DHAP) and a 4-carbon erythrose-4-phosphate (E4P),
where each has one carbon labeled with 13
C. Thus, the seven carbon compound SBP is doubly
labeled with 13
C. Sedoheptulose-7-phosphate (S7P) derived from SBP through Shb17 protein is
consequently doubly labeled with 13
C, while S7P derived from the pentose phosphate pathway
initially is singly labeled with 13
C. The contribution to S7P by Shb17 as measured by mass
spectrometry in this labeling experiment (quantitated as the ratio of labeled fractions of S7P and
SBP) was the flux assay. Reproduced from Clasquin et al (2011) with permission from Elsevier.
7
2 Methods
2.1 Strains used
Table 1. List of strains used.
Strain Name Genotype Source
RCY609 Matα his3Δ leu2Δ lyp1Δ This study
RCY2115 Matα his3Δ leu2Δ lyp1Δ SHB17-T2A-
ZsGreen::HygMX
This study
RCY1714 Mata his3Δ This study
RCY2116 Matα his3Δ SHB17-T2A-ZsGreen::HygMX This study
RFP prototrophic
deletion set
Mata his3Δ lyp1Δ can1Δ::STE2pr-SpHis5
yfgΔ::KanMX TDH2-TagRFP::NatMX
This study
RFP prototrophic
deletion set with
SHB17-T2A-
ZsGreen
Mata his3Δ lyp1Δ SHB17-T2A-
ZsGreen::HygMX can1Δ::STE2pr-SpHis5
yfgΔ::KanMX TDH2-TagRFP::NatMX
This study
RCY1395 Mata TDH2-TagBFP::NatMX his3Δ lyp1Δ
hoΔ::KanMX can1Δ::STE2pr-SpHis5
This study
RCY2117 Mata TDH2-TagBFP::NatMX his3Δ
SHB17-T2A-ZsGreen::HygMX
can1Δ::STE2pr-SpHis5
This study
RCY2118 Mata/α his3/HIS3 lyp1/LYP1 TDH2-
TagRFP::KanMX SHB17-T2A-
ZsGreen::HygMX
This study
RCY616
(FY4/FY5 diploid)
Mata/α This study
RCY308 (FY4) Mata Winston et al,
1995
RCY1488 Mata ura3Δ ydl242W:SbTDH2pr-ZsGreen-
tADH1
This study
RCY1271 Mata TDH2-ZsGreen::KanMX This study
8
RCY1742 Mata/α his3Δ/his3Δ This study
2.2 Construction of ZsGreen reporter
Strains expressing a ZsGreen reporter were made by transforming the RCY609 strain (Matα
his3Δ leu2Δ lyp1Δ) with 2 PCR products: 1) a fragment with 40-bp homology to the 3’-end of
SHB17 without the stop codon, the 60-bp T2A sequence, the ZsGreen sequence, and homology
to the TEF promoter, and; 2) HygMX cassette with homology to the TEF promoter, as well as
homology to sequences downstream of the 3’-end of SHB17. Transformation yielded a strain
with SHB17-T2A-ZsGreen::HygMX under its endogenous promoter in the RCY609 background
(Strain RCY2115). Transformants were checked for the ZsGreen reporter protein expression
under SHB17 promoter control by measuring green fluorescence on the flow cytometer. Forty-
eight transformant colonies were picked and inoculated into 200 μl of YNB + glucose + leucine
+ histidine in a 96 well plate and grown overnight at 30°C. Culture was diluted 1:50 in fresh
medium in a new 96 well plate, grown for 5 hours at 30°C, and sonicated for 1 minute at 30
amplitude to separate the cells. Flow cytometry on each well for checking green fluorescence of
the colonies was performed with the help of Dr. Amy Caudy. The same construct with
ZsYellow instead of ZsGreen was attempted but transformants did not fluoresce yellow as
measured by the flow cytometer; thus, ZsGreen transformants were used for further steps (For
full description of flow cytometry procedure, see below).
The ZsGreen transformants that were confirmed to be green fluorescent were crossed to
RCY1714 (Mata his3Δ) and sporulated. For crossing to the RFP prototrophic deletion set for
fluorescent screens, an SGA-ready strain was identified from the sporulation and named
RCY2116 (Matα his3Δ SHB17-T2A-ZsGreen::HygMX). Strain RCY2116 was crossed to the
yeast prototrophic deletion collection with RFP (Mata his3Δ lyp1Δ can1Δ::STE2pr-SpHis5
yfgΔ::KanMX TDH2-TagRFP::NatMX). After SGA, more than 5000 strains were obtained
(Mata his3Δ lyp1Δ SHB17-T2A-ZsGreen::HygMX can1Δ::STE2pr-SpHis5 yfgΔ::KanMX
TDH2-TagRFP::NatMX) and grown as colonies on rectangular agar plates (YPD and
YNB+glucose). This initial set was scanned on the Typhoon fluorescence imager (see Typhoon
fluorescence imaging section).
For construction of the in-well control strain for flow cytometry, strain RCY2116 was
crossed to RCY1395 (Mata TDH2-TagBFP::NatMX his3Δ lyp1Δ hoΔ::KanMX can1Δ::STE2pr-
SpHis5). The resulting BFP strain called RCY2117 (Mata TDH2-TagBFP::NatMX his3Δ
9
SHB17-T2A-ZsGreen::HygMX can1Δ::STE2pr-SpHis5) was similar to the RFP prototrophic
deletion strains in that both had SHB17-T2A-ZsGreen construct to report on SHB17 expression
levels, but differed in that the BFP strain was wild-type for the gene that is mutated in the RFP
set; the blue fluorescence as measured in the flow cytometer as opposed to the red fluorescence
could be used to distinguish the wild-type from the mutant populations.
2.3 Flow cytometry and Typhoon image of follow-up sets
For preparation for flow cytometry of the follow-up validation sets of candidate regulators in
comparison with the control strain in the same well in the 96 well plate, the control RCY2117
strain (Mata TDH2-TagBFP::NatMX his3Δ SHB17-T2A-ZsGreen::HygMX can1Δ::STE2pr-
SpHis5) was first inoculated into 10 ml of liquid YPD media and grown at 30°C. The liquid
culture was distributed to 96 well plates in 100 μl aliquots, and pinned to YPD and
YNB+glucose rectangular agar plates which were grown at 30°C. Follow-up sets of candidate
regulators in the RFP prototrophic deletion set with SHB17-T2A-ZsGreen were pinned from
frozen stock to liquid YPD in 96 well plates and grown at 30°C, then pinned to YPD and
YNB+glucose rectangular agar plates for further growth at 30°C.
For Typhoon scanning, the follow-up RFP sets were pinned to full plates of YPD and
YNB+glucose rectangular agar plates (full plates denote a higher level of agar than usual in
order to scan ~3 mm above the platen on the Typhoon imager). The plates were at room
temperature and scanned over time on the Typhoon imager (see Typhoon fluorescence imaging
section). Constant growth at room temperature minimized shifts in temperature between 30°C
and room temperature for scanning (30°C plates not equilibrated to room temperature fog up the
platen on the Typhoon imager and affect fluorescence readings considerably). Images were
extracted for green and red fluorescence from each colony at each time point with GenePixPro
software and subsequently compiled and analyzed with self-made Perl and R codes for
performing analysis and graphing. Normalization was performed to the red fluorescence from
the same colony in case of RFP normalization, and a second type of normalization was
performed to the green fluorescence of the wild-type from the same row of the same plate using
self-made Perl codes. The two normalization methods were compared through graphing in R.
For flow cytometry, the follow-up RFP sets and the control BFP set were pinned to the
same plate with corresponding fresh media (YPD and YNB+glucose rectangular agar media). In
order to minimize takeover from potential growth differences between the two populations, the
10
mixed populations were grown for no longer than one overnight in 30°C. In the morning the
mixed population from agar solid plates were pinned to liquid YPD and YNB+glucose in 96
well plates, growth for ~4 hours (for YPD) and ~6 hours (for YNB+glucose). To fix the cells,
190 μl of 95% cold ethanol (-20°C) was added. This was named “liquid” growth set. The mixed
populations from agar solid plates were also pinned to water and immediately fixed with
ethanol; this was named the “solid” growth set in order to capture fluorescence from strains
grown on solid media. Both liquid and solid growth sets fixed in ethanol were stored at -20°C
with a plate lid. One day before running on the flow cytometer, plates were centrifuged 8 plates
at a time for 20 minutes at 2000 rpm at 4°C in Sorvall RC-4. Samples were resuspended using a
Beckman Coulter Biomek FX in sodium citrate buffer (50 mM NaCitrate, pH 7.2 and 0.036
mg/ml RNaseA) and two 96 well plates were combined to one 384 well plate for flow
cytometry. RNaseA was not required for this particular experiment but was a part of previous
experiment using Sytox stains from which the current method was derived; RNaseA was
recommended to be kept in the mixture as the method was not tested without RNaseA. Resulting
384 well plates were sealed and vortexed for 15 seconds at 2000 rpm (Eppendorf MixMate, 384
well setting), and sonicated for 1 minute at 50 Amp (QSonica sonicators). Plates were
centrifuged for 1 minute at 1000 rpm in a Sorvall RC-4, then placed at 37°C overnight. Plates
were stored at 4°C and sonicated again before running on the flow cytometer.
Analysis was performed with FlowJo software (detailed below) and data were compiled
and statistically analyzed with self-made Perl codes in order to find the top candidate regulators
of SHB17. The two readouts from Typhoon and flow cytometry were normalized and compared
using various self-made Perl codes, and graphed with self-made R codes.
2.4 Typhoon fluorescence imaging
For the initial screen of the entire RFP prototrophic deletion set with SHB17-T2A-ZsGreen (See
Table 1), and for the follow-up candidate regulator sets of strains, colonies were grown on
rectangular plates (YPD and YNB+glucose media) overnight and scanned over time. Eight
plates were scanned at a time in the Typhoon fluorescence scanner (Typhoon Trio Variable
mode imager). For ZsGreen fluorescence, excitation 488 nm (Blue) and emission 520 nm BP 40
were used. For TagRFP red fluorescence, excitation 532 nm (Green) and emission 580 BP 30
Cy3, TAMARA, AlexaFluo546 were used. Both were scanned ranging from 400-450 Volts for
the PMT, and resolutions were 100 μm. GenePixPro software was used to separate and measure
11
the green and red fluorescent channels from the Typhoon scans. Self-made sets of Perl codes
were used for analyzing the output files and for finding candidate regulators of SHB17.
2.5 FlowJo method
For extracting green, red, and blue fluorescence levels to obtain the green fluorescence level
from both the control BFP and mutant RFP populations in the same well, FlowJo X software
was used. In a plot of SSC-A (Side scatter area) vs FSC-A (Forward scatter area), typically three
populations were seen: yeast population, small debris, big debris. Gating was performed to make
contour plots around the yeast cells and exclude debris from further gating and analysis (usually
~96% of population was included after this gating). The gated population was viewed in a FSC-
W vs FSC-A plot; sonication does not always separate attached cells and FSC-W vs FSC-A
allows visualization of the length of cells, where cells attached to each other have wider and
typically higher fluorescence. Gating was used to remove these potentially multiple cell events
(usually ~1% of population were gated out at this step). The gated populations were then plotted
as SSC-W (Side scatter width) vs SSC-A where the main population was selected as well (~1%
of population gated out). As there were both budded and unbudded yeast cells, the gated cells
were examined in an SSC-W vs FSC-A plot in order to exclude the budded cells (typically ~50-
60% passed this gating). The unbudded cells were viewed as a histogram of FSC-A and
relatively small to medium cells were selected (excludes ~50% of relatively smaller and larger
cells that may contribute to fluorescence bias due to their size). Blue (control) and red (mutant)
populations were separated in view by graphing in V460-36A (blue) vs YG582_15_A (red). The
two populations were individually selected as distinct populations and the mean green
fluorescence was extracted from each population. The mean red or blue fluorescence was
extracted from the corresponding population as well. Typically the same gating was applied
across the sample within the plate run on the same day (unless population shifts from the
standard gating occurred when looked at each plot individually; then a separate gating was
performed for deviated populations). For analysis within FlowJo, graphs of the blue and red
populations were plotted on axis with green fluorescence to compare the level of green
fluorescence between the two populations, and subsequently analyses for statistical differences -
see below. In addition, both populations were also plotted on SSC-A as well to visualize the size
differences of the two populations. The differences in the green fluorescence between the blue
control and red mutant populations due to similar pattern of difference in the size (SSC-A)
12
between the two populations were disregarded and were not considered candidate regulators of
SHB17. Finally, wells with cell counts fewer than 500 from each population were disqualified
from subsequent analysis.
2.6 Statistical analysis methods
2.6.1 Normalization of Typhoon data
Two different normalization methods were used for flow cytometry data. First, the green
fluorescence of each colony was normalized to the red fluorescence from the same colony. As
the variation in RFP level within the same plate and across the plates was greater than the
variation seen in the green fluorescence, a second normalization method was devised. In this
method, the green fluorescence of the colony was normalized to the green fluorescence of the
wild-type colony from the same row in the same plate. Z-scores for the initial screen with 5000
strains were then calculated. In the RFP normalization method, a positive Z-score means SHB17
was upregulated in that RFP deletion mutant, and negative Z-score means SHB17 was
downregulated in that RFP deletion mutant. In the wild-type normalization method, a positive
Z-score means SHB17 was higher in mutant than in WT (upregulation), and a negative Z-score
means SHB17 was lower in mutant than in WT (downregulation). Z-scores for the follow-up
strains were performed with Benjamini-Hochberg FDR method along with flow cytometry data
(see below).
2.6.2 Benjamini-Hochberg FDR and Z-score calculation for flow cytometry
data
For flow cytometry of cell populations with a mixture of control BFP and mutant RFP strains,
Z-scores were calculated for each strain based on the Benjamini-Hochberg method (VanderSluis
et al, 2014). This method essentially compared the green fluorescence of the blue control
normalized to that of the red mutant (yfg deleted) from each well to the average green
fluorescence of the blue control normalized to that of the red control (wild-type from the RFP
protrophic deletion set). For this statistical analysis, the Z-score was calculated by:
[Green fluorescence (blue/red) for mutant – Average Green(blue/red) for wild-type]/
Sqrt2(stdev(green(blue/red) for mutant )^2 + stdev(green(blue/red) for wild-type)^2)
13
As the blue control was normalized to the mutant, for flow cytometry Z-scores, the positive Z-
score means expression was higher in WT (downregulation of SHB17) and negative Z-score
means SHB17 expression was higher in the mutant than in WT (upregulation of SHB17); it was
noted during analysis that the interpretation of the z-score depended on the normalization
method.
The calculation of Z-scores was performed within the same plate (this allowed plate by plate
FDR threshold that differed between plates for finding putative regulators) and P-value of each
strain was calculated based on the Z-scores in Excel. Both negative and positive Z-scores were
accounted for in the P-value calculations. For false discovery rate (FDR) analysis, the strains
were sorted by their P-values from least to highest value, then ranked where the most significant
P-value was given the rank of 1. The Pj (the expected P value based on the rank and the number
of strains) was calculated for each strain by:
Pj = (rank/number of strains in plate including WT)*FDR threshold
For data from the follow-up set scanned on the Typhoon, blank spots were excluded from the
analysis and the false discovery rate (FDR) threshold was arbitrarily set to 5% initially. If the
null hypothesis was rejected (ie. P-value is lower or more significant than the Pj expected P-
value), then the strains were considered potential regulators of SHB17. The cutoff value (FDR
threshold) was changed up to maximum of the 30% false discovery rate if the plate did not yield
significant candidates with lower FDR values. Typically a FDR of 5-20% was used for most
plates.
Similar analysis was performed for the red fluorescence instead of the green fluorescence in
order to find the potential regulators of TDH2 (tagged to red fluorescence).
2.7 Regular flow cytometry method
For assessing strain integrity by flow cytometry without ethanol fixation, strains were first
grown overnight at 30°C in liquid medium. Two microliters of the overnights were diluted in
100 μl of fresh media in 96 well plates and grown at 30°C with shaking. Wild-type strains
(RCY616 diploid for diploid transformants or RCY308 for haploids and sporulations) were non-
fluorescent and grown in the same plate. After growth (~5-7 hours depending on media
conditions), the plates were sonicated for 1 minute at 30 amplitude. Samples were run on the
flow cytometer with the help of Dr. Amy Caudy and Dr. Adam Rosebrock. Data were acquired
real time and threshold for fluorescence was set up compared to the wild-type controls.
14
Subsequent analyses and graphing of the 96 well plates for sporulation was performed in R with
self-made codes.
2.8 Nutrient conditions on flow cytometry
To check the ZsGreen fluorescence reporter for SHB17 expression under conditions known to
change flux through riboneogenesis, the reporter strain RCY2118 (mata/α his3/HIS3 lyp1/LYP1
TDH2-TagRFP::KanMX SHB17-T2A-ZsGreen::HygMX), as well as other diploids with the
same genotype, were inoculated and grown in appropriate media: 1) YNB+glucose (minimal
media), 2) YNB+glucose+20 amino acids+adenine+uracil, 3) YNB+glucose+20 amino
acids+adenine+uracil+Tween80, 4) YNB+glucose+20 amino
acids+adenine+uracil+ergosterol+Tween80, 5) YP galactose, 6) YPD and 7) YPD+DTT. For
making 20 amino acid mix, a 18 amino acid mix (Studier, 2005) was first made (ie. amino acid
mix without cysteine and tyrosine). Adenine and uracil were then added to final concentrations
of 50 mg/L and 20 mg/L, respectively (Clasquin et al, 2011). Tween80 (polysorbate 80) was
added to final concentration of 1% 50/50 (v/v) Tween 80/ethanol and ergosterol was
supplemented to final concentration of 20 μl/mL dissolved in 50/50 (v/v) Tween80/ethanol. YP
galactose was made with 2% (w/v) final concentration of galactose (adapted from Gasch et al,
2000 where large scale study implied differential expression of SHB17 in YP galactose).
YPD+DTT made to 2.5 mM of DTT that was added before running on cytometry; see below
(adapted from Barreto et al, 2012 method where 120 minute exposure to DTT seemed to reduce
expression of SHB17).
ZsGreen reporter diploids and the control strain (RCY616, wild-type diploid) were
inoculated into 2 ml of media 1 to 7 described above and grown in 30°C shaker overnight. In the
morning, 100 μl of the overnight was diluted into 5 ml of media and grown for 4 hours in 30°C
shaker. For YPD+DTT condition, DTT was added to 2.5 mM and cultures were returned to
30°C shaker. Two hours later 100 μl of the cultures were moved to 96 well plates for flow
cytometry, sonicated at 30 Amp for 1 minutes. The samples were then run on the flow cytometer
and fluorescence of the green and red were measured.
Two milliliters of the same cultures used for cytometry (around the same time when used
for flow cytometry) were collected by vacuum filtration onto Nylaflo filters and put into 2 ml
safelock tubes and frozen immediately in liquid nitrogen. The samples were saved in -80°C for
preparation of RNA from the samples using my working procedure for RNA extraction.
15
2.9 Fluorescence activated cell sorting
For identifying the top highest and lowest fluorescent strains from the population with the
SHB17 fluorescent reporter, the RFP prototrophic deletion strains with SHB17-T2A-ZsGreen
were first grown on agar plates (YPD+G418 and YNB+glucose). The cells from ~5000 strains
were scraped twice, each with 1 ml YPD+G418 per plate or 1 ml YNB+glucose per plate, and
pooled separately according to the media conditions. Glycerol was added to 20% final
concentration and mixed well. Optical density was measured and 1 ml aliquots were made and
all stocks were frozen before thawing for inoculation (to maximize consistent results for future
experiments using the stocks). The thawed stock from each media type was inoculated to
YPD+G418 or YNB+glucose liquid media in the evening and grown at 30°C with doubling time
considerations taken into account. Cultures with various higher and lower titrations were made
to meet the target of ~0.4 OD/ml in the morning. For collecting cells that were sorted, media
(YPD+G418 and YNB+glucose) with carbenicillin (to final concentration of 0.1 mg/ml) were
prepared as sorting itself is not a sterile procedure. Cells that were sorted with wider width of
threshold were inoculated into liquid media and grown at 30°C. Cells were centrifuged,
collected, and frozen (also for purposes of genomic DNA extraction from the cells). Cells that
were sorted with a narrow % threshold were plated to achieve ~1000 cells/plate, and grown at
30°C. Colonies were counted, confirmed for individual fluorescence of selected colonies (96
colonies per replicate per sorted fluorescence range were confirmed) by flow cytometry, then
pooled together and frozen; these cell populations were ready to use for barcode sequencing and
identification of the top and bottom fluorescent strains.
2.10 Polysome profiling
Prototrophic mutant (ypl080cΔ, bud20Δ, rpl19aΔ, shb17Δ) and control (wild-type RCY308)
strains were inoculated in 2 ml YPD liquid media in triplicates and shaken at 30°C overnight. In
the afternoon, Optical Density (OD) was checked with Ultrospec10 spectrophotometer
(Amersham Biosciences) and cultures were diluted to 0.15 OD/ml in 5 ml YPD in duplicate.
After 5-6 hours of growth, the OD was checked and each culture was diluted into fresh YPD to
a final volume of 100 ml. Three inoculating volumes (1/5x, 1/8x, and 1/10x titrations) were used
for each culture in order to obtain 0.4-0.5 OD/ml at extraction the next morning from growth at
25°C. When yeast cultures reached 0.4-0.5 OD/ml, cycloheximide was added to a final
16
concentration of 200 μg/ml. Cultures were immediately cooled on ice and all subsequent steps
were performed at 0°C. Each 100 ml culture was centrifuged in two 50 ml tubes in Sorvall RC-6
centrifuge with SLA-600TC rotor, at 7000 rpm for 5 minutes. Pellets were washed with 4 ml of
1x lysis buffer (0.1 M Tris pH 7.5, 100 mM MgCl2*6H2O, 0.1 M KCl ) with fresh 200 μg/ml
cycloheximide and 200 μg/ml heparin. Split cultures were collected into one tube and spun 7000
rpm for 5 minutes. Pellet was resuspended in 0.5 ml of 1x lysis buffer with fresh 200 μg/ml
cycloheximide, 200 μg/ml heparin, 500 μM DTT and protease inhibitor (one tablet from Roche
COmplete Ultra tablet mini EDTA-free). Resuspensions were moved to chilled 2 ml screw cap
tubes with ~600 μl zirconia beads and grinded for 30 minutes at 500 speed 1x rate in
GenoGrinder for lysis. The bottoms of 2 ml tubes were punctured with a 27G precision needle
and placed into a 5 ml tube. The contents were briefly centrifuged into 5 ml tubes and
resuspended with a P1000 before moving to 1.5 ml tubes. A Zeiss phase contrast microscope
was used to check for ghosting (indicative of cell lysis) and the lysate was centrifuged for 10
min at 13,000 rpm at 4°C to clear insoluable material; the supernatant was used. RNA
concentration was determined using a Nanodrop Spectrophotometer (RNA setting, A260).
Sucrose gradients (7%-47%) were made in 6 centrifuge tubes (Beckman, Polyallomer
Centrifuge Tubes, 14x89 mm, 331372) using filter sterilized sucrose solutions in 1x lysis buffer.
Samples were loaded onto the top of the sucrose gradients and centrifuged at 35 000 rpm, 3
hours, 4°C in Beckman SW 41 T1, Acceleration 1 and Deceleration 5. Fractionation was
performed with a density gradient fractionation system (Brandel) and UV absorbance (A254)
was recorded with PeakTrak Software (Teledyne Isco). Peaks were visualized, analyzed, and
labeled in Adobe Illustrator CS6.
2.11 Immunoblotting
Proteins were prepared first by inoculating mutant and control strains in 150 μl of media in 96
well plates and growing at 30°C overnight. In the morning, 62 ul of the overnight cultures were
diluted into 1 ml of fresh media in deep 96 well plates and grown at 25°C shaker (~6 hours for
YPD). Thirteen hours before protein extraction was to be performed, cultures were diluted using
a Beckman Coulter Biomek FX and Biomek NXp robot in two 24 well plates with fresh media
to 5 ml per well. Automated dilution cultures were shaken at 25°C in order to reach mid-log
phase of 0.4 to 0.6 OD/ml the next morning. At similar OD600 ranges, cells were harvested by
centrifugation of 4 ml cultures for 5 min at 12500 rcf at 4°C. Pellets were resuspended in 267 ul
17
of 2.0 M lithium acetate, chilled on ice for 3 minutes, and centrifuged for 5 min at 12500 rcf at
4°C. Pellets were resuspended in 267 ul of 0.4 M sodium hydroxide, chilled on ice for 5
minutes, and centrifuged for 5 min at 12500 rcf at 4°C (adapted from Zhang, T., et al, 2011).
Pellets were resuspended to the same protein concentration (determined by OD600 at the time of
harvest) in 2x Laemmli buffer (125 mM Tris-HCl, pH 6.8, 4% (v/v) SDS, 20% (v/v) glycerol,
25 mM EDTA, 0.4 mg/ml bromophenol blue, 2% (v/v) 2-mercaptoethanol). Samples were
heated for 5 min at 95°C and centrifuged for 10 min at 13,000 rpm at room temperature.
Supernatants containing proteins were moved to fresh tubes and stored at -20°C. For assessment
by electrophoresis, proteins were mixed 1:1 with 2x Laemmli and then 1:1 with 2x SDS PAGE
loading buffer (100 mM Tris-HCl, pH 6.8, 4% (w/v) SDS, 0.2% (w/v) bromophenol blue, 20%
(v/v) glycerol, fresh 200 mM DTT). Samples were vortexed, boiled for 3 minutes at 95°C, and
loaded 8 ul per well.
Gels were made fresh with 10% resolving gel and 4% stacking gel. 1x TGS buffer was used as
running buffer (0.3% (w/v) Tris, 1.44% (w/v) glycine, 0.1% (w/v) SDS). Samples were run for 1
hour at 150V at room temperature. For western blotting, proteins were transferred onto PVDF
membrane for 1.5 hours at 70V with a stir bar in 2:7:1 of methanol, water, and 10x transfer
buffer (5.02% (w/v) Tris, 2.93% (w/v) glycine, 3.75% (v/v) SDS).
Membranes were washed in fresh 1x TBST (0.605% (w/v) Tris, 0.876% (w/v) sodium chlorine,
0.1% (v/v) Tween-20, adjusted to pH 7.5) and blocked with gentle shaking for 1 hour at room
temperature in 1x TNT blocking buffer + azide (10 mM Tris-Cl pH 7.5, 0.15 M sodium
chloride, 0.05% (v/v) Tween-20, 5% (w/v) milk, 0.05% (w/v) sodium azide). Gels were checked
for adequate transfer using Commassie stain (0.25% (w/v) Coomassie R-250, 45% methanol,
10% glacial acetic acid).
Primary antibody incubation of blocked membranes was performed in 50 ml Falcon tubes
overnight on a rotator at 4°C with 1:2,500 dilution of Shb17 Antibody no.3 (affinity-purified
peptide, polyclonal antibody from rabbit, raised to the sequence CEIQNVKSYDDDTVP),
1:10,000 dilution of alpha-Tubulin Antibody (from rabbit), and 0.1% (w/v) azide in 5%
blocking solution (5% (w/v) milk in 1X TBST).
Membranes were washed in 1X TBST for 5 times, 5 minutes each at room temperature. Washed
membranes were incubated for 1 hour at room temperature on a rotator with fresh secondary
antibody solution (1:25,000 ECL Anti-rabbit IgG horseradish peroxide-linked whole antibody
(from donkey), no. NA934V in 10 mM Tris-Cl, pH 7.5, 150 mM sodium chloride, 0.05% (v/v)
18
Tween-20, 5% (w/v) milk). Membranes were washed in 1X TBST for 5 times, 5 minutes each at
room temperature with gentle shaking in designated or fresh tip boxes.
Proteins on membranes were visualized with West Pico detection reagent and developed on x-
ray films. ImageJ software was used for quantitation of the Shb17 and Tub1 bands.
2.12 Pseudonative gels
To extract non-denatured proteins from yeast cells, fluorescently tagged and control strains were
inoculated into 2 ml media (YNB + Leucine + histidine) in replicates and grown in a 30°C
shaker overnight. Two mL of cells were pelleted (3000 g or 6000 rpm, 5 min, in 4°C) and
resuspended in Y-PER reagent (Thermo-Fisher) and PMSF was added to 1 mM to prevent
protein degradation. The mixture was vortexed at room temperature for 20 minutes and the
lysate was centrifuged at 14,000 g for 10 minutes at room temperature. Supernatants were
obtained and mixed 1:1 with 2x NEB buffer 2 to prevent denaturation. For loading, samples
were mixed 1:1 with 2x loading dye + DTT (100 mM Tris-Cl, pH 6.8, 4% (w/v) SDS, 0.2%
(w/v) bromophenol blue, 20% (v/v) glycerol, fresh 200 mM DTT) (adapted from Baird et al,
2000). Without boiling, 5-15 μl samples were immediately loaded onto 2% SDS-PAGE pre-cast
Pierce 12 well gels with 1x Tris-HEPES-SDS running buffer.
2.13 Metabolite extraction
Regular methods for metabolite extraction were carried out using the standard metabolite
extraction protocol developed in the lab by Dr. Amy Caudy (Caudy, A.A., Budding Yeast: A
Laboratory Manual, 2016, Chapter 33). Briefly, yeast strains were inoculated into liquid media
(typically YNB+glucose and supplemented as necessary) and grown overnight at 30°C. In the
morning, cultures were diluted to OD600 of 0.1 OD/ml in 5 ml fresh media in 24 well plates, then
further grown in 30°C shaker. Once cultures reached ~0.4 OD/ml, typically 4 ml of cells were
harvested in a vacuum manifold over Nylon filters. Metabolism was quenched by immediately
placing the filters in -20°C extraction solvent (40:40:20 methanol:acetonitrile:water) and cells
were taken off the filters by vortexing. Samples underwent three cycles of freeze-thaws from -
20°C to -80°C in order to thoroughly lyse the cells. Filters were removed and cell debris was
pelleted by centrifuging at 4000 rpm for 10 minutes (Sorvall RC-4 centrifuge). One milliliter of
the supernatant containing metabolites was obtained and stored at -80°C. For preparation of
19
samples for mass spectrometry, metabolite extract samples of 200 μl aliquots were made and
then dried in a nitrogen dryer (TurboVap LV evaporator) at 5 psi (higher pressure exposes
metabolites to too much oxygen in the air) for minimum ~2 hours. Dried extracts were vortexed
for 1 minute at 4°C to resuspend in HPLC H2O to 0.3 OD cells per 40 μl H2O (calculated using
the OD600 before extraction), typically within ~60 μl final resuspension volume. Tubes were
briefly centrifuged and vacuum filtered on Pall LifSci AcroPrep 96 Filter Plates 0.45 μm PN
5030 onto PP microplate. The filtered extracts were moved to mass spectrometer vials. Sample
information was prepared and the samples were run on the liquid chromatography and mass
spectrometer by Dr. Amy Caudy and Dr. Adam Rosebrock. Metabolite levels were visualized
and areas under the peak for desired metabolites were determined by Agilent Q-TOF
Quantitative Analysis, Agilent QQQ Quantitative Analysis, and Agilent Qualitative Analysis.
Data were compiled, analyzed, and graphed with self-made R codes.
20
3 Results
3.1 Construction of a customized deletion collection expressing
an SHB17 reporter
For identifying regulators of Shb17, I wanted to measure the level of the Shb17 enzyme across
the yeast deletion collection (Vandersluis et al, 2014; Giaever et al, 2002). I used a Synthetic
Genetic Array (SGA) approach (Tong et al, 2001) to introduce an SHB17-T2A-ZsGreen reporter
gene integrated at the SHB17 locus (see Materials and Methods) into the prototrophic deletion
collection (VanderSluis et al, 2014) expressing a red fluorescent marker (TDH2-TagRFP,
unpublished). This yielded over 5000 haploid strains, each carrying a different non-essential
gene knockout, TDH2 cotranslationally fused to a red fluorescent protein TagRFP, and a
construct of SHB17 fusion to T2A and ZsGreen (a green fluorescent protein) (Figure 3). Tdh2
was chosen as this is the yeast equivalent of mammalian GAPDH, one of the most widely used
internal controls. Tdh2 is also one of the most abundant proteins in the cell (Ho et al, 2017).
Other common controls include genes encoding ribosomal proteins, such as RPL39 (Pinay and
Andrews, 2010; Fillingham et al, 2009); however, the metabolic cycle of SHB17 oscillates with
ribosomal proteins (Clasquin et al, 2011, and Tu, B.,et al, 2005), and so RPL39 was not chosen
for our study. When the TDH2 promoter is activated, this drives the expression of TagRFP,
which is used as an internal control. When Shb17 is expressed (by the action of its yet unknown
regulators), the T2A small peptide co-translationally cleaves itself and separates the ZsGreen
protein from the Shb17 protein. T2A is from foot-and-mouth disease virus (Aphthoviruses) and
is a 20 amino acid peptide that can co-translationally cleave itself (Trichas, G., et al, 2008). I
chose to include the T2A peptide in my fusion protein, since ZsGreen is a tetramer
(Yanushevich, Y., et al 2002), while Shb17 from gel filtration assay and x-ray crystallography
looks to be monomeric or likely dimeric (Kuznetsova, E., et al. 2010). If the Shb17 protein was
fused to the ZsGreen protein, the latter fluorescent protein would drive the tetramerization of
Shb17 and possibly affect its function. T2A allows ZsGreen to form a functional tetramer, and
Shb17 to be free from its fusion protein. I elected to use ZsGreen rather than the Shb17-GFP
fusion because my goal was to scan the strains in the flow cytometer to find the green and red
fluorescence, and treatment of cells with various fixatives such as formaldehyde is known to
change the fluorescence of GFP (Wong, B., and Rosebrock, A., unpublished result) while it
21
leaves ZsGreen fluorescence unaffected (Savidge, T., and Pothoulakis, C., 2005). Also,
subsequent immunoblotting of SHB17-GFP and SHB17-T2A-ZsGreen by Yutong Ma (from
Caudy lab) shows that Shb17 expression is reduced by the cotranslational fusion with GFP but
more similar to wild type levels when expressed with the T2A-ZsGreen fusion tag (Figure 4).
With the strain configuration described above, if the deletion of a non-essential yeast open
reading frame causes changes in Shb17 levels, I could detect this by changes in the green
fluorescence levels indicative of changes in SHB17 expression levels.
22
Figure 3. Protein fusion reporter construct. Synthetic Genetic Array (SGA) method (Tong et
al, 2001) and the prototrophic yeast deletion collection (VanderSluis et al, 2014) were used to
produce a customized deletion collection of over 5000 strains, where each haploid strain (a
representation strain shown above) has a deletion in a non-essential gene marked by KanMX
marker (yorf::KanMX), a fusion construct of TDH2-TagRFP, and the reporter construct SHB17-
T2A-ZsGreen. The arrow on T2A indicates co-translational cleavage of T2A from its
downstream protein, ZsGreen. Four ZsGreen units form a functional fluorescent tetramer. Tdh2
functional tetramer is fused to TagRFP protein that fluoresces red. The level of SHB17
expression in each strain is determined by the fluorescence intensity of the deletion collection
through imaging on a Typhoon fluorescence imager or by flow cytometry.
23
Figure 4. Immunoblot of SHB17-GFP and SHB17-T2A-ZsGreen fusion proteins. The
original figure was kindly provided by Yutong Ma (from Caudy lab), which was then rearranged
and cropped for the simplification of presentation as shown here. All strains were grown in YPD.
Protein was extracted from the WT (RCY308) culture at OD600 of 0.99, OD600 = 0.66 for
SHB17-GFP (left panel), OD600 = 0.41 for the SHB17-T2A-ZsGreen (middle panel). For
extracts shown in the right panel, the OD of extraction of WT (RCY308) was 0.74, and 0.54 for
SHB17-GFP (information provided by Yutong Ma). Levels of tubulin are shown as a loading
control. The strain for protein extraction is labeled above the lanes, and the primary antibody
used for analysis is labeled at the left of the lanes.
24
3.2 Confirmation of T2A cleavage
To test whether T2A was functional in cleaving ZsGreen from Shb17 and whether the functional
fluorescent ZsGreen tetramers of the expected fully cleaved size were formed, I ran cell lysates
from strains with T2A-ZsGreen tag and ZsGreen alone on a pseudonative gel. This type of gel
allows separation of proteins in their native non-denatured state. Figure 5 shows the
pseudonative gel that I scanned for green fluorescence on the Typhoon imager. The strain
without the T2A tag has ~100 kDa ZsGreen tetramer, as expected (Yanushevich, Y., et al,
2002). I observed the same size band in extracts from the strain expressing a T2A-ZsGreen tag.
Extracts from the strain without the T2A tag (TDH2-ZsGreen) had a band with shifted mobility,
showing that T2A was indeed functional in my Shb17 reporter construct. Although a small
proportion of higher molecular weight species were detected in the gel images, the majority of
the species in the cell extracts were the cleaved version of my reporter protein. The higher
molecular weight species may be aggregates which are thought to occur in fluorescent proteins
(Yanushevich, Y., et al, 2002). The lower molecular weight band may be monomers of ZsGreen,
although there is no current evidence in the literature that monomeric ZsGreen can form a
functional fluorescent protein.
After T2A cleavage of ZsGreen from Shb17, there was a small amount of T2A peptide
left attached to the Shb17 protein. I tested whether this ‘leftover’ T2A tag affected the activity
of Shb17. I extracted metabolites from the strains expressing the SHB17-T2A-ZsGreen and a
control strain without the tag (wild-type) and the samples were run on the mass spectrometer
(Figure 6). I found that the levels of S7P and SBP in the two extracts were comparable, and I
conclude that the T2A tag is likely to have a minimal effect on Shb17 enzymatic function.
25
Figure 5. Confirmation of T2A cleavage of the ZsGreen protein from Shb17 protein using
a pseudonative gel. Lane 1: extract from strain RCY2116 (has SHB17-T2A-ZsGreen); Lane 2:
biological replicate of lane 1 (RCY2116); Lane 3: extract from strain RCY2171 (has TDH2-
ZsGreen, a positive control for a shift in band); Lane 4: extract from strain RCY1488 (Untagged
ZsGreen protein expression); Lane 5: biological replicate of lane 4 (RCY1488); Lane 6: extract
from strain RCY1742 (non-fluorescent negative control). The position of migration of the Tdh2-
ZsGreen tetramer (~240 kDa) and the ZsGreen tetramer (~100 kDa) are marked with ** and *,
respectively.
26
Figure 6. Tests for enzymatic activity of the SHB17-T2A-ZsGreen and Shb17-T2A
proteins. The top plot shows sedoheptulose-7-phosphate (S7P) levels on the y-axis as the area
under the peak for S7P value as units with the x-axis indicating strains, and the bottom plot
shows sedoheptulose-1,7-bisphosphate (SBP) levels on the y-axis as the area under the peak for
SBP as units with the x-axis for strains from which metabolites were extracted. S7P and SBP
levels were measured via mass spectrometry. Blank: No cell extract control; Sample SHB17-
T2A-ZsGreen_1: extracts from RCY2116 (has SHB17-T2A-ZsGreen), from 3 biological
replicates; Sample SHB17-T2A-ZsGreen_2: extract from RCY2116, from 3 biological replicates
(extraction performed on a different day from that of Sample SHB17-T2A-ZsGreen_1); Sample
WT: metabolites extracted from RCY308 (wild-type), from 2 biological replicates.
27
3.3 Shb17 activity correlates with Shb17 protein levels
In riboneogenesis (Clasquin et al, 2011), the flux through Shb17, as measured by the degree of
conversion from SBP to S7P, increases as nutrient supplements are added to minimal media (see
Figure 2). To test whether the increased activity through Shb17 was due to a higher level of
Shb17 enzyme, I constructed a ZsGreen fluorescent reporter for the SHB17 gene and measured
Shb17 protein levels by fluorescence using flow cytometry (Figure 7). I grew my Shb17-T2A-
ZsGreen strain under different nutrient conditions supplemented with amino acids, nucleotides,
and fatty acids that were similar to the conditions used in the previously published flux assay
(Clasquin et al, 2011). In this prior work, flux through Shb17 was measured by determining how
much of the S7P pool was derived from riboneogenesis as opposed to the PPP. In this
experiment, 13
C-labeled glucose-6-phosphate (G6P with 13
C on its 6th
carbon position) was fed
to wild-type cells. Sedoheptulse-1,7-bisphosphate (SBP) was found by Clasquin et al (2011) to
be made from a 4-carbon compound erythrose-4-phosphate (E4P) and a 3-carbon compound
dihydroxyacetone-phosphate (DHAP), where both compounds are derived from the labeled G6P
and thus each are labeled with one 13
C. The seven carbon compound SBP is hence labeled with
two 13
C, and S7P made from SBP through Shb17 protein is therefore also doubly labeled with
13C. Meanwhile, S7P made through the pentose phosphate pathway from singly labeled G6P is
initially mostly singly labeled. As the mass of the doubly labeled and singly labeled S7P differ,
the contribution or flux from SBP to S7P via Shb17 protein can be found by measuring the
portion of S7P that is doubly labeled compared to the singly labeled. The dissimilarity in the
mass allows quantitation for each of the differentially labeled compounds by running the cell
extracts on the mass spectrometer. In summary, S7P becomes differentially labeled depending
on the pathway (riboneogenesis or PPP) that was used to make S7P, and the differentially
labeled S7P was measured on a mass spectrometer to determine the metabolic flow or flux
through Shb17. A comparison of Figures 2 and 7 suggest that Shb17 enzyme levels that I
measured by green fluorescence in the flow cytometer mirrored the enzymatic Shb17 flux levels
under similar conditions. I observed that Shb17 enzyme levels were doubled in supplemented
versus minimal media, a pattern also observed in the Shb17 flux assay (Clasquin et al, figure
reproduced in Figure 2). However, the graded response that was observed in the flux
measurement was not seen. These results show that different levels of Shb17 can be detected in
the flow cytometer, and that Shb17 enzyme levels are regulated by nutrient conditions.
Although the results do not preclude the possibility that Shb17 may be influenced by allosteric
28
regulation and other mechanisms, it appears that the increased metabolic flux through Shb17
(Clasquin et al, 2011) is at least in part due to increased levels of Shb17. Addition of DTT
during growth approximately 2 hours before measurement on the flow cytometer resulted in a
high level of fluorescence (not shown), which was unexpected from the SHB17 transcript data
(Gasch et al, 2000); we suspect that the reducing agent may be affecting the fluorophore itself in
this condition.
29
Figure 7. Flow cytometry measurement of ZsGreen reporter for SHB17 expression across
nutrient conditions. Supplementation of strain RCY2118 with nutrients similar to that used in
Clasquin et al (2011) (see Figure 2) is shown. Green fluorescence was measured in the flow
cytometer and quantified in arbitrary fluorescence units (y-axis) for the nutrient conditions in
which RCY2118 was grown (x-axis). Error bars refer to two biological replicates per nutrient
condition. Minimal refers to yeast nitrogen base (YNB) + glucose; aa = 20 amino acid mix (see
Methods for full description of the nutrient supplements).
30
3.4 Typhoon fluorescent scanning for initial screen of potential
regulators of SHB17
Having validated my experimental system, I next used both YNB (yeast nitrogen base) minimal
media and YPD rich media for growing my customized deletion set. I scanned my reporter
deletion arrays on the Typhoon fluorescent imager to detect the red and green fluorescence from
each colony (see Figure 8 for an example plate). After scanning, I normalized the raw data in
two ways: first, I normalized the green to the red fluorescence from each colony (Haass et al,
2007); and second, I normalized the green fluorescence from each colony to the green
fluorescence of the wild-type colony that was present in each row of the same plate. I calculated
4 Z-scores for each strain, from YPD and YNB media, and for RFP and WT normalizations. An
example of the distribution of normalized green fluorescence from deletion strains that showed
increased Shb17 levels, and deletion strains with decreased Shb17 levels is shown in Figure 9. I
then followed up on 183 candidates with Z-scores below -2 or above 2. I also followed up on
100 genes from my deletion set that were candidate regulators of SHB17, as suggested by
previous high-throughput studies; included in this list were the genes from recent large scale
mRNA expression profiling experiment (Kemmeren et al, 2014), which revealed three genes,
XRN1, GRR1, and YAP1, whose deletion affected SHB17 transcript levels. I also mined
Yeastract (a compilation of transcription factor binding site sequences and proteins that bind the
SHB17 promoter region in large scale studies using chromatin immunoprecipitation) (Teixeria et
al, 2014), BioGrid (Chatr-Aryamontri et al, 2012), DRYGIN (genes with genetic interactions
with SHB17) (Koh et al, 2010), and GeneMania (protein-protein interaction with Shb17 based
on large scale studies) (Warde-Farley et al, 2010). In combination, I selected 283 strains for my
first candidate array, which I analyzed with the Typhoon imager.
31
Figure 8. Identifying regulators of SHB17 through Typhoon readout. Image of a typical
plate of strains assessed in a high-throughput fashion for green and red fluorescence from my
customized deletion collection (contains SHB17-T2A-ZsGreen and TDH2-TagRFP; see Figure
3) as a readout from a Typhoon imager. The green and red fluorescence channels are overlaid in
this image. Broadly three types of colonies are shown: 1) green colonies for strains with higher
green fluorescence than the red fluorescence signal; 2) red colonies for strains with higher red
fluorescence than green fluorescence; and 3) orange colonies for strains with approximately
equal red and green fluorescence.
32
Figure 9. Z-scores of the entire deletion collection scanned on the Typhoon and
visualization of the cut offs for downstream analysis. Distribution of log2 GFP:RFP ratios
from the genome-wide analysis of SHB17 (Z less than -2) or increase in SHB17-T2A-ZsGreen
reporter expression (Z greater than 2). The y-axis represents the Log2 of Z-scores found by
calculating the standard deviation away from the mean in the entire customized deletion
collection.
33
3.5 Comparison of normalization methods
During data analysis, I realized that there was high variation in measurements from the RFP
channel from the Typhoon imager, when using normalization of the green to red channels
(Figure 10). Also, when I used the signal of mutant versus wild-type in the green channel to
normalize, I found that certain putative regulators – tal1, xrn1, and grr1 – were more
pronounced hits than found using the ‘Green/Red’ method. Therefore, I compared the Z-scores
obtained using the two normalization methods for the follow-up set that I scanned on the
Typhoon. As shown in Figures 11 and 12, the correlation between Z-scores of the two
normalization methods was not very high. Since the red fluorescence from my screen had a
higher variation, I thought the variation in red fluorescence may be driving the changes seen in
‘Green/Red’ normalization. The correlations between the Z-scores of Red only (Red
fluorescence from the WT normalized to red fluorescence from the mutant) and ‘Green/Red’
were quite high (Figures 11 and 12).
In Figure 13, I show the overlap of genes that are putative positive and negative regulators of
Shb17, predicted by the two normalization methods applied to my Typhoon data. I observed no
overlap between the two datasets, and the effect of some deletions of Shb17 enzyme levels was
dependent on the growth condition in the experiment (Figures 13), suggesting that they are
regulators responding to nutrient levels that affect Shb17 abundance. I also analyzed the follow-
up candidate genes for functional relatedness using the Gene Ontology.
Gene deletions that caused an increase in Shb17 levels were highly enriched in processes
such as mitochondrial organization, and ubiquinone metabolic processes. In addition to these
pathways, I saw that deletion of transaldolase and transketolase caused an increase in Shb17
abundance. Deletion of transaldolase is known to increase flux through Shb17 (Clasquin et al,
2011), although it was not previously known whether this was the result of a change in protein
levels. It remains to be seen whether and how transketolase is also having an effect on Shb17
levels. In addition, I observed that mutation of several potential positive regulators resulted in
decreased levels of Shb17. This group of genes showed functional enrichment for roles in RNA
metabolic processes, and macromolecule biosynthetic processes. This group included two of the
three genes whose deletion caused a decrease in SHB17 transcript levels (XRN1, GRR1, YAP1)
(Kemmeren et al, 2014). My deletion reporter screen revealed decreased Shb17 abundance in
xrn1 or grr1, but not for yap1 mutant strains. The reason YAP1 was not a strong hit in my screen
may be due to differences in media conditions used between my screen and the media used by
34
Kemmeren et al (2014) study (rich media). Also YAP1 is only needed as a transcription factor
upon oxidative stress, and Yap1 is cytoplasmic in normoxia (Gulshan, K., et al, 2005), so I did
not necessarily expect to pick up yap1 as a regulator under the normoxic conditions that I used.
For further follow-up, I created a new array (“follow-up” set) of 445 strains to be
scanned in just 5 plates (96 well x 4 replicates per strain) that included the 283 strains mutated
for the genes of interest from the previous follow-up set, as well as an additional 88 wild-type
strains that were placed in a dispersed fashion across the five 96 well plates to facilitate
normalization of green fluorescence of mutants against the green fluorescence from the wild-
type colonies from the same row of the same plate. In this set, additional mutants were added
(74 strains), such as the strains with deletions in genes involved in broad biochemical pathways
that seemed to have been indicated in some of the top hits from the initial and previous follow-
up screen results. These pathways include the PPP, glycolysis, tricarboxylic acid cycle, amino
acid and fatty acid metabolism, pseudohyphal growth, and oxidative stress pathways. The plates
that I refer to as plates A – E in the follow-up set scanned for flow cytometry and Typhoon were
these 5 plates made this way.
35
Figure 10. Variability of fluorescence of red and green fluorescent proteins.
A typical 384-well plate with 4 replicates per strain from the follow-up set (plate ‘A’ shown
where colonies were grown on yeast nitrogen base + glucose agar plate) scanned for green and
red fluorescence. On the top shows the count (frequency) of the strains (the y-axis) with
fluorescence that falls within GenePix processed fluorescence bins (Background fluorescence
subtracted from the Median red fluorescence) of the red channel for all strain on the plate (the x-
axis). On the bottom shows counts (the y-axis) for the GenePix processed fluorescence value
(Background fluorescence subtracted from the Median green fluorescence) of the green channel
for all strains on the plate (the x-axis). The 4 replicate colonies were averaged in fluorescence
and green and red fluorescence channels for the same colonies from the same plate were
separately plotted.
36
YPD
Green (mut/WT)
Green/Red
Figure 11. Green normalization method has a low correlation with Green/Red method
from the Typhoon scans. Fluorescence found from YPD plate growth and expressed as Z-
scores are shown. On the top is a plot of untransformed Z-scores (Benjamini-Hochberg analysis)
for normalization of green fluorescence of mutant colony by the green fluorescence of wild-type
colony in the same row of the same plate (the y-axis), compared with the untransformed Z-
scores (Benjamini-Hochberg analysis) for normalization of the green fluorescence to the red
fluorescence from the same colony. On the bottom is a plot of untransformed Z-scores
Red
(WT/Mut)
Green/Red
37
(Benjamini-Hochberg analysis) for normalization of red fluorescence from mutant colony by the
red fluorescence from the wild-type colony in the same row of the same plate (the y-axis),
compared with the untransformed Z-scores (Benjamini-Hochberg analysis) for normalization of
the green fluorescence to the red fluorescence from the same colony. For both red and green
channels, if more than two wild-types were present per row, the values were averaged and
subsequently used for normalization.
38
Figure 12. Green normalization method has a low correlation with Green/Red method
from the Typhoon scans. Fluorescence found from YNB plate growth and expressed as Z
scores are shown. The Green and Red refer to single channel fluorescence normalized to the
wild types on the plate. See the figure description for Figure 11 for normalization methods
indicated in the y- and x-axes.
YNB
Green/Red
Green (mut/WT)
Red
(WT/Mut)
Green/Red
39
Figure 13. Venn diagram of significant Z-scores that overlap between media and
normalization methods. The follow-up deletion collection grown on YPD and YNB was
scanned on a Typhoon imager, and 283 strains with decreased (NEG) or increased (POS) Shb17
levels were selected. The number of strains with a significant Z-score after growth on YNB and
YPD are shown, and the diagram illustrates overlap of hits in the two conditions. The results
after normalization to RFP levels in the colonies (left diagram) and normalization to WT Shb17
expression levels (right diagram) are shown.
40
3.6 Complementary fluorescence measurement with flow
cytometry
We decided to use flow cytometry for measuring fluorescence from the mutants as an
orthogonal approach for validation and to better understand the inconsistent results between the
two normalization methods for the Typhoon scanned data (Green (mut/WT) and Green/Red). I
fixed the sets of follow-up strains with ethanol (fixation with formaldehyde was tested and
thought inferior to fixation with ethanol due to technical reasons on the flow cytometer), which
were run on the flow cytometer. The reporter for Shb17 was ZsGreen, which is compatible with
fixation and flow cytometry. I constructed a wild-type control strain that also had the SHB17-
T2A-ZsGreen reporter, but with a blue fluorescent reporter TagBFP fused to TDH2. As
illustrated in Figure 14, for the flow cytometry experiments, each well of a 96 well plate
contained two populations of cells grown in the same conditions: [1] the experimental mutant
haploid from the prototrophic deletion set that I used for Typhoon experiments; [2] the control
TagBFP strain mentioned above of the same mating type. The red mutant and blue control were
differentiated by the red TagRFP (indicative of the mutant strain) and blue TagBFP (indicative
of the WT strain) fluorescence measured by the flow cytometer, and from each red or blue cell,
the green fluorescence was extracted. The mean of green fluorescence for all cells in the well
was compared between the mutant and control to find differences in Shb17 levels between the
two populations. Shown in Figure 14 are examples of readouts from the flow cytometer. For a
deletion that did not change Shb17 levels, the green fluorescence between the red mutant
population and the blue control population was similar and is shown as an overlay. For a
deletion that caused an increase in Shb17 levels, the green fluorescence from the red mutant
population was higher than from the blue control population, consistent with a role for the
deleted gene as a repressor of Shb17 (and vice versa in fluorescence for deletions that caused a
decrease in Shb17, the putative activators).
41
Figure 14. Validation of the putative hits from high-throughput screening by flow
cytometry. In 96 well plates, two populations were grown in each well: 1) the prototrophic
deletion strain with SHB17-T2A-ZsGreen marked by TDH2-TagRFP, and 2) control population
with the same reporter for SHB17, marked with blue color (TDH2-TagBFP), and with wild-type
(WT) version of the yeast ORF (YORF). The flow cytometry measures red, blue, and green
fluorescence from each well. Right panel shows representative examples of the different levels
of green fluorescence in the two populations, as measured by flow cytometry. The x-axis is the
green fluorescence from ZsGreen indicating Shb17 protein level from both red and blue control
from the same well as measured by flow cytometery. The y-axis is the number of cells
(normalized to mode) (See Figure 21 for explanation of the y-axis).
42
Considering some future downstream metabolite experiments that are typically
performed in liquid culture, I was interested in assessing whether growth in liquid medium as
opposed to growth as colonies on plates affected Shb17 expression. I grew the prototrophic
deletion (with SHB17-T2A-ZsGreen, and TDH2-TagRFP) and the control colonies (with
SHB17-T2A-ZsGreen, TDH2-TagBFP, and wild-type version of the gene knocked out in the
prototrophic deletion strain) separately on agar (YNB or YPD), and pinned them together onto
the same agar solid media (YNB or YPD). The mix of population was then grown overnight to
minimize the chances of the faster growing populations overtaking the slower growing
populations. To find the liquid growth effect, I pinned the colonies into liquid media (YNB or
YPD) in 96 well plates, and after growth, I fixed them with ethanol. To test for a solid media
growth effect, I pinned the colonies that were grown overnight on a solid medium in water and
fixed immediately with ethanol. The samples were run on the flow cytometer to find the average
green fluorescence from the red and blue populations. I found the ratio of green from red to
green from blue (Green (red/blue)), and compared the ratio to the WT distribution to find Z-
scores and P-values, from which I performed false discovery rate (FDR) analysis (see Methods
and section 3.7) to find putative regulators with significantly up-regulated or down-regulated
Shb17. For flow cytometry, we found that the in-well blue control was important for increasing
reproducibility between biological replicates, as the normalization of the green from each red
strain to the average green fluorescence of the plate had lower reproducibility than using the
green fluorescence from the blue population as control (Figure 15).
For the follow-up set, I compared the Z-scores found from the Typhoon for the WT
normalization (Green (mut/WT)) and RFP normalization (Green/Red) with the Z-scores found
from the flow cytometry method (Green (red/blue)). Figures 16 and 17 shows the correlation
plots, where the green (mut/WT) normalization for the Typhoon showed higher correlation with
the flow cytometry normalization for both solid and liquid grown samples than the green/red
normalization. From this, it seems that for my experimental system using TDH2-TagRFP as an
internal control, the Green/Red normalization for Typhoon data may be less reliable than Green
(mut/WT) normalization. It seems likely that some of the deletions are affecting Tdh2 levels as
well. The correlation between liquid grown and solid grown strains was also high (Figure 18).
All flow cytometry data were gated as shown in Figure 19.
43
Figure 15. In-well control cells increases reproducibility. Unbudded cells were gated for and
analyzed for all biological replicates, two of which are shown. A) Shown on the x- and y-axes
are green fluorescence normalized to average green fluorescence of the plate in log2 of SHB17
expression level from liquid growths on YPD as measured by fluorescence in flow cytometer.
B) In-well control was incorporated into the analyses. Shown on the x- and y-axes are ratio of
Green (mutant/control) in log2 of SHB17 expression level from liquid growths on YPD as
measured by fluorescence in flow cytometer. Four biological replicates were run on flow
cytometer, from which two were randomly picked and shown as representatives.
A. B.
44
Figure 16. Flow cytometry solid growth results correlate with Typhoon Green
normalization. Shb17 levels were measured by Typhoon and flow cytometry after growth on
YPD media; the Solid vs Solid refers to solid (sld) growth for the typhoon imager measured
against strains grown on solid agar medium for flow cytometer. The x- and y-axes are expressed
45
in Z-scores (Benjamini-Hochberg analysis). On the top is the comparison of the Z-scores of
typhoon scanned data where the green from mutant colony was normalized to the green from
wild-type from the same row of the same plate (the y-axis) to the Z-scores of flow cytometry
measured data where the red population was normalized to the blue control population (the x-
axis). On the bottom is the comparison of the Z-scores of typhoon scanned data where the green
fluorescence from the colony was normalized to the red fluorescence from the same colony (the
y-axis), to the Z-score of fluorescence measured by flow cytometry, where the red population
was normalized to the blue control population (the x-axis). The top and bottom plots used data
collected from the same strains that have undergone different analysis procedures.
46
Figure 17. Flow cytometry liquid growth results correlate with Typhoon Green
normalization. See above legend – shown is growth from YPD media; the Solid vs Liquid
refers to solid (sld) growth for the typhoon imager measured against strains grown on liquid
(liq) medium for flow cytometer. The axes are expressed in Z scores. See figure description for
Figure 16 for explanation of the y- and x-axes.
47
Figure 18. Flow cytometry solid correlates with liquid growth.
The Z-score (Benjamini-Hochberg analysis) found for strains in the follow-up set was found
after quantitating fluorescence from the readout of flow cytometer and plotted on the x- and y-
axes. For each strain, the red population was normalized to the blue control population for
strains grown in solid media and in liquid media, then compared. Growth on YPD is shown.
48
49
Figure 19. FlowJo gating example. From SSC-A vs FSC-A view of the cell populations per
well, the majority of the cells are gated or selected for (“Cells by scatter”) and both large and
small debris are excluded from subsequent analyses. The FSC-W vs FSC-A views of the gated
population were further gated as shown (this view shows the width of how long a cell was in the
laser, so cells stuck together have a wider, bigger pulse that has higher fluorescence; these cells
were removed by gating). Next, SSC-W vs SSC-A views were used to select the main
population. From this population, unbudded cells were gated from SSC-W vs FSC-A view
(empirically, the gated population are known to be unbudded; Adam Rosebrock, personal
communication). Then the histogram of FSC-A was chosen to select small cells as shown by the
width of the line on the histogram (bigger cells are more autofluorescent especially in green and
presumably more protein of interest is expressed in bigger cells, where the major source of
cellular autofluorescence comes from small molecules such as NADPH and Flavin (Tzur, A., et
al, 2011)). From the small cells gate, view of V460-36A (blue) vs YG582_15_A (red) was
chosen (In this example, the red population is not visible). Note that the blue and the red came
out of the same small cell population. These gating were applied across wells and checked
across wells for gating precision and quantitative analyses were performed (not shown in figure).
50
The subset of follow-up strains on agar plates was scanned on the Typhoon imager and
were also analyzed on the flow cytometer. For analysis on reproducibility between scanning
times on the Typhoon and possible effects of growth time (and colony sizes, etc) on
fluorescence, I scanned the plates 4 times over 3 days. Figure 19 shows the results from
scanning representative plates with the distribution of green fluorescence from 4 replicate
colonies from each follow-up plate among the blank spots (no colony, a negative fluorescence
control), the mutant strains (the mutants that were subject to a follow-up), and WT colonies (all
different WT strains from the customized collection; various biological replicates represented
per follow-up plate). It seems that about 6 hours after pinning onto agar (the starting point I
typically took for reading colonies over time to observe fluorescence from small colonies), the
colonies are not yet large or developed enough, as scans after 3 days show higher reproducibility
between the replicate colonies in the same plate as well as higher dynamic range of the
fluorescence detected on the Typhoon imager. The distribution of WT, mutant, and blank
overall had the same pattern across the time points. These results highlights the importance of
scanning colonies on a Typhoon imager at time points where colonies have developed
sufficiently for a higher dynamic range of measurement across the strains.
51
Figure 19. Reproducibility and dynamic range for Typhoon increases with the time of
growth from pinning and to scanning; Growth on YNB plates is plotted. PlateA refers to a
plate with colony growth (randomly chosen from plates A to E). Green_A and Green_B on the
x- and y-axes refer to GenePix processed (Background fluorescence subtracted from the Median
52
fluorescence of green for each colony) from the Typhoon scanned fluorescence of replicates in
arbitrary fluorescence units (chosen randomly from 4 replicates, Green_A to Green_D). On the
left panel shows the fluorescence comparison between biological replicates scanned on the first
day (upper panel), and on the third day (lower panel). The same plate was scanned over days
after further growth at room temperature. On the right panel shows the distribution of
fluorescence (GenePix processed values where background fluorescence was subtracted from
the Median fluorescence of green for the colony and averaged for the four replicates) in
arbitrary fluorescence units on the x-axis and counts on the y-axis. The colour represents the
groups on the plate: 1) WT (blue bars); 2) mutant (“Strain”, green bars); and 3) blank (empty
spot on plates, red bars), for plate that was scanned on the first day (upper panel), and on the
third day (lower panel).
53
3.7 False discovery rate analysis
I performed false discovery rate (FDR) analysis on the liquid and solid flow cytometry dataset
to determine the top putative regulators of Shb17. The top hits included GRR1, which appeared
to be an activator of SHB17 from a microarray study (Kemmeren et al, 2014), and my screen
also suggested an activator role. In the yeast metabolic cycle, we know that SHB17 expression
oscillates with ribosome biosynthesis (Clasquin et al, 2011, and Tu et al, 2005) and a large
number of hits were ribosomal subunits or required for ribosomal assembly. It seems likely that
the ribosomal related genes from my top hits such as RPL19A, RPL20A, EFG1, HMO1, PUF6,
MRT4, SRP40, BUD20, and possibly YPL080C, are involved in regulating SHB17 possibly
through a feedback mechanism in response to nutrients and growth conditions as discussed
below. PUF6, MRT4, and SRP40 are involved in ribosome biogenesis (RiBi) and assembly, and
deletions of these genes caused a ~2-fold upregulation of SHB17.
As shown in Figure 20, comparison between the data from my flow cytometry analysis
and the mRNA levels in the published microarray data (Kemmeren et al, 2014) shows that,
interestingly, my screen identified genes that had no SHB17 transcript changes between the
deletion and WT.
One example of this is LYS14, whose deletion caused a decrease in Shb17 level from my
screen. Lys14 is known to be a transcriptional activator involved in lysine biosynthesis and was
predicted to be a potential Shb17 regulator based on transcription factor binding site sequence
(Yeastract). LYS14 was unexpected as a regulator in terms of its biology, however, as lysine
biosynthesis consumes 2 NADPH per lysine produced (SGD biochemical pathways). If we
assume that the riboneogenesis pathway provides ribose when the oxidative PPP is reduced in
demand (such as when NADPH levels are high), the observed reduction in SHB17 upon deletion
of a lysine biosynthesis activator would not be expected. It seems that redox state alone may not
be sufficient for predicting the regulation of SHB17.
Ribosome biogenesis is one of the most energetically demanding activities in the cell as
exponentially growing yeast need to make about 2000 ribosomes per minute, and over half
(~60%) of total cellular transcription levels are devoted to ribosomal RNA transcription
(Woolford et al, 2013). My results fit with the hypothesized role of riboneogenesis in supplying
ribose for ribosome biogenesis. I hypothesize that the riboneogenesis pathway may be
responding to the levels of ribosome present in the cell, where a defect in ribosomal biogenesis
causes an increase in Shb17 levels, possibly for production of more ribose to offset the ribosome
54
deficiency. It may also be possible that certain ribosomal subunits are involved with SHB17
expression.
One candidate activator, BUD20, is a known RiBi gene, where Bud20 is an export factor
that shuttles pre-60S ribosomal subunit from the nucleus into the cytoplasm for further
processing. The C2H2-zinc finger binding domain of Bud20 is thought to be important for
binding to rRNA in the pre-60S subunit (Bassler et al, 2012).
YPL080C was an interesting hit that consistently showed up as a robust and strong
candidate. When deleted, there was a strong increase in Shb17 levels by fluorescence and also
by immunoblot. YPL080C is a dubious ORF; however, YPL080C is flanked by divergently
transcribed ribosomal protein genes RPL21B and RPS9A, and it is possible that the deletion of
ypl080c caused an inadvertent deletion of regulatory regions for either or both of the flanking
genes.
Efg1 was a candidate activator from my screen. EFG1 is known to be a RiBi gene that is
likely one of the last RiBi assembly factors to fall off the 90S ribosome and aid in the
maturation of 18S rRNA (Wang et al, 2017; Schilling et al, 2012). It is interesting how these
function at molecular level with respect to Shb17, as EFG1 and BUD20 are among the few top
candidates that are putative activators of SHB17, while other candidates that were RiBi genes
were candidate repressors.
TUF1 might be an interesting gene; it is a mitochondrial translation elongation factor,
but a study by Hayano et al (2003) showed that the human ortholog of Tuf1 was one of the
unexpected non-ribosomal proteins and was found to be associated with hNop56-rRNP
complexes that participate in RiBi. The authors speculated that the results might indicate its
involvement in RiBi, as Tuf1 was also found to be localized to the nucleolus in addition to the
mitochondrion (Hayano et al, 2003).
Figures 21 and 22 show the raw flow cytometry readings for the green fluorescence
from the red (mutant) and blue (control) populations, for some top hits of putative repressors
and putative activators.
Comparison of the green normalized Typhoon data and the flow cytometry data from my
follow-up strains showed that the datasets are well correlated (Figure 23) and from these
analyses I was able to pick out regulators of interest (Figure 24), consisting of 10 putative
activators and 10 putative repressors of SHB17 for further validations.
55
Figure 20. Flow cytometry data compared with mRNA levels from large scale screen.
Green arrows indicate ribosomal genes. Blue arrows indicate potential SHB17 regulators that
passed the threshold (fold-change > 1.5 and p-value < 0.01) from Kemmeren et al (2014) large
scale mRNA data for rich media. On the y-axis are the follow-up set grown on YPD and
measured by flow cytometer, where the green fluorescence from red cells were normalized to
the green fluorescence from blue control cells from the same well, then log2 transformed. On
the x-axis are the mRNA levels from the Kemmeren et al (2014) data that were log2
transformed.
Log2 S
hb
17
pro
tein
lev
el b
y f
low
cy
tom
etry
(my d
ata
)
Log2 SHB17 mRNA levels in deletion strains
(Kemmeren et al, 2014)
56
Figure 21. Raw flow cytometry plots for a set of putative repressors.
puf6 rpl19a
puf6 rpl19a
ypl080c
ypl080c
pin4
pin4
57
Plots from FlowJo analyses are shown for typical putative repressors from the follow-up set
measured by flow cytometer. B510_20 filter on the x-axis refers to the emission wavelength
with bandwidth of 20 nm; this captures green fluorescence from ZsGreen protein indicative of
Shb17 levels. SSC-A on x-axis refers to the area of the side scatter that is positively correlated
with the granularity or complexity of the cells (and the size of the cells to some extent (Tzur et
al, 2011)). The y-axes is the number of cells (normalized to mode; represents data as percentage
of maximum, which was found by the number of cells in each bin (values on the x-axis) divided
by the number of cells in the bin with the largest number of cells (Yin et al, 2015)). Comparison
of the side scatters between the two populations along with the green fluorescence
measurements were necessary as level of protein may be positively correlated with the cell size
and/or complexity.
58
Figure 22. Raw flow cytometry plots for a set of putative activators. B510_20 filter refers to
the green fluorescence. SSC-A refers to the granularity of cells. See figure description of Figure
21 for more detailed explanation of SSC-A and B510_20.
bud20
bud20
grr1
grr1
gcn5
gcn5
tuf1
tuf1
59
Figure 23. Flow cytometry measurement of reporter expression correlates with whole
colony imaging. Fluorescence intensities from colonies scanned on the Typhoon and population
averages of cells measured on the flow cytometer from the follow-up set are shown. On the y-
axis is the Z-scores (Benjamini-Hochberg analysis) of typhoon scanned colonies where the
green fluorescence from mutant colony was normalized to the green fluorescence from the wild-
type(s) from the same row of the same plate. On the x-axis is the Z-scores (Benjamini-Hochberg
analysis) of flow cytometry measured strains where colonies were first grown on solid YPD
media, then transferred to liquid for measurement on flow cytometer; the red fluorescence was
normalized to the blue control fluorescence from the same well. The strains used for Typhoon
imaging and flow cytometry were grown from the same follow-up colonies.
60
Figure 24. The putative regulators from the screens and validation screens. Dark blue
circles indicate ribosome related genes, and the light blue circle indicates a possibly ribosome
related gene. The candidates were chosen from comparisons of normalized fluorescence from
the follow-up strains used for the Typhoon and flow cytometry (both solid and liquid growth)
that were grown on YPD and YNB media.
61
3.8 FACS screening of deletion collection
I also grew the deletion collection on YPD and YNB agar, and pooled the deletion collection
from the same media. This pool was grown to log phase, and with the help of Dr. Adam
Rosebrock, was sorted using Fluorescence Activated Cell Sorting (FACS). I obtained cells with
the highest and lowest Shb17 levels by selecting for the 1% top-most and 1% bottom-most
fluorescence indicative of Shb17 levels, as well as for a wider selection of fluorescent cells.
Since these strains were bar-coded, we will be able to sequence the strains that fell into each of
these categories as further validation of our screening methods (Figure 25). I grew the sorted
populations of cells on both liquid and agar media. To determine whether reporter changes were
stable following sorting, cells from the sorted populations grown on agar media (YPD and YNB)
were each picked into 96 well plates (for high number of replicates) with liquid YPD or YNB.
For this analysis, I picked 4 x 96 colonies from the agar plates into a 96 well plate (96 colonies
per replicate per high or low fluorescence). The replicates were biological replicates and
represent different colonies that were picked from the growth plates. Picked colonies in 4 x 96
well plates in liquid media were regrown at 30°C and retested for reporter fluorescence by flow
cytometry. Figure 26 shows that the strains that were sorted to be high fluorescence and low
fluorescence indeed stayed high and low fluorescence after regrowing and retesting, indicating
that the sorting itself did not yield spuriously high or low fluorescent strains in sorted pools. As
the strains are barcoded, it is possible to extract the genomic DNA of the high and low
fluorescent strains from each pool and sequence them to find putative SHB17 regulators.
62
Figure 25. Schematic of fluorescence activated cell sorting procedure and validation of
sorted cells. First the entire customized deletion collection grown on YNB or YPD were pooled,
each expressing a level of ZsGreen fluorescence indicative of Shb17 levels. The populations
were FACS sorted into bottom and top tier fluorescence. These were separately plated to agar
plate for further growth. 96 colonies from each group of fluorescence was picked and grown in
liquid for further validation of fluorescence. The plates were pooled and frozen for barcode
sequencing. For results of FACS and flow cytometry validation data, see Figure 26.
63
64
Figure 26. Reporter expression changes are stable following sorting. Cells from the deletion
pool (from YPD growth) were sorted into pools of high and low fluorescence using FACS.
These sorted high and low fluorescence pools were regrown on agar media (YPD + G418) and
65
individual colonies were picked into 96 well plates with liquid YPD + G418 to retest the picked
colonies for reporter fluorescence by flow cytometry. Each replicate shows measurements from
96 picked colonies (4 x 96 colonies measured in flow cytometry as validation; the replicates
represent different colonies picked and are biological replicates). Red dashed lines indicate
average fluorescence of control cells. The x-axis shows the green fluorescence as measured by
the flow cytometer, in arbitrary fluorescence units, and the y-axis shows the count or frequency
of data falling in the bins (range of values on the x-axis).
66
3.9 Direction into ribose as further validations
As noted earlier, ribose is important for making the ribosomal RNA component of ribosomes.
Ribosome biogenesis (RiBi) requires a significant amount of cellular resources; as Shb17 links
glycolysis to non-oxidative PPP, the ribose provided through the riboneogenic route via Shb17
may be used for RiBi. Hence, SHB17 may be regulated in conditions that alter the need for RiBi.
Tu et al (2005) data of metabolically cycling cells under glucose limited conditions showed that
SHB17 transcription coincides with transcription of ribosomal proteins (Figure 27). Data
analysis of gene expression after stress also point to co-regulation of SHB17 and some
ribosome-related genes. In microarray data from the stress conditions such as osmotic stress
(O’Rourke et al, 2004), general stress response (Hao et al, 2011), oxidative stress by hydrogen
peroxide (Guan et al, 2012), and by lack of potassium (Barreto et al, 2012) SHB17 shows
coordinated expression with some RiBi or ribosomal protein genes, such as SNU13 (an RNA
binding protein involved in rRNA processing), RPL6B (60S subunit protein), and ZUO1
(ribosome chaperone involved in RiBi), and possibly NOP56 (involved with 60S subunit
assembly) (not shown).
67
Figure 27. SHB17 transcription coincides with ribosomal proteins. Graph derived from
Kudlicki et al (2007) visualization of Tu et al (2005) large scale data measuring mRNA
expression levels (microarray, expressed as concentration of mRNA on linear scale, normalized
to unit average) on the y-axis for ribosomal protein genes and SHB17 levels temporally across
the yeast metabolic cycle in minutes on the x-axis.
SHB17
68
3.10 Polysome Profiles for probing ribosome side of the story
I decided to focus on the ribosome related genes from the screen result, as ribosomal genes were
enriched in my set of potential SHB17 repressors. In addition to the expression data discussed
above, a genome-wide chromatin immunoprecipitation study by Jorgensen et al (2004) found
that Sfp1 binds the SHB17 promoter. This is interesting as Sfp1 is a known regulator of RiBi
and ribosomal proteins and it may point to SHB17 being involved with RiBi process or that it
may be a RiBi gene. From my Typhoon initial screen, the Z score of sfp1 deletion was also
significant on both YPD and YNB agar media (-2.6 to -4.0); however, upon re-scanning on the
typhoon and measurement on the flow cytometer with new replicates, sfp1 did not reproduce
this initial result. The Target of Rapamycin (TOR) pathway regulates ribosome biogenesis
partly through the transcription factor Sfp1 (Loewith et al, 2011), and TOR inhibition reduces
ribosomal protein levels (Steffen et al, 2008). To investigate possible links between SHB17 and
the TOR pathway, transcriptomic data that are available from GEO from NCBI were analyzed.
From looking at several GEO microarray datasets rapamycin had barely any effect on SHB17
levels; the difference was generally less than 0.1 fold difference (data not shown). Nevertheless,
my top hits were enriched in ribosome biogenesis factors and ribosomal proteins, and I wanted
to investigate the polysome profiles of my top hits along with shb17 deletion.
As my screen identified RiBi factors as enriched in candidate regulators, we performed
polysome profiles for the mutants of interest to test if hits including BUD20, YPL080C, PUF6,
and RPL19A have a role in RiBi. Polysome profiling is one method to test if there is disruption
in RiBi, as it measures and detects the ribosomal subunits 40S and 60S, as well as assembled
80S ribosomes and polysomes (multiple 80S ribosomes bound on an mRNA). For the polysome
profiling experiment, we used the puf6 deletion as a positive control since it was one of the
putative repressors from my screen, and Zhihua et al (2009) showed that Puf6 sediments with
the 60S fraction and that deletion of puf6 caused a decrease in 60S (and an increase in 40S
subunits) compared to the WT at 20°C as seen in their polysome profiles (lower temperature
allows observation of enhanced ribosomal defect).
Once the ribosomal RNA has been processed, the production of 40S and 60S occur
separately, allowing accumulation of single subunits as a result of ribosome biogenesis defects
that are associated with the steps required for ribosome assembly and processing of individual
subunits (Zhihua et al, 2009). We measured the levels of ribosomal subunits and polysomes in
the WT, and mutants in shb17, puf6, rpl19a, and bud20 (Figures 28 to 33). As expected, rpl19a
69
had a marked decrease in the level of 60S; this makes sense as RPL19A is known to be a 60S
large ribosomal subunit protein. Since a paralog of RPL19A exists (rpl19b) (Byrne et al, 2005
and SGD), I think it is possible that the two paralogs rpl19a and rpl19b may be transcribed at
different levels (which can be measured by quantitative RT-qPCR with primers specific for each
paralog), or it may be that the two paralogs are regulated differently such as at the level of
mRNA splicing and protein degradation. Since rpl19a showed a remarkable decrease in 60S
levels in my polysome profiles, it may be possible that RPL19A may be the main contributor
over its paralog for Rpl19 protein levels and/or function in ribosome biogenesis of the 60S
subunit. It is also possible that the two paralogs are transcribed and regulated similarly but have
acquired neofunctionalization for one of the pairs, where RPL19A presumably has a greater role
in the 60S biogenesis. Since the majority of RPL paralog pairs are >98% identical at the amino
acid level (Steffen et al, 2008), it may be possible that a functional diversification plays no or a
small role for RPL19. We also observed halfmers for rpl19a, which is in accordance with the
reduced 60S that lead to more 40S that is associated with the 80S particles.
It is interesting that the bud20 deletion strain also showed a similar defect as rpl19a
deletion in terms of the ribosome ratios, as there was a depletion in 60S, 80S, and polysomes.
Interestingly, ypl080c might be acting similarly to bud20 or puf6 according to the polysome
profiles and it may be that the deletion of ypl080c alters ribosome biogenesis (as discussed
below, it likely disrupts the regulation of flanking ribosomal genes).
One of my hypotheses was that Puf6 may be binding SHB17 mRNA to explain why Puf6
may be a repressor for SHB17. As it is known that Puf6 binds mRNAs such as ASH1 and
represses translation (Shahbabian et al, 2014), I thought this could be one mechanism of SHB17
regulation. Dr. Frederick Roth suggested I look at the ChIP-chip and genome-wide RNA-
immunoprecipitation data from his lab by Suzanne Komili, who studied the association of Puf6
with mRNAs, and performed transcriptional profiling of the cells lacking Puf6 to test if Puf6
regulates the transcription levels of their targets. In this study, Puf6 was tested for genomic
binding in the presence of RNase, as ChIP-chip crosslinking is known to cause mRNA-binding
proteins to immunoprecipitate with the bound mRNA and the DNA loci, regardless of whether
the proteins bind the DNA directly or only with the nascent mRNA transcript. Their findings
suggest that Puf6 binds via the mRNA for the majority of their novel targets. From my search on
this Puf6 RNA immunoprecipitation data, it did not look like Puf6 bound to SHB17 mRNA.
Hence, I looked at Puf6 from another perspective; perhaps Puf6’s function as a repressor of
70
SHB17 had to do with its function as a RiBi gene. Puf6 is a member of the Pumilio/fem-3
mRNA binding factor (PUF) family of RNA binding proteins that act in post-transcriptional
regulation (Yang et al, 2016). Puf6 is a known 60S assembly factor that is localized primarily in
the nucleolus, recognizing structured RNA but not specific RNA sequences using the unique L-
like shape in its PUM repeats, and interacting with Loc1 and the maturing 60S transiently to
incorporate Rpl43 into the maturing 60S (Wang et al, 2017; Yang et al, 2016). Deletion of puf6
causes reduced biogenesis and export of the 60S subunit in yeast (Li et al, 2009). My polysome
profiles for the prototrophic puf6 deletion grown in YPD at room temperature showed depletion
of 60S and the presence of halfmers as expected (Figure 30).
YPL080C is an interesting dubious ORF as the prototrophic deletion mutant in this ORF
caused a robust high level of Shb17 as measured by my fluorescence assays and immunoblots.
From YEASTRACT, YPL080C is thought to be bound in the promoter region by some
regulators of ribosome biogenesis such as Sfp1 (microarray in YPD medium in mid-log phase),
Fhl1 (ChIp-on-chip in YPD and SC in mid-log phase), and Ifh1 (ChIp-on-chip, YPD medium).
However, as can be seen in Figure 34, the promoter region of YPL080C is in close proximity to
the promoter region of one of its flanking genes, RPL21B (YPL079W), and seems to share the
transcription binding sites for Sfp1, Fhl1, and Ifh1. In addition, genome-wide sequence-specific
RNA sequencing (Walters et al, 2017) indicates that YPL080C is likely not expressed. In both
minimal and rich media (YEPD), the expression level of YPL080C was around ~0 to 20. While
the nearby dubious ORF such as YPL073C had expression of 0 to 1, the flanking genes for
YPL080C had high level of mRNA expression; RPS9A had levels around 400, while for
RPL21B the measured levels were around 2000. It is likely that YPL080C is not actually
expressed as the flanking genes are high in expression, and it is known that active chromatin can
lead to divergent transcription and more transcription around it due to nucleosome-free region
around the promoter (Seila et al, 2009). Consistent with this idea, the mRNA levels of the genes
flanking the other dubious ORF (YPL073C) were considerably less (0 to ~140). YPL080C
region has some sequence similarity with other organisms. From BLAST analysis, 5
Saccharomyces species (pastorianus, paradoxus, mikatae, arboricola, kudriavzevii) had 67 to
99% similarity in sequence with S. cerevisiae YPL080C. This may be explained by close
evolutionary relationship between these Saccharomyces species; for example, the upstream
sequence of SHB17 (promoter region) has 71% to 95% sequence similarity to other fungal
species such as Saccharomyces pastoranus, paradoxus, mikatae, arboricola, and kudriavzevii
71
(among other fungal species) as well. Another part in considering whether YPL080C is
functional is to consider if it can be translated to protein. The Kozak sequence on mRNA helps
ribosomes to bind the AUG start codon in eukaryotes, and has a consensus sequence of
(A/G)CCAUGG in the context of the AUG start codon (Martin et al, 2016). For YPL080C, it
contains the G after AUG start codon but its upstream sequence does not seem to contain the
Kozak sequence. Hence, it is likely that the effect on Shb17 seen in the ypl080c deletion in my
data is due to the deletion of the regions flanking ypl080c. Although the location of the deletion
is known, sequencing of ypl080c deletion strain would confirm the identity of the strain. In
addition, it may be that YPL080C was called a gene in the first place because it had a start and a
stop codon that encompassed a 100 amino acid length of putative peptide by chance. It may be
likely that deletion in YPL080C was causing deletions in certain promoter elements of the
flanking gene, RPL21B. In particular, since the 60S subunit level is decreased in the polysome
profiles for ypl080c deletion (Figure 32), I would think the ypl080c deletion affected RPL21B
more extensively than the other flanking gene (RPS9A). It is interesting to note that deletion in
rpl21b or rps9a had less of an effect on SHB17 expression in my initial screen than ypl080c
deletion, suggesting that the ypl080c deletion may alter the expression of both flanking genes,
leading to greater phenotypes. Validating ypl080c by complementation assay is one way to test
this idea (See below for more details).
An interesting pattern is that puf6, ypl080c, and rpl19a all have low 60S compared to
40S and the control strain, contain halfmers, and are putative repressors of SHB17. bud20 on the
other hand has high 60S compared to the 40S and the control strain, contains halfmers in its
profile, and is a putative activator of SHB17. From my data so far, it looks as though having low
60S to 40S is indicative of a repressor, while high 60S to 40S is indicative of an activator. More
polysome profiles on other putative activators and repressors may be needed to test the
hypothesis that levels of 60S (and perhaps the presence of halfmers and the biological
implications this pertains) are indicative of the regulation of SHB17. For example, Mrt4 is a
nuclear paralog of P0 stalk protein in the ribosome and is an assembly factor for the pre-60S at
early stage of 60S biogenesis in the nucleolus (Lo et al, 2009). Mrt4 is a putative repressor of
SHB17 from my screen and immunoblot (Figure 36) and although I have not performed
polysome profiling on the mrt4 deletion strain in my growth conditions, published polysome
profiles in the mrt4 deletion gene show a significant deficit in 60S subunits in the W303 strain
background grown in YP galactose (Rodriguez-Mateos et al, 2009), consistent with repressors
72
of SHB17 possibly being associated with low 60S levels. On the other hand, gcn5 deletion, one
of the top hits from my candidate regulators, was found to decrease SHB17 levels from my
fluorescence assays. In 2009 study by Bonander et al, deletion in gcn5 was found to increase the
transcript level of a RiBi gene BMS1, which when overexpressed and analyzed by polysome
profiling, was found to increase 60S levels in a dose-dependent manner. Hence, deletion of gcn5,
which was found to be a strong candidate for an activator of Shb17 from my assays, may also
cause increase in 60S, similar to the bud20 deletion polysome profile observed in this work. It
will be interesting to explore how Shb17 level may be regulated by the 60S level.
A known example in yeast where the frequency of translation is increased specifically
due to the lack of 60S subunits is GCN4. In this case, the initiation of translation at the
inhibitory upstream ORF (uORF) present in the GCN4 5’ leader region is thought to be reduced
by a lack of the 60S subunit, causing increased translation and production of Gcn4 (Steffen et al,
2008). There is yet no reported uORF for SHB17 (uORFdb from Wethmar et al, 2014), and
SHB17 is not one of the genes with conserved uORFs (Cvijovic et al, 2007). Hence, a more
likely explanation for the increased Shb17 levels possibly being associated with a reduced level
of 60S subunits may be that change in the ratio of ribosomal subunits results in altered SHB17
translation. This type of regulation is known to occur as described by Bonander et al (2009). In
this study, the authors found that increasing the transcript level of a ribosome biogenesis factor
BMS1 by doxycycline-induced expression caused a perturbation in the ratio of 60S to 40S
subunits (increased 60S relative to the 40S) and altered the translation and yield of recombinant
proteins, Fps1 and green fluorescent protein. Hence, it is possible that Shb17 is regulated
through the ratio of 60S to 40S. One way this hypothesis can be tested is to use the 60S inhibitor
diazaborine or Rbins (Kawashima et al, 2016) (see Future Directions for details).
It is also interesting to note that the deletion in the nuclear export factor BUD20 caused
alteration in the polysome profiles compared to the WT, as it is known that several export
factors play a redundant role in ribosomal subunit export (Konikkat et al, 2017). It may be
possible that Bud20 has a predominant role over other export factors with respect to SHB17
expression.
To test if Shb17 protein might be binding to ribosomal subunits or ribosomes, I
performed immunoblotting on the collected fractions after protein precipitation for the WT and
puf6 deletion, which were samples that were fractionated for the polysome profiling assay. The
binding between Shb17 and the ribosomal subunits were detected highly at the sample peak at
73
the top of the gradient. There is a significantly fainter band for Shb17 at the beginning of the
60S fraction where Tub1 is no longer detected (Figure 35); however, it is likely that this
detection of Shb17 may be a carryover from the sample peak fractions as we would expect a
stronger Shb17 band for the 60S fraction and likely a continuation of Shb17 band in the 80S and
the polysome fractions if Shb17 was binding the 60S ribosomal subunit. It is possible that
Shb17 may be binding transiently to the maturing 60S (or other subunits) similarly to an
assembly factor that could not be detected by polysome profiling in the puf6 deletion; one may
have to probe this question by using specific mutants in ribosome biogenesis components and
finding whether Shb17 is associated with the development stage of the ribosomal subunits.
Published studies using immunoprecipitation-mass spectrometry in yeast that purified pre-60S
components in several deletion mutants of early to late acting RPL genes identified assembly
factors associated with the maturing 60S from early to late assembly phases (Gamalinda et al,
2013). From this list (that include known assembly factors such as Bud20, Mrt4, and Puf6),
Shb17 did not seem to be binding the pre-60S complex or the assembly factors associated with
the pre-60S.
In conclusion, the level of 60S is thought to be regulating Shb17 levels; higher 60S
levels seem to be decreasing Shb17 levels and lower 60S levels seem to be increasing Shb17
levels. If defect in ribosome biogenesis and assembly is not causing a general defect in protein
synthesis (see Future Directions), it would be interesting to understand how differences in the
levels of 60S regulate Shb17 protein level. One theory I have is that the regulation is through
another component in the cell. If there are candidate regulators of Shb17 that are regulated by a
uORF, then perhaps this influences Shb17 levels in turn. As mentioned above, it is known that
the production of Gcn4 protein levels differ by the change in the 60S levels via regulation on
GCN4 uORF; low 60S levels lead to more Gcn4, and high 60S levels lead to lower Gcn4
production (Steffen et al, 2008). My theory is that if there is a high 60S level in mutants such as
bud20 deletion, then there may be low level of the secondary protein that is regulated by the
high 60S level, and (assuming this secondary protein is an activator of Shb17) lead to low levels
of SHB17 expression. Conversely, if there is a low 60S level in mutants such as ypl080c, puf6,
and rpl19a deletion strains, then there may be high level of the secondary protein, and
consequently lead to high level of SHB17 expression.
74
Figure 28. Polysome profile of wild-type cells. Polysome profiles separate ribosomal subunits
40S, 60S, 80S, and polysomes using a sucrose gradient; UV absorbance is used to detect and
quantify the ribonucleoprotein complexes. UV 260 nm signal in arbitrary units (on the y-axis)
was measured over time as fractions were collected (on the x-axis) for detection of ribosomal
subunits. Briefly, strain RCY308 (wild-type) was extracted at 0.4-0.5 OD/ml, lysed, loaded to 7-
47% sucrose gradients, centrifuged, and fractionated for detection of subunits.
40S
60S
80S
polysome
75
Figure 29. Polysome profile of shb17 deletion strain.
UV 260 nm signal in arbitrary units (on the y-axis) was measured over time in minutes or
fractions collected (on the x-axis) for detection of ribosomal subunits. Strain RCY1097 (shb17
deletion) was extracted at 0.4-0.5 OD/ml, lysed, loaded to 7-47% sucrose gradients, centrifuged,
and fractionated.
40S
60S
80S
shb17Δ
76
Figure 30. Polysome profile of puf6 deletion mutant. UV 260 nm signal in arbitrary units (on
the y-axis) was measured over time in minutes or fractions collected (on the x-axis) for
detection of ribosomal subunits. Prototrophic puf6 deletion was extracted at 0.4-0.5 OD/ml,
lysed, loaded to 7-47% sucrose gradients, centrifuged, and fractionated. Dark blue arrow marks
the diminished 60S in comparison to 40S level and wild-type polysome profile. Orange triangles
indicate halfmers.
40S
60S
80S
puf6Δ
77
Figure 31. Polysome profile of ypl080c deletion mutant. UV 260 nm signal in arbitrary units
(on the y-axis) was measured over time in minutes or fractions collected (on the x-axis) for
detection of ribosomal subunits. Prototrophic ypl080c deletion was extracted at 0.4-0.5 OD/ml,
lysed, loaded to 7-47% sucrose gradients, centrifuged, and fractionated. Dark blue arrow marks
diminished 60S in comparison to 40S level and wild-type polysome profile. Orange triangles
indicate halfmers.
40S
60S
80S
ypl080cΔ
78
Figure 32. Polysome profile of bud20 deletion mutant. UV 260 nm signal in arbitrary units
(on the y-axis) was measured over time in minutes or fractions collected (on the x-axis) for
detection of ribosomal subunits. Prototrophic bud20 deletion was extracted at 0.4-0.5 OD/ml,
lysed, loaded to 7-47% sucrose gradients, centrifuged, and fractionated. Light blue arrow marks
increased 60S in comparison to 40S level and wild-type polysome profile. Orange triangles
indicate halfmers.
40S
60S
80S
bud20Δ
79
Figure 33. Polysome profile of rpl19a deletion mutant. UV 260 nm signal in arbitrary units
(on the y-axis) was measured over time in minutes or fractions collected (on the x-axis) for
detection of ribosomal subunits. Prototrophic rpl19a deletion was extracted at 0.4-0.5 OD/ml,
lysed, loaded to 7-47% sucrose gradients, centrifuged, and fractionated. Dark blue arrow marks
the diminished 60S in comparison to 40S level and wild-type polysome profile. Orange triangles
indicate halfmers.
40S
60S
80S
rpl19aΔ
80
Figure 34. Location and orientation of YPL080C. The ORF YPL080C (in blue) is flanked by
two ribosomal protein genes RPS9A (YPL081W) and RPL21B (YPL079W) (in grey). The x-axis
denotes the position on the chromosome. Figure derived from the SGD database.
81
Figure 35. Immunoblot of polysome profiles.
Fractions collected after protein precipitation for polysome profiling of WT and puf6 deletion
strain were immunoblotted for Shb17 and Tub1 control. A) WT strain. Lane 1: ladder; lane 2:
sample peak; lane 3: fraction before 40S, after the sample peak; lane 4: beginning of 60S; lane 5:
beginning of 80S; further lanes are polysomes. B) puf6 deletion. Lane 1: sample peak; lane 2:
ladder; lane 3: fraction before 40S, after the sample peak; lane 4: beginning of 60S; lane 5:
beginning of 80S; further lanes are polysomes.
Shb17
Tub1
A.
Tub1
Shb17
B.
1 2 3 4 5 6 7 8
1 2 3 4 5 6 7 8
82
3.11 Immunoblotting for validation of SHB17 regulators
To confirm the results of my fluorescent screens, I performed immunoblots using an antibody
we raised to a peptide from Shb17 to measure protein levels for the 20 candidate genes in the
prototrophic deletion set without the fluorescent markers (Figure 36). The values were
normalized to a Tub1 loading control and the WT strain. The mutants that had robust data on the
immunoblot results include the gene deletions tuf1, gcn5, bud20, rpl19a, and ypl080c, where the
results validate the fluorescence measurement data from my Typhoon and flow cytometry
screens.
One hypothesis I have is that riboneogenesis may provide ribose for cells growing under
hypoxic conditions in which the oxidative PPP is known to be down-regulated (Celton, M., et al,
2012). For cells that are not in anaerobic conditions, riboneogenesis may still be modulated in
response to the cellular need of RiBi, which requires a significant amount of cellular resources
for its production (Thomson, E., et al, 2013). The results from my screen picked out a large
number of genes involved in RiBi and assembly as putative regulators of Shb17, supporting the
hypothesis that the riboneogenesis pathway may be associated with providing ribose for RiBi.
As hypoxia has been reported to downregulate oxidative PPP (Celton, M., et al, 2012), I
hypothesized that hypoxia may cause significant upregulation of Shb17 in order to
accommodate lowered flux through the oxidative PPP. Contrary to my hypothesis, hypoxic
conditions did not greatly change SHB17 transcript levels compared to the normoxic conditions
according to GEO microarray data (GSE9514 (Protchenko et al, 2008), GSE12004 (Chan and
Roth, 2008), GSE30046 (Vizoso-Vazquez et al, 2012), GSE34286 (Rachfall et al, 2013), not
shown). There is a rather small ~1.4 fold difference in SHB17 levels between normoxia and
hypoxia/anoxia, where SHB17 mRNA was lower in hypoxic conditions. My immunoblots
showed that there is about a small fold change of ~1.2 fold difference in Shb17 levels between
normoxia and hypoxia conditions, where Shb17 was higher in abundance in normoxia (data not
shown). These cells I used for immunoblotting were prepared with galactose as carbon source
and NH4 or glucose as nitrogen source, and grown to 0.4-0.5 OD/ml by Olga Zaslaver in the
same lab. The mRNA data is consistent with my immunoblot where the fold changes were
around the same range and Shb17 was lower in hypoxic conditions than in normoxia; however,
it is unlikely that hypoxic conditions drive upregulation of Shb17 as I initially hypothesized. In
fact, my immunoblots and the transcript data suggest that Shb17 behaves in the opposite
direction to my expectation, where its levels were found to be slightly decreased in hypoxia.
83
Figure 36. Immunoblots of candidate activators and repressors. Candidate regulators whose
effects on Shb17 levels are robustly reproduced in immunoblots are shown. From primary and
validation screens, the putative activators are TUF1 and GCN5; the putative repressors are
YPL080C, IRA2, and MRT4. All strains were grown in YPD in this plot and grown to 0.4-0.6
OD/ml before protein extraction and immunoblotting. The bands from x-ray film was
quantitated with ImageJ software, where the Shb17 to Tub1 ratio was found. This was then
normalized to the Shb17 to Tub1 ratio from the RCY308 wild-type (also extracted at 0.4-0.6
OD/ml range) that was run on the same gel on the same day. The y-axis indicates the ratio of the
Shb17/Tub1 from mutant to the ratio of Shb17/Tub1 from wild-type. Error bars refer to the
replicate immunoblots per strain (at least 2 and up to 6 biological replicates per strain, where the
protein samples were prepared from different days as replicates) performed across different
days. The horizontal line refers to the value at which a strain would have the same Shb17/Tub1
ratio as the wild-type (in which case it would be neither a repressor nor an activator of SHB17).
84
4 Summary of significance and future directions
My project delved into identifying and validating the regulators of riboneogenesis. Currently,
we know that nutrient and metabolism have a regulatory effect on ribosome biogenesis, such as
through the target of rapamycin (TOR) pathway (Loewith and Hall, 2011); however, whether
the ribosome has a regulatory effect on metabolism has not been well characterized. The results
from my screen and ribosome profiling suggest that the ribosome biogenesis, specifically the
level of the 60S subunit, regulates the level of Shb17. The deletion of my putative repressors
that had increased Shb17 levels seem to also have low 60S levels, while the deletion of my
putative activator that had decreased Shb17 levels was associated with high 60S levels. In
addition, my hits were enriched for genes that have function in ribosome biogenesis. It seems
that both my putative repressors and putative activators have a commonality in terms of the
pattern of the 60S levels, leading to the hypothesis that the ribosome may be involved in
regulating riboneogenesis.
As ribosome biogenesis seems to be associated with riboneogenesis, further experiments
can focus on studying how inhibiting the specific assembly of 60S subunit via the use of
diazaborine or the newly discovered ribozinoindoles (Rbins) (Kawashima et al, 2016) would
affect Shb17 production. Diazaborine is known to bind a ribosomal assembly factor Drg1 and
inhibit 60S assembly in the cytoplasm, leading to reduced level of the 60S (Pertschy et al,
2004). Rbins is a specific and reversible inhibitor of Midasin (Rea1 in S. cerevisiae) that is
involved with 60S assembly; this inhibition was shown to occur both in vitro and in vivo
(Kawashima et al, 2016). First, WT strains will be tested with Rbins treatment to see if there is
an accumulation or depletion of the 60S subunit, as there were accumulation or depletion of the
60S in 60S biogenesis defects in my polysome profiles of mutants (I speculate that a defect in a
cytoplasmic assembly step as opposed to a defect in nuclear maturation of pre-60S may
determine the extent of degradation of incorrect pre-60S during quality control and possibly
affect the level of 60S accumulation or depletion). The use of Rbins on the wild-type strain and
assessing its polysome profiles (to check Rbins activity) and Shb17 protein levels (via
immunoblotting and/or fluorescence imaging compared to the wild-type untreated with Rbins)
may be able to test my hypothesis that alteration of the ribosomal subunits (especially the 60S)
regulates the production level of Shb17. If there is a dose-dependent response of Shb17 levels to
the amount of Rbins added, this can further support the hypothesis. If Rbins treatment depletes
85
the 60S, the 60S inhibitors can also be used to specifically target the 60S in mutants where the
60S is already compromised (such as puf6 and it would be interesting to test ypl080c deletion as
well). I would expect that treating these mutants with a dose of 60S inhibitor may not
significantly increase Shb17 levels further than the level of Shb17 in the untreated mutants,
while the 60S inhibitor treatment in the WT may possibly increase Shb17 levels to the level
observed in puf6, ypl080c, or other mutants with reduced 60S. If this inhibitor experiment
confirms my results from the polysome profiles, think it is possible that a feedback loop may be
present in yeast; specifically, my hypothesis would be that the ribosome influences Shb17 levels
in order to control how much ribose is made through Shb17, which may in turn affect the
production of ribosome. Previous studies on recombinant proteins has shown that changes in
ribosomal subunit ratios can lead to differences in recombinant protein yield (Bonander et al,
2009), and it will be interesting to see if Shb17 production control falls under such regulation.
The riboneogenesis pathway may be conserved with some bacteria and other fungal
organisms including fungal pathogens such as Candida albicans (Kim et al, unpublished). Fungi
are eukaryotic, and have close evolutionary relationships with their human host, rendering many
antifungal drugs toxic to humans, as well as limiting the number of drug targets (Cowen, L,E., et
al, 2002). Sedoheptulose bisphosphatases in the SHB17 family are not present in mammals (nor
has the lab observed isotopic labeling patterns consistent with a riboneogenic pathway) and
presents a novel therapeutic target to control fungal growth. Our collaborators have found that
deletion of one or both of the SHB17 homologs in C. albicans conferred defects in hyphal
morphology and reduced biofilm mass, and that Shb17 function in ribose synthesis was helping
to maintain biofilm hyphal morphology (Desai, J.V., and Mitchell, A., unpublished result). It
remains to be seen how targeting SHB17 homologs in C. albicans may influence virulence of
pathogenic yeast.
A future experiment may be to use shb17 deletion in the Σ1278b (Sigma) strain
background and for the deletion of the regulators of SHB17 in the Sigma strain (the Sigma strain
can undergo filamentous growth and allows study of this morphology). These can be compared
in terms of their morphology to observe whether they are able to undergo pseudohyphal growth.
From what we know from the C. albicans data from our collaborator, I think deletion of shb17
or deletion in positive regulator of SHB17 would result in defective pseudohyphal growth under
conditions conducive to pseudohyphal growth. One may test the connection between the level of
Shb17 and the filamentation capacity. I constructed SHB17-T2A-ZsGreen reporter in the Sigma
86
1278b strain background. This reporter can also be introduced into the deletion background of
my putative regulators in Sigma strains. The Shb17 levels can be detected by green fluorescence
and/or immunoblotting, and filament morphology can also be concurrently observed with
microscopy. As filmentation process requires changes in intracellular redox status (Guedouari,
H., et al, 2014), one may try to find the redox levels in these strains and try to answer how
riboneogenesis, which does not have a redox cost, reconciles with the filamentation process. To
check redox level, one can use the enzyme cycling assay (Gibon and Larher, 1997) that I have
been developing in the lab to quantitate the levels of NAD(P)(H).
The story of the hyphal growth and possible link to pathogenic yeast C. albicans is
currently underway. We intend to extract metabolites from the deletions in the SHB17 orthologs
in C. albicans and measure the levels of SBP and S7P. Once we receive the proper strains from
our collaborator, it will be possible to measure SBP and S7P metabolite levels using methods of
C. albicans growth and extraction procedures that I have developed and optimized previously. It
would be interesting to use Calcofluor white stain for example to see whether the hyphae
thickness correlates with the levels of SBP and S7P.
YAP1 was one of the three hits from Kemmeren et al (2014) genome-wide dataset that is
a putative regulator of SHB17. As mentioned before, YAP1 is only needed as a transcription
factor upon oxidative stress, and Yap1 is cytoplasmic in normoxia (Gulshan, K., et al, 2005).
One reason my screen may not have found YAP1 as a regulator of SHB17 may be due to the
growth condition I used, which were performed in normoxic conditions. To test whether YAP1
is a regulator of SHB17 as it was predicted to be from transcription factor binding sites and large
scale study (Kemmeren et al, 2014), one could use oxidative conditions such as hydrogen
peroxide in cells with WT YAP1 and yap1 deletion and test whether cells with WT YAP1 treated
with oxidative stress have high Shb17 levels, then compare this to yap1 deletion cells with
oxidative stress. I expect that in yap1 deletion strain, Shb17 level would be the same as the
condition without oxidative stress in yap1 deletion.
Further validation on the YPL080C can be performed by complementing the deletion
version with WT version of YPL080C. I have made MOBY versions (CEN plasmid, keeps 1-3
plasmid per cell at a low copy (Cheuk et al, 2009)) of YPL080C as well as a separate plasmid
that express the flanking regions encompassing YPL079W to YPL081W (Figure 34) as we
suspect ypl080c deletion from the collection likely has deletion across the flanking region. I am
87
also making this in another plasmid background for different copy numbers and expression
levels. The complemented strains can be run on the immunoblots to test for the level of Shb17.
FACS sorted cells can be useful for validating the other assays I used, as well as finding
regulators of SHB17, which will allow a three-factor approach to further pinpoint potential
interesting regulators. The barcoding method once developed in the lab will be available for the
barcode sequencing of my FACS sorted and pooled strains with top and bottom fluorescence
from the deletion collection. This may validate the results from my flow cytometry and typhoon
screens and provide other insights such as the comparability between the three methods.
Once a regulator is deemed a true regulator of SHB17 through consistent and robust
evidence from experiments such as immunoblotting and quantitative RT-PCR among other
validation steps, I can leverage this knowledge in order to find the condition in which Shb17 is
required. This can be used for further experiments such as when a more pronounced phenotype
upon shb17 deletion is desired. Previous experiments showed that identifying conditions in
which Shb17 is needed proved to be difficult to find due to the redundancy of metabolism.
Deletion in shb17 did not cause much growth defect (~1% growth defect compared to WT in
YPD, unpublished results) and hypoxic versus normoxic conditions also did not show growth
differences in shb17 deletion and WT (Caudy, A.). It is thought that conditions that are normally
used in the lab likely do not require much Shb17. To identify the conditions in which Shb17
may be more required, growth phenotypes of shb17 can be tested across various conditions of
interest, and the growth phenotype can be measured as a readout. Also, as the Andrews lab is
performing genome-wide screen on different conditions, data from their study may be valuable
for finding conditions that affect shb17 deletion strains.
To test the hypothesis that SHB17 expression is modulated by proteins that are sensitive
to the 60S level, one could determine the regulation of the regulators of Shb17. For example,
one could search for the presence of uORF in the putative regulators of Shb17. As gcn4 deletion
was one of the top putative regulator of Shb17 from my fluorescence assays, probing the
relationship between GCN4 and SHB17 may be illuminating. To test if uORF regulation for
GCN4 is the medium through which changes in 60S level influence Shb17 levels, one may use a
mutant where the uORF of GCN4 is modified, in the background of the deletion in an activator
such as bud20 or a repressor such as rpl19a, puf6, or ypl080c. With the change in 60S due to
deletion in an activator or repressor, the modified uORF version of GCN4 may have defective
Gcn4 production, and the level of SHB17 expression in this context can be compared with the
88
level of SHB17 expression in the deletion of bud20, rpl19a, puf6, or ypl080c without the
modified uORF.
One concern is that Shb17 may be affected in the putative regulators I studied because
ribosome and protein synthesis was affected in general. To address this, I am currently
performing experiments and analysis for a “reverse” screen. In this screen, the yeast GFP
collection (Huh et al, 2003) is currently being crossed to the deletion of the putative SHB17
regulators (rpl19a, ypl080c, bud20). After SGA, the strains (for example would be the genotype
Mata can1::STE2pr-LEU2 HTA2-mCherry-URA3 his3 leu2 ura3 met15 rpl19a::NatMX YFG-
GFP-HisMX) will be scanned on a Typhoon imager for fluorescence, where the GFP level will
be indicative of the protein level from the entire GFP collection in the deletion of putative
SHB17 regulators. For SGA, we intend to filter out autofluorescent colonies (yeast colonies
autofluoresce green and protein-GFP based assays are known to have lower sensitivity than
mass spec based assays especially for the low fluorescent strains due to autofluorescence (Ho et
al, 2017)) using normalized protein levels (in molecules per cell) from a collection of 19
published proteome data sets (published mass spec, fluorescence, immunoblotting) analyzed by
Brandon Ho from Dr. Grant Brown’s lab (Ho et al, 2017). We also intend to have wild-type
colonies per row in the plates so that I can normalize the green fluorescence of strains to that of
wild-types from the same row, and I can also normalize green fluorescence of strains within
colony to the red fluorescence from mCherry. The effect of deleting rpl19a, ypl080c, or bud20
on the GFP collection will be compared to the effect of the control ho deletion (homothallic
switching endonuclease, used as a control knockout) on the GFP collection. This screen can: 1)
verify the level of Shb17 in the deletion of regulators by measuring Shb17-GFP levels in the
colonies; 2) find other proteins that are regulated together with Shb17-GFP in the array (similar
to the idea of “guilt-by-association”) and for example uncover pathways that may turn out to be
co-regulated with riboneogenesis; and 3) observe how disruption of ribosome biogenesis affect
the proteome compared to the disruption of non-RiBi gene (ho deletion). This approach may
help to further characterize ribose metabolism and the regulation of riboneogenesis, as well as
provide ideas as to why the riboneogenesis pathway evolved in organisms such as the yeast.
I
5 References
Barreto, L., et al. (2012). The short-term response of yeast to potassium starvation. 14(11),
3026-2042.
Bassler, J., et al. (2012). The conserved Bud20 zinc finger protein is a new component of the
ribosomal 60S subunit export machinery. Mol Cell Biol. 32(24), 4898-4912.
Bonander, N., et al. (2009). Altering the ribosomal subunit ratio in yeast maximizes recombinant
protein yield. Microb Cell Fact. 8, 10.
Byrne, K.P., et al. (2005). The Yeast Gene Order Browser: combining curated homology and
syntenic context reveals gene fate in polyploid species. Genome Res. 15(10), 1456-61.
Caudy, A.A. (2016). Budding Yeast: A Laboratory Manual, Chapter 33: Metabolomics in Yeast.
Cold Spring Harbor Laboratory Press.
Celton, M., et al. (2012). A constraint-based model analysis of the metabolic consequences of
increased NADPH oxidation in Saccharomyces cerevisiae. Metab Eng. 14, 366-379.
Chan, K., and Roth, M.B. (2008). Anoxia-induced suspended animation in budding yeast as an
experimental paradigm for studying oxygen-regulated gene expression. Eukaryot Cell. 7(10),
1795-808.
Chatr-Aryamontri, A., et al. (2012). The BioGRID interaction database: 2013 update. Nucl
Acids Res. 41, D816-D823.
Cheuk, H., et al. (2009). A molecular barcoded yeast ORF library enables mode-of-action
analysis of bioactive compounds. Nat Biotechnol. doi: 10.1038/nbt.1534
Clasquin, M.F. et al. (2011). Riboneogenesis in yeast. Cell. 145(6), 959-980.
Cowen, L.E., et al. (2002). Evolution of drug resistance in Candida albicans. Annu Rev
Microbiol. 56, 139-65.
Cvijovic, M., et al. (2007). Identification of putative regulatory upstream ORFs in the yeast
genome using heuristics and evolutionary conservation. BMC Bioinformatics. 8, 295.
Fillingham, J., et al. (2009). Two-color cell array screen reveals interdependent roles for histone
chaperones and a chromatin boundary regulator in histone gene repression. Molecular Cell.
35(3), 340-351.
Giaever, G., et al. (2002). Functional profiling of the Saccharomyces cerevisiae genome. Nature.
218, 387-391.
II
Gibon, Y., and Larher, F. (1997). Cycling assay for nicotinamide adenine dinucleotides: NaCl
precipitation and ethanol solubilization of the reduced tetrazolium. Analytical Biochemistry.
251, 153-157.
Gimeno, C.J., et al. (1992). Unipolar cell divisions in the yeast S. cerevisiae lead to filmantous
growth: regulation by starvation and RAS. Cell. 68, 1077-1090.
Grabowska, d., and Chelstwoska, A. (2003). The ALD6 gene product is indispensable for
providing NADPH in yeast cells lacking glucose-6-phosphate dehydrogenase activity. J Biol
Chem. 278(16), 13984-13988.
Grant, C., et al. (1996). Yeast glutathione reductase is required for protection against oxidative
stress and is a target gene for yAP-1 transcriptional regulation. Mol Microbiol. 21(1), 171-9.
Guedouari, H., et al. (2014). Changes in glutathione-dependent redox status and mitochondrial
energetic strategies are part of the adaptive response during the filamentous process in Candida
albicans. Biochim Biophys Acta. 1842(9), 1855-69.
Gulshan, K., et al. (2005). Oxidant-specific folding of Yap1p regulates both transcriptional
activation and nuclear localization. J Biol Chem. 280(49), 40524-33.
Haass, F.A., et al. (2007). Identification of yeast proteins necessary for cell-surface function of a
potassium channel. PNAS. 104(46), 18079-18084.
Hao, N., et al. (2011). Single-dependent dynamics of transcription factor translocation controls
gene expression. Nat Struct Mol Biol. 19(1), 31-39.
Haschemi, A., et al. (2012). The sedoheptulose kinase CARKL directs macrophage polarization
through control of glucose metabolism. Cell Metab. 15(6), 813-826.
Hayano, T., et al. (2003). Proteomic analysis of human Nop56p-associated pre-ribosomal
ribonucleoprotein complexes. Possible link between Nop56p and the nucleolar protein treacle
responsible for Treacher Collins syndrome. J Biol Chem. 278(36), 34309-34319.
Ho, B., et al. (2017). Comparative analysis of protein abundance studies to quantify the
Saccharomyces cerevisiae proteome. [preprint in bioRxiv, doi: https://doi.org/10.1101/104919]
Huh, W., et al. (2003). Global analysis of protein localization in budding yeast. Nature. 425,
686-691.
Jorgensen, P., et al. (2004). A dynamic transcriptional network communicates growth potential
to ribosome synthesis and critical cell size. Genes Dev. 18(20), 2491-2505.
Kardon, T., et al. (2008). Characterization of mammalian sedoheptulokinase and mechanism of
formation of erythritol in sedoheptulokinase deficiency. FEBS Letters. 582, 3330-3334.
III
Kawashima, S., et al. (2016). Potent, reversible, and specific chemical inhibitors of eukaryotic
ribosome biogenesis. Cell. 167(2), 512-524.
Kemmeren, P., et al. (2014). Large-scale genetic perturbations reveal regulatory networks and
an abundance of gene-specific repressors. Cell. 157, 740-752.
Koh, J.L.Y., et al. (2010). DRYGIN: a database of quantitative genetic interaction networks in
yeast. Nucl Acids Res. 38, D502-D507.
Konikkat, S., and Woolford, J. L. (2017) Principles of 60S ribosomal subunit assembly
emerging from recent studies in yeast. Biochemical Journal. 474: 195-214.
Kudlicki, A., et al. (2007). SCEPTRANS: an online tool for analyzing periodic transcription in
yeast. Bioinformatics. 23(12), 1559-1561.
Kuznetsova, E., et al. (2010). Structure and activity of the metal-independent fructose-1,6-
bisphosphatase YK23 from Saccharomyces cerevisiae. J Biol Chem. 285(27), 21049-21059.
Li, Z., et al. (2009). Rational extension of the ribosome biogenesis pathway using network-
guided genetics. PLoS Biol. 7, e1000213.
Lo, K., et al. (2009). Ribosome stalk assembly requires the dual-specificity phosphatase Yvh1
for the exchange of Mrt4 with P0. J Cell Biol. 186(6), 849-62.
Loewith, R., and Hall, M.N. (2011). Target of Rapamycin (TOR) in nutrient signaling and
growth control. Genetics. 189, 1177-1201.
Martin, F., et al. (2016). Ribosomal 18S rRNA base pairs with mRNA during eukaryotic
translation initiation. Nature Communications. 7, 12622.
O’Rourke, S.M., et al. (2004). Unique and redundant roles for HOG MAPK pathway
components as revealed by whole-genome expression analysis. Mol Biol Cell. 15(2), 532-542.
Pertschy, B., et al. (2004). Diazaborine treatment of yeast cells inhibits maturation of the 60S
ribosomal subunit. Mol Cell Biol. 24(14), 6476-6487.
Pinay, K., and Andrews, B. (2010). Illuminating transcription pathways using fluorescent
reporter genes and yeast functional genomics. Transcription. 1(2), 76-80.
Protchenko, O., and Philpott, C. (2008). Role of PUG1 in inducible porphyrin and heme
transport in Saccharomyces cerevisiae. Eukaryot Cell. 7(5), 859-71.
Rachfall, N., et al. (2013). RACK1/Asc1p, a ribosomal node in cellular signaling. Mol Cell
Proteomics. 12(1), 87-105.
IV
Rodriguez-Mateos, M., et al. (2009). Role and dynamics of the ribosomal protein P0 and its
related trans-acting factor Mrt4 during ribosome assembly in Saccharomyces cerevisiae. Nucleic
Acids Res. 37(22), 7519-7532.
Savidge, T., and Pothoulakis, C. (2005). Microbial imaging. Elsevier. p37.
Schilling, V., et al. (2012). Genetic interactions of yeast NEP1 (EMG1), encoding an essential
factor in ribosome biogenesis. Yeast. 29(5), 167-183.
Seila, A.C., et al. (2009). Divergent transcription: A new feature of active promoters. Cell
Cycle, 8(16), 2557-2564.
Shahbabian, K., et al. (2014). Co-transciptional recruitment of Puf6 by She2 couples
translational repression to mRNA localization. Nucl Acids Res. 42(13), 8692-8704.
Steffen, K., et al. (2008). Yeast lifespan extension by depletion of 60S ribosomal subunits is
mediated by Gcn4. Cell. 133(2), 292-302.
Stincone, A., et al. (2014). The return of metabolism: biochemistry and physiology of the
pentose phosphate pathway. Biol Rev. 90(3), 927-963.
Studier, F.W. (2005). Protein production by auto-induction in high-density shaking cultures.
Protein Expression and Purification. 41, 207-234.
Su, C., et al. (2013). Reduced TOR signaling sustains hyphal development in Candida albicans
by lowering Hog1 basal activity. Mol Biol Cell. 24(3), 385-397.
Teixeria, M.C., et al. (2014). The YEASTRACT database: an upgraded information system for
the analysis of gene and genomic transcription regulation in Saccharomyces cerevisiae. Nucl
Acids Res. 42, D161-D166.
Thomson, E., et al. (2013). Eukaryotic ribosome biogenesis at a glance. J Cell Sci. 126, 4815-
21.
Tong, A.H.Y., et al. (2001). Systematic genetic analysis with ordered arrays of yeast deletion
mutants. Science. 294, 2364-2368.
Trichas, G., et al. (2008). Use of the viral 2A peptide for bicistronic expression in transgenic
mice. BMC Biol. 6(40).
Tu, B., et al. (2005). Logic of the yeast metabolic cycle: temporal compartmentalization of
cellular processes. Science. 310, 1152-1158.
Tzur, A., et al. (2011). Optimizing optical flow cytometry for cell volume-based sorting and
analysis. Plos ONE. 6(1), e16053.
V
VanderSluis, B., et al. (2014). Braod metabolic sensitivity profiling of a prototrophic yeast
deletion collection. Genome Biology. 15, R64.
Vizoso-Vazquez, A., et al. (2012). Ixr1p and the control of the Saccharomyces cerevisiae
hypoxic response. Appl Microbiol Biotechnol. 94(1), 173-84.
Walters, R.W., et al. (2017). Identification of NAD+ capped mRNAs in Saccharomyces
cerevisiae. PNAS. 114(3), 480-485.
Wamelink, M., et al. (2008). Sedoheptulokinase deficiency due to a 57-kb deletion in cystinosis
patients causes urinary accumulation of sedoheptulose: elucidation of the CARKL gene. Human
Mut. 29, 532-536.
Wang, B., and Ye, K. (2017). Nop9 binds the central pseudoknot region of 18S rRNA. Nucleic
Acids Res. doi: 10.1093/nar/gkw1323. [Epub ahead of print]
Warde-Farley, D., et al. (2010). The GeneMANIA prediction server: biological network
integration for gene prioritization and prediction gene function. Nucl Acids Res. 38 Suppl,
W214-220.
Wethmar, K., et al. (2014). uORFdb – a comprehensive literature database on eukaryotic uORF
biology. Nucleic Acids Res. 43, D60-D67.
Winston, F. et al. (1995). Construction of a set of convenient Saccharomyces cerevisiae strains
that are isogenic to S288C. Yeast. 11(1), 53-5.
Woolford, J.L., et al. (2013). Ribosome biogenesis in the yeast Saccharomyces cerevisae.
Genetics. 195(5), 643-681.
Yang, Y., et al. (2016). The roles of Puf6 and Loc1 in 60S biogenesis are interdependent and
both are required for efficient accommodation of Rpl43. J Biol Chem. 9;291(37): 19312-23.
Yanushevich, Y., et al. (2002). A strategy for the generation of non-aggregating mutants of the
Anthozoa fluorescent proteins. FEBS Lett. 511, 11-14.
Yin, J., et al. (2015). Preparation of a cyanine-based fluorescent probe for highly selective
detection of glutathione and its use in living cells and tissues of mice. Nature Protocols. 10,
1742-1754.
Zhihua, Li., et al. (2009). Rational extension of the ribosome biogenesis pathway using network-
guided genetics. PLoS Biol. 7(10), e1000213.
