EFFECTS OF CODON USAGE ON MRNA
TRANSLATION AND DECAY
by
VLADIMIR PRESNYAK
Submitted in partial fulfillment of the requirements for the degree
of Doctor of Philosophy
Dissertation Adviser: Dr. Jeffery Coller
Department of Biochemistry
CASE WESTERN RESERVE UNIVERSITY
May, 2015
CASE WESTERN RESERVE UNIVERSITY
SCHOOL OF GRADUATE STUDIES
We hereby approve the thesis/dissertation of
Vladimir Presnyak
Candidate for the Doctor of Philosophy degree*.
Committee chair
Timothy Nilsen
Dissertation advisor
Jeff Coller
Committee members
Maria Hatzoglou
Donny Licatalosi
Nathan Morris
Date of defense
March 18, 2015
* We also certify that written approval has been obtained
for any proprietary material contained therein.
2
TABLE OF CONTENTS
TABLE OF CONTENTS .................................................................................................... 3
INDEX OF FIGURES ........................................................................................................ 5
INDEX OF TABLES .......................................................................................................... 6
ACKNOWLEDGEMENTS ................................................................................................ 7
ABSTRACT ........................................................................................................................ 8
CHAPTER 1: BACKGROUND ....................................................................................... 10
Cellular context of mRNA decay.................................................................................. 10
Overview of mRNA decay............................................................................................ 12
Pathways of mRNA decay ............................................................................................ 14
Quality control pathways .............................................................................................. 18
mRNA translation is intricately linked to decay ........................................................... 20
Mechanism is insufficient to explain decay rate ........................................................... 23
Codon bias and optimality ............................................................................................ 23
CHAPTER 2: RARE CODON ANALYSIS..................................................................... 26
Position of rare codons changes effects on decay ......................................................... 26
Destabilization by rare codons depends on translation ................................................. 28
Decay of reporters occurs through the major decay pathway ....................................... 29
Ribosome association of constructs remains unchanged .............................................. 32
CHAPTER 3: GENOMIC ANALYSIS ............................................................................ 34
Determination of half-lives by RNA-seq ...................................................................... 34
The search for features correlating with decay ............................................................. 37
Codon usage correlates with mRNA stability............................................................... 39
Codon usage in genome falls into distinct patterns ...................................................... 45
Optimal codons increase average ribosome density ..................................................... 49
Related genes appear correlated through codon usage ................................................. 52
CHAPTER 4: EXPERIMENTAL VALIDATION ........................................................... 55
Changes in codon content leads to changes in stability ................................................ 55
Regulation through codon usage dominates over UTR regulation ............................... 56
Codon content affects the major decay pathway .......................................................... 58
HIS3 reporter system allows for fine tuning of mRNA stability .................................. 60
Codon content impacts translation beyond changes in mRNA .................................... 62
Affected step of translation is elongation ..................................................................... 65
3
Changes in codon content can impact cellular fitness .................................................. 68
CHAPTER 5: DISCUSSION ............................................................................................ 70
Overview ....................................................................................................................... 70
Considerations of rare codon experiments .................................................................... 71
Considerations of RNA-seq study ................................................................................ 72
Considerations of codon content experiments .............................................................. 75
Possible roles of DHH1 ................................................................................................ 76
Codon optimality in yeast and other organisms ............................................................ 77
Ribosome as monitor of all mRNA fates ...................................................................... 83
Future directions ........................................................................................................... 84
APPENDIX A: BIOINFORMATICS ............................................................................... 86
Half-life fitting .............................................................................................................. 86
CSC calculation ............................................................................................................ 88
APPENDIX B: MATERIALS AND METHODS ............................................................ 93
Yeast strains and growth ............................................................................................... 93
Plasmids and strain construction ................................................................................... 94
Northern RNA analysis ................................................................................................. 97
Polyribosome analysis .................................................................................................. 98
Asymmetric PCR probes............................................................................................... 99
Plating assays .............................................................................................................. 100
RNA-seq ..................................................................................................................... 100
Alignment and half-life calculation ............................................................................ 101
Statistical techniques ................................................................................................... 102
Heat map generation ................................................................................................... 102
Tables .......................................................................................................................... 103
BIBLIOGRAPHY ........................................................................................................... 111
4
INDEX OF FIGURES
Figure 1: Overview of 5’-3’ decay ................................................................................. 15
Figure 2: Structure and features of the rare codon constructs .................................. 26
Figure 3: mRNAs bearing rare codons display reduction in stability dependent on
the position of the rare codons ....................................................................................... 28
Figure 4: mRNA translation is required for position dependence ............................. 29
Figure 5: Inclusion of rare codons accelerates both deadenylation and degradation
........................................................................................................................................... 30
Figure 6: Decapping is required for position dependent mRNA destabilization by
rare codons ...................................................................................................................... 31
Figure 7: mRNAs bearing rare codons do not change in ribosome distribution ...... 33
Figure 8: Sequencing of poly(A)+ enriched and total mRNA sets produces different
halflives ............................................................................................................................ 35
Figure 9: Occurrence of codons correlates with differential effects on mRNA halflife ..................................................................................................................................... 39
Figure 10: Influence on stability correlated with optimality for virtually all codons
........................................................................................................................................... 41
Figure 11: Frequency of occurrence is not equivalent to optimality for codons ....... 42
Figure 12: Data from an independent source show codon effects similar to ours .... 43
Figure 13: Codon effects are dependent on reading frame ......................................... 44
Figure 14: Genes cluster into distinct patterns of codon usage .................................. 46
Figure 15: Individual gene clusters show effects of codon usage................................ 48
Figure 16: Codons impact average ribosome density similarly to effects on mRNA
decay ................................................................................................................................. 51
Figure 17: Previously observed similarity in half-life can be explained by similar
codon usage ...................................................................................................................... 53
Figure 18: Changes in codon composition lead directly to changes in decay ............ 56
Figure 19: Regulation of stability through the UTR appears weaker than codon
effects ................................................................................................................................ 58
Figure 20: Regulation of decay through codon usage utilizes the major decay
pathway ............................................................................................................................ 59
Figure 21: Changes in optimal codon content accelerates both deadenylation and
decapping ......................................................................................................................... 60
Figure 22: Codon usage impacts translation beyond its effects on mRNA decay ..... 62
Figure 23: Changes in codon usage do not produce changes in ribosomal association
........................................................................................................................................... 64
Figure 24: Ribosomal translocation is directly affected by codon usage ................... 67
Figure 25: Regulation through codon usage is potent enough to impact cellular
fitness................................................................................................................................ 69
Figure 26: Codon usage bias varies greatly between organisms ................................ 79
Figure 27: Codon usage within degenerate groups displays lower bias in higher
eukaryotes ........................................................................................................................ 81
5
INDEX OF TABLES
Table 1: Yeast strains used in this study..................................................................... 103
Table 2: Plasmids used in this study ........................................................................... 105
Table 3: Oligonucleotides used in this study .............................................................. 106
Note:
The data presented herein are reproduced with permission from: Presnyak V,
Alhusaini N, Chen YH, Martin S, Morris N, Kline N, Olson S, Weinberg D, Baker KE,
Graveley BR, Coller J. Codon optimality is a major determinant of mRNA stability. Cell.
2015 Mar 12;160(6):1111-24.
6
ACKNOWLEDGEMENTS
All of the work presented herein would not be possible without the help and
support of a great number of people. First and most importantly, I would like to thank
my wife, Jennifer. The unfailing love and support she has offered me, whether here by
my side or 2000 miles away completing her schooling as a physician, has been a
crucial source of motivation and stability though the ups and downs of graduate
work. I would also like to thank my parents, Oleg and Nataliya, whose long journey
from their homeland and many sacrifices have given me the chance to be here.
I would like to thank my adviser, Dr. Jeff Coller. His insights into many areas,
from data interpretation to the values of collaboration and presentation skills, have
been indispensable. I would like to acknowledge Dr. Kristian Baker as well, whose
contributions have helped me advance my research with advice and direction over
many years. I would also like to thank the members of my committee, Drs. Timothy
Nilsen, Maria Hatzoglou, Donny Licatalosi, and Nathan Morris for their guidance.
Other researchers in the lab have all contributed to this work in many different
ways. TJ Sweet was my mentor when I first started in the lab, showing me techniques
and procedures as well as informing me about the background in the field. His
knowledge and his approach to science were inspirational. Najwa Alhusaini’s support
in the lab has been invaluable with her vast expertise in molecular biology
techniques. Ying-Hsin Chen, Sophie Martin, and Nicholas Kline have all been kind
enough to dedicate large amounts of their time in support of my work. The work of
collaborators beyond the lab has been equally crucial; especially that of Dr. Brenton
Graveley, whose expertise in the area of RNA sequencing was the very basis of this
project.
7
Effects of Codon Usage on mRNA
Translation and Decay
Abstract
by
VLADIMIR PRESNYAK
Gene expression is a complex process regulated at many steps. One
important step is the degradation of mRNA. The major pathways and enzymes in
mRNA decay have been identified and described, but this has not yet led to a good
understanding of the mechanisms for the observed differences in mRNA half-lives.
Previous research has elucidated several examples of regulation through 3’ UTR
elements, but general mechanisms are not clear. The translation of mRNA is
intricately linked to decay, thus the two processes must be evaluated together to
uncover regulation that frequently affects both.
We show that the choice of codons in the body of an mRNA can dictate the
stability of the mRNA. Messages with a high percentage of optimal codons are
relatively stable, whereas messages with a high percentage of non-optimal codons
are relatively unstable. Reducing the optimal codon content of a stable message with
naturally high occurrence of optimal codon leads to a reduction in stability, and
8
conversely, increasing the optimal codon content of a naturally unstable message
leads to an increase in stability. Similarly, inclusion of rare codons (a subset of nonoptimal codons) in a message leads to a reduction in stability. Importantly, these
effects appear to be dominant over previously-described 3’ UTR regulatory elements.
Finally, we show that optimal codon content is shared among groups of genes
encoding proteins of related function, which have previously been found to have
similar half-lives. Taken together, this evidence indicates that codon content can be
an intrinsic regulatory feature of mRNAs affecting stability.
Consistent with the role of codons in translation, we show that this method of
regulation directly affects steps of translation. The reduction of mRNA stability
caused by rare codon inclusion is dependent on translation. Concordantly, inclusion
of non-optimal codons in reporters leads to reduction in protein output that is greater
than the decrease in mRNA levels. These strongly suggest that translation is the
primary target of this method of regulation. To determine the precise step affected,
we demonstrate that the changes in optimal codon content have a direct effect on
the rate of ribosome translocation on these messages. The coupling of translation to
decay makes the changes in half-lives possible and further amplifies the effect of the
regulation.
This work demonstrates the powerful mechanisms of regulation possible
through codon usage, suggesting that this may be an evolutionary feature that allows
cells to regulate expression of proteins without changes to the sequence or the need
for external control sequences. Additionally, it implicates the ribosome as the key
point of regulation, not only for translation, but for mRNA stability as well.
9
CHAPTER 1: BACKGROUND
Cellular context of mRNA decay
Gene expression and its regulation are central to the basic functioning of all
cells. Virtually all processes in the cell depend on changes in gene expression;
progression through the cell cycle, responses to environmental variations, and even
apoptosis are all examples of this. The core processes of gene expression fit into the
central dogma of molecular biology (Crick, 1970). The two cornerstone events are the
transfer of genetic information from DNA to messenger RNA (mRNA) through
transcription in the nucleus and subsequent transfer of that information from mRNA
into protein by translation. This, of course, is an oversimplification of the process, as
there are a great number of steps surrounding the processing of each of the three
molecules involved, which can therefore be divided into three areas relating to the
molecules involved: steps that affect DNA, such as activation and binding of
transcription factors, and chromatin modifications; steps that affect RNA, such as
splicing, polyadenylation, and export; and steps that affect protein, such as protein
folding and modification. The two processes that transfer information from one
molecule to another, transcription and translation, reside at the interfaces of these
areas.
Rather than simple transfers of information, transcription and translation are
incredibly complex events, with multiple layers of regulation (Dever and Green, 2012;
Hinnebusch and Lorsch, 2012; Shandilya and Roberts, 2012). They are also points of
signal amplification, as multiple transcription events can create multiple mRNAs from
a single DNA locus, and multiple rounds of translation can create multiple proteins
from a single mRNA molecule. Regulation of these steps yields a range of functional
10
gene product levels that has been estimated to be around 6 orders of magnitude in
yeast, with reported protein counts ranging from single digit molecules per cell for the
most rare to over a million for the most abundant. (Ghaemmaghami et al., 2003) This
regulation can occur from the earliest steps, like chromatin modification (Shilatifard,
2006), which happens before transcription even begins, to the latest steps like the
vast array of post-translational protein modifications (Lothrop et al., 2013), occurring
after translation has completed.
mRNA holds a special position in gene expression, as it is the only molecule
involved in both transcription and translation. Thus, regulation of mRNA metabolism
plays a critical role in regulating levels of gene expression. A majority of regulatory
events occur in the nucleus and those mRNAs that are exported to the cytoplasm are
competent to attempt translation. Once in the cytoplasm, the key regulatory
mechanism acting on mRNAs is mRNA decay. It represents the default fate of
mRNAs, as any molecule that is not actively protected from it by another process will
be destroyed. mRNA decay serves as a counterbalance to both transcription and
translation by controlling levels of the intermediate molecule.
The rate of mRNA decay is highly variable and can be adjusted to meet the
needs of the cell, allowing for great precision and flexibility when combined with
regulation at the level of transcription and translation. While mRNA decay is the
direct negative counterpart of transcription, destroying the molecules produced by
that process, it’s even more tightly intertwined with the process of translation (Huch
and Nissan, 2014). Translation appears to be the primary process which protects
mRNAs from decay. These processes compete for the same pool of mRNAs in the
cytoplasm, ensuring the survival of the fittest among those mRNAs. Those that
11
translate poorly, especially mRNAs with features that prevent normal translation (e.g.
mRNAs harboring nonsense mutations or breaks), are rapidly eliminated (Shoemaker
and Green, 2012). This competition amplifies the effects of regulation of translation
– mRNAs that are poorly translated also tend to be highly susceptible to mRNA
degradation. This is possible because of the competition between these processes,
which manifests as a tight-knit relationship between the two that has been observed
for some time, but the details of which remain elusive (Jacobson and Peltz, 1996;
Roy and Jacobson, 2013).
Overview of mRNA decay
From a molecular standpoint, a regulated process of mRNA decay is
facilitated through the protection of mRNAs by two features added during the process
of its maturation in the nucleus: the cap that is added to the 5’ end of the message
by the capping enzyme complex, known as the 5’ cap, (Topisirovic et al., 2011) and
the long stretch of adenosine residues added by the polyadenylation machinery,
known as the poly(A) tail (Proudfoot, 2011). The addition of these features is coupled
to transcription, assuring that the message is protected in the early steps of
processing.
The cap is added to the mRNA immediately as it emerges from the RNA
polymerase – the capping complex is associated directly with the RNA polymerase
(Cho et al., 1997). The cap is a unique 7mGpppN structure, consisting of a guanine
residue methylated at the 7-nitrogen, which is coupled to the first nucleotide of the
message through a unique 5’-5’ triphosphate linkage (Shatkin, 1976). This unique
configuration confers resistance to enzymes that would normally degrade RNAs from
the 5’ end (5’-3’ exonucleases), necessitating removal of the structure if the mRNA is
12
to be degraded in this manner (Furuichi et al., 1977). The cap is also bound by a cap
binding complex in the nucleus and will eventually be bound by eIF4E in the
cytoplasm to promote translation. These proteins further stabilize the cap and protect
the message (Topisirovic et al., 2011).
The poly(A) tail is a long stretch of adenosines, ranging from a fully-adenylated
length of about 70 nucleotides in yeast to over 200 in mammals. This is added to the
mRNA immediately after transcription, with factors involved in the process also
interacting directly with the polymerase (Glover-Cutter et al., 2008). A set of
cleavage/specificity factors cleave the mRNA and stimulate addition of adenosines
by polyadenylate polymerase (PAP) until the tail is sufficiently long and the interaction
between the cleavage factors and the PAP is disrupted. The newly-formed poly(A) tail
is then bound by a protective protein known as polyadenylate binding protein (PAB).
This machinery also interacts with the RNA polymerase to promote termination, as
well as the spliceosome to facilitate splicing of the nascent transcript (Colgan and
Manley, 1997; Proudfoot, 2011).
Decay of mRNAs occurs through two pathways. The pathways are defined by
the exonucleases that carry out the destruction of the mRNA. The major decay
pathway focuses on recruiting a 5’ to 3’ exonuclease, and is thus defined as the 5’-3’
decay pathway. The minor decay pathway works in the opposite direction, being
defined as the 3’-5’ decay pathway (Parker, 2012; Schoenberg and Maquat, 2012).
In addition to these basic decay pathways, there are several quality control pathways
affecting specific mRNAs that cause aberrancies in translation. The processes of
quality control mRNA decay are nonsense-mediated mRNA decay, which affects
primarily mRNAs with nonsense (termination) codons encountered earlier than
13
expected; non-stop mRNA decay, which affects mRNAs lacking a functional
termination codon; and no-go mRNA decay, which specifically targets mRNAs with
stalled ribosomes, a situation classically caused by strong secondary structure. These
pathways use some of the same proteins as the default decay processes, but tend to
recruit the decay machinery in ways that are specific to the aberrancy that triggers
the pathway (Isken and Maquat, 2007; Shoemaker and Green, 2012).
Pathways of mRNA decay
The 5’-3’ decay pathway is the major pathway of mRNA degradation in yeast
(Figure 1). This pathway is divided into three steps: deadenylation, the removal of the
poly(A) tail; decapping, the removal of the 5’ cap; and exonucleolytic degradation, the
destruction of the message body (Tucker and Parker, 2000).
Deadenylation generally occurs first, and is thought to be rate-limiting for most
mRNAs (Franks and Lykke-Andersen, 2008). In this step, the poly(A) tail of the
message is shortened to a length of about 10 nucleotides, which is thought to be
sufficient to remove the PAB molecules that otherwise function as a protective signal
(Tucker et al., 2002). This is carried out by a large complex of proteins known as the
CCR4-NOT complex, consisting of the CCR4 poly(A)-specific exonuclease, POP2 (Caf1
in higher eukaryotes) putative exonuclease, the NOT1 structural scaffold protein, and
several proteins of unknown function, including other NOT and CAF proteins. In yeast,
CCR4 provides all of the nucleolytic activity of the complex, but in other organisms
Caf1 is known to be active or even dominant (Chen et al., 2002; Goldstrohm and
Wickens, 2008). Specific triggers for deadenylation are not well described, but a
number of proteins have been described to interact with both the deadenylation
complex and the translational machinery, suggesting a direct link between the
14
processes (Gray et al., 2000; Hoshino, 2012; Wilusz et al., 2001). On some
messages, deadenylation can be triggered by recruitment of the complex by factors
associated with the messages through sequence-specific interactions, such as the
PUF family of proteins or even miRNA-mediated complexes in higher eukaryotes
(Eulalio et al., 2009; Goldstrohm et al., 2007; Nilsen, 2007).
15
Decapping of normal messages is a deadenylation-dependent event, thought
to occur directly afterwards and to be coordinated by preferential binding of the
decapping activators to deadenylated mRNAs (Chowdhury et al., 2007; Decker and
Parker, 1993; Tharun and Parker, 2001). The basic function of decapping is to
remove the protective 5’ cap structure, allowing access to the mRNA body by
exonucleases. This means that the decapping factors compete for access to the cap
with other proteins, most importantly eIF4E (Schwartz and Parker, 2000). Because
eIF4E is a central factor in translation initiation (see below), decapping activators
tend to function as translational repressors. Additionally, many of them interact with
PAB1, a poly(A) binding protein known to stimulate translation (Vilela et al., 2000;
Wyers et al., 2000). For some decapping cofactors, translational repression may
actually be their primary function (Coller and Parker, 2005; Sweet et al., 2012). The
core set of decapping activators include DHH1, a DEAD-box helicase with multiple
functions (Coller et al., 2001); PAT1, an mRNA-binding protein thought to coordinate
the activity of the decapping complex (Nissan et al., 2010); and the LSM1-7 complex,
a heptameric RNA-binding ring thought to associate with the mRNA late in the
process and consolidate the decapping signals (Tharun and Parker, 2001). The act of
removing the cap structure itself is carried out by the DCP1/DCP2 holoenzyme, with
DCP2 contributing the enzymatic activity (Coller and Parker, 2004). Several other
proteins contribute to decapping activity, but are not required, including the
enhancers of decapping EDC1-3, proteins that have been described to interact with
the decapping enzyme and promote decapping in vitro (Dunckley et al., 2001;
Kshirsagar and Parker, 2004).
16
Exonucleolytic degradation is the last step in the pathway and appears to be
the least regulated; it is carried out by the XRN1 exonuclease, which acts on mRNAs
without cofactors. Its known interactions are with the decapping machinery, as well
as some components of quality control mRNA decay pathways, which are thought to
provide its recruitment mechanism (Nagarajan et al., 2013). This enzyme rapidly
removes nucleotides from the 5’ end of any RNA with a 5’ monophosphate in a highly
processive manner, which is attributable to an unwinding activity inherent to the
active site (Jinek et al., 2011). This feature additionally allows the enzyme to degrade
structured mRNAs without a helicase cofactor.
For most studied mRNAs in yeast, 3’-5’ decay pathway appears to contribute
little to overall half-life, with decay intermediates only observable when the 5’-3’
pathway is blocked (Anderson and Parker, 1998). However, the pathway is important
in cases where the 5’-3’ decay pathway fails to function and in specific cases of
regulation through the pathway (Lin et al., 2007; Orban and Izaurralde, 2005). The
pathway begins with an identical deadenylation step as the 5’-3’ pathway; however,
this is followed by direct digestion of the message body from the 3’ end by the
exosome rather than decapping and decay from the 5’ end. The exosome consists of
several proteins that are homologous to nucleases, but it appears that only one
subunit has nuclease activity in the cytoplasm, RRP44. The other nuclease-like
proteins, as well as the accessory factors in this complex are thought to function in
recruitment, binding, and channeling of the RNA. This complex is recruited by a group
of proteins known as the SKI proteins, which interact with the exosome and serve to
facilitate its degradation of mRNAs (Chlebowski et al., 2013). After the degradation of
17
the message body, the cap of the message is left to be degraded by the scavenging
decapping enzyme DCS1 (Wang and Kiledjian, 2001).
Quality control pathways
Nonsense-mediated decay (NMD) is a quality control decay pathway that
degrades mRNAs which terminate translation prematurely, typically due to the
presence of a nonsense codon early in the message, though other conditions exist
under which this pathway may become active, such as the presence of multiple
reading frames or abnormally long 3’ untranslated regions (UTRs) (Baker and Parker,
2004; Losson and Lacroute, 1979). It is proposed to stop the production of truncated
proteins, which can be deleterious in several ways, including aggregation, gain of
function, and dominant negative activity (Frischmeyer and Dietz, 1999; Pulak and
Anderson, 1993). This process functions by recruiting a host of decay-related
proteins to the message, including the deadenylation components, the decapping
enzyme, and exosomal components, which accelerate degradation in multiple ways
e.g. bypassing the deadenylation requirement to recruit the decapping machinery
and the exosome. This leads to the removal of the cap from the message and its
subsequent degradation by XRN1 as well as degradation by the exosome. (Lejeune et
al., 2003; Swisher and Parker, 2011). The main components of the pathway are the
UPF proteins, specifically UPF1-3, with an accessory group of proteins known as the
SMG proteins, which are required in some organisms. The UPF proteins carry out
both recognition of NMD targets and recruitment of decay machinery (Chang et al.,
2007). It should be noted that NMD can destabilize messages very strongly, with
changes of 100-fold or more possible upon introduction of a premature nonsense
codon (Baumann et al., 1985).
18
Non-stop decay (NSD) is also a quality control pathway, but its targets are
those mRNAs lacking a stop codon. These occur in cases of aborted transcription,
premature polyadenylation, or damage to the mRNA. This process is proposed to
rescue ribosomes that have reached the end of an mRNA without encountering a
stop codon and thus have not gone through termination (Frischmeyer et al., 2002;
van Hoof et al., 2002). As termination facilitates release of the ribosome from the
mRNA, these ribosomes require rescuing to be removed from the mRNA. Factors
closely related to eRF3 (SKI7 in yeast or Hbs1 in mammals) are thought to recognize
the ribosome in the same way as the traditional termination factors, though the
mechanism of targeting to stalled ribosomes are unclear. Upon recognition, the
ribosome can be release from the message through the action of Dom34 (Saito et
al., 2013) and the message itself can be degraded through the action of the
exosome, which is recruited through interactions with the SKI complex (Klauer and
van Hoof, 2012).
No-go decay (NGD) is a quality control pathway responsible for degradation of
mRNAs that cause ribosomes to stall during translation. This can occur when a
ribosomes encounters a very stable structure in the open reading frame (ORF) of the
mRNA (Harigaya and Parker, 2010). Similarly to NSD, this pathway facilitates the
release of ribosomes through the function of HBS1 and DOM34. This promotes
endonucleolytic cleavage of the message by unidentified nucleases. It is unclear
whether HBS1-DOM34 stimulates the nuclease activity in some way or whether the
removal of the ribosome is sufficient to allow access to the mRNA (Doma and Parker,
2006; Shoemaker et al., 2010). Once the mRNA is cleaved, the fragments now
possess unprotected ends, which can be processed by XRN1 and the exosome.
19
mRNA translation is intricately linked to decay
While the quality control pathways are clearly dependent on translation for
their function, the interplay between translation and decay of normal messages is
much less clear. Translation makes use of many of the same features of the mRNA
that are relevant to decay, such as the 5’ cap and 3’ poly(A) tail and their associated
proteins (Hinnebusch and Lorsch, 2012). Translation can be divided into three
separate processes – initiation, which is the recruitment of a ribosome to the mRNA;
elongation, which is the progressive assembly of a protein molecule; and finally
termination, which is the removal of the protein product and the ribosome from the
mRNA. The process entails enormous complexity, with numerous regulatory and
accessory factors at each step – in addition to the ribosome itself, which contains 3
RNAs and over 70 proteins. Regulation of this process has traditionally been thought
to occur primarily during initiation, though there is some emerging evidence that
regulation may also occur during elongation and termination (Sonenberg and
Hinnebusch, 2009).
In eukaryotes, the translation cycle begins with loading of the small 40S
ribosomal subunit with an initiator Met tRNA by the action of eukaryotic initiation
factors eIF1 and eIF2. The ribosomal subunit is then bound by eIF3 to make the 43S
pre-initiation complex. eIF3 serves to prevent premature association of the small
subunit with the large one and to facilitate its recruitment to mRNAs (Jackson et al.,
2010). Meanwhile, the mRNA is bound by the eIF4 complex, made up of the eIF4E
cap-binding protein, the eIF4A RNA helicase, and the eIF4G structural scaffold.
Assembly of this complex on the mRNA allows for interactions with eIF3 and
recruitment of the pre-initiation complex to the 5’ of the message. At this point, the
20
pre-initiation complex can scan along the 5’ end of the mRNA until it finds an
appropriate AUG initiation codon. Once it is positioned at the start site, eIF5 then acts
to facilitate the recruitment of the large ribosomal subunit, which is bound by eIF6
that serves to prevent premature ribosome association and aids in recruitment
(Jackson et al., 2010). The subunits are joined and the initiation factors are released,
allowing for the ribosome to begin elongation and for the initiation factors to repeat
the process with a new ribosome.
Interactions of both eIF4E and eIF4G are critical to the interplay between
translation and decay. eIF4E strongly binds and protects the 5’ cap, thus inhibiting 5’
decay. Its removal is thought to be regulated as a potential rate-limiting step for the
pathway (von der Haar et al., 2004). eIF4G is a scaffold that interacts with many
proteins involved in both processes, with documented connections to PAB1 at the 3’
of the message, in addition to its role in coordinating the members of the eIF4
complex (Tarun and Sachs, 1996). It has also been found to interact with many
different accessory factors involved in various facets of decay (Rajyaguru et al.,
2012). This observation is part of the basis of the closed-loop model of translation,
where the 3’ ends of messages are found close to the 5’ end, facilitating stimulation
of translation by PAB1 and allowing efficient recycling of ribosomes while protecting
the transcripts from decay (Jacobson, 1996). These claims are supported by
evidence that inhibition of translation initiation leads to accelerated decay of
messages.
Elongation is conceptually a simpler process, with eukaryotic translation
elongation factor (eEF) 1 facilitating recruitment of tRNAs to the ribosome as it
progresses, allowing for decoding and protein assembly. The other elongation factor,
21
eEF2, allows for movement of the ribosome along the mRNA in a ratcheting fashion
(Dever and Green, 2012). The complexity in this process comes from the difficulty in
matching the correct tRNA to the codon currently in the amino-acyl tRNA site of the
ribosome (A-site). Recruitment of tRNAs is a stochastic process, requiring exquisite
sensitivity from the ribosome to rapidly incorporate correct matches and reject
incorrect ones (Rodnina and Wintermeyer, 2001). The effects of perturbations at this
step on decay are unclear – inhibiting elongation with cycloheximide has led to
stabilization of mRNAs (Beelman and Parker, 1994), but inhibiting elongation by
recruitment of translational repressors such as DHH1 has led to destabilization
(Sweet et al., 2012). It should be noted that both experimental approaches have
caveats, as the drug treatment is likely to have pleotropic effects and the tethering of
DHH1 may promote occurrence of abnormal interactions.
Translation termination occurs when a ribosome reaches the termination
codon of an mRNA. In this case, no tRNA is available to match the stop codon, so the
A-site must be filled by eukaryotic release factor (eRF) 1, which acts with its cofactor
eRF3 to stimulate release of the protein product and disassembly of the ribosome
through the ribosome recycling pathway (Dever and Green, 2012). The termination
factors have been shown to interact with PAB1 and UPF1, and thus may play an
important role in decay, especially in NMD (Ivanov et al., 2008). Perturbations at this
step, such as mutations that change the conformation of the A-site or changes in the
concentrations of the release factors, typically lead to read-through translation,
where a non-cognate tRNA is used to decode the stop codon and then translate on
past the expected site of termination (Bertram et al., 2001).
22
Mechanism is insufficient to explain decay rate
Despite the fact that normal mRNAs are degraded by a common decay
pathway, turnover rates for individual yeast mRNAs differ dramatically with half-lives
ranging from <1 minute to 60 minutes or greater (Coller and Parker, 2004). Specific
modes of regulation are not well known and it is postulated that features of the
mRNA itself or composition of its associated proteins may lead to differences in
entering the mRNA decay pathway. Currently, some sequence and/or structural
elements located within 5’ and 3’ UTRs have been implicated in contributing to the
decay of a subset of mRNAs (Geisberg et al., 2014; Lee and Lykke-Andersen, 2013;
Muhlrad and Parker, 1992). In yeast, the primary example of this type of regulation is
the effect of the PUF proteins, which promote deadenylation of messages containing
specific sequences to which these proteins can bind upon recognition. However, PUF
proteins are thought to regulate only 10% of yeast genes, thus failing to account for
the wide variety of half-lives in the transcriptome. In higher eukaryotes, miRNAmediated complexes may function in the same way, potentially impacting a large
fraction in the genome, but the premise holds that these features regulate mRNA
stability predominantly in a transcript-specific manner (Geisberg et al., 2014).
Therefore, it seems likely that additional and more general features which act to
modulate transcript stability could exist within mRNAs.
Codon bias and optimality
One such feature of mRNAs has been suggested through experiments relating
to codon usage (Hoekema et al., 1987). Hoekama et al. showed that synonymous
substitution of minor codons (also called rare codons) into the highly expressed
PGK1 gene would lead to a reduction in protein output. Part of the reduction came
23
from reduced translational efficiency caused by the substitutions, but changes in
mRNA steady state levels contributed as well. Rare codons are defined as codons
that occur infrequently in the genome and have been ascribed a variety of functions
in many systems, though the relationship between rarity of codons and their effects
is best described in bacterial systems (Plotkin and Kudla, 2011). The concept that
codon usage could lead to effects on translation was first elucidated by research in
codon usage bias, which showed that rather than a consistent pattern of usage
across the genome, different codons were preferentially used in different genes.
(Grantham et al., 1981; Ikemura, 1985; Sharp and Li, 1987). Codon usage bias was
quickly characterized to affect the speed and accuracy of translation, as well as
levels of gene expression (Akashi, 1994; Sharp and Li, 1986). Over time, these
attributes were ascribed to a broader concept, known as codon optimality.
Codon optimality is a concept related to codon usage bias, born of the
understanding that the frequency of occurrence and effects on translation and other
processes need not always be linked (Pechmann and Frydman, 2013; dos Reis et al.,
2004). Conceptually, codon optimality is a scale that reflects the balance between
the supply of charged tRNA molecules in the cytoplasmic pool and the demand of
tRNA usage by translating ribosomes, representing a measure of translation
efficiency. Specifically, optimal codons are postulated to be decoded faster and more
accurately by the ribosome than non-optimal codons (Akashi, 1994; Drummond and
Wilke, 2008), which are hypothesized to slow translation elongation (Novoa and
Ribas de Pouplana, 2012; Tuller et al., 2010).
Codon optimality has been shown to play an important role in wide variety of
processes related to translation, modulating factors such as expression level,
24
translation elongation rates, kinetics of protein folding, and translational accuracy
(Akashi, 1994; Hudson et al., 2011; Kri Ko et al., 2014; Novoa and Ribas de
Pouplana, 2012; Pechmann and Frydman, 2013; dos Reis et al., 2004; Zhou et al.,
2009). Further, there has been some work showing that codon selection is under
significant evolutionary pressure, with cells preferring to express tRNAs at levels
tightly coordinated with relative demands of codon usage (Doherty and McInerney,
2013; Yona et al., 2013). The link between codon usage and translational effects
has been well documented, but the effects on decay, a process intricately tied to
translation, have not been explored in depth.
25
CHAPTER 2: RARE CODON ANALYSIS
Position of rare codons changes effects on decay
In our lab’s previous work, we showed that inclusion of a cluster of rare
arginine codons within the open reading frame (ORF) of a reporter mRNA dramatically
enhanced its turnover (Hu et al., 2009; Sweet et al., 2012). This destabilization
affected both the deadenylation and decapping steps of the major decay pathway.
We additionally showed that the destabilization was not dependent on known
components of several quality control pathways.
It was clear that the presence of rare codons leads to destabilization of the
reporter mRNA used in those experiments, but the mechanism remained a mystery.
To get more insight into a possible mechanism, we sought to characterize the effect
further. Guided by evidence that showed some mRNA decay pathways can exhibit
strong position dependence (specifically NMD), we created a series of constructs
where an identical rare codon stretch was inserted into the mRNA into several
different locations, at 5% into the reading frame, 25%, 50%, 63%, 77% (this is
identical to the original construct), and 94%, referred to as RC 5, RC 25, RC 50, RC
26
63, RC 77, and RC 94 respectively, and a control construct without the rare codons
referred to as –RC (diagram of codon insertion presented in (Figure 2). This allowed
us to test a spectrum of reporters that varied only in the position of the rare codon
stretch.
The half-lives of the reporters were tested by placing the reporter mRNAs into
a plasmid under the control of the GAL UAS, a system that allows for half-life
determination through transcriptional shut-off. The GAL-controlled mRNAs are highly
expressed in the presence of galactose, but strongly and quickly repressed upon
transferring the cells into a glucose-rich medium. We can then collect samples
throughout a time course and evaluate the levels of mRNA remaining in the absence
of transcription to calculate a decay rate and a half-life. Performing the experiment in
wild-type cells with all of the reporters (Figure 3) demonstrated that the reporters do
indeed exhibit a dependence on position, with stretches of rare codons inserted later
in the message producing greater decreases in half-life.
This was surprising for two main reasons. First, the other pathway that
displays similar behavior, NMD, has an opposite polarity – stop codons earlier in the
message produce greater decreases in half-life for that pathway. Second, it was
entirely unclear why rare codons inserted later in the message would lead to greater
destabilization. This called into question the previously established results (Hoekema
et al., 1987), as their methodology involved a series of reporters with an increasingly
long stretch of rare codons at the 5’ of the message that produced increasing
destabilization of the message. Their interpretation was that more rare codons added
to a message led to more destabilization. However, if the position of the rare codons
is important to the extent of destabilization, the reported destabilization seen in that
27
work may be due to introduction of rare codons later in the message, rather than the
increasing numbers of the rare codons.
Destabilization by rare codons depends on translation
To further characterize this behavior, we verified its dependence on
translation. This allowed us to ascertain that the observed effect is due to the rare
nature of the codons, as alternate explanations for destabilization could include
changes in properties of the mRNA itself, such as structure or GC content. To do this,
we inserted a strong stem-loop structure into the 5’ UTRs of several of the RC
28
constructs. This strong structure has been shown to reduce translation of the
message to below 1%, likely due to blocking of ribosomal scanning. With a steadystate analysis, we showed that whereas the rare codon constructs without the stem
loop demonstrated a reduction in accumulation (stemming from a reduction in halflife) for the construct with rare codons positioned late in the message, the rare codon
constructs with the stem loop showed little difference in accumulation between the
constructs (Figure 4). As expected, the stem-loop containing constructs are
expressed at a lower level than their well-translated counterparts, due to the typical
coupling between translation and stability.
Decay of reporters occurs through the major decay pathway
With the translational dependence of this phenomenon established, we tested
these constructs on a high-resolution acrylamide northern gel to verify that
deadenylation and decapping are both being impacted, as would be expected if the
rare codons affected the major decay pathway rather than triggering a quality control
pathway. Rapid decapping without deadenylation could be a tell-tale sign of a quality
control pathway such as NMD, which can uncouple decapping from deadenylation.
This experiment indicated that the rare codon bearing messages do appear to
29
undergo both deadenylation and decapping faster than the control (Figure 5),
suggesting that this is likely due to regulation of the major decay pathway. To further
exclude the possible activation of other quality control pathways, we tested the levels
of these reporters in cells deleted for key proteins in those factors – upf1∆ for NMD
and dom34∆ for NGD. In both cases, the polarity remained intact (though somewhat
diminished in the dom34∆ strain), solidifying the idea that this phenomenon was due
to the action of the major decay pathway (data not shown).
Having determined that these differences are most likely due to the action of
the major decay pathway, we tested the stability of the constructs in a series of
30
deletion mutants to find the members of the pathway that are responsible. We found
that deletion of the decapping enzyme DCP2 (Figure 6) leads to a situation where the
mRNAs bearing rare codons are less stable than the control (though much more
stable than in wild type cells), but the position of the rare codons no longer dictates
the degree of destabilization. This indicated to us that the polarity was due to the
action of the 5’-3’ decay pathway, as in the absence of DCP2, the normally minor 3’5’ decay pathway is thought to become the main method of decay. Further narrowing
down the factors involved in the 5’-3’ decay pathway, we found the constructs
behaved similarly in dhh1∆ cells (not shown, very similar to dcp2∆ below). By
contrast, in cells deleted for other decay cofactors (specifically lsm1∆ and pat1∆),
constructs were stabilized overall, but retained the dependence on position polarity
(data not shown, similar to WT above). We concluded that DHH1 acts on DCP2
independently of the other known decapping cofactors to create the position
dependence in these reporters.
31
Ribosome association of constructs remains unchanged
To explain the mechanism behind the increased destabilization of mRNAs with
late rare codons, and presumably late disruptions in translation, we hypothesized
that the clearance of these messages may be caused by accumulation of slowed
ribosomes. We termed this the “traffic jam” model, where areas of slow translation
late in the message would lead to accumulation of a larger number of ribosomes on
the message than areas of slow translation early in the message, since they could
just pile up onto the message before the slowdown. To test this theory, we analyzed
the polysomal association of these mRNAs to ascertain whether the ones with the
late rare codons would indeed harbor more ribosomes than the ones with early rare
codons or without rare codons at all. This was done by sucrose gradient fractionation,
which allows separation of cellular complexes by weight. This in turn allowed us to
estimate the number of ribosomes that are associated with a message by looking for
the message in fractions whose weight corresponds to clusters of a certain number
of ribosomes. We found that the mRNAs all appear to settle in similar fractions,
suggesting that the number of ribosomes associated with each is similar (Figure 7).
The caveat is that they all settle in heavy ribosomal fractions, where resolution
becomes poor beyond about 6 ribosomes. It is possible that the numbers of
ribosomes are different on the messages, but the differences are unlikely to be
dramatic, as we do not see movement into extraordinarily deep fractions of the
gradient, which can be seen in cases of heavy ribosome accumulation (Sweet et al.,
2012).
32
These experiments with rare codons gave us very strong indications that there
was an unexplored relationship between codon usage and mRNA half-life. Codon
usage has been implicated in some translational regulation, allowing highly
expressed genes to translate quickly and efficiently or allowing time for proteins to
fold when necessary. A role in mRNA decay would introduce a totally new and widereaching role for codon usage – it may help establish and influence mRNA half-lives,
which taken together with its role in translation would give it a dual role in the control
of gene expression.
33
CHAPTER 3: GENOMIC ANALYSIS
Determination of half-lives by RNA-seq
Extending analyses described above, we turned to a whole-transcriptome
approach to analyzing the role of codon usage in mRNA decay regulation. We started
by obtaining mRNA half-lives for as many genes in yeast as possible. We chose to do
our own decay measurements rather than relying on previously published data sets
due to some concerns about the effects of deadenylation on those half-lives
presented in many studies. Measuring global mRNA decay rates using methods that
either enrich for polyA+ RNA from total RNA samples and/or synthesize
complementary DNA (cDNA) using oligonucleotides annealed to the poly(A) tail may
fail to capture important information for several reasons. Although it is firmly
established that deadenylation is the rate limiting step in mRNA turnover, we and
others have observed that specific mRNAs persist in cells as relatively long-lived
deadenylated species (Hu et al., 2009; Muhlrad et al., 1995). For such transcripts,
decapping and subsequent decay is delayed and decapping becomes the rate
defining step for mRNA degradation. Moreover, some mRNAs may contain structures
that impede poly(A) tail function (Geisberg et al., 2014). Lastly, the overall level of
information gained may vary with the level of poly(A) enrichment achieved in the
protocol used, creating further uncertainty. With this in mind, we sought to determine
how prevalent these phenomena are on a transcriptome-wide level. For this purpose,
we performed an experiment similar to a GAL transcriptional shut-off described
above, except the rapid repression of transcription was achieved across the whole
genome by inactivation of RNA polymerase II (Nonet et al., 1987). A set of samples
across a time course was collected, similar to standard shut-off experiments. At each
34
time point, libraries were prepared from either oligo-dT selected mRNAs or rRNAdepleted whole cell RNA and subjected to Illumina sequencing (see experimental
procedures).
35
This approach allowed us to compare the half-lives of species captured by
oligo dT selection (referred to as poly(A)+) with the half-lives of the total mRNA decay
rates, calculated from samples the rRNA depleted samples (Figure 8A). Remarkably,
the vast majority (92%) of transcripts for which we could confidently calculate halflives (3969) had longer half-lives when the rRNA depleted libraries were analyzed
relative to the half-lives determined from poly(A) selected libraries (Figure 8B and C).
In fact, a majority of transcripts demonstrated half-lives that were more than twice as
long when calculated from total mRNA than from poly(A)+ mRNA. It is important to
note that not all of these transcripts need to exist as completely deadenylated RNAs.
Oligo-dT selection typically uses resins bound to oligonucleotides of dT around 18 nt
in length. mRNAs with poly(A) tails shorter than that number may not be captured
efficiently, and so may be lost from analysis of the poly(A)+ pool without undergoing
complete deadenylation. These data do indicate that mRNA half-lives determined
from poly(A)+ data sets may give skewed values for many genes. This observation
expands upon previous experiments that showed certain mRNAs, such as PGK1,
could remain as a largely deadenylated species for extended periods, visible on highresolution northern gels in shut-off experiments (Muhlrad et al., 1995). Our
observations here show that this is not an isolated phenomenon, but actually a
typical mode of regulation, where mRNAs may not undergo decapping immediately
follow deadenylation. Going forward, we analyzed mRNA half-lives calculated from
the total mRNA data to avoid complications of interpreting differential effects of
deadenylation.
36
The search for features correlating with decay
With this data in hand, we attempted to identify sequence motifs that might
dictate stability or instability. We used the MEME suite (Bailey et al., 2009) to search
through the 5’ UTRs, ORFs, and 3’ UTRs of the most stable 10% of all mRNAs in an
attempt to find conserved motifs that may explain their stability. We repeated the
analysis with the 10% least stable of all mRNAs to look for destabilizing elements in
the same fashion. We concluded in both cases that there did not appear to be any
conserved sequence motifs shared among the most or least stable groups of mRNAs.
Similarly, we analyzed features like length, abundance, and GC content of the most
stable and least stable groups. Of these, the abundance was the only one that
showed a notable difference between the groups, with stable mRNAs being more
abundant than their unstable counterparts – median FPKM was 87 for 10% most
stable and only 30 for the 10% least stable. This observation served as a control and
confirmed the ab initio assumption that the rate of decay of mRNAs contributes
strongly to establishing overall level of expression. This observation is not indicative
of a novel relationship, as half-life and abundance are not independent variables. In
sum, we were not able to find significant correlations between these general mRNA
features and half-life.
Searching for other features, we returned to the observations we had
previously made with our rare codon constructs. Thus, we inspected our
transcriptome-wide mRNA half-life data to determine whether codon content within
ORFs could affect mRNA stability. To do so, we began by analyzing the extent of
correlation between occurrence of individual rare codons and changes in stability. We
found that occurrence of the very rare codons individually did not seem associated
37
with a broad reduction in stability. To address this problem, we analyzed the rare
codons as a group. As a group, rare codon occurrence did correlate with reduction of
mRNA stability, but exhibited inexplicable variability based on the codons included in
the group. We hypothesized that if rare codons caused destabilization of messages,
as we included increasingly common codons into the test group, the significance of
association with half-life reduction would decrease. Instead, the significance
fluctuated as we included more codons, rising with the inclusion of some, falling with
the inclusion of others. This contradicted our hypothesis that rarity of codons was
predictive of mRNA stability.
To form a new hypothesis, we took an unbiased approach – we determined if
mRNAs enriched in any individual codon demonstrated greater or lesser stability. In
general, we defined mRNAs as stable if they show a positive 2-fold difference from
the median (~20 min), and unstable if they show a negative 2-fold difference from
the median (~5 min). For each of the 61 translated codons, we calculated a
frequency of occurrence in each mRNA, resulting in a list of 3969 frequencies, which
we compared to our list of mRNA half-lives. A Pearson correlation calculation was
used to generate an R-value, representing the level of correlation between the
occurrences of that codon and the half-lives. We refer to this metric as the Codon
occurrence to mRNA Stability Correlation coefficient (CSC). Repeating the calculation
for all the codons, we could compare the CSC values for all codons to each other
(Figure 9). It is clear from that comparison that some codons are associated with
stabilization of mRNAs and others are associated with destabilization. This indicates
that some preferentially occurred in stable mRNAs while others occurred
preferentially in unstable mRNAs (overall p-value = 1.496e-14, permutation p-value <
38
10-4). For example, the GCT alanine codon was highly enriched in stable transcripts
as defined by our RNA-seq analysis, while its synonymous codons, GCG and GCA
were preferentially present in unstable transcripts (Figure 9). Approximately one-third
of all codons were over-represented in stable mRNAs, while the remaining two-thirds
appeared to predominate in unstable mRNAs. As a consequence of the large dataset
and significance of the observed correlation, these data strongly suggest that codon
usage influences mRNA degradation rates.
Codon usage correlates with mRNA stability
To gain insight into this phenomenon, we analyzed the overall relationship
between our CSC metric and existing methods for evaluating the optimality of
codons. A previous publication (dos Reis et al., 2004) had established a metric for
39
the translational efficiency of codons. This metric was termed the tRNA Adaptive
Index (tAI). It attempts to describe the availability of decoding potential in the cell – it
is based on the tRNA gene copy number, which was found to be a good proxy for
tRNA concentration (Percudani et al., 1997), and a factor that accounts for the
strength of interaction between the codon and anticodon. This metric is meant to
reflect the efficiency of tRNA usage by the ribosome. To keep consistency with recent
literature regarding optimality, we defined our optimal and non-optimal codons based
on the definitions presented by Frydman and colleagues, which are based on a tAI
cutoff around .5 combined with an accounting of the over- and under-representation
of certain codons in the genome (Figure 10A) (Pechmann and Frydman, 2013; Zhou
et al., 2009). Strikingly, we found that codons associated with stable or unstable
mRNAs nearly perfectly mirrored their assignment as optimal or non-optimal,
respectively (Figure 10B). Direct comparison between our CSC metric and the tAI
metric revealed quantitatively illustrates the relationship between codon optimality
and effects on revealed very good overall agreement between these values (Figure
13A; R = 0.753, p-value = 2.583e-12, permutation p-value < 10-4), suggesting that
there is a significant link between the translation efficiency of a codon and its ability
to stabilize mRNA.
40
It is important to note that while codon optimality is somewhat associated in
genomic codon usage (Figure 11), both commonly occurring and uncommonly
occurring codons can be optimal or non-optimal. Overall, rare codons tend to be nonoptimal, but important exceptions exist in the form of both optimal uncommon
codons and non-optimal common ones.
41
Further, the relationship between optimal codon content and mRNA half-life is
independent of the method used to determine half-life. Our method, while based on a
classical and dependable method of achieving transcriptional repression, has some
downsides that could in theory affect the half-life calculations. For example, the rpb11 mutation itself could affect mRNA decay rates or shifting the cells to a nonpermissive temperature may disturb the pathway in some way. To mitigate these
risks, we repeated our analysis of codon usage vs. mRNA half-life using mRNA decay
rates obtained by a different laboratory using a different method. In contrast to our
own, these data were obtained with a steady state approach calculation using
metabolic labelling that minimally perturbs the cell and is completely distinct from
our method (Miller et al., 2011). Both datasets show very similar final numbers for
the CSC metric, indicating that method has not significantly skewed the calculations
(Figure 12).
42
To determine if the codon optimality correlation was possibly masking other
features that might actually be determining mRNA half-life (e.g. sequence content,
GC percentage, or secondary structure), we reanalyzed our data after
computationally introducing +1 and +2 frameshifts. In the analysis of these
frameshifted ORFs, the correlation between codon content and stability completely
disappears, thus eliminating other variables as determinative (Figure 13B: R = 0.127, p-value = 0.3303, permutation p-value = 0.8847; and Figure 13C: R= -0.288,
p-value = 0.0242, permutation p-value = 0.0012).
43
As shown above, computational analysis of our global mRNA stability data
revealed a relationship between codon occurrence and mRNA half-life. There are
three possibilities that emerge from this analysis: first, stabilizing or destabilizing
effects may come from clusters of optimal or non-optimal codons respectively, as the
rare codon constructs would suggest; second, there may be a small number of key
codons which can stabilize or destabilize messages on their own – these would likely
be those codons that fall at the extremes of the CSC metric; third, simple codon
content ratio may be at work – higher prevalence of optimal codons leads to
stabilization and higher prevalence of non-optimal codons leads to destabilization. To
evaluate the first possibility, we computationally analyzed mRNA sequences in an
attempt to identify clustering patterns of non-optimal codons in an effort to identify
effects similar to those seen with the rare codon cluster. However, we found that
there is little tendency for non-optimal codons to cluster. Indeed, we found that non-
44
optimal codons tended to be evenly distributed within each individual transcript. For
those few mRNAs where non-optimal codon clusters could be found, those clusters
did not appear to be predictive of particularly short half-lives. Thus, we ruled out
codon clustering as a primary mechanism of regulation through optimality.
Codon usage in genome falls into distinct patterns
To distinguish between the other two possibilities, we analyzed the pattern of
codon usage across the transcriptome to identify specific trends across genes. To do
this, we evaluated the relative codon composition of each mRNA and then applied
clustering analysis to identify similar patterns of usage. This analysis revealed that
there are several different mRNA classes that differ strikingly in preferred codon
usage within the transcriptome (Figure 14). There was a limited number of distinct
patterns that emerged, suggesting that these groups may represent cohorts of coregulated genes that can respond to changes in tRNA availability. Possibilities include
changes in the cell cycle, such as switching between states of proliferation and
quiescence, which has recently been shown to produce a switch in tRNA expression
patterns (Gingold et al., 2014); activation of stress response pathways, which have
been shown to affect tRNA modification pathways (Chan et al., 2012); or changes in
nutrient availability, which have been shown to change subcellular distribution of
tRNAs (Whitney et al., 2007).
45
46
Within these classes, two stand out because they specifically prefer either
optimal or non-optimal codons (Figure 15A & B). Further, this preferred usage
correlates well with overall transcript stability (Figure 15C). The average half-life of
the genes in these two groups differs by approximately 2-fold. We concluded that this
was most likely due to the widespread preference towards inclusion of optimal
codons in one group. This supported the hypothesis that inclusion of a large number
of optimal codons, rather than any individual codon, lead to a stabilizing effect in this
group. Closer inspection of several stable mRNAs further supported that conclusion,
as none of these was enriched in any particular codon, but an overwhelming
proportion (>80%) of codons fell into the category of optimal (Figure 15D). By
contrast, unstable mRNAs appeared to contain a mix of optimal and non-optimal
codons. These were marked by a lack of enrichment for any identifiable group of
codons at all (Figure 15E). These analyses demonstrated that in this set of mRNAs,
the stable mRNAs are biased towards harboring predominately optimal codons and
the unstable mRNAs include non-optimal codons in greater number, though the
specific codon identities vary between individual transcripts. Applying the conclusion
that optimal codon content is the defining factor for stabilization, we divided mRNAs
into groups based on their optimal codon content. mRNAs with less than 40% optimal
codons were found to be typically unstable, with a median half-life close to 5
minutes. In contrast, mRNAs with 70% optimal codon content or greater were found
to be much more stable on average, with a median half-life of 17.8 minutes (Figure
15F). It should be noted that each of those groups represent a relatively small
fraction of the genome with a majority falling in between (about 6% and 10%
respectively).
47
48
Optimal codons increase average ribosome density
As the primary impact of codon effects would be expected to be at the level of
translation, we reasoned that effects similar to those described for stability should be
visible for measures of translation. Currently, there are no direct genome-wide
translation rate measurements, but one widely-used proxy is ribosomal profiling. In
this assay, mRNAs engaged by ribosomes are digested with nucleases and the
remaining protected fragments are analyzed to extrapolate a ribosomal density
across the mRNAs. While this gives no information about the speed of ribosomal
transit, it allows an estimation of the average ribosomal density for each mRNA.
Previous studies have found that messages known to be highly translated tend to
correlate with higher ribosome occupancies (Ingolia et al., 2009).
Specifically, we analyzed a previously published ribosome profiling data set
(Ingolia et al., 2009) from wild-type yeast cells and compared this to mRNA codon
usage. Translational efficiency of each mRNA was calculated by normalizing the
number of ribosome-protected fragments (RPF) per mRNA from profiling data to the
total number of reads for each transcript from whole-cell RNA-seq analysis performed
in parallel to generate a translational efficiency index (TEI). TEI from mRNAs was
analyzed for correlations with codon usage, using the same algorithm as the CSC
calculation described above to create Codon occurrence to TEI Correlation coefficient
49
(CTC). This revealed a strong relationship between TEI and codon usage (Figure
16A).
While this relationship was not identical to the one seen with mRNA stability
previously observed, the association of optimal codons with positive effects and nonoptimal codons with negative effects held true. Comparing our calculated CSC values
to the CTC values, we obtain a very strong relationship, demonstrating that the two
measures are closely linked (Figure 16B). Accordingly, transcripts with high TEI
scores were enriched in optimal codons while transcripts with low TEI scores
predominantly harbored non-optimal codon triplets. These data provide support for
the premise that codon optimality dramatically influences translation efficiency
(Tuller et al., 2010) and argue that codon optimality influences mRNA translation and
stability nearly identically, implicating codon usage in the linkage between the two
processes.
As with the mRNA stability analysis, we sought to verify that these
observations were not unique to the data set we chose; thus, we extended our
analysis to include several more datasets found in the literature (Artieri and Fraser,
2014; Brar et al., 2012; Gerashchenko et al., 2012; Guydosh and Green, 2014;
Ingolia et al., 2009; McManus et al., 2014; Zinshteyn and Gilbert, 2013). Plotting the
CTC values for all of these data sets together (Figure 16C) demonstrates that all of
these data sets are consistent with the data set used for our analysis.
50
51
Related genes appear correlated through codon usage
Consistent with the idea of co-regulation through codon usage, a previous
analysis of mRNA stability in yeast revealed that the decay rates of some mRNAs
encoding proteins that function in the same pathway or are part of the same complex
were similar. Turnover of individual mRNAs appeared to be based on the
physiological function and cellular requirement of the proteins they encode (Wang et
al., 2002). We hypothesized that codon composition may provide a mechanism for
the cell to coordinate the metabolism of transcripts expressing proteins of common
function, in line with the grouping of genes into large blocks with similar usage. We
assessed codon usage for genes whose protein products function in common
pathways and/or complexes. We observed that mRNAs encoding the enzymes
involved in glycolysis (n=10) had a similar and extraordinarily high proportion of
optimal codons (mean=86%; Figure 17A). These transcripts were determined to be
stable in the previous study and were confirmed to have a long half-life in our
genome-wide analysis (median half-life=43.4 min). By contrast, mRNAs encoding
polypeptides involved in pheromone response in yeast cells (n = 14) were all
unstable in both studies (median half-life=5.6 min in our data) and harbored an
average of only 43% optimal codons (Figure 17A).
Our analysis revealed that other groups of transcripts behave similarly. The
stable large and small cytosolic ribosomal subunit protein mRNAs (n=70 and 54,
respectively; median half-life=18.9 min and 20.2 min, respectively) demonstrated an
average optimal codon content of 89% and 88% respectively. However, mRNAs that
encode ribosomal proteins functioning in the mitochondria are unstable (n=42;
median half-life=4.8 min), consistent with the observation that they have 45%
52
optimal codon content (Figure 17A & B). Other families of genes that have similar
decay rates include those whose protein products are involved in ribosomal
processing, tRNA modification, the TCA cycle, RNA processing, and components of
the translational machinery (Figure 17 and data not shown). These data provide
evidence that transcripts expressing proteins of related function are coordinated at
the level of optimal codon content as well as decay rate, suggesting that these genes
may have evolved specific codon contents as a mechanism to facilitate precise
synchronization of expression based on their function in the cell.
53
In this section, we have established that optimal codon content is a general
property of mRNAs that correlates significantly with half-life. It must be noted that the
correlation we observe between optimal codon content and mRNA half-life is modest
– the trend is clear, but there are many outliers where the optimal codon content
does not match the observed half-life. While codon optimality can clearly influence
decay rates, other factors will also play major roles in regulation of mRNA decay.
Players such as translation initiation rate, 5’ UTR and 3’UTR sequence, and RNA
binding proteins work together to achieve the vast continuum of observable mRNA
half-lives.
54
CHAPTER 4: EXPERIMENTAL VALIDATION
Changes in codon content leads to changes in stability
To experimentally validate the relationship observed in the computational
analysis, we tested the effects of altering optimal codon content within an mRNA. We
designed two complimentary sets of reporters. In the first case, we started with a
natural mRNA that has relatively few optimal codons and modified its coding
sequence to contain a much higher percentage of optimal codons without strongly
affecting other variables like GC content and secondary structure. At the same time,
we took a natural mRNA with a high percentage of optimal codons and modified it to
harbor many more non-optimal codons in the same fashion. We then expressed the
two reporters in their native context (using their natural flanking sequences and
promoters).
Specifically, we modified the codon content of the unstable LSM8 mRNA (halflife = 4.65 min) by making synonymous optimal substitutions in 52 of its 60 nonoptimal codons. Similarly, we replaced the majority of optimal codons (108 of 113)
within the coding region of the stable RPS20 mRNA (half-life = 25.3 min) with
synonymous, non-optimal codons. This methodology ensured that the polypeptides
encoded by these sequences were unchanged from the native form. Northern blot
analysis of transcriptional shut-off experiments using rpb1-1 revealed that alteration
of the codons within these two transcripts resulted in dramatic changes in their
stability. Specifically, the half-life of LSM8 mRNA was increased greater than 7-fold
as a consequence of the conversion of non-optimal codons into synonymous optimal
codons in its ORF (half-life = 18.7 min; Figure 18A). In contrast, substitution of nonoptimal for optimal codons within the stable RPS20 mRNA resulted in a sharp (10
55
fold) reduction in its stability (half-life = 2.5 min; Figure 18B). These experiments
demonstrate that codon usage is a critical determinant of mRNA stability. To put
these findings into context, a 10-fold change in half-life is similar to the changes seen
when introducing nonsense mutations into a message to trigger the powerful NMD
pathway in yeast (Zhang et al., 1995) and is at the maximal end of the regulation
seen by other factors such as the PUF proteins (Olivas and Parker, 2000) in yeast.
Thus, optimal codon content within an mRNA can strongly influence stability in a way
that can be manipulated within the native context.
Regulation through codon usage dominates over UTR regulation
To further understand the relationship between regulation of mRNA stability
by codon usage and regulation by methods described previously, e.g. 3’ UTR
56
elements, we evaluated the behavior of high and low optimal codon content reporters
in well-studied genomic contexts. To do this, we generated two synthetic open
reading frames which encode the same 59 amino acid polypeptide, but differ in the
optimality at each codon (SYN reporters). This was done to avoid any possible
intrinsic regulation that may occur in the ORF of genes. The synthetic sequences
have no similarity to any yeast genes at either the RNA or the protein level. We
introduced the synthetic ORFs into a reporter bearing the 5’ and 3‘ UTRs of MFA2, a
well-studied mRNA which is rapidly degraded in the cell (half-life = 3.0 min), a
phenomenon shown to be mediated, in part, by elements encoded within its 3’ UTR
(LaGrandeur and Parker, 1999; Muhlrad and Parker, 1992). We also introduced the
synthetic ORFs into a reporter with the 5’ and 3’ UTRs of PGK1, a well-characterized
and stable mRNA (half-life = 25 min) (LaGrandeur and Parker, 1999; Muhlrad et al.,
1995). When the stability of the four reporter mRNAs was measured by
transcriptional shut-off analysis using the GAL expression system, the SYN transcripts
encoded with optimal codons were found to be significantly more stable (~4-fold)
than their counterparts bearing the non-optimal codons (Figure 19). In this
experiment, we can see the influence of the regulatory flanking sequences on the
stability of these messages – both reporters had about 1.5-fold longer half-life in the
stabilizing context of PGK1 than in the destabilizing context of MFA2. However, the
magnitude of regulation by optimal codon content of the ORF was much greater than
that of the flanking sequences. While this was an extreme case with polar opposite
optimal codon contents, it clearly made the point that regulation at this level is
potentially more potent than the previously described methods of regulation in the
UTRs.
57
Codon content affects the major decay pathway
Importantly, degradation of both the optimally and non-optimally encoded SYN
reporter mRNAs was determined to occur through the 5’-3’ deadenylation-dependent
major mRNA decay pathway, rather than the aberrant decay pathways. To establish
this, we tested the decay of these reporters in a series of deletion mutants (Figure
20). Only the reporters with PGK1 context were utilized for these assays, as they
produce better signal on a northern blot and behave the same as reporters with the
MFA2 context. The non-stop decay pathway was not tested here, as it has the bestdefined targets of all of the aberrant mRNA decay pathways and absolutely requires a
58
message lacking a stop codon. The dom34∆ strain is not competent for no-go decay,
which could result if a large number of non-optimal codon produced a block in
translation similar to the stalling at strong mRNA structures. This strain does not
show significant stabilization of the reporters, implying that the no-go decay pathway
is not involved in the decay of these messages. The upf1∆ strain is not competent for
nonsense-mediated decay, and a lack of stabilization here demonstrates that this
pathway is not involved in the decay of these messages either. As a positive control
for the major decay pathway, both ccr4∆ and dcp2∆ strains were tested to ensure
that disruptions of the major decay pathway would stabilize the reporters.
59
Further, high-resolution northern analysis of the decay of these mRNAs
confirmed that the rates of both deadenylation and decapping, the regulated steps in
the major decay pathway, were affected as a consequence of changes in codon
composition within the reporter ORFs (Figure 21). These data demonstrate that
optimal codon content is a critical determinant of mRNA stability influencing both the
rate of deadenylation and decapping during turnover of the mRNA independently of
5’ and 3’ UTRs. Regulation at the level of the UTRs can act in parallel with regulation
by the optimal codon of the ORF.
HIS3 reporter system allows for fine tuning of mRNA stability
In conjunction with regulation at the level of mRNA decay, we expected that
optimal codon content should have effects on translation, as proposed by multiple
60
previous studies on codon optimality. To evaluate these effects in vivo, we
established a new reporter system which produces a well-characterized protein
product encoded by different amounts of optimal codons. Specifically, we engineered
the ORF of the HIS3 gene to contain either all optimal (HIS3 opt) or all non-optimal
codons (HIS3 non-opt), with the wild-type HIS3 gene providing an intermediate point
at 43% optimal codons (Figure 22A). The HIS3 gene was chosen because it is a
protein with a well-characterized function that could be used to screen for expression
by growth and has a relatively long ORF (220 amino acids) compared to our other
synonymous mutation constructs, allowing us to effectively monitor ribosome
association by sucrose density gradients (see below).
We then determined the mRNA decay rate of the three HIS3 constructs by
transcriptional shutoff analysis using an rpb1-1 strain. Consistent with our previous
results, it was observed that changing optimal codon content produced a dramatic
effect on mRNA half-life (Figure 22B). Notably, the effect on HIS3 mRNA decay was
commensurate with change in optimal codon content. The native HIS3 mRNA is more
stable than average, especially given its relatively low optimal codon content. It has a
half-life of 9.5 min, above the median of 7.3 min for the entire genome. For this
experiment, the native UTRs and transcriptional context of the gene were maintained
to avoid disrupting any regulatory elements which may be responsible for its relative
stability. Decreasing the optimal codon content to nearly 0 produced a marked
decrease in the half-life of the mRNA to 2 minutes. Conversely, increasing the optimal
content to near maximum produced a large increase in the stability of the message,
with a half-life of greater than 60 minutes. Thus, within this single native genomic
context, we can achieve a full range of mRNA half-lives, from the minimum observed
61
to beyond the scope of our measurements, without introducing any regulatory
changes outside of the open reading frame and without altering protein sequence.
Codon content impacts translation beyond changes in mRNA
With the range of regulation of mRNA half-life and accumulation within this
reporter system established, we assayed the effects of this regulation on translation
in the system. To ascertain that protein output of the reporters was reduced as well
as the mRNA levels, we monitored the protein output from the HIS3 construct with a
high optimal codon content by western blotting and compared it to the inverted
62
reporter. To account for changes in mRNA levels previously described, we normalized
the amount of protein detected to the levels of mRNA for each reporter, as
determined by northern blot. We observed that the non-optimal construct had fourfold less protein output than the optimal construct (Figure 22C). This confirmed our
expectation that translation should be impacted by changes in optimal codon content
in addition to changes at the mRNA expression level.
At the level of mechanism, there were several possibilities for the reduction in
protein output per mRNA molecule in the case of low optimal codon content. In broad
terms, each molecule can either engage fewer ribosomes, with the rate of ribosomal
transit roughly equivalent for both constructs, or the ribosomes can progress through
the non-optimal codons slower than the optimal codons, which may or may not be
combined with a slowdown in initiation to balance the numbers of ribosomes on the
message. There has been significant controversy in the field surrounding the
question of whether translation rates vary on different codons, with different labs
reporting different results (Charneski and Hurst, 2013; Gardin et al., 2014; Qian et
al., 2012). Based on the experiments with rare codons, we hypothesized that the
number of ribosomes on the constructs should not vary, with the reduction in protein
output then having to come from slower rates of translation elongation on the
constructs with low optimal codon content.
63
To measure this, we evaluated the ribosome occupancy of the HIS3 mRNA
constructs. Ribosome occupancy was monitored using sucrose gradients, followed by
fractionation and northern blotting of the isolated fractions (Hu et al., 2009). In a
critical validation of our hypothesis, it was observed that the ribosome occupancy
64
was nearly identical for all three HIS3 reporter mRNAs (Figure 23), suggesting that
each construct engaged a very similar number of ribosomes. In this case, the HIS3
construct is of appropriate size to resolve ribosome counts, so the ambiguity of the
rare codon experiment is avoided (see above). The quantitation of the northern blots
presented below further confirms that there is no significant shift in ribosome
occupancy between the messages. Thus, we propose that the lack of change in
ribosomal association must mean that the observed four-fold decrease in protein
output is likely due to lower protein output per ribosome, implying a decrease in
ribosome translocation rate on the construct encoded with low optimal codon
content.
Affected step of translation is elongation
To directly determine whether ribosome translocation rate is indeed the
affected step, we monitored ribosomal run-off of these two reporters. Ribosomal runoff measurements are performed by blocking initiation and monitor the ribosomal
occupancy of messages as ribosomes progress through elongation and termination.
In the case of ribosomal run-off experiments in yeast, glucose deprivation can be
used to induce rapid inhibition of translational initiation (Coller and Parker, 2005).
Under these conditions, ribosomes will complete their elongation and termination
cycles and a large fraction of messages that were engaged in polyribosomal
complexes then transition to lighter parts of the gradient as they can no longer reenter translation (Figure 24A). We monitored this transition for both our wellcharacterized HIS3 reporter system and for the previously identified endogenous
mRNAs, RPS20 and LSM8, which naturally have very high (92%) and very low (45%)
optimal codon content respectively. We extracted mRNA-ribosome complexes before
65
and after glucose deprivation, separated the material with a sucrose gradient,
collected fractions, and monitored the presence of the mRNAs in each fraction by
northern analysis.
Importantly, under normal conditions the ribosome occupancy of both of the
HIS3 was determined to be similar (Figure 24B & C), with the short RPS20 and LSM8
constructs engaging fewer ribosomes than the longer HIS3 mRNAs. However, upon
inhibition of translation initiation and induction of ribosome run-off, a large fraction of
the high optimal codon construct mRNA relocated to the light fractions of the
gradient in the ribosome-free area, whereas the low optimal codon content HIS3
mRNA remained virtually undisturbed in the polyribosomal fraction (Figure 24B & C).
This situation is mirrored in the endogenous messages, with RPS20 mRNA shifting
significantly in their distribution upon inhibition of initiation, but LSM8 mRNA
remaining largely associated with polyribosomes. We this conclude that the reduction
in output per mRNA is most likely due to a change in rate of elongation of the
associated ribosomes. This is demonstrated by the inability of the mRNAs to
efficiently transition to the non-translating pool upon induction of ribosomal run-off.
This observation supports recent work that has found that more highly available
tRNAs (i.e. ones that score highly on the tAI optimality scale) tend to translate more
rapidly (Gardin et al., 2014). Thus, we have shown that regulation by optimal codon
content sits at the junction of mRNA expression and protein translation – it has
effects on both, with evidence pointing to a direct involvement in translation. It has
long been observed that perturbations in translation can lead to destabilization of
messages (Coller and Parker, 2004), creating a landscape with quickly and efficiently
translated stable messages at one extreme and poorly translated unstable messages
66
at the other. The main variable in this scenario appears to be the optimal codon
content of the messages, with contributions from regulatory factors that interact with
UTR sequences and potentially other factors that influence the balance between
translation and decay (Sweet et al., 2012).
67
Changes in codon content can impact cellular fitness
Finally, to evaluate the cellular impact of this range of regulation, we assayed
the growth of our cell lines bearing the HIS3 constructs on 3-AT. As our cell
background is deficient for histidine synthesis due to disruption of the
imidazoleglycerol-phosphate dehydratase enzyme (IGPD) encoded by its native HIS3
gene, the copy of HIS3 encoded by the reporters is the sole source of histidine in the
absence of supplementation. 3-aminotriazole (3-AT) is a competitive inhibitor of IGPD
that can be precisely titrated to challenge the cells (Glaser and Houston, 1974). In
this experiment, we plated cells carrying our reporters in a dcp2∆ background in 5fold dilution series on plates containing no histidine and 40 mM 3-AT (Figure 25). The
dcp2∆ background was used to keep the mRNAs at similar levels by blocking
enhanced degradation of the low optimal codon content transcript. Results of a
parallel experiment using a wild-type background showed very similar results, but
required higher concentrations of 3-AT and higher dilutions (data not shown). The
results show that the reduction of protein output by the low optimal codon content
message produces a dramatic decrease in the fitness of the cells under these
challenging conditions. This directly demonstrates that the regulation of expression
by codon usage can have dramatic consequences to the cell. Thus, this method of
regulation carries potential for both a wide range of regulation, especially when the
stability and translational phenotypes are considered together, and for dramatic
consequences on fitness.
68
69
CHAPTER 5: DISCUSSION
Overview
In this work, we present six lines of evidence in support of the finding that
codon usage is a general mechanism regulating both mRNA stability and translation
to achieve powerful and precise regulation of gene expression. First, analysis of rare
codon reporters indicates that rare codons can induce accelerated decay in yeast
through the major decay pathway. Second, global analysis of RNA decay rates reveals
that mRNA half-life correlates with optimal codon content. Many stable mRNAs
demonstrate a strong preference towards the inclusion of optimal codons within their
coding regions, while many unstable mRNAs harbor non-optimal codons. Third, we
observe tightly coordinated optimal codon content in genes encoding proteins with
common physiological function. We hypothesize that this finding explains the
previously observed similarity in mRNA decay rates for these gene families. Fourth,
we demonstrate that mRNA half-life can be predictably manipulated by changes in
optimal codon content. This argues against the idea that optimal codon content may
be masking other features responsible for changes in stability. Fifth, we directly
demonstrate that changes in optimal codon content impact rates of change the rate
of ribosome translocation of a transcript, indicating that the effect on mRNA decay
occurs through modulation of mRNA translation elongation. Finally, we demonstrate
that changes in optimal codon content under conditions where the gene product is
limiting for growth lead to significant impacts on cellular fitness. Taken together, our
data suggest that the optimal codon content of genes is a key features of the open
reading frames that determines both their capability to produce protein and their
70
stability as transcripts. There is likely evolutionary pressure on protein coding regions
to coordinate gene expression at the level of protein synthesis and mRNA decay.
Considerations of rare codon experiments
The work began with an exploratory project based on previous observations
that rare codons negatively impact mRNA stability when present in mRNA coding
regions made by our group and others (Caponigro et al., 1993; Hoekema et al.,
1987; Hu et al., 2009; Sweet et al., 2012), we expanded our analysis to include
questions of dependence on position as well as evaluation of factors involved in
these phenomena. We found a striking position dependence associated with the
degree of destabilization conferred by a stretch of rare codons. Rare codons early in
the message provided less destabilization than rare codons later in the message in a
largely linear fashion. There are some potential concerns regarding the interpretation
of this experiment, since the exogenous codons were introduced into the mRNA
without regard for disruption of local mRNA or protein structure. The rare codoncontaining constructs all produce protein at a greatly reduced rate as compared to
the parent mRNA (data not shown), but there does not appear to be a polarity
associated with the protein production. One possible explanation is that the protein is
misfolding due to the new codons disrupting local structure, which has been shown
to lead to disruptions in translation and message decay (Hollien and Weissman,
2006; Kirstein-Miles et al., 2013). However, this is unlikely because this type of
response should inhibit cell growth, which was not observed with these constructs.
The dependence of this phenomenon on DCP2 and DHH1 suggests that this
pathway functions through the 5’-3’ major decay pathway rather than quality control
mRNA decay pathways, but curiously, disruptions of other components of that
71
pathway do not disrupt the position dependence. This suggests that perhaps DHH1
acts on DCP2 independently of the other decay factors. This model is supported by
the observations that DHH1 appears to have a limited set of substrates, whereas
other decapping activators such as LSM1 and PAT1 function broadly to affect
virtually every mRNA (Coller and Parker, 2005; Sweet et al., 2012). Additionally,
DHH1 is known to be a translational repressor independent of its role in decay,
functioning to restrict ribosomal run-off (Sweet et al., 2012). The observations of
unchanged ribosome association with the rare codon constructs in the presence of
significant protein output reduction suggests that these constructs may exhibit
slower translocation of ribosomes, which could be mediated by DHH1. Overall, the
reasons behind the polarity displayed by these constructs (which was not
recapitulated by whole-genome analysis, discussed below) remain a mystery.
Considerations of RNA-seq study
In the subsequent experiments, we combined a transcriptional inhibition time
course with genome-wide RNA-Seq analysis of poly(A)+ versus total RNA (depleted of
rRNA) and demonstrated that total mRNA half-lives are significantly longer than
poly(A)+ half-lives. These data suggest either that methods used to enrich poly(A)+
mRNA are inefficient or that upon removal of the poly(A) tail, a large fraction of
mRNAs are maintained at some level in the cell as deadenylated transcripts, which is
a striking observation, as deadenylation in yeast and other eukaryotes represents not
only the initial event in mRNA degradation, but for many mRNAs has been suggested
to be the rate-limiting step (Decker and Parker, 1993; Franks and Lykke-Andersen,
2008). From the perspective of a technical limitation, oligo-dT based purifications are
biased towards mRNAs with longer tails, as short tails allow for fewer binding sites for
72
the oligonucleotides; studies across decades have reported significant drops in
signal once the length of the poly(A) tail approached 20-30 nucleotides (Blower et al.,
2013; Cabada et al., 1977) From the perspective of poly(A)- mRNAs existing in the
cell as a biological molecule, some studies have estimated that as many as 35% of
mRNA species in the cytoplasm could be found in the poly(A)- state (Cheng et al.,
2005), though other studies place that number lower (Yang et al., 2011). These
could occur due to a lack of polyadenylation activity rather than deadenylation, but
current understanding of mRNA transcription and processing dictates that the
cleavage and polyadenylation steps are intricately woven into transcription
termination and export (Proudfoot, 2011), making it unlikely that they do not occur
for such a large group of mRNAs. Regardless of the reason, data from our study as
well as previous ones (Wang et al., 2002) indicate that sole use of poly(A)+ methods
to determine mRNA decay rates could lead to the underestimation of half-lives.
Our global mRNA decay analysis was performed by inhibiting mRNA
transcription using a well-characterized temperature-sensitive allele of the gene for
the large subunit of RNA polymerase II, rpb1-1 (Nonet et al., 1987). While this
method has its drawbacks, it has been shown to produce mRNA half-life
measurements comparable to those produced by other methods, such as shut-off of
regulated promoters such as the GAL promoter, approach to steady-state by
metabolic labelling, and inhibition of transcription with drugs such as thiolutin
(Herrick et al., 1990; Wang et al., 2002). Consistent with these assumptions, mRNA
half-life values obtained in this study are in agreement with many mRNAs whose
decay has been measured experimentally and published in the literature (Decker and
Parker, 1993; Herrick et al., 1990; Miller et al., 2011) despite the fact that rpb1-1
73
shut-off does induce a stress that could alter mRNA decay rates. Importantly, our
dataset also correlates moderately with half-lives generated by Miller et al., who used
a steady state approach methodology with metabolic labelling by 4-thiouracil to
determine mRNA half-lives in an effort to minimize perturbation to the cells. Their
approach has its own limitations, including the need to express an exogenous
nucleoside transporter for proper uptake, poorly-understood kinetics of uptake and
incorporation of their label, and inherent limitations of microarray technology in halflife calculation. Nonetheless, their method is completely distinct from ours and
provides a proper control data set. Using the Miller et. al., data set we observe strong
a correlation between mRNA half-life and codon optimality (Figure 12). In fact, the
correlation between half-life and codon optimality observed in the Miller et al. (2011)
dataset was higher than our own (overall correlation R=.533 vs R=.313 respectively).
It should be noted that the correlation of half-life values between our two data sets is
limited (R=.448). Thus, we conclude that the influence of codon optimality on mRNA
half-life is observable in data from independent sources, using independent
methodologies, even in the absence of perfect agreement in the half-life values.
Based on previous observations with rare codons, we evaluated our genomewide half-life data for any effect codon content might have on mRNA turnover. Our
global analysis revealed correlations between the enrichment of individual codons
and the stability (or instability) of mRNA in yeast (Figure 9). This analysis
demonstrated that the pattern of codon usage bias among synonymous codons had
specific repercussions for mRNA stability. We observed enrichment of optimal codons
within the coding region of stable mRNAs, while non-optimal codons are found to
predominate within unstable mRNAs. It is important to note that while codon content
74
is clearly a major determinant of mRNA stability, it does not predict half-lives of all
mRNAs. For example, mRNAs for several histone components, such as HHF2 and
HHT1, contain 85% optimal codons, but yet are very unstable with half-lives of 2.4
and 3.5 minutes, respectively. The half-lives of such mRNAs could be dictated by
their ability to initiate translation efficiently (or inefficiently) and/or by elements in 5’
or 3’ UTRs. It is also possible that features within the ORFs might explain some of the
outliers, such as the distribution and placement of optimal and non-optimal codons.
Curiously, we were unable to recapitulate the position-dependent polarity seen in our
experiments with rare codons through a cluster search in our sequencing data, thus
more nuanced exploration of this concept may be warranted.
Considerations of codon content experiments
As a translation efficiency scale, codon optimality reflects a balance between
supply of available charged tRNA and demand of translating ribosomes. In an
extension of this concept, Tuller et al (2010) theorized that coadaptation between
coding sequences and tRNA pools can influence translation speed and as a
consequence, ribosomal density. However, studies of ribosomal profiling data have
found conflicting evidence of differences in ribosome density at individual codons –
some reporting no changes between codons at all, and some finding meaningful
differences (Charneski and Hurst, 2013; Gardin et al., 2014; Ingolia et al., 2009;
Qian et al., 2012). Thus, the contribution of codon optimality to elongation rate is not
clear. We propose that codon optimality can powerfully alter ribosome translocation
rate. We have shown that protein output is greater from an mRNA containing optimal
codons than from an analogous mRNA containing synonymous non-optimal codons.
These mRNAs have similar ribosome association patterns in sucrose gradients, but
75
produce different amounts of protein, suggesting that ribosome translocation rate is
different between these mRNAs. Additionally, upon blockage of translational
initiation, we observe that an mRNA with optimal codons clears from polyribosomes
much more efficiently that the synonymous mRNA containing non-optimal codons.
Together, these data argue that codon identity can have a powerful influence on
ribosome translocation rate. The failure to see this influence by some ribosome
profiling experiments may indicate that the effect of each individual codon on
elongation rate is minute and undetectable by analysis of individual fragments.
Indeed, while codon identity may have a small influence on ribosome decoding rate
when measured individually, our data suggest that codon optimality is powerfully
additive and can result in dramatic changes to mRNA metabolism. Though the
precise mechanism connecting effects on translation elongation with effects on
deadenylation and decapping remains unknown, we have previously posited that
slowing of ribosomal transit leads to the association of decay factors that promote
entry of the mRNA into decay (Sweet et al., 2012).
Possible roles of DHH1
The DEAD-box RNA helicase DHH1 is an intriguing candidate for the
mechanism of monitoring translation elongation by the ribosome for multiple
reasons. First, DHH1 is an integral component of the mRNA decay machinery
(Presnyak and Coller, 2013) that has been shown to act as an activator of decapping
though its role in promoting translational repression (Coller and Parker, 2005; Sweet
et al., 2012). Secondly, DHH1 is an abundant protein (>50,000 per cell), far
exceeding the levels of all other mRNA decay factors. Third, DHH1 orthologs in yeast
and Xenopus has been shown to associate broadly across an mRNA transcript,
76
including sites within the coding region (Minshall and Standart, 2004; Mitchell et al.,
2013). Fourth, our recent findings have shown that DHH1 protein can modulate
translation of mRNA at the level of translational elongation when directed to a
transcript (Sweet et al., 2012). Lastly, our findings with the rare codon constructs
implicate DHH1 in regulation of mRNA stability conferred by codon effects. Taken
together, these findings spell out a model wherein DHH1 associates directly with
transcripts through its intrinsic RNA-binding activity and influences ribosome transit
in some way. The simplest model of this event can be based on residence time,
where a failure to displace DHH1 by quickly translating ribosomes eventually leads to
activation of further translational repression and/or recruitment of a decay complex.
Further studies are needed to determine the mode of influence on mRNA decay by
translation elongation rate, including understanding of the role of DHH1 in the
process; and whether this conserved protein represents the link between codon
usage and the array of mRNA decay rates observed in yeast and other eukaryotic
cells.
Codon optimality in yeast and other organisms
We show that codon optimality strongly influences mRNA stability regardless
of the nature of the transcript’s untranslated regions (Figure 19). We suggest that
codon usage is a general mechanism intrinsic to all mRNAs, which facilitates the finetuning of gene expression. It works in concert with message-specific mRNA regulators
commonly found in UTR regions to fulfill that function (Goldstrohm et al., 2007;
Olivas and Parker, 2000; White et al., 2013). Codon usage may serve to establish the
base decay rate and expression level for a given mRNA, while UTR-based regulation
may play more specific roles for regulating individual mRNAs as needed for a given
77
intra- and extracellular environment. Interestingly, these two pathways may function
upon a single point of regulation - UTR regulators are described to be able to induce
translational repression rather than directly recruit the decay machinery, which is
consistent with the alterations of translation elongation effected by optimal and nonoptimal codons.
Codon optimality as described in this work and previously is a relatively
straightforward concept in the context of simple, single-celled organisms such as
yeast and bacteria, which spend the largest portions of their existence in either an
exponentially growing phase or in a quiescent phase while waiting for proper
conditions for exponential growth. It’s clear that in these cells, it is most beneficial to
tweak the production of proteins that are rate-limiting for growth to levels that are as
high as possible. Thus, in these systems, optimality is well-defined and easily
observable. Gene products like the ribosomal proteins and metabolic enzymes have
immediately distinct codon usage signatures that are highly biased towards codons
readily identifiable as optimal. The entire cell is a machine with a singular purpose –
to grow as quickly as possible as soon as conditions allow.
In higher organisms, the concept of optimality becomes much murkier. Cells
are no longer single-purpose growth machines, but are instead cogs in a much larger
mechanism, which must be finely coordinated and tuned to perform multiple
functions. Indeed, even the codon bias is reduced in these cases. For example, the
ribosomal genes of yeast are among the most highly expressed and most biased
genes in yeast, showing extreme preference for optimal codons. This preference is
strongly reduced in higher organisms (Figure 26). In S. cerevisiae, there is a very
small number of very frequently used codons, with a majority of codons showing
78
relatively low usage. In S. pombe, a yeast with a much slower rate of growth, the bias
is significantly less pronounced. As the chart moves to metazoans, including D.
melanogaster and M. musculus, the bias continues to decreases, with the mouse
ribosomal genes showing little bias compared to yeast genes.
Codon bias of ribosomal genes by organism
70
Usage/1000 codons
60
50
40
30
20
10
0
S. cer
S. pom
D. mel
M. mus
By limiting the analysis to single amino acids, we can demonstrate that bias
among degenerately encoded groups also decreases along with overall genomic bias
(Figure 27). Presented here are the breakdowns of codon usage within the 6-fold
degenerate amino acids in the ribosomal genes of several organisms. Ribosomal
genes were chosen for this analysis both because they are well-conserved between
organisms and because they need to be expressed at very high levels to achieve
79
rapid growth. Within this group of organisms, S. cerevisiae follows a consistent
patterns; one codon is always used for a majority of occurrences, with a second
accounting for a majority of the remainder. These patterns extend to other groups of
degenerate codons beyond those that are shown, though some of the groups with
fewer members only have a single preferred codon. The usage patterns correspond
tightly to tRNA expression levels, e.g. for Arg, the tRNA decoding the most common
AGA codons is present at 11 copies (Iben and Maraia, 2012), the tRNA decoding the
second most common CGT is present at 6 copies, and the other 4 codons only have
2 corresponding tRNA genes combined. There are some variations in the way that the
decoding potential is provided, e.g. for serine, the second most common codon, TCC,
lacks a direct decoding tRNA and is decoded by I-C wobble pairing with the tRNA for
the most common codon, TCT (present at 11 copies). These preferences persist at a
lower level in fission yeast and fruit flies, but are largely absent from the mouse
ribosomal genes, particularly in the case of arginine. Thus, the definition of optimality
in higher eukaryotes becomes a daunting task, as there is a lack of clear preference
for specific codons. This may reflect the need for the more diversified roles that cells
play within these organisms; different cell types in different tissues may express
tRNAs at different levels, affecting sets of genes enriched in the cognate codons.
80
AA
Arg
Arg
Arg
Arg
Arg
Arg
Leu
Leu
Leu
Leu
Leu
Leu
Ser
Ser
Ser
Ser
Ser
Ser
Codon
AGA
AGG
CGA
CGC
CGG
CGT
CTA
CTC
CTG
CTT
TTA
TTG
AGC
AGT
TCA
TCC
TCG
TCT
S. cer
80%
1%
0%
0%
0%
18%
6%
0%
0%
2%
16%
75%
2%
2%
4%
39%
0%
53%
S. pom
9%
1%
1%
19%
0%
70%
1%
17%
2%
36%
7%
37%
10%
7%
6%
23%
3%
51%
D. mel
4%
8%
3%
50%
3%
33%
3%
14%
58%
6%
1%
17%
17%
4%
3%
43%
23%
11%
M. mus
16%
19%
15%
20%
18%
11%
4%
20%
45%
15%
3%
13%
21%
10%
9%
29%
5%
27%
Additionally, in our experiments, we use exponentially growing yeast cells, thus
providing a steady and unchanging environment for our assay. However, both long
and short time scales provide important opportunities for the reassignment of codon
optimality in the cell. In the short term, changes in cellular growth conditions and
nutrient availability could significantly impact individual (or subsets of) charged tRNA
levels. As a consequence of this reduction in supply, translation elongation rates of
mRNAs enriched in the cognate codons of these tRNAs would be slowed and mRNA
levels decreased due to enhanced turnover. In this way, codon optimality provides
the cell not only with a general mechanism to hone mRNA levels, but also with a
mechanism to sense environmental conditions and rapidly tailor global patterns of
gene expression.
81
Long term genetic changes that introduce synonymous mutations into proteincoding genes do not alter the amino acid sequence of the encoded polypeptide;
however, such changes would impact mRNA and protein expression levels if the
mutations significantly altered the proportion of optimal codons within the open
reading frame of the mRNA. Synonymous gene mutation can thus be envisioned as a
method to evolve mRNA stability rates that are advantageous to the cell. We find that
mRNAs encoding proteins that act together in similar pathways or stoichiometric
complexes, which have been previously observed to decay at similar rates (Wang et
al., 2002), encode nearly identical proportions of optimal codons (Figure 17). We
suggest that codon optimality has been finely tuned for these gene sets as an
elegant mechanism to ensure coordinated post-transcriptional regulation and
parsimonious expression of proteins at the precise levels required by the cell.
Interestingly, similar levels of codon optimality would ensure not only similarity of
stability and translation rates for related mRNAs, but also coordination of response to
changes in tRNA levels (e.g. nutrient availability, stress, cell type, etc.). Recent
studies reveal that tRNA concentrations within the cell are not static but are
constantly undergoing change, sometimes dramatically. For instance, large scale
RNA profiling experiments have demonstrated that tRNA concentrations vary widely
between proliferating and differentiating cells (Gingold et al., 2014). Based on our
analysis, we would argue that significant alterations in tRNA concentrations could
alter the mRNA expression profile within a cell, even without any changes in
transcription by dynamically changing message stability.
82
Ribosome as monitor of all mRNA fates
As a final implication, our work suggests that co-translational mRNA
surveillance by the ribosome is not only important to target aberrant mRNAs to
rapidly decay, but also to tune the degradation rates of normal mRNAs. In
eukaryotes, aberrations in mRNAs lead to aberrant translation events such as
premature termination, lack of translation termination, and ribosome stalling, which
result in the accelerated turnover of the mRNA by the nonsense-mediated, non-stop,
and no-go decay pathways, respectively (Shoemaker and Green, 2012). All of these
pathways are critically dependent on the ribosome. Events surrounding aberrant
translation anchor each pathway, making the ribosome the sole sensor able to trigger
the entry of mRNA into these pathways. We find here that codon usage within normal
mRNAs also influences translating ribosomes and can have profound effects on
mRNA stability. Thus, the ribosome acts as the master sensor for all mRNA decay,
determining the fate of all mRNA through modulation of its elongation and/or
termination processes. The use of the ribosome as a sensor is ideal for proteincoding genes, whose primary function in the cell is to be translated. We suggest that
a component of mRNA stability is built into all mRNAs as a function of codon
composition. The elongation rate of translating ribosomes is communicated to the
general decay machinery, which affects the rate of deadenylation and decapping.
Individually, identity of codons within an mRNA would be predicted to have tiny
influence on overall ribosomal decoding; however, within the framework of an entire
mRNA, we show that codon optimality can have profound effects on translation
elongation and mRNA turnover. We conclude that codon identity represents a general
property of mRNAs and is a critical determinant of their stability.
83
Future directions
At this point, there are three direct lines of experimentation that can be done
to understand the mechanism of regulation though codon usage and expand the
application of these findings.
First, the relationship between tRNA expression and codon usage needs to be
explored. It is clear that optimal codons are those that match up well with the
available pool of charged tRNAs. This is advantageous to the cell, whose growth may
be limited by production of proteins, particularly components of the ribosome and
metabolic pathways. This also presents a potential mechanism of regulation, as
altering the availability of tRNAs will lead to changes in effective optimality of the
cognate codons, which can strongly alter both translation and stability of mRNAs. It
has been shown that tRNA pools can change in conditions of stress or with changes
in cellular programs (Chan et al., 2012; Gingold et al., 2014), making it likely that this
mechanism is utilized by cells with some regularity. One way to discover this
regulation is to monitor the tRNA pool of cells under a variety of conditions, while
testing the stability of mRNAs in that scenario. The best test would be a direct
measurement of translation rates for all the mRNAs in the cell to ascertain that the
changes in tRNA availability actually affect translation as expected, but as such an
assay is not available, mRNA stability is a good proxy for this measurement. Another
way to test these effects would be to artificially alter the available tRNA concentration
in the cell by inhibiting modification or changing nutrient conditions such that some
amino acids become limiting. Measurement of mRNA stability under these conditions
would similarly allow for shifts in regulation.
84
Similarly extending the idea that optimality is a fluid concept, patterns of tRNA
expression and mRNA stability in multicellular organisms are an important area to
research. In a complex organism with a multitude of cellular programs ranging from
developmental roles to maintenance of mature tissues, preferred sets of codons
could vary widely, depending on tRNA expression patterns and cellular function at the
time. One important project would be to understand those relationships – the
differential tRNA expression patterns could play roles in regulating large rafts of
genes in programs like development, helping explain developmental regulation such
as some fetal splice isoforms. Further, changes in tRNA expression could be
important to maintaining cellular identity, for example, reinforcing the differentiated
behavior of mature cells and the proliferative behavior of stem cells. The first
analysis of this kind has been published in recent studies (Gingold et al., 2014).
From a mechanistic point of view, it is not clear what regulates the entry of
non-optimal messages into the decay pathway and the coupling to translational
events. From some of our experiments, we suspect that the RNA helicase DHH1 can
participate in this process, but further study would be necessary to identify the
specifics of this interaction. We can look at the effects of changes in optimal codon
content on the stability on messages in the absence of DHH1 in the cell. Similarly, we
can evaluate the effects of DHH1 deletion on mRNA stability of normal messages for
which we have previously established half-lives. Finally, we can look at direct
association of DHH1 with messages harboring predominantly optimal or non-optimal
codons, as we would expect the association to differ between messages that are
targets of regulation and those that are not.
85
APPENDIX A: BIOINFORMATICS
This section provides a highlight of the code used in the project. It is not a list
of code used, rather it is meant to illustrate some of the techniques and coding
structures utilized for the project. Additionally, it provides some convenient code
sections that could be adapted for future scripts. Code is presented below with line
by line comments in grey.
Half-life fitting
This code was written in R, using the RStudio environment for Windows. It
takes an Excel file of genes names and quantitation across a time course. Our input
for this script was a file containing our normalized FPKM reads. The data presented
above uses least absolute deviation fitting for half-life calculation, which was chosen
because we had a relatively dense time course, with 10 time points. This situation
required a robust method, as we wanted to minimize impact of outliers. The
particular example below uses ordinary least squares fitting, as that should be
suitable to a wider range of applications.
#
#
#
#
Half-life calculation by OLS method
Requires xlsx, foreach, and doParallel packages
Requires an installed and set up version of the
Java Runtime (JRE) - this is required for xlsx only
######################################################
## IMPORTANT - ADJUST THESE PARAMETERS FOR YOUR RUN ##
######################################################
# input data file location (xlsx format)
# expected file arrangement is gene names in first column,
# FPKM at given time points in subsequent columns
# extra columns are okay, they will be ignored
# remember to use \\ for path separator
input_file <- c("C:\\User\\Experiment 1.xlsx")
86
# output file for calculated k values
# remember to use \\ for path separator
output_file <- c("C:\\User\\exp_1_results.txt")
# time points to be used for fitting in minutes
time_points <- c(0,2,4,6,8,10,15,20,30,40,60)
# number of threads to use for calculation
threads <- c(4)
###################################
## DATA PROCESSING AND FUNCTIONS ##
###################################
# import packages
library(xlsx)
library(foreach)
library(doParallel)
# read xlsx file with FPKM of timepoints into data frame
read_data <- read.xlsx(input_file, sheetIndex = 1)
# convert data frame to matrix without gene names
temp_matrix <- as.matrix(cbind(read_data[,-1]))
# discard columns beyond pre-defined time points
data_matrix <- temp_matrix[,1:length(time_points)]
# define main function - this takes a row of data from
# matrix above, normalizes it to the 0 time point,
# and fits a k value to it using the supplied distance
# function and OLS fitting method
main <- function (data_row){
# normalize data by dividing through by time point 0
norm_data <- data_row / data_row[1]
# pass data to optimization function, which will minimize
# the distance function (between 0 and 1)
final_k <- (optimize(function(prop_k) dist(norm_data,
prop_k), c(0,1)))
# return the optimised k value along with minimized error
return(c(as.numeric(final_k[1]), as.numeric(final_k[2])))
}
# define distance function (exponential decay fit with OLS)
87
dist <- function(data, proposed_k) {
# proposed values calculated from passed proposed k value
# and pre-defined time points in the format of e^-kx
prop_values <- exp(-proposed_k*time_points)
# returns the sum of the squares of the distances between
# the actual data and proposed values
return(sum((data - prop_values)^2))
}
# set up parallelization back-end – sets previously defined
# number of threads and registers the parallel backend
cluster <- makeCluster(threads)
registerDoParallel(cluster)
# set up function loop - simply calls the main function on
# each row of data and captures output
loop_output <- foreach(i = 1:(nrow(data_matrix))) %dopar% {
main(data_matrix[i,]) }
# returns to the sequential back-end
registerDoSEQ()
# format output of function loop into matrix
# add row labels and headers
final_output <- do.call(rbind,loop_output)
rownames(final_output) <- read_data[,1]
colnames(final_output) <- c("k", "dist")
# write output to file (tsv format)
write.table(final_output, output_file, quote = FALSE, sep =
"\t")
The output produced from this can then be easily imported into Excel and
sorted or filtered as necessary. Excel was chosen for filtering as it allows faster
visualization and adjustment than doing the same with a script.
CSC calculation
The CSC calculation code was originally created in Perl 5 using the Ubuntu
linux command line, and later adapted in R. The code presented below is from the
88
original Perl script. Much of this code has been adapted into other scripts, as it is a
good set of techniques and snippets to handle this type of sequencing information.
The basic workflow of the script begins with two files as input. The first is a FASTA
document with the sequences of all coding regions (CDS) of the genome. For yeast,
this file can be obtained from the Saccharomyces Genome Database
(http://www.yeastgenome.org) or through the UCSC genome table browser
(http://genome.ucsc.edu/cgi-bin/hgTables). UCSC has many more organisms, and
most species-specific databases will be able to provide this as well. The second is a
listing of genes and their corresponding calculated half-lives, provided by the script
described above. The FASTA file is read into memory, and the occurrence of each
codon in that file is counted. The script then goes through each codon one by one
and evaluates the correlation between the occurrence of that codon and the half-life
provided by the second file. These correlations are printed out to the command line
to be piped to a file.
# CSC calculation script
#!/usr/bin/perl
# load modules – requires basic stats and list utils
use strict;
use warnings;
use Statistics::Basic;
use List::Util;
###########################
## COMMAND LINE HANDLING ##
###########################
# Read file names from command line
# will throw an error if it cannot do it
my $CDS_file = shift or die;
my $T12_file = shift or die;
############################
89
## GENERAL FUNCTION CALLS ##
############################
# Establish variables used in body
# hash of strings – stores half-lives
my %halflives;
# hash of strings – stores raw sequences
my %sequences;
# hash of hashes – stores codon counts for each sequence
my %counted_genes;
# this is the hard-coded list of codons for the script
my @codon_list = split ', ', "TTT, CTT, ATT, GTT, TCT, CCT,
ACT, GCT, TAT, CAT, AAT, GAT, TGT, CGT, AGT, GGT, TTC, CTC,
ATC, GTC, TCC, CCC, ACC, GCC, TAC, CAC, AAC, GAC, TGC, CGC,
AGC, GGC, TTA, CTA, ATA, GTA, TCA, CCA, ACA, GCA, CAA, AAA,
GAG, TGG, CGA, AGA, GGA, TTG, CTG, ATG, GTG, TCG, CCG, ACG,
GCG, CAG, AAG, GAA, CGG, AGG, GGG";
# Call subroutine to parse FASTA file
my %sequences = FASTA_parse( $CDS_file );
# Call subroutine to parse half-lives
%halflives = T12_PARSE( $T12_file );
# looks for mismatches between files, omitting any half# life that does not have a sequence associated with it
foreach ( keys %halflives ){
delete $halflives{$_} if not defined $sequences{$_}
}
# Uses a loop to call the codon-counting subroutine
foreach my $key ( keys %sequences ) {
# stores each set of counted codons into hash
$counted_genes{$key} = { CODONS( $sequences{$key} ) }
}
# Print header
print "Codon\tCorrelation\n";
# loops through the array of codons defined above,
# creates two vectors (arrays) populated with codon count
# and halflife, then calculates a correlation
# note that codon count is normalized to length of sequence
for (my $i = 0; $i < scalar @codon_list; $i++) {
# declare variables used in the loop
my @codon_count;
my @halflife;
90
# loop generates the arrays
# takes one gene from the half-life list at a time
foreach my $key ( keys %halflives ) {
# places half-life of current gene into array
push @halflife, $halflives{$key};
# takes the count of the current gene for the codon
# defined by outer loop
# defaults to 0 if value not present
my $hits = $counted_genes{$key}{$codon_list[$i]} || 0;
# normalizes codon count as fraction of total and
# places normalized count into array
push @codon_count, ($hits / (List::Util::sum values
%{$counted_genes{$key}})) ;
}
# calculates correlation between the two ordered arrays
# for the current codon
my $correlation = Statistics::Basic::correlation(
\@codon_count, \@halflife );
# prints the correlation for the current codon
print $codon_list[$i]."\t".$correlation."\n";
}
# Create a hash from a FASTA file
# lines starting with > become hash key
# following lines converted into hash value
sub FASTA_parse {
# take passed argument as file name
my $filename = shift;
# define temporary variables used in the sub
my %temp_hash;
my $seq_name;
# open file for reading
open CDS_FILE, $filename;
# pull lines from file one by one until end of file
while ( my $line = <CDS_FILE> ) {
# check if file name starts with >
# and matches expected naming
if ( $line =~ m/^>(.+?)\s/ ) {
# if line does start with >, capture first “word”
$seq_name = $1;
# trim identifier from name to match HL file
91
$seq_name =~ s/sacCer3_sgdGene_//;
}
# if line doesn’t start with >, capture as sequence
else {
# take off return character
chomp $line;
# put sequence into hash under the name defined above
$temp_hash{$seq_name} .= $line;
}
}
# close file handle
close CDS_FILE;
# returns hash of name/sequence pairs
return %temp_hash;
}
# Parses a simple half-life file - 2-column TSV input
sub T12_PARSE {
# take passed argument as file name
my $filename = shift;
# define temporary variables used in the sub
my %temp_hash;
# open file for reading
open T12_FILE, $filename;
# pull lines from file one by one until end of file
while ( my $line = <T12_FILE> ) {
# regex splits line, checks for proper formatting
# of half-life and captures both sides
if ( $line =~ m/(^\w+-*\w*)\t((-*\d+\.\d+|0))/ ) {
# stores half-life in hash under gene name
$temp_hash{$1} = $2
}
}
# closes file handle
close T12_FILE;
# returns hash of gene/half-life pairs
return %temp_hash;
}
# Function for splitting sequence into codons
# input is a sequence, output is a codon hash
sub CODONS {
# take passed argument as sequence
92
my $curr_seq = shift;
# declare temporary variables used in sub
my %codons;
# regex tokenizes sequence into 3-character pieces
# and creates a hash using the codon itself as the key
# the values are incremented as new codons are found
$codons{$_}++ foreach ($curr_seq =~ m/\w{3}/g);
# returns hash of codon/count pairs
return %codons;
}
This code has been adapted for numerous scripts during this project,
highlighting the adaptability of the basic subroutines, such as FASTA handling and
codon counting. Nevertheless, care is required when adapting this code, for example
the FASTA handler needs to be adjusted for the expected gene naming formats,
which requires some expertise to tune the regular expression to capture the gene
name properly.
APPENDIX B: MATERIALS AND METHODS
Yeast strains and growth
The genotypes of all yeast strains used in this study are listed below in Table
1. Unless otherwise indicated, all strains are based on BY4741. Cells were grown in
standard synthetic medium (pH 6.5) supplemented with appropriate amino acids and
either 2% glucose, 2% galactose/1% sucrose, 2% raffinose/1% sucrose or 2%
sucrose as the carbon source. All cells were grown at 24 C and collected at mid-log
phase (3 x 107 cells ml-1).
93
Plasmids and strain construction
The plasmids and oligonucleotides used in this study are listed in Table 2 and
Table 3 respectively.
Rare codon reporters: To make the negative control for the rare codon
experiments, cloning sites were introduced into the 3’ UTR of pJC296 (Hu et al.,
2009) by site-directed mutagenesis using oligos oJC1011/oJC1012 and
oJC1013/oJC1014. MS2 sites were introduced by annealing oligos
oJC1015/oJC1016 and cloning them into the newly created SpeI/XhoI sites to create
the base plasmid without RC (pJC408). Constructs used in the experiments were
additionally tagged with HA at the 3’ terminal using oJC1196/oJC1197 to create the
–RC construct (pJC441). Three other constructs were created in the same way from
pJC408, amplifying the ORF in two pieces, with the rare codon stretch encoded on
the primers at the junction of the two pieces. HA tags were also added on the far 3’
primer. Oligos used were: oJC1261/oJC1244/oJC1245/oJC559 for RC 25 (pJC469),
oJC1261/oJC1246/oJC1247/oJC559 for RC 50 (pJC470),
oJC1261/oJC1248/oJC1249/oJC559 for RC 63 (pJC471). The RC 77 (pJC443)
plasmid was generated from the previously used pJC314 (Hu et al., 2009) in the
same way that –RC was made from pJC296 above (SDM to add restriction sites,
insertion of MS2 sites, then addition of HA tag). RC 94 (pJC489) was made by 2
rounds of PCR, inserting half of the codon stretch at a time into pJC441, using oligos
oJC1318/oJC1319 and oJC1320/oJC1321. The last one, RC 5 (pJC468) was made
by direct amplification from pJC296 with oJC558/oJC877 and oJC559/oJC876.
These amplicons were combined by PCR reaction with oJC558/oJC559 and then
cloned back into pJC296.
94
For the stem-loop (SL) constructs, the stem loop construct without RC
(pJC442) was made from pJC134 (Hu et al., 2009) as described above (SDM to add
restriction sites, insertion of MS2 sites, then addition of HA tag). The SL constructs
bearing the rare codons were made by inserting the RC stretch with 2 rounds of PCR
as for pJC489 above. Oligos used were: oJC1370-3 for SL RC 5 (pJC497) and
oJC1374-7 for SL RC 77 (pJC498).
LSM8 & RPS20 reporters: To construct the base reporter plasmids bearing
LSM8 (pJC663) and RPS20 (pJC666), DNA was amplified from the LSM8 locus with
oJC2357/oJC2358 and from the RPS20 locus with oJC2366/oJC2367. Restriction
sites were inserted by site-directed mutagenesis to facilitate further cloning. XhoI
sites were introduced directly upstream of the start codon in both using
oJC2415/oJC2416 and oJC2417/oJC2418 respectively. SphI sites were introduced
directly downstream of the stop codon using oJC2431/oJC2432 and
oJC2433/oJC2434. Several point mutations were introduced into the 3’ UTRs to
facilitate detection using oJC2435/oJC2436 and oJC2437/oJC2438 respectively.
These were then cloned into pJC69 (Gietz and Sugino, 1988) to create pJC663, 666.
The optimality-inverted plasmids (pJC667, 668 respectively) were constructed by
synthesizing the ORF in two parts by annealing oJC2421/oJC2422 and amplifying
with oJC2423/oJC2424 for LSM8 and annealing oJC2427/oJC2428 and amplifying
with oJC2427/oJC2428 for RPS20. These inserts were cloned back into the
XhoI/SphI sites of pJC663, 666. These reporters were transformed into yJC244 to
make yJC1888-91.
SYN reporters: To construct the plasmids bearing the synthetic reporters,
restriction sites were introduced directly before the start codon and after the stop
95
codon of a PGK1-bearing plasmid (pJC296) as well as an MFA2-bearing plasmid
(pJC312). Both of these plasmids are under the control of a GAL1 UAS. SpeI and XhoI
sites were inserted into pJC296, using oJC2377/oJC2378 and oJC2379/oJC2380
respectively. XbaI and XhoI sites were introduced into pJC312, using
oJC2381/oJC2382 and oJC2383/oJC2384 respectively. The SYN-opt sequence was
synthesized as two complementary oligonucleotides (oJC2385/oJC2409), then
annealed and digested with SpeI/XhoI, then ligated into similarly digested plasmids
prepared as above to make the SYN-opt reporters with PGK1 context (pJC672) and
MFA2 context (pJC674). The SYN-nonopt oligonucleotides (oJC2386/oJC2410) were
processed identically to generate the SYN-nonopt reporter with PGK1 context
(pJC673) and MFA2 context (pJC675). These reporters were transformed into yJC151
to make yJC1892-95.
HIS3 reporters: For the HIS3 reporters, the endogenous reporter (pJC712) was
made by amplifying the URA3 selectable marker from pJC390 with oJC2508/2509
and inserting it into the cloning site of pJC387, which already contained the HIS3
ORF under the control of its native promoter. This was transformed into yJC151 to
make yJC2031 and into yJC1883 to make yJC2033. The non-optimal ORF was
synthesized by annealing 4 oligonucleotides (oJC2500-3), then amplifying with
oJC2518/oJC2519, and replacing the existing ORF of the pJC387 plasmid using
PacI/AscI to make pJC710. Selectable marker URA3 was then added as described
above to make pJC711. This was transformed into yJC151 to make yJC2030 and into
yJC1883 to make yJC2032. The optimal ORF was constructed by annealing 4
oligonucleotides (oJC2605-8), amplifying with pJC2611/2612, and then replacing
the ORF of pJC711 using PacI/AscI to make pJC716. This was transformed into
96
yJC151 to make yJC2088 and into yJC244 to make yJC2090. FLAG-tagged versions
were produced by introducing the FLAG tag via site-directed mutagenesis into
pJC711 using oligonucleotides oJC2620/2621 to make pJC719 and into pJC716
using oligonucleotides oJC2622/2623 to make pJC720. These were transformed
into yJC151 to make yJC2135 and yJC2137 respectively. All of the HIS3 constructs
were designed to retain a short invariant region in the ORF (positions 337-359),
which was used for detection by northern oligonucleotide probe oJC2564.
Northern RNA analysis
Northern RNA analysis of shutoffs was performed essentially as previously
described (Hu et al., 2009). Briefly, for analysis of the RC and SYN reporters, cells
carrying the SYN reporters were grown in 2% galactose, 1% sucrose synthetic media
and collected at mid-log phase. Transcription repression was achieved by
resuspending collected cells in media containing 4% glucose. After transcriptional
repression, cell aliquots were removed, total RNA was isolated by (30 mg) was
analyzed by electrophoresis through 1.4% formaldehyde agarose gel or 6%
denaturing polyacrylamide gel. For analysis of LSM8, RPS20, and HIS3 reporters,
rpb1-1 shut-offs were performed as described below in the first paragraph of the
RNA-seq section, then loaded onto 1.4% formaldehyde agarose gels instead of library
construction and following steps.
Northern analyses were performed using oligonucleotide radiolabelled with T4
PNK. Specifically, the LSM8 reporters were detected using oJC2450, RPS20 with
oJC2451, HIS3 with oJC2564, and SYN RNAs with oJC168. Northern signal
quantitation was performed using ImageQuant software.
97
Polyribosome analysis
Sucrose density gradients for polyribosome analysis were performed
essentially as described previously (Hu et al., 2009). Specifically, cells were grown
until mid-log phase (OD600 = 0.4-0.45) at 24°C in synthetic media with the
appropriate amino acids and 2% glucose. For glucose deprivation experiments, cells
were centrifuged and resuspended in media with or without glucose for 10 min
before harvesting. All cells were treated with cycloheximide to a final concentration of
100 µg ml-1 and collected by centrifugation. Cell pellets were lysed in buffer (10 mM
Tris, pH 7.4, 100 mM NaCl, 30 mM MgCl2, 1 mM DTT, 100 µg ml-1 cycloheximide) by
vortexing with glass beads, and cleared using the hot needle puncture method
followed by centrifugation at 2,000 rpm for 2 min at 4°C. After centrifugation of the
supernatants at 29,000 r.p.m. for 10 min with a TLA 120.2 rotor, Triton X-100 was
added to a final concentration of 1%. Sucrose gradients were made on a Biocomp
gradient maker and were 15–45% weight/weight (sucrose to buffer (50 mM
TrisAcetate pH 7.0, 50 mM NH4Cl, 12 mM MgCl2, 1 mM DTT)). 10 units (OD260) of cell
lysate were loaded onto each gradient. Gradients were centrifuged at 41,000 r.p.m.
for 2 h and 26 min at 4 °C in a Beckman SW-41Ti rotor and fractionated using a
Brandel Fractionation System and an Isco UA-6 ultraviolet detector. Fractions were
precipitated overnight at −20°C using 2 volumes 95% ethanol. RNA/protein was
pelleted at 14,000 rpm for 30 min, then pellets were resuspended in 500 µL LET (25
mM Tris pH 8.0, 100 mM LiCl, 20 mM EDTA) with 1% SDS. Fractions were then
extracted once with phenol/LET, once with phenol/chloroform/LET, and then were
precipitated with one-tenth volume of 7.5 M CH3COONH4 and 2 volumes 95%
ethanol. After centrifugation at 14,000 rpm for 20 min, pellets were washed once
98
with 700 µL 75% ethanol, air dried, and resuspended in 1× LET. Half of each sample
was loaded on 1.4% agarose-formaldehyde gels and Northern analysis carried out as
above. Northern blots of RNA from cells without stress were probed with
oligonucleotide oJC2564. Northern blots of RNA from cells with stress were probed
with probes generated by radiolabeled asymmetric PCR for increased sensitivity.
Asymmetric PCR probes
Plasmids pJC711 and pJC716 were used as templates to amplify non-optimal
and optimal His3 sequences, respectively, in a first PCR using oJC2540/oJC2541
and Phusion Taq polymerase (BioLabs). The PCR products were run on 1% agarose
gel and the single amplicons were extracted using a GenElute Gel extraction kit
(Sigma) and resuspended in 30 µL of water. 4 µL were added to a final 50 µL PCR
mix containing dATP, dGTP, dTTP (200 µM each), dCTP (3 µM), the reverse primer
oJC2564 (His3 ORF, 1 µM), 50 µCi of [α-32P]dCTP (3000 Ci/mmol; 10 µCi/µL) and 5
units of Taq polymerase. After denaturation at 94°C for 5’, asymmetric amplification
was performed for 40 cycles (15 sec at 94°C, 30 sec at 58°C, 30 sec at 72°C)
followed by 10 min at 72°C. The obtained radiolabelled probes were purified on
Micro Bio-Spin 6 Chromatography Columns (BioRad) following the manufacturer’s
instructions. Blots were pre-hybridized 1 h at 42°C in 50 % formamide, 5 X SSC, 1 X
Denhardt’s, 0.5 mg/mL salmon sperm DNA, 10 mM EDTA and 0.2 % SDS, and
probed with the optimal or non-optimal single-stranded probes generated by
asymmetric PCR overnight at 42°C in the same buffer. They were washed twice for 5
min at room temperature in 2 X SSC, 0.1 % SDS, and once for 45 min at 50°C in 0.1
X SSC, 0.1 % SDS, and then placed on phosphorimager screens for overnight
exposure.
99
Plating assays
For assays of growth on 3-AT, the HIS3 constructs pJC710, pJC711, pJC716
were transformed into the dcp2∆ strain to make yJC2040/yJC2041/uJC2089. These
were then grown at 24 C in complete synthetic medium overnight, collected, and
resuspended in medium lacking histidine at a density of OD600 = 0.2. They were then
serially diluted and 4 µl of each dilution was plated onto plates lacking histidine and
supplemented with 3-AT. These were then grown at 24 C and photographed.
RNA-seq
rpb1-1 mutant cells (Nonet et al., 1987) (yJC244) were grown to mid-log
phase at 24°C as described above. To achieve transcriptional repression, cells were
shifted to 37°C, then cell aliquots were removed and isolated total RNA was used for
library construction. 10 time points were collected over 60 minutes, including an
initial aliquot collected at time 0, before the temperature shift.
Total RNA libraries were then prepared using the Illumina TruSeq Stranded
Total RNA library prep kit. The starting material consisted of 1 μg of total RNA and 1
ng of ERCC Phage NIST spike-ins.
Poly(A)+ RNA libraries were prepared using the Illumina TruSeq Stranded
mRNA library prep kit. The starting material for these libraries consisted of 4 μg of
RNA and 1 ng of ERCC Phage NIST spike-ins.
The libraries were quantitated using an Agilent Bioanalyzer and sequenced on
an Illumina HiSeq2000 using paired-end 100 bp reads with an index read.
Sequencing data and the processed data for each gene are available at the Gene
Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo) under accession number
GSE57385.
100
Alignment and half-life calculation
Reads were aligned to the SacCer2 S. cerevisiae reference genome using
Bowtie v0.12.7 (Langmead et al., 2009) using the parameters ‘-m 1 -v 2 -p 8’. The
remaining unaligned reads were then aligned to a reference file containing the
sequences of the spike-in controls using the same parameters. The aligned reads
were then converted into bam format and indexed using samtools v0.1.18 (Li et al.,
2009). Gene FPKM values were calculated with Cufflinks v1.3.0 (Trapnell et al.,
2010) using default parameters and a gtf file of the SacCer2 SGD gene annotation
downloaded from the UCSC browser. The raw FPKM numbers were then normalized
to the number of reads aligning to the spike-ins to adjust for the amplification
resulting from a smaller pool of mRNA at later time points.
To estimate the half-life for each gene, we normalized each of the expression
levels for each gene and each time series to the initial expression level. We then fit
an exponential decay curve to the data by minimizing the sum of the absolute
residuals for each gene. We filtered the list to exclude dubious and unverified ORFs,
genes for which the average absolute residual was greater than .14, and genes
which had an estimated half-life longer than the measured time course. To get a very
rough idea of the variability in our estimates of the gene half-lives we performed a
bootstrap type procedure. The un-normalized residuals from the original data were
resampled for each gene and added to the un-normalized fitted curve values to
repeatedly simulate new sample data sets. The 95% confidence intervals were based
on the 2.5% and 97.5% quantiles of the half-life estimates calculated from the
simulated data sets.
101
Statistical techniques
The Codon occurrence to mRNA Stability Correlation coefficient (CSC) was
determined by calculating a Pearson correlation coefficient between the frequency of
occurrence of individual codons and the half-lives of the messages containing them.
To determine the statistical significance of the association between codon optimality
and the CSC, we first categorized the CSC as either positive or negative. We then
used a chi-squared test of association. We also used linear regression as another
measure. Similarly, to look at association in between the categories of optimal codon
content and mRNA half-life, we used an ANOVA f-test with mRNA half-life on the log
scale.
Any test of association between codon optimality and transcript stability may
show artificial statistical significance due to confounding with the base pair content
of the genes. To help mitigate this possibility, for each test statistic, we randomly
permuted the base pairs of the genes and recalculated the test statistic for each of
10,000 permutations. We calculated the base pair permutation p-value as the
number of permuted data sets with a test of association stronger than the chisquared test in the un-permuted data. Statistical calculations were done using the R
environment. Percent optimal codon values were calculated by generating a list of
optimal and non-optimal codons as previously described (Pechmann and Frydman,
2013).
Heat map generation
For all mRNA with reliable half-lives, rates of usage of each of the 61 codons
was calculated by using an in-house Perl script. These values were then input into an
Excel spreadsheet, assigned ranks using the RANK.AVG function, and then exported
102
to a tsv file. These were then evaluated using a Spearman distance metric and
clustered using k-means clustering in Cluster3 (de Hoon et al., 2004). The clustered
output was visualized and color coded using the log-scale option of Java Treeview
(Saldanha, 2004).
Tables
Table 1
Yeast Strains
Name
Genotype
Source
yJC151
MATa, ura3, leu2, his3, met15
yJC244
MATa, ura3-52, his3-200, leu2-3.112, rpb1-1
This study
(Nonet et
al., 1987)
yJC1093 MATa, ura3, leu2, his3, met15, [PGK1-HA-MS2, URA3], [MS2, LEU2]
This study
yJC1095 MATa, ura3, leu2, his3, met15, [SL-PGK1-HA-MS2, URA3], [MS2, LEU2]
This study
MATa, ura3, leu2, his3, met15, [PGK1-RC77%-HA-MS2,
yJC1097 URA3], [MS2, LEU2]
This study
MATa, ura3, leu2, his3, met15, dcp2::NEO, [PGK1-HA-MS2,
yJC1099 URA3], [MS2, LEU2]
yJC1101
yJC1103
yJC1221
yJC1223
yJC1225
yJC1227
yJC1229
yJC1231
yJC1233
yJC1235
yJC1237
yJC1239
yJC1304
This study
MATa, ura3, leu2, his3, met15,
MS2, URA3], [MS2, LEU2]
MATa, ura3, leu2, his3, met15,
HA-MS2, URA3], [MS2, LEU2]
MATa, ura3, leu2, his3, met15,
URA3], [MS2, LEU2]
MATa, ura3, leu2, his3, met15,
URA3], [MS2, LEU2]
MATa, ura3, leu2, his3, met15,
URA3], [MS2, LEU2]
MATa, ura3, leu2, his3, met15,
URA3], [MS2, LEU2]
MATa, ura3, leu2, his3, met15,
URA3], [MS2, LEU2]
MATa, ura3, leu2, his3, met15,
MS2, URA3], [MS2, LEU2]
MATa, ura3, leu2, his3, met15,
HA-MS2, URA3], [MS2, LEU2]
MATa, ura3, leu2, his3, met15,
HA-MS2, URA3], [MS2, LEU2]
MATa, ura3, leu2, his3, met15,
HA-MS2, URA3], [MS2, LEU2]
MATa, ura3, leu2, his3, met15,
HA-MS2, URA3], [MS2, LEU2]
MATa, ura3, his3, leu2, met15,
URA3], [MS2, LEU2]
dcp2::NEO, [SL-PGK1-HA-
This study
dcp2::NEO, [PGK1-RC77%-
This study
[PGK1-RC2%-HA-MS2,
This study
[PGK1-RC25%-HA-MS2,
This study
[PGK1-RC50%-HA-MS2,
This study
[PGK1-RC63%-HA-MS2,
This study
[PGK1-RC94%-HA-MS2,
This study
dcp2::NEO, [PGK1-RC2%-HA-
This study
dcp2::NEO, [PGK1-RC25%-
This study
dcp2::NEO, [PGK1-RC50%-
This study
dcp2::NEO, [PGK1-RC63%-
This study
dcp2::NEO, [PGK1-RC94%-
This study
[SL-PGK1-RC5%-HA-MS2,
103
This study
MATa, ura3, his3, leu2, met15, [SL-PGK1-RC77%-HA-MS2,
yJC1306 URA3], [MS2, LEU2]
yJC1334
yJC1336
This study
MATa, ura3, leu2, his3, lys2, dcp2::NEO [MS2, LEU2] [SLPGK1-RC5%, URA3]
MATa, ura3, leu2, his3, lys2, dcp2::NEO [MS2, LEU2] [SLPGK1-RC77%, URA3]
MATa, ura3-52, his3-200, leu2-3.112, rpb1-1,
yJC1888 [WT (nonoptimal) LSM8, URA3]
MATa, ura3-52, his3-200, leu2-3.112, rpb1-1,
yJC1889 [mutant (optimal) LSM8, URA3]
MATa, ura3-52, his3-200, leu2-3.112, rpb1-1,
yJC1890 [WT (optimal) RPS20, URA3]
MATa, ura3-52, his3-200, leu2-3.112, rpb1-1,
yJC1891 [mutant (nonoptimal) RPS20, URA3]
This study
This study
This study
This study
This study
This study
yJC1892 MATa, ura3, leu2, his3, met15, [pGAL-PGK1pG/SYN opt, URA3]
This study
yJC1893 MATa, ura3, leu2, his3, met15, [pGAL-PGK1pG/SYN non-opt, URA3]
This study
yJC1894 MATa, ura3, leu2, his3, met15, [pGAL-MFA2pG/SYN opt, URA3]
This study
yJC1895 MATa, ura3, leu2, his3, met15, [pGAL-MFA2pG/SYN non-opt, URA3]
MATa, ura3, leu2, his3, met15, dcp2::NEO, [pGAL-PGK1pG/SYN opt,
yJC1917 URA3]
MATa, ura3, leu2, his3, met15, dcp2::NEO, [pGAL-PGK1pG/SYN nonyJC1918 opt, URA3]
MATa, ura3, leu2, his3, met15, ccr4::NEO, [pGAL-PGK1pG/SYN opt,
yJC1961 URA3]
MATa, ura3, leu2, his3, met15, ccr4::NEO, [pGAL-PGK1pG/SYN nonyJC1962 opt, URA3]
MATa, ura3, leu2, his3, met15, dom34::NEO, [pGAL-PGK1pG/SYN opt,
yJC1963 URA3]
MATa, ura3, leu2, his3, met15, dom34::NEO, [pGAL-PGK1pG/SYN nonyJC1964 opt, URA3]
MATa, ura3, leu2, his3, met15, upf1::NEO, [pGAL-PGK1pG/SYN opt,
yJC1996 URA3]
MATa, ura3, leu2, his3, met15, upf1::NEO, [pGAL-PGK1pG/SYN nonyJC1997 opt, URA3]
This study
yJC2030 MATa, ura3, leu2, his3, met15, [HIS3-non-optimal, URA3]
This study
yJC2031 MATa, ura3, leu2, his3, met15, [HIS3-endogenous, URA3]
This study
yJC2032 MATa, ura3, leu2, his3, met15, rpb1-1, [HIS3-non-optimal, URA3]
This study
yJC2033 MATa, ura3, leu2, his3, met15, rpb1-1, [HIS3-endogenous, URA3]
This study
yJC2040 MATa, ura3, leu2, his3, lys2, dcp2::NEO, [HIS-non-optimal, URA3]
This study
yJC2041 MATa, ura3, leu2, his3, lys2, dcp2::NEO, [HIS-endogenous, URA3]
This study
yJC2088 MATa, ura3, leu2, his3, met15, [HIS3-optimal, URA3]
This study
yJC2089 MATa, ura3, leu2, his3, lys2, dcp2::NEO, [HIS3-optimal, URA3]
This study
yJC2090 MATa, ura3-52, his3-200, leu2-3.112, rpb1-1, [HIS3-optimal, URA3]
This study
104
This study
This study
This study
This study
This study
This study
This study
This study
yJC2135 MATa, ura3, leu2, his3, met15, [FLAG-HIS3-non-optimal, URA3]
This study
yJC2137 MATa, ura3, leu2, his3, met15, [FLAG-HIS3-optimal, URA3]
This study
yJC1883 MATa, ura3, leu2, his3, met15, rpb1-1
This study
Table 2
Plasmids
Name
Description
pJC69
YCpLac33
pJC296
PGK1pG reporter under control of GAL1 promoter
pJC312
MFA2–pG reporter under control of GAL1 promoter
pJC387
pRS413
pJC390
pJC659
pRS416
Reference
(Gietz and
Sugino, 1988)
(Decker and
Parker, 1993)
(Decker and
Parker, 1993)
(Brachmann
et al., 1998)
(Brachmann
et al., 1998)
WT LSM8 +/–500 bp in pJC69
This study
pJC660
WT RPS20 +/–500 bp in pJC69
This study
pJC661
pJC659 with an XhoI site upstream of the LSM8 start codon.
pJC661 with an SphI site downstream of the LSM8 stop codon
(and an additional single nucleotide mutation within the–3' UTR).
This study
pJC663
pJC662 with additional LSM8 3' UTR mutations.
This study
pJC664
This study
pJC675
pJC660 with an XhoI site upstream of the RPS20 start codon.
pJC664 with an SphI site downstream of the RPS20 stop codon
(and an additional single nucleotide mutation within the–3' UTR).
pJC665 with additional RPS20 3' UTR mutations.
pJC663 in which the mutant (optimal) LSM8 gene replaced
the WT LSM8 gene.
pJC666 in which the mutant (nonoptimal) RPS20 gene replaced
the WT RPS20 gene.
PGK1pG reporter with SYN opt ORF (under control of GAL1
promoter).
PGK1pG reporter with SYN non–opt ORF (under control of GAL1
promoter).
MFA2pG reporter with SYN opt ORF (under control of GAL1
promoter).
MFA2pG reporter with SYN non–opt ORF (under control of GAL1
promoter).
pJC710
HIS3 non-optimal ORF (own promoter) without marker
This study
pJC711
HIS3 non-optimal ORF (own promoter)
This study
pJC712
HIS3 endogenous ORF (own promoter)
This study
pJC716
HIS3 optimal ORF (own promoter)
This study
pJC719
Flag-tagged HIS3 non-optimal (own promoter)
This study
pJC720
Flag-tagged HIS3 optimal (own promoter)
This study
pJC662
pJC665
pJC666
pJC667
pJC668
pJC672
pJC673
pJC674
105
This study
This study
This study
This study
This study
This study
This study
This study
This study
Table 3
Oligonucleotides
Name
oJC168
(oRP121)
Sequence
5'-AATTCCCCCCCCCCCCCCCCCCA-3'
Reference
(Hu et al.,
2009)
oJC558
5'-CCGGGGATCCGTACTGTTACTCTCTCTC-3'
This study
oJC559
This study
oJC877
5'-GTGCCAAGCTTTAACGAACGCAGAAT-3'
5'-TTAATAGCGCGGCGGCGGCGGCGGGCGACGGACGGTAAG
AAGATCACTTCTAACCAAAGAATTGTTGCTGC-3'
5'-CGTCGCCCGCCGCCGCCGCCGCGCTATTAAGACACGCTTG
TCCTTCAAGTCCAAATCTTG-3'
oJC1011
5'-TTGAATTGAATTGAAACTAGTAATTTGGGGGGGGGG-3'
This study
oJC1012
5'-CCCCCCCCCCAAATTACTAGTTTCAATTCAATTCAA-3'
This study
oJC1013
5'-GGGGGGGGGGGGGGGCTCGAGTAGATCAATTTTTTTC-3'
This study
oJC1014
5'-GAAAAAAATTGATCTACTCGAGCCCCCCCCCCCCCCC-3'
5'-CTAGACATGAGGATCACCCATGTCTGCAGGTCGACTCTAGA
AAACATGAGGATCACCCATGT-3'
5'-TCGAACATGGGTGATCCTCATGTTTTCTAGAGTCGACCTGC
AGACATGGGTGATCCTCATGT-3'
5'-GTGTTGCTTTCTTATCCGAATTAATTAACGGCGCGCCGAATT
GAAACTAGACATG-3'
5'-CATGTCTAGTTTCAATTCGGCGCGCCGTTAATTAATTCGGAT
AAGAAAGCAACAC-3'
5'-CGTCGCCCGCCGCCGCCGCCGCGCTATTAATTCAACTTCT
GGACCGACAC-3'
5'-TTAATAGCGCGGCGGCGGCGGCGGGCGACGGTTATTTTGT
TGGAAAACTTG-3'
5'-CGTCGCCCGCCGCCGCCGCCGCGCTATTAAGGCCAAGAAT
GGTCTGGTTG-3'
5'-TTAATAGCGCGGCGGCGGCGGCGGGCGACGATTCAATTGA
TTGACAACTTG-3'
5'-CGTCGCCCGCCGCCGCCGCCGCGCTATTAAACCAGCCTTG
TCGAAGATGG-3'
5'-TTAATAGCGCGGCGGCGGCGGCGGGCGACGGCCAAGGCC
AAGGGTGTCGAAG-3'
This study
5'-GAGCTCGGTACCCGGGGATCCGTAC-3'
5'-CATGTCTCTACTGGTTTAATAGCGCGGCGGGAATTATTGGA
AGG-3'
5'-CCTTCCAATAATTCCCGCCGCGCTATTAAACCAGTAGAGAC
ATG-3'
5'-GTCTCTACTGGTTTAATAGCGCGGCGGCGGCGGCGGGCGA
CGAAGGAATTGCCAG-3'
5'-CTGGCAATTCCTTCGTCGCCCGCCGCCGCCGCCGCGCTAT
TAAACCAGTAGAGAC-3'
5'-GAAGGACAAGCGTGTCTTAATAGCGCGGCGGTTCAACGTC
CCATTG-3'
5'-CAATGGGACGTTGAACCGCCGCGCTATTAAGACACGCTTGT
CCTTC-3'
5'-GTGTCTTAATAGCGCGGCGGCGGCGGCGGGCGACGGACG
GTAAGAAGATC-3'
This study
oJC876
oJC1015
oJC1016
oJC1196
oJC1197
oJC1244
oJC1245
oJC1246
oJC1247
oJC1248
oJC1249
oJC1261
oJC1318
oJC1319
oJC1320
oJC1321
oJC1370
oJC1371
oJC1372
106
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
oJC1377
5'-GATCTTCTTACCGTCCGTCGCCCGCCGCCGCCGCCGCGCT
ATTAAGACAC-3'
5'-CCAGAATCTAGAAAGTTAATAGCGCGGCGGGTTGCAAAGG
CTAAG-3'
5'-CTTAGCCTTTGCAACCCGCCGCGCTATTAACTTTCTAGATTC
TGG-3'
5'-CTAGAAAGTTAATAGCGCGGCGGCGGCGGCGGGCGACGA
CCATTGTCTGG-3'
5'-CCAGACAATGGTCGTCGCCCGCCGCCGCCGCCGCGCTATT
AACTTTCTAG-3'
oJC2357
5'-CAGGTCAAGCTTTCCAGTAGCTGGTTAAACTTG-3'
This study
oJC2358
5'-CAGGTCGGATCCTTGCTATTTGGCGATGAGTTC-3'
This study
oJC2366
5'-CGGTCCAAGCTTATTGTGACTAGAATACTATTG-3'
This study
oJC2367
5'-CAGGTCGGATCCGCGTGAAACATTTATCAGC-3'
5'-CTACTTTTTACAACAAATATACTAGTATGTCTTTATCTTCAAA
GTT-3’
5'-AACTTTGAAGATAAAGACATACTAGTATATTTGTTGTAAAAA
GTAG-3’
5'-CTTATCCGAAAAGAAATAACTCGAGTTGAATTGAACGAAGG
AATTT-3’
5'-AAATTCCTTCGTTCAATTCAACTCGAGTTATTTCTTTTCGGA
TAAG-3’
5'-TCATACAACAATAACTACCATCTAGAATGCAACCGATCACCA
CTGC-3’
5'-GCAGTGGTGATCGGTTGCATTCTAGATGGTAGTTATTGTTG
TATGA-3’
5'-CCGCCTGTGTTATCGCTTAACTCGAGACGACAACCAAGAGA
TCTAG-3’
5'-CTAGATCTCTTGGTTGTCGTCTCGAGTTAAGCGATAACACA
GGCGG-3’
5'-GAATACTAGTATGCCACCAAAGGCTTCCCCAACCGGTGCTT
CCTCCGTTTTGAAGGCTAAGGCTCCATCCATCCCAGCTAAGAC
CGTTGGTAAGACCTTGCCAAAGACCGTTATCACCAAGTTGTCC
ACCGTTATCACCTTGGGTGCTGCTGGTTTGATCGTTCCATTGT
CCATCGGTATCGGTGTTTAACTCGAGCTAA-3’
5'-GAATACTAGTATGCCGCCGAAAGCAAGTCCGACAGGAGCA
AGTAGTGTACTGAAAGCAAAAGCACCGAGTATACCGGCAAAA
ACAGTAGGAAAAACACTGCCGAAAACAGTAATAACAAAACTGA
GTACAGTAATAACACTGGGAGCAGCAGGACTGATAGTACCGC
TGAGTATAGGAATAGGAGTATAACTCGAGCTAA-3’
5'-TTAGCTCGAGTTAAACACCGATACCGATGGACAATGGAACG
ATCAAACCAGCAGCACCCAAGGTGATAACGGTGGACAACTTG
GTGATAACGGTCTTTGGCAAGGTCTTACCAACGGTCTTAGCTG
GGATGGATGGAGCCTTAGCCTTCAAAACGGAGGAAGCACCGG
TTGGGGAAGCCTTTGGTGGCATACTAGTATTC-3’
5'-TTAGCTCGAGTTATACTCCTATTCCTATACTCAGCGGTACTA
TCAGTCCTGCTGCTCCCAGTGTTATTACTGTACTCAGTTTTGTT
ATTACTGTTTTCGGCAGTGTTTTTCCTACTGTTTTTGCCGGTAT
ACTCGGTGCTTTTGCTTTCAGTACACTACTTGCTCCTGTCGGA
CTTGCTTTCGGCGGCATACTAGTATTC-3’
This study
oJC1373
oJC1374
oJC1375
oJC1376
oJC2377
oJC2378
oJC2379
oJC2380
oJC2381
oJC2382
oJC2383
oJC2384
oJC2385
oJC2386
oJC2409
oJC2410
107
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
oJC2422
5'-CAAGAAGAACACACTATCGCTCGAGATGTCAGCCACCTTGA
AAGAC-3’
5'-GTCTTTCAAGGTGGCTGACATCTCGAGCGATAGTGTGTTCT
TCTTG-3’
5'-AATAAACAAAAAGGTATACTCGAGATGTCTGACTTTCAAAAG
G-3’
5'-CCTTTTGAAAGTCAGACATCTCGAGTATACCTTTTTGTTTAT
T-3’
5'-CAGGATCTCGAGATGTCCGCCACCTTGAAGGACTACTTGAA
CAAGAGAGTTGTTATCATCAAGGTTGACGGCGAATGTTTGATC
GCTTCTTTGAACGGCTTCGACAAGAACACTAACTTGTTCATCA
CCAACGTTTTCAACCGTATCTCTAAGGAATTCATCTGTAAGGC
TCAATTGTTGCGTGGCTCCGAAATTGC-3'
5'-CAGGATGCATGCTTACTTAGTCTTAGATTCGTAAACCTTTTC
CCAGATAACGTGTTCGTTTTCGATCTTGTTCTTGGTGTCCTTCA
ACATTGGGACCTTCTTTTCGTCGATTGGAGCCAAAGAGTCGTC
GTTTTCGGCGTCGATCAAGCCAACCAAAGCAATTTCGGAGCC
ACGCAACAATTGAGCCTTAC-3'
oJC2423
5'-CAGGATCTCGAGATGTCCGCC-3'
This study
oJC2424
This study
oJC2426
5'-CAGGATGCATGCTTACTTAGTCTTAGATTCG-3'
5'-ATGAGTGATTTTCAGAAAGAGAAAGTAGAGGAGCAGGAGC
AGCAGCAGCAGCAGATAATAAAAATTAGGATAACACTGACAAG
CACAAAAGTAAAACAGCTGGAGAATGTAAGCTCAAATATTGTA
AAAAATGCAGAGCAGCATAATCTGGTAAAAAAAGGACCGGTAA
GGCTACCGACAAAAGTACTGAAAATAAGCAC-3'
5'-TGCTTAATTTGATGCTACTACTACCTCTACATCCACTCCAGG
CTCAATTGTTATCTGTGTTATCCTTTTTACTATCTGTACAGGTG
CCTCAAGATCTATATACCTTTTATGTATCCTCATCTCATATGTC
TCCCATGTTTTACTTCCCTCTCCATTCGGTGTTTTCCTTGTGCT
TATTTTCAGTACTTTTGTCGGTAGCC-3'
oJC2427
5'-CAGGATCTCGAGATGAGTGATTTTCAGAAAGAG-3'
This study
oJC2428
5'-CAGGATGCATGCTTAATTTGATGCTACTACTACC-3'
5'-GTGTACGAATCAAAGACAAAATAAGCATGCAGCAATAATAG
TAATAATAATA-3’
5'-TATTATTATTACTATTATTGCTGCATGCTTATTTTGTCTTTGA
TTCGTACAC-3’
5'-GTTGTTGTTGCTTCCAACTAAGCATGCTGTAACTGGAAATAA
TTTC-3’
5'-GAAATTATTTCCAGTTACAGCATGCTTAGTTGGAAGCAACAA
CAAC-3’
5'-CAAAGACAAAATAAGCATGCAGGTAACCAAGTAATAATAATA
ATAATAA-3’
5'-TTATTATTATTATTATTACTTGGTTACCTGCATGCTTATTTTG
TCTTTG-3’
5'-CTTCCAACTAAGCATGCTGTTACGCGTAATAATTTCCATTAG
ATTCC-3’
5'-GGAATCTAATGGAAATTATTACGCGTAACAGCATGCTTAGTT
GGAAG-3’
This study
oJC2415
oJC2416
oJC2417
oJC2418
oJC2421
oJC2425
oJC2431
oJC2432
oJC2433
oJC2434
oJC2435
oJC2436
oJC2437
oJC2438
108
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
oJC2450
5'-TACTTGGTTACCTGCATGC-3'
This study
oJC2451
This study
oJC2509
5'-ATTACGCGTAACAGCATGC-3’
5'-CAAGTTAATTAAATGACAGAGCAGAAAGCGCTAGTAAAACG
AATAACAAATGAGACGAAAATACAGATAGCGATATCATTAAAA
GGAGGACCCCTAGCGATAGAGCATTCGATATTTCCGGAGAAA
GAGGCAGAGGCAGTAGCAGAGCAGGCGACACAGTCGCAGGT
GATAAATGTGCATACAGGAATAGGGTTTCTG-3'
5'-CCTTTTTACTCCTCGCACCGCCCCTAGCGCCTCTTTAAATG
CCTGTCCGAGTGCTATCCCGCAATCCTCTGTCGTATGATGATC
ATCTATATGTAAATCTCCTATGCACTCTACTATTAGCGACCAGC
CCGAATGTTTCGCCAGTGCATGTATCATATGATCCAGAAACCC
TATTCCTGTATGCACATTTATCACCTG-3'
5'-GCATTTAAAGAGGCGCTAGGGGCGGTGCGAGGAGTAAAAA
GGTTTGGATCAGGATTTGCGCCTCTGGATGAGGCACTTTCGA
GGGCGGTGGTAGATCTTTCGAATAGGCCGTATGCAGTAGTGG
AGCTTGGATTACAGAGGGAGAAAGTAGGAGATCTCTCATGCG
AGATGATACCGCATTTTCTTGAGAGCTTTGCAGA-3'
5'-GTTAGGCGCGCCCTACATAAGTACTCCTTTCGTCGAGGGTA
CATCATTCGTTCCATTGGGCGACGTCGCCTCCCTTATCGCTAC
CGCAAGTGCTTTAAACGCACTCTCACTTCGATGATGATCATTT
TTGCCTCGCAGACAATCTACATGGAGCGTTATCCTGCTTGCCT
CTGCAAAGCTCTCAAGAAAATGCGGTATCA-3'
5'-GAGTGCACCATACCACAGCTCTCGAGTTCAATTCATCATTTT
TTTT-3'
5'-ATCTGTGCGGTATTTCACACGAATTCGGGTAATAACTGATAT
AATT-3'
oJC2518
5'-CAAGTTAATTAAATGACAGAGCAG-3'
This study
oJC2519
5'-GTTAGGCGCGCCCTACATAAGTACT-3'
This study
oJC2540
5'-AGTAATGTGATTTCTTCGAA-3'
This study
oJC2541
5'-ATTCATAGGTATACATATAT-3'
This study
oJC2564
This study
oJC2607
5'-CCTGATCCAAACCTTTTTACTCC-3'
5'-CCTACGTTAATTAAATGACTGAACAAAAGGCCTTGGTTAAG
CGTATTACTAACGAAACCAAGATTCAAATTGCCATCTCTTTGAA
GGGTGGTCCATTGGCCATTGAACACTCCATCTTCCCAGAAAA
GGAAGCTGAAGCTGTTGCTGAACAAGCCACTCAATCCCAAGT
CATTAACGTCCACACTGGTATTGGTTTCTTG-3'
5'-AACCTTTTTACTCCACGGACGGCACCCAAGGCTTCCTTGAA
AGCTTGACCCAAAGCAATACCACAGTCTTCAGTGGTGTGGTG
GTCGTCAATGTGCAAGTCACCAATACATTCAACGATCAAGGAC
CAACCGGAGTGCTTGGCCAAAGCGTGAATCATGTGGTCCAAG
AAACCAATACCAGTGTGGACGTTAATGACTTG-3'
5'-GGAAGCCTTGGGTGCCGTCCGTGGAGTAAAAAGGTTTGGA
TCAGGTTTCGCCCCATTGGACGAAGCTTTGTCCAGAGCCGTC
GTTGACTTGTCCAACAGACCATACGCTGTTGTCGAATTGGGTT
TGCAAAGAGAAAAGGTTGGTGACTTGTCTTGTGAAATGATCCC
ACACTTCTTGGAATCCTTCGCTGAAGCTTCCA-3'
oJC2608
5'-GGTTCAGGCGCGCCCTACATCAAAACACCCTTGGTGGATG
GAACGTCGTTGGTACCGTTTGGGGAGGTGGCTTCTCTAATGG
CAACGGCCAAAGCCTTGAAGGCAGATTCAGAACGGTGGTGGT
oJC2500
oJC2501
oJC2502
oJC2503
oJC2508
oJC2605
oJC2606
109
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
CGTTCTTACCACGCAAACAGTCAACGTGCAAGGTAATTCTGGA
AGCTTCAGCGAAGGATTCCAAGAAGTGTGGGA-3'
oJC2611
5'-CCTACGTTAATTAAATGACTGAACAAAAGGCCTTGG-3'
This study
oJC2612
5'-GGTTCAGGCGCGCCCTACATCAAAAC-3'
5'-GAGCAGGCAAGATAAACGATTAATTAAATGGATTATAAAGAT
GATGATGATAAAACAGAGCAGAAAGCGCTAGTAAAACGAATA3'
This study
oJC2620
This study
oJC2622
5'-TATTCGTTTTACTAGCGCTTTCTGCTCTGTTTTATCATCATCA
TCTTTATAATCCATTTAATTAATCGTTTATCTTGCCTGCTC-3'
5'-GAGCAGGCAAGATAAACGATTAATTAAATGGATTATAAAGAT
GATGATGATAAAACTGAACAAAAGGCCTTGGTTAAGCGTATT-3
'
oJC2623
5'-AATACGCTTAACCAAGGCCTTTTGTTCAGTTTTATCATCATC
ATCTTTATAATCCATTTAATTAATCGTTTATCTTGCCTGCTC-3'
This study
oJC2632
5'-TGGTTGGTAGTCTGACTGGACCCT-3'
This study
oJC2633
5'-GCTTGCTATGAGGCATTCGCCG-3'
This study
oJC2621
110
This study
This study
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