EVOLUTIONARY AND FUNCTIONAL CHRACTERIZATION OF OXIDATIVE
STRESS PROTEINS IN DENDROCTONUS PONDEROSAE HOPKINS
(CURCULIONIDAE: SCOLYTINAE)
by
Luke Spooner
B.Sc., University of Northern British Columbia, 2012
B. Ed, Simon Fraser University, 2014

THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
IN
NATURAL RESOURCES AND ENVIRONMENTAL STUDIES (BIOLOGY)

UNIVERSITY OF NORTHERN BRITISH COLUMBIA
December 2020
© Luke Spooner, 2020

I

Abstract
Understanding the mountain pine beetle detoxification systems is vital for
predicting its continued spread into the novel jack pine host. Phylogenetic analyses were
conducted for mountain pine beetle catalase, glutathione peroxidase, superoxide
dismutase, and peroxiredoxin. These proteins were generally conserved, but there were
differences in some key functional motifs. Specifically, a peroxiredoxin (DPPrx1)
contained a unique combination of hyperoxidation motifs. DPPrx1 and a superoxide
dismutase (DPSOD1) were selected for further functional analyses and demonstrated
higher reactivity when compared to other SOD and Prx proteins. Also, DPPrx1
experiences hyperoxidation at a lower H2O2 concentration (~0.06 mM) than human
peroxiredoxin (~0.12 mM). In other systems, hyperoxidized peroxiredoxin does act as a
signal molecule for the expression of other oxidative stress proteins. Therefore, due to its
relatively high reactivity and potential role as a cellular signal, DPPrx1 could serve as a
future pest management target.

II

Table of Contents
Abstract

II

Table of Contents

III

List of Tables

IV

List of Figures

V

Chapter 1: Introduction
1.1. Mountain pine beetle ecology and outbreak
1.2 Pine Chemical Defenses
1.3 Mountain Pine Beetle detoxification
1.4 Mountain pine beetle Oxidative stress proteins
1.5 Investigation of recombinant oxidative stress proteins
1.6 Research question
1.7 Objectives
1.8 Hypothesis

8
8
9
11
13
18
18
18
19

Chapter 2 Phylogenetics and Sequence Analyses
2.1 Introduction
2.2 Research Question
2.3 Objectives
2.4 Hypothesis
2.5 Materials and methods
2.6 Results
2.7 Discussion
2.8 Conclusions and Future Directions

20
20
27
27
28
28
31
45
52

Chapter 3: Functional Characterization of DPPRx1 and DPSOD1
3.1 Introduction
3.2 Research Question
3.3 Objectives
3.4 Hypothesis
3.5 Materials and Methods
3.6 Results
3.7 Discussion
3.8 Conclusions and Future Directions

54
54
62
63
63
63
76
82
89

Chapter 4: Conclusions

92

Works Cited

97

III

List of Tables
Table 2.1 - Major primary oxidative stress proteins (superoxide dismutase, peroxiredoxin,
glutathione peroxidase, and catalase) for Tribolium castaneum (TC), Anoplophora
glabripennis (AG), Bombyx mori (BM), Bicyclus anynana (BA), Apis mellifera
(AM), Pseudomyrmex gracilis (PG), Drosophila melanogaster (DM), and Anopheles
gambiae (AGa). ......................................................................................................... 29
Table 2.2 - Primary oxidative stress proteins of Dendroctonus ponderosae and their key
features....................................................................................................................... 32
Table 3.1 - Cloning primers for DPPrx1 and DPSOD2, ATTB sequences are bolded .... 64
Table 3.2 - The proportion and type of reagent in each SOD sample and blank in the
Sigma-Aldrich WST-1 plate assay ............................................................................ 76

IV

List of Figures
Figure 1.1 -Physical, chemical, and protein-based defense mechanisms employed by pine
trees, mountain pine beetles, and their fungal and bacterial symbionts. ................... 12
Figure 1.2 - Key oxidative stress proteins (Superoxide dismutase, Peroxiredoxin,
Catalase, and Glutathione peroxidase) used by the mountain pine beetle ................. 16
Figure 2.1 - Alignment for catalase sequences from Dendroctonus ponderosae (DP),
Drosophila melanogaster (DM), Bombyx mori (BM), Bicyclus anynana (BA),
Tribolium castaneum (TC), Apis mellifera (AM), Pseudomyrmex gracilis (PG),
Anopheles gambiae (Aga), and Anoplophora glabripennis (AG). ............................ 34
Figure 2.2 - Neighbour-joining tree of insect catalase sequences .................................... 35
Figure 2.3 - Alignments for glutathione peroxidase sequences ........................................ 36
Figure 2.4 - Neighbour-joining tree of insect glutathione peroxidase sequences ............. 37
Figure 2.5 - Alignments for superoxide dismutase sequences .......................................... 38
Figure 2.6 - Neighbour-joining tree of insect superoxide dismutase sequences ............... 39
Figure 2.7 - Alignments for peroxiredoxin 1 and peroxiredoxin 6 sequences................. 41
Figure 2.8 - Neighbour-joining tree of insect peroxiredoxin ............................................ 43
Figure 2.9 - Alignments for peroxiredoxin 5 sequences ................................................... 44
Figure 2.10 - Neighbour-joining tree of insect peroxiredoxin 5 sequences ...................... 45
Figure 3.1 - The reaction mechanism for a typical 2-cysteine peroxiredoxin .................. 59
Figure 3.2 - Primary protein structure of DPPrx1. ............................................................ 60
Figure 3.3 - Overview of the LR and BP steps ................................................................. 65
Figure 3.4 – A standard curve generated from BSA standards of differing concentrations
and their respective absorbance readings at 595 nm. ................................................ 71
Figure 3.5 - Antioxidant activity of DPPrx1. .................................................................... 77
Figure 3.6 - Formation of HRP compound 1 in varying concentrations of DPPrx1 ........ 78
Figure 3.7 - Determination of second-order rate constant (9.8 x 107 M-1s-1) for the
reaction of reduced DPPrx1 with H2O2 through competition with horseradish
peroxidase. ................................................................................................................. 79
Figure 3.8. - Determination of pKa (5.4213) for reduced DPPrx1 with H2O2 ................. 80
Figure 3.9. Nonreducing SDS-PAGE analysis of purified, reduced DPPrx1. .................. 81

V

Figure 3.10 - (a) Inhibition percentages of WST-1 reduction for differing concentrations
of DPSOD1 ................................................................................................................ 82

VI

Acknowledgements
I would first of all like to thank my supervisor Dr. Dezene Huber for providing
me with this opportunity and trusting me to conduct this research. In addition, my
wonderful committee members, Dr. Philip Batista and Dr. Brent Murray. My first
introduction to research was as an undergraduate student in both Dezene and Brent’s labs,
so getting to continue my education with both of them has been very special. As well,
Phil spent many hours helping me troubleshoot my PCR experiments and refine my
phylogenetic tree analyses. I will always be grateful for his time, insights, and supportive
nature. I would also like to express my gratitude to Dr. Nicole Sukdeo who has helped me
multiple times in this project, particularly during the protein expression portion where
without her guidance and support I am not sure whether I would have ever managed to
generate sufficient protein yields. As well, Caitlin Pitt at the UNBC genetics lab could
always be counted on to provide quick and reliable sequencing results and Victoria
Rezendes, Alexander Douglas, and Kian Draper spent many hours assisting me in the lab.
I am thankful for my friends and colleagues at the Academic Success Center and Prince
George Public Library, both of which provided me with meaningful and reliable
employment throughout my masters. Finally, to all of my family and friends who have
always supported me in my academic pursuits, to quote Dr. Andrew H. Knoll, “In both
nature and in nurture, I am lucky.”

VII

Chapter 1: Introduction
1.1. Mountain pine beetle ecology and outbreak
Bark beetles (Curculionidae: Scolytinae) are a group of phloeophagous insects
that have a major impact on conifer health in forest ecosystems and contribute to overall
tree mortality. This is particularly true in the case of the mountain pine beetle,
Dendroctonus ponderosae Hopkins, which is endemic to western North America.
Historically, the range of this species extends from northern Mexico (latitude 31° north)
to central British Columbia (BC) (latitude 56° north). It can feed on and reproduce in all
North American species of pine, with the exception of Pinus jeffreyi (Jeffrey pine), but its
primary host species is lodgepole pine, Pinus contorta Douglas (Safranyik et al. 2010).
Populations of the mountain pine beetle fluctuate in four phases: endemic (low
population density), incipient-endemic (population density increases), epidemic
(populations reach outbreak level), and post-epidemic (populations decline) (Carroll et al.
2006). Typically, an epidemic phase lasts five years, however in 1994 the largest
outbreak in recorded history began, and to date, has killed 750 million cubic meters of
lodgepole pine stands in British Columbia and now in Alberta (Janes et al. 2014; Aukema
et al. 2006). Despite the severity of this outbreak, at normal low population levels
mountain pine beetle are generally restricted to trees in a weakened condition (Saab et al.
2014).
With the massive spike in population that occurred from this most recent
outbreak, a major expansion in the range of the mountain pine beetle also took place. The
mountain pine beetle travelled farther north than previously observed and also crossed the
Canadian Rocky Mountains east into Alberta (Janes et al. 2014; Cullingham et al. 2011).
There the beetle was able to proliferate within a hybrid zone comprised of lodgepole pine
8

and jack pine, Pinus banksiana (Lusebrink et al. 2013; Cullingham et al. 2011).
Eventually the mountain pine beetle was able to progress into forests comprised primarily
of jack pines, which extend as far as to eastern Canada and the United States. The
mountain pine beetle alongside its fungal and other microbial symbionts have been
coevolving with the lodgepole pine for eons, but this is not the case for jack pines (Clark
et al. 2014). Jack pines have different resin chemistry compared to lodgepole pine, and
continued success of the mountain pine beetle within this novel host species is contingent
upon many factors, but particularly on how it will be able to overcome the trees’ defenses
(Clark et al. 2014; Janes et al. 2014). Therefore, developing a better understanding of
mechanisms that mountain pine beetle use to overcome the host tree’s defense systems
could be critical towards predicting the continued spread of infestation across novel jack
pine host extents of range.
1.2 Pine Chemical Defenses
The two main groups of secondary metabolites produced by pines are the phenols
and terpenes. Phenols are a key part of the pine defense system, and they are important in
disrupting the progress of affecting fungal or bacterial infection (Vornam et al. 2019;
Tholl 2015). Therefore, in terms of bark beetle defenses, terpenes are usually considered
the much more noteworthy group. They are the largest group of secondary metabolites,
with 40,000 unique compounds found in nature (Tholl 2015). They serve a number of
functions, ranging from growth and development to facilitating specific interactions
between conifers and their environments (Tholl 2015).
The relationship and impact pine terpenes have on the mountain pine beetle is a
complicated one. Seybold et al. (2006) discussed how the mountain pine beetle uses

9

monoterpenes in the biosynthesis of its aggregation pheromones. Aggregation
pheromones are essential to the success of the mountain pine beetle, as they help to
recruit more individuals to the host tree, ultimately overwhelming the host tree’s defenses
(Chiu et al. 2019). That said, terpenes are also harmful to the mountain pine beetle, but
their exact impact is still not fully understood. Many are toxic and cause death, while
others appear to disrupt ovary and egg development, serve as food deterrents, or even
mimic the juvenile hormone of mountain pine beetle and hinder maturation (Chiu et al.
2019; Seybold et al. 2006). Additionally, it has been found that the monoterpenes, αpinene and β-pinene, serve as kairomones for parasitoids and predaceous insects like the
wrinkled bark beetle, Lasconotus complex (Seybold et al. 2006). The role of terpenes in
this tri-trophic interaction is an area that requires more in-depth study and is another
intriguing potential function of these chemicals (Shikano et al. 2017). In addition to the
initial toxicity of these compounds, the breakdown of terpenes by invading bark beetles
produces highly degradative reactive oxygen species (ROS) that the invading beetles then
have to contend with (Mason et al. 2018; Birben et al. 2012).
Conifer trees are believed to have evolved over 200 million years ago, and shortly
after (~65 million years later) the first wood boring beetles evolved (Seybold et al. 2006).
Therefore, as the primary host of mountain pine beetle, lodgepole pine has had millions
of years to adapt and refine the aforementioned defenses to specifically contend with the
mountain pine beetle. However, the mountain pine beetle has in turn been able to
optimize its own set of strategies to overcome these obstacles (Seybold et al. 2006).

10

1.3 Mountain Pine Beetle detoxification
Mountain pine beetles have a number of pathways dedicated to isolation and
degradation - a multigenic process that has been the focus of many studies (Robert et al.
2016; Pitt et al. 2014; Keeling et al. 2013; Robert et al. 2013; Bonnett et al. 2012). With
the successful sequencing of the mountain pine beetle genome, it is now possible to
identify specific genes and gene families involved in these different pathways (Keeling et
al. 2013). Particularly, studies investigating the global mountain pine beetle production of
RNA transcripts (transcriptome) and proteins (proteome) (Pitt et al. 2014; Robert et al.
2013), that occur before and after host colonization, have particularly garnered a
considerable amount of attention.

11

Physical/Chemical%defenses%
Phenolic"lignin"layer"

DehydraEon%

J"

Pine%Tree%
Host%
(Pinus&Spp.)"

Chemical%Defenses%
MonoterpenesP"*"
DiterpenesP"*"
Sesquiterpenes V"
Phenols S"

J*"

Bark%Beetle%
Predators%

monoterpenes%

OxidaEve%Stress%
Super"oxide"dismutase"(EIM)U"
Ferri n U"
Peroxiredoxin"
Glutathione"peroxidase"
CatalaseU"

Free%%
Radicals%

Physical%Breakdown%

T"

FV"

Mountain%Pine%Beetle%
(Dendroctonus&ponderosae)%

Proteins%
Chi nase"
Gluconase"
Porins"
Lec ns"

Chemicall%BreakdownU"
Cytochrome"P4 0"
Glutathione"S5transferase"
Glucosyl"transferase"
Esterase"
ABC"Transporter"
Alcohol"degydrogenase"

Mechanical%Defenses%
Stone"Cells"
Calcium"oxalate"crystals"
Resin"Ducts"(Trauma c/axial*)"
Fiber"lignifica on"
Trichomes"

JE*"

12

Figure 1.1 -Physical, chemical, and protein-based defense mechanisms employed by pine trees, mountain pine beetles, and their fungal
and bacterial symbionts. Red arrows represent predators of the mountain pine beetle that are attracted to monoterpene kairomones.
Thick green arrows represent induced defenses, thinner arrows represent constitutive defenses, green curved arrows indicate the plant
growth regulators (J=methyl jasmonate and E=ethylene). The curved orange arrows represent the aggregation pheromones
(T=transverbenone) and the anitaggregation pheromones (V=verbenone and F=frontalin) derived from the pine monterpenes.
Chemical defenses are paired with a superscript indicating which pathway, MVA (V), MEP (P), or shikimic acid (S), produces the
specific compound. For the mountain pine beetle and their fungal symbionts, a superscript U is used to indicate genes that were
unregulated in previous transcriptomic or proteomic work.

Fungal%Symbionts%
(Grossmania&clavigera)"

Free%%
Radicals%

J"

J"
Chemical%Defenses%
MonoterpenesP"
DiterpenesP"
Sesquiterpene V"
Phenols S"

Physical/Chemical%defenses%
Phenolic"lignin"layer"

OxidaEve%Stress%
Cu/Zn5"Superoxide"dismutase U"
PeroxidaseU"
Thioredoxin U"
Thioredoxin"reducataseU"

Chemical%%Breakdown%
Oxidoreductase U"
Flavoprotein"monoxygenaseU"
Dehydrogenase/reductaseU"

35hydroxyacyl5CoA"dehydrogenases"
Aldehyde"dehydrogenases"
Oxidoreducatse"
Enoyl5CoA"hydratase"

Chemical%BreakdownM"

Bacterial%Symbionts:%
(Pseudomonas,&Rahnella,&
Serra:a,&and&Burkholderia%

Proteins%
Chi nase"
Gluconase"
Porins"
Lec ns"

When comparing the results of the studies analyzing the mountain pine beetle
transcriptome and proteome, it was found that gene products involved in detoxification
were upregulated. Specifically, several gene families displayed upregulation: cytochrome
p450s, ABC transporters, and oxidative stress proteins (Pitt et al. 2014;Robert et al.
2013). Despite that, the number of products upregulated from each gene family varied
between the transcriptome and the proteome, it is likely that these genes play a
considerable role in detoxification (Pitt et al. 2014, Robert et al. 2013). The p450 family
of proteins has been investigated for their suspected role in trans-verbenol production,
but their suspected primary role is breaking down the secondary metabolites of plants.
ABC transporters function to actively pump toxins out of the cell, somewhat in
opposition to the porin proteins utilized by the trees. The other major family of proteins
that was upregulated was the oxidative stress proteins and will therefore be the focus of
this project (Figure 1.1) (Pitt et al. 2014; Robert et al. 2013).
1.4 Mountain pine beetle Oxidative stress proteins
As discussed, the factors influencing mountain pine beetle colonization of pine
trees are many and complicated. This study’s primary focus was on the oxidative stress
system. Specifically, it was conducted with the intention of functionally and
phylogenetically characterizing the mountain pine beetle’s primary oxidative stress
proteins with a focus on those that displayed upregulation in previous transcriptomic and
proteomic analyses.
Oxidative stress proteins moderate the harmful effects of reactive oxygen species.
All aerobic organisms continually produce reactive oxygen species, also known as free

13

radicals. However, when exposed to toxic chemicals, like the secondary metabolites of
pine trees, exposure to reactive oxygen species increases considerably (Birben et al.
2012). This is believed to be due to the breakdown of the lignin polymer, and the number
of phenolic compounds generated as a result of this process. In other species these
compounds have been shown to lead to the formation of the free radicals quinone and
aglycone (Mason and Bowers 2017). Free radicals can be extremely harmful to the
mountain pine beetle because they deplete fat stores, degrade DNA and protein, and
damage gut tissue (Mason 2016; Dmochowska-Ślęzak et al. 2016). Therefore, being able
to properly metabolize such compounds is important to the mountain pine beetle’s
survival during host colonization. This is further supported by the upregulation of these
proteins in blue stain fungus, Grossmania clavigera, that was exposed to detoxification
conditions (DiGuistini et al. 2011). The oxidative stress proteins of mountain pine beetle
have not been studied in terms of functional characteristics. However, within other
organisms, there is a general understanding of what substrates are degraded by each
enzyme (Ajuwon et al. 2015).
Primary oxidative stress proteins are those that directly break down free radicals
and include superoxide dismutase (SOD), peroxiredoxin (Prx), glutathione peroxidase
(GPx), and catalase. Many of these proteins displayed upregulation in the mountain pine
beetle transcriptome and proteome during host colonization (Pitt et al. 2014; Robert et al.
2013). In addition to these four are thioredoxin and thioredoxin reductase (TR) which are
essential oxidative stress proteins but are designated as ‘peripheral’ to the main pathways
because they do not directly interact with the free radicals, so were not examined in this
study (Sheng et al. 2014). They were also not upregulated during host colonization in the

14

transcriptome and proteome of the mountain pine beetle (Pitt et al. 2014; Robert et al.
2013).
One of the most notable of these upregulated oxidative stress proteins is
superoxide dismutase, which processes the harmful superoxide anion (O2-) into freefloating oxygen and H2O2. There are three main families of animal SOD – extracellular,
intracellular, and mitochondrial (Broxton and Culotta 2019). All three families are
present within the mountain pine beetle, but only the intracellular variant demonstrated
upregulation during host colonization, when the insect is likely to be detoxifying host
secondary metabolites (Pitt et al. 2014; Robert et al. 2013). Mitochondrial SOD differs
from the other SOD types in terms of structure and that its metal cofactor is a manganese
ion rather than copper or zinc. The primary function of mitochondrial SOD is to
breakdown the free radicals produced from aerobic respiration, and therefore it would
make sense that its expression would not be heavily impacted during host colonization
(Zelko et al. 2002). It is curious that the extracellular variant did not show any
upregulation (Pitt et al. 2014; Robert et al. 2013), considering that Colinet et al. (2011)
found that these proteins are found in the venom and digestive juices of some parasitic
wasps and flies as they have antivirulence properties that protect against the host’s
immune response.
SOD breaks down the superoxide anion into H2O2, which if left to build up within
the cells could prove lethal, but instead it is readily processed by peroxiredoxin, catalase,
and GPx (Ajuwon et al. 2015). In contrast to the multiple proteins for degrading H2O2,
the superoxide anion is only broken down by SOD (Figure 1.1; Figure 1.2). Therefore,

15

SOD is likely critical to successful mitigation of free radicals in the mountain pine beetle
(Dmochowska-Ślęzak et al. 2016).

Figure 1.2 - Key oxidative stress proteins (Superoxide dismutase, Peroxiredoxin,
Catalase, and Glutathione peroxidase) used by the mountain pine beetle, their free radical
substrates, and primary electron donors, derived from Ajuwon et al. (2015).
The peroxiredoxins are a family of thiol-specific proteins containing no metal
cofactors. In addition to their antioxidant activities, they are involved with cell signaling
cascades, cell proliferation, and immune responses (Shi et al. 2014). Currently there are
six identified isoforms of peroxiredoxin (Shi et al. 2014). The most notable way of
distinguishing the different peroxiredoxin types is by their number of cysteine groups:
Prx1–4 are classified as the typical 2-cysteine type, Prx5 is the atypical 2 cysteine type,
and Prx6 is the one cysteine type. Prx1,2, and 6 are found in the cystosol; Prx4 contains
16

an extracellular N-terminal signal; Prx3 is located within the mitochondria; Prx5 can be
found in the cytosol, mitochondria, and peroxisomes; and Prx 6 is cytosolic but primarily
localized to the outer membranes of the peroxisomes, mitochondria, and nucleus (Rhee et
al. 2016; Shi et al. 2014). All six isoforms are present in the mountain pine beetle and
five were upregulated during host colonization: Prx1, Prx2, Prx4, Prx5 and Prx6 (Pitt et
al. 2014; Robert et al. 2013).
Despite being a key component of the antioxidant systems of mammals (with
there being six isoforms in humans), glutathione peroxidases were not originally
considered a part of insect systems. However, isoforms were initially identified in
Drosophilia melanogaster, and have since been discovered in many other insect species
(Corona and Robinson 2006). GPx's are a family of antioxidant protein that use
glutathione or thioredoxins as electron donors, to reduce H2O2. Which electron donor
they use is dependent on whether they contain a cysteine or a selenocysteine as the
electron acceptor (Dias et al. 2016). As will be discussed in Chapter 2, mountain pine
beetles only have two GPx isoforms that are both localized within the cytosol and both
use cysteine as their main electron acceptor. Neither protein demonstrated upregulation in
the transcriptome or proteome during host colonization (Pitt et al. 2014; Robert et al.
2013), but since GPx is a primary protein in the antioxidant pathway, they were included
among the proteins deemed suitable for further analyses.
In contrast to these other proteins, there is only one isoform of catalase that has
been identified within other vertebrate and invertebrate systems. The one known
exception is Caenorhabditis elegans where the catalase gene is believed to have
duplicated (Corona and Robinson 2006). Consistent with these other systems, mountain

17

pine beetle has a single catalase that is intracellular and was not upregulated in the
transcriptome and proteome during host colonization (Pitt et al. 2014; Robert et al. 2013).
However, it is also a key part of the oxidative stress pathway, so it was also deemed
suitable for further analyses.
1.5 Investigation of recombinant oxidative stress proteins
The information garnered from this process, and the resulting phylogenetic and
functional assay data could be useful for other researchers working in metabolomics and
looking to functionally characterize proteins. As well, the findings from this study could
help to confirm the role of these proteins within mountain pine beetle. This research is
also valuable because the findings could be used in designing outbreak models that could
be predictive of the mountain pine beetle’s continued range expansion.
1.6 Research question
1) Do the mountain pine beetle’s primary oxidative stress proteins (catalase,
glutathione peroxidase, superoxide dismutase, and peroxiredoxin) have unique
evolutionary histories or functional features compared to those found in other
organisms?
1.7 Objectives
1) Construct amino acid alignments and neighbor joining trees for mountain pine
beetle catalase, glutathione peroxidase, peroxiredoxin, and superoxide dismutase
and their homologs found in other insect species with well-characterized genomes
(Chapter 2).

18

2) Design and optimize protocols allowing for the expression and purification of
mountain pine beetle catalase, glutathione peroxidase, peroxiredoxin, and
superoxide dismutase (Chapter 3).
3) Design and optimize protocols allowing for the functional characterization of
mountain pine beetle superoxide dismutase, peroxiredoxin, glutathione
peroxidase, and catalase (Chapter 3).
1.8 Hypothesis
1) The mountain beetle’s primary oxidative stress proteins will have evolved unique
functional features enabling them to mitigate the high oxidative stress
environments produced by their host trees.

19

Chapter 2 Phylogenetics and Sequence Analyses
2.1 Introduction
Mitigating the damaging effects of ROS is essential to an organism’s metabolic
functioning. Therefore, the important role of oxidative stress proteins is ubiquitous across
all life forms. However, across different taxonomic groups, there are differences in
abundance and reactivity for each protein type. For instance, glutathione reductase is
common in vertebrates but mainly absent in many insect groups; instead analogous genes
like thioredoxin and thioredoxin peroxidase have evolved to act as functional equivalents
(Couto et al. 2016). Catalase, glutathione peroxidase, superoxide dismutase, and
peroxiredoxin play a primary role in reducing oxidative stress, but taxonomic variations
are still present.
Corona and Robinson (2006) conducted a phylogenetic analysis of the primary
and secondary antioxidant system of Apis mellifera (western honeybee). They found that
although the basic components of the system remained consistent (mainly the specific
antioxidant proteins present) there were considerably more paralogs present for each
protein in the western honeybee. The authors attributed this to the differences between
social and solitary insects, and the different selective pressures that each type of insect
may be exposed to (Corona and Robinson 2006). With the completion of the mountain
pine beetle genome (Keeling et al. 2013), a similar analysis can now be done on the
mountain pine beetle antioxidant system. With bark beetles living the majority of their
lives exposed to their host tree’s defensive compounds, it is likely that their primary
oxidative stress proteins had to undergo unique adaptions in order to survive those harsh
conditions
Proposed Evolution of the Primary antioxidant proteins
20

The evolution of oxidative stress proteins is believed to be directly linked to the
Earth’s Great Oxidation Event (Sheng et al. 2014). As cyanobacteria began to introduce
oxygen gas to the environment, the production of the superoxide anion soon followed.
Superoxide is a free radical that can be highly degradative to DNA and other biologically
essential molecules, however, it displays varying reactivity within the different regions of
the cell. When compartmentalized within organelles like mitochondria, that express their
own superoxide dismutase, superoxide levels are maintained at minimal and nondamaging levels. Without this antioxidant system, the ROS generated would be
extremely damaging when in contact with other cellular regions, ultimately resulting in
cell death (Turrens 2003). The superoxide radical had a drastic impact on the metabolic
processes used by the organisms of the time, resulting in the evolution of the enzymes in
the antioxidant pathways. In order to catalyze the reduction and oxidation the of the
superoxide anion it is believed that superoxide dismutase (SOD) was the first of the
oxidative stress proteins to develop in the common ancestor of modern Archaeans ~2.42.0 billion years ago (Sheng et al. 2014).
The evolution of oxidative stress pathways was not only key for withstanding the
degradative impacts from ROS like superoxide and H2O2, but likely also for the
development of some of the first cellular signaling pathways (Inupakutika et al. 2016).
Once these ROS could be maintained at distinct REDOX levels, they could then be
coopted by the cells for signal transduction. These molecules could be useful for
indicating when the cell is experiencing stress conditions, and because different ROS
have different properties (e.g. reactivity, diffusion rate, etc.), each ROS could trigger a
unique response in the cell. It is likely that ROS could have been some of the first signal

21

molecules to be incorporated for cellular signaling – and oxidative stress proteins, like
SOD, would have been crucial for their regulation (Inupakutika et al. 2016). Furthermore,
there is support that the oxidative stress proteins themselves can serve as signal
molecules within the cell, particularly the hyperoxidized form of peroxiredoxin. This
peroxiredoxin conformation occurs when oxidative stress is high and is believed to act as
some sort of signal, resulting in the expression of additional oxidative stress proteins
(Neumann et al. 2009).
Superoxide dismutase
The evolution of SOD is believed to have occurred not only in response to
increased oxygen in the environment but also to increased accessibility to REDOX-active
transition metals which are transition metals that enable the SOD proteins to catalyze
their redox cycling reactions (Jomova et al. 2012). The evolution of SOD enzymes has
resulted in a classification of these enzymes based on their associated cofactors.
Originally, iron (Fe2+) occurred in abundance in the world’s oceans and was used as the
first metal cofactor of these original SODs, as well as the similar superoxide reductases
(SOR), that evolved separately in prokaryotes (Sheng et al. 2014). However, as the
atmosphere became increasingly oxygenated, the available Fe2+ was converted into
insoluble ferric oxyhydroxides. Manganese (Mn2+) and Nickel (Ni2+) cofactors were then
employed as alternatives to iron (Inupakutika et al. 2016).
As the atmospheric concentration of oxygen increased, copper (Cu) and zinc (Zn)
were gradually oxidized into the soluble Cu2+ and Zn2+ ions and became the primary
metal cofactors used by most SOD and SOR variants (Sheng et al. 2014). These are the
metal cofactors still used by cytosolic and perixosomic SODs, with cytosolic SODs

22

evolving much earlier than peroxisomic SODs. Fe-SOD is found within the chloroplasts
and Mn-SOD within the mitochondria, a characteristic that can be attributed to the
bacterial origins of these organelles. There is also a Ni-SOD variant that is restricted to
gram-negative bacteria and cyanobacteria (Inupakutika et al. 2016).
Mammalian SODs are among the most thoroughly characterized enzymes of this
type because of their essential role in human health. For instance, a mutation in one of the
subunits of the human Cu/Zn-SOD1 gene has been linked to an increase amyotrophic
lateral sclerosis (ALS), or Lou Gehrig’s disease (Sheng et al. 2014). However, in insect
systems, such as mountain pine beetle, these enzymes are not as well characterized. That
said, although it is not specific to insects, generally it is understood that mitochondrial
Mn-SODs primarily break down the superoxide anion surges referred to as “superoxide
flashes” that occur as a by-product from cellular respiration. Some cytosolic Cu/Zn
SODs are located near the mitochondria and are believed to further assist with this, but
others are located in other regions of the cytosol and could break down the superoxide
anions produced by peroxisomes and other degradative structures (Sheng et al. 2014).
The exact role of extracellular Cu/Zn SOD in insects is even more tentative. Their
function could be particularly important for an organism like the mountain pine beetle
that is actively breaking down high concentrations of toxic compounds (e.g. terpenes and
phenols) and consistently needing to mitigate the damaging effects of the resulting free
radicals that are produced (Parker et al. 2017). Furthermore, the fungal and microbial
symbionts are also actively breaking down these compounds, so it is likely that there
would be a large quantity of superoxide radicals produced in the extracellular matrix that
the beetles would need to contend with (Gretscher et al. 2016).

23

When SOD breaks down the superoxide anion, it produces oxygen and hydrogen
peroxide (H2O2). Although less damaging than the superoxide anion, H2O2 is still a
reactive oxygen species and required the evolution of new oxidative stress proteins in
order to mitigate its damaging effects. There is evidence to support that shortly after its
evolution, the original gene encoding SOD underwent a duplication event in the initial
ancestral organism. Subsequent mutations in each of the duplicates led to one becoming
the original Cu/Zn-SOD gene, and the other becoming the first catalase protein (Klotz &
Loewen 2003).
Catalase
Originally discovered and named in 1900 (Loew 1900), catalases have become
the most commonly studied antioxidant proteins due to their presence across all life
forms and relatively straightforward reaction mechanisms. Catalase breaks down two
H2O2 molecules into two water molecules and one oxygen molecule, and as such is a key
pH regulator. Disrupted catalase activity has been linked to a number of diseases (e.g.
Alzheimer's disease) (Nandi et al. 2019). With the exception of C. elegans, which has
two catalase genes, most organisms are known to only contain one catalase gene (Petriv
and Rachubinski 2004). Typically, catalase proteins lack any kind of signal peptide,
restricting them to the cytosol, and are large tetramers typically comprised of four
polypeptide chains that are ~500 amino acids long (Ashokan et al. 2011). Three families
of catalase have been defined: typical heme containing catalases, atypical heme
containing catalases, and manganese catalases (Zamocky et al. 2009).
Manganese catalases represent only a small portion of catalase proteins (30
proteins) found within bacteria. Phylogenetic studies show that this family diverged very

24

early on in the evolution of catalases (Mhamdi et al. 2010; Zamocky et. al. 2009). The
atypical heme containing catalases are absent in Animalia and Plantae. The most
abundant type of catalase – the typical heme containing catalases – are found across all
Archaea, Bacteria, Eukarya (including Plantae and Animalia). The spread of this family
of catalase across these disparate life forms is believed to be due to multiple instances of
lateral gene transfer events between eukaryotes and their microbial symbionts (Zamocky
et al. 2009).
Glutathione peroxidase
A study conducted by Margis et al. (2008), supported that glutathione peroxidase
does not have a linear evolutionary history. The evidence indicates that several lateral
gene transfers resulted in the formation of the different GPx genes. Margis et al. (2008)
were able to organize the different glutathione peroxidases into three polyphyletic
groups: Group 1 (metazoans), Group 2 (fungi, bacteria, cyanobacteria), Group 3 (plants).
GPx catalyzes the breakdown of H2O2 using either the glutathione or thioredoxin
(TPX) molecule as an electron donor. In mammals, glutathione is the primary electron
reductant, and initially it was believed that genes encoding enzymes with GPx activity
were absent from insect genomes. However, with the completion of the Drosophila
melanogaster genome, two GPx homologs were discovered that use TPX as the primary
reductant. As a result, they were labeled as GPx homologs with TPX activity. Two GPx
homologs were also identified within the Apis mellifera and Anopheles gambiae genomes
(Corona and Robinson 2006). Most mammals have six distinct GPx isoforms, with GPx4
being the most similar to those found in insects. Mammalian GPx4 shows more similarity
to the GPx4 isoforms found in other organisms (insects, plants, and bacteria), indicating

25

that the other mammalian GPx proteins share a distinct phylogenetic origin from the
GPx4 proteins which are generally regarded as being phylogenetically ancient and having
first evolved prior to the divergence of these different organismal groups (Margis et al.
2008).
Peroxiredoxin
Peroxiredoxins (Prx's), also known as thioredoxin peroxidases, are a family of
proteins that are ubiquitous across all three domains of life (Knoops et al. 2007). In the
last couple decades, the Prx family has become one of the most widely studied families of
oxidative stress proteins, due to their prevalence and their many different functions (e.g.
protein chaperones, oxidative stress mitigation, cellular signaling, etc.) (McGonigle et al.
1998). In addition, this group is unique among the previously mentioned oxidative stress
proteins in that they lack metal cofactors, relying solely on the cysteines in their active
site to facilitate catalysis. Although similar to GPx, they exclusively use thioredoxin as an
electron donor instead of the glutathione molecule. Despite having a similar threedimensional structure and having functional similarities to other oxidative stress proteins,
there is little sequence similarity between the peroxiredoxins and the other primary
oxidative stress proteins (Chae et al. 1994).
The Prx family of proteins is large and diverse in terms of their functional role
and sequence similarity, making it challenging to categorize individual proteins. Nelson
et al. (2010) were able to classify Prx’s based on the similarity of the Prx active site into
six subfamilies. When constructing a tree with both insect and human homologs, it was
found that the different Prx types formed distinct clades regardless of species. As a result,
Nelson et al. (2010) came to the conclusion that different Prx types were present in the

26

common ancestor of both of these groups. This was further supported when it was
determined that each member of these particular clades had the same subcellular location.
There are more Prx proteins in eukaryotes than in prokaryotes, and they appear to
have other functional roles in addition to reducing oxidative stress. When in their
hyperoxidized stated, Prx proteins are believed to play a crucial role in cellular signaling.
Bacterial Prx (Ahpc) shows limited susceptibility to hyperoxidation, indicating that it is
probably not involved in cellular signaling. In contrast, many of the Prx proteins found in
eukaryotes do demonstrate this susceptibility. Therefore, the expansion in the number of
unique Prx proteins in eukaryotes is likely a reflection of the expansion in function that is
required in more complex eukaryotic organisms (Bolduc et al. 2018). Despite their
importance in other systems, all of the previously discussed proteins have yet to be
characterized in mountain pine beetle.
2.2 Research Question
1) Are the primary oxidative stress proteins (catalase, glutathione peroxidase,

superoxide dismutase, and peroxiredoxin) of the mountain pine beetle
phylogenetically similar to the primary oxidative stress proteins of other insects
with well characterized genomes?
2.3 Objectives
1) Conduct phylogenetic and bioinformatic analyses comparing the antioxidant
genes of mountain pine beetle to two other Coleopterans and two species from
Lepidoptera, Diptera, and Hymenoptera.

27

2.4 Hypothesis
1) The Oxidative stress proteins of the mountain pine beetle will cluster most closely

with species that have a similar life history, such as Asian longhorn beetle.
2.5 Materials and methods
Annotation of Dendroctonus ponderosae genes
In order to conduct phylogenetic analyses on the mountain pine beetle oxidative
stress proteins, homologous protein sequences were identified from eight other insect
species. The following species used were selected based on availability of their respective
annotated genomes: Tribolium castaneum (Herbst) (red flour beetle), Anoplophora
glabripennis (Motschulsky) (Asian longhorn beetle), Bombyx mori (L.) (silk moth),
Bicyclus anynana (Butler) (squinting bush brown), Apis mellifera (L.) (honey bee),
Pseudomyrmex gracilis (Fabricius) (elongate twig ant), Drosophila melanogaster (fruit
fly), and Anopheles gambiae (Giles) (African malaria mosquito). Each homologous
sequence was obtained by inputting the accession number of the mountain pine beetle
protein of interest into the BLASTp program at NCBI (Altschul et al. 1990). To further
support that the generated protein was actually that particular homolog, the proteins that
were generated from BLASTp were cross referenced with previous studies that worked
with them to confirm that those authors also identified the protein as that particular
homolog. The accession number for each protein is listed in Table 2

28

XP_624806

Prx5

-

NP_001171540

GPx2

CAT

KMQ89785

XP_020288373

XP_020280704

XP_020300222

XP_395319

NP_001171492

XP_020282120

XP_020289705

XP_020300009

NP_001164444

GPx1

Prx6b

Prx6a

XP_003249289

Prx1

NP_536731

-

NP_728870

NP_523463

ABV32570

NP_0010271
91

NP_477510

NP_0011631
08

XP_016766983

SOD
CCS

XP_020295228

NP_476735

SOD3 NP_001037084 XP_0202291397

DM
CAA35210

PG
XP_020282033

SOD1 NP_001171498

AM

BA

-

-

NP_001037084 XP_023935668

BM

NP_001037083 XP_023942413

ABV32570

-

XP_023941442

-

ABL09376

-

NP_001036912 XP_023940851

-

XP_003436755 NP_001036999 XP_023941251

XP_320690

XP_308753

XP_001238545 NP_001040386 XP_023942413

XP_308081

XP_018574477 XP_004922164 XP_023955178

AAS17758

XP_311594

AGa

XP_018575508

XP_018566914

XP_018566915

XP_018580062

XP_018569307

XP_018575310

XP_018578437

XP_018574477

XP_0185694

XP_018579184

AG

NP_001164309

NP_001164309

XP_008198961

XP_970660

XP_968419

XP_969254

XP_018578437

XP_975577

XP_975577

XP_968284

TC

Table 2.1 - Major primary oxidative stress proteins (superoxide dismutase, peroxiredoxin, glutathione peroxidase, and catalase) for
Tribolium castaneum (TC), Anoplophora glabripennis (AG), Bombyx mori (BM), Bicyclus anynana (BA), Apis mellifera (AM),
Pseudomyrmex gracilis (PG), Drosophila melanogaster (DM), and Anopheles gambiae (AGa).

29

Determination of protein characteristics
For the mountain pine beetle oxidative stress proteins, the number of exons was
obtained from the NCBI summary for each gene and the open reading frame size was
determined using the NCBI ORF finder (NCBI Resource Coordinators 2018). Subcellular localization of the proteins was determined using WoLF PSORT (Horton et al.
2007) and confirmed with SignalP-5.0 (Almagro et al. 2019). Protein weight was
determined using BioSuite Sequence Manipulation Suite (Stothard 2000) and confirmed
in SWISS-MODEL (Guex et al. 2009). It was difficult to determine the specific
subfamily of some of the typical one-cysteine Prx proteins, so the Deacon Active Site
(Genbank 9nr, Nov 2010 edition) was used to determine the particular subfamily for
these Prx proteins. The N-terminal mitochondrial sequences in the Prx5 sequences were
identified using TargetP-2.0 (Almagro et al. 2019).
Phylogenetic analysis
The different types of each protein were grouped together for the different
phylogenetic investigations. Following the example of Wang et al. (2008) and Wang et
al. (2016) it was deemed prudent to separate the atypical peroxiredoxins (the oxidizing
and resolving cysteines are on the same subunit) and typical peroxiredoxins (the
oxidizing and resolving cysteines are on different subunits) as these proteins are
dissimilar enough that grouping them together makes it difficult for either family to
resolve properly.
Multiple sequence alignments were created using CLUSTALW 2.1 in Geneious
Prime 2020.0, under the following conditions: gap opening=15.0, gap extension= 0.3 and
using a protein weight matrix of the Gonnet series. Any gaps in the alignments were

30

removed as missing information in a pairwise manner. Amino acid substitutions were
identified across the key protein domains, and deemed conserved, semiconserved, or
nonconserved using the NCBI BLOSUM62 database.
Based on the generated amino acid alignments, ProtTest 2.4 (Oakley et al. 2014)
was used to determine the most suitable best-fit model to use for each protein family’s
phylogenetic tree. A Whelan And Goldman model (WAG) was determined to be the most
suitable for Prx, SOD, and GP. LG was deemed to be the most suitable model for
catalase. Using these matrices derived from the best fit model, each neighbour joining
tree was constructed using the PAUP 4.0 b10 (Swofford 2003) program implemented in
Geneious Prime 9. In order to assess the significance of the branch distribution, a
bootstrapping value based on 1000 replications was generated for each alignment.
2.6 Results
Ten genes of interest were identified in the mountain pine beetle genome (Table
2.2). Both mountain pine beetle GPx proteins were cytosolic and roughly the same size,
differing at only a few amino acids. There was no information on NCBI about the number
of exons present for either of the mountain pine beetle’s GPx proteins, but all of the other
proteins contained multiple exons. Two of the peroxiredoxin genes were assigned to the
Prx6 subfamily, so one was labeled Prx6a (extracellular) and the other Prx6b (cytosolic).
One mountain pine beetle SOD (AEE61971) was found to contain a copper chaperone
domain (CCS) resulting in the relabeling of this protein from a SOD to a SOD-CCS to
highlight the differing function of this enzyme (Table 2.2). Due to this reclassification,
the mountain pine beetle SOD-CCS was not included in any phylogenetic analyses with
the mountain pine beetle SOD proteins.
31

Table 2.2 - Primary oxidative stress proteins of Dendroctonus ponderosae and their key
features. Information includes accession numbers, number of base pairs in their
associated gene, number of base pairs in the open reading frame of their associated gene,
number of exons, number of amino acids, weight in kiloDaltons, and subcellular
localization. Nd is used to indicate unknown information. C is used to indicate protein
found in the cytoplasm, E is used to indicate a protein found in the extracellular matrix,
and m is used to indicate a protein found in the mitochondria.
Gene

Accession
number

Gene
length
(bp)

ORF
length
(ORF)

Number
of
Exons

AA

Protein size
(kDa)

Cellular
localization

SOD1

AEE63298

887

263

2

152

15.57

C

SOD3

AEE63612

2107

285

5

204

21.05

E

SODCCS

AEE61971

2917

324

4

231

24.63

C

Prx1

AEE61832

1157

571

3

195

21.57

C

Prx5

AEE62552

1059

369

4

122

13.42

M

Prx6a

AEE61766

1141

813

4

270

26.48

E

Prx6b

AEE62426

1280

195

4

64

7.58

C

GPx1

AEE61713

723

504

nd

167

19.09

C

GPx2

AEE61775

930

504

nd

167

18.89

C

CAT

AEE61710

1649

1536

8

511

58.6

C

Figure 2.1 is an alignment of the different insect catalase sequences. The
alignment shows a high degree of conservation among the three catalytic amino acids,
with no variation occurring. Although the heme ligand binding
(F*RERI*ERVVHAKGAG) and active site motifs (R*F*YAD*H) also showed a high
degree of conservation, there was variation in the heme ligand-binding motif at the
second amino acid in the motif (the 65th site in the alignment). At this position there
appears to be a conservative replacement between the hymenopterans (both species),
Asian longhorn beetle, and mountain pine beetle, which contained an asparagine residue,
32

with the remaining sequences that contained aspartic acid. Within the active site motif
there was more variation between the insect catalases. However, the mountain pine beetle
sequences did not have any substitutions within this region.

33

H

N

Y

Figure 2.1 - Alignment for catalase sequences from Dendroctonus ponderosae (DP),
Drosophila melanogaster (DM), Bombyx mori (BM), Bicyclus anynana (BA), Tribolium
castaneum (TC), Apis mellifera (AM), Pseudomyrmex gracilis (PG), Anopheles gambiae
(Aga), and Anoplophora glabripennis (AG). The heme-ligand binding motif
(F*RERI*ERVVHAKGAGA) and the active site (R*F*YADT) are highlighted in a
black box. The three catalytic amino acids are identified with red letters above their
locations (Qin et al. 2016).

34

The phylogenetic analysis of catalase showed the proteins separated based on
order (Figure 2.2). Mountain pine beetle catalase was most similar to that of Asian long
horn beetle (Figure 2.2). As the lowest bootstrapping value was 73, these groupings have
relatively high statistical support.

Figure 2.2 - Neighbour-joining tree of insect catalase sequences: Dendroctonus
ponderosae (DP), Drosophila melanogaster (DM), Bombyx mori (BM), Bicyclus
anynana (BA), Tribolium castaneum (TC), Apis mellifera (AM), Pseudomyrmex gracilis
(PG), Anopheles gambiae (AGa), and Anoplophora glabripennis (AG). The tree was
constructed using the distance matrix based on the LG model derived from catalase
amino acid sequences obtained from NCBI. The tree was prepared using Geneious Prime.
The GenBank accession number is displayed for each sequence and the bolded values
above each node represent the bootstrap support, and the values below each branch
indicate branch length.
Comparison of insect glutathione peroxidase amino acid sequences showed that
there were some amino acid differences within the three conserved glutathione
peroxidase domains (Figure 2.3). The most notable of these was in the domain closest to

35

the N-terminus where both mountain pine beetle GPx’s had a lysine substitution. Many of
the other species also had a lysine at this site, but the four other coleopteran GPx proteins
had a glutamine (Figure 2.3).

Figure 2.3 - Alignments for glutathione peroxidase sequences from Dendroctonus
ponderosae (DP), Drosophila melanogaster (DM), Bombyx mori (BM), Bicyclus
anynana (BA), Tribolium castaneum (TC), Apis mellifera (AM), Pseudomyrmex gracilis
(PG), Anopheles gambiae (AGa), and Anoplophora glabripennis (AG). The conserved
glutathione peroxidase domains (A, B, and C respectively) are highlighted in hollow
black boxes (Jiu et al. 2015).
Phylogenetic analysis of insect glutathione peroxidases showed that when both
were present, the GPx1 and GPx2 sequences formed a species-specific cluster (Figure
2.4). An exception was the Asian longhorn beetle where a species-specific clade did not
form. Asian longhorn beetle did still group with the rest of the coleopterans, forming a
distinct clade, the rest of the insects sequences also grouped together based on order. The
mountain pine beetle clade was basal to the other coleopterans. That said, the bootstrap
value associated with the divergence of the coleopteran clade is low (45).

36

Figure 2.4 - Neighbour-joining tree of insect glutathione peroxidase sequences
Dendroctonus ponderosae (DP), Drosophila melanogaster (DM), Bombyx mori (BM),
Bicyclus anynana (BA), Tribolium castaneum (TC), Apis mellifera (AM), Pseudomyrmex
gracilis (PG), Anopheles gambiae (AGa), and Anoplophora glabripennis (AG). The tree
was constructed using the distance matrix based on the Whelan and Goldman (WAG)
model from glutathione peroxidase amino acid sequences obtained from NCBI. The tree
was prepared using Geneious Prime. The tree was prepared using Geneious Prime. The
GenBank accession number is displayed for each sequence and the bolded values above
each node represent the bootstrap support, and the values below each branch indicate
branch length.
The alignment of insect SODs did showcase some variability within the
conserved SOD domains (Figure 2.5). DP SOD1 had an amino that differed from the
consensus in each of those domains, but overall, DPSOD3 contained more substitutions
that deviated from the consensus within these domains.

37

C C

SS

Z

SS

SS

Z ZZ

C

SS

Figure 2.5 - Alignments for superoxide dismutase sequences from Dendroctonus
ponderosae (DP), Drosophila melanogaster (DM), Bombyx mori (BM), Bicyclus anynana
(BA), Tribolium castaneum (TC), Apis mellifera (AM), Pseudomyrmex gracilis (PG),
Anopheles gambiae (AGa), and Anoplophora glabripennis (AG). Conserved superoxide
dismutase domains are highlighted in a black box. Amino acids involved in zinc and
copper binding are identified with red letters above their locations, labeled with a Z or a
C, respectively. The amino acids identified with a red S are involved in disulphide
formation (Parker et al. 2004).
The phylogeny of insect SOD’s showed a distinction between the SOD1 and
SOD3 groups, with each forming a separate monophyletic clade (Figure 2.6). Generally,
the proteins separated based on order. The main exception being African malarial
mosquito SOD1, which was basal to the rest of the proteins. The mountain pine beetle
38

isoforms of each protein showed the most similarity with the respective Asian longhorn
beetle isoforms. There was a high degree of bootstrap support associated with the branch
order of the SOD3 clade (97) but not with the SOD1 clade (32). The bootstrap support
within each clade was low.

Figure 2.6 - Neighbour-joining tree of insect superoxide dismutase sequences
Dendroctonus ponderosae (DP), Drosophila melanogaster (DM), Bombyx mori (BM),
Bicyclus anynana (BA), Tribolium castaneum (TC), Apis mellifera (AM), Pseudomyrmex
gracilis (PG), Anopheles gambiae (AGa), and Anoplophora glabripennis (AG). The tree
was constructed using the distance matrix based on the Whelan and Goldman (WAG)
model derived from superoxide dismutase amino acid sequences obtained from NCBI.
The tree was prepared using Geneious Prime. The tree was prepared using Geneious
Prime. The GenBank accession number is displayed for each sequence and the bolded
values above each node represent the bootstrap support, and the values below each
branch indicate branch length.
Comparison of the Prx amino acid sequences showed that the GGLG and YF
hyperoxidation motifs were only present in the Prx1 sequences (Figure 2.7). However,
the mountain pine beetle Prx1 sequence was the only Prx sequence that did not contain
39

the traditional YF motif, but instead underwent an amino acid substitution resulting in a
FF motif. Hypersensitivity motif A was present also present in only the Prx1 sequences,
excluding the fruit fly Prx sequence. Hyperoxidation motif B was not present in any of
the sequences.

40

Motif A

Motif A

Motif B

GGLG

YF

Figure 2.7 - Alignments for peroxiredoxin 1 and peroxiredoxin 6 sequences from
Dendroctonus ponderosae (DP), Drosophila melanogaster (DM), Bombyx mori (BM),
Bicyclus anynana (BA), Tribolium castaneum (TC), Apis mellifera (AM), Pseudomyrmex
gracilis (PG), Anopheles gambiae (AGa), and Anoplophora glabripennis (AG). The YF
and GGLG hypersensitivity motifs are identified with red letters above their locations.
The locations of hypersensitivity motifs A (D-X8-N/G-X10-H-X27-S/G) and B (T-X3-S/T)
are highlighted in black boxes and labelled with their respective names (Bolduc et al.
2018).

41

The different Prx1 and Prx6 proteins form distinct phylogenetic groups based on
protein type rather than species (Figure 2.8). For each protein type, the proteins formed
species-specific clades, with the exception of the Prx1 group where the Asian longhorn
beetle’s sequence is basal to the rest of the Prx1 sequences. The branch order of each of
the Prx6b, Prx6a, and Prx1 clades indicated a high degree of bootstrap support at 96, 78,
and 100 respectively.

42

Prx6b

Prx6a

Prx1

Figure 2.8 - Neighbour-joining tree of insect peroxiredoxin sequences Dendroctonus
ponderosae (DP), Drosophila melanogaster (DM), Bombyx mori (BM), Bicyclus anynana
(BA), Tribolium castaneum (TC), Apis mellifera (AM), Pseudomyrmex gracilis (PG),
Anopheles gambiae (AGa), and Anoplophora glabripennis (AG). The tree was
constructed using the distance matrix based on the Whelan and Goldman (WAG) model
derived from peroxiredoxin amino acid sequences obtained from NCBI. The tree was
prepared using Geneious Prime. The GenBank accession number is displayed for each
sequence and the bolded values above each node represent the bootstrap support, and the
values below each branch indicate branch length.
Among the Prx5 proteins, there was some variability in the composition and
length of the mitochondrial leader sequences, but none of the proteins had a peroxisomal

43

target sequence at their C-terminal (Figure 2.9). The conserved active site residues
showed no variation among the different sequences.
V

T

R

Figure 2.9 - Alignments for peroxiredoxin 5 sequences from Dendroctonus ponderosae
(DP), Drosophila melanogaster (DM), Bombyx mori (BM), Bicyclus anynana (BA),
Tribolium castaneum (TC), Apis mellifera (AM), Pseudomyrmex gracilis (PG),
Anopheles gambiae (AGa), and Anoplophora glabripennis (AG). Predicted mitochondrial
presequences are highlighted in the hollow boxes and conserved active site residues are
marked with a red letter (Cox et al. 2010).
The Prx5 proteins separated based on species, except for both of the Prx5 proteins
from the Dipterans, which displayed more of a ladderlike structure, but formed a general
monophyletic clade with the Lepidopteran proteins. The coleopteran and hymenopteran
clades demonstrated a high degree of bootstrap support with 100 and 91 respectively, but
the dipteran and lepidopteran clade only had an associated bootstrap value of 50.

44

Figure 2.10 - Neighbour-joining tree of insect peroxiredoxin 5 sequences Dendroctonus
ponderosae (DP), Drosophila melanogaster (DM), Bombyx mori (BM), Bicyclus anynana
(BA), Tribolium castaneum (TC), Apis mellifera (AM), Pseudomyrmex gracilis (PG),
Anopheles gambiae (AGa), and Anoplophora glabripennis (AG). The tree was
constructed using the distance matrix based on the Whelan and Goldman (WAG) model
derived from peroxiredoxin 5 amino acid sequences obtained from NCBI. The tree was
prepared using Geneious Prime. The GenBank accession number is displayed for each
sequence and the bolded values above each node represent the bootstrap support, and the
values below each branch indicate branch length.
2.7 Discussion
In this study, I conducted a comparative and phylogenetic analysis of the
mountain pine beetle oxidative stress proteins. Some of the trees that I generated resolved
based on insect order, with Dipterans and Lepidopterans typically grouping together and
separately from the Coleopterans and Hymenopterans. These findings suggest that the
evolution of these antioxidant proteins parallel the species evolution and that these
proteins have an important conserved function (Misof et al. 2014). Misof et al. (2014)
conducted phylogenetic analyses on 1478 protein-coding insect genes, looking at both
nucleotide and amino acid sequences. From their generated trees, they proposed that
Hymenopterans first evolved during the Permian period with the Coleopterans appearing
shortly after. The Lepidopterans and Dipterans did not appear until the Triassic;
therefore, these two orders are more closely related to each other than to Coleoptera or
45

Hymenoptera. A configuration that supports this hypothesis was observed in the trees for
catalase, SOD, Prx5, and Prx6b.
Mountain pine beetle catalase, SOD, and Prx6b also demonstrated the greatest
similarity to their respective Asian longhorn beetle counterparts. The similarity between
the mountain pine beetle and Asian longhorn isoforms for each of these proteins was
supported by both high bootstrap values and similar branch lengths. The exception was
SOD1 which had a lower bootstrap value of 32. Catalase and SOD are two of the primary
proteins involved in free radical breakdown; therefore, the fact that these proteins show
the most similarity to the Asian longhorn beetle homolog is not surprising in that the two
insects do share a common life history, with the majority of their development occurring
under the bark of trees (Faccoli et al. 2014). As a result, they would both require key
antioxidant proteins like SOD and catalase to function similarly to mitigate the defensive
chemicals of their respective host trees.
DPPrx1, DPPrx5, and DPPrx6a did not group with their counterparts from Asian
longhorn beetle, and with the exception of Prx6a, these groupings had high bootstrap
support. This could potentially be attributed to the diversity in function that has been
described within peroxiredoxin proteins (Bolduc et al. 2018). Peroxiredoxins have been
found to play many roles in addition to reducing oxidative stress. Therefore, they may
serve different physiological roles in mountain pine beetles and Asian longhorn beetles,
despite the similar environments these organisms share. In particular, this could be the
case for mountain pine beetle Prx1 as it did contain a variation of the YF hypersensitivity
motif, which will be further discussed in Chapter 3. As previously discussed,
hyperoxidized peroxiredoxin has been found to act as a cellular signal. Therefore, the

46

presence of this motif in mountain pine beetle and not in Asian longhorn beetle supports
that Prx1 may play a more multifaceted role in the mountain pine beetle or the Asian
longhorn beetle.
Regarding catalase, there is only one isoform of this protein present in most
organisms, and it is regarded as one of the primary proteins involved in the mitigation of
H2O2 (Nandi et al. 2019). Consequently, it is especially true that the respective mountain
pine beetle and Asian longhorn beetle catalases would need to function similarly to
manage the similar environments both organisms inhabit. This was reflected in the amino
acid alignment of these two proteins, where among key amino acid residues, variation
was only seen for two residues (Figure 2.1). Within the Heme ligand-binding motif,
mountain pine beetles and Asian longhorns showed variation when compared to the other
sequences, but even then they both contained identical conserved aspartic acid to
asparagine substitutions. Massoumi-Alamouti et al. (2014) looked at specific genes
coding for detoxification proteins in several pine pathogen species found within the
genus Grossmania. Their findings supported that species adapted to a particular pine tree
shared certain gene variants, which could be reflective of the specific pine chemistries
they are exposed to. As supported by the work done by Massoumi-Alamouti et al. (2014),
the presence of these identical substitutions in the catalase sequences of both mountain
pine beetle and Asian longhorn beetle could be due to the shared selective pressures each
of these beetles’ experiences within their associated host trees. This sequence similarity
supports that the proteins would function similarly under oxidative stress conditions.
Mountain pine beetle catalase is the largest of its primary oxidative stress
proteins. A reason for the dramatic size difference between catalase and the other

47

oxidative stress proteins that perform a similar role, is that the catalase gene is believed to
have initially evolved from a duplication mutation within the Cu/Zn-SOD gene, resulting
in a larger gene and gene product (Corona and Robinson 2006)(Table 2.1). Originally,
catalase was considered to be the sole scavenger of H2O2 in insects. This was attributed to
the reduced number of insect glutathione peroxidases that had been identified
(Yamamoto et al. 2005). However, despite that the number of identified glutathione
peroxidase isoforms in insects is reduced, these proteins in addition to peroxiredoxins
have been identified within most insect orders, so catalase no longer has the distinction
of being regarded as the sole H2O2 scavenger in insects (Yamamoto et al. 2005).
All of the trees containing different protein isoforms demonstrated the formation
of distinct clades based on the specific protein isoform, with the exception of the GPx
tree. Instead, the GPx tree demonstrated distinct clustering by species when both GPx1
and GPx2 sequences were present (Figure 2.4). This species grouping perhaps is
indicative of functional diversification of the different GPx proteins that would have
occurred after the species diverged from one another.
Wang et al. (2001) studied the antioxidant systems of insect species and found
that GPx activity was generally highly reduced. This could potentially account for our
inability to generate so many GPx2 orthologs. Aside from the elongated twig ant, the
coleopterans were the only species to have both GPx1 and GPx2. Potentially, the
duplication event that resulted in both orthologs may have occurred after the divergence
of coleoptera from the other insect groups (Wang et al. 2001). In particular, the
lepidopterans that I examined were found to have a smaller suite of available antioxidant

48

proteins as neither species had extracellular SOD or Prx homologs (Table 2.2). This
suggests that this insect order may have evolved a different oxidative stress strategy.
From the mountain pine beetle genome (Keeling et al. 2013), a putative SOD
protein (AEE61971) was queried against a sequence database to find homologous
proteins in other organisms. Interestingly all of the matches were identified as copper
chaperone proteins (CCS) and when these sequences were aligned, this AEE61971 gene
and its homologous proteins did have some of the conserved SOD domains, but others
were missing, most notably the zinc binding domains. Based on these findings, it is likely
that AEE61971 is not actually a SOD protein but actually a copper chaperone for SOD
(CCS). CCS proteins have some conserved domains associated with SOD proteins, amino
acid residues involved in copper binding, and an N-terminal heavy metal associated
domain (Corona & Robinson 2006). The latter feature is unique to these proteins and
present in the AEE61971 protein.
The function of the CCS protein is to bring and incorporate the copper cofactor to
the Cu/Zn SOD as it is folding. The structure of the CCS protein generally mirrors that of
a Cu/Zn SOD protein – a heterodimer with copper binding residues (Figure 2.5). This
similarity in structure is believed to facilitate proper recognition, binding, and activation
between the CCS and SOD proteins (Boyd et al. 2018). As a result, the CCS genes share
similar sequence similarities with that of SOD proteins. Based on this similarity, when
the mountain pine beetle draft genome was initially characterized, this gene may have
been misidentified as a SOD protein.
Zhang et al. (2017) investigated the SOD proteins of Dastarcus helophoroides
(Coleoptera: Bothrideridae) and found two distinct groups consistent with the

49

intracellular SOD1 and extracellular SOD3 groups. This distribution is consistent with
the SOD phylogenetic tree that I constructed for this study. As discussed by Corona and
Robinson (2006), this indicates that these proteins evolved independently of one another.
The general separation between the extracellular SOD3 group and intracellular SOD1
group is consistent with other studies that have been conducted looking at the phylogeny
of SOD proteins (Zhang et al. 2017; Corona and Robinson 2006) Based on the branch
lengths associated with each tree, the extracellular SOD3 family showed less
diversification in their amino acid sequences than the SOD 1 family. This was surprising,
as Laukkanen et al. (2015) found that the SOD3 family is directly involved in mediating
oxidative stress from the environment as well as in cellular signalling, so I was expecting
a greater degree of variation to be observed within this group, based on the differing
conditions found within the environments of each organism.
Due to the general reduction in measurable GPx protein activity in insect cells
(Wang et al. 2001), the overall evolutionary trend is to see a greater number of
peroxiredoxin isoforms in insects compared to other organisms. This appears to be the
case in mountain pine beetle as there are two Prx6 isoforms (an extracellular and
intracellular variant) in its genome with homologous Prx6 proteins being found in the
other insect genomes as well. In addition, these proteins often display a higher reactivity,
carrying out their associated reactions much faster (Wang et al. 2016). Consistent with
the work done by Wang et al. (2016) looking at silk moth peroxiredoxin proteins, the
typical one-cysteine peroxiredoxins (Prx6), and the typical two-cysteine peroxiredoxins
(Prx1) formed distinct groups, with the latter group containing the conserved FVCP and
EVCP regions characteristic of these families (Figure 2.7; Figure 2.8). Corona and

50

Robinson (2006) phylogenetically compared honeybee peroxiredoxins to other
invertebrates and vertebrates and also found that the proteins separated based on the
specific protein homolog rather than species and suggest that these peroxiredoxin
families may have diverged prior to the speciation of invertebrates and vertebrates. This
conclusion is further supported by the distribution of the peroxiredoxin tree in Figure 2.8.
Some Prx5 proteins have a peroxisomal SQL sequence near their C-terminus,
such as PMP20 from Candida boidinii (a species of yeast). The presence of this sequence
can result in the peroxiredoxin being localized to the mitochondria or the peroxisomes.
All the Prx5 sequences investigated in this study had a mitochondrial leader sequence and
lacked an SQL peroxisomal sequence, supporting that these proteins are exclusively
mitochondrial (Knoops et al. 2011). With Prx5 located within the mitochondria, its
origins are likely bacterial having been incorporated into the eukaryote genome around
the same time that the mitochondrion was also incorporated into the eukaryote cell
(Djuika et al. 2015, Wang et al. 2016).
In addition to mediating oxidative stress from H2O2, the hyperoxidized form of
some peroxiredoxins have been found to play an important role in cellular signalling. In
general, the typical two-cysteine peroxiredoxins are more susceptible to hyperoxidation
than the atypical or typical one-cysteine peroxiredoxins. The YF and GGLG motifs have
long been supported to increase susceptibility of peroxiredoxins to hypersensitivity in
Prx1 proteins (Wood et al. 2003). Consistent with the current knowledge on these motifs,
none of the typical or atypical peroxiredoxins from mountain pine beetle contained these
motifs. All of the insect Prx1 proteins did have the GGLG motif. They all also had the
YF motif except for the Prx1 from mountain pine beetle. In this protein the tyrosine in the

51

motif was substituted with a phenylalanine, making it FF instead of YF. A previous study
by Schneider et al. (2008) found that substituting a phenylalanine with a tyrosine in the
active site of a fructose-6-phosphate aldolase protein resulted in a >70 fold increase in
protein activity when compared to the wild type. This substitution` in such a key
regulatory domain of the DPPrx1 protein, could have a considerable impact on the
susceptibility of that protein to hyperoxidation (Figure 2.7).
Recently Bolduc et al. (2018) identified two novel motifs that drastically impact
the overall sensitivity of 2-cysteine Prx proteins to hypersensitivity: Motif A and Motif B.
It was found that in human and bacterial Prx proteins the different combinations of the
four motifs could have considerable impacts on the overall hypersensitivity of the
protein. Despite being present in bacterial and human Prx proteins, motif B has not been
identified in any of the other insect sequences. Bolduc et al. (2018) compared the relative
hypersensitivity of 2-cysteine Prx proteins with varying combinations of these
hypersensitivity motifs. However, they did not look at the reactivity of a peroxiredoxin
with just GGLG and Motif A. Therefore, mountain pine beetle Prx1 could offer a unique
opportunity to further expand on how these domains impact overall reactivity,
particularly when the modified YF motif is also taken into account.
2.8 Conclusions and Future Directions
In this study I examined the sequence structure and phylogenetic relationships of
the primary oxidative stress proteins of the mountain pine beetle, which to my knowledge
has previously not been done for a species of bark beetle. Overall, I found that the key
components of the antioxidant system in insects are also conserved in the mountain pine
beetle. By examining the sequence structure of these genes, it allowed for any unique
52

features in the mountain pine beetle oxidative stress proteins to be identified. One of the
most notable differences was that the mountain pine beetle was the only insect to have its
Prx1 containing a FF instead of the conserved YF. A mutation in such a key regulatory
motif could have critical impacts on the functionality of this protein and could be
indicative of the unique life history of the mountain pine beetle compared to the other
insects in this study.

53

Chapter 3: Functional Characterization of DPPRx1 and DPSOD1
3.1 Introduction
Reactive oxygen species (ROS), including the superoxide anion (O2-) and
hydrogen peroxide (H2O2) are considered primary lines of chemical defense in pine trees
(Li et al. 2016). ROS are produced by trees as defensive compounds and are by-products
of lignin breakdown during mountain pine beetle colonization (Birben et al. 2012). These
molecules can be incredibly harmful as they can damage lipids, protein, and DNA.
Therefore, mitigating ROS damage is essential to mountain pine beetle survival during
colonization of pine trees. To my knowledge, no studies have been performed on the
function of oxidative stress proteins in bark beetles. The mountain pine beetle has a
number of oxidative stress proteins, including catalase, glutathione peroxidase,
peroxiredoxin and superoxide dismutase, many of which showed upregulation during
host colonization (Pitt et al. 2014; Robert et al. 2013). I was able to generate sufficient
quantities of heterologously expressed mountain pine beetle peroxiredoxin-1 (DPPrx1)
and superoxide dismutase-1 (DPSOD1), for functional characterization. SOD is essential
as it is one of the only proteins able to breakdown the superoxide free radical (O2-) and
peroxiredoxin performs a variety of vital antioxidant and signal transduction roles
(Tasaki et al. 2018; Winterbourn 2008).
One recent study by Li et al. (2016) investigated the role of a typical 2-cysteine
peroxiredoxin (BxPrx) in the pine wood nematode, Bursaphelenchus xylophilus. Like the
mountain pine beetle, pine wood nematodes feed on the parenchymal cells of pine
species, and their population has reached epidemic levels in some regions, drastically
impacting pine forests across Asia and Europe. In addition, under infestation conditions,
Li et al. (2016) found that BxPrx expression was elevated, similar to the increased
54

expression of DPPrx1. The pine wood nematode is responsible for the pine wilt disease,
which has had considerable ecological and economic impacts on Asian and European
pine stands. A previous study found that 2-cysteine peroxiredoxin proteins are
overexpressed in C. elegans (parasitic nematode), as they play a critical role in the
breakdown of free radicals produced both metabolically within the nematode and as an
immune response by the host organism (McGonigle et al. 1998). Although Li et al.
(2016) did not determine a rate constant for BxPrx, they were able to assess its activity
using a metal catalyzed oxidation (MCO) system, which assays for the ability of
peroxiredoxins to prevent DNA degradation by H 2O2, and via disc assays, which compare
the survival rate of bacterial cells grown in high concentrations of H2O2 when BxPrx is
present and when it is absent. They found that BxPrx has a high antioxidant activity,
indicating that it likely plays a role in mitigating the damage from free radicals that build
up during host colonization.
To date there has been no attempt to characterize the activity of any bark beetle
peroxiredoxins. That said, Zhang and Lu (2015) looked at the antioxidant functions of an
atypical peroxiredoxin (BMPrx5) from Bombyx mori (silk moth). Using quantitative real
time PCR, they found that BMPrx5 was upregulated in the haemocytes of the silk moth
under conditions triggering oxidative stress (i.e. bacterial infection and H2O2 injection).
This response is consistent with the work done by Pitt et al. (2014) and Robert et al.
(2013) who observed the upregulation of these genes in the transcriptome and proteome,
respectively, of the mountain pine beetle under detoxification conditions. Using a
combination of approaches that include an MCO system, disc diffusion, and viability
assays (which also evaluates the bacterial survival when grown in high H2O2

55

concentrations), it was confirmed that silk moth peroxiredoxin displays considerable
antioxidant functions (Zhang and Lu 2015). However, the authors did not characterize
BMPrx5 by determining rate constants or pKa values.
Based on these findings it is proposed that the physiological purpose of DPPx1
are to mitigate the damaging impacts of H2O2 through the cysteine residues within the
active site of the protein having a high oxidizing tendency. However, the threedimensional structure of DPPx1 and its active site can greatly influence its overall
activity and oxidizing potential. Similar to the previously described studies, the general
affinity of DPPx1 for H2O2 can also be confirmed using a MCO system, but in order to
gain a more in depth understand of the physiological role of DPPrx1 and its affinity for
H2O2, a rate constant must be determined for its reaction with H2O2 (Nelson et al. 2008).
Rate constant determination
A competition-based assay with horseradish peroxidase is a relatively
straightforward and affordable approach to generate a second order rate constant for a
peroxiredoxin with H2O2 (Nelson and Parsonage 2011). Horseradish peroxidase (HRP)
reacts with hydrogen peroxide (H2O2) resulting in an oxidized form of HRP called HRP
compound 1, which absorbs less light at 403 nm compared to unreacted HRP.
Peroxiredoxin has a higher affinity for H2O2 than horseradish peroxidase (HRP), as a
result there is a reduction in the net decrease in absorbance at 403 nm (from HRP
compound 1 formation) when a functional peroxiredoxin is present in a solution
containing horseradish peroxidase and H 2O2. This allows for the fractional HRP
inhibition to be calculated because it is proportional to the concentrations of
peroxiredoxin in the reaction mix (Ogusucu et al. 2007).

56

Originally, rate constants for this reaction were acquired using steady state
kinetics (kcat/kM). Using this approach, the rate constants for the typical 2-cysteine
peroxiredoxins, Tsa1 and Tsa2 from Saccharomyces cerevisiae, were determined to be
104 to 105 M−1s−1, respectively (Park et al. 2000). This was surprising because for a
protein that appears to be as essential as peroxiredoxin, it is expected that it would react
more rapidly with its substrate and have a second order rate constant that is larger than
105 M−1s−1. However, using the horseradish competition assay, Ogusucu et al. (2007)
measured rate constants that were two orders of magnitude larger than originally
reported. These findings were further supported by kinetic approaches performed in a
stopped-flow instrument, indicating that the horseradish competition assay garnered a
more accurate rate constant than the steady state kinetic approach previously employed.
Therefore, I used a horseradish competition assay to determine rate constants for DPPrx1
because of its accuracy and its ease of implementation. Due to DPPrx1’s seemingly
important role during detoxification, based on transcriptomic and proteomic data, it was
suspected that the generated rate constant would be close to the value of 107 M−1s−1
(Ogusucu et al. 2007).
pKa determination
The second order rate constant of horseradish peroxidase with H 2O2 is pH
independent from pH 4.5 to 7.0, which makes the horseradish peroxidase competition
assay a useful tool for determining the pKa of the active site of a peroxiredoxin protein, as
it allows for a range of pH values to be tested (Dunford 1999; Job et al. 1978). This pK a
value should be indicative of the pKa value of the oxidizing cysteine (CP); whereas, the

57

pKa of the resolving cysteine (CR) should be more comparable to that of a free cysteine
group (pKa=8.4) (Ogusucu et al. 2007).
Sensitivity to Hyperoxidation
A physiologically important aspect of DPPx1 could be its potential role as a
molecular signal. As discussed in Chapter 2, DPPx1 is a cytosolic homodimer, with each
monomer containing a CP and a CR. It is critical to the catalytic function of 2-cysteine
peroxiredoxin that the Cp is quickly deprotonated at the neutral pH of the cytosol, becoming
a thiolate (CPS-). Therefore, the pKa of Cp is relatively low compared to that of free
cysteines (Nelson et al. 2008). This thiolate has a high affinity for reacting with H2O2, to
become a sulfenic acid (CPSOH). Condensation then occurs between the sulfenic acid and
the resolving cysteine (SR), forming a disulfide linkage. Therefore, each peroxiredoxin
protein is able to form two disulfide linkages and reduce four H2O2 molecules. The
homodimer will eventually be reduced via disulfide exchange facilitated by the
thioredoxin-thioredoxin reductase system (Ledgerwood et al. 2017).
However, when free radical concentrations are high the sulfenic acid can be
further oxidized to form a sulfinic acid (CPSO2H) and sulfonic acid (CpSO3H). These
hyperoxidized states can no longer be reduced by thioredoxin, but sulfiredoxin, an ATPdependent enzyme that is much slower, can reduce sulfinic acid. The conversion of the
thiolate to sulfenic acid is believed to be an irreversible reaction. As hyperoxidized
peroxiredoxin is catalytically inactive, this eliminates any antioxidant capacity of the
enzyme (Ledgerwood et al. 2017).

58

!CpS% %%HSCR!%
!CRSH% %SCP!%
(Reduced*State)%
H2O2
H2O

ThioredoxinThioredoxin Reductase
System

Sulfiredoxin

!CPSOH%%HSCR!%

H

!CRSH%%HOSCp!%
%%%%%%%(Sulfenic*Acid)*
Low H2O2
Concentration

!CpS%!SCR!%

High H2O2
Concentration

!CPSO2H% %HSCR!%
!CRSH%%HOS2Cp!%

!CRS%!SCp!%
(Disulfide*Forma0on)*

(Hyperoxidized*State)%

Figure 3.1 - The reaction mechanism for a typical 2-cysteine peroxiredoxin, adapted from
Ledgerwood et al. (2017). The homodimer is represented by the yellow and green blocks
with their respective cysteine residues. The fork in the diagram indicates the two
pathways that lead to either the disulfide or hyperoxidized state. The reduction pathways
for each of the oxidized forms and the main enzymes involved are represented by the
curved arrows.
Initially, the propensity for peroxiredoxin to undergo hyperoxidation under
extreme oxidative stress seems counterintuitive to the role of an oxidative stress protein.
However, there is evidence that hyperoxidized peroxiredoxin serves a valuable role as a
messenger within the greater antioxidant response systems of a cell. In S. cerevisiae,
thiol-based peroxidases have been found to act as ‘peroxide sensors’, with their
hyperoxidized forms oxidizing the transcription factor Yap1 (Delaunay 2002). This
transcription factor regulates the expression of a glutathione peroxidase protein called
Orp1. This protein has a much higher affinity for H2O2 than peroxiredoxin and will
59

therefore be more effective at reducing damage from free radicals (Delaunay 2002). A
similar mechanism happens with Pap1, a homolog of Yap 1, found in
Schizosaccharomyces pombe (Delaunay 2002). It is regulated by a hyperoxidized
peroxiredoxin called Tpx1 (Vivancos et al. 2005). Similar mechanisms are also found in
plants, such as Arabidopsis thaliana, where a 2-cysteine peroxiredoxin was shown to
regulate the transcriptional activity of Rap24, a transcription factor that was seen to
upregulate the expression of the plant’s antioxidant defense (Rudnik et al. 2017; Rhee et
al. 2012).
As discussed in Chapter 2, there are unique motifs that provide a peroxiredoxin
molecule with varying degrees of sensitivity to hyperoxidation. Two key motifs that
convey sensitivity to hyperoxidation are the primary sequences Glycine-GlycineLeucine-Glycine (GGLG) located near the middle of the peroxiredoxin active site, and
Tyrosine-Phenylalanine (YF), located near the C-terminus of the proteins (Figure 3.1).
These motifs restrict bonding between the sulfenic acid and the resolving cysteine, thus
leaving the cysteine residues susceptible to the formation of sulfinic and sulfonic acids. It
is common for the conserved YF sequence to be missing from typical 2-cysteine
peroxiredoxins, which is the case for DPPrx1 (Kumagai et al. 2009).
M S P A L QK P A P S F K GT A V V D GQ F K D I S L ED Y K GQ Y V V L F F Y P L D F T F V C P T E I I A F SD R I E

D"

E F K K I K T A V I GA ST D SH F SH L AW I N T P R K QGG L G SMN I P L L A D K N L E I A R S YGV L D E ST G

*"

H"

*"

I A F RG L F I I D P K G I L RQV T I N D L P V GR SV D E T L R L V Q A FQ F T D EH G E V C P A GWT PGK K T I
K PQV D A SK E F F S ST N

Figure 3.2 - Primary protein structure of DPPrx1. Functional cysteines, flanked by a valine
and a proline (VCP), are boxed in blue. The conserved sensitivity motif GGLG is boxed in
purple. Hyperoxidation motif A is underlined in red. Sites where a lysine was found in
place of conserved glycine are highlighted with a *.
60

Because DPPrx1 is missing the YF motif that is associated with sensitivity to
hyperoxidation (see Chapter 2) it is likely that the protein may not show susceptibility to
hyperoxidation. However, Bolduc et al. (2018) did an intensive sequence analysis of
bacterial and human peroxiredoxins. In addition to the YF and GGLG motifs, they
identified two novel protein motifs associated with sensitivity to hyperoxidation – Motif
A (See Figure 3.2) and Motif B (T-X3-S/T). In addition to sequence analysis, Bolduc et
al. (2018) performed functional analysis on different peroxiredoxins and found it difficult
to predict how these particular motifs impact catalytic function.
Bolduc et al. (2018) found that the peroxiredoxins that displayed the highest
susceptibility to hyperoxidation were those that contained the GGLG and YF motifs, with
only either Motif A or Motif B. Consequently, the peroxiredoxins containing both Motif
A and Motif B showed the greatest resistance to hyperoxidation, regardless of whether
the GGLG and YF motifs were present. We can infer based on the presence of motif A,
that the sensitivity of DPPrx1 to hyperoxidation is likely high. However, the presence of
motif A with GGLG in DPPrx1, is a combination that was not seen by Boluc et al.
(2018), so the exact effect it will have on hyperoxidation is unknown.
Another unique aspect of the specific motif A present in DPPrx1 is that in the
sites where bacterial and human peroxiredoxins have glycines, DPPrx1 has lysines. In
other proteins that have experienced a mutation resulting in the conversion of a glycine to
lysine, there is almost always an impact on the overall function, except in cases where the
amino acids are not located near the active site (Tsuzuki et al. 2003). However, Motif A
completely overlaps with the active site of DPPrx1 and includes one of its functional
cysteines (Figure 3.2). Therefore, these mutations could have a considerable impact on

61

both the antioxidant and/or signal transduction functions of DPPrx1 (Haijema et al.
1996).
Functional analysis of DPSOD1
Similar to peroxiredoxin, there is no prior research literature describing the
activity of Cu/Zn-SOD in bark beetles. However, there have been studies looking at a
Tribolium castaneum (red flour beetle) SODs (Ferro et al. 2017; Behrens et al. 2014).
The detoxification system of the red flour beetle is an interesting one in that it the insect
has experienced extensive selective pressures from insecticide usage. This is similar to
how the mountain pine beetle has existed alongside the selection pressure context of host
tree defensive metabolites. Using transcriptomic data collected from red flour beetle
populations infected by Bacillus thuringiensis, a common bioinsecticide used in
agriculture, Behrens et al. (2014) determined that oxidative stress proteins experienced
significant upregulation. Based on this upregulation, red flour beetle SOD, as one of the
primary oxidative stress proteins, was deemed a suitable selection for functional analysis.
Ferro et al. (2017) observed that the red flour beetle SOD, when ‘primed’ via exposure to
Bacillus thuringiensis infection, displayed a distinct increase in SOD activity which was
determined based on its ability to inhibit nitrolium blue tetrazolium.
3.2 Research Question
1) Do the primary oxidative stress proteins (catalase, glutathione peroxidase,

superoxide dismutase, and peroxiredoxin) of the mountain pine beetle have
unique functional features (i.e. increased reactivity)?

62

3.3 Objectives
1) Design and optimize a protocol allowing for the expression and purification of
mountain pine beetle superoxide dismutase 1 and peroxiredoxin 1.
2) Functionally characterize mountain pine beetle superoxide dismutase 1 by
performing a water-soluble tetrazolium (WST-1) salt competition assay to
determine how much is required to achieve 50% inhibition of xanthine oxidase
(IC50).
3) Determine the pKa and second order rate constant values for mountain pine beetle

peroxiredoxin 1 by performing a competition-based horseradish peroxidase assay.
In addition, perform a nonreducing SDS-PAGE to determine the susceptibility of
mountain pine beetle peroxiredoxin 1 to hyperoxidation.
3.4 Hypothesis
1) Based on the unique life history of the mountain pine beetle, their oxidative stress

proteins will demonstrate unique functional features compared to the oxidative
stress proteins of other organisms.
3.5 Materials and Methods
Entry Clone Construction
ElectroMAX DH10B T1 Phage-Resistant electro-competent cells containing the
genes of interest were retrieved from a mountain pine beetle cDNA library glycerol stock
and were grown at 37°C on lysogeny broth (LB) agar plates containing chloramphenicol
(30 μg/ml). The following day, four colonies were selected and grown overnight at 37°C
in 5 mL of LB containing chloramphenicol (30μg/ml). Plasmids were extracted using the
QIAprep ® Spin Miniprep kit (Qiagen).
63

The Gateway® Cloning Procedure TM (Invitrogen) was selected to introduce genes
of interest into One Shot® BL21-AI™ E. coli (Invitrogen) competent cells via the
plasmid pDEST-17 (Invitrogen) as it employs a single recombination reaction. To insert
the genes of interest, specific recombination sites (attB sites) that are 25 base pairs long
are needed to flank the genes. These were incorporated into the primers (Table 3.1) prior
to polymerase chain reaction (PCR) amplification. Primers for DPPrx1 and DPSOD1
were designed and purchased from Integrated DNA technologies (Table 3.1).
Table 3.1 - Cloning primers for DPPrx1 and DPSOD2, ATTB sequences are bolded
Primer
DPPrx1F
DPPrx1R
DPSOD1F
DPSOD1R

Sequence (5’–3’)
GGGGACAAGTTTGTACAAAAAAGCAGGCTTAATGTCTCCTGCACTTCAGAA
GGGGACCACTTTGTACAAGAAAGCTGGGTACTATTAGTTAGTTGAGCTGAAAA
GGGGACAAGTTTGTACAAAAAAGCAGGCTTCATGGTTAAAGCCGTTGCCGTGTTG
GGGGACCACTTTGTACAAGAAAGCTGGGTCCTACTAGTTGGGCTGTGCAAGG

PCR reaction conditions were: 3 μL of extracted plasmid, 1X reaction buffer, 0.5
μL of dNTP’s, 2.5 μM of paired forward and reverse primers (containing attB sites),
(Table 3.1), 0.25 μL (1.25 units) of Platinum Taq high fidelity DNA polymerase
(Invitrogen), and 17.75 μL of milliQ-grade water. Reactions (25 μL) were run at 94°C for
1 minute; followed by 34 cycles of 94°C for 30 seconds, 66°C (DPPrx1) or 69°C
(DPSOD1) for 30 seconds, and 72°C for 90 seconds; 72°C for 10 minutes.
PCR product gene sequences were confirmed through restriction digestion
analysis and dye-terminator Sanger sequencing at the UNBC NALS Genetics Laboratory.
For the restriction digest, the purified plasmids (1 µg) were mixed with HindIII (1 unit)
and digested at 37°C for 4 hours, and then run on a 1% agarose gel at 1.5 volts for 1.5
hours. Upon confirming that proper amplification and cloning had occurred, the

64

Gateway® Cloning Procedure was employed to transfer the plasmid to the One Shot®
BL21-AI E. coli expression system (Figure 3.3).

Gene*of*interest*

+"

pDONRTM221*

1"
pDESTTM17*

pDonr*

+"

2"

pDONRTM221*

1)"BP"reac on"
*****Crea?ng*an**
*****entry*clone*

+"
pDESTTM17*

2)"LR"reac on"
*****Crea?ng*an**
*****expression*
*****clone*

3"

BL213A1*E.#coli*cells*

3)"Plasmid"Transforma on*

Figure 3.3 - Overview of the LR and BP steps adapted from the Gateway® Technology
Cloning protocol (2003).
The first step of the Gateway® Cloning Procedure, referred to as the BP reaction,
is designed to incorporate the gene of interest to a transfer vector (pDONR™) (Figure
3.3). Depending on the amplicon concentration, which was measured using a Nanodrop
spectrophotometer, 1–10 μL of the PCR product was mixed with 2 μL of pDONR™ (150
ng/μL) and 1–9 μL of TE buffer to make a final volume of 10 μL. Lastly, 4 μL of BP
Clonase™ II (thawed on ice for 2 minutes and vortexed twice for 2 seconds) was added
to the mixture. The reaction was then incubated at room temperature (~21°C) for 1 hour.
To stop the reaction Proteinase K (2 mg/mL) was added and the reaction was incubated
for ten minutes at 37°C.
The transfer vectors generated during the BP reaction were transformed into
competent cells. Frozen 50 μL aliquots of TOP10 library efficiency cells (stored at 80°C) were kept on ice until thawed, and then 1 μL of BP reaction mix was added, mixed

65

by lightly flicking the closed tube, and left on ice for 30 minutes. The cells were then heat
shocked in a 42°C water bath for 30 seconds, and the tube was immediately transferred
back to the ice and left to sit for one minute. SOC medium (450 μL) was added and the
tube was shaken horizontally at 200 rpm for 1 hour at 37°C. Selection of transformed
cells relies on the kanamycin resistance gene found in the pDONR™ plasmid. Therefore,
20 μL and 100 μL of the above cell mix were streaked on LB agar plates containing
kanamycin (30μg/ml). Plates were incubated overnight at 37°C. From each plate four
colonies were transferred to 5 mL of LB treated with kanamycin (30μg/ml) and incubated
at 37°C overnight in a shaker (300 rpm). To confirm that the BP reaction had occurred
successfully, pDONR™ plasmids were purified using the Qiagen QIAprepâ Spin
Miniprep Kit and submitted to the UNBC NALS Genetics Laboratory for sequencing.
The second part of the Gateway® Cloning Procedure is called the LR reaction and
involves the transfer of the gene of interest from pDONR to the final expression vector
(pDEST-17) by LR Clonase™ II. The steps of the LR reaction are similar to those of the
BP reaction but using purified pDONR containing either DPPrx1 or DPSOD in place of
PCR product (Figure 3.3). For cell transformation steps the BL21-AI™ strain of E. coli
was used in place of library efficiency cells with ampicillin (30μg/ml) or carbenicillin
(30μg/ml) instead of kanamycin for selection, as this is the antibiotic pDEST™-17
confers resistance to. Of the potential expression systems that could have been employed,
BL21-AI™ cells were selected for their affordability, ease of use, and because they are
deficient in two key proteases (lon and OmpT) that would otherwise reduce the overall
yield of protein product (Amid et al. 2011).
DPPrx1 induction

66

In order to optimize the amount of soluble protein produced, different induction
parameters (e.g. temperature, rotary shaker rpm setting, induction time, and heavy metal
supplementation) were compared. For DPPRx1, 50 μL of BL21-AI™ cell stock was
grown overnight and added to a 250 mL Erlenmeyer containing 50 mL of LB growth
media, and 500 μL of ampicillin (30μg/ml). Cultures were shaken at 300 rpm in a rotary
shaker-incubator (Shel Lab) at a temperature of 37°C. The tops of the flasks were
covered with tin foil to allow for air-exchange. After two hours, the optical density (OD)
at 600 nm was measured using a SmartSpec™ Plus Spectrophotometer (Bio-rad).
Following the protocol described by Wang et al. (2016), if they reached an OD of 0.4–0.6
the cultures were induced with 0.4 mM of arabinose. If the desired optical density had not
yet been reached then the cultures were left to incubate further, and the OD was checked
every 30 minutes and induced after the desired value was reached. After arabinose
induction, the cultures were then left to shake and incubate at 300 rpm at 37°C for six
hours. After the induction was finished, the cultures were aliquoted into both 15 mL
conical and 1.5 mL microcentrifuge tubes and centrifuged at 13,000 rpm for three
minutes using a Sorvall Legend Micro 17R centrifuge (Thermo Fisher) and a Sorvall
Legend XIR centrifuge (Thermo Fisher) respectively. The supernatant was poured off,
and the cell pellets were stored at -20°C until they were used for further analysis.
DPSOD1 Induction
Inductions were performed on the BL21-AI™ cell cultures containing DPSOD1
in LB growth media but did not result in the production of any soluble protein. Therefore,
the minimal media induction conditions employed by Asmail (2008) were employed. The
minimal media was made of 5 g/L glucose, 1 mg/L of biotin, 2 mg/L of thiamin, 1g/L of

67

ammonium sulphate, 0.125% Casamino acids, 25 mM phosphate buffer, 12.5 ng/mL of
ampicillin, and 0.004% of heavy metal stock solution. Heavy metal stock solution was
prepared with the following components: 100 mg MoNa2O4•2H2O, 90.4 mg CoCl2•
6H2O, 35 mg CuSO4• 5H2O, 200 mg MnSO4•H2O, 854.6 mg MgSO4• 7H2O, 250 mg
ZnSO4• 7H2O, 500 mg CaCl2• 2H2O, 200 mg H3BO3, and FeCl2• 4H2O all mixed in 1
liter of 1M HCl. The mixtures were stirred overnight with a magnetic stir bar and filtered
using WhatmanTM Qualitative filter paper: Grade 1 Circles (GE Healthcare). 50 mL of
overnight cell culture, grown in LB media, was mixed with 50 mL of minimal media in a
300 mL Erlenmeyer flask covered with tinfoil and shaken at 37°C at 300 rpm until an OD
of 0.6–0.8 was reached. At this point, the flasks were put on ice for five minutes and then
arabinose was added to a final concentration of 0.5 mM. The cultures were then
incubated at room temperature (~20°C), and mixed with a magnetic stir bar for 18 hours.
After that, the cultures were both 15 mL falcon and 1.5 mL microcentrifuge ,tubes and
centrifuged at 13,000 rpm for three minutes. The cultures collected in the 1.5 mL
microcentrifuge tubes were run on an SDS-PAGE, using NuPAGE™ 4-12%
Bis/Trispolyacrylamide gels (Invitrogen), to confirm protein presence and size. In
addition, to assess protein solubility, cultures were centrifuged and SDS-PAGE was
performed on both the pellet and supernatant. The subsequent purifications and assays
were performed on the cultures stored in the 15 mL falcon tubes. The resulting
supernatant was poured away, and the cell pellets were stored at -20°C for further
analysis.
Protein Purification

68

Pellets from the 15 mL aliquots were resuspended in 1 mL of GE His SpinTrap
Binding Buffer and 50 μL of 200 mM phenylmethylsulfonyl fluoride (PMSF) and left to
sit on ice for 30 minutes with regular intervals of stirring using a glass rod. Resuspended
cell cultures were transferred to precooled 15 mL conical tubes and, while still on ice,
sonicated using an Model 150T ultrasonic dismembranator (Fisher Scientific). The cell
suspension was sonicated on ice using intervals of five pulses of 30 second duration on
output five with a 30 second break in between pulsing cycles. The supernatant containing
soluble SOD was recovered after sonication by centrifugation at 13,000 x g for 10
minutes. SDS-PAGE was performed on sub-samples of the pellets and lysates to compare
levels of soluble and insoluble recombinant SOD produced during induction. The rest of
the lysate was transferred to a new microcentrifuge tube and used for chromatographic
purification of SOD after SDS-PAGE confirmation of solubility.
To assess the size and quantity of the expressed protein, SDS-PAGE was
performed with NuPAGE™ 4-12% Bis/Trispolyacrylamide gels (Invitrogen) and run for
1.5 hours at 125 V, under reducing conditions, and run with a 1 Kb protein ladder
(Thermo Scientific).
The protein purification protocol was adapted from a general protocols for
purification with GE Healthcare His SpinTrap column. Before finalizing the purification
protocol, five different imidazole concentrations were screened for the binding buffer
conditions: 0 mM, 20 mM, 40 mM, 60 mM, 100 mM, and 300 mM. 20 mM resulted in
the highest yield, which was also the concentration recommended in the GE purification
protocol. His SpinTrap columns were inverted and shaken to disperse the medium and
then centrifuged for 30 seconds at 70 x g. To prime the column, 600 µl of 20 mM binding

69

buffer was added to the column and spun for 30 seconds at 70 x g. 600 µl of lysate was
applied to the column and incubated at room temperature for 30 minutes to promote
maximum protein binding before centrifugation at 70 x g for 30 seconds. The column
was washed with 600 µl of 20 mM binding buffer to remove protein contaminants, and
then the protein was eluted with 200 µl of 500 mM elution buffer pH 7. This step was
repeated in a separate tube to collect any remaining protein attached to the column.
Protein quantification
I used the Bradford protein assay (Bio-Rad) to quantify the amount of protein
collected at different steps of the purification. Concentrated bovine serum albumen
(BSA) was diluted with MilliQ-grade water to prepare BSA standards in the following
concentrations: 0.0 mg/ml, 0.05 mg/ml, 0.1 mg/ml, 0.2 mg/ml, 0.4 mg/ml, 0.6 mg/ml, 0.8
mg/ml and 1.0 mg/ml. In a 96 well plate, 250 μL of Quick Start™ Bradford 1X reagent
(Bio-Rad) was added to each well followed by 10 μL of the appropriate BSA standard or
purified protein. The contents of each well were mixed by pipetting up and down, with
care being taken to ensure that no bubbles formed, and then then the absorbance was read
at 595 nm using a FilterMax F5 Multi-Mode Microplate Reader (Molecular Devices).
Each Bradford standard and protein sample were run in triplicate. Using the absorbance
reading from the different BSA standards, a standard curve was generated and could be
used to determine the protein concentration of the unknown samples (Figure 3.4).

70

1.6

y = 1.0009x + 0.3694
R² = 0.978

Absorbance at 595 nm

1.4
1.2
1

0.8
0.6
0.4
0.2
0

0

0.2

0.4
0.6
0.8
Concentration (mg/ml)

1

1.2

Figure 3.4 – A standard curve generated from BSA standards of differing concentrations
and their respective absorbance readings at 595 nm.
Metal-catalyzed Oxidation System
In order to confirm the antioxidant function of DPPrx1, an MCO (metal-catalyzed
oxidation) system was employed (Latifi et al. 2007). The following protocol was adapted
from Lim et al. (1993). 10 mM of Dithiothreitol (DTT) and 40 μM of FeCl3 were mixed
to produce free oxidant radicals (i.e. hydroxyl radical and hydroperoxyl radical species).
In addition, 20 mM of EDTA was added with varying amounts of DPPrx1 (0–8 μM). The
final volume for each reaction was 50 μL. Differing volumes of MilliQ-grade water were
added to each reaction in order to bring the total volume of each reaction to 60 μL. These
reactions were then incubated at 37°C for one hour in a shaking incubator (300 rpm).
Plasmid DNA (500ng pUC19) was then added to the reactions and incubated for 4 hours
at 20°C The impact of the MCO on the structure of the DNA was then analyzed by
loading 20 μL of each reaction on to a 1% agarose gel which was then electrophoresed at
100 volts for 30 minutes and 50 volts for one hour (Zhang and Lu 2015).
Horseradish peroxidase competition assay for DPPrx1
71

A 0.5 mM HRP stock solution was prepared by dissolving HRP in 25 mM
standard Prx buffer, pH 7.0 (25 mM potassium phosphate and 1 mM, EDTA [pH
7.0])(Nelson and Parsonage 2011). The final concentration of HRP working stock was
determined by reading the absorbance using a SmartSpec™ Plus Spectrophotometer
FilterMax F5 Multi-Mode Microplate Reader (Bio-Rad) at 403 nm. As well, a cuvette
with a 1 cm pathlength and a molar extinction coefficient of 1.02 x 105 M-1cm-1 were
used. Aliquots were then stored at -80°C. To reduce purified DPPrx1 (active
conformation), it was mixed with 10mM of dithiothreitol (DTT) and incubated at room
temperature for 1 hour. From the reduced peroxiredoxin, five different peroxiredoxin
working stocks were prepared with concentrations of 2 μg/ml, 4 μg/mL, 6 μg/ml, 8
μg/mL, and 12 μg/mL.
2.25 μL of HRP stock solution was added to each well of the plate and DPPrx1
and MilliQ-grade water were then added to achieve the desired final DPPrx1
concentration and a total reaction volume of 150 μL. The reactions for each concentration
was run in triplicate. To initiate the reaction between HRP and H2O2 , 10 μL of 45 μM
H2O2 was added to each well of the plate. Due to the instability of compound 1, the
absorbance at 403 nm was then measured within 30 seconds of adding H2O2 in order to
determine the extent to which HRP compound 1 had formed. The mixture was pipetted
up and down and care was taken to prevent the formation of bubbles and the absorbance
at 403 nm was recorded (Nelson and Parsonage 2011).
DPPrx1 rate constant calculation
The proportion of H2O2 reacting with horseradish peroxidase was determined
from the initial observed absorbance (ΔAobvs) reading at 403 nm. The concentration of

72

DPPrx1 is proportional to the difference between that observed absorbance (ΔAobvs) of
each sample and the maximum change in absorbance (ΔAmax) at 403 nm (ΔAmax -ΔAobvs)
after adding DPPrx1. The ΔAmax can be calculated from averaging all the absorbance
readings from the wells containing 0 mg/mL of peroxiredoxin (i.e. no inhibition of the
HRP H2O2 reaction). Fractional HRP inhibition can then be calculated by the ratio of
H2O2 reacting with horseradish peroxidase to H2O2 reacting with peroxiredoxin: ([ΔAmaxΔAobvs]/ ΔAobvs). Therefore, based on this formula and the rate equations for
peroxiredoxin and HRP’s reactions with H2O2 we can generate equation 1.
[ΔAmax − ΔAobvs]
[(Px3 Px3)]
=
012 4567 )
[(HRP HRP)]
ΔAobvs

This equation was reorganized to solve for kPx3, allowing for a k-value to be calculated
for each concentration of DPPrx1.
9

ΔAmax − ΔAobvs
: [(HRP HRP)] = [(Px3 Px3)] 012 4567 )
ΔAobvs

The kPx3 values were plotted against the DPPrx1 concentrations and a linear regression
was performed. The generated slope is equivalent to the second order rate constant for
DPPrx1. Based on values documented by Nelson et al. (2008), the kHRP used in these
calculations was 1.8 × 107 M-1 s-.1
pKa analysis of DPPrx1
In order to determine the functional pKa of DPPrx1, the HRP competition assay
was conducted at seven different pH values (4.5, 5, 5.5, 6, 6.5, 7, 7.5) with each
concentration of DPPrx1 again being run in triplicate. The previously described DPPrx1
expression protocol was conducted again with purified DPPrx1 eluted and stored in
elution buffers at each of the desired pH values. To achieve the desired pH values for the

73

elution buffer, pH was measured using a Cyberscan pH 1100/2100 pH calibrator (Eutech
Instruments) and adjusted using 1M hydrochloric acid (pH 2.1). The only difference in
the protocol for the horseradish competition assay used for pKa determination was that
1.8 × 107 M-1 s-1 was only used as the kHRP value for pH values of 5 and higher. For pH
values less than 5, the k HRP was calculated using equation 3.
KHRP =

1.78 x 107 M-1 s-1
012 4567 3)
[H+] [H+]
+ K +K K
2
1 2

The K-values used for these calculations in Equation 3, as recommended by
Nelson et al. (2008), were K1 = 5.64 X 10-4 and K2 = 1.29 x 10-4. The resulting second
order rate constants were then plotted against their associated pH values and fitted
directly to Equation 4 using CurveExpert Professional Software v.2.6.5 (Hyams 2018).
Y=

[ A x 10 pH)+ B x 10 pKa)]
10pKa + 10pH

012 4567 )

In Equation 4, A indicates the upper plateau from the plotted data associated with
the higher pH values, and B is the lower plateau from the plotted data associated with the
lower pH values. Y corresponds to the ε 240 and was calculated from the amount of protein
in solution as described using the Bradford assay (Nelson et al. 2008). The HRP
competition assay was conducted three times at each of the pH values being investigated,
with the exception of pH 4.5 (run once), pH 5 (run in duplicate), and pH 7.5 (run in
duplicate) where it was difficult to generate enough protein yield to run the assays in
triplicate.
DPPrx1 Hyperoxidation analyses
To determine whether the conformation of reduced DPPrx1 changed when
exposed to higher concentrations of H2O2, a SDS-PAGE was performed under
74

nonreducing conditions. First DPPrx1 was reduced with 10mM of dithiothreitol (DTT)
and incubated at room temperature for 1 hour. Nine 5 μM aliquots of DPPrx1 were
prepared and each was mixed with a different concentration of H2O2 ranging from 0–0.96
mM. These reactions were then mixed with 20-40 μL of SDS loading buffer (Laemmeli’s
solution) and boiled for ten minutes and then run on 4-12% bis/trispolyacrylamide gels
for 1.5 hours at 125 V (Peskin et al. 2013).
Xanthine Oxidase SOD Assay
In order to functionally characterize DPSOD1, a water-soluble tetrazolium (WST-1)
salt competition assay with xanthine oxidase and tetrazolium was conducted using the
WST-1 SOD assay kit (Sigma-Aldrich), scaled for microplate readers. Bovine SOD
standards were prepared using superoxide dismutase from bovine erythrocytes () in the
following concentrations: 200 U/mL, 100 U/ml, 50 U/mL, 20 U/mL, 10 U/mL, 5 U/mL, 1
U/mL, 0.1 U/mL, 0.05 U/mL, 0.01 U/mL, and 0.001 U/mL. An enzyme working solution
containing xanthine oxidase, was prepared by diluting 15 µL of enzyme stock solution
with 2.5 mL of dilution buffer. Both solutions were included with the Sigma-Aldrich kit.
WST working solution was also prepared by diluting 1 mL working stock solution in 19
mL of dilution buffer. Both the enzyme working solution and working solution were
stored for long-term use (up to one month) at -20°C and 4°C respectively.
The DPSOD1 assay and the subsequent inhibition rate (%) calculations were
conducted using the protocol provided with the WST-1 plate SOD assay kit (SigmaAldrich). The components of the sample, standard and blank solutions were made as
described in Table 2 using a multichannel pipette and all were run in triplicate.

75

Absorbance readings at 560 nm were obtained using a FilterMax F5 Multi-Mode
Microplate Reader.
Table 3.2 - The proportion and type of reagent in each SOD sample and blank in the
Sigma-Aldrich WST-1 plate assay
Sample solution
ddH2O
WST working solution
Enzyme working solution
Dilution buffer

Sample
20 µL
–
200 µL
20 µL
–

Blank 1
–
20 µL
200 µL
20 µL
–

Blank 2
20 µL
–
200 µL
–
20 µL

Blank 3
–
20 µL
200 µL
–
20 µL

The assay was performed with 1ng, 2ng, 4 ng, 6 ng, and 8 ng of DPSOD1. Each
concentration was run in triplicate, and the mean percent inhibition for each DPSOD1
concentration was determined. These mean percent inhibition values were plotted against
the mass of SOD used, and an exponential curve was fitted to the data using CurveExpert
Professional Software (Equation 5).
Y = a

– e-bx)

(Equation 5)

From these plots the amount of DPSOD1 and bovine SOD that produced 50%
inhibition of xanthine oxidase (IC50) or one SOD unit was determined. The activity of
DPSOD1 can then be expressed in terms of its IC50 value or in terms of bovine SOD
equivalents where the final value is expressed as a ratio of the DPSOD1 IC50 value to the
Bovine SOD IC50 value (Peskin and Winterbourn 2017).
3.6 Results
DNA cleavage by the metal-catalyzed oxidation system (MCO)
A MCO system was used to establish the antioxidant-based ability of DPRX1 to
protect DNA from cleavage, in an investigation of the likely physiological role of this
76

protein in the cellular context. The mixture of DTT and Fe3+ produces DNA damaging
free radicals, which degrade the supercoiled form (SF) of DNA, producing the more
linear nicked form (NF) (Figure 3.5). The SF form travels further down the gel compared
to the NF form. If DPPrx3 has antioxidant activity, a brighter SF band should be
observed in the mixtures with a higher concentration of DPPrx3 compared to those with a
lower concentration (Zhang and Lu 2015). In the wells with 0 μg/mL of peroxiredoxin,
there was no distinct lower band visible, only a bright upper band. However, as the
concentration of DPPrx1 increased the intensity of the upper NF band decreased and the
intensity of the lower SF band increased as a result of more of the SF form remaining
intact (Figure 3.5).

NF!
SF!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!0!!!!!!!!!!!!!25!!!!!!!!!!!!50!!!!!!!!!!!75!!!!!!!!!!100!!!!!!!!200!!!!!!!!!!!–!!!!!!!!!!!!!!!–!!!!!!!!!!!!!–!!!!!!!!!!!!!!!–!!!!!!!!!!!DPPrx1(μg/mL)!

!!!!!!!!!!!!!!!!!!!!!!!!!!!+!!!!!!!!!!!!!!+!!!!!!!!!!!!!!!+!!!!!!!!!!!!!+!!!!!!!!!!!!!+!!!!!!!!!!!!!+!!!!!!!!!!!!!+!!!!!!!!!!!!!!!+!!!!!!!!!!!!!+!!!!!!!!!!!!!!!+!!!!!!!!!!!DTT!
!!!!!!!!!!!!!!!!!!!!!!!!!!!+!!!!!!!!!!!!!!+!!!!!!!!!!!!!!!+!!!!!!!!!!!!!+!!!!!!!!!!!!!+!!!!!!!!!!!!!+!!!!!!!!!!!!!–!!!!!!!!!!!!!!!+!!!!!!!!!!!!!+!!!!!!!!!!!!!!!–!!!!!!!!!!!FeCl3!
!!!!!!!!!!!!!!!!!!!!!!!!!!!+!!!!!!!!!!!!!!+!!!!!!!!!!!!!!!+!!!!!!!!!!!!!+!!!!!!!!!!!!!+!!!!!!!!!!!!!+!!!!!!!!!!!!!+!!!!!!!!!!!!!!!+!!!!!!!!!!!!!–!!!!!!!!!!!!!!!+!!!!!!!!!!!pUC19!

Figure 3.5 - Antioxidant activity of DPPrx1. DTT and Fe3+ were mixed to generate free
radicals and incubated for 1 hour for 37°C. Afterwards pUC19 plasmid DNA (500 ng)
and DPPrx1 in concentrations ranging 0-200 µg/mL were added to the reaction and
incubated for another 4 hours at 37°C. The resulting reaction mixes were run (100 volts
for 0.5 hours, followed by 50 volts for 1 hour) on a 1.0% agarose gel stained with
ethidium bromide. NF and SF indicate the nicked and supercoiled plasmid DNA
respectively.
Second order rate constant for DPPrx1
To further characterize the enzymatic activity of DPPrx1, a competition assay
assessing the ability of DPPrx1 to outcompete horseradish peroxidase for H2O2 was
employed. HRP has its maximum absorbance at a wavelength of 403 nm. Therefore, the
77

degree to which HRP compound 1 production was inhibited was first determined by
measuring the absorbance at 403nm with increasing concentrations of DPPrx1. The
difference between these values and the end point absorbance at 403 nm for reactions
containing only HRP and H2O2 was then determined (Figure 3.6; Figure 3.7).

Decrease in Absorbance at 403 nm ±1 SE

0.12
0.1
0.08
0.06
0.04
0.02
0

0

2

4

DPPrx1 [μM]

6

8

12

Figure 3.6 - Formation of HRP compound 1 in varying concentrations of DPPrx1, as
determined by the difference obtained by subtracting absorbance at 403nm of H2O2
reacting with just HRP (∆AObs) and that of H2O2 reacting with HRP in the presence of
varying concentrations of DPPrx1 (∆Amax-∆AObs). Error bars indicate standard error
for samples that were run in triplicate with protein from three separate DPPrx1
inductions.

78

1600

{[F/(1-F)]}kHRP(HRP)s-1 ±1 SE

1400

y = 98.479x + 66.083
R² = 0.9705

1200
1000
800
600
400
200
0

0

2

4

6

DPPrx1 [μM]

8

10

12

14

Figure 3.7 - Determination of second-order rate constant (9.8 x 107 M-1s-1) for the
reaction of reduced DPPrx1 with H2O2 through competition with horseradish peroxidase.
The second order rate constants were calculated using Equation 1 and were plotted
against their associated DPPrx1 concentration and fit by linear regression. Samples were
run in triplicate with protein from three separate DPPrx1 inductions, and the calculated
second order rate constants were averaged. The error bars represent standard error.
As the concentration of DPPrx1 increased, the absorbance at 403 nm decreased
(Figure 3.6). The difference in absorbance before and after DPPrx1 was added and was
used to calculate the fractional inhibition of each reaction mixture, and a linear regression
was performed on the plot of fractional inhibition versus DPPrx1 concentration. The
resulting slope provided a second order rate constant of 9.8 x 107 M-1s-1 for DPPrx1
(Figure 3.7).
pKa analysis of DPPrx1
To assess the pH dependence of DPPrx1 with H2O2, the HRP competition assay
was again employed but across a pH range. The resulting second order rate constants
were plotted against their corresponding pH values (Figure 3.8).

79

Second order rate constants for DPPrx1

120
100
80
60
40
20
0

-20

4.3

4.8

5.3

5.8

pH

6.3

6.8

7.3

Figure 3.8. - Determination of pKa (5.4213) for reduced DPPrx1 with H2O2. Second order
rate constants for the reaction of H2O2 with DPPrx1 were plotted against the pH in which
they were generated; data was fit to Equation 2 as described in the methods. The number
of points at each pH is the number of replicates at that particular pH.
As the pH for the assay increased, the resulting k values also increased. After a
pH of 6 the rate constants leveled out and were relatively similar. Overall, a functional
pKa value of 5.4 was determined from fitting the data to Equation 2 (Figure 3.8).
Hyperoxidation analyses for DPPrx1
In order to determine the structure of DPPrx1 under differing oxidative stress
conditions, reduced DPPrx1 was mixed with varying concentrations of H2O2 and the
resulting band patterns were compared via SDS-PAGE (Figure 3.9). At H2O2
concentrations of 0.02 mM and 0.04 mM, three bands were generated: a major band at
~40 kDa, a smaller band just underneath it, and another minor band at ~22 kDa. At H2O2
concentration of 0 mM and above 0.06 mM, only two bands formed, a major band at ~40
kDa and a minor one at ~22 kDa (Figure 3.9).

80

Figure 3.9. Nonreducing SDS-PAGE analysis of purified, reduced DPPrx1. 5 µM of
DPPrx1 was mixed with varying concentration of H2O2 (0–0.96 mM). 1 kb ladder is in
lane 1.
WST-1 assay on DPSOD1
In order to assess the relative activity of DPSOD1, an inhibition curve was
generated form varying DPSOD1 concentrations using the WST-1 SOD assay (Figure
3.10). From the inhibition curves, the amount of DPSOD1 that results in 50% inhibition
of WST-1 (IC50) reduction was determined to be 0.95 ng (Figure 3.10). This value was
less than the amount of Bovine SOD, which was 1.62 ng. As the activity of bovine SOD
is often used a common reference point when characterizing SOD activity, the activity of
DPSOD1 can be expressed as a ratio of its IC50 to that of the bovine standard – 1.62 ng
per 0.95 ng of protein.

81

Inhibition of WST-1 reduction (%)
±1 SE

120.00
100.00
80.00
60.00
40.00
20.00
0.00
-0.50
-20.00

1.50

3.50

5.50

7.50

9.50

DPSOD1 (ng)

100.00
80.00
60.00

±1 SE

Inhibition of WST-1 reduction (%)

120.00

40.00
20.00

0.00
-0.50

0.50

1.50

-20.00

2.50

3.50

4.50

5.50

6.50

7.50

Bovine SOD (ng)

Figure 3.10 - (a) Inhibition percentages of WST-1 reduction for differing concentrations
of DPSOD1, fitted to an exponential curve to generate an inhibition curve. (b) Inhibition
percentages of WST-1 reduction for differing concentrations of bovine Cu, Zn-SOD
fitted to an exponential curve to generate an inhibition curve. Each point is the mean
value for the three wells containing that specific standard, and the error bars represent
standard error.
3.7 Discussion
MCO DNA protection assay
One of the key physiological roles of peroxiredoxin is to mitigate the damaging
effects of H2O2, as it is particularly damaging to proteins, lipids, and most notably DNA
82

(Mason 2016; Dmochowska-Ślęzak et al. 2016). As demonstrated in the MCO system,
when DPPrx1 was present in higher concentrations, the accumulation of nicked DNA
was minimized, supporting that DPPrx1 possesses antioxidant activity (Figure 3.5). This
is consistent with the previously mentioned work done by Li et al. (2016) looking at a
typical 2-cysteine peroxiredoxin from the pine wood nematode, which lives in a similar
host environment. The results from this MCO experiment support the important
antioxidant role of DPPrx1 for the mountain pine beetle. Preventing this kind of
molecular damage would be essential to the success of the mountain pine beetle when
first entering the phloem and surviving the flood of defensive compounds produced by
the tree that can initiate the generation of free radicals (Mason 2016).
Peroxiredoxin functional assay
The second order rate constant generated for DPPrx1 with H2O2 was 9.8 x 107 Ms (Figure 3.6). This value is two orders of magnitude higher than the rate constant

1 -1

reported for human typical 2-cysteine peroxiredoxin, 4.9 x 105 M-1s-1 (Carvalho et al.
2017). However, it is more comparable to another human peroxiredoxin, 4 x 107 M-1s-1
(Ogusucu et al. 2007), and to those found in Salmonella typhimurium, AhpC - 2.3 x 107
M-1s-1 (Parsonage et al. 2008), and S. cerevisae, Tsa1 - 2.2 × 107 M−1 s −1 and Tsa2 - 1.3 ×
107 M−1 s −1 (Nelson et al. 2008). However, of these five peroxiredoxins, DPPrx1 has the
highest second order rate constant, indicating that DPPrx1 is more reactive with H 2O2
than these other peroxiredoxins. The reactivity of the protein likely plays a key part in
mountain pine beetle survival. This high reactivity would better enable the protein to
protect the cell against oxidative damage, but also more readily participate in any
hydrogen peroxide-mediated cellular signaling.

83

The horseradish competition assay is a useful tool to elucidate the reactivity of
DPPrx1 with H2O2 but cannot be used to study the relationship between DPPrx1 and
organic peroxides. There are several proposed signal transduction pathways that use
peroxiredoxin as the initial messenger that convey the message through both covalent and
noncovalent interactions (Ogusucu et al. 2007). Therefore, DPPrx1 is likely involved in
other pathways, but future research would be required to determine the particular
pathways, protein interaction partners, and DPPrx1’s reactivity with the associated target
molecules.
One such study would involve a variant of the horseradish peroxidase competition
assay that could be used to assess the role of DPPrx1 in other pathways as described by
Peschenko and Shichi (2001). In this approach, another competition assay called the
selenium glutathione peroxidase/glutathione reductase/NADPH coupled assay is
incorporated into the protocol. Initially, peroxynitrite and organic peroxides are mixed
with the peroxiredoxin of interest. If the peroxiredoxin has a high affinity for the organic
peroxides, then there should be a reduction in free organic peroxides in the mixture.
Those free organic peroxides can be by quantified with the glutathione
peroxidase/glutathione reductase/NADPH coupled assay. The difference in organic
peroxides can then be used to generate a second order rate constant between
peroxiredoxin and the organic peroxides (Ogusucu et al. 2007; Peshenko and Schichi
2001). Potentially, this approach could be employed with DPPrx1 to better assess the role
of this protein in other detoxification pathways used by the mountain pine beetle, with
more ecologically relevant substrate contexts.
Peroxiredoxin pKa analysis

84

DPPrx1 has a functional pKa of 5.4, which is consistent with the pKa value for
Tsa1 from S. cerevisae, which also has a pKa of 5.4. This pKa is higher than that of other
proteins that have thiolate containing active sites, such as E. coli DsbA at 3.5. However,
the pK a for DPPx1 is lower than that of free cysteine (6.7–7.1). A pKa value lower than
that of free cysteine is important because at a pKa of 6 there would be 93% deprotonation
of the cysteine at a pH of 7, but at a pH of 8.5 only 3% of the cysteine would show
deprotonation (Nelson et al. 2008). As the deprotonated thiolate form is much more
reactive with H2O2, having a pKa below 6 highly increases the reactivity of the
peroxiredoxin protein. However, the impact this would have on the overall function of
the protein is somewhat balanced by the fact that once the thiolate is formed, the
nucleophilicity diminishes as the pK a value decreases (Nelson et al. 2008).
Physiologically this becomes relevant, as DPPrx1 is a cytosolic protein. The cytoplasm of
the cells is approximately neutral, so the combination of the lower pKa value and the
decreased nucleophilicity after thiolate formation, allows for it to be able to carry out
antioxidant activities in the cytosol (Nelson et al. 2008).
Hyperoxidation of DPPrx1
In the absence of H2O2, DPPrx1 ran as a major and minor band, likely
corresponding to the ~22 kDa monomer and the ~43kDa dimer, respectively. Initially as
the H 2O2 concentration increased, the homodimer (both the single disulfide and the
double disulfide forms) became the major bands, as this is the functionally relevant
conformation of the protein. At 40 µM, the monomer once again becomes the dominant
band (Figure 3.9), corresponding to the hyperoxidized form (Figure 3.1) (Peskin et al.
2013).

85

The hyperoxidation of typical 2-cysteine peroxiredoxins in insects has not been
characterized. It was observed that DPPrx1 underwent hyperoxidation at a lower
concentration of H2O2 (~0.06 mM) than human peroxiredoxin (~0.12 mM). This can
potentially be attributed to the higher rate constant of DPPrx1 (Figure 3.7) and the
presence of hyperoxidation sensitivity motifs (Figure 3.2). This hyperoxidized
peroxiredoxin has been found to serve many signalling purposes in other organisms
suggesting that hyperoxidized DPPrx1 could play a similar role in the mountain pine
beetle during host colonization (Delaunay 2002). This signal transduction could be
essential during host colonization when mountain beetles are undergoing considerable
oxidative stress which would require more proteins to mitigate the damaging impact of
free radicals like catalase and glutathione peroxidase which could more effectively
breakdown H2O2.
α-pinene is a terpene produced by pine trees and is used as a defensive toxin
against mountain pine beetles during colonization (Seybold et al. 2006). However, the
mountain pine beetle has evolved a mechanism to incorporate the α-pinene molecule into
the structure of its primary aggregation pheromone, trans-verbenol (Chiu et al. 2019).
Therefore, a compound that was originally produced by trees to withstand mountain pine
beetle attacks has been coopted by the mountain pine beetle to serve as a key component
for overwhelming the tree defenses. Potentially, a similar situation could be occurring
with H2O2, where a compound originally produced to defend against mountain pine beetle
attack has been incorporated by the beetle’s defense systems as a means for better
overcoming the host tree’s defenses.

86

H2O2 is produced in high quantities during the breakdown of phenols and terpenes
that occurs during tree colonization and can freely cross the plasma membrane of cells.
The results from the hyperoxidation experiments support that when exposed to high
concentration of H2O2, DPPrx1 undergoes a hyperoxidized change in oligomeric state. As
discussed by Delaunay (2002), hyperoxidized peroxiredoxin acts as a signaling molecule
in other organisms. One of the most likely signaling targets for hyperoxidized DPPrx1
would be proteins that lead to the expression of oxidative stress proteins like glutathione
peroxidase and catalase that typically have a higher affinity for H2O2. That said, it is
likely that DPPrx1 is involved in many different signaling pathways because there are
196 unique protein interactors for human peroxiredoxin 1 (Ledgerwood et al. 2017)
Ledgerwood et al. (2017) have proposed three different signal transduction
pathways caused by hyperoxidized peroxiredoxin. The first proposed mechanism is
known as the ‘disulfide relay’, where hyperoxidized peroxiredoxin triggers disulfide
formation in a targeted protein or proteins, which then proceed to pass on this signal
through other proteins (Okazaki et al. 2007; Ichijo et al. 1997). This signal transduction
was original studied in S. cerevisiae but there is evidence that supports its presence in
mammalian cells as well. As reviewed in Ledgerwood et al. (2017), this mechanism is
carried out between thiol containing proteins.
The second mechanism that has been proposed for peroxiredoxin signaling is noncovalent interactions of the hyperoxidized peroxiredoxin with its target proteins. These
mechanisms would allow the peroxiredoxin to pass the signal on to proteins without thiol
groups. The exact mechanism in which this signal is passed on is not fully known, but it
appears peroxiredoxins are able to impact non-redox proteins like phosphatase, a tensin

87

homologue (PTEN), and epidermal growth factor (EGF) without changing their redox
states (Kwon et al. 2004; Lee et al. 2002). These proteins have been referred to as
peroxiredoxin interacting partners and identifying them within the mountain pine beetle
is key to elucidating the exact signaling roles of DPPrx1 (Ledgerwood et al. 2017).
The third type of peroxiredoxin signal transduction is where the hyperoxidized
peroxiredoxin is not actually acting as the signal, but because it is catalytically inactive,
the subsequent build-up of H 2O2 is acting as the initial signal. This mechanism is referred
to as the floodgate model and has been proposed as playing a key part during the cell
cycle (Wood et al. 2003). In this specific scenario, it is not the build-up of H 2O2 that
causes the inactivation of the peroxiredoxin but rather inhibitory phosphorylation by
another protein (Ledgerwood et al. 2017; Wood et al. 2003). This allows for the build-up
of H2O2 and the oxidation and inhibition of a centrosome-bound phosphatase. This would
prevent the early degradation of mitotic activators and allows for the cell cycle to
continue (Lim et al. 2015). However, the exact mechanism for how H2O2 channels and
accumulates at the centrosome is still not understood (Woo et al. 2010).
Each of the three previously mentioned pathways is dependent on involvement of
a hyperoxidized peroxiredoxin. Therefore, the fact that DPPrx1 shows sensitivity to
hyperoxidation (Figure 3.9) indicates that it could potentially be a part of many cellsignaling systems.
Superoxide dismutase functional assay
Upon conducting a WST-1 assay on DPSOD1, an IC50 value of 0.95 ng was
generated. This is lower than that of the SOD obtained from bovine erythrocytes -1.62 ng
(Figure 3.10). In addition, this IC50 is much lower than that of a Cu, Zn SOD from S

88

cerivisiae, that has an IC50 value of 1.5 μg (Peskin and Winterbourn 2017). Therefore, this
suggests that DPSOD1 may be a relatively more reactive SOD. This is similar to the
higher rate constant generated for DPPrx1 where the increased reactivity could be
attributed to the unique life history of the mountain pine beetle, immersing itself in toxic
compounds during host tree colonization. Other insects that are found in environments
with a high concentration of free radicals, such as Tribolium castaneum and
Reticulitermes speratus, also appear to have more reactive SOD (Tasaki et al. 2018; Ferro
et al. 2017). Therefore, it is likely that the life history of mountain pine beetle requires a
highly reactive suite of oxidative stress proteins to successfully survive the host tree’s
defenses
3.8 Conclusions and Future Directions
The findings of this study support the likely physiological role of peroxiredoxin
within the mountain pine beetle, as a key part in preventing biomacromolecule damage,
such as DNA nicking, due to the presence of reactive oxygen species. As well, the
functional profile of DPPrx1 was further expanded through characterizing the reaction
constant and pKa value for DPPrx1 with a horseradish peroxidase competition assay. A
potential aspect of DPPrx1’s functionality that was not tested for in this study is its ability
to mitigate H 2O2 levels independent of its functional cysteines. This catalytic activity is
similar to that of catalase and associated with a GVN motif located in the active site (Sun
et al. 2017). This particular motif is present in DPPrx1 making this protein a suitable
candidate for further tests determining whether DPPrx1 also has this catalytic function
(Sun et al. 2017).

89

Furthermore, my findings support that DPPrx1 is sensitive to hyperoxidation; this
susceptibility to hyperoxidation makes sense with the relatively high second order rate
constant that we determined for the enzyme. This potential role in signal transduction
could be essential when the mountain beetle cells are undergoing considerable oxidative
stress, as it could trigger the expression of other oxidative stress proteins as discussed
previously. However, there are many other signaling pathways hyperoxidized
peroxiredoxin is believed to be a part of (i.e. the H2O2 floodgate model, noncovalent
signaling interactions, and the disulfide relay) (Ledgerwood et al. 2017). Therefore, it is
likely that cellular signaling is the primary role of DPPrx1 during host colonization, even
more so than its role as an antioxidant. At this point, the signaling roles of peroxiredoxin
in the mountain pine beetle are mainly speculative, making it an area for future research.
However, characterizing some of the other pathways and molecules that may be
interacting with peroxiredoxin may be challenging as currently most approaches have
only been conducted with high throughput mass spectrometric-based methods and
overexpression of tagged peroxiredoxins (Portillo-Ledesma 2018). The former approach
is highly technical and the second is problematic as the tag often impacts the activity of
the peroxiredoxin. Conducting a study of this type could require designing a novel
experimental approach.
In addition to the different pathways that hyperoxidized DPPrx1 may be involved
in, the unique active site of this enzyme is another potential focal point for future
investigation. As discussed in the introduction, the glycine residues that are present in the
motif A’s of human and bacterial Prx1s are not present in DPPrx1 (Bolduc et al. 2018).
There is a potential link between the presence of these lysines and the high reactivity of

90

DPPrx1, particularly when considering why DPPrx1 shows such a high sensitivity to
hyperoxidation, despite missing the YR motif typically associated with 2-cysteine
peroxiredoxin being prone to hyperoxidation (Figure 3.2; Figure 3.9).
An interesting line of investigation would be to compare the DPSOD1 IC50 value
to some of the other mountain pine beetle SODs. Most notably DPSOD3, which is an
extracellular protein, meaning it is secreted into the periplasmic space. Other studies
support that extracellular SODs are often secreted to break down superoxide before it can
enter the cell and cause any damage either on its own or from its breakdown and
subsequent formation of H 2O2. In addition, because there is evidence that extracellular
SOD may also be involved in redox signaling pathways (Fukai & Fukai 2011), it would
be interesting to see how the difference in cellular localization and function might impact
the overall activity of these two SOD proteins.

91

Chapter 4: Conclusions
Mitigating the destructive impacts of reactive oxygen species (ROS) is essential
to the mountain pine beetle’s success during host colonization. A better understanding of
these oxidative stress proteins and their unique functionality will lead to an overall
increase in the understanding of bark beetles and the molecular mechanisms underlying
host detoxification. Therefore, findings from this study could potentially be applied to
other bark beetles like, Dendroctonus rufipennis, the spruce beetle or other economically
important Dendroctonus spp. Spruce beetle currently of growing concern in British
Columbia as its population is increasing in some areas, considerably impacting the local
forest ecosystems and economies (Campbell et al. 2019) and various other bark beetles
are also important in BC and elsewhere in the mountain west.
In this study I selected five key oxidative stress proteins from the mountain pine
beetle – superoxide dismutase (SOD), catalase, glutathione peroxidase (GPx), and
peroxiredoxin (Prx) – for functional and phylogenetic analyses. Including the sequence
and phylogenetic data in this study enabled me to compare the active sites of these
proteins to those of other insect species, thus providing a molecular context for the
functional data. From the generated sequence alignments, I found a variation in the YF
hyperoxidation motif of DPPRx1 (Chapter 2). A non-conserved amino acid substitution
in such a key reactivity motif could have a considerable impact on that protein’s
reactivity.
In fact, this impact on reactivity was potentially observed in this study as the
generated second-order rate constant was 9.8 x 107 M-1s-1, which was two orders of
magnitude higher than the human homolog (Ogusucu et al. 2007). As well, DPPrx1

92

displayed a higher susceptibility to hyperoxidation compared to human Prx proteins. This
is notable as hyperoxidized Prx is known to act as a signal for the expression of other
oxidative stress proteins (Delaunay 2002). This increase in activity and susceptibility to
hyperoxidation indicates that DPPrx1 could be a key component of the mountain pine
beetle primary antioxidant system. Future studies could investigate DPPrx1 homologs in
other bark beetles, such as spruce beetle or Ips spp., to see if they have the same unique
FF motif and function similarly. Additionally, one could do site-specific mutagenesis,
changing the motif to YF, and then conduct comparative kinetic assays. These DPPrx1
features could be unique to bark beetles and could have evolved to enable them to better
overcome the host tree’s defenses. If this is the case, disrupting these proteins and the
molecular pathways that they are part of could be a potential target for future pest
management strategies.
To gather this functional data, I obtained the coding region for each protein from
a mountain pine beetle cDNA library and inserted into an expression vector using the
Gateway Cloning ProcedureTM. This approach generated enough DPPrx1 and DPSOD1
to proceed with functional analyses. Competition assays were used to functionally
characterize these proteins, a xanthine oxidase assay for DPSOD1 and a horseradish
peroxidase for DPPrx1. Competition-based assays do not directly measure the activity of
a protein but do so indirectly through the disruption of a competing protein’s reaction,
and as a result, a reaction constant can be successfully generated.
In order to acquire a sufficient protein yield for enzymological characterization, a
systematic stepwise approach had to be employed. This was effective, but very time
consuming. Therefore, these finished protocols could be very useful to other researchers

93

undertaking functional analyses on similar proteins. At the very least, they would provide
a general framework. These protocols could be used in functional analyses related to a
variety of research fields (agriculture, medicine, ecology, etc.).
Despite generating purified recombinant enzyme for DPPRx1 and DPSOD1
characterization, I was unable to do so for the other oxidative stress proteins being
analyzed. Most of these were expressed as insoluble inclusion bodies. There are a number
of solutions that future researchers could employ to remedy this problem. Wingfield
(2015) discussed denaturing misfolded proteins and refolding them under the conditions
optimal for that specific protein. However, the yield is usually reduced (5%-20%) as a
result of this procedure.
Another approach would be to use an expression system other than E. coli. As
these are eukaryotic proteins, potentially a eukaryotic expression system could be
preferable. Although complex molecular processes vary considerably amongst eukaryotic
organisms, protein modifications like glycosylation, phosphorylation, and fatty acid
addition can be better facilitated in eukaryotic systems than prokaryotic ones (Possee
1997; Giesse et al. 1996). Cereghino and Clegg (1999) found success with yeast when
trying to express proteins that had been clustering into inclusion bodies. As mentioned in
the introductory chapter of this thesis, insect cell lines are also an option and would best
replicate the cellular conditions of the mountain pine beetle, and insect cell lines can also
be quickly scaled up, increasing the total protein yield (Grennan et al. 1996).
Another method to promote proper protein folding, particularly for any of the
extracellular proteins (DPSOD2 and DPPrx6) and membrane bound proteins (DPPrx5),
would be to fuse a signal sequence to the recombinant proteins. These signal sequences
94

are key for directing proteins to their respective organelles and cellular regions. Zhang et
al. (2018) compared the effect of a number of signal peptides on a thermostable lipase,
lipBJ10, from Pseudomonas fluorescens. Although there was variation based on the
specific signal peptide that was being used, in general they found that attaching these
signal peptides to the N-terminus increased protein solubility. That said, the success of
this approach is highly dependent, on the particular proteins being expressed and their
associated secretory pathways. Singh et al. (2013) compared the expression yields of a
thioredoxin protein that had two different attached leader sequences to wild types, and
found that in general, those with leader sequences demonstrated decreased
thermostability and increased likelihood for protein aggregation.
Another potential solution to this problem would be to express the associated
chaperone with each protein. For instance, in chapter 2 I found that one of the proteins
that was designated as ‘SOD-like’ was most likely a copper chaperone for SOD (CCS); a
protein which functions to ensure the proper insertion of the copper cofactor into the fully
folded SOD. I supplemented the DPSOD1 inductions with copper and obtained a
sufficient yield of soluble DPSOD1 (Williams et al. 2016). However, the majority of the
protein was still in the insoluble state, and all of the DPSOD2 was insoluble. Potentially,
if the expression vector contained both the DPSOD gene of interest and the CCS protein
then there would be a greater yield of properly folded protein. A study by
Eiamphungporn et al. (2018) compared the co-expression of a Cu/Zn SOD with its
corresponding CCS protein and found that not only was their an increase in the yield of
soluble protein, but that there was also a three-fold increase in specific activity.
Therefore, now that I have identified a potential CCS protein, co-expressing it with
95

mountain pine beetle SOD proteins may not only change the protein yield but also the
functional characterization of the proteins.
The mountain pine beetle is an organism with a unique life history, spending the
majority of its existence in a host tree that defends itself with resin-based toxins.
Therefore, continuing to examine the proteins it uses for detoxification, like the oxidative
stress proteins, will provide further intriguing insights as to what molecular modifications
had to evolve in order for it to not only survive, but flourish in these conditions. As these
functional and bioinformatics protocols become more refined, the more in-depth our
understanding of these systems becomes. This is noteworthy because the better we
understand these symptoms, the better we will be able to predict future outbreak patterns
and create targeted pest management strategies.

96

Works Cited
Ajuwon, O. R., Marnewick, J., & Davids, L. (2015). Rooibos (Aspalathus linearis) and its Major
Flavonoids — Potential Against Oxidative Stress-Induced Conditions. INTEC Open
Science, 171–218. http://dx.doi.org/10.5772/61614
Almagro Armenteros, J. J., Tsirigos, K. D., Sønderby, C. K., Petersen, T. N., Winther, O.,
Brunak, S., von Heijne, G., & Nielsen, H. (2019). SignalP 5.0 improves signal peptide
predictions using deep neural networks. Nature biotechnology, 37(4), 420–423.
https://doi.org/10.1038/s41587-019-0036-z
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. (1990) Basic local alignment
search tool. J. Mol. Biol. 215:403–410. PubMed.
Amid, A., Ismail, N. A., Yusof, F., Salleh, H. M. (2011). Expression, purification, and
characterization of a recombinant stem bromelain from Ananas comosus. Process
Biochemistry, 46(12), 2232–2239.
Asmail, N. (2008). Cloning and characterization of superoxide dismutase from the ectoparasite
Sarcoptes scabiei. Uppsala University.
Ashokan, S. K. V., Mundagunar, D. S., & Mundagunar, Y. D. (2010). Catalase: Phylogenetic
Characterization to explore protein cluster. Journal of Research in Bioinformatics, 1(1),
001-008.
Aukema, B., Carroll, A., Zhu, J., Raffa, K., Sickley, T. A., & Taylor, S. W. (2006). Landscape
level analysis of mountain pine beetle in British Columbia, Canada: spatiotemporal
development and spatial synchrony within the present outbreak. Ecography, 29, 427–441.
Behrens, S., Peuß, R., Milutinović, B., Eggert, H., Esser, D., Rosenstiel, P., Schulenburg, H.,
Bornberg-Bauer, E., & Kurtz, J. (2014). Infection routes matter in population-specific
responses of the red flour beetle to the entomopathogen Bacillus thuringiensis. BMC
genomics, 15(1), 445.
Birben, E., Sahiner, U. M., Sackesen, C., Erzurum, S., & Kalayci, O. (2012). Oxidative stress
and antioxidant defense. The World Allergy Organization journal, 5(1), 9–19.
https://doi.org/10.1097/WOX.0b013e3182439613
Bolduc, J. A., Nelson, K. J., Haynes, A. C., Lee, J., Reisz, J. A., Graff, A. H., Clodfelter, J. E.,
Parsonage, D., Poole, L. B., Furdui, C. M., & Lowther, W. T. (2018). Novel
hyperoxidation resistance motifs in 2-Cys peroxiredoxins. The Journal of biological
chemistry, 293(30), 11901–11912. https://doi.org/10.1074/jbc.RA117.001690
Bonnett, T. R., Robert J. A., Pitt C., Fraser J. D., Keeling C. I., Bohlmann J., & Huber D. P.
(2012). Global and comparative proteomic profiling of overwintering and developing

97

mountain pine beetle, Dendroctonus ponderosae (Coleoptera: Curculionidae), larvae.
Insect Biochem. Mol Biol., 42(12), 890–901. https://doi.org/10.1016/j.ibmb.2012.08.003
Boyd, S. D., Liu, L., Bulla, L., & Winkler, D. D. (2018). Quantifying the Interaction between
Copper-Zinc Superoxide Dismutase (Sod1) and its Copper Chaperone (Ccs1). Journal of
proteomics & bioinformatics, 11(4), 473. https://doi.org/10.4172/jpb.1000473
Broxton, C. N., & Culotta, V. C. (2016). SOD Enzymes and Microbial Pathogens: Surviving the
Oxidative Storm of Infection. PLoS pathogens, 12(1), e1005295.
https://doi.org/10.1371/journal.ppat.1005295
Campbell, E. M., Antos, J. A., vanAkker, L. (2019). Resilience of southern Yukon boreal forests
to spruce beetle outbreaks. Forest Ecology and Management, 433, 52–63.
https://doi.org/10.1016/j.foreco.2018.10.037
Carroll, A.L., Aukema, B.H., Raffa, K.F., Linton, D.A., Smith, G.D., Lindgren, B.S. (2006).
Mountain pine beetle outbreak development: the endemic-incipient transition. Mountain
Pine Beetle Initiative Project #1.03 Working Paper. Canada Forest Services, Pacific
Forestry Centre, Victoria, BC, Canada.
Carvalho, L., Truzzi, D. R., Fallani, T. S., Alves, S. V., Toledo, J. C., Jr, Augusto, O., Netto, L.,
& Meotti, F. C. (2017). Urate hydroperoxide oxidizes human peroxiredoxin 1 and
peroxiredoxin 2. The Journal of biological chemistry, 292(21), 8705–8715.
https://doi.org/10.1074/jbc.M116.767657
Cereghino, G. P., & Cregg, J. M. (1999). Applications of yeast in biotechnology: protein
production and genetic analysis. Current opinion in biotechnology, 10(5), 422–427.
https://doi.org/10.1016/s0958-1669(99)00004-x
Chae, H. Z., Chung, S. J., & Rhee, S. G. (1994). Thioredoxin-dependent peroxide reductase from
yeast. The Journal of biological chemistry, 269(44), 27670–27678.
Chiu, C.C., Keeling, C. I. & Bohlmann, J. (2019) The cytochrome P450 CYP6DE1 catalyzes the
conversion of α-pinene into the mountain pine beetle aggregation pheromone transverbenol. Sci. Rep. 9(1477). https://doi.org/10.1038/s41598-018-38047-8
Clark, E. L., Pitt, C., Carroll, A., Lindgren, B., & Huber, D. P. (2014). Comparison of lodgepole
and jack pine resin chemistry: implications for range expansion by the mountain pine
beetle, Dendroctonus ponderosae (Coleoptera: Curculionidae). PeerJ, 2.
Colinet, D., Cazes, D., Belghazi, M., Gatti, J. L., & Poirié, M. (2011). Extracellular superoxide
dismutase in insects: characterization, function, and interspecific variation in parasitoid
wasp venom. The Journal of biological chemistry, 286(46), 40110–40121.
https://doi.org/10.1074/jbc.M111.288845

98

Corona, M., & Robinson, G. E. (2006). Genes of the antioxidant system of the honey bee:
annotation and phylogeny. Insect molecular biology, 15(5), 687–701.
https://doi.org/10.1111/j.1365-2583.2006.00695.x
Couto, N., Wood, J. L., & Barber, J. (2016). The role of glutathione reductase and related
enzymes on cellular redox homoeostasis network. Free radical biology & medicine, 95,
27–42.
Cox, A. G., Winterbourn, C. C., & Hampton, M. B. (2009). Mitochondrial peroxiredoxin
involvement in antioxidant defence and redox signalling. The Biochemical
journal, 425(2), 313–325. https://doi.org/10.1042/BJ20091541
Cullingham, C. I., Cooke, J. E., Dang, S., Davis, C. S., Cooke, B. J., & Coltman, D. W. (2011).
Mountain pine beetle host-range expansion threatens the boreal forest. Molecular
ecology, 20(10), 2157–2171. https://doi.org/10.1111/j.1365-294X.2011.05086.x
Delaunay, A., Pflieger, D., Barrault, M. B., Vinh, J., & Toledano, M. B. (2002). A thiol
peroxidase is an H2O2 receptor and redox-transducer in gene activation. Cell, 111(4),
471–481. https://doi.org/10.1016/s0092-8674(02)01048-6
Dias, F. A., Gandara, A. C., Perdomo, H. D., Gonçalves, R. S., Oliveira, C. R., Oliveira, R. L.,
Citelli, M., Polycarpo, C. R., Santesmasses, D., Mariotti, M., Guigó, R., Braz, G. R.,
Missirlis, F., & Oliveira, P. L. (2016). Identification of a selenium-dependent glutathione
peroxidase in the blood-sucking insect Rhodnius prolixus. Insect biochemistry and
molecular biology, 69, 105–114. https://doi.org/10.1016/j.ibmb.2015.08.007
DiGuistini, S., Wang, Y., Liao, N. Y., Taylor, G., Tanguay, P., Feau, N., Henrissat, B., Chan, S.
K., Hesse-Orce, U., Alamouti, S. M., Tsui, C. K., Docking, R. T., Levasseur, A., Haridas,
S., Robertson, G., Birol, I., Holt, R. A., Marra, M. A., Hamelin, R. C., Hirst, M., …
Breuil, C. (2011). Genome and transcriptome analyses of the mountain pine beetle-fungal
symbiont Grosmannia clavigera, a lodgepole pine pathogen. Proceedings of the National
Academy of Sciences of the United States of America, 108(6), 2504–2509.
https://doi.org/10.1073/pnas.1011289108
Djuika, C. F., Huerta-Cepas, J., Przyborski, J. M., Deil, S., Sanchez, C. P., Doerks, T., Bork, P.,
Lanzer, M., & Deponte, M. (2015). Prokaryotic ancestry and gene fusion of a dual
localized peroxiredoxin in malaria parasites. Microbial cell (Graz, Austria), 2(1), 5–13.
https://doi.org/10.15698/mic2015.01.182
Dmochowska-Ślęzak, K. , Dmitryjuk, M. , Zaobidna, E. , & Żółtowska, K. (2016). The
antioxidant defense system of Varroa destructor mites facilitates the infestation of Apis
mellifera. Journal of Apicultural Science, 60(1). https://doi.org/10.1515/jas-2016-0010
Dunford. H. B. (1999). Heme Peroxidases. Wiley-VCH, New York. 507 pp.

99

Eiamphungporn, W., Yainoy, S., & Prachayasittikul, V. (2016). Enhancement of Solubility and
Specific Activity of a Cu/Zn Superoxide Dismutase by Co-expression with a Copper
Chaperone in Escherichia coli. Iranian journal of biotechnology, 14(4), 243–249.
https://doi.org/10.15171/ijb.1465
Faccoli, M., Favaro, R., Concheri, G., Squartini, A., & Battisti, A. (2016). Tree colonization by
the Asian longhorn beetle, Anoplophora glabripennis (Coleoptera: Cerambycidae): effect
of habitat and tree suitability. Insect science, 23(2), 288–296.
https://doi.org/10.1111/1744-7917.12192
Ferro, K., Ferro, D., Corrà, F., Bakiu, R., Santovito, G., & Kurtz, J. (2017). Cu,Zn Superoxide
Dismutase Genes in Tribolium castaneum: Evolution, Molecular Characterisation, and
Gene Expression during Immune Priming. Frontiers in immunology, 8, 1811.
https://doi.org/10.3389/fimmu.2017.01811
Fukai, T., & Ushio-Fukai, M. (2011). Superoxide dismutases: role in redox signaling, vascular
function, and diseases. Antioxidants & redox signaling, 15(6), 1583–1606.
https://doi.org/10.1089/ars.2011.3999
Giesse, S., Gram, H., Kleuser, B., & Kocher, H. P. (1996). Eukaryotic Expression Systems: A
Comparison. Protein Expression and Purification, 8(3), 271–282.
https://doi.org/10.1006/prep.1996.0101
Grennan Jones, F., Wolstenholme, A., Fowler, S., Smith, S., Ziemnicka, K., Bradbury, J.,
Furmaniak, J., & Rees Smith, B. (1996). High-level expression of recombinant
immunoreactive thyroid peroxidase in the High Five insect cell line. Journal of molecular
endocrinology, 17(2), 165–174. https://doi.org/10.1677/jme.0.0170165
Gretscher, R., Streicher, P., Strauß, A.S., Wielsch, N., Stock, M., Wang, D., Boland, W., Burse,
A. (2016). A common theme in extracellular fluids of beetles: extracellular superoxide
dismutases crucial for balancing ROS in response to microbial challenge. Scientific
Reports, 6, 24082. https://doi.org/10.1038/srep24082
Guex, N., Peitsch, M. C., & Schwede, T. (2009). Automated comparative protein structure
modeling with SWISS-MODEL and Swiss-PdbViewer: a historical perspective.
Electrophoresis, 30(1), S162–S173. https://doi.org/10.1002/elps.200900140
Gui-Qin, S., Ze, Z., Kun-Lun, J., Kun, Z., Dong-Xu, A., Gang, W., Bao-Long, Z., & He-Nan, Y.
(2014)
Characterization and expression analysis of peroxiredoxin family genes from the
silkworm Bombyx mori in response to phoxim and chlorpyrifos. Pesticide Biochemistry
and Physiology, 114, 24–31. https://doi.org/10.1016/j.pestbp.2014.07.007
Haijema, B. J., Noback, M., Hesseling, A., Kooistra, J., Venema, G., & Meima, R. (1996).
Replacement of the lysine residue in the consensus ATP-binding sequence of the AddA
subunit of AddAB drastically affects chromosomal recombination in transformation and
100

transduction of Bacillus subtilis. Molecular microbiology, 21(5), 989–999.
https://doi.org/10.1046/j.1365-2958.1996.601424.x
Ichijo, H., Nishida, E., Irie, K., ten Dijke, P., Saitoh, M., Moriguchi, T., Takagi, M., Matsumoto,
K., Miyazono, K. and Gotoh, Y. (1997). Induction of apoptosis by ASK1, a mammalian
MAPKKK that activates SAPK/JNK and p38 signaling pathways. Science, 275, 90–94.
Horton, P., Park, K. J., Obayashi, T., Fujita, N., Harada, H., Adams-Collier, C. J., & Nakai, K.
(2007). WoLF PSORT: protein localization predictor. Nucleic acids research, 35, W585–
W587. https://doi.org/10.1093/nar/gkm259
Hyams, D. G., CurveExpert software, http://www.curveexpert.net, 2010.
Inupakutika, M. A., Sengupta, S., Devireddy, A. R., Azad, R. K., & Mittler, R. (2016). The
evolution of reactive oxygen species metabolism. Journal of experimental
botany, 67(21), 5933–5943. https://doi.org/10.1093/jxb/erw382
Janes, J.K., Li, Y., Keeling, C.I., Yuen, M.M.S., Boone, C.K., Cooke, J.E.K., Bohlmann, J.,
Huber, D.P.W., Murray, B.W., Coltman, D.W., & Sperling, F.A.H. (2014). How the
mountain pine beetle (Dendroctonus ponderosae) breached the Canadian Rocky
Mountains. Molecular Biology and Evolution, 31, 1803–1815.
Jiu, M., Li, J., Gao, X., Wang, L., Wang, X., & Liu, S. (2015). Identification and characterization
of two phospholipid hydroperoxide glutathione peroxidase genes from the Mediterranean
species of the whitefly Bemisia tabaci complex. Archives of insect biochemistry and
physiology, 89 1, 54–67.
Jomova, K., Baros, S. & Valko, M. (2012). Redox active metal-induced oxidative stress in
biological systems. Transition Metal Chemistry, 37, 127–134.
https://doi.org/10.1007/s11243-012-9583-6
Job, D., & Dunford, H. (1978). Horseradish peroxidase. XXVIII. Formation and reactivity of the
alkaline form. Evidence for an enzyme–substrate complex in compound 1 formation.
Canadian Journal of Chemistry, 56 (10)
Keeling, C. I., Yuen, M. M., Liao, N. Y., Roderick Docking, T., Chan, S. K., Taylor, G. A.,
Palmquist, D. L., Jackman, S. D., Nguyen, A., Li, M., Henderson, H., Janes, J. K., Zhao,
Y., Pandoh, P., Moore, R., Sperling, F. A., W Huber, D. P., Birol, I., Jones, S. J., &
Bohlmann, J. (2013). Draft genome of the mountain pine beetle, Dendroctonus
ponderosae Hopkins, a major forest pest. Genome Biology, 14(3).
https://doi.org/10.1186/gb-2013-14-3-r27
Klotz, M. G., & Loewen, P. C. (2003). The molecular evolution of catalatic hydroperoxidases:
evidence for multiple lateral transfer of genes between prokaryota and from bacteria into
eukaryota. Molecular biology and evolution, 20(7), 1098–1112.
https://doi.org/10.1093/molbev/msg129
101

Knoops, B., Loumaye, E., & Van Der Eecken, V. (2007). Evolution of the Peroxiredoxins.
Peroxiredoxin Systems.
Knoops, B., Goemaere, J., Van der Eecken, V., & Declercq, J. P. (2011). Peroxiredoxin 5:
structure, mechanism, and function of the mammalian atypical 2-Cys
peroxiredoxin. Antioxidants & redox signaling, 15(3), 817–829.
https://doi.org/10.1089/ars.2010.3584
Kobayashi, Y., Nojima, Y., Sakamoto, T., Iwabuchi, K., Nakazato, T., Bono, H., Toyoda, A.,
Fujiyama, A., Kanost, M., & Tabunoki, H. (2019). Comparative analysis of seven types
of superoxide dismutases for their ability to respond to oxidative stress in Bombyx mori.
Scientific Reports, 9(1):2170.
Kumagai, T., Osada, Y., Ohta, N., & Kanazawa, T. (2009). Peroxiredoxin-1 from Schistosoma
japonicum functions as a scavenger against hydrogen peroxide but not nitric
oxide. Molecular and biochemical parasitology, 164(1), 26–31.
https://doi.org/10.1016/j.molbiopara.2008.11.002
Kwon J, Lee SR, Yang KS, Ahn Y, Kim YJ, Stadtman ER, Rhee SG. Reversible oxidation and
inactivation of the tumor suppressor PTEN in cells stimulated with peptide growth
factors. Proc Natl Acad Sci U S A. 2004 Nov 23;101(47):16419-24. doi:
10.1073/pnas.0407396101. Epub 2004 Nov 8. PMID: 15534200; PMCID: PMC534546.
Latifi, A., Ruiz, M., Jean, R., & Zhang, C. C. (2007). PrxQ-A, a member of the peroxiredoxin Q
family, plays a major role in defense against oxidative stress in the cyanobacterium
Anabaena sp. strain PCC7120. Free radical biology & medicine, 42(3), 424–431.
Laukkanen M. O., Cammarota F., Esposito T., Salvatore M., & Castellone M. D. (2015)
Extracellular Superoxide Dismutase Regulates the Expression of Small GTPase
Regulatory Proteins GEFs, GAPs, and GDI. PLoS ONE 10(3).
https://doi.org/10.1371/journal.pone.0121441
Ledgerwood E. C. , Marshall J. W. , & Weijman, J. F. (2017). The role of peroxiredoxin 1 in
redox sensing and transducing. Archives of Biochemistry and Biophysics, 617, 60–67.
https://doi.org/10.1016/j.abb.2016.10.009
Lee, S. R., Yang, K. S., Kwon, J., Lee, C., Jeong, W., & Rhee, S. G. (2002). Reversible
inactivation of the tumor suppressor PTEN by H 2O2. The Journal of biological
chemistry, 277(23), 20336–20342. https://doi.org/10.1074/jbc.M111899200
Li, Z., Zhang, Q., & Zhou, X. (2016). A 2-Cys peroxiredoxin in response to oxidative stress in
the pine wood nematode, Bursaphelenchus xylophilus. Scientific Reports, 6.
Lim, Y. S., Cha, M. K., Kim, H. K., Uhm, T. B., Park, J. W., Kim, K., & Kim, I. H. (1993).
Removals of hydrogen peroxide and hydroxyl radical by thiol-specific antioxidant protein
102

as a possible role in vivo. Biochemical and biophysical research communications, 192(1),
273–280.
Lim, J. M., Lee, K. S., Woo, H. A., Kang, D., & Rhee, S. G. (2015). Control of the
pericentrosomal H2O2 level by peroxiredoxin I is critical for mitotic progression. The
Journal of cell biology, 210(1), 23–33. https://doi.org/10.1083/jcb.201412068
Loew O. (1900). A New Enzyme OF General Occurence in Organismis. Science, 11(279), 701–
702. https://doi.org/10.1126/science.11.279.701
Lusebrink, I., Erbilgin, N., & Evenden, M. L. (2013). The lodgepole x jack pine hybrid zone in
Alberta, Canada: a stepping stone for the mountain pine beetle on its journey east across
the boreal forest. J. Chem. Ecol. 39, 1209–1220.
Margis, R., Dunand, C., Teixeira, F. K., & Margis-Pinheiro, M. (2008). Glutathione peroxidase
family - an evolutionary overview. The FEBS journal, 275(15), 3959–3970.
https://doi.org/10.1111/j.1742-4658.2008.06542.x
Mason R. P. (2016). Imaging free radicals in organelles, cells, tissue, and in vivo with immunospin trapping. Redox biology, 8, 422–429. https://doi.org/10.1016/j.redox.2016.04.0
Mason, P. A., & Bowers, M. D. (2017). Localization of Defensive Chemicals in Two Congeneric
Butterflies (Euphydryas, Nymphalidae). Journal of Chemical Ecology, 43, 480–486.
Mason, C. J., Keefover-Ring, K., Villari, C., Klutsch, J., Cook, S., Bonello, P., Erbilgin, N.,
Raffa, K., & Townsend, P. (2018). Anatomical defences against bark beetles relate to
degree of historical exposure between species and are allocated independently of
chemical defences within trees. Plant, cell & environment, 42(2), 633–646.
Massoumi-Alamouti, S., Haridas, S., Feau, N., Robertson, G., Bohlmann, J., & Breuil, C. (2014).
Comparative genomics of the pine pathogens and beetle symbionts in the genus
Grosmannia. Molecular biology and evolution, 31(6), 1454–1474.
https://doi.org/10.1093/molbev/msu102
McGonigle, S., Dalton, J.P., & James, E. R. 1998. Peroxiredoxins: A New Antioxidant Family.
Parasitology Today, 14(4), 139–145.
Mhamdi, A., Queval, G., Chaouch, S., Vanderauwera, S., Van Breusegem, F., & Noctor, G.
(2010). Catalase function in plants: a focus on Arabidopsis mutants as stress-mimic
models. Journal of experimental botany, 61(15), 4197–4220.
https://doi.org/10.1093/jxb/erq282
Misof, B., Liu, S., Meusemann, K., Peters, R. S., Donath, A., Mayer, C., Frandsen, P. B., Ware,
J., Flouri, T., Beutel, R. G., Niehuis, O., Petersen, M., Izquierdo-Carrasco, F., Wappler,
T., Rust, J., Aberer, A. J., Aspöck, U., Aspöck, H., Bartel, D., Blanke, A., … Zhou, X.

103

(2014). Phylogenomics resolves the timing and pattern of insect
evolution. Science, 346(6210), 763–767. https://doi.org/10.1126/science.1257570
Nandi, A., Yan, L. J., Jana, C. K., & Das, N. (2019). Role of Catalase in Oxidative Stress- and
Age-Associated Degenerative Diseases. Oxidative medicine and cellular longevity, 2019,
9613090. https://doi.org/10.1155/2019/9613090
National Center for Biotechnology Information (NCBI)[Internet]. Bethesda (MD): National
Library of Medicine (US), National Center for Biotechnology Information; [1988] –
[cited 2020 Dec 18]. Available from: https://www.ncbi.nlm.nih.gov/
NCBI Resource Coordinators (2018). Database resources of the National Center for
Biotechnology Information. Nucleic acids research, 46(D1), D8–D13.
https://doi.org/10.1093/nar/gkx1095
Nelson, K. J., & Parsonage, D. (2011). Measurement of peroxiredoxin activity. Current
protocols in toxicology, Chapter 7, Unit7.10.
https://doi.org/10.1002/0471140856.tx0710s49
Nelson, K. J., Parsonage, D., Hall, A., Karplus, P. A., & Poole, L. B. (2008). Cysteine pK(a)
values for the bacterial peroxiredoxin AhpC. Biochemistry, 47(48), 12860–12868.
https://doi.org/10.1021/bi801718d
Nelson, K. J., Knutson, S. T., Soito, L., Klomsiri, C., Poole, L. B., & Fetrow, J. S. (2011).
Analysis of the peroxiredoxin family: using active-site structure and sequence
information for global classification and residue analysis. Proteins, 79(3), 947–964.
https://doi.org/10.1002/prot.22936
Neumann, C. A., Cao, J., & Manevich, Y. (2009). Peroxiredoxin 1 and its role in cell
signaling. Cell cycle (Georgetown, Tex.), 8(24), 4072–4078.
https://doi.org/10.4161/cc.8.24.10242
Oakley, T. H., Alexandrou, M. A., Ngo, R., Pankey, M. S., Churchill, C. C. K., & Lopker, K. B.
(2014). Osiris: Accessible and reproducible phylogenetic and phylogenomic analyses
within the Galaxy workflow management system. BMC Bioinformatics, 15, 230.
Ogusucu, R., Rettori, D., Munhoz, D. C., Netto, L. E., & Augusto, O. (2007). Reactions of yeast
thioredoxin peroxidases I and II with hydrogen peroxide and peroxynitrite: rate constants
by competitive kinetics. Free radical biology & medicine, 42(3), 326–334.
https://doi.org/10.1016/j.freeradbiomed.2006.10.042
Okazaki, S., Tachibana, T., Naganuma, A., Mano, N., & Kuge, S. (2007). Multistep disulfide
bond formation in Yap1 is required for sensing and transduction of H2O2 stress
signal. Molecular cell, 27(4), 675–688. https://doi.org/10.1016/j.molcel.2007.06.035

104

Park, S. G., Cha, M. K., Jeong, W., & Kim, I. H. (2000). Distinct physiological functions of thiol
peroxidase isoenzymes in Saccharomyces cerevisiae. The Journal of biological
chemistry, 275(8), 5723–5732. https://doi.org/10.1074/jbc.275.8.5723
Parker, J., Parker, K.M., & Keller, L. (2004). Molecular phylogenetic evidence for an
extracellular Cu Zn superoxide dismutase gene in insects. Insect Molecular Biology, 13.
Parsonage, D., Nelson, K. J., Ferrer-Sueta, G., Alley, S., Karplus, P. A., Furdui, C. M., & Poole,
L. B. (2015). Dissecting peroxiredoxin catalysis: separating binding, peroxidation, and
resolution for a bacterial AhpC. Biochemistry, 54(7), 1567–1575.
https://doi.org/10.1021/bi501515w
Peshenko, I. V., & Shichi, H. (2001). Oxidation of active center cysteine of bovine 1-Cys
peroxiredoxin to the cysteine sulfenic acid form by peroxide and peroxynitrite. Free
radical biology & medicine, 31(3), 292–303. https://doi.org/10.1016/s08915849(01)00579-2
Peskin, A. V., Dickerhof, N., Poynton, R. A., Paton, L. N., Pace, P. E., Hampton, M. B., &
Winterbourn, C. C. (2013). Hyperoxidation of peroxiredoxins 2 and 3: rate constants for
the reactions of the sulfenic acid of the peroxidatic cysteine. The Journal of biological
chemistry, 288(20), 14170–14177. https://doi.org/10.1074/jbc.M113.460881
Peskin, A. V., & Winterbourn, C. C. (2017). Assay of superoxide dismutase activity in a plate
assay using WST-1. Free radical biology & medicine, 103, 188–191.
https://doi.org/10.1016/j.freeradbiomed.2016.12.033
Petriv, O. I., & Rachubinski, R. (2004). Lack of Peroxisomal Catalase Causes a Progeric
Phenotype in Caenorhabditis elegans*. Journal of Biological Chemistry, 279, 19996–
20001.
Pitt, C., Robert, J. A., Bonnett, T. R., Keeling, C. I., Bohlmann, J., & Huber, D. P. (2014).
Proteomics indicators of the rapidly shifting physiology from whole mountain pine
beetle, Dendroctonus ponderosae (Coleoptera: Curculionidae), adults during early host
colonization. PloS one, 9(10), e110673. https://doi.org/10.1371/journal.pone.0110673
Portillo-Ledesma, S., Randall, L. M., Parsonage, D., Dalla Rizza, J., Karplus, P. A., Poole, L. B.,
Denicola, A., & Ferrer-Sueta, G. (2018). Differential Kinetics of Two-Cysteine
Peroxiredoxin Disulfide Formation Reveal a Novel Model for Peroxide
Sensing. Biochemistry, 57(24), 3416–3424. https://doi.org/10.1021/acs.biochem.8b00188
Possee R. D. (1997). Baculoviruses as expression vectors. Current opinion in
biotechnology, 8(5), 569–572. https://doi.org/10.1016/s0958-1669(97)80030-4
Qin, J., Lu, M., Zheng, Y., & Du, Y. (2016). Molecular Cloning, Characterization, and
Functional Analysis of Catalase in Frankliniella occidentalis (Thysanoptera:
Thripidae). Annals of the Entomological Society of America, 110, 212–220.
105

Rhee S. G. (2016). Overview on Peroxiredoxin. Molecules and cells, 39(1), 1–5.
https://doi.org/10.14348/molcells.2016.2368
Rhee, S. G., Woo, H. A., Kil, I. S., & Bae, S. H (2012). Peroxiredoxin functions as a peroxidase
and a regulator and sensor of local peroxides. J. Biol. Chem., 287, 4403–4410.
Robert, J. A., Bonnett, T., Pitt, C., Spooner, L. J., Fraser, J., Yuen, M. M., Keeling, C. I.,
Bohlmann, J., & Huber, D. P. (2016). Gene expression analysis of overwintering
mountain pine beetle larvae suggests multiple systems involved in overwintering stress,
cold hardiness, and preparation for spring development. PeerJ, 4, e2109.
https://doi.org/10.7717/peerj.2109
Robert, J. A., Pitt, C., Bonnett, T. R., Yuen, M. M., Keeling, C. I., Bohlmann, J., & Huber, D. P.
(2013). Disentangling detoxification: gene expression analysis of feeding mountain pine
beetle illuminates molecular-level host chemical defense detoxification
mechanisms. PloS one, 8(11), e77777.
Rudnik, R., Bulcha, J. T., Reifschneider, E., Ellersiek, U., & Baier, M. (2017). Specificity versus
redundancy in the RAP2.4 transcription factor family of Arabidopsis thaliana:
transcriptional regulation of genes for chloroplast peroxidases. BMC plant biology, 17(1),
144. https://doi.org/10.1186/s12870-017-1092-5
Saab, V. A., Latif, Q. S., Rowland, M.M., Johnson, T. N., Chalfoun, A. D., Buskirk, S. W.,
Heyward, J. E., Dresser, M. A. (2014). Ecological Consequences of Mountain Pine
Beetle Outbreaks for Wildlife in Western North American Forests. Forest Science,
60(3)3, 539–559. https://doi.org/10.5849/forsci.13-022
Seybold, S., Huber, D.P., Lee, J., Graves, A.D., & Bohlmann, J. (2006). Pine monoterpenes and
pine bark beetles: a marriage of convenience for defense and chemical
communication. Phytochemistry Reviews, 5, 143–178.
Sheng, Y., Abreu, I. A., Cabelli, D. E., Maroney, M. J., Miller, A. F., Teixeira, M., & Valentine,
J. S. (2014). Superoxide dismutases and superoxide reductases. Chemical
reviews, 114(7), 3854–3918. https://doi.org/10.1021/cr4005296
Safranyik, L., Carroll, A. L., Régnière, J., Langor, D. W., Riel, W. G., Shore, T. L., Peter,
B., Cooke, B. J., Nealis, V. J., & Taylor, S. W. 2010. Potential for Range Expansion of
Mountain Pine Beetle into the Boreal Forest of North America. The Canadian
Entomologist, 142(5), 415–442. https://doi.org/10.4039/n08-CPA01
Schneider, S., Sandalova, T., Schneider, G., Sprenger, G. A., & Samland, A. K. (2008).
Replacement of a phenylalanine by a tyrosine in the active site confers fructose-6phosphate aldolase activity to the transaldolase of Escherichia coli and human origin. The
Journal of biological chemistry, 283(44), 30064–30072.
https://doi.org/10.1074/jbc.M803184200
106

Shikano, I., Rosa, C., Tan, C. W., & Felton, G. W. (2017). Tritrophic Interactions: MicrobeMediated Plant Effects on Insect Herbivores. Annual review of phytopathology, 55, 313–
331. https://doi.org/10.1146/annurev-phyto-080516-035319
Singh, P., Sharma, L., Kulothungan, S. R., Adkar, B. V., Prajapati, R. S., Ali, P. S., Krishnan, B.,
& Varadarajan, R. (2013). Effect of signal peptide on stability and folding of Escherichia
coli thioredoxin. PloS one, 8(5), e63442. https://doi.org/10.1371/journal.pone.0063442
Stadtman, E. R., & Oliver, C. N. (1991). Metal-catalyzed oxidation of proteins. Physiological
consequences. The Journal of biological chemistry, 266(4), 2005–2008.
Stothard, P. (2000) The Sequence Manipulation Suite: JavaScript programs for analyzing and
formatting protein and DNA sequences. Biotechniques, 28, 1102–1104.
Sun, C. C., Dong, W. R., Shao, T., Li, J. Y., Zhao, J., Nie, L., Xiang, L. X., Zhu, G., & Shao, J.
Z. (2017). Peroxiredoxin 1 (Prx1) is a dual-function enzyme by possessing Cysindependent catalase-like activity. The Biochemical journal, 474(8), 1373–1394.
https://doi.org/10.1042/BCJ20160851
Swofford, D.L. (2003) PAUP*. Phylogenetic analysis using parsimony (* and other methods).
Version 4. Sinauer Associates, Sunderland.
Tasaki, E., Kobayashi, K., Matsuura, K., & Luchi, Y. (2018). Long-Lived Termite Queens
Exhibit High Cu/Zn-Superoxide Dismutase Activity. Oxidative medicine and cellular
longevity, 2018, 5127251. https://doi.org/10.1155/2018/5127251
Tholl D. (2015). Biosynthesis and biological functions of terpenoids in plants. Advances in
biochemical engineering/biotechnology, 148, 63–106.
https://doi.org/10.1007/10_2014_295
Tsuzuki, D., Hichiya, H., Okuda, Y., Yamamoto, S., Tamagake, K., Shinoda, S., & Narimatsu, S.
(2003). Alteration in catalytic properties of human CYP2D6 caused by substitution of
glycine-42 with arginine, lysine and glutamic acid. Drug metabolism and
pharmacokinetics, 18(1), 79–85.
Turrens, J. F. (2003). Mitochondrial formation of reactive oxygen species. The Journal of
physiology, 552(Pt 2), 335–344. https://doi.org/10.1113/jphysiol.2003.049478
Vivancos, A. P., Castillo, E. A., Biteau, B., Nicot, C., Ayté, J., Toledano, M. B., & Hidalgo, E.
(2005). A cysteine-sulfinic acid in peroxiredoxin regulates H2O2-sensing by the
antioxidant Pap1 pathway. Proceedings of the National Academy of Sciences of the
United States of America, 102(25), 8875–8880.
Vornam, B., Leinemann, L., Peters, F.S., Wolff, A., Leha, A., Salina-Riester, G., Shumacher, J.,
& Gaiing, O. (2019). Response of Scots pine (Pinus sylvestris) seedlings subjected to
107

artificial infection with the fungus Sphaeropsis sapinea. Plant Mol. Biol. Rep., 37, 214–
223. https://doi.org/10.1007/s11105-019-01149-2
Wang, Q., Chen, K., Yao, Q., Zhao, Y., Li, Y., Shen, H., & Mu, R. (2008). Identification and
characterization of a novel 1-Cys peroxiredoxin from silkworm, Bombyx mori.
Comparative Biochemistry and physiology. Biochemistry & Molecular Biology, 149(1),
176–182. https://doi.org/10.1016/j.cbpb.2007.09.005.
Wang, Q., Chen, K., Yao, Q., Zhao, Y., Li, Y., Shen, H., & Mu, R. (2008). Identification and
characterization of a novel 1-Cys peroxiredoxin from silkworm, Bombyx mori.
Comparative Biochemistry and physiology. Biochemistry & Molecular Biology, 149(1),
176–182. https://doi.org/10.1016/j.cbpb.2007.09.005.
Wang, Q., Zhou, Y., Chen, K., & Ju, X. (2016). Identification and characterization of an atypical
2‐cys peroxiredoxin from the silkworm, Bombyx mori. Insect Molecular Biology, 25(4),
347–354.
Wang, Y., Oberley, L. W., & Murhammer, D. W. (2001). Evidence of oxidative stress following
the viral infection of two lepidopteran insect cell lines. Free radical biology & medicine,
31(11), 1448–1455. https://doi.org/10.1016/s0891-5849(01)00728-6
Williams, J. R., Trias, E., Beilby, P. R., Lopez, N. I., Labut, E. M., Bradford, C. S., Roberts, B.
R., McAllum, E. J., Crouch, P. J., Rhoads, T. W., Pereira, C., Son, M., Elliott, J. L.,
Franco, M. C., Estévez, A. G., Barbeito, L., & Beckman, J. S. (2016). Copper delivery to
the CNS by CuATSM effectively treats motor neuron disease in SOD(G93A) mice coexpressing the Copper-Chaperone-for-SOD. Neurobiology of disease, 89, 1–9.
https://doi.org/10.1016/j.nbd.2016.01.020
Wingfield P. T. (2015). Overview of the purification of recombinant proteins. Current protocols
in protein science, 80, 6.1.1–6.1.35. https://doi.org/10.1002/0471140864.ps0601s80
Winterbourn, C. C. (2008). Reconciling the chemistry and biology of reactive oxygen
species. Nature chemical biology, 4(5), 278–286. https://doi.org/10.1038/nchembio.85
Winterbourn, C., & Peskin, A. (2016). Kinetic Approaches to Measuring Peroxiredoxin
Reactivity. Molecules and Cells, 39, 26–30.
Woo, H. A., Yim, S. H., Shin, D. H., Kang, D., Yu, D. Y., & Rhee, S. G. (2010). Inactivation of
peroxiredoxin I by phosphorylation allows localized H 2O2 accumulation for cell
signaling. Cell, 140(4), 517–528. https://doi.org/10.1016/j.cell.2010.01.009
Wood, Z. A., Poole, L. B., & Karplus, P. A. (2003). Peroxiredoxin evolution and the regulation
of hydrogen peroxide signaling. Science, 300(5619), 650–653.
https://doi.org/10.1126/science.1080405

108

Xikeranmu, Z., Abdunasir, M., Ma, J., Tusong, K., & Liu, X. (2019). Characterization of two
copper/zinc superoxide dismutases (Cu/Zn-SODs) from the desert beetle Microdera
punctipennis and their activities in protecting E. coli cells against cold. Cryobiology, 87,
15–27. https://doi.org/10.1016/j.cryobiol.2019.03.006
Yamamoto, K., Banno, Y., Fujii, H., Miake, F., Kashige, N., & Aso, Y. (2005). Catalase from
the silkworm, Bombyx mori: Gene sequence, distribution, and overexpression. Insect
Biochemistry and Molecular Biology, 35(4), 277–283.
https://doi.org/10.1016/j.ibmb.2005.01.00
Zamocky, M., Furtmüller, P. G., Bellei, M., Battistuzzi, G., Stadlmann, J., Vlasits, J., & Obinger,
C. (2009). Intracellular catalase/peroxidase from the phytopathogenic rice blast fungus
Magnaporthe grisea: expression analysis and biochemical characterization of the
recombinant protein. The Biochemical journal, 418(2), 443–451.
https://doi.org/10.1042/BJ20081478
Zelko, I. N., Mariani, T. J., & Folz, R. J. (2002). Superoxide dismutase multigene family: a
comparison of the CuZn-SOD (SOD1), Mn-SOD (SOD2), and EC-SOD (SOD3)
genhang, L., & Lu, Z. (2015). Expression, purification and characterization of an atypical
2-Cys peroxiredoxin from the silkworm, Bombyx mori. Insect molecular biology, 24(2),
203–212. https://doi.org/10.1111/imb.12149
Zhang, L, & Lu, Z. (2015). Expression, purification and characterization of an atypical 2-Cys
peroxiredoxin from the silkworm, Bombyx mori. Insect Molecular Biology, 24(2), 203–
212.
Zhang, Z., Guo, R., & Li, M. (2017). Identification, characterization and expression analysis of
superoxide dismutase genes in Dastarcus helophoroides (Coleoptera:
Bothrideridae). Gene, 606, 25–34. https://doi.org/10.1016/j.gene.2017.01.003

109