EVOLUTION OF INDEPENDENT GENETIC PATHWAYS FOR PATHOGEN 
RESISTANCE WITHIN THE NEMATODE CAENORHABDITIS REMANEI 
 
 
 
 
 
 
 
 
 
 
 
 
 
by 
 
HEATHER ARCHER 
 
 
 
 
 
 
 
 
 
 
 
 
 
A THESIS 
 
Presented to the Department of Biology 
and the Graduate School of the University of Oregon 
in partial fulfillment of the requirements 
for the degree of 
Master of Science  
September 2013 
 ii 
THESIS APPROVAL PAGE 
 
Student: Heather Archer 
 
Title: Evolution of Independent Genetic Pathways for Pathogen Resistance within the 
Nematode Caenorhabditis remanei 
 
This thesis has been accepted and approved in partial fulfillment of the requirements for 
the Master of Science degree in the Department of Biology by: 
 
Karen Guillemin Chairperson 
Patrick Phillips Member 
John Willis Member 
 
and 
 
Kimberly Andrews Espy Vice President for Research and Innovation; 
 Dean of the Graduate School  
 
Original approval signatures are on file with the University of Oregon Graduate School. 
 
Degree awarded September 2013 
  
 iii 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
© 2013 Heather Marie Archer  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 iv 
THESIS ABSTRACT 
 
Heather Archer 
 
Master of Science 
 
Department of Biology 
 
September 2013 
 
Title: Evolution of Independent Genetic Pathways for Pathogen Resistance within the 
Nematode Caenorhabditis remanei 
 
 
 
Pathogenic host-microbe interactions can result from continuous evolution of a 
host’s ability to resist infection and a pathogen’s ability to survive and replicate. 
Pseudomonas aeruginosa is a versatile and opportunistic pathogen, ubiquitous in soil, and 
capable of damaging plants, vertebrates, and invertebrates. Previous studies in nematodes 
suggest that the pathogenic effects of P. aeruginosa can result from multiple distinct 
pathways: a toxin-based effect that kills within a few hours and a generalized virulence that 
kills over the course of multiple days. Using experimental evolution in the highly 
polymorphic nematode Caenorhabditis remanei, I show that nematode resistance to the 
two modes of pathogenesis in P. aeruginosa evolves through genetically independent 
pathways. These results demonstrate that multiple virulence factors in a pathogen can result 
in multiple responses in the host, and the genetic lines established here create resources for 
further exploration of the genetic basis for resistance to P. aeruginosa. 
 v 
CURRICULUM VITAE 
 
NAME OF AUTHOR:  Heather Archer 
 
 
GRADUATE AND UNDERGRADUATE SCHOOLS ATTENDED: 
 
 University of Oregon, Eugene 
 University of Phoenix, Phoenix, Arizona 
  
 
DEGREES AWARDED: 
 
 Master of Science, Biology, 2013, University of Oregon 
 Bachelor of Science, Psychology, 2009, University of Phoenix 
  
 
AREAS OF SPECIAL INTEREST: 
 
 Evolution 
 Genetics 
 
 
PROFESSIONAL EXPERIENCE: 
 
 Teaching Assistant, University of Oregon, 2011-2013 
 BIO132: Introduction to Animal Behavior 
 BIO212: General Biology II: Organisms 
 BIO214: General Biology IV: Mechanisms 
 
 Research Assistant, University of Oregon, 2012-2013 
 Phillips Lab Research Assistant 
 
 
 
  
 
  
 
 vi 
ACKNOWLEDGMENTS 
 
I wish to express my gratitude to Professor Patrick Phillips for assistance in the 
preparation of this manuscript. In addition, many profound thanks are due to Drs. Rose 
Reynolds and Ann Peterson, whose enthusiasm and encouragement was essential to the 
genesis of this project. I also thank the members of my committee for their valuable 
insights. Most of all, thanks to my family for their patience and support of many long 
days spent in the lab.  
 vii 
 
 
 
Dedicated to Trudy Scott Bradley, 1948 – 2012. The infinite possibilities each day 
holds should stagger the mind. The sheer number of experiences available is uncountable, 
breathtaking. Yet so many live trapped in loops. Reliving the same days over and over, 
envisioning only a handful of paths that have been laid out ahead. Each day seeing the 
same things, responding the same way, a slight variation of yesterday. Every moment 
smoothly following the gentle curves of norms and expectations. Thinking that tomorrow 
our dreams can become reality (modified from XKCD: Dreams). 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 viii 
TABLE OF CONTENTS 
Chapter Page 
 
 
I. INTRODUCTION .................................................................................................... 1 
II. RESULTS................................................................................................................ 8 
 P. aeruginosa Is Pathogenic to C. remanei............................................................ 8 
 C. remanei Resistance to P. aeruginosa Is an Evolvable Trait ............................. 8 
 Evolved Resistance to One Environment Does Not Correlate to Resistance in  
Another ............................................................................................................ 9 
 Evolution under Alternating Slow- and Fast-Killing Environments ..................... 10 
III. DISCUSSION ........................................................................................................ 11 
 Contrasting Kinetics of Pathogenicity in the Ancestral C. remanei Population .... 11 
 Evolutionary Independence of Resistance Pathways ............................................. 12 
IV. MATERIALS AND METHODS .......................................................................... 16 
APPENDIX: FIGURES AND TABLES ..................................................................... 19 
REFERENCES CITED ................................................................................................ 25 
 ix 
LIST OF FIGURES 
 
Figure Page 
 
 
1. Experimental evolution design .............................................................................. 19 
 
2. Survival kinetics of PX443. ................................................................................... 20 
 
3. Percent survival of PX443 ..................................................................................... 20 
4. Slow-killing environment log fold change in survival of evolved populations ..... 21 
5. Fast-killing environment log fold change in survival of evolved populations ...... 22 
 x 
LIST OF TABLES 
 
Table Page 
 
 
1. Differences between replicates of evolved populations in fast-kill survival. ........ 23 
2. Differences between replicates of evolved populations in slow-kill survival. ...... 24 
 
 1 
CHAPTER I 
INTRODUCTION 
Pathogenic host-microbe interactions can be likened to an ongoing battle, a 
continuous evolution between host ability to restrict infecting microbes and pathogen 
ability to survive and replicate. If an important long term goal of health research is to 
sway the battle in favor of hosts, then it is fundamentally important to understand the 
genetic mechanisms underlying this exchange. Many insights into host immunity have 
been made during the past decade, but much of this work has been conducted in cell 
culture (Baxt, et al., 2013). Studies of this type are somewhat oversimplified due to lack 
of the contextual and systemic complexity present in vivo. Evidence increasingly shows, 
for example, that multiple pathogen response pathways exist, interact, and regulate other 
pathways (Saitoh, et al., 2008; Nakahira, et al., 2011; Irazoqui, et al., 2010). This 
potential for interaction among otherwise independent pathways underscores a critical 
need for pathogen-host interaction studies based on intact biological systems that allow 
analysis of host responses in conditions such that interactions among immune pathways 
can be observed. 
Nematodes provide one such system. These microscopic worms can be free-living 
or parasitic. Many of the free-living species, such as Caenorhabditis elegans, are soil-
dwelling bacteriovores. As a model organism, C. elegans has been considered ideal due 
to its small size, transparent body, ability to survive freezing, short generation time, 
completely mapped cellular differentiation pattern, self-fertilizing reproductive system 
which can produce genetically identical offspring, ease of targeted genetic disruption via 
double-stranded RNA-mediated interference (RNAi), and fully sequenced genome with 
 2 
homology to humans approaching 80% (Kaletta & Hengartner, 2006; Lai, et al., 2000). 
C. elegans is also uniquely tractable for the study of pathogen interactions. In addition to 
being a bacteriovore, it is susceptible to many of the same pathogens that sicken and kill 
mammals and humans (O'Callaghan & Vergunst, 2010). Additionally, these worms have 
interacted with and adapted to bacteria for greater than 600 million years (Irazoqui, et al., 
2010). Presumably, during this time bacteria have evolved to avoid predation at least in 
part via the same mechanisms utilized to avoid detection in humans, while at the same 
time worms have evolved immune characteristics to overcome pathogenic effects of the 
bacteria.  
Remarkably, many of the genetic elements involved in immune responses were 
already established when worms and humans last shared a common ancestor nearly one 
billion years ago (Ruvkun & Hobert, 1998). Several of these components, such as toll-
like receptors (TLRs) and anti-microbial peptides (AMPs), have already been shown to 
be highly conserved across many diverse taxa including insects, mammals, and plants 
(Ganz & Lehrer, 1994; Broekaert, et al., 1995; Hoffman, 1995; Takeda & Akira, 2003). 
Moreover, it has also been shown that over the course of evolutionary history that genetic 
pathways with important contributions to fitness persist and tend to adopt only slightly 
varied function in related species (De Robertis, 2008). It is plausible then that currently 
existing components of nematode immunity have been conserved across taxa, including 
humans, despite the billion years of separation. 
For example, studies conducted in Drosophila melanogaster first revealed the 
functional role of Toll receptors as part of a signaling pathway governing immune 
response with a highly conserved homology to human interleukin 1 (IL-1) receptors 
 3 
(Rock, et al., 1998; Wu & Anderson, 1998). In many insect systems the Toll receptor is 
activated by the cytokine Spaetzle, which is produced in response to detection of 
pathogen-associated molecular patterns (PAMPs) while vertebrate TLRs are directly 
activated by PAMPs. However in both cases the downstream pathways are largely the 
same: TLRs initiate the production of transcription factors belonging to the nuclear 
factor-κβ (NF-κβ) family through signals transmitted by scaffold proteins of the Mal and 
Myd88 classes (Akira, et al., 2006). The downstream genes regulated by NF-κβ have 
been well-characterized to include conserved antimicrobial peptides (AMPs) and pro-
inflammatory cytokines (Vallabhapurapu & Karin, 2009). All together this is indicative 
of a high degree of conservation within the structure and function of immune systems in 
addition to the sequence homology of individual components. 
A reverse genetic analysis of the C. elegans genome identified components with 
similarity to insect and mammalian TLRs and associated signaling pathways (Pujol, et 
al., 2001). In particular is the presence of TOL-1 (a protein containing a Toll/IL-1R 
protein-protein interaction, aka TIR, domain), TRF-1 (similar to tumor necrosis factor 
receptor-associated factor, part of a signal cascade), PIK-1 (IL-1R-associated kinase), and 
IKB-1 (inhibitor of NF-κβ) (Irazoqui, et al., 2010). Additionally, TIR-1 was identified as a 
scaffold protein containing a TIR domain homologous to human sterile α- and armadillo-
motif-containing protein (SARM), which functions in humans as a negative regulator 
downstream of TLR3 and TLR4 (Carty, et al., 2006). Moreover, despite the lack of any 
clear homologs of MyD-88 or NF-κβ-like transcription factors (intermediary signaling 
components in mammalian systems) worms are still capable of producing conserved 
 4 
AMPs in response to pathogens (Zhu, 2008) indicating they do indeed possess 
evolutionarily shared immune functions (Irazoqui, et al., 2010). 
The high degree of conservation begs the question: how does the immune system 
evolve to recognize and mount a defense against pathogenic bacteria? In a biological 
system as complex as humans it is very difficult to study the genetic basis of the immune 
response, first and foremost due to ethical and logistical issues (e.g. long reproduction 
times) (Glavis-Bloom, et al., 2012). To address these issues, simpler vertebrate model 
organisms such as the mouse Mus musculus and the zebrafish Danio rerio have been 
widely used (Irazoqui, et al., 2010). But, vertebrates possess evolutionarily novel immune 
system functions, collectively referred to as the adaptive immune system. However, for 
an adaptive immune response to occur, evolutionarily conserved, a.k.a. innate, immune 
functions must first be activated (Song, 2012; Kaisho, et al., 2001; Pasare & Medzhitov, 
2004; Iwasaki & Medzhitov, 2004; Akira, et al., 2001; Barton & Medzhitov, 2002; Prinz, 
et al., 2006). Therefore, understanding the evolution and function of innate immunity is 
important but is often complicated by crosstalk between the innate and adaptive systems 
(Parish & O'Neill, 1997; Trinchieri, 1995; Reschner, et al., 2008). A seemingly 
straightforward solution to this particular issue is the use of invertebrate model organisms 
as they are lacking adaptive immune components (Irazoqui, et al., 2010). But, 
compounding this problem is that the natural variety of phenotypes that accompany the 
high levels of genetic variation in complex systems makes it quite difficult to forecast 
what the relevant bioactivity will be when observations are based on much simpler 
organisms with lesser phenotypic variety despite the degree of conservation (Kammenga, 
et al., 2008; Irazoqui, et al., 2010). 
 5 
The lack of phenotypes is a particular problem for relating the immune function 
of C. elegans to other biological systems. The obligately selfing hermaphroditic mating 
system of C. elegans has resulted in greatly reduced genetic and phenotypic diversity 
(Andersen, et al., 2012; Phillips, 2012). This is of particular note because most previous 
studies examining the genetic basis of pathogen response in nematodes have been 
conducted using C. elegans, with a common conclusion being that the amount of genetic 
diversity limits evolutionary diversification and the resultant functional bioactivity (Pujol, 
et al., 2008; Schulenburg & Boehnisch, 2008). Further, it has been shown that                                                                                                              
coevolution of a host organism with pathogenic bacteria drives self-fertilizing 
populations to extinction while selecting for increased outcrossing in mixed mating 
populations (Morran, et al., 2011). Presumably this arises out of a selection-imposed 
requirement for allelic and phenotypic variation. This suggests that despite the shared 
elements present in the C. elegans genome, the primary strategies employed for pathogen 
resistance may be unique. Given these evolutionary dynamics, the relevance of C. 
elegans as a general model for host-pathogen dynamics may be somewhat limited. 
To overcome these limitations, I have created a model system using 
Caenorhabditis remanei and Pseudomonas aeruginosa. The nematode C. remanei is a 
soil-dwelling bacteriovore akin to C. elegans. It also is easily maintained and 
manipulated in lab environments, is transparent so that phenotypes can be easily scored, 
survives indefinitely when frozen, and has a short generation time that readily facilitates 
multi-generational examination of evolutionary genetic responses to controlled 
environments. Additionally, it possesses relatively facile genetic architecture and exhibits 
immune characteristics without any known orthologs of specific ligand-binding pathogen 
 6 
receptors (pattern-recognition receptors, PRRs). In contrast to C. elegans, however, C. 
remanei is a gonochoristic (male-female) species. As such, the mode and tempo for its 
population demography is distinct from that of hermaphroditic nematode species, and the 
resulting amount of genetic diversity present in natural populations is extraordinarily high 
relative to C. elegans (Phillips, 2006).  
Pseudomonas aeruginosa is a highly virulent and opportunistic mammalian 
pathogen that is also damaging to plants and invertebrates such as C. remanei. It is a 
frequent cause of death for individuals with cystic fibrosis, severe burns, and other forms 
of compromised immunity and is resistant to conventional treatments (Tan, et al., 1999). 
Under normal circumstances, the innate arm of the human immune system is capable of 
recognizing the presence of P. aeruginosa and initiating an effective immune response 
before an infection can become established. Pseudomonas aeruginosa is ubiquitous in 
soil but possesses a highly variable metabolism enabling it to survive in a wide variety of 
environments and to produce a number of environment-specific virulence factors 
(Cezairliyan, et al., 2013; Mahajan-Miklos, et al., 1999; Tan, et al., 1999).  Presumably, 
the shared ecology between P. aeruginosa and C. remanei has led to historical host-
microbe encounters between these organisms resulting in the presence of natural genetic 
variation at a relevant scale.  
My model system is based on this interaction and takes advantage of the greater 
diversity present in C. remanei along with the variable metabolism of P. aeruginosa to 
investigate the genetic basis of pathogen resistance using an experimental evolution 
approach. In the lab environment, P. aeruginosa can kill C. elegans in either a slow 
infectious process (slow-kill) or by a toxin-mediated mechanism (fast-kill) depending on 
 7 
the strain and culture conditions (Tan, et al., 1999; Mahajan-Miklos, et al., 1999). Slow-
kill conditions require P. aeruginosa to be grown on nematode growth medium (NGM) 
and have a slower rate of death where fast-kill requires a high osmolarity medium and 
has faster kinetics. Observations indicate that on slow-kill media bacteria begin 
accumulating in the intestine of C. elegans approximately 36 hours after feeding begins 
(Tan, et al., 1999). Worms removed from slow-kill P. aeruginosa within 12 hours of 
exposure or placed on heat-killed bacteria with slow-kill media do not appear to exhibit 
any reduction in fitness (Tan, et al., 1999). Under fast-kill conditions, P. aeruginosa 
produces diffusible toxins and killing does not require contact with live bacteria 
(Mahajan-Miklos, et al., 1999). Recently, fast-killing has been shown to be mediated by 
production of phenazine-1-carboxylic-acid in a pH dependent manner (Cezairliyan, et al., 
2013). In order to assess the evolutionary response of a natural population of C. remanei 
to P. aeruginosa, I performed a 40-generation study under slow- and fast-killing 
conditions, as well as their combination (Figure 1). The results from this work show that 
at least some of the components of these response pathways are independent from one 
another and that experimental evolution with C. remanei can be a powerful tool for 
exploring the genetic basis of pathogen resistance within natural populations. 
 
 
  
 8 
CHAPTER II 
RESULTS 
P. aeruginosa is pathogenic to C. remanei. When exposed to P. aeruginosa 
under slow-kill conditions, C. remanei recently collected from nature showed no obvious 
symptoms or increased mortality after 24 hours of exposure. Continued exposure results 
in a gradual cessation of pharyngeal pumping and loss of mobility followed by a 
significant increase in mortality by 72 hours (Figure 2). Differences between the 
pathogenic and control bacteria are most pronounced after 120 hours (5 days), with only 
half of the population exposed to P. aeruginosa remaining at that time (Figure 3;   
  = 
67.5, p < 0.0001). For simplicity, further tests of pathogen-induced mortality focus on 
this 5-day time point.  
C. remanei populations are unable to persist for any significant length of time 
when continuously exposed to P. aeruginosa that have been induced to produce the fast-
kill response (Figure 2). Subsequent fast-kill responses therefore involve an initial four 
hour exposure to the fast-kill environment, followed by an assessment of individual 
viability 24 hours later. Approximately 70% of the ancestral population died within 24 
hours of exposure to P. aeruginosa under the fast-kill conditions (Figure 3,   
  = 135.0, p 
< 0.0001).  
 
C. remanei resistance to P. aeruginosa is an evolvable trait. After 40 
generations of experimental evolution in either slow- or fast-killing environments 
(denoted as SK or FK, respectively), the resulting C. remanei populations showed higher 
resistance to P. aeruginosa than the ancestral population when compared to the 
 9 
appropriate matched control environment (Figure 4, SK:   
  = 13.5, p = 0.0002; Figure 5, 
FK,   
  = 373.1, p < 0.0001). There were no significant differences among replicates (SK, 
  
  = 1.782, p = 0.1820; FK,   
  = 1.195, p = 0.2743), so data was pooled across 
replicates in all subsequent analyses (see Tables 1 and 2 for replicate specific 
information).  
 
Evolved resistance to one environment does not correlate to resistance in 
another. Individuals evolved in the fast-kill environment (FK) do not display any 
increase in survival when placed on slow-killing media (Figure 4,   
  = 0.9, p = 0.3317). 
Control lines (Ctrl) survive significantly worse than either of the evolved resistance 
conditions (Figure 4,   
 = 42.8, p < 0.0001), and indeed perform marginally worse than 
the ancestors (Figure 4;   
  = 3.4, p = 0.0665). 
Survival under fast-kill environmental conditions increased significantly for both 
the slow-kill and control populations (Figure 5;   
  = 200.9, p < 0.0001) but is still 
substantially less than that observed for the fast-kill populations (Figure 5;   
  = 16.2, p = 
0.0003).  
The increased resistance to the fast-kill environment is not the result of adaptation 
to the high-osmolarity of the fast-kill inducing medium, as controls evolved under the 
same conditions but with the standard E. coli strain OP50 replacing P. aeruginosa (FKC) 
do not significantly differ in resistance from controls evolved under standard lab 
conditions (Figure 5,   
  = 0.2, p = 0.6854). Although, slow-kill evolved populations 
(SK) survive fast-killing significantly better than ancestral individuals, they do not differ 
from lab adapted controls (Figure 5,   
  = 4.2, p = 0.1212). This indicates that a portion 
 10 
of the mechanism underlying resistance to fast-killing arises from exposure to standard 
lab conditions and is independent of interactions with P. aeruginosa. Moreover, it 
appears to have a negative influence on the mechanism giving rise to slow-killing 
resistance. 
 
Evolution under alternating slow- and fast-killing environments. Lines 
evolved under an alternating exposure to slow- and fast-killing (SKFK) display no 
significant change in resistance in the slow-kill environment (Figure 4,   
  = 0.4, p = 
0.5464). In the fast-kill environment, SKFK populations have a significant increase in 
resistance compared to the ancestor (Figure 4,   
  = 52.7, p < 0.0001), but the degree of 
resistance is significantly lower than all other evolved populations (Figure 7,   
  = 33.0, p 
< 0.0001). 
 
  
 11 
CHAPTER III 
DISCUSSION 
Contrasting kinetics of pathogenicity in the ancestral C. remanei population. 
In slow- and fast-killing assays conducted with C. elegans, consumption of live bacteria 
was required for slow-killing while fast-killing occurred on PGS media on which P. 
aeruginosa had been grown and removed, suggesting that fast-killing is mediated by a 
diffusible toxin (Tan, et al., 1999; Cezairliyan, et al., 2013). The kinetics of pathogenicity 
in these studies is highly similar to the kinetics shown here with C. remanei (Figure 2; 
Tan, et al., 1999). Studies of P. aeruginosa mutants with attenuated killing abilities in C. 
elegans showed that many of the bacterial genes underlying pathogenicity in slow- or 
fast-killing are environment specific, primarily producing phenazines with relatively 
narrow and largely non-overlapping parameters for toxicity (Cezairliyan, et al., 2013; 
Mahajan-Miklos, et al., 1999; Tan, et al., 1999). From the bacterial perspective then, 
pathogenicity in the slow-killing and fast-killing environments clearly arises from 
distinctly different genetic and biochemical mechanisms. This is suggestive of a 
possibility for distinct genetic mechanisms to form the basis of response to slow-killing 
independently from fast-killing and vice versa in C. elegans. However, another 
possibility is that the basis of resistance in C. elegans is not specific to the environment 
or to toxins produced by P. aeruginosa, but is instead the manifestation of a generalized 
stress response shared across all environments. If this were the case, it is possible that the 
fast-killing environment is simply more taxing to the worm’s system, thus generating the 
faster kinetic, or that slow-killing is equally taxing but requires more time to develop out 
of a bacterial dependence on quorum sensing or some other density-dependent 
 12 
mechanism to initiate pathogenicity. Killing of C. remanei by P. aeruginosa also appears 
to take place through two distinct mechanisms. In the slow-killing environment exposure 
of C. remanei to P. aeruginosa for a minimum of 24 hours is required with significant 
mortality beginning at 72 hours of continuous exposure, whereas pathogenicity in the 
fast-kill environment requires only four hours of exposure to achieve 70% mortality at 
the 24-hour time point. Experimental evolution in C. remanei is therefore consistent with 
the suggestion that the genetic pathways underlying resistance to P. aeruginosa may 
operate independently from one another. 
 
Evolutionary independence of resistance pathways. If the genetic mechanism 
underlying resistance consisted of a single stress response activated in both environments, 
the expected outcome would be for a strongly correlated and reciprocal resistant 
phenotype in the populations evolved under P. aeruginosa conditions. As my results 
show, evolution in the fast-kill environment did not confer any significant increase in 
resistance to the slow-kill environment. On the other hand, populations evolved under 
slow-kill conditions do show a significant increase in resistance to fast-killing when 
compared to the ancestral population. However, the degree of increase is significantly 
less than the fast-kill evolved group (Figure 5). 
At first glance this appears to suggest that evolution in slow-killing conditions 
does offer some protection against fast-killing. However, both control populations, 
evolved in the same manner but with the OP50 strain of E. coli replacing P. aeruginosa, 
display the same level of resistance to fast-killing as the slow-kill evolved populations. 
This suggests that the increase in resistance to fast-killing displayed by the slow-kill 
 13 
population is the result of adaptation to an effect that is not dependent specifically on P. 
aeruginosa. It seems plausible then that, because all of the aforementioned evolved lines 
(but not the ancestor) were routinely exposed to OP50 during the experimental evolution, 
this portion of increased fast-kill resistance is likely due to adaptation to OP50. This is 
supported by evidence demonstrating that the OP50 strain of E. coli is mildly pathogenic 
to C. elegans (So, et al., 2011). Like P. aeruginosa, OP50 is a gram-negative bacterium 
and thereby they have many genetic features in common, some of which have been 
shown to attenuate fast-killing virulence in P. aeruginosa mutants (Mahajan-Miklos, et 
al., 1999). For example, a bacterial gene, MdoH, identified to be important for full 
pathogenicity in fast-killing by P. aeruginosa is also present in OP50 (Mahajan-Miklos, 
et al., 1999). On the other hand, the small molecule phenazine-1-carboxylic-acid (PCA), 
a toxin necessary for fast-killing by P. aeruginosa (Cezairliyan, et al., 2013), is not 
produced by OP50. This suggests that fast-killing is a multifactorial process and that 
some of the sub-lethal pathogenic properties are shared between OP50 and P. aeruginosa. 
Moreover, the genes for the presumed primary toxic compounds produced by P. 
aeruginosa in the slow-killing environment, phenazines (and in particular the phenazine 
pyocyanin), are generally not present in the genus Escherichia and therefore are not 
found in OP50 (Reszka, et al., 2012; Kuznetsova, et al., 2012; Nowroozi, et al., 2012), 
lending credence to the hypothesis that the mode of toxicity employed by P. aeruginosa 
in the slow-kill environment may be wholly separate from OP50. This would account for 
the lack of any correlated increase in resistance from other environments. Taken together 
these data suggest the genetic pathways underlying resistance to slow-killing and fast-
 14 
killing by P. aeruginosa are largely independent of each other with an overlap in 
resistance to fast-killing mediated by adaptation to OP50. 
An additional perspective on the evolutionary independence of the genetic 
pathways for slow-killing and fast-killing resistance comes from examination of the 
population evolved under alternating slow-killing and fast-killing conditions (SKFK). 
The SKFK populations display no significant increase in resistance to slow-killing but, 
despite having no exposure to OP50 during the experimental evolution period, do have a 
significantly increased resistance to fast-killing relative to the ancestral population. 
However, the level of resistance is significantly lower than the degree displayed by the 
populations evolved under slow-kill and control conditions (Figure 5). This is suggestive 
of an evolutionary antagonistic interaction between the slow-kill and fast-kill resistance 
pathways. Additionally, evidence from assays in C. elegans demonstrates that the 
transcription of antibacterial immune effectors is selectively repressed during infection 
with the fungal pathogen Candida albicans and that production of an antimicrobial 
peptide required for defense against fungal pathogens during infection by a bacterial 
pathogen increases susceptibility (Pukkila-Worley & Ausubel, 2012; Marsh, et al., 2011). 
This suggests that the worms are capable of distinguishing between pathogen types and 
coordinating responses among independent and antagonistic genetic pathways. Another 
possible factor is that the kinetics of pathogenicity in the slow-kill versus the fast-kill 
environment skewed the strength of selection in each environment to favor selection for 
fast-kill resistance. In other words, because fast-killing mortality was at 70% within 24 
hours, non-resistant individuals exposed as L4s were likely to die before producing 
offspring whereas mortality in the slow-killing environment reaches 50% around day 5 
 15 
giving individuals exposed as L4s ample time to produce offspring. It is also possible for 
both the kinetic skew and antagonistic interactions between pathways to be present.  
As a whole this work suggests that C. remanei responds to virulence mechanisms 
of P. aeruginosa by at least two distinct genetic pathways. These two independent 
pathways are likely initiated in response to environment-specific factors through as-yet 
unknown interfaces. Also, these pathways appear to interact in an antagonistic fashion. In 
the context of host-pathogen interactions and bacteriovore feeding systems, this may 
reflect strategies evolved by the host to balance energy investment in antimicrobial 
defenses while addressing the capabilities of pathogenic bacteria to evade detection and 
predation. However, more work is needed to address this question. 
 
 
  
 16 
CHAPTER IV 
MATERIALS AND METHODS 
Bacterial Strains, Growth Conditions, and Media. The P. aeruginosa strain 
PA14 (Rahme, et al., 1995) and the E. coli strain OP50 were used. Strains were grown at 
37°C and the media used for bacterial culture and maintenance were Luria-Bertani broth 
(LB) (Miller, 1972) and King’s broth (KB) (King, et al., 1954). PA14 was cultured on 
nematode growth medium lite (NGM lite) (US Biological catalog #N1005) for slow-
killing assays or peptone-glucose-sorbitol (PGS) media (1% Bacto-Peptone, 1% NaCl, 
1% glucose, 1.7% Bacto-Agar, 0.15 M sorbitol) for fast-killing assays (Mahajan-Miklos, 
et al., 1999; Tan, et al., 1999). OP50 cultured on NGM lite was used for a low-osmolarity 
control and OP50 cultured on PGS was used for a high-osmolarity control. 
 
Nematode Strains. All C. remanei strains were maintained by standard culturing 
conditions on NGM lite agar with E. coli OP50 or P. aeruginosa PA14 as a food source 
at 20°C (Stiernagle, 1999). The strain used in this work was PX443, a derivative of 39 
natural isolates collected from wood lice found in the same tract of forest detritus in the 
Koffler Scientific Reserve at Jokers Hill, King Township, Ontario by Rose Reynolds. The 
isolates were intercrossed in a scheme designed to equalize the genomic contribution 
from each strain to generate PX443. 
 
Bacterial Virulence Survival Assays. Both the fast-killing and slow-killing 
assays were based on protocols described in Mahajan-Miklos et al. (1999). The fast 
killing assay was conducted by spreading 60 μL of liquid from a PA14 culture grown for 
 17 
18 hours in KB on 11 cm plates containing peptone-glucose-sorbitol medium. After the 
culture was spread, plates were incubated at 37°C for 24 hours then placed at room 
temperature for 2-4 hours. Approximately 200 age-synchronized L4 individuals per plate 
were placed on fast kill media for survival or both PGS and NGM lite seeded with OP50 
as controls and incubated at room temperature for 4 hours. The plates were then washed 
with S-basal buffer solution (Stiernagle, 1999), worms transferred onto NGM lite-OP50 
plates, and surviving individuals counted 24 hours later. A total of three replicate plates 
per test condition were used. 
The slow killing assay was conducted by spreading 20μL of liquid from a PA14 
culture grown for 18 hours in KB on 3 cm plates containing NGM lite. The plates were 
then incubated at 37°C for 24 hours and placed at room temperature for 2-4 hours. A total 
of 90 age-synchronized L4 females per line were picked to said plates at a density of 3 
females per plate. Resistance was scored as the fraction surviving after 5 days of 
exposure to the slow-kill environment. An individual was considered dead when a nose-
touch failed to elicit any movement. 
 
Experimental Evolution.  
Selection in fast-kill environment. Two population replicates of C. remanei 
(PX443) were grown on NGM lite plates with OP50 as a food source for 40 generations. 
This population was exposed to PA14 on PGS solid media for 4 hours every other 
generation. High-osmolarity controls were passaged in the same manner but with OP50 
substituted for PA14. A subset of each population was frozen every fifth generation for 
later analysis. 
 18 
Selection in slow-kill environment. Two population replicates of C. remanei 
(PX443) were grown on NGM lite plates alternating PA14 and OP50 as the food source 
every other generation for 40 generations. Low-osmolarity (lab-adapted) controls were 
passaged in the same manner but with OP50 substituted for PA14. A subset of each 
population was frozen every fifth generation for later analysis. 
Selection in slow-kill/fast-kill environments. Two population replicates of C. 
remanei (PX443) were grown on NGM lite plates with PA14 as the only food source for 
40 generations. This population was also exposed to PA14 on PGS solid media for 4 
hours every other generation. A subset of the population was frozen every fifth 
generation for later analysis. 
 
Statistical Analysis. Slow-killing survival was calculated as the proportion of 
individuals surviving after five days of exposure. Individuals who had left the plates were 
removed from the data set. Fast-killing survival was calculated as the proportion of 
individuals surviving after 24 hours of removal from fast-killing relative to the number of 
individuals surviving after 24 hours of removal from control plates. Specific hypotheses 
were tested via categorical data analysis of weighted survival frequencies using JMP 
Pro10 (SAS Institute Inc, 2013). Differences among groups was tested using the chi-
square resulting from the appropriate likelihood-ratio test.  
 
  
 19 
APPENDIX 
FIGURES AND TABLES 
Figure 1: Experimental evolution design. Layout of environments followed by 
experimental evolution cycle. 
 
 
  
PX443 
(Ancestor) 
NGM 
(Low Osmo) 
OP50 
(control) 
PA14 
(Slow Kill) 
PGS 
(High Osmo) 
PA14 
(Fast Kill) 
PA14 
(SKFK) 
OP50 
(Fast Kill Ctrl) 
Gen 1 
E. coli 
16K 
Gen 2 
PA14 
10 1K 
Gen 3 E. 
coli 16K 
Gen 4 
PA14 
10 1K 
Gen 5 E. 
coli 16K 
+Freeze 
Gen 6 
PA14 
10 1K 
Gen 7 E. 
coli 16K 
Gen 8 
PA14 
10 1K 
Gen 9 E. 
coli 16K 
Gen 10 
PA14 
10 1K 
+Freeze 
Beginning with ancestor (in freezer) 
 
Cycle continued 4 times – Up to Gen 
40 (numbers reflect population size) 
 
Two replicates for experimental lines  
 
For Slow-kill/Fast-kill evolved line:  
E. coli = PA14 on slow-killing media 
PA14 = PA14 on fast-killing media 
 20 
Figure 2: Survival kinetics of PX443 (Ancestor) L4 life-stage individuals in OP50 
control, slow-killing, and fast-killing environments. 
                                      
 
 
Figure 3: Percent survival of PX443 (Anc). Slow-killing measurements were taken at 5-
days post-exposure and fast-killing measurement is 24 hours after completion of a 4-hour 
exposure. Error bars reflect standard error. 
 
 
0.00
0.25
0.50
0.75
1.00
P
er
ce
n
t 
S
u
rv
iv
in
g
 
OP50       Slow-Kill     Fast-Kill 
Anc
Control 
Slow-Kill 
Fast-Kill 
Percent Surviving 
Days Alive 
 21 
Figure 4: Slow-killing environment log fold change in survival of evolved populations 
relative to PX443. Error bars reflect standard error. 
* = significant difference from PX443 
 
  
* 
-0.25
-0.13
0.00
0.13
0.25
L
o
g
 F
o
ld
 C
h
an
g
e 
in
 S
u
rv
iv
al
 
Slow-Kill Environment 
SK
FK
SKFK
FKC
Ctrl
 22 
Figure 5: Fast-killing environment log fold change in survival of evolved populations 
relative to PX443. Error bars reflect standard error. 
* = significant difference from PX443 
† = significant difference from FK 
‡ = significant difference from SKFK 
 
  
 * ‡ 
* † ‡ 
* † 
* † ‡ 
* † ‡ 
0
0.2
0.4
0.6
L
o
g
 F
o
ld
 C
h
an
g
e 
in
 S
u
rv
iv
al
 
Fast-Kill Environment 
FK
SK
SKFK
FKC
Ctrl
 23 
Table 1: Differences between replicates of experimentally evolved populations in fast-kill 
survival. Shaded cells have p > 0.05. 
Fast-Kill 
ChiSquare 
Analysis FK1 FK2 SK1 SK2 SKFK1 SKFK2 FKC1 FKC2 Ctrl 1 Ctrl 2 Anc 
FK1 
           
FK2 
Χ
2
1 = 
1.2,        
p = 
0.2743 
          
SK1 
Χ
2
1 = 
37.3,        
p < 
0.0001 
Χ
2
1 = 
52.5,        
p < 
0.0001 
      
  
  
SK2 
Χ
2
1 = 
14.2,        
p = 
0.0002 
Χ
2
1 = 
23.0,        
p < 
0.0001 
Χ
2
1 = 
2.8,     
p = 
0.0916 
        
SKFK1 
Χ
2
1 = 
99.6,        
p < 
0.0001 
Χ
2
1 = 
119.1,        
p < 
0.0001 
Χ
2
1 = 
30.7,     
p < 
0.0001 
Χ
2
1 = 
38.7,     
p < 
0.0001 
       
SKFK2 
Χ
2
1 = 
115.5,        
p < 
0.0001 
Χ
2
1 = 
138.2,        
p < 
0.0001 
Χ
2
1 = 
35.5,        
p < 
0.0001 
Χ
2
1 = 
42.8,        
p < 
0.0001 
Χ
2
1 = 
0.2,        
p = 
0.6989 
      
FKC1 
Χ
2
1 = 
237.9,        
p < 
0.0001 
Χ
2
1 = 
268.3,        
p < 
0.0001 
Χ
2
1 = 
130.5,        
p < 
0.0001 
Χ
2
1 = 
123.5,        
p < 
0.0001 
Χ
2
1 = 
16.2,        
p < 
0.0001 
Χ
2
1 = 
25.2,        
p < 
0.0001 
     
FKC2 
Χ
2
1 = 
263.8,        
p < 
0.0001 
Χ
2
1 = 
298.8,        
p < 
0.0001 
Χ
2
1 = 
147.4,        
p < 
0.0001 
Χ
2
1 = 
128.8,        
p < 
0.0001 
Χ
2
1 = 
11.6,        
p = 
0.0006 
Χ
2
1 = 
20.5,        
p < 
0.0001 
Χ
2
1 = 
1.5,        
p = 
0.2146 
    
Ctrl 1 
Χ
2
1 = 
43.9,        
p < 
0.0001 
Χ
2
1 = 
58.5,        
p < 
0.0001 
Χ
2
1 = 
2.1,        
p = 
0.1508 
Χ
2
1 = 
7.4,        
p = 
0.0066 
Χ
2
1 = 
14.0,        
p  = 
0.0002 
Χ
2
1 = 
14.4,        
p = 
0.0002 
Χ
2
1 = 
72.3,        
p < 
0.0001 
Χ
2
1 = 
71.4,        
p < 
0.0001 
   
Ctrl 2 
Χ
2
1 = 
27.3,        
p < 
0.0001 
Χ
2
1 = 
38.3,        
p < 
0.0001 
Χ
2
1 = 
0.1,        
p = 
0.8018 
Χ
2
1 = 
2.6,        
p = 
0.1082 
Χ
2
1 = 
19.3,        
p < 
0.0001 
Χ
2
1 = 
19.9,        
p < 
0.0001 
Χ
2
1 = 
77.4,        
p < 
0.0001 
Χ
2
1 = 
75.7,        
p < 
0.0001 
Χ
2
1 = 
0.9,        
p = 
0.3470 
  
Anc 
Χ
2
1 = 
265.4,     
p < 
0.0001 
Χ
2
1 = 
295.1,     
p < 
0.0001 
Χ
2
1 = 
159.2,     
p < 
0.0001 
Χ
2
1 = 
152.0,     
p < 
0.0001 
Χ
2
1 = 
33.3,     
p < 
0.0001 
Χ
2
1 = 
46.6,     
p < 
0.0001 
Χ
2
1 = 
5.3,     
p = 
0.0218 
Χ
2
1 = 
13.3,     
p = 
0.0003 
Χ
2
1 = 
136.4,     
p < 
0.0001 
Χ
2
1 = 
103.4,     
p < 
0.0001 
 
 
 
 24 
Table 2: Differences between replicates of experimentally evolved populations in slow-
kill survival. Shaded cells have p > 0.05. 
Slow-Kill 
ChiSquare 
Analysis FK1 FK2 SK1 SK2 SKFK1 SKFK2 FKC1 FKC2 Ctrl 1 Ctrl 2 Anc 
FK1                       
FK2 
Χ
2
1 = 
0.3,    
p = 
0.6350 
                    
SK1 
Χ
2
1 = 
16.5,   
p < 
0.0001 
Χ
2
1 = 
13.1,  
p = 
0.0003 
                  
SK2 
Χ
2
1 = 
12.0,  
p = 
0.0005 
Χ
2
1 = 
9.2,    
p = 
0.0024 
Χ
2
1 = 
0.2,    
p = 
0.6461 
                
SKFK1 
Χ
2
1 = 
1.4,    
p = 
0.2310 
Χ
2
1 = 
0.5,    
p = 
0.4618 
Χ
2
1 = 
8.0,    
p = 
0.0046 
Χ
2
1 = 
5.2,    
p = 
0.0222 
              
SKFK2 
Χ
2
1 = 
0.1,    
p = 
0.7940 
Χ
2
1 = 
0.5,    
p = 
0.4629 
Χ
2
1 = 
18.4,   
p < 
0.0001 
Χ
2
1 = 
13.7,   
p = 
0.0002 
Χ
2
1 = 
2.1,    
p = 
0.1475 
            
FKC1 
Χ
2
1 = 
5.5,    
p = 
0.0189 
Χ
2
1 = 
8.0,    
p = 
0.0047 
Χ
2
1 = 
39.8,  
p < 
0.0001 
Χ
2
1 = 
32.0,  
p < 
0.0001 
Χ
2
1 = 
12.2,   
p = 
0.0005 
Χ
2
1 = 
4.3,    
p = 
0.0380 
          
FKC2 
Χ
2
1 = 
4.9,    
p = 
0.0272 
Χ
2
1 = 
7.2,    
p = 
0.0073 
Χ
2
1 = 
37.9,  
p < 
0.0001 
Χ
2
1 = 
30.4,  
p < 
0.0001 
Χ
2
1 = 
11.2,  
p = 
0.0008 
Χ
2
1 = 
3.8,    
p = 
0.0524 
Χ
2
1 = 
0.02,  
p = 
0.9030 
        
Ctrl 1 
Χ
2
1 = 
2.8,    
p = 
0.0947 
Χ
2
1 = 
4.6,    
p = 
0.0314 
Χ
2
1 = 
32.3,  
p < 
0.0001 
Χ
2
1 = 
25.5,  
p < 
0.0001 
Χ
2
1 = 
8.1,    
p = 
0.0045 
Χ
2
1 = 
2.0,    
p = 
0.1614 
Χ
2
1 = 
0.5,    
p = 
0.4876 
Χ
2
1 = 
0.3,    
p = 
0.5711 
      
Ctrl 2 
Χ
2
1 = 
0.8,    
p = 
0.3849 
Χ
2
1 = 
1.8,    
p = 
0.1775 
Χ
2
1 = 
24.2,  
p < 
0.0001 
Χ
2
1 = 
18.5,  
p < 
0.0001 
Χ
2
1 = 
4.2,    
p = 
0.0399 
Χ
2
1 = 
0.4,    
p = 
0.5470 
Χ
2
1 = 
2.2,    
p = 
0.1355 
Χ
2
1 = 
1.8,    
p = 
0.1740 
Χ
2
1 = 
0.6,    
p = 
0.4203 
    
Anc 
Χ
2
1 = 
1.6,    
p = 
0.2073 
Χ
2
1 = 
0.6,    
p = 
0.4215 
Χ
2
1 = 
7.5,    
p = 
0.0063 
Χ
2
1 = 
4.8,    
p = 
0.0281 
Χ
2
1 = 
0.01,    
p = 
0.9409 
Χ
2
1 = 
2.3,    
p = 
0.1314 
Χ
2
1 = 
12.5,    
p = 
0.0004 
Χ
2
1 = 
11.5,   
p = 
0.0007 
Χ
2
1 = 
8.3,    
p = 
0.0039 
Χ
2
1 = 
4.4,    
p = 
0.0349 
  
 
 
 25 
REFERENCES CITED 
Akira, S., Takeda, K., & Kaisho, T. (2001). Toll-like receptors: Critical proteins linking 
innate and acquired immunity. Nat Immunol, 2, 675-80. 
Akira, S., Uematsu, S., & Takeuchi, O. (2006). Pathogen recognition and innate 
immunity. Cell, 124, 783-801. 
Andersen, E., Gerke, J., Shapiro, J., Crissman, J., Ghosh, R., Bloom, J., Félix, M.-A., 
Kruglyak, L. (2012). Chromosome-scale selective sweeps shape Caenorhabditis 
elegans genomic diversity. Nat Genet, 44(3), 285-90. 
Barton, G., & Medzhitov, R. (2002). Control of adaptive immune responses by Toll-like 
receptors. Curr Opin Immunol, 14, 380-83. 
Baxt, L. A., Garza-Mayers, A. C., & Goldberg, M. B. (2013). Bacterial subversion of 
host innate immune pathways. Science, 340(6133), 697-701. 
Begun, J., Sifri, C., Goldman, S., Calderwood, S., & Ausubel, F. (2005). Staphylococcus 
aureus virulence factors identified by using a high-throughput Caenorhabditis 
elegans-killing model. Infect Immun, 73, 872-77. 
Broekaert, W., Terras, F., Cammue, B., & Osborn, R. (1995). Plant defensins: novel 
antimicrobial peptides as components of the host defense system. Plant Physiol, 
108(4), 1353-1358. 
Carty, M., Goodbody, R., Schroder, M., Stack, J., Moynagh, P., & Bowie, A. (2006). The 
human adaptor SARM negatively regulates adaptor protein TRIF-dependent Toll-
like receptor signaling. Nat Immunol, 7(10), 1074-81. 
Cezairliyan, B., Vinayavekhin, N., Grenfell-Lee, D., Yuen, G. J., Saghatelian, A., & 
Ausubel, F. M. (2013). Identification of Pseudomonas aeruginosa Phenazines that 
Kill Caenorhabditis elegans. PLOS Pathogens, 9(1), e1003101. 
Couillault, C., Pujol, N., Reboul, J., Sabatier, L., Guichou, J., Kohara, Y., & Ewbank, J. 
(2004). TLR-independent control of innate immunity in Caenorhabditis elegans 
by the TIR domain adaptor protein TIR-1, an ortholog of human SARM. Nat 
Immunol, 5(5), 488-94. 
Darby, C., Cosma, C.L., Thomas, J.H., & Manoli, C. (1999). Lethal paralysis of 
Caenorhabditis elegans by Pseudomonas aeruginosa. Proc Natl Acad Sci USA, 
96(26), 15202-7. 
 26 
De Robertis, E. (2008). Evo-Devo: Variations on ancestral themes. Cell, 132(2), 185-95. 
Estes, K., Dunbar, T., Powell, J., Ausubel, F., & Troemel, E. (2010). bZIP transcription 
factor zip-2 mediates an early response to Pseudomonas aeruginosa infection in 
Caenorhabditis elegans. Proc Natl Acad Sci USA, 107(5), 2153-8. 
Ganz, T., & Lehrer, R. (1994). Defensins. Curr Opin Immunol, 6(4), 584-9. 
Garvis, S., Munder, A., Ball, G., de Bentzmann, S., Wiehlmann, L., Ewbank, J., . . . 
Filloux, A. (2009). Caenorhabditis elegans semi-automated liquid screen reveals a 
specialized role for the chemotaxis gene cheB2 in Pseudomonas aeruginosa 
virulence. PLoS Pathog, 5(8), e1000540. 
Glavis-Bloom, J., Muhammed, M., & Mylonakis, E. (2012). Of Model Hosts and Man: 
Using Caenorhabditis elegans, Drosophila melanogaster, and Galleria mellonella 
as model hosts for infectious disease research. In E. Mylonakis, F. Ausubel, & A. 
Casadevall, Recent Advances on Model Hosts (pp. 11-17). New York: Springer. 
Hoffman, J. (1995). Innate immunity of insects. Curr Opin Immunol, 7(1), 4-10. 
Irazoqui, J. E., Urbach, J. M., & Ausubel, F. M. (2010). Evolution of host innate defence: 
insights from C. elegans and primitive invertebrates. Nat Rev Immunol, 10(1), 47-
58. 
Irazoqui, J., Urbach, J., & Ausubel, F. (2010). Evolution of host innate defence: insights 
from Caenorhabditis elegans and primitive invertebrates. Nar Rev Immunol, 
10(1), 47-58. 
Iwasaki, A., & Medzhitov, R. (2004). Toll-like receptor control of the adaptive immune 
responses. Nat Immunol, 5, 987-95. 
Kaisho, T., Takeuchi, O., Kawai, T., Hoshino, K., & Akira, S. (2001). Endotoxin-induced 
maturation of MyD88-deficient dendritic cells. J Immunol, 166, 5688-694. 
Kaletta, T., & Hengartner, M. (2006). Finding function in novel targets: C. elegans as a 
model organism. Nat Rev Drug Discov, 5, 387-399. 
Kammenga, J., Phillips, P., De Bono, M., & Doroszuk, A. (2008). Beyond induced 
mutants: using worms to study natural variation in genetic pathways. Trends in 
Genetics, 24(4), 178-85. 
 27 
King, E., Ward, M., & Raney, D. (1954). Two simple media for the demonstration of 
pyocyanin and fluorescin. J. Lab.Clin. Med., 44, 301-307. 
Kurz, C., Chauvet, S., Andres, E., Aurouze, M., Vallet, I., Michel, G., . . . Ewbank, J. 
(2003). Virulence factors of the human opportunistic pathogen Serratia 
marcescens identified by in vivo screening. EMBO J, 22(7), 1451-60. 
Kuznetsova, M., Karpunina, T., Maslennikova, I., Nesterova, L., & Demakov, V. (2012). 
Effect of Pseudomonas aeruginosa exometabolites on planktonic and biofilm 
cultures of Escherichia coli. Zh Mikrobiol Epidemiol Immunobiol, 6, 17-22. 
Lai, C.-H., Chou, C.-Y., Lan-Yang, C., Liu, C.-S., & Lin, W.-c. (2000). Identification of 
novel human genes evolutionarily conserved in caenorhabditis elegans by 
comparative proteomics. Genome Res, 10, 703-713. 
Liberati, N., Fitzgerald, K., Kim, D., Feinbaum, R., Golenbock, D., & Ausubel, F. 
(2004). Requirement for a conserved Toll/interleukin-1 resistance domain protein 
in the Caenorhabditis elegans immune response. Proc Natl Acad Sci USA, 
101(17), 6593-8. 
Mahajan-Miklos, S., Tan, M.-W., Rahme, L. G., & Ausubel, F. M. (1999). Molecular 
Mechanisms of Bacterial Virulence Elucidated Using a Pseudomonas aeruginosa-
Caenorhabditis elegans Pathogenesis Model. Cell, 96(1), 47-56. 
Marsh, E., van den Berg, M., & May, R. (2011). A two-gene balance regulates 
Salmonella typhimurium tolerance in the nematode Caenorhabditis elegans. PLoS 
ONE, 6, e16839. 
Medzhitov, R. (2001). Toll-like receptors and innate immunity. Nat Rev Immunol, 1(2), 
135-45. 
Miller, J. (1972). Experiments in Molecular Genetics. Cold Spring Harbor, New York: 
Cold Spring Harbor Laboratory. 
Morran, L., Schmidt, O., Gelarden, I., Parrish, R. 2., & Lively, C. (2011). Running with 
the Red Queen: host-parasite coevolution selects for biparental sex. Science, 
333(6039), 216-8. 
Nakahira, K., Haspel, J., Rathinam, V., Lee, S., Dolinay, T., Lam, H., . . . Choi, A. 
(2011). Autohpagy proteins regulate innate immune responses by inhibiting the 
release of mitochondrial DNA mediated by the NALP3 inflammasome. Nat 
Immunol, 12(3), 222-57. 
 28 
Nomiyama, H., & Osada, N. (2013). Systematic classification of vertebrate chemokines 
based on conserved synteny and evolutionary history. Genes Cells, 18(1), 1-16. 
Nomiyama, H., Osada, N., & Yoshie, O. (2010). The evolution of mammaliam 
chemokine genes. Cytokine & Growth Factor Reviews, 21(4), 253-262. 
Nowroozi, J., Akhavan Sepahi, A., & Rashnonejad, A. (2012). Pyocyanin biosynthetic 
genes in clinical and environmental isolates of Pseudomonas aeruginosa and 
detection of pyocyanin's antimicrobial effects with or without colloidal silver 
nanoparticles. Cell J, 14(1), 7-18. 
O'Callaghan, D., & Vergunst, A. (2010). Non-mammalian animal models to study 
infectious disease: worms or fly fishing? Curr Opin Microbiol, 13, 79-85. 
Parish, C., & O'Neill, E. (1997). Dependence of the adaptive immune response on innate 
immunity: some questions answered but new paradoxes emerge. Immunol Cell 
Biol, 75(6), 523-7. 
Pasare, C., & Medzhitov, R. (2004). Toll-dependent control mechanisms of CD4 T cell 
activation. Immunity, 21, 733-41. 
Phillips, P. (2006). One Perfect Worm. TRENDS in Genetics, 22(8), 405-407. 
Phillips, P. (2012). Serf-fertilization sweeps up variation in the worm genome. Nat Genet, 
44(3), 237-8. 
Powell, J. R., Kim, D. H., & Ausubel, F. M. (2009). The G protein-coupled receptor 
FSHR-1 is required for the Caenorhabditis elegans innate immune response. Proc 
Nat Acad Sci USA, 106(8), 2782-2787. 
Prinz, M., Garbe, F., Schmidt, H., Mildner, A., Gutcher, I., Wolter, K., . . . Becher, B. 
(2006). Innate immunity mediated by TLR9 modulates pathogenicity in an animal 
model of multiple sclerosis. J Clin Invest, 116, 456-64. 
Pujol, N., Cypowyi, S., Ziegler, K., Millet, A., Astrain, A., Goncharov, A., . . . Ewbank, 
J. (2008). Distinct innate immune responses to infection and wounding in the C. 
elegans epidermis. Curr Biol, 18(7), 481-9. 
Pujol, N., Link, E., Liu, L., Kurz, C., Alloing, G., Tan, M., . . . Ewbank, J. (2001). A 
reverse genetic analysis of components of the Toll signaling pathway in 
Caenorhabditis elegans. Curr Biol, 11(11), 809-21. 
 29 
Pujol, N., Zugasti, O., Wong, D., Couillault, C., Kurz, C. L., Schulenburg, H., & Ewbank, 
J. (2008). Anti-fungal innate immunity in C. elegans is enhanced by evolutionary 
diversification of antimicrobial peptides. PLoS Pathogens, 4, e1000105. 
Pukkila-Worley, R., & Ausubel, F. (2012). Immune defense mechanisms in the 
Caenorhabditis elegans intenstinal epithelium. Curr Opin Immunol, 24(1), 3-9. 
Rahme, L., Stevens, E., Wolfort, S., Shao, J., Tompkins, R., & Ausubel, F. (1995). 
Common virulence factors for bacterial pathogenicity in plants and animals. 
Science, 268, 1899-1902. 
Reschner, A., Hubert, P., Delvenne, P., Boniver, J., & Jacobs, N. (2008). Innate 
lymphocyte and dendritic cell cross-talk: a key factor in the regulation of the 
immune response. Clin Exp Immunol, 152(2), 219-226. 
Reszka, K., Xiong, Y., Sallans, L., Pasula, R., Olakanmi, O., Hassett, D., & Britigan, B. 
(2012). Inactivation of the potent Pseudomonas aeruginosa cytotoxin pyocyanin 
by airway peroxidases and nitrite. Am J Physiol Lung Cell Mol Physiol, 302(10), 
L1044-56. 
Rock, F., Hardiman, G., Timans, J., Kastelein, R., & Bazan, J. (1998). A family of human 
receptors structurally related to Drosophila Toll. Proc Natl Acad Sci USA, 95(2), 
588-93. 
Ruvkun, G., & Hobert, O. (1998). The taxonomy of developmental control in 
Caenorhabditis elegans. Science, 282, 2033-41. 
Saitoh, T., Fujita, N., Jang, M., Uematsu, S., Yang, B., Satoh, T., . . . Akira, S. (2008). 
Loss of the autophagy protein Atg16L1 enhances endotoxin-induced IL-1B 
production. Nature, 456(7219), 264-68. 
SAS Institute Inc. (2013). JMP Pro 10. Cary, NC, USA. 
Schulenburg, H., & Boehnisch, C. (2008). Diversification and adaptive sequence 
evolution of Caenorhabditis lysozymes (Nematoda: Rhabditidae). BMC Evol. 
Biol., 8, 114. 
Shivers, R., Kooistra, T., Chu, S., Pagano, D., & Kim, D. (2009). Tissue-specific 
activities of an immune signaling module rgulate physiological responses to 
pathogenic and nutritional bacteria in C. elegans. Cell Host & Microbe, 6, 321-30. 
 30 
So, S., Tokumaru, T., Miyahara, K., & Ohshima, Y. (2011). Control of lifespan by food 
bacteria, nutrient limitation and pathogenicity of food in C. elegans. Mech Ageing 
Dev, 132(4), 210-2. 
Song, W. (2012). Crosstalk between complement and toll-like receptors. Toxicol Pathol, 
40(2), 174-82. 
Stiernagle, T. (1999). Maintenance of C. elegans. In I. Hope, C. elegans: A practical 
approach (pp. 51-67). Oxford: Oxford University Press. 
Takeda, K., & Akira, S. (2003). Toll receptors and pathogen resistance. Cell Microbiol, 
5(3), 143-53. 
Takeda, K., Kaisho, T., & Akira, S. (2003). Toll-like receptors. Annu Rev Immunol, 21, 
335-376. 
Tan, M., Mahajan-Miklos, S., & Ausubel, F. (1999). Killing of Caenorhabditis elegans by 
Pseudomonas aeruginosa used to model mammalian bacterial pathogenesis. 
PNAS, 96, 715-720. 
Tenor, J., & Aballay. (2008). A conserved Toll-like receptor is required for 
Caenorhabditis elegans innate immunity. EMBO Rep, 9, 103-9. 
Trinchieri, G. (1995). Natural killer cells wear different hats: effector cells of innate 
resistance and regulatory cells of adaptive immunity and of hematopoiesis. Semin 
Immunol, 7(2), 83-8. 
Troll, J., Bent, E., Pacquette, N., Wier, A., Goldman, W., Silverman, N., & McFall-Ngai, 
M. (2010). Taming the symbiont for coexistence: a host PGRP neutralizes a 
symbiont toxin. Environ Microbiol, 12(8), 2190-203. 
Vallabhapurapu, S., & Karin, M. (2009). Regulation and function of NF-kappaB 
transcription factors in the immune system. Annu Rev Immunol, 27, 693-733. 
Wu, L., & Anderson, K. (1998). Regulated nuclear import of Rel proteins in the 
Drosophila immune response. Nature, 392(6671), 93-7. 
Zhu, S. (2008). Discovery of six families of fungal defensin-like peptides provides 
insights into origin and evolution of the CSalphabeta defensins. Mol Immunol, 45, 
828-38.