Download Avoiding Chronic Inflammation

Avoiding Chronic Inflammation
Cytokine Networks, Bacteria and Bacteriokines
By Will Koning
Supervisors: Professors Robert Seymour and Brian Henderson
case study
11 February 2005
In this essay I give a basic introduction to the inflammatory response paying
particular attention to a cytokine network that modulates this response. I describe a
model of a pro-inflammatory / anti-inflammatory cytokine network and I discuss
how the model relates to a few examples of inflammatory responses. I present some
novel research that provides a possible example of bacteria subverting the immune
response and I conclude with a summary.
Last century during the antibiotic age the United States Surgeon General, William
Stewart, announced that it was, “time to close the book on infectious disease”. It is more
than 30 years since this delightfully arrogant statement was made and bacteria are far
from being under our control. They have evolved antibiotic resistance because of the
prevalence of antibiotics in their environment due to unnecessary human and livestock
intake. We develop more and more antibiotics yet more and more cases of bacteria that
are resistant to all our repertoire of antibiotics are evolving. These bacteria that are
resistant to most, if not all, antibiotics are aptly named ‘super-bugs’.
Bacteria are heavier, more numerous, and more diverse than all plants and animals
combined. There are very few described species of bacteria because due to their small
size they are difficult to classify without using molecular techniques (this will soon
change). The human body comprises about 1013 cells yet we carry about 1014 bacteria
(Seymour and Henderson, 2001), even when we eat our five-plus servings of fruit and
shower daily. These bacteria are called the ‘normal microflora’ and live symbiotically
with us. They protect us by covering all of our exposed external and internal surfaces and
excluding pathogenic bacteria from these (Hornef et al., 2002; Seymour and Henderson,
2001). Bacteria employ a few other competitive mechanisms as well (some of which are
the initial source of antibiotics; Birch, 1993). Bacteria have a generation time of a few
hours while humans have a minimum generation time of over 10 years so bacteria can go
through approximately 104 generations for every human one. This allows a huge amount
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of evolutionary change. Microbes have co-evolved with their hosts (Hornef et al., 2002)
and we simply can not compete with them in an evolutionary arms race. We are a home
for them and they will only damage us if it improves their fitness, and it is in these cases,
and when our immune system overreacts, that pathology occurs. Every generation of
bacteria that lives with us either symbiotically or pathogenically is being selected and
those that are better able to thrive and survive off us persist. Symbiotic bacteria get better
at excluding other bacteria but also at living with us. Commensal bacteria evolve their
surface antigens in response to significant antibacterial responses throughout the life of
the host (Young et al., 2002). Some even develop ways to manipulate our immune system
(Hornef et al., 2002). We need to learn to live with commensal microflora while we
destroy pathogens and avoid chronic inflammation.
Plants and animals protect themselves in a variety of ways (innate immunity in plants and
animals and acquired immunity as well in vertebrates). Innate immunity is the natural
immunity an organism has to fight infection while acquired immunity is developed while
an organism is alive and it uses very specific antibodies to target infective agents. The
inflammatory response occurs when any pro-inflammatory components (PAMPS –
pathogen-associated molecular patterns) are detected. The most common bacterial PAMP
is lipopolysaccaride (LPS), a major cell membrane protein of a type of bacteria. The
inflammatory response involves an increased blood flow and attraction of cells to the
infected site and the activation of many components of the immune system. The innate
immune response is critical in the early stages of infection. In vertebrates it is also linked
to the adaptive response, which assists in fighting the infection while building specific
immunity with a memory component (Hornef et al., 2002). Normal microflora do not
cause inflammation but paradoxically these bacteria contain many pro-inflammatory
components (PAMPS; Hornef et al., 2002).
We desire to understand the process of infection and inflammation to treat ‘pathologies’
in humans and other animals and plants. We are interested in getting the balance right;
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targeting pathogenic bacteria with enough of a response to destroy them without causing
unnecessary cellular and tissue damage. Some bacterial pathogens can adapt their
environment to favour survival and subvert immune mechanisms to reduce pathology, or
even increase pathology to aid in their infection of the host. If bacteria deliberately
inflame the host, once the invasion is completed they may change their strategy and
dampen the immune response (Young et al., 2002; Hornef et al., 2002).
Cytokines are the most important mediators of the inflammatory response. Cytokines are
soluble proteins released by nearly all (if not all) of the cells in the body but primarily
cells of the immune system, which act non-enzymatically with specific receptors to
regulate immune responses (Clemens, 1991; Nicola 1994) They have no documented
inherent activity. The major interest in studying cytokines is their role in disease. PAMPS
stimulate the release of pro-inflammatory cytokines, which are crucial to both the innate
and adaptive immune response (Hornef et al., 2002). As we come to the end of the
antibiotic age it is important we improve our understanding of the networks of interacting
components between bacteria and their hosts. Major players in these networks are
cytokines.
Cytokine networks are incredibly complex. If too few or too many cytokines are
produced the host dies. If the correct amount and combination of cytokines are produced
the host overcomes infection. We want to understand what is right for the system and
what the system actually is. It is very important to understand the balance between proinflammatory and anti-inflammatory responses of cytokine networks and the networks’
roles in inflammatory disease (Seymour and Henderson 2001). Cytokines can display
autocrine and paracrine functionality. An autocrine action is where a cell produces
molecules that affect that cell while a paracrine action is where a cell produces molecules
that affect other cells. We know very little about the kinetics and dynamics of
inflammatory networks (Seymour and Henderson 2001).
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Models are developed to gain insight in to systems, to determine how systems work, to
determine how they fail and to determine how they could be changed. Once a model is
constructed, the model can be explored and manipulated. If it is a good model it will
behave in the same way as the underlying real system. We want to understand how
cytokines interact to produce an appropriate response and also to understand how they
could function to produce known disorders. Once we understand how the system
malfunctions we can try to prevent this from happening, and cure or treat it when it does.
Seymour and Henderson (2001) modeled the Interleukin-1 / Tumor Necrosis Factor alpha
/ Interleukin-10 (IL-1/TNF-/IL-10) cytokine network. This is the core network involved
in the initial response to bacterial infection and is central to the pathology of rheumatoid
arthritis and systemic inflammatory response syndrome. Aside from being clinically
important this network has a few nodes with a clear relationship making it more
amenable to modeling (Seymour and Henderson, 2001). They used a few different
models and variations on these but primarily a six dimensional (reduced down to four; by
invoking a steady state hypothesis to model stimulated receptor synthesis) continuoustime dynamical system. This looked at the autocrine response of an immune cell to its
own cytokine production after a transient or sustained insult. This factored in the ‘insult’
(transitory/persistent stimulus with TNF- or LPS), the pro-inflammatory cytokine IL-1,
the anti-inflammatory cytokine IL-10 and the complicated dynamics of the cell surface
receptors (dissociation, synthesis, endocytosis, and shedding). This model showed that a
variety of outcomes are possible. With a sustained stimulus; multiple equilibria, runaway
production, stable limit cycles and quasi-periodic behaviour are all possible and depend
on the precise nature of the response functions of the cytokines. With a transient stimulus
the cell may return to its initial quiescent state or remain activated and find an appropriate
asymptotic state of inflammation if a threshold was crossed (Seymour and Henderson,
2001).
The interesting dynamics this model displays cannot occur with a totally concave IL-1
autocrine response. To get multiple equilibria requires ‘ultrasensitive’ responses, but the
global IL-1 autocrine response is concave when tested experimentally using LPS
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stimulation. This was determined by testing the dose response curve on a population of
cells using flow cytometry (Rapecki’s data presented in both of the ‘case presentations’;
Seymour and Henderson, 2005). However if each cell has an ‘ultrasensitive’ response and
the threshold is variable the global response can be concave. Mean field models where
every cell behaves as an average cell are inadequate, particularly as only a few
macrophages are around an infected site. The response occurs at a local cellular level so
the cells ‘ultrasensitive’ response can give rise to multiple equilibria. It would be nice to
experimentally determine the dose response characteristics of individual cells but cells
die so can only be tested a few times.
To analyse the model it was reduced from six dimensions to four by invoking a quasisteady-state hypothesis to model stimulated receptor-synthesis. This hypothesis could be
wrong but it seems reasonable to assume it is valid as it is based on an estimate from
published kinetic data for IL-1 (which could be made for IL-10 due to a lack of data) and
the apparent weak effect of stimulated up-regulation of receptors (Takii et al., 1992;
Seymour and Henderson, 2001).
The different behaviours of the model are potentially the dynamics of a range of immune
responses. The most common response to the detection of pathogens is modeled by a
transitory stimulus where the inflammatory response returns to its initial quiescent state.
Septic shock, chronic inflammation, inflammatory variability and periodic systems are all
possible within the model. Runaway IL-1 production where IL-1 is stimulated but no
equilibrium is found could be the underlying process in septic shock (Seymour and
Henderson, 2001). A stable limit cycle could explain bouts of inflammatory response and
remission. The model can be explored to predict possible effects of treatments of
inflammation. The main parameter controlling the dynamics of the model is the strength
and duration of stimulus (the bacteria), but adding or subtracting (of which there are a
variety of methods) from the pool of cytokines could effect a bifurcation to a different
equilibrium potentially treating the inflammation (Seymour and Henderson, 2001).
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Jit et al. (2004) used this model to provide an explanation of the effectiveness of
treatment by blockading TNF in Rheumatoid Arthritis, which is an equilibrium
condition, as opposed to the ineffectiveness of this treatment in systemic inflammatory
response syndrome (SIRS), because it is a non-equilibrium condition. Treating SIRS with
anti-TNF- therapy may keep TNF in the system and release it slowly as it decouples (Jit
et al. 2004). SIRS would benefit from a model that uses several cytokines that could
predict accurately the form and timing of drug delivery, as it exists in a non-equilibrium
condition with many different phases of interactions taking place simultaneously.
Modeling mulitiple cytokine connected subnetworks that are out of phase is important for
SIRS (Jit et al. 2004).
Possible Bacteriokines with Similarity to IL-10 (novel research)
I am particularly interested in Interleukin-10, the main cytokine involved in downregulating the immune response, and the possibility of bacterial versions of this cytokine.
Bacteria may mediate, resist, counteract or subvert the host immune system so I went
looking for IL-10 similarity in the published bacterial protein sequence databases. A
BLAST search looks for similarity between sequences (BLAST 2.0; Altschul et al., 1997;
Basic Local Alignment Search Tool). It also calculates the expected number of sequences
that are more similar than the matches it finds that could occur by chance within the
searched database. The ‘region C’ IL-10 receptor combining site is essential for
immunosuppressive but not JAK-STAT activity of IL-10 (Reineke et al., 1998; Riley et
al., 1999). I ran a BLAST search on this receptor-combining site and two Lactobacillae
were returned with very strong hits (expected occurrence of 0.045 times within database;
see Appendix 1 for sequence alignments and Figure 1 for a picture of IL-10; Altschul et
al., 1997). This is very interesting as Lactobacillae are lactic acid producing bacteria that
are commonly found living symbiotically in the human digestive tract.
It is possible these bacterial proteins are bacterial cytokines (bacteriokines; Wilson et al.,
1998) that down-regulate the human immune response (possibly that of other mammals
with similar IL-10 regions as well). However cytokines bind to very high affinity
receptors and there are multiple binding regions on the IL-10/IL-10R combining site so
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similarity at the sequence level does not necessarily mean there is similarity at the
functional level (Nicola 1994; Pestka et al., 2004). The Lactobacillae proteins may
interact with the IL-10 receptor, or other proteins that interact with IL-10. Bacteria
produce many molecules that affect the cytokine network and these proteins may be part
of the cytokine network but not actual bacteriokines (Wilson et al., 1998). The BLAST
search reports the Lactobacillae proteins function as acetate kinases, which may be
incorrect, but any immunomodulatory effects may be in addition to their kinase function.
As the Lactobacillae are non pathogenic it is likely that if they do have a function they
will work agonistically to produce an immunosuppressive response but they could
theoretically function as an antagonist of IL-10. It is very hard to predict possible
functions, if any, based on a small amount of sequence similarity. The bacterial protein
sequences are different and are not identical even at the receptor-combining site as they
have extra amino acids. IL-10 functions as a homodimer and the bacterial protein is much
larger and may or may not be able to, or need to pair up with, another copy of itself.
Viral homologues (virokines) have been found which are very similar to IL-10. The first
virus-encoded cytokine identified was from Epstein-Barr virus (EBV), which shares
significant homology with IL-10 (Barry and McFadden, 1997). It is easier to find viral
versions because viruses can readily take host DNA up and produce the products. If they
are useful the viruses will increase their fitness and the proteins will be maintained but in
the majority of the time the proteins will be of no use to the virus. Any viral versions that
are maintained will share high homology with the host proteins and can be readily
detected by homology searches. Technically, to define a match as homologous requires
descent from a common ancestor, and if this is not the case it should be called similar. It
is harder for bacteria to integrate host DNA as they normally live outside the cell and do
not use the cell’s nuclear machinery. Bacterial proteins are more likely to be present
through convergent evolution and these are harder to detect through similarity searches
because the proteins do not share common ancestry but only common function.
Bacteriokines and virokines are collectively called microkines. Microkines add an extra
level of complexity to the super-networks of host and microbial molecules that interact to
determine the manifestations of disease (Wilson et al., 1998).
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Viral cytokines have reduced binding efficacy with human IL-10 (Liu et al., 1997). As
viruses tend to steal genetic material from their hosts, it is very similar to the hosts
version. Bacteria have larger genomes than viruses and these may have more potential
evolutionary trajectories. Bacteria may be able to make versions of host proteins that are
more functional than virus homologues because they have more genetic material to work
with and can explore more adaptive space. They may avoid sub-optimal adaptive peaks if
they can evolve any sort of functionality at all. Bacteria do have a range of mechanisms
to spread genes horizontally among themselves and the BLAST search mentioned above
also returned a bacteriophage with a match to the same region (this is not investigated in
this essay).
The BLAST search returned Lactobacillus proteins that have highly-significant similarity
with a small but important region if IL-10 so the next step to determine if these are nodes
in the cytokine network by testing their function experimentally. It would be very
interesting to test the affinity of the bacterial proteins to IL-10R and to see if they have an
immunosuppressive effect. Running a competitive ELISA to see if soluble IL-10 receptor
inhibits binding of an antibody to the Lactobacillae proteins could test affinity of the
proteins for IL-10R. Purifying the proteins and investigating their effect on human
macrophages that are exposed to LPS could assess their immuno-activity.
A different species of bacteria, Actinobacillus actinomycetemcomitans, produces a gene
product that is toxic to primate leucocytes, an immunosuppressive protein, a superantigen
that kills T-cells, proteins that inhibit the cell cycle, a protein that may bind to the IL-10
receptor and an inhibitor of neutrophil chemotaxis, thus clearly demonstrating the
possibilities available to bacteria to modulate the immune response (Henderson et al.
2002). A bacterial cytokine is already known but this is for a species of bacteria to
communicate with dormant conspecifics to promoting their resuscitation (Mukamolova et
al., 1998).
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In summary this essay is about dealing with bacteria without becoming chronically
or excessively inflamed. It describes a model of the pro-inflammatory-antiinflammatory cytokine network and explores its normal and clinical dynamics. It
also describes ways these are affected. This essay also presents a possible example of
a human-bacterial cytokine symbiosis, where humans get the benefits of lactose
degradation and the Lactobacillus bacteria get a safe home. In this case humans and
bacteria both benefit from the absence of an inappropriate human immune
response.
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REFERENCES
Altschul, S. F., T. L. Madden, A. A. Schaeffer, J. Zhang, Z. Zhang, W. Miller, and
D. J. Lipman. 1997, Gapped BLAST and PSI-BLAST: a new generation of protein
database search programs, Nucleic Acids Research. 25:3389-3402.
Barry, M., and G. McFadden. 1997. Virus encoded cytokines and cytokine receptors.
Parasitology. 115:S89-S100.
Birch, B. 1993. Alexander Fleming. Bitter Verlag
Clemens, M. J. 1991. Cytokines. BIOS Scientific Publishers.
Henderson, B., M. Wilson, L. Sharp, and J. M. Ward. 2002. Actinobacillus
actinomycetemcomitans. Journal of Medical Microbiology. 51:1013-1020.
Hornef, M. W., M. J. Wick, M. Rhen, and S. Normark. 2002. Bacterial strategies for
overcoming host innate and adaptive immune responses. Nature Immunology. 3:10331040.
Jit, M., B. Henderson, M. Stevens, and R. M. Seymour. 2004. TNF- neutralization in
cytokine-driven diseases: a mathematical model to account for therapeutic success in
rheumatoid arthritis but therapeutic failure in systemic inflammatory response syndrome.
Rheumatology. (Advance Access published on December 7, 2004, DOI
10.1093/rheumatology/keh491).
Liu, Y., R. de Waal Malefyt, F. Briere, C. Parham, J. M. Bridon, J. Banchereau, K.
W. Moore, and J. Xu. 1997. The EBV IL-10 homologue is a selective agonist with
impaired binding to the IL-10 receptor. Journal of Immunology. 158:604-613.
Mukamolova, G. V., A. S. Kaprelyants, D. I. Young, M. Young, and D. B. Kell. 1998.
A bacterial cytokine. Proceedings of the National Academy of Sciences. 95:8916-8921.
Nicola, N. A. 1994. Guidebook to cytokines and their receptors. Oxford University Press.
Pestka, S., C. D. Krause, D. Sarkar, M. R. Walter, Y. Shi, and P. B. Fisher. 2004.
Interleukin-10 and related cytokines and receptors. Annual Review of Immunology.
22:927-979.
Reineke, U., R. Sabat, H. D. Volk, and J. Schneider-Mergener. 1998. Mapping of the
interleukin-10/interleukin-10 receptor combining site. Protein Science. 7:951-960.
Riley J. K., K. Takeda, S. Akira, and R. D. Schreiber. 1999. Interleukin-10 receptor
signaling through the JAK-STAT pathway. Requirement for two distinct receptor-derived
signals for anti-inflammatory action. Journal of Biological Chemistry. 274:16513-16521.
Seymour, R. M., and B. Henderson. 2001. Pro-inflammatory-anti-inflammatory
cytokine dynamics mediated by cytokine-receptor dynamics in monocytes.. IMA Journal
of Mathematics Applied in Medicine and Biology. 18:159-192.
Takii, T., T. Akahoshi, K. Kato, H. Hayashi, T. Marunouchi, K. Onozaki. 1992.
Interleukin-1 up-regulates transcription of its own receptor in a human fibroblast cell line
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Wilson, M., R. Seymour, and B. Henderson. 1998. Bacterial perturbation of cytokine
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The cover illustrates a fatal case of chronic inflammation in a straw man.
The cover photo of a burning man is by James Wang and is reproduced with permission
http://www.sims.berkeley.edu/~jwang/gallery/Burning%20Man/images/Inflammation.jpg
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QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
Figure 1. (Modified from Pestka et al. 2004). Ribbon diagram of IL-10 depicting helices
A-D coloured cyan and helices E-F coloured yellow. The 1a-binding site is coloured red
and the 1b site is coloured green. Adjacent to the ribbon diagram is a space filling model
of the domain with ball and stick receptor residues (white bonds) that contact site 1a and
site 1b shown. The region of similarity between human IL-10 and the Lactobacillus
johnsonii and L. gasseri kinases is at the carboxyl-terminal end of human IL-10 and is
encased by a line ‘can’. This region is known to be essential for anti-inflammatory
activity (Riley et al., 1999; Pestka et al., 2004).
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Appendix 1
Homo sapiens Interleukin-10. 1ILK
nscthfpgnlpnmlrdlrdafsrvktffqmkdqldnlllkeslledfkgylgcqalsemiqfyleevmpqa
enqdpdikahvnslgenlktlrlrlrrchrflpcenkskaveqvknafnklqekgiykamsefdifinyie
aymtmkirn
Acetate kinase [Lactobacillus johnsonii NCC533]
mkkvlavnsgsssfkyklfsldneeviasgmadrvglpgsvftmtladgsqhdeqsdianqeeavqkllsw
lkeynvidslediagvghrvvaggeeftdstvitednlwkiynmsdyaplhnpaeadgiyafmkvlpnvpe
vavfdtsfhqsldpvqylysvpykyyekfrarkygahgtsaryvsrrtadllnkpvedlkmvlchlgsgas
vtaikdgksfdtsmgfspvagitmstrsgdvdpsllqfimkkgnitsfnevikmlntesgllglsgispdm
rdiekaikngdkqaqltkdifinrivryigaymtemggldvlvftagigehdasvrkqimdgltwlgleyd
eeankanhesvittpnskitamivptneelmiardvvrlakldklnk
>gi|42518837|ref|NP_964767.1|
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene&cmd=search&term=4
2518837%5BPUID%5D acetate kinase [Lactobacillus johnsonii NCC 533]
gi|41583123|gb|AAS08733.1|
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene&cmd=search&term=4
1583123%5BPUID%5D acetate kinase [Lactobacillus johnsonii NCC 533]
Length = 402
Score = 35.8 bits (77), Expect = 1.4
(Expect 0.13 if searching for binding site)
(Expect 0.045 if searching for binding site within bacterial database)
Identities = 12/18 (66%), Positives = 12/18 (66%), Gaps = 5/18 (27%)
IL-10: 135 DIFIN----YIEAYMT-M 147
DIFIN
YI AYMT M
Sbjct: 303 DIFINRIVRYIGAYMTEM 320
COG0282: Acetate kinase [Lactobacillus gasseri]
Mkkvlavnsgsssfkyklfsldnekviasgmadrvglpgsvftmtladgsqhdeqsdianqeeavqkllsw
lkeynvidsladiagvghrvvaggeeftdstvitddnlwkiynmsdyaplhnpaeadgiyafmkvlpnvpe
vavfdtsfhqsldpvqylysvpykyyekfrarkygahgtsaryvsrrtadllkkpvedlkmvlchlgsgas
vtaikdgksfdtsmgfspvagitmstrsgdvdpsllqfimkkgnitsfnevikmlntksgllglsgispdm
rdiekaikngdkqakltkdifinrivryigaymtemggldvlvftagigehdasvrkqimdgltwlgleyd
ekankankegiittpkskitamivptneelmiardvvrlakldk
>gi|23002543|ref|ZP_00046218.1|
COG0282: Acetate kinase
[Lactobacillus gasseri]
Length = 399
Score = 35.8 bits (77), Expect = 1.4
(Expect 0.13 if searching for binding site)
(Expect 0.045 if searching for binding site within bacterial database)
Identities = 12/18 (66%), Positives = 12/18 (66%), Gaps = 5/18 (27%)
IL-10: 135 DIFIN----YIEAYMT-M 147
DIFIN
YI AYMT M
Sbjct: 303 DIFINRIVRYIGAYMTEM 320
(There were a few other strong hits for bacteria, including other symbionts, and notably
also for bacteriophages which are not addressed in this paper.)
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