The evolution and prevention of
Antibiotic resistance in Human pathogens
Bachelorthesis Marlies Oomen
Supervisors: Jan- Willem Veening Robin A. Sorg
Molecular Genetics, GBB, University of Groningen
Index
Abstract 3
Introduction 4
Current use of antibiotics 4
Evolution of antibiotics 4
Evolutionary pressure 6
Resistance genes 7
Antibiotic resistance in the human microbiome 8
Monotherapy vs. synergistic use 8
Conclusion 10
References 11
Abstract
Antibiotic resistance is a big problem worldwide. If we do not learn how to prevent resistance from occurring, we might go back to the times before the discovery of antibiotics. However, the more is known about the evolution of antibiotic resistance in pathogens, the more difficult it seems to prevent it. Antibiotic resistance is not new since the discovery of penicillin, however the spread is new. Antibiotic resistance is spreading rapidly, because most genes, which acquire resistance, are located at mobile elements. Therefore bacteria can pass it on easily via horizontal gene transfer to cells in their environment. Because a lot of cells are exposed to concentrations of antibiotic, by therapeutically use in humans, but also in agriculture and animal farming, the evolutionary pressure is high. This makes that the resistance cells have a big advantage and makes them spread easily in pathogens and in non-‐pathogenic cells. Nowadays resistant cells are all around us and it is inevitable that there will be resistance in pathogens for each antibiotic if it is biochemically possible. It is still guessing how to prevent this, however it is most likely that the answer lies in altering the dose, the combination and the structures of the antibiotics we use.
Introduction
Antibiotic resistance in human pathogens is a growing worldwide problem. The European centre for disease prevention and control (ECDC) estimated that 250000 people in the European union die annually due to an infection by multi resistant bacteria (ECDC, 2009). The antibiotic resistance is caused by the excessive use of antibiotics for treatment of humans, but also in cattle and other animals. Although the demand for new antibiotics against the multi resistant bacteria is growing everyday, the development of new antibiotics has slowed down.
This is because it is expensive and difficult to get new antibiotics approved in clinical trials. Also, the new antibiotics, which are getting approved currently, are often second of third generation agents of an already used antibiotic. But resistance for those follow-‐up generation antibiotics is easy to gain when the bugs are already resistant to the first generation of the antibiotic.
Daptomycin was the only new class of new antibiotics to be discovered in the past 50 years, and there are no indications that pharmaceutical companies are trying to find them (Walsh, 2003)(Lewis, 2012).
Nowadays there is a lot known about the development of resistance in bacteria, however it is difficult to apply this knowledge in the therapeutically use of antibiotics. This will be necessary to prevent the new development of resistance in pathogens. In this thesis will be described what the mechanisms behind the development of antibiotics are and will be proposed how the pathogens could be stopped in becoming multi resistance.
Current use of antibiotics
Antibiotics are drugs that kill or inhibit the growth of bacteria and are effective on growing cells (Madigan M. , Martinko, Stahl, & Clark). Antibiotics are used for the treatment of a bacterial infection, but also to prevent bacterial infections in a lower dose. The concentration of antibiotic used in therapy mainly depends on the minimal inhibitory concentration (MIC).
This is the lowest concentration possible at which the growth of the bacterial cells is inhibited (Lambert & Pearson, 2000). The concentration of antibiotic given in therapy is always above the MIC value. The MIC value can be determined by an epsilon test (Etest). The Etest is an experiment by which a gradient of the antibiotic of interest is created over a plate with the organism of interest. After incubation the plate will show for which concentration of the gradient the cells will grow and were not (Jacobs, Bajaksouziana, & Appelbaumb, 1992). The borderline is the MIC value. A MIC value can be different for species, but even for different strain in one species. This is because the strains have different mechanisms to adapt themselves for the antibiotic pressure. The mean MIC value for one species is represented in the MIC50 value. This is the concentration of an antibiotic for which 50 percent of the strains is inhibited in growth by the antibiotic (Goldstein, Soussy, & Thabaut, 1996). Although the MIC value inhibits the cells, it is still possible for the pathogens to evolve antibiotic resistance for antibiotic concentrations above the MIC value (Zhao & Drlica,
2003).
Evolution of antibiotic resistance For the development of antibiotic resistance are two things necessary. First of all, evolutionary pressure (Davies & Davies, 2010);
as long as becoming resistant isn’t a advantage, the new characteristic wont spread over the population. However, when there is an advantage for the resistant cells over the non-‐
adapted cells, the resistant cells
will take over the population, Figure 1 - Illustration mutant selection window (Drlica & Zhao, 2007)
because they survive longer and divide faster in higher doses of the antibiotic. The new resistant population will have a new niche in which they can survive and other cells cannot.
However the concentration of the antibiotic in the environment is important in the evolution of becoming resistant. If the concentration is too low, usually the antibiotic does not affect non-‐resistant cells and the advantage for the resistant cells is not present anymore.
However, if the concentration of the antibiotic is too high, the cells do not have enough time to adjust and to gain resistance. For the lower concentration is the MIC value used and the upper concentration is defined as the mutant prevention concentration (MPC). The concentration window in between those values is the mutant selection window (MSW) (Drlica & Zhao, 2007).
This is illustrated in figure 1. Drlica et al give the example in their article of the behaviour of a fluoroquinolone, but this principle is applicable for all classes of antibiotics. Illustration A shows the behaviour of different concentrations of the antibiotic versus the fraction of affected cells, which can still recover. The MIC value is at the point where the fraction of survival cells is getting lower. The higher the concentration of antibiotic, the less cells survive. However it also shown that there is a clear ‘plateau’ phase and that the fraction of survival cells is not linear going down. The cells that are adapted to the environment with antibiotic cause this plateau phase; the cells are resistant. The curve drops down again after the plateau phase, because the antibiotic concentration is too high, even for the cells that gain resistance. The concentration for which the curve drops down and the fraction of survival cells is close to zero, is the MPC value(Drlica K. , 2003). The MSW window is clearly visualized as the concentration window between MIC and MPC. This includes the plateau phase. Illustration B visualizes how the concentration of the antibiotic in the body can change over time. This causes that however the initial dose is above the mutant selection window, over time the serum concentration becomes less and possible in between the MSW (Drlica & Zhao, 2007).
Figure 2 - Mechanisms for gaining resistance(Allen, Donato, Wang, Cloud-Hansen, Davies, & Handelsman, 2010)
The second important requirement for gaining antibiotic resistance is the presence of a way to gain this new property (Davies & Davies, 2010). There are different mechanisms for bacteria to become resistant (figure 2). The cell could prevent the antibiotic from inserting in the cell (2a) by altering the cell wall or cell membrane properties. The cell could also actively transport the antibiotic molecules out the cells via efflux pumps (2b). Both changing the properties of the cell wall and transporting antibiotic out of the cells, is not specific for one antibiotic, but could work for multiple antibiotics. However there are also mechanisms for resistance, which are specific for one antibiotic only. For instance changing the target of the antibiotic and therefore making the antibiotic non-‐effective (2c). Another mechanism for specific resistance is the expression of an enzyme, which can inactivate the antibiotic molecules (2d) (Allen, Donato, Wang, Cloud-‐Hansen, Davies, & Handelsman, 2010). Officially only the third mechanism, in which the target is changed, is a mutation, which results in resistance. However, the other mechanisms have of course also an underlying genetic change by which the cells gain resistance.
Depending on the antibiotic, the target and the way of gaining resistance, the resistance is evolved by only one or two mutations, but for other combinations of drug and bacterium, a complete set of genes is necessary. An example of an antibiotic that only need one mutation to gain resistance, is rifampicin in S. pneumoniae (Katzung, Masters, & Trevor, 2012). As one can imagine, the likelihood of this happening is very high opposed to the gaining of a complete set of genes. The genes, which cause resistance, are r genes and are classed as the resistome (Wright, 2007). It is known that these r genes can easily spread via integrons or plasmids between different cells and even different species via horizontal gene transfer (Davies J. , 1994; Drlica &
Zhao, 2007). This is also why antibiotic resistance is such a big problem, one cell does not have to go through all steps of evolution by them self, one step of horizontal gene transfer (if the r genes are present in cells in the environment) is enough (Wright, 2010).
Evolutionary pressure
As described above is a concentration in the MSW a key ingredient for the evolution of antibiotic resistance. In this concentration window is the evolutionary pressure high, but not high enough to instantly kill all the bacteria. The MSW is different for each combination of antibiotic and bacterium (Drlica & Zhao, 2007). However the dose for therapy is for most antibiotics set in respect to the MIC value. Besides that, the concentration of the antibiotic in a human body is not constant as shown in figure 1B. This results in the fact that the concentration of antibiotic in the environment of the bacteria that cause the infection, is most times in the MSW and therefore enriches the change for the bacteria to become resistant. This is of course not preferable (Drlica K. , 2003). To prevent that the concentration of the antibiotic gets in the MSW, the initial dose given to the patient can be set to a higher concentration. However the more antibiotic you give to a, sick, person, the higher the changes for severe unwanted side effects of the antibiotic (Madigan M. , Martinko, Stahl, & Clark).
Another option minimise the changes of the antibiotic concentration getting in between the MSW, is to narrow the MSW. Narrowing the MSW has as a result that the changes of the antibiotic concentration get lower, and therefore the changes that the pathogens gain resistance.
Narrowing the MSW therefore minimises the changes of the spread of resistant pathogens (Drlica K. , 2003). Lowering the mutant prevention concentration could be one way to narrow the MSW. It is known that altering the molecule of the antibiotic agent could make the MPC lower. This has for example already been done for fluoroquinolones. Zhao et all compared fluoroquinolones, which only differed in one extra functional group. They discovered that only one functional group could make a big difference in the MPC of the antibiotics and therefore the MSW. For example moxifloxacin and the compound Bay y3114 only differ a methoxy-‐group on the C8 of a quinolone, however the MSW of moxifloxacin is one third of Bay y3114 in E. coli (Zhao & Drlica, 2003). This could be important in designing second or third generations of an antibiotic or in identifying and choosing the compound with the most narrow MSW. A second way to narrow the MSW is by using multiple antibiotics at the same time. The bacteria have to gain resistance for both antibiotics, of which the changes will be smaller of course (Yeh,
Hegreness, Presser Aiden, & Kishony, 2009). This will be described more extensively later on in the section monotherapy vs. synergistic use.
Furthermore, the MIC value, the mutant prevention concentration and therefore the MSW, differs for each antibiotic bacterial strain combination. One could argue that only antibiotics with low MIC and MPC concentrations and narrow MSW should be used for the bacterium that causes the infection. However to do this it is necessary to know exactly which bacterial strain it is. This is called streamlining and is already now done for hospitalized persons to check whether the bacteria are resistant to some antibiotics (Kerremans, et al., 2007). The testing to identify the bacterial strain takes time, which is not always possible when someone is critically ill because of the infection. Therefore a broad-‐spectrum antibiotic is administered to the patient. A broad-‐spectrum antibiotic is an antibiotic, which affects a wide variety of bacteria, to prevent the condition of the patient from getting even more severe. After the cause of the infection is identified, the broad-‐spectrum antibiotic is switched to an antibiotic, which is specific for the bacterium causing the infection (Madigan, Martinko, & Stahl).
Resistance genes
To understand the evolution of resistance genes, it is important to know about the evolution of antibiotics. Antibiotics are natural products, produced by organisms to protect their environment from enemies or competitive organisms (Madigan M. , Martinko, Stahl, & Clark).
Although the discovery of the first antibiotic, penicillin, dates from 1929 (Flemming, 1929), natural antibiotics are much older. This implies that not only antibiotics, but also antibiotic resistance are older than the discovery of antibiotics. D’Costa et al proved this by taking samples from late Pleistocene permafrost sediments. After DNA sequence analysis, they conclude that already then there were resistance genes present in microorganisms. And most of all, they proved that antibiotic resistance is an ancient and natural occurring phenomenon (D'Costa, et al., 2011). Also, the resistant genes are widely spread. Glad et all proved that even environments isolated from the extensive use of antibiotic, contain the r genes. They found genes that can cause resistance are present in the faeces of polar bears in arctic areas (Glad, et al., 2010)(Wright, 2010).
So the resistance genes are no new phenomenon and the genes are present even in the most deserted places. However, the combination of the presence of the resistance genes and the evolutionary pressure arise from the use of antibiotics as described above, is new. This could explain the quick and widely spread of resistance for multiple antibiotics. This is also why Davies states that if antibiotic resistance is biochemically possible, it is inevitable that it will emerge (Davies & Davies, 2010).
As described before, all the resistance genes and precursors for r genes in pathogens and in non-‐pathogens form the resistome (Wright, 2007). As said, the resistance genes are ancient, but were did they come from originally? It is not very likely that specific antibiotic resistance evolved in non-‐producers. This evolution of resistance should have been quick enough to prevent the cells from dying. Especially for the highly adapted resistance mechanisms, this does not seem very likely. A more likely origin of antibiotic resistance is the producer of the antibiotic itself (Davies & Davies, 2010). To produce antibiotics in toxic concentration, the bacteria should not be affected by it. The hypothesis is that the properties for antibiotic production emerged at the same time as antibiotic resistance. However, most of the producers of antibiotics used in clinic are fungi (Madigan M. , Martinko, Stahl, & Clark). Fungi do not need the resistance mechanism when they produce agents, which are specific against bacteria.
The hypothesis also does not explain the wide spread of the resistance genes. However the localisation of the resistance genes in the genome can explain this. If these features to produce antibiotic and in the same time protect themselves from it, was placed on a mobile genetic elements, the characteristic can pass on to the neighbouring cells. In this way the cells, which did have the genes for antibiotic production and resistance, take over the cells, which did not have the adaptation. However, not only the cells of the same species can take over this features, but also neighbouring cells of other species. The cells of the other species probably do not have the right abilities to produce the antibiotic themselves, but the resistance genes can be
used for defence against the antibiotic in their environment. As already said in the introduction, many of the resistance genes identified, are originated on a mobile element, such as an integron or a plasmid. This way the spread of the r genes is fairly quick via horizontal gene transfer (Davies & Davies, 2010) (Ochman, Lawrence, & Groisman, 2000). Only one cell with the resistance genes is needed, to make the entire microbial biome resistant.
Antibiotic resistance in the human microbiome
Humans have a big microbiome, for example on their skin and in their gut. This microbiome contains mainly non-‐pathogenic bacteria and are good for our health. However, when someone takes an antibiotic to cure an infection, it affects the microbiome as well, especially when a broad-‐spectrum antibiotic is used. And just as pathogens can gain resistance, the non-‐pathogenic bacteria of the microbiome can gain resistance for the used antibiotic.
Sommer et al found a lot of known and unknown resistance genes in the microbiome of healthy individuals (Sommer, Dantas, & Church, 2009). At first sight, this seems as a good thing, because of this the most common side effect of antibiotics, that it kills the microbial flora as well, is prevented. However, because those resistance genes are also mobile elements, the r genes can also be passed on to pathogens, if these are near the resistant cells. This makes our microbiome in a resistance reservoir for pathogens, which cause new infections. This is a big problem when someone uses the same antibiotic multiple times for new infections. When the new non-‐resistant pathogens are in the environment of the resistant microbial flora, it is likely that they take over the resistant genes under antibiotic pressure. This makes that specific antibiotic useless for that individual. Also, less harmful bacteria such as opportunistic bacteria get more dangerous when they are antibiotic resistant. Another important aspect of this resistant reservoir as that we as humans play a crucial role in the spread of the resistant genes. The resistant human microbiome is in contact with other cells via our faeces and sewage system.
A way to be able to predict the spread of the resistance genes is to make ecological models and map the entire genetic framework of resistance (MacLean, Hall, & Perron, 2010). In combining the information of the working mechanisms with the behaviour of resistance genes, will help predict the occurrence of resistance in antibiotics that are used right now, and in future antibiotics. If resistance can be predicted, the use of antibiotics can be optimized as well, in order to slow down the evolution of resistance.
Monotherapy vs. synergistic use
It is getting more and more standardised to give a combination of two classes of antibiotics to severe ill patients. Most used is a combination of beta-‐lactams, such as amoxicillin, and macrolides, as clarithromycin (Lim, et al., 2009). However, studies to the effect of combination therapy in those patients are ambiguous. Some studies show that the death rate does get lower when combination therapy is used and some studies does not show any differences in monotherapy versus combination therapy (Rodrigo, Mckeever, Woodhead, & Lim, 2013). This is mainly because these studies are done for patient populations, which are not
Figure 3 - Illustration synergistic (A) and antagonistic (B) combination of antibiotics and the chances for occurance of resistance (Yeh, Hegreness, Presser Aiden, & Kishony, 2009).
always comparable. Besides that, the results differ per combination of antibiotics, dose and the pathogen.
The main reason to use combination therapy is to increase the effect of the antibiotics without using high concentrations of one antibiotic. High concentrations of one antibiotic are unwanted, because than the unwanted side effects of the antibiotic are more severe as well. By using multiple antibiotics at once, the concentration of the antibiotics can stay low, while the effect is high. This way the mortality rate because of an infection and the change that antibiotic resistance occur should be lower. Combinations of broad-‐spectrum antibiotics are also used when it is uncertain what the infection in a critically ill patient is causing (Katzung, Masters, &
Trevor, 2012).
When using multiple drugs at the same time the concepts of antagonism and synergy should be taken into account. Synergy is what happens when the effect of two agents used at the same time is more than just the sum of the effects when taken as a monotherapeutic agent. The agents work even better when they are taken together. Antagonism is the phenomenon what happens when the effect of two agents is less than the sum of the effects. This concept could also be applied on genes or compounds in cells. When these concepts are applied on drugs, another aspect is important, namely the dose both the agents. Also it is not a binairy concept, it is a gradient from strongly antagonistic, in which the effects of both the agents can be even less than the effect of one individual agent, to a strong synergistic effect (Katzung, Masters, & Trevor, 2012) (Yeh, Hegreness, Presser Aiden, & Kishony, 2009).
Until now combinations of antibiotic are made for antibiotics that work synergistically, this is also the most intuitively. However Yeh et al propose that antagonistic instead of synergistic use should have the preference (Yeh, Hegreness, Presser Aiden, & Kishony, 2009).
This is because antagonistic combinations are the best combinations to prevent antibiotic resistance. When a synergistic combination is used, the cells that gain resistance are immediately better off than the non-‐resistant cells, because the synergistic effect is undone.
However, when a strong antagonistic combination is used, the cells that gain resistance are at first more affected than the non-‐resistant cells. Therefore the chances for gaining resistance for the second antibiotic as well are very low. This is represented in figure 3, in which for 3A the combination of agent A and B is a synergistic combination and for 3B an antagonistic combination. As illustrated results the resistance for one of the agents of a synergistic combination in a lower inhibition percentage. However, for the antagonistic situation results resistance in an even higher inhibition, which makes is unlikely that the cells gain resistance for both the antibiotics (Yeh, Hegreness, Presser Aiden, & Kishony, 2009).
This can also be seen in the light of the concept of mutant selection window, which is introduced by Drlica and Zhao (Zhao & Drlica, 2003). The main reason why a synergistic combination is now mostly used is because the MIC value is low. However, because the MPC of an antagonistic combination is generally much lower. Because the MIC value is high and the MPC is low, the MSW is much more narrow. This explains also why it is so unlikely that resistance occurs.
Another point to take into account is the way the agents are antagonistic to each other.
This can be because of induction of enzymatic inhibition, but also because one of the agents inhibits the growth of the cells, while the other agent requires growing cells to kill them (Katzung, Masters, & Trevor, 2012). There are two groups of antibiotics, bactericidal and bacteriostatic agents. Bacteriostatic agents inhibit the growth of bacteria, while cidal agents kill the cells. However, for bactericidal agents to be effective, the cells need to be growing. When the combination of antibiotics is antagonistic because one is a static agent and the other is a cidal agent, the likelihood for resistance to occur is minimal, however the effectiveness is also very low. Therefore it is important to use antibiotic form the same group to prevent resistance, and in the same time cure the infection.
Conclusion
Despite we now know a lot about the consequences and the evolution of antibiotic resistance, we still do not know how to prevent antibiotic resistance. The ideal situation, by which we can prevent antibiotic resistance completely at once, is not very presumably. Resistant bacteria, pathogenic and non-‐pathogenic, are and will be all around us. Until we find a way to prevent antibiotic resistance or a complete new type of antibiotic, for which no resistance can be evolved, we need to try to postpone for each antibiotic the moment that all the pathogenic bacteria are resistant. If we do not do that, we will be set back in time, before the discovery of antibiotics or we need to start pushing the pharmaceutical companies in developing new antibiotics (Lewis, 2012).
Ways to postpone the moment of complete resistance lie in finding the right combinations of drug and bug, the right doses and the right combinations of antibiotics. The easiest way to slow down the spread of resistance is to not use the antibiotics for those bacteria that only need a few mutations to gain resistance, like rifampicin in S. pneumoniae. Another way is to not use the antibiotic bacterium combinations with a broad mutant selection window. By doing this, the spread of the specific resistance gene is slowed down, because the evolutionary pressure is not there anymore. However, this is of course only possible if there are alternatives.
That brings us to developing new alternatives. It is expensive and risky to develop a completely new antibiotic. Although it is for the long time important that new classes of antibiotics will developed for therapy, there are also still possibilities for new generations of classes (Walsh, 2003). When designing new structures, the mutant selection window should be taken into account, like Zhao and Drlica did in their comparative study to different structures and their MSW for fluoroquinolones (Zhao & Drlica, 2003). Though the MSW differs for each species and antibiotic combination, the MSW can be more optimized for a narrower window for multiple species. It is also important to optimize the dose in which a specific antibiotic is given.
Now the dose is mainly depending on the MIC value. However, if possible without to many side effects, this should be higher, to minimize the chances that the serum concentration will be under the mutant prevention concentration.
More research should be done for the effect of resistance in our microbiome (Sommer, Dantas, & Church, 2009). How easy can pathogens interact with the resistant microbiome and what happens to opportunistic bacteria once they are resistant? This is also important when looking at the use of broad-‐spectrum antibiotics. If they are used too elaborately, our microbiome will get adapted to it and so will all the bacteria that are in, indirect, contact with them and the antibiotic. If we still want to be able to use the broad-‐spectrum antibiotics in emergency situations, we need to strictly use it for emergency situations, and make the streamlining to a narrow-‐spectrum antibiotic as quickly as possible.
Furthermore, combinations of antibiotics could be very useful. However, the combination should be chosen, with the effect of the combination taken into account (Yeh, Hegreness, Presser Aiden, & Kishony, 2009). With regards to minimising the chances for resistance to occur, an antagonistic combination should be chosen. However, it is not always possible to use an antagonistic combination and still maintain the antibiotic effect, without side effects. The combinations that are used now are mainly synergistic combinations. These drug combinations should be checked properly again. If they have the effect as they were intended to have, otherwise an antagonistic combination or even monotherapy should have the preference.
To conclude, despite taken all these factors into account, antibiotic resistance is inevitable. And although we know a lot about the occurrence of resistance, we cannot predict how and when it will happen. More research is necessary to make ecological models and a genetic framework, to analyse the evolution of resistance, which is already in pathogens, and to predict future resistance for new antibiotics. Only then we will have enough information to optimize the use of antibiotics and to be able to cure infections.
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