Microorganisms need to sense and respond to constantly changing microenvironments,
and adapt their transcriptome, proteome, and metabolism accordingly to survive [1].
However, microbes sometimes react in a way which does not make immediate biological
sense in light of the current environment—for example, by up-regulating an iron acquisition
system in times of metal abundance. The reason for this seemingly nonsensical behavior
can lie in the microbe's ability to predict a coming change in conditions by cues
from the current environment. If the microbe (pre-)adapts accordingly, it will increase
its fitness and chances of survival under subsequent selection pressures—a concept
known as adaptive prediction (Figure 1) [2].
10.1371/journal.ppat.1004356.g001
Figure 1
The basis of adaptive prediction.
(A) The conditions in environment 1 (red circles) activate a distinct response (green
circles) in a microbe. After changing to environment 2, altering, e.g., the expression
pattern to respond to the new conditions (triangles) requires time, during which the
microbe is not well adapted. (B) If the sequential temporal order of the two environments
from (A) is kept over many generations, a new signal pathway can form. Now, the conditions
in environment 1 induce responses to both the first and the second environment. When
changing to environment 2, the microbe is hence already pre-adapted.
In metazoans with complex neural network architecture, the capacity to anticipate
changes in the environment is understandable. It can be achieved in a single multicellular
organism, e.g., by classical conditioning. In unicellular organisms, however, this
type of learning normally requires generations of selection pressure to connect one
predictor to a coming condition.
Why Is Adaptive Prediction Relevant for Human Pathogens?
The human host is, to a certain extent, a highly predictable environment. In its different
niches, pH values, ion concentrations, temperature, and many other factors are normally
kept within small ranges. Transiting from one niche to another usually follows a predetermined
pattern—entering the host from the environment is associated with an increase in temperature;
in the gastrointestinal (GI) tract, the neutral gut will follow the strongly acidic
stomach; invasion into tissue and entering the bloodstream will likely lead to engulfment
by immune cells, followed by oxidative stress and starvation for micronutrients such
as iron or zinc; and passaging through the gut means decreasing oxygen and glucose
levels. These cues can be used by commensals and potential pathogens to optimize their
fitness by predicting the next stage in host–microbe interaction.
Sensing and Making Sense—The Example of Escherichia coli and Other Enteric Bacteria
A good example for adaptive prediction comes from the gut bacterium Escherichia coli.
In this microbe, an increase in temperature elicits a transcriptional response typical
for low oxygen levels [3]. This makes biological sense, as the increase in temperature
can indicate the bacterium's arrival in the gut, where oxygen will soon become limiting.
Interestingly, this predictive function can be disrupted if temperature and oxygen
levels are dissociated over evolutionary timescales. In a laboratory microevolution
experiment with a reversed temperature–oxygen relationship (i.e., high temperature
is followed by high oxygen), Tagkopoulos et al. obtained E. coli strains where the
predictive quality of temperature for oxygen was largely lost [3]. Similarly, maltose
utilization genes are activated in E. coli upon exposure to lactose, reflecting the
sequential abundance of these sugars in the gut [2]. Again, disruption of this sequence
over hundreds of generations was able to abolish this adaptive prediction in vitro
[2]. These two examples show how strongly an evolved adaptive prediction response
can impact microbial fitness.
As many pathogens are gut-associated, similar patterns can be found in pathogenic
enteric bacteria. The enterohemorrhagic E. coli (EHEC) serotype O157:H7, for example,
can use the presence of bile as a signal to induce transcription of iron acquisition
genes, independent of actual iron levels [4]. This can be useful in the iron-sequestering
environment of the small intestine, where bile abounds. On the other hand, pathogenicity-island
encoded genes that are specifically expressed at later stages of the intestinal passage
by EHECs were found to be repressed by bile in the upper part of the small intestine
[4]. Many other enteric bacteria, like Salmonella, Shigella, and Vibrio spp. also
use bile as a signal to regulate virulence programs, which are biologically unlinked
to bile salts but are advantageous at later stages in their mammalian hosts (reviewed
in [5]). Vibrio cholerae is also known to induce genes late in its infection cycle
that are of no immediate use in the host. These genes, for example those involved
in chitin binding and degradation, should benefit the bacteria only after they are
released into the aquatic environment where crustaceans provide ample chitin [6]—although
it is tempting to speculate that chitin degradation may play an additional role in
competition with resident fungi in the gut. In summary, sensing certain host-specific
factors can herald changing conditions, and pathogens can use these signals in their
(pre-)adaptation to the host or for transition from the host.
(Re-)interpreting Old Cues—The Candida albicans Example
Candida albicans is a fungal pathogen that can transit from a commensal state in the
gut to an aggressive pathogen that invades tissue and disseminates via the bloodstream.
Tissue invasion is linked to a specific morphology change, the yeast-to-hypha transition
(Figure 2). The hyphal program is triggered by multiple stimuli, including contact
with epithelial cells and body temperature [7]. Part of this program is the expression
of the multipurpose, hypha-associated cell wall protein, Als3, which enables the fungus
to attach to and invade host cells and use the intracellular host iron storage protein
ferritin as an iron source after invasion [8]. Other hypha-associated factors are
the Sap4-6 proteases, which can degrade host proteins during invasion, and the cell
surface localized superoxide dismutase Sod5, which can be used to detoxify reactive
oxygen species likely to be produced by attracted immune effector cells when tissue
is damaged. Therefore, by triggering hyphal morphogenesis, C. albicans produces factors
that are required during or after tissue invasion even before the actual invasion
process is initiated [7].
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Figure 2
Candida albicans as an example for adaptive prediction of pathogens in the host.
When attaching to epithelial cells, environmental signals trigger hyphae formation.
The hyphae start to express a set of proteins which are not apparently beneficial
for the fungus in its current situation (green symbols). Only when penetrating into
the host tissue and during encounters with host immune cells like neutrophils, the
stresses (red symbols) occur under which these proteins give C. albicans an advantage
in survival and growth (see text for more details).
In the blood, C. albicans seems to interpret the presence of (even low) amounts of
glucose as a cue for potential encounters with immune cells. While the related baker's
yeast, Saccharomyces cerevisiae, down-regulates most stress-response genes in the
presence of glucose, C. albicans up-regulates oxidative and osmotic stress responses
when encountering glucose levels similar to the bloodstream [9]. While not necessary
for growth on glucose, these adaptations would allow better survival of attacks by
blood-borne phagocytic cells after leaving the glucose-poor gut. Accordingly, the
signaling networks leading from glucose to stress response differ significantly between
the two species, allowing C. albicans to reinterpret glucose as a pre-indicator of
possible future dangers [9].
In a similar vein, C. albicans responds to neutral or alkaline pH by expressing genes
involved in iron and zinc uptake via an alkaline-induced transcription factor, Rim101
[10], [11]. As these metals are generally less soluble at high pH, this connection
makes biological sense and may help in a timely response, even before the full effect
of metal limitation is felt by the cell. Thus, common environmental cues like presence
of carbon sources or pH changes can obtain a new, additional meaning and allow the
pathogen to predict conditions in different host niches. To this end, established
signaling pathways for these conditions can be rewired to novel outputs and thus allow
an adaptive prediction response.
Weighing Costs and Risks—The Plasmodium Example
Predicting the future environment comes with a risk. A “false positive” prediction—in
which the pathogen falsely predicts a future environment that it will not encounter
in reality—will leave the pathogen in a state less adapted to the current environment,
with all the associated fitness costs. A “false negative” prediction (in which the
pathogen does not interpret the signal correctly to prepare for a future change) will
lead to a severe loss in fitness in the coming environment [12]. Because of this trade-off,
any (costly) adaptation must rely on robust and reliable signals before a population
of cells commits to a new phenotype. Alternatives exist in the form of stochastic
switching and phenotypic heterogeneity, in which only a random subpopulation expresses
a certain trait [13]. This strategy is more common in unpredictable and fluctuating
environments [14].
The causative agents of malaria, Plasmodium spp., normally replicate asexually inside
the bloodstream of their host. However, at every replication cycle, a portion of the
parasites develops into gametocytes instead. For these sexual stages, the mammalian
host is a dead end, as the gametocytes cannot replicate asexually anymore. However,
after a mosquito bite, only gametocytes can enter this new, suitable host to differentiate
and mate [15]. Thus, in every replication cycle, there is a trade-off between investing
resources into forming the sexual stage for propagation between hosts and asexual
reproduction within a host. Interestingly, the rate of conversion to the sexual stage
varies between Plasmodium species, and antimalarial treatment, as well as an increase
in young reticulocytes, increases the number of sexual gametocytes [16]. The malaria
parasites use these indicators as signs of imminent host death or clearance of infection.
In a “terminal investment,” the sexual between-host transmission strategy is then
followed. Similarly, in a freshly infected naive host, investment in sexual forms
is possible since the associated fitness costs are low. In contrast, in the presence
of low levels of stress, for example caused by parasites of different genotypes competing
for the same host resources, fitness costs for not replicating asexually are high,
and asexual reproduction, hence, dominates (discussed in [17]). Overall, environmental
cues allow the pathogen to weigh the risks for committing to a pre-adapted phenotype.
Adaptive Prediction and Coordinated Regulation
Adaptive prediction seems, in many aspects, similar to the concept of coordinate regulation,
in which several genes, often including virulence factors, are controlled by a common
regulatory system in response to an environmental trigger [18]. Conceptually, however,
coordinate regulation responds to environmental factors that are linked by their simultaneous
occurrence rather than their temporal succession. A good example is the iron-starvation–induced
expression of the siderophore synthesis machinery, siderophore binding proteins, and
cytolytic toxins in many bacteria. In that process, iron starvation indicates a host
environment or activities by the host, and a coordinated transcriptional regulation
allows immediate destruction of host cells, binding, and finally, uptake of iron in
response. In a sense, signal and bacterial adaptation responses are spatially linked,
as they occur in the immediate environment of the microbe. In contrast, in adaptive
prediction, signal and responses are temporally linked.
It may prove difficult, however, to draw a precise dividing line between the two concepts,
as many intermediate forms likely exist. Furthermore, a coordinated regulation could
feasibly evolve into an adaptive prediction system. Coordinately regulated genes come
under control of one or a few transcription factors or regulatory pathways. If an
independent signal (nearly) always predictively precedes the coordinated expression,
these few signal pathways (or the single pathway) can easily evolve to accept this
signal for a “pre-emptive” response [3]. Adding a predictive to the existing immediate
trigger, hence, allows a complex and fully coordinated response to take place in anticipation
of a new environment. This way, coordinated regulation could make the appearance of
adaptive prediction evolutionary more likely.
On the other hand, the expression of many genes can come with a higher fitness cost.
Mathematical models show that this kind of adaptive prediction is more likely to occur
in environments where stresses (rather than future improvement in growth conditions)
are able to be predicted well and may be even modified to include a partial response
(for details, see [12]).
Prevalence and Possible Medical Applications of Adaptive Prediction
How prevalent is this phenomenon in pathogens? It seems likely that adaptive prediction
processes are more common than is currently appreciated. In the laboratory, microbes
are rarely exposed to two or more consecutive environments that reflect the natural
progression through habitats. Unusual (i.e., predictive) transcriptional responses
occur, but without a biological explanation these may not be followed up when investigating
the microbe's response to a specific environment. Especially in environmental microbes,
which are not known to be generally associated with animal hosts, a host-adaptive
response to certain environmental stresses may indicate potential for pathogenicity.
Such adaptations would likely be different to commensal organisms, and may result
from transient but repeated exposure to animal hosts.
In simulations, predictive behavior of genetic networks appears fast and frequently
[3]. In directed evolution experiments, yeast can acquire the ability to predict one
stress from the presence of another remarkably quickly [19]. Finally, without the
need for evolutionary processes spanning generations, associative learning is considered
feasible in individual single cell organisms [20] and even in simple chemical networks
[21]. While still mostly hypothetical, this would allow microbes to expand beyond
evolutionarily acquired adaptive prediction into responses shaped by individual cell
life histories.
It therefore seems highly likely that many pathogens can switch to a (currently) non-adaptive
phenotype when external cues indicate a coming change in environment. Using these
signals to “trick” a pathogen into a phenotypic conversion may be exploited to render
microbes maladapted to their current surroundings. As an avenue for future treatment
options, adaptive prediction responses may therefore deserve deeper consideration.