Collective motion of bacteria in a temperature gradient

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Trade-ošs in adaptation to high temperature

Bennett and Lenski (ÕÉÉì) reports no signicant trade-oš in temperature of adaptation aŸer 2000 generations (Fig. Õ.ß): increase in relative tness is signicant at adaptation temperature but variations (and potential decrease) at other temperatures are not, meaning that the improvement selected at a given temperature does not come at a cost at other temperatures. Similarly, in P. pseudoalcaligenes, increase in growth rate at 45XC does not impair growth at 35XC (Shi and Xia, óþþì).
However, aŸer 2000 generations of adaptation at 20XC, E. coli shows signicant trade-oš between relative tness at 20XC (ca. 1.1) and at 40XC (ca. 0.8) (Mongold et al., ÕÉÉä).is trade-oš was conrmed later on although no quantitative association was shown between tness increase at low temperature and decrease at high temperature (Bennett and Lenski, óþþß). Moreover massive trade-ošs are reported in a qualitative manner by Dallinger (՘˜ß): algae evolved at 70XC are no longer able to grow at 15.5XC while the ancestral line dies if taken from 15.5XC to 65.5XC. Consequently trade-ošs may depend on the selective pressure and on the species of interest and are probably more likely to appear along with more substantial changes acquired over long times.

Genetic modications following adaptation to high temperature

While full-genome sequencing analysis of strains adapted to high temperature are still under work, two dišerent genomic approaches give some insight on the genetic basis of adaptation:
– DNA arrays allow to study genome rearrangement. Several duplications were observed repeatedly and interpreted as a way to increase the level of gene expression (Riehle et al., óþþÕ) although this was not conrmed in further studies (Riehle et al., óþþì).
– DNA microarrays reveal that 12% of whole genome expression is modied with 39 genes beingmodied repeatedly in dišerent replicates.
Among those are found stress response and heat-inducible genes (Riehle et al., óþþ¢). Unfortunately, no dynamics of these changes over time is available. It would be very interesting to see if gradual steps of evolution as reported by Lenski and Travisano (ÕÉɦ) and Elena et al. (ÕÉÉä) for adaptation to low nutrient environment and by Dallinger (՘˜ß) for adaptation to high
temperature coincide with xation of new mutations.
A recent molecular evolution study focuses on substitutions during E. coli adaptation to increasing temperature from 37XC to 45XC by 2XC steps (Kishimoto et al., óþÕþ). Interestingly, there is a transition from positive to neutral selection of substitutions during the experiment. In addition, even in the positive selection phase, there is no clear correlation between the rate of tness increase and the rate of substitution xation.

Alterned selection at dišerent temperatures

Alternated temperature regimes allow to address two related issues: what drives adaptation toward specialist vs. generalist types? Is it possible to select for more e›cient acclimation as one possible generalist strategy?.
In the alternated regime of Bennett’s experiment, bacteria were switched daily from 32 to 42XC over 2000 generations. Compared with constant temperature regimes, these evolved lines show signicant relative tness increase along all the range of temperatures of growth (notably at 27 and 37XC) while the lines evolved at a constant temperature only show an increase in relative tness in their own niches (Bennett and Lenski, ÕÉÉì) ( Fig. Õ.ß).ese data suggest that alternated regime selects for generalists while constant selection favors specialists. Interestingly, adaptation was not slowed down by this alternance of selective pressure; although slower adaptationmay be expected with goals in opposite directions, alternating selective pressures have rather been proposed to fasten adaptation by reducing the probability to be trapped in a local adaptation optima (Kashtan et al., óþþß).
Subsequent analysis of the lines evolved in this alternated regime identi ed that adaptation occurs mostly at constant temperature and that no or very limited improvement in the ability to face temperature change occurred (Leroi et al., ÕÉɦa). It is worth asking how these results would be ašected by dišerent alternance frequencies. More frequent transitions could favor selection of improved response to environmental switches themselves.

Acclimation & survival at high temperature

Survival to high temperature depends on the physiological state of bacteria, in particular it is lower in exponential phase than in stationary phase. In E. coli H-¢ó grown in milk, this dišerence in survival between exponential and stationary phases is stronger when bacteria are grown below the optimal growth temperature at 28XC than above it at 38.5XC (Elliker and Frazier, ÕÉì˜). In another experiment, exponentially growing E. coli MC¦Õþþ cells treated at 43XC during 20 min survive a 30 min heat shock at 50XC while cells in exponential phase growing at 37XC die (Shigapova et al., óþþ¢). In stationary phase, more detailed descriptions are available:
– Survival to heat shock at 57.5XC increases with temperature of growth (6-fold increase between 10 and 42XC) in E. coli WìÕÕþ grown in tryptone soya broth supplemented with yeast extract (Cebrián et al., óþþ˜). No acclimation is observed below 30XC.
– E. coli H-¢ó grown in milk survive better to a 30 min heat shock at 53XC when preliminarily incubated above the optimal growth temperature than below: survival reaches almost 70% at 38.5XC while it is as low as 15% at 28XC (Elliker and Frazier, ÕÉì˜).
– Survival to heat shock at 50XC is almost twice higher for bacteria grown at 41.5XC than at 32XC with E. coli B in Davis minimal medium (DM) (Leroi et al., ÕÉɦb).
– Survival to heat ramp from 30 to 55XC for E. coli MGÕ䢢 grown in Brain Heart Infusion broth is claimed to be accurately described from heat shock survival data assuming (i) no growth occurs, (ii)
inactivation by temperature starts from 49.5XC and (iii) heat resistance does not increase due to gradual temperature change (from 0.15 to 1.64XC/min) (Valdramidis et al., óþþä). is is interpreted
as a demonstration of limited acclimation to heat stress in stationary phase. However, direct survival rate estimation using their data suggests that death rate increases with slope and almost doubles between 0.15 to 1.64XC/min.
– E. coli MMó˜ grown in Luria Bertani rich medium (LB) at 30XC survive better a one hour heat shock at 50XC when temperature increases slowly (0.5XC/min) than aŸer instantaneous heat shock (Guyot et al., óþÕþ). is is a marked ešect as survival rate is 90× higher. Interestingly the primary cause of mortality is the loss of envelope integrity as shown by permeability and membrane žuidity measurements. Moreover this increase relies on protein synthesis during the heat treatment resulting in higher membrane stabilóä ™• ±„u †•±u§£Zí fu±ëuu• Zhh†“Z±†™• Z•o ZoZ£±Z±†™• ity rather than in expression of heat-inducible chaperones and proteases.

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Table of contents :

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Õ.Õ Introduction
Õ.Õ.Õ Spatial structure in ecology and evolution
Õ.Õ.ó Impact of high temperature on bacteria physiology ÕÕ
Õ.Õ.ì Adaptation to high temperature in evolutionary
Õ.Õ.¦ Acclimation to temperature variations: ešects of temperature history
Õ.ó Adaptation and acclimation to a temperature gradient in a chemostat
Õ.ó.Õ Experimental design
Õ.ó.ó Building the setup
Õ.ó.ì Short-term ecological dynamics
Õ.ó.¦ Reducing convection
Õ.ì Interplay of chemotaxis and temperature
Õ.ì.Õ Microchannels setup principle
Õ.ì.ó Collective motion of bacteria in a temperature gradient
Õ.ì.ì Characterization of thermotaxis
Õ.¦ Ešect of temperature history on growth and survival
Õ.¦.Õ Survival to heat shock
Õ.¦.ó Acclimation and growth at high temperature
Õ.¢ Discussion
Õ.¢.Õ Challenges of evolutionary experiments in a spatial temperature gradient
Õ.¢.ó On the nature of acclimation
Õ.¢.ì Costs and benets of acclimation in bacteria
Õ.¢.¦ Interplay between acclimation and adaptation in evolution experiments
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ó.Õ Introduction
ó.Õ.Õ Context and aims
ó.Õ.ó Methods to measure microbial population size xii h™•±u•±«
ó.ó Device monitoring microbial growth at low density
ó.ó.Õ Principle and setup
ó.ó.ó Validating the approach
ó.ì Biological applications
ó.ì.Õ Methods
ó.ì.ó Calibrations with microbes
ó.ì.ì Population dynamics
ó.ì.¦ Discussion and perspectives
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ì.Õ Introduction
ì.Õ.Õ Bacterial siderophores: a model system to study public goods games
ì.Õ.ó Pyoverdine metabolism in Pseudomonas aeruginosa
ì.ó Methods and results
ì.ó.Õ Protocols
ì.ó.ó Dynamic variability of pyoverdine concentration within a clonal microcolony
ì.ó.ì Molecular mechanisms underlying pyoverdine variability
ì.ì Discussion
ì.ì.Õ Implications of a phenotypic switch in pyoverdine metabolism
ì.ì.ó On the evolution of pyoverdine


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