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Metabolomics – finding the players of physiological responses to stress exposure Metabolites are
both substrates and the end product of cellular processes. As such, they represent useful descriptors of phenotypes, and can be measured more easily than genetic or proteomic responses in non-model species. Moreover, changes in gene or protein expression does not always correlate with variation in protein activity while an alteration in metabolites does (Kuile and Westerhoff 2001). Therefore, metabolomic studies can provide molecular-level compilation of upstream genomic, transcriptomic and proteomic responses of an organism to a given stimulus or change (Kell 2004). Metabolic profiling, or “fingerprinting”, has the purpose of attaining as much of the total metabolomic information as possible from different systems to compare their metabolome, and thereby infer a relationship between treatment and changes in metabolite composition/concentrations (Tyagi et al. 2010). Since this method quantifies a lot of metabolites at the same time, it generates a dataset with more observed variables than the number of observations, making traditional linear regression methods unsuitable for the analyses (Worley and Powers 2013). The most popular multivariate analysis methods for analysing metabolomics data are principal component analysis (PCA) and partial least squares projection to latent structures (PLS) (Abdi and Williams 2010; Wold, Sjöström, and Eriksson 2001). These methods produce scores that can be seen as good “summaries” of observations, and help to identify biologically relevant spectral features and differentiate groups even when datasets are complex (Worley and Powers 2013). Initial observation of groups’ separation in a PCA followed by a confirmation and more detailed PLS classification have a greater likelihood of producing biologically relevant results. This is because separation only is observed between groups in PCA scores when within-group variation is significantly less than between-group variation in the data, while separation in PLS analysis has a tendency to over-fit models to the data. In addition, efforts have been made to standardise the processing of raw spectral data and analysis interpretation, enabling researchers to generate reliable and comparable models (van den Berg et al. 2006; Goodacre et al. 2007; Kjeldahl and Bro 2010; Lindon et al. 2005; Sysi-Aho et al. 2007; Wongravee et al. 2009). The covariance (or correlation) matrix of principal components in a PCA can be split into a scale part (eigenvalues, the variance explained by the principal components) and a direction part (eigenvectors, coefficient of projection). By multiplying the eigenvector by the square root of the eigenvalue, the coefficient is “loaded” by the amount of variance, giving what is called the loadings, representing the covariance between the original variables and the principal components. Therefore, it is usually beneficial to use loadings or eigenvectors to complement PCA scores, as they represent a measure of the association between variables on which the PCA is based. Unfortunately, researchers and other sources (including some R packages) use these two terms interchangeably, causing confusion, although different meanings of “loadings” generally lead to the same interpretations of the components (Abdi and Williams 2010). How easily loadings can be interpreted is directly affected by the number of variables and the scaling method used (if any). Variables whose loadings are placed close together (away from the origin) in a loadings plot, can be assumed to be correlated. Moreover, variables with loadings in a given position in a loading plot contribute greatly to observations whose scores are found in a similar position in a scores plot (van den Berg et al. 2006). In addition, for PCA and validated PLS scores, quantitative measures must be applied to reliably infer significant separations between classes within a scores plot. One simple method of inferring class membership is through 95% confidence ellipses calculated from scores, which then completes an analysis of metabolomics data (Worley, Halouska, and Powers 2013). Metabolites that have been observed to fluctuate in various organisms after exposure to an environmental stressor can serve as valuable bioindicators of environmental stress. (Lankadurai, Nagato, and Simpson 2013). As is often the case, the fruit fly Drosophila melanogaster has been used in several studies to investigate their metabolic responses to environmental stressors (Colinet et al. 2012; Feala et al. 2007; Kostal et al. 2011; Overgaard et al. 2007; Pedersen et al. 2008; Vesala et al. 2012). For example, Colinet et al. (2012) showed that cold acclimation resulted in increased concentrations of the sugars sucrose, fructose and trehalose, which could be potential biomarkers of cold acclimation. In addition, Colinet et al. (2012) found a decrease in metabolic intermediates glycerate, citrate, fumarate and malate, which suggests a mechanism for the flies energy saving strategy when cold acclimated. In such ways, metabolomics can be used to unravel the biochemistry of thermal tolerance.
Pesticide effect on temperature sensitivity in insects
Effects of pesticides on temperature sensitivity is even less documented than that of temperature on pesticides. A few of the studies mentioned above also showed a decrease in temperature tolerance as a consequence of pesticide exposure. Specifically, Meng, Delnat, and Stoks (2020b) showed a reduction in CTmax after exposing C. pipiens to a heat spike and then to chlorpyrifos, or to chlorpyrifos before the heat spike, the former resulting in the largest reduction in heat tolerance. Delnat et al. (2019) also showed a decrease in temperature tolerance of C. pipiens larvae and male adults following chlorpyrifos exposure. However, females’ temperature tolerance was not affected, possibly because exposure to the used pesticide concentration was observed to be less toxic to females than males. The authors also speculate whether the decrease in heat tolerance observed was due to increased oxygen demand and decreased oxygen supply caused by multiple stressors, leading to a metabolic mismatch at a lower temperature; the occurrence of such mismatch has been shown to often limit thermal tolerance in arthropods (Delnat et al. 2019; Verberk et al. 2016). Op de Beeck, Verheyen, and Stoks (2018) found that heat tolerance was reduced after chlorpyrifos exposure in I. elegans, but not in the closely related, but comparably small and fast paced damselfly species Ischnura pumilio, further underlining the importance of considering species, or their specific traits, when predicting outcomes of stress exposures. Op de Beeck, Verheyen, and Stoks (2017) showed that the reduction in CTmax after pesticide exposure occurred in I. elegans larvae from different latitudes and temperature regimes (constant 20 vs 24 °C), but the reduction was larger in animals kept at lower temperatures, again because of higher accumulation of pesticide in the water at lower temperature.
The research presented above indicate that global warming can be expected mainly to affect pesticide tolerance by influencing environmental degradation as well as animal uptake and elimination, and not through resource depletion (unless food supply is scarce). Furthermore, it seems that pesticide exposure can reduce heat tolerance; the underlying mechanisms are very poorly investigated, and the ultimate increase in oxygen consumption when coping or recently having coped with pesticide exposure could be one of the main drivers of this observed reduction in heat tolerance. For specialist species, who generally live close to their tolerance thermal threshold, this interaction could prove to be all the more harmful (Deutsch et al. 2008).
Effects in insects of freezing temperatures on pesticides and vice versa have not been described, which could be because it is less relevant to investigate, as pesticide applications usually occur at times of warmer temperatures (plant growth season). However, many pesticides persist in the environment, making winter exposure studies important. Another possible explanation is the bias towards publication of effect studies, as for example a study testing effects of two pesticides (abamectin and carbendazim) on freeze tolerance in an earthworm showed no interaction (Bindesbøl et al. 2009). In conclusion, especially effects of pesticides on thermal tolerance are scarcely documented, and more effect studies should be published for ecologists to get a realistic idea of effects of these interactions, for extreme cold and hot weather scenarios, in the future.
Altitudinal and lowland gradients
At Saint Malo and Molloy, insects were collected along an altitudinal gradient up to 250 m above sea level (a.s.l.). At Saint Malo, three populations were collected from different elevations (0-5 m, 95-110 m and 190-210 m a.s.l.) and given the corresponding names: Saint Malo 0, Saint Malo 100 and Saint Malo 200. At Molloy, four populations were collected at three different elevations (0-5 m, 110-130 m and 245-250 m a.s.l.), with two distinct habitats being sampled at around 125 m a.s.l., one having less vegetation (Molloy 125) than the other (Molloy 125 Vegetation).
At Papous, a horizontal seashore – inland transect was completed, with sampling at different distances from the coast (approximately 2-15 m, 185-215 m, 385-415 m and 785-815 m): Papous 0, Papous 200, Papous 400 and Papous 800 respectively.
Measurement of the duration of recovery from cold and heat exposures
Empty Petri dishes lined with paper were placed into an incubator. After approximately 10 min, five individuals were added into each of 4-5 Petri dishes, and covered with lids; the cabinet was closed and a timer started. After 15 min exposure to a given temperature, the dishes were removed from the incubator, rapidly transferred into a walk-in chamber at 9 ± 1.5 °C, and a new timer was started. The number of insects in coma, and number of recovered insects (walking ability restored) were subsequently noted every minute, over 30 min (The recovery period was monitored for up to 120 min in a preliminary test, and no change in the number of recovered beetles was observed as compared with 30 min). Any individual that had not recovered after 30 min was considered dead. This procedure was repeated for each population and temperature treatment. All populations were exposed to -6, -7 or 37 °C for 15 min. The duration of exposure of 15 min was selected after having performed preliminary tests, with insects being exposed at these three temperatures for different durations (from 5 to 120 min; data not shown). Depending on the number of insects available for each population, the remaining adults were exposed to up to eight distinct temperatures (-5, -6, -7, -8, 35, 36, 37 and 38 °C) for 15 min; this additional experiment ensured that we investigated temperatures that were close to the thermal limits of the species, and that any difference in temperature tolerance would be identified.
To test for the effect of prolonged duration of exposure on the subsequent recovery, further assays conducted at -6 °C were completed with duration of exposure of the adult M. soledadinus of 1 or 2 h in addition to the 15 min (0.25 h) treatment for the populations of the altitudinal (Molloy, St Malo) and inland-shore (Papous) gradients. During the experiments, each insect was only used once (i.e. exposed to one temperature and duration combination on a single occasion).
Table of contents :
Chapter 1 Background
Thermal performance curve
What is stress?
Ecological consequences of climate stress
Increased temperature and thermal fluctuations
Invasive species
Mild winters
Climate stress tolerance strategies
Heat shock proteins and chaperones
Oxidative stress
Desiccation
Other extreme temperature tolerance strategies
Metabolomics – finding the players of physiological responses to stress exposure
Pesticides
Measuring toxicity
Types of insecticides
Pathways of uptake and excretion
Ecological consequences of pesticides in the environment
Pesticide and temperature interactions in insects
Effect of temperature on pesticide sensitivity in insects
Pesticide effect on temperature sensitivity in insects
This thesis
Aims and hypotheses
Biological models
Merizodus soledadinus
Alphitobius diaperinus
References
Chapter 2 Thermal tolerance patterns of a carabid beetle sampled along invasion and altitudinal gradients at a sub-Antarctic island
1 Introduction
2 Methods
2. 1 Insect collection
2. 1. 1 Invasion gradient
2. 1. 2 Altitudinal and lowland gradients
2. 2 Measurement of the duration of recovery from cold and heat exposures
2. 3 Statistical analysis
3 Results
3. 1 Recovery of adult M. soledadinus sampled along an invasion gradient transect
3. 2 Recovery of adult M. soledadinus sampled along an altitudinal gradient
3. 3 Duration of recovery of adult M. soledadinus exposed for different time periods
3. 4 Duration of recovery of adult M. soledadinus sampled along a seashore-inland transect 64
4 Discussion
4. 1 Thermal sensitivity of M. soledadinus adults sampled along an invasion gradient
4. 2 Adult M. soledadinus sampled along an altitudinal gradient
4. 3 Adult M. soledadinus sampled along a seashore-inland transect and exposed for different time periods
5 Conclusions
Supplementary material
References
Chapter 3 Thermal plasticity and sensitivity to insecticides in populations of an invasive beetle: Cyfluthrin increases vulnerability to extreme temperature
1. Introduction
2. Materials and methods
2.1. Rearing of the insects
2.2 Experimental design
2.2.1. Characterization of the thermal tolerance of the insects from different populations
2.2.2. Effect of daily heat spikes and cyfluthrin exposure
2.2.3. Effect of daily heat spikes and cyfluthrin exposure on recovery from exposure to extreme temperatures
2.3. Data treatment
3. Results
3.1. Characterization of the thermal tolerance of the insects from different populations
3.2. Effect of daily heat spikes and cyfluthrin exposure
3.3. Effect of daily heat spikes and cyfluthrin exposure on recovery from exposure to extreme temperatures
4. Discussion
4.1 Characterization of the thermal tolerance of the insects from different populations
4.2. Effect of daily heat spikes and cyfluthrin exposure
4.3. Effect of daily heat spikes and cyfluthrin exposure on recovery from exposure to extreme temperatures
5. Conclusion
Supplementary File 1
Supplementary File 2
Supplementary File 3
Effect of heat and pesticide exposure on Alphitobius diaperinus
1 Introduction
2 Methods
2.1. Rearing of the insects
2.2 Experimental chamber preparation
2.3 Exposure treatment
2.4 Physiological Analyses
2.5 Reproduction
3 Results
4 Discussion
References
Chapter 4 Desiccation in the lesser mealworm Alphitobius diaperinus
1 Introduction
2 Methods
2.1 Collection and rearing of insects
2.2 Effects of desiccating conditions on the survival and physiology of the insects
2.2.1 Survival of the insects exposed to desiccating conditions
2.2.2 Measurements of body water and sugar content
2.2.3 Metabolomics
2.3 Effects of desiccating conditions on activity and reproductive capacities of the insects
2.3.1 Measurement of locomotor activity
2.3.2 Measurement of the reproduction capacities of the beetles
2.4 Statistics
3 Results
3.1 Survival of the insects exposed to desiccating conditions
3.2 Effects of desiccating conditions on sugar content and metabolic profiles
3.3 Locomotor activity of the beetles
3.4 Reproductive capacities
4 Discussion
Conclusion
Supplementary Material
References
Chapter 5 General discussion on the scientific contributions of the thesis’ results and future perspectives
Warming and geographic expansion of alien species
Thermal tolerance breadth
Effects of climate change and pesticides on A. diaperinus
References
