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Taking extreme measures: A quantitative study of multiple stress interactions at the limits of life

Environments exposed to simultaneously occurring extremes are prevalent in the natural world, yet analysis of such settings tends to focus on the effect of single environmental stresses. In this study, quantitative multiplicative and minimising models previously used to study nutrient limitation were applied to the growth of the hydrothermal vent-dwelling organism Halomonas hydrothermalis when subjected to combined nutrient limitation and NaCl-salt stress. Results showed an interactive effect from both salt and nutrient stresses under optimal conditions. However, the fit became more non-interactive as salinity is increased; at which point NaCl-salt had a more dominating effect on growth than inorganic phosphate (Pi). We discuss biochemical hypotheses to explain these data. This work shows that models developed to understand nutrient limitation can be used to quantify and separate the contributions of stresses under other physical and chemical extremes, such as extreme salinity, and facilitate the development of biochemical hypotheses of how extremes may be influencing cell physiology. Importance Very few environments in the natural world are exposed to just one extreme or stress at a time. To understand life’s ability to survive in multiple-extreme environments, we must be able to quantify how different extremes interact. Using methods developed for the study of multiple nutrient limitation, this study uses kinetic growth models to investigate at the effect of extreme environments on bacterial growth. Results show that closer to the extremes of life, individual stresses dominate growth; whereas under optimal conditions there is a multiplicative effect from both salt and nutrient stresses. This approach offers a new way to quantify and potentially understand and develop hypotheses for how life operates under multiple extremes.

2 Abstract 22 Environments exposed to simultaneously occurring extremes are prevalent in the natural 23 world, yet analysis of such settings tends to focus on the effect of single environmental 24 stresses. In this study, quantitative multiplicative and minimising models previously used to 25 study nutrient limitation were applied to the growth of the hydrothermal vent-dwelling 26 organism Halomonas hydrothermalis when subjected to combined nutrient limitation and 27 NaCl-salt stress. Results showed an interactive effect from both salt and nutrient stresses 28 under optimal conditions. However, the fit became more non-interactive as salinity is 29 increased; at which point NaCl-salt had a more dominating effect on growth than inorganic 30 phosphate (P i ). We discuss biochemical hypotheses to explain these data. This work shows  Very few environments in the natural world are exposed to just one extreme or stress at a 39 time. To understand life's ability to survive in multiple-extreme environments, we must be 40 able to quantify how different extremes interact. Using methods developed for the study of 41 multiple nutrient limitation, this study uses kinetic growth models to investigate at the effect Introduction 49 50 Micro-organisms are able to tolerate a variety of stresses which has enabled them to 51 colonise many environments once thought to be uninhabitable, known as extreme 52 environments [1]. For decades, laboratory studies into extreme environments have tended 53 focused on single extremes, such as temperature, pressure, pH and salinity [2]. Although life to just the one stress [2]. Most microorganisms are exposed to multiple extremes and yet 57 there have been relatively few studies conducted to research them. Those studies that do 58 investigate multiple extremes tend to measure microbial growth parameters, but provide little 59 analysis of how the component extremes influence the combined extreme responses.

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However, methods have been used in other areas of microbiology to quantify the relative 62 contributions of individual stresses to a combined stress response. This is particularly the 63 case in the study of nutrient limitation. The concept of co-limitation has been explored by 64 several studies in the fields of oceanography and food industry [4], [5], reviewing the effects 65 of nutrient co-limitation on bacterial growth. Furthermore, studies such as Darvehei     shows the effect of low, optimum and high salinities (defined here as 0, 3-4 and 8% [wt/vol] 5 respectively), each at low (0.3 M) and optimum (varying with NaCl concentration) P i 105 concentrations on growth rate. In this study, optical density data was transferred to Microsoft 106 Excel where growth curves were produced, using the exponential phase to produce growth 107 rates, acquired using equation 1 (See Figure 2 (b)).

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Growth rate results show a general positive correlation between growth rate and increase in 110 NaCl up until an optimal growth rate was reached, followed by a negative correlation as 111 growth rates decreased. Optimum growth was achieved at 4% (wt/vol) NaCl and 0.18 M P i .

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Under optimal conditions for both stressors, the organism exhibited an average growth rate 113 of 0.278. No growth was observed below 0.3M P i or above 8% (wt/vol) NaCl at non-optimal 114 P i concentrations.

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A multiple-regression ANOVA was implemented (

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To present the data obtained in this study, we have organised it into a four-plot schematic.

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The fit of the two quantitative methods is visualised as four heat maps ( Figure 2

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Similarity between the fit of both multiplicative and minimizing models can be seen in Map 145 (c). A highly similar fit, i.e. both multiplicative and minimising models closely fit (light blue) is 146 generally observed at the edges of the heat map at more extreme salinities, and more 147 centrally under optimal salt and P i concentrations. Map (d) displays whether P i or salinity is 148 having the greatest effect on growth at any given combination of the two stresses. Given that 149 the majority of fit is to the multiplicative model, this shows where one stress is having more 150 of an effect than the other. It can be seen that at high P i and both high and low salt 151 concentrations that the effect of high/low salinity has a greater effect on growth. At lower P i 152 concentrations and optimal salinities, P i generally affects growth more than salinity. The majority of combinations of P i and salt stresses have best fit to the multiplicative model.

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We interpret this to show that most combinations of salt and P i limitation stress are 193 interactive when not at the absolute limit. This can be understood at the biochemical level.

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We would expect that when an organism is not growing at the limit of the imposed stresses

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In support of this, Rai et al, 2005 [18] found that P i uptake in the cyanobacterium Anabaena 225 doliolum was significantly reduced with an increase in salinity, suggesting that P i could not 226 be efficiently transported into cells. The study concluded that energy constraints that may 227 have caused an absence of strategies to uptake P i under high salt concentrations, as P i 228 uptake is an energy-dependant process. Thus although under these conditions, phosphate 229 would be limiting, high salinity would ultimately be the controlling factor in growth.

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Another explanation could be nutrient over-exposure under high salt concentrations.

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Previous studies have focussed on P i limitation at high salinities, but not on high 233 concentrations of the nutrient. The general bacterial response mechanism for P i uptake in 234 conditions with an abundance of P i is the P i -Inorganic Transport (Pit) model [19]. An excess  Yet another hypothesis could be linked to the uptake of potassium (K). In saline 242 environments, some cells accumulate K + ions to balance osmotic concentrations inside and 10 out of cells -the 'salt-in strategy' [22]. In addition, P i transport systems are dependant upon 244 the presence of K + [19]. This indicates that at higher salt concentrations, cells may be taking 245 up K + for osmoregulation processes, and depleting available P i . Uptake of potassium ions 246 might change the ionic associations of K + with P i ions, indirectly affecting the ease by which 247 P i can be taken up by cells. If the presence of high Na + and Clconcentrations influences P i 248 concentrations, then at extremes of NaCl, it might be observed to dominate growth.  The bacterium Halomonas hydrothermalis was selected for this study. It was isolated from 286 an extreme hydrothermal environment [23] and is adapted to a wide range of stresses [15].

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From the family Halomonadacacae, popular in research on osmotic adaptation due to their 288 moderately halophilic nature [16], this bacterium has well-documented boundaries of growth, 289 exhibiting cell division across temperatures of 2 to 40°C (optimal growth reported at 30°C), 290 total salt concentrations of 0.5% to 22% (wt/vol) (optimal range of 4% to 7% (wt/vol)), and pH 291 values of 5 to 12 (optimal range of 7 to 8) [15], [23]. Although the salt-tolerance of this 292 organism has been constrained, nothing is known about required nutrient levels for cell 293 division to occur.

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The organism was cultured in a marine-minimal media as described by Ostling et al., 1991

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To predict a standard deviation for the multiplicative approach, a Gaussian, normal 370 distribution was assumed for simplicity. The product of variables in relation to dataset 2 and 371 dataset 3 was calculated to give a predicted multiplicative variance of the two combined. For 372 the minimizing approach, the distribution of the minimum of random Gaussian variables, 373 moments Y=min(X 1 , X 2 ) [33] were applied using probability density function and cumulative 374 distribution functions to predict an overall variance.

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For investigations into which of the two stresses had a greater effect on growth, a one-sided 377 students t-test was carried out between the two distributions in R-studio. This showed 378 whether the average of the salinity factor for replicates is greater than average of the nutrient 379 limitation factor. Results were visualised using heat maps produced in R Studio; showing the 15 fit of the two methods, growth rate values, which stress dominated at which combination of 381 stresses, and similarity in fit of the two models. The latter was calculated using the difference