In-situ chemical oxidation is an effective groundwater remediation approach for delivering oxidants to the subsurface environment where various contaminants of concern, natural organic matter, and other reduced species within the soil consume the oxidants. The addition of these oxidants alters microbial activity changing the physical and chemical structure of the soil. This paper studied the effects of chemical oxidation on microbial activity with and without toluene. Several oxidants were used as part of the study: sodium percarbonate, hydrogen peroxide, potassium permanganate, and sodium persulfate evaluated at low, medium, and high concentrations. A series of biometer experiments seeded with microbe Pseudomonas putida F1 and soil sample and aqueous toluene solution for each oxidant was monitored by CO2 production as a function of incubation days to evaluate the effects of oxidation on the microbial activity. Of the oxidants tested, permanganate oxidation resulted in the highest increase in microbial activity post oxidation based on CO2 production both with and without the addition of toluene. The other oxidants exhibited a direct correlation between oxidant concentration and the change in permanganate chemical oxidant demand of the soil. However, there was no correlation between oxidant concentration and microbial activity. Each of the oxidants was shown to increase CO2 yield except for sodium percarbonate, which had an adverse effect on microbial activity. It is likely that the increased microbial activity associated with permanganate oxidation was the result of chemical reactions between the oxidant and natural organic matter in the soil. In-situ chemical oxidation; Biometer study; Pseudomonas putida F1; Bioavailable carbon; Toluene; Catalase. Several strong oxidants can be used in the ISCO process; however, this paper focuses on permanganate . Under aerobic conditions, hydrogen peroxide can decompose into molecular oxygen (−) and hydroxide ion in the process. Fenton's reagent is an even stronger oxidizing agent than hydrogen peroxide, capable of oxidizing a wide range of organic contaminants (2+/H2O2/O3) as a part of the advanced oxidation process (Hydrogen peroxide is one of the most used oxidants for the remediation of organic contaminants in wastewater systems . It can r oxygen . When caaminants . Fenton' process . However process .2S2O8). Persulfate degrades into sulfate which, according to the United States Environmental Protection Agency (USEPA), has a secondary maximum contaminant level (MCL) of 250 mg/L, as compared to manganese which has a secondary MCL of 0.05 mg/L. Like permanganate, persulfate has also not been shown to significantly hinder microbial activity  and has become more popular as an alternative for conventional H2O2  with various oxidants added at different concentrations  to evaluate the effects of in-situ chemical oxidation on microbial activity. This effect was monitored by establishing the direct correlation between microbial activity and the production of carbon dioxide gas over time. The post-oxidation biometers were used to determine if ISCO with the various oxidants altered the soil in ways that increased or decreased microbial activity. Previous studies were also performed using pre-and post-oxidation biometers to further assess the changes in microbial activity , and non-target  and other reduced species) compounds  and it hactivity .2The chemicals used were reagent grade potassium permanganate (Aldrich), sodium percarbonate (Aldrich), hydrogen peroxide , sodium persulfate (Aldrich), monosodium potassium phosphate (Fisher), dibasic potassium phosphate (Fisher), glycerol (Fisher), 1 N barium chloride (Fisher), phosphate-buffered saline, and phenolphthalein pH indicator. Permanganate chemical oxidant demand (PCOD) removal was determined to evaluate the amount of NOD of the soil following chemical oxidation with permanganate, hydrogen peroxide, sodium persulfate, and percarbonate using a modified test developed by Pseudomonas putida F1 (ATCC strain 700007) from ATCC: The Global Bioresource Center  and was shipped freeze-dried .The biometer tests were performed with soil samples previously treated with the various oxidants. The soil samples were oxidized by adding 3 g of soil and 30 mL of each oxidant at low, medium, and high concentrations and tumbled, using a Barnstead lab-quake tumbler for two weeks. The samples were then added to biometer flasks both with and without the addition of an aqueous toluene solution. The study aimed to correlate the microbial degradation of toluene as a sole carbon substrate following oxidant treatment with the rate of CO2.1Pseudomonas putida F1 culture to inoculate a beaker of sterile nutrient broth. The nutrient broth and nutrient agar solutions were sterilized by autoclaving at 120 °C for 20 min. The inoculated broth was streaked for isolation to ensure a pure culture and then grown in a glycerol and nutrient broth solution, preserved in a nitrogen freezer at -70 °C for future use.The experiments used a Once the bacterium had been successfully grown in nutrient broth and agar, the experiment determined bacterial growth kinetics and populations to establish an effective and consistent inoculation process. The first step was developing a consistent inoculation procedure that would minimize the lag time and produce a sufficient cell count. A flask containing 150 mL of nutrient broth was inoculated with a single pure culture collected from a nutrient agar plate. This sample was grown in an incubator for fifteen hours, allowing the bacteria to progress through the lag phase into the exponential growth phase. Then, three other flasks containing 150 mL of nutrient broth were inoculated using one milliliter of the freshly grown bacteria. Optical density measurements were taken every two hours with a spectrophotometer at 600 ηm. The samples were also measured periodically using plate counts to determine the number of viable cells at points along the growth curve.12 per mL). It also provided the most consistent results from batch to batch. The following procedure was used to ensure that the biometers were inoculated consistently for all experiments while minimizing additional carbon added to the system. First, the bacteria were grown and isolated on nutrient agar plates. A single pure culture of the organism was then added to a 150 ml beaker of sterile nutrient broth and grown for 15 h at 30 °C. Then, 1 ml of this bacterial culture was added to 150 ml of clean nutrient broth  and grown for approximately 30 h to an optical density reading of 1.5 at 600 ηm. An aliquot of 20 ml of cells was washed by centrifuging at 4000 rpm. The experiment finally decanted the excess nutrient broth. The cells were re-suspended in a solution of phosphate-buffered saline to remove residual carbon from the unused Haas Broth to produce a low residual carbon inoculum.The optimal time for the bacteria used to inoculate the biometers was determined to be 30 h  when the maximum number of viable cells was present (3×102.22 produced by the microorganisms as a function of time. See 2 gas was then trapped when it dissolved into the potassium hydroxide (KOH) solution (10 ml of 0.05 M) to fill the sidearm. 2 produced (This experiment used a series of biometer flasks to determine the amount of COproduced . Table 32 (at the most), equal to 29 mg of oxygen consumed or roughly 60% of the available oxygen in the closed biometer flask. It was sufficient oxygen for the test and confirmed that oxygen was not a limiting factor.Sorenson phosphate buffer  was used for the experiments as phosphate buffers are found in living systems and are typically non-toxic to cell growth . This ex2 when completely degraded to CO2 by the microorganisms. See 2 produced, the KOH solution was removed from the sidearm of the flask using a 12 mL syringe. The KOH solution was added to 0.2 mL of 1 N barium chloride solution to force any remaining potassium bicarbonate species into potassium carbonate species. The solution was then titrated using 0.05 N hydrochloric acid to a color change, using phenolphthalein as the pH indicator. The sidearm was then refilled with a new KOH solution and placed back in the incubator until the next round of sampling.The following steps were performed for each microcosm experiment. First, the media solution  was adde2 production as a function of incubation days to determine the statistical significance of each oxidant treatment.Several experimental controls with un-oxidized soil were run with and without toluene, Haas broth, and microbial inoculums, as shown in 32 and water. Eqs. 2 with KOH. Finally, 2 produced. bc = volume of the KOH consumed (mL), Vbi = initial volume of KOH (mL), Na = normality of the titrant (N), Va = volume of titrant (HCL) used to produce a color change (mL), and Nb = normality of the KOH solution (N).Gas Chromatography  measured the toluene concentration. Toluene concentrations were measured using GC-FID  . Eq. (1)44.12 produced for each oxidant as a function of incubation times (days) using a 95% confidence interval. The experiments offer a baseline for comparing the post-oxidation effects on microbial activity. The amount of CO2 produced by the experiments containing soil, Haas broth, microbes, and toluene and the experiments including soil, Haas broth, and microbes without toluene was the baseline for comparing post-oxidation conditions. Each system shows different levels of CO2 production for every experimental state, and as the assumed carbon content for the respective system increases, CO2 also increases. Observations could neglect the CO2 present in the biometer flask and the CO2 produced from the death and utilization of microbial cells.The biometer control experiments provided the baseline for evaluating the microbial activity change between pre-and post-oxidized soil samples. This data provides the insight to assess changes in microbial activity post-oxidation with each oxidant and statistically assess the significance of CO4.22 production as a function of time for each soil samples oxidized with permanganate with/without toluene addition. The figure depicts a direct correlation between the concentration of permanganate and the amount of CO2 produced, suggesting that the extent of oxidation increased microbial activity. 2 production from pre-to post-oxidation conditions at day six for the cases with and without toluene addition. It also shows the amount of CO2 available from the total consumption of toluene by the bacteria. Besides, it shows the actual CO2 produced from the addition of toluene for the pre-oxidation control and oxidized soil.2 produced than the soil pre-oxidation. In the soils oxidized with permanganate, the microbes made much more CO2 than in the pre-oxidation control. Based on the difference between the pre-and post-oxidized soil with toluene, it appears that the microbes were much better able to degrade the toluene in the post-oxidation environment than in the pre-oxidation environment. However, the toluene degradation was not quantitatively measured as a function of time. Experiments adjusted the exact amount of toluene in both instances. The CO2 production from the addition of toluene is much higher in the post-oxidation samples than in pre-oxidation controls. The percent increase in CO2 output between the pre-and post-oxidized soils was very similar. Nevertheless, the amount of CO2 produced from toluene addition is higher than what was added to the system as toluene. The microbes produced as much as twice the amount of CO2 from toluene addition in the permanganate oxidized samples than unoxidized soils. Because the addition of toluene is the only difference between the samples with and without toluene for the post-oxidized soils, this indicates that the microbes were better able to utilize toluene as a carbon source following oxidation.The samples oxidized with permanganate indicated double the amount of CO4.32 production of soil samples oxidized with three different hydrogen peroxide concentrations along with the pre-oxidation control. It is interesting to note that the samples oxidized with hydrogen peroxide had a slight increase in microbial activity from the pre-to post-oxidized cases with toluene addition and a slight decrease in microbial activity without toluene addition. 2 production from pre-to post-oxidation conditions on day six for the cases with and without toluene addition. It also shows the amount of CO2 available from the total consumption of toluene by the bacteria and the actual CO2 produced from the addition of toluene for the pre-oxidation control and oxidized soil.The experimental assessment also evaluated the effects of hydrogen peroxide on enhancing microbial activity in a post-oxidation environment. 2 production that was not meaningfully different. Each increase in oxidation had similar CO2 output and was consistent both with and without toluene addition. The decrease of the CO2 production for the hydrogen-oxidized soil without toluene addition indicates that the oxidation could have a detrimental effect on microbial activity. The difference between the samples with toluene addition and the samples without toluene addition shows more outstanding CO2 production from the addition of the toluene post-oxidation. The difference between the samples with and without toluene addition provides that in these oxidized soils, the addition of toluene significantly increased the amount of CO2 produced. It indicates that the oxidation with hydrogen peroxide altered the soil to an environment where the microbes, although not as active, were much better able to utilize the toluene. The oxidation of the soil made the conditions less desirable for microbial growth. Yet, with the addition of toluene, the microbes could produce more CO2 than can be accounted for from toluene consumption, as shown in The concentration of hydrogen peroxide suggested CO4.42 production for the post-oxidized soils with toluene addition. Without toluene addition, there is still an increase in CO2 output for one oxidant concentration and a slight decrease for the others. The CO2 increase rate for the systems without toluene addition post-oxidation is higher than in the pre-oxidation case early in the test. After day one, however, the rate for the oxidized soil plateaus. 2 production from pre-to post-oxidation conditions on day six for the cases with and without toluene addition. It also includes the amount of CO2 available from the total consumption of toluene by the bacteria and the actual CO2 produced from the addition of toluene for the pre-oxidation control and oxidized soil.2 production with toluene addition in the post-oxidized soils. The gains were all very similar, as oxidation increased CO2 production, but the oxidant concentration did not significantly influence the difference. However, the oxidant concentration in the samples without toluene addition exhibited a correlation with the increase or decrease in CO2 production from pre-to post-oxidation conditions. The effects of the oxidant concentration on the amount of CO2 output are well defined in the case without toluene addition. The difference in CO2 production between the samples with/without toluene addition is likely due to the addition of toluene stimulating microbial activity. Thus, the microbes are better at utilizing bioavailable carbon.These samples all revealed significant increases in CO2 production from toluene supplements was also significant. The post-oxidized soils saw more CO2 production increases from the addition of toluene than what experiments observed in the pre-oxidation condition. The CO2 production from toluene addition for each system increases as the initial oxidant concentration decreases. The minimal amount of increased CO2 production from the addition of toluene is still almost twice as much post-oxidation. The amount of CO2 produced from toluene addition is more than can be accounted for from the amount of toluene added. It again indicates that the microbes used carbon provided by toluene to utilize the soil's carbon better.The difference in CO4.52 production than the other oxidants. However, the CO2 production from the microbial activity was not as significant. 2 produced by the systems oxidized with percarbonate with toluene addition was almost three times higher than pre-oxidation. This high increase raised the question of carbonate species being transformed into CO2 with the pH buffer's addition. The post-oxidized solution's pH ranged from ∼9.5 to 10.5, and it went ∼7 and 7.5 after pH buffer addition. This decrease in pH was likely responsible for some of the increased CO2 production observed. For the soils oxidized with percarbonate, another test was conducted with no microbial addition to the biometer flask to determine the CO2 output from only the microbes.Oxidation with sodium percarbonate resulted in much more CO2 production. This increased production is most likely due to dropping the pH and causing some carbonate species to transform into CO2. When CO2 produced from the percarbonate oxidized soils without microbial inoculation is ignored, the systems produced far less CO2 solely from microbial activity. There is very little difference between the post-oxidized systems without toluene addition both with and without the microbes' addition. Both higher oxidized samples (6.25 g/L and 3.13 g/L) showed the same trend, with the highest initial concentration (6.25 g/L) producing less CO2 early. At roughly five days for both conditions, the more oxidized sample begins to make more CO2. This trend reinforces just how little of an effect the addition of the microbes had on the production of CO2. These percarbonate-oxidized soils altered the soil, thus adversely affecting the microbial activity, showing a high decrease in CO2 output from microbial activity. It could be due to the carbon not being transformed into a more bioavailable form.The biometer experiments without microbial addition indicated high amounts of CO2 production from pre-to post-oxidation conditions on day six for the cases with and without toluene addition. It also includes the amount of CO2 available from the total consumption of toluene by the bacteria and the actual CO2 produced from the addition of toluene for the pre-oxidation control and oxidized soil. With toluene addition, there was more CO2 production. However, the CO2 production in the samples without toluene addition produced minimal CO2 from the microbes. This table illustrates just how influenced the CO2 production was by merely the percarbonate and buffer reaction. The amount of CO2 produced from the microbes in the system was almost non-existent. The sample with toluene addition did show some CO2 production generated from the microbial population. When the CO2 produced from the system without microbes is removed from the sample, CO2 production remaining from the microbes is still far below that of observed pre-oxidation. The carbonate species transforming to CO2 controls the CO2 production in these samples.2 produced from the addition of toluene was more significant than that found in the pre-oxidized soil samples. As shown in 2 increase from the addition of toluene is quite substantial even though the total amount of CO2 production is lower than in the pre-oxidation case. Adding toluene to the system resulted in almost twice the amount of CO2 output than in the pre-oxidation system from toluene's addition. Again, the amount of CO2 produced is higher than what can be accounted for from the addition of toluene.The samples oxidized with percarbonate did not have an overall increase in microbial activity. However, the amount of CO4.62 production between the pre-and post-oxidized soils also increased, showing a direct correlation. These results suggest that the biodegradability of the natural organic matter in soil increased as the permanganate concentration increased. Each oxidant treatment also showed statistically different levels of CO2 production. Hydrogen peroxide and sodium persulfate had very similar impacts on CO2 production. These two oxidants showed a slight increase in CO2 production over the control samples but the concentration of the oxidant had little impact on CO2 production. Sodium percarbonate was the only oxidant to show a deleterious effect. Tables 2 production with and without toluene.2 produced from the addition of toluene was frequently higher than the stoichiometric equivalent indicating that the microbes grew better and were more active in the presence of toluene. In each case, the chemical oxidation did not harm the microbe's ability to degrade toluene. In soils oxidized with hydrogen peroxide, the addition of toluene resulted in the most significant increase in CO2 production from toluene addition. The amount of CO2 produced from toluene was as much as double the amount available from toluene. It suggests that in each post-oxidation case, the microbes also better utilized the toluene than in the pre-oxidation control. The oxidation of NOM likely forced the microbes to use toluene as some of the available carbon was not bioavailable. It also may indicate that a contaminant would be better-degraded post-oxidation even if the overall microbial activity decreased. A comparison of the CO2 produced from the toluene added is shown as the amount higher and lower than 63 μmol of CO2, which is the amount of CO2 available from toluene's addition.The amount of CO52 production, both with and without the toluene's addition. The permanganate oxidized soils also indicated the strongest direct correlation between increasing oxidant concentration and increased CO2 production. Permanganate had the most significant effect on reducing NOD and exhibited the greatest increase in CO2 production. It is likely that the soil oxidized by permanganate was more suitable for overall microbial activity as a result of previously unavailable organic carbon being oxidized into more biodegradable forms. Unlike permanganate, oxidation with hydrogen peroxide did not show a strong direct correlation between increasing oxidant concentration and increased CO2 production. Although hydrogen peroxide oxidation did show an increase in microbial activity following toluene addition it decreased without toluene from the pre-oxidation control. The addition of toluene likely stimulated increased microbial growth and the microbes utilized the carbon in the soil at a faster rate.Oxidation with permanganate revealed the highest increase in microbial activity post oxidation based on CO2 production increase. The addition of toluene stimulated CO2 production and showed more CO2 than what is available from toluene. The post-oxidized system using persulfate was more suitable for microbial activity with toluene and less convenient without toluene. The post-oxidation environment was better for natural attenuation. The oxidant sodium percarbonate showed the only adverse effects post-oxidation. The addition of toluene stimulated microbial growth in the system, allowing microbes to utilize the bioavailable carbon in the soil more quickly. It likely increased CO2 production from toluene compared to the available CO2 from the toluene addition. The system, however, was overall less suited for microbial activity.Sodium persulfate oxidation showed increased microbial activity post oxidation after toluene's addition and decreased without toluene. There was no correlation between initial oxidant concentration and CO2 production from the bacteria Pseudomonas putida F1 than can be accounted for by the addition of toluene alone. It also indicates that microbes more completely degraded the toluene during post-oxidation. The microbes used toluene and bioavailable CO2 more quickly during post oxidation. It suggests that soil's carbon was more bioavailable in the post-oxidized soil. It indicates that the microbes used the toluene and efficiently consumed the bioavailable carbon in the oxidized system. Although there was a direct correlation between oxidant concentration and PCOD removal in soil, permanganate was the only oxidant that showed a correlation between PCOD removal and microbial activity. It would appear that permanganate oxidation of the natural organic matter in soil may actually result in greater bioavailability thus stimulating microbial growth.The oxidation with potassium permanganate, hydrogen peroxide, and sodium persulfate resulted in an increased or statistically unchanged microbial activity. The oxidation with sodium percarbonate showed the only harmful effects on microbial activity. Regardless of the oxidant, oxidation resulted in more COMohan B. Dangi & Michael A. Urynowicz: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.Christopher L. Schultz: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data.Samir Budhathoki: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.Sadikshya R. Dangi: Analyzed and interpreted the data; Wrote the paper.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.Data will be made available on request.The authors declare no conflict of interest.No additional information is available for this paper.


Drosophila melanogaster. Using fed and starved larvae that lack prothoracic glands, which synthesize ecdysone, we show that nutrition regulates growth both via ecdysone and via an ecdysone-independent mechanism, while nutrition regulates patterning only via ecdysone. We then demonstrate that growth shows a graded response to ecdysone concentration, while patterning shows a threshold response. Collectively, these data support a model where nutritionally regulated ecdysone fluctuations confer plasticity by regulating disc growth in response to basal ecdysone levels and confer robustness by initiating patterning only once ecdysone peaks exceed a threshold concentration. This could represent a generalizable mechanism through which hormones coordinate plastic growth with robust patterning in the face of environmental change.Animals develop in unpredictable, variable environments. In response to environmental change, some aspects of development adjust to generate plastic phenotypes. Other aspects of development, however, are buffered against environmental change to produce robust phenotypes. How organ development is coordinated to accommodate both plastic and robust developmental responses is poorly understood. Here, we demonstrate that the steroid hormone ecdysone coordinates both plasticity of organ size and robustness of organ pattern in the developing wings of the fruit fly  Developing animals respond to changes in their environment in a multitude of ways, for example, altering how long and how fast they grow, the time it takes them to mature, and their reproductive output . Other aIf we want to extract general principles of how organisms regulate their development in variable environments, we need to understand how developmental processes unfold over time. Several recent studies that have applied systems approaches to development offer excellent examples, frequently employing methods to quantify how gene expression patterns change over time. These studies have used the dynamic changes in expression patterns to uncover the rules governing how insects build their segments , how theThe success of these studies is, in part, due to the fact that the gene regulatory networks underlying each of these processes have been well described in their respective developmental contexts. In contrast, the gene regulatory networks governing growth and patterning at later stages of development, even at later stages of embryonic development, are not as well resolved. If we further complicate this by comparing development across environmental conditions and even across traits, approaches that rely on understanding the configuration of gene regulatory networks become much more difficult to implement.Nevertheless, we can still use the principle of comparing the dynamics of developmental processes across environments to gain useful insights into the relationship between plasticity and robustness. Many types of environmental conditions impact organ development to induce changes in body and organ size. Malnutrition or starvation reduces growth rates in all animals, resulting in smaller body and organ sizes . SimilarWe already have some understanding of the mechanisms that regulate growth and patterning in response to changing environmental conditions. The genetic mechanisms underlying plasticity in growth are best elucidated in insects. In insects, changes in available nutrition affect the synthesis and secretion of the conserved insulin-like peptides . InsulinDrosophila melanogaster have shown that, at least in this insect, insulin acts to control the synthesis of a second developmental hormone, the steroid hormone ecdysone , are ablated, these imaginal discs grow at greatly reduced rates  to drive an apoptosis-inducing gene (UAS-GRIM). GAL80ts is active at 17°C, where it represses GAL4 action, but inactive above 25°C, which allows phm-GAL4 to drive expression of UAS-GRIM and ablate the PG  . This tee the PG . Becausephm-GAL4; GAL80ts or UAS-GRIM parental strain crossed to w1118) were pooled in all analyses.PGX larvae had significantly reduced ecdysteroid titres than control genotypes . This meF > 65, p<0.001 for discs from both PGX and control larvae). Growth of the discs, however, followed a significantly different trajectory in PGX versus control larvae    and was p=0.002) b. The prp=0.002) c.We found no evidence of temporal separation between wing disc growth and the progression of pattern compare  and 3. BTo confirm that reduced ecdysone titres were responsible for delayed patterning, and not a systemic response to the death of the glands, we performed a second experiment where we added either the active form of ecdysone, 20-hydroxyecdysone (20E), or ethanol (the carrier) back to the food. PGX and control larvae were transferred onto either 20E or ethanol food and allowed to feed for 42 hr, after which we dissected their wing discs and examined their size and pattern. On the control (ethanol) food, wing discs from PGX larvae were smaller d and shoCollectively, these data indicate that ecdysone is necessary for the normal progression of growth and patterning in wing imaginal discs. The loss of ecdysone has a more potent effect on patterning, however, which is effectively shutdown in PGX larvae, than on disc growth, which continues, albeit at a slower rate, for the first 24 hr of the third instar in PGX larvae.The observation that ecdysone is necessary to drive both normal growth and patterning suggests that it may play a role in coordinating growth and patterning across environmental conditions. However, to do so it must lie downstream of the physiological mechanisms that sense and respond to environmental change. As discussed above, ecdysone synthesis is regulated by the activity of the insulin-signalling pathway, which is in turn regulated by nutrition. Starving larvae of yeast early in the third instar both suppress insulin signalling and inhibit growth and patterning of organs . We explAn important aspect of these data is that in PGX larvae either supplementing the 1% sucrose food with 20E or feeding them on normal food both rescued wing disc growth , albeit The data above suggest a model of growth and patterning, where both ecdysone and nutrition can drive growth, but where patterning is driven by ecdysone. We next focused on exploring how ecdysone regulates both growth and patterning. Patterning genes, particularly morphogens, are known to regulate growth, so one hypothesis is that ecdysone promotes patterning, which in turn promotes the ecdysone-driven component of disc growth. A second related hypothesis is that ecdysone-driven growth is necessary to promote patterning. Under either of these hypotheses, because the mechanisms regulating patterning and growth are interdependent, we would expect that changes in ecdysone levels would not change the relationship between disc size and disc pattern. An alternative hypothesis, therefore, is that ecdysone promotes growth and patterning through at least partially independent mechanisms. Under this hypothesis, the relationship between size and patterning may change at different levels of ecdysone.phm>InR) and reduced insulin signalling by overexpressing the negative regulator of insulin signalling PTEN (P0206>PTEN).To distinguish between these two hypotheses, we increased or decreased the activity of the insulin-signalling pathway in the PG, which is known to increase or decrease the level of circulating ecdysone, respectively . We thenphm>InR: AIClinear – AIClogistic = 22, ANOVA, F = 1.71, p=0.2018) or decrease ecdysone synthesis rates (P0206>PTEN). Changing ecdysone levels, however, significantly changed the parameters of the linear model and altered the relationship between disc size and Achaete pattern. Specifically, increasing ecdysone level shifted the relationship so that later stages of Achaete patterning occurred in smaller discs  = 25.8, p<0.001). Changing ecdysone levels significantly changed the parameters of the logistic model and altered the relationship between disc size and Senseless pattern (The relationship between Senseless pattern and disc size is best fit using a four-parameter logistic (threshold) function, which provides a significantly better fit to the data than a linear function . However, as noted above, disc growth early in the third larval instar is only moderately affected by ablation of the PG, potentially obfuscating the effects of supplemental 20Esupplemental 20ETo quantify the effects of 20E concentration in the wing disc growth, we dissected discs at 5 hr intervals starting immediately after the moult to the third instar (0 hr AL3E) to 20 hr AL3E. Because male and female larvae show differences in wing disc growth , we sepaAs before, in both PGX and control larvae, wing disc growth was suppressed by starvation i. To expTo test whether wing disc growth rates show either a graded or threshold response to 20E concentration in starved PGX larvae, we extracted the linear growth rate coefficients from our models. We then modelled the relationship between growth rate and 20E concentration with three nonlinear functions: a graded Michaelis–Menten function, and threshold three- and four-parameter log-logistic functions. Finally, we tested which model best fit the data using Akaike information criteria (AIC) and Bayesian information criteria (BIC) for model selection. The model with the lowest AIC and BIC values best fits the data.When wing disc growth rate was modelled with the graded Michaelis–Menten function, both the AIC and BIC values were lower than when it was modelled with either threshold function k. This sThe effect of 20E concentration on Achaete patterning was qualitatively different to its effect on growth. As before, Achaete patterning did not progress in either starved or fed PGX larvae . In contTo compare the progression of Achaete at different levels of 20E with the progression of disc growth, we modelled the relationship between Achaete pattern, time after third-instar ecdysis, and 20E concentration as second-order polynomial regression for fed and starved PGX larvae. As for disc growth, we then extracted the linear coefficients from this model at each level of 20E and modelled the relationship between patterning rate and 20E using a Michaelis–Menten function and a three- and four-parameter log-logistic function. Both the AIC and BIC indicated that that a threshold four-parameter log-logistic function fit the data better than a graded Michaelis–Menten function k. Thus, Comparing the timing of Achaete patterning in 20E-supplemented PGX larvae versus fed controls provides some indication of when in normal development the threshold level of 20E necessary to initiate Achaete patterning is reached. Discs from fed control larvae began to reach Achaete stage 4 by 15 hr AEL3 , while dSenseless patterning did not progress as far as Achaete patterning, only achieving an average of stage 3 in fed and stage 4 in starved larvae at 20 hr AL3E when supplemented with 20E. In both fed and starved larvae, supplemental 20E We again used a second-order polynomial regression to model the relationship between Senseless pattern, time after third-instar ecdysis, and 20E concentration. The relationship between the linear coefficient from our model for Senseless patterning and 20E was best fit with a log-logistic rather than a Michaelis–Menten function. For fed PGX larvae, the four-parameter logistic function provided the best fit to the data k, whereaCollectively, these data support a model of disc growth and patterning where ecdysone regulates disc growth as a graded response to basal levels of ecdysone, while ecdysone regulates disc patterning as a single threshold response.D. melanogaster use the same hormonal signal to coordinate both their growth and progression of pattern. We found that ecdysone simultaneously regulates the plastic growth and robust patterning of the wing disc through independent mechanisms: plastic growth responds to ecdysone with a graded response, while robust patterning responds with a single threshold response. We propose that these differences in response represent a potentially general mechanism through which high levels of variation in one organ characteristic, for example, size, could be coordinated with low levels of variation in another characteristic of the same organ, for example, pattern.Organs are remarkably good at achieving correct pattern across a broad range of environmental conditions that generate variation in size. While this might seem a simple feat when growth and patterning occur at separate times or are regulated by different hormones, it is considerably less simple if both growth and patterning occur at the same time and are regulated by the same endocrine signal. In this work, we explored how the wing discs of developing Drosophila. During normal development, circulating ecdysone levels are low during the first 8 hr of the third larval instar until attainment of a critical size initiates a hormonal cascade that causes ecdysone to fluctuate through a series of characteristic peaks. Each of these peaks is associated with key developmental transitions and prepares the larva for metamorphosis. Low nutrition delays attainment of critical size and the initiation of these peaks, but also appears to raise basal levels of circulating ecdysone between these peaks  act to regulate growth in an organ-autonomous manner. The extent to which morphogen gradients respond to these systemic cues is unclear, although the activity of morphogens is known to interact with those of systemic signals at the level of the target genes. For example, insulin/TOR signalling regulates the activity of Yorkie, a downstream effector of patterning morphogens, including Wingless and Dpp, which controls the rate of cell division . SimilarD. melanogaster . We decreased rates of ecdysone synthesis by crossing P0206-GAL4, which drives expression throughout the ring gland, with yw; UAS PTEN (P0206>PTEN). Even though P0206-GAL4 is a weaker GAL4 driver for the PG and also drives expression in the corpora allata, we chose to use it to drive UAS PTEN because phm>PTEN larvae die as first-instar larvae  and yw; UAS PTEN (+>PTEN) were used as a reference for the phm>InR and P0206>PTEN genotypes, respectively.We manipulated growth rates and developmental timing by altering the rates of ecdysone synthesis in developing r larvae . The parFlies of the above genotypes were raised from timed egg collections (2–6 hr) on cornmeal/molasses medium containing 45 g of molasses, 75 g of sucrose, 70 g of cornmeal, 10 g of agar, 1100 ml of water, and 25 ml of a 10% Nipagin solution per litre. Larvae were reared at low density (200 eggs per 60 × 15 mm Petri dish) in a 12 hr light-dark cycle with 70% humidity and maintained at 25°C unless stated otherwise.ts, phantom GAL4tub-GAL80 strain with UAS Grim to generate PGX progeny  or UAS Grim (+>Grim), were used as controls for genetic background effects. The parental controls were reared under the same thermal conditions as PGX larvae.We used a transgenic combination that allowed us to genetically ablate the PG and eliminate native ecdysone synthesis specifically in the third larval instar. We crossed a  progeny . GAL80tshan 22°C . Rearingorphosis . Changing the PG . The proCrosses, egg collections, and larval rearing were done on the cornmeal/molasses medium (above) for the experiments in To measure the effects of changes in the rates in ecdysone synthesis on wing disc growth and wing disc patterning, larvae were staged into 1 hr cohorts at ecdysis to the third larval instar as in phm>, and >Grim genotypes, larvae were raised from egg to second instar at 17°C. Larvae were staged into 2 hr cohorts at ecdysis to the third larval instar using the methods described above. We separated female and male larvae by examining them for the presence of testes, which are significantly larger than the ovaries and visible even in newly moulted males.For the experiments in To show that ecdysone could rescue patterning and growth in PGX larvae , we addeTo determine the relative contributions of nutrition-dependent signalling or ecdysone to growth and patterning, we fed newly moulted PGX and control larvae 1 ml of starvation medium (1% sucrose with 1% agar) supplemented with either 0.15 mg of 20E dissolved in ethanol or an equivalent volume of ethanol . Supplemented food was left at room temperature for at least 4 hr to evaporate excess off ethanol before use. Larvae were collected at 24 hr AL3E for tissue dissection.For the 20E dose–response experiments, we conducted an initial pilot that showed that supplementing the food with 100 ng of ecdysone/mg food could rescue most of the Achaete and Senseless patterning in PGX wing discs. We collected newly moulted third instar larvae, separated the sexes, and then transferred 10–20 larvae to either sucrose food  or SYA food (fed) at 29°C. We fed these larvae on one of six 20E concentrations: 0, 6.25, 12.5, 25, 50, or 100 ng of 20E/mg food. We added the same volume of ethanol to all treatments.To quantify the relationship between the concentration of 20E administered and the concentration of ecdysteroids in the hemolymph, we allowed newly ecdysed larvae to feed on either sucrose or SYA food that had been supplemented with one of the six concentrations of 20E for 20 hr at 29°C. We then transferred them onto either sucrose food or SYA food that did not contain ecdysone but was dyed blue. They were left to feed for 2 hr until their guts were filled with blue food. This extra step was taken so that we could be sure that our hemolymph ecdysone titres were not contaminated with ecdysone from the food. 30–40 larvae were then weighed as a group and transferred to five times their weight in volume of ice-cold methanol. Larvae were homogenized and ecdysone titres were determined using a 20-Hydroxyecdysone Enzyme ImmunoAssay Kit  as per the manufacturer’s instructions.l-lysine-coated coverslips using Fluoromount-G (SouthernBiotech). Tissues were imaged using either a Leica LSM 510 or a Nikon C1 upright confocal microscope and processed using ImageJ (version 2.0) and Adobe Photoshop CC 2017.For each sample, 10–20 larvae were dissected on ice-cold phosphate-buffered saline (PBS) and fixed in 4% formaldehyde in PBS overnight at 4°C. After fixation, the tissue was washed four times (15 min per wash) with 0.3% Triton X-100 in PBS (PBT), then blocked for 30 min at room temperature in 2% heat-inactivated normal donkey serum in PBT. After blocking, the tissue was incubated in a primary antibody solution diluted with 2% heat-inactivated normal donkey serum in PBT overnight at 4°C. We used the guinea pig anti-Senseless , 1:1000 We quantified wing disc size using disc area as a proxy. All quantifications were done using ImageJ. Wing discs show exponential growth in the third instar. Thus, we studied the growth trajectories of the discs by ln-transforming disc area.Achaete and Senseless stage was quantified using the staging scheme developed by 10.26180/13393676).All the analyses were conducted in R and the annotated R markdown scripts, and data for the analyses are deposited on Figshare (doi: 2) or disc pattern (Achaete or Senseless), we fit either linear or Gompertz models and selected the model that best fit the data using ANOVA and AIC. The Gompertz model was parameterized as y is disc size/pattern, x is time, a is the asymptote of y, b controls where along the x-axis the curve is positioned, and c is the scaling constant, such that c = eg, where g is the growth/patterning rate . To compare the parameters of linear models between treatments and genotypes, we used ANOVA. To compare the parameters of Gompertz models between treatments and genotypes, we used ANOVA to compare the fit of models that assign the same constants across groups versus models that assigned group-specific constants.For the relationship between time after third-instar ecdysis and disc size (log µm2) and Senseless pattern, we fit a four-parameter logistic model parametrized as y is disc pattern, x is disc size, c is the minimum asymptote, d is the maximum asymptote, b is the inflection point, and a is the scaling constant, such that a = 1 /k, where k is the logistic growth rate. We again used ANOVA to compare the fit of models that assign the same parameters across groups versus models that assigned group-specific parameters. The relationship between disc size and Achaete pattern was fit using a linear model and compared across treatments using ANOVA.For the relationship between disc size  a threshold three-parameter log-logistic function: d is the maximum asymptote, b is the rate of increase, and a is the inflection point; and (3) a threshold four-parameter log-logistic function: c is the minimum asymptote, d is the maximum asymptote, b is the rate of increase, and a is the inflection point. For each model, we calculated the AIC and BIC to allow model selection. The model that produces the lowest AIC and BIC value best fits the data.Finally, to compare the effects of 20E supplementation in the diet on the progression of wing disc growth, Achaete patterning, and Senseless patterning, we fit a second-order orthogonal polynomial regression using disc size/patterning stage as our dependent variable, and 20E concentration and linear and quadratic terms for time as fixed effects. Fitting a single model to the data allowed us to compare the same model parameters for growth and patterning. We then extracted the linear rate of change at each 20E concentration using the age in R . The chaFor all parametric tests, we checked for homoscedasticity and normality of errors. Drosophila with nutritional input. Importantly, the authors find that growth is only partially dependent on the ecdysteroid titre, whereas the onset of bristle patterning is dependent on a threshold level that is different for different genes.This article is a carefully done assessment of the role of the moulting hormone ecdysone in coordinating growth and patterning of the wing imaginal disc in the final larval instar of  public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, including Lynn M. Riddiford as Reviewing Editor and Reviewer #1, and the evaluation has been overseen by K VijayRaghavan as the Senior Editor.Thank you for submitting the article "Ecdysone coordinates plastic growth with robust pattern in the developing wing" for consideration by The reviewers have discussed their reviews with one another, and I have drafted this to help you prepare a revised submission.Essential revisions:Please see the Recommendations for the authors from each of the 3 reviewers below. Please try to answer all the comments in making your revision. For those comments with which you disagree, please write a rebuttal in your cover letter when you submit the revised manuscript.Reviewer #1 (Recommendations for the authors):Specific details that need clarification before publication:1) It would help readers not familiar with advanced statistics to define the Gompertz function.2) Although the authors use "ecdysone" to discuss the general function of the hormone, they should use 20E throughout the experimental section and discussion where they are using specific amounts of the hormone in the food to avoid possible reader confusion with the chemical "ecdysone", sometimes known as α-ecdysone.3) Figure 6: The legend does not match the figure as to (A) and (B).4) In Supplementary Figure 1, the data for + > grim appears to be nearly flat rather than showing a significantly positive correlation between ecdysone concentration in the food and the ecdysone concentration in the blood as the authors claim. You need to indicate that Supplementary Table 6 has the statistical parameters to support your claim.5) Indicate in the figure legends for Figures 2, 3, and 7 and Supplementary Figures 1, 2, 4, and 5, how many animals were used for each point. Because of the overlap of many points, particularly the solid points so that there are different shades of gray or red, it is impossible to tell.6) P24: The first full paragraph discussing the threshold response of patterning to ecdysone seems cumbersome and difficult to understand. Rewriting with some condensation would help.7) Be consistent in formatting the references. In some the journal names are written out and in others they are abbreviated. Use this journal's format throughout.Reviewer #2 (Recommendations for the authors):– The data plotted in Figure 7 are somewhat noisy across 20E concentrations. The 12.5mg/mL and 25mg/mL concentrations are particularly ambiguous in their effects on disc size. This limits confidence in the conclusion that disc growth occurs through a graded response to 20E. While the models employed to fit the data result in a small p value, the shapes of the curves in Figure 7 and Figure 8 are not terribly dissimilar, despite different conclusions being drawn.– A related comment, the authors state that 6.25mg/mL of 20E was insufficient to rescue Ac patterning, whereas 25mg/mL of 20E and above rescued Ac patterning, and from these data, they conclude that there is a threshold response to ecdysone. However, the authors also state that 12.5mg/mL showed intermediate levels of patterning. It would be helpful to explain how an intermediate level of patterning is distinct from a graded response.– It is unclear why the authors focused on the first 20-hours after 3rd larval ecdysis for the experiments with defined concentrations of supplemental 20E, especially when they state elsewhere in the manuscript that wing disc growth seems to be independent of ecdysone at early 3rd instar stages. Would there be a more pronounced difference between 20E concentrations on wing disc growth at later stages of development?– While the data suggest that wing disc growth exhibits a graded response to 20E, these data remain correlative. There is no demonstration that plasticity of wing disc growth is caused by a graded response to ecdysone. An alternative hypothesis is that growth plasticity is a consequence of ecdysone-independent signals. Interestingly, Figure 5A suggests that the PG secretes an inhibitory growth signal because starved larvae supplemented with 20E have smaller discs than PGX starved larvae supplemented with 20E. It is possible that ecdysone-independent nutritional signaling could determine plasticity of wing growth.Reviewer #3 (Recommendations for the authors):p9, Hypothesis 1 is Figure 1B not 1C.In order to show the effectiveness of ablating prothoracic glands in reducing ecdysone level it would be good to extract and present from the experience represented in Supplementary figure 1, the ecdysone concentration in PGX and control larvae raised on normal diet. Reviewer #1 (Recommendations for the authors):Specific details that need clarification before publication:1) It would help readers not familiar with advanced statistics to define the Gompertz function.This is an excellent point. To try to clarify our description of the Gompertz function, we have added the following description to lines 210-212.“Insect wing discs show damped exponential, or fast-then-slow, growth dynamics . These types of growth dynamics have frequently been modelled using a Gompertz function, which assumes that exponential growth rates slow down with time.”2) Although the authors use "ecdysone" to discuss the general function of the hormone, they should use 20E throughout the experimental section and discussion where they are using specific amounts of the hormone in the food to avoid possible reader confusion with the chemical "ecdysone", sometimes known as α-ecdysone.Thank you for raising this, we had indeed overlooked this detail. We have changed the figures and text to use 20E whenever we refer to experimental manipulations of ecdysteroids.3) Figure 6: The legend does not match the figure as to (A) and (B).Thanks very much, we have corrected this error.4) In Supplementary Figure 1, the data for + > grim appears to be nearly flat rather than showing a significantly positive correlation between ecdysone concentration in the food and the ecdysone concentration in the blood as the authors claim. You need to indicate that Supplementary Table 6 has the statistical parameters to support your claim.We have reanalysed this data, grouping the control genotypes as we have done throughout the manuscript for consistency. We further found that this data was better fit with second order polynomial function for ecdysone concentration, which allows the relationship between 20E in the hemolymph and diet to curve. We have also referred to the slope terms in Supplementary Table 8 to support our claim:“There is a significant positive relationship between 20E concentration in the food and the concentration of ecdysteroids in the larval blood, as indicated by a significant 20E term (Supplementary Table 8).”5) Indicate in the figure legends for Figures 2, 3, and 7 and Supplementary Figures 1, 2, 4, and 5, how many animals were used for each point. Because of the overlap of many points, particularly the solid points so that there are different shades of gray or red, it is impossible to tell.The number of discs sampled/genotype have now been added to the legend of all plots.6) P24: The first full paragraph discussing the threshold response of patterning to ecdysone seems cumbersome and difficult to understand. Rewriting with some condensation would help.We have worked to streamline this paragraph, in the hopes that it improves its readability. Lines 918 – 931 now read:“At first glance, the observation that patterning shows a threshold response to ecdysone may not be surprising. In any given cell, patterning is inherently regulated by threshold responses because the expression of the patterning gene product is either on or off in that cell. However, our patterning scheme considers the progression of patterning across the entire field of cells that make up the wing disc. Cells across the wing disc turn on Achaete and Senseless expression at different times, resulting in a continuous progression of pattern with time [42]. Furthermore, like growth, the progression of pattern can vary in rate depending on environmental and hormonal conditions [42]. Consequently, the progression of patterning could, in principle, also show a graded response to ecdysone levels. The observation that once ecdysone concentrations are above threshold, the rate of patterning for Achaete and Senseless is independent of ecdysone provides evidence that the rate of patterning across an entire organ can also show a threshold response: an assumption that, hitherto, has not been tested.”7) Be consistent in formatting the references. In some the journal names are written out and in others they are abbreviated. Use this journal's format throughout.Thanks very much for picking this up. We have corrected the references.Reviewer #2 (Recommendations for the authors):– The data plotted in Figure 7 are somewhat noisy across 20E concentrations. The 12.5mg/mL and 25mg/mL concentrations are particularly ambiguous in their effects on disc size. This limits confidence in the conclusion that disc growth occurs through a graded response to 20E. While the models employed to fit the data result in a small p value, the shapes of the curves in Figure 7 and Figure 8 are not terribly dissimilar, despite different conclusions being drawn.It’s not the shape of the curves that matter for determining graded versus threshold responses, but rather the distribution of the curves. To clarify this point, we have now plotted the linear growth rate for each 20E concentration, and fit this data with a Michaelis Menten model . This model shows that as 20E concentrations increase, the linear growth rate also increases, although the rate of increase slows down at concentrations greater than 25 ng/ml. In contrast, Achaete and Senseless patterning rates of increase show switch -like responses that are best fit with a log-logistic models .– A related comment, the authors state that 6.25mg/mL of 20E was insufficient to rescue Ac patterning, whereas 25mg/mL of 20E and above rescued Ac patterning, and from these data, they conclude that there is a threshold response to ecdysone. However, the authors also state that 12.5mg/mL showed intermediate levels of patterning. It would be helpful to explain how an intermediate level of patterning is distinct from a graded response.We agree, the way this was originally phrased was confusing. We have addressed this by altering our analysis to focus on the key point highlighted above: does ecdysone after rates of growth and patterning as a graded or threshold response? By extracting the linear rates of increase from the growth and patterning dataset, we show that growth rates increase gradually with 20E concentration . In contrast, Achaete and Senseless patterning rates are best fit with a threshold response function .– It is unclear why the authors focused on the first 20-hours after 3rd larval ecdysis for the experiments with defined concentrations of supplemental 20E, especially when they state elsewhere in the manuscript that wing disc growth seems to be independent of ecdysone at early 3rd instar stages. Would there be a more pronounced difference between 20E concentrations on wing disc growth at later stages of development?The reason for we focused on the first 20 hours of the third instar is that wing disc patterning becomes ecdysone independent around 10-15 hours. It is important to note that wing disc growth does depend on ecdysone in starved larvae in the first 20 hours, but the effects are not as strong in fed larvae.– While the data suggest that wing disc growth exhibits a graded response to 20E, these data remain correlative. There is no demonstration that plasticity of wing disc growth is caused by a graded response to ecdysone. An alternative hypothesis is that growth plasticity is a consequence of ecdysone-independent signals. Interestingly, Figure 5A suggests that the PG secretes an inhibitory growth signal because starved larvae supplemented with 20E have smaller discs than PGX starved larvae supplemented with 20E. It is possible that ecdysone-independent nutritional signaling could determine plasticity of wing growth.If we have understood the reviewer’s comments correctly, they state that the relationship between wing disc growth and 20E concentration is correlative. If we had measured ecdysteroid titres in the hemolymph and related these to wing disc growth rates, we would agree that the data is correlative. However, we experimentally manipulated 20E concentration and demonstrated that this had a graded response on wing disc growth rates in starved PGX larvae. Thus, we conclude that the effect of 20E on wing disc growth is causal.To clarify this point, we have extracted the linear rate coefficients for wing disc growth, Achaete patterning, and Senseless patterning. We have then modelled the relationship between linear rates and 20E concentration for wing disc growth , Achaete patterning rate  and Senseless patterning rate . This is now discussed in Lines 522-807of the revised manuscript.We agree that ecdysone-independent signals are also important for regulating plasticity of the wing disc, and make the point that nutrition is important for growth via ecdysone independent mechanisms throughout the manuscript. However, this manuscript focusses on how plasticity and robustness can be achieved through the action of the same developmental hormone, in this case ecdysone. The potential growth-inhibitory factor from the PG is very interesting, and is a topic of future research in the lab.Reviewer #3 (Recommendations for the authors):p9, Hypothesis 1 is Figure 1B not 1C.Yes, you are correct. Thank you for catching this.In order to show the effectiveness of ablating prothoracic glands in reducing ecdysone level it would be good to extract and present from the experience represented in Supplementary figure 1, the ecdysone concentration in PGX and control larvae raised on normal diet.Thank you very much for this recommendation, we have analysed the ecdysone titres in fed PGX and control larvae and found that PGX produce significantly lower titres than controls .