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Organ growth is tightly regulated across environmental conditions to generate appropriate final size. While the size of some organs is free to vary, others need to maintain constant size to function properly. This poses a unique problem: how is robust final size achieved when environmental conditions alter the rates and the duration of growth? Brain growth is known to be "spared" from the effects of the environment from humans to fruit flies. Here, we explore how this robustness in brain size is achieved by examining differences in growth dynamics between the larval body, the brain, and the mushroom bodies – a brain compartment – in Drosophila melanogaster across thermal and nutritional conditions. We identify key differences in patterns of growth between the whole brain and mushroom bodies, as well as differences in the signalling pathways implicated, that are likely to underlie robustness of final organ shape. Further, we show that these differences produce distinct brain shapes across environments.
How are the shapes and sizes of growing organs regulated throughout development to generate fully-functional multicellular organisms with highly specialized parts? This seems particularly difficult to understand given that body parts initiate growth at different times, and further grow at different rates and with differing dynamics (Huxley, 1932; Andersen et al., 2013; Eder et al., 2017). In addition, some organs show exquisite sensitivity to environmental conditions, known as plasticity, changing their shape and size with changes in nutrition, temperature, and other conditions (Bateson, 2017). In contrast, other organs maintain relatively constant final sizes across conditions (Nijhout, 2002; Bateson, 2017). Properties that allow growth to resist perturbations in environmental conditions contribute to robustness in development (Nijhout, 2002; Bateson, 2017; Mirth & Shingleton, 2019). As a result of differing degrees of plasticity or robustness across organs, organisms that develop in different environments, such as rich versus poor nutritional environments, will differ in their body size and shape (Mirth & Shingleton, A. W., 2012). Understanding the properties of organ growth that allow them to be either plastic or robust to environmental conditions is key to uncovering how correct, functional body form is achieved.
Extensive studies in insects have described how the patterns of growth across organs generate variation in size and shape of the adult body (Mirth & Shingleton, A. W., 2012; Andersen et al., 2013; Nijhout et al., 2014; Mirth & Shingleton, 2019). Organs differ in their growth dynamics because they grow at different rates, grow with different dynamics, or grow for differing amounts of time. This can occur either at the level of an individual organ or through coordinating growth processes among organs relative to the growing body (Huxley, 1932; Shingleton & Frankino, 2018). Environmental conditions can alter each of these growth properties. For instance, in lepidopterans like the tobacco horn worm Manduca sexta and the buckeye butterfly Junonia coenia, and in fruit flies like Drosophila melanogaster, poor nutrition results in reduced growth rates in the developing wing and also extends the larval development period (Miner et al., 2000; Shingleton et al., 2008; Shingleton et al., 2009; Nijhout & Grunert, 2010). Poorly nourished larvae give rise to adults with small wings because wing growth is sufficiently suppressed that the extended growth period cannot compensate to restore wing size to fully-fed levels (Shingleton et al., 2009). In D. melanogaster, rearing larvae at lower temperatures also reduces wing growth rates, albeit to a lesser degree than poor nutrition, and extends developmental time. However, in this case even though growth rates are slower, the extended developmental period results in adults with larger wings (McDonald et al., 2018). These findings illustrate that if we want to understand how organs achieve either plasticity or robustness in their final size, we first need to understand how these properties of organ growth change across environmental conditions.
Across a wide variety of animals, including mammals and insects, the brain is generally less sensitive to changes in environmental conditions than other organs of the body (Cusick & Georgieff, 2016). This is commonly referred to as brain sparing. In humans, reduced nutrient availability or reduced oxygen supply during late fetal life reduces fetal growth (Cohen et al., 2015). However, oxygen and nutrient supplies to the brain are maintained at the expense of the rest of the body. As such, at birth newborns have reduced weight and body sizes and disproportionately large heads (Cox & Marton, 2009; Cohen et al., 2015). This illustrates that the brain has built-in mechanisms to ensure its size is not compromised.
The larval central nervous system of the fruit fly Drosophila melanogaster, hereafter referred to as the brain, provides an excellent system for how organ growth can be robust to environmental perturbation. Drosophila larvae reared under poor nutrient conditions have proportionally larger brains than those reared under nutrient rich conditions (Cheng et al., 2011). In Drosophila, the brain differentiates in the embryo, a stage that is protected from nutrient restriction as the embryos not feed. However, reactivation of quiescent neuroblast and glial stem cells occurs early in the first larval stage of development and requires cell autonomous nutrient signals (Yuan et al., 2020). After reactivation, most brain growth occurs in the three larval stages, and nutrition plays an important role. Poor nutrition, especially in the later stages of larval development, produces in small sized adults (Mirth & Shingleton, 2012), but brain growth is spared against poor nutrition via the action of a tyrosine kinase-like insulin receptor called Alk and its ligand Jelly Belly (Jeb). Jeb is secreted by glial cells under poor nutrient conditions (Cheng et al., 2011). Once bound to its ligand, Alk activates downstream effectors of the insulin signalling pathway and also regulates downstream targets of the TOR kinase bypassing amino-acid sensing in the absence of nutrient cues. By maintaining the insulin and TOR pathways active, Jeb ensures that the size of the brain is maintained even when larvae are starved, and that the composition of cells in the brain is maintained for proper functioning (Cheng et al., 2011; Lanet & Maurange, 2014).
While these findings highlight a genetic mechanism through which brain sparing occurs, they do not explain how brain growth adjusts with extended larval growth periods caused by poor nutrition. If Alk caused growth rates to remain as high in starved larvae as it does in fed, the extension in developmental time caused by starvation would cause brains to overgrow. Because this does not happen, it suggests that the brains of starved larvae adjust their growth dynamics to avoid overshooting their size with longer growth periods.
This could happen in several ways. Firstly, when larvae were starved, their brains could maintain constant growth rates but then stop growing once they reach a target size (Figure 1A). This could be modelled using non-linear models that approach an asymptote. Alternatively, brains from starved larvae could delay the time at which they initiate growth, but then maintain constant rates once growth is initiated (Figure 1A). A change in the lag term of a lagged-exponential model between starved and fed larval brains would indicate that this was the method through which brains adjust with environmental conditions. Finally, Jeb and Alk might not act to ensure insulin and TOR signalling are maintained at constant levels. Instead this pathway might tune growth rates, and the timing at which growth is initiated, to adjust for the extended growth period (Figure 1A). This would result in changes in both the lag and the growth rate terms from a lagged-exponential model. These differences in growth dynamics are important, as each implies a different mechanism for adjusting brain size with environmental conditions.
Like all brains, the Drosophila larval brain is composed of distinct functional compartments, each containing specific populations of neurons (Truman & Bate, 1988; Prokop & Technau, 1991). The growth properties of neurons are known to differ across compartments (Hartenstein, V. et al., 2008). The neuroblasts undergo three waves of proliferation at distinct developmental stages - embryonic, larval, and pupal stages - to generate all the neurons in the larval and adult brain (Hartenstein, Volker et al., 1985). Neuroblast progenitors produced during the embryonic wave go into quiescence (Hartenstein, Volker et al., 2008), and are reactivated during larval development to form the neural cells that will contribute to the adult brain. The timing of reactivation of neuroblasts varies between different populations of cells in the brain, determined by both the activity of proneural genes (Hartenstein, V. & Wodarz, 2013) and by environmental conditions (Chell & Brand, 2010; Speder & Brand, 2018)
Supporting this idea, different neuronal subclasses change their rates of cell division in response to nutrition and other environmental conditions like temperature, light, and population densities during larval stages of growth, although the overall brain size is still maintained (Prokop & Technau, 1994; Heisenberg et al., 1995; Lin et al., 2013; Wang et al., 2018). While other neuronal populations enter quiescence in the early larval stages, the mushroom body neurons – paired neuronal structures important for olfactory processing and learning – continue to divide from the first instar (L1) stage onwards, differentiating two distinct lineages across larval development: the g and α'/β' lineages (Kunz et al., 2012). A third lineage, the α/β lineage, is established during pupal development (Kunz et al., 2012). When faced with extremely poor nutritional conditions that reduce larval growth, the mushroom body neurons maintain division of the same neuronal lineage – the g lineage – over longer developmental periods (Lin et al., 2013; Rossi et al., 2017). In contrast, the optic lobe neurons, which receive sensory input from the visual system, are activated late in larval development and highly sensitive to changes in the nutritional environment, and the size of the neuron pool involved in initial proliferation depends on nutrient availability (Lanet & Maurange, 2014). This suggests that specific brain regions differ in how they protect the brain from environmental perturbations.
These findings allow us to further propose a model of how the mushroom body compartments of the brain might remain constant in size in the face of changing environmental conditions. Firstly, because neuroblasts such as those that give rise to the mushroom body neurons begin proliferating much earlier than the majority of the brain neuroblasts, and proliferate throughout the larval instars, we might expect the size of these structures to increase constantly, or linearly, throughout larval development (Figure 1B). Most of the remaining neuroblasts of the brain initiate proliferation late in the second instar. Thus, we would expect a period of little or no discernible growth across the whole brain in the first two instars, punctuated by a rapid onset of growth in later development that would be best characterized by exponential growth with a time lag to its onset (Figure 1B). This would mean that growth dynamics in the mushroom body could differ significantly from that of the whole brain. Differences in their dynamics could indicate that growth is mediated by differing mechanisms between compartments, potentially dictating their response to environmental cues. As an alternative hypothesis, the dynamics of mushroom body growth could also be exponential but could have a much shallower slope.
This work aims to determine how brains regulate their growth to ensure robust size across different environmental conditions. To address this, we first compared the growth patterns of whole brains and mushroom bodies, relative to the larval body, under standard rearing conditions. We then used altered nutritional and thermal conditions to explore how the dynamics of brain growth respond to environmental change. Finally, we manipulated the activity of three signalling pathways known to regulate organ growth rates and the length of the larval growth period. These studies reveal differences in the way the mushroom body compartment regulates its growth when compared to the whole brain, and highlights how growth dynamics are tuned by nutrition and temperature within the mushroom body and across the whole brain. With these studies, we deepen our understanding of how different brain regions maintain robustness across environmental conditions.
Comparing the growth dynamics of the larval body, whole brain, and mushroom bodies across larval development
Given that the mushroom body neuroblasts show different patterns of growth to the majority of other neuroblasts in the brain, our first goal was to devise methods to compare mushroom body growth to whole brain and larval body growth across all three larval instars. To ensure that we compared the growth of the same structures across developmental time, we required a marker that would be expressed throughout all three instars. Using the expression data available from the Janelia FlyLight project (http://flweb.janelia.org/cgi-bin/flew.cgi), we selected lines with GAL4 expression in the mushroom bodies. We then crossed these GAL4 lines to UAS GFP lines, and selected animals immediately after hatch to the first instar (L1) larvae, or after the moult to the second instar (L2) and third instar (L3) larvae. We found that the GMR38E10 GAL4 line drove GFP expression in the vertical and medial lobes of the mushroom body neurons from hatch through to pupariation (Figure 2). In the late L3 stage, GFP expression was not apparent in the mushroom body calyx (Figure 2), which is the dendritic projections of Kenyon cell bodies (Supplementary Figure 1). Thus, to be able to compare measurements across all stages of development we excluded the calyx and peduncles from our analyses and measured only the ventral and medial lobes for mushroom body volume (Supplementary Figure 1).
We next sought to compare the dynamics of larval, whole brain, and mushroom body growth. For each sample collected, we imaged the larvae before dissecting out their brains. By quantifying larval length and width, we could approximate larval volume using the formula length x width2. Counter-staining the brains with phalloidin, which marks the actin cytoskeleton of all cells, allowed us to derive whole brain volume and mushroom body volume in the same sample.
Because growth is typically exponential, the standard method of looking at growth dynamics is to log-transform the data. In the simplest of cases, this linearizes the data allowing us to use linear regression analyses to explore growth dynamics. Non-linear growth dynamics in log-transformed data indicate that growth rates change with time.
Larval growth increased steadily throughout the first, second, and third instar stages (Figure 3A-C, Table 1). Linear models explain 68%, 55%, and 78% of the variation in larval volume over time for L1, L2, and L3 respectively (Table 1, adjusted R2 values). Similarly, the mushroom body displayed steady linear growth throughout all three instars (Figure 3G-I, Table 1), with linear models explaining 43%, 55%, and 77% of the variance in mushroom body volume over time for the L1, L2, and L3 respectively (Table 1, adjusted R2 values). In contrast, for whole brain volume we observed a slight, but significant, decrease in whole brain volume with time in the L1 (Figure 3D, Table 1). In this case, the linear model explained only 4% of the variance in whole brain volume in the L1 (Table 1, adjusted R2 values). There was no significant change in brain volume with time across the L2 stage (Figure 3E, Table 1). In the L3, whole brain volume shows a non-linear relationship with time, curving upwards. This suggests that whole brain growth speeds up as the third instar progresses (Figure 3F). Curiously, at 0 hours after the moult to both L2 and L3, brain volume appears to increase despite no evidence of positive growth during the L2 instar. We cannot tell whether this is a random sampling effect or if this results from a burst of growth during the moult cycle itself, which we could not accurately sample.
Our results thus far suggest that whole brain growth is regulated differently to that of the larval body and mushroom bodies. To formally test this, we fit our growth data with both linear models and a range of non-linear models commonly used to describe growth dynamics, including second order polynomial, exponential, lagged exponential, and power models (Karkach, 2006). Each of these models infers something different about growth. The second order polynomial model assumes that there are both linear and quadratic components to growth over time; exponential models describe growth that speeds up exponentially over time; lagged exponential models are similar to exponential models, but infer the presence of a slow period of growth followed by a switch to exponential growth; and the power model implies that growth increases according to a power function. We assessed which model best fit our growth data for each trait using two different model selection methods: Akaike's Information Criteria (AIC) and Bayesian Information Criteria (BIC), both of which estimate the quality of each model relative to the others, penalizing models with a higher number of parameters to avoid overfitting the data. The model with the lowest AIC and BIC values provides the best fit for the data. Where these values were close between models, we selected the simplest model (i.e. the model with the fewest parameters). We restricted these comparisons to L3 growth, since the whole brains did not show significant positive growth in the L1 and L2 stages.
For growth in the larval body and mushroom body, we found that linear models provided the best fit to our data (Table 2). This means that the growth rates in the larval body and mushroom body do not change over time in the third instar. Whole brain growth, on the other hand, was best fit with a lagged exponential model. This indicates that in the early stages of the third instar the whole brain grew slowly. After this lag phase, the rate of whole brain growth increased exponentially. Taken together, these data suggest that while the larval body and mushroom body growth rates do not change with time over the third instar, the whole brain undergoes a period of little growth, followed by a second phase of rapidly increased growth in the L3.
Developmental time and growth dynamics are modulated by changes in nutrition and temperature
We next sought to determine how brain size remains robust when developmental time was extended as a result of altered environmental conditions. To do so, we reared larvae on one of three food dilutions, 10%, 25%, or 100%, and at one of two temperatures, 25°C or 29°C. We compared growth rates in the L3 across these six environmental conditions. Changing the diet and/or rearing temperature altered the time it took for animals to initiate metamorphosis at pupariation (white pre-pupae). Compared to animals grown under standard conditions (25°C and 100% food), animals reared on food with only 10% of the normal caloric content took the longest to pupariate (90 and 80 hours after the moult at 25°C and 29°C respectively, compared to 42 hours at 25°C on 100% food). At 25°C, pupariation was delayed to 50 hours after the moult when larvae were reared on 25% food. Development time was similar between the 25% and 100% food conditions at 29°C (42 hours from moult to white pre-pupae).
Given these differences in development time across nutritional and thermal conditions, we next defined how this changed growth dynamics of the mushroom body, whole brain, and larval body. For each condition, we sampled 5-7 time points across the L3 stage, with the last 2 time points corresponding to the wandering and white prepupal stages, respectively. Diluting the food reduced growth rates of the larval body at both temperatures (Figure 4A, B, Table 3). Overall, the larval body grew more slowly when larvae were reared at 29°C compared to 25°C (Figure 4A, B, Table 3). Larvae grew slowest on 10% food at 29°C and fastest on 100% food at 25°C (Figure 4A, B, Table 3), resulting in a significant interaction between time, food, and temperature. These data provide a convenient proof-of-principle that we can alter growth dynamics by manipulating food and temperature.
Changing developmental time allowed us to directly test our different models. We predicted that brain structures would remain robust to changes in developmental time in one of three ways (Figure 1A). Our first model predicted that when developmental time was extended, brain structures would maintain their growth rates, grow to their final size, and then stop growing and remain the same size until pupariation. This would be modelled best using an asymptotic regression, but could also be approximated by a negative quadratic term from a second order polynomial regression – indicating growth is flattening out. In our second model, we predicted that brain structures would remain robust against changes in developmental time by altering the time at which growth initiated, but maintaining constant growth rates. This hypothesis would be best supported by a change in the lag constant of a lagged exponential regression. Our final hypothesis proposed that brain structures would carefully tune both their rates of growth and the time they initiated exponential growth, supported by a change in both the scaling and lag constants of a lagged exponential regression or by a change in slope in a linear regression in the case of the mushroom bodies.
In the mushroom body, we found that diluting the food reduced growth rates (Figure 4E, F, Table 3), but that rearing temperature did not affect the rate of growth in this structure. This resulted in a significant decrease in growth rates for larvae grown on 10% food when compared to 25% food, as well as reduced growth rates on 25% food when compared to 100% food at both temperatures. Under all conditions, the mushroom bodies maintained linear growth trajectories. This best supports our model that at least the mushroom body compartment of the brain achieves robustness of size by carefully tuning its growth rates to adjust for changes in developmental time.
Because the whole brain showed non-linear growth patterns, we initially modelled whole brain growth using second order polynomials (Figure 4C, D, Table 2). Similar to the larval body and mushroom bodies, diluting the food reduced the growth rates of the whole brain with the slowest growth on 10% food for both temperatures. Rearing temperature also reduced growth rates in the whole brain (Figure 4C, D, Table 3). In addition, the way that food affects growth rates depends on the rearing temperature. For larvae reared at 25°C, growth rates differed depending on whether they were given 25% or 100% food. At 29°C, there was no difference in growth rate between the 25% and 100% food. Thus, the whole brain shows complex responses to the combined effects of temperature and diet.
These models allowed us to further distinguish between our hypotheses. If whole brains grew to a target size and then stopped, we would expect the quadratic terms from our polynomial regressions to be negative as growth flattens out. In all cases where the quadratic term was significant in our models, we found that the value was positive (Table 4). This suggests that our first model – that brains should grow to a target size then stop – is not supported by our data.
We can distinguish between our second and third models using the lagged exponential growth models using the formula , where a is the intercept, b is the lag constant, and c is the scaling constant. If brains remain robust to changes in developmental time by altering the time at which they turn on growth (hypothesis 2), we would expect the lag constant (b) to change, but not the scaling constant (c). Hypothesis 3 would be supported if both the lag constant (b) and scaling constant (c) changed with altered developmental time. We fit our whole brain growth data with lagged exponential curves and explored whether the lag and scaling constants differed across our six environmental conditions (Table 5). We then conducted pairwise comparisons between whole brain growth curves either at the same temperature but across different diets, or on the same diet but across the two temperatures. We asked whether fitting specific lag and scaling constants for the curves for each condition improved the fit to the data. For the comparisons between the 10% food and either the 25% or the 100% food, the lag constants were too dissimilar to find a common coefficient, resulting in a failure to resolve a null model. While this suggests that the lag constants differ in these comparisons, we cannot formally test for this. However, we find definitive evidence that both the lag constants (1 instance) and the scaling constants (5 instances) differ significantly between conditions for whole brain growth (Table 5). Taken together, our data best supports a model where both the timing at which exponential growth begins and the growth rate are carefully tuned to adjust for differences in developmental time.
Changing environmental conditions affects size traits in the prepupae
We have shown that the growth dynamics of the larval body, mushroom body, and whole brain are all sensitive to environmental perturbation, but that they respond in different ways to changes in diet and temperature. We next extended these findings by examining the effects of changed environmental conditions on their final size at pupariation.
Pupal body volume decreased as the food was diluted and also decreased at the higher temperature (Figure 5A, Table 6). This is what we would have expected given previously published data on the effects of diet and temperature on pupal body size (Loeb & Northrop, 1917; Davidowitz et al., 2003; Couret et al., 2014). At pupariation, we did not observe a significant effect of diet on its own for whole brain volume (Figure 5B, Table 6). However, whole brains were smaller at 29°C than at 25°C, and there was a significant temperature by diet interaction (Figure 5B, Table 6). This is due to the fact that at 25°C larval diet had no effect on brain volume while at 29°C, brain volume decreased with diet concentration. Mushroom body volumes at pupariation varied with diet and temperature, with increasing food concentrations and increasing temperatures negatively impacting mushroom body volume (Figure 5C, Table 6). The significant interaction between diet and temperature results from the fact that while food concentration correlates negatively with mushroom body volume at 25°C, it correlates positively with mushroom body volume at 29°C.
These differences in the way the whole brain and mushroom body volumes respond to diet and temperature has interesting implications for brain shape. While mushroom body volumes are remarkably robust in size on 25% and 100% foods, on 10% food they are larger for their brain size at 25°C and smaller for their whole brain size at 29°C (Figure 5D, Table 6). This highlights that brain shape changes across environmental conditions, as compartments of the brain differ in how they grow in response to these conditions. This leads us to ask how these differences in response across brain compartments are achieved.
The role of nutrition-sensitive signalling pathways in regulating whole brain and mushroom body growth
Developmental timing is ultimately regulated by ecdysone (Yamanaka et al., 2013), and the timing of ecdysone production is modified by the activity of nutrition-sensitive pathways including insulin and TOR signalling (Caldwell et al., 2005; Colombani et al., 2005; Mirth et al., 2005; Koyama et al., 2014). Ecdysone, insulin, and TOR pathways are also known to regulate the growth of the larval body and of the brain (Ikeya et al., 2002; Chell & Brand, 2010; Sousa-Nunes et al., 2011; Lanet & Maurange, 2014; Rossi et al., 2017; Syed et al., 2017). We sought to understand which of these pathways changes growth dynamics in the structures of the brain, thus potentially uncovering the signalling mechanisms through which the whole brain and mushroom bodies adjust their growth with dietary and thermal conditions.
Ecdysone affects developmental timing but not growth rates of the brain structures
We first explored whether ecdysone signalling changed growth dynamics in the larval body, whole brain, and mushroom body. To understand the role of ecdysone in regulating growth acros nutritional conditions, we supplemented either 100% or 10% food with 50 ng/mg of 20-Hydroxyecdysone (20E). As a control, we added the same volume of the vehicle (ethanol). Animals were staged at 0 h L3 and transferred from the standard fly food (100% food) to the treatment diets. L3 staged animals were left in each diet until they reached the white prepupal stage. As in previous experiments, we analysed five time points from the L3 moult through to pupariation. We did not have sufficient data to fit lagged exponential models for the brain volume growth curves. In addition, we found that for these experiments linear models fit the data better than second order polynomial models, and we therefore opted for the simpler models (Supplementary table 1).
Supplementing 20E in the diet resulted in significant reductions in the time to pupariation when compared to animals fed with the vehicle control. Animals placed on control 100% food reached pupariation at 52 h after L3 moult while those on 100% food supplemented with 20E reached pupation at 40 h after L3 moult. This 12-hour increase in the control treatment appearred to be due to ethanol addition. As expected, in the 10% diet condition, the addition of 20E significantly reduced the time to pupariation. Animals that were reared on the 10% food supplemented with 20E reached pupariation at 60 h after L3 moult, but pupariated at 80 h when reared on the 10% food with ethanol alone. (Figure 6).
As in our previous experiment, the larval body, the whole brain, and the mushroom bodies grew at slower rates on the 10% food supplemented with ethanol. Adding 20E to the food significantly reduced larval volume on both 10% and 100% diets (Figure 6A, Table 7), corroborating previous findings that ecdysone suppresses the growth of larval tissues (Caldwell et al., 2005; Colombani et al., 2005; Mirth et al., 2005). In contrast, 20E had no significant effects on the growth rates of either the whole brain or the mushroom body volumes on either diet (Figure 6B, C, Table 7).
Diet however, did have a significant effect on whole brain volume at pupariation (Figure 6B, Table 7). Further, because it reduced total development time adding 20E to the diets reduced pupal volume, and the whole brain and mushroom body volumes in the white prepupae (Supplementary Figure 3, Table S3). Taken together, while 20E can control the size of brain structures by regulating total developmental time, ecdysone signalling is unlikely to control changes in growth rates in response to diet and temperature in the brain.
Insulin affects the growth rates of the larval body and mushroom body, while TOR signalling regulates the growth rates of the whole brain
We next tested whether inhibiting either the insulin or TOR signalling pathways could change whole brain and mushroom body growth rates. We first performed dose response curves for both the inhibitors of insulin (wortmannin) and the TOR (rapamycin) to determine the minimum dose that would affect body size and developmental timing (Supplementary Figure 4). From these results, we selected doses of 100μM of wortmannin/ml of food and 30μM of rapamycin/ml of food, which were dissolved in ethanol and supplemented into the standard 100% food. We did not have sufficient data to accurately fit lagged exponential models for the brain volume growth curves. Thus, for each measure of size, we tested whether linear or second order polynomials best fit the data, and selected the simpler model in cases where the fit was similar.
Adding Wortmannin to the food extended larval development time from 52 (control) to 72 hours. Wortmannin decreased the growth rate of the larval body, as indicated by a significant treatment by time interaction (Figure 7A, Table 8), resulting in decreased prepupal volumes (Supplementary Figure 5, Supplementary Table 3). When we observed the whole brain, the prepupal volume was also significantly decreased in animals treated with wortmannin (Supplementary Figure 5, Supplementary Table 3). However, we could not detect significant decreases in growth rate of the whole brains from wortmannin-treated larvae (Figure 7B, Table 8). In contrast, the growth rate of the mushroom bodies was significantly affected the by the addition of wortmannin, resulting in smaller prepupal mushroom bodies than controls. Indeed, the mushroom body volumes appeared to shrink after the larvae were transferred to wortmannin-supplemented food. This suggests that the growth of the larval body and mushroom bodies is more sensitive to insulin signalling than that of the whole brain.
Rapamycin treatment did not affect larval body growth rates (Figure 8A, Table 9). Because the addition of rapamycin to the food extended larval development time to 86 hours, these larvae pupariated with larger larval body volumes (Supplementary Figure 6, Supplementary Table 4). Similarly, mushroom body growth rates were not significantly affected by rapamycin treatment, although the treated larvae had lower mean mushroom body volumes across larval stages (Figure 8C, Table 9), the mushroom body volumes in the prepupal stage was not affected with rapamycin treatment (Supplementary Figure 6, Supplementary Table 4). Again, this is in contrast to what we expect as increasing developmental time and keeping the growth rate constant should result in an increase in the final size, as organs continue to grow at the same rate for longer durations than the control group. For the whole brain, rapamycin significantly reduced growth rates when compared to controls (Figure 8B, Table 9) resulting in smaller prepupal brain volumes (Supplementary Figure 6, Supplementary Table 4). These results indicate that TOR signalling plays a more important role in regulating whole brain growth rates than in regulating the growth rates of the larval body and mushroom body.
Individual organs vary in their response to the environmental conditions that affect adult body size (Shingleton et al., 2009). Organs like the brain and genital discs are known to be nutritionally insensitive when compared to organs like the wing (Chell & Brand, 2010; Shingleton, 2010; Cheng et al., 2011; Tang et al., 2011; Shingleton & Frankino, 2018). While we have some understanding of the genetic mechanisms underpinning robustness in size in these organs, our understanding of how these mechanisms affect the dynamics of growth was poorly understood. Further, the brain is a complex organ whose compartments do not all behave in the same manner. Functional compartments like the Drosophila mushroom body differ in their growth patterns as well as their nutritional plasticity from the rest of the brain. In this study, we aimed to test our predictions that the differences in proliferation between neurons of the whole brain and mushroom bodies would confer distinct growth dynamics, which could impart differences in their sensitivity to environmental conditions.
Previous studies had suggested that brain sparing occurs under stressful conditions because Jeb/Alk maintain high levels of insulin and TOR signalling in the neuroblasts (Cheng et al., 2011). These same conditions also affect development time of the larva (Beadle et al., 1938; Robertson & Reeve, 1952; Robertson, 1962, 1966; Partridge et al., 1994; Nunney & Cheung, 1997). If insulin and TOR levels are at the same level in the brains of starved and fed larvae, then the brain must have additional mechanisms to prevent overgrowth when development time is extended. In this study, we altered development time by changing both nutrition and temperature. We proposed three hypotheses that would allow brain size to remain robust against environmental conditions. These posited that in response to changes in total development time the brain would either 1) grow to a target size and stop growing for the remainder of the growth period, 2) delay the onset of its growth, but maintain constant growth rates even under stressful conditions, or 3) regulate both its growth rate and the time at which it switches growth on to adjust for changes in developmental time. Our data supports our third hypothesis, that robustness of brain size is possible because both the time at which exponential growth is initiated and the rates of growth of the brain have been altered.
Our results imply that Jeb/Alk signalling, which is responsible for brain sparing in Drosophila, plays a more nuanced role than previously described. Rather than simply maintaining high levels of insulin and/or TOR signalling, signalling from Alk could be acting to adjust growth rates of the brain to match changes in developmental time. Precisely how this occurs is unknown, however given that both insulin and ecdysone signalling are key regulators of the length of the growth period (Caldwell et al., 2005; Colombani et al., 2005; Mirth et al., 2005; Shingleton et al., 2005; Koyama et al., 2014), these systemic cues could be regulating the concentration of Jeb secreted by the glial cells in accordance with the degree to which development is delayed. Other organs that show robustness in final size could be responding to environmental conditions in a similar fashion. For example, we would predict the genital discs maintain robust final size by tuning their growth rates to account for extended growth periods under poor nutrition or thermal stress.
While the size of the pupal brain is robust against environmental conditions, this does not mean that brain growth is insensitive to environmental stress. Nutritional signals are important for neuroblasts to exit quiescence and re-initiate proliferation in the larval stages (Britton & Edgar, 1998; Chell & Brand, 2010; Yuan et al., 2020). Cues from the fat body, the insect equivalent of the adipose tissue and liver, signal to glial cells in the brain, which in turn produce insulin-like peptides that induce the neuroblasts to recommence cell divisions (Britton & Edgar, 1998; Chell & Brand, 2010; Yuan et al., 2020). Starving larvae in early instars causes most neuroblasts and glia, with the exception of the mushroom body neuroblasts, to remain quiescent (Britton & Edgar, 1998; Chell & Brand, 2010; Yuan et al., 2020). This is owing to the cell-autonomous and non-autonomous growth coordination activity of PI3Kinase in the early larval stages of development (Yuan et al., 2020). After they exit quiescence, neuroblasts no longer depend on nutritional cues to maintain proliferation (Cheng et al., 2011; Lanet & Maurange, 2014). It is unclear how this change in the impact of nutrition relates to the growth dynamics measured throughout larval development in the current study, however it is likely to be responsible for reduced rates of exponential growth in the third instar on poor diets.
While the growth dynamics of the whole brain change to accommodate additional developmental time, our findings also demonstrate that not all compartments of the brain should be expected to respond in the same way. Our comparisons between whole brain and the mushroom bodies highlight how the growth dynamics of specific brain compartments can differ from the patterns observed across the brain as a whole. Some of these differences arise simply due to differences in the timing of neuroblast reactivation. While the neurons of the mushroom body continue to proliferate throughout larval development, most other neuroblasts reinitiate proliferation after the late second instar. This alone should be sufficient to generate differences in growth dynamics between the mushroom bodies and the rest of the brain.
However, differences in growth patterns are not unique to the mushroom body. Unlike most of the other regions of the brain, the optic lobe shows extensive plasticity in size with nutritional conditions (Lanet et al., 2013; Lanet & Maurange, 2014). This is presumably to compensate for changes in eye size across environmental conditions, and is facilitated due to their unique mode of development. Instead of arising from embryonic neuroblasts, the optic lobe is formed from neuroepithelium that continues to divide and expand until early in the third instar (Farkas & Huttner, 2008; Brand & Livesey, 2011). Proliferation of the optic lobe neuroepithelium remains sensitive to nutrition until the early third instar, where a small pulse of ecdysone induces the cells in this neuroepithelium to become neuroblasts (Lanet et al., 2013). After this transition, starvation no longer impacts cell divisions in this brain region, and each neuroblast proceeds to divide and generate the full complement of neuronal cells types necessary for the function of the optic lobe (Lanet et al., 2013). This ensures that while the total number of neurons in the optic lobe is plastic, the diversity of cell types is held constant (Lanet et al., 2013). Given its mode of development and persistent sensitivity to nutrition, we would expect that the optic lobes would also exhibit different growth dynamics from the whole brain.
The growth of the whole brain and mushroom bodies also differed in their sensitivity to insulin and TOR. We observed that while mushroom body growth appeared to be more sensitive to insulin signalling, the growth of the whole brain was more sensitive to TOR signalling. Our results for whole brain growth are consistent with other studies in Drosophila (Sousa-Nunes et al., 2010; Yuan et al., 2020), but also in mammals (Cloetta et al., 2013), where TOR signalling controls cell cycle progression and neuronal exit from quiescence respectively, ultimately regulating final brain size.
Our results showing that the mushroom bodies were sensitive to reductions in insulin signalling is contrary to previous research (Sipe & Siegrist, 2017). Sipe and Siegrit (2017) found that the Pax-6 orthologue, Eyeless, is specifically expressed in the mushroom body. Eyeless expression allows mushroom body neurons to continue proliferating independent of PI3Kinase activity, a central regulator of insulin signalling, under conditions of poor nutrition (Sipe & Siegrist, 2017). This discrepancy in results is likely to be one of degree: while eyeless undoubted plays a role in maintaining proliferation, insulin signalling in the mushroom body neurons has its own independent effects on proliferation and in controlling the size of the arbour.
Finally, the majority of studies of brain growth have focused on nutritional stress. However, a number of other conditions are known to extend developmental time, including temperature, oxygen limitation, and larval density (Partridge et al., 1994; Peck & Maddrell, 2005; Mirth & Shingleton, A. W., 2012). The mechanisms regulating extended developmental time under these conditions are less well understood, but ultimately culminate in changing the rate of ecdysone production and secretion. While we found that ecdysone did not change growth rates in our experiments, it did change larval, whole brain, and mushroom body size by truncating the length of the larval period. Previous studies have documented that reducing or eliminating ecdysone or ecdysone signalling also reduces brain size (Lanet et al., 2013; Herboso et al., 2015). It is likely that the conditions under which we reared larvae were insufficient to reduce ecdysone concentrations enough to affect growth rates.
In this research, we sought to understand how organs achieve robust final size by exploring the growth dynamics of the brain across nutritional and thermal conditions. We found that at least one compartment of the brain can differ in its growth patterns from the rest of the brain, and speculate that this might be true of other compartments. These distinct growth patterns allow specific brain regions to vary their response to changing environmental conditions via modulating the action of different molecular mechanism that regulate their growth. Taken together, our findings demonstrate that brain compartments achieve robustness in final size via different trajectories. Furthermore, by probing the growth dynamics of organs under environmental stress, we fill in important gaps in our knowledge of how these organs achieve robustness of final size.
Materials and Methods
Fly strains and husbandry
Drosophila stocks were reared at 25°C with 65% humidity, on a 12-hour light/dark cycle and maintained on sucrose-yeast (SY) diet (detailed below). To drive the expression of green fluorescent protein (GFP) in the mushroom body neurons, we used the R21B06-GAL4 line (BDSC 68318), known to be expressed in the mushroom bodies of larval and adult brains (http://flweb.janelia.org/cgi-bin/flew.cgi; (Pfeiffer et al., 2008; Jenett et al., 2012). This line was crossed with a membrane-tagged GFP reporter (w[*]; P[y[+t7.7] w[+mC]=10XUAS-IVS-myr::GFP]attP2). These stocks were obtained from the Bloomington Drosophila Stock Center, Indiana University, Bloomington.
Sucrose/Yeast (SY) diet was prepared as reported by (Toivonen et al., 2007), with 100g autolyzed Brewer's Yeast, 50g sucrose, 10g agar, 1.5ml propionic acid, 15ml Nipagin M solution dissolved in one liter of distilled water. In addition to the standard diet (100% SY), we exposed larvae to additional experimental diets, which contained 10% and 25% of the caloric content of the standard SY diet. These diets were made by adding appropriate amounts of the original Brewer's yeast and sucrose to the same concentration of agar and water. 25% food contained 25g autolyzed Brewer's Yeast and 12.5g sucrose, while 10% food contained 10g autolyzed Brewer's Yeast and 5g sucrose, dissolved in one litre of distilled water. All diets were allowed to cool to 60° before the preservatives (propionic acid and Nipagin M) were added and the food dispensed.
Treatment with ecdysone and inhibitors
The concentrations of both inhibitors, Wortmannin and Rapamycin, to be used was derived following a dosage response curve on pupal volumes (Supplementary Figure 4). For Wortmannin, doses ranging from 1 – 80μM/ml of food produced no significant effect on pupal volumes, but 1000μM/ml of food significantly reduced pupal volumes, and at 120μM/ml of food, we observed a significant increase in larval lethality. Similarly, there was no significant effect of doses ranging from 1 – 25μM/ml of food, but 30μM/ml of Rapamycin in food significantly reduced pupal volumes, and doses higher than 40μM/ml of food, produced significant larval lethality. This way, we selected 30μM/ml of food of Rapamycin and 100μM /ml of food of Wortmannin for the experiment.
The standard medium was then supplemented with solutions containing one of the following substances dissolved in 100% ethanol:
- 30μM Rapamycin (Sigma-Aldrich, USA), TOR signalling inhibitor
- 100μM wortmannin (Sigma-Aldrich, USA), PI3Kinase inhibitor (Neri et al., 1999)
- 50ng/mg 20E (Sigma-Aldrich, USA) Ecdysone supplement.
Body size measurement, organ dissection, and immunocytochemistry
Animals picked at the relevant time points were first placed in cold PBS solution, to immobilize them, and then imaged using a Zeiss Stemi 508 dissecting microscope before dissection. These images were analyzed using the FIJI (ImageJ, Version 2.0.0-rc-69/1.52i, 2019) software. The length and width of the larva or pupa was measured using the straight-line tool, and volume was calculated using the formula lw2 (length x width2).
After measuring each larva/pupa, we dissected out their brain in cold 1x Phosphate Buffered Saline (1xPBS) under a Leica S9E dissecting microscope according to methods previously described (Hafer & Schedl, 2006; Daul et al., 2010). Isolated brains were fixed overnight in 4% paraformaldehyde at 4°C. After four washes in a solution of cold 0.3% Triton X-100 in PBS (PBT) for 20 minutes each, samples were incubated in 2% normal donkey serum block solution prior to immunostaining. The blocked tissue samples were then transferred to Acti-stain™ 670 Phalloidin (1:1200, Cytoskeleton Cat#: PHDN1) reagent diluted in PBT and normal donkey serum, and incubated on a rocking platform shaker in the dark for 2-3days at 4°C. Prior to imaging, samples were rinsed in cold PBS, and PBS was replaced with 25% KY jelly in water solution. Samples were imaged using the Leica SP8 HyD microscope with 40x water immersion objective.
Image processing and statistical analysis
Images were analyzed using the Imaris© (Bitplane) software. Image normalization was performed to ensure standardized measurements across images with different signal intensities, and 3D analysis of the brain was done by software's in-built wizard. Images were rendered as volumes using the surface analysis tool on Imaris.
Data analyses were carried out in R Studio (Version 1.2.5019© 2009-2019 RStudio, Inc.). We fit our body and organ size data with both linear, using the lm function, and non-linear models, using the nls package (Baty et al., 2015). We used AIC and BIC to assess model fit, selecting the simplest models when these values were similar. Data visualization was conducted using the ggplot package (Wickham, 2016) in R Studio (Version 1.2.5019© 2009-2019 RStudio, Inc.).
Author contributions
AEC, BN and CKM conceived and designed the experiments. AEC performed the experiments. AEC and CKM analysed the data. All authors contributed to interpretation of data and final manuscript preparation.
Declaration of interests
The authors declare no competing interests.
We are grateful to Keith Schulze, Alex Fulcher and Stephen Firth of the Monash MicroImaging (MMI) for their technical support. This work was supported by an Australian Research Council Future Fellowship (FT170100259) to CKM and the school of Biological Sciences, Monash University, Australia.


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Posted in: Beverage, Drink | Cuisines: Middle Eastern
This is a tasty appetizer with Lebanese roots.
Bright with flavor, this salad makes for an easy summer dish. Serve alone or as a side to grilled chicken. Add a shiny glass of rosé to enjoy alongside lunch or dinner!
A quick and very delicious fix to any chocolate craving.
Chili has a rich heritage in the state of Texas, beginning in San Antonio as far back as the early 1800s and even featured at the World's Fair in Chicago in 1893. With deep Texas roots, even a U.S. president, LBJ, was a big fan and had their own recipe -- Pedernales River Chili -- printed cards to share during their stay in the White House. We hope you enjoy our family's chili, named after one of the founders' sons, that has a small Mediterranean twist by using chickpeas and fava beans.
Gingerbread may have been brought to Europe in 992 AD by the Armenian monk Gregory of Nicopolis (in modern-day western Greece). Leaving Nicopolis to live in Bondaroy (north-central France), near the town of Pithiviers, he stayed there for seven years and taught gingerbread baking to French Christians. Throughout the centuries, many cultures have celebrated the art of gingerbread making, with even a gingerbread guild established in Germany in the 15th century.
Rolled up YUMMY Goodness in the form of Finger Baklava made daily at Phoenicia Specialty Foods!
This homemade vanilla extract is oh-so-good and also incredibly easy to make. For your own use, skip the need for little containers and just steep the vanilla beans right into the entire whisky bottle. I was lucky enough to sample a variety of bourbon whiskeys before choosing Giant Bourbon 95 Proof Small Batch locally crafted in Houston by Gulf Coast Distillers. As bourbon is derived from corn mash while rye whiskey tends to taste drier with more spices, this local bourbon imparts a sweetly warm and full-bodied taste. I thought, how perfect for cooking and baking. When it's ready, the uses are endless, from adding a teaspoon to heavy cream before whisking it into freshly whipped cream to infusing magic into chocolate chip cookie dough batter. Let the goodness begin!
With festive potlucks and dinner gatherings right around the corner, this is a great side dish or even as a main one for vegetarians or anyone looking to eat less meat. Infused with vibrantly fruity and nutty flavors, spruce up a meal with this easy to make pilaf!

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How to Launch Startup Applications With A Delay On Linux
This article explains how to delay startup applications, with 2 ways of achieving this.Read More
This article explains how to delay startup applications, with 2 ways of achieving this.Read More
ImageMagick is an open-source image manipulation command-line tool. It is used to create, edit, transform, and convert .png, .jpg, .gif and other types of images. You can use it to resize and transform images, add text overlays, draw shapes, add […]
SOGo is a free, open-source and collaborative groupware server released under the GNU GPL/LGPL v2 and above. It comes with a simple and easy to use web-based interface and supports multiple native clients.Read More
Linux hardware maker has revamped one of their most popular laptops.Read More
After years of open development, Isovalent formally launches with $29M in funding for its eBPF-powered Cilium networking service.Read More
The ABS or Arch Build System is a package building system native to the Arch Linux distributionRead More
by Ramon Persaud
I've been playing around with the Raspberry Pi Zero W lately and having so much fun on the command line. For those uninitiated it's a tiny Arm computer running Raspbia…Read More
Mozilla is already working on the next major version of the popular and open-source web browserRead More
who is a command-line utility that prints a list of currently logged in users.Read More
SUSE, the major European Linux company, may soon be spun out in a $6 billion IPO.Read More
Rocket.Chat is a web-based chat application written in JavaScript, using the Meteor full-stack framework. It is an open-source chat collaboration platform that allows you to communicate securely in real-time across multiple devices.Read More
Every time you power on your Linux PC, it goes through a series of stages before finally displaying a login screen that prompts for your username or password. There are 4 distinct stages that
The post A Basic Guide to Linux Boot Process first appeared …
the Open Invention Network has been protecting Linux and open-source software developers and companies from open-source's enemies and patent trolls.Read More
Dirk Hohndel, VMWare's chief open source officer, talked about the "symbiotic relationship" between open source and the enterprise.Read More
The following are two reasons why you would want to keep track of what network services are running on your system: To ensure that no legitimate network services that you don't need are running To ensure that you don't have any malware that's listening for network connections from its master You can use the following […]
The post Auditing network services with netstat in linux appeared first on The Linux Juggernaut.Read More
Nikto is a special-purpose tool with only one purpose; that is, it's meant to scan web servers, and only web servers. It's easy to install, easy to use, and capable of doing a comprehensive scan of a web server fairly quickly. Nikto is built on LibWhisker2 (by RFP) and can run on any platform which […]
The post Web server scanning with Nikto in Linux appeared first on The Linux Juggernaut.Read More
the brief history of ping network administrationRead More
There are multitude of Linux variants exists. Developing and maintaining applications for all Linux…
The post Manage AppImages, AUR, Flatpaks And Snaps With Bauh appeared first on OSTechNix.


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Tag "Wadhwani Foundation"
Wadhwani Foundation invites early stage startups to a free 12-Week Growth Program; Apply now
By : CIOL Bureau
Wadhwani Foundation is a not-for-profit organisation. Its primary mission is to accelerate economic development in emerging economies by driving job creation in India and other emerging economies through large-scale initiatives in entrepreneurship, small business growth, innovation, and skilling. Silicon Valley


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- 2020
- Spatial patterns of tau deposition are associated with amyloid, ApoE, sex, and cognitive decline in older adults
- Low vitamin d levels and frailty status in older adults : A systematic review and meta-analysis
- Older person's experiences of benefits gained from the support and advice given during preventive home visits
- Multiple indicators focusing on different alcohol-related domains should be used to describe alcohol use profiles among older patients
- A Multidimensional Latent Class Analysis of Harmful Alcohol Use Among Older Adults : Subtypes Within the Swedish Addiction Severity Index Registry
- Association of inflammatory mediators with frailty status in older adults : results from a systematic review and meta-analysis
- 2019
- Cystatin C and creatinine-based eGFR levels and their correlation to long-term morbidity and mortality in older adults
- Maintaining cognitive function with internet use : A two-country, six-year longitudinal study
- Assessment of cognition in ageing. Investigating internal validity, occurrence and reversion of Mild Cognitive Impairment. Data from the general population study "Good Aging in Skåne"
- Longitudinal evaluation of periodontitis and tooth loss among older adults


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You may have been told that you are a people person. You may be the kind of person that people naturally turn to for advice. If so, there is a strong possibility that you may be asking yourself this question. Should I become a life coach?
This choice is more of a "calling" than a career. But, realistically, many people might wonder if they can afford to take on this profession. The salary of a life coach varies with experience and personal expertise. To help understand the range a life coach salary falls into it is important to examine the existing levels of opportunity.
There are 8-10 major life coach giants. Many people start with one of these established organizations for security. Here you will receive free training, have instant clients, and an immediate escolhasegura income. Coaches are trained using a carefully honed template unique to the company. This might be compared to a fast food chain rather than being a chef. Every aspect is precisely designed and formulas are followed without deviation. The overall effect of the brand is instilled in clients. You can learn a lot and perhaps pave the way to start your own practice while you collect a pay check. The established provider may not have many advancement opportunities and without promotions you may be locked into a particular earning framework for years.
You can perfect your personal approach and start up your own coaching service. If you have 10-20 years experience in the workforce there is a good chance of making 75 to 95,000 dollars per year. Your experience around the issues of marriage, divorce, children, health, finances, and spirituality may contribute to your effectiveness. You should allow a year and a half to two years to establish a consistent stream of income. You should also be prepared to invest 12-15 thousand to create your business. This may include marketing materials, website, training, certification, and other start up costs. You will have to generate your leads, offer some free sessions, and create a reputation that leads to referrals.


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We always try to get you the best price. However, there are a more ways to increase your savings. Be sure to:
Even a little knowledge can go a long way to getting you the most savings.
From budget-friendly brands like Fox and Budget to global names like Hertz and Sixt, CarRentals.com has some of the best brands in the business. Read through the online reviews to get an idea of which is best for you.
Whether you want a roomy family vehicle or a low-cost economy car, the sheer wealth of options in this metropolis means you're certain to find an automobile that suits your needs. Some options include:
Normally, the age limit for renting a car in Arica is 21—the same as it is across most of the country. Additionally, many suppliers charge an added fee if you are under 25. Be sure to review the terms to discover any additional charges is before you reserve your rental.
Almost all countries require driver's insurance. You may be able to buy interim insurance with the rental car company.Several credit card issuers (Visa, MasterCard, American Express, and Discover) will provide some coverage. But there may be limitations, such as not covering large automobiles like premium models or some SUVs. Make sure to call and find out what kind of coverage is offered before you head to Arica.
Most suppliers give you a choice of fuel policies. Below is a list of the most common ones.You will have to find a gas station near your rental car supplier
We suggest a full tank policy. You should receive your car topped off. If you return your rental with less than the amount you received it with, most companies will charge a premium to top it back off. If you want to keep your expenses lower, it is best to find a filling station before bringing the rental. Stations near or around airports or car rental places tend to have higher prices.
Unlimited miles is a good option if you plan to do a little sightseeing or visit a lot of clients all over Arica. You can cruise wherever you like without incurring additional charges. Read over your policy to see if there are any limitations.
Be sure you go over a few items before you leave the car rental lot. It's important to:
Use CarRentals.com to get the best wheels Arica has to offer. Before you know it, you'll be cruising between the highly-rated restaurants, renowned landmarks, and all the terrific sites in Arica.


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Steel Panther
Luna Park
Sydney, Australia
June 17, 2016
They are the biggest glam rock band that never was from 1985, in 2016.
Steel Panther has become bigger than any act that served as their source material.
Histrionic comedy rock? Metal cabaret?
Subversion of the entertainment industry?
Performance art?
Expert banter without cynism, stereotypes of self-aggrandizing machismo rock conventions lead ad absurdum.
Virtuosic musicianship.
Seasoned jokers.
Wigs and hair sprayed coiffures.
Aware of their roots and signifying them.
Itch for publicity.
Pushing boundaries.
Ever feeding on the lowest common denominators.
Likeable personas.
All out sexism and misogyny, which gets uncomfortable with the audience taking it for face value.
Memorable hooks.
Suggestively taunting.
Attentive to detail.
Painting with broad brushes.
See more than a few contradictions there?
Steel Panther embodies it all.
Jokes do get old but Steel Panther's one is well crafted and perfectly executed.
Do you need more than one trick if it is executed well?
Does the fact that perfectly tasteless jokes are made on a meta-level justify them?
Would I even ask the same questions if I reviewed a Will Ferrell / Ben Stiller movie with exactly the same comedic content?
Steel Panther is a fun act.
If it ain't broke, don't fix it:
Their staple opener "Eye of the Panther" kicked off the proceedings and opened a tour de force through the highlights of their 3 studio albums.
Expertly taking the local context into consideration, the scandalously high prices that Australians have to dish out for Peruvian marching powder were denounced and tribute was paid to Satchel's crystal meth cooking clan in Adelaide, which may or may not have inspired his trademark mid-show solo performance to culminate doing the locals proud with his Hendrix-ian take on the national anthem of Australia.
As time goes by it seems to take less and less prompting to have loose females jump on stage to frame SP's performance of the song "17 Girls in a Row" in various states of undress and dry humping the band in the most outrageous manners.
"Party All Day" and the sing-a-long crowd pleaser "Community Property" ended the show on a high note.
Even after having seen them various times with largely the same show, an intelligent band with great songs and likeable characters never gets old.
Photos by KAVV
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