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Medley, J. K.

Publications and source records attributed to Medley, J. K..

3 recordsLinked to original sources

Metabolic reprogramming underlies cavefish muscular endurance despite loss of muscle mass and contractility

Physical inactivity - specifically the lack of moderate-to-vigorous activity - is a scourge to human health, promoting metabolic disease and muscle wasting. Interestingly, multiple ecological niches have relaxed investment into physical activity, providing unique evolutionary insight into adaptive physical inactivity. The Mexican cavefish Astyanax mexicanus lost moderate-to-vigorous activity following cave colonization, reaching basal swim speeds ~3-fold slower than their river-dwelling counterpart. We found that this was accompanied by a marked shift in body composition, decreasing muscle mass by 30% and increasing fat mass by 40%. This shift persisted at the single muscle fiber level via increased lipid and sugar accumulation at the expense of myofibrillar volume. Transcriptomic analysis of laboratory-reared and wild-caught cavefish indicated this shift in investment is driven by increased expression of ppar{gamma} - the master regulator of adipogenesis - with a simultaneous decrease in fast myosin heavy chain expression. Ex vivo and in vivo analysis confirmed these investment strategies come with a functional trade-off, decreasing cavefish muscle fiber shortening velocity, time to maximal force, and ultimately maximal swimming velocity. Despite this, cavefish displayed a striking degree of muscular endurance, reaching maximal swim speeds ~3.5-fold faster than their basal swim speeds. Multi-omics analysis indicated metabolic reprogramming, specifically increased phosphoglucomutase-1 abundance, phosphorylation, and activity, as contributing mechanisms enhancing cavefish glycogen utilization under metabolically strenuous conditions. Collectively, we reveal broad skeletal muscle reprogramming following cave colonization, displaying an adaptive skeletal muscle phenotype reminiscent to mammalian disuse and high-fat models while simultaneously maintaining a unique capacity for sustained muscle contraction under fatiguing conditions. SignificanceThe evolutionary consequence of decreased physical activity upon skeletal muscle physiology remains unexplored. Using the Mexican cavefish, we find loss of moderate-to-vigorous swimming following cave colonization has resulted in broad shifts in skeletal muscle investment - away from muscle mass and instead toward fat and sugar accumulation - ultimately decreasing muscle fiber twitch kinetics. Surprisingly though, cavefish possessed marked muscular endurance, reaching maximal swimming speeds rivaling their river-dwelling counterpart. Multi-omics analysis revealed carbohydrate metabolic reprogramming as a contributing component, most notably elevated abundance and phosphorylation of the glycogenolytic enzyme Phosphoglucomutase-1 - a likely adaptation to cave-specific hypoxia. These findings emphasize the impact multiple selective pressures have on skeletal muscle physiology, providing the first evolutionary insight into skeletal muscle adaptation following decreased activity.

evolutionary biology↗

Untargeted Metabolomics of the Cavefish Astyanax mexicanus Reveals the Basis of Metabolic Strategies in Adaptation to Extreme Conditions

Insights from extreme-adapted organisms, which have evolved natural strategies for promoting survivability under severe environmental pressures, may help guide future research into novel approaches for enhancing human longevity. The cave-adapted Mexican tetra,Astyanax mexicanus, has attracted interest as a model system for metabolic resilience, a term we use to denote the property of maintaining robust health and longevity under conditions that would have highly deleterious effects in other organisms (Fig 1). Cave-dwelling populations of Mexi-can tetra exhibit elevated blood glucose and possess a mutation in the insulin receptor that in humans has been linked to Rabson-Mendenhall syndrome, a condition characterized by severe insulin resistance that causes numerous developmental abnormalities, is highly associated with debilitating progression, and drastically reduces lifespan. In addition, cavefish develop large numbers of hypertrophic visceral adipocytes and possess vastly enriched stores of body fat compared to surface-dwelling counterparts. However, cavefish appear to avoid the progression of the respective pathologies typically associated with these conditions, such as accumulation of advanced glycation end products (AGEs), chronic tissue inflammation, impaired growth due to insulin dysregulation, and low survivability due to arterial disease. The metabolic strategies underlying the resilience properties of A. mexicanus cavefish, and how they relate to environmental challenges of the cave environment, are poorly understood. Here, we provide an untargeted metabolomics study of long- and short-term fasting in two A. mexicanus cave populations and one surface population. We find that, although cave-fish share many similarities with metabolic syndrome normally associated with the human state of obesity, important differences emerge, including a reduction in cholesteryl esters and intermediates of protein glycation, and an increase in antioxidants and metabolites associated with hypoxia and longevity. We find important overlaps between metabolic alterations in cave-dwelling Mexican tetra and other models of resilience and extreme longevity, such as naked mole-rats, including enhanced reliance on sugars as an energy source and a trend toward more potent antioxidant activity. This work suggests that certain metabolic features associated with human pathologies are not intrinsically harmful, but are rather consequences of suboptimal adaptation of humans to survival under adverse metabolic conditions, and suggests promising avenues for future investigation into the role of metabolic strategies in evolutionary adaptation and health. We provide a transparent pipeline for reproducing our analysis and a Shiny app for other researchers to explore and visualize our dataset. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/358077v3_fig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@bcaff5org.highwire.dtl.DTLVardef@1c40b89org.highwire.dtl.DTLVardef@1d17d69org.highwire.dtl.DTLVardef@80a483_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig. 1.C_FLOATNO Metabolic resilience survivability under a variety of extreme conditions. Certain populations of cavefish have adaptations that cause increased appetite (1) and increased fat accumulation (2) (in cases where nutrients are plentiful, such as in lab-raised populations). These same populations also exhibit robust health and longevity (2, 3) and do not suffer ill-effects due to high levels of visceral fat and hyper-glycemia, both of which are features of most cave populations. However, visceral fat accumulation in cave populations is highly dependent on nutrient availability and is not displayed in wild-caught specimens (4). Thus, cavefish paradoxically appear to tolerate both extremely low and extremely high levels of triglycerides, glucose, and other energy storage metabolites. We argue that these differences can be reconciled under a hypothesis whereby the cave environment selects not for resistance to nutrient deprivation per se, but rather resilience to a variety of nutrient availability states (such as seasonal floods). Survival under such challenging conditions ostensibly favors the ability to tolerate extreme metabolic states, including not only starvation but also high levels of potentially deleterious metabolites such as triglycerides and reactive oxygen species (ROS). We find evidence for elevated antioxidant levels and altered cholesterol / cholesteryl ester homeostasis in cavefish, suggesting that cavefish may use these mechanisms to offset potentially harmful metabolites and tolerate a broad range of metabolic conditions. C_FIG

systems biology↗

Accelerated Biochemical Kinetic Model Fitting via the Asynchronous, Generalized Island Method

Mechanistic kinetic models of biological pathways are an important tool for understanding biological systems. Constructing kinetic models requires fitting the parameters to experimental data. However, parameter fitting on these models is a non-convex, non-linear optimization problem. Many algorithms have been proposed to addressing optimization for parameter fitting including globally convergent, population-based algorithms. The computational complexity of the this optimization for even modest models means that parallelization is essential. Past approaches to parameter optimization have focused on parallelizing a particular algorithm. However, this requires re-implementing the algorithm usinga distributed computing framework, which requires a significant investment of time and effort. There are two major drawbacks of this approach: First, the choice of best algorithm may depend on the model. Given the large variety of optimization algorithms available, it is difficult to re-implement every potentially useful algorithm. Second, when new advances are made in a given optimization algorithm, the parallel implementation must be updated to take advantage of these advantages. Thus, there is a continual burden placed on the parallel implementation. The drawbacks of re-implementing algorithms lead us to a different approach to parallelizing parameter optimization. Instead of parallelizing the algorithms themselves, we run many instances of the algorithm on single cores. This provides great flexibility as to the choice of algorithms by allowing us to reuse previous implementations. Also, it does not require the creation and maintenance of parallel versions of optimization algorithms. This approach is known as the island method. To our knowledge, the utility of the island method for parameter fitting in systems biology has not been previously demonstrated. For the parameter fitting problem, we allow islands to exchange information about their \"best\" solutions so that all islands leverage the discoveries of the few. This turns out to be avery effective in practice, leading to super-linear speedups. That is, if a single processor finds the optimal value of parameters in time t, then N processors exchanging information in this way find the optimal value much faster than t/N. We show that the island method is able to consistently provide good speedups for these problems. We also benchmark the island method against a variety of large, challenging kinetic models and show that it is able to consistently improve the quality of fit in less time than a single-threaded implementation.Our software is available at https://github.com/sys-bio/sabaody under a Apache 2.0 license.\n\nContactmailto:medjk@comcast.net

systems biology↗