The hidden costs of dietary restriction: Implications for its evolutionary and mechanistic origins.
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ABSTRACT:
SUBMITTER: McCracken AW
PROVIDER: S-EPMC7034997 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Dietary restriction (DR) is the most successful and widespread means of extending organismal lifespan. However, the evolutionary basis of life extension under DR remains uncertain. The traditional evolutionary explanation is that when organisms experience DR, they allocate endogenous resources to survival and postpone reproduction until conditions improve. However, this life-extension strategy should be maladaptive if DR continues for multiple generations due to trade-offs between longevity and reproduction. To test this prediction, we subjected the budding yeast Saccharomyces cerevisiae to 1800 generations of evolution on restricted versus non-restricted diets. Adaptation to a non-restricted diet improved reproductive fitness by 57%, but provided a much smaller (14%) advantage on a restricted diet. By contrast, adaptation to DR resulted in an approximately 35% increase in reproductive fitness on both restricted and non-restricted diets. Importantly, the life-extending effect of DR did not decrease following long-term evolution on the restricted diet. Thus, contrary to theoretical expectations, we found no evidence that the life-extending DR response became maladaptive during multigenerational DR. Together, our results suggest that the DR response has a low cost and that this phenomenon may have evolved as part of a generalist strategy that extends beyond the benefits of postponing reproduction.
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Project description:The high energetic costs of human brain development have been hypothesized to explain distinctive human traits, including exceptionally slow and protracted preadult growth. Although widely assumed to constrain life-history evolution, the metabolic requirements of the growing human brain are unknown. We combined previously collected PET and MRI data to calculate the human brain's glucose use from birth to adulthood, which we compare with body growth rate. We evaluate the strength of brain-body metabolic trade-offs using the ratios of brain glucose uptake to the body's resting metabolic rate (RMR) and daily energy requirements (DER) expressed in glucose-gram equivalents (glucosermr% and glucoseder%). We find that glucosermr% and glucoseder% do not peak at birth (52.5% and 59.8% of RMR, or 35.4% and 38.7% of DER, for males and females, respectively), when relative brain size is largest, but rather in childhood (66.3% and 65.0% of RMR and 43.3% and 43.8% of DER). Body-weight growth (dw/dt) and both glucosermr% and glucoseder% are strongly, inversely related: soon after birth, increases in brain glucose demand are accompanied by proportionate decreases in dw/dt. Ages of peak brain glucose demand and lowest dw/dt co-occur and subsequent developmental declines in brain metabolism are matched by proportionate increases in dw/dt until puberty. The finding that human brain glucose demands peak during childhood, and evidence that brain metabolism and body growth rate covary inversely across development, support the hypothesis that the high costs of human brain development require compensatory slowing of body growth rate.
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Project description:The cancer atavistic theory suggests that carcinogenesis is a reverse evolution process. It is thus of great interest to explore the evolutionary origins of cancer driver genes and the relevant mechanisms underlying the carcinogenesis. Moreover, the evolutionary features of cancer driver genes could be helpful in selecting cancer biomarkers from high-throughput data. In this study, through analyzing the cancer endogenous molecular networks, we revealed that the subnetwork originating from eukaryota could control the unlimited proliferation of cancer cells, and the subnetwork originating from eumetazoa could recapitulate the other hallmarks of cancer. In addition, investigations based on multiple datasets revealed that cancer driver genes were enriched in genes originating from eukaryota, opisthokonta, and eumetazoa. These results have important implications for enhancing the robustness of cancer prognosis models through selecting the gene signatures by the gene age information.
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Project description:Dietary restriction (DR), limiting calories or specific nutrients without malnutrition, extends lifespan across diverse taxa. Traditionally, this lifespan extension has been explained as a result of diet-mediated changes in the trade-off between lifespan and reproduction, with survival favored when resources are scarce. However, a recently proposed alternative suggests that the selective benefit of the response to DR is the maintenance of reproduction. This hypothesis predicts that lifespan extension is a side effect of benign laboratory conditions, and DR individuals would be frailer and unable to deal with additional stressors, and thus lifespan extension should disappear under more stressful conditions. We tested this by rearing outbred female fruit flies (Drosophila melanogaster) on 10 different protein:carbohydrate diets. Flies were either infected with a bacterial pathogen (Pseudomonas entomophila), injured with a sterile pinprick, or unstressed. We monitored lifespan, fecundity, and measures of aging. DR extended lifespan and reduced reproduction irrespective of injury and infection. Infected flies on lower protein diets had particularly poor survival. Exposure to infection and injury did not substantially alter the relationship between diet and aging patterns. These results do not provide support for lifespan extension under DR being a side effect of benign laboratory conditions.
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Project description:Hierarchical organization-the recursive composition of sub-modules-is ubiquitous in biological networks, including neural, metabolic, ecological, and genetic regulatory networks, and in human-made systems, such as large organizations and the Internet. To date, most research on hierarchy in networks has been limited to quantifying this property. However, an open, important question in evolutionary biology is why hierarchical organization evolves in the first place. It has recently been shown that modularity evolves because of the presence of a cost for network connections. Here we investigate whether such connection costs also tend to cause a hierarchical organization of such modules. In computational simulations, we find that networks without a connection cost do not evolve to be hierarchical, even when the task has a hierarchical structure. However, with a connection cost, networks evolve to be both modular and hierarchical, and these networks exhibit higher overall performance and evolvability (i.e. faster adaptation to new environments). Additional analyses confirm that hierarchy independently improves adaptability after controlling for modularity. Overall, our results suggest that the same force-the cost of connections-promotes the evolution of both hierarchy and modularity, and that these properties are important drivers of network performance and adaptability. In addition to shedding light on the emergence of hierarchy across the many domains in which it appears, these findings will also accelerate future research into evolving more complex, intelligent computational brains in the fields of artificial intelligence and robotics.
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Project description:A central biological question is how natural organisms are so evolvable (capable of quickly adapting to new environments). A key driver of evolvability is the widespread modularity of biological networks--their organization as functional, sparsely connected subunits--but there is no consensus regarding why modularity itself evolved. Although most hypotheses assume indirect selection for evolvability, here we demonstrate that the ubiquitous, direct selection pressure to reduce the cost of connections between network nodes causes the emergence of modular networks. Computational evolution experiments with selection pressures to maximize network performance and minimize connection costs yield networks that are significantly more modular and more evolvable than control experiments that only select for performance. These results will catalyse research in numerous disciplines, such as neuroscience and genetics, and enhance our ability to harness evolution for engineering purposes.
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Project description:BackgroundSurgery risks increase when electricity is accessible but unreliable. During unreliable electricity events and without data on increased risk to patients, medical professionals base their decisions on anecdotal experience. Decisions should be made based on a cost-benefit analysis, but no methodology exists to quantify these risks, the associated hidden costs, nor risk charts to compare alternatives.MethodsTwo methodologies were created to quantify these hidden costs. In the first methodology through research literature and/or measurements, the authors obtained and analyzed a year's worth of hour-by-hour energy failures for four energy healthcare system (EHS) types in four regions (SolarPV in Iraq, Hydroelectric in Ghana, SolarPV+Wind in Bangladesh, and Grid+Diesel in Uganda). In the second methodology, additional patient risks were calculated according to time and duration of electricity failure and medical procedure impact type. Combining these methodologies, the cost from the Value of Statistical Lives lost divided by Energy shortage ($/kWh) is calculated for EHS type and region specifically. The authors define hidden costs due to electricity failure as VSL/E ($/kWh) and compare this to traditional electricity costs (always defined in $/kWh units), including Levelized Cost of Electricity (LCOE also in $/kWh). This is quantified into a fundamentally new energy healthcare system risk chart (EHS-Risk Chart) based on severity of event (probability of deaths) and likelihood of event (probability of electricity failure).ResultsVSL/E costs were found to be 10 to 10,000 times traditional electricity costs (electric utility or LCOE based). The single power source EHS types have higher risks than hybridized EHS types (especially as power loads increase over time), but all EHS types have additional risks to patients due to electricity failure (between 3 to 105 deaths per 1,000 patients).ConclusionsThese electricity failure risks and hidden healthcare costs can now be calculated and charted to make medical decisions based on a risk chart instead of anecdotal experience. This risk chart connects public health and electricity failure using this adaptable, scalable, and verifiable model.
| S-EPMC7641375 | biostudies-literature