Carlsson, H. | bioRxiv Science

bioRxiv2018

Reduced insulin/IGF-1 signalling in adult parents increases offspring fitness

Classical theory maintains that ageing evolves via energy trade-offs between reproduction and survival leading to accumulation of unrepaired cellular damage with age. In contrast, the emerging new theory postulates that ageing evolves because of deleterious late-life hyper-function of reproduction-promoting genes leading to excessive biosynthesis in late-life. The hyper-function theory uniquely predicts that optimizing nutrient-sensing molecular signalling in adulthood can simultaneously postpone ageing and increase Darwinian fitness. Here we show that reducing evolutionarily conserved insulin/IGF-1 nutrient-sensing signalling via daf-2 RNA interference (RNAi) fulfils this prediction in Caenorhabditis elegans nematodes. Long-lived daf-2 RNAi parents showed normal fecundity as self-fertilizing hermaphrodites and improved late-life reproduction when mated to males. Remarkably, the offspring of daf-2 RNAi parents had higher Darwinian fitness across three different genotypes. Thus, reduced nutrient-sensing signalling in adulthood improves both parental longevity and offspring fitness supporting the emerging view that sub-optimal gene expression in late-life lies at the heart of ageing. Impact StatementUnderstanding mechanisms underpinning ageing is fundamental to improving quality of life in an increasingly long-lived society. Recent breakthroughs have challenged the long-standing paradigm that the energy trade-off between reproduction and somatic maintenance causes organismal senescence via slow accumulation of unrepaired cellular damage with age. The emerging new theory of ageing provides a conceptually novel framework by proposing that ageing is a direct consequence of physiological processes optimized for early-life function, such as growth and early-life reproduction, that are running too high (i.e. at hyperfunction) in late adulthood. Contrary to the classic view based on damage accumulation, the hyperfunction theory proposes that suboptimal gene expression in late-life causes ageing via excessive biosynthesis. Thus, the hyperfunction theory uniquely predicts that longevity and Darwinian fitness can be simultaneously increased by reducing unnecessarily high levels of nutrient-sensing signalling in adulthood. Here we show that reducing evolutionarily conserved nutrient-sensing signalling pathway fulfils this prediction in Caenorhabditis elegans nematodes. We found that downregulation of the insulin/IGF-1 signalling in adult C. elegans nematodes not only improves longevity but, most intriguingly, increases fitness of the resulting offspring in the next generation. We found support for increase in offspring fitness across different genetic backgrounds. Our findings contradict the theoretical conjecture that energy trade-offs between growth, reproduction and longevity is the universal cause of senescence and provide strong experimental support for the emerging hyperfunction theory of ageing.

genetics↗

bioRxiv2018

Data-adaptive pipeline for filtering and normalizing metabolomics data.

IntroductionUntargeted metabolomics datasets contain large proportions of uninformative features and are affected by a variety of nuisance technical effects that can bias subsequent statistical analyses. Thus, there is a need for versatile and data-adaptive methods for filtering and normalizing data prior to investigating the underlying biological phenomena.\n\nObjectivesHere, we propose and evaluate a data-adaptive pipeline for metabolomics data that are generated by liquid chromatography-mass spectrometry platforms.\n\nMethodsOur data-adaptive pipeline includes novel methods for filtering features based on blank samples, proportions of missing values, and estimated intra-class correlation coefficients. It also incorporates a variant of k-nearest-neighbor imputation of missing values. Finally, we adapted an RNA-Seq approach and R package, scone, to select an appropriate normalization scheme for removing unwanted variation from metabolomics datasets.\n\nResultsUsing two metabolomics datasets that were generated in our laboratory from samples of human blood serum and neonatal blood spots, we compared our data-adaptive pipeline with a traditional filtering and normalization scheme. The data-adaptive approach outperformed the traditional pipeline in almost all metrics related to removal of unwanted variation and maintenance of biologically relevant signatures. The R code for running the data-adaptive pipeline is provided with an example dataset at https://github.com/courtneyschiffman/Data-adaptive-metabolomics.\n\nConclusionOur proposed data-adaptive pipeline is intuitive and effectively reduces technical noise from untargeted metabolomics datasets. It is particularly relevant for interrogation of biological phenomena in data derived from complex matrices associated with biospecimens.

bioinformatics↗