An alternative food source for metabolism and longevity studies in Caenorhabditis elegans.

Caenorhabditis elegans is an instrumental research model used to advance our knowledge in areas including development, metabolism, and aging. However, research on metabolism and/or other measures of health/aging are confounded by the nematode's food source in the lab, live E. coli bacteria.

Modular metabolite assembly in depends on carboxylesterases and formation of lysosome-related organelles.

Signaling molecules derived from attachment of diverse metabolic building blocks to ascarosides play a central role in the life history of and other nematodes; however, many aspects of their biogenesis remain unclear. Using comparative metabolomics, we show that a pathway mediating formation of intestinal lysosome-related organelles (LROs) is required for biosynthesis of most modular ascarosides as well as previously undescribed modular glucosides. Similar to modular ascarosides, the modular glucosides are derived from highly selective assembly of moieties from nucleoside, amino acid, neurotransmitter, and lipid metabolism, suggesting that modular glucosides, like the ascarosides, may serve signaling functions.

Hypoxic response regulators RHY-1 and EGL-9/PHD promote longevity through a VHL-1-independent transcriptional response.

HIF-1-mediated adaptation to changes in oxygen availability is a critical aspect of healthy physiology. HIF is regulated by a conserved mechanism whereby EGLN/PHD family members hydroxylate HIF in an oxygen-dependent manner, targeting it for ubiquitination by Von-Hippel-Lindau (VHL) family members, leading to its proteasomal degradation. The activity of the only C.

Computational tools for geroscience.

The rapid progress of the past three decades has led the geroscience field near a point where human interventions in aging are plausible. Advances across scientific areas, such as high throughput "-omics" approaches, have led to an exponentially increasing quantity of data available for biogerontologists. To best translate the lifespan and healthspan extending interventions discovered by basic scientists into preventative medicine, it is imperative that the current data are comprehensively utilized to generate testable hypotheses about translational interventions.