Tyvelose Archives - The NOMIS Foundation

Organismal aging is often characterized as a steady, monotonic decline of organ and tissue function. However, recent studies indicate spatial and temporal variations of aging rates across the lifespan. We consider these variations from the perspective of underlying cellular changes. Cells in certain tissues may age earlier and produce signals that accelerate the aging of other cells, locally or distantly, acting as drivers for organismal aging and leading to a lack of synchronous aging between tissues. As cells adopt new homeostatic states, cellular aging can be viewed, at least in part, as a quantal process we refer to as digital aging. Analog declines of tissue function with age may be the sum of underlying digital events. Cellular aging, digital or otherwise, is not uniform across time or space within organisms or between organisms of the same species. Advanced systems-level and single-cell methodologies will refine our understanding of cell and tissue aging, and how these processes integrate to produce the complexities of individual, organismal aging.

We previously identified 529 proteins that had been reported by multiple different studies to change their expression level with age in human plasma. In the present study, we measured the q-value and age coefficient of these proteins in a plasma proteomic dataset derived from 4263 individuals. A bioinformatics enrichment analysis of proteins that significantly trend toward increased expression with age strongly implicated diverse inflammatory processes. A literature search revealed that at least 64 of these 529 proteins are capable of regulating life span in an animal model. Nine of these proteins (AKT2, GDF11, GDF15, GHR, NAMPT, PAPPA, PLAU, PTEN, and SHC1) significantly extend life span when manipulated in mice or fish. By performing machine-learning modeling in a plasma proteomic dataset derived from 3301 individuals, we discover an ultra-predictive aging clock comprised of 491 protein entries. The Pearson correlation for this clock was 0.98 in the learning set and 0.96 in the test set while the median absolute error was 1.84 years in the learning set and 2.44 years in the test set. Using this clock, we demonstrate that aerobic-exercised trained individuals have a younger predicted age than physically sedentary subjects. By testing clocks associated with 1565 different Reactome pathways, we also show that proteins associated with signal transduction or the immune system are especially capable of predicting human age. We additionally generate a multitude of age predictors that reflect different aspects of aging. For example, a clock comprised of proteins that regulate life span in animal models accurately predicts age.

Publications in Tyvelose by NOMIS researchers

Asynchronous, contagious and digital aging

Data mining of human plasma proteins generates a multitude of highly predictive aging clocks that reflect different aspects of aging