DNA methylation clocks struggle to distinguish inflammaging from healthy aging, but feature rectification improves coherence and enhances detection of inflammaging.

Biological age estimation from DNA methylation and determination of relevant biomarkers is an active research problem which has predominantly been tackled with black-box penalized regression. Machine learning is used to select a small subset of features from hundreds of thousands CpG probes and to increase generalizability typically lacking with ordinary least-squares regression. Here, we show that such feature selection lacks biological interpretability and relevance in the clocks of the first- and next-generations, and clarify the logic by which these clocks systematically exclude biomarkers of aging and disease. Moreover, in contrast to the assumption that regularized linear regression is needed to prevent overfitting, we demonstrate that hypothesis-driven selection of biologically relevant features in conjunction with ordinary least squares regression yields accurate, well-calibrated, generalizable clocks with high interpretability. We further demonstrate that the interplay of disease-related shifts of predictor values and their corresponding weights, which we term feature shifts, contributes to the lack of resolution between health and disease in conventional linear models. Lastly, we introduce a method of feature rectification, which aligns these shifts to improve the distinction of age predictions for healthy people vs. patients with various diseases. Key Findings: There is no apparent biological significance of the CpGs selected by first- and next-generation clocksThe range of residuals for first- and next-generation clock predications on healthy samples is very large; for all models tested, a prediction error of +/-10-20 years is within the 95% range of variation for healthy controls and does not signify age accelerationThere is no significant shift in the mean of residuals for patient populations relative to healthy populations for most studied first- and next-generation clocks. For those with significance, the effect size is very small.Hypothesis-driven feature pre-selection, coupled with modified forward step-wise selection yields age predictors on par with first and next-generation clocks. EN/ML is not needed.Disease-related shifts at different CpG probes, along with learned model weights, can be either positive or negative; their combination leads to de-coherence effect in linear models.Model coherence can be induced by rectifying features to have only positive shifts in patient samples; this provides a better resolution between health and disease in DNAm age models, and expectedly, introduces more non-linearity to the input data.

Small-animal blood exchange is an emerging approach for systemic aging research.

We describe a small-animal blood exchange approach developed for aging research as an alternative to heterochronic parabiosis or plasma injections. In parabiosis, animals are surgically coupled, which has several disadvantages, including difficulty controlling experimental procedure, the effects of shared organs, environmental enrichment from jointly exploring the housing enclosure, involuntary exercise and an imprecise onset of blood sharing. Likewise, in plasma injections, the added volumes need to be small, and there is little flexibility in changing the relative contributions of ectopic to endogenous blood components. These factors complicate the conclusions and interpretations, including the identification of key mechanisms and molecular or cellular determinants. Our approach, where blood is exchanged between animals without them being surgically coupled, is less invasive than parabiosis. The percentage of exchanged blood or other exchanged fluids is known and precise. The age of plasma and cells can be mixed and matched at all desired relative contributions to the endogenous systemic milieu, and the onset of the effects can be accurately delineated. In this protocol, we describe the preparatory and animal surgery steps required for small-animal blood exchange in mice and compare this process with parabiosis and plasma injections. We also provide the design, hardware and software for the blood exchange device and compare automated and manual exchange methods. Lastly, we report mathematical modeling of the dilution of blood factors. The fluid exchange takes ~30 min when performed by a well-trained biomedical scientist; the entire process takes ~2 h.

Plasma dilution improves cognition and attenuates neuroinflammation in old mice.

Our recent study has established that young blood factors are not causal, nor necessary, for the systemic rejuvenation of mammalian tissues. Instead, a procedure referred to as neutral blood exchange (NBE) that resets signaling milieu to a pro-regenerative state through dilution of old plasma, enhanced the health and repair of the muscle and liver, and promoted better hippocampal neurogenesis in 2-year-old mice (Mehdipour et al., Aging 12:8790-8819, 2020). Here we expand the rejuvenative phenotypes of NBE, focusing on the brain. Namely, our results demonstrate that old mice perform much better in novel object and novel texture (whisker discrimination) tests after a single NBE, which is accompanied by reduced neuroinflammation (less-activated CD68+ microglia). Evidence against attenuation/dilution of peripheral senescence-associated secretory phenotype (SASP) as the main mechanism behind NBE was that the senolytic ABT 263 had limited effects on neuroinflammation and did not enhance hippocampal neurogenesis in the old mice. Interestingly, peripherally acting ABT 263 and NBE both diminished SA-ßGal signal in the old brain, demonstrating that peripheral senescence propagates to the brain, but NBE was more robustly rejuvenative than ABT 263, suggesting that rejuvenation was not simply by reducing senescence. Explaining the mechanism of the positive effects of NBE on the brain, our comparative proteomics analysis demonstrated that dilution of old blood plasma yields an increase in the determinants of brain maintenance and repair in mice and in people. These findings confirm the paradigm of rejuvenation through dilution of age-elevated systemic factors and extrapolate it to brain health and function.

Skeletal muscle as an experimental model of choice to study tissue aging and rejuvenation.

Skeletal muscle is among the most age-sensitive tissues in mammal organisms. Significant changes in its resident stem cells (i.e., satellite cells, SCs), differentiated cells (i.e., myofibers), and extracellular matrix cause a decline in tissue homeostasis, function, and regenerative capacity. Based on the conservation of aging across tissues and taking advantage of the relatively well-characterization of the myofibers and associated SCs, skeletal muscle emerged as an experimental system to study the decline in function and maintenance of old tissues and to explore rejuvenation strategies. In this review, we summarize the approaches for understanding the aging process and for assaying the success of rejuvenation that use skeletal muscle as the experimental system of choice. We further discuss (and exemplify with studies of skeletal muscle) how conflicting results might be due to variations in the techniques of stem cell isolation, differences in the assays of functional rejuvenation, or deciding on the numbers of replicates and experimental cohorts.