An integrative module analysis of DNA methylation landscape in aging

Noninvasive Analysis Using Data-Independent Acquisition Mass Spectrometry: New Epidermal Proteins That Reveal Sex Differences in the Aging Process

The development of mass spectrometry has provided a method with extremely high sensitivity and selectivity that can be used to identify protein biomarkers. Epidermal proteins, lipids, and cornified envelopes are involved in the formation of the skin epidermal barrier. The epidermal protein composition changes with age. Therefore, quantitative proteomic changes may be indicative of skin aging. We sought to utilize data-independent acquisition mass spectrometry for noninvasive analysis of epidermal proteins in healthy Chinese individuals of different age groups and sexes. In our study, we completed high-throughput protein detection, analyzed protein differences with MaxQuant software, and performed statistical analyses of the proteome. We obtained interesting findings regarding ceruloplasmin (CP), which exhibited significant differences and is involved in ferroptosis, a signaling pathway significantly associated with aging. There were also several proteins that differed between sexes in the younger group, but the sex differences disappeared with aging. These proteins, which were associated with both aging processes and sex differences, are involved in signaling pathways such as apoptosis, oxidative stress, and genomic stability and can serve as candidate biomarkers for sex differences during aging. Our approach for noninvasive detection of epidermal proteins and its application to accurately quantify protein expression can provide ideas for future epidermal proteomics studies.

The Human Body as a Super Network: Digital Methods to Analyze the Propagation of Aging

Biological aging is a complex process involving multiple biological processes. These can be understood theoretically though considering them as individual networks-e.g., epigenetic networks, cell-cell networks (such as astroglial networks), and population genetics. Mathematical modeling allows the combination of such networks so that they may be studied in unison, to better understand how the so-called "seven pillars of aging" combine and to generate hypothesis for treating aging as a condition at relatively early biological ages. In this review, we consider how recent progression in mathematical modeling can be utilized to investigate aging, particularly in, but not exclusive to, the context of degenerative neuronal disease. We also consider how the latest techniques for generating biomarker models for disease prediction, such as longitudinal analysis and parenclitic analysis can be applied to as both biomarker platforms for aging, as well as to better understand the inescapable condition. This review is written by a highly diverse and multi-disciplinary team of scientists from across the globe and calls for greater collaboration between diverse fields of research.