Age-induced and photoinduced changes in gene expression profiles in facial skin of Caucasian females across 6 decades of age.

BACKGROUND: Intrinsic and extrinsic factors, including ultraviolet irradiation, lead to visible signs of skin aging. OBJECTIVE: We evaluated molecular changes occurring in photoexposed and photoprotected skin of white women 20 to 74 years of age, some of whom appeared substantially younger than their chronologic age. METHODS: Histologic and transcriptomics profiling were conducted on skin biopsy samples of photoexposed (face and dorsal forearm) or photoprotected (buttocks) body sites from 158 women. 23andMe genotyping determined genetic ancestry. RESULTS: Gene expression and ontologic analysis revealed progressive changes from the 20s to the 70s in pathways related to oxidative stress, energy metabolism, senescence, and epidermal barrier; these changes were accelerated in the 60s and 70s. The gene expression patterns from the subset of women who were younger-appearing were similar to those in women who were actually younger. LIMITATIONS: Broader application of these findings (eg, across races and Fitzpatrick skin types) will require further studies. CONCLUSIONS: This study demonstrates a wide range of molecular processes in skin affected by aging, providing relevant targets for improving the condition of aging skin at different life stages and defining a molecular pattern of epidermal gene expression in women who appear younger than their chronologic age.

Aging-Associated Changes in the Adult Human Skin Microbiome and the Host Factors that Affect Skin Microbiome Composition.

Understanding the changes in the skin microbiome and their relationship to host skin factors during aging remains largely unknown. To better understand this phenomenon, we collected samples for metagenomic and host skin factor analyses from the forearm, buttock, and facial skin from 158 Caucasian females aged 20‒24, 30‒34, 40‒44, 50‒54, 60‒64, and 70‒74 years. Metagenomics analysis was performed using 16S ribosomal RNA gene sequencing, whereas host sebocyte gland area, skin lipids, natural moisturizing factors, and antimicrobial peptides measurements were also performed. These analyses showed that skin bacterial diversity increased at all the skin sites with increasing age. Of the bacterial genera with an average relative abundance >1%, only Lactobacillus and Cutibacterium demonstrated a significant change (decrease) in abundance at all sampled skin sites with increasing age. Additional bacterial genera demonstrated significant age- and site-specific changes in abundance. Analysis of sebocyte area, natural moisturizing factors, lipids, and antimicrobial peptides showed an age-related decrease in sebocyte area and increases in natural moisturizing factors/antimicrobial peptides/skin lipids, all of which correlated with changes in specific bacterial genera. In conclusion, the human skin microbiome undergoes age-associated alterations that may reflect underlying age-related changes in cutaneous biology.

Age-Related Changes in Hair Shaft Protein Profiling and Genetically Variant Peptides.

Recent reports highlight possible improvements in individual identification using proteomic information from human hair evidence. These reports have stimulated investigation of parameters that affect the utility of proteomic information. In addition to variables already studied relating to processing technique and anatomic origin of hair shafts, an important variable is hair ageing. Present work focuses on the effect of age on protein profiling and analysis of genetically variant peptides (GVPs). Hair protein profiles may be affected by developmental and physiological changes with age of the donor, exposure to different environmental conditions and intrinsic processes, including during storage. First, to explore whether general trends were evident in the population at different ages, hair samples were analyzed from groups of different subjects in their 20's, 40's and 60's. No significant differences were seen as a function of age, but consistent differences were evident between European American and African American hair profiles. Second, samples collected from single individuals at different ages were analyzed. Mostly, these showed few protein expression level differences over periods of 10 years or less, but samples from subjects at 44 and 65 year intervals were distinctly different in profile. The results indicate that use of protein profiling for personal identification, if practical, would be limited to decadal time intervals. Moreover, batch effects were clearly evident in samples processed by different staff. To investigate the contribution of storage (at room temperature) in affecting the outcomes, the same proteomic digests were analyzed for GVPs. In samples stored over 10 years, GVPs were reduced in number in parallel with the yield of identified proteins and unique peptides. However, a very different picture emerged with respect to personal identification. Numbers of GVPs sufficed to distinguish individuals despite the age differences of the samples. As a practical matter, three hair samples per person provided nearly the maximal number obtained from 5 or 6 samples. The random match probability (where the log increased in proportion to the number of GVPs) reached as high as 1 in 108. The data indicate that GVP results are dependent on the single nucleotide polymorphism profile of the donor genome, where environmental/processing factors affect only the yield, and thus are consistent despite the ages of the donors and samples and batchwise effects in processing. This conclusion is critical for application to casework where the samples may be in storage for long periods and used to match samples recently collected.

Data requirements for protein identification using chemically-assisted fragmentation and tandem mass spectrometry.

Many laboratories identify proteins by searching tandem mass spectrometry data against genomic or protein sequence databases. These database searches typically use the measured peptide masses or the derived peptide sequence and, in this paper, we focus on the latter. We study the minimum peptide sequence data requirements for definitive protein identification from protein sequence databases. Accurate mass measurements are not needed for definitive protein identification, even when a limited amount of sequence data is available for searching. This information has implications for the mass spectrometry performance (and cost), data base search strategies and proteomics research.