Love is in the hair: arginine methylation of human hair proteins as novel cardiovascular biomarkers

Biological importance of arginine: A comprehensive review of the roles in structure, disorder, and functionality of peptides and proteins

Arginine shows Jekyll and Hyde behavior in several respects. It participates in protein folding via ionic and H-bonds and cation-pi interactions; the charge and hydrophobicity of its side chain make it a disorder-promoting amino acid. Its methylation in histones; RNA binding proteins; chaperones regulates several cellular processes. The arginine-centric modifications are important in oncogenesis and as biomarkers in several cardiovascular diseases. The cross-links involving arginine in collagen and cornea are involved in pathogenesis of tissues but have also been useful in tissue engineering and wound-dressing materials. Arginine is a part of active site of several enzymes such as GTPases, peroxidases, and sulfotransferases. Its metabolic importance is obvious as it is involved in production of urea, NO, ornithine and citrulline. It can form unusual functional structures such as molecular tweezers in vitro and sprockets which engage DNA chains as part of histones in vivo. It has been used in design of cell-penetrating peptides as drugs. Arginine has been used as an excipient in both solid and injectable drug formulations; its role in suppressing opalescence due to liquid-liquid phase separation is particularly very promising. It has been known as a suppressor of protein aggregation during protein refolding. It has proved its usefulness in protein bioseparation processes like ion-exchange, hydrophobic and affinity chromatographies. Arginine is an amino acid, whose importance in biological sciences and biotechnology continues to grow in diverse ways.

Recent omics advances in hair aging biology and hair biomarkers analysis

Aging is a complex natural process that leads to a decline in physiological functions, which is visible in signs such as hair graying, thinning, and loss. Although hair graying is characterized by a loss of pigment in the hair shaft, the underlying mechanism of age-associated hair graying is not fully understood. Hair graying and loss can have a significant impact on an individual's self-esteem and self-confidence, potentially leading to mental health problems such as depression and anxiety. Omics technologies, which have applications beyond clinical medicine, have led to the discovery of candidate hair biomarkers and may provide insight into the complex biology of hair aging and identify targets for effective therapies. This review provides an up-to-date overview of recent omics discoveries, including age-associated alterations of proteins and metabolites in the hair shaft and follicle, and highlights the significance of hair aging and graying biomarker discoveries. The decline in hair follicle stem cell activity with aging decreased the regeneration capacity of hair follicles. Cellular senescence, oxidative damage and altered extracellular matrix of hair follicle constituents characterized hair follicle and hair shaft aging and graying. The review attempts to correlate the impact of endogenous and exogenous factors on hair aging. We close by discussing the main challenges and limitations of the field, defining major open questions and offering an outlook for future research.

Development and evaluation of a novel fluorescent chiral derivatization reagent DBD-S-M-Pro: first observation of four chiral amino acids in human hair

This study reports a novel fluorescent chiral derivatization reagent, 4-(N,N-dmethylaminosulfonyl)-2,1,3-benzoxadiazole-(2-succinimidoxy)-trans-2-methyl-L-proline (DBD-S-M-Pro), with a benzoxadiazole structure containing an N-hydroxysuccinimide activation group. DBD-S-M-Pro targets chiral amino-functional compounds under alkaline conditions without a condensation agent. Gradient elution was performed on a BEH C18 (100 × 2.1 mm, 1.7 μm) column with a mobile phase of 0.05% formic acid (FA) in 10 mM ammonium acetate (CH₃COONH₄) and 0.1% FA in acetonitrile or methanol. The efficiency of the chiral resolution was evaluated under excitation and emission wavelengths of 450 nm and 560 nm, respectively. The 19 chiral amino acids were separated in the range of 1.45-14.84. The resolutions of almost all DL-amino acids exceeded 1.5; the exceptions were serine (Ser) and lysine (Lys), with resolutions of 1.45 and 1.46, respectively. In addition, a new approach was devised for the simultaneous analysis of four chiral amino acids (DL-Glu, DL-Ala, DL-Val, and DL-Phe) in human hair. These amino acids were analyzed in the range of 12.5-400 pmol, with R² ≥ 0.9990, limits of detection (S/N = 3) of 4-10 pmol, and intraday and interday precisions of 0.57-6.23%. The average spikes in the hair recoveries were 89.76-111.54%, and the matrix effects were 92.47-102.40%. Next, the contents of free chiral amino acids in the hair samples of 10 healthy volunteers (five males and five females) were analyzed with this method, and the differences were compared. The developed DBD-S-M-Pro provides a novel strategy for the sensitive determination of free chiral amino acids in living organisms.

Utilization of Host and Microbiome Features in Determination of Biological Aging

The term ‘old age’ generally refers to a period characterized by profound changes in human physiological functions and susceptibility to disease that accompanies the final years of a person’s life. Despite the conventional definition of old age as exceeding the age of 65 years old, quantifying aging as a function of life years does not necessarily reflect how the human body ages. In contrast, characterizing biological (or physiological) aging based on functional parameters may better reflect a person’s temporal physiological status and associated disease susceptibility state. As such, differentiating ‘chronological aging’ from ‘biological aging’ holds the key to identifying individuals featuring accelerated aging processes despite having a young chronological age and stratifying them to tailored surveillance, diagnosis, prevention, and treatment. Emerging evidence suggests that the gut microbiome changes along with physiological aging and may play a pivotal role in a variety of age-related diseases, in a manner that does not necessarily correlate with chronological age. Harnessing of individualized gut microbiome data and integration of host and microbiome parameters using artificial intelligence and machine learning pipelines may enable us to more accurately define aging clocks. Such holobiont-based estimates of a person’s physiological age may facilitate prediction of age-related physiological status and risk of development of age-associated diseases.