Gremlin 2 suppresses differentiation of stem/progenitor cells in the human skin

Abstract

Introduction The skin is comprised of various kinds of cells and has three layers, the epidermis, dermis and subcutaneous adipose tissue. Stem cells in each tissue duplicate themselves and differentiate to supply new cells that function in the tissue, and thereby maintain the tissue homeostasis. In contrast, senescent cells accumulate with age and secrete senescence-associated secretory phenotype (SASP) factors that impair surrounding cells and tissues, which lowers the capacity to maintain homeostasis in each tissue. Previously, we found Gremlin 2 (GREM2) as a novel SASP factor in the skin and reported that GREM2 suppressed the differentiation of adipose-derived stromal/stem cells. In the present study, we investigated the effects of GREM2 on stem cells in the epidermis and dermis. Methods To examine whether GREM2 expression and the differentiation levels in the epidermis and dermis are correlated, the expressions of GREM2, stem cell markers, an epidermal differentiation marker Keratin 10 (KRT10) and a dermal differentiation marker type 3 procollagen were examined in the skin samples (n = 14) randomly chosen from the elderly where GREM2 expression level is high and the individual differences of its expression are prominent. Next, to test whether GREM2 affects the differentiation of skin stem cells, cells from two established lines (an epidermal and a dermal stem/progenitor cell model) were cultured and induced to differentiate, and recombinant GREM2 protein was added. Results In the human skin, the expression levels of GREM2 varied among individuals both in the epidermis and dermis. The expression level of GREM2 was not correlated with the number of stem cells, but negatively correlated with those of both an epidermal and a dermal differentiation markers. The expression levels of epidermal differentiation markers were significantly suppressed by the addition of GREM2 in the three-dimensional (3D) epidermis generated with an epidermal stem/progenitor cell model. In addition, by differentiation induction, the expressions of dermal differentiation markers were induced in cells from a dermal stem/progenitor cell model, and the addition of GREM2 significantly suppressed the expressions of the dermal differentiation markers. Conclusions GREM2 expression level did not affect the numbers of stem cells in the epidermis and dermis but affects the differentiation and maturation levels of the tissues, and GREM2 suppressed the differentiation of stem/progenitor cells in vitro . These findings suggest that GREM2 may contribute to the age-related reduction in the capacity to maintain skin homeostasis by suppressing the differentiation of epidermal and dermal stem/progenitor cells.

ATAC-clock: An aging clock based on chromatin accessibility

“…3 d). Among these OCRs were both the promoter and enhancer of GREM2, a gene that encodes a senescence-associated secretory phenotype (SASP) factor with a known association with aging in adipose tissue and skin [ 35 , 36 ]. The promoter of GREM2 was also selected in every nested cross-validation model, highlighting its robustness to predict age.…”

Effects of Erythropoietin-Treatment on Wound Healing in Burn Patients. Investigated Using Immunohistochemical and Histological Methods, Analyzed Using AI. Post Hoc Analysis of the Randomized, Placebo-Controlled Clinical Trial “EPO in Burns”

“…The results of histological staining using Hematoxylin and Eosin (H&E) are shown in Figure 7. epidermis and may also be used to detect neo-epidermal structures and epithelialization [11,16,17]. In Figure 8, the CK10 marker expression is plotted to test if CK10 marker expression is affected by EPO.…”

Development of a novel aging clock based on chromatin accessibility

“…Instead, we manually investigated the clock OCRs with the 20 largest absolute coefficients in the model (Figure 4c). One of these OCRs was the promoter of GREM2 , a gene that encodes a senescence-associated secretory phenotype (SASP) factor with known association with aging in adipose tissue and skin [32,33]. This OCR was also selected in every nested cross-validation model, highlighting its robustness to predict age.…”