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Abstract The skin is our outer permeability and immune defense barrier against myriad external assaults. Aryl hydrocarbon receptor (AhR) senses environmental factors and regulates barrier robustness and immune homeostasis. AhR agonists have been approved by the FDA for psoriasis treatment and are in clinical trials for the treatment of atopic dermatitis (AD), but the underlying mechanism of action remains poorly defined. Here, we report thatOVOL1/Ovol1is a conserved and direct transcriptional target of AhR in epidermal keratinocytes. We show that OVOL1/Ovol1 influences AhR-mediated regulation of keratinocyte gene expression and thatOVOL1/Ovol1ablation in keratinocytes impairs the barrier-promoting function of AhR, exacerbating AD-like inflammation. Mechanistically, we have identified Ovol1’s direct downstream targets genome-wide and provided in vivo evidence supporting the role ofId1as a functional target in barrier maintenance, disease suppression, and neutrophil accumulation. Furthermore, our findings reveal that an IL-1/dermal γδT cell axis exacerbates type 2 and 3 immune responses downstream of barrier perturbation inOvol1-deficient AD skin. Finally, we present data suggesting the clinical relevance of OVOL1 and ID1 functions in human AD skin. Our study highlights a keratinocyte-intrinsic AhR-Ovol1-Id1 regulatory axis that promotes both epidermal and immune homeostasis in the context of skin inflammation, identifying new therapeutic targets. 

Summary Allocation of leaf phosphorus (P) among different functional fractions represents a crucial adaptive strategy for optimizing P use. However, it remains challenging to monitor the variability in leaf P fractions and, ultimately, to understand P‐use strategies across diverse plant communities.We explored relationships between five leaf P fractions (orthophosphate P, P_i; lipid P, P_L; nucleic acid P, P_N; metabolite P, P_M; and residual P, P_R) and 11 leaf economic traits of 58 woody species from three biomes in China, including temperate, subtropical and tropical forests. Then, we developed trait‐based models and spectral models for leaf P fractions and compared their predictive abilities.We found that plants exhibiting conservative strategies increased the proportions of P_Nand P_M, but decreased the proportions of P_iand P_L, thus enhancing photosynthetic P‐use efficiency, especially under P limitation. Spectral models outperformed trait‐based models in predicting cross‐site leaf P fractions, regardless of concentrations (R² = 0.50–0.88 vs 0.34–0.74) or proportions (R² = 0.43–0.70 vs 0.06–0.45).These findings enhance our understanding of leaf P‐allocation strategies and highlight reflectance spectroscopy as a promising alternative for characterizing large‐scale leaf P fractions and plant P‐use strategies, which could ultimately improve the physiological representation of the plant P cycle in land surface models. 

Summary Leaf dark respiration (R_dark), an important yet rarely quantified component of carbon cycling in forest ecosystems, is often simulated from leaf traits such as the maximum carboxylation capacity (V_cmax), leaf mass per area (LMA), nitrogen (N) and phosphorus (P) concentrations, in terrestrial biosphere models. However, the validity of these relationships across forest types remains to be thoroughly assessed.Here, we analyzedR_darkvariability and its associations withV_cmaxand other leaf traits across three temperate, subtropical and tropical forests in China, evaluating the effectiveness of leaf spectroscopy as a superior monitoring alternative.We found that leaf magnesium and calcium concentrations were more significant in explaining cross‐siteR_darkthan commonly used traits like LMA, N and P concentrations, but univariate trait–R_darkrelationships were always weak (r² ≤ 0.15) and forest‐specific. Although multivariate relationships of leaf traits improved the model performance, leaf spectroscopy outperformed trait–R_darkrelationships, accurately predicted cross‐siteR_dark(r² = 0.65) and pinpointed the factors contributing toR_darkvariability.Our findings reveal a few novel traits with greater cross‐site scalability regardingR_dark, challenging the use of empirical trait–R_darkrelationships in process models and emphasize the potential of leaf spectroscopy as a promising alternative for estimatingR_dark, which could ultimately improve process modeling of terrestrial plant respiration. 

Proton transfer is crucial in various chemical and biological processes. Because of significant nuclear quantum effects, accurate and efficient description of proton transfer remains a great challenge. In this Communication, we apply constrained nuclear–electronic orbital density functional theory (CNEO-DFT) and constrained nuclear–electronic orbital molecular dynamics (CNEO-MD) to three prototypical shared proton systems and investigate their proton transfer modes. We find that with a good description of nuclear quantum effects, CNEO-DFT and CNEO-MD can well describe the geometries and vibrational spectra of the shared proton systems. Such a good performance is in significant contrast to DFT and DFT-based ab initio molecular dynamics, which often fail for shared proton systems. As an efficient method based on classical simulations, CNEO-MD is promising for future investigations of larger and more complex proton transfer systems.