Essential Role of STAT3 Signaling in Hair Follicle Homeostasis.

Dominant-negative mutations associated with signal transducer and activator of transcription 3 (STAT3) signaling, which controls epithelial proliferation in various tissues, lead to atopic dermatitis in hyper IgE syndrome. This dermatitis is thought to be attributed to defects in STAT3 signaling in type 17 helper T cell　specification. However, the role of STAT3 signaling in skin epithelial cells remains unclear. We found that STAT3 signaling in keratinocytes is required to maintain skin homeostasis by negatively controlling the expression of hair follicle-specific keratin genes. These expression patterns correlated with the onset of dermatitis, which was observed in specific pathogen-free conditions but not in germ-free conditions, suggesting the involvement of Toll-like receptor-mediated inflammatory responses. Thus, our study suggests that STAT3-dependent gene expression in keratinocytes plays a critical role in maintaining the homeostasis of skin, which is constantly exposed to microorganisms.

A combination approach of pseudotime analysis and mathematical modeling for understanding drug-resistant mechanisms.

Cancer cells acquire drug resistance through the following stages: nonresistant, pre-resistant, and resistant. Although the molecular mechanism of drug resistance is well investigated, the process of drug resistance acquisition remains largely unknown. Here we elucidate the molecular mechanisms underlying the process of drug resistance acquisition by sequential analysis of gene expression patterns in tamoxifen-treated breast cancer cells. Single-cell RNA-sequencing indicates that tamoxifen-resistant cells can be subgrouped into two, one showing altered gene expression related to metabolic regulation and another showing high expression levels of adhesion-related molecules and histone-modifying enzymes. Pseudotime analysis showed a cell transition trajectory to the two resistant subgroups that stem from a shared pre-resistant state. An ordinary differential equation model based on the trajectory fitted well with the experimental results of cell growth. Based on the established model, it was predicted and experimentally validated that inhibition of transition to both resistant subtypes would prevent the appearance of tamoxifen resistance.

Hunt for the tipping point during endocrine resistance process in breast cancer by dynamic network biomarkers.

Acquired drug resistance is the major reason why patients fail to respond to cancer therapies. It is a challenging task to determine the tipping point of endocrine resistance and detect the associated molecules. Derived from new systems biology theory, the dynamic network biomarker (DNB) method is designed to quantitatively identify the tipping point of a drastic system transition and can theoretically identify DNB genes that play key roles in acquiring drug resistance. We analyzed time-course mRNA sequence data generated from the tamoxifen-treated estrogen receptor (ER)-positive MCF-7 cell line, and identified the tipping point of endocrine resistance with its leading molecules. The results show that there is interplay between gene mutations and DNB genes, in which the accumulated mutations eventually affect the DNB genes that subsequently cause the change of transcriptional landscape, enabling full-blown drug resistance. Survival analyses based on clinical datasets validated that the DNB genes were associated with the poor survival of breast cancer patients. The results provided the detection for the pre-resistance state or early signs of endocrine resistance. Our predictive method may greatly benefit the scheduling of treatments for complex diseases in which patients are exposed to considerably different drugs and may become drug resistant.