Spatial transition tensor of single cells.

Spatial transcriptomics and messenger RNA splicing encode extensive spatiotemporal information for cell states and transitions. The current lineage-inference methods either lack spatial dynamics for state transition or cannot capture different dynamics associated with multiple cell states and transition paths. Here we present spatial transition tensor (STT), a method that uses messenger RNA splicing and spatial transcriptomes through a multiscale dynamical model to characterize multistability in space. By learning a four-dimensional transition tensor and spatial-constrained random walk, STT reconstructs cell-state-specific dynamics and spatial state transitions via both short-time local tensor streamlines between cells and long-time transition paths among attractors. Benchmarking and applications of STT on several transcriptome datasets via multiple technologies on epithelial-mesenchymal transitions, blood development, spatially resolved mouse brain and chicken heart development, indicate STT's capability in recovering cell-state-specific dynamics and their associated genes not seen using existing methods. Overall, STT provides a consistent multiscale description of single-cell transcriptome data across multiple spatiotemporal scales.

exFINDER: identify external communication signals using single-cell transcriptomics data.

Cells make decisions through their communication with other cells and receiving signals from their environment. Using single-cell transcriptomics, computational tools have been developed to infer cell-cell communication through ligands and receptors. However, the existing methods only deal with signals sent by the measured cells in the data, the received signals from the external system are missing in the inference. Here, we present exFINDER, a method that identifies such external signals received by the cells in the single-cell transcriptomics datasets by utilizing the prior knowledge of signaling pathways. In particular, exFINDER can uncover external signals that activate the given target genes, infer the external signal-target signaling network (exSigNet), and perform quantitative analysis on exSigNets. The applications of exFINDER to scRNA-seq datasets from different species demonstrate the accuracy and robustness of identifying external signals, revealing critical transition-related signaling activities, inferring critical external signals and targets, clustering signal-target paths, and evaluating relevant biological events. Overall, exFINDER can be applied to scRNA-seq data to reveal the external signal-associated activities and maybe novel cells that send such signals.

DURIAN: an integrative deconvolution and imputation method for robust signaling analysis of single-cell transcriptomics data.

Single-cell RNA sequencing trades read-depth for dimensionality, often leading to loss of critical signaling gene information that is typically present in bulk data sets. We introduce DURIAN (Deconvolution and mUltitask-Regression-based ImputAtioN), an integrative method for recovery of gene expression in single-cell data. Through systematic benchmarking, we demonstrate the accuracy, robustness and empirical convergence of DURIAN using both synthetic and published data sets. We show that use of DURIAN improves single-cell clustering, low-dimensional embedding, and recovery of intercellular signaling networks. Our study resolves several inconsistent results of cell-cell communication analysis using single-cell or bulk data independently. The method has broad application in biomarker discovery and cell signaling analysis using single-cell transcriptomics data sets.

Integrating single-cell datasets with ambiguous batch information by incorporating molecular network features.

With the rapid development of single-cell sequencing techniques, several large-scale cell atlas projects have been launched across the world. However, it is still challenging to integrate single-cell RNA-seq (scRNA-seq) datasets with diverse tissue sources, developmental stages and/or few overlaps, due to the ambiguity in determining the batch information, which is particularly important for current batch-effect correction methods. Here, we present SCORE, a simple network-based integration methodology, which incorporates curated molecular network features to infer cellular states and generate a unified workflow for integrating scRNA-seq datasets. Validating on real single-cell datasets, we showed that regardless of batch information, SCORE outperforms existing methods in accuracy, robustness, scalability and data integration.

spliceJAC: transition genes and state-specific gene regulation from single-cell transcriptome data.

Extracting dynamical information from single-cell transcriptomics is a novel task with the promise to advance our understanding of cell state transition and interactions between genes. Yet, theory-oriented, bottom-up approaches that consider differences among cell states are largely lacking. Here, we present spliceJAC, a method to quantify the multivariate mRNA splicing from single-cell RNA sequencing (scRNA-seq). spliceJAC utilizes the unspliced and spliced mRNA count matrices to constructs cell state-specific gene-gene regulatory interactions and applies stability analysis to predict putative driver genes critical to the transitions between cell states. By applying spliceJAC to biological systems including pancreas endothelium development and epithelial-mesenchymal transition (EMT) in A549 lung cancer cells, we predict genes that serve specific signaling roles in different cell states, recover important differentially expressed genes in agreement with pre-existing analysis, and predict new transition genes that are either exclusive or shared between different cell state transitions.

Inference and multiscale model of epithelial-to-mesenchymal transition via single-cell transcriptomic data.

Rapid growth of single-cell transcriptomic data provides unprecedented opportunities for close scrutinizing of dynamical cellular processes. Through investigating epithelial-to-mesenchymal transition (EMT), we develop an integrative tool that combines unsupervised learning of single-cell transcriptomic data and multiscale mathematical modeling to analyze transitions during cell fate decision. Our approach allows identification of individual cells making transition between all cell states, and inference of genes that drive transitions. Multiscale extractions of single-cell scale outputs naturally reveal intermediate cell states (ICS) and ICS-regulated transition trajectories, producing emergent population-scale models to be explored for design principles. Testing on the newly designed single-cell gene regulatory network model and applying to twelve published single-cell EMT datasets in cancer and embryogenesis, we uncover the roles of ICS on adaptation, noise attenuation, and transition efficiency in EMT, and reveal their trade-off relations. Overall, our unsupervised learning method is applicable to general single-cell transcriptomic datasets, and our integrative approach at single-cell resolution may be adopted for other cell fate transition systems beyond EMT.

Pharmacological inhibition of the Notch pathway enhances the efficacy of androgen deprivation therapy for prostate cancer.

Although androgen deprivation therapy (ADT) is a standard treatment for metastatic prostate cancer, this disease inevitably recurs and progresses to ADT-resistant stage after this therapy. Accordingly, understanding the mechanism of resistance to ADT and finding new approach to enhance the efficacy of ADT may provide a major benefit to PCa patients. In our study, we found upregulated expression of Notch receptors is positive associated with ADT-resistance progression. Using fluorescent Notch signaling reporter system, we observed that endogenous Notch signaling could be activated after treatment of androgen deprivation in LNCaP cells via activation of Notch3. In addition, exogenous activation of the Notch signaling though Dox-induced overexpression of any Notch intracellular domains (NICD1-4) could enhance the resistance of PCa cells to ADT under ex vivo 3D culture conditions and upregulate expression of ADT resistance-associated phospho-p38 and Bcl-2 in LNCaP cells. As a result, pharmacological inhibition of the Notch pathway using γ-secretase inhibitor (GSI), DAPT, downregulated both phospho-p38 and Bcl-2 expression and significantly enhanced the efficacy of ADT in androgen sensitive PCa cells with impaired proliferation and 3D colony formation, increased apoptosis and remarkable inhibition of tumor growth in murine subcutaneous xenograft model. These results indicate that activated Notch signaling contributes to ADT resistance, and suggest that inhibition of the Notch pathway may be a promising adjuvant therapy of ADT for PCa.

Construction of the landscape for multi-stable systems: Potential landscape, quasi-potential, A-type integral and beyond.

Motivated by the famous Waddington's epigenetic landscape metaphor in developmental biology, biophysicists and applied mathematicians made different proposals to construct the landscape for multi-stable complex systems. We aim to summarize and elucidate the relationships among these theories from a mathematical point of view. We systematically investigate and compare three different but closely related realizations in the recent literature: the Wang's potential landscape theory from steady state distribution of stochastic differential equations (SDEs), the Freidlin-Wentzell quasi-potential from the large deviation theory, and the construction through SDE decomposition and A-type integral. We revisit that the quasi-potential is the zero noise limit of the potential landscape, and the potential function in the third proposal coincides with the quasi-potential. We compare the difference between local and global quasi-potential through the viewpoint of exchange of limit order for time and noise amplitude. We argue that local quasi-potentials are responsible for getting transition rates between neighboring stable states, while the global quasi-potential mainly characterizes the residence time of the states as the system reaches stationarity. The difference between these two is prominent when the transitivity property is broken. The most probable transition path by minimizing the Onsager-Machlup or Freidlin-Wentzell action functional is also discussed. As a consequence of the established connections among different proposals, we arrive at the novel result which guarantees the existence of SDE decomposition while denies its uniqueness in general cases. It is, therefore, clarified that the A-type integral is more appropriate to be applied to the decomposed SDEs rather than its primitive form as believed by previous researchers. Our results contribute to a deeper understanding of landscape theories for biological systems.

Response to "Comment on 'Construction of the landscape for multi-stable systems: Potential landscape, quasi-potential, A-type integral and beyond"' [J. Chem. Phys. 145, 147104 (2016)].

The uniqueness issue of SDE decomposition theory proposed by Ao and his co-workers has recently been discussed. A comprehensive study to investigate connections among different landscape theories [J. Chem. Phys. 144, 094109 (2016)] has pointed out that the decomposition is generally not unique, while Ao et al. recently argue that such conclusions are "incorrect" because the uniqueness of the decomposition for Ornstein-Uhlenbeck (O-U) process has been claimed before. In this response, we will demonstrate that the claimed "uniqueness" of the O-U process decomposition is invalid to serve as a counterexample according to the original definition of SDE decomposition. The absence of effective and concrete boundary conditions in previous SDE decomposition papers will be pointed out, and some other issues in the comment will also be responded.

Reconstructing growth and dynamic trajectories from single-cell transcriptomics data.

Time-series single-cell RNA sequencing (scRNA-seq) datasets provide unprecedented opportunities to learn dynamic processes of cellular systems. Due to the destructive nature of sequencing, it remains challenging to link the scRNA-seq snapshots sampled at different time points. Here we present TIGON, a dynamic, unbalanced optimal transport algorithm that reconstructs dynamic trajectories and population growth simultaneously as well as the underlying gene regulatory network from multiple snapshots. To tackle the high-dimensional optimal transport problem, we introduce a deep learning method using a dimensionless formulation based on the Wasserstein-Fisher-Rao (WFR) distance. TIGON is evaluated on simulated data and compared with existing methods for its robustness and accuracy in predicting cell state transition and cell population growth. Using three scRNA-seq datasets, we show the importance of growth in the temporal inference, TIGON's capability in reconstructing gene expression at unmeasured time points and its applications to temporal gene regulatory networks and cell-cell communication inference.

Volatile Oil of Magnolia biondii Pamp. for Transnasal Administration: Its Preparation, Characterization, And Mechanism of Action in the Treatment of Allergic Rhinitis.

BACKGROUND: Allergic Rhinitis (AR) is a common chronic nasal condition usually caused by allergens. The immune system overreacts when the body is exposed to allergens, releasing a lot of tissue chemicals that cause congestion, more secretions, and an inflammatory reaction in the nasal mucosa. METHOD: In clinical practice, it remains a significant public health issue. Modern pharmacological studies have demonstrated that Magnolia Volatile Oil (MVO) has good anti-inflammatory, antibacterial, immunomodulatory, and other pharmacological effects. Previous research and literature reports have reported that MVO has good therapeutic effects on allergic rhinitis. However, due to the poor water solubility of Magnolia, its bioavailability is low. The purpose of this present work is to develop a new microemulsion formulation to improve the stability and bioavailability of MVO. RESULTS: The droplet size, PDI, and zeta potential of Magnolia volatile oil microemulsion (MVOME) were characterized along with its physical characteristics, and these values were found to be 14.270.03 nm, 0.09410.31, and -0.35850.12 mV, respectively, demonstrating the successful formation of microemulsion. In OVA-induced AR rats, MVO-ME dramatically reduced the serum levels of TNF-α, IL-1ß, and IL-6 inflammatory factors. In addition, MVO-ME significantly inhibited the expression of protein levels of PPAR-γ and P65 in the nasal mucosa of AR rats. In this regard, we hypothesized that MVO-ME may play a therapeutic role in AR by activating the PPAR signaling pathway as well as inhibiting the activation of the NF/κB signaling pathway. CONCLUSION: MVO-ME has systematic advantages, such as high solubility, bioavailability, etc. It is expected to be an efficient nano-drug delivery system for the clinical treatment of allergic rhinitis.

Uncovering Minimal Pathways in Melanoma Initiation.

Cutaneous melanomas are clinically and histologically heterogeneous. Most display activating mutations in Braf or Nras and complete loss of function of one or more tumor suppressor genes. Mouse models that replicate such mutations produce fast-growing, pigmented tumors. However, mice that combine Braf activation with only heterozygous loss of Pten also produce tumors and, as we show here, in an Albino background this occurs even with Braf activation alone. Such tumors arise rarely, grow slowly, and express low levels of pigmentation genes. The timing of their appearance was consistent with a single step stochastic event, but no evidence could be found that it required de novo mutation, suggesting instead the involvement of an epigenetic transition. Single-cell transcriptomic analysis revealed such tumors to be heterogeneous, including a minor cell type we term LNM ( L ow-pigment, N eural- and extracellular M atrix-signature) that displays gene expression resembling "neural crest"-like cell subsets detected in the fast-growing tumors of more heavily-mutated mice, as well as in human biopsy and xenograft samples. We provide evidence that LNM cells pre-exist in normal skin, are expanded by Braf activation, can transition into malignant cells, and persist with malignant cells through multiple rounds of transplantation. We discuss the possibility that LNM cells not only serve as a pre-malignant state in the production of some melanomas, but also as an important intermediate in the development of drug resistance.

Deciphering the antidepressant effects of Rosa damascena essential oil mediated through the serotonergic synapse signaling pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: Rosa damascena is an ancient plant with significance in both medicine and perfumery that have a variety of therapeutic properties, including antidepressant, anti-anxiety, and anti-stress effects. Rose damascena essential oil (REO) has been used to treat depression, anxiety and other neurological related disorders in Iranian traditional medicine. However, its precise mechanism of action remains elusive. AIM OF THE STUDY: The aim of this study was to investigate the impact and mechanism underlying the influence of REO on chronic unpredictable mild stress (CUMS) rats. MATERIALS AND METHODS: Gas chromatography-mass spectrometry (GC-MS) technique coupling was used to analyze of the components of REO. A CUMS rat model was replicated to assess the antidepressant effects of varying doses of REO. This assessment encompassed behavioral evaluations, biochemical index measurements, and hematoxylin-eosin staining. For a comprehensive analysis of hippocampal tissues, we employed transcriptomics and incorporated weighting coefficients by means of network pharmacology. These measures allowed us to explore differentially expressed genes and biofunctional pathways affected by REO in the context of depression treatment. Furthermore, GC-MS metabolomics was employed to assess metabolic profiles, while a joint analysis in Metscape facilitated the construction of a network elucidating the links between differentially expressed genes and metabolites, thereby elucidating potential relationships and clarifying key pathways regulated by REO. Finally, the expression of relevant proteins in the key pathways was determined through immunohistochemistry and Western blot analysis. Molecular docking was utilized to investigate the interactions between active components and key targets, thereby validating the experimental results. RESULTS: REO alleviated depressive-like behavior, significantly elevated levels of the neurotransmitter 5-hydroxytryptamine (5-HT), and reduced hippocampal neuronal damage in CUMS rats. This therapeutic effect may be associated with the modulation of the serotonergic synapse signaling pathway. Furthermore, REO rectified metabolic disturbances, primarily through the regulation of amino acid metabolic pathways. Joint analysis revealed five differentially expressed genes (EEF1A1, LOC729197, ATP8A2, NDST4, and GAD2), suggesting their potential in alleviating depressive symptoms by modulating the serotonergic synapse signaling pathway and tryptophan metabolism. REO also modulated the 5-HT2A-mediated extracellular regulated protein kinases-cAMP-response element binding protein-brain-derived neurotrophic factor (ERK-CREB-BDNF) pathway. In addition, molecular docking results indicated that citronellol, geraniol and (E,E)-farnesol in REO may serve as key active ingredients responsible for its antidepressant effects. CONCLUSIONS: This study is the first to report that REO can effectively alleviate CUMS-induced depression-like effects in rats. Additionally, the study offers a comprehensive understanding of its intricate antidepressant mechanism from a multi-omics and multi-level perspective. Our findings hold promise for the clinical application and further development of this essential oil.

exFINDER: identify external communication signals using single-cell transcriptomics data.

Cells make decisions through their communication with other cells and receiving signals from their environment. Using single-cell transcriptomics, computational tools have been developed to infer cell-cell communication through ligands and receptors. However, the existing methods only deal with signals sent by the measured cells in the data, the received signals from the external system are missing in the inference. Here, we present exFINDER, a method that identifies such external signals received by the cells in the single-cell transcriptomics datasets by utilizing the prior knowledge of signaling pathways. In particular, exFINDER can uncover external signals that activate the given target genes, infer the external signal-target signaling network (exSigNet), and perform quantitative analysis on exSigNets. The applications of exFINDER to scRNA-seq datasets from different species demonstrate the accuracy and robustness of identifying external signals, revealing critical transition-related signaling activities, inferring critical external signals and targets, clustering signal-target paths, and evaluating relevant biological events. Overall, exFINDER can be applied to scRNA-seq data to reveal the external signal-associated activities and maybe novel cells that send such signals.

The multiple activations in budding yeast S-phase checkpoint are Poisson processes.

Eukaryotic cells activate the S-phase checkpoint signal transduction pathway in response to DNA replication stress. Affected by the noise in biochemical reactions, such activation process demonstrates cell-to-cell variability. Here, through the analysis of microfluidics-integrated time-lapse imaging, we found multiple S-phase checkpoint activations in a certain budding yeast cell cycle. Yeast cells not only varied in their activation moments but also differed in the number of activations within the cell cycle, resulting in a stochastic multiple activation process. By investigating dynamics at the single-cell level, we showed that stochastic waiting times between consecutive activations are exponentially distributed and independent from each other. Finite DNA replication time provides a robust upper time limit to the duration of multiple activations. The mathematical model, together with further experimental evidence from the mutant strain, revealed that the number of activations under different levels of replication stress agreed well with Poisson distribution. Therefore, the activation events of S-phase checkpoint meet the criterion of Poisson process during DNA replication. In sum, the observed Poisson activation process may provide new insights into the complex stochastic dynamics of signal transduction pathways.

Stimuli-Responsive Nanoadjuvant Rejuvenates Robust Immune Responses to Sensitize Cancer Immunotherapy.

Despite their immense therapeutic potential, cancer immunotherapies such as immune checkpoint blockers (ICBs) benefit only a small subset of patients. Toll-like receptor agonists reverse the immunosuppressive tumor microenvironment (TME) to enhance antitumor immunity, but their systemic administration induces side effects. This work describes a TME-responsive nanotherapeutic platform for the site-specific release of drug candidates in tumors with a significant antitumor efficacy. Imidazoquinoline (IMQ)-derived liposomal nanovesicles (LN-IMQ) triggered the antitumor ability of macrophages, mobilized T-cell immunity, and promoted the secretion of antitumor cytokines, explaining the synergistic effect of LN-IMQ with ICBs. LN-IMQ monotherapy observed complete tumor regression in 6/8 of 4T1-bearing mouse, and cured mice resisted secondary tumor challenge. Besides, LN-IMQ decreased the occurrence of lung metastases, being effective against advanced metastases. On the other hand, neoantigen-based cancer vaccine has very low immune responses. Here, we also verified that LN-IMQ can serve as an ideal tumor antigen delivery vector. Cancer cells in vitro treated with chemotherapeutic drugs included multiple neoantigens and high levels of damage-associated molecular patterns, which were then successfully encapsulated in LN-IMQ to obtain a "personalized nanovaccine" with artificially amplified antigenicity and adjuvant properties. This study developed an attractive potential personalized nanovaccine for chemotherapeutic-drug-induced tumor neoantigens and immunotherapy.

Evaluation of the Mechanism of Action of Rosemary Volatile Oil in the Treatment of Alzheimer's Disease Using Gas Chromatography -mass Spectrometry Analysis and Network Pharmacology.

OBJECTIVE: This study aimed to investigate the active components and mechanism of action of rosemary volatile oil for treating Alzheimer's disease (AD) using network pharmacology. METHODS: We obtained the constituents of the rosemary volatile oil by searching Chinese herbal systemic pharmacological databases and analytical platforms and constructed the constituent-target networks by predicting and screening the action targets of the rosemary volatile oil constituents using SwissTargetPrediction, metaTarFisher, and Pubchem. We obtained the AD-related targets using the Genecards, OMIM, and DisGeNET databases and constructed the protein-protein interaction networks (PPI) using the STRING database in Venny 2.1.0 graph to identify the cross-targets by screening the core-acting targets. Cytoscape 3.8.2 software was used to construct a componenttarget- pathway network to screen the potential active components of the rosemary volatile oil for the treatment of AD and predict the mechanism of action of the rosemary volatile oil for the treatment of AD in combination with existing pharmacological studies. We performed a gene ontology (GO) biological process and a Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the targets of the rosemary volatile oil for the treatment of AD using R language and molecular docking using Discovery Studio 4.0 software to validate their biological activities. RESULTS: A network constructed using gas chromatography-mass spectrometry (GC-MS) analysis identified 26 potentially active ingredients in the rosemary volatile oil. We retrieved a total of 10762 AD targets from Genecards and other databases. Our GO enrichment analysis yielded 39 entries (P < 0.05), including 14 entries for biological processes, five entries for cellular composition, and 20 entries for molecular function. A total of 14 entries (P < 0.05) were then enriched in the KEGG pathway that primarily involved the IL-17 signaling pathway and the AGE-RAGE pathway. CONCLUSION: The active components of rosemary volatile oil had good inhibition of the inflammatory response. This study provides a reference and guidance for the in-depth study on rosemary volatile oil for the treatment of AD.

Investigation of the Molecular Mechanism Underlying the Therapeutic Effect of Perilla frutescens L. Essential Oil on Acute Lung Injury Using Gas Chromatography Mass Spectrometry and Network Pharmacology.

OBJECTIVE: The present study aimed to investigate the molecular mechanism through which Perilla essential oil treats acute lung injury (ALI) through network pharmacology, molecular docking, and in vitro assays. METHODS: Relevant ALI targets of the active ingredients of Perilla essential oil were predicted using the SwissTargetPrediction database and meta TarFisher database. These ALI targets were then screened using GeneCards and DisGeNET, and differentially expressed ALI target genes were identified using the Gene Expression Omnibus (GEO) database. Next, key targets were enriched using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Protein-protein interaction network analysis was performed to obtain targets with the highest degree values for molecular docking with Perilla essential oil active ingredients. For in vitro experiments, lipopolysaccharide (LPS) was used to induce an ALI inflammation model using RAW264.7 cells. The model cells were then treated with Perilla essential oil to detect the protein expression levels of vascular endothelial factor (NO), tumor necrosis factor (TNF-α), and p65 nuclear transcription factor in them. RESULTS: Sixty-eight key targets of Perilla oil were identified for the treatment of ALI. These targets were found to be involved in biological processes related to peptides, response to lipopolysaccharides, the positive regulation of cytokine production, etc., using GO. The signaling pathways found to be associated with the targets included the AGE-RAGE signaling pathway in diabetic complications, the NF-kappa B signaling pathway, and small cell lung cancer and other inflammatory signaling pathways. The five key targets that showed good binding activity with Perilla oil active ingredients included TNF, RELA, PARP1, PTGS2, and IRAK4. In vitro assays showed that Perilla essential oil could significantly reduce NO and TNF-α levels and inhibit the phosphorylation of nuclear transcription factor P65, thus inhibiting the activation of NF-κB signaling pathway. Conclusion Perilla essential oil can play a role in the treatment of ALI by inhibiting the activation of the NF-κB signaling pathway and preventing an excessive inflammatory response. This study thus provides a reference for the in-depth study of the mechanisms through which Perilla essential oil treats ALI.

The JAK/STAT/NF-κB signaling pathway can be regulated by rosemary essential oil, thereby providing a potential treatment for DNCB-induced in mice.

OBJECTIVE: The purpose of this study was to investigate the mechanism through which rosemary essential oil treats atopic dermatitis. METHODS: A dinitrochlorobenzene (DNCB)-induced atopic dermatitis mouse model was established and treated with low (1%), medium (2%), and high (4%) doses of Rosmarinus officinalis essential oil (EORO). Serum levels of interleukin (IL)-6 and tumor necrosis factor-alpha (TNF-α) in each group were determined using enzyme-linked immunosorbent assay (ELISA). Skin tissues were stained with hematoxylin-eosin and toluidine blue. We used network pharmacology and molecular docking techniques to verify the biological activity of essential proteins and their corresponding compounds in the pathway. Gas chromatography-mass spectrometry (GC-MS) was used for metabolomics analysis and multivariate statistical analysis of mouse serum to screen differential metabolites and metabolic pathway analysis. Protein expression of p-JAK1, CD4+ cells, and IL-4 in the skin tissue was detected by immunohistochemistry analysis. Protein levels of STAT3, p-STAT3, P65, and p-P65 in damaged skin tissues were detected using western blotting. RESULT: The skin of mice in the model group showed different degrees of erythema, dryness, scratches, epidermal erosion and shedding, and crusting. After treatment, the serum levels of IL-6 and TNF-α in EORO group were significantly decreased, and the expression of p-JAK1,CD4 + cells, IL-4, p-P65 / P65 and p-STAT3 / STAT3 proteins in skin tissues were decreased. CONCLUSION: EORO can effectively improve DNCB-induced AD-like skin lesions in mice by regulating the JAK/STAT/NF-κB signaling pathway, thereby reducing the production of downstream arachidonic acid metabolites, inhibiting skin inflammation, and restoring epidermal barrier function.

Preneoplastic stromal cells promote BRCA1-mediated breast tumorigenesis.

Women with germline BRCA1 mutations (BRCA1+/mut) have increased risk for hereditary breast cancer. Cancer initiation in BRCA1+/mut is associated with premalignant changes in breast epithelium; however, the role of the epithelium-associated stromal niche during BRCA1-driven tumor initiation remains unclear. Here we show that the premalignant stromal niche promotes epithelial proliferation and mutant BRCA1-driven tumorigenesis in trans. Using single-cell RNA sequencing analysis of human preneoplastic BRCA1+/mut and noncarrier breast tissues, we show distinct changes in epithelial homeostasis including increased proliferation and expansion of basal-luminal intermediate progenitor cells. Additionally, BRCA1+/mut stromal cells show increased expression of pro-proliferative paracrine signals. In particular, we identify pre-cancer-associated fibroblasts (pre-CAFs) that produce protumorigenic factors including matrix metalloproteinase 3 (MMP3), which promotes BRCA1-driven tumorigenesis in vivo. Together, our findings demonstrate that precancerous stroma in BRCA1+/mut may elevate breast cancer risk through the promotion of epithelial proliferation and an accumulation of luminal progenitor cells with altered differentiation.