Role of follistatin-like protein 1 in liver diseases

Exosomes Derived from Human Palatal Mesenchymal Cells Mediate Intercellular Communication During Palatal Fusion by Promoting Oral Epithelial Cell Migration

Purpose

Exosomes are important "messengers" in cell-cell interactions, but their potential effects on palatal fusion are still unknown. This study aimed to explore the role and mechanism of exosomes derived from palatal mesenchymal cells in epithelial-mesenchymal communication during palatogenesis.

Methods

The expression of exosome marker CD63 and CD81 in palatal cells during palatogenesis was detected by immunofluorescence staining. After being purified from the supernatant of human embryonic palatal mesenchymal (HEPM) cells, exosomes (HEPM-EXO) were characterized by nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM) and Western blot. HEPM-EXO were co-cultured with human immortalized oral epithelial cells (HIOEC). The effects of HEPM-EXO on the cell proliferation, migration, apoptosis and epithelial-mesenchymal transition (EMT) of HIOEC were evaluated. The proteins encapsulated in HEPM-EXO were analyzed by proteomic analysis.

Results

The extensive expression of CD63 and CD81 in palatal epithelial and mesenchymal cells were continuously detected during E12.5~E14.5, suggesting that exosomes were involved in the process of palatal fusion. The expression of CD63 was also observed in the acellular basement membrane between the palatal epithelium and the mesenchyme in vivo, and HEPM-EXO could be internalized by HIOEC in vitro, suggesting that exosomes are potent to diffuse through the cellular tissue boundary to mediate palatal cell-cell communication. Exposure of HEPM-EXO to HIOEC substantially inhibited the proliferation and stimulated the migration of HIOEC, but had no significant effect on cell apoptosis and EMT. Proteomic analysis revealed the basic characteristics of the proteins in HEPM-EXO and that exosomal THBS1 may potentially regulate the cell behaviors of HIOEC, which needs further verification. Gene ontology (GO) analysis uncovered that the proteins highly expressed in HEPM-EXO are closely related to wound healing, implying a promising therapeutic opportunity of HEPM-EXO in tissue injury treatment with future studies.

Conclusion

HEPM-EXO mediated cell-cell communication by regulating cell proliferation and migration of oral epithelial cells during palatogenesis.

Identification of integrated proteomics and transcriptomics signature of alcohol-associated liver disease using machine learning

Distinguishing between alcohol-associated hepatitis (AH) and alcohol-associated cirrhosis (AC) remains a diagnostic challenge. In this study, we used machine learning with transcriptomics and proteomics data from liver tissue and peripheral mononuclear blood cells (PBMCs) to classify patients with alcohol-associated liver disease. The conditions in the study were AH, AC, and healthy controls. We processed 98 PBMC RNAseq samples, 55 PBMC proteomic samples, 48 liver RNAseq samples, and 53 liver proteomic samples. First, we built separate classification and feature selection pipelines for transcriptomics and proteomics data. The liver tissue models were validated in independent liver tissue datasets. Next, we built integrated gene and protein expression models that allowed us to identify combined gene-protein biomarker panels. For liver tissue, we attained 90% nested-cross validation accuracy in our dataset and 82% accuracy in the independent validation dataset using transcriptomic data. We attained 100% nested-cross validation accuracy in our dataset and 61% accuracy in the independent validation dataset using proteomic data. For PBMCs, we attained 83% and 89% accuracy with transcriptomic and proteomic data, respectively. The integration of the two data types resulted in improved classification accuracy for PBMCs, but not liver tissue. We also identified the following gene-protein matches within the gene-protein biomarker panels: CLEC4M-CLC4M, GSTA1-GSTA2 for liver tissue and SELENBP1-SBP1 for PBMCs. In this study, machine learning models had high classification accuracy for both transcriptomics and proteomics data, across liver tissue and PBMCs. The integration of transcriptomics and proteomics into a multi-omics model yielded improvement in classification accuracy for the PBMC data. The set of integrated gene-protein biomarkers for PBMCs show promise toward developing a liquid biopsy for alcohol-associated liver disease.

Echinacoside: A promising active natural products and pharmacological agents

Echinacoside, a natural phenylethanoid glycoside, was discovered and isolated from the garden plant Echinacea angustifolia DC., belonging to the Compositae family, approximately sixty years ago. Extensive investigations have revealed that it possesses a wide array of pharmacologically beneficial activities for human health, particularly notable for its neuroprotective and anticancer activity. Several crucial concerns surfaced, encompassing the recognition of active metabolites that exhibited inadequate bioavailability in their prototype form, the establishment of precise molecular signal pathways or targets associated with the aforementioned effects of echinacoside, and the scarcity of dependable clinical trials. Hence, the question remains unanswered as to whether scientific research can effectively utilize this natural compound. To support future studies on this natural product, it is imperative to provide a systematic overview and insights into potential future prospects. The current review provides a comprehensive analysis of the existing knowledge on echinacoside, encompassing its wide distribution, structural diversity and metabolism, diverse therapeutic applications, and improvement of echinacoside bioavailability for its potential utilization.