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38. Transcriptome Analysis Reveals the Molecular Mechanism of PLIN1 in Goose Hierarchical and Pre-Hierarchical Follicle Granulosa Cells.

Author

He, Hua, Lin, Yueyue, Zhang, Xi, Xie, Hengli, Wang, Zhujun, Hu, Shenqiang, Li, Liang, Liu, Hehe, Han, Chunchun, Xia, Lu, Hu, Jiwei, Wang, Jiwen, Liao, Lin, and Yuan, Xin

Subjects
GRANULOSA cells, GENE expression, EVIDENCE gaps, AGRICULTURAL egg production, CELL cycle
Abstract

Simple Summary: The low laying performance of geese has significantly hindered the development of the goose breeding industry. The growth and development of goose follicle granulosa cells (GCs) directly influence normal ovulation in geese, which in turn impacts egg production. Our previous data suggest that de novo lipogenesis (DNL) occurs within the GCs of goose follicles. In this study, we investigated the potential mechanism of action of the PLIN1 gene in goose GCs for the first time, using cell culture, transfection, and transcriptome sequencing technologies. The results of the transcriptome analysis indicated that PLIN1 may regulate oxidative stress in goose follicular granulosa cells through the TGF-β signaling pathway, thereby inducing cell proliferation or apoptosis. Additionally, nine candidate genes were identified as potentially involved in this process. These findings enhance the understanding of the molecular regulatory mechanisms in goose follicular GCs and provide a theoretical foundation for future in-depth studies on these molecular mechanisms. PLIN1, a member of the PAT family, is expressed in both adipocytes and steroidogenic cells. In this study, we used cell transfection technology combined with transcriptome sequencing to investigate the regulatory mechanism of PLIN1 in goose follicular GCs. Gene Ontology (GO) analysis revealed that in the four groups (phGC: over_vs_over-NC; hGC: over_vs_over-NC; phGC: si_vs_si-NC; hGC: si_vs_si-NC), most differentially expressed genes (DEGs) were significantly enriched (p < 0.05) in pathways related to biological processes (BPs), particularly those associated with the regulation of cellular lipid metabolism and oxidative stress. KEGG analysis further identified significant enrichment (p < 0.05) in pathways related to cell apoptosis and the cell cycle. A joint analysis of KEGG and PPI on the upregulated and downregulated DEGs revealed that the TGF-β signaling pathway was the only pathway significantly enriched among both upregulated and downregulated DEGs after PLIN1 overexpression in hGCs and phGCs. Based on these findings, we hypothesize that PLIN1 overexpression may promote granulosa cell proliferation and apoptosis by activating the TGF-β signaling pathway in goose follicular GCs. Additionally, nine potential candidate genes were identified: PPARγ, MGLL, PTEN, BAMBI, BMPR2, JUN, FST, ACSF3, and ACSL4. These results address a significant research gap concerning the role of this gene in granulosa cells and contribute to the understanding of its molecular regulatory mechanisms. [ABSTRACT FROM AUTHOR]

Published
2025
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39. Quantifying Spatiotemporal Changes in Supraglacial Debris Cover in Eastern Pamir from 1994 to 2024 Based on the Google Earth Engine.

Author

Liu, Hehe, Zhang, Zhen, Liu, Shiyin, Xie, Fuming, Ding, Jing, Li, Guolong, and Su, Haoran

Subjects
*RANDOM forest algorithms, *EVIDENCE gaps, *HAZARD mitigation, *ABLATION (Glaciology), *CONSERVATION of mass, *GLACIERS, *MASS budget (Geophysics)
Abstract

Supraglacial debris cover considerably influences sub-debris ablation patterns and the surface morphology of glaciers by modulating the land–atmosphere energy exchange. Understanding its spatial distribution and temporal variations is crucial for analyzing melting processes and managing downstream disaster mitigation efforts. In recent years, the overall slightly positive mass balance or stable state of eastern Pamir glaciers has been referred to as the "Pamir-Karakoram anomaly". It is important to note that spatial heterogeneity in glacier change has drawn widespread research attention. However, research on the spatiotemporal changes in the debris cover in this region is completely nonexistent, which has led to an inadequate understanding of debris-covered glacier variations. To address this research gap, this study employed Landsat remote sensing images within the Google Earth Engine platform, leveraging the Random Forest algorithm to classify the supraglacial debris cover. The classification algorithm integrates spectral features from Landsat images and derived indices (NDVI, NDSI, NDWI, and BAND RATIO), supplemented by auxiliary factors such as slope and aspect. By extracting the supraglacial debris cover from 1994 to 2024, this study systematically analyzed the spatiotemporal variations and investigated the underlying drivers of debris cover changes from the perspective of mass conservation. By 2024, the area of supraglacial debris in eastern Pamir reached 258.08 ± 20.65 km2, accounting for 18.5 ± 1.55% of the total glacier area. It was observed that the Kungey Mountain region demonstrated the largest debris cover rate. Between 1994 and 2024, while the total glacier area decreased by −2.57 ± 0.70%, the debris-covered areas expanded upward at a rate of +1.64 ± 0.10% yr−1. The expansion of debris cover is driven by several factors in the context of global warming. The rising temperature resulted in permafrost degradation, slope destabilization, and intensified weathering on supply slopes, thereby augmenting the debris supply. Additionally, the steep supply slope in the study area facilitates the rapid deposition of collapsed debris onto glacier surfaces, with frequent avalanche events accelerating the mobilization of rock fragments. [ABSTRACT FROM AUTHOR]

Published
2025
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