bims-lycede Biomed News
on Lysosome-dependent cell death
Issue of 2025–02–02
two papers selected by
Sofía Peralta, Universidad Nacional de Cuyo
  1. The Type III Intermediate Filament Protein Peripherin Regulates Lysosomal Degradation Activity and Autophagy.
  2. Integrating mitochondrial and lysosomal gene analysis for breast cancer prognosis using machine learning.
  1. Int J Mol Sci. 2025 Jan 10. pii: 549. [Epub ahead of print]26(2):
    The Type III Intermediate Filament Protein Peripherin Regulates Lysosomal Degradation Activity and Autophagy.
    Roberta Romano, Paola Cordella, Cecilia Bucci.
      Peripherin belongs to heterogeneous class III of intermediate filaments, and it is the only intermediate filament protein selectively expressed in the neurons of the peripheral nervous system. It has been previously discovered that peripherin interacts with proteins important for the endo-lysosomal system and for the transport to late endosomes and lysosomes, such as RAB7A and AP-3, although little is known about its role in the endocytic pathway. Here, we show that peripherin silencing affects lysosomal abundance but also positioning, causing the redistribution of lysosomes from the perinuclear area to the cell periphery. Moreover, peripherin silencing affects lysosomal activity, inhibiting EGFR degradation and the degradation of a fluorogenic substrate for proteases. Furthermore, we demonstrate that peripherin silencing affects lysosomal biogenesis by reducing the TFEB and TFE3 contents. Finally, in peripherin-depleted cells, the autophagic flux is strongly inhibited. Therefore, these data indicate that peripherin has an important role in regulating lysosomal biogenesis, and positioning and functions of lysosomes, affecting both the endocytic and autophagic pathways. Considering that peripherin is the most abundant intermediate filament protein of peripheral neurons, its dysregulation, affecting its functions, could be involved in the onset of several neurodegenerative diseases of the peripheral nervous system characterized by alterations in the endocytic and/or autophagic pathways.
    Keywords:  autophagy; cytoskeleton; intermediate filaments; lysosome; peripherin
    DOI:  https://doi.org/10.3390/ijms26020549
  2. Sci Rep. 2025 Jan 27. 15(1): 3320
    Integrating mitochondrial and lysosomal gene analysis for breast cancer prognosis using machine learning.
    Huilin Chen, Zhenghui Wang, Jiale Shi, Jinghui Peng.
      The impact of mitochondrial and lysosomal co-dysfunction on breast cancer patient outcomes is unclear. The objective of this study is to develop a predictive machine learning (ML) model utilizing mitochondrial and lysosomal co-regulators in order to provide a foundation for future studies focused on breast cancer (BC) patients' stratification and personalized interventions. Firstly, Differences and correlations of mitochondrial and lysosome related genes were screened and validated by differential analysis, copy number variation (CNV), single nucleotide polymorphism (SNPs) and correlation analysis. WGCNA and univariate Cox regression were employed to identify prognostic mitochondrial and lysosomal co-regulators. ML was utilized to further selected these regulators and then the coxboost + Survivor-SVM model was identified as the most suitable model for predicting outcomes in BC patients. Subsequently, the association between the immune and mlMSGs score was investigated through scRNA-seq. We found that the overall immunoinfiltration of immune cells was decreased in the high-risk group, it was specifically noted that B cell mlMSGs activity remained diminished in high-risk patients. Finally, the expression and function of the key gene SHMT2 were confirmed through in vitro experiments. This study shows that the ML model demonstrated a strong association with patient outcomes. Analysis conducted through the model has identified decreased B-cell immune infiltration and increased mlMSGs activity as significant factors influencing patient prognosis. These results may offer novel approaches for early intervention and prognostic forecasting in BC.
    Keywords:  Breast cancer; Immunotherapy; Machine learning; Mitochondrial and lysosomal dysfunction; Sc-RNA
    DOI:  https://doi.org/10.1038/s41598-025-86970-4
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