Mir22hg facilitates ferritinophagy-mediated ferroptosis in sepsis by recruiting the m6A reader YTHDC1 and enhancing Angptl4 mRNA stability.

BACKGROUND: Ferritinophagy-mediated ferroptosis plays a crucial role in fighting pathogen aggression. The long non-coding RNA Mir22hg is involved in the regulation of ferroptosis and aberrantly overexpression in lipopolysaccharide (LPS)-induced sepsis mice, but whether it regulates sepsis through ferritinophagy-mediated ferroptosis is unclear. METHODS: Mir22hg was screened by bioinformatics analysis. Ferroptosis was assessed by assaying malondialdehyde (MDA), reactive oxygen species (ROS), and Fe2+ levels, glutathione (GSH) activity, as well as ferroptosis-related proteins GPX4 and SLC3A2 by using matched kits and performing western blot. Ferritinophagy was assessed by Lyso tracker staining and FerroOrange staining, immunofluorescence analysis of Ferritin and LC-3, and western blot analysis of LC-3II/I, p62, FTH1, and NCOA4. The bind of YTH domain containing 1 (YTHDC1) to Mir22hg or angiopoietin-like-4 (Angptl4) was verified by RNA pull-down and/or immunoprecipitation (RIP) assays. RESULTS: Mir22hg silencing lightened ferroptosis and ferritinophagy in LPS-induced MLE-12 cells and sepsis mouse models, as presented by the downregulated MDA, ROS, Fe2+, NCOA4, and SLC3A2 levels, upregulated GPX4, GSH, and FTH1 levels, along with a decrease in autophagy. Mir22hg could bind to the m6A reader YTHDC1 without affecting its expression. Mechanistically, Mir22hg enhanced Angptl4 mRNA stability through recruiting the m6A reader YTHDC1. Furthermore, Angptl4 overexpression partly overturned Mir22hg inhibition-mediated effects on ferroptosis and ferritinophagy in LPS-induced MLE-12 cells. CONCLUSION: Mir22hg contributed to in ferritinophagy-mediated ferroptosis in sepsis via recruiting the m6A reader YTHDC1 and strengthening Angptl4 mRNA stability, highlighting that Mir22hg may be a potential target for sepsis treatment based on ferroptosis.

LESS: Label-efficient multi-scale learning for cytological whole slide image screening.

In computational pathology, multiple instance learning (MIL) is widely used to circumvent the computational impasse in giga-pixel whole slide image (WSI) analysis. It usually consists of two stages: patch-level feature extraction and slide-level aggregation. Recently, pretrained models or self-supervised learning have been used to extract patch features, but they suffer from low effectiveness or inefficiency due to overlooking the task-specific supervision provided by slide labels. Here we propose a weakly-supervised Label-Efficient WSI Screening method, dubbed LESS, for cytological WSI analysis with only slide-level labels, which can be effectively applied to small datasets. First, we suggest using variational positive-unlabeled (VPU) learning to uncover hidden labels of both benign and malignant patches. We provide appropriate supervision by using slide-level labels to improve the learning of patch-level features. Next, we take into account the sparse and random arrangement of cells in cytological WSIs. To address this, we propose a strategy to crop patches at multiple scales and utilize a cross-attention vision transformer (CrossViT) to combine information from different scales for WSI classification. The combination of our two steps achieves task-alignment, improving effectiveness and efficiency. We validate the proposed label-efficient method on a urine cytology WSI dataset encompassing 130 samples (13,000 patches) and a breast cytology dataset FNAC 2019 with 212 samples (21,200 patches). The experiment shows that the proposed LESS reaches 84.79%, 85.43%, 91.79% and 78.30% on the urine cytology WSI dataset, and 96.88%, 96.86%, 98.95%, 97.06% on the breast cytology high-resolution-image dataset in terms of accuracy, AUC, sensitivity and specificity. It outperforms state-of-the-art MIL methods on pathology WSIs and realizes automatic cytological WSI cancer screening.