Germline stem cells play an essential role in establishing the fertility of an organism. Although extensively characterized, the regulatory mechanisms that govern the fundamental properties of mammalian female germline stem cells remain poorly understood. We generate genome-wide profiles of the histone modifications H3K4me1, H3K27ac, H3K4me3, and H3K27me3, DNA methylation, and RNA polymerase II occupancy and perform transcriptome analysis in mouse female germline stem cells. Comparison of enhancer regions between embryonic stem cells and female germline stem cells identifies the lineage-specific enhancers involved in germline stem cell features. Additionally, our results indicate that DNA methylation primarily contributes to female germline stem cell unipotency by suppressing the somatic program and is potentially involved in maintenance of sexual identity when compared with male germline stem cells. Moreover, we demonstrate down-regulation of Prmt5 triggers differentiation and thus uncover a role for Prmt5 in maintaining the undifferentiated status of female germline stem cells. The genome-wide epigenetic signatures and the transcription regulators identified here provide an invaluable resource for understanding the fundamental features of mouse female germline stem cells.

Motivation: Antibiotic resistance presents a formidable global challenge to public health and the environment. While considerable endeavors have been dedicated to identify antibiotic resistance genes (ARGs) for assessing the threat of antibiotic resistance, recent extensive investigations using metagenomic and metatranscriptomic approaches have unveiled a noteworthy concern. A significant fraction of proteins defies annotation through conventional sequence similarity-based methods, an issue that extends to ARGs, potentially leading to their under-recognition due to dissimilarities at the sequence level. Results: Herein, we proposed an Artificial Intelligence-powered ARG identification framework using a pretrained large protein language model, enabling ARG identification and resistance category classification simultaneously. The proposed PLM-ARG was developed based on the most comprehensive ARG and related resistance category information (>28K ARGs and associated 29 resistance categories), yielding Matthew’s correlation coefficients (MCCs) of 0.983 ± 0.001 by using a 5-fold cross-validation strategy. Furthermore, the PLM-ARG model was verified using an independent validation set and achieved an MCC of 0.838, outperforming other publicly available ARG prediction tools with an improvement range of 51.8%–107.9%. Moreover, the utility of the proposed PLM-ARG model was demonstrated by annotating resistance in the UniProt database and evaluating the impact of ARGs on the Earth's environmental microbiota. Availability and implementation: PLM-ARG is available for academic purposes at https://github.com/Junwu302/PLM-ARG, and a user-friendly webserver (http://www.unimd.org/PLM-ARG) is also provided.

As a noninvasive blood testing, the detection of cell-free DNA (cfDNA) methylation in plasma has raised an increasing interest due to diagnostic applications. Although extensively used in cfDNA methylation analysis, bisulfite sequencing is less cost-effective. In this study, we investigated the cfDNA methylation patterns in lung cancer patients by MeDIP-seq. Compared with the healthy individuals, 330 differentially methylated regions (DMRs) at gene promoters were identified in lung cancer patients with 33 hypermethylated and 297 hypomethylated regions, respectively. Moreover, these hypermethylated genes were validated with the publicly available DNA methylation data, yielding a set of ten significant differentially methylated genes in lung cancer, including B3GAT2, BCAR1, HLF, HOPX, HOXD11, MIR1203, MYL9, SLC9A3R2, SYT5, and VTRNA1-3. Our study demonstrated MeDIP-seq could be effectively used for cfDNA methylation profiling and identified a set of potential biomarker genes with clinical application for lung cancer.