Overexpression of pyrroline-5-carboxylate reductase 1 (PYCR1) has been associated with the development of certain cancers; however, no studies have specifically examined the role of PYCR1 in hepatocellular carcinoma (HCC). Based on The Cancer Genome Atlas expression array and meta-analysis conducted using the Gene Expression Omnibus database, we determined that PYCR1 was upregulated in HCC compared to adjacent nontumor tissues (P < 0.05). These data were verified using quantitative real-time polymerase chain reaction, western blotting, and immunohistochemistry analysis. Additionally, patients with low PYCR1 expression showed a higher overall survival rate than patients with high PYCR1 expression. Furthermore, PYCR1 overexpression was associated with the female sex, higher levels of alpha-fetoprotein, advanced clinical stages (III and IV), and a younger age (< 45 years old). Silencing of PYCR1 inhibited cell proliferation, invasive migration, epithelial-mesenchymal transition, and metastatic properties in HCC in vitro and in vivo. Using RNA sequencing and bioinformatics tools for data-dependent network analysis, we found binary relationships among PYCR1 and its interacting proteins in defined pathway modules. These findings indicated that PYCR1 played a multifunctional role in coordinating a variety of biological pathways involved in cell communication, cell proliferation and growth, cell migration, a mitogen-activated protein kinase cascade, ion binding, etc. The structural characteristics of key pathway components and PYCR1-interacting proteins were evaluated by molecular docking, and hotspot analysis showed that better affinities between PYCR1 and its interacting molecules were associated with the presence of arginine in the binding site. Finally, a candidate regulatory microRNA, miR-2355-5p, for PYCR1 mRNA was discovered in HCC. Overall, our study suggests that PYCR1 plays a vital role in HCC pathogenesis and may potentially serve as a molecular target for HCC treatment.

Polygenic risk scores (PRSs) assess genetic susceptibility to alcohol use disorder (AUD), yet their molecular implications remain underexplored. Neuroimmune interactions, particularly in microglia, are recognized as notable contributors to AUD pathophysiology. We investigated the interplay between AUD PRS and ethanol in human microglia derived from iPSCs from individuals with AUD high-PRS (diagnosed with AUD) or low-PRS (unaffected). Ethanol exposure induced elevated CD68 expression and morphological changes in microglia, with differential responses between high-PRS and low-PRS microglial cells. Transcriptomic analysis revealed expression differences in MHCII complex and phagocytosis-related genes following ethanol exposure; high-PRS microglial cells displayed enhanced phagocytosis and increased CLEC7A expression, unlike low-PRS microglial cells. Synapse numbers in cocultures of induced neurons with microglia after alcohol exposure were lower in high-RPS cocultures, suggesting possible excess synapse pruning. This study provides insights into the intricate relationship between AUD PRS, ethanol, and microglial function, potentially influencing neuronal functions in developing AUD.

Cancer cell phenotypes evolve during a tumor's treatment. In some cases, tumor cells acquire cancer stem cell-like (CSL) traits such as resistance to chemotherapy and diminished differentiation; therefore, targeting these cells may be therapeutically beneficial. In this study we show that in progressive estrogen receptor positive (ER+) metastatic breast cancer tumors, resistant subclones that emerge following chemotherapy have increased CSL abundance. Further, in vitro organoid growth of ER+ patient cancer cells also shows that chemotherapy treatment leads to increased abundance of ALDH+/CD44+ CSL cells. Chemotherapy induced CSL abundance is blocked by treatment with a pan-HDAC inhibitor, belinostat. Belinostat treatment diminished both mammosphere formation and size following chemotherapy, indicating a decrease in progenitor CSL traits. HDAC inhibitors specific to class IIa (HDAC4, HDAC5) and IIb (HDAC6) were shown to primarily reverse the chemo-resistant CSL state. Single-cell RNA sequencing analysis with patient samples showed that HDAC targets and MYC signaling were promoted by chemotherapy and inhibited upon HDAC inhibitor treatment. In summary, HDAC inhibition can block chemotherapy-induced drug resistant phenotypes with 'one-two punch' strategy in refractory breast cancer cells.

Many bacteria utilize environmentally widespread C2 sulfonate sulfoacetate as a sulfur source, and the disease-linked human gut sulfate- and sulfite-reducing bacteria can use it as a terminal electron receptor for anaerobic respiration generating toxic H ₂ S. However, the mechanism of sulfoacetate formation is unknown, although it has been proposed that sulfoacetate originates from bacterial degradation of sulfoquinovose (SQ), the polar head group of sulfolipids present in all green plants.

The tyrosine kinase receptor ephrin receptor A2 (EPHA2) is overexpressed in lung (LSCC) and head and neck (HNSCC) squamous cell carcinomas. Although EPHA2 can inhibit tumorigenesis in a ligand-dependent fashion via phosphorylation of Y588 and Y772, it can promote tumorigenesis in a ligand-independent manner via phosphorylation of S897. Here, we show that EPHA2 and Roundabout Guidance Receptor 1 (ROBO1) interact to form a functional heterodimer. Furthermore, we show that the ROBO1 ligand Slit Guidance Ligand 2 (SLIT2) and ensartinib, an inhibitor of EPHA2, can attenuate growth of HNSCC cells and act synergistically in LSCC cells. Our results suggest that patients with LSCC and HNSCC may be stratified and treated based on their EPHA2 and ROBO1 expression patterns. Although ~73% of patients with LSCC could benefit from SLIT2+ensartinib treatment, ~41% of patients with HNSCC could be treated with either SLIT2 or ensartinib. Thus, EPHA2 and ROBO1 represent potential LSCC and HNSCC theranostics.

The advent of single-cell RNA sequencing (scRNA-seq) technologies has enabled gene expression profiling at the single-cell resolution, thereby enabling the quantification and comparison of transcriptional variability among individual cells. Although alterations in transcriptional variability have been observed in various biological states, statistical methods for quantifying and testing differential variability between groups of cells are still lacking. To identify the best practices in differential variability analysis of single-cell gene expression data, we propose and compare 12 statistical pipelines using different combinations of methods for normalization, feature selection, dimensionality reduction and variability calculation. Using high-quality synthetic scRNA-seq datasets, we benchmarked the proposed pipelines and found that the most powerful and accurate pipeline performs simple library size normalization, retains all genes in analysis and uses denSNE-based distances to cluster medoids as the variability measure. By applying this pipeline to scRNA-seq datasets of COVID-19 and autism patients, we have identified cellular variability changes between patients with different severity status or between patients and healthy controls.