Immunomodulation by glucocorticoid-induced leucine zipper in macrophages: enhanced phagocytosis, protection from pyroptosis, and altered mitochondrial function.

Glucocorticoids, which have long served as fundamental therapeutics for diverse inflammatory conditions, are still widely used, despite associated side effects limiting their long-term use. Among their key mediators is glucocorticoid-induced leucine zipper (GILZ), recognized for its anti-inflammatory and immunosuppressive properties. Here, we explore the immunomodulatory effects of GILZ in macrophages through transcriptomic analysis and functional assays. Bulk RNA sequencing of GILZ knockout and GILZ-overexpressing macrophages revealed significant alterations in gene expression profiles, particularly impacting pathways associated with the inflammatory response, phagocytosis, cell death, mitochondrial function, and extracellular structure organization activity. GILZ-overexpression enhances phagocytic and antibacterial activity against Salmonella typhimurium and Escherichia coli, potentially mediated by increased nitric oxide production. In addition, GILZ protects macrophages from pyroptotic cell death, as indicated by a reduced production of reactive oxygen species (ROS) in GILZ transgenic macrophages. In contrast, GILZ KO macrophages produced more ROS, suggesting a regulatory role of GILZ in ROS-dependent pathways. Additionally, GILZ overexpression leads to decreased mitochondrial respiration and heightened matrix metalloproteinase activity, suggesting its involvement in tissue remodeling processes. These findings underscore the multifaceted role of GILZ in modulating macrophage functions and its potential as a therapeutic target for inflammatory disorders, offering insights into the development of novel therapeutic strategies aimed at optimizing the benefits of glucocorticoid therapy while minimizing adverse effects.

EpiSegMix: a flexible distribution hidden Markov model with duration modeling for chromatin state discovery.

MOTIVATION: Automated chromatin segmentation based on ChIP-seq (chromatin immunoprecipitation followed by sequencing) data reveals insights into the epigenetic regulation of chromatin accessibility. Existing segmentation methods are constrained by simplifying modeling assumptions, which may have a negative impact on the segmentation quality. RESULTS: We introduce EpiSegMix, a novel segmentation method based on a hidden Markov model with flexible read count distribution types and state duration modeling, allowing for a more flexible modeling of both histone signals and segment lengths. In a comparison with existing tools, ChromHMM, Segway, and EpiCSeg, we show that EpiSegMix is more predictive of cell biology, such as gene expression. Its flexible framework enables it to fit an accurate probabilistic model, which has the potential to increase the biological interpretability of chromatin states. AVAILABILITY AND IMPLEMENTATION: Source code: https://gitlab.com/rahmannlab/episegmix.

Enhanced medullary and extramedullary granulopoiesis sustain the inflammatory response in lupus nephritis.

OBJECTIVES: In SLE, deregulation of haematopoiesis is characterised by inflammatory priming and myeloid skewing of haematopoietic stem and progenitor cells (HSPCs). We sought to investigate the role of extramedullary haematopoiesis (EMH) as a key player for tissue injury in systemic autoimmune disorders. METHODS: Transcriptomic analysis of bone marrow (BM)-derived HSPCs from patients with SLE and NZBW/F1 lupus-prone mice was performed in combination with DNA methylation profile. Trained immunity (TI) was induced through ß-glucan administration to the NZBW/F1 lupus-prone model. Disease activity was assessed through lupus nephritis (LN) histological grading. Colony-forming unit assay and adoptive cell transfer were used to assess HSPCs functionalities. RESULTS: Transcriptomic analysis shows that splenic HSPCs carry a higher inflammatory potential compared with their BM counterparts. Further induction of TI, through ß-glucan administration, exacerbates splenic EMH, accentuates myeloid skewing and worsens LN. Methylomic analysis of BM-derived HSPCs demonstrates myeloid skewing which is in part driven by epigenetic tinkering. Importantly, transcriptomic analysis of human SLE BM-derived HSPCs demonstrates similar findings to those observed in diseased mice. CONCLUSIONS: These data support a key role of granulocytes derived from primed HSPCs both at medullary and extramedullary sites in the pathogenesis of LN. EMH and TI contribute to SLE by sustaining the systemic inflammatory response and increasing the risk for flare.

Machine learning for deciphering cell heterogeneity and gene regulation.

Epigenetics studies inheritable and reversible modifications of DNA that allow cells to control gene expression throughout their development and in response to environmental conditions. In computational epigenomics, machine learning is applied to study various epigenetic mechanisms genome wide. Its aim is to expand our understanding of cell differentiation, that is their specialization, in health and disease. Thus far, most efforts focus on understanding the functional encoding of the genome and on unraveling cell-type heterogeneity. Here, we provide an overview of state-of-the-art computational methods and their underlying statistical concepts, which range from matrix factorization and regularized linear regression to deep learning methods. We further show how the rise of single-cell technology leads to new computational challenges and creates opportunities to further our understanding of epigenetic regulation.