E3 ubiquitin ligase TRIM38 regulates macrophage polarization to reduce hepatic inflammation by interacting with HSPA5

Metabolic dysfunction-associated steatotic liver disease (MASLD) encompasses pathologies from simple steatosis and steatohepatitis (MASH) to cirrhosis. Hepatic inflammation is a common cause of liver pathogenesis, with macrophage activation as a key indicator of both acute and chronic liver dysfunction. While M1 macrophages promote inflammation and M2 macrophages suppress it, their roles in MASLD are dynamic and shift according to disease stage and liver microenvironment. Tripartite motif (TRIM) family proteins, which possess E3 ubiquitin ligase activity, are involved in various cellular processes, including intracellular signaling, development, apoptosis, protein quality control, innate immunity, autophagy, and carcinogenesis. TRIM38 negatively regulates innate immunity and inflammation triggered by viruses, Toll-like receptor 3 and 4, and tumor necrosis factor α/interleukin-1β signaling; however, its role in liver pathogenesis remains unclear. This study investigates the role of macrophage TRIM38 in metabolic liver disease to identify key targets for controlling inflammation. TRIM38 overexpression suppressed lipopolysaccharide-induced macrophage activation and metabolic stress-induced hepatic lipid accumulation. Mechanistically, TRIM38 interacted with heat shock protein family A member 5 (HSPA5) and stabilized it via K63-dependent ubiquitination. This TRIM38-HSPA5 axis promoted the expression of M2 macrophage markers (arginase 1 and retinoic acid-related orphan receptor α), thereby ameliorating liver steatosis. Single-cell RNA sequencing revealed significant downregulation of TRIM38 expression in the liver macrophages of patients with MASLD and negative regulation of liver inflammation via modulation of macrophage polarization. Hence, macrophage TRIM38 suppresses metabolic liver disease progression via HSPA5-mediated M2 macrophage polarization and provides insights into potential therapeutic targets.

Spatiotemporally transcriptomic analyses of floral buds reveal the high-resolution landscape of flower development and dormancy regulation in peach

The spatiotemporal transcriptome dataset reported here provides the peach flower bud's gene expression atlas at spatiotemporal resolution level using the 10x Genomics Visium platform. This dataset can be used to define transcript accumulation for any interesting genes across several flower bud cells. It was generated using three peach flower bud samples during the activity-dormancy period, providing valuable insight into gene expression profiling and developmental stages under different environmental contexts or conditions. Importantly, we found that different cell types are related to specific regulatory programs, including signal transduction, environment and stress responses, and flower development. Our research provides insight into the transcriptomic landscape of the key cell types for flower buds and opens new avenues to study cell-type specification, function, and differentiation in Rosaceae fruit trees. A series of pivotal genes (e.g. AMS, MS188, MS1) for flower bud development were identified. These results provide a valuable reference for the activity-dormancy transition in perennial deciduous fruit trees.

Inflammatory Response of THP1 and U937 Cells: The RNAseq Approach

THP1 and U937 are monocytic cell lines that are common bioassays to reflect monocyte and macrophage activities in inflammation research. However, THP-1 is a human monocytic leukemia cell line, and U937 originates from pleural effusion of histiocytic lymphoma; thus, even though they serve as bioassay in inflammation research, their response to agonists is not identical. Consequently, there has yet to be a consensus about the panel of strongly regulated genes in THP1 and U937 cells representing the inflammatory response to LPS and IFNG. Therefore, we have performed an RNAseq of THP1 and U937 exposed to LPS and IFNG to identify the most sensitive genes and the unique properties of each individual cell line. When applying a highly stringent threshold, we could identify 43, 8 up and 94, 103 down-regulated genes in THP1 and U937 cells, respectively. In THP1 cells, among the most strongly up-regulated genes are CCL1, CXCL2, CXCL3, IL1A, IL1B, IL6, and PTGES. In U937 cells, the strongest up-regulated genes include CSF2, CSF3, CXCL2, CXCL5, CXCL6, IL1A, IL19, IL36G, IL6, ITGA1, ITGA2, and PTGS2. Even though THP1 is considerably less responsive than U937, there are genes commonly upregulated by LPS and IFNG, including the CCL1, CCL3, CCL20, CXCL2, CXCL3, CXCL8, as well as IL1A, IL1B, IL23A, IL6, and genes of prostaglandin synthesis PTGES and PTGS2. Downregulated genes are limited to NRGN and CD36. This head-to-head comparison revealed that THP1 is less responsive than U937 cells to LPS and IFNG and identified a panel of highly regulated genes that can be applied in bioassays in inflammation research. Our data further propose bulk RNAseq as a standard method in bioassay research.

Principles of bacterial innate immunity against viruses

All organisms must defend themselves against viral predators. This includes bacteria, which harbor immunity factors such as restriction-modification systems and CRISPR-Cas systems. More recently, a plethora of additional defense systems have been identified, revealing a richer, more sophisticated immune system than previously appreciated. Some of these newly identified defense systems have distant homologs in mammals, suggesting an ancient evolutionary origin of some facets of mammalian immunity. An even broader conservation exists at the level of how these immunity systems operate. Here, we focus at this level, reviewing key principles and high-level attributes of innate immunity in bacteria that are shared with mammalian immunity, while also noting key differences, with a particular emphasis on how cells sense viral infection.

Mechanisms of Toll-like receptor tolerance induced by microbial ligands

Some microorganisms can develop tolerance. On the one hand, it allows pathogenic microbes to escape immune surveillance, on the other hand, it provides the possibility to microbiota representatives to colonize different biotopes and build a symbiotic relationship with the host. Complex regulatory interactions between innate and adaptive immune systems as well as stimulation by antigens help microbes control and maintain immunological tolerance. An important role in this process belongs to innate immune cells, which recognize microbial components through pattern-recognition receptors. Toll-like receptors (TLRs) represent the main class of these receptors. Despite the universality of the activated signaling pathways, different cellular responses are induced by interaction of TLRs with microbiota representatives and pathogenic microbes, and they vary during acute and chronic infection. The research on mechanisms underlying the development of TLR tolerance is significant, as the above receptors are involved in a wide range of infectious and noninfectious diseases; they also play an important role in development of allergic diseases, autoimmune diseases, and cancers. The knowledge of TLR tolerance mechanisms can be critically important for development of TLR ligand-based therapeutic agents for treatment and prevention of multiple diseases.

Immune Regulation in Time and Space: The Role of Local- and Long-Range Genomic Interactions in Regulating Immune Responses

Mammals face and overcome an onslaught of endogenous and exogenous challenges in order to survive. Typical immune cells and barrier cells, such as epithelia, must respond rapidly and effectively to encountered pathogens and aberrant cells to prevent invasion and eliminate pathogenic species before they become overgrown and cause harm. On the other hand, inappropriate initiation and failed termination of immune cell effector function in the absence of pathogens or aberrant tissue gives rise to a number of chronic, auto-immune, and neoplastic diseases. Therefore, the fine control of immune effector functions to provide for a rapid, robust response to challenge is essential. Importantly, immune cells are heterogeneous due to various factors relating to cytokine exposure and cell-cell interaction. For instance, tissue-resident macrophages and T cells are phenotypically, transcriptionally, and functionally distinct from their circulating counterparts. Indeed, even the same cell types in the same environment show distinct transcription patterns at the single cell level due to cellular noise, despite being robust in concert. Additionally, immune cells must remain quiescent in a naive state to avoid autoimmunity or chronic inflammatory states but must respond robustly upon activation regardless of their microenvironment or cellular noise. In recent years, accruing evidence from next-generation sequencing, chromatin capture techniques, and high-resolution imaging has shown that local- and long-range genome architecture plays an important role in coordinating rapid and robust transcriptional responses. Here, we discuss the local- and long-range genome architecture of immune cells and the resultant changes upon pathogen or antigen exposure. Furthermore, we argue that genome structures contribute functionally to rapid and robust responses under noisy and distinct cellular environments and propose a model to explain this phenomenon.

Single Cell RNA-Seq and Machine Learning Reveal Novel Subpopulations in Low-Grade Inflammatory Monocytes With Unique Regulatory Circuits

Subclinical doses of LPS (SD-LPS) are known to cause low-grade inflammatory activation of monocytes, which could lead to inflammatory diseases including atherosclerosis and metabolic syndrome. Sodium 4-phenylbutyrate is a potential therapeutic compound which can reduce the inflammation caused by SD-LPS. To understand the gene regulatory networks of these processes, we have generated scRNA-seq data from mouse monocytes treated with these compounds and identified 11 novel cell clusters. We have developed a machine learning method to integrate scRNA-seq, ATAC-seq, and binding motifs to characterize gene regulatory networks underlying these cell clusters. Using guided regularized random forest and feature selection, our method achieved high performance and outperformed a traditional enrichment-based method in selecting candidate regulatory genes. Our method is particularly efficient in selecting a few candidate genes to explain observed expression pattern. In particular, among 531 candidate TFs, our method achieves an auROC of 0.961 with only 10 motifs. Finally, we found two novel subpopulations of monocyte cells in response to SD-LPS and we confirmed our analysis using independent flow cytometry experiments. Our results suggest that our new machine learning method can select candidate regulatory genes as potential targets for developing new therapeutics against low grade inflammation.

The versatile plasmacytoid dendritic cell: Function, heterogeneity, and plasticity

Since their identification as the natural interferon-producing cell two decades ago, plasmacytoid dendritic cells (pDCs) have been attributed diverse functions in the immune response. Their most well characterized function is innate, i.e., their rapid and robust production of type-I interferon (IFN-I) in response to viruses. However, pDCs have also been implicated in antigen presentation, activation of adaptive immune responses and immunoregulation. The mechanisms by which pDCs enact these diverse functions are poorly understood. One central debate is whether these functions are carried out by different pDC subpopulations or by plasticity in the pDC compartment. This chapter summarizes the latest reports regarding pDC function, heterogeneity, cell conversion and environmentally influenced plasticity, as well as the role of pDCs in infection, autoimmunity and cancer.

New connections: Decoding macrophages in disease

Deciphering how macrophages are reprogrammed in disease may lead to better patient therapies.