Biology subjects

Yuan, R.

Publications and source records attributed to Yuan, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Uncertainty-Guided Decision-Making in Bumble Bees

The evolutionary origins of cognitive monitoring remain contested. Metacognition, the capacity to monitor one's own cognitive states, has long been linked to complex vertebrate brains, yet whether a miniature brain can evaluate the reliability of its internal representations is unknown. Here we demonstrate that bumble bees engage in uncertainty-guided decision-making. Bees dynamically adjusted opt-out choices according to perceptual difficulty, settling for a smaller guaranteed reward to avoid errors, and actively paid a reward cost to seek information under uncertainty. Without any retraining, bees transferred this 'opt-out-under-uncertainty' rule to novel tactile and working-memory tasks under an all-probe design. This strategy was stable across individuals and accurately captured by a confidence-based decision model. Our findings suggest that a brain of approximately one million neurons can support an abstract domain-general uncertainty-monitoring policy, indicating that the neural substrates for uncertainty-guided decision-making may be far more ancient and widely distributed than previously assumed.

animal behavior and cognition↗

bioRxiv2026

Synovial transcriptional clusters link cartilage degeneration to cell-type-specific gene expression in knee osteoarthritis

ObjectivesTo identify synovial transcriptional clusters in human knee osteoarthritis (OA) and determine how these relate to synovial histologic features, cell-type-associated gene expression, and cartilage degeneration severity. MethodsBulk RNA sequencing (RNA-seq) of synovial tissue from n = 135 patients with knee OA was analyzed using consensus clustering. Clusters were compared by clinical and histologic features, including cartilage degeneration severity (OARSI score). Single-cell RNA-seq (n = 18) and spatial transcriptomics were used to relate cartilage degeneration-associated gene expression patterns to synovial cell populations. ResultsFour synovial transcriptional clusters that differed in synovial histologic features and cartilage degeneration severity were identified. Greater cartilage degeneration was associated with enrichment of lining fibroblast- and inflammatory myeloid-associated gene expression, whereas lesser cartilage degeneration was associated with enrichment of sublining fibroblast, endothelial, mural cell, and adipocyte-associated gene expression. ConclusionsHuman knee OA synovium segregates into transcriptional clusters associated with cartilage degeneration severity. Synovial transcriptional heterogeneity corresponds to cell-type-associated gene expression. Key messagesO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIOsteoarthritis synovium exhibits marked histologic and molecular heterogeneity. C_LIO_LISynovial inflammation detected by MRI correlates with cartilage degeneration and predicts progressive cartilage loss in knee OA. C_LIO_LIPrior transcriptomic studies have identified molecular subsets of OA synovium, but their relationship to cartilage degeneration severity remains unclear. C_LI What this study addsO_LIOA synovium segregates into four transcriptional clusters: Sublining (C1), Lymphomyeloid (C2), Myeloid (C3), and Major trauma (C4). C_LIO_LIGreater cartilage degeneration is associated with enrichment of inflammatory myeloid and lining fibroblast gene expression, whereas lesser degeneration is associated with enrichment of adipocyte, sublining fibroblast, endothelial, and mural cell-associated gene expression. C_LI How this study might affect research, practice or policyO_LIProvides a framework for a clinically relevant biological stratification of OA patients based on synovial molecular features. C_LIO_LIInforms future efforts to link synovial biology with OA prognosis, cartilage degeneration, treatment allocation, and development of targeted therapeutic strategies. C_LI

genomics↗

bioRxiv2026

Single-cell carbon storage dynamics drive conditional fitness in microbes

Microbes frequently encounter fluctuating environments, requiring dynamic energy management strategies for survival. While carbon storage polymers like polyhydroxybutyrate (PHB) are ubiquitous across bacterial taxa, their precise ecological advantage remains poorly understood.1 Here we show that carbon storage drives conditional fitness during environmental transitions. Using a high-throughput single-cell microfluidic platform, we tracked tens of thousands of Cupriavidus necator cells under precisely controlled carbon and nitrogen fluctuations. We found that PHB provides no advantage under nutrient abundance but becomes decisive at starvation boundaries: during carbon starvation, it enables [~]30% more progeny before arrest; during recovery from nitrogen starvation, it shortens lag and accelerates regrowth. Strikingly, at the single-cell level, PHB granules are inherited in an asymmetric, all-or-nothing fashion, concentrating resources into specific lineages to overcome the discrete energetic threshold required for cell division. Despite this single-cell variance, at the population level, PHB fractions robustly return to a common setpoint after nutrient shifts--a homeostatic behavior consistent with integral feedback control. These findings reveal that while PHB does not increase the basal exponential growth rate, it confers a distinct fitness advantage by prolonging the proliferative phase during nutrient depletion and facilitating successful recovery from starvation, explaining the evolutionary persistence of carbon storage in environments with pulsed resource availability.

microbiology↗

bioRxiv2025

Steric Gating Dictates Selective Activation of BIRC6 by UBA6 Over UBA1

The giant E2-E3 chimera BIRC6 is specifically activated by the ubiquitin-activating enzyme UBA6, but not by UBA1. This specificity allows BIRC6 to ubiquitinate downstream substrates (e.g., caspases) and thereby regulate apoptosis. However, the molecular mechanism that underlies UBA6mediated specific activation of BIRC6 remains elusive. Here, we used a UbDha (Ub residue 76 dehydroalanine) probe to achieve one-step, activity-based trapping of the transient transthioesterification intermediate, and resolved the cryo-EM structures of two key complexes: 1) the doubly loaded UBA6-BIRC6UBC-UbT-UbA complex (3.4 [A] resolution) and 2) the singly loaded UBA6-BIRC6UBC-UbT complex (3.3 [A] resolution). Structural analysis reveals that the specific BIRC6-UBA6 pairing is dictated by steric compatibility between an insertion loop (residues 4649- 4653) in BIRC6s UBC domain and UBA6s gate helix. Unlike UBA1, UBA6 avoids steric clash with this insertion loop via two key conformational changes: a ~30{degrees} clockwise rotation and a 19 [A] displacement of its gate helix. These conformational changes collectively create a compatible cavity to accommodate the insertion loop. Biochemical validation confirms two key findings: 1) truncating BIRC6s insertion loop rescues UBA1-mediated ubiquitin charging of BIRC6UBC; 2) swapping UBA6s gate helix with that of UBA1 abolishes UBA6s transthioesterification activity towards BIRC6UBC. Our study provides key structural insights into the molecular basis of E1-E2 pairing specificity and advances our mechanistic understanding of apoptotic signalling.

biochemistry↗

bioRxiv2025

Modeling the structure-conditioned sequence landscape for large-scale protein design with TriFlow

Generative models have revolutionized computational protein design, and the design of high-quality sequences given backbone structure is a critical component for success. Current state-of-the-art design pipelines utilize sequence design methods with local structural context and autoregressive generation. To improve efficiency and quality of sequence design, we developed TriFlow, a model that combines a RoseTTAFold-like three-track architecture for global structural context with discrete flow-matching for efficient few-step sequence generation. We trained TriFlow on a large dataset of interacting protein chains from Protein Data Bank and interacting domains from AlphaFold protein structure Database to enrich its knowledge of natural protein and domain interfaces. TriFlow outperforms existing sequence design methods like ProteinMPNN across diverse benchmarks, including de novo binder design, where it boosts the in silico success rate of state-of-the-art design pipelines such as BindCraft. We demonstrated this by conducting a large-scale benchmark, generating and computationally validating binders for over 500 diverse protein targets. Experimental validation on a small set of targets also suggests that the performance of TriFlow is on par with BindCraft. By leveraging the model to explore the designed sequence landscape, we discovered that we can effectively highlight functional sites, by contrasting designed sequences that reflect structure constraints with natural evolutionary profiles. As a practical demonstration of its capabilities, we applied our pipeline to systematically design specific binders against human class I cytokines, computationally optimizing for on-target affinity while minimizing off-target interactions, demonstrating that specificity also scales with inference time and computational budget. TriFlow thus provides a robust framework both for large-scale protein engineering and for exploring the fundamental principles of the structure-conditioned sequence landscape. TriFlow software and generated resources are available at https://triflow.zhoulab.io/.

bioinformatics↗

bioRxiv2025

Ferrapoptosis: Discovery of a Hybrid Programmed Cell Death

Traditional taxonomies classify ferroptosis and apoptosis as distinct forms of regulated cell death. Here, we challenge this view by defining and validating a previously unrecognized neuronal death entity in traumatic brain injury (TBI), which we term "Ferrapoptosis". This hybrid mode of death is characterized by the coexistence of ferroptotic and apoptotic molecular and ultrastructural features within the same neuron-- ultrastructural alterations that cannot be fully characterized by any known cell death modalities. By integrating single-cell transcriptomics with multi-layered functional assays, we show that Ferrapoptosis dominates in severe injury and the acute phase, and is critically driven by mitochondrial oxidative stress. Over time, its predominance is gradually replaced by death modes in which either ferroptosis or apoptosis alone becomes the major pathway. Genome-wide CRISPR screening further identifies Smg7 as a key regulator that synchronously activates both death programs to drive the hybrid phenotype, whereas genetic or viral inhibition of Smg7 reduces Ferrapoptosis and promotes neurological recovery after TBI in mice. Our work systematically delineates a previously unrecognized form of cell death and its pathogenetic mechanisms, providing experimental evidence to refine cell death classification, and suggesting a conceptual strategy for treating complex diseases such as TBI by targeting shared regulatory nodes. HIGHLIGHTSO_LIDefined and validated a previously unrecognized cell death modality, termed "Ferrapoptosis". C_LIO_LIElucidated the ordered pattern of Ferrapoptosis across key stages of TBI pathogenesis. C_LIO_LIRevealed mitochondrial oxidative stress as the pivotal hub integrating ferroptosis and apoptosis. C_LIO_LIIdentify Smg7 as a core driver of Ferrapoptosis and a promising neuroprotective target. C_LI

cell biology↗

bioRxiv2025

Sexual dimorphism in zymosan-induced arthritis is linked with a higher IFN response in myeloid cell subsets that faithfully recapitulate RA synovial cell clusters

A key feature of rheumatoid arthritis (RA) is sexual dimorphism, with a higher incidence of RA in females. Myeloid cells are key drivers of the inflammatory effector phase of RA, but little is known about their contribution to a sexually dimorphic arthritis phenotype. We wished to utilize an arthritis model that recapitulates RA pathogenic myeloid cell subsets defined by single cell transcriptome analysis to investigate sex differences in human disease-relevant cell types and related molecular pathways. We developed a computational strategy that rigorously maps scRNAseq-defined mouse arthritis myeloid cells and clusters onto previously defined RA subsets. Synovial myeloid cells in the zymosan-induced arthritis (ZIA) model closely recapitulated four pathogenic RA myeloid cell subsets, strongly supporting the disease-relevance of the ZIA model. ZIA also effectively modeled myeloid cells in immune checkpoint inhibitor arthritis (ICI-A). These RA, ICI-A and ZIA myeloid cells express genes in TNF-NF-kB, PGE2, and IFN-STAT pathways. In ZIA, an induction phase dominated by cell clusters expressing NF-kB and PGE2 pathways transitioned to peak arthritis characterized by cell subsets co-expressing NF-kB and IFN pathways, as occurs in RA and various inflammatory diseases. Female mice exhibited increased arthritis linked with early induction of interferon-stimulated genes and increased expansion of IFN signature-expressing cell subsets. Our study identifies a computational strategy and animal model that enables investigation of recently described pathogenic myeloid cell subsets, and links increased arthritis in female mice with specific myeloid cell subsets that exhibit a stronger IFN response.

immunology↗

bioRxiv2025

Cross-chemical and cross-species toxicity prediction: benchmarkingand a novel 3D-structure-based deep learning model

Prediction of a compounds toxicity is a key step toward realizing animal-free testing of chemical compounds. Recent advances have yielded significant progress in computational toxicity prediction, including machine learning methods that utilize chemical fingerprints and deep learning-based latent representations. However, challenges remain, primarily due to the lack of clean training datasets and the inconsistent model performance. To address these challenges, we curated a comprehensive dataset of aquatic toxicity from seven data sources, which contains 50,603 records for 5,889 compounds across 2,285 different species, much larger than similar datasets used in previous studies. We also developed tox-learn, a Python library featuring tools for automated dataset cleaning, machine learning methods and performance evaluation. The library places special emphasis on avoiding overestimation of prediction accuracy caused by improper train-test data splitting. Based on this toolbox, we benchmarked various predictive models using different train-test splitting strategies on the curated dataset. Our results showed that the choice of machine learning method, molecular fingerprint, and train-test splitting strategy all significantly affect performance. We demonstrated that incorporating species information generally improved predictions, although the degree of improvement depended on how this information was represented. In addition, we developed a new 3D structure-based deep-learning model, 3DMol-Tox, which achieves regression accuracy comparable to the best 2D-structure based model (GPBoost) while exhibiting consistently higher within-one-bin (W1B) classification accuracy. Finally, we analyzed the impact of different train-test splitting strategies and provide recommendations based on our benchmarking, such as using structure-aware splitting to mitigate information leakage, a common issue that inflates reported model performance.

bioinformatics↗

bioRxiv2025

Pathologic profibrotic activation of distinct myeloid cell subsets in a model of impaired healing after anterior cruciate ligament reconstruction

Anterior cruciate ligament reconstruction (ACLR) may fail due to poor bone-to-tendon integration, yet the cellular mechanisms underlying this process remain incompletely understood. Using a murine model of ACLR, we show that host-derived immune cells are the primary drivers of early healing, while tendon graft-derived stromal cells also contribute at the tendon-bone interface. We developed a model of impaired post-ACLR healing by a surgical approach, that led to increased infiltration of immune cells at the tendon-bone interface, particularly CX3CR1+ monocytes/macrophages (MonoMacs). Single-cell RNA sequencing revealed dynamic monocyte-to-macrophage transitions, with an enrichment of fibrotic, mechanosensitive CX3CR1+ MonoMacs in the impaired healing model. These findings suggest that excessive recruitment or functional shifts in host-derived MonoMacs may disrupt normal healing dynamics. Our study identifies CX3CR1+ MonoMacs as a key cell population related to inferior bone-to-tendon healing after ACLR and potential therapeutic targets to improve surgical outcomes.

immunology↗

bioRxiv2025

Beyond Antagonism: IL-4 Exploits TNF signaling to Shape Its Gene Expression Signature in Monocytes and Macrophages.

Investigation of crosstalk between antagonistic pro- and anti-inflammatory cytokines has focused on mechanisms and functional consequences of cross-inhibition. We investigated cross-regulation between proinflammatory TNF and anti-inflammatory IL-4 in primary human monocytes and in a skin wound-healing model. Surprisingly, TNF functioned mainly as a costimulator of IL-4-induced gene expression, whereas IL-4 selectively inhibited the TNF-induced IFN response, leaving inflammatory gene expression mostly intact. TNF and IL-4 synergistically induced gene sets important for regulating inflammation and tissue repair, which were highly induced during the phase of wound healing when these cytokines are co-expressed. Crosstalk between TNF and IL-4 was mediated by epigenetic chromatin-mediated mechanisms associated with cooperation between NF-{kappa}B and STAT6 transcription factors, erasure of negative histone mark H3K27me3, and selective inhibition of IRF1. These results identify a long-sought mechanism for expansion of the IL-4 response, and highlight the complexity of crosstalk between antagonistic cytokines that includes cooperation for select gene responses important in immune response and tissue repair.

immunology↗

bioRxiv2025

CASP16 protein monomer structure prediction assessment

The assessment of monomer targets in the Critical Assessment of Structure Prediction Round 16 (CASP16) underscores that the problem of single-domain protein fold prediction is nearly solved--no target folds were incorrectly predicted across all Evaluation Units. However, challenges remain in accurately modeling truncated sequences, irregular secondary structures, and interaction-induced conformational changes. The release of AlphaFold3 (AF3) during CASP16, and its effective integration by many groups, demonstrated its superiority over AlphaFold2 (AF2), particularly in confidence estimation and model selection. Additional improvements in multiple sequence alignments (MSAs) and fragment-based prediction, i.e., selecting the optimal fragment of the full sequence for modeling, also contributed to enhanced prediction accuracy. The top three groups--all from the Yang lab--consistently outperformed others across CASP16 monomer targets, reflecting their robust modeling pipelines and successful adoption of AF3. CASP16 also introduced three new challenges: Phase 0, in which stoichiometry was withheld; Phase 2, which supplied [~]8,000 MassiveFold models per target to test model selection strategies; and Model 6, which limited predictors to using MSAs provided by the organizers. While we evaluated group performance in these additional challenges, the insights gained were limited due to low participation and caveats in the design of experiments. We suggest improvements for the organization of these challenges and encourage broader engagement from the prediction community. The progress in monomer modeling from CASP15 to CASP16 was subtle, but more groups in CASP16 were able to outperform ColabFold, reflecting the communitys improved ability in optimizing AF2 and the growing adoption of AF3. We anticipate that the recent release of the AF3 source code will stimulate future progress through user-driven optimization and innovations in model architecture. Finally, model ranking remains a persistent weakness across most groups, highlighting a critical area for future development.

biophysics↗

bioRxiv2025

Assessment of Protein Complex Predictions in CASP16: Are we making progress?

The assessment of oligomer targets in the Critical Assessment of Structure Prediction Round 16 (CASP16) suggests that complex structure prediction remains an unsolved challenge. More than 30% of targets, particularly antibody-antigen targets, were highly challenging, with each group correctly predicting structures for only about a quarter of such targets. Most CASP16 groups relied on AlphaFold-Multimer (AFM) or AlphaFold3 (AF3) as their core modeling engines. By optimizing input MSAs, refining modeling constructs (using partial rather than full sequences), and employing massive model sampling and selection, top-performing groups were able to significantly outperform the default AFM/AF3 predictions. CASP16 also introduced two additional challenges: Phase 0, which required predictions without stoichiometry information, and Phase 2, which provided participants with thousands of models generated by MassiveFold (MF) to enable large-scale sampling for resource-limited groups. Across all phases, the MULTICOM series and Kiharalab emerged as top performers based on the quality of their best models per target. However, these groups did not have a strong advantage in model ranking, and thus their lead over other teams, such as Yang-Multimer and kozakovvajda, was less pronounced when evaluating only the first submitted models. Compared to CASP15, CASP16 showed moderate overall improvement, likely driven by the release of AF3 and the extensive model sampling employed by top groups. Several notable trends highlight key frontiers for future development. First, the kozakovvajda group significantly outperformed others on antibody-antigen targets, achieving over a 60% success rate without relying on AFM or AF3 as their primary modeling framework, suggesting that alternative approaches may offer promising solutions for these difficult targets. Second, model ranking and selection continue to be major bottlenecks. The PEZYFoldings group demonstrated a notable advantage in selecting their best models as first models, suggesting that their pipeline for model ranking may offer important insights for the field. Finally, the Phase 0 experiment indicated reasonable success in stoichiometry prediction; however, stoichiometry prediction remains challenging for high-order assemblies and targets that differ from available homologous templates. Overall, CASP16 demonstrated steady progress in multimer prediction while emphasizing the urgent need for more effective model ranking strategies, improved stoichiometry prediction, and the development of new modeling methods that extend beyond the current AF-based paradigm.

biophysics↗

bioRxiv2025

Assessment of nucleic acid structure prediction in CASP16

Consistently accurate 3D nucleic acid structure prediction would facilitate studies of the diverse RNA and DNA molecules underlying life. In CASP16, blind predictions for 42 targets canvassing a full array of nucleic acid functions, from dopamine binding by DNA to formation of elaborate RNA nanocages, were submitted by 65 groups from 46 different labs worldwide. In contrast to concurrent protein structure predictions, performance on nucleic acids was generally poor, with no predictions of previously unseen natural RNA structures achieving TM-scores above 0.8. Even though automated server performance has improved, all top-performing groups were human expert predictors: Vfold, GuangzhouRNA-human, and KiharaLab. Good performance on one template-free modeling target (OLE RNA) and accurate global secondary structure prediction suggested that structural information can be extracted from multiple sequence alignments. However, 3D accuracy generally appeared to depend on the availability of closely related 3D structure templates, and predictions still did not achieve consistent recovery of pseudoknots, singlet Watson-Crick-Franklin pairs, non-canonical pairs, or tertiary motifs like A-minor interactions. For the first time, blind predictions of nucleic acid interactions with small molecules, proteins, and other nucleic acids could be assessed in CASP16. As with nucleic acid monomers, prediction accuracy for nucleic acid complexes was generally poor unless 3D templates were available. Accounting for template availability, there has not been a notable increase in nucleic acid modeling accuracy between previous blind challenges and CASP16.

biophysics↗

bioRxiv2025

Attention Guided Mechanism Interpretable Drug-Gene Interaction (MIDI) Modeling for Cancer Drug Response Prediction and Target Effect Explanation

Cancer drug discovery using genetic information is still poorly developed. Precisely locating drug atoms and explaining the targeting effect is crucial in precision medicine since it helps understand the drugs mechanism of action. Much data has been collected regarding drug response against cancer cell lines, and many models predict the drug response based on genomic information. However, to our knowledge, none of the data-driven techniques propose to detect the targeting mechanism of small drug molecules against genetic targets. In this work, we propose MIDI (Mechanism Interpretable Drug-Gene Interaction) model to delve deep into the targeting relation between drug molecules against genetic patterns. We show that purely based on a data-driven approach, the attention mechanism in our model could capture the important binding effect of small molecules towards gene targets. We provide both theoretical derivation and experiment results to show the information flow regarding the attention mechanism. In the meantime, we demonstrate that our model presents much higher prediction performance with the interpretation mechanism than the other state-of-the-art drug response prediction models.

cancer biology↗

bioRxiv2025

Exosomal miR-145-5p promotes apoptosis of renal tubule epithelial cells through the JNK signalling pathway

Acute kidney injury (AKI) is the most prevalent extrapulmonary organ failure observed in patients with acute respiratory distress syndrome (ARDS). However, the underlying mechanisms leading to AKI in ARDS remain unclear. Exosomes facilitate intercellular and inter-organ communication via exosomal microRNAs (miRNAs), which potentially contribute to the crosstalk between the lung and kidney in ARDS. Therefore, the purposes of our study were to investigate the involvement of exosomal miRNAs in the development of AKI in ARDS. In current study, Sprague-Dawley rats were induced to develop ARDS via hydrochloric acid aspiration. Nanoparticle tracking analysis, Transmission electron microscopy and western blotting were conducted to analyse the exosomes. Quantitative reverse transcription-polymerase chain reaction and in situ hybridisation showed elevated expression level of miR-145-5p in the circulating exosomes and lung tissue of ARDS rats. Overexpression of miR-145-5p disrupted the proteomic profile of renal proximal tubule epithelial cells, leading to impacts on proteins associated with various biological processes, including apoptosis. We further demonstrate that miR-145-5p overexpression enhanced JNK phosphorylation by directly repressing RBM3 expression and promoted cell apoptosis. In vivo, administration of agomiR-145-5p injection via intravenous route, which subsequently induced renal tubular cell apoptosis. These findings have revealed that miR-145-5p may serve as a novel mediator for the apoptosis of renal cell in the context of ARDS.

cell biology↗

bioRxiv2024

Leveraging the Human Panproteome to Enhance Peptide and Protein Identification in Proteomics and Metaproteomics

In this paper, we developed a novel approach to utilize the human pangenome to improve peptide and protein identification from proteomic data (MS/MS spectra). We propose a new data structure called panproteome graph (PPG), in which nodes are tryptic peptides, to represent the human pangenome. The PPG can be built in linear time and can be utilized via graph traversal using a depth-first search algorithm to generate potential peptides for peptide identification in proteomics. The PPG built using the 47 human proteomes from the Human Pangenome Reference Consortium (HPRC) coupled with UniProt human proteins resulted in more than 4.2M tryptic peptides, a 26% increase as compared to when only the UniProt proteins were included. Graph-based analysis of the PPG revealed a giant disconnected component with about 3M nodes, suggesting substantial sharing of tryptic peptides among proteins. We applied tryptic peptides derived from PPG to characterize three collections of human proteomic and metaproteomic datasets, and our results showed that by exploiting the human pangenome, we were able to increase the number of identified peptides on all datasets we tested (about 8% increase across all three collections). We also showed that using more complete human proteome would be useful for reducing potential misidentification of human peptides as microbial peptides, a problem that was previously studied but based on genomic sequencing data. Our tool for building PPG is available in a GitHub repo PPGpep, and PPG-derived tryptic peptides can be utilized by MetaProD, a pipeline for both human and bacterial peptide and protein identification from (meta)proteomics datasets.

bioinformatics↗

bioRxiv2024

Opposing Regulation of TNF Responses and IL-1b+ Macrophages by PGE2-cAMP and IFN-g Signaling

IL-1{beta}-expressing macrophages have been described in rheumatoid arthritis (RA), immune checkpoint inhibitor-induced inflammatory arthritis (ICI-arthritis), and pancreatic cancer and proposed to be pathogenic. In RA and pancreatic cancer IL-1{beta}+ macrophages express genes cooperatively induced by PGE2 and TNF signaling, but mechanisms that induce these cells and the extent to which they contribute to arthritic phenotypes are not known. In this study we used an integrated transcriptomic and epigenomic analysis in primary human monocytes to study PGE2-TNF crosstalk, and how it is regulated by IFN-{gamma}, as occurs in RA synovial macrophages. We identified a TNF + PGE2 (TP) induced gene expression signature that is enriched in a previously identified IL1{beta}+ RA monocytic subset defined by scRNAseq and includes genes in pathogenic IL-1, Notch and neutrophil chemokine pathways. A similar TP gene expression signature was apparent in an IL-1{beta}+ macrophage subset newly identified by scRNAseq in ICI-arthritis. Reference mapping revealed that ICI-arthritis myeloid cells map primarily onto four previously defined RA synovial monocytic clusters, and the TP signature extends beyond IL-1{beta}+ cells to subsets of adjacent clusters, suggestive of a new functional monocyte subset. TP signature genes are distinct from canonical inflammatory NF-{kappa}B target genes such as TNF, IL6 and IL12B and are activated by cooperation of PGE2-induced AP-1, CEBP and NR4A family transcription factors with TNF-induced NF-{kappa}B activity. Unexpectedly, IFN-{gamma} suppressed induction of AP-1, CEBP and NR4A activity to ablate induction of IL-1, Notch and neutrophil chemokine genes, while promoting expression of distinct inflammatory genes such as TNF and T cell chemokines like CXCL10. The opposing cross-regulation of PGE2 and IFN signaling in vitro was reflected in vivo in mutually exclusive expression of TP and IFN signatures in different cell clusters in RA and ICI-arthritis monocytes. These results reveal the basis for synergistic induction of inflammatory genes by PGE2 and TNF, and a novel regulatory axis whereby IFN-{gamma} and PGE2 oppose each other to determine the balance between two distinct TNF-induced inflammatory gene expression programs relevant for RA and ICI-arthritis.

immunology↗

bioRxiv2024

IL-10 Targets IRFs to Suppress IFN and Inflammatory Response Genes by Epigenetic Mechanisms

Interleukin-10 (IL-10) is pivotal in suppressing inflammation and innate immune activation, in large part by suppressing induction of genes by potent inflammatory factors such as TLR ligands. Despite decades of research, molecular mechanisms underlying this inhibition have not been resolved. This study utilized an integrated epigenomic analysis of gene transcription, chromatin accessibility, histone modifications and transcription factor binding to investigate IL-10-mediated suppression of LPS and TNF responses in primary human monocytes. Instead of inhibiting core TLR4-activated pathways such as NF-{kappa}B, MAPK-AP-1 and TBK1-IRF3 signaling, IL-10 targeted IRF transcription factor activity and DNA binding, particularly IRF5 and an IRF1-mediated amplification loop that is operative in monocytes. This resulted in suppression of inflammatory NF-{kappa}B target genes, in whose activation IRFs play an amplifying role, and near-complete suppression of interferon-stimulated genes. Mechanisms of TLR4 and TNFR target gene inhibition included downregulation of chromatin accessibility, de novo enhancer formation, and IRF1-associated H3K27ac activating histone marks. These results provide a mechanism by which IL-10 suppresses inflammatory NF-{kappa}B target genes, highlight the role of IRF1 in inflammatory gene expression, and describe an underappreciated suppression of IFN responses by epigenetic mechanisms.

immunology↗