MRGM: an enhanced catalog of mouse gut microbial genomes substantially broadening taxonomic and functional landscapes.

Mouse gut microbiome research is pivotal for understanding the human gut microbiome, providing insights into disease modeling, host-microbe interactions, and the dietary influence on the gut microbiome. To enhance the translational value of mouse gut microbiome studies, we need detailed and high-quality catalogs of mouse gut microbial genomes. We introduce the Mouse Reference Gut Microbiome (MRGM), a comprehensive catalog with 42,245 non-redundant mouse gut bacterial genomes across 1,524 species. MRGM marks a 40% increase in the known taxonomic diversity of mouse gut microbes, capturing previously underrepresented lineages through refined genome quality assessment techniques. MRGM not only broadens the taxonomic landscape but also enriches the functional landscape of the mouse gut microbiome. Using deep learning, we have elevated the Gene Ontology annotation rate for mouse gut microbial proteins from 3.2% with orthology to 60%, marking an over 18-fold increase. MRGM supports both DNA- and marker-based taxonomic profiling by providing custom databases, surpassing previous catalogs in performance. Finally, taxonomic and functional comparisons between human and mouse gut microbiota reveal diet-driven divergences in their taxonomic composition and functional enrichment. Overall, our study highlights the value of high-quality microbial genome catalogs in advancing our understanding of the co-evolution between gut microbes and their host.

MRGM: An enhanced catalog of mouse gut microbial genomes substantially broadening taxonomic and functional landscapes.

Mouse gut microbiome research is pivotal for understanding the human gut microbiome, providing insights into disease modeling, host-microbe interactions, and the dietary influence on the gut microbiome. To enhance the translational value of mouse gut microbiome studies, we need detailed and high-quality catalogs of mouse gut microbial genomes. We introduce the Mouse Reference Gut Microbiome (MRGM), a comprehensive catalog with 42,245 non-redundant mouse gut bacterial genomes across 1,524 species. MRGM marks a 40% increase in the known taxonomic diversity of mouse gut microbes, capturing previously underrepresented lineages through refined genome quality assessment techniques. MRGM not only broadens the taxonomic landscape but also enriches the functional landscape of the mouse gut microbiome. Using deep learning, we have elevated the Gene Ontology annotation rate for mouse gut microbial proteins from 3.2% with orthology to 60%, marking an over 18-fold increase. MRGM supports both DNA- and marker-based taxonomic profiling by providing custom databases, surpassing previous catalogs in performance. Finally, taxonomic and functional comparisons between human and mouse gut microbiota reveal diet-driven divergences in their taxonomic composition and functional enrichment. Overall, our study highlights the value of high-quality microbial genome catalogs in advancing our understanding of the co-evolution between gut microbes and their host.

Gut Microbiome Wellness Index 2 enhances health status prediction from gut microbiome taxonomic profiles.

Recent advancements in translational gut microbiome research have revealed its crucial role in shaping predictive healthcare applications. Herein, we introduce the Gut Microbiome Wellness Index 2 (GMWI2), an enhanced version of our original GMWI prototype, designed as a standardized disease-agnostic health status indicator based on gut microbiome taxonomic profiles. Our analysis involves pooling existing 8069 stool shotgun metagenomes from 54 published studies across a global demographic landscape (spanning 26 countries and six continents) to identify gut taxonomic signals linked to disease presence or absence. GMWI2 achieves a cross-validation balanced accuracy of 80% in distinguishing healthy (no disease) from non-healthy (diseased) individuals and surpasses 90% accuracy for samples with higher confidence (i.e., outside the "reject option"). This performance exceeds that of the original GMWI model and traditional species-level α-diversity indices, indicating a more robust gut microbiome signature for differentiating between healthy and non-healthy phenotypes across multiple diseases. When assessed through inter-study validation and external validation cohorts, GMWI2 maintains an average accuracy of nearly 75%. Furthermore, by reevaluating previously published datasets, GMWI2 offers new insights into the effects of diet, antibiotic exposure, and fecal microbiota transplantation on gut health. Available as an open-source command-line tool, GMWI2 represents a timely, pivotal resource for evaluating health using an individual's unique gut microbial composition.

Comprehensive assessment of machine learning methods for diagnosing gastrointestinal diseases through whole metagenome sequencing data.

The gut microbiome, linked significantly to host diseases, offers potential for disease diagnosis through machine learning (ML) pipelines. These pipelines, crucial in modeling diseases using high-dimensional microbiome data, involve selecting profile modalities, data preprocessing techniques, and classification algorithms, each impacting the model accuracy and generalizability. Despite whole metagenome shotgun sequencing (WMS) gaining popularity for human gut microbiome profiling, a consensus on the optimal methods for ML pipelines in disease diagnosis using WMS data remains elusive. Addressing this gap, we comprehensively evaluated ML methods for diagnosing Crohn's disease and colorectal cancer, using 2,553 fecal WMS samples from 21 case-control studies. Our study uncovered crucial insights: gut-specific, species-level taxonomic features proved to be the most effective for profiling; batch correction was not consistently beneficial for model performance; compositional data transformations markedly improved the models; and while nonlinear ensemble classification algorithms typically offered superior performance, linear models with proper regularization were found to be more effective for diseases that are linearly separable based on microbiome data. An optimal ML pipeline, integrating the most effective methods, was validated for generalizability using holdout data. This research offers practical guidelines for constructing reliable disease diagnostic ML models with fecal WMS data.

A metabolic switch orchestrated by IL-18 and the cyclic dinucleotide cGAMP programs intestinal tolerance.

Tissues are exposed to diverse inflammatory challenges that shape future inflammatory responses. While cellular metabolism regulates immune function, how metabolism programs and stabilizes immune states within tissues and tunes susceptibility to inflammation is poorly understood. Here, we describe an innate immune metabolic switch that programs long-term intestinal tolerance. Intestinal interleukin-18 (IL-18) stimulation elicited tolerogenic macrophages by preventing their proinflammatory glycolytic polarization via metabolic reprogramming to fatty acid oxidation (FAO). FAO reprogramming was triggered by IL-18 activation of SLC12A3 (NCC), leading to sodium influx, release of mitochondrial DNA, and activation of stimulator of interferon genes (STING). FAO was maintained in macrophages by a bistable switch that encoded memory of IL-18 stimulation and by intercellular positive feedback that sustained the production of macrophage-derived 2'3'-cyclic GMP-AMP (cGAMP) and epithelial-derived IL-18. Thus, a tissue-reinforced metabolic switch encodes durable immune tolerance in the gut and may enable reconstructing compromised immune tolerance in chronic inflammation.

Genome-resolved metagenomics: a game changer for microbiome medicine.

Recent substantial evidence implicating commensal bacteria in human diseases has given rise to a new domain in biomedical research: microbiome medicine. This emerging field aims to understand and leverage the human microbiota and derivative molecules for disease prevention and treatment. Despite the complex and hierarchical organization of this ecosystem, most research over the years has relied on 16S amplicon sequencing, a legacy of bacterial phylogeny and taxonomy. Although advanced sequencing technologies have enabled cost-effective analysis of entire microbiota, translating the relatively short nucleotide information into the functional and taxonomic organization of the microbiome has posed challenges until recently. In the last decade, genome-resolved metagenomics, which aims to reconstruct microbial genomes directly from whole-metagenome sequencing data, has made significant strides and continues to unveil the mysteries of various human-associated microbial communities. There has been a rapid increase in the volume of whole metagenome sequencing data and in the compilation of novel metagenome-assembled genomes and protein sequences in public depositories. This review provides an overview of the capabilities and methods of genome-resolved metagenomics for studying the human microbiome, with a focus on investigating the prokaryotic microbiota of the human gut. Just as decoding the human genome and its variations marked the beginning of the genomic medicine era, unraveling the genomes of commensal microbes and their sequence variations is ushering us into the era of microbiome medicine. Genome-resolved metagenomics stands as a pivotal tool in this transition and can accelerate our journey toward achieving these scientific and medical milestones.

Single-cell analysis reveals cellular and molecular factors counteracting HPV-positive oropharyngeal cancer immunotherapy outcomes.

BACKGROUND: Oropharyngeal squamous cell carcinoma (OPSCC) induced by human papillomavirus (HPV-positive) is associated with better clinical outcomes than HPV-negative OPSCC. However, the clinical benefits of immunotherapy in patients with HPV-positive OPSCC remain unclear. METHODS: To identify the cellular and molecular factors that limited the benefits associated with HPV in OPSCC immunotherapy, we performed single-cell RNA (n=20) and T-cell receptor sequencing (n=10) analyses of tonsil or base of tongue tumor biopsies prior to immunotherapy. Primary findings from our single-cell analysis were confirmed through immunofluorescence experiments, and secondary validation analysis were performed via publicly available transcriptomics data sets. RESULTS: We found significantly higher transcriptional diversity of malignant cells among non-responders to immunotherapy, regardless of HPV infection status. We also observed a significantly larger proportion of CD4+ follicular helper T cells (Tfh) in HPV-positive tumors, potentially due to enhanced Tfh differentiation. Most importantly, CD8+ resident memory T cells (Trm) with elevated KLRB1 (encoding CD161) expression showed an association with dampened antitumor activity in patients with HPV-positive OPSCC, which may explain their heterogeneous clinical outcomes. Notably, all HPV-positive patients, whose Trm presented elevated KLRB1 levels, showed low expression of CLEC2D (encoding the CD161 ligand) in B cells, which may reduce tertiary lymphoid structure activity. Immunofluorescence of HPV-positive tumors treated with immune checkpoint blockade showed an inverse correlation between the density of CD161+ Trm and changes in tumor size. CONCLUSIONS: We found that CD161+ Trm counteracts clinical benefits associated with HPV in OPSCC immunotherapy. This suggests that targeted inhibition of CD161 in Trm could enhance the efficacy of immunotherapy in HPV-positive oropharyngeal cancers. TRIAL REGISTRATION NUMBER: NCT03737968.

A Phase II Open-Label Randomized Clinical Trial of Preoperative Durvalumab or Durvalumab plus Tremelimumab in Resectable Head and Neck Squamous Cell Carcinoma.

PURPOSE: Clinical implications of neoadjuvant immunotherapy in patients with locally advanced but resectable head and neck squamous cell carcinoma (HNSCC) remain largely unexplored. PATIENTS AND METHODS: Patients with resectable HNSCC were randomized to receive a single dose of preoperative durvalumab (D) with or without tremelimumab (T) before resection, followed by postoperative (chemo)radiotherapy based on multidisciplinary discretion and 1-year D treatment. Artificial intelligence (AI)-powered spatial distribution analysis of tumor-infiltrating lymphocytes and high-dimensional profiling of circulating immune cells tracked dynamic intratumoral and systemic immune responses. RESULTS: Of the 48 patients enrolled (D, 24 patients; D+T, 24 patients), 45 underwent surgical resection per protocol (D, 21 patients; D+T, 24 patients). D±T had a favorable safety profile and did not delay surgery. Distant recurrence-free survival (DRFS) was significantly better in patients treated with D+T than in those treated with D monotherapy. AI-powered whole-slide image analysis demonstrated that D+T significantly reshaped the tumor microenvironment toward immune-inflamed phenotypes, in contrast with the D monotherapy or cytotoxic chemotherapy. High-dimensional profiling of circulating immune cells revealed a significant expansion of T-cell subsets characterized by proliferation and activation in response to D+T therapy, which was rare following D monotherapy. Importantly, expansion of specific clusters in CD8+ T cells and non-regulatory CD4+ T cells with activation and exhaustion programs was associated with prolonged DRFS in patients treated with D+T. CONCLUSIONS: Preoperative D±T is feasible and may benefit patients with resectable HNSCC. Distinct changes in the tumor microenvironment and circulating immune cells were induced by each treatment regimen, warranting further investigation.

Transcriptomic and metabolomic analysis unveils nanoplastic-induced gut barrier dysfunction via STAT1/6 and ERK pathways.

The widespread prevalence of micro and nanoplastics in the environment raises concerns about their potential impact on human health. Recent evidence demonstrates the presence of nanoplastics in human blood and tissues following ingestion and inhalation, yet the specific risks and mechanisms of nanoplastic toxicity remain inadequately understood. In this study, we aimed to explore the molecular mechanisms underlying the toxicity of nanoplastics at both systemic and molecular levels by analyzing the transcriptomic/metabolomic responses and signaling pathways in the intestines of mice after oral administration of nanoplastics. Transcriptome analysis in nanoplastic-administered mice revealed a notable upregulation of genes involved in pro-inflammatory immune responses. In addition, nanoplastics substantially reduced the expression of tight junction proteins, including occludin, zonula occluden-1, and tricellulin, which are crucial for maintaining gut barrier integrity and function. Importantly, nanoplastic administration increased gut permeability and exacerbated dextran sulfate sodium-induced colitis. Further investigation into the underlying molecular mechanisms highlighted significant activation of signaling transsducer and activator of transcription (STAT)1 and STAT6 by nanoplastic administration, which was in line with the elevation of interferon and JAK-STAT pathway signatures identified through transcriptome enrichment analysis. Additionally, the consumption of nanoplastics specifically induced nuclear factor kappa-B (NF-κB) and extracellular signal-regulated kinase (ERK)1/2 signaling pathways in the intestines. Collectively, this study identifies molecular mechanisms contributing to adverse effects mediated by nanoplastics in the intestine, providing novel insights into the pathophysiological consequences of nanoplastic exposure.

Integrative single-cell characterization of a frugivorous and an insectivorous bat kidney and pancreas.

Frugivory evolved multiple times in mammals, including bats. However, the cellular and molecular components driving it remain largely unknown. Here, we use integrative single-cell sequencing (scRNA-seq and scATAC-seq) on insectivorous (Eptesicus fuscus; big brown bat) and frugivorous (Artibeus jamaicensis; Jamaican fruit bat) bat kidneys and pancreases and identify key cell population, gene expression and regulatory differences associated with the Jamaican fruit bat that also relate to human disease, particularly diabetes. We find a decrease in loop of Henle and an increase in collecting duct cells, and differentially active genes and regulatory elements involved in fluid and electrolyte balance in the Jamaican fruit bat kidney. The Jamaican fruit bat pancreas shows an increase in endocrine and a decrease in exocrine cells, and differences in genes and regulatory elements involved in insulin regulation. We also find that these frugivorous bats share several molecular characteristics with human diabetes. Combined, our work provides insights from a frugivorous mammal that could be leveraged for therapeutic purposes.

The effects and challenges of alcohol use disorder peer support service in South Korea: A focus group study.

Although alcohol use disorder (AUD) is associated with a high disease burden due to medical consequences, people with AUD often do not receive appropriate treatment or community health service support. Peer-support services, intended to bridge the gap between patients with AUD and mental health providers have produced successful results. However, the number of studies on peer supporters in South Korea, especially those with AUD, is limited. Therefore, research on the effectiveness or challenges faced by AUD peer supporters in Korea is required to meet the challenges posed by increases in the numbers and capabilities of peer supporters in the mental health field. This qualitative study (N = 13), included focus group interviews of AUD peer supporters (n = 7), and supervisors of peer supporters (n = 6). Focus group data were analysed using thematic analysis methods. These analyses identified themes including 'Life before AUD peer supporters: The moment of change', 'Effects of peer support services: Working as peer supporters changed our lives', 'Challenges of peer supporter services: Things experienced as a team' and 'Future directions: How we can improve peer support services'? Findings indicate that healthcare professionals, educated to maintain clear boundaries, are discouraged from disclosing personal information or developing personal relationships with patients. On the other hand, peer supporters discuss personal histories and prioritize personal connections. By acting as comrades, peer supporters have positive effects. By sharing their experiences, peer supporters formed appropriate relationships with AUD patients and families resulting in more active engagement in treatment programmes and increased numbers of patients receiving outpatient treatment.

Gut Microbiome Wellness Index 2 for Enhanced Health Status Prediction from Gut Microbiome Taxonomic Profiles.

Recent advancements in human gut microbiome research have revealed its crucial role in shaping innovative predictive healthcare applications. We introduce Gut Microbiome Wellness Index 2 (GMWI2), an advanced iteration of our original GMWI prototype, designed as a robust, disease-agnostic health status indicator based on gut microbiome taxonomic profiles. Our analysis involved pooling existing 8069 stool shotgun metagenome data across a global demographic landscape to effectively capture biological signals linking gut taxonomies to health. GMWI2 achieves a cross-validation balanced accuracy of 80% in distinguishing healthy (no disease) from non-healthy (diseased) individuals and surpasses 90% accuracy for samples with higher confidence (i.e., outside the "reject option"). The enhanced classification accuracy of GMWI2 outperforms both the original GMWI model and traditional species-level α-diversity indices, suggesting a more reliable tool for differentiating between healthy and non-healthy phenotypes using gut microbiome data. Furthermore, by reevaluating and reinterpreting previously published data, GMWI2 provides fresh insights into the established understanding of how diet, antibiotic exposure, and fecal microbiota transplantation influence gut health. Looking ahead, GMWI2 represents a timely pivotal tool for evaluating health based on an individual's unique gut microbial composition, paving the way for the early screening of adverse gut health shifts. GMWI2 is offered as an open-source command-line tool, ensuring it is both accessible to and adaptable for researchers interested in the translational applications of human gut microbiome science.

Imputation of single-cell transcriptome data enables the reconstruction of networks predictive of breast cancer metastasis.

Single-cell transcriptome data provide a unique opportunity to explore the gene networks of a particular cell type. However, insufficient capture rate and high dimensionality of single-cell RNA sequencing (scRNA-seq) data challenge cell-type-specific gene network (CGN) reconstruction. Here, we demonstrated that the imputation of scRNA-seq data enables reconstruction of CGNs by effective retrieval of gene functional associations. We reconstructed CGNs for seven primary and nine metastatic breast cancer cell lines using scRNA-seq data with imputation. Key genes for primary or metastatic cell lines were prioritized based on network centrality measures and CGN hub genes that were presumed to be the major determinant of cell type characteristics. To identify novel genes in breast cancer metastasis, we used the average rank difference of centrality between the primary and metastatic cell lines. Genes predicted using CGN centrality analysis were more enriched for known breast cancer metastatic genes than those predicted using differential expression. The molecular chaperone CCT2 was identified as a novel gene for breast metastasis during knockdown assays of several candidate genes. Overall, our study demonstrated an effective CGN reconstruction technique with imputation of scRNA-seq data and the feasibility of identifying key genes for particular cell subsets using single-cell network analysis.

Integrative single-cell characterization of frugivory adaptations in the bat kidney and pancreas.

Frugivory evolved multiple times in mammals, including bats. However, the cellular and molecular components driving it remain largely unknown. Here, we used integrative single-cell sequencing on insectivorous and frugivorous bat kidneys and pancreases and identified key cell population, gene expression and regulatory element differences associated with frugivorous adaptation that also relate to human disease, particularly diabetes. We found an increase in collecting duct cells and differentially active genes and regulatory elements involved in fluid and electrolyte balance in the frugivore kidney. In the frugivorous pancreas, we observed an increase in endocrine and a decrease in exocrine cells and differences in genes and regulatory elements involved in insulin regulation. Combined, our work provides novel insights into frugivorous adaptation that also could be leveraged for therapeutic purposes.

scHumanNet: a single-cell network analysis platform for the study of cell-type specificity of disease genes.

A major challenge in single-cell biology is identifying cell-type-specific gene functions, which may substantially improve precision medicine. Differential expression analysis of genes is a popular, yet insufficient approach, and complementary methods that associate function with cell type are required. Here, we describe scHumanNet (https://github.com/netbiolab/scHumanNet), a single-cell network analysis platform for resolving cellular heterogeneity across gene functions in humans. Based on cell-type-specific gene networks (CGNs) constructed under the guidance of the HumanNet reference interactome, scHumanNet displayed higher functional relevance to the cellular context than CGNs built by other methods on single-cell transcriptome data. Cellular deconvolution of gene signatures based on network compactness across cell types revealed breast cancer prognostic markers associated with T cells. scHumanNet could also prioritize genes associated with particular cell types using CGN centrality and identified the differential hubness of CGNs between disease and healthy conditions. We demonstrated the usefulness of scHumanNet by uncovering T-cell-specific functional effects of GITR, a prognostic gene for breast cancer, and functional defects in autism spectrum disorder genes specific for inhibitory neurons. These results suggest that scHumanNet will advance our understanding of cell-type specificity across human disease genes.

Deletion of PD-1 destabilizes the lineage identity and metabolic fitness of tumor-infiltrating regulatory T cells.

Regulatory T (Treg) cells have an immunosuppressive function and highly express the immune checkpoint receptor PD-1 in the tumor microenvironment; however, the function of PD-1 in tumor-infiltrating (TI) Treg cells remains controversial. Here, we showed that conditional deletion of PD-1 in Treg cells delayed tumor progression. In Pdcd1fl/flFoxp3eGFP-Cre-ERT2(+/-) mice, in which both PD-1-expressing and PD-1-deficient Treg cells coexisted in the same tissue environment, conditional deletion of PD-1 in Treg cells resulted in impairment of the proliferative and suppressive capacity of TI Treg cells. PD-1 antibody therapy reduced the TI Treg cell numbers, but did not directly restore the cytokine production of TI CD8+ T cells in TC-1 lung cancer. Single-cell analysis indicated that PD-1 signaling promoted lipid metabolism, proliferation and suppressive pathways in TI Treg cells. These results suggest that PD-1 ablation or inhibition can enhance antitumor immunity by weakening Treg cell lineage stability and metabolic fitness in the tumor microenvironment.

HiFi metagenomic sequencing enables assembly of accurate and complete genomes from human gut microbiota.

Advances in metagenomic assembly have led to the discovery of genomes belonging to uncultured microorganisms. Metagenome-assembled genomes (MAGs) often suffer from fragmentation and chimerism. Recently, 20 complete MAGs (cMAGs) have been assembled from Oxford Nanopore long-read sequencing of 13 human fecal samples, but with low nucleotide accuracy. Here, we report 102 cMAGs obtained by Pacific Biosciences (PacBio) high-accuracy long-read (HiFi) metagenomic sequencing of five human fecal samples, whose initial circular contigs were selected for complete prokaryotic genomes using our bioinformatics workflow. Nucleotide accuracy of the final cMAGs was as high as that of Illumina sequencing. The cMAGs could exceed 6 Mbp and included complete genomes of diverse taxa, including entirely uncultured RF39 and TANB77 orders. Moreover, cMAGs revealed that regions hard to assemble by short-read sequencing comprised mostly genomic islands and rRNAs. HiFi metagenomic sequencing will facilitate cataloging accurate and complete genomes from complex microbial communities, including uncultured species.

COVID-GWAB: A Web-Based Prediction of COVID-19 Host Genes via Network Boosting of Genome-Wide Association Data.

Host genetics affect both the susceptibility and response to viral infection. Searching for host genes that contribute to COVID-19, the Host Genetics Initiative (HGI) was formed to investigate the genetic factors involved in COVID-19 via genome-wide association studies (GWAS). The GWAS suffer from limited statistical power and in general, only a few genes can pass the conventional significance thresholds. This statistical limitation may be overcome by boosting weak association signals through integrating independent functional information such as molecular interactions. Additionally, the boosted results can be evaluated by various independent data for further connections to COVID-19. We present COVID-GWAB, a web-based tool to boost original GWAS signals from COVID-19 patients by taking the signals of the interactome neighbors. COVID-GWAB takes summary statistics from the COVID-19 HGI or user input data and reprioritizes candidate host genes for COVID-19 using HumanNet, a co-functional human gene network. The current version of COVID-GWAB provides the pre-processed data of releases 5, 6, and 7 of the HGI. Additionally, COVID-GWAB provides web interfaces for a summary of augmented GWAS signals, prediction evaluations by appearance frequency in COVID-19 literature, single-cell transcriptome data, and associated pathways. The web server also enables browsing the candidate gene networks.

The nucleolus is the site for inflammatory RNA decay during infection.

Inflammatory cytokines are key signaling molecules that can promote an immune response, thus their RNA turnover must be tightly controlled during infection. Most studies investigate the RNA decay pathways in the cytosol or nucleoplasm but never focused on the nucleolus. Although this organelle has well-studied roles in ribosome biogenesis and cellular stress sensing, the mechanism of RNA decay within the nucleolus is not completely understood. Here, we report that the nucleolus is an essential site of inflammatory pre-mRNA instability during infection. RNA-sequencing analysis reveals that not only do inflammatory genes have higher intronic read densities compared with non-inflammatory genes, but their pre-mRNAs are highly enriched in nucleoli during infection. Notably, nucleolin (NCL) acts as a guide factor for recruiting cytosine or uracil (C/U)-rich sequence-containing inflammatory pre-mRNAs and the Rrp6-exosome complex to the nucleolus through a physical interaction, thereby enabling targeted RNA delivery to Rrp6-exosomes and subsequent degradation. Consequently, Ncl depletion causes aberrant hyperinflammation, resulting in a severe lethality in response to LPS. Importantly, the dynamics of NCL post-translational modifications determine its functional activity in phases of LPS. This process represents a nucleolus-dependent pathway for maintaining inflammatory gene expression integrity and immunological homeostasis during infection.