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Lai, W.

Publications and source records attributed to Lai, W..

At least 19 recordsLinked to original sources

Multi-modal in situ profiling of human cells using the AVITI24 platform characterizes single-cell responses to acute stress

Cells begin to respond to stress within minutes, but how this early response depends on cell type, neighboring cells, or cell cycle phase has been difficult to determine, because bulk assays average across cells and synchronization can induce stress. We measured 338 transcripts, 48 proteins and phosphoproteins, and six cell paint stains in situ in 1.18 million single cells from four human cell lines exposed to H2O2, alone and in co-culture, and in A549 cells exposed to TNF-, cadmium, dithiothreitol or lipopolysaccharide. In A549 cells, five distinct stressors changed the same targets in the same direction within 15 min, differing mainly in magnitude, and modestly increased JNK phosphorylation. By 30 min of H2O2 treatment, immediate-early genes (FOS, JUN, DDIT3, DUSP1, NR4A1) were induced most strongly in HCT116 and A549 cells. These changes occurred across the whole cell population rather than in a responsive subset. Cell line-specific differences persisted in co-culture, and the response of MCF7 cells did not increase with the number of adjacent, strongly-responding A549 cells. Ordering cells along the cell cycle from the same measurements identified 106 cycle-associated transcripts, and the response varied more between cell lines than among cell cycle phases. Overall, the measured regulators respond to stress within 30 minutes through a largely shared program whose magnitude depends more on cell identity than on cell cycle state or neighboring cell type.

genomics↗

DECIPHER integrates disentangled representation learning and prototype-based cell-type deconvolution across molecular modalities

The growing availability of single-cell resources creates new opportunities to extract cell-type-resolved information from the vast body of existing bulk omics data through cell-type deconvolution. Conventional deconvolution methods often rely on linear mixture models or specific probabilistic assumptions and can be sensitive to batch effects, whereas many deep-learning approaches are modality-specific or lack a unified end-to-end learning framework. Here, we developed DECIPHER, an end-to-end representation-learning framework for cell-type deconvolution that can be applied across multiple molecular modalities. DECIPHER learns a domain-constant representation (Zc) for deconvolution and a domain-specific representation (Zs) to model domain-associated variation. By integrating nonlinear representation learning with differentiable non-negative least-squares optimization, DECIPHER estimates cell-type proportions from Zc. Across simulated datasets, experimentally generated bulk-cell mixtures, real-world datasets, and multiple molecular modalities, DECIPHER showed robust and competitive cell-type deconvolution performance. Beyond cell-type proportion estimation, the learned Zc supported chronological age prediction across independent cohorts and prognostic stratification in lung adenocarcinoma, demonstrating that DECIPHER can transform high-dimensional bulk omics data into low-dimensional, biologically informative representations. DECIPHER thus broadens the utility of existing bulk omics resources for biological discovery and clinical research.

bioinformatics↗

Differential LncRNA expression profile in the plasma of preeclampsia and normal pregnancies

Background: Early detection of preeclampsia with noninvasive and reliable biomarkers is the foremost step for minimizing adverse effects during pregnancy. However, none maternal serum analyte evaluated so far is sufficiently accurate to allow recommending their routine use. Methods: Microarray was used to first identify differentially expressed LncRNA and mRNA. Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to determine bio-functions and signaling pathways. Quantitative real-time polymerase chain reaction was used to validate the results of microarray. Finally, the lncRNA-mRNA co-expression network was constructed to find the interaction of genes. Result: A total of 1476 differentially expressed lncRNAs and 594 mRNAs were identified. Both RNA-seq and RT-qPCR showed the expression of T241171,T338586, uc002ywy.3 was up-regulated, and the expression of ENST00000524858, T131416, T357032, uc.335+ was down regulated in the plasma of patients with preeclampsia. Pathway analysis showed the targeted mRNAs were enriched in apoptosis, sulfur metabolism, starch and sucrose metabolism. ceRNA network found that LncRNA-uc002ywy.3 might be the upstream regulator of miRNA-4498. LncRNA-uc002ywy.3/miRNA-4498 was predicted to interplay with genes involved in programmed cell death-1and its ligand (PD-1/PD-L1) pathway by KEGG analysis. Conclusion s PD-1/PD-L1 signaling pathway may be involved in the development of preeclampsia. The dysregulated LncRNA-uc002ywy.3/miRNA-4498 shed light on a new layer involved in the regulatory network of preeclampsia.

physiology↗

Integrative Multi-Omics Analysis of Gut Microbiota Dysbiosis and Host-Microbiome Interaction Mechanisms in Hypertension

ObjectiveTo characterize gut microbiota dysbiosis in hypertension and investigate its multilevel interactions with the host immune system. MethodsIntegrated multi-cohort microbiome data were used to evaluate microbial diversity, differential abundance, and co-occurrence network features between individuals with hypertension and healthy controls. The scBPS framework was applied to analyze microbiome-cell associations, enabling the resolution of relationships between key microbial taxa and functional states of immune cells at single-cell resolution. ResultsSeveral potentially protective genera reduced in hypertension and occupied central topological positions in the co-occurrence networks. Single-cell analyses further demonstrated that multiple key genera were closely associated with the functional states of monocytes and T cells (p<0.05). Specifically, Bacteroides and Bifidobacterium were associated with the proliferation and repair of classical monocytes; Butyricimonas showed a negative association with antigen processing and presentation pathways in monocytes; and Oscillospira promoted the transition of dnT cells toward an immunoregulatory state, suggesting its potential role in immune homeostasis. ConclusionsIntegrated multi-omics analyses reveal that hypertension-associated gut microbes may contribute to disease development through immune regulation, providing insights into microbiome-immune interaction mechanisms and potential targets for precision interventions.

microbiology↗

A unified framework for causal gene regulatory network inference grounded in orthogonal molecular evidence

Gene regulatory networks (GRNs) govern gene expression, cellular differentiation, and stable transcriptional states. Yet inferring GRNs that integrate molecular regulatory mechanisms and reproduce transcriptional states as stable outcomes remains a central challenge. Here we present SETIA, a framework that infers GRNs whose explicit dynamical models reproduce transcriptional profiles as one or more stable states across conditions. Applied to RNA-seq data from wild-type and transcription factor knockout strains in Saccharomyces cerevisiae, SETIA infers GRNs that accurately reproduce held-out transcriptional states in cross-validation experiments. Incorporating TF-promoter binding and protein-protein interaction priors, SETIA yields GRNs ranging from mechanistically grounded architectures to flexible models that capture indirect regulatory influences. SETIA reveals that gene expression organizes into discrete stable states that represent distinct transcriptional programs, all emerging as stable attractors of a single underlying GRN whose dynamics are predominantly explained by TF-DNA binding and protein-protein interactions from orthogonal molecular evidence. HighlightsO_LISETIA infers causal gene regulatory networks whose dynamics reproduce and generalize to held-out transcriptional profiles as stable attractors C_LIO_LIGenes occupy multiple discrete, reproducible expression states C_LIO_LIA ChIP-exo-derived protein-protein/DNA interaction network provides structural priors that ground the GRN in molecular mechanisms C_LIO_LIMolecular structural priors improve mechanistic interpretability while maintaining dynamical performance C_LIO_LISETIA generalizes across bulk and single-cell data and scales to a semi-genome-scale regulatory network C_LI

systems biology↗

eIF4G2-dependent translation restrains pancreatic cancer progression

Pancreatic ductal adenocarcinoma (PDA) is among the most lethal cancers, driven by cellular plasticity that fuels therapeutic resistance and early dissemination. The contribution of translational control to this plasticity remains poorly understood. Through an in vivo CRISPR/Cas9 screen, we identify the non-canonical initiation factor eIF4G2 (DAP5/NAT1) as a translational checkpoint restraining PDA progression. Loss of eIF4G2 accelerated tumor growth, induced poorly differentiated, basal-like histology, and triggered widespread metastasis. Ribosome profiling revealed that eIF4G2 loss does not alter bulk protein synthesis but instead impairs translation of a selective regulon, including tumor suppressors such as PTEN and CREBBP. Functional studies confirmed that PTEN loss was sufficient to drive dedifferentiation but insufficient to promote metastasis, implicating the broader eIF4G2-dependent program, including translational control of transcriptional regulators like CREBBP, in limiting dissemination. Consistently, eIF4G2-deficient tumors exhibited transcriptomic enrichment of programs related to migration and wound healing. Computational inference from human PDA datasets revealed reduced eIF4G2 activity in metastases, aligning with basal-like features and predicting poorer survival. These results support a model in which eIF4G2 maintains epithelial identity and restrains metastatic potential, highlighting selective translation as a determinant of PDA subtype and clinical outcome.

cancer biology↗

CNOT10 is involved in TTP-mediated AU-rich element containing mRNA metabolism, independent of mRNA decay regulation

Tristetraprolin (TTP)/ ZFP36 is an RNA binding protein that is involved in the turnover regulation of target adenylate-uridylate-rich RNA element (ARE) containing mRNAs. AREs are present in the 3-untranslated region of transcripts expressed from many immediate-early genes, including cytokines and chemokines. It has been demonstrated that TTP-mediated post-transcriptional mRNA decay regulation is crucial for modulating physiological control, particularly in response to inflammatory stimulation. TTP is associated with the CCR4-NOT deadenylation complex through the TTP C-terminus to promote mRNA decay. However, it is not fully understood whether there are additional sites within TTP that contribute to its function through interacting with other factors. We analyzed the functionality of the unique tryptophan residues located in the TTP N-terminus using a cell-based assay system that consists of a tetracycline-responsive CMV promoter-driven, intron-inserted luciferase (LUC). This system enabled us to analyze TTP activity during both the early phase and the steady-state phase of gene expression, as well as in the post-transcriptional mRNA decay following the treatment of tetracycline analogs. Meanwhile, we identified putative TTP associates using a proximity labeling method. We found that tryptophan residues in the TTP N-terminus together with CNOT10, a component of the CCR4-NOT complex, were involved specifically in the reduction of the ARE-containing LUC mRNA level during the early phase of gene expression. However, they were not involved in the decay of LUC mRNA in the steady-state phase. We propose a novel post-transcriptional TTP functionality in the reduction of ARE-containing mRNA level, which differs from the well-characterized mRNA decay activity.

molecular biology↗

The FERM Guild: A Differentially Correlated Microbial Module Drives Hypertension via Metabolic Flux Perturbations

BackgroundHypertension is a major risk factor for cardiovascular diseases, with changes in gut microbiota composition and function being closely associated with its onset and progression. However, the high inter-individual variability in gut microbiota complicates the identification of pathogenic mechanisms using traditional methods. In contrast, the smaller variability in gut microbial metabolites offers a more reliable and consistent basis for cross-individual comparisons. ResultsParsimonious Flux Balance Analysis (pFBA), integrated with Double Machine Learning (DoubleML), identified 17 metabolites significantly associated with hypertension (p < 0.05, RobustnessV alue > 0.1). These included meso-2,6-diaminoheptanedioate, p-hydroxyphenylacetic acid, cellobiose, dextran 40 (1,6--D-glucan), L-glutamic acid, kestopentaose, among others. Differential microbial correlation network analysis identified a key microbial sub-network, the FERM guild, consisting of 19 species, with prominent genera including Faecalibacterium,Enterobacter,Roseburia, and Methanobrevibacter. Using GSEA, the dysregulation of this guild was found to be strongly associated with a set of 17 hypertension-related metabolites(p = 0.017). Further analysis revealed that the contribution of FERM genus to key metabolites is significantly associated with blood pressure (p < 0.05), even without significant differences in their abundance; additionally, an imbalance exists between FERM genus and other species. ConclusionsOur findings reveal that hypertension is associated with a disruption of gut microbial diversity, structure, and metabolic function. Seventeen key metabolites related to blood pressure regulation were identified, exhibiting pro- or anti-hypertensive potential and linked to functional microbial modules. These results highlight the gut microbiota and its metabolites as promising targets for therapeutic intervention in hypertension.

bioinformatics↗

3-Phenylpropionic acid, a microbiota-derived polyphenol metabolite linked to hippuric acid, ameliorates colitis as a colon-enriched PPAR-γ agonist

Polyphenols are promising therapeutics for Crohns disease (CD), yet their bioavailability is largely dependent on the gut microbiome. The bioactive metabolites, metabolic pathways, and anti-inflammatory mechanisms underlying their effects remain poorly defined. We identified hippuric acid (HIPA) as a biomarker of polyphenol-microbiome interaction. HIPA is a meta-organismal metabolite produced through a cross-organ pathway that involves synthesis from gut microbial polyphenol metabolism via 3-phenylpropionic acid (3-PPA), followed by renal clearance. We found that CD is characterised by impaired polyphenol metabolism, resulting in deficient colonic 3-PPA and reduced serum hippuric acid relative to healthy individuals. Furthermore, we demonstrate that 3-PPA is a colon-enriched, mild PPAR-{gamma} agonist that ameliorates colitis in murine model. Systemic bioinformatic profiling of metagenomic data suggested the existence of undiscovered 3-PPA producers. Through targeted microbial incubation with polyphenols, we identified the probiotic Bifidobacterium breve and a lab isolate Escherichia coli FAH as novel 3-PPA producers. Collectively, our findings establish 3-PPA as a diet-dependent, colon-produced, mild PPAR-{gamma} agonist with the potential to minimise adverse effects associated with traditional PPAR-{gamma} agonists. This work establishes a foundation for developing microbiota-targeted, polyphenol-based therapeutic strategies for CD patients.

microbiology↗

Multi-omics characterization of the skin microbiota reveals the anti-aging roles of Stenotrophomonas maltophilia

Shifts in the skin microbiome have shown a close link to chronological age. However, the contribution of skin microbiome in skin aging phenotypes remains unclear. To explore this, we performed phenotypic, metabolomic, metagenomic, and functional analyses on a cohort with divergent skin aging phenotypes. Genome-scale metabolic models (GEMs) integrated with metabolomic analysis revealed that Stenotrophomonas maltophilia, enriched in the younger group (categorized by AI-predicted age and skin elasticity), utilizes the glutathione cycle to maintain redox homeostasis. Cellular experiments showed its metabolites enhanced GSH synthesis and alleviated oxidative stress-induced skin aging by upregulating key genes in fibroblasts, including GCLM, PGD, SOD2, and NQO1. Additionally, GEMs highlighted its potential anti-aging roles in regulating host metabolic pathways involving betaine, lysolecithin, and porphyrin. In parallel, Acinetobacter guillouiae was found to influence host melanin metabolism by degrading dopamine (DA) and 3-methoxytyramine (3-MT), offering potential therapeutic strategies for mitigating pigmentation. Our findings highlight the dynamic interplay between skin microbiota and the host in aging, offering new insights for designing targeted anti-aging interventions.

microbiology↗

OmniCorr: An R-package for visualizing putative host-microbiota interactions using multi-omics data

Holo-omics leverages omics datasets to explore the interactions between hosts and their associated microbiomes. Although the generation of omics data from matching host and microbiome samples is steadily increasing, there remains a scarcity of computational tools capable of integrating and visualizing this data to facilitate the interpretation and prediction of host-microbiota interactions. We present OmniCorr, an R package designed to: (1) manage the complexity of omics data by clustering similar observations into modules, (2) visualize correlations of these modules across different omics layers, host-microbiota interfaces, and meta-data, and (3) identify statistically significant associations indicative of putative host-microbiota interactions. OmniCorrs utility is demonstrated using datasets from two systems: (i) Atlantic salmon, integrating host transcriptomics with metagenomics and metatranscriptomics to explore dietary impacts, and (ii) cattle, combining host proteomics with metaproteomics and metabolomics to investigate methane emission variability. Availability and ImplementationOmniCorr is freely available at: https://github.com/shashank-KU/OmniCorr.

bioinformatics↗

Extreme high-diversity barcoded HIV library enables single cell multi-omics analysis of viral expression and fate determination

HIV-1 persists as a global health challenge due to its ability to establish latent reservoirs that evade eradication by antiretroviral therapy (ART). This study introduces a high-diversity barcoded HIV-1 library combined with a novel peptide barcode system to enable single-cell multi-dimensional analysis of viral integration, transcription, splicing, and translation of distinct viral mRNA isoform. Our method revealed over 80 distinct HIV-1 splicing isoforms and uncovered significant temporal variation in mRNA diversity, driven by integration site and chromatin context. By linking HIV-1 5UTR with peptide barcoding, we quantified the translation efficiency of alternative 5' UTRs and demonstrated correlations with abundance of each viral mRNA isoform. Additionally, we showed that latency reversal agents, such as SAHA and Bryostatin, selectively reactivate proviruses depending on their genomic and chromatin context and leads to distinct viral RNA splicing program. This platform revealed the diversity of HIV-infected cells at levels of transcripts as well as at proteins, and subsequently the fate determination. The detailed understanding of the stochastic diversity and dynamic nature of HIV-1 latency, inform the development of targeted therapies to eliminate latent reservoirs.

microbiology↗

Protozoal populations drive system-wide variation in the rumen microbiome

While rapid progress has been made to characterize the bacterial and archaeal populations of the rumen microbiome, insight into how they interact with keystone protozoal species remains elusive. Here, we reveal two distinct rumen community types (RCT-A and RCT-B) that are not strongly associated with host phenotype nor genotype but instead linked to protozoal community patterns. We leveraged a series of multi-omic datasets to show that the dominant Epidinium spp. in animals with RCT-B employ a plethora of fiber-degrading enzymes that present enriched Prevotella spp. a favorable carbon landscape to forage upon. Conversely, animals with RCT-A, dominated by genera Isotricha and Entodinium, harbor a more even distribution of fiber, protein, and amino acid metabolizers, reflected by higher detection of metabolites from both protozoal and bacterial activity. We reveal microbiome variation across key protozoal and bacterial populations is interlinked, which should act as an important consideration for future development of microbiome-based technologies.

microbiology↗

Influenza Virus Enhances Bacterial Growth in the Lungs through Weakening Capsule Receptor-Mediated Bacterial Capture by Neutrophils

Certain serotypes of encapsulated bacteria are more associated with severe pneumonia than others, but the biological reason behind this association has remained largely unknown. This study has uncovered a causal relationship between capsule serotype and virulence phenotype of Streptococcus pneumoniae, a leading cause of community-acquired pneumonia, in mice using isogenic bacteria. Certain high-virulence (HV) serotypes persist in the lungs and lead to severe pneumonia, whereas the low-virulence (LV) serotypes are effectively cleared. The capsule type-dependent difference in virulence is determined by the capsule-binding receptors, which recognize the capsular polysaccharides of the LV serotypes (but not the HV serotypes) and enable pathogen capture by neutrophils through complement activation. The capsule receptor-driven immunity of lung neutrophils is significantly compromised by pre-infection of influenza virus, which enhances the survival and virulence of the LV S. pneumoniae serotypes, and recurs in the heightened pathogenicity of Haemophilus influenzae. This study not only unravels the long-sought mechanistic mystery of serotype-dependent virulence in bacterial lung infections, but also provides an explanation for the well-known pathogenesis synergism between influenza virus and respiratory bacteria. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=160 SRC="FIGDIR/small/622589v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@1464164org.highwire.dtl.DTLVardef@16f34e5org.highwire.dtl.DTLVardef@1f9ad46org.highwire.dtl.DTLVardef@df104d_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

Deciphering the Gut Microbiome's Influence on Depression: A Community-Level Constraint-Based Metabolic modelling Approach

BackgroundDepression is a common mental disorder worldwide, and its pathogenesis remains incompletely understood. However, increasing evidence suggests that gut microbiota play a significant role in the development of depression through the gut-brain-microbiota axis. However, due to the substantial individual variability in gut microbiota, while metabolic functions and metabolites between individuals are relatively similar, analyzing functional profiles and metabolic products yields more accurate results. ResultsIn this study, gut microbiota abundance data were collected from 354 depression patients and 5575 healthy individuals in the GMrepo v2 database. After matching for age and BMI, 271 paired samples from each group were retained. Alpha diversity analysis revealed significant differences between the two groups in the Observed, Shannon, and Chao1 indices, while beta diversity analysis indicated only subtle differences in gut microbiota composition. LEfSe analysis identified 23 differential species, with 18 enriched in the healthy group and 5 in the depression group. Further alpha diversity analysis of reaction abundance showed significant differences in the Observed and Chao1 indices, while beta diversity analysis did not reveal significant differences in reaction abundance. Differential and enrichment analyses identified 89 reactions that were significantly different between the groups, which were enriched in 4 metabolic pathways. Wilcoxon signed-rank test of COBRA-predicted metabolic fluxes revealed significant differences in the fluxes of 21 metabolites. Although the abundance of six species did not differ significantly between the two groups, their contributions to metabolic fluxes were significantly different. Mediation analysis indicated that gut microbiota influence the progression of depression by modulating various metabolites. ConclusionsThis study combined species abundance with COBRA metabolic flux predictions and mediation analysis, identified several differential species, as well as multiple differential metabolic fluxes, such as Cu2+ and L-Dopa, which were attributed to species like Dorea. Species such as Acholeplasma and Acidovorax were found to influence the progression of depression by affecting various metabolites, including Cu2+. These findings contribute to a deeper understanding of the relationship between gut microbiota and depression, offering new directions for the diagnosis and treatment of depression.

bioinformatics↗

Systematic Mining of gut microbiota biomarkers for IBD

BackgroundInflammatory bowel disease (IBD), encompassing ulcerative colitis (UC) and Crohns disease (CD), represents a chronic inflammatory condition with an incompletely understood etiology. Emerging evidence suggests that alterations in gut microbiota composition play a pivotal role in disease development. Here, we leveraged gut fecal metagenomic data (3044 samples: 2248 IBD and 796 healthy) from publicly available sources to explore microbiome biomarkers related to IBD to provide new ideas for clinical treatment. ResultsOur analyses revealed marked disparities in microbial species abundance and composition between IBD and healthy samples in both male and female subjects from the United States, whereas such distinctions were absent in subjects from Spain. Hierarchical and systematic investigations uncovered microbial phyla, such as Verrucomicrobia and Firmicutes, associated with IBD in US cohorts. Furthermore, we identified 127 highly correlated pathways with these differential microbes, covering functions such as peptidoglycan biosynthesis III, dTDP-L-rhamnose biosynthesis I, starch degradation, and glucose-1-phosphate degradation. Gut microbial metabolites were predicted based on metagenomic data using the MelonnPan workflow and 16 metabolites with significant differences were identified that collectively contribute to energy metabolism, digestion, skin integrity and general bodily function. ConclusionsThis study identified differences in microbial species and metabolic pathways related to Inflammatory Bowel Disease (IBD) through hierarchical and systematic analysis, which can aid in the clinical diagnosis of IBD. Pathways such as Peptidoglycan Biosynthesis III and dTDP-L-Rhamnose Biosynthesis I are associated with the generation of bacterial cell walls, and disruptions in these pathways affect bacterial activity, leading to an imbalance in the hosts gut microbiota. Based on the hypotheses regarding the pathogenesis of IBD derived from the above mining results, it provides a theoretical basis for selecting precise treatment options.

bioinformatics↗

MIST: an interpretable and flexible deep learning framework for single-T cell transcriptome and receptor analysis

Joint analysis of transcriptomic and T cell receptor (TCR) features at single-cell resolution provides a powerful approach for in-depth T cell immune function research. Here, we introduce a deep learning framework for single-T cell transcriptome and receptor analysis, MIST (Multi-Insight for T cell). MIST features three latent spaces: gene expression, TCR, and a joint latent space. Through analyses of antigen- specific T cells and T cells related to lung cancer immunotherapy, we demonstrate MISTs interpretability and flexibility. MIST easily and accurately resolves cell function and antigen-specificity by vectorizing and integrating transcriptome and TCR data of T cells. In addition, using MIST, we identified the heterogeneity of CXCL13+ subsets in lung cancer infiltrating CD8+ T cells and their association with immunotherapy, providing additional insights into the functional transition of CXCL13+ T cells related to anti-PD-1 therapy that were not reported in the original study. MIST is available at https://github.com/aapupu/MIST.

bioinformatics↗

Histone deacetylase 1 and 2 play essential roles in alveolar macrophage homeostasis

Alveolar macrophages (AMs) are the primary lung-resident macrophages that play a pivotal role in pathogen clearance and surfactant homeostasis. Although the molecular regulation of AM development at the transcriptional level has been more widely studied, the epigenetic mechanisms that maintain AM homeostasis remain incompletely understood. Here, we demonstrate that histone deacetylases HDAC1 and HDAC2 play essential roles in preserving AM integrity. Dual deletion of HDAC1 and HDAC2 resulted in severe defect in AM development, leading to pulmonary alveolar proteinosis (PAP) and increased susceptibility to influenza infection in mice. Mechanistically, HDAC1 and HDAC2 act through their deacetylase activity to suppress the expression of pro-apoptotic protein Bim, thereby enhancing cell survival. Furthermore, HDAC1 and HDAC2 form a functional complex with the histone demethylase LSD1 (KDM1A) to co-regulate a transcriptional program governing AM differentiation. Our findings identify HDAC1 and HDAC2 as central epigenetic regulators essential for alveolar macrophage homeostasis.

immunology↗