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Spatiotemporal diversity in molecular and functional abnormalities in the mdx dystrophic brain.

Duchenne muscular dystrophy (DMD) is characterized by progressive muscle degeneration and neuropsychiatric abnormalities. Loss of full-length dystrophins is both necessary and sufficient to initiate DMD. These isoforms are expressed in the hippocampus, cerebral cortex (Dp427c), and cerebellar Purkinje cells (Dp427p). However, our understanding of the consequences of their absence, which is crucial for developing targeted interventions, remains inadequate. We combined RNA sequencing with genome-scale metabolic modelling (GSMM), immunodetection, and mitochondrial assays to investigate dystrophic alterations in the brains of the mdx mouse model of DMD. The cerebra and cerebella were analysed separately to discern the roles of Dp427c and Dp427p, respectively. Investigating these regions at 10 days (10d) and 10 weeks (10w) followed the evolution of abnormalities from development to early adulthood. These time points also encompass periods before onset and during muscle inflammation, enabling assessment of the potential damage caused by inflammatory mediators crossing the dystrophic blood-brain barrier. For the first time, we demonstrated that transcriptomic and functional dystrophic alterations are unique to the cerebra and cerebella and vary substantially between 10d and 10w. The common anomalies involved altered numbers of retained introns and spliced exons across mdx transcripts, corresponding with alterations in the mRNA processing pathways. Abnormalities in the cerebra were significantly more pronounced in younger mice. The top enriched pathways included those related to metabolism, mRNA processing, and neuronal development. GSMM indicated dysregulation of glucose metabolism, which corresponded with GLUT1 protein downregulation. The cerebellar dystrophic transcriptome, while significantly altered, showed an opposite trajectory to that of the cerebra, with few changes identified at 10 days. These late defects are specific and indicate an impact on the functional maturation of the cerebella that occurs postnatally. Although no classical neuroinflammation markers or microglial activation were detected at 10 weeks, specific differences indicate that inflammation impacts DMD brains. Importantly, some dystrophic alterations occur late and may therefore be amenable to therapeutic intervention, offering potential avenues for mitigating DMD-related neuropsychiatric defects.

Animals↗

Developing a disease-specific accessible transcriptional signature as a biomarker for ataxia with oculomotor apraxia type 2.

BACKGROUND: Genetic ataxias are clinically heterogenous neurodegenerative conditions often involving rare or private mutations and it is often difficult to assign pathogenicity to rare gene variants solely based on DNA sequencing. An effective functional assay from an easy-to-obtain biospecimen would aid this assessment and be of high clinical value. SETX encodes a ubiquitous DNA/RNA helicase crucial for resolving R-loops and maintaining genome stability. Loss-of-function mutations cause a recessive disorder, Ataxia with Oculomotor Apraxia Type 2 (AOA2). METHODS: Here we utilize Weighted Gene Co-expression Network Analysis (WGCNA) from patient blood to construct an AOA2-specific transcriptomic signature as a biomarker to evaluate SETX variants in patients clinically suspected of having AOA2. RESULTS: WGCNA from peripheral blood RNA of 11 AOA2 patients from 7 families initially identified a single gene module that was modestly effective in distinguishing individuals with AOA2 from controls (sensitivity 73%, specificity 97%) and was able to robustly differentiate AOA2 patients from those with genetically distinct, yet phenotypically similar, neurological disorders (sensitivity 100%, specificity 100%). An independent derivation of the transcriptional biomarker identified a dual module model that was able to better distinguish individuals with AOA2 from controls (sensitivity 100%, specificity 97%). As validation, we examined a second cohort of 21 patients from 13 families and demonstrate that this dual module transcriptional biomarker could discriminate patients clinically suspected of AOA2 from controls (57%, 95%CI: 34%-78%). Overall, the transcriptional biomarker was able to separate AOA2 subjects (n = 32) from controls (n = 35) with 72% sensitivity and 97% specificity. Notably, this transcriptomic biomarker enabled verification of the first pathogenic SETX mutation found in a non-canonical transcript, expanding the spectrum of mutations that contribute to AOA2. CONCLUSIONS: Our study identified a transcriptional biomarker that was able to differentiate AOA2 from controls and from other related neurological disorders, consequently expanding the spectrum of known pathogenic mutations. This proof-of-concept study illustrates that transcriptional biomarkers may be used to validate variants of uncertain significance in known genetic diseases.

Humans↗

LungGENIE: the lung gene-expression and network imputation engine.

BACKGROUND: Few cohorts have study populations large enough to conduct molecular analysis of ex vivo lung tissue for genomic analyses. Transcriptome imputation is a non-invasive alternative with many potential applications. We present a novel transcriptome-imputation method called the Lung Gene Expression and Network Imputation Engine (LungGENIE) that uses principal components from blood gene-expression levels in a linear regression model to predict lung tissue-specific gene-expression. METHODS: We use paired blood and lung RNA sequencing data from the Genotype-Tissue Expression (GTEx) project to train LungGENIE models. We replicate model performance in a unique dataset, where we generated RNA sequencing data from paired lung and blood samples available through the SUNY Upstate Biorepository (SUBR). We further demonstrate proof-of-concept application of LungGENIE models in an independent blood RNA sequencing data from the Genetic Epidemiology of COPD (COPDGene) study. RESULTS: We show that LungGENIE prediction accuracies have higher correlation to measured lung tissue expression compared to existing cis-expression quantitative trait loci-based methods (median Pearson's r = 0.25, IQR 0.19-0.32), with close to half of the reliably predicted transcripts being replicated in the testing dataset. Finally, we demonstrate significant correlation of differential expression results in chronic obstructive pulmonary disease (COPD) from imputed lung tissue gene-expression and differential expression results experimentally determined from lung tissue. CONCLUSION: Our results demonstrate that LungGENIE provides complementary results to existing expression quantitative trait loci-based methods and outperforms direct blood to lung results across internal cross-validation, external replication, and proof-of-concept in an independent dataset. Taken together, we establish LungGENIE as a tool with many potential applications in the study of lung diseases.

Humans↗

Identification and characterization of the HSP gene family in the Chinese giant salamander: Expression patterns under combined environmental stress.

BACKGROUND: The Chinese giant salamander (Andrias davidianus) is a critically endangered living fossil species that is highly sensitive to changes in water temperature. However, systematic studies on the heat shock protein (HSP) gene family and its response mechanisms to environmental stress in this species remain limited. This study utilized transcriptome data from captive-bred salamanders exposed to combined temperature and pathogen stress. Bioinformatics tools were employed to identify the HSP gene family of A. davidianus (AndHSP) and to analyze their evolution, structure, and function, thereby revealing their regulatory mechanisms in response to environmental stress. RESULTS: A total of 72 AndHSPs were identified and classified into five subfamilies. Phylogenetic analysis revealed that each subfamily is evolutionarily conserved and functionally related. Gene expression analysis demonstrated that pathogen infection induced the expression of AndHSPs, and elevated temperature significantly intensified this response. Nine key differentially expressed genes were identified, predominantly from the AndHSP70 subfamily, with AndHSP70-18 exhibiting rapid heat-induced expression. Tissue-specific analysis showed high expression of AndHSP60 in the spleen. A qPCR validation confirmed the reliability of the transcriptome expression results. CONCLUSIONS: This study presents the first systematic identification of the AndHSP gene family and elucidates its cooperative stress response mechanisms under combined temperature and pathogen stress. These findings provide a molecular basis for understanding the species' environmental adaptation and have important implications for its conservation and artificial breeding.

Animals↗

Integrated multi-omics analysis of fluoroquinolone tolerance mechanisms induced by enrofloxacin in Pasteurella multocida.

BACKGROUND: The global prevalence of multidrug-resistant bacteria has been rising at an alarming rate, posing a serious threat to both human and animal health. However, the mechanisms by which bacteria acquire antibiotic tolerance and subsequently develop resistance remain incompletely understood. METHODS: In this study, Pasteurella multocida, a common pathogen in the animal husbandry industry, was exposed to enrofloxacin, and genome resequencing, transcriptomic, and metabolomic analyses were performed to elucidate the adaptive mechanisms of P. multocida under fluoroquinolone-induced stress. RESULTS: Compared with the wild-type strain, the enrofloxacin-tolerant strain exhibited an extended lag phase, a prolonged logarithmic phase, reduced sensitivity to polymyxin B, reduced biofilm formation, and an elongated cellular morphology. Multi-omics analysis revealed a deletion in the dusB gene of the tolerant strain, resulting in a truncated non-functional protein. The deletion of dusB enhanced tolerance by prolonging the lag phase and reducing the growth rate. Moreover, the expression of genes in the CAMP pathway was up-regulated, and deletion of cpxR further promoted tolerance by modulating ribosome-associated genes. Integrated transcriptomic and metabolomic analyses indicated activation of the tricarboxylic acid (TCA) cycle during tolerance development. CONCLUSION: This study identified dusB and cpxR as key genes mediating enrofloxacin tolerance in P. multocida, elucidated the association between the antibiotic tolerance, growth, and gene expression, and may provide potential targets for future strategies aimed at limiting tolerance-associated resistance development.

Enrofloxacin↗

Construction of a prognostic model for gastric cancer based on immune infiltration and microenvironment, and exploration of MEF2C gene function.

BACKGROUND: Advanced gastric cancer (GC) exhibits a high recurrence rate and a dismal prognosis. Myocyte enhancer factor 2c (MEF2C) was found to contribute to the development of various types of cancer. Therefore, our aim is to develop a prognostic model that predicts the prognosis of GC patients and initially explore the role of MEF2C in immunotherapy for GC. METHODS: Transcriptome sequence data of GC was obtained from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO) and PRJEB25780 cohort for subsequent immune infiltration analysis, immune microenvironment analysis, consensus clustering analysis and feature selection for definition and classification of gene M and N. Principal component analysis (PCA) modeling was performed based on gene M and N for the calculation of immune checkpoint inhibitor (ICI) Score. Then, a Nomogram was constructed and evaluated for predicting the prognosis of GC patients, based on univariate and multivariate Cox regression. Functional enrichment analysis was performed to initially investigate the potential biological mechanisms. Through Genomics of Drug Sensitivity in Cancer (GDSC) dataset, the estimated IC50 values of several chemotherapeutic drugs were calculated. Tumor-related transcription factors (TFs) were retrieved from the Cistrome Cancer database and utilized our model to screen these TFs, and weighted correlation network analysis (WGCNA) was performed to identify transcription factors strongly associated with immunotherapy in GC. Finally, 10 patients with advanced GC were enrolled from Sun Yat-sen University Cancer Center, including paired tumor tissues, paracancerous tissues and peritoneal metastases, for preparing sequencing library, in order to perform external validation. RESULTS: Lower ICI Score was correlated with improved prognosis in both the training and validation cohorts. First, lower mutant-allele tumor heterogeneity (MATH) was associated with lower ICI Score, and those GC patients with lower MATH and lower ICI Score had the best prognosis. Second, regardless of the T or N staging, the low ICI Score group had significantly higher overall survival (OS) compared to the high ICI Score group. For its mechanisms, consistently, for Camptothecin, Doxorubicin, Mitomycin, Docetaxel, Cisplatin, Vinblastine, Sorafenib and Paclitaxel, all of the IC50 values were significantly lower in the low ICI Score group compared to the high ICI Score group. As a result, based on univariate and multivariate Cox regression, ICI Score was considered to be an independent prognostic factor for GC. And our Nomogram showed good agreement between predicted and actual probabilities. Based on CIBERSORT deconvolution analysis, there was difference of immune cell composition found between high and low ICI Score groups, probably affecting the efficacy of immunotherapy. Then, MEF2C, a tumor-related transcription factor, was screened out by WGCNA analysis. Higher MEF2C expression is significantly correlated with a worse OS. Moreover, its higher expression is also negatively correlated with tumor mutation burden (TMB) and microsatellite instability (MSI), but positively correlated with several immunosuppressive molecules, indicating MEF2C may exert its influence on tumor development by upregulating immunosuppressive molecules. Finally, based on transcriptome sequencing data on 10 paired tumor tissues from Sun Yat-sen University Cancer Center, MEF2C expression was significantly lower in paracancerous tissues compared to tumor tissues and peritoneal metastases, and it was also lower in tumor tissues compared to peritoneal metastases, indicating a potential positive association between MEF2C expression and tumor invasiveness. CONCLUSIONS: Our prognostic model can effectively predict outcomes and facilitate stratification GC patients, offering valuable insights for clinical decision-making. The identified transcription factor MEF2C can serve as a biomarker for assessing the efficacy of immunotherapy for GC.

Humans↗

Multiomics analysis reveals that senescent CXCL16+ macrophages promote lung adenocarcinoma progression through TGF-β signalling.

BACKGROUND: Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer and remains a leading cause of cancer-related mortality worldwide. Although, immunotherapy has become a cornerstone of first-line treatment, only 20-30% of patients achieve a durable clinical benefit, largely because of the complexity and heterogeneity of the tumour immune microenvironment. Emerging evidence indicates that cellular senescence, particularly within immune cells, contributes to tumour progression by impairing antitumour immunity; however, its mechanistic role in LUAD remains incompletely understood. METHODS: We performed an integrative multiomics analysis incorporating genome-wide association studies (GWASs), bulk RNA sequencing, single-cell RNA sequencing, and spatial transcriptomics to characterize immune heterogeneity in LUAD. Cellular senescence was validated by performing staining for senescence-associated β-galactosidase and the canonical markers p16 and p21. SHAP analysis was applied to evaluate the contribution of CXCL16+ macrophages. Functional roles were assessed using coculture assays, in vitro and in vivo tumour models, orthotopic tumour implantation, and multiplex immunofluorescence staining of clinical specimens. RESULTS: A summary data-based on Mendelian randomization analysis integrating GWAS and TCGA data identified CXCL16 as a senescence-associated gene that is causally linked to the LUAD risk. Single-cell RNA sequencing revealed that CXCL16 is predominantly expressed in macrophages, and the pseudotime analysis together with β-galactosidase staining confirmed its association with macrophage senescence. Spatial transcriptomics and immunofluorescence staining showed the marked enrichment of CXCL16+ macrophages in LUAD tissues. The cell-cell communication analysis further revealed a strong association between the number of CXCL16+ macrophages and the activation of the TGF-β signalling pathway within the tumour microenvironment. Functionally, CXCL16+ macrophages promoted LUAD progression via TGF-β signalling, as validated in vitro and in subcutaneous and orthotopic tumour models. Molecular dynamics simulations additionally suggested that LUAD patients with high levels of CXCL16+ macrophage infiltration may exhibit increased sensitivity to bosutinib. CONCLUSIONS: CXCL16 promotes macrophage senescence, and senescent CXCL16+ macrophages drive LUAD progression through TGF-β signalling. These findings identify CXCL16+ macrophages as a biologically and therapeutically relevant immune cell population, highlighting a potential target for precision intervention in LUAD.

Humans↗

MOADE: a multimodal autoencoder for dissociating bulk multi-omics data.

In single cell biology, the complexity of tissues may hinder lineage cell mapping or tumor microenvironment decomposition, requiring digital dissociation of bulk tissues. Many deconvolution methods focus on transcriptomic assay, not easily applicable to other omics due to ambiguous cell markers and reference-to-target difference. Here, we present MOADE, a multimodal autoencoder pipeline linking multi-dimensional features to jointly predict personalized multi-omic profiles and cellular compositions, using pseudo-bulk data constructed by internal non-transcriptomic reference and external scRNA-seq data. MOADE is evaluated through rigorous simulation experiments and real multi-omic data from multiple tissue types, outperforming nine deconvolution pipelines with superior generalizability and fidelity.

Humans↗

MEANtools integrates multi-omics data to identify metabolites and predict biosynthetic pathways.

During evolution, plants have developed the ability to produce a vast array of specialized metabolites, which play crucial roles in helping plants adapt to different environmental niches. However, their biosynthetic pathways remain largely elusive. In the past decades, increasing numbers of plant biosynthetic pathways have been elucidated based on approaches utilizing genomics, transcriptomics, and metabolomics. These efforts, however, are limited by the fact that they typically adopt a target-based approach, requiring prior knowledge. Here, we present MEANtools, a systematic and unsupervised computational integrative omics workflow to predict candidate metabolic pathways de novo by leveraging knowledge of general reaction rules and metabolic structures stored in public databases. In our approach, possible connections between metabolites and transcripts that show correlated abundance across samples are identified using reaction rules linked to the transcript-encoded enzyme families. MEANtools thus assesses whether these reactions can connect transcript-correlated mass features within a candidate metabolic pathway. We validate MEANtools using a paired transcriptomic-metabolomic dataset recently generated to reconstruct the falcarindiol biosynthetic pathway in tomato. MEANtools correctly anticipated five out of seven steps of the characterized pathway and also identified other candidate pathways involved in specialized metabolism, which demonstrates its potential for hypothesis generation. Altogether, MEANtools represents a significant advancement to integrate multi-omics data for the elucidation of biochemical pathways in plants and beyond.

Metabolomics↗

Genetic variation influences food-sharing sociability in honey bees.

Individual variation in sociability is a central feature of every society. This includes honey bees, with some individuals well connected and sociable, and others at the periphery of their colony's social network. However, the genetic and molecular bases of sociability are poorly understood. Trophallaxis-a behavior involving sharing liquid with nutritional and signaling properties-comprises a social interaction and a proxy for sociability in honey bee colonies: more sociable bees engage in more trophallaxis. Here, we identify genetic and molecular mechanisms of trophallaxis-based sociability by combining genome sequencing, brain transcriptomics, and automated behavioral tracking. A genome-wide association study (GWAS) identified 18 single nucleotide polymorphisms (SNPs) associated with variation in sociability. Several SNPs were localized to genes previously associated with sociability in other species, including in the context of human autism, suggesting shared molecular mechanisms of sociability. Variation in sociability also was linked to differential brain gene expression, particularly genes associated with neural signaling and development. Using comparative genomic and transcriptomic approaches, we also detected evidence for divergent mechanisms underpinning sociability across species, including those related to reward sensitivity and encounter probability. These results highlight both potential evolutionary conservation of the molecular roots of sociability and points of divergence.

Animals↗

Integrative omics analysis identifies biomarkers of septic cardiomyopathy.

Septic Cardiomyopathy (SCM) is a syndrome of acute cardiac dysfunction in septic patients, unrelated to cardiac ischemia. Multiomics studies including transcriptomics and proteomics have provided new insights into the mechanisms of SCM. In here, a rat model of SCM was established by intraperitoneal injection of lipopolysaccharide (LPS). Biomarkers of SCM were characterized via a multi-omics analysis. The differentially expressed (DE) mRNAs predominantly appeared in pathways linked to the immune response, inflammatory response, and the complement and coagulation cascades, while DE proteins were mainly enriched in pathways associated with the complement and coagulation cascades. On this basis, the integrated analysis was performed between transcriptome and proteome. The potential biomarkers were further verified by RT-qPCR and WB. The current proteotranscriptomic research has furnished a valuable dataset and fresh perspectives that will enhance our comprehension of the development of SCM. This, in turn, is expected to expedite the formulation of novel approaches for the prevention and management of SCM in patients.

Cardiomyopathies↗

Enterocutaneous Fistula-Associated Sepsis and Mortality: Development and Validation of a Multimodal Artificial Intelligence Prediction Model.

BACKGROUND: Predicting enterocutaneous fistula (ECF)-associated sepsis and mortality poses significant challenges in digital health care due to the disease's complexity and heterogeneous clinical manifestations. Current approaches that rely on single-modal data or traditional scoring systems often fail to capture the intricate immune-inflammatory dynamics and multisystem involvement in patients with ECF. OBJECTIVE: This study aims to develop an artificial intelligence (AI)-driven multimodal fusion model integrating clinical, imaging, and transcriptomic data for early prediction of ECF-associated sepsis and 28-day mortality, addressing the limitations of conventional single-dimensional models. METHODS: This study leveraged publicly available datasets (Medical Information Mart for Intensive Care III [MIMIC-III], electronic Intensive Care Unit [eICU], and The Cancer Genome Atlas) to construct a multimodal framework. Clinical parameters were processed using Extreme Gradient Boosting, abdominal imaging features were extracted via convolutional neural networks, and transcriptomic profiles were analyzed with variational autoencoders. A Transformer-based fusion network was employed for joint prediction and validated through cross-validation and external testing. Key features were identified using Shapley Additive Explanations and Local Interpretable Model-Agnostic Explanations interpretability algorithms, while immune regulatory mechanisms were explored via weighted gene co-expression network analysis. RESULTS: The multimodal model achieved an area under the curve (AUC) of 0.89 for predicting sepsis and 28-day mortality, outperforming unimodal models (clinical-only model, AUC 0.72, and imaging-only model, AUC 0.78). Critical predictors included Sequential Organ Failure Assessment score, lactate levels, intra-abdominal free fluid on imaging, and immunoregulatory genes (programmed death-ligand 1 [PD-L1] and indoleamine 2,3-dioxygenase 1 [IDO1]). Mechanistic analysis revealed distinct immune reprogramming in patients with sepsis, characterized by increased regulatory T cells and M2 macrophages, along with downregulated cluster of differentiation 8+ (CD8+) T cells. CONCLUSIONS: This multimodal AI model offers an innovative digital solution in medical informatics, enabling precise early risk stratification for ECF-associated sepsis. By integrating multisource data and providing interpretable insights into immune-inflammatory pathways, the model enhances health care quality for patients with ECF and paves the way for personalized intervention strategies.

Humans↗

Integrative single-cell and genomic analysis reveals NMB as a driver of metastatic adaptation in esophageal squamous cell carcinoma via metabolic rewiring and immune evasion.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) has high mortality, and metastasis is the leading cause of patient death. Neuromedin B (NMB) promotes tumor development in various cancers, yet its role in ESCC metastasis remains unclear. METHODS: We integrated single-cell transcriptomic data from matched primary and metastatic ESCC lesions (GSE309392) with bulk transcriptomic cohorts from TCGA and GSE53624. In silico gene perturbation, ligand-receptor communication analysis, and single-cell prognostic model construction were performed, followed by functional validation through siRNA-mediated NMB knockdown in TE-1 and KYSE30 cell lines. RESULTS: NMB was identified as a key gene enriched in metastatic ESCC lesions, and its high expression was associated with coordinated upregulation of oxidative phosphorylation pathway genes and aldo-keto reductase family antioxidant enzymes (AKR1C1, AKR1C2, AKR1B10). Genomic analysis revealed that NMB-high tumors carried a higher clonal mutation burden and a markedly increased frequency of NFE2L2 activating mutations (23% vs. 8%, P = 0.04). In silico knockout and correlation analysis identified AKR1C1 as a downstream effector of NMB. NMB expression was negatively correlated with CD8+ T cell and activated NK cell infiltration. CellChat analysis revealed communication between NMB-positive cells and monocytes via the TGM2-ADGRG1 axis, and specifically detected IFNG signaling. In the single-cell prognostic model, NMB-positive cells accounted for 50% of the high-risk group but only 20% of the low-risk group. TCGA-based survival analysis demonstrated that high NMB expression was associated with shorter overall survival (HR = 2.98, P = 0.03). In vitro NMB-targeted RNA interference markedly inhibited proliferation, colony formation, and migration in TE-1 and KYSE30 cells. CMap screening identified the endothelin-PDE5-cGMP axis as a potential therapeutic target. CONCLUSION: NMB serves as a key driver of metastatic adaptation in ESCC, conferring a survival advantage to tumor cells during metastatic colonization through genomic evolution and immune remodeling, with metabolic adaptation as a downstream consequence of genomic alterations.

NMB↗

Integrated multi-omics profiling identifies aging-related molecular signatures and convergent interferon signaling in systemic lupus erythematosus.

BACKGROUND: Systemic lupus erythematosus (SLE) is characterized by chronic immune activation and molecular alterations that overlap with aging-related biological processes. However, how these alterations are organized across molecular layers and whether they converge on shared regulatory networks remain incompletely understood. METHODS: We performed an integrative multi-omics analysis combining in-house proteomic and phosphoproteomic data from 130 patients with SLE and 90 healthy controls (HCs) and publicly available transcriptomic datasets comprising 1,461 SLE patients. Proteins and phosphorylation sites were annotated using established aging-related gene resources. Differential protein abundance and phosphorylation changes were analyzed across disease-status and disease-activity comparisons. Nominal P-value thresholds were used for exploratory feature selection, whereas FDR-adjusted P values were used to assess robustness after multiple-testing correction. Kinase-substrate enrichment, transcription factor annotation, and cell-type-resolved transcriptomic comparison were used to explore potential regulatory programs. RESULTS: We identified 128 nominally altered proteins annotated to aging-related biological processes, including genomic instability, mitochondrial dysfunction, and epigenetic alterations. Phosphoproteomic analysis revealed 36 nominally altered phosphorylation sites, including previously unreported sites in IFI16 (S153, S780) and PKCδ (S507, S664). Clustering analysis demonstrated heterogeneous protein co-regulation patterns across disease states. Kinase activity inference suggested altered activity of TBK1 and IKKβ. TF analysis further highlighted STAT1, RELA, and PML as potential central nodes within the inferred regulatory network. Notably, these multi-omic alterations were not randomly distributed but showed convergence toward shared signaling pathways, particularly those related to interferon responses. CONCLUSIONS: This integrative multi-omics study identifies inflammatory and interferon-dominated molecular alterations in SLE PBMCs that overlap with aging-related biological processes and converge on shared regulatory networks. These findings provide a hypothesis-generating framework for investigating the intersection between chronic immune activation and aging-related molecular remodeling in SLE.

Humans↗

SSB deficiency-induced R-loop accumulation triggers podocyte inflammation in DKD.

INTRODUCTION: Diabetic kidney disease (DKD) is fundamentally a podocytopathy in which sterile inflammation plays a central pathogenic role, yet the upstream triggers that initiate inflammatory cascades in podocytes remain elusive. R-loops are critical regulators of genomic stability, and their pathological accumulation triggers DNA damage and innate immune activation. Whether R-loop dysregulation contributes to podocyte-driven inflammation in DKD is unknown. METHODS: We integrated single-cell transcriptomic profiling, dual machine learning algorithms, and functional experiments to dissect the R-loop regulatory network in the diabetic kidney. RESULTS: Integrated analysis of human diabetic kidney single-cell RNA-seq data revealed a globally compromised R-loop regulatory network selectively within podocytes. Intersection of podocyte-specific transcriptomic shifts with validated R-loop regulators identified 93 candidate genes, from which dual machine learning algorithms pinpointed SSB (Sjögren syndrome antigen B) as the principal podocyte-selective R-loop resolver and a superior diagnostic biomarker (AUC = 0.983). SSB expression was selectively downregulated in diabetic podocytes and showed the strongest positive correlation with the R-loop resolution module. Mechanistically, SSB loss impaired RNA splicing and stability pathways, leading to aberrant R-loop accumulation that activated the cGAS-dependent inflammatory signaling in podocytes. In two murine DKD models and high glucose-challenged podocytes, SSB was markedly reduced. Remarkably, SSB knockdown in podocytes alone sufficed to trigger R-loop accumulation and pro-inflammatory cytokine expression, whereas both RNase H1-mediated R-loop removal and cGAS co-depletion blunted this response. DISCUSSION: These findings suggest that an SSB-governed R-loop -cGAS -inflammatory signaling axis may link genomic instability to podocyte inflammation and contribute to DKD progression, nominating R-loop homeostasis as a previously unrecognized potential therapeutic target.

Podocytes↗

Characterization of carbon metabolism in a highly adhesive bacterium Acinetobacter sp. Tol 5 capable of assimilating diverse hydrocarbons and aromatic compounds.

Sustainable bioproduction requires developing robust microbial chassis with broad metabolic versatility and suitability for industrial applications. Acinetobacter sp. Tol 5 is a highly adhesive bacterium capable of utilizing various hydrocarbons, making it a promising chassis candidate for immobilized whole-cell catalysis. In this study, we characterized the carbon metabolism of Tol 5 by reconstructing metabolic pathway maps from its genomic data and analyzing the transcriptomes of cells grown on ethanol, hexadecane, toluene, and phenol. Genomic analysis revealed that Tol 5 has limited capacity for sugar utilization but possesses a wide range of metabolic pathways for alkane and aromatic compounds, including five distinct aromatic degradation routes that expand the known metabolic diversity of the genus Acinetobacter. Transcriptome analysis identified the specific pathway genes induced in response to each carbon source. During growth on phenol, alkylbenzene degradation genes were upregulated alongside phenol monooxygenase genes, suggesting possible substrate-dependent cross-regulation between aromatic degradation pathways. Gene disruption experiments indicated that phenol monooxygenase is required for phenol assimilation, whereas toluene dioxygenase may contribute to earlier entry into exponential growth while potentially limiting final biomass accumulation. These findings provide a comprehensive view of the carbon metabolism of Tol 5 and a basis for assessing its potential in bioprocesses using non-sugar carbon sources.

Acinetobacter↗

Multi-omics approaches in idiopathic pulmonary fibrosis: from molecular mechanisms to therapeutic targets and precision medicine.

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease with limited therapeutic options and marked molecular heterogeneity. Despite available antifibrotic therapies, disease progression remains poorly predictable, highlighting the need for improved mechanistic understanding and therapeutic targeting. This review summarizes recent advances in multi-omics research to elucidate the molecular mechanisms underlying IPF and to identify potential biomarkers and pharmacological targets. Multi-omics studies, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiome profiling, and single-cell sequencing, have revealed key pathogenic mechanisms in IPF. Genetic susceptibility factors such as MUC5B promoter variants and telomere-related genes contribute to disease risk. Epigenetic regulation, including DNA methylation, histone modifications, and non-coding RNAs, plays a central role in fibrotic remodeling. Transcriptomic and proteomic analyses have identified dysregulated signaling pathways, including TGF-β, mTOR, cellular senescence, and extracellular matrix remodeling. Metabolomic alterations indicate disrupted lipid and amino acid metabolism. Importantly, integration of multi-omics datasets enables the identification of molecular endotypes, candidate biomarkers, and potential therapeutic targets. However, challenges including data integration, tissue heterogeneity, limited cohort size, and the need for functional validation remain important barriers to clinical translation. Continued development of multi-omics approaches may facilitate more accurate disease classification and support the development of personalized therapeutic strategies for IPF.

biomarkers↗

Comprehensive characterization of the genes in AP2/ERF family and their involvement in salt-alkali stress response during Nelumbo nucifera seed germination.

Nelumbo nucifera Gaertn. is an economically and ecologically important aquatic plant, but its growth and productivity are severely constrained by soil salinization and alkalization. AP2/ERF transcription factors are key regulators of plant abiotic stress responses; however, their roles in salt-alkali tolerance in N. nucifera remain largely unclear. In this study, we performed a genome-wide identification and characterization of the AP2/ERF gene family in N. nucifera, followed by phylogenetic, structural, and physicochemical analyses. A total of 101 AP2/ERF genes were identified and classified into five subfamilies, showing both evolutionary conservation and species-specific divergence compared with Arabidopsis thaliana. Physiological analyses during seed germination under salt-alkali stress revealed significant changes in malondialdehyde content, proline accumulation, and antioxidant enzyme activities, suggesting activation of oxidative stress defense and osmotic adjustment mechanisms. Transcriptome profiling of seedlings treated with 150 mM salt-alkali solution for 5 and 10 days identified 7,350 differentially expressed genes, including 29 AP2/ERF members responsive to stress. Among them, 13 genes, including AP2-9, ERF23, ERF15, ERF31, ERF34, and DREB21, were consistently upregulated under both treatments, indicating their potential roles in stress adaptation. qRT-PCR validation further confirmed the sustained upregulation of key genes AP2-9, ERF23, ERF34, and DREB21, consistent with transcriptome data. Overall, this study provides the first comprehensive overview of the AP2/ERF gene family in N. nucifera and identifies candidate regulators involved in salt-alkali stress responses, offering valuable insights into the molecular mechanisms of stress adaptation and potential genetic resources for breeding salt-alkali tolerant aquatic plants.

AP2/ERF transcription factors↗