From Adipose Tissue to Alveoli: Bioinformatic Mapping of Obesity-Associated Lung Injury Pathways

From Adipose Tissue to Alveoli: Bioinformatic Mapping of Obesity-Associated Lung Injury Pathways | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report From Adipose Tissue to Alveoli: Bioinformatic Mapping of Obesity-Associated Lung Injury Pathways Luis Jesuino de Oliveira Andrade, Gabriela Correia Matos de Oliveira, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7729527/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: Obesity represents a systemic inflammatory state predisposing individuals to enhanced acute respiratory distress syndrome susceptibility, yet molecular mechanisms linking adipose tissue dysfunction to lung injury remain poorly characterized. Objective: To establish a bioinformatics framework mapping molecular pathways connecting obesity-associated adipose tissue dysfunction to lung injury susceptibility through cross-tissue transcriptomic analysis. Methods: We analyzed publicly available 23 high-quality datasets from GEO database comprising 1,247 adipose tissue samples (683 obese, 564 lean) and 834 lung samples (445 ARDS patients, 389 controls). Differential expression analysis employed limma-voom and DESeq2 frameworks with study-specific blocking factors. Gene Set Enrichment Analysis utilized human-specific databases with significance thresholds of FDR q1.0. Protein-protein interaction networks were constructed using STRING database with confidence scores >0.7. Statistical analyses included hypergeometric tests for pathway overlap, meta-analysis with random-effects models, and logistic regression for clinical correlations. External validation employed three independent cohorts with forest plot analysis and Egger's regression for publication bias assessment. Results: Analysis identified 2,847 dysregulated genes in obese adipose tissue and 1,892 in lung injury samples, with 347 genes commonly altered (p<0.001). Key hub genes included IL6, TNF, STAT3, and NFKB1, orchestrating inflammatory cascades. Cross-tissue analysis revealed 43 shared pathways, predominantly TNF-NF-κB signaling (NES=2.84) and IL6-STAT3 pathways (NES=2.63). The inflammatory pathway score correlated with mechanical ventilation duration (r=0.67, p<0.001) and predicted 30-day mortality (OR=2.34, 95% CI: 1.45-3.78). Conclusion: Our results demonstrated that obesity-induced adipose inflammation promotes lung injury susceptibility through TNF-NF-κB and IL6-STAT3 pathway activation, revealing therapeutic targets for respiratory complications. Internal Medicine Obesity Transcriptomics Lung injury Bioinformatics Figures Figure 1 Figure 2 Figure 3 INTRODUCTION The escalating global obesity epidemic has fundamentally transformed respiratory medicine, establishing adipose tissue as an active endocrine organ whose inflammatory mediators profoundly influence pulmonary homeostasis. 1 Obesity constitutes a systemic inflammatory state wherein proinflammatory mediators produced in adipose tissue predispose individuals to enhanced susceptibility to acute respiratory distress syndrome (ARDS) and other forms of lung injury. 2 This paradigm shift necessitates comprehensive understanding of molecular pathways through which adipose tissue-derived factors modulate alveolar epithelial cell function. 3 Despite established clinical associations between obesity and respiratory pathology, critical knowledge gaps persist regarding the mechanistic connections between adipose tissue dysfunction and lung injury. While obesity represents a confirmed risk factor for ARDS, 4,5 paradoxical findings demonstrate improved survival rates in obese patients with established lung injury compared to normal-weight individuals. 6 , 7 The precise molecular mechanisms underlying these complex relationships remain poorly characterized, particularly the transcriptomic signatures that bridge adipose inflammation and alveolar epithelial pathology. Furthermore, current understanding lacks integrative frameworks to comprehensively map the molecular networks connecting systemic metabolic dysfunction to localized pulmonary pathology. The application of bioinformatics approaches to decipher obesity-associated lung injury pathways addresses the inherent complexity of multi-organ crosstalk and limitations of traditional methodologies. Recent advances in spatial transcriptomics and single-cell RNA sequencing have revolutionized our understanding of tissue-specific molecular signatures. 8 , 9 Transcriptomic profiling of adipose tissue has revealed distinct inflammatory profiles in obesity that may influence distant organ systems, 10 while spatial analysis of lung tissue provides unprecedented insights into cellular interactions during injury and repair processes. 11 The central of this study question focuses on identifying specific molecular pathways through which obesity-associated adipose tissue dysfunction contributes to enhanced lung injury susceptibility, emphasizing transcriptomic signatures that connect adipose inflammation to alveolar pathology. Understanding these mechanisms is critical for developing targeted therapeutic interventions that address both metabolic and respiratory dysfunction simultaneously. METHODOLOGY Study Design and Data Sources This retrospective bioinformatics study utilized transcriptomic data from published human studies to investigate molecular pathways connecting obesity-associated adipose tissue inflammation to lung injury mechanisms. A systematic search of the Gene Expression Omnibus (GEO) database was conducted to identify relevant datasets from peer-reviewed publications examining gene expression profiles in human adipose tissue from obese versus lean individuals, and lung tissue from patients with acute lung injury or ARDS versus healthy controls. Search terms included "obesity," "adipose tissue," "lung injury," "ARDS," "human," and "transcriptome" with publication dates from 2010 to 2024. Dataset Selection Criteria and Subject Characteristics Human datasets were selected based on predefined inclusion criteria: (1) original research articles with raw transcriptomic data deposited in GEO; (2) studies involving adult human subjects (≥18 years); (3) clearly defined obesity status (BMI ≥30 kg/m²) or lung injury diagnosis according to Berlin criteria for ARDS 12 ; (4) minimum sample size of 10 subjects per group; and (5) comprehensive clinical metadata including demographic and anthropometric data. Exclusion criteria encompassed studies with pediatric populations, mixed animal-human data, or insufficient clinical phenotyping. Selected datasets underwent thorough review of original publications to extract relevant clinical characteristics including age, sex, BMI, comorbidities, and lung injury severity scores. Clinical Data Integration and Patient Stratification Clinical metadata from original studies were systematically extracted and harmonized across datasets. Obesity classification followed World Health Organization criteria (BMI ≥30 kg/m²), 13 while lung injury severity was assessed using available clinical scores including Sequential Organ Failure Assessment (SOFA) 14 or Acute Physiology and Chronic Health Evaluation (APACHE) II 15 scores when reported in source publications. Patient stratification considered relevant confounding variables including age, sex, diabetes status, and smoking history based on information available in original study reports. Transcriptomic Data Processing from Published Studies Raw transcriptomic data from selected human studies underwent standardized preprocessing protocols. For microarray datasets (Affymetrix, Illumina platforms), raw CEL or IDAT files were processed using platform-specific normalization methods including Robust Multi-array Average (RMA) for Affymetrix arrays and quantile normalization for Illumina BeadChips. RNA-sequencing datasets were reprocessed from raw FASTQ files using a unified pipeline: quality assessment with FastQC, adapter trimming via Trimmomatic, alignment to human reference genome GRCh38 using HISAT2, and gene quantification with featureCounts. Batch effects between different studies were identified through principal component analysis and corrected using the ComBat-seq algorithm. Human Tissue-Specific Differential Expression Analysis Differential gene expression analysis was performed separately for human adipose tissue (subcutaneous and visceral when specified) comparing obese versus lean individuals, and lung tissue comparing ARDS patients versus controls. Linear mixed-effects models implemented in limma-voom framework accounted for potential study-specific effects while identifying consistent expression changes across datasets. For RNA-seq data, DESeq2 was employed with study as a blocking factor. Statistical significance thresholds were set at adjusted p-value < 0.05, and absolute log2 fold change ≥ 1.0, ensuring robust identification of clinically relevant expression differences. Cross-Tissue Pathway Analysis Using Human Data Pathway enrichment analysis utilized gene sets from human-specific databases including KEGG Homo sapiens pathways, Reactome human pathways, and Gene Ontology biological processes. Gene Set Enrichment Analysis (GSEA) was conducted using ranked gene lists from human adipose tissue and lung tissue comparisons, with significance assessed at FDR q-value 1.0. Cross-tissue pathway overlap analysis identified shared inflammatory and metabolic pathways between obese adipose tissue and injured lung tissue from human studies. Human Protein-Protein Interaction Network Construction Protein-protein interaction (PPI) networks were constructed using human-specific databases including STRING (Homo sapiens), BioGRID, and Human Protein Reference Database. Only experimentally validated interactions with confidence scores > 0.7 were included to ensure biological relevance. Network topology analysis identified hub genes based on degree centrality and betweenness centrality metrics, with particular focus on genes consistently altered across multiple human studies. Validation Using Independent Human Cohorts External validation employed independent human datasets not used in primary analysis, when available in GEO database. Meta-analysis approaches using random-effects models assessed consistency of gene expression changes across different human populations and study designs. Publication bias was evaluated using funnel plots and Egger's regression test for key genes identified in the analysis. Clinical Relevance Assessment Identified molecular signatures were evaluated for clinical relevance by correlating gene expression patterns with available clinical outcomes from original studies, including length of mechanical ventilation, intensive care unit (ICU) stay duration, and mortality when reported. Pathway scores derived from human data were tested for associations with clinically relevant endpoints using logistic regression models adjusted for age, sex, and comorbidity burden. Statistical Analysis Statistical analyses employed R 4.3.0 (public domain software). Continuous variables were assessed using ANOVA with Tukey's post-hoc tests; categorical variables used chi-square or Fisher's exact tests. Differential expression utilized limma-voom and DESeq2 with Benjamini-Hochberg FDR correction (p<0.05). Hypergeometric tests assessed pathway overlaps. Meta-analysis employed random-effects models with I² heterogeneity assessment. Network topology calculated degree and betweenness centrality metrics. Clinical correlations used Pearson or Spearman coefficients with logistic regression modeling. Ethical Considerations All analyses utilized publicly available, de-identified human data from previously published studies that received appropriate institutional review board approval as reported in original publications. Therefore, it was not necessary to have the study reviewed by the ethics committee. RESULTS Systematic search of the GEO database using predefined criteria yielded 847 initial datasets from peer-reviewed publications spanning 2010-2024. Following rigorous screening and application of inclusion criteria, 23 high-quality datasets were selected for analysis, comprising 12 adipose tissue studies encompassing 1,247 human subjects (683 obese individuals with BMI ≥30 kg/m² and 564 lean controls) and 11 lung tissue datasets containing 834 samples (445 from ARDS/acute lung injury patients and 389 healthy controls). Selected studies utilized diverse transcriptomic platforms including 13 RNA-sequencing and 10 microarray datasets, with robust quality metrics demonstrating RNA-seq mapping rates exceeding 82% and microarray signal-to-noise ratios above 12, wan Comprehensive clinical metadata extraction revealed well-characterized patient populations across both tissue types. Adipose tissue studies included subjects with mean age 47.3 ± 8.4 years (obese) and 43.7 ± 9.1 years (lean controls), with 62% female representation. Obese cohorts demonstrated mean BMI of 34.2 ± 4.7 kg/m² versus 22.1 ± 2.3 kg/m² in lean controls (p<0.001). Lung injury datasets encompassed patients with mean age 58.1 ± 12.4 years, 43% female representation, and ARDS severity distribution of 34% mild, 48% moderate, and 18% severe cases according to Berlin criteria. Clinical severity scores revealed mean APACHE II scores of 18.4 ± 6.2 and SOFA scores of 8.7 ± 3.1. Comorbidity analysis identified diabetes mellitus in 28.3% of obesity studies and 31.7% of lung injury cohorts, while smoking history was documented in 34% and 67% of subjects, respectively (Table 2, 3 and 4). Standardized preprocessing protocols successfully harmonized heterogeneous transcriptomic datasets across multiple platforms and studies. RNA-sequencing datasets demonstrated median mapping rates of 87.3% (range: 82.1-94.6%) with mean sequencing depths of 42.8 million reads per sample. Gene detection sensitivity ranged from 12,847 to 18,234 expressed genes per RNA-seq dataset. Microarray platforms exhibited robust quality metrics with median signal-to-noise ratios of 15.7 (range: 12.3-21.4) and background-corrected intensities exceeding detection thresholds in >85% of probes, capturing 11,245 to 15,892 detectable transcripts. Principal component analysis revealed minimal within-study batch effects, while ComBat-seq correction effectively addressed inter-study variability. Gene symbol mapping achieved 94.7% concordance across platforms after application of updated annotation databases (Graphic 1). Differential expression analysis identified 2,847 significantly dysregulated genes in obese adipose tissue compared to lean controls (adjusted p<0.05, |log2FC|≥1.0), with 1,523 upregulated and 1,324 downregulated transcripts. Key upregulated genes included proinflammatory mediators (IL6, TNF, IL1B), chemokines (CCL2, CXCL10), and matrix remodeling factors (MMP9, MMP2). Conversely, 1,892 genes were significantly altered in lung tissue from ARDS patients versus healthy controls, comprising 1,147 upregulated and 745 downregulated genes. Notable upregulated transcripts encompassed inflammatory response genes (STAT3, NFKB1, IRF7), epithelial barrier dysfunction markers (OCLN, TJP1), and acute phase response proteins (SAA1, CRP). Cross-tissue comparison revealed 347 genes commonly dysregulated between obese adipose tissue and injured lung tissue, representing significant molecular overlap (hypergeometric test, p<0.001) (Graphic 2). Gene Set Enrichment Analysis identified 127 significantly enriched pathways in obese adipose tissue (FDR q1.0), with inflammatory response pathways demonstrating the highest enrichment scores: TNF signaling via NF-κB (NES=2.84), inflammatory response (NES=2.71), and IL6-JAK-STAT3 signaling (NES=2.63). Lung injury samples exhibited 94 significantly enriched pathways, including epithelial-mesenchymal transition (NES=2.91), complement cascade (NES=2.45), and interferon-α response (NES=2.38). Cross-tissue pathway overlap analysis revealed 43 shared pathways between obese adipose tissue and injured lung tissue, predominantly involving inflammatory cascades (TNF signaling, IL6-STAT3, complement activation), oxidative stress responses, and lipid metabolism dysregulation. Metabolic pathway analysis demonstrated significant downregulation of fatty acid oxidation (NES=-1.87) and oxidative phosphorylation (NES=-2.12) in both tissue types (Figure 1). PPI network analysis of commonly dysregulated genes yielded a densely connected network comprising 284 nodes and 1,567 edges (average degree=11.04). Network topology analysis identified key hub genes based on centrality metrics: IL6 (degree=34, betweenness=0.087), TNF (degree=31, betweenness=0.092), STAT3 (degree=28, betweenness=0.074), and NFKB1 (degree=26, betweenness=0.083). Molecular complex detection identified five distinct functional modules: (1) cytokine-mediated inflammatory signaling (23 genes), (2) transcriptional regulation of immune response (18 genes), (3) extracellular matrix remodeling (15 genes), (4) complement cascade activation (12 genes), and (5) lipid metabolism regulation (11 genes). Network centralization analysis revealed a scale-free topology (R²=0.912), indicating the presence of highly connected hub genes driving cross-tissue pathological communication (Figure 2). External validation using three independent datasets (GSE73034, GSE47460, GSE32540) confirmed the consistency of identified gene signatures across different populations and study designs. Meta-analysis of key hub genes demonstrated robust effect sizes: IL6 (pooled log2FC=1.47, 95% CI: 1.23-1.71, p<0.001), TNF (pooled log2FC=1.32, 95% CI: 1.08-1.56, p<0.001), and ADIPOQ (pooled log2FC=-0.89, 95% CI: -1.15 to -0.63, p<0.001) (Table 5). Forest plots revealed consistent directionality across all validation cohorts with minimal between-study heterogeneity (I²0.05 for all tested genes). Clinical correlation analysis revealed significant associations between identified molecular signatures and patient outcomes. The inflammatory pathway score (derived from IL6, TNF, STAT3 expression) demonstrated strong positive correlation with mechanical ventilation duration (r=0.67, p<0.001) and ICU length of stay (r=0.58, p=0.003). Logistic regression models adjusted for age, sex, and comorbidity burden showed that high inflammatory pathway scores were independently associated with increased 30-day mortality risk (OR=2.34, 95% CI: 1.45-3.78, p<0.001). The adipokine dysregulation score (based on LEP, ADIPOQ, RETN expression) correlated significantly with ARDS severity (Spearman's ρ=0.52, p<0.001) and demonstrated predictive value for prolonged mechanical ventilation (AUC=0.73, 95% CI: 0.65-0.81) (Graphic 4). Integration of differential expression and network analyses revealed a distinct mechanistic pathway connecting obesogenic adipose tissue inflammation to lung injury susceptibility. The identified signaling cascade initiates with inflammatory adipocyte activation, characterized by elevated expression of proinflammatory cytokines (IL6↑, TNF↑, IL1B↑) and dysregulated adipokine production (LEP↑, ADIPOQ↓, RETN↑). These inflammatory mediators activate downstream transcriptional programs in pulmonary epithelial cells through NF-κB (NFKB1↑, RELA↑) and STAT3 signaling pathways. Subsequent upregulation of chemokine receptors (CXCR4↑, CCR5↑) and matrix metalloproteinases (MMP9↑, MMP2↑) promotes epithelial barrier dysfunction and enhanced inflammatory cell recruitment, ultimately increasing lung injury susceptibility. This mechanistic framework was supported by pathway enrichment analysis showing coordinated activation of TNF-NF-κB signaling (p<0.001) and IL6-STAT3 pathways (p<0.001) across both tissue compartments (Figure 3). DISCUSSION Our study with a comprehensive bioinformatics analysis demonstrates that obesity-induced adipose tissue inflammation creates molecular susceptibility to ARDS through coordinated TNF-NF-κB and IL6-STAT3 pathway activation. We demonstrate that cross-tissue transcriptomic mapping identified shared dysregulated genes with inflammatory pathway scores correlating significantly with clinical outcomes, establishing precision therapeutic targets for obesity-associated respiratory complications. While existing literature predominantly focuses on mechanical aspects of obesity's pulmonary impact, recent investigations suggest adipose tissue inflammation significantly contributes to respiratory dysfunction through molecular mechanisms. 1 Clinical studies consistently demonstrate obesity as a significant risk factor for acute respiratory distress syndrome development. 16 However, comprehensive transcriptomic inflammatory mediators create complex cross-tissue signaling networks. 17 Our systematic analysis of twenty-three high-quality datasets substantially expands this understanding by providing molecular evidence for obesity-associated respiratory pathology mechanisms. Current literature consistently emphasizes the importance of detailed clinical phenotyping in metabolic and pulmonary research. While published studies often report demographic and anthropometric data distributions in obesity-related adipose investigations, lung injury datasets typically prioritize severity stratification. 18 The clinical metadata analysis confirms established distinctions in adipose tissue profiles between obese and lean subjects, consistent with previous studies highlighting increased inflammation and metabolic alterations in obesity. 19 Likewise, lung injury severity classified by ARDS criteria aligns well with validated clinical scores, reinforcing their prognostic value. 20 Our results align with established studies demonstrating that well-characterized populations are fundamental for biomarker discovery. The demographic and comorbidity profiles we describe, particularly the expected prevalence of metabolic comorbidities in obesity studies and smoking history in lung injury cohorts, demonstrate the known epidemiology of these conditions, thereby validating the clinical relevance of the patient groups under investigation. Transcriptomic data preprocessing in both microarray and RNA-sequencing studies typically includes rigorous quality control, normalization, and batch effect correction to ensure data comparability and reliability. Established methods such as RMA for microarrays and standardized RNA-seq pipelines involving alignment and quantification are widely adopted. Batch effects, often detected via principal component analysis, are effectively mitigated using algorithms like ComBat, preserving biological signal integrity across datasets. 21,22 Our transcriptomic data processing outcomes demonstrate a high level of concordance with published literature, notably in achieving robust gene detection sensitivity and reliable mapping rates for RNA-sequencing datasets. Comparably, microarray quality metrics align with established benchmarks regarding signal-to-noise ratios and transcript capture. The minimal batch effects observed post-ComBat-seq correction reaffirm the effectiveness of such harmonization strategies widely endorsed in transcriptomic studies. Gene symbol concordance across platforms further highlights the strength of updated annotation protocols. These consistent results emphasize the methodological rigor and reproducibility fundamental for integrating heterogeneous datasets in obesity and lung injury research, complementing broader findings on preprocessing impacts documented in recent comparative analyses. Differential gene expression analyses reported in the literature consistently emphasize the importance of robust statistical frameworks to discern meaningful transcriptomic alterations in obesity and lung injury contexts. 23 Differential expression profiling in adipose and pulmonary tissues has revealed distinct transcriptional signatures associated with metabolic dysfunction and ARDS. 24 Approaches utilizing linear mixed-effects models or DESeq2 effectively control for study-specific effects while highlighting pathophysiologically relevant genes, maintaining statistical rigor in identifying pathologically relevant transcriptional changes. 25 The adoption of stringent thresholds for significance and fold change is widely supported to ensure clinical relevance. These methodologies align with our results, where consistent expression patterns emerged across adipose and lung tissue datasets, reinforcing the biological validity while demonstrating superior technical reproducibility and cross-platform concordance compared to conventional analytical frameworks reported in contemporary literature. Cross-tissue pathway analysis integrates transcriptomic data from multiple human tissues to elucidate shared and tissue-specific molecular mechanisms underlying complex diseases. 26 Advanced methods incorporating pathway crosstalk and gene interaction networks enhance identification of disease-relevant pathways with higher accuracy and biological relevance. 27 These methodologies have successfully uncovered tissue-independent pathways in metabolic disorders, inflammatory conditions, and cancer progression and has been instrumental in revealing novel risk pathways, improving mechanistic understanding beyond single-tissue studies. 28 Our findings align closely with literature demonstrating significant pathway overlaps across tissues, particularly in inflammatory and metabolic processes. The enrichment of TNF, IL6-STAT3, and complement pathways reinforce established cross-tissue immune signaling patterns, while metabolic downregulation echoes common observations in obesity and lung injury studies, supporting the translational relevance of these shared molecular mechanisms. Recent literature on Human PPI Networks highlights transformative advances driven by deep learning, integrating architectures like GNNs, CNNs, and Transformers for precise interaction prediction. 29 The STRING database enhances network resolution by differentiating functional, physical, and regulatory interactions, incorporating cross-species protein embeddings to improve predictive accuracy and biological insights. 30 Compared to recent literature emphasizing dynamic, directionally annotated PPI networks like STRING, our analysis reveals a densely connected network with 284 nodes, highlighting key inflammatory and immune regulatory hubs (IL6, TNF, STAT3, NFKB1) and distinct functional modules, supporting a scale-free topology that aligns with STRING's findings on pronounced hub centrality driving biological processes. Our study exhibited inherent methodological constraints including dataset heterogeneity, absent temporal causality assessment, and lack of experimental validation, which remained unresolvable due to computational analysis design limitations and reliance on pre-existing transcriptomic repositories. FINAL CONSIDERATIONS Our study offers a bioinformatics approach demonstrating that obesity-induced adipose tissue inflammation drives lung injury susceptibility through TNF-NF-κB and IL6-STAT3 pathway activation. The densely connected PPI network and identification of key hub genes underscore cross-tissue pathological communication. Despite dataset heterogeneity, integration of multiple transcriptomic datasets strengthens mechanistic insights, guiding targeted therapeutic development for obesity-associated respiratory complications. CONCLUSION Our study demonstrates that obesity-induced adipose tissue inflammation establishes molecular susceptibility to lung injury through coordinated TNF-NF-κB and IL6-STAT3 pathway activation, highlighting potential molecular targets for obesity-associated respiratory disease treatment. Declarations Conflicts of interest: None declared. References Palma G, Sorice GP, Genchi VA, Giordano F, Caccioppoli C, D'Oria R, et al. Adipose Tissue Inflammation and Pulmonary Dysfunction in Obesity. Int J Mol Sci. 2022;23(13):7349. Kallinos E, Chung KP, Torres LK, Bhatia D, Ersoy B, Carmeliet P, et al. High-fat diet obesity exacerbates acute lung injury-induced dysregulation of fatty acid oxidation in alveolar epithelial type 2 cells. Am J Physiol Lung Cell Mol Physiol. 2025;329(3):L343-L356. Plataki M, Fan L, Sanchez E, Huang Z, Torres LK, Imamura M, et al. Fatty acid synthase downregulation contributes to acute lung injury in murine diet-induced obesity. JCI Insight. 2019;5(15):e127823. 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Tables Tables 1 to 5 are available in the Supplementary Files section. Graphics Graphics 1 to 4 are available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files G1.png Graphic 1. Comprehensive quality metrics and batch effect correction efficacy across heterogeneous transcriptomic platforms G2.png Graphic 2. Multi-tissue differential gene expression analysis revealing shared inflammatory pathways and tissue-specific dysregulation patterns. G3.png Graphic 3. Forest plot of key hub genes across independent cohorts G4.png Graphic 4. Clinical Correlation Analysis of Molecular Signatures and Patient Outcomes Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Multi-tissue differential gene expression analysis revealing shared inflammatory pathways and tissue-specific dysregulation patterns.\u003c/p\u003e","description":"","filename":"G2.png","url":"https://assets-eu.researchsquare.com/files/rs-7729527/v1/875c606e0837e17d00775e0e.png"},{"id":92492574,"identity":"a55479ab-aeca-4a08-8408-4eb465f81b66","added_by":"auto","created_at":"2025-09-30 09:50:39","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":82604,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphic 3\u003c/strong\u003e. Forest plot of key hub genes across independent cohorts\u003c/p\u003e","description":"","filename":"G3.png","url":"https://assets-eu.researchsquare.com/files/rs-7729527/v1/6ada031426f67dd95db09726.png"},{"id":92491742,"identity":"20e77206-3e7c-4a07-9c6b-687accb7775e","added_by":"auto","created_at":"2025-09-30 09:42:39","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":68936,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphic 4\u003c/strong\u003e. Clinical Correlation Analysis of Molecular Signatures and Patient Outcomes\u003c/p\u003e","description":"","filename":"G4.png","url":"https://assets-eu.researchsquare.com/files/rs-7729527/v1/3c6208edffd7aba61c88c361.png"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eFrom Adipose Tissue to Alveoli: Bioinformatic Mapping of Obesity-Associated Lung Injury Pathways\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe escalating global obesity epidemic has fundamentally transformed respiratory medicine, establishing adipose tissue as an active endocrine organ whose inflammatory mediators profoundly influence pulmonary homeostasis.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Obesity constitutes a systemic inflammatory state wherein proinflammatory mediators produced in adipose tissue predispose individuals to enhanced susceptibility to acute respiratory distress syndrome (ARDS) and other forms of lung injury.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e This paradigm shift necessitates comprehensive understanding of molecular pathways through which adipose tissue-derived factors modulate alveolar epithelial cell function.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eDespite established clinical associations between obesity and respiratory pathology, critical knowledge gaps persist regarding the mechanistic connections between adipose tissue dysfunction and lung injury. While obesity represents a confirmed risk factor for ARDS,\u003csup\u003e4,5\u003c/sup\u003e paradoxical findings demonstrate improved survival rates in obese patients with established lung injury compared to normal-weight individuals.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e The precise molecular mechanisms underlying these complex relationships remain poorly characterized, particularly the transcriptomic signatures that bridge adipose inflammation and alveolar epithelial pathology. Furthermore, current understanding lacks integrative frameworks to comprehensively map the molecular networks connecting systemic metabolic dysfunction to localized pulmonary pathology.\u003c/p\u003e\u003cp\u003eThe application of bioinformatics approaches to decipher obesity-associated lung injury pathways addresses the inherent complexity of multi-organ crosstalk and limitations of traditional methodologies. Recent advances in spatial transcriptomics and single-cell RNA sequencing have revolutionized our understanding of tissue-specific molecular signatures.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Transcriptomic profiling of adipose tissue has revealed distinct inflammatory profiles in obesity that may influence distant organ systems,\u003csup\u003e10\u003c/sup\u003e while spatial analysis of lung tissue provides unprecedented insights into cellular interactions during injury and repair processes.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThe central of this study question focuses on identifying specific molecular pathways through which obesity-associated adipose tissue dysfunction contributes to enhanced lung injury susceptibility, emphasizing transcriptomic signatures that connect adipose inflammation to alveolar pathology. Understanding these mechanisms is critical for developing targeted therapeutic interventions that address both metabolic and respiratory dysfunction simultaneously.\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Data Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective bioinformatics study utilized transcriptomic data from published human studies to investigate molecular pathways connecting obesity-associated adipose tissue inflammation to lung injury mechanisms. A systematic search of the Gene Expression Omnibus (GEO) database was conducted to identify relevant datasets from peer-reviewed publications examining gene expression profiles in human adipose tissue from obese versus lean individuals, and lung tissue from patients with acute lung injury or ARDS versus healthy controls. Search terms included \u0026quot;obesity,\u0026quot; \u0026quot;adipose tissue,\u0026quot; \u0026quot;lung injury,\u0026quot; \u0026quot;ARDS,\u0026quot; \u0026quot;human,\u0026quot; and \u0026quot;transcriptome\u0026quot; with publication dates from 2010 to 2024.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDataset Selection Criteria and Subject Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuman datasets were selected based on predefined inclusion criteria: (1) original research articles with raw transcriptomic data deposited in GEO; (2) studies involving adult human subjects (\u0026ge;18 years); (3) clearly defined obesity status (BMI \u0026ge;30 kg/m\u0026sup2;) or lung injury diagnosis according to Berlin criteria for ARDS\u003csup\u003e12\u003c/sup\u003e; (4) minimum sample size of 10 subjects per group; and (5) comprehensive clinical metadata including demographic and anthropometric data. Exclusion criteria encompassed studies with pediatric populations, mixed animal-human data, or insufficient clinical phenotyping. Selected datasets underwent thorough review of original publications to extract relevant clinical characteristics including age, sex, BMI, comorbidities, and lung injury severity scores.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Data Integration and Patient Stratification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical metadata from original studies were systematically extracted and harmonized across datasets. Obesity classification followed World Health Organization criteria (BMI \u0026ge;30 kg/m\u0026sup2;),\u003csup\u003e13\u003c/sup\u003e while lung injury severity was assessed using available clinical scores including Sequential Organ Failure Assessment (SOFA)\u003csup\u003e14\u003c/sup\u003e or Acute Physiology and Chronic Health Evaluation (APACHE) II\u003csup\u003e15\u003c/sup\u003e scores when reported in source publications. Patient stratification considered relevant confounding variables including age, sex, diabetes status, and smoking history based on information available in original study reports.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscriptomic Data Processing from Published Studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw transcriptomic data from selected human studies underwent standardized preprocessing protocols. For microarray datasets (Affymetrix, Illumina platforms), raw CEL or IDAT files were processed using platform-specific normalization methods including Robust Multi-array Average (RMA) for Affymetrix arrays and quantile normalization for Illumina BeadChips. RNA-sequencing datasets were reprocessed from raw FASTQ files using a unified pipeline: quality assessment with FastQC, adapter trimming via Trimmomatic, alignment to human reference genome GRCh38 using HISAT2, and gene quantification with featureCounts. Batch effects between different studies were identified through principal component analysis and corrected using the ComBat-seq algorithm.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Tissue-Specific Differential Expression Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferential gene expression analysis was performed separately for human adipose tissue (subcutaneous and visceral when specified) comparing obese versus lean individuals, and lung tissue comparing ARDS patients versus controls. Linear mixed-effects models implemented in limma-voom framework accounted for potential study-specific effects while identifying consistent expression changes across datasets. For RNA-seq data, DESeq2 was employed with study as a blocking factor. Statistical significance thresholds were set at adjusted p-value \u0026lt; 0.05, and absolute log2 fold change \u0026ge; 1.0, ensuring robust identification of clinically relevant expression differences.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCross-Tissue Pathway Analysis Using Human Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePathway enrichment analysis utilized gene sets from human-specific databases including KEGG Homo sapiens pathways, Reactome human pathways, and Gene Ontology biological processes. Gene Set Enrichment Analysis (GSEA) was conducted using ranked gene lists from human adipose tissue and lung tissue comparisons, with significance assessed at FDR q-value \u0026lt; 0.25 and normalized enrichment score |NES| \u0026gt; 1.0. Cross-tissue pathway overlap analysis identified shared inflammatory and metabolic pathways between obese adipose tissue and injured lung tissue from human studies.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Protein-Protein Interaction Network Construction\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProtein-protein interaction (PPI) networks were constructed using human-specific databases including STRING (Homo sapiens), BioGRID, and Human Protein Reference Database. Only experimentally validated interactions with confidence scores \u0026gt; 0.7 were included to ensure biological relevance. Network topology analysis identified hub genes based on degree centrality and betweenness centrality metrics, with particular focus on genes consistently altered across multiple human studies.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation Using Independent Human Cohorts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExternal validation employed independent human datasets not used in primary analysis, when available in GEO database. Meta-analysis approaches using random-effects models assessed consistency of gene expression changes across different human populations and study designs. Publication bias was evaluated using funnel plots and Egger\u0026apos;s regression test for key genes identified in the analysis.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Relevance Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIdentified molecular signatures were evaluated for clinical relevance by correlating gene expression patterns with available clinical outcomes from original studies, including length of mechanical ventilation, intensive care unit (ICU) stay duration, and mortality when reported. Pathway scores derived from human data were tested for associations with clinically relevant endpoints using logistic regression models adjusted for age, sex, and comorbidity burden.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses employed R 4.3.0 (public domain software). Continuous variables were assessed using ANOVA with Tukey\u0026apos;s post-hoc tests; categorical variables used chi-square or Fisher\u0026apos;s exact tests. Differential expression utilized limma-voom and DESeq2 with Benjamini-Hochberg FDR correction (p\u0026lt;0.05). Hypergeometric tests assessed pathway overlaps. Meta-analysis employed random-effects models with I\u0026sup2; heterogeneity assessment. Network topology calculated degree and betweenness centrality metrics. Clinical correlations used Pearson or Spearman coefficients with logistic regression modeling.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll analyses utilized publicly available, de-identified human data from previously published studies that received appropriate institutional review board approval as reported in original publications. Therefore, it was not necessary to have the study reviewed by the ethics committee.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eSystematic search of the GEO database using predefined criteria yielded 847 initial datasets from peer-reviewed publications spanning 2010-2024. Following rigorous screening and application of inclusion criteria, 23 high-quality datasets were selected for analysis, comprising 12 adipose tissue studies encompassing 1,247 human subjects (683 obese individuals with BMI \u0026ge;30 kg/m\u0026sup2; and 564 lean controls) and 11 lung tissue datasets containing 834 samples (445 from ARDS/acute lung injury patients and 389 healthy controls). Selected studies utilized diverse transcriptomic platforms including 13 RNA-sequencing and 10 microarray datasets, with robust quality metrics demonstrating RNA-seq mapping rates exceeding 82% and microarray signal-to-noise ratios above 12, wan\u003c/p\u003e\n\u003cp\u003eComprehensive clinical metadata extraction revealed well-characterized patient populations across both tissue types. Adipose tissue studies included subjects with mean age 47.3 \u0026plusmn; 8.4 years (obese) and 43.7 \u0026plusmn; 9.1 years (lean controls), with 62% female representation. Obese cohorts demonstrated mean BMI of 34.2 \u0026plusmn; 4.7 kg/m\u0026sup2; versus 22.1 \u0026plusmn; 2.3 kg/m\u0026sup2; in lean controls (p\u0026lt;0.001). Lung injury datasets encompassed patients with mean age 58.1 \u0026plusmn; 12.4 years, 43% female representation, and ARDS severity distribution of 34% mild, 48% moderate, and 18% severe cases according to Berlin criteria. Clinical severity scores revealed mean APACHE II scores of 18.4 \u0026plusmn; 6.2 and SOFA scores of 8.7 \u0026plusmn; 3.1. Comorbidity analysis identified diabetes mellitus in 28.3% of obesity studies and 31.7% of lung injury cohorts, while smoking history was documented in 34% and 67% of subjects, respectively (Table 2, 3 and 4).\u003c/p\u003e\n\u003cp\u003eStandardized preprocessing protocols successfully harmonized heterogeneous transcriptomic datasets across multiple platforms and studies. RNA-sequencing datasets demonstrated median mapping rates of 87.3% (range: 82.1-94.6%) with mean sequencing depths of 42.8 million reads per sample. Gene detection sensitivity ranged from 12,847 to 18,234 expressed genes per RNA-seq dataset. Microarray platforms exhibited robust quality metrics with median signal-to-noise ratios of 15.7 (range: 12.3-21.4) and background-corrected intensities exceeding detection thresholds in \u0026gt;85% of probes, capturing 11,245 to 15,892 detectable transcripts. Principal component analysis revealed minimal within-study batch effects, while ComBat-seq correction effectively addressed inter-study variability. Gene symbol mapping achieved 94.7% concordance across platforms after application of updated annotation databases (Graphic 1).\u003c/p\u003e\n\u003cp\u003eDifferential expression analysis identified 2,847 significantly dysregulated genes in obese adipose tissue compared to lean controls (adjusted p\u0026lt;0.05, |log2FC|\u0026ge;1.0), with 1,523 upregulated and 1,324 downregulated transcripts. Key upregulated genes included proinflammatory mediators (IL6, TNF, IL1B), chemokines (CCL2, CXCL10), and matrix remodeling factors (MMP9, MMP2). Conversely, 1,892 genes were significantly altered in lung tissue from ARDS patients versus healthy controls, comprising 1,147 upregulated and 745 downregulated genes. Notable upregulated transcripts encompassed inflammatory response genes (STAT3, NFKB1, IRF7), epithelial barrier dysfunction markers (OCLN, TJP1), and acute phase response proteins (SAA1, CRP). Cross-tissue comparison revealed 347 genes commonly dysregulated between obese adipose tissue and injured lung tissue, representing significant molecular overlap (hypergeometric test, p\u0026lt;0.001) (Graphic 2).\u003c/p\u003e\n\u003cp\u003eGene Set Enrichment Analysis identified 127 significantly enriched pathways in obese adipose tissue (FDR q\u0026lt;0.25, |NES|\u0026gt;1.0), with inflammatory response pathways demonstrating the highest enrichment scores: TNF signaling via NF-\u0026kappa;B (NES=2.84), inflammatory response (NES=2.71), and IL6-JAK-STAT3 signaling (NES=2.63). Lung injury samples exhibited 94 significantly enriched pathways, including epithelial-mesenchymal transition (NES=2.91), complement cascade (NES=2.45), and interferon-\u0026alpha; response (NES=2.38). Cross-tissue pathway overlap analysis revealed 43 shared pathways between obese adipose tissue and injured lung tissue, predominantly involving inflammatory cascades (TNF signaling, IL6-STAT3, complement activation), oxidative stress responses, and lipid metabolism dysregulation. Metabolic pathway analysis demonstrated significant downregulation of fatty acid oxidation (NES=-1.87) and oxidative phosphorylation (NES=-2.12) in both tissue types (Figure 1).\u003c/p\u003e\n\u003cp\u003ePPI network analysis of commonly dysregulated genes yielded a densely connected network comprising 284 nodes and 1,567 edges (average degree=11.04). Network topology analysis identified key hub genes based on centrality metrics: IL6 (degree=34, betweenness=0.087), TNF (degree=31, betweenness=0.092), STAT3 (degree=28, betweenness=0.074), and NFKB1 (degree=26, betweenness=0.083). Molecular complex detection identified five distinct functional modules: (1) cytokine-mediated inflammatory signaling (23 genes), (2) transcriptional regulation of immune response (18 genes), (3) extracellular matrix remodeling (15 genes), (4) complement cascade activation (12 genes), and (5) lipid metabolism regulation (11 genes). Network centralization analysis revealed a scale-free topology (R\u0026sup2;=0.912), indicating the presence of highly connected hub genes driving cross-tissue pathological communication (Figure 2).\u003c/p\u003e\n\u003cp\u003eExternal validation using three independent datasets (GSE73034, GSE47460, GSE32540) confirmed the consistency of identified gene signatures across different populations and study designs. Meta-analysis of key hub genes demonstrated robust effect sizes: IL6 (pooled log2FC=1.47, 95% CI: 1.23-1.71, p\u0026lt;0.001), TNF (pooled log2FC=1.32, 95% CI: 1.08-1.56, p\u0026lt;0.001), and ADIPOQ (pooled log2FC=-0.89, 95% CI: -1.15 to -0.63, p\u0026lt;0.001) (Table 5). \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eForest plots revealed consistent directionality across all validation cohorts with minimal between-study heterogeneity (I\u0026sup2;\u0026lt;25%) (Graphic 3). Funnel plot analysis and Egger\u0026apos;s regression test indicated no significant publication bias for primary hub genes (p\u0026gt;0.05 for all tested genes).\u003c/p\u003e\n\u003cp\u003eClinical correlation analysis revealed significant associations between identified molecular signatures and patient outcomes. The inflammatory pathway score (derived from IL6, TNF, STAT3 expression) demonstrated strong positive correlation with mechanical ventilation duration (r=0.67, p\u0026lt;0.001) and ICU length of stay (r=0.58, p=0.003). Logistic regression models adjusted for age, sex, and comorbidity burden showed that high inflammatory pathway scores were independently associated with increased 30-day mortality risk (OR=2.34, 95% CI: 1.45-3.78, p\u0026lt;0.001). The adipokine dysregulation score (based on LEP, ADIPOQ, RETN expression) correlated significantly with ARDS severity (Spearman\u0026apos;s \u0026rho;=0.52, p\u0026lt;0.001) and demonstrated predictive value for prolonged mechanical ventilation (AUC=0.73, 95% CI: 0.65-0.81) (Graphic 4).\u003c/p\u003e\n\u003cp\u003eIntegration of differential expression and network analyses revealed a distinct mechanistic pathway connecting obesogenic adipose tissue inflammation to lung injury susceptibility. The identified signaling cascade initiates with inflammatory adipocyte activation, characterized by elevated expression of proinflammatory cytokines (IL6\u0026uarr;, TNF\u0026uarr;, IL1B\u0026uarr;) and dysregulated adipokine production (LEP\u0026uarr;, ADIPOQ\u0026darr;, RETN\u0026uarr;). These inflammatory mediators activate downstream transcriptional programs in pulmonary epithelial cells through NF-\u0026kappa;B (NFKB1\u0026uarr;, RELA\u0026uarr;) and STAT3 signaling pathways. Subsequent upregulation of chemokine receptors (CXCR4\u0026uarr;, CCR5\u0026uarr;) and matrix metalloproteinases (MMP9\u0026uarr;, MMP2\u0026uarr;) promotes epithelial barrier dysfunction and enhanced inflammatory cell recruitment, ultimately increasing lung injury susceptibility. This mechanistic framework was supported by pathway enrichment analysis showing coordinated activation of TNF-NF-\u0026kappa;B signaling (p\u0026lt;0.001) and IL6-STAT3 pathways (p\u0026lt;0.001) across both tissue compartments (Figure 3).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur study with a comprehensive bioinformatics analysis demonstrates that obesity-induced adipose tissue inflammation creates molecular susceptibility to ARDS through coordinated TNF-NF-\u0026kappa;B and IL6-STAT3 pathway activation. We demonstrate that cross-tissue transcriptomic mapping identified shared dysregulated genes with inflammatory pathway scores correlating significantly with clinical outcomes, establishing precision therapeutic targets for obesity-associated respiratory complications.\u003c/p\u003e\n\u003cp\u003eWhile existing literature predominantly focuses on mechanical aspects of obesity\u0026apos;s pulmonary impact, recent investigations suggest adipose tissue inflammation significantly contributes to respiratory dysfunction through molecular mechanisms.\u003csup\u003e1\u003c/sup\u003e Clinical studies consistently demonstrate obesity as a significant risk factor for acute respiratory distress syndrome development.\u003csup\u003e16\u003c/sup\u003e However, comprehensive transcriptomic inflammatory mediators create complex cross-tissue signaling networks.\u003csup\u003e17\u003c/sup\u003e Our systematic analysis of twenty-three high-quality datasets substantially expands this understanding by providing molecular evidence for obesity-associated respiratory pathology mechanisms.\u003c/p\u003e\n\u003cp\u003eCurrent literature consistently emphasizes the importance of detailed clinical phenotyping in metabolic and pulmonary research. While published studies often report demographic and anthropometric data distributions in obesity-related adipose investigations, lung injury datasets typically prioritize severity stratification.\u003csup\u003e18\u003c/sup\u003e The clinical metadata analysis confirms established distinctions in adipose tissue profiles between obese and lean subjects, consistent with previous studies highlighting increased inflammation and metabolic alterations in obesity.\u003csup\u003e19\u003c/sup\u003e Likewise, lung injury severity classified by ARDS criteria aligns well with validated clinical scores, reinforcing their prognostic value.\u003csup\u003e20\u003c/sup\u003e Our results align with established studies demonstrating that well-characterized populations are fundamental for biomarker discovery. The demographic and comorbidity profiles we describe, particularly the expected prevalence of metabolic comorbidities in obesity studies and smoking history in lung injury cohorts, demonstrate the known epidemiology of these conditions, thereby validating the clinical relevance of the patient groups under investigation.\u003c/p\u003e\n\u003cp\u003eTranscriptomic data preprocessing in both microarray and RNA-sequencing studies typically includes rigorous quality control, normalization, and batch effect correction to ensure data comparability and reliability. Established methods such as RMA for microarrays and standardized RNA-seq pipelines involving alignment and quantification are widely adopted. Batch effects, often detected via principal component analysis, are effectively mitigated using algorithms like ComBat, preserving biological signal integrity across datasets.\u003csup\u003e21,22\u003c/sup\u003e Our transcriptomic data processing outcomes demonstrate a high level of concordance with published literature, notably in achieving robust gene detection sensitivity and reliable mapping rates for RNA-sequencing datasets. Comparably, microarray quality metrics align with established benchmarks regarding signal-to-noise ratios and transcript capture. The minimal batch effects observed post-ComBat-seq correction reaffirm the effectiveness of such harmonization strategies widely endorsed in transcriptomic studies. Gene symbol concordance across platforms further highlights the strength of updated annotation protocols. These consistent results emphasize the methodological rigor and reproducibility fundamental for integrating heterogeneous datasets in obesity and lung injury research, complementing broader findings on preprocessing impacts documented in recent comparative analyses.\u003c/p\u003e\n\u003cp\u003eDifferential gene expression analyses reported in the literature consistently emphasize the importance of robust statistical frameworks to discern meaningful transcriptomic alterations in obesity and lung injury contexts.\u003csup\u003e23\u003c/sup\u003e Differential expression profiling in adipose and pulmonary tissues has revealed distinct transcriptional signatures associated with metabolic dysfunction and ARDS.\u003csup\u003e24\u003c/sup\u003e Approaches utilizing linear mixed-effects models or DESeq2 effectively control for study-specific effects while highlighting pathophysiologically relevant genes, maintaining statistical rigor in identifying pathologically relevant transcriptional changes.\u003csup\u003e25\u003c/sup\u003e The adoption of stringent thresholds for significance and fold change is widely supported to ensure clinical relevance. These methodologies align with our results, where consistent expression patterns emerged across adipose and lung tissue datasets, reinforcing the biological validity while demonstrating superior technical reproducibility and cross-platform concordance compared to conventional analytical frameworks reported in contemporary literature.\u003c/p\u003e\n\u003cp\u003eCross-tissue pathway analysis integrates transcriptomic data from multiple human tissues to elucidate shared and tissue-specific molecular mechanisms underlying complex diseases.\u003csup\u003e26\u003c/sup\u003e Advanced methods incorporating pathway crosstalk and gene interaction networks enhance identification of disease-relevant pathways with higher accuracy and biological relevance.\u003csup\u003e27\u003c/sup\u003e These methodologies have successfully uncovered tissue-independent pathways in metabolic disorders, inflammatory conditions, and cancer progression and has been instrumental in revealing novel risk pathways, improving mechanistic understanding beyond single-tissue studies.\u003csup\u003e28\u003c/sup\u003e Our findings align closely with literature demonstrating significant pathway overlaps across tissues, particularly in inflammatory and metabolic processes. The enrichment of TNF, IL6-STAT3, and complement pathways reinforce established cross-tissue immune signaling patterns, while metabolic downregulation echoes common observations in obesity and lung injury studies, supporting the translational relevance of these shared molecular mechanisms.\u003c/p\u003e\n\u003cp\u003eRecent literature on Human PPI Networks highlights transformative advances driven by deep learning, integrating architectures like GNNs, CNNs, and Transformers for precise interaction prediction.\u003csup\u003e29\u003c/sup\u003e The STRING database enhances network resolution by differentiating functional, physical, and regulatory interactions, incorporating cross-species protein embeddings to improve predictive accuracy and biological insights.\u003csup\u003e30 \u003c/sup\u003eCompared to recent literature emphasizing dynamic, directionally annotated PPI networks like STRING, our analysis reveals a densely connected network with 284 nodes, highlighting key inflammatory and immune regulatory hubs (IL6, TNF, STAT3, NFKB1) and distinct functional modules, supporting a scale-free topology that aligns with STRING\u0026apos;s findings on pronounced hub centrality driving biological processes.\u003c/p\u003e\n\u003cp\u003eOur study exhibited inherent methodological constraints including dataset heterogeneity, absent temporal causality assessment, and lack of experimental validation, which remained unresolvable due to computational analysis design limitations and reliance on pre-existing transcriptomic repositories.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFINAL CONSIDERATIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study offers a bioinformatics approach demonstrating that obesity-induced adipose tissue inflammation drives lung injury susceptibility through TNF-NF-\u0026kappa;B and IL6-STAT3 pathway activation. The densely connected PPI network and identification of key hub genes underscore cross-tissue pathological communication. Despite dataset heterogeneity, integration of multiple transcriptomic datasets strengthens mechanistic insights, guiding targeted therapeutic development for obesity-associated respiratory complications.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eOur study demonstrates that obesity-induced adipose tissue inflammation establishes molecular susceptibility to lung injury through coordinated TNF-NF-\u0026kappa;B and IL6-STAT3 pathway activation, highlighting potential molecular targets for obesity-associated respiratory disease treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u0026nbsp;\u003c/strong\u003eNone declared.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003ePalma G, Sorice GP, Genchi VA, Giordano F, Caccioppoli C, D\u0026apos;Oria R, et al. Adipose Tissue Inflammation and Pulmonary Dysfunction in Obesity. Int J Mol Sci. 2022;23(13):7349.\u003c/li\u003e\n \u003cli\u003eKallinos E, Chung KP, Torres LK, Bhatia D, Ersoy B, Carmeliet P, et al. High-fat diet obesity exacerbates acute lung injury-induced dysregulation of fatty acid oxidation in alveolar epithelial type 2 cells. Am J Physiol Lung Cell Mol Physiol. 2025;329(3):L343-L356.\u003c/li\u003e\n \u003cli\u003ePlataki M, Fan L, Sanchez E, Huang Z, Torres LK, Imamura M, et al. Fatty acid synthase downregulation contributes to acute lung injury in murine diet-induced obesity. JCI Insight. 2019;5(15):e127823.\u003c/li\u003e\n \u003cli\u003eMcCallister JW, Adkins EJ, O\u0026apos;Brien JM Jr. Obesity and acute lung injury. Clin Chest Med. 2009;30(3):495-508, viii.\u003c/li\u003e\n \u003cli\u003eLiu QY, Chen Y, He Y, Zhu RL. Impact of obesity on outcomes in patients with acute respiratory syndrome. J Int Med Res. 2021;49(6):3000605211024860.\u003c/li\u003e\n \u003cli\u003eMaia LA, Cruz FF, de Oliveira MV, Samary CS, Fernandes MVS, Trivelin SAA, et al. Effects of Obesity on Pulmonary Inflammation and Remodeling in Experimental Moderate Acute Lung Injury. Front Immunol. 2019;10:1215.\u003c/li\u003e\n \u003cli\u003eHibbert K, Rice M, Malhotra A. Obesity and ARDS. Chest. 2012;142(3):785-790.\u003c/li\u003e\n \u003cli\u003eReyfman PA, Walter JM, Joshi N, Anekalla KR, McQuattie-Pimentel AC, Chiu S, et al. Single-Cell Transcriptomic Analysis of Human Lung Provides Insights into the Pathobiology of Pulmonary Fibrosis. Am J Respir Crit Care Med. 2019;199(12):1517-1536.\u003c/li\u003e\n \u003cli\u003eKohda H, Tanaka M, Shichino S, Arakawa S, Komori T, Ito A, et al. Novel Cell-to-Cell Communications Between Macrophages and Fibroblasts Regulate Obesity-Induced Adipose Tissue Fibrosis. Diabetes. 2025;74(7):1135-1152.\u003c/li\u003e\n \u003cli\u003eO\u0026ntilde;ate B, Vilahur G, Camino-L\u0026oacute;pez S, D\u0026iacute;ez-Caballero A, Ballesta-L\u0026oacute;pez C, Ybarra J, et al. Stem cells isolated from adipose tissue of obese patients show changes in their transcriptomic profile that indicate loss in stemcellness and increased commitment to an adipocyte-like phenotype. BMC Genomics. 2013;14:625.\u003c/li\u003e\n \u003cli\u003eJoshi PR, Sadre S, Guo XA, McCoy JG, Mootha VK. Lipoylation is dependent on the ferredoxin FDX1 and dispensable under hypoxia in human cells. J Biol Chem. 2023;299(9):105075.\u003c/li\u003e\n \u003cli\u003eARDS Definition Task Force; Ranieri VM, Rubenfeld GD, Thompson BT, Ferguson ND, Caldwell E, et al. Acute respiratory distress syndrome: the Berlin Definition. JAMA. 2012;307(23):2526\u0026ndash;33.\u003c/li\u003e\n \u003cli\u003eObesity. and overweight. In: Department of Sustainable Development and Healthy Environments World Health Organization; 2021.\u003c/li\u003e\n \u003cli\u003eMoreno R, Rhodes A, Piquilloud L, Hernandez G, Takala J, Gershengorn HB, et al. The Sequential Organ Failure Assessment (SOFA) Score: has the time come for an update? Crit Care. 2023;27(1):15.\u003c/li\u003e\n \u003cli\u003eKnaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13(10):818-29.\u003c/li\u003e\n \u003cli\u003eShah D, Romero F, Guo Z, Sun J, Li J, Kallen CB, et al. Obesity-Induced Endoplasmic Reticulum Stress Causes Lung Endothelial Dysfunction and Promotes Acute Lung Injury. Am J Respir Cell Mol Biol. 2017;57(2):204-215.\u003c/li\u003e\n \u003cli\u003eWang C. Obesity, inflammation, and lung injury (OILI): the good. Mediators Inflamm. 2014;2014:978463.\u003c/li\u003e\n \u003cli\u003eTang W, Wu S, Tang Y, Ma J, Ao Y, Liu L, et al. Microarray analysis identifies lncFirre as a potential regulator of obesity-related acute lung injury. Life Sci. 2024;340:122459.\u003c/li\u003e\n \u003cli\u003ePeters U, Suratt BT, Bates JHT, Dixon AE. Beyond BMI: Obesity and Lung Disease. Chest. 2018;153(3):702-709.\u003c/li\u003e\n \u003cli\u003eFan E, Brodie D, Slutsky AS. Acute Respiratory Distress Syndrome: Advances in Diagnosis and Treatment. JAMA. 2018;319(7):698-710.\u003c/li\u003e\n \u003cli\u003eVan R, Alvarez D, Mize T, Gannavarapu S, Chintham Reddy L, Nasoz F, et al. A comparison of RNA-Seq data preprocessing pipelines for transcriptomic predictions across independent studies. BMC Bioinformatics. 2024;25(1):181.\u003c/li\u003e\n \u003cli\u003eFederico A, Serra A, Ha MK, Kohonen P, Choi JS, Liampa I, et al. Transcriptomics in Toxicogenomics, Part II: Preprocessing and Differential Expression Analysis for High Quality Data. Nanomaterials (Basel). 2020;10(5):903.\u003c/li\u003e\n \u003cli\u003eZhang SJ, Qin XZ, Zhou J, He BF, Shrestha S, Zhang J, et al. Adipocyte dysfunction promotes lung inflammation and aberrant repair: a potential target of COPD. Front Endocrinol (Lausanne). 2023;14:1204744.\u003c/li\u003e\n \u003cli\u003edos Santos CC, Okutani D, Hu P, Han B, Crimi E, He X, et al. Differential gene profiling in acute lung injury identifies injury-specific gene expression. Crit Care Med. 2008;36(3):855-65.\u003c/li\u003e\n \u003cli\u003eRosati D, Palmieri M, Brunelli G, Morrione A, Iannelli F, Frullanti E, et al. Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review. Comput Struct Biotechnol J. 2024;23:1154-1168.\u003c/li\u003e\n \u003cli\u003eCastresana-Aguirre M, Sonnhammer ELL. Pathway-specific model estimation for improved pathway annotation by network crosstalk. Sci Rep. 2020;10(1):13585.\u003c/li\u003e\n \u003cli\u003eLin D, Wu S, Li W, Ye P, Pan X, Zheng T, et al. A cross-tissue transcriptome-wide association study identifies new susceptibility genes for frailty. Front Genet. 2024;15:1404456.\u003c/li\u003e\n \u003cli\u003eMorrow JD, Chase RP, Parker MM, Glass K, Seo M, Divo M, et al. RNA-sequencing across three matched tissues reveals shared and tissue-specific gene expression and pathway signatures of COPD. Respir Res. 2019;20(1):65.\u003c/li\u003e\n \u003cli\u003eCui J, Yang S, Yi L, Xi Q, Yang D, Zuo Y. Recent advances in deep learning for protein-protein interaction: a review. BioData Min. 2025;18(1):43.\u003c/li\u003e\n \u003cli\u003eSzklarczyk D, Nastou K, Koutrouli M, Kirsch R, Mehryary F, Hachilif R, et al. The STRING database in 2025: protein networks with directionality of regulation. Nucleic Acids Res. 2025 Jan 6;53(D1):D730-D737.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 5 are available in the Supplementary Files section.\u003c/p\u003e"},{"header":"Graphics","content":"\u003cp\u003eGraphics 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Obesity, Transcriptomics, Lung injury, Bioinformatics","lastPublishedDoi":"10.21203/rs.3.rs-7729527/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7729527/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003eObesity represents a systemic inflammatory state predisposing individuals to enhanced acute respiratory distress syndrome susceptibility, yet molecular mechanisms linking adipose tissue dysfunction to lung injury remain poorly characterized.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo establish a bioinformatics framework mapping molecular pathways connecting obesity-associated adipose tissue dysfunction to lung injury susceptibility through cross-tissue transcriptomic analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We analyzed publicly available 23 high-quality datasets from GEO database comprising 1,247 adipose tissue samples (683 obese, 564 lean) and 834 lung samples (445 ARDS patients, 389 controls). Differential expression analysis employed limma-voom and DESeq2 frameworks with study-specific blocking factors. Gene Set Enrichment Analysis utilized human-specific databases with significance thresholds of FDR q\u0026lt;0.25 and |NES|\u0026gt;1.0. Protein-protein interaction networks were constructed using STRING database with confidence scores \u0026gt;0.7. Statistical analyses included hypergeometric tests for pathway overlap, meta-analysis with random-effects models, and logistic regression for clinical correlations. External validation employed three independent cohorts with forest plot analysis and Egger's regression for publication bias assessment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Analysis identified 2,847 dysregulated genes in obese adipose tissue and 1,892 in lung injury samples, with 347 genes commonly altered (p\u0026lt;0.001). Key hub genes included IL6, TNF, STAT3, and NFKB1, orchestrating inflammatory cascades. Cross-tissue analysis revealed 43 shared pathways, predominantly TNF-NF-κB signaling (NES=2.84) and IL6-STAT3 pathways (NES=2.63). The inflammatory pathway score correlated with mechanical ventilation duration (r=0.67, p\u0026lt;0.001) and predicted 30-day mortality (OR=2.34, 95% CI: 1.45-3.78).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Our results demonstrated that obesity-induced adipose inflammation promotes lung injury susceptibility through TNF-NF-κB and IL6-STAT3 pathway activation, revealing therapeutic targets for respiratory complications.\u003c/p\u003e","manuscriptTitle":"From Adipose Tissue to Alveoli: Bioinformatic Mapping of Obesity-Associated Lung Injury Pathways","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 09:42:34","doi":"10.21203/rs.3.rs-7729527/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ab6e42e9-7d0a-4b1a-b6b5-00f76d505028","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55438849,"name":"Internal Medicine"}],"tags":[],"updatedAt":"2025-09-30T09:42:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-30 09:42:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7729527","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7729527","identity":"rs-7729527","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}