Latent Transforming Growth Factor-β Binding Protein-2 Regulates Lung Fibroblast-to-Myofibroblast Differentiation in Pulmonary Fibrosis via NF-κB Signaling

The Role of Vimentin in Human Corneal Fibroblast Spreading and Myofibroblast Transformation

Vimentin has been reported to play diverse roles in cell processes such as spreading, migration, cell-matrix adhesion, and fibrotic transformation. Here, we assess how vimentin impacts cell spreading, morphology, and myofibroblast transformation of human corneal fibroblasts. Overall, although knockout (KO) of vimentin did not dramatically impact corneal fibroblast spreading and mechanical activity (traction force), cell elongation in response to PDGF was reduced in vimentin KO cells as compared to controls. Blocking vimentin polymerization using Withaferin had even more pronounced effects on cell spreading and also inhibited cell-induced matrix contraction. Furthermore, although absence of vimentin did not completely block TGFβ-induced myofibroblast transformation, the degree of transformation and amount of αSMA protein expression was reduced. Proteomics showed that vimentin KO cells cultured in TGFβ had a similar pattern of protein expression as controls. One exception included periostin, an ECM protein associated with wound healing and fibrosis in other cell types, which was highly expressed only in Vim KO cells. We also demonstrate for the first time that LRRC15, a protein previously associated with myofibroblast transformation of cancer-associated fibroblasts, is also expressed by corneal myofibroblasts. Interestingly, proteins associated with LRRC15 in other cell types, such as collagen, fibronectin, β1 integrin and α11 integrin, were also upregulated. Overall, our data show that vimentin impacts both corneal fibroblast spreading and myofibroblast transformation. We also identified novel proteins that may regulate corneal myofibroblast transformation in the presence and/or absence of vimentin.

Curcumin relieves arecoline-induced oral submucous fibrosis via inhibiting the LTBP2/NF-κB axis

BACKGROUND

Submucosal fibrosis (OSF) of the oral cavity is a chronic scarring disease. Arecoline (Are) is the driving factor for the occurrence and deterioration of OSF. Curcumin plays a vital anti-inflammatory role in Are-induced OSF development. However, its potential pharmacological mechanism needs to be elucidated.

METHODS

The relative molecular level was measured via qRT-PCR or Western blot. MTT assay, transwell assay and flow cytometry detected cell proliferation, migration, and apoptosis. The correlation between hypoxia-inducible factor-1α (HIF-1α) and LTBP2 promoter was confirmed through dual-luciferase reporter assay. ELISA was performed to detect inflammatory cytokines levels.

RESULTS

Curcumin alleviated Are-induced oral mucosal fibroblast cells fibrosis by reducing oral mucosa fibroblasts viability, promoting cell apoptosis, suppressing cell migration, and down-regulating the levels of fibrosis markers and inflammatory factors. Curcumin relieved Are-induced OSF via inhibiting HIF-1α. Mechanically, HIF-1α bound to the promoter of LTBP2 to transcriptionally activated LTBP2. LTBP2 knockdown relieved Are-induced OSF, and curcumin down-regulated LTBP2 via inhibiting HIF-1α to relieve Are-induced OSF. Moreover, curcumin decreased NF-κB signal associated proteins via inhibiting LTBP2 to relieve Are-induced OSF.

CONCLUSION

Curcumin reduced the transcription level of LTBP2 by inhibiting HIF-1α, thereby inactivating NF-κB pathway to alleviate Are-induced OSF.

Proteomic Biomarkers of Survival in Idiopathic Pulmonary Fibrosis

Rationale: Idiopathic pulmonary fibrosis (IPF) causes progressive lung scarring and high mortality. Reliable and accurate prognostic biomarkers are urgently needed. Objectives: To identify and validate circulating protein biomarkers of IPF survival. Methods: High-throughput proteomic data were generated using prospectively collected plasma samples from patients with IPF from the Pulmonary Fibrosis Foundation Patient Registry (discovery cohort) and the Universities of California, Davis; Chicago; and Virginia (validation cohort). Proteins associated with three-year transplant-free survival (TFS) were identified using multivariable Cox proportional hazards regression. Those associated with TFS after adjustment for false discovery in the discovery cohort were advanced for testing in the validation cohort, with proteins maintaining TFS association with consistent effect direction considered validated. After combining cohorts, functional analyses were performed, and machine learning was used to derive a proteomic signature of TFS. Measurements and Main Results: Of 2,921 proteins tested in the discovery cohort (n = 871), 231 were associated with differential TFS. Of these, 140 maintained TFS association with consistent effect direction in the validation cohort (n = 355). After cohorts were combined, the validated proteins with the strongest TFS association were latent-transforming growth factor β-binding protein 2 (hazard ratio [HR], 2.43; 95% confidence interval [CI] = 2.09-2.82), collagen α-1(XXIV) chain (HR, 2.21; 95% CI = 1.86-2.39), and keratin 19 (HR, 1.60; 95% CI = 1.47-1.74). In decision curve analysis, a proteomic signature of TFS outperformed a similarly derived clinical prediction model. Conclusions: In the largest proteomic investigation of IPF outcomes performed to date, we identified and validated 140 protein biomarkers of TFS. These results shed important light on potential drivers of IPF progression.

Unagi: Deep Generative Model for Deciphering Cellular Dynamics and In-Silico Drug Discovery in Complex Diseases

Human diseases are characterized by intricate cellular dynamics. Single-cell sequencing provides critical insights, yet a persistent gap remains in computational tools for detailed disease progression analysis and targeted in-silico drug interventions. Here, we introduce UNAGI, a deep generative neural network tailored to analyze time-series single-cell transcriptomic data. This tool captures the complex cellular dynamics underlying disease progression, enhancing drug perturbation modeling and discovery. When applied to a dataset from patients with Idiopathic Pulmonary Fibrosis (IPF), UNAGI learns disease-informed cell embeddings that sharpen our understanding of disease progression, leading to the identification of potential therapeutic drug candidates. Validation via proteomics reveals the accuracy of UNAGI's cellular dynamics analyses, and the use of the Fibrotic Cocktail treated human Precision-cut Lung Slices confirms UNAGI's predictions that Nifedipine, an antihypertensive drug, may have antifibrotic effects on human tissues. UNAGI's versatility extends to other diseases, including a COVID dataset, demonstrating adaptability and confirming its broader applicability in decoding complex cellular dynamics beyond IPF, amplifying its utility in the quest for therapeutic solutions across diverse pathological landscapes.

Plasma LTBP2 as a potential biomarker in differential diagnosis of connective tissue disease-associated interstitial lung disease and idiopathic pulmonary fibrosis: a pilot study

Few biomarkers distinguish connective tissue disease-associated interstitial lung disease (CTD-ILD) from idiopathic pulmonary fibrosis (IPF). Latent transforming growth factor-β binding protein-2 (LTBP2), a secreted extracellular matrix protein, is involved in pulmonary fibrosis. However, the role of LTBP2 in differentially diagnosing CTD-ILD and IPF is unclear. In this study, enzyme-linked immunosorbent assays quantified plasma LTBP2 concentrations in 200 individuals (35 healthy controls, 42 CTD patients without ILD, 89 CTD-ILD patients, and 34 IPF patients). CTD-ILD and IPF were further classified based on chest imaging pattern and pulmonary function test results. Plasma LTBP2 levels were significantly elevated in the IPF group compared with the CTD-ILD group. ROC analysis further suggested the possible value of LTBP2 in differentially diagnosing CTD-ILD and IPF. Additionally, CTD-ILD patients with progressive lung fibrosis had higher plasma LTBP2 concentrations than those who did not. Similarly, patients with IPF developing acute exacerbation showed higher plasma LTBP2 levels than those with stable IPF. This is the first study showing that LTBP2 was closely associated with the usual interstitial pneumonia (UIP) pattern in rheumatoid arthritis-associated ILD (RA-ILD). Moreover, the optimal cutoff values of LTBP2 for distinguishing IPF from CTD-UIP/RA-UIP were 33.75 and 38.33 ng/mL with an AUC of 0.682 and 0.681, respectively. Our findings suggest that plasma LTBP2 levels may differentially diagnose CTD-ILD and IPF, and assess their fibrotic activity. Additionally, clinical LTBP2 evaluation may be a great aid to identifying the presence of the UIP pattern in RA-ILD and to discriminating IPF from CTD-UIP, particularly RA-UIP.

Identification and Validation of Genes Exhibiting Dynamic Alterations in Response to Bleomycin-Induced Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) carries a high mortality rate and has a poor prognosis. The pathogenesis of pulmonary fibrosis (PF) is highly related to dysregulation of multiple RNAs. This study aims to identify and validate dysregulated RNAs that exhibited dynamic alterations in response to bleomycin (BLM)-induced PF. The results will provide therapeutic targets for patients suffering from IPF. Whole transcriptomic profiles of BLM-induced PF were obtained through high-throughput RNA sequencing. miRNA profiling was downloaded from GSE45789 database in the Gene Expression Omnibus (GEO). We identified the differentially expressed RNAs (DERNAs) that exhibited dynamic alterations in response to BLM-induced PF. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analysis were conducted to discovery regulatory processes of PF. Weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, and co-expression analysis were performed to identify key genes and pathogenic pattern during the progression of PF. MiRanda, miRcode, and TargetScan were utilized to predict target relationships in the potential competing endogenous RNA (ceRNA) network. The results were verified by qRT-PCR analysis. In the context of BLM-induced PF, this study identified a total of 167 differentially expressed messenger RNAs (DEmRNAs), 115 differentially expressed long non-coding RNAs (DElncRNAs), 45 differentially expressed circular RNAs (DEcircRNAs), and 87 differentially expressed microRNAs (DEmiRNAs). These RNA molecules showed dynamic alterations in response to BLM-induced PF. These DEmRNAs exhibited a predominant association with the biological processes pertaining to the organization of extracellular matrix. A regulatory network was built in PF, encompassing 31 DEmRNAs, 18 DE lncRNAs, 13 DEcircRNAs, and 13 DEmiRNAs. Several DERNA molecules were subjected to validate using additional BLM-induced PF model. The outcomes of this validation process shown a strong correlation with the results obtained from RNA sequencing analysis. The GSE213001 dataset was utilized to validate the expression levels and diagnostic efficacy of four specific hub mRNAs (CCDC80, CLU, COL5A1, and COL6A3) in individuals diagnosed with PF. In this study, we identified and validated several RNA molecules that exhibited dynamic alternations in response to BLM-induced PF. These dysregulated RNAs participated in the pathogenesis of PF and can be used as therapeutic targets for early-stage IPF. Although more work must be done to confirm the results, our study may provide directions for future studies.

Etiology and Pathogenesis of Rheumatoid Arthritis-Interstitial Lung Disease

Interstitial lung disease (ILD) is one of the most serious extra-articular complications of rheumatoid arthritis (RA), which increases the mortality of RA. Because the pathogenesis of RA-ILD remains poorly understood, appropriate therapeutic strategies and biomarkers have not yet been identified. Thus, the goal of this review was to summarize and analyze the reported data on the etiology and pathogenesis of RA-ILD. The incidence of RA-ILD increases with age, and is also generally higher in men than in women and in patients with specific genetic variations and ethnicity. Lifestyle factors associated with an increased risk of RA-ILD include smoking and exposure to pollutants. The presence of an anti-cyclic citrullinated peptide antibody, high RA disease activity, and rheumatoid factor positivity also increase the risk of RA-ILD. We also explored the roles of biological processes (e.g., fibroblast-myofibroblast transition, epithelial-mesenchymal transition, and immunological processes), signaling pathways (e.g., JAK/STAT and PI3K/Akt), and the histopathology of RA involved in RA-ILD pathogenesis based on published preclinical and clinical models of RA-ILD in animal and human studies.

Inhibitory effects of Schisandrin C on collagen behavior in pulmonary fibrosis

Pulmonary fibrosis (PF) is a serious progressive fibrotic disease that is characterized by excessive accumulation of extracellular matrix (ECM), thus resulting in stiff lung tissues. Lysyl oxidase (LOX) is an enzyme involved in fibrosis by catalyzing collagen cross-linking. Studies found that the ingredients in schisandra ameliorated bleomycin (BLM)-induced PF, but it is unknown whether the anti-PF of schisandra is related to LOX. In this study, we established models of PF including a mouse model stimulated by BLM and a HFL1 cell model induced by transforming growth factor (TGF)-β1 to evaluate the inhibition effects of Schisandrin C (Sch C) on PF. We observed that Sch C treatment decreased pulmonary indexes compared to control group. Treatment of Sch C showed a significant reduction in the accumulation of ECM as evidenced by decreased expressions of α-SMA, FN, MMP2, MMP9, TIMP1 and collagen proteins such as Col 1A1, and Col 3A1. In addition, the expression of LOX in the lung tissue of mice after Sch C treatment was effectively decreased compared with the MOD group. The inhibition effects in vitro were consistent with those in vivo. Mechanistic studies revealed that Sch C significantly inhibited TGF-β1/Smad2/3 and TNF-α/JNK signaling pathways. In conclusion, our data demonstrated that Sch C significantly ameliorated PF in vivo and vitro, which may play an important role by reducing ECM deposition and inhibiting the production of LOX.

What Do We Need to Know About Rising Rates of Idiopathic Pulmonary Fibrosis? A Narrative Review and Update

The most common type of idiopathic interstitial pneumonia is idiopathic pulmonary fibrosis (IPF), an irreversible, progressive disorder that has lately come into question for possible associations with COVID-19. With few geographical exceptions, IPF is a rare disease but its prevalence has been increasing markedly since before the pandemic. Environmental exposures are frequently implicated in IPF although genetic factors play a role as well. In IPF, healthy lung tissue is progressively replaced with an abnormal extracellular matrix that impedes normal alveolar function while, at the same time, natural repair mechanisms become dysregulated. While chronic viral infections are known risk factors for IPF, acute infections are not and the link to COVID-19 has not been established. Macrophagy may be a frontline defense against any number of inflammatory pulmonary diseases, and the inflammatory cascade that may occur in patients with COVID-19 may disrupt the activity of monocytes and macrophages in clearing up fibrosis and remodeling lung tissue. It is unclear if COVID-19 infection is a risk factor for IPF, but the two can occur in the same patient with complicating effects. In light of its increasing prevalence, further study of IPF and its diagnosis and treatment is warranted.

Fibrosis: Types, Effects, Markers, Mechanisms for Disease Progression, and Its Relation with Oxidative Stress, Immunity, and Inflammation

Most chronic inflammatory illnesses include fibrosis as a pathogenic characteristic. Extracellular matrix (ECM) components build up in excess to cause fibrosis or scarring. The fibrotic process finally results in organ malfunction and death if it is severely progressive. Fibrosis affects nearly all tissues of the body. The fibrosis process is associated with chronic inflammation, metabolic homeostasis, and transforming growth factor-β1 (TGF-β1) signaling, where the balance between the oxidant and antioxidant systems appears to be a key modulator in managing these processes. Virtually every organ system, including the lungs, heart, kidney, and liver, can be affected by fibrosis, which is characterized as an excessive accumulation of connective tissue components. Organ malfunction is frequently caused by fibrotic tissue remodeling, which is also frequently linked to high morbidity and mortality. Up to 45% of all fatalities in the industrialized world are caused by fibrosis, which can damage any organ. Long believed to be persistently progressing and irreversible, fibrosis has now been revealed to be a very dynamic process by preclinical models and clinical studies in a variety of organ systems. The pathways from tissue damage to inflammation, fibrosis, and/or malfunction are the main topics of this review. Furthermore, the fibrosis of different organs with their effects was discussed. Finally, we highlight many of the principal mechanisms of fibrosis. These pathways could be considered as promising targets for the development of potential therapies for a variety of important human diseases.

Identification of LTBP2 gene polymorphisms and their association with thoracolumbar vertebrae number, body size, and carcass traits in Dezhou donkeys

The number of thoracolumbar vertebrae in Dezhou donkeys varies from 22 to 24 and is associated with body size and carcass traits. In mammals, the latent transforming growth factor beta binding protein 2 (LTBP2) has been found to have some functions in the development of thoracolumbar vertebrae. The relationship between LTBP2 and TLN (the number of thoracolumbar vertebrae) of Dezhou donkeys is yet to be reported. The purposes of this study are as follows: 1) to quantify the effect of thoracolumbar vertebrae number variation of Dezhou donkeys on body size and carcass trait; 2) to study the distribution of single nucleotide variants (SNVs) in the LTBP2 gene of Dezhou donkeys; and 3) to explore whether these SNVs can be used as candidate sites to study the mechanism of Dezhou donkey muti-thoracolumbar vertebrae development. The TLN, body size, and carcass traits of 392 individuals from a Dezhou donkey breed were recorded. All animals were sequenced for LTBP2 using GBTS liquid chip and 16 SNVs were used for further analysis. We then analyzed the relationship between these SNVs with TLN, body size, and carcass traits. The results showed that: 1) c.5547 + 860 C > T, c.5251 + 281 A > C, c.3769 + 40 C > T, and c.2782 + 3975 A > G were complete genetic linkages and significantly associated with thoracic vertebrae number (TN) (p < 0.05) (wild-type homozygotes had more TN than heterozygotes); 2) c.1381 + 768 T > G and c.1381 + 763 G > T were significantly associated with lumber vertebrae number (LN) (p < 0.05); 3) c.1003 + 704 C > T, c.1003 + 651 C > T, c.1003 + 626 A > G, and c.812 + 22526 T > G were significantly associated with chest circumference (CHC), front carcass weight (CWF), after carcass weight (CWA), and carcass weight (CW) (p < 0.05) (wild-type homozygotes were larger than other genotypes in CHC, CWF, CWA, and CW); and 4) the effect of variation is not consistent in c.565 + 11921 A > G, c.565 + 6840 A > G, c.565 + 3453 C > T, and c.494 + 5808 C > T. These results provide useful information that the polymorphism of LTBP2 is significantly associated with TLN, body size, and carcass traits in Dezhou donkeys, which can serve as a molecule marker to improve donkey production performance.

Combining Deep Phenotyping of Serum Proteomics and Clinical Data via Machine Learning for COVID-19 Biomarker Discovery

The persistence of long-term coronavirus-induced disease 2019 (COVID-19) sequelae demands better insights into its natural history. Therefore, it is crucial to discover the biomarkers of disease outcome to improve clinical practice. In this study, 160 COVID-19 patients were enrolled, of whom 80 had a “non-severe” and 80 had a “severe” outcome. Sera were analyzed by proximity extension assay (PEA) to assess 274 unique proteins associated with inflammation, cardiometabolic, and neurologic diseases. The main clinical and hematochemical data associated with disease outcome were grouped with serological data to form a dataset for the supervised machine learning techniques. We identified nine proteins (i.e., CD200R1, MCP1, MCP3, IL6, LTBP2, MATN3, TRANCE, α2-MRAP, and KIT) that contributed to the correct classification of COVID-19 disease severity when combined with relative neutrophil and lymphocyte counts. By analyzing PEA, clinical and hematochemical data with statistical methods that were able to handle many variables in the presence of a relatively small sample size, we identified nine potential serum biomarkers of a “severe” outcome. Most of these were confirmed by literature data. Importantly, we found three biomarkers associated with central nervous system pathologies and protective factors, which were downregulated in the most severe cases.