Targeting N4BP2L1 for Therapy in IPF and SSc-ILD: Evidence from Mendelian Randomization and Multi-Omics Analysis

Targeting N4BP2L1 for Therapy in IPF and SSc-ILD: Evidence from Mendelian Randomization and Multi-Omics Analysis | 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 Research Article Targeting N4BP2L1 for Therapy in IPF and SSc-ILD: Evidence from Mendelian Randomization and Multi-Omics Analysis Xiaodi Lv, Weifeng Tang, Silin Ou, Fangyong Yang, Wenqian Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4307133/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 Background: Systemic sclerosis-associated interstitial lung disease (SSc-ILD) and idiopathic pulmonary fibrosis (IPF) are paradigmatic conditions characterized by similar pathogenic mechanisms involving inflammation and fibrosis. Both disorders present considerable challenges due to their elevated mortality rates and the difficulty in identifying effective treatments. Consequently, it is imperative to explore potential targets and deepen our understanding of the initiation and progression of these diseases. Methods: To address this, we utilized a combination of Mendelian randomization (MR) analysis, single-cell RNA sequencing (scRNA-seq) analysis, and other multi-omics analysis. Results: Our investigation confirmed the involvement of N4BP2L1 in CD8+ effector T (Teff) cells and its causal relationship with SSc-ILD. Subsequent multi-omics analyses were conducted to validate the role of N4BP2L1+ CD8+ Teff cells in the pathogenesis of both IPF and SSc-ILD. Through enrichment analysis, we unveiled the complex interplay among programmed necrosis, autophagy, and ferroptosis, highlighting their pivotal role in modulating the activity of N4BP2L1+ CD8+ Teff cells. Conclusions: In essence, the heightened activity of N4BP2L1+ CD8+ Teff cells is implicated in the development of inflammation, fibrosis, and epithelial-mesenchymal transition (EMT), emphasizing its significance in the pathogenesis of both SSc-ILD and IPF. target Mendelian Randomization single-cell RNA sequencing IPF SSc-ILD Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Systemic sclerosis (SSc) is a connective tissue disease of unknown origin characterized by widespread fibrosis, changes in blood vessels, inflammation, and the presence of autoantibodies targeting various cellular antigens as its primary features. Fibrotic tissue remodeling and the resulting complications, such as organ failure, constitute significant contributors to morbidity and mortality in SSc (1). Pulmonary fibrosis is characterized by the gradual replacement of alveolar tissue with fibrotic scars, posing a threat to alveolar gas exchange and diminishing lung compliance. This process leads to an increased workload for breathing and hypoxemia, resulting in the progression of respiratory failure and eventual mortality. While the root cause of pulmonary fibrosis is frequently unknown, as seen in idiopathic pulmonary fibrosis (IPF), it can also be linked to connective tissue diseases, environmental factors such as hypersensitivity pneumonitis, occupational exposures like silicosis and asbestosis, or certain medications in some individuals. Diagnostic methods aimed at distinguishing the various causes of pulmonary fibrosis, including surgical lung biopsy, remain imprecise, and there are limited laboratory indicators predicting responsiveness to treatment (2, 3). Lung fibrosis, a frequent complication seen in SSc and the defining characteristic of idiopathic pulmonary fibrosis (IPF), is linked to significant mortality and currently lacks an approved treatment. Researchers consistently direct their attention to understanding the connection between these two diseases, aiming to delve into the essence of both conditions to determine if the immune response differs between SSc-associated interstitial lung disease (ILD) and IPF. The inquiry revolves around whether SSc-associated ILD (SSc-ILD) and IPF are genuinely distinct entities or merely different points along the same clinical spectrum. Even though research has delved into identifying common targets for SSc-ILD and IPF, and polypharmacology has emerged as an effective therapeutic strategy, exemplified by drugs like the multiple tyrosine kinase inhibitor nintedanib, the search for robust and predictive biomarkers for SSc--ILD and IPF remains a pivotal inquiry (4). Furthermore, there is ongoing examination into the potential role of personalized medicine approaches in directing targeted therapy for SSc-ILD and IPF. To enhance the prognosis of individuals with SSc-ILD or IPF and to uncover new targets that could enrich therapeutic strategies, we carried out single-cell RNA sequencing on lung tissues obtained from two patients diagnosed with IPF, two patients with SSc- ILD, and two healthy donors. We identified noteworthy potential regulatory mechanisms for treating both SSc-ILD and IPF through Mendelian randomization (MR). MR refers to the utilization of genetic variations to investigate causal relationships between modifiable exposures and various outcomes (5). Methods We compiled all the resources, software, and algorithms utilized for analysis in this article, along with their respective identifiers listed in the key resources table (table 1). Table 1: Key resources table Reagent or Resource Source Identifier Deposited data ScRNA-seq GEO GSE122960 Bulk RNA-seq GEO GSE231693 ILD GWAS IEU OpenGWAS project ebi-a- GCST90018643 ILD GWAS IEU OpenGWAS project ebi-a-GCST90018863 Software and algorithms Bioconductor Open source https://www.bioconductor.org Seurat versions Open source https://satijalab.org/seurat/ TwoSample MR Open source https://github.com/MRCIEU/TwoSampleMR Harmony package Open source https://cran.rproject.org/web/packages/harmony/index.html plot1cell package Open source https://github.com/TheHumphreysLab/plot1cell slingshot package Open source https://github.com/kstreet13/slingshot CellChat package Open source https://github.com/sqjin/CellChat coloc package Open source https://cran.r-project.org/web/packages/coloc/index.html scMetabolism package Open source https://github.com/wu-yc/scMetabolism GeneSwitches Open source https://github.com/SGDDNB/GeneSwitches Metascape Open source https://metascape.org/gp/index.html Method details MR analysis To convert the gene IDs of DEGs to Ensembl IDs, we utilized the "org.Hs.eg.db" R package. Subsequently, the eQTL data corresponding to the DEGs were retrieved from the IEU OpenGWAS Project database (https://gwas.mrcieu.ac.uk/). Based on the exposure and outcome data, we explored the potential roles of DEGs of CD8+ Teff cells in SSc-ILD using MR. Initially, we identified independent SNPs associated with eQTLs as genetic instruments. The selection of genetic instruments for MR hinges on three core assumptions: (1) they must be strongly associated with the exposure, (2) they should be independent of any confounder of the exposure-outcome association, and (3) they should not directly influence the outcome, except through their association with the exposure. These assumptions indicate that the instruments should only affect the outcome through their association with the risk factor (6). SNPs failing to meet the significance threshold of genome-wide association (p < 5E-08) and with a minor allele frequency (MAF) < 0.01 were excluded. Following the harmonization of exposure and outcome summary data, we selected independent SNPs with low linkage disequilibrium (LD) (R 2 < 0.01 with strand alignment = 10,000 kb). The R 2 and F-statistic of each variable were used to assess the strength of genetic instruments, calculated using the formula: R 2 = 2 × EAF × (1-EAF) × β 2 F=(N-K-1) / K × (R 2 /1 - R 2 ) ,where R^2 represents the proportion of variability in gene levels explained by each genetic instrument, and EAF denotes the effect allele frequency (7). Genetic instruments with an F-statistic < 10 were excluded. Subsequently, we employed the "TwoSampleMR" R package to assess causal inference between the exposure and outcome (8). For the primary analysis, the Wald ratio method or IVW method was used for MR estimates if only one SNP was available for the gene, while MR-Egger method, simple mode, weighted median method, and weighted mode method were used when two or more instruments were available. The IVW method, considered the most powerful under instrumental variable assumptions, was selected as the primary analysis method if all genetic variants met the necessary criteria (9). A sensitivity analysis was then conducted, including a heterogeneity test based on Cochran’s Q test and an intercept test using MR-Egger regression to assess horizontal pleiotropy of instrumental variables (10). To visualize the MR results, a scatter plot illustrating the effect of SNPs on the exposure versus their effect on the outcome was generated. Additionally, a forest plot was utilized to visualize estimates from multiple instruments. A funnel plot was created to visually evaluate heterogeneity, and a leave-one-out plot was generated to visualize MR estimates when each instrument was left out sequentially. Reverse causality detection Applying identical screening criteria for expression quantitative trait loci (eQTLs), an additional set of genetic instruments for SSc-ILD was chosen from GWAS for bidirectional MR analysis, aimed at detecting potential reverse causality (11). Furthermore, Steiger filtering was conducted to validate the directionality of the association between genes and SSc-ILD (12). Statistical significance was determined at a threshold of P < 0.05. Bayesian co-localization analysis Bayesian co-localization analyses were employed to evaluate the likelihood that two traits share a common causal variant, utilizing the 'coloc' package (https://github.com/chr1swallace/coloc) with default arguments. Bayesian co-localization offers posterior probabilities for five hypotheses concerning the potential sharing of a single variant between two traits (13). Both the coloc.abf and coloc.susie algorithms were utilized in this analysis. ScRNA-seq analysis Data acquisition and processing ScRNA-seq data pertaining to human IPF and SSc-ILD patients and healthy controls were obtained from the GEO database under accession ID GSE122960 (www.ncbi.nlm.nih.gov/geo). We selected sequencing data of two IPF patients (GSM3489183/ GSM3489184), two SSc-ILD patients (GSM3489194/ GSM3489198) and two healthy lung donors (GSM3489182/ GSM3489185) from this database. Using the "Seurat" R package (14), we conducted data preprocessing and transformation, excluding unqualified cells based on specific criteria (gene counts per cell ≤ 200 or ≥ 4000, percent of mitochondrial genes per cell ≥ 10%). Following standard quality control procedures, we normalized and scaled the raw RNA counts using the "NormalizeData" and "ScaleData" functions for downstream analysis. PCA was employed to reduce the data dimensions. The "Harmony" R package facilitated dataset integration and removal of dataset-specific variation within each cluster (15). Subsequently, we applied the "FindNeighbors" and "FindClusters" functions to cluster cells, performed UMAP, and visualized the landscape of all cells and the proportion of each cell type in IPF and SSc-ILD patients and healthy controls using the "plot1cell" R package, annotating cell clusters' marker genes with the "SingleR" R package based on the "Human Primary Cell Atlas Data". To identify cell subtypes within T cells, a second round of clustering was performed on T cells using the same procedure as the first round. After re-clustering T cells, we cross-referenced common T cell markers manually for the annotation of T cell types. Pseudotime analysis and cell-cell interaction analysis Pseudotime analysis was performed using the Slingshot package, following methodologies outlined in prior studies (16). In summary, size factors and dispersions were estimated, and highly variable features were identified within the SingleCellExperiment object. Subsequently, dimensional reduction techniques were applied, and cells were ordered to facilitate pseudotime visualization. Cell-cell interactions were investigated using Cellchat (17, 18), with a focus on including secreted signaling in humans for the analysis of cell-cell interactions (19). Gene enrichment analysis and protein-protein interaction enrichment analysis The Metascape (https://metascape.org/gp/index.html#/main/step1) was used to conduct gene enrichment analysis and protein-protein interaction enrichment analysis with DEGs. Single-cell metabolism analysis R packages were employed to analyze metabolic activity, encompassing wu-yc/scMetabolism and YosefLab/VISION obtained from GitHub, along with AUCell, GSVA, scMetabolism, and rsvd. Bulk RNA-seq analysis We obtained bulk RNA-seq data (GSE231693) from GEO database. The relevant R packages were used to conduct bulk RNA-seq analysis including IOBR/IOBR, preprocessCore, biomaRt, DESeq2 and limma. Switch gene analysis We implemented a comprehensive workflow to analyze gene switches. Firstly, we filtered out genes expressed in less than 5 cells and constructed a SingleCellExperiment object with log-normalized single-cell data. Subsequently, we incorporated pseudotime information to capture the temporal dynamics of gene expression. To handle the high-dimensional nature of the data, we applied dimensionality reduction techniques including PCA, UMAP, and tSNE. Following this, we carefully determined the binarization threshold and conducted binarization analysis to convert continuous gene expression values into binary states. Then, we identified gene switches by filtering the binarized data based on predefined criteria. Additionally, we integrated relevant genes identified from MR results to enrich our analysis. Finally, we presented the outcomes of our investigation. Quantification and statistical analysis All statistical analyses were conducted using R software (version 4.2). IVW, MR-Egger, weighted median, and weighted mode analyses were executed utilizing the R package "TwoSampleMR". To mitigate weak instrumental variable bias, the F-statistic was required to exceed 10. A significance level of p < 0.05 was adopted to establish causal relationships between the two traits. The expression levels of N4BP2L1 in CD8+ Teff cells were compared between SSc-ILD patients and healthy controls using the Wilcoxon test. The overall study design was illustrated in figure 1. Results ScRNA-seq uncovers the subset of T cells in the pathogenesis of both IPF and SSc-ILD We examined single-cell RNA sequencing (scRNA-seq) data derived from samples within the GEO datasets, specifically GSE122960. We selected six lung samples from this dataset, encompassing two patients with IPF, two patients with SSc-ILD, and two lung transplant donors. Following meticulous preprocessing with stringent quality control metrics, we employed the Uniform Manifold Approximation and Projection (UMAP) technique on the high-dimensional scRNA-seq data, focusing on the top 15 principal components. Subsequently, we proficiently classified the cells into 8 subclusters and assigned annotations to discernible cell types using the SingleR R package (figure 2a). The major cell types included B cells, T cells, NK cells, macrophages, monocytes, epithelial cells, endothelial cells and tissue stem cells. Figure2a reveals an upregulation in the number of T cells in both the SSc-ILD group and the IPF group when compared to the donor group. Hence, we further focused on the T cells and again applied the mutual nearest neighbor approach, followed by PCA dimension reduction, to the subset of genes with the highest variability as identified by the Seurat (ver. 3.1.2) FindVariableGenes function (20). Subsequently, we performed clustering at a resolution of 0.6 and sub-clustering at a resolution of 0.2, utilizing Seurat's FindClusters and UMAP visualization. This time, we manually characterized the subtypes of T cells using the markers compiled in the figure2b. Recognizing the distinctive features of CD8+ effector memory T cells (CD8+ Tem) characterized by high GZMK expression and low GAMA expression, we designated cluster 4 as CD8+ Tem. Cluster 5, marked by elevated GZMH, PRF1, and KLRG1 expression, earned the designation CD8+ effector T (CD8+ Teff) cells. Although lacking the defining characteristics of CD8+ Teff cells, cluster 3 exhibited lower GZMK expression and higher GZMA and CCL5 expression, leading us to name them CD8+ central memory T cells. In comparison to cluster 5, cells in cluster 7 displayed almost identical characteristic markers, except for the T cell marker CD3E. Consequently, we identified them as NK cells. Cluster 8 showcased significant expression of XCL1, XCL2, and CD69, indicative of markers for CD8+ resident memory T cells (CD8+ Trm). Additionally, cluster six exhibited distinct expression of Foxp3 and IL2RA, leading us to designate them as CD4+ regulatory T (Treg) cells. Finally, cluster 0, characterized by CD69 and high expression of IL7R and KLRB1, was named CD4+ resident memory T (CD4+ Trm) cells. All the markers were shown in figure 2d. Annotating Cluster 1 and 2 with the selected markers proved challenging. Consequently, we conducted pseudotime developmental trajectory analysis on T cells from three groups, elucidating the relationships among various T cell subsets (figure 2c). Cluster 1 and 2, positioned at the outset of the T cell developmental trajectory, likely represent undifferentiated T cells with a naïve characteristic. Since our subsequent research primarily focused on differentiated T cells, we chose to categorize these clusters simply as T cells without delving deeper into their specific marker details. Subsequently, we employed a bar plot to illustrate the changes in the number of T cell subsets across the control, IPF, and SSc-ILD groups (figure 3c). Our emphasis was on CD8+ Teff cells, as both the IPF and SSc-ILD groups exhibited an upward trend in the quantity of this subset compared to the control group. The upregulation of CD8+ Teff cells suggests their potential promotional role in the pathogenesis of IPF and SSc-ILD. To gain deeper insights into the regulatory function of this subset, we utilized CellChat for an analysis, examining changes in cellular communication between CD8+ Teff cells and other cell types, as depicted in the figure 2e and 2f. Interestingly, CD8+ Teff cells in different disease groups impact similar cells, including macrophages, monocytes, and endothelial cells. However, distinct intercellular communications exist. For instance, in IPF, CD8+ Teff cells act as sources, relying on CCL5 to communicate with the target CCR1, a pattern not observed in the SSc-ILD group (figure 3b). Conversely, in the SSc-ILD group, CD8+ Teff cells can interact with macrophages when ANXA1 combines with the target FPR2, a communication not observed in the IPF group (figure 3a). The shared characteristics in cellular communication of CD8+ Teff cells in both groups, such as interactions with monocytes and macrophages mediated by ANXA1 and FPR1, communication with monocytes through ANXA1 and FPR2, and communication with endothelial cells through CCL5 and ACKR1, raise the possibility that the function of CD8+ Teff cells not only promotes the development of IPF and SSc-ILD but may also have some underlying connections. Identifying a key gene that influences the development of IPF and, simultaneously, serves as a dynamic factor in the generation and development of SSc-ILD becomes a pivotal question for our research. Discovering such a key gene could offer a meaningful therapeutic target for the treatment of both diseases, which share high mortality rates from an immune regulation perspective. Moreover, uncovering this key gene may contribute to a more precise understanding of the relationship between SSc-ILD and IPF, enhancing the accuracy of the cognitive paradigm and prognosis for these two diseases. MR analysis uncovers a target gene in IPF that elucidates the mechanisms underlying the generation and development of SSc-ILD Marker genes for CD8+ Teff cells were identified in comparison to various cell types, as depicted in the table S1 in Document S1. The single nucleotide polymorphisms (SNPs) of these marker genes were utilized as instrumental variables (IV) for the exposure, with ILD disease serving as the outcome. The IV for the exposure were identified within the SNPs associated with ILD, using the dataset from the IEU OpenGWAS project with the accession number ebi-a- GCST90018643. Heterogeneity (Table S2) and pleiotropy tests (Table S3) were carried out, and the outcomes have been documented in the Document S2. Later, we gathered the SNP and gene data with P-values lower than 0.05, signifying that the involvement of the specific CD8+ Teff gene in the pathogenesis of IPF can play a role in regulating SSc-ILD (as shown in table). The volcano plot depicting these genes was presented in the figure 3e. Utilizing MR analysis with the inverse variance-weighted and Wald ratio methods, the forest plot illustrating the SNP and their associated genes exerting a causal impact on SSc-ILD outcomes is presented in the figure 4a (also shown in Table S8 in Document S2). Notably, our findings revealed that the genes NCALD (odds ratio [OR], 1.9628; 95% confidence interval [CI], 1.2830-3.0028; P=0.002) and N4BP2L1 (OR, 1.9761; 95% CI, 1.0881-3.5723; P=0.025) exhibited a promoting effect on the pathogenesis of SSc-ILD. Conversely, the genes GZMH (OR, 0.6635; 95% CI, 0.4613-0.9542; P=0.029) and THOC5 (OR, 0.6828; 95% CI, 0.5339-0.8731; P=0.002) demonstrated a protective effect against the development of SSc-ILD. Subsequently, we conducted MR analysis once more, using the dataset of SNPs associated with ILD(8) from the IEU OpenGWAS project (accession number ebi-a-GCST90018863), to further validate the specific SNP and its corresponding gene accurately (figure 4b and Table S9 in Document S2). We observed that the exposure to N4BP2L1 unequivocally exhibited a promotional effect on the pathogenesis of SSc-ILD (OR, 1.6094; 95% CI, 1.1350-2.2823; P=0.008). Conversely, exposure to THOC5 (OR, 0.7780; 95% CI, 0.6605-0.9163; P=0.003) and GZMH (OR, 0.7209; 95% CI, 0.5986-0.8683; P=0.001) unquestionably demonstrated a protective effect against the development of SSc-ILD. The scatter plot, forest plot, funnel plot, and leave-one-out plot for the genes NCALD, THOC5, and GZMH were compiled in the figure 4d, 4e and 4f. Regarding the dataset with the accession number ebi-a-GCST90018643, it was confirmed through MR analysis that only two SNPs (rs26528, rs20633) associated with the gene N4BP2L1 exhibited a causal effect on the pathogenesis of SSc-ILD. Consequently, scatter plots, forest plots, funnel plots, and leave-one-out plots for this gene were not generated. Given the upregulation of the cellular proportion of CD8+ Teff cells in both IPF and SSc-ILD diseases compared to the healthy lung, our focus centered on genes that may contribute to the promotion of SSc-ILD pathogenesis so that we can better understand the impact of CD8+ Teff cells in fostering SSc-ILD and uncover potential therapeutic targets. Consequently, the gene N4BP2L1 became the primary focus of our following investigation. Sensitive analysis for SSc-ILD causal gene Subsequently, we performed a bidirectional MR analysis, which verified the absence of a causal relationship between SSc-ILD and the gene N4BP2L1 level (OR, 1.0099; 95% CI, 0.9373-1.0882; P=0.795). The SNPs associated with SSc-ILD exposure were obtained from the dataset ebi-a-GCST90018643, and the corresponding forest plot is illustrated in the figure 4c. Then we performed Bayesian co-localization analysis for the primary SNP associated with the causal gene N4BP2L1, namely rs206333 (located on chromosome 13 at position 32997745), exhibiting a lower p-value and higher F-statistics, as illustrated in the Table S3 in Document S2. All SNPs associated with this gene (ID=eqtl-a-ENSG00000139597) were sourced from the IEU OpenGWAS project. The findings of the Bayesian co-localization are detailed in the Table S5 in Document S2. The analysis indicated that the SNP rs206333 within the N4BP2L1 gene may share the same variant with SSc-ILD, as denoted by coloc.abf-PPH4 = 0.108. The regional association plot (figure 3d) illustrates that the SNP rs17635503, situated on chromosome 13 between positions 31997745 and 33997745, may exhibit a robust regional association with SSc-ILD. Finally, Steiger filtering was employed to validate that the established causal relationships between N4BP2L1 and SSc-ILD were unaffected by reverse causation, aligning with the findings from bidirectional MR analysis (as shown in table S6 in Document S2). The combination of scRNA-seq and bulk RNA-seq analyses to confirm the role of N4BP2L1 in the pathogenesis of the disease The feature plot in the figure illustrates a higher density of the gene N4BP2L1 in CD8+ Teff cells compared to other T cell subtypes (figure 5c). Hence, our focus shifted to the gene expression dynamics of CD8+ Teff cells, revealing a correlation between N4BP2L1 expression and CD8+ Teff development. The figure 5d indicates that the presence of the N4BP2L1 gene coincides with a pseudo-timeline around 10.0. This observation prompts us to consider exploring ways to downregulate N4BP2L1 expression based on the regulatory mechanisms during disease onset. Such exploration may unveil additional therapeutic targets for treating IPF and SSc-ILD. Then we conducted intercellular communication analysis between N4BP2L1+ CD8+ Teff cells and other cell types, as well as N4BP2L1- CD8+ Teff cells and other cells. The bubble plot illustrates the intercellular communication involving either N4BP2L1- CD8+ Teff cells or N4BP2L1+ CD8+ Teff cells interacting with other cells. Figure 5a exclusively focuses on the intercellular communication between N4BP2L1- CD8+ Teff cells and other cells due to our random selection of 2000 targets from the data frame containing scRNA-seq information on cellular communication between N4BP2L1- CD8+ Teff cells or N4BP2L1+ CD8+ Teff cells and other cells. To assess the variance between N4BP2L1+ CD8+ Teff cells and N4BP2L1- CD8+ Teff cells, we reanalyzed intercellular communication using the complete dataset, ensuring inclusion of cell-cell communication even in cell groups with minimal representation (as shown in figure 5b). Additionally, we illustrated the metabolic differences between N4BP2L1+ CD8+ Teff cells and N4BP2L1- CD8+ Teff cells, as presented in the figure 6a. These two subsets of CD8+ Teff cells exhibit notable metabolic distinctions. Thiamine metabolism, riboflavin metabolism, glycosphingolipids biosynthesis, and folate biosynthesis are upregulated in N4BP2L1+ CD8+ Teff cells, whereas terpenoid backbone biosynthesis is upregulated in N4BP2L1- CD8+ Teff cells. The metabolic differences may provide insights into the potential role of N4BP2L1+ CD8+ Teff cells in promoting the pathogenesis of IPF and SSc-ILD, while also suggesting potential therapeutic strategies based on metabolic regulation to mitigate the impact of N4BP2L1- CD8+ Teff cells. We generated a heatmap illustrating the differentially expressed gene (DEG) using the GSE231693 bulk RNA-seq dataset (figure 6c). We specifically selected the healthy group and SSc-ILD group for analysis. The results clearly indicate an upregulation of the N4BP2L1 gene in the SSc-ILD group when compared to the healthy group. The analysis of gene enrichment in N4BP2L1+ CD8+ Teff cells We compiled a list of genes that were upregulated in N4BP2L1+ CD8+ Teff cells when compared to N4BP2L1- CD8+ Teff cells (see Table S7 in Document S1). Subsequently, these upregulated genes were employed for conducting gene enrichment analysis. As depicted in Figure 6d, the top 20 clusters were presented along with their respective enriched terms. Our attention was directed towards the TNFR1-mediated ceramide production, lipid biosynthetic process, and autophagy, which could suggest the involvement of N4BP2L1-expressing CD8+ Teff cells in the pathogenesis of IPF and SSc-ILD. The relevant analysis will be demonstrated in detail in discussion section. We also conducted protein-protein interaction network analysis (figure 6b). For each specified gene list, we conducted protein-protein interaction enrichment analysis using the following databases: STRING (21), BioGrid (22), OmniPath (23), and InWeb_IM (23). Only physical interactions meeting a stringent criterion in STRING (physical score > 0.132) and those from BioGrid were considered. The resulting network comprises proteins forming physical interactions with at least one other member within the list. Should the network consist of between 3 and 500 proteins, we applied the Molecular Complex Detection (MCODE) algorithm to identify densely connected network components (24). Subsequently, MCODE networks identified for individual gene lists were collated and are presented in table 2. Pathway and process enrichment analyses were then independently conducted for each MCODE component. The three terms with the most significant p-values were selected as the functional description for the respective components, displayed in table 2. Table 1: The top 20 clusters are accompanied by their representative enriched terms, with one term per cluster. "Count" indicates the number of genes within the lists that belong to the specified ontology term. "%" represents the percentage of all genes found in the specified ontology term (only input genes with at least one ontology term annotation are considered in the calculation). "Log10(P)" denotes the p-value in base 10, while "Log10(q)" indicates the multi-test adjusted p-value in base 10. GO Category Description Count % Log10(P) Log10(q) GO:0070988 GO Biological Processes demethylation 7 2.15 -6.31 -1.96 GO:0006325 GO Biological Processes chromatin organization 24 7.38 -5.24 -1.47 GO:0051043 GO Biological Processes regulation of membrane protein ectodomain proteolysis 5 1.54 -5.04 -1.47 R-HSA-5626978 Reactome Gene Sets TNFR1-mediated ceramide production 3 0.92 -4.62 -1.18 GO:0008610 GO Biological Processes lipid biosynthetic process 19 5.85 -4.57 -1.18 R-HSA-948021 Reactome Gene Sets Transport to the Golgi and subsequent modification 10 3.08 -4.47 -1.14 GO:0072529 GO Biological Processes pyrimidine-containing compound catabolic process 5 1.54 -4.34 -1.14 GO:0007623 GO Biological Processes circadian rhythm 8 2.46 -3.96 -0.82 GO:0009991 GO Biological Processes response to extracellular stimulus 16 4.92 -3.89 -0.8 R-HSA-383280 Reactome Gene Sets Nuclear Receptor transcription pathway 5 1.54 -3.63 -0.69 GO:0006482 GO Biological Processes protein demethylation 3 0.92 -3.6 -0.69 GO:0033866 GO Biological Processes nucleoside bisphosphate biosynthetic process 5 1.54 -3.59 -0.69 GO:0006644 GO Biological Processes phospholipid metabolic process 12 3.69 -3.25 -0.38 GO:0014857 GO Biological Processes regulation of skeletal muscle cell proliferation 3 0.92 -3.21 -0.37 GO:0032259 GO Biological Processes methylation 9 2.77 -2.78 -0.15 GO:0008380 GO Biological Processes RNA splicing 12 3.69 -2.75 -0.15 GO:0006914 GO Biological Processes autophagy 10 3.08 -2.68 -0.15 GO:0032288 GO Biological Processes myelin assembly 3 0.92 -2.68 -0.15 GO:0040029 GO Biological Processes epigenetic regulation of gene expression 7 2.15 -2.66 -0.15 R-HSA-9716542 Reactome Gene Sets Signaling by Rho GTPases, Miro GTPases and RHOBTB3 17 5.23 -2.65 -0.14 Table 2: MCODE networks identified for individual gene lists and the three terms with the most significant p-values. Color MCODE GO Description Log10(P) Red MCODE_1 R-HSA-5626978 TNFR1-mediated ceramide production -8.1 Red MCODE_1 M128 PID TNF PATHWAY -5.2 Red MCODE_1 R-HSA-75893 TNF signaling -5 Blue MCODE_2 R-HSA-199977 ER to Golgi Anterograde Transport -9.4 Blue MCODE_2 R-HSA-948021 Transport to the Golgi and subsequent modification -9 Blue MCODE_2 R-HSA-204005 COPII-mediated vesicle transport -8.5 Green MCODE_3 R-HSA-5625886 Activated PKN1 stimulates transcription of AR (androgen receptor) regulated genes KLK2 and KLK3 -9.5 Green MCODE_3 R-HSA-2262752 Cellular responses to stress -9.5 Green MCODE_3 R-HSA-5625740 RHO GTPases activate PKNs -8.9 Purple MCODE_4 R-HSA-212165 Epigenetic regulation of gene expression -7.7 Purple MCODE_4 GO:0045943 positive regulation of transcription by RNA polymerase I -7.5 Purple MCODE_4 GO:0006356 regulation of transcription by RNA polymerase I -7.1 Orange MCODE_5 R-HSA-72163 mRNA Splicing - Major Pathway -8.7 Orange MCODE_5 R-HSA-72172 mRNA Splicing -8.6 Orange MCODE_5 GO:0000398 mRNA splicing, via spliceosome -8.2 Brown MCODE_7 R-HSA-6811438 Intra-Golgi traffic -8.5 Brown MCODE_7 R-HSA-6811442 Intra-Golgi and retrograde Golgi-to-ER traffic -6.5 Brown MCODE_7 R-HSA-199991 Membrane Trafficking -5 Discussion In recent years, therapeutic strategies for IPF and SSc-ILD have shifted towards specific target exploration due to the multiple adverse reactions and poor prognosis associated with polypharmacology drugs. For example, the c-ABL/SRC tyrosine kinase inhibitor bosutinib (SKI-606) has demonstrated reduced fibrotic gene expression both in vitro and in TBR-induced pulmonary fibrosis (25, 26). Another multitarget tyrosine kinase inhibitor, anlotinib (AL3818), approved for IPF treatment, has shown attenuation of bleomycin-induced pulmonary fibrosis by suppressing TGFβ signaling (27). However, both drugs have exhibited multiple adverse reactions. While dasatinib treatment was well tolerated in SSc-ILD patients, only 39% showed improvement (28). Given the cost-effectiveness of clinical experiments to confirm drugs for potential targets and the limited population of SSc patients with progressive fibrotic manifestations, it is essential to develop additional strategies to explore effective drug candidates. According to the MR analysis findings, we confirmed the involvement of N4BP2L1 in CD8+ Teff cells in the onset of IPF and its contribution to the progression of SSc-ILD. To delve deeper into the potential significance of N4BP2L1, we initially examined the role of CD8+ Teff cells in either IPF or SSc-ILD. In fibrotic human lungs, the distribution of CD8+ T cells tends to be more widespread throughout the lung parenchyma and alveolar walls (29). Elevated CD8+ T cell levels in lung biopsies from IPF patients have been associated with reduced total lung capacity and forced vital capacity. Additionally, an increase in CD28-negative CD8+ T cells has been observed in IPF patient lung samples, showing a transcriptional profile that favors fibrosis and inflammation, alongside heightened PD-1 expression, indicating chronic activation (30). The severity of dyspnea and functional parameters associated with disease severity are correlated with CD8+ T cells (29, 31). Studies in a bleomycin-induced pulmonary fibrosis model have revealed that CD8+ T cells can transform into profibrotic IL-13–producing cells via IL-21 receptor stimulation. Furthermore, treatment with IL-4 and IL-21 prompts these cells to produce more IL-21, further fostering IL-13 production in an autocrine manner (32). Collectively, evidence on CD8+ T cells underscores their detrimental role in the initiation and advancement of pulmonary fibrosis, suggesting a potential avenue for therapeutic intervention. Moreover, additional discoveries indicate that CD8+ T cells play a role not only in promoting fibrosis but also in contributing to microvasculopathy (33). Additionally, the cytotoxic activity facilitated by granzyme B and perforin can generate autoantigens, which trigger and/or sustain immune responses. Notably, fragments of self-proteins produced by granzyme B were targeted by autoantibodies in systemic sclerosis (34). A study investigating the connection between ILD and CD8+ T cells found that lung tissues affected by ILD had higher levels of CD8+ T cells compared to those without ILD. Additionally, an examination of T cell function in lung tissues revealed a positive association between CD103+CD8+ T cells and IFNγ production (35). Although these findings did not clarify the direct participation of CD8+ Teff cells, they did affirm the potential contribution of CD8+ T cells to the pathogenesis of IPF and SSc-ILD. This is consistent with our sc-RNA sequencing results, which revealed changes in the proportion of CD8+ Teff cells. It further bolsters the hypothesis that the functions of T cell subsets may parallel those of previously reported CD8+ T cells. To further elucidate the potential impact of CD8+ Teff on the pathogenesis of IPF and SSc-ILD, we conducted an analysis of intercellular communication between this T cell subset and others. ANXA1, originating from CD8+ Teff, has been demonstrated to elicit various effects concerning the adhesion and migration of leukocytes, which are crucial processes in the initiation of the inflammatory response. The dual role of ANXA1 in either promoting or mitigating inflammation has been documented in numerous studies (36-38). These investigations underscore the multifaceted nature of the actions of ANXA1 and its breakdown products, emphasizing the need for comprehensive research within the intricate in vivo milieu to fully comprehend their roles in inflammatory processes. The expression of CCL5 plays a significant role in lung fibrosis, as it is associated with the presence of intrapulmonary fibrocytes and the accumulation of collagen (39). Collectively, through the analysis of sc-RNA seq, we initially identified the specific subtype of T cells of interest, which we then subjected to further MR analysis to uncover a deeper understanding of the relationship between IPF and SSc-ILD. The MR analysis led us to identify the N4BP2L1 gene, which appears to potentially promote the development of SSc-ILD, while also demonstrating a significant association with IPF. N4BP2 belongs to the NEDD4 binding partner family and interacts with the B cell lymphoma 3-encoded protein (Bcl-3) (40, 41). While N4BP2L1 shares homology with N4BP2, its specific function and expression in relation to fibrosis and inflammation are still unclear. The N-terminal region of N4BP2 contains a polynucleotide kinase domain, while its C terminus is characterized by a small mutator S (MutS)-related (Smr) domain (41). A previous study has established that miR-448 can modulate the expression of N4BP2L1, offering the possibility for subsequent validation via animal or cell experiments (42). Additionally, the suppression of miR-448 promotes the initiation of epithelial–mesenchymal transition (EMT) (43). Further investigation into the relationship between this miRNA and the potential role of N4BP2L1 in promoting fibrosis and inflammation by CD8+ Teff cells may be pursued by confirming the induction of EMT by CD8+ Teff cells. We will concentrate on exploring the potential role of N4BP2L1+ CD8+ Teff cells in inducing EMT. The relevant discussion will be presented subsequently. While our primary focus was on investigating the involvement of genes in promoting the pathogenesis of either IPF or SSc-ILD, the analysis of MR also revealed two genes, THOC5 and GZMH, which exhibited an evidently protective role in the development of these diseases. The THO complex, a constituent of the TREX (transcription/export) complex, was initially recognized as a five-protein ensemble (Tho2p, Hpr1p, Mft1p, Thp2p, and Tex1), serving roles in transcriptional elongation, nuclear RNA export, and maintaining genome stability (44, 45). The THO complex regulates RNA 3′ processing, preventing R-loop formation and exporting a specific set of mRNAs. The absence of the THO complex in yeast cells initially revealed evidence implicating R-loops as contributors to genome instability (46). Additionally, THOC5 is pivotal in stem cell and cancer cell biology, subject to post-translational regulation by stem cell ligands (CXCL12), oxidative stress, and downstream effects of oncogenes (47-50). The perforin/granzyme (Gzm) pathway stands as a prominent mechanism utilized by cytotoxic T lymphocytes for target cell destruction (51, 52). These Gzms represent a collection of serine proteases that are deeply conserved across both humans and rodents. While GzmH maps to the identical gene cluster and exhibits considerable similarity to GzmB, it possesses distinct enzymatic specificity. A forthcoming study has demonstrated that GzmH induces a gradual form of cell death irrespective of caspase activation (53). Additionally, another investigation illustrated that GzmH induces swift cell demise in target tumor cells, marked by DNA fragmentation and mitochondrial impairment (54). Further exploration is required to understand the involvement of THOC5 and GzmH in protecting against the pathogenesis of IPF or SSc-ILD. It could be conjectured that THOC5 plays a role in maintaining genome stability in CD8+ Teff, while GzmH may regulate the cellular death of CD8+ Teff, potentially aiding in transforming the inflammatory, oxidative stress, and pro-fibrosis environment into a state of homeostasis. Whether promoting these mechanisms could serve as therapeutic strategies for patients with IPF or SSc-ILD remains subject to further investigation. We successfully demonstrated the involvement of N4BP2L1 in CD8+ Teff. As depicted in Figure 5, the expression of N4BP2L1 initiates during the maturation phase of CD8+ Teff. Upon closer examination of metabolic pathway alterations between N4BP2L1+ CD8+ Teff and N4BP2L1- CD8+ Teff, it becomes apparent that metabolic pathways associated with glycosphingolipid biosynthesis are markedly upregulated in N4BP2L1+ CD8+ Teff compared to N4BP2L1- CD8+ Teff. Glycosphingolipids (GSLs) consist of sugar structures that are hydrophilic and ceramide, which is hydrophobic (55). The cis interactions among the sugar moiety of GSLs promote lateral associations of these molecules with other membrane components. These interactions culminate in the creation of specialized cellular membrane microdomains, known as lipid rafts. Lipid rafts are enriched regions comprising cholesterol, GSLs, sphingomyelin, glycosylphosphatidylinositol, and membrane-anchored molecules (56). Gangliosides play a role in the function and activation of CD8+ T cell receptors (TCRs) (57). In CD8+ Teff cells, o-series gangliosides assist in recruiting TCRs to specific regions of the cell membrane, namely lipid rafts, where they contribute to the activation process of these cells. Furthermore, GSLs play critical roles in the apoptotic pathways of adaptive immune cells. Moreover, GSLs are integral in the formation of lipid rafts not only on plasma membranes but also on subcellular compartments. These GSL-enriched lipid rafts within subcellular domains facilitate essential signaling pathways involved in various physiological functions. Prior findings indicate that lipid raft-like domains in mitochondria-associated membranes (MAM) are significant for the organelle scrambling activity, which is essential for autophagosome biogenesis (58). The previous study confirmed that the activation and cytolytic granule secretion of cytotoxic lymphocytes (CLs) necessitate the presence of the glycosylphosphatidylinositol (GPI) biosynthetic pathway (59). In N4BP2L1+ CD8+ Teff cells, there is a clear upregulation of these metabolic pathways, suggesting a potential amplification of the pro-inflammatory effect through the modulation of CD8+ Teff TCR function and activation, autophagy, and cytolytic granule secretion. Further investigation is needed to determine if additional regulatory mechanisms are at play in this CD8+ Teff subset and to elucidate their impact on neighboring cells. Our understanding of their role is derived from an analysis of intercellular communication analysis. The distinction between figures 6 and 7 serves as a reminder that N4BP2L1+ CD8+ T cells likely play a significant role in the pathogenesis of SSc-ILD by regulating monocytes through ANXA1. The interaction between ANXA1 and the receptor FPR2 on monocytes may trigger the activation of the p38 MAPK/MAPKAPK/HSP27 signaling cascade (60). Further exploration is required to understand how the regulation of monocytes influences the inflammatory or pro-fibrotic environment. Moreover, the heightened intercellular communication between N4BP2L1- CD8+ T cells and tissue stem cells facilitated by the GZMA and FR2 interaction implies a potential protective role of N4BP2L1- CD8+ T cells in targeting stem cells to combat fibrosis. It's worth noting that the presence of N4BP2L1 may potentially reverse this condition, but further investigation is warranted to confirm this hypothesis. The discovery of mutual interaction through MIF between N4BP2L1- CD8+ T cells and the co-expression of CD74 and CXCR4 on B cells indicates that elevated MIF levels attract B cells expressing CXCR4 and CD74. Upon binding to their ligands, this interaction forms a complex that activates survival pathways, thus inhibiting cell death (61). Additional investigation is essential to grasp the significance of B cell survival in the regulation of IPF or SSc-ILD. It has been verified that regulatory B cells (Bregs) are notably reduced in SSc-ILD (62). Breg cells demonstrate phenotypical and functional impairment in SSc patients. Moreover, in SSc, B cells show impaired activation of p38 MAPK and STAT-3 upon stimulation via BCR and TLR-9. Further exploration is needed to determine whether the function of N4BP2L1- CD8+ T cells can mitigate the dysfunction observed in B cells, and whether the presence of N4BP2L1 once again inhibits this functional role. The upregulated genes in N4BP2L1+ CD8+ Teff cells may potentially play a role in promoting the pathogenesis of IPF or SSc-ILD. Therefore, we performed gene enrichment analysis to identify the unique functions of these cells. TNFR1-mediated ceramide production was obviously upregulated in N4BP2L1+ CD8+ Teff cells compared to N4BP2L1- CD8+ Teff cells. In numerous studies, TNF-mediated programmed necrosis has been linked to the biological and mechanistic roles of TNF-α and its interaction with TNFR-1, particularly in the context of pharmacological or genetic apoptosis inhibition (63, 64). Elevated intracellular ceramide levels have been associated with heightened redox reactions within cells, hinting at potential crosstalk among ceramide, ROS, and TNF-α pathways in this phenomenon (65, 66). The increased pathway activity suggests a close association between heightened mitochondrial ROS production, activation of programmed necrosis, and the pathogenic role of N4BP2L1+ CD8+ Teff cells in IPF and SSc-ILD. Compared to N4BP2L1- CD8+ Teff cells, both lipid biosynthetic processes and autophagy were upregulated in N4BP2L1+ CD8+ Teff cells. The previous study showed that mice with Atg7-deficient CD8+ T cells exhibited impaired memory development in influenza or murine cytomegalovirus models, along with reduced recall responses to secondary influenza infection (67). Likewise, in a granzyme-Cre Atg7fl/fl mouse model, CD8+ T cells demonstrated intrinsic deficiencies in memory formation following lymphocytic choriomeningitis virus infection (68). Autophagy activity may surge at the peak of CD8+ Teff responses, suggesting its potential importance during the transition from Teff to memory (Tmem) CD8+ cells. Both studies also associated defects in CD8+ T cell memory formation with the inability to trigger metabolic shifts, particularly the failure to enhance fatty acid oxidation, a critical process in CD8+ T cell memory formation (69). Therefore, the heightened pathway involving both lipid biosynthetic processes and autophagy suggests a heightened activity of CD8+ Teff cells and a substantial transition to CD8+ Tmem cells, characterized by increased expression of N4BP2L1. It is reasonable to deduce that N4BP2L1+ CD8+ Teff cells exhibit enhanced activity and contribute to inflammation and fibrosis. This deduction aligns with previous findings indicating that depletion of CD8+ T cells or perforin deficiency significantly attenuated bleomycin-induced pulmonary fibrosis (70, 71). Moreover, earlier research demonstrated that CD8+ T cell cytotoxicity, alongside IL-13 production, could play a pivotal role in inflammation and fibrosis, further supporting our interpretation (32). To deepen our comprehension of the function of CD8+ Teff cell activity and its role in pathogenesis, we undertake a comprehensive analysis of the potential involvement of lipid biosynthetic processes, autophagy, and TNFR1-mediated ceramide production in regulating regulated cellular death (RCD) within CD8+ Teff cells. The increased production of mitochondrial ROS triggered by TNFR1-mediated ceramide synthesis and the elevation of autophagy levels might suggest the involvement of mitophagy in the development of IPF or SSc-ILD influenced by CD8+ Teff cells. The heightened production of mitochondrial ROS may stimulate mitophagy, as mitophagy selectively degrades mitochondria to eliminate dysfunctional organelles and reduce ROS levels. Mitophagy, a form of selective autophagy, selectively targets mitochondria for degradation, clearing dysfunctional organelles and reducing ROS levels. Identified cargo receptors involved in mitophagy include CALCOCO2, OPTN, SQSTM1, TAX1BP1, and others. Meanwhile, mitochondrial ROS plays a crucial role in inducing autophagy (72). Overall, our speculation suggests that TNFR1-mediated ceramide synthesis induces ROS production, potentially promoting mitophagy in CD8+ Teff cells, as evidenced by the upregulation of autophagy observed in gene enrichment analysis. Moreover, mitophagy can facilitate the development of effector memory in antigen-specific CD8+ T cells, suggesting that the heightened activity of N4BP2L1+ CD8+ T cells might contribute to the onset of inflammation and fibrosis (73). Furthermore, considering the induction of ROS generation and the upregulation of autophagy and lipid biosynthetic processes, we further speculated that ferroptosis may also play a crucial in the regulating activity of N4BP2L1+ CD8+ T cells. Ferroptosis ensues from uncontrolled and lethal lipid peroxidation, leading to the rupture of the plasma membrane facilitated by iron catalysis, which encompasses both non-enzymatic (Fenton reaction) and enzymatic (lipoxygenases) mechanisms. Consequently, lipid peroxidation emerges as a pivotal signaling event. And mitophagy can regulate lipid peroxidation by modulating mitochondrial function. The upregulation of ROS may induce the upregulation in mitophagy, while concurrently promoting ferroptosis, as evidenced by the upregulation of lipid biosynthetic processes. However, it is noteworthy that the upregulation of mitophagy itself can also promote ferroptotic cell death, as demonstrated in previous studies (74, 75). Further exploration is needed to determine whether the upregulation of ferroptosis, mitophagy, and programmed necrosis may directly regulate the activity of CD8+ Teff cells, or if proinflammatory or profibrotic cytokines contribute to the development of inflammation or fibrosis. It is also important to investigate whether these factors influence the phenotype of CD8+ Teff cells or attract other cell types, leading to inflammatory injury and fibrosis, thereby promoting the pathogenesis of IPF or SSc-ILD. Following the protein-protein interaction enrichment analysis, our focus shifted to nuclear receptor co-repressor 1 (NCOR1), chromobox homolog 5 (CBX5), and O-glcNAc transferase (OGT), as these proteins were deemed more pivotal in interacting and regulating compared to others in the enrichment analysis. The involvement of CBX5 and OGT in the pathogenesis of pulmonary fibrosis has been previously established (76, 77). However, further exploration is required to determine their role in regulating the activity of CD8+ Teff cells. Intriguingly, NCOR1, known as a negative regulator of the nuclear receptor PPARα, has previously been shown to serve a physiological role in selective autophagy, enabling the regulation of lipid metabolism through selective turnover (78). The activation of the protein appears to have a strong association with the upregulated pathways of lipid biosynthetic processes and autophagy, suggesting the significant involvement of ferroptosis in CD8+ N4BP2L1 Teff cells. All the evident directed us towards to the enhanced activity of CD8+ Teff cells. Is there any connection between this result and EMT? The EMT program confers various immunosuppressive properties to mesenchymal cells (79). Therefore, it is reasonable to speculate that epithelial cells, under the heightened activity of CD8+ Teff cells, initiate the EMT program to shield themselves from immune damage. However, this mechanism ultimately promotes the pathogenesis of IPF or SSc-ILD. Conclusions Overall, our study, employing scRNA-seq and MR analysis, has identified N4BP2L1 as a gene target and CD8+ Teff cells as a cell type target for the development of therapeutic strategies in patients with SSc-ILD. Utilizing gene enrichment analysis, protein-protein interaction enrichment analysis, cell-cell interaction analysis, switch gene analysis, single-cell metabolism analysis, and bulk RNA-seq analysis, we have elucidated the role of N4BP2L1 in CD8+ Teff cells in regulating the pathogenesis of IPF and SSc-ILD. On one hand, our findings confirm potential regulatory pathways, proteins, alterations in gene expression, metabolism pathways, and interactions with other cell types that may be influenced by N4BP2L1 in CD8+ Teff cells. On the other hand, we have provided insights into multiple potential downstream regulatory mechanisms that may be modulated by N4BP2L1 in CD8+ Teff cells, warranting further investigation. The elucidation of connections between these downstream regulatory networks and inflammation or fibrosis promoted by CD8+ Teff cells holds promise for the development of novel therapeutic strategies for these challenging diseases. Our primary focus on elucidating the causal relationship between N4BP2L1 and SSc-ILD, rather than IPF, stemmed from several factors. SSc is recognized as a rare and heterogeneous chronic connective tissue disease characterized by progressive fibrosis affecting both the skin and internal organs (80, 81). Importantly, ILD is a common manifestation of SSc, impacting approximately 35–52% of patients (82). By concentrating on SSc-ILD, we aimed to delve deeper into understanding the relationship between N4BP2L1 and the pathogenesis of systemic sclerosis, a broader condition encompassing various organ involvements beyond the lungs. Our intention was to identify targets that could shed light on the underlying mechanisms of SSc, ultimately paving the way for the development of comprehensive treatment algorithms for this complex disease. Limitations of the study The study conducted herein relied on bioinformatic approaches. Further experiments are warranted to validate the role of N4BP2L1 in CD8+ Teff cells as a therapeutic target for SSc-ILD and potentially IPF. Experimental validation could involve gene knockout or downregulation of N4BP2L1 in CD8+ Teff cells to ascertain its impact on inflammation and fibrosis. Additionally, investigating whether downregulation of this gene affects pathways such as autophagy, lipid biosynthesis, and intercellular interactions, which may contribute to inflammation and fibrosis generation, is essential. Moreover, understanding how various forms of RCD such as programmed necrosis, mitophagy, and fibrosis are influenced by N4BP2L1, and how they influence the activity of CD8+ Teff cells, requires further experimental confirmation. Abbreviations CD8+ Tem: CD8+ effector memory T cells; CI: Confidence Interval; DEG: Differentially Expressed Gene; ILD: Interstitial Lung Disease; IPF: Idiopathic Pulmonary Fibrosis; IV: Instrumental Variables; MR: Mendelian Randomization; NK cells: Natural Killer cells; OR: Odds Ratio; SSc-ILD: Systemic Sclerosis-associated Interstitial Lung Disease; SNP: Single Nucleotide Polymorphism; TCRs: T Cell Receptors; TGFβ: Transforming Growth Factor Beta; scRNA-seq: Single-cell RNA sequencing Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials Lead contact Further information and requests should be directed to the lead contact, Ying Wei ( [email protected] ). Materials availability This study did not generate new unique reagents. Data and code availability Publicly available data can be downloaded from the sources provided from “Key resources table”. Upon request, the lead contact can provide the necessary code for reanalyzing the data presented in this report. Competing interests All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding: This work was supported by grants from the National Natural Science Foundation of China (Grant No. 82174495). Author contributions Conceptualization: X.L., W.T. Formal analysis: X.L., W.T.. Investigation: S.O., F.Y., W.W., X.D. and Y.W. Methodology: X.L., W.T., S.O. Supervision: W.W., X.D. and Y.W.. Writing—original draft: X.L. Writing—review & editing: W.W., X.D. and Y.W. Acknowledgements We thank Shaocong Mo for guiding the MR analysis process. The datasets supporting the conclusions of this article are included within the article and supplemental information. 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Journal of clinical pathology. 2004;57(12):1292-8. Li C, Zhang Y, Liu J, Kang R, Klionsky DJ, Tang D. Mitochondrial DNA stress triggers autophagy-dependent ferroptotic death. Autophagy. 2021;17(4):948-60. Gupta SS, Sharp R, Hofferek C, Kuai L, Dorn GW, Wang J, et al. NIX-mediated mitophagy promotes effector memory formation in antigen-specific CD8+ T cells. Cell reports. 2019;29(7):1862-77. e7. Basit F, Van Oppen LM, Schöckel L, Bossenbroek HM, Van Emst-de Vries SE, Hermeling JC, et al. Mitochondrial complex I inhibition triggers a mitophagy-dependent ROS increase leading to necroptosis and ferroptosis in melanoma cells. Cell death & disease. 2017;8(3):e2716-e. Chang L-C, Chiang S-K, Chen S-E, Yu Y-L, Chou R-H, Chang W-C. Heme oxygenase-1 mediates BAY 11–7085 induced ferroptosis. Cancer letters. 2018;416:124-37. Vang S, Helton E, Thannickal V, Krick S, Barnes J. O-GlcNAc Transferase Regulates TGFb1 Signaling in Idiopathic Pulmonary Fibrosis. C98 ADVANCES IN IDIOPATHIC PULMONARY FIBROSIS: FROM MECHANISMS TO PROGRESSION: American Thoracic Society; 2020. p. A5991-A. Ligresti G, Caporarello N, Meridew JA, Jones DL, Tan Q, Choi KM, et al. CBX5/G9a/H3K9me-mediated gene repression is essential to fibroblast activation during lung fibrosis. JCI insight. 2019;4(12). Saito T, Kuma A, Sugiura Y, Ichimura Y, Obata M, Kitamura H, et al. Autophagy regulates lipid metabolism through selective turnover of NCoR1. Nature communications. 2019;10(1):1567. Camp FA, Brunetti TM, Williams MM, Christenson JL, Sreekanth V, Costello JC, et al. Antigens Expressed by Breast Cancer Cells Undergoing EMT Stimulate Cytotoxic CD8+ T Cell Immunity. Cancers. 2022;14(18):4397. Initiative ARC. 2013 Classification Criteria for Systemic Sclerosis. Arthritis & Rheumatism. 2013;65(11):2737-47. Jaeger VK, Wirz EG, Allanore Y, Rossbach P, Riemekasten G, Hachulla E, et al. Incidences and risk factors of organ manifestations in the early course of systemic sclerosis: a longitudinal EUSTAR study. PloS one. 2016;11(10):e0163894. Bergamasco A, Hartmann N, Wallace L, Verpillat P. Epidemiology of systemic sclerosis and systemic sclerosis-associated interstitial lung disease. Clinical epidemiology. 2019:257-73. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.zip 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4307133","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":294455176,"identity":"59c5e0e7-8d98-4e7f-bed6-3ecafd32a8a7","order_by":0,"name":"Xiaodi Lv","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Xiaodi","middleName":"","lastName":"Lv","suffix":""},{"id":294455177,"identity":"2aa364e2-9340-4632-8540-5cf2498ba179","order_by":1,"name":"Weifeng Tang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Weifeng","middleName":"","lastName":"Tang","suffix":""},{"id":294455178,"identity":"7c535132-020c-46b8-9f3b-9e6b1501720a","order_by":2,"name":"Silin Ou","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Silin","middleName":"","lastName":"Ou","suffix":""},{"id":294455180,"identity":"a32d0c4e-a1cc-4409-8a93-f28af1903fd6","order_by":3,"name":"Fangyong Yang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Fangyong","middleName":"","lastName":"Yang","suffix":""},{"id":294455182,"identity":"6094a55e-5fde-4dbb-b9bd-bb5da7d0acbe","order_by":4,"name":"Wenqian Wang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Wenqian","middleName":"","lastName":"Wang","suffix":""},{"id":294455184,"identity":"81a7e19c-626a-46b1-8a40-a4ec192eb70f","order_by":5,"name":"Xiaohong Duan","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Xiaohong","middleName":"","lastName":"Duan","suffix":""},{"id":294455186,"identity":"7b1092a1-72f3-4f08-9e76-0e9965f1c366","order_by":6,"name":"Ying Wei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYFACxgaGBBj7g4GNHGlaGGcUpBmTZiEzz4fDiQRVGRxvbpN4uKOWgb/9jNljGwPmBAb2w0c34NVy5mCbROKZ4wwSZ3LMjXMM2PIYeNLSbuDTYnYjEail7RgDww0eM+kcA55iBgkeM/xa7j+EaJEHabEwkEhsIKjlBiNISw2DAUgLg4EBYS32ZxKbLRLbDjAYnkkrN+wxSDBmI+QXyfbjD2/+bKtjkDt+eNuDH3/+y/GzHz6GVwsQsEgwMByub2BgYANz2QgoBwHmDwwMdcQqHgWjYBSMgpEIAMMqSfGCjo91AAAAAElFTkSuQmCC","orcid":"","institution":"Fudan University","correspondingAuthor":true,"prefix":"","firstName":"Ying","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2024-04-22 16:06:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4307133/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4307133/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55394781,"identity":"b6855713-9111-4099-8dd7-50dcd4ae5de9","added_by":"auto","created_at":"2024-04-26 16:44:13","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":716032,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design for validating N4BP2L1 as a gene target\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4307133/v1/487f0c17c26179f176964329.jpg"},{"id":55394839,"identity":"2555c022-5bec-4fa9-bba6-bba2305f8d8e","added_by":"auto","created_at":"2024-04-26 16:44:15","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1016849,"visible":true,"origin":"","legend":"\u003cp\u003ea. The clusters of entire cells using scRNA-seq; b. The subtypes of T cells using scRNA-seq; c. Pseudotime analysis of T cells; d. The markers of T cells; e. Cell-cell interaction analysis in IPF group; f. Cell-cell interaction analysis in SSc-ILD group\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4307133/v1/5080f1bd6350d83185288ea3.jpg"},{"id":55394838,"identity":"d8af6b0d-f31a-4409-82b9-e3fbd3791acf","added_by":"auto","created_at":"2024-04-26 16:44:15","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":598516,"visible":true,"origin":"","legend":"\u003cp\u003ea. Analysis of cell-cell interactions between CD8+ Teff cells and receptors on other cells in the SSc-ILD group; b. Analysis of cell-cell interactions between CD8+ Teff cells and receptors on other cells in the IPF group; c. The changes in the number of T cell subsets across the control, IPF, and SSc-ILD groups; d. Bayesian co-localization analysis for the SNPs associated with the causal gene N4BP2L1 and SSc-ILD e. The volcano plot depicting MR results\u003c/p\u003e","description":"","filename":"figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4307133/v1/5ac7aa18b4918591c9a16135.jpg"},{"id":55394834,"identity":"8e51e703-512e-4019-adcb-fbd9dc7023e5","added_by":"auto","created_at":"2024-04-26 16:44:14","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1077758,"visible":true,"origin":"","legend":"\u003cp\u003ea. Forest plot depicting MR results; b. The validation of the causal relationship between causal SNPs and SSc-ILD MR analysis using outcome data from another datasets; c. Reverse causality by MR analysis; d. Sensitive analysis of NCALD gene; e. Sensitive analysis of THOC5 gene; f. Sensitive analysis of GZMH gene\u003c/p\u003e","description":"","filename":"figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4307133/v1/ef1e0939e0699a5a4d39356c.jpg"},{"id":55394835,"identity":"31e0f127-5d1d-4089-a406-c833ede7116e","added_by":"auto","created_at":"2024-04-26 16:44:14","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":549333,"visible":true,"origin":"","legend":"\u003cp\u003ea. Cellular communication between N4BP2L1- CD8+ Teff cells and other cells; b. Cellular communication between N4BP2L1- CD8+ Teff cells or N4BP2L1+ CD8+ Teff cells and other cells; c. The feature plot illustrating the density of the gene N4BP2L1 in T cells; d. Switch gene analysis for N4BP2L1+ CD8+ Teff cells\u003c/p\u003e","description":"","filename":"figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4307133/v1/de3a7b1bb684e53340ff708d.jpg"},{"id":55394755,"identity":"17f77ad6-43bd-4b29-a223-272659989406","added_by":"auto","created_at":"2024-04-26 16:44:10","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1154400,"visible":true,"origin":"","legend":"\u003cp\u003ea. Single-cell metabolism analysis for T cells; b. Protein-protein interaction network analysis; c. Bulk RNA-seq analysis to illustrate DEG between SSc-ILD and healthy group; d. Gene enrichment analysis of upregulated genes in N4BP2L1+ CD8+ Teff cells\u003c/p\u003e","description":"","filename":"figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4307133/v1/7d92c675f16cae8bc38d3480.jpg"},{"id":55604875,"identity":"b1bebe91-e907-4689-ba8f-ed1d5b73aa4c","added_by":"auto","created_at":"2024-04-30 12:46:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1507040,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4307133/v1/e91fc136-118e-414d-bb72-0ad9f3485e08.pdf"},{"id":55394836,"identity":"e7468ef6-fe29-4e5e-b2ee-a3ceafcc01bc","added_by":"auto","created_at":"2024-04-26 16:44:15","extension":"zip","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":140552,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.zip","url":"https://assets-eu.researchsquare.com/files/rs-4307133/v1/3898ffa9be312e479d7993bf.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Targeting N4BP2L1 for Therapy in IPF and SSc-ILD: Evidence from Mendelian Randomization and Multi-Omics Analysis","fulltext":[{"header":"Background","content":"\u003cp\u003eSystemic sclerosis (SSc) is a connective tissue disease of unknown origin characterized by widespread fibrosis, changes in blood vessels, inflammation, and the presence of autoantibodies targeting various cellular antigens as its primary features. Fibrotic tissue remodeling and the resulting complications, such as organ failure, constitute significant contributors to morbidity and mortality in SSc\u0026nbsp;(1).\u003c/p\u003e\n\u003cp\u003ePulmonary fibrosis is characterized by the gradual replacement of alveolar tissue with fibrotic scars, posing a threat to alveolar gas exchange and diminishing lung compliance. This process leads to an increased workload for breathing and hypoxemia, resulting in the progression of respiratory failure and eventual mortality. While the root cause of pulmonary fibrosis is frequently unknown, as seen in idiopathic pulmonary fibrosis (IPF), it can also be linked to connective tissue diseases, environmental factors such as hypersensitivity pneumonitis, occupational exposures like silicosis and asbestosis, or certain medications in some individuals. Diagnostic methods aimed at distinguishing the various causes of pulmonary fibrosis, including surgical lung biopsy, remain imprecise, and there are limited laboratory indicators predicting responsiveness to treatment\u0026nbsp;(2, 3).\u003c/p\u003e\n\u003cp\u003eLung fibrosis, a frequent complication seen in SSc and the defining characteristic of idiopathic pulmonary fibrosis (IPF), is linked to significant mortality and currently lacks an approved treatment. Researchers consistently direct their attention to understanding the connection between these two diseases, aiming to delve into the essence of both conditions to determine if the immune response differs between SSc-associated interstitial lung disease (ILD) and IPF. The inquiry revolves around whether SSc-associated ILD (SSc-ILD) and IPF are genuinely distinct entities or merely different points along the same clinical spectrum. Even though research has delved into identifying common targets for SSc-ILD and IPF, and polypharmacology has emerged as an effective therapeutic strategy, exemplified by drugs like the multiple tyrosine kinase inhibitor nintedanib, the search for robust and predictive biomarkers for SSc--ILD and IPF remains a pivotal inquiry\u0026nbsp;(4). Furthermore, there is ongoing examination into the potential role of personalized medicine approaches in directing targeted therapy for SSc-ILD and IPF.\u003c/p\u003e\n\u003cp\u003eTo enhance the prognosis of individuals with SSc-ILD or IPF and to uncover new targets that could enrich therapeutic strategies, we carried out single-cell RNA sequencing on lung tissues obtained from two patients diagnosed with IPF, two patients with SSc- ILD, and two healthy donors. We identified noteworthy potential regulatory mechanisms for treating both SSc-ILD and IPF through Mendelian randomization (MR). MR refers to the utilization of genetic variations to investigate causal relationships between modifiable exposures and various outcomes (5).\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe compiled all the resources, software, and algorithms utilized for analysis in this article, along with their respective identifiers listed in the key resources table (table 1).\u003c/p\u003e\n\u003cp\u003eTable 1: Key resources table\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.349005424954793%\" valign=\"top\"\u003e\n \u003cp\u003eReagent or Resource\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.74864376130199%\" valign=\"top\"\u003e\n \u003cp\u003eIdentifier\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eDeposited data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.349005424954793%\" valign=\"top\"\u003e\n \u003cp\u003eScRNA-seq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003eGEO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.74864376130199%\" valign=\"top\"\u003e\n \u003cp\u003eGSE122960\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.349005424954793%\" valign=\"top\"\u003e\n \u003cp\u003eBulk RNA-seq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003eGEO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.74864376130199%\" valign=\"top\"\u003e\n \u003cp\u003eGSE231693\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.349005424954793%\" valign=\"top\"\u003e\n \u003cp\u003eILD GWAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003eIEU OpenGWAS project\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.74864376130199%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a- GCST90018643\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.349005424954793%\" valign=\"top\"\u003e\n \u003cp\u003eILD GWAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003eIEU OpenGWAS project\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.74864376130199%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90018863\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eSoftware and algorithms\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.349005424954793%\" valign=\"top\"\u003e\n \u003cp\u003eBioconductor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003eOpen source\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.74864376130199%\" valign=\"top\"\u003e\n \u003cp\u003ehttps://www.bioconductor.org\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.349005424954793%\" valign=\"top\"\u003e\n \u003cp\u003eSeurat versions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003eOpen source\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.74864376130199%\" valign=\"top\"\u003e\n \u003cp\u003ehttps://satijalab.org/seurat/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.349005424954793%\" valign=\"top\"\u003e\n \u003cp\u003eTwoSample MR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003eOpen source\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.74864376130199%\" valign=\"top\"\u003e\n \u003cp\u003ehttps://github.com/MRCIEU/TwoSampleMR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.349005424954793%\" valign=\"top\"\u003e\n \u003cp\u003eHarmony package\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003eOpen source\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.74864376130199%\" valign=\"top\"\u003e\n \u003cp\u003ehttps://cran.rproject.org/web/packages/harmony/index.html\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.349005424954793%\" valign=\"top\"\u003e\n \u003cp\u003eplot1cell \u0026nbsp;package\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003eOpen source\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.74864376130199%\" valign=\"top\"\u003e\n \u003cp\u003ehttps://github.com/TheHumphreysLab/plot1cell\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.349005424954793%\" valign=\"top\"\u003e\n \u003cp\u003eslingshot package\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003eOpen source\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.74864376130199%\" valign=\"top\"\u003e\n \u003cp\u003ehttps://github.com/kstreet13/slingshot\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.349005424954793%\" valign=\"top\"\u003e\n \u003cp\u003eCellChat package\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003eOpen source\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.74864376130199%\" valign=\"top\"\u003e\n \u003cp\u003ehttps://github.com/sqjin/CellChat\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.349005424954793%\" valign=\"top\"\u003e\n \u003cp\u003ecoloc package\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003eOpen source\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.74864376130199%\" valign=\"top\"\u003e\n \u003cp\u003ehttps://cran.r-project.org/web/packages/coloc/index.html\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.349005424954793%\" valign=\"top\"\u003e\n \u003cp\u003escMetabolism package\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003eOpen source\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.74864376130199%\" valign=\"top\"\u003e\n \u003cp\u003ehttps://github.com/wu-yc/scMetabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.349005424954793%\" valign=\"top\"\u003e\n \u003cp\u003eGeneSwitches\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003eOpen source\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.74864376130199%\" valign=\"top\"\u003e\n \u003cp\u003ehttps://github.com/SGDDNB/GeneSwitches\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.349005424954793%\" valign=\"top\"\u003e\n \u003cp\u003eMetascape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003eOpen source\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.74864376130199%\" valign=\"top\"\u003e\n \u003cp\u003ehttps://metascape.org/gp/index.html\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch3\u003eMethod details\u003c/h3\u003e\n\u003ch4\u003eMR analysis\u003c/h4\u003e\n\u003cp\u003eTo convert the gene IDs of DEGs to Ensembl IDs, we utilized the \u0026quot;org.Hs.eg.db\u0026quot; R package. Subsequently, the eQTL data corresponding to the DEGs were retrieved from the IEU OpenGWAS Project database (https://gwas.mrcieu.ac.uk/).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the exposure and outcome data, we explored the potential roles of DEGs of CD8+ Teff cells in SSc-ILD using MR. Initially, we identified independent SNPs associated with eQTLs as genetic instruments. The selection of genetic instruments for MR hinges on three core assumptions: (1) they must be strongly associated with the exposure, (2) they should be independent of any confounder of the exposure-outcome association, and (3) they should not directly influence the outcome, except through their association with the exposure. These assumptions indicate that the instruments should only affect the outcome through their association with the risk factor\u0026nbsp;(6). SNPs failing to meet the significance threshold of genome-wide association (p \u0026lt; 5E-08) and with a minor allele frequency (MAF) \u0026lt; 0.01 were excluded. Following the harmonization of exposure and outcome summary data, we selected independent SNPs with low linkage disequilibrium (LD) (R\u003csup\u003e2\u003c/sup\u003e \u0026lt; 0.01 with strand alignment = 10,000 kb). The R\u003csup\u003e2\u003c/sup\u003e and F-statistic of each variable were used to assess the strength of genetic instruments, calculated using the formula:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e= 2 \u0026times; EAF \u0026times; (1-EAF) \u0026times;\u0026nbsp;\u0026beta;\u003csup\u003e2\u003c/sup\u003e\u0026nbsp; \u0026nbsp; F=(N-K-1) / K \u0026times; (R\u003csup\u003e2\u003c/sup\u003e/1 - R\u003csup\u003e2\u003c/sup\u003e)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e,where R^2 represents the proportion of variability in gene levels explained by each genetic instrument, and EAF denotes the effect allele frequency\u0026nbsp;(7). Genetic instruments with an F-statistic \u0026lt; 10 were excluded.\u003c/p\u003e\n\u003cp\u003eSubsequently, we employed the \u0026quot;TwoSampleMR\u0026quot; R package to assess causal inference between the exposure and outcome\u0026nbsp;(8). For the primary analysis, the Wald ratio method or IVW method was used for MR estimates if only one SNP was available for the gene, while MR-Egger method, simple mode, weighted median method, and weighted mode method were used when two or more instruments were available. The IVW method, considered the most powerful under instrumental variable assumptions, was selected as the primary analysis method if all genetic variants met the necessary criteria\u0026nbsp;(9). A sensitivity analysis was then conducted, including a heterogeneity test based on Cochran\u0026rsquo;s Q test and an intercept test using MR-Egger regression to assess horizontal pleiotropy of instrumental variables\u0026nbsp;(10).\u003c/p\u003e\n\u003cp\u003eTo visualize the MR results, a scatter plot illustrating the effect of SNPs on the exposure versus their effect on the outcome was generated. Additionally, a forest plot was utilized to visualize estimates from multiple instruments. A funnel plot was created to visually evaluate heterogeneity, and a leave-one-out plot was generated to visualize MR estimates when each instrument was left out sequentially.\u003c/p\u003e\n\u003ch4\u003eReverse causality detection\u003c/h4\u003e\n\u003cp\u003eApplying identical screening criteria for expression quantitative trait loci (eQTLs), an additional set of genetic instruments for SSc-ILD was chosen from GWAS for bidirectional MR analysis, aimed at detecting potential reverse causality\u0026nbsp;(11). Furthermore, Steiger filtering was conducted to validate the directionality of the association between genes and SSc-ILD\u0026nbsp;(12). Statistical significance was determined at a threshold of P \u0026lt; 0.05.\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003eBayesian co-localization analysis\u003c/h4\u003e\n\u003cp\u003eBayesian co-localization analyses were employed to evaluate the likelihood that two traits share a common causal variant, utilizing the \u0026apos;coloc\u0026apos; package (https://github.com/chr1swallace/coloc) with default arguments. Bayesian co-localization offers posterior probabilities for five hypotheses concerning the potential sharing of a single variant between two traits\u0026nbsp;(13). Both the coloc.abf and coloc.susie algorithms were utilized in this analysis.\u003c/p\u003e\n\u003ch4\u003eScRNA-seq analysis\u003c/h4\u003e\n\u003ch5\u003eData acquisition and processing\u003c/h5\u003e\n\u003cp\u003eScRNA-seq data pertaining to human IPF and SSc-ILD patients and healthy controls were obtained from the GEO database under accession ID GSE122960 (www.ncbi.nlm.nih.gov/geo). We selected sequencing data of two IPF patients (GSM3489183/ GSM3489184), two SSc-ILD patients (GSM3489194/ GSM3489198) and two healthy lung donors (GSM3489182/ GSM3489185) from this database. Using the \u0026quot;Seurat\u0026quot; R package\u0026nbsp;(14), we conducted data preprocessing and transformation, excluding unqualified cells based on specific criteria (gene counts per cell \u0026le; 200 or \u0026ge; 4000, percent of mitochondrial genes per cell \u0026ge; 10%). Following standard quality control procedures, we normalized and scaled the raw RNA counts using the \u0026quot;NormalizeData\u0026quot; and \u0026quot;ScaleData\u0026quot; functions for downstream analysis. PCA was employed to reduce the data dimensions. The \u0026quot;Harmony\u0026quot; R package facilitated dataset integration and removal of dataset-specific variation within each cluster\u0026nbsp;(15). Subsequently, we applied the \u0026quot;FindNeighbors\u0026quot; and \u0026quot;FindClusters\u0026quot; functions to cluster cells, performed UMAP, and visualized the landscape of all cells and the proportion of each cell type in IPF and SSc-ILD patients and healthy controls using the \u0026quot;plot1cell\u0026quot; R package, annotating cell clusters\u0026apos; marker genes with the \u0026quot;SingleR\u0026quot; R package based on the \u0026quot;Human Primary Cell Atlas Data\u0026quot;. To identify cell subtypes within T cells, a second round of clustering was performed on T cells using the same procedure as the first round. After re-clustering T cells, we cross-referenced common T cell markers manually for the annotation of T cell types.\u0026nbsp;\u003c/p\u003e\n\u003ch5\u003ePseudotime analysis and cell-cell interaction analysis\u0026nbsp;\u003c/h5\u003e\n\u003cp\u003ePseudotime analysis was performed using the Slingshot package, following methodologies outlined in prior studies\u0026nbsp;(16). In summary, size factors and dispersions were estimated, and highly variable features were identified within the SingleCellExperiment object. Subsequently, dimensional reduction techniques were applied, and cells were ordered to facilitate pseudotime visualization. Cell-cell interactions were investigated using Cellchat\u0026nbsp;(17, 18), with a focus on including secreted signaling in humans for the analysis of cell-cell interactions\u0026nbsp;(19).\u003c/p\u003e\n\u003ch5\u003eGene enrichment analysis and protein-protein interaction enrichment analysis\u003c/h5\u003e\n\u003cp\u003eThe Metascape (https://metascape.org/gp/index.html#/main/step1) was used to conduct gene enrichment analysis and protein-protein interaction enrichment analysis with DEGs.\u003c/p\u003e\n\u003ch4\u003eSingle-cell metabolism analysis\u0026nbsp;\u003c/h4\u003e\n\u003cp\u003eR packages were employed to analyze metabolic activity, encompassing wu-yc/scMetabolism and YosefLab/VISION obtained from GitHub, along with AUCell, GSVA, scMetabolism, and rsvd.\u003c/p\u003e\n\u003ch4\u003eBulk RNA-seq analysis\u0026nbsp;\u003c/h4\u003e\n\u003cp\u003eWe obtained bulk RNA-seq data (GSE231693) from GEO database. The relevant R packages were used to conduct bulk RNA-seq analysis including IOBR/IOBR, preprocessCore, biomaRt, DESeq2 and limma.\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003eSwitch gene analysis\u0026nbsp;\u003c/h4\u003e\n\u003cp\u003eWe implemented a comprehensive workflow to analyze gene switches. Firstly, we filtered out genes expressed in less than 5 cells and constructed a SingleCellExperiment object with log-normalized single-cell data. Subsequently, we incorporated pseudotime information to capture the temporal dynamics of gene expression. To handle the high-dimensional nature of the data, we applied dimensionality reduction techniques including PCA, UMAP, and tSNE. Following this, we carefully determined the binarization threshold and conducted binarization analysis to convert continuous gene expression values into binary states. Then, we identified gene switches by filtering the binarized data based on predefined criteria. Additionally, we integrated relevant genes identified from MR results to enrich our analysis. Finally, we presented the outcomes of our investigation.\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003eQuantification and statistical analysis\u003c/h4\u003e\n\u003cp\u003eAll statistical analyses were conducted using R software (version 4.2). IVW, MR-Egger, weighted median, and weighted mode analyses were executed utilizing the R package \u0026quot;TwoSampleMR\u0026quot;. To mitigate weak instrumental variable bias, the F-statistic was required to exceed 10. A significance level of p \u0026lt; 0.05 was adopted to establish causal relationships between the two traits. The expression levels of N4BP2L1 in CD8+ Teff cells were compared between SSc-ILD patients and healthy controls using the Wilcoxon test.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe overall study design was illustrated in figure 1.\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003eScRNA-seq uncovers the subset of T cells in the pathogenesis of both IPF and SSc-ILD\u003c/h3\u003e\n\u003cp\u003eWe examined single-cell RNA sequencing (scRNA-seq) data derived from samples within the GEO datasets, specifically GSE122960. We selected six lung samples from this dataset, encompassing two patients with IPF, two patients with SSc-ILD, and two lung transplant donors. Following meticulous preprocessing with stringent quality control metrics, we employed the Uniform Manifold Approximation and Projection (UMAP) technique on the high-dimensional scRNA-seq data, focusing on the top 15 principal components. Subsequently, we proficiently classified the cells into 8 subclusters and assigned annotations to discernible cell types using the SingleR R package (figure 2a). The major cell types included B cells, T cells, NK cells, macrophages, monocytes, epithelial cells, endothelial cells and tissue stem cells. Figure2a reveals an upregulation in the number of T cells in both the SSc-ILD group and the IPF group when compared to the donor group. Hence, we further focused on the T cells and again applied the mutual nearest neighbor approach, followed by PCA dimension reduction, to the subset of genes with the highest variability as identified by the Seurat (ver. 3.1.2) FindVariableGenes function\u0026nbsp;(20). Subsequently, we performed clustering at a resolution of 0.6 and sub-clustering at a resolution of 0.2, utilizing Seurat\u0026apos;s FindClusters and UMAP visualization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis time, we manually characterized the subtypes of T cells using the markers compiled in the figure2b. Recognizing the distinctive features of CD8+ effector memory T cells (CD8+ Tem) characterized by high GZMK expression and low GAMA expression, we designated cluster 4 as CD8+ Tem. Cluster 5, marked by elevated GZMH, PRF1, and KLRG1 expression, earned the designation CD8+ effector T (CD8+ Teff) cells. Although lacking the defining characteristics of CD8+ Teff cells, cluster 3 exhibited lower GZMK expression and higher GZMA and CCL5 expression, leading us to name them CD8+ central memory T cells. In comparison to cluster 5, cells in cluster 7 displayed almost identical characteristic markers, except for the T cell marker CD3E. Consequently, we identified them as NK cells. Cluster 8 showcased significant expression of XCL1, XCL2, and CD69, indicative of markers for CD8+ resident memory T cells (CD8+ Trm). Additionally, cluster six exhibited distinct expression of Foxp3 and IL2RA, leading us to designate them as CD4+ regulatory T (Treg) cells. Finally, cluster 0, characterized by CD69 and high expression of IL7R and KLRB1, was named CD4+ resident memory T (CD4+ Trm) cells. All the markers were shown in figure 2d.\u003c/p\u003e\n\u003cp\u003eAnnotating Cluster 1 and 2 with the selected markers proved challenging. Consequently, we conducted pseudotime developmental trajectory analysis on T cells from three groups, elucidating the relationships among various T cell subsets (figure 2c). Cluster 1 and 2, positioned at the outset of the T cell developmental trajectory, likely represent undifferentiated T cells with a na\u0026iuml;ve characteristic. Since our subsequent research primarily focused on differentiated T cells, we chose to categorize these clusters simply as T cells without delving deeper into their specific marker details.\u003c/p\u003e\n\u003cp\u003eSubsequently, we employed a bar plot to illustrate the changes in the number of T cell subsets across the control, IPF, and SSc-ILD groups (figure 3c). Our emphasis was on CD8+ Teff cells, as both the IPF and SSc-ILD groups exhibited an upward trend in the quantity of this subset compared to the control group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe upregulation of CD8+ Teff cells suggests their potential promotional role in the pathogenesis of IPF and SSc-ILD. To gain deeper insights into the regulatory function of this subset, we utilized CellChat for an analysis, examining changes in cellular communication between CD8+ Teff cells and other cell types, as depicted in the figure 2e and 2f. Interestingly, CD8+ Teff cells in different disease groups impact similar cells, including macrophages, monocytes, and endothelial cells. However, distinct intercellular communications exist. For instance, in IPF, CD8+ Teff cells act as sources, relying on CCL5 to communicate with the target CCR1, a pattern not observed in the SSc-ILD group (figure 3b). Conversely, in the SSc-ILD group, CD8+ Teff cells can interact with macrophages when ANXA1 combines with the target FPR2, a communication not observed in the IPF group (figure 3a). The shared characteristics in cellular communication of CD8+ Teff cells in both groups, such as interactions with monocytes and macrophages mediated by ANXA1 and FPR1, communication with monocytes through ANXA1 and FPR2, and communication with endothelial cells through CCL5 and ACKR1, raise the possibility that the function of CD8+ Teff cells not only promotes the development of IPF and SSc-ILD but may also have some underlying connections. Identifying a key gene that influences the development of IPF and, simultaneously, serves as a dynamic factor in the generation and development of SSc-ILD becomes a pivotal question for our research. Discovering such a key gene could offer a meaningful therapeutic target for the treatment of both diseases, which share high mortality rates from an immune regulation perspective. Moreover, uncovering this key gene may contribute to a more precise understanding of the relationship between SSc-ILD and IPF, enhancing the accuracy of the cognitive paradigm and prognosis for these two diseases.\u003c/p\u003e\n\u003ch3\u003eMR analysis uncovers a target gene in IPF that elucidates the mechanisms underlying the generation and development of SSc-ILD\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eMarker genes for CD8+ Teff cells were identified in comparison to various cell types, as depicted in the table S1 in Document S1. The single nucleotide polymorphisms (SNPs) of these marker genes were utilized as instrumental variables (IV) for the exposure, with ILD disease serving as the outcome. The IV for the exposure were identified within the SNPs associated with ILD, using the dataset from the IEU OpenGWAS project with the accession number ebi-a- GCST90018643.\u003c/p\u003e\n\u003cp\u003eHeterogeneity (Table S2) and pleiotropy tests\u0026nbsp;(Table S3) were carried out, and the outcomes have been documented in the Document S2. Later, we gathered the SNP and gene data with P-values lower than 0.05, signifying that the involvement of the specific CD8+ Teff gene in the pathogenesis of IPF can play a role in regulating SSc-ILD (as shown in table). The volcano plot depicting these genes was presented in the figure 3e.\u003c/p\u003e\n\u003cp\u003eUtilizing MR analysis with the inverse variance-weighted and Wald ratio methods, the forest plot illustrating the SNP and their associated genes exerting a causal impact on SSc-ILD outcomes is presented in the figure 4a (also shown in Table S8 in Document S2). Notably, our findings revealed that the genes NCALD (odds ratio [OR], 1.9628; 95% confidence interval [CI], 1.2830-3.0028; P=0.002) and N4BP2L1 (OR, 1.9761; 95% CI, 1.0881-3.5723; P=0.025) exhibited a promoting effect on the pathogenesis of SSc-ILD. Conversely, the genes GZMH (OR, 0.6635; 95% CI, 0.4613-0.9542; P=0.029) and THOC5 (OR, 0.6828; 95% CI, 0.5339-0.8731; P=0.002) demonstrated a protective effect against the development of SSc-ILD. Subsequently, we conducted MR analysis once more, using the dataset of SNPs associated with ILD(8)\u0026nbsp;from the IEU OpenGWAS project (accession number ebi-a-GCST90018863), to further validate the specific SNP and its corresponding gene accurately (figure 4b and Table S9 in Document S2). We observed that the exposure to N4BP2L1 unequivocally exhibited a promotional effect on the pathogenesis of SSc-ILD (OR, 1.6094; 95% CI, 1.1350-2.2823; P=0.008). Conversely, exposure to THOC5 (OR, 0.7780; 95% CI, 0.6605-0.9163; P=0.003) and GZMH (OR, 0.7209; 95% CI, 0.5986-0.8683; P=0.001) unquestionably demonstrated a protective effect against the development of SSc-ILD.\u003c/p\u003e\n\u003cp\u003eThe scatter plot, forest plot, funnel plot, and leave-one-out plot for the genes NCALD, THOC5, and GZMH were compiled in the figure 4d, 4e and 4f. Regarding the dataset with the accession number ebi-a-GCST90018643, it was confirmed through MR analysis that only two SNPs (rs26528, rs20633) associated with the gene N4BP2L1 exhibited a causal effect on the pathogenesis of SSc-ILD. Consequently, scatter plots, forest plots, funnel plots, and leave-one-out plots for this gene were not generated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGiven the upregulation of the cellular proportion of CD8+ Teff cells in both IPF and SSc-ILD diseases compared to the healthy lung, our focus centered on genes that may contribute to the promotion of SSc-ILD pathogenesis so that we can better understand the impact of CD8+ Teff cells in fostering SSc-ILD and uncover potential therapeutic targets. Consequently, the gene N4BP2L1 became the primary focus of our following investigation.\u003c/p\u003e\n\u003ch3\u003eSensitive analysis for SSc-ILD causal gene\u003c/h3\u003e\n\u003cp\u003eSubsequently, we performed a bidirectional MR analysis, which verified the absence of a causal relationship between SSc-ILD and the gene N4BP2L1 level (OR, 1.0099; 95% CI, 0.9373-1.0882; P=0.795). The SNPs associated with SSc-ILD exposure were obtained from the dataset ebi-a-GCST90018643, and the corresponding forest plot is illustrated in the figure 4c.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThen we performed Bayesian co-localization analysis for the primary SNP associated with the causal gene N4BP2L1, namely rs206333 (located on chromosome 13 at position 32997745), exhibiting a lower p-value and higher F-statistics, as illustrated in the Table S3 in Document S2. All SNPs associated with this gene (ID=eqtl-a-ENSG00000139597) were sourced from the IEU OpenGWAS project. The findings of the Bayesian co-localization are detailed in the Table S5 in Document S2. The analysis indicated that the SNP rs206333 within the N4BP2L1 gene may share the same variant with SSc-ILD, as denoted by coloc.abf-PPH4 = 0.108. The regional association plot (figure 3d) illustrates that the SNP rs17635503, situated on chromosome 13 between positions 31997745 and 33997745, may exhibit a robust regional association with SSc-ILD. Finally, Steiger filtering was employed to validate that the established causal relationships between N4BP2L1 and SSc-ILD were unaffected by reverse causation, aligning with the findings from bidirectional MR analysis (as shown in table S6 in Document S2).\u003c/p\u003e\n\u003ch3\u003eThe combination of scRNA-seq and bulk RNA-seq analyses to confirm the role of N4BP2L1 in the pathogenesis of the disease\u003c/h3\u003e\n\u003cp\u003eThe feature plot in the figure illustrates a higher density of the gene N4BP2L1 in CD8+ Teff cells compared to other T cell subtypes (figure 5c). Hence, our focus shifted to the gene expression dynamics of CD8+ Teff cells, revealing a correlation between N4BP2L1 expression and CD8+ Teff development. The figure 5d indicates that the presence of the N4BP2L1 gene coincides with a pseudo-timeline around 10.0. This observation prompts us to consider exploring ways to downregulate N4BP2L1 expression based on the regulatory mechanisms during disease onset. Such exploration may unveil additional therapeutic targets for treating IPF and SSc-ILD.\u003c/p\u003e\n\u003cp\u003eThen we conducted intercellular communication analysis between N4BP2L1+ CD8+ Teff cells and other cell types, as well as N4BP2L1- CD8+ Teff cells and other cells. The bubble plot illustrates the intercellular communication involving either N4BP2L1- CD8+ Teff cells or N4BP2L1+ CD8+ Teff cells interacting with other cells. Figure 5a exclusively focuses on the intercellular communication between N4BP2L1- CD8+ Teff cells and other cells due to our random selection of 2000 targets from the data frame containing scRNA-seq information on cellular communication between N4BP2L1- CD8+ Teff cells or N4BP2L1+ CD8+ Teff cells and other cells. To assess the variance between N4BP2L1+ CD8+ Teff cells and N4BP2L1- CD8+ Teff cells, we reanalyzed intercellular communication using the complete dataset, ensuring inclusion of cell-cell communication even in cell groups with minimal representation (as shown in figure 5b). Additionally, we illustrated the metabolic differences between N4BP2L1+ CD8+ Teff cells and N4BP2L1- CD8+ Teff cells, as presented in the figure 6a. These two subsets of CD8+ Teff cells exhibit notable metabolic distinctions. Thiamine metabolism, riboflavin metabolism, glycosphingolipids biosynthesis, and folate biosynthesis are upregulated in N4BP2L1+ CD8+ Teff cells, whereas terpenoid backbone biosynthesis is upregulated in N4BP2L1- CD8+ Teff cells. The metabolic differences may provide insights into the potential role of N4BP2L1+ CD8+ Teff cells in promoting the pathogenesis of IPF and SSc-ILD, while also suggesting potential therapeutic strategies based on metabolic regulation to mitigate the impact of N4BP2L1- CD8+ Teff cells.\u003c/p\u003e\n\u003cp\u003eWe generated a heatmap illustrating the differentially expressed gene (DEG) using the GSE231693 bulk RNA-seq dataset (figure 6c). We specifically selected the healthy group and SSc-ILD group for analysis. The results clearly indicate an upregulation of the N4BP2L1 gene in the SSc-ILD group when compared to the healthy group.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eThe analysis of gene enrichment in N4BP2L1+ CD8+ Teff cells\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eWe compiled a list of genes that were upregulated in N4BP2L1+ CD8+ Teff cells when compared to N4BP2L1- CD8+ Teff cells (see Table S7 in Document S1). Subsequently, these upregulated genes were employed for conducting gene enrichment analysis. As depicted in Figure 6d, the top 20 clusters were presented along with their respective enriched terms. Our attention was directed towards the TNFR1-mediated ceramide production, lipid biosynthetic process, and autophagy, which could suggest the involvement of N4BP2L1-expressing CD8+ Teff cells in the pathogenesis of IPF and SSc-ILD.\u0026nbsp;The relevant analysis will be demonstrated in detail in discussion section.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe also conducted protein-protein interaction network analysis (figure 6b). For each specified gene list, we conducted protein-protein interaction enrichment analysis using the following databases: STRING (21), BioGrid (22), OmniPath (23), and InWeb_IM (23). Only physical interactions meeting a stringent criterion in STRING (physical score \u0026gt; 0.132) and those from BioGrid were considered. The resulting network comprises proteins forming physical interactions with at least one other member within the list. Should the network consist of between 3 and 500 proteins, we applied the Molecular Complex Detection (MCODE) algorithm to identify densely connected network components (24). Subsequently, MCODE networks identified for individual gene lists were collated and are presented in table 2. Pathway and process enrichment analyses were then independently conducted for each MCODE component. The three terms with the most significant p-values were selected as the functional description for the respective components, displayed in table 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1: The top 20 clusters are accompanied by their representative enriched terms, with one term per cluster. \u0026quot;Count\u0026quot; indicates the number of genes within the lists that belong to the specified ontology term. \u0026quot;%\u0026quot; represents the percentage of all genes found in the specified ontology term (only input genes with at least one ontology term annotation are considered in the calculation). \u0026quot;Log10(P)\u0026quot; denotes the p-value in base 10, while \u0026quot;Log10(q)\u0026quot; indicates the multi-test adjusted p-value in base 10.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"545\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.431192660550458%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGO\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.394495412844037%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.55045871559633%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.376146788990825%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCount\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.577981651376147%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog10(P)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.660550458715596%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog10(q)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.431192660550458%\"\u003e\n \u003cp\u003eGO:0070988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.394495412844037%\"\u003e\n \u003cp\u003eGO Biological Processes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.55045871559633%\"\u003e\n \u003cp\u003edemethylation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.376146788990825%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.577981651376147%\"\u003e\n \u003cp\u003e-6.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.660550458715596%\"\u003e\n \u003cp\u003e-1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.431192660550458%\"\u003e\n \u003cp\u003eGO:0006325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.394495412844037%\"\u003e\n \u003cp\u003eGO Biological Processes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.55045871559633%\"\u003e\n \u003cp\u003echromatin organization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.376146788990825%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\"\u003e\n \u003cp\u003e7.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.577981651376147%\"\u003e\n \u003cp\u003e-5.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.660550458715596%\"\u003e\n \u003cp\u003e-1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.431192660550458%\"\u003e\n \u003cp\u003eGO:0051043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.394495412844037%\"\u003e\n \u003cp\u003eGO Biological Processes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.55045871559633%\"\u003e\n \u003cp\u003eregulation of membrane protein ectodomain proteolysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.376146788990825%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.577981651376147%\"\u003e\n \u003cp\u003e-5.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.660550458715596%\"\u003e\n \u003cp\u003e-1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.431192660550458%\"\u003e\n \u003cp\u003eR-HSA-5626978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.394495412844037%\"\u003e\n \u003cp\u003eReactome Gene Sets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.55045871559633%\"\u003e\n \u003cp\u003eTNFR1-mediated ceramide production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.376146788990825%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.577981651376147%\"\u003e\n \u003cp\u003e-4.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.660550458715596%\"\u003e\n \u003cp\u003e-1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.431192660550458%\"\u003e\n \u003cp\u003eGO:0008610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.394495412844037%\"\u003e\n \u003cp\u003eGO Biological Processes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.55045871559633%\"\u003e\n \u003cp\u003elipid biosynthetic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.376146788990825%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\"\u003e\n \u003cp\u003e5.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.577981651376147%\"\u003e\n \u003cp\u003e-4.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.660550458715596%\"\u003e\n \u003cp\u003e-1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.431192660550458%\"\u003e\n \u003cp\u003eR-HSA-948021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.394495412844037%\"\u003e\n \u003cp\u003eReactome Gene Sets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.55045871559633%\"\u003e\n \u003cp\u003eTransport to the Golgi and subsequent modification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.376146788990825%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\"\u003e\n \u003cp\u003e3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.577981651376147%\"\u003e\n \u003cp\u003e-4.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.660550458715596%\"\u003e\n \u003cp\u003e-1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.431192660550458%\"\u003e\n \u003cp\u003eGO:0072529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.394495412844037%\"\u003e\n \u003cp\u003eGO Biological Processes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.55045871559633%\"\u003e\n \u003cp\u003epyrimidine-containing compound catabolic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.376146788990825%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.577981651376147%\"\u003e\n \u003cp\u003e-4.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.660550458715596%\"\u003e\n \u003cp\u003e-1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.431192660550458%\"\u003e\n \u003cp\u003eGO:0007623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.394495412844037%\"\u003e\n \u003cp\u003eGO Biological Processes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.55045871559633%\"\u003e\n \u003cp\u003ecircadian rhythm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.376146788990825%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.577981651376147%\"\u003e\n \u003cp\u003e-3.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.660550458715596%\"\u003e\n \u003cp\u003e-0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.431192660550458%\"\u003e\n \u003cp\u003eGO:0009991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.394495412844037%\"\u003e\n \u003cp\u003eGO Biological Processes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.55045871559633%\"\u003e\n \u003cp\u003eresponse to extracellular stimulus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.376146788990825%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\"\u003e\n \u003cp\u003e4.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.577981651376147%\"\u003e\n \u003cp\u003e-3.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.660550458715596%\"\u003e\n \u003cp\u003e-0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.431192660550458%\"\u003e\n \u003cp\u003eR-HSA-383280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.394495412844037%\"\u003e\n \u003cp\u003eReactome Gene Sets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.55045871559633%\"\u003e\n \u003cp\u003eNuclear Receptor transcription pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.376146788990825%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.577981651376147%\"\u003e\n \u003cp\u003e-3.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.660550458715596%\"\u003e\n \u003cp\u003e-0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.431192660550458%\"\u003e\n \u003cp\u003eGO:0006482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.394495412844037%\"\u003e\n \u003cp\u003eGO Biological Processes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.55045871559633%\"\u003e\n \u003cp\u003eprotein demethylation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.376146788990825%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.577981651376147%\"\u003e\n \u003cp\u003e-3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.660550458715596%\"\u003e\n \u003cp\u003e-0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.431192660550458%\"\u003e\n \u003cp\u003eGO:0033866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.394495412844037%\"\u003e\n \u003cp\u003eGO Biological Processes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.55045871559633%\"\u003e\n \u003cp\u003enucleoside bisphosphate biosynthetic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.376146788990825%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.577981651376147%\"\u003e\n \u003cp\u003e-3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.660550458715596%\"\u003e\n \u003cp\u003e-0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.431192660550458%\"\u003e\n \u003cp\u003eGO:0006644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.394495412844037%\"\u003e\n \u003cp\u003eGO Biological Processes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.55045871559633%\"\u003e\n \u003cp\u003ephospholipid metabolic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.376146788990825%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\"\u003e\n \u003cp\u003e3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.577981651376147%\"\u003e\n \u003cp\u003e-3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.660550458715596%\"\u003e\n \u003cp\u003e-0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.431192660550458%\"\u003e\n \u003cp\u003eGO:0014857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.394495412844037%\"\u003e\n \u003cp\u003eGO Biological Processes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.55045871559633%\"\u003e\n \u003cp\u003eregulation of skeletal muscle cell proliferation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.376146788990825%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.577981651376147%\"\u003e\n \u003cp\u003e-3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.660550458715596%\"\u003e\n \u003cp\u003e-0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.431192660550458%\"\u003e\n \u003cp\u003eGO:0032259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.394495412844037%\"\u003e\n \u003cp\u003eGO Biological Processes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.55045871559633%\"\u003e\n \u003cp\u003emethylation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.376146788990825%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\"\u003e\n \u003cp\u003e2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.577981651376147%\"\u003e\n \u003cp\u003e-2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.660550458715596%\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.431192660550458%\"\u003e\n \u003cp\u003eGO:0008380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.394495412844037%\"\u003e\n \u003cp\u003eGO Biological Processes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.55045871559633%\"\u003e\n \u003cp\u003eRNA splicing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.376146788990825%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\"\u003e\n \u003cp\u003e3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.577981651376147%\"\u003e\n \u003cp\u003e-2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.660550458715596%\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.431192660550458%\"\u003e\n \u003cp\u003eGO:0006914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.394495412844037%\"\u003e\n \u003cp\u003eGO Biological Processes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.55045871559633%\"\u003e\n \u003cp\u003eautophagy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.376146788990825%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\"\u003e\n \u003cp\u003e3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.577981651376147%\"\u003e\n \u003cp\u003e-2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.660550458715596%\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.431192660550458%\"\u003e\n \u003cp\u003eGO:0032288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.394495412844037%\"\u003e\n \u003cp\u003eGO Biological Processes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.55045871559633%\"\u003e\n \u003cp\u003emyelin assembly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.376146788990825%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.577981651376147%\"\u003e\n \u003cp\u003e-2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.660550458715596%\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.431192660550458%\"\u003e\n \u003cp\u003eGO:0040029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.394495412844037%\"\u003e\n \u003cp\u003eGO Biological Processes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.55045871559633%\"\u003e\n \u003cp\u003eepigenetic regulation of gene expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.376146788990825%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.577981651376147%\"\u003e\n \u003cp\u003e-2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.660550458715596%\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.431192660550458%\"\u003e\n \u003cp\u003eR-HSA-9716542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.394495412844037%\"\u003e\n \u003cp\u003eReactome Gene Sets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.55045871559633%\"\u003e\n \u003cp\u003eSignaling by Rho GTPases, Miro GTPases and RHOBTB3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.376146788990825%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\"\u003e\n \u003cp\u003e5.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.577981651376147%\"\u003e\n \u003cp\u003e-2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.660550458715596%\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2: MCODE networks identified for individual gene lists and the three terms with the most significant p-values.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"359\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.991643454038996%\"\u003e\n \u003cp\u003e\u003cstrong\u003eColor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.827298050139277%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMCODE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.055710306406684%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.57660167130919%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.54874651810585%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog10(P)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.991643454038996%\"\u003e\n \u003cp\u003eRed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.827298050139277%\"\u003e\n \u003cp\u003eMCODE_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.055710306406684%\"\u003e\n \u003cp\u003eR-HSA-5626978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.57660167130919%\"\u003e\n \u003cp\u003eTNFR1-mediated ceramide production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.54874651810585%\"\u003e\n \u003cp\u003e-8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.991643454038996%\"\u003e\n \u003cp\u003eRed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.827298050139277%\"\u003e\n \u003cp\u003eMCODE_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.055710306406684%\"\u003e\n \u003cp\u003eM128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.57660167130919%\"\u003e\n \u003cp\u003ePID TNF PATHWAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.54874651810585%\"\u003e\n \u003cp\u003e-5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.991643454038996%\"\u003e\n \u003cp\u003eRed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.827298050139277%\"\u003e\n \u003cp\u003eMCODE_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.055710306406684%\"\u003e\n \u003cp\u003eR-HSA-75893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.57660167130919%\"\u003e\n \u003cp\u003eTNF signaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.54874651810585%\"\u003e\n \u003cp\u003e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.991643454038996%\"\u003e\n \u003cp\u003eBlue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.827298050139277%\"\u003e\n \u003cp\u003eMCODE_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.055710306406684%\"\u003e\n \u003cp\u003eR-HSA-199977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.57660167130919%\"\u003e\n \u003cp\u003eER to Golgi Anterograde Transport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.54874651810585%\"\u003e\n \u003cp\u003e-9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.991643454038996%\"\u003e\n \u003cp\u003eBlue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.827298050139277%\"\u003e\n \u003cp\u003eMCODE_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.055710306406684%\"\u003e\n \u003cp\u003eR-HSA-948021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.57660167130919%\"\u003e\n \u003cp\u003eTransport to the Golgi and subsequent modification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.54874651810585%\"\u003e\n \u003cp\u003e-9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.991643454038996%\"\u003e\n \u003cp\u003eBlue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.827298050139277%\"\u003e\n \u003cp\u003eMCODE_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.055710306406684%\"\u003e\n \u003cp\u003eR-HSA-204005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.57660167130919%\"\u003e\n \u003cp\u003eCOPII-mediated vesicle transport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.54874651810585%\"\u003e\n \u003cp\u003e-8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.991643454038996%\"\u003e\n \u003cp\u003eGreen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.827298050139277%\"\u003e\n \u003cp\u003eMCODE_3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.055710306406684%\"\u003e\n \u003cp\u003eR-HSA-5625886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.57660167130919%\"\u003e\n \u003cp\u003eActivated PKN1 stimulates transcription of AR (androgen receptor) regulated genes KLK2 and KLK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.54874651810585%\"\u003e\n \u003cp\u003e-9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.991643454038996%\"\u003e\n \u003cp\u003eGreen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.827298050139277%\"\u003e\n \u003cp\u003eMCODE_3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.055710306406684%\"\u003e\n \u003cp\u003eR-HSA-2262752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.57660167130919%\"\u003e\n \u003cp\u003eCellular responses to stress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.54874651810585%\"\u003e\n \u003cp\u003e-9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.991643454038996%\"\u003e\n \u003cp\u003eGreen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.827298050139277%\"\u003e\n \u003cp\u003eMCODE_3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.055710306406684%\"\u003e\n \u003cp\u003eR-HSA-5625740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.57660167130919%\"\u003e\n \u003cp\u003eRHO GTPases activate PKNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.54874651810585%\"\u003e\n \u003cp\u003e-8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.991643454038996%\"\u003e\n \u003cp\u003ePurple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.827298050139277%\"\u003e\n \u003cp\u003eMCODE_4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.055710306406684%\"\u003e\n \u003cp\u003eR-HSA-212165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.57660167130919%\"\u003e\n \u003cp\u003eEpigenetic regulation of gene expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.54874651810585%\"\u003e\n \u003cp\u003e-7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.991643454038996%\"\u003e\n \u003cp\u003ePurple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.827298050139277%\"\u003e\n \u003cp\u003eMCODE_4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.055710306406684%\"\u003e\n \u003cp\u003eGO:0045943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.57660167130919%\"\u003e\n \u003cp\u003epositive regulation of transcription by RNA polymerase I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.54874651810585%\"\u003e\n \u003cp\u003e-7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.991643454038996%\"\u003e\n \u003cp\u003ePurple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.827298050139277%\"\u003e\n \u003cp\u003eMCODE_4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.055710306406684%\"\u003e\n \u003cp\u003eGO:0006356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.57660167130919%\"\u003e\n \u003cp\u003eregulation of transcription by RNA polymerase I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.54874651810585%\"\u003e\n \u003cp\u003e-7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.991643454038996%\"\u003e\n \u003cp\u003eOrange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.827298050139277%\"\u003e\n \u003cp\u003eMCODE_5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.055710306406684%\"\u003e\n \u003cp\u003eR-HSA-72163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.57660167130919%\"\u003e\n \u003cp\u003emRNA Splicing - Major Pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.54874651810585%\"\u003e\n \u003cp\u003e-8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.991643454038996%\"\u003e\n \u003cp\u003eOrange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.827298050139277%\"\u003e\n \u003cp\u003eMCODE_5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.055710306406684%\"\u003e\n \u003cp\u003eR-HSA-72172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.57660167130919%\"\u003e\n \u003cp\u003emRNA Splicing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.54874651810585%\"\u003e\n \u003cp\u003e-8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.991643454038996%\"\u003e\n \u003cp\u003eOrange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.827298050139277%\"\u003e\n \u003cp\u003eMCODE_5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.055710306406684%\"\u003e\n \u003cp\u003eGO:0000398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.57660167130919%\"\u003e\n \u003cp\u003emRNA splicing, via spliceosome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.54874651810585%\"\u003e\n \u003cp\u003e-8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.991643454038996%\"\u003e\n \u003cp\u003eBrown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.827298050139277%\"\u003e\n \u003cp\u003eMCODE_7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.055710306406684%\"\u003e\n \u003cp\u003eR-HSA-6811438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.57660167130919%\"\u003e\n \u003cp\u003eIntra-Golgi traffic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.54874651810585%\"\u003e\n \u003cp\u003e-8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.991643454038996%\"\u003e\n \u003cp\u003eBrown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.827298050139277%\"\u003e\n \u003cp\u003eMCODE_7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.055710306406684%\"\u003e\n \u003cp\u003eR-HSA-6811442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.57660167130919%\"\u003e\n \u003cp\u003eIntra-Golgi and retrograde Golgi-to-ER traffic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.54874651810585%\"\u003e\n \u003cp\u003e-6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.991643454038996%\"\u003e\n \u003cp\u003eBrown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.827298050139277%\"\u003e\n \u003cp\u003eMCODE_7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.055710306406684%\"\u003e\n \u003cp\u003eR-HSA-199991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.57660167130919%\"\u003e\n \u003cp\u003eMembrane Trafficking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.54874651810585%\"\u003e\n \u003cp\u003e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn recent years, therapeutic strategies for IPF and SSc-ILD have shifted towards specific target exploration due to the multiple adverse reactions and poor prognosis associated with polypharmacology drugs. For example, the c-ABL/SRC tyrosine kinase inhibitor bosutinib (SKI-606) has demonstrated reduced fibrotic gene expression both in vitro and in TBR-induced pulmonary fibrosis\u0026nbsp;(25, 26). Another multitarget tyrosine kinase inhibitor, anlotinib (AL3818), approved for IPF treatment, has shown attenuation of bleomycin-induced pulmonary fibrosis by suppressing TGF\u0026beta; signaling\u0026nbsp;(27). However, both drugs have exhibited multiple adverse reactions. While dasatinib treatment was well tolerated in SSc-ILD patients, only 39% showed improvement\u0026nbsp;(28). Given the cost-effectiveness of clinical experiments to confirm drugs for potential targets and the limited population of SSc patients with progressive fibrotic manifestations, it is essential to develop additional strategies to explore effective drug candidates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to the MR analysis findings, we confirmed the involvement of N4BP2L1 in CD8+ Teff cells in the onset of IPF and its contribution to the progression of SSc-ILD. To delve deeper into the potential significance of N4BP2L1, we initially examined the role of CD8+ Teff cells in either IPF or SSc-ILD.\u003c/p\u003e\n\u003cp\u003eIn fibrotic human lungs, the distribution of CD8+ T cells tends to be more widespread throughout the lung parenchyma and alveolar walls\u0026nbsp;(29). Elevated CD8+ T cell levels in lung biopsies from IPF patients have been associated with reduced total lung capacity and forced vital capacity. Additionally, an increase in CD28-negative CD8+ T cells has been observed in IPF patient lung samples, showing a transcriptional profile that favors fibrosis and inflammation, alongside heightened PD-1 expression, indicating chronic activation\u0026nbsp;(30). The severity of dyspnea and functional parameters associated with disease severity are correlated with CD8+ T cells\u0026nbsp;(29, 31).\u003c/p\u003e\n\u003cp\u003eStudies in a bleomycin-induced pulmonary fibrosis model have revealed that CD8+ T cells can transform into profibrotic IL-13\u0026ndash;producing cells via IL-21 receptor stimulation. Furthermore, treatment with IL-4 and IL-21 prompts these cells to produce more IL-21, further fostering IL-13 production in an autocrine manner\u0026nbsp;(32). Collectively, evidence on CD8+ T cells underscores their detrimental role in the initiation and advancement of pulmonary fibrosis, suggesting a potential avenue for therapeutic intervention. Moreover, additional discoveries indicate that CD8+ T cells play a role not only in promoting fibrosis but also in contributing to microvasculopathy\u0026nbsp;(33). Additionally, the cytotoxic activity facilitated by granzyme B and perforin can generate autoantigens, which trigger and/or sustain immune responses. Notably, fragments of self-proteins produced by granzyme B were targeted by autoantibodies in systemic sclerosis\u0026nbsp;(34). A study investigating the connection between ILD and CD8+ T cells found that lung tissues affected by ILD had higher levels of CD8+ T cells compared to those without ILD. Additionally, an examination of T cell function in lung tissues revealed a positive association between CD103+CD8+ T cells and IFN\u0026gamma; production\u0026nbsp;(35).\u003c/p\u003e\n\u003cp\u003eAlthough these findings did not clarify the direct participation of CD8+ Teff cells, they did affirm the potential contribution of CD8+ T cells to the pathogenesis of IPF and SSc-ILD. This is consistent with our sc-RNA sequencing results, which revealed changes in the proportion of CD8+ Teff cells. It further bolsters the hypothesis that the functions of T cell subsets may parallel those of previously reported CD8+ T cells.\u003c/p\u003e\n\u003cp\u003eTo further elucidate the potential impact of CD8+ Teff on the pathogenesis of IPF and SSc-ILD, we conducted an analysis of intercellular communication between this T cell subset and others. ANXA1, originating from CD8+ Teff, has been demonstrated to elicit various effects concerning the adhesion and migration of leukocytes, which are crucial processes in the initiation of the inflammatory response. The dual role of ANXA1 in either promoting or mitigating inflammation has been documented in numerous studies\u0026nbsp;(36-38). These investigations underscore the multifaceted nature of the actions of ANXA1 and its breakdown products, emphasizing the need for comprehensive research within the intricate in vivo milieu to fully comprehend their roles in inflammatory processes. The expression of CCL5 plays a significant role in lung fibrosis, as it is associated with the presence of intrapulmonary fibrocytes and the accumulation of collagen\u0026nbsp;(39). Collectively, through the analysis of sc-RNA seq, we initially identified the specific subtype of T cells of interest, which we then subjected to further MR analysis to uncover a deeper understanding of the relationship between IPF and SSc-ILD.\u003c/p\u003e\n\u003cp\u003eThe MR analysis led us to identify the N4BP2L1 gene, which appears to potentially promote the development of SSc-ILD, while also demonstrating a significant association with IPF. N4BP2 belongs to the NEDD4 binding partner family and interacts with the B cell lymphoma 3-encoded protein (Bcl-3)\u0026nbsp;(40, 41). While N4BP2L1 shares homology with N4BP2, its specific function and expression in relation to fibrosis and inflammation are still unclear. The N-terminal region of N4BP2 contains a polynucleotide kinase domain, while its C terminus is characterized by a small mutator S (MutS)-related (Smr) domain\u0026nbsp;(41). A previous study has established that miR-448 can modulate the expression of N4BP2L1, offering the possibility for subsequent validation via animal or cell experiments\u0026nbsp;(42). Additionally, the suppression of miR-448 promotes the initiation of epithelial\u0026ndash;mesenchymal transition (EMT)\u0026nbsp;(43). Further investigation into the relationship between this miRNA and the potential role of N4BP2L1 in promoting fibrosis and inflammation by CD8+ Teff cells may be pursued by confirming the induction of EMT by CD8+ Teff cells. We will concentrate on exploring the potential role of N4BP2L1+ CD8+ Teff cells in inducing EMT. The relevant discussion will be presented subsequently.\u003c/p\u003e\n\u003cp\u003eWhile our primary focus was on investigating the involvement of genes in promoting the pathogenesis of either IPF or SSc-ILD, the analysis of MR also revealed two genes, THOC5 and GZMH, which exhibited an evidently protective role in the development of these diseases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe THO complex, a constituent of the TREX (transcription/export) complex, was initially recognized as a five-protein ensemble (Tho2p, Hpr1p, Mft1p, Thp2p, and Tex1), serving roles in transcriptional elongation, nuclear RNA export, and maintaining genome stability\u0026nbsp;(44, 45). The THO complex regulates RNA 3\u0026prime; processing, preventing R-loop formation and exporting a specific set of mRNAs. The absence of the THO complex in yeast cells initially revealed evidence implicating R-loops as contributors to genome instability\u0026nbsp;(46). Additionally, THOC5 is pivotal in stem cell and cancer cell biology, subject to post-translational regulation by stem cell ligands (CXCL12), oxidative stress, and downstream effects of oncogenes\u0026nbsp;(47-50). The perforin/granzyme (Gzm) pathway stands as a prominent mechanism utilized by cytotoxic T lymphocytes for target cell destruction\u0026nbsp;(51, 52). These Gzms represent a collection of serine proteases that are deeply conserved across both humans and rodents. While GzmH maps to the identical gene cluster and exhibits considerable similarity to GzmB, it possesses distinct enzymatic specificity. A forthcoming study has demonstrated that GzmH induces a gradual form of cell death irrespective of caspase activation\u0026nbsp;(53). Additionally, another investigation illustrated that GzmH induces swift cell demise in target tumor cells, marked by DNA fragmentation and mitochondrial impairment\u0026nbsp;(54). Further exploration is required to understand the involvement of THOC5 and GzmH in protecting against the pathogenesis of IPF or SSc-ILD. It could be conjectured that THOC5 plays a role in maintaining genome stability in CD8+ Teff, while GzmH may regulate the cellular death of CD8+ Teff, potentially aiding in transforming the inflammatory, oxidative stress, and pro-fibrosis environment into a state of homeostasis. Whether promoting these mechanisms could serve as therapeutic strategies for patients with IPF or SSc-ILD remains subject to further investigation.\u003c/p\u003e\n\u003cp\u003eWe successfully demonstrated the involvement of N4BP2L1 in CD8+ Teff. As depicted in Figure 5, the expression of N4BP2L1 initiates during the maturation phase of CD8+ Teff. Upon closer examination of metabolic pathway alterations between N4BP2L1+ CD8+ Teff and N4BP2L1- CD8+ Teff, it becomes apparent that metabolic pathways associated with glycosphingolipid biosynthesis are markedly upregulated in N4BP2L1+ CD8+ Teff compared to N4BP2L1- CD8+ Teff.\u003c/p\u003e\n\u003cp\u003eGlycosphingolipids (GSLs) consist of sugar structures that are hydrophilic and ceramide, which is hydrophobic\u0026nbsp;(55). The cis interactions among the sugar moiety of GSLs promote lateral associations of these molecules with other membrane components. These interactions culminate in the creation of specialized cellular membrane microdomains, known as lipid rafts. Lipid rafts are enriched regions comprising cholesterol, GSLs, sphingomyelin, glycosylphosphatidylinositol, and membrane-anchored molecules\u0026nbsp;(56). Gangliosides play a role in the function and activation of CD8+ T cell receptors (TCRs)\u0026nbsp;(57). In CD8+ Teff cells, o-series gangliosides assist in recruiting TCRs to specific regions of the cell membrane, namely lipid rafts, where they contribute to the activation process of these cells. Furthermore, GSLs play critical roles in the apoptotic pathways of adaptive immune cells. Moreover, GSLs are integral in the formation of lipid rafts not only on plasma membranes but also on subcellular compartments. These GSL-enriched lipid rafts within subcellular domains facilitate essential signaling pathways involved in various physiological functions. Prior findings indicate that lipid raft-like domains in mitochondria-associated membranes (MAM) are significant for the organelle scrambling activity, which is essential for autophagosome biogenesis\u0026nbsp;(58).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe previous study confirmed that the activation and cytolytic granule secretion of cytotoxic lymphocytes (CLs) necessitate the presence of the glycosylphosphatidylinositol (GPI) biosynthetic pathway\u0026nbsp;(59).\u003c/p\u003e\n\u003cp\u003eIn N4BP2L1+ CD8+ Teff cells, there is a clear upregulation of these metabolic pathways, suggesting a potential amplification of the pro-inflammatory effect through the modulation of CD8+ Teff TCR function and activation, autophagy, and cytolytic granule secretion. Further investigation is needed to determine if additional regulatory mechanisms are at play in this CD8+ Teff subset and to elucidate their impact on neighboring cells. Our understanding of their role is derived from an analysis of intercellular communication analysis.\u003c/p\u003e\n\u003cp\u003eThe distinction between figures 6 and 7 serves as a reminder that N4BP2L1+ CD8+ T cells likely play a significant role in the pathogenesis of SSc-ILD by regulating monocytes through ANXA1. The interaction between ANXA1 and the receptor FPR2 on monocytes may trigger the activation of the p38 MAPK/MAPKAPK/HSP27 signaling cascade\u0026nbsp;(60). Further exploration is required to understand how the regulation of monocytes influences the inflammatory or pro-fibrotic environment. Moreover, the heightened intercellular communication between N4BP2L1- CD8+ T cells and tissue stem cells facilitated by the GZMA and FR2 interaction implies a potential protective role of N4BP2L1- CD8+ T cells in targeting stem cells to combat fibrosis. It\u0026apos;s worth noting that the presence of N4BP2L1 may potentially reverse this condition, but further investigation is warranted to confirm this hypothesis. The discovery of mutual interaction through MIF between N4BP2L1- CD8+ T cells and the co-expression of CD74 and CXCR4 on B cells indicates that elevated MIF levels attract B cells expressing CXCR4 and CD74. Upon binding to their ligands, this interaction forms a complex that activates survival pathways, thus inhibiting cell death\u0026nbsp;(61). Additional investigation is essential to grasp the significance of B cell survival in the regulation of IPF or SSc-ILD. It has been verified that regulatory B cells (Bregs) are notably reduced in SSc-ILD\u0026nbsp;(62). Breg cells demonstrate phenotypical and functional impairment in SSc patients. Moreover, in SSc, B cells show impaired activation of p38 MAPK and STAT-3 upon stimulation via BCR and TLR-9. Further exploration is needed to determine whether the function of N4BP2L1- CD8+ T cells can mitigate the dysfunction observed in B cells, and whether the presence of N4BP2L1 once again inhibits this functional role.\u003c/p\u003e\n\u003cp\u003eThe upregulated genes in N4BP2L1+ CD8+ Teff cells may potentially play a role in promoting the pathogenesis of IPF or SSc-ILD. Therefore, we performed gene enrichment analysis to identify the unique functions of these cells.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTNFR1-mediated ceramide production was obviously upregulated in N4BP2L1+ CD8+ Teff cells compared to N4BP2L1- CD8+ Teff cells. In numerous studies, TNF-mediated programmed necrosis has been linked to the biological and mechanistic roles of TNF-\u0026alpha; and its interaction with TNFR-1, particularly in the context of pharmacological or genetic apoptosis inhibition\u0026nbsp;(63, 64). Elevated intracellular ceramide levels have been associated with heightened redox reactions within cells, hinting at potential crosstalk among ceramide, ROS, and TNF-\u0026alpha; pathways in this phenomenon\u0026nbsp;(65, 66). The increased pathway activity suggests a close association between heightened mitochondrial ROS production, activation of programmed necrosis, and the pathogenic role of N4BP2L1+ CD8+ Teff cells in IPF and SSc-ILD.\u003c/p\u003e\n\u003cp\u003eCompared to N4BP2L1- CD8+ Teff cells, both lipid biosynthetic processes and autophagy were upregulated in N4BP2L1+ CD8+ Teff cells. The previous study showed that mice with Atg7-deficient CD8+ T cells exhibited impaired memory development in influenza or murine cytomegalovirus models, along with reduced recall responses to secondary influenza infection\u0026nbsp;(67). Likewise, in a granzyme-Cre Atg7fl/fl mouse model, CD8+ T cells demonstrated intrinsic deficiencies in memory formation following lymphocytic choriomeningitis virus infection\u0026nbsp;(68). Autophagy activity may surge at the peak of CD8+ Teff responses, suggesting its potential importance during the transition from Teff to memory (Tmem) CD8+ cells. Both studies also associated defects in CD8+ T cell memory formation with the inability to trigger metabolic shifts, particularly the failure to enhance fatty acid oxidation, a critical process in CD8+ T cell memory formation\u0026nbsp;(69). Therefore, the heightened pathway involving both lipid biosynthetic processes and autophagy suggests a heightened activity of CD8+ Teff cells and a substantial transition to CD8+ Tmem cells, characterized by increased expression of N4BP2L1. It is reasonable to deduce that N4BP2L1+ CD8+ Teff cells exhibit enhanced activity and contribute to inflammation and fibrosis. This deduction aligns with previous findings indicating that depletion of CD8+ T cells or perforin deficiency significantly attenuated bleomycin-induced pulmonary fibrosis\u0026nbsp;(70, 71). Moreover, earlier research demonstrated that CD8+ T cell cytotoxicity, alongside IL-13 production, could play a pivotal role in inflammation and fibrosis, further supporting our interpretation\u0026nbsp;(32).\u003c/p\u003e\n\u003cp\u003eTo deepen our comprehension of the function of CD8+ Teff cell activity and its role in pathogenesis, we undertake a comprehensive analysis of the potential involvement of lipid biosynthetic processes, autophagy, and TNFR1-mediated ceramide production in regulating regulated cellular death (RCD) within CD8+ Teff cells.\u003c/p\u003e\n\u003cp\u003eThe increased production of mitochondrial ROS triggered by TNFR1-mediated ceramide synthesis and the elevation of autophagy levels might suggest the involvement of mitophagy in the development of IPF or SSc-ILD influenced by CD8+ Teff cells. The heightened production of mitochondrial ROS may stimulate mitophagy, as mitophagy selectively degrades mitochondria to eliminate dysfunctional organelles and reduce ROS levels. Mitophagy, a form of selective autophagy, selectively targets mitochondria for degradation, clearing dysfunctional organelles and reducing ROS levels. Identified cargo receptors involved in mitophagy include CALCOCO2, OPTN, SQSTM1, TAX1BP1, and others. Meanwhile, mitochondrial ROS plays a crucial role in inducing autophagy\u0026nbsp;(72). Overall, our speculation suggests that TNFR1-mediated ceramide synthesis induces ROS production, potentially promoting mitophagy in CD8+ Teff cells, as evidenced by the upregulation of autophagy observed in gene enrichment analysis. Moreover, mitophagy can facilitate the development of effector memory in antigen-specific CD8+ T cells, suggesting that the heightened activity of N4BP2L1+ CD8+ T cells might contribute to the onset of inflammation and fibrosis\u0026nbsp;(73).\u003c/p\u003e\n\u003cp\u003eFurthermore, considering the induction of ROS generation and the upregulation of autophagy and lipid biosynthetic processes, we further speculated that ferroptosis may also play a crucial in the regulating activity of N4BP2L1+ CD8+ T cells. Ferroptosis ensues from uncontrolled and lethal lipid peroxidation, leading to the rupture of the plasma membrane facilitated by iron catalysis, which encompasses both non-enzymatic (Fenton reaction) and enzymatic (lipoxygenases) mechanisms. Consequently, lipid peroxidation emerges as a pivotal signaling event. And mitophagy can regulate lipid peroxidation by modulating mitochondrial function. The upregulation of ROS may induce the upregulation in mitophagy, while concurrently promoting ferroptosis, as evidenced by the upregulation of lipid biosynthetic processes. However, it is noteworthy that the upregulation of mitophagy itself can also promote ferroptotic cell death, as demonstrated in previous studies\u0026nbsp;(74, 75). Further exploration is needed to determine whether the upregulation of ferroptosis, mitophagy, and programmed necrosis may directly regulate the activity of CD8+ Teff cells, or if proinflammatory or profibrotic cytokines contribute to the development of inflammation or fibrosis. It is also important to investigate whether these factors influence the phenotype of CD8+ Teff cells or attract other cell types, leading to inflammatory injury and fibrosis, thereby promoting the pathogenesis of IPF or SSc-ILD.\u003c/p\u003e\n\u003cp\u003eFollowing the protein-protein interaction enrichment analysis, our focus shifted to nuclear receptor co-repressor 1 (NCOR1), chromobox homolog 5 (CBX5), and O-glcNAc transferase (OGT), as these proteins were deemed more pivotal in interacting and regulating compared to others in the enrichment analysis. The involvement of CBX5 and OGT in the pathogenesis of pulmonary fibrosis has been previously established\u0026nbsp;(76, 77). However, further exploration is required to determine their role in regulating the activity of CD8+ Teff cells.\u003c/p\u003e\n\u003cp\u003eIntriguingly, NCOR1, known as a negative regulator of the nuclear receptor PPAR\u0026alpha;, has previously been shown to serve a physiological role in selective autophagy, enabling the regulation of lipid metabolism through selective turnover\u0026nbsp;(78). The activation of the protein appears to have a strong association with the upregulated pathways of lipid biosynthetic processes and autophagy, suggesting the significant involvement of ferroptosis in CD8+ N4BP2L1 Teff cells.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll the evident directed us towards to the enhanced activity of CD8+ Teff cells. Is there any connection between this result and EMT? The EMT program confers various immunosuppressive properties to mesenchymal cells (79). Therefore, it is reasonable to speculate that epithelial cells, under the heightened activity of CD8+ Teff cells, initiate the EMT program to shield themselves from immune damage. However, this mechanism ultimately promotes the pathogenesis of IPF or SSc-ILD.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOverall, our study, employing scRNA-seq and MR analysis, has identified N4BP2L1 as a gene target and CD8+ Teff cells as a cell type target for the development of therapeutic strategies in patients with SSc-ILD. Utilizing gene enrichment analysis, protein-protein interaction enrichment analysis, cell-cell interaction analysis, switch gene analysis, single-cell metabolism analysis, and bulk RNA-seq analysis, we have elucidated the role of N4BP2L1 in CD8+ Teff cells in regulating the pathogenesis of IPF and SSc-ILD. On one hand, our findings confirm potential regulatory pathways, proteins, alterations in gene expression, metabolism pathways, and interactions with other cell types that may be influenced by N4BP2L1 in CD8+ Teff cells. On the other hand, we have provided insights into multiple potential downstream regulatory mechanisms that may be modulated by N4BP2L1 in CD8+ Teff cells, warranting further investigation. The elucidation of connections between these downstream regulatory networks and inflammation or fibrosis promoted by CD8+ Teff cells holds promise for the development of novel therapeutic strategies for these challenging diseases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur primary focus on elucidating the causal relationship between N4BP2L1 and SSc-ILD, rather than IPF, stemmed from several factors. SSc is recognized as a rare and heterogeneous chronic connective tissue disease characterized by progressive fibrosis affecting both the skin and internal organs (80, 81). Importantly, ILD is a common manifestation of SSc, impacting approximately 35\u0026ndash;52% of patients (82). By concentrating on SSc-ILD, we aimed to delve deeper into understanding the relationship between N4BP2L1 and the pathogenesis of systemic sclerosis, a broader condition encompassing various organ involvements beyond the lungs. Our intention was to identify targets that could shed light on the underlying mechanisms of SSc, ultimately paving the way for the development of comprehensive treatment algorithms for this complex disease.\u003c/p\u003e\n\u003ch2\u003eLimitations of the study\u003c/h2\u003e\n\u003cp\u003eThe study conducted herein relied on bioinformatic approaches. Further experiments are warranted to validate the role of N4BP2L1 in CD8+ Teff cells as a therapeutic target for SSc-ILD and potentially IPF. Experimental validation could involve gene knockout or downregulation of N4BP2L1 in CD8+ Teff cells to ascertain its impact on inflammation and fibrosis. Additionally, investigating whether downregulation of this gene affects pathways such as autophagy, lipid biosynthesis, and intercellular interactions, which may contribute to inflammation and fibrosis generation, is essential. Moreover, understanding how various forms of RCD such as programmed necrosis, mitophagy, and fibrosis are influenced by N4BP2L1, and how they influence the activity of CD8+ Teff cells, requires further experimental confirmation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCD8+ Tem: CD8+ effector memory T cells; CI: Confidence Interval; DEG: Differentially Expressed Gene; ILD: Interstitial Lung Disease; IPF: Idiopathic Pulmonary Fibrosis; IV: Instrumental Variables; MR: Mendelian Randomization; NK cells: Natural Killer cells; OR: Odds Ratio; SSc-ILD: Systemic Sclerosis-associated Interstitial Lung Disease; SNP: Single Nucleotide Polymorphism; TCRs: T Cell Receptors; TGF\u0026beta;: Transforming Growth Factor Beta; scRNA-seq: Single-cell RNA sequencing\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u0026nbsp;\u003c/h2\u003e\n\u003ch3\u003eLead contact\u003c/h3\u003e\n\u003cp\u003eFurther information and requests should be directed to the lead contact, Ying Wei ([email protected]).\u003c/p\u003e\n\u003ch3\u003eMaterials availability\u003c/h3\u003e\n\u003cp\u003eThis study did not generate new unique reagents.\u003c/p\u003e\n\u003ch3\u003eData and code availability\u003c/h3\u003e\n\u003cp\u003ePublicly available data can be downloaded from the sources provided from \u0026ldquo;Key resources table\u0026rdquo;. Upon request, the lead contact can provide the necessary code for reanalyzing the data presented in this report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by grants from the National Natural Science Foundation of China (Grant No. 82174495).\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eConceptualization: X.L., W.T. Formal analysis: X.L., W.T.. Investigation: S.O., F.Y., W.W., X.D. and Y.W. Methodology: X.L., W.T., S.O. Supervision: W.W., X.D. and Y.W.. Writing\u0026mdash;original draft: X.L. Writing\u0026mdash;review \u0026amp; editing: W.W., X.D. and Y.W.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe thank Shaocong Mo for guiding the MR analysis process. The datasets supporting the conclusions of this article are included within the article and supplemental information.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eExperimental models and subject details\u003c/h3\u003e\n\u003cp\u003eThis study did not involve the use of experimental models or the enrollment of human subjects.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVarga J, Abraham D. Systemic sclerosis: a prototypic multisystem fibrotic disorder. The Journal of clinical investigation. 2007;117(3):557-67.\u003c/li\u003e\n\u003cli\u003eRicheldi L. Idiopathic pulmonary fibrosis: current challenges and future perspectives. 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Clinical epidemiology. 2019:257-73.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"target, Mendelian Randomization, single-cell RNA sequencing, IPF, SSc-ILD","lastPublishedDoi":"10.21203/rs.3.rs-4307133/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4307133/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Systemic sclerosis-associated interstitial lung disease (SSc-ILD) and idiopathic pulmonary fibrosis (IPF) are paradigmatic conditions characterized by similar pathogenic mechanisms involving inflammation and fibrosis. Both disorders present considerable challenges due to their elevated mortality rates and the difficulty in identifying effective treatments. Consequently, it is imperative to explore potential targets and deepen our understanding of the initiation and progression of these diseases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eTo address this, we utilized a combination of Mendelian randomization (MR) analysis, single-cell RNA sequencing (scRNA-seq) analysis, and other multi-omics analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Our investigation confirmed the involvement of N4BP2L1 in CD8+ effector T (Teff) cells and its causal relationship with SSc-ILD. Subsequent multi-omics analyses were conducted to validate the role of N4BP2L1+ CD8+ Teff cells in the pathogenesis of both IPF and SSc-ILD. Through enrichment analysis, we unveiled the complex interplay among programmed necrosis, autophagy, and ferroptosis, highlighting their pivotal role in modulating the activity of N4BP2L1+ CD8+ Teff cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e In essence, the heightened activity of N4BP2L1+ CD8+ Teff cells is implicated in the development of inflammation, fibrosis, and epithelial-mesenchymal transition (EMT), emphasizing its significance in the pathogenesis of both SSc-ILD and IPF.\u003c/p\u003e","manuscriptTitle":"Targeting N4BP2L1 for Therapy in IPF and SSc-ILD: Evidence from Mendelian Randomization and Multi-Omics Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-26 16:43:54","doi":"10.21203/rs.3.rs-4307133/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":"d91ab28c-df1b-4a8b-bd53-2e4d4a5c84f6","owner":[],"postedDate":"April 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-30T12:37:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-26 16:43:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4307133","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4307133","identity":"rs-4307133","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}