ZDHHC5 and ZDHHC14 promote depression via the mediation of double- negative T cells

ZDHHC5 and ZDHHC14 promote depression via the mediation of double- negative T cells | 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 ZDHHC5 and ZDHHC14 promote depression via the mediation of double- negative T cells Gengchen Lu, Yining Zhou, Lingwei Song, Zhiwei Xu, Bin Cheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6681518/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Dec, 2025 Read the published version in Mammalian Genome → Version 1 posted 9 You are reading this latest preprint version Abstract This study aims to identify palmitoylation modification-related genes involved in depression and to explore the mechanism of gene action through the mediation of immune cells. We analyzed the latest reviews on palmitoylation, integrated palmitoylation-related gene loci, and extracted expression quantitative trait locus(eQTL) data for palmitoylation genes associated with depression. Through batch analysis, we initially identified positive genes and validated them using Mendelian randomization based on summary data(SMR) to pinpoint the target genes. Subsequently, we further conducted mediation analysis to explore the downstream mechanism of action of the target gene. In the results,Thirty-one palmitoylated genes were screened from the literature. After extracting eQTL data to obtain 22 co-located loci, batch analysis with depression yielded seven positive genes.Validation using SMR analysis identified ZDHHC5 (OR = 1.136755, P = 1.14×10⁻⁹, P SMR = 6.88×10⁻¹⁰) and ZDHHC14 (OR = 1.055997, P = 2.29×10⁻⁹, P SMR =0.02409) as the final target genes. The downstream mechanisms of action were explored using 731 immune cells. The results showed that: ZDHHC5 promotes depression through the mediating effects of effector memory double negative (CD4-negative and CD8-negative) (mediation effect = 0.065386) and CD28-negative double negative (CD4-negative and CD8-negative) percentage of T cells (CD28- DN (CD4-CD8-) % T cells) (mediation effect = 0.086404). ZDHHC14 promoted depression through EM DN (CD4-CD8-) AC (mediation effect = 0.129494). Our results indicated that double negative T cells played an important role in this study. Thus, we conclude that ZDHHC5 and ZDHHC14 promote depression via the mediation of double-negative T cells. Palmitoylation modification Depression eQTL SMR Immune cells Downstream mechanism of action Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Depression is a common mental disorder characterized by high prevalence and recurrence rates, posing a significant risk to global public health. Globally, the lifetime prevalence of depression ranges from 10–20%, with higher rates observed in early adulthood and middle age, and a greater prevalence in women than in men (Hirschfeld, 2012; Salk et al., 2017). Prevalence rates vary significantly across different regions due to socio-economic, cultural, and environmental factors (First, 2013). Depression is primarily characterized by persistent and significant depressed mood. Patients often exhibit low mood, negative pessimism, low self-esteem, and depressive symptoms; in severe cases, they may develop suicidal tendencies or behaviors. Additionally, some patients experience prominent anxiety and motor agitation, as well as psychotic symptoms such as hallucinations and delusions (Rakel, 1999). Although the exact pathogenesis of depression remains unclear, genetic studies have identified several genes that may be closely related to its development. These include genes associated with monoamine neurotransmitters, the hypothalamic-pituitary-adrenal (HPA) axis stress hormones, inflammation-related cytokines, and circadian rhythm regulation (Shadrina et al., 2018). Recent research has highlighted the potential role of palmitoylation in the onset and progression of depression. For instance, palmitoylation may influence depressive behaviors by modulating the function of the 5-hydroxytryptamine 1A receptor (5-HT1AR) (Gorinski et al., 2019). Moreover, palmitoylation can affect immune responses by altering the function of various immune-related proteins, potentially contributing to disease development (Zhang et al., 2021; Z. Zhang, C. Ren, et al., 2024). Meanwhile, the role of the immune system in the pathological mechanisms of depression has been extensively validated (Perry et al., 2021; Xue et al., 2024), suggesting that immune abnormalities may serve as an important mediator linking palmitoylation to depression. Exploring the interaction between palmitoylation and the immune system could provide new insights into the clinical diagnosis and treatment of depression. Palmitoylation is an important post-translational modification (PTM) of proteins that has been shown to play a crucial role in regulating gene expression, cellular metabolism, and immune responses (Ma et al., 2024; N. Zhang et al., 2024). At the cellular level, it precisely controls protein membrane localization, stability, and protein-protein interactions, thereby mediating signal transduction (Liang et al., 2023; H. Zhang et al., 2024). At the disease level, palmitoylation is implicated in the development of tumours (Mo et al., 2024), neurodegenerative diseases (Cho & Park, 2016), and metabolic disorders (Dong et al., 2023). In the brain, palmitoylation is the most common lipid modification(Fukata & Fukata, 2010), making palmitoylated gene expression closely associated with neurological and psychiatric disorders (Y. Z. Chen et al., 2024). Immune cells are a core component of the human immune system, responsible for immune surveillance, memory, and regulation. They recognize, attack, and eliminate foreign pathogens as well as abnormal cells(Janeway CA, 2012). The interplay between the immune system and neuropsychiatric disorders has been extensively studied in recent years. Palmitoylation may influence the development of neurological disorders by modulating immune cell function. For example, palmitoylation of TRPV2 ion channels regulates microglial phagocytosis, affecting the progression of Alzheimer's disease (Yang et al., 2024); palmitoylation of Toll-like receptors (TLRs) in immune cells may impact the development of Parkinson's disease (Chesarino et al., 2014; da Silva et al., 2016). These studies not only reveal the role of palmitoylation in neurological diseases but also suggest that immune cells may serve as important mediators in palmitoylation-mediated disease processes. However, the mechanisms underlying the interaction between palmitoylation-related genes and immune cell function in depression remain incompletely understood. Direct evidence for a causal relationship among these factors is still lacking, possibly due to the high cost and complexity of large-scale observational or rigorous experimental studies with more difficult-to-control research protocols. In contrast, novel biomedical approaches that explore the causal relationship between palmitoylation, immune cells, and depression using abundant genetic variation offer significant advantages in elucidating the interconnections and underlying mechanisms among these factors. Mendelian randomisation (MR) is a genetic epidemiological study design that excels in investigating causal relationships between traits and diseases (Sekula et al., 2016). Genome-wide association studies (GWAS) identify genetic associations of traits based on single nucleotide polymorphisms (SNPs). By integrating GWAS data with epigenetic modification data, such as methylation, researchers can further identify expression quantitative trait loci (eQTLs) and methylation quantitative trait loci (mQTLs) (Lawlor, 2016; Y. Li et al., 2023).As an expanded approach to MR, Mendelian randomisation based on summary data(SMR), leverages pooled GWAS and QTL data to efficiently screen for disease-associated regulatory genes, thereby identifying potential therapeutic targets (Zhu et al., 2016).MR analyses mimic the design of randomised controlled trials (RCTs) but with reduced bias, avoiding confounding factors and reverse causality issues (Emdin et al., 2017). SMR analyses are currently applied to study a variety of diseases, including cancer (Z. Zhang, T. Fang, et al., 2024), ankylosing spondylitis (Dai et al., 2024), and psychiatric disorders (Li et al., 2024; X. Li et al., 2023; Luo et al., 2024). Expression quantitative trait loci (eQTLs) are genetic variants, including single nucleotide polymorphisms (SNPs) loci, that influence gene expression levels and can regulate the expression of specific genes in an individual's genome (Tang, 2023). Studying eQTLs can provide insights into the genetic factors that regulate gene expression and further elucidate the mechanisms linking gene function to expression traits (Tao et al., 2024). Given the potential role of palmitoylation in immune regulation and psychiatric disorders, this paper aims to investigate the mechanisms by which palmitoylation-related genes mediated by immune cells contribute to the development of depression. This research seeks to offer new perspectives on the pathological mechanisms and targeted interventions for depression. Materials and methods Research design The research design of this study is shown in Fig. 1 (Fig. 1 ): Clearly defined palmitoylated genes were extracted by collating the latest palmitoylation reviews (step1). Potential palmitoylated gene targets for depression were mined from 15,695 eQTL genes (genes encoding drug targets or proteins related to drug targets) using the MR method (step2). Target palmitoylated gene targets were identified through SMR verification (step3). Immune cells were selected as intermediate factors to explore the mechanism of action of palmitoylated genes on depression (step4). Data Source Data for blood cis eQTL are available from the eQTLGen consortium. The eQTL data are obtained from ( https://molgenis26.gcc.rug.nl/downloads/eqtlgen/cis-eqtl/2019-12-11-cis-eQTLsFDR-ProbeLevel-CohortInfoRemoved-BonferroniAdded.txt.gz ), and all raw data were locally culled for chaining imbalances to obtain 15695 usable eQTL data; Depression data were sourced from the FinnGenc database. T The GWAS data of Depression were sourced from the FinnGen database ( https://storage.googleapis.com/finngen-public-data-r12/summary_stats/release/finngen_F5_DEPRESSIO.gz ), Disease ID: R12_F5_DEPRESSIO, and the database was adjusted for covariates including gender, age, 10 principal components (PCs), Finngen chip version 1 or 2, and traditional genotyping batch (Kurki et al., 2023). A total of 59,333 depression cases and 434,831 controls were included in the study; SMR from( https://yanglab.westlake.edu.cn/software/smr/#DataResource ), ( https://www.eqtlgen.org/cis-eqtls.html ); Data for 731 immune cell traits (Ebi-a-GCST0001391 to Ebi-a-GCST0002121) were retrieved from the IEU OpenGWAS project database ( https://gwas.mrcieu.ac.uk/ ). These traits include seven cell panels: B cells, dendritic cells (DCs), mature T cells, monocytes, myeloid cells, TBNK, and regulatory T cells (Treg). In addition, 731 immune cell traits include absolute cell count (AC), relative cell count (RC), median fluorescence intensity (MFI) reflecting surface antigen levels and morphological parameters (MP) (Orrù et al., 2020). All samples were obtained from Western populations. And this study was a secondary study of open-source data and the databases used were publicly available, so no additional ethical review was required. Literature Search Strategy and Palmitoylation Gene Collation Relevant literature was searched through databases (e.g., PubMed) for palmitoylated genes published before the cut-off date. Searches were conducted using the following keywords: 'palmitoylation' and 'gene'. The inclusion criteria for the literature were as follows: (1) Strong evidence supporting the authenticity of the palmitoylated genes; (2) Information including the first author, year of publication, country or region, and type of study; (3) Authenticity of the article with an impact factor of 2 or higher. The exclusion criteria were: (1) Palmitoylated genes not clearly named; (2) Genes that could not be clearly categorized or were ambiguous; (3) Case reports or conference abstracts. All literature meeting the criteria was collated, and the palmitoylated genes were clearly listed along with their sources, including the first author, publication date, country, study type, and journal information. MR Design In this study, genome-wide eQTL data were localized to exclude linkage disequilibrium (LD). MR analyses were conducted to investigate the relationships between palmitoylated eQTL genes and depression, 731 immune cell types and depression, and palmitoylated genes and 731 immune cell types. SMR approach was used to estimate whether the effects of SNPs on traits are mediated by molecular traits such as gene expression and mQTLs. Selection of Genetic Instrumental Variables MR uses genetic variation to represent risk factors, and the genetic instrumental variables (IVs) used in the analyses must satisfy three key assumptions(Fig. 1 )(Davies et al., 2018): (1) The instrumental variables are significantly associated with the exposure factors. (2) The instrumental variables are independent of any confounders of the outcome association. (3) The instrumental variables affect the outcome only through their influence on the exposure factors and not by other means. Meaningful SNPs were selected as instrumental variables based on the following criteria: (1) A strict threshold was applied to ensure that only SNPs with a p-value below the genome-wide significance threshold (5.0 × 10 − 8 ) were considered. (2) A local culling of chained imbalances was used, with the (LD) threshold of r² < 0.1 and a clustering window of 10,000 kb (Gkatzionis et al., 2023). (3) SNPs incompatible between exposure and outcome datasets (e.g., A/G versus A/C) were excluded. For palindromic SNPs, allele frequencies were used to infer the positive strand allele; if no allele frequencies were available, these SNPs were directly excluded. (4) The F statistic was calculated to assess the strength of the IVs in MR analysis and to determine the presence of weak instrumental bias (Burgess et al., 2017). To further validate the association hypothesis, an F statistic greater than 10 indicates that weak instrumental variable bias is unlikely, and thus, IVs with F < 10 were excluded (Burgess & Thompson, 2011). The formula for the F statistic is: F = ((N - K − 1) / K) * (R² / (1 - R²)), where N denotes the sample size of the exposure factor, R² denotes the proportion of the exposure variance explained by the instrumental variable, and K denotes the number of instrumental variables(Pierce et al., 2011). The formula for R² is: R² = 2 × (1 - MAF) × MAF × β², where β is the allele effect value, MAF is the minor allele frequency, and EAF is the effect allele frequency. Since MAF + EAF = 1 and MAF = min(EAF, 1 - EAF), EAF can be considered equivalent to MAF when calculating R² (Davey Smith & Hemani, 2014). Statistical Analyses The STROBE-MR guidelines were followed to conduct the analyses (Skrivankova et al., 2021). The inverse variance weighting (IVW) method was used as the primary analytical approach for MR. IVW is generally considered the gold standard for assessing causality and is the most reliable method for testing causal effects (Burgess et al., 2019). Results were deemed potential candidates for causal relationships with the exposure factor when the IVW analysis yielded significant differences (P < 0.05). To provide robust causal estimates under various assumptions, the study also employed several complementary methods, including MR-Egger, weighted median, simple mode, and weighted mode. These methods provided β-values and 95% confidence intervals to interpret the causality between the exposure factors and outcome variables. Sensitivity Analysis Sensitivity analysis included tests for heterogeneity and horizontal pleiotropy. In this study, the MR-Egger regression model intercept term was used to assess horizontal pleiotropy. A P-value > 0.05 for the intercept indicated the absence of significant horizontal pleiotropy. Heterogeneity among SNPs was evaluated using the Cochrane's Q-test within the MR-Egger framework(Bowden et al., 2018). If the P-value of the Q-statistic was < 0.05, it suggested significant heterogeneity, and the results of the random-effects IVW method should be prioritized. The MR-PRESSO (MR Pleiotropy RESidual Sum and Outlier) test was applied to detect horizontal pleiotropy by identifying outlier SNPs. Any identified outlier SNPs were removed, and the analyses were re-conducted to ensure robustness. Mediated MR Analysis To identify immune cell traits strongly causally associated with depression, a two-sample MR approach was used to calculate the total effect of palmitoylated genes on depression (betaT). A two-step mediated analysis was then conducted to explore immune cell traits that mediate the causal effect of palmitoylated genes on depression. Step 1: Using the previously identified target palmitoylated genes and immune cell traits, two-sample MR was performed to screen for palmitoylated genes with strong causal associations with immune cell traits. The effect values were calculated (beta A). Step 2: Using the immune cell traits identified in Step 1 as exposure factors, the effect values for these traits on depression were calculated (beta B), adjusting for the effect of palmitoylated genes by removing the IVs used in the palmitoylated gene-immune cell trait MR analysis. Finally, the mediating effect of the immune cell traits in the palmitoylation gene-depression pathway was calculated using the ‘product of coefficients’ method (beta A × beta B) and its ratio (beta A ×beta B / beta T). For the mediation effect to be considered significant, the mediating effect ratio should be at least 5% and statistically significant. Analysis Software and Technical Support All data processing was conducted using R (version 4.4.1), with analyses performed using R packages such as ‘MendelianRandomisation’, ‘MRPRESSO’, and ‘TwoSampleMR’. PLINK software (version v1.90) was utilized for local linkage disequilibrium pruning, and statistical significance was set at p < 0.05. SMR software version 1.3.1 ( https://cnsgenomics.com/software/smr/#Overview ) was employed for allele coordination and analysis, with default system commands used for SMR validation. Results Through a retrospective analysis of the palmitoylation literature, 31 palmitoylation genes that have been confidently associated with palmitoylation(Chamberlain & Shipston, 2015; Y. Chen et al., 2024; M. Li et al., 2023) were identified. These genes were intersected with 15,695 eQTL data points obtained after local exclusion of LD, resulting in 22 co-located genes(Fig. 2 )as follows: PPT1,PPT2,ZDHHC1,ZDHHC11,ZDHHC12,ZDHHC13,ZDHHC14,ZDHHC16,ZDHHC17,ZDHHC18,ZDHHC19,ZDHHC2,ZDHHC20,ZDHHC21,ZDHHC23,ZDHHC24,ZDHHC3,ZDHHC4,ZDHHC5,ZDHHC6,ZDHHC7,ZDHHC8. Table.1: The gene obtained by the intersection of palmitoylation gene and eQTL data The 22 identified palmitoylated eQTL genes were subjected to batch MR analysis with depression. Based on the IVW results, a total of 7 loci with P < 0.05 were extracted.And visual representation using forest plots (Fig. 3 ) Replace the ensemble IDs of the genes obtained above and perform SMR analysis for validation (Table.1): Table.1: Depression SMR validation results. Gene CHR TopSNP Beta SE P SMR P HEIDI Nsnp OR(95%CI) ZDHHC5 11 rs7116341 0.1610 0.0261 6.88×10⁻¹⁰ 0.2845 20 1.175(1.116 ~ 1.236) ZDHHC14 6 rs2135213 0.0616 0.0273 0.0241 0.0335 20 1.063(1.008 ~ 1.122) ZDHHC8 22 rs73877146 0.0413 0.0334 0.2172 0.0034 20 1.042(0.976 ~ 1.113) ZDHHC13 11 rs11025029 0.0232 0.0214 0.2787 0.2616 20 1.023(0.981 ~ 1.067) ZDHHC19 3 rs11185516 0.0130 0.031 0.6741 0.0025 20 1.013(0.953 ~ 1.077) ZDHHC20 13 rs7981970 -0.0473 0.0279 0.0898 0.0219 20 0.954(0.903 ~ 1.007) ZDHHC6 10 rs1887139 -0.0755 0.04 0.0589 0.568 20 0.927(0.857 ~ 1.003) Beta is the estimated effect size in the SMR analysis, SE is the corresponding standard error, PSMR is the P-value of the SMR analysis, P HEIDI is the P-value of the HEIDI test, and Nsnp is the number of SNPs involved in the HEIDI test.( CHR , chromosome; HEIDI , heterogeneity in dependent instruments; SNP , single-nucleotide polymorphism; SMR , summary data-based Mendelian randomization; QTL , quantitative trait loci; FDR , false discovery rate; GWAS , genome-wide association studies.) Validation by SMR showed that ZDHHC5 (P SMR = 6.88×10⁻¹⁰) and ZDHHC14 (P SMR =0.0241) had statistically significant differences, with the odds ratio (OR) values meeting the requirement of a 95% confidence interval. The OR values obtained from eQTL analysis were greater than 1, indicating that these genes are risk factors for depression. Accordingly, ZDHHC5 and ZDHHC14 were identified as the final target genes. Screening of Immune Cells Associated with Depression: A batch two-sample MR study was conducted, using immune cells as the exposure and depression as the outcome. This analysis identified a total of 38 immune cell types associated with depression (Fig. 4 ). Screening of Immune Cells Associated with the Target Genes ZDHHC5 and ZDHHC14 Using ZDHHC5 and ZDHHC14 as exposures, batch two-sample MR analyses were conducted with the 38 immune cell types identified in the previous step as outcomes. These analyses revealed a total of nine immune cell types associated with ZDHHC5 and eight immune cell types associated with ZDHHC14(Fig. 5 ). Analyzing the Effect of Target Genes on Depression via Immune Cell Traits (Table.2). Table.2: Mediator MR analysis of the impact of target genes on depression through immune cells. Gene Immune cell phenotype betaT betaA betaB betaAB se Z P betaAB ZDHHC14 IgD- CD27- %lymphocyte 0.0545 0.0924 -0.0817 -0.0075 0.0041 -1.8470 -0.1385 Effector Memory CD4-CD8- T cell 0.0545 0.1171 0.0603 0.0071 0.0066 1.0712 0.1295 CD20 on IgD + CD38- 0.0545 -0.1171 0.0441 -0.0052 0.0048 -1.0732 -0.0947 CD20 on IgD- CD38- 0.0545 -0.1292 0.0429 -0.0055 0.0053 -1.0469 -0.1018 CD20 on memory B cell 0.0545 -0.1470 0.0364 -0.0053 0.0060 -0.8978 -0.0982 IgD on IgD + CD38- 0.0545 -0.0974 0.0328 -0.0032 0.0040 -0.7925 -0.0586 CD33 on CD33br HLA DR+ 0.0545 -0.1572 0.0075 -0.0012 0.0097 -0.1216 -0.0216 CD33 on CD33br HLA DR + CD14- 0.0545 -0.1461 0.0074 -0.0011 0.0091 -0.1193 -0.0198 ZDHHC5 Effector Memory CD4-CD8- T cell 0.1282 0.1390 0.0603 0.0084 0.0084 1.0018 0.0654 CD28- DN (CD4-CD8-) %T cell 0.1282 0.2322 0.0477 0.0111 0.0148 0.7484 0.0864 IgD on IgD + CD24+ 0.1282 -0.1456 0.0290 -0.0042 0.0089 -0.4758 -0.0329 CD16-CD56 on NK 0.1282 0.1959 0.0186 0.0036 0.0129 0.2816 0.0284 CCR7 on naive CD8br 0.1282 -0.1407 -0.0490 0.0069 0.0094 0.7321 0.0538 CD33 on CD14 + monocyte 0.1282 0.2921 0.0068 0.0020 0.0290 0.0686 0.0155 CD33 on CD66b + + myeloid cell 0.1282 0.2158 0.0117 0.0025 0.0213 0.1191 0.0198 CD33 on CD33br HLA DR+ 0.1282 0.2761 0.0075 0.0021 0.0324 0.0639 0.0162 CD33 on CD33br HLA DR + CD14- 0.1282 0.2570 0.0074 0.0019 0.0302 0.0630 0.0148 betaT: the total effect of the target gene to depression, betaA: the effect of the target gene to immune cells, betaB: the effect value of immune cells to depression, betaAB: the mediating effect, that is, the introduction effect, represents the effect of the target gene to depression mediated by immune cells, se is the standard error, Z is the statistic, and P betaAB is the proportion of the mediating effect. Based on the results of screening data for a mediating effect proportion of not less than 5% (P betaAB ≥ 0.05), it can be preliminarily concluded that ZDHHC5 promotes the development of depression through the mediating effects of EM DN (CD4-CD8-) AC ((P betaAB = 0.0654) and CD28- DN (CD4-CD8-) T cells ((P betaAB =0.0864). Additionally, ZDHHC14 promotes depression through the mediation of EM DN (CD4-CD8-) AC ((P betaAB = 0.1295). Disscusion To investigate the relationship between palmitoylation genes, immune cells, and depression, this study compiled 31 palmitoylation-related genes from literature reports. For the first time, we utilized publicly available blood eQTL data and Finnish database depression samples in a two-sample MR and mediation analysis to examine the causal relationship between palmitoylation genes and depression through immune cells. Through SMR validation, we identified ZDHHC5 and ZDHHC14 as potentially associated with depression. Elevated expression of ZDHHC5 may contribute to the onset of depression via immune cell traits such as EM DN (CD4-CD8-) AC and CD28- DN (CD4-CD8-) T cells. Elevated expression of ZDHHC14 may promote depression through EM DN (CD4-CD8-) AC. These findings suggest that double-negative T cells are important mediators between palmitoylation genes and depression. Additionally, by comparing downstream results side-by-side, we found that mediating effects on other immune cells were either not significant or did not align with the total effect. This discrepancy may be due to the low percentage of palmitoylation modification effects of the target genes on these immune cells. Therefore, we propose that the robust effect of ZDHHC5 and ZDHHC14 on depression is primarily mediated through their catalysis of palmitoylation modifications in double-negative T cells. Double-negative T cells (DNTs) are a subset of immune cells that do not express CD4 or CD8. Constituting approximately 3–5% of the peripheral blood T cell subpopulation (Fischer et al., 2005), DNTs have garnered attention in recent years for their roles in inflammation, immune response, and cancer. The high expression of memory T cells in patients with major depressive disorder (MDD) can be interpreted as a sign of premature senescence of T cells (Schiweck et al., 2020). CD4 and CD8 are co-receptors on T cells, and disruption of complex immune homeostasis leads to enhanced immune escape via suppressive immune cells in the microenvironment, promoting disease onset and progression(H. Chen et al., 2024). An experimental animal study related to depression showed that CD4 expression was significantly lower in the chronic unpredictable mild stress (CUMS) model group compared to the control group, while CD4 expression increased in all treatment groups at different doses (Gong et al., 2024). Additionally, a RCT of depression demonstrated that the expression of CD4 and CD8 markers in peripheral blood serum was lower in the depressed group than in the control group. After treatment, the expression of these markers was significantly higher in the depressed group compared to pre-treatment levels (Goyal et al., 2017). These findings suggest that the expression of CD4 and CD8 is negatively correlated with depression and provide strong support for double-negative T cells as a risk factor for depression, consistent with the current MR results. Moreover, palmitoylation sites were clearly present in both CD4 and CD8 within the cytoplasmic structural domain (Bijlmakers, 2009), providing evidence that double-negative T cells may act as mediators of palmitoylation genes affecting disease. ZDHHC5 and ZDHHC14 are palmitoyl acyltransferases (PATs) that are highly expressed in the hippocampus. ZDHHC5 has been identified as an independent segregating genetic variant associated with depression in the most recent and comprehensive meta-analysis of GWAS data on depression (Howard et al., 2019). However, there is a relative paucity of studies correlating ZDHHC14 with depression. The current study suggests that ZDHHC14 is the only PAT predicted to bind proteins containing the Type-I PDZ structural domain (Sanders et al., 2020). It has been reported that the carboxy-terminal PDZ ligand (CAPON) association of hippocampal neuronal nitric oxide synthase (nNOS) mediates synapses that modulate anxiety-depressive behavior (Shi et al., 2024). In the cellular realm, overexpression of ZDHHC14 can lead to apoptosis (Yeste-Velasco et al., 2014), potentially due to its overcatalysis of palmitoylation modifications of intracellular proteins. This may explain the premature senescence of T cells observed in patients with depression, providing novel insights into the relationship between ZDHHC14, immune cells, and depression. Based on the results of this study and given that ZDHHC14 is one of the most highly expressed PATs in the hippocampusand even in this brain retion (Cajigas et al., 2012; Cembrowski et al., 2016), we propose that ZDHHC14 has a significant impact on double-negative T cells in this region. Collectively, our findings suggest that aberrant expression of ZDHHC5 and ZDHHC14 in the hippocampus leads to excessive palmitoylation modification of double-negative T cells, disrupting immune homeostasis in this brain region and contributing to the development of depression. In this study, we identified depression-associated palmitoylation gene targets ZDHHC5 and ZDHHC14 by reviewing palmitoylation literature and conducting Mendelian randomisation analyses. We then performed mediation analyses to explore the downstream mechanisms by which these target genes contribute to depression through immune cells. To our knowledge, this is the first large-scale correlation analysis of palmitoylation genes, immune cells, and depression. The results suggest that double-negative T cells mediate the promotional effect of ZDHHC5 and ZDHHC14 on the development of depression. Specifically, the expression of CD4 and CD8, as T-cell surface co-receptors, is negatively correlated with the onset of depression. This negative correlation may be due to aberrant expression of ZDHHC5 and ZDHHC14 in the hippocampus, leading to excessive palmitoylation modification of CD4- and CD8- in double-negative T cells. These findings provide new directions for understanding the pathogenesis of depression and developing novel targeted therapies. However, further objective evidence is needed to elucidate their specific relationships and mechanisms of action. Limitations The GWAS data included in this study were derived from European populations in high-income countries, limiting the generalizability of the findings to low- and middle-income countries and other ethnicities. Future studies should aim to enrich GWAS data to facilitate MR analyses across diverse ethnic and socioeconomic groups. Additionally, we look forward to validate the expression levels of ZDHHC5 and ZDHHC14 identified in this study through animal experiments to provide stronger biological evidence. Finally, the design of high-quality clinical research protocols is essential to enhance the evidence-based support for the causal effects of palmitoylated genes and immune cells on depression. Conclusions By employing a variety of research methods, we systematically investigated the causal relationship between palmitoylation modification and depression, and explored the downstream mechanism of palmitoylation genes with 731 types of immune cells as mediators. Our findings provide new insights into the mechanism by which palmitoylation modification leads to depression. Further research should focus on rigorous basic experimental validation of our results. Meanwhile, targeted therapeutic approaches should also be developed to mitigate the impact of depression on patients' health. Declarations Data Availability Statement The datasets generated and analyzed in this study are available in online repositories. The names and accession numbers of the relevant libraries/repositories can be found in the article or supplementary material. Ethics Statement Ethical approval was not required for this study involving humans, in accordance with local legislation and institutional requirements. Written informed consent to participate in this study from the subjects or their legal guardians/next of kin was not required, as per national legislation and institutional guidelines. Authors' Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Gengchen Lu,Yining Zhou, Lingwei Song and Zhiwei Xu. The first draft of the manuscript was written by Gengchen Lu.Bin Cheng revised it critically for important intellectual content and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding The authors declare that no financial support was received for the research, writing, and/or publication of this paper. Conflict of Interest The authors declare that this study was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Acknowledgements The authors would like to thank the MiBioGen consortium for providing GWAS data summary statistics and the authors of the palmitoylation review for data sharing. We also thank the BioLetter Maniacs team for their advice on manuscript preparation. Supplementary Material Supplementary materials related to this article can be found online. References Bijlmakers, M. J. (2009). Protein acylation and localization in T cell signaling (Review). Mol Membr Biol , 26 (1), 93-103. https://doi.org/10.1080/09687680802650481 Bowden, J., Spiller, W., Del Greco, M. F., Sheehan, N., Thompson, J., Minelli, C., & Davey Smith, G. (2018). Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. Int J Epidemiol , 47 (6), 2100. https://doi.org/10.1093/ije/dyy265 Burgess, S., Davey Smith, G., Davies, N. M., Dudbridge, F., Gill, D., Glymour, M. M., Hartwig, F. P., Kutalik, Z., Holmes, M. 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Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet , 48 (5), 481-487. https://doi.org/10.1038/ng.3538 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 12 Dec, 2025 Read the published version in Mammalian Genome → Version 1 posted Editorial decision: Revision requested 21 Oct, 2025 Reviews received at journal 04 Jul, 2025 Reviewers agreed at journal 13 Jun, 2025 Reviews received at journal 02 Jun, 2025 Reviewers agreed at journal 19 May, 2025 Reviewers invited by journal 19 May, 2025 Editor assigned by journal 19 May, 2025 Submission checks completed at journal 17 May, 2025 First submitted to journal 16 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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14:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6681518/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6681518/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00335-025-10173-5","type":"published","date":"2025-12-12T15:58:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83223324,"identity":"31f42ca7-09e9-494a-b475-b662c4806fdf","added_by":"auto","created_at":"2025-05-21 10:50:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":158846,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMendel schematic diagram and research flow chart.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6681518/v1/e7fdacceed9c51475eeaccb2.png"},{"id":83222158,"identity":"0f1d6e1d-b807-455f-8786-d297a49d709b","added_by":"auto","created_at":"2025-05-21 10:34:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":39439,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVenn diagram of eQTL and palmitoylation gene\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6681518/v1/9e4118eb67267fd7324e7e12.png"},{"id":83222160,"identity":"9d44ca1d-a88e-448d-b33b-552f6d583860","added_by":"auto","created_at":"2025-05-21 10:34:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":83007,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of palmitoylated genes associated with depression\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6681518/v1/ff79a17b2e6fff6e7d26a524.png"},{"id":83222164,"identity":"92d60249-3313-49a5-87f3-543d01c59f19","added_by":"auto","created_at":"2025-05-21 10:34:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1761799,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of immune cells associated with depression\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6681518/v1/af61b6c68694951df36748a3.png"},{"id":83222167,"identity":"3da297e9-5ebf-4fe7-ac19-27618b03dc18","added_by":"auto","created_at":"2025-05-21 10:34:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":168297,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of immune cells associated with the target gene\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6681518/v1/ffadb84eb2452e82740202c5.png"},{"id":98243985,"identity":"0224a82a-7357-466b-9169-5dad90fb30ec","added_by":"auto","created_at":"2025-12-15 16:12:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3231185,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6681518/v1/fbd53a4d-80dc-483a-98b5-69551b956953.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"ZDHHC5 and ZDHHC14 promote depression via the mediation of double- negative T cells","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDepression is a common mental disorder characterized by high prevalence and recurrence rates, posing a significant risk to global public health. Globally, the lifetime prevalence of depression ranges from 10\u0026ndash;20%, with higher rates observed in early adulthood and middle age, and a greater prevalence in women than in men (Hirschfeld, 2012; Salk et al., 2017). Prevalence rates vary significantly across different regions due to socio-economic, cultural, and environmental factors (First, 2013). Depression is primarily characterized by persistent and significant depressed mood. Patients often exhibit low mood, negative pessimism, low self-esteem, and depressive symptoms; in severe cases, they may develop suicidal tendencies or behaviors. Additionally, some patients experience prominent anxiety and motor agitation, as well as psychotic symptoms such as hallucinations and delusions (Rakel, 1999).\u003c/p\u003e \u003cp\u003eAlthough the exact pathogenesis of depression remains unclear, genetic studies have identified several genes that may be closely related to its development. These include genes associated with monoamine neurotransmitters, the hypothalamic-pituitary-adrenal (HPA) axis stress hormones, inflammation-related cytokines, and circadian rhythm regulation (Shadrina et al., 2018). Recent research has highlighted the potential role of palmitoylation in the onset and progression of depression. For instance, palmitoylation may influence depressive behaviors by modulating the function of the 5-hydroxytryptamine 1A receptor (5-HT1AR) (Gorinski et al., 2019). Moreover, palmitoylation can affect immune responses by altering the function of various immune-related proteins, potentially contributing to disease development (Zhang et al., 2021; Z. Zhang, C. Ren, et al., 2024).\u003c/p\u003e \u003cp\u003eMeanwhile, the role of the immune system in the pathological mechanisms of depression has been extensively validated (Perry et al., 2021; Xue et al., 2024), suggesting that immune abnormalities may serve as an important mediator linking palmitoylation to depression. Exploring the interaction between palmitoylation and the immune system could provide new insights into the clinical diagnosis and treatment of depression.\u003c/p\u003e \u003cp\u003ePalmitoylation is an important post-translational modification (PTM) of proteins that has been shown to play a crucial role in regulating gene expression, cellular metabolism, and immune responses (Ma et al., 2024; N. Zhang et al., 2024). At the cellular level, it precisely controls protein membrane localization, stability, and protein-protein interactions, thereby mediating signal transduction (Liang et al., 2023; H. Zhang et al., 2024). At the disease level, palmitoylation is implicated in the development of tumours (Mo et al., 2024), neurodegenerative diseases (Cho \u0026amp; Park, 2016), and metabolic disorders (Dong et al., 2023). In the brain, palmitoylation is the most common lipid modification(Fukata \u0026amp; Fukata, 2010), making palmitoylated gene expression closely associated with neurological and psychiatric disorders (Y. Z. Chen et al., 2024).\u003c/p\u003e \u003cp\u003eImmune cells are a core component of the human immune system, responsible for immune surveillance, memory, and regulation. They recognize, attack, and eliminate foreign pathogens as well as abnormal cells(Janeway CA, 2012). The interplay between the immune system and neuropsychiatric disorders has been extensively studied in recent years. Palmitoylation may influence the development of neurological disorders by modulating immune cell function. For example, palmitoylation of TRPV2 ion channels regulates microglial phagocytosis, affecting the progression of Alzheimer's disease (Yang et al., 2024); palmitoylation of Toll-like receptors (TLRs) in immune cells may impact the development of Parkinson's disease (Chesarino et al., 2014; da Silva et al., 2016). These studies not only reveal the role of palmitoylation in neurological diseases but also suggest that immune cells may serve as important mediators in palmitoylation-mediated disease processes.\u003c/p\u003e \u003cp\u003eHowever, the mechanisms underlying the interaction between palmitoylation-related genes and immune cell function in depression remain incompletely understood. Direct evidence for a causal relationship among these factors is still lacking, possibly due to the high cost and complexity of large-scale observational or rigorous experimental studies with more difficult-to-control research protocols. In contrast, novel biomedical approaches that explore the causal relationship between palmitoylation, immune cells, and depression using abundant genetic variation offer significant advantages in elucidating the interconnections and underlying mechanisms among these factors.\u003c/p\u003e \u003cp\u003eMendelian randomisation (MR) is a genetic epidemiological study design that excels in investigating causal relationships between traits and diseases (Sekula et al., 2016). Genome-wide association studies (GWAS) identify genetic associations of traits based on single nucleotide polymorphisms (SNPs). By integrating GWAS data with epigenetic modification data, such as methylation, researchers can further identify expression quantitative trait loci (eQTLs) and methylation quantitative trait loci (mQTLs) (Lawlor, 2016; Y. Li et al., 2023).As an expanded approach to MR, Mendelian randomisation based on summary data(SMR), leverages pooled GWAS and QTL data to efficiently screen for disease-associated regulatory genes, thereby identifying potential therapeutic targets (Zhu et al., 2016).MR analyses mimic the design of randomised controlled trials (RCTs) but with reduced bias, avoiding confounding factors and reverse causality issues (Emdin et al., 2017). SMR analyses are currently applied to study a variety of diseases, including cancer (Z. Zhang, T. Fang, et al., 2024), ankylosing spondylitis (Dai et al., 2024), and psychiatric disorders (Li et al., 2024; X. Li et al., 2023; Luo et al., 2024).\u003c/p\u003e \u003cp\u003eExpression quantitative trait loci (eQTLs) are genetic variants, including single nucleotide polymorphisms (SNPs) loci, that influence gene expression levels and can regulate the expression of specific genes in an individual's genome (Tang, 2023). Studying eQTLs can provide insights into the genetic factors that regulate gene expression and further elucidate the mechanisms linking gene function to expression traits (Tao et al., 2024). Given the potential role of palmitoylation in immune regulation and psychiatric disorders, this paper aims to investigate the mechanisms by which palmitoylation-related genes mediated by immune cells contribute to the development of depression. This research seeks to offer new perspectives on the pathological mechanisms and targeted interventions for depression.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch design\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe research design of this study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): Clearly defined palmitoylated genes were extracted by collating the latest palmitoylation reviews (step1). Potential palmitoylated gene targets for depression were mined from 15,695 eQTL genes (genes encoding drug targets or proteins related to drug targets) using the MR method (step2). Target palmitoylated gene targets were identified through SMR verification (step3). Immune cells were selected as intermediate factors to explore the mechanism of action of palmitoylated genes on depression (step4).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Source\u003c/h3\u003e\n\u003cp\u003eData for blood cis eQTL are available from the eQTLGen consortium. The eQTL data are obtained from (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://molgenis26.gcc.rug.nl/downloads/eqtlgen/cis-eqtl/2019-12-11-cis-eQTLsFDR-ProbeLevel-CohortInfoRemoved-BonferroniAdded.txt.gz\u003c/span\u003e\u003cspan address=\"https://molgenis26.gcc.rug.nl/downloads/eqtlgen/cis-eqtl/2019-12-11-cis-eQTLsFDR-ProbeLevel-CohortInfoRemoved-BonferroniAdded.txt.gz\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and all raw data were locally culled for chaining imbalances to obtain 15695 usable eQTL data;\u003c/p\u003e \u003cp\u003eDepression data were sourced from the FinnGenc database. T The GWAS data of Depression were sourced from the FinnGen database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://storage.googleapis.com/finngen-public-data-r12/summary_stats/release/finngen_F5_DEPRESSIO.gz\u003c/span\u003e\u003cspan address=\"https://storage.googleapis.com/finngen-public-data-r12/summary_stats/release/finngen_F5_DEPRESSIO.gz\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Disease ID: R12_F5_DEPRESSIO, and the database was adjusted for covariates including gender, age, 10 principal components (PCs), Finngen chip version 1 or 2, and traditional genotyping batch (Kurki et al., 2023). A total of 59,333 depression cases and 434,831 controls were included in the study;\u003c/p\u003e \u003cp\u003eSMR from(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://yanglab.westlake.edu.cn/software/smr/#DataResource\u003c/span\u003e\u003cspan address=\"https://yanglab.westlake.edu.cn/software/smr/#DataResource\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.eqtlgen.org/cis-eqtls.html\u003c/span\u003e\u003cspan address=\"https://www.eqtlgen.org/cis-eqtls.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e);\u003c/p\u003e \u003cp\u003eData for 731 immune cell traits (Ebi-a-GCST0001391 to Ebi-a-GCST0002121) were retrieved from the IEU OpenGWAS project database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These traits include seven cell panels: B cells, dendritic cells (DCs), mature T cells, monocytes, myeloid cells, TBNK, and regulatory T cells (Treg). In addition, 731 immune cell traits include absolute cell count (AC), relative cell count (RC), median fluorescence intensity (MFI) reflecting surface antigen levels and morphological parameters (MP) (Orr\u0026ugrave; et al., 2020). All samples were obtained from Western populations. And this study was a secondary study of open-source data and the databases used were publicly available, so no additional ethical review was required.\u003c/p\u003e\n\u003ch3\u003eLiterature Search Strategy and Palmitoylation Gene Collation\u003c/h3\u003e\n\u003cp\u003eRelevant literature was searched through databases (e.g., PubMed) for palmitoylated genes published before the cut-off date. Searches were conducted using the following keywords: 'palmitoylation' and 'gene'. The inclusion criteria for the literature were as follows: (1) Strong evidence supporting the authenticity of the palmitoylated genes; (2) Information including the first author, year of publication, country or region, and type of study; (3) Authenticity of the article with an impact factor of 2 or higher. The exclusion criteria were: (1) Palmitoylated genes not clearly named; (2) Genes that could not be clearly categorized or were ambiguous; (3) Case reports or conference abstracts.\u003c/p\u003e \u003cp\u003eAll literature meeting the criteria was collated, and the palmitoylated genes were clearly listed along with their sources, including the first author, publication date, country, study type, and journal information.\u003c/p\u003e\n\u003ch3\u003eMR Design\u003c/h3\u003e\n\u003cp\u003eIn this study, genome-wide eQTL data were localized to exclude linkage disequilibrium (LD). MR analyses were conducted to investigate the relationships between palmitoylated eQTL genes and depression, 731 immune cell types and depression, and palmitoylated genes and 731 immune cell types. SMR approach was used to estimate whether the effects of SNPs on traits are mediated by molecular traits such as gene expression and mQTLs.\u003c/p\u003e\n\u003ch3\u003eSelection of Genetic Instrumental Variables\u003c/h3\u003e\n\u003cp\u003eMR uses genetic variation to represent risk factors, and the genetic instrumental variables (IVs) used in the analyses must satisfy three key assumptions(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)(Davies et al., 2018): (1) The instrumental variables are significantly associated with the exposure factors. (2) The instrumental variables are independent of any confounders of the outcome association. (3) The instrumental variables affect the outcome only through their influence on the exposure factors and not by other means.\u003c/p\u003e \u003cp\u003eMeaningful SNPs were selected as instrumental variables based on the following criteria: (1) A strict threshold was applied to ensure that only SNPs with a p-value below the genome-wide significance threshold (5.0 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) were considered. (2) A local culling of chained imbalances was used, with the (LD) threshold of r\u0026sup2; \u0026lt; 0.1 and a clustering window of 10,000 kb (Gkatzionis et al., 2023). (3) SNPs incompatible between exposure and outcome datasets (e.g., A/G versus A/C) were excluded. For palindromic SNPs, allele frequencies were used to infer the positive strand allele; if no allele frequencies were available, these SNPs were directly excluded. (4) The F statistic was calculated to assess the strength of the IVs in MR analysis and to determine the presence of weak instrumental bias (Burgess et al., 2017). To further validate the association hypothesis, an F statistic greater than 10 indicates that weak instrumental variable bias is unlikely, and thus, IVs with F\u0026thinsp;\u0026lt;\u0026thinsp;10 were excluded (Burgess \u0026amp; Thompson, 2011). The formula for the F statistic is: F = ((N - K \u0026minus;\u0026thinsp;1) / K) * (R\u0026sup2; / (1 - R\u0026sup2;)), where N denotes the sample size of the exposure factor, R\u0026sup2; denotes the proportion of the exposure variance explained by the instrumental variable, and K denotes the number of instrumental variables(Pierce et al., 2011). The formula for R\u0026sup2; is: R\u0026sup2; = 2 \u0026times; (1 - MAF) \u0026times; MAF\u0026thinsp;\u0026times;\u0026thinsp;β\u0026sup2;, where β is the allele effect value, MAF is the minor allele frequency, and EAF is the effect allele frequency. Since MAF\u0026thinsp;+\u0026thinsp;EAF\u0026thinsp;=\u0026thinsp;1 and MAF\u0026thinsp;=\u0026thinsp;min(EAF, 1 - EAF), EAF can be considered equivalent to MAF when calculating R\u0026sup2; (Davey Smith \u0026amp; Hemani, 2014).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analyses\u003c/h2\u003e \u003cp\u003eThe STROBE-MR guidelines were followed to conduct the analyses (Skrivankova et al., 2021). The inverse variance weighting (IVW) method was used as the primary analytical approach for MR. IVW is generally considered the gold standard for assessing causality and is the most reliable method for testing causal effects (Burgess et al., 2019). Results were deemed potential candidates for causal relationships with the exposure factor when the IVW analysis yielded significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). To provide robust causal estimates under various assumptions, the study also employed several complementary methods, including MR-Egger, weighted median, simple mode, and weighted mode. These methods provided β-values and 95% confidence intervals to interpret the causality between the exposure factors and outcome variables.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSensitivity Analysis\u003c/h3\u003e\n\u003cp\u003eSensitivity analysis included tests for heterogeneity and horizontal pleiotropy. In this study, the MR-Egger regression model intercept term was used to assess horizontal pleiotropy. A P-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for the intercept indicated the absence of significant horizontal pleiotropy. Heterogeneity among SNPs was evaluated using the Cochrane's Q-test within the MR-Egger framework(Bowden et al., 2018). If the P-value of the Q-statistic was \u0026lt;\u0026thinsp;0.05, it suggested significant heterogeneity, and the results of the random-effects IVW method should be prioritized. The MR-PRESSO (MR Pleiotropy RESidual Sum and Outlier) test was applied to detect horizontal pleiotropy by identifying outlier SNPs. Any identified outlier SNPs were removed, and the analyses were re-conducted to ensure robustness.\u003c/p\u003e\n\u003ch3\u003eMediated MR Analysis\u003c/h3\u003e\n\u003cp\u003eTo identify immune cell traits strongly causally associated with depression, a two-sample MR approach was used to calculate the total effect of palmitoylated genes on depression (betaT). A two-step mediated analysis was then conducted to explore immune cell traits that mediate the causal effect of palmitoylated genes on depression.\u003c/p\u003e \u003cp\u003eStep 1: Using the previously identified target palmitoylated genes and immune cell traits, two-sample MR was performed to screen for palmitoylated genes with strong causal associations with immune cell traits. The effect values were calculated (beta A). Step 2: Using the immune cell traits identified in Step 1 as exposure factors, the effect values for these traits on depression were calculated (beta B), adjusting for the effect of palmitoylated genes by removing the IVs used in the palmitoylated gene-immune cell trait MR analysis.\u003c/p\u003e \u003cp\u003eFinally, the mediating effect of the immune cell traits in the palmitoylation gene-depression pathway was calculated using the \u0026lsquo;product of coefficients\u0026rsquo; method (beta A \u0026times; beta B) and its ratio (beta A \u0026times;beta B / beta T). For the mediation effect to be considered significant, the mediating effect ratio should be at least 5% and statistically significant.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis Software and Technical Support\u003c/h2\u003e \u003cp\u003eAll data processing was conducted using R (version 4.4.1), with analyses performed using R packages such as \u0026lsquo;MendelianRandomisation\u0026rsquo;, \u0026lsquo;MRPRESSO\u0026rsquo;, and \u0026lsquo;TwoSampleMR\u0026rsquo;. PLINK software (version v1.90) was utilized for local linkage disequilibrium pruning, and statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. SMR software version 1.3.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cnsgenomics.com/software/smr/#Overview\u003c/span\u003e\u003cspan address=\"https://cnsgenomics.com/software/smr/#Overview\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was employed for allele coordination and analysis, with default system commands used for SMR validation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThrough a retrospective analysis of the palmitoylation literature, 31 palmitoylation genes that have been confidently associated with palmitoylation(Chamberlain \u0026amp; Shipston, 2015; Y. Chen et al., 2024; M. Li et al., 2023) were identified. These genes were intersected with 15,695 eQTL data points obtained after local exclusion of LD, resulting in 22 co-located genes(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)as follows: PPT1,PPT2,ZDHHC1,ZDHHC11,ZDHHC12,ZDHHC13,ZDHHC14,ZDHHC16,ZDHHC17,ZDHHC18,ZDHHC19,ZDHHC2,ZDHHC20,ZDHHC21,ZDHHC23,ZDHHC24,ZDHHC3,ZDHHC4,ZDHHC5,ZDHHC6,ZDHHC7,ZDHHC8.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable.1: The gene obtained by the intersection of palmitoylation gene and eQTL data\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe 22 identified palmitoylated eQTL genes were subjected to batch MR analysis with depression. Based on the IVW results, a total of 7 loci with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were extracted.And visual representation using forest plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eReplace the ensemble IDs of the genes obtained above and perform SMR analysis for validation (Table.1):\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable.1: Depression SMR validation results.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTopSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003csub\u003eSMR\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003csub\u003eHEIDI\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNsnp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZDHHC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers7116341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.88\u0026times;10⁻\u0026sup1;⁰\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.2845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.175(1.116\u0026thinsp;~\u0026thinsp;1.236)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZDHHC14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers2135213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.063(1.008\u0026thinsp;~\u0026thinsp;1.122)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZDHHC8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers73877146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.042(0.976\u0026thinsp;~\u0026thinsp;1.113)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZDHHC13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers11025029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.2616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.023(0.981\u0026thinsp;~\u0026thinsp;1.067)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZDHHC19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers11185516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.013(0.953\u0026thinsp;~\u0026thinsp;1.077)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZDHHC20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers7981970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.954(0.903\u0026thinsp;~\u0026thinsp;1.007)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZDHHC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers1887139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.927(0.857\u0026thinsp;~\u0026thinsp;1.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eBeta is the estimated effect size in the SMR analysis, SE is the corresponding standard error, PSMR is the P-value of the SMR analysis, P\u003c/b\u003e \u003csub\u003e \u003cb\u003eHEIDI\u003c/b\u003e \u003c/sub\u003e \u003cb\u003eis the P-value of the HEIDI test, and Nsnp is the number of SNPs involved in the HEIDI test.(\u003c/b\u003e\u003cb\u003eCHR\u003c/b\u003e, \u003cb\u003echromosome;\u003c/b\u003e \u003cb\u003eHEIDI\u003c/b\u003e, \u003cb\u003eheterogeneity in dependent instruments;\u003c/b\u003e \u003cb\u003eSNP\u003c/b\u003e, \u003cb\u003esingle-nucleotide polymorphism;\u003c/b\u003e \u003cb\u003eSMR\u003c/b\u003e, \u003cb\u003esummary data-based Mendelian randomization;\u003c/b\u003e \u003cb\u003eQTL\u003c/b\u003e, \u003cb\u003equantitative trait loci;\u003c/b\u003e \u003cb\u003eFDR\u003c/b\u003e, \u003cb\u003efalse discovery rate;\u003c/b\u003e \u003cb\u003eGWAS\u003c/b\u003e, \u003cb\u003egenome-wide association studies.)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eValidation by SMR showed that ZDHHC5 (P\u003csub\u003eSMR\u003c/sub\u003e = 6.88\u0026times;10⁻\u0026sup1;⁰) and ZDHHC14 (P\u003csub\u003eSMR\u003c/sub\u003e =0.0241) had statistically significant differences, with the odds ratio (OR) values meeting the requirement of a 95% confidence interval. The OR values obtained from eQTL analysis were greater than 1, indicating that these genes are risk factors for depression. Accordingly, ZDHHC5 and ZDHHC14 were identified as the final target genes.\u003c/p\u003e \u003cp\u003eScreening of Immune Cells Associated with Depression: A batch two-sample MR study was conducted, using immune cells as the exposure and depression as the outcome. This analysis identified a total of 38 immune cell types associated with depression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eScreening of Immune Cells Associated with the Target Genes ZDHHC5 and ZDHHC14\u003c/p\u003e \u003cp\u003eUsing ZDHHC5 and ZDHHC14 as exposures, batch two-sample MR analyses were conducted with the 38 immune cell types identified in the previous step as outcomes. These analyses revealed a total of nine immune cell types associated with ZDHHC5 and eight immune cell types associated with ZDHHC14(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnalyzing the Effect of Target Genes on Depression via Immune Cell Traits (Table.2).\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable.2: Mediator MR analysis of the impact of target genes on depression through immune cells.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmune cell phenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ebetaT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ebetaA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ebetaB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ebetaAB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ese\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP\u003csub\u003ebetaAB\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eZDHHC14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIgD- CD27- %lymphocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.8470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.1385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffector Memory CD4-CD8- T cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.0712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.1295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD20 on IgD\u0026thinsp;+\u0026thinsp;CD38-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.1171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.0732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.0947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD20 on IgD- CD38-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.1292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.0469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.1018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD20 on memory B cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.1470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.8978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.0982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIgD on IgD\u0026thinsp;+\u0026thinsp;CD38-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.7925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.0586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD33 on CD33br HLA DR+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.1572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.1216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.0216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD33 on CD33br HLA DR\u0026thinsp;+\u0026thinsp;CD14-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.1461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.1193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.0198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eZDHHC5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffector Memory CD4-CD8- T cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.0018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD28- DN (CD4-CD8-) %T cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.7484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIgD on IgD\u0026thinsp;+\u0026thinsp;CD24+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.1456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.4758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.0329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD16-CD56 on NK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCR7 on naive CD8br\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.1407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.7321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD33 on CD14\u0026thinsp;+\u0026thinsp;monocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD33 on CD66b\u0026thinsp;+\u0026thinsp;+\u0026thinsp;myeloid cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD33 on CD33br HLA DR+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD33 on CD33br HLA DR\u0026thinsp;+\u0026thinsp;CD14-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ebetaT: the total effect of the target gene to depression, betaA: the effect of the target gene to immune cells, betaB: the effect value of immune cells to depression, betaAB: the mediating effect, that is, the introduction effect, represents the effect of the target gene to depression mediated by immune cells, se is the standard error, Z is the statistic, and P\u003c/b\u003e \u003csub\u003e \u003cb\u003ebetaAB\u003c/b\u003e \u003c/sub\u003e \u003cb\u003eis the proportion of the mediating effect.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBased on the results of screening data for a mediating effect proportion of not less than 5% (P\u003csub\u003ebetaAB\u003c/sub\u003e \u0026ge; 0.05), it can be preliminarily concluded that ZDHHC5 promotes the development of depression through the mediating effects of EM DN (CD4-CD8-) AC ((P\u003csub\u003ebetaAB\u003c/sub\u003e = 0.0654) and CD28- DN (CD4-CD8-) T cells ((P\u003csub\u003ebetaAB\u003c/sub\u003e =0.0864). Additionally, ZDHHC14 promotes depression through the mediation of EM DN (CD4-CD8-) AC ((P\u003csub\u003ebetaAB\u003c/sub\u003e = 0.1295).\u003c/p\u003e"},{"header":"Disscusion","content":"\u003cp\u003eTo investigate the relationship between palmitoylation genes, immune cells, and depression, this study compiled 31 palmitoylation-related genes from literature reports. For the first time, we utilized publicly available blood eQTL data and Finnish database depression samples in a two-sample MR and mediation analysis to examine the causal relationship between palmitoylation genes and depression through immune cells. Through SMR validation, we identified ZDHHC5 and ZDHHC14 as potentially associated with depression.\u003c/p\u003e \u003cp\u003eElevated expression of ZDHHC5 may contribute to the onset of depression via immune cell traits such as EM DN (CD4-CD8-) AC and CD28- DN (CD4-CD8-) T cells. Elevated expression of ZDHHC14 may promote depression through EM DN (CD4-CD8-) AC. These findings suggest that double-negative T cells are important mediators between palmitoylation genes and depression.\u003c/p\u003e \u003cp\u003eAdditionally, by comparing downstream results side-by-side, we found that mediating effects on other immune cells were either not significant or did not align with the total effect. This discrepancy may be due to the low percentage of palmitoylation modification effects of the target genes on these immune cells. Therefore, we propose that the robust effect of ZDHHC5 and ZDHHC14 on depression is primarily mediated through their catalysis of palmitoylation modifications in double-negative T cells.\u003c/p\u003e \u003cp\u003eDouble-negative T cells (DNTs) are a subset of immune cells that do not express CD4 or CD8. Constituting approximately 3\u0026ndash;5% of the peripheral blood T cell subpopulation (Fischer et al., 2005), DNTs have garnered attention in recent years for their roles in inflammation, immune response, and cancer. The high expression of memory T cells in patients with major depressive disorder (MDD) can be interpreted as a sign of premature senescence of T cells (Schiweck et al., 2020). CD4 and CD8 are co-receptors on T cells, and disruption of complex immune homeostasis leads to enhanced immune escape via suppressive immune cells in the microenvironment, promoting disease onset and progression(H. Chen et al., 2024).\u003c/p\u003e \u003cp\u003eAn experimental animal study related to depression showed that CD4 expression was significantly lower in the chronic unpredictable mild stress (CUMS) model group compared to the control group, while CD4 expression increased in all treatment groups at different doses (Gong et al., 2024). Additionally, a RCT of depression demonstrated that the expression of CD4 and CD8 markers in peripheral blood serum was lower in the depressed group than in the control group. After treatment, the expression of these markers was significantly higher in the depressed group compared to pre-treatment levels (Goyal et al., 2017).\u003c/p\u003e \u003cp\u003eThese findings suggest that the expression of CD4 and CD8 is negatively correlated with depression and provide strong support for double-negative T cells as a risk factor for depression, consistent with the current MR results. Moreover, palmitoylation sites were clearly present in both CD4 and CD8 within the cytoplasmic structural domain (Bijlmakers, 2009), providing evidence that double-negative T cells may act as mediators of palmitoylation genes affecting disease.\u003c/p\u003e \u003cp\u003eZDHHC5 and ZDHHC14 are palmitoyl acyltransferases (PATs) that are highly expressed in the hippocampus. ZDHHC5 has been identified as an independent segregating genetic variant associated with depression in the most recent and comprehensive meta-analysis of GWAS data on depression (Howard et al., 2019). However, there is a relative paucity of studies correlating ZDHHC14 with depression. The current study suggests that ZDHHC14 is the only PAT predicted to bind proteins containing the Type-I PDZ structural domain (Sanders et al., 2020). It has been reported that the carboxy-terminal PDZ ligand (CAPON) association of hippocampal neuronal nitric oxide synthase (nNOS) mediates synapses that modulate anxiety-depressive behavior (Shi et al., 2024).\u003c/p\u003e \u003cp\u003eIn the cellular realm, overexpression of ZDHHC14 can lead to apoptosis (Yeste-Velasco et al., 2014), potentially due to its overcatalysis of palmitoylation modifications of intracellular proteins. This may explain the premature senescence of T cells observed in patients with depression, providing novel insights into the relationship between ZDHHC14, immune cells, and depression.\u003c/p\u003e \u003cp\u003eBased on the results of this study and given that ZDHHC14 is one of the most highly expressed PATs in the hippocampusand even in this brain retion (Cajigas et al., 2012; Cembrowski et al., 2016), we propose that ZDHHC14 has a significant impact on double-negative T cells in this region. Collectively, our findings suggest that aberrant expression of ZDHHC5 and ZDHHC14 in the hippocampus leads to excessive palmitoylation modification of double-negative T cells, disrupting immune homeostasis in this brain region and contributing to the development of depression.\u003c/p\u003e \u003cp\u003eIn this study, we identified depression-associated palmitoylation gene targets ZDHHC5 and ZDHHC14 by reviewing palmitoylation literature and conducting Mendelian randomisation analyses. We then performed mediation analyses to explore the downstream mechanisms by which these target genes contribute to depression through immune cells. To our knowledge, this is the first large-scale correlation analysis of palmitoylation genes, immune cells, and depression.\u003c/p\u003e \u003cp\u003eThe results suggest that double-negative T cells mediate the promotional effect of ZDHHC5 and ZDHHC14 on the development of depression. Specifically, the expression of CD4 and CD8, as T-cell surface co-receptors, is negatively correlated with the onset of depression. This negative correlation may be due to aberrant expression of ZDHHC5 and ZDHHC14 in the hippocampus, leading to excessive palmitoylation modification of CD4- and CD8- in double-negative T cells. These findings provide new directions for understanding the pathogenesis of depression and developing novel targeted therapies. However, further objective evidence is needed to elucidate their specific relationships and mechanisms of action.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe GWAS data included in this study were derived from European populations in high-income countries, limiting the generalizability of the findings to low- and middle-income countries and other ethnicities. Future studies should aim to enrich GWAS data to facilitate MR analyses across diverse ethnic and socioeconomic groups. Additionally, we look forward to validate the expression levels of ZDHHC5 and ZDHHC14 identified in this study through animal experiments to provide stronger biological evidence. Finally, the design of high-quality clinical research protocols is essential to enhance the evidence-based support for the causal effects of palmitoylated genes and immune cells on depression.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBy employing a variety of research methods, we systematically investigated the causal relationship between palmitoylation modification and depression, and explored the downstream mechanism of palmitoylation genes with 731 types of immune cells as mediators. Our findings provide new insights into the mechanism by which palmitoylation modification leads to depression. Further research should focus on rigorous basic experimental validation of our results. Meanwhile, targeted therapeutic approaches should also be developed to mitigate the impact of depression on patients' health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed in this study are available in online repositories. The names and accession numbers of the relevant libraries/repositories can be found in the article or supplementary material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was not required for this study involving humans, in accordance with local legislation and institutional requirements. Written informed consent to participate in this study from the subjects or their legal guardians/next of kin was not required, as per national legislation and institutional guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Gengchen Lu,Yining Zhou, Lingwei Song and Zhiwei Xu. The first draft of the manuscript was written by Gengchen Lu.Bin Cheng revised it critically for important intellectual content and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no financial support was received for the research, writing, and/or publication of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that this study 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\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the MiBioGen consortium for providing GWAS data summary statistics and the authors of the palmitoylation review for data sharing. We also thank the BioLetter Maniacs team for their advice on manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary materials related to this article can be found online.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBijlmakers, M. J. (2009). Protein acylation and localization in T cell signaling (Review). \u003cem\u003eMol Membr Biol\u003c/em\u003e,\u003cem\u003e 26\u003c/em\u003e(1), 93-103. https://doi.org/10.1080/09687680802650481 \u003c/li\u003e\n\u003cli\u003eBowden, J., Spiller, W., Del Greco, M. F., Sheehan, N., Thompson, J., Minelli, C., \u0026amp; Davey Smith, G. (2018). 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Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. \u003cem\u003eNat Genet\u003c/em\u003e,\u003cem\u003e 48\u003c/em\u003e(5), 481-487. https://doi.org/10.1038/ng.3538 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"mammalian-genome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mage","sideBox":"Learn more about [Mammalian Genome](http://link.springer.com/journal/335)","snPcode":"335","submissionUrl":"https://submission.nature.com/new-submission/335/3","title":"Mammalian Genome","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Palmitoylation modification, Depression, eQTL, SMR, Immune cells, Downstream mechanism of action","lastPublishedDoi":"10.21203/rs.3.rs-6681518/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6681518/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aims to identify palmitoylation modification-related genes involved in depression and to explore the mechanism of gene action through the mediation of immune cells. We analyzed the latest reviews on palmitoylation, integrated palmitoylation-related gene loci, and extracted expression quantitative trait locus(eQTL) data for palmitoylation genes associated with depression. Through batch analysis, we initially identified positive genes and validated them using Mendelian randomization based on summary data(SMR) to pinpoint the target genes. Subsequently, we further conducted mediation analysis to explore the downstream mechanism of action of the target gene. In the results,Thirty-one palmitoylated genes were screened from the literature. After extracting eQTL data to obtain 22 co-located loci, batch analysis with depression yielded seven positive genes.Validation using SMR analysis identified ZDHHC5 (OR\u0026thinsp;=\u0026thinsp;1.136755,\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.14\u0026times;10⁻⁹,\u003cem\u003eP\u003c/em\u003e\u003csub\u003eSMR\u003c/sub\u003e= 6.88\u0026times;10⁻\u0026sup1;⁰) and ZDHHC14 (OR\u0026thinsp;=\u0026thinsp;1.055997,\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.29\u0026times;10⁻⁹,\u003cem\u003eP\u003c/em\u003e\u003csub\u003eSMR\u003c/sub\u003e=0.02409) as the final target genes. The downstream mechanisms of action were explored using 731 immune cells. The results showed that: ZDHHC5 promotes depression through the mediating effects of effector memory double negative (CD4-negative and CD8-negative) (mediation effect\u0026thinsp;=\u0026thinsp;0.065386) and CD28-negative double negative (CD4-negative and CD8-negative) percentage of T cells (CD28- DN (CD4-CD8-) % T cells) (mediation effect\u0026thinsp;=\u0026thinsp;0.086404). ZDHHC14 promoted depression through EM DN (CD4-CD8-) AC (mediation effect\u0026thinsp;=\u0026thinsp;0.129494). Our results indicated that double negative T cells played an important role in this study. Thus, we conclude that ZDHHC5 and ZDHHC14 promote depression via the mediation of double-negative T cells.\u003c/p\u003e","manuscriptTitle":"ZDHHC5 and ZDHHC14 promote depression via the mediation of double- negative T cells","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-21 10:34:51","doi":"10.21203/rs.3.rs-6681518/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-21T13:06:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-04T15:06:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92681530984687534608460302882177091706","date":"2025-06-14T02:12:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-03T00:45:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"141454748602836563283927554829063090029","date":"2025-05-19T12:50:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-19T12:40:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-19T12:15:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-17T04:21:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Mammalian Genome","date":"2025-05-16T14:19:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"mammalian-genome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mage","sideBox":"Learn more about [Mammalian Genome](http://link.springer.com/journal/335)","snPcode":"335","submissionUrl":"https://submission.nature.com/new-submission/335/3","title":"Mammalian Genome","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"db913407-53b0-4444-8b50-c1e89ff58d45","owner":[],"postedDate":"May 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:04:26+00:00","versionOfRecord":{"articleIdentity":"rs-6681518","link":"https://doi.org/10.1007/s00335-025-10173-5","journal":{"identity":"mammalian-genome","isVorOnly":false,"title":"Mammalian Genome"},"publishedOn":"2025-12-12 15:58:53","publishedOnDateReadable":"December 12th, 2025"},"versionCreatedAt":"2025-05-21 10:34:51","video":"","vorDoi":"10.1007/s00335-025-10173-5","vorDoiUrl":"https://doi.org/10.1007/s00335-025-10173-5","workflowStages":[]},"version":"v1","identity":"rs-6681518","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6681518","identity":"rs-6681518","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}