Combination of multidisciplinary approaches reveals potential causal associations between influenza and immune cells: Single-cell RNA sequencing and Mendelian randomization

Combination of multidisciplinary approaches reveals potential causal associations between influenza and immune cells: Single-cell RNA sequencing and Mendelian randomization | 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 Combination of multidisciplinary approaches reveals potential causal associations between influenza and immune cells: Single-cell RNA sequencing and Mendelian randomization Ziwei Guo, Dongjie Wu, Xiaohan Chen, Jiuchong Wang, Wenliang Lv This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4276363/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The relationship between immune cells and influenza is a battle between the host immune system and viral invaders, however, the causality and underlying mechanisms have not been fully elucidated. Methods This study first analysed disability-adjusted life years (DALYs) and mortality of influenza using descriptive epidemiology based on the Global Burden of Disease (GBD) data from 1990 to 2019. Potential causal associations between 731 immune cells and influenza were then explored using univariate Mendelian randomization (UVMR), followed by validation of the cellular subpopulations to which the immune cells identified by UVMR belonged at the single-cell level, and then enrichment analysis has been performed. Finally, we also performed MR of key genes in cellular subpopulations, reverse MR analysis, colocalization analysis, potential drug prediction and molecular docking for genes satisfying causal associations. Results Joinpoint regression trend analysis showed a general downward trend in the change of influenza DALYs rate and mortality rate, and then UVMR results showed a strong association between the immune cell HLA-DR on CD14+ CD16- monocyte and influenza ( P IVW = 5.47E-05, P FDR = 0.03). The single-cell sequencing (scRNA-Seq) results verified that the immune cell HLA-DR on CD14+ CD16- monocyte identified by UVMR belonged to the Classical monocytes (CMs) subpopulation. MR analysis of key genes in the cellular subpopulation identified a total of 7 genes as causally associated with influenza, and no reverse causal association was found. The 3 genes were identified as druggable by drug prediction, namely VIM, CTSA and CSF3R. Finally, molecular docking results demonstrated the strong potential of the CSF3R gene as a drug target. Conclusions Our study provides new insights into future prevention and treatment strategies for influenza from epidemiology to genetics to bioinformatic analyses and genomic. Epidemiology Single-cell RNA sequencing Mendelian randomization Genetic Influenza Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Influenza is an acute respiratory illness caused by influenza A, B and C viruses, with fever, sore throat, runny nose, cough, headache, muscle aches and general malaise as the main clinical manifestations[ 1 ]. Most patients present only with upper respiratory symptoms, while severe cases may develop pneumonia, bacterial infections and a wide range of non-respiratory complications[ 2 ].According to the World Health Organization (WHO), there are 1 billion influenza virus infections worldwide each year, 3–5 million severe influenza cases, and 290,000-650,000 deaths from influenza-associated respiratory illnesses[ 3 ]. Currently, the main treatment for influenza is vaccination and antiviral medication, however, the high variability of the virus limits the use of medication, and drug resistance and side effects also affect the efficacy of the disease[ 4 – 6 ]. Therefore, the prevention and control of influenza virus infections remains a global public health challenge[ 7 ]. The Global Burden of Disease (GBD) study is a systematic scientific study designed to quantify the health losses associated with a range of diseases and disabilities[ 8 , 9 ]. A number of studies report long-term trends in influenza incidence or prevalence at the global and regional levels[ 3 , 10 , 11 ]. However, this overall trend may not accurately reflect the actual global burden of disease due to updates in data. In addition, there have been significant changes in the risk factors associated with influenza, and a better understanding of influenza mortality, disability-adjusted life years (DALYs) and future trends will be critical to further refining national health systems in order to meet the future challenges posed by influenza. Influenza is closely linked to the body's immune system, which kicks in to fight the infection when the influenza virus invades the body[ 12 , 13 ]. This includes recognition of virus particles, activation of immune cells (such as B and T lymphocytes), and production of antibodies to neutralise the virus[ 14 ]. Viral mutation, however, is associated with immune escape, and influenza viruses are able to evade the immune system by altering their surface proteins (e.g., haemagglutinin and neuraminidase) through antigenic drift and antigenic switching[ 15 ]. This means that even if an individual has been previously infected or vaccinated against influenza, they can still be infected by the new mutated strain[ 14 ]. However, in some cases, the immune response itself can lead to an exacerbation of the condition, particularly in what is known as a cytokine storm, which is a form of widespread inflammation due to an overreaction of the immune system[ 13 ]. This response can cause damage to body tissues (such as the lungs), sometimes more severely than the virus itself. Based on this, Mendelian randomization (MR) uses genetic variation as an instrumental variable for immune cells, making it possible to assess the causal relationship between immune cells and influenza[ 16 ]. In recent years, single-cell RNA sequencing (scRNA-Seq) technology has provided powerful tools to gain a deeper understanding of the complex interactions of influenza at the molecular and cellular levels[ 17 , 18 ]. ScRNA-Seq can be used to identify cellular heterogeneity of influenza virus infections and to analyse host cellular responses to influenza virus infections, including the activation and regulation of immune cells as well as the production of cytokines and chemokines[ 19 , 20 ]. It is also possible to better understand the role of specific immune cell populations (e.g. T and B cells) in influenza, including how these cells differentiate and develop immune memory, by studying their single-cell gene expression[ 21 – 23 ]. This study combines for the first time the GBD studies of influenza from 1990 to 2019.Data on influenza in all populations were collected from the GBD database from 1990 to 2019 to describe changes in the burden of influenza in all populations, with the main analyses being DALYs and mortality. Potential causal associations between 731 immune cells and influenza were then explored using univariate Mendelian randomization (UVMR), followed by validation of the cellular subpopulations to which the immune cells identified by UVMR belonged at the single-cell level using scRNA-Seq. Finally, we performed MR of key genes in cellular subpopulations, reverse MR analysis, colocalization analysis, potential drug prediction and molecular docking for genes satisfying causal associations. Materials and methods Study overview The dataset encompassing all the data from this study is publicly accessible on the database website ( Additional file 1: Table S1 ). For MR analysis, we adhered to the guidelines of Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization (STROBE-MR) for the reporting of MR outcomes[ 24 ]. The flowchart of the study is presented in Fig. 1 . GBD data sources GBD 2019 provides the most up-to-date estimation of the epidemiological data of 369 diseases and injuries in 21 GBD regions and 204 countries and territories from 1990 to 2019. We obtained repeated cross-sectional data from the Global Health Data Exchange (GHDx) query tool ( https://www.healthdata.org/research-analysis/gbd ), which includes DALYs count and rate (per 100,000 person-year), and mortality cases and rate (per 100,000 population) by sex, age, region, and country. Details of the methodology used in the GBD 2019 can be found in previous studies[ 25 , 26 ]. DALYs and mortality data We calculated mortality rates using data from official vital statistics, which follow the International Classification of Diseases (ICD) system, or from household mortality assessments, commonly referred to as verbal autopsies. Disability-Adjusted Life Years (DALYs) represent a comprehensive measure of disease impact, combining the total number of years lost due to early death (Years of Life Lost, YLLs) with the number of years lived with a disability (Years Lived with Disability, YLDs) for the existing disease cases within a population. We adjusted for comorbidities by accounting for the separate likelihood of encountering each condition, using simulation models for 40,000 individuals, across varying ages, genders, countries, and years. The Global Burden of Disease Study (GBD) 2019 introduced bias adjustment procedures to ensure more accurate comparisons across varying case definitions and research methodologies. Additionally, age standardization was performed by adopting a direct method, aligned with the global population's age distribution in 2019. Data processing and disease model First, we assessed the global trends in influenza’s DALYs and mortality rate. We used the Joinpoint regression model with logarithm-transformed rates to calculate the average annual percent changes (AAPCs) between 1990 and 2019, as the dependent variable and year as the independent variable[ 27 ]. The AAPC is a pre-specified aggregate measure of trends over fixed intervals and is calculated as a weighted average of annual percentage change (APC), allowing a single number to be used to describe the average APCs over multiple years. We calculated AAPCs using the geometrically weighted average of the various APC values in the regression analysis[ 28 , 29 ]. The AAPCs indicate the number of APC values that change annually (e.g. an increase, decrease, or no change). DALYs rate of influenza analyses were performed using the Joinpoint Regression Program (version 4.9.1.0, New York, USA). A two-sided P-value < 0.05 was considered statistically significant. GWAS summary statistics for exposure and outcomes GWAS summary statistics for each immune trait are publicly available from the GWAS Catalog (accession numbers from GCST0001391 to GCST0002121) [ 30 ]. The original GWAS on immune traits was performed using data from 3,757 European individuals and there were no overlapping cohorts. Approximately 22 million SNPs genotyped with high-density arrays were imputed with Sardinian sequence-based reference panel and associations were tested after adjusting for covariates (i.e., sex, age and age2)[ 31 ]. The influenza data were extracted from the FinnGen R10 ( https://www.finngen ), which consisted of 9,204 cases and 344,010 controls. This study was conducted in collaboration with multiple organizations and correlated with electronic health record data. The ethics committees of each institutional review board approved the written informed consent obtained from all participants in the separate studies. No additional ethical approval or informed consent was necessary ( Additional file 1: Table S1 ). Instrumental variables (IVs) identification and univariable mendelian randomization (UVMR) For the data of 731 immune cells, the criteria for the genetic instruments were as follows: P < 5× 10 − 6 , r 2 0.01 and kb < 10,000[ 32 ]. For influenza, we adjusted the significance level to P < 5×10 − 8 .The "TwoSampleMR" package was employed to perform UVMR analysis. The proportion of phenotypic variation explained (PVE) and F statistic were calculated for each IV to evaluate IV strength and avoid weak instrumental bias. The inverse variance weighted (IVW) method was used in the primary analysis to assess the causal relationships[ 33 ]. Cochran’s Q statistic and corresponding P values were used to test the heterogeneity among selected IVs. To exclude the effect of pleiotropy, a common method was used (i.e., MR-Egger), which implies the presence of horizontal multiplicity if its intercept term is significant[ 34 , 35 ]. In addition, scatter plots showed that the results were not affected by outliers, leave-one-out plots were used to indicated the stability of the results. ScRNA-Seq data analysis We obtained scRNA-seq data of influenza patients from the GEO database (GSE182123), which includes human peripheral blood mononuclear cells from 4 healthy controls, and 5 influenza patients[ 36 ]. The publicly available dataset used in this study had received the necessary ethical approvals. The "Seurat" R package was utilized for the analysis of scRNA-seq data[ 37 ]. After filtering the low-quality data, we employed the "NormalizeData" function for "LogNormalize" normalization of the data, followed by conversion into a Seurat object. We identified the top 2000 highly variable genes using the "FindVariableFeatures" function. Next, principal component analysis (PCA) was performed on the highly variable genes using the "RunPCA" function. Cell clustering analysis was performed using the "FindNeighbors" and "FindClusters" functions. Visualisation was then performed using the "RunUMAP" and "RunTSNE" functions and cell clustering experiments were performed according to UMAP-1 and UMAP-2[ 38 ]. To annotate the cell clusters with cell types, we utilized the "SingleR" R package and performed cell annotation using the Human Primary Cell Atlas as the reference dataset. The expression patterns of the aforementioned genes in various cell types were visualized based on the UMAP plot and tSNE plot. Furthermore, a detailed examination of key cellular subgroups was carried out to identify receptor and ligand expression at the individual cell level, providing a clear understanding of intricate interactions in the cellular microenvironment[ 39 , 40 ]. Lastly, the identified key cell subgroups were screened and extracted for differential genes using the "FindMarkers" function (Threshold = 0.5) . Enrichment Analysis To investigate the functional characteristics and biological relevance of the identified marker genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment studies were done using the "ClusterProfiler" package, and the "ggplot2" package for bubble plots visualization. The GO enrichment study includes three primary categories: biological process (BP), molecular function (MF), and cellular component (CC). This comprehensive approach provides a holistic perspective on the connections between genes and pertinent terms, facilitating a deeper comprehension of the functional relationships among the identified marker genes. Simultaneously, the KEGG analysis was employed to elucidate the associations between the marker genes and functional pathways, providing valuable insights into the biological pathways implicated by these genes[ 41 , 42 ]. Mendelian randomization analysis of key cell subpopulations IV were selected based on SNPs associated with eQTLs [ 43 ]. These eQTLs were linked to marker genes specific to the identified key cellular subpopulations[ 44 , 45 ] ( Additional file 1: Table S1 ). Summaries of genetic association data for influenza were obtained from FinnGen R10 ( https://www.finngen ), which consisted of 9,204 cases and 344,010 controls. Screening criteria for instrumental variables were consistent with the methodology used in previous UVMR analysis. The IVW method was served as the primary analysis to assess the causal relationships. We also performed sensitivity analyses to test for heterogeneity and pleiotropy. Reverse causality detection and colocalization analysis Using the same screening criteria as for eQTL, we performed reverse MR analysis. Bidirectional MR analysis was used to identify potential reverse causality signals. Effects were estimated using IVW, MR-Egger, weighted median, simple mode and weighted mode. Colocalization analysis was used to determine whether genetic associations within a particular region point to a common causal variant, i.e. whether there is evidence that a particular exposure and a particular outcome are caused by the same SNP or a set of closely linked SNPs. Colocalization analyses of significant influenza-associated genes were performed using the "coloc" software package( https://github.com/chr1swallace/coloc ). In this study, colocalization was considered significant when PPH4 > 0.80[ 46 , 47 ]. Potential therapeutic drugs prediction We then performed the drug prediction analysis, with the list of available drug genes coming from a previous study[ 48 ]. It was designed as a computational method and combined with data from many existing global genomic studies to identify druggable genes and associate them with known drugs, with a total of 4,479 druggable genes proposed. The key genes identified in the MR analysis were extracted and analysed among the druggable genes. We then further searched for potential therapeutic targets by searching for interactions between these genes and drugs using the DGIdb[ 49 ], ChEMBL[ 50 ], DrugBank[ 51 ] and PharmSnap databases. These databases prioritise potential drug targets by integrating information on drug-gene interactions, gene function, text mining and expert management. Molecular docking In order to assess the affinity of drug candidates for their targets and from this to understand the drug target’s druggability, molecular docking was performed in this study[ 52 ]. Firstly, protein numbers were searched from the uniport database ( https://www.uniprot.org/ ), and then 2D structures of each small molecule ligand drug were obtained from the PubChem database (PDB, https://www.rcsb.org/ ). These structures were then imported into the Chem3D software in order to calculate the minimum free energy and convert them into 3D structures. This structures were imported into PyMOL to remove water molecules and ligands. The AutoDock tool (version 1.5.7) was used to obtain the PDBQT format of the receptor and ligand and to create a 3D mesh frame for the receptor for subsequent molecular docking simulations[ 53 ].Visualization was performed using PyMOL ( https://www.pymol.org/ , version 2.6). A binding energy of less than − 5 kcal/mol was defined as indicative of effective ligand-receptor binding, and the binding energy less than − 7 kcal/mol indicated strong binding activity. The entire molecular docking process was visualized in the model by AutoDock Vina (version 1.2.2). Results DALYs rate and mortality rate of influenza (1990–2019) The global influenza DALYs and mortality rate were analysed by building a Joinpoint regression model to establish segmented regression on the temporal characteristics of the influenza pandemic, fitting and optimising the trend for each interval, identifying the number and location of turning points where the trend changed, specifying the direction and speed of change in the whole and locally through AAPCs and APC values, respectively, and stratified by male and female gender. Results based on Joinpoint regression analyses showed a decrease in DALYs for influenza between 1990 and 2019 (AAPC = -3.14), with DALYs for male influenza cases decreasing from 786, 4858 in 1990 to 410, 5953 in 2019, with an AAPC = -2.96 (Fig. 2 A). In 2019, the rate of change in DALYs for influenza for all regions globally, based on a population of 100,000, was − 0.60, of which the rate of change for males was − 0.58 (Table 1 ). Mortality cases of influenza declined from 257,155 in 1990 to 243,671 in 2019 (Change rate (%) = -0.05), according to the Joinpoint regression analysis, the overall AAPC=-1.88 (Fig. 2 B).In 2019, the global rate of change of influenza mortality cases for all regions, based on a population of 100,000, was − 0.42.Considering the gender groups, the rate of change was − 0.39 for males, and the rate of change for females was − 0.45(Table 2 ). This implies that there has been an overall downward trend in influenza mortality over the past 30 years. Table 1 Cases and age-standardized of influenza DALYs and their AAPCs from 1990 to 2019 at the global level. Group DALY Number DALY rate (per 100,000) Age-standardized DALY rate (per 100,000) 1990 2019 Change rate (%) 1990 2019 Change rate (%) 1990 2019 Change rate (%) Both 15023631 7512768 -0.50 280.82 97.10 -0.65 259.15 103.25 -0.60 Male 7864858 4105953 -0.48 291.97 105.80 -0.64 274.91 114.93 -0.58 Female 7158773 3406815 -0.52 269.52 88.34 -0.67 246.55 93.50 -0.62 Table 2 Cases and age-standardized of influenza mortality and their AAPCs from 1990 to 2019 at the global level. Group Death Number Death rate (per 100,000) Age-standardized death rate (per 100,000) 1990 2019 Change rate (%) 1990 2019 Change rate (%) 1990 2019 Change rate (%) Both 257155 243671 -0.05 4.81 3.15 -0.34 5.68 3.29 -0.42 Male 134005 129892 -0.03 4.97 3.35 -0.33 6.60 4.00 -0.39 Female 123151 113779 -0.08 4.64 2.95 -0.36 5.07 2.78 -0.45 UVMR analysis To explore the causal effects of influenza on 731 immune cells, UVMR analysis was performe. We screened the SNPs included in the UVMR analyses for screening and removal of pleiotropy according to previous criteria, then calculated the F-statistical for the remaining SNPs, therefor the included SNPs had strong statistical effects ( Additional file 1: Table S2 ). After multiple test adjustment based on the FDR method( P FDR <0.05), the UVMR results showed a strong association between the immune cell HLA-DR on CD14 + CD16- monocyte and influenza ( P IVW =5.47E-05, P FDR = 0.03) (Fig. 3 A). In addition, the Cochran’s Q test ( P Heterogeneity = 0.84) and MR-Egger intercept test ( P Pleiotropy = 0.96) provided strong evidence for the absence of heterogeneity and pleiotropy ( Additional file 1: Table S3 ). The scatter and leave-one-out plots indicated the stability of the results (Fig. 3 B-C). ScRNA-seq data analysis We analysed scRNA-seq data from 9 samples, including peripheral blood mononuclear cells from 4 healthy controls, and 5 influenza patients. After preprocessing the data using stringent quality control metrics (Fig. 4 A-C), a total of 39,245 cells were included in the analysis, and the cells were classified into 19 subpopulations (Fig. 4 D) and annotated to identifiable cell types using the "SingleR" package. The major cell types included B cells, monocytes, T cells, natural killer cells, and platelets (Fig. 4 E-F). The proportion of monocytes was significantly higher in patients diagnosed with influenza(Fig. 4 G-H). Monocytes were isolated, downscaled, and clustered to identify their distinct cellular subpopulations. These subpopulations were annotated as monocytes: Classical monocytes (CMs), Non classical monocytes, Intermediate monocytes, Myeloid dendritic cells and Plasmacytoid dendritic cells (Fig. 5 A). Notably, the expression level of CMs was higher in monocyte subpopulations in influenza patients compared to healthy controls (Fig. 5 B). Next, cellular communication was modelled using the "CellChat" function, which combines the interaction of ligand receptors and their cofactors using gene expression data as input. Inferred trajectories depicted the ability of one or more cells to differentiate into other cells, and bubble plots illustrated ligand receptor pairs signaling from specific cells to other cell populations. The results highlight the importance of monocyte developmental trajectories, with more significant communication between CMs and Non classical monocytes (Fig. 5 C-D), and bubble plots showing that ANXA1-FPR1 is significantly enriched in CMs, which regulate inflammatory mediators and immune responses by influencing the behavior of leukocytes in to exert anti-inflammatory effects(Fig. 5 E).After clustering the cellular subpopulations, characteristic genes specific to this subpopulation that are associated with other subpopulations and cells were calculated. A total of 191 genes were crossed and plotted on a Venn diagram (Fig. 5 F). These genes are essential for characterizing key cellular subpopulations associated with influenza( Supplementary Table 4 ). Finally, enrichment analyses of these subpopulation-specific genes were performed to reveal the underlying biological mechanisms associated with them. Enrichment analysis GO enrichment analysis showed that in BP function, the key genes were significantly correlated with regulation of inflammatory response, chemotaxis and taxis. In BP function, the key genes were significantly associated with secretory granule lumen, cytoplasmic vesicle lumen and vesicle lumen. In MF function, the key genes were significantly associated with cytokine binding and immune receptor activity, etc(Fig. 5 G). KEGG pathway enrichment analysis revealed that the key genes were involved in Neutrophil extracellular trap formation, IL-17 signaling pathway and Hematopoietic cell lineage (Fig. 5 H). Mendelian randomization analysis of key cell subpopulations A total of 191 genes were analyzed as key marker genes for MR analysis. The F-statistics of all gene tools were higher than 10, indicating a strong tool strength. A total of 7 genes were potentially causally associated with the risk of influenza incidence (VIM, CTSA, UBE2D1, CSF3R, AHNAK, DPYD and FAM200B) through the use of the Wald ratio or IVW method ( Additional file1: Table S5 ). The association volcano and frost plots (Fig. 6 A-B ) illustrates the relationship between key marker genes and influenza. According to gene prediction of MR analysis, elevated levels of VIM, CTSA, UBE2D1, AHNAK and FAM200B were negatively associated with influenza, and these 5 genes may be protective genes for the risk of developing influenza. In contrast, the other genes, CTSA and DPYD, were positively associated with influenza, i.e., elevated levels of the genes were associated with an increased risk of influenza, and these 2 genes may be risk genes for the risk of influenza ( Additional file 1: Table S6 ). Of these 7 genes, 4 had associations consistent with the directionality observed by the weighted median method: CTSA, UBE2D1, CSF3R, AHNAK Fig. 6 C ) . In addition, sensitivity analyzed showed no evidence of heterogeneity ( P > 0.05) or pleiotropy ( P > 0.05) for any of the 7 genes ( Additional file 1: Table S7 ). Reverse causality detection and colocalization analysis Testing for reverse causality did not reveal any causal effect of influenza on these genes. Next, we performed colocalization analysis of the seven genes using the "coloc" R package.However, the results of the colocalization analysis indicated that there was insufficient evidence to support that these genes shared the same variant as influenza (PPH4 < 0.80) ( Additional file 1: Table S7 ). The results of this analysis may be related to factors such as insufficient sample size or weak effects of genetic tools, genetic heterogeneity, confounders and bias, and complex biological pathways, which resulted in the failure of the analysis to detect colocalization signals. Thus, the genetic association with influenza may have been achieved through other pathways. Potential therapeutic drugs prediction We identified 3 genes (VIM, CTSA and CSF3R) in the list of druggable genes that have been targeted for drug development ( Additional file 1: Table S8-9 ). The content of the data is divided into 3 layers, and instructions on how to define them can be found further in the methods section of the main text [ 5 ]. The identified VIM and CSF3R genes both belong to Tier 1, which includes the efficacy targets of approved small molecule and biotherapeutic drugs, as well as the efficacy targets of clinical-stage drug candidates. CTSA belongs to Tier 2, a tier of genes encoding targets with known biologically active drug-like small molecule binding partners as well as ≥ 50% identity (over ≥ 75% of the sequence) to approved drug targets. Furthermore, we found that a number of drugs targeting the CSF3R have been developed for the treatment of neutropenia (e.g., PEGFILGRASTIM-JMDB and TBO-FILGRASTIM) and developed as anti-inflammatory and anti-tumor agents (e.g. RUXOLITINIB). TBO-FILGRASTIM is a recombinant human granulocyte colony-stimulating factor used to induce granulocyte production and reduce the risk of infection following myelosuppressive therapy, whereas PEGFILGRASTIM-JMDB possesses pharmacological activity comparable to that of TBO-FILGRASTIM and binds to the G-CSF receptor to stimulate the proliferation, differentiation and activation of neutrophils to increase the drug's effect[ 54 ]. A number of drugs targeting VIM have been developed for their anti-inflammatory, analgesic, and cell-penetrating effects, such as DIMETHYL SULFOXIDE. The above-listed drugs are all approved by the U.S. Food and Drug Administration (FDA). However, no relevant approved drugs targeting the CTSA gene have been found in the DGIdb database. Molecular docking The binding sites and interactions of the 3 drug candidates with the proteins encoded by the corresponding genes were obtained using Autodock, and binding energies for each interaction were generated, yielding a total of 2 effective docking results for the proteins and drugs. Each drug candidate was connected to the protein target through visible hydrogen bonding and strong electrostatic interactions. Of these, CSF3R and RUXOLITINIB exhibited the lowest binding capacity (-7.4 kcal/mol), indicating strong binding activity(Fig. 6 D ) . Discussion The WHO released the Global Influenza Prevention and Control Strategy (2019–2030), and the importance of influenza prevention and control has attracted global attention[ 1 , 11 ]. In this study, the global influenza epidemic characteristics and trends were analyzed using descriptive epidemiology in conjunction with the GBD database from 1990 to 2019. The Joinpoint regression trend analysis shows that the overall trend of influenza DALYs rate and mortality rate is decreasing, which can be attributed to the following reasons: firstly, with the wider influenza vaccination and public acceptance of the vaccine, the number of influenza infections can be reduced, which can reduce the speed and severity of the disease; secondly, increased awareness of personal hygiene, effective dissemination of public health information, and better preventive measures during the influenza season can help reduce infection rates; thirdly, the availability of more rapid influenza tests, more effective antiviral therapeutic drugs on the market, and improvements in care and treatment regimens; fourth, improvements in disease surveillance systems globally have facilitated earlier identification and respond to influenza epidemics; and fifth, increased international collaboration in influenza virus surveillance and vaccine production has helped predict changes in influenza virus strains in advance, leading to the manufacture of more effective seasonal influenza vaccines. Our findings can raise awareness among doctors and patients, improve the health care system, and provide a basis for effective measures to prevent and control influenza. Next, based on a large amount of publicly available genetic data, we explored causal relationships between 731 immune cells and influenza. To the best of our knowledge, this is the first MR analysis to explore causal relationships between multiple immune phenotypes and influenza. In this study, we found that HLA-DR on the monocyte cell group CD14 + CD16- has been shown to be associated with an increased risk of influenza. HLA-DR is an important Major Histocompatibility Complex (MHC) class II molecule commonly known as Human Leukocyte Antigen in humans[ 32 , 55 ]. HLA-DR surface molecules play an important role in the immune response, particularly in assisting in the activation of specific immune responses[ 56 ]. CD14 + CD16- monocytes are a typical class of inflammatory response monocytes that mainly phagocytose pathogens and present antigens in the immune response[ 57 ]. CD14 + CD16- monocytes with high HLA-DR expression play a crucial role in the development of influenza. In influenza infection, these monocytes may be involved in the role of antigen presentation. When influenza virus infects the organism, CD14 + CD16- monocytes phagocytose viral particles, process and present peptides of the virus on their HLA-DR molecules[ 58 , 59 ]. These peptides are presented to CD4 + T cells, inducing activation and differentiation of the latter. These cells also produce and release a variety of pro-inflammatory factors, such as TNF-α and IL-1, which contribute to the immune system response and cause local and systemic inflammatory responses[ 60 ]. ScRNA-seq, a new technique for transcriptome sequencing of isolated single cells, is now being used to analyse virus-host interactions at the single-cell level. We verified at the single-cell level that the immune cells identified by UVMR HLA-DR on CD14 + CD16- monocyte belong to the CMs subpopulation. And the expression level of CMs was higher in monocyte subpopulations from influenza patients compared to healthy controls. MR analysis of key genes in the cellular subpopulation identified 7 genes as causally associated with influenza, and 3 genes were identified as druggable genes by drug prediction, namely VIM, CTSA and CSF3R. Among them, the high binding activity of CSF3R and RUXOLITINIB by molecular docking demonstrated the strong potential of the CSF3R gene as a drug target. The protein encoded by the VIM gene is vimentin, a structural protein belonging to the intermediate fibronectin family. This protein supports the structural integrity of cells and is involved in a variety of cellular functions such as cell attachment, cell migration and signaling[ 61 ]. Although the VIM gene is not an immune gene that directly interacts with influenza viruses, some studies suggest that intermediate fibre proteins may play a role during viral infection, especially during the invasion and replication phases of the virus. For example, VIM may act as a non-specific viral attachment site during viral entry into cells, or during viral replication and assembly[ 62 ]. Some viruses are able to modulate host cell backbone proteins to aid in their replication and propagation[ 63 ]. The CTSA gene encodes galactose neuraminidase 1 (Cathepsin A), a protective protein of the lysosomal enzyme class that is implicated in a variety of biochemical processes[ 64 ]. It has been suggested that lysosomes may play a role in the life cycle of viral infections, where influenza virus replication may require lysosomal function. After the influenza virus invades a cell, its RNA must be released from the inner vesicles that encapsulate them in order to begin the process of viral replication, and the acidification process of this inner vesicle is associated with lysosomal function[ 65 ]. On the other hand, lysosomal proteins, such as galactose neuraminidase 1, may also be involved in regulating the immune response of host cells to viral invasion. For example, they may influence the production of inflammatory mediators or activate other cellular pathways involved in viral defense[ 66 ]. CSF3R encodes a receptor whose primary function is to regulate the production of white blood cells (granulocytes)[ 67 ]. This receptor is essential for the growth and differentiation of granulocytes (mainly neutrophils), which are one of the body's main white blood cells and play an important role in warding off infections[ 68 ]. During infection, neutrophils act as a front-line defense for the body's immune system, and they respond rapidly to help fight the invasion of the influenza virus through the regulation of the CSF3R receptor. Several studies have suggested that differences in the immune system response to the virus in different individuals may be associated with variations in specific genes, which may include the CSF3R gene[ 69 , 70 ]. CSF3R is associated with may indirectly affect the course, severity, and duration of influenza infection[ 71 , 72 ]. Gene variants may affect an individual's susceptibility to viral infections as well as the ability to recover. Thus, individual differences in the CSF3R gene may affect their response to influenza[ 73 , 74 ]. However, the onset and progression of influenza is multifactorial, involving host genetic background, viral strain specificity, and other genetic and environmental factors. Genetic variation is only one part of the equation, and these interactions and effects are often very complex. In addition, influenza virus-host cell interactions are complex and often involve the synergistic action of multiple genes and metabolic pathways. Therefore, identifying the roles of the aforementioned genes in influenza infection will require studies that include virology, molecular biology and host genetics to more fully understand the potential functions of these genes in the influenza virus life cycle. In summary, this is the first time that descriptive epidemiology has been used to analyze the epidemiological characteristics and trends of influenza based on the GBD database. This study is also the first to identify potential causal associations between HLA-DR on CD14 + CD16- monocyte and influenza by UVMR. Gene expression of different cell types in blood samples from influenza patients was also analyzed and annotated using scRNA-seq, and potential drug targets for influenza were initially identified from genetic insights. However, there are some limitations to this study. First, only global influenza DALYs rates and mortality rates were analyzed, and subgroup analyses of trends in influenza DALYs rates and mortality rates for different age groups were not conducted. In addition, the influenza data information for this study was derived from sources only up to 2019, and updated stream GBD information is not yet available. Additionally, the applicability of the study's findings is limited due to its focus on predominantly European-descended individuals. To extend these results to people of different ethnic backgrounds, further research and thorough validation are needed to confirm their universal relevance. Despite meticulous attempts to eradicate bias, MR analysis is still susceptible to confounding by unobserved variables or pleiotropy, which may distort the outcomes. Also, while enrichment analysis provides meaningful insights, it is not without its confines, as it depends on predetermined groups of genes or biological pathways that may not fully capture the entire spectrum of potential biological processes or their interplay. Precision in molecular docking analysis is contingent on the high quality of the protein structures and ligands involved. Although this method is instrumental in pinpointing likely drug candidates, it cannot ensure their success in a clinical environment. It is therefore imperative that further laboratory research and clinical trials be conducted to substantiate the medical viability of these proposed targets. Acknowledging and addressing these limitations will pave the way for future research to improve the understanding of influenza and its potential treatments, and will help to create a more holistic perspective and advance relevant research in the field in a meaningful way. Conclusion In conclusion, this study encompasses a combination of multidisciplinary approaches ranging from epidemiology to genetics to bioinformatic analyses and genomics, which not only enables us to further understand the characteristics of the global influenza pandemic and its changing trends, but also provides a comprehensive understanding of the complex relationship between immune cell populations and influenza. In addition, observing and comparing the expression patterns of these immune cell-associated genes at the cellular level provides insights into their functions and regulatory mechanisms in the development of influenza, as well as potential possibilities for the prediction and development of drug targets for influenza in the future. Abbreviations GBD The Global Burden of Disease Study DALYs Disability-adjusted life years AAPCs The average annual percent changes APC Annual percentage change PVE Phenotypic variation explained IV Instrumental variables SNP Single Nucleotide Polymorphism ScRNA-Seq Single-cell RNA-Sequencing PCA principal component analysis UMAP Uniform Manifold Approximation and Projection IVW Inverse variance weighted UVMR Univariable Mendelian randomization FDR False discovery rate Declarations Acknowledgments The data used in this study were obtained from genome-wide association study summary statistics that were publicly released by genetic consortia. Interested parties may obtain the data by submitting a request to the corresponding author. Furthermore, all datasets generated for this study have been included in the article or additional files. Authors’ contributions GZW has full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. GZW, WDJ, CXH, WJC and LWL conceived and designed the study. GZW wrote the first draft of the manuscript. WDJ undertook the statistical analyses. GZW, WDJ, CXH, WJC and LWL interpreted data, reviewed the paper, and made critical revision of the manuscript for important intellectual content. All authors read and approved the final version of the manuscript. Funding This work was supported by Scientific and technological innovation project of China Academy of Chinese Medical Sciences (CI2021A00801 and CI2021A00806). Data sharing statement All data analyzed in this study can be obtained by a reasonable request to corresponding authors. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors have no relevant financial or non-financial interests to disclose. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References Uyeki TM: Influenza . Annals of internal medicine 2021, 174 (11):Itc161-itc176. Neumann G, Kawaoka Y: Seasonality of influenza and other respiratory viruses . EMBO molecular medicine 2022, 14 (4):e15352. Iuliano AD, Roguski KM, Chang HH, Muscatello DJ, Palekar R, Tempia S, Cohen C, Gran JM, Schanzer D, Cowling BJ et al : Estimates of global seasonal influenza-associated respiratory mortality: a modelling study . Lancet (London, England) 2018, 391 (10127):1285-1300. 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Yamaya M, Kikuchi A, Sugawara M, Nishimura H: Anti-inflammatory effects of medications used for viral infection-induced respiratory diseases . Respiratory investigation 2023, 61 (2):270-283. Lin SJ, Lin KM, Chen SJ, Ku CC, Huang CW, Huang CH, Gale M, Jr., Tsai CH: Type I Interferon Orchestrates Demand-Adapted Monopoiesis during Influenza A Virus Infection via STAT1-Mediated Upregulation of Macrophage Colony-Stimulating Factor Receptor Expression . Journal of virology 2023, 97 (4):e0010223. Zhang J, Wang J, Gong Y, Gu Y, Xiang Q, Tang LL: Interleukin-6 and granulocyte colony-stimulating factor as predictors of the prognosis of influenza-associated pneumonia . BMC infectious diseases 2022, 22 (1):343. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialtables.xls Supplementary Information The online version contains supplementary material available at: Additional file1: Table S1. GWAS summary statistics: source and description. Table S2. Summary information on SNPs for univariate Mendelian randomization (UVMR) results of 731 immune cells and influenza. Table S3. The UVMR and sensitivity analysis results of 731 immune cells and influenza ( P FDR <0.05). Table S4. List of key marker genes for classical monocyte subpopulations identified by single-cell RNA sequencing. Table S5. Information on SNPs for causal association of key genes and influenza. Table S6. Results of MR analysis of key genes and influenza. Table S7. Sensitivity analysis and colocalization results of eQTLs for seven genes with influenza-associated SNPs. Table S8. List of 4,479 druggable genes. Table S9. Drug target prediction information for 3 druggable genes. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4276363","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":293781339,"identity":"fff2b9a8-45e8-4b6e-89b5-8b0a00814431","order_by":0,"name":"Ziwei Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIie2QMUvDQBTHLwSiw6uuByneV3hHwKmo38DV8Y5AurTQsWNL4bIorpn8EsLNrwSc8gEEF7M4p1sGsaZbifTs2OF+y3vD/8ef9xjzeE4VxRjsJjU4uorOcjpaCepiliUXUKmjy8LkvCn1C79DZ0w8pGVTm9FQ3FfXMcNwbHjX2s7tQUW+Zwq1yUBSp8wwmpp4ScFj9XFYKSaotC1BLp9sXCBMzZBUGBi3QtpuQa4GNgbk44grdCqCT+RCWwIRDV4TQFT/KghfKdM/KSBcbuoClTTdk9euW0Selpu2urkVz2+Kmu+tEHm+/mznjhbqLzvoT26/ZdFfPB6Px9PnF2qRWNIuejOTAAAAAElFTkSuQmCC","orcid":"","institution":"China Academy of Chinese Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Ziwei","middleName":"","lastName":"Guo","suffix":""},{"id":293781340,"identity":"61839f48-ab3d-4eaa-91cf-a3c70da5309f","order_by":1,"name":"Dongjie Wu","email":"","orcid":"","institution":"China Academy of Chinese Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Dongjie","middleName":"","lastName":"Wu","suffix":""},{"id":293781341,"identity":"cea5dd22-006e-408a-a573-173ac0e3d00b","order_by":2,"name":"Xiaohan Chen","email":"","orcid":"","institution":"China Academy of Chinese Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xiaohan","middleName":"","lastName":"Chen","suffix":""},{"id":293781342,"identity":"ec4eabf1-51d9-41b9-81a5-fd2d3de630bf","order_by":3,"name":"Jiuchong Wang","email":"","orcid":"","institution":"China Academy of Chinese Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jiuchong","middleName":"","lastName":"Wang","suffix":""},{"id":293781343,"identity":"46371b6c-e29e-470d-b261-ced1f1ff6be8","order_by":4,"name":"Wenliang Lv","email":"","orcid":"","institution":"China Academy of Chinese Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Wenliang","middleName":"","lastName":"Lv","suffix":""}],"badges":[],"createdAt":"2024-04-16 13:32:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4276363/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4276363/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55098560,"identity":"e7ef2b2b-3724-4ed7-b0ac-27a27581851d","added_by":"auto","created_at":"2024-04-22 15:05:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":208366,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the study design.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4276363/v1/4706d1fd80a3feb2397c911a.png"},{"id":55098562,"identity":"2500e374-df58-4317-a232-2127e423836a","added_by":"auto","created_at":"2024-04-22 15:05:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":77887,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in global male and female DALYs and mortality from 1990 to 2019. A. Age-standardised DALY rate Joinpoint regression analysis for influenza from 1990 to 2019. B. Age-standardised death rate Joinpoint regression analysis for influenza from 1990 to 2019. AAPCs, average annual percent changes; DALYs, Disability-adjusted life-years.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4276363/v1/83f1e78de05a832f32718f2a.png"},{"id":55098198,"identity":"6fa4a2c8-619c-4dd3-8fa4-41aa65d702f1","added_by":"auto","created_at":"2024-04-22 14:57:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":41861,"visible":true,"origin":"","legend":"\u003cp\u003eThe results of UVMR analysis. A. Volcano plot for the causal association between immune cells and influenza. B. Scatter plot for the causal association between HLA-DR on CD14+ CD16- and influenza. C. Leave-one-out plots for the causal association between HLA-DR on CD14+ CD16- and influenza.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4276363/v1/fa2894a4e2cc0888e1b9bfcb.png"},{"id":55098199,"identity":"ea401c7b-d1b5-443a-84e1-71cedcaa9a9f","added_by":"auto","created_at":"2024-04-22 14:57:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":156500,"visible":true,"origin":"","legend":"\u003cp\u003eHealthy control and influenza samples of single-cell RNA sequencing. A-C.Quality control of scRNA-seq data. D. Visualising the overall cell type composition of 39,245 cells from healthy controls and influenza patients using UMAP and tSEN. E-G. Visualisation of major cell types from healthy controls and influenza patients.H.Plot of cell proportions for the two groups of samples.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4276363/v1/8c93e85d286d85ebfb97fa7f.png"},{"id":55098561,"identity":"72612886-55e6-43d9-bf08-317f74607657","added_by":"auto","created_at":"2024-04-22 15:05:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":87350,"visible":true,"origin":"","legend":"\u003cp\u003eSmall cell subpopulation analysis and enrichment analysis. A-B.UMAP plots and cell scale plots showed elevated expression of classical monocytes in monocyte subpopulations in influenza patients compared to healthy controls.C-D.Intercellular communication network plot shows that CMs play an important role in intercellular communication with other cells. E.Bubble plots showing that ANXA1-FPR1 is significantly enriched in CMs. F.Venn diagram showing the number of genes in CMs relative to other cells and the marker genes. G-H. KEGG pathway enrichment analysis and GO enrichment analysis.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4276363/v1/29086f0729fe6a979a6724b3.png"},{"id":55098203,"identity":"03f27991-c9ec-48f6-9941-dc437250022d","added_by":"auto","created_at":"2024-04-22 14:57:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":180521,"visible":true,"origin":"","legend":"\u003cp\u003eResults of MR analysis and molecular docking.A-B.Volcano and forest plots of MR results for key genes and influenza.C.Scatter plots for the causal associations between CTSA, UBE2D1, CSF3R, AHNAK and influenza (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eIVW\u003c/sub\u003e\u0026lt;0.05, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e\u0026lt;0.05). D.Docking results of available proteins small molecules.WM:Weighted median method.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4276363/v1/a2895223aaffdbf8d3587ab8.png"},{"id":55100592,"identity":"fe636bde-fb10-487b-9680-a936752d036a","added_by":"auto","created_at":"2024-04-22 15:21:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2650058,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4276363/v1/9df13e8c-0380-4a3d-9fb7-ef381ca394ed.pdf"},{"id":55098204,"identity":"29ae7f95-69e4-4233-afeb-438993767f19","added_by":"auto","created_at":"2024-04-22 14:57:20","extension":"xls","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7849472,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe online version contains supplementary material available at:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional file1:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S1.\u003c/strong\u003e GWAS summary statistics: source and description.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S2.\u003c/strong\u003e Summary information on SNPs for univariate Mendelian randomization (UVMR) results of 731 immune cells and influenza.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S3. \u003c/strong\u003eThe UVMR and sensitivity analysis results of 731 immune cells and influenza (\u003cem\u003eP\u003c/em\u003e \u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S4\u003c/strong\u003e. List of key marker genes for classical monocyte subpopulations identified by single-cell RNA sequencing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S5.\u003c/strong\u003e Information on SNPs for causal association of key genes and influenza.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S6.\u003c/strong\u003e Results of MR analysis of key genes and influenza.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S7\u003c/strong\u003e. Sensitivity analysis and colocalization results of eQTLs for seven genes with influenza-associated SNPs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S8.\u003c/strong\u003e List of 4,479 druggable genes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S9.\u003c/strong\u003e Drug target prediction information for 3 druggable genes.\u003c/p\u003e","description":"","filename":"SupplementaryMaterialtables.xls","url":"https://assets-eu.researchsquare.com/files/rs-4276363/v1/5811d2053d57da312fdb4b5b.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Combination of multidisciplinary approaches reveals potential causal associations between influenza and immune cells: Single-cell RNA sequencing and Mendelian randomization","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInfluenza is an acute respiratory illness caused by influenza A, B and C viruses, with fever, sore throat, runny nose, cough, headache, muscle aches and general malaise as the main clinical manifestations[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Most patients present only with upper respiratory symptoms, while severe cases may develop pneumonia, bacterial infections and a wide range of non-respiratory complications[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].According to the World Health Organization (WHO), there are 1\u0026nbsp;billion influenza virus infections worldwide each year, 3\u0026ndash;5\u0026nbsp;million severe influenza cases, and 290,000-650,000 deaths from influenza-associated respiratory illnesses[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Currently, the main treatment for influenza is vaccination and antiviral medication, however, the high variability of the virus limits the use of medication, and drug resistance and side effects also affect the efficacy of the disease[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, the prevention and control of influenza virus infections remains a global public health challenge[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Global Burden of Disease (GBD) study is a systematic scientific study designed to quantify the health losses associated with a range of diseases and disabilities[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A number of studies report long-term trends in influenza incidence or prevalence at the global and regional levels[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, this overall trend may not accurately reflect the actual global burden of disease due to updates in data. In addition, there have been significant changes in the risk factors associated with influenza, and a better understanding of influenza mortality, disability-adjusted life years (DALYs) and future trends will be critical to further refining national health systems in order to meet the future challenges posed by influenza.\u003c/p\u003e \u003cp\u003eInfluenza is closely linked to the body's immune system, which kicks in to fight the infection when the influenza virus invades the body[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This includes recognition of virus particles, activation of immune cells (such as B and T lymphocytes), and production of antibodies to neutralise the virus[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Viral mutation, however, is associated with immune escape, and influenza viruses are able to evade the immune system by altering their surface proteins (e.g., haemagglutinin and neuraminidase) through antigenic drift and antigenic switching[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This means that even if an individual has been previously infected or vaccinated against influenza, they can still be infected by the new mutated strain[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, in some cases, the immune response itself can lead to an exacerbation of the condition, particularly in what is known as a cytokine storm, which is a form of widespread inflammation due to an overreaction of the immune system[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This response can cause damage to body tissues (such as the lungs), sometimes more severely than the virus itself. Based on this, Mendelian randomization (MR) uses genetic variation as an instrumental variable for immune cells, making it possible to assess the causal relationship between immune cells and influenza[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, single-cell RNA sequencing (scRNA-Seq) technology has provided powerful tools to gain a deeper understanding of the complex interactions of influenza at the molecular and cellular levels[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. ScRNA-Seq can be used to identify cellular heterogeneity of influenza virus infections and to analyse host cellular responses to influenza virus infections, including the activation and regulation of immune cells as well as the production of cytokines and chemokines[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. It is also possible to better understand the role of specific immune cell populations (e.g. T and B cells) in influenza, including how these cells differentiate and develop immune memory, by studying their single-cell gene expression[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study combines for the first time the GBD studies of influenza from 1990 to 2019.Data on influenza in all populations were collected from the GBD database from 1990 to 2019 to describe changes in the burden of influenza in all populations, with the main analyses being DALYs and mortality. Potential causal associations between 731 immune cells and influenza were then explored using univariate Mendelian randomization (UVMR), followed by validation of the cellular subpopulations to which the immune cells identified by UVMR belonged at the single-cell level using scRNA-Seq.\u0026nbsp;Finally, we performed MR of key genes in cellular subpopulations, reverse MR analysis, colocalization analysis, potential drug prediction and molecular docking for genes satisfying causal associations.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy overview\u003c/h2\u003e \u003cp\u003eThe dataset encompassing all the data from this study is publicly accessible on the database website (\u003cb\u003eAdditional file 1: Table S1\u003c/b\u003e). For MR analysis, we adhered to the guidelines of Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization (STROBE-MR) for the reporting of MR outcomes[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The flowchart of the study is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGBD data sources\u003c/h2\u003e \u003cp\u003eGBD 2019 provides the most up-to-date estimation of the epidemiological data of 369 diseases and injuries in 21 GBD regions and 204 countries and territories from 1990 to 2019. We obtained repeated cross-sectional data from the Global Health Data Exchange (GHDx) query tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.healthdata.org/research-analysis/gbd\u003c/span\u003e\u003cspan address=\"https://www.healthdata.org/research-analysis/gbd\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which includes DALYs count and rate (per 100,000 person-year), and mortality cases and rate (per 100,000 population) by sex, age, region, and country. Details of the methodology used in the GBD 2019 can be found in previous studies[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDALYs and mortality data\u003c/h2\u003e \u003cp\u003eWe calculated mortality rates using data from official vital statistics, which follow the International Classification of Diseases (ICD) system, or from household mortality assessments, commonly referred to as verbal autopsies. Disability-Adjusted Life Years (DALYs) represent a comprehensive measure of disease impact, combining the total number of years lost due to early death (Years of Life Lost, YLLs) with the number of years lived with a disability (Years Lived with Disability, YLDs) for the existing disease cases within a population. We adjusted for comorbidities by accounting for the separate likelihood of encountering each condition, using simulation models for 40,000 individuals, across varying ages, genders, countries, and years. The Global Burden of Disease Study (GBD) 2019 introduced bias adjustment procedures to ensure more accurate comparisons across varying case definitions and research methodologies. Additionally, age standardization was performed by adopting a direct method, aligned with the global population's age distribution in 2019.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData processing and disease model\u003c/h2\u003e \u003cp\u003eFirst, we assessed the global trends in influenza\u0026rsquo;s DALYs and mortality rate. We used the Joinpoint regression model with logarithm-transformed rates to calculate the average annual percent changes (AAPCs) between 1990 and 2019, as the dependent variable and year as the independent variable[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The AAPC is a pre-specified aggregate measure of trends over fixed intervals and is calculated as a weighted average of annual percentage change (APC), allowing a single number to be used to describe the average APCs over multiple years. We calculated AAPCs using the geometrically weighted average of the various APC values in the regression analysis[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The AAPCs indicate the number of APC values that change annually (e.g. an increase, decrease, or no change). DALYs rate of influenza analyses were performed using the Joinpoint Regression Program (version 4.9.1.0, New York, USA). A two-sided P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGWAS summary statistics for exposure and outcomes\u003c/h2\u003e \u003cp\u003eGWAS summary statistics for each immune trait are publicly available from the GWAS Catalog (accession numbers from GCST0001391 to GCST0002121) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The original GWAS on immune traits was performed using data from 3,757 European individuals and there were no overlapping cohorts. Approximately 22\u0026nbsp;million SNPs genotyped with high-density arrays were imputed with Sardinian sequence-based reference panel and associations were tested after adjusting for covariates (i.e., sex, age and age2)[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The influenza data were extracted from the FinnGen R10 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.finngen\u003c/span\u003e\u003cspan address=\"https://www.finngen\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ), which consisted of 9,204 cases and 344,010 controls. This study was conducted in collaboration with multiple organizations and correlated with electronic health record data. The ethics committees of each institutional review board approved the written informed consent obtained from all participants in the separate studies. No additional ethical approval or informed consent was necessary (\u003cb\u003eAdditional file 1: Table S1\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eInstrumental variables (IVs) identification and univariable mendelian randomization (UVMR)\u003c/h2\u003e \u003cp\u003eFor the data of 731 immune cells, the criteria for the genetic instruments were as follows: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, MAF\u0026thinsp;\u0026gt;\u0026thinsp;0.01 and kb\u0026thinsp;\u0026lt;\u0026thinsp;10,000[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. For influenza, we adjusted the significance level to \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e.The \"TwoSampleMR\" package was employed to perform UVMR analysis. The proportion of phenotypic variation explained (PVE) and F statistic were calculated for each IV to evaluate IV strength and avoid weak instrumental bias. The inverse variance weighted (IVW) method was used in the primary analysis to assess the causal relationships[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Cochran\u0026rsquo;s Q statistic and corresponding P values were used to test the heterogeneity among selected IVs. To exclude the effect of pleiotropy, a common method was used (i.e., MR-Egger), which implies the presence of horizontal multiplicity if its intercept term is significant[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In addition, scatter plots showed that the results were not affected by outliers, leave-one-out plots were used to indicated the stability of the results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eScRNA-Seq data analysis\u003c/h2\u003e \u003cp\u003eWe obtained scRNA-seq data of influenza patients from the GEO database (GSE182123), which includes human peripheral blood mononuclear cells from 4 healthy controls, and 5 influenza patients[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The publicly available dataset used in this study had received the necessary ethical approvals. The \"Seurat\" R package was utilized for the analysis of scRNA-seq data[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. After filtering the low-quality data, we employed the \"NormalizeData\" function for \"LogNormalize\" normalization of the data, followed by conversion into a Seurat object. We identified the top 2000 highly variable genes using the \"FindVariableFeatures\" function. Next, principal component analysis (PCA) was performed on the highly variable genes using the \"RunPCA\" function. Cell clustering analysis was performed using the \"FindNeighbors\" and \"FindClusters\" functions. Visualisation was then performed using the \"RunUMAP\" and \"RunTSNE\" functions and cell clustering experiments were performed according to UMAP-1 and UMAP-2[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. To annotate the cell clusters with cell types, we utilized the \"SingleR\" R package and performed cell annotation using the Human Primary Cell Atlas as the reference dataset. The expression patterns of the aforementioned genes in various cell types were visualized based on the UMAP plot and tSNE plot. Furthermore, a detailed examination of key cellular subgroups was carried out to identify receptor and ligand expression at the individual cell level, providing a clear understanding of intricate interactions in the cellular microenvironment[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Lastly, the identified key cell subgroups were screened and extracted for differential genes using the \"FindMarkers\" function (Threshold\u0026thinsp;=\u0026thinsp;0.5) .\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment Analysis\u003c/h2\u003e \u003cp\u003eTo investigate the functional characteristics and biological relevance of the identified marker genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment studies were done using the \"ClusterProfiler\" package, and the \"ggplot2\" package for bubble plots visualization. The GO enrichment study includes three primary categories: biological process (BP), molecular function (MF), and cellular component (CC). This comprehensive approach provides a holistic perspective on the connections between genes and pertinent terms, facilitating a deeper comprehension of the functional relationships among the identified marker genes. Simultaneously, the KEGG analysis was employed to elucidate the associations between the marker genes and functional pathways, providing valuable insights into the biological pathways implicated by these genes[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMendelian randomization analysis of key cell subpopulations\u003c/h2\u003e \u003cp\u003eIV were selected based on SNPs associated with eQTLs [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. These eQTLs were linked to marker genes specific to the identified key cellular subpopulations[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] (\u003cb\u003eAdditional file 1: Table S1\u003c/b\u003e). Summaries of genetic association data for influenza were obtained from FinnGen R10 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.finngen\u003c/span\u003e\u003cspan address=\"https://www.finngen\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ), which consisted of 9,204 cases and 344,010 controls. Screening criteria for instrumental variables were consistent with the methodology used in previous UVMR analysis. The IVW method was served as the primary analysis to assess the causal relationships. We also performed sensitivity analyses to test for heterogeneity and pleiotropy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eReverse causality detection and colocalization analysis\u003c/h2\u003e \u003cp\u003eUsing the same screening criteria as for eQTL, we performed reverse MR analysis. Bidirectional MR analysis was used to identify potential reverse causality signals. Effects were estimated using IVW, MR-Egger, weighted median, simple mode and weighted mode. Colocalization analysis was used to determine whether genetic associations within a particular region point to a common causal variant, i.e. whether there is evidence that a particular exposure and a particular outcome are caused by the same SNP or a set of closely linked SNPs. Colocalization analyses of significant influenza-associated genes were performed using the \"coloc\" software package(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/chr1swallace/coloc\u003c/span\u003e\u003cspan address=\"https://github.com/chr1swallace/coloc\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In this study, colocalization was considered significant when PPH4\u0026thinsp;\u0026gt;\u0026thinsp;0.80[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePotential therapeutic drugs prediction\u003c/h2\u003e \u003cp\u003eWe then performed the drug prediction analysis, with the list of available drug genes coming from a previous study[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. It was designed as a computational method and combined with data from many existing global genomic studies to identify druggable genes and associate them with known drugs, with a total of 4,479 druggable genes proposed. The key genes identified in the MR analysis were extracted and analysed among the druggable genes. We then further searched for potential therapeutic targets by searching for interactions between these genes and drugs using the DGIdb[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], ChEMBL[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], DrugBank[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] and PharmSnap databases. These databases prioritise potential drug targets by integrating information on drug-gene interactions, gene function, text mining and expert management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMolecular docking\u003c/h2\u003e \u003cp\u003eIn order to assess the affinity of drug candidates for their targets and from this to understand the drug target\u0026rsquo;s druggability, molecular docking was performed in this study[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Firstly, protein numbers were searched from the uniport database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ), and then 2D structures of each small molecule ligand drug were obtained from the PubChem database (PDB, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These structures were then imported into the Chem3D software in order to calculate the minimum free energy and convert them into 3D structures. This structures were imported into PyMOL to remove water molecules and ligands. The AutoDock tool (version 1.5.7) was used to obtain the PDBQT format of the receptor and ligand and to create a 3D mesh frame for the receptor for subsequent molecular docking simulations[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].Visualization was performed using PyMOL (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.pymol.org/\u003c/span\u003e\u003cspan address=\"https://www.pymol.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version 2.6). A binding energy of less than \u0026minus;\u0026thinsp;5 kcal/mol was defined as indicative of effective ligand-receptor binding, and the binding energy less than \u0026minus;\u0026thinsp;7 kcal/mol indicated strong binding activity. The entire molecular docking process was visualized in the model by AutoDock Vina (version 1.2.2).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDALYs rate and mortality rate of influenza (1990\u0026ndash;2019)\u003c/h2\u003e \u003cp\u003eThe global influenza DALYs and mortality rate were analysed by building a Joinpoint regression model to establish segmented regression on the temporal characteristics of the influenza pandemic, fitting and optimising the trend for each interval, identifying the number and location of turning points where the trend changed, specifying the direction and speed of change in the whole and locally through AAPCs and APC values, respectively, and stratified by male and female gender. Results based on Joinpoint regression analyses showed a decrease in DALYs for influenza between 1990 and 2019 (AAPC = -3.14), with DALYs for male influenza cases decreasing from 786, 4858 in 1990 to 410, 5953 in 2019, with an AAPC = -2.96 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In 2019, the rate of change in DALYs for influenza for all regions globally, based on a population of 100,000, was \u0026minus;\u0026thinsp;0.60, of which the rate of change for males was \u0026minus;\u0026thinsp;0.58 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Mortality cases of influenza declined from 257,155 in 1990 to 243,671 in 2019 (Change rate (%) = -0.05), according to the Joinpoint regression analysis, the overall AAPC=-1.88 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).In 2019, the global rate of change of influenza mortality cases for all regions, based on a population of 100,000, was \u0026minus;\u0026thinsp;0.42.Considering the gender groups, the rate of change was \u0026minus;\u0026thinsp;0.39 for males, and the rate of change for females was \u0026minus;\u0026thinsp;0.45(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This implies that there has been an overall downward trend in influenza mortality over the past 30 years.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCases and age-standardized of influenza DALYs and their AAPCs from 1990 to 2019 at the global level.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eDALY Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eDALY rate (per 100,000)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eAge-standardized DALY rate (per 100,000)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChange rate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eChange rate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eChange rate (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15023631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7512768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e280.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e259.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e103.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7864858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4105953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e291.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e105.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e274.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e114.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7158773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3406815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e269.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e88.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e246.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e93.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.62\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCases and age-standardized of influenza mortality and their AAPCs from 1990 to 2019 at the global level.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eDeath Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eDeath rate (per 100,000)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eAge-standardized death rate (per 100,000)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChange rate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eChange rate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eChange rate (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e257155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e243671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e134005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e123151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eUVMR analysis\u003c/h2\u003e \u003cp\u003eTo explore the causal effects of influenza on 731 immune cells, UVMR analysis was performe. We screened the SNPs included in the UVMR analyses for screening and removal of pleiotropy according to previous criteria, then calculated the F-statistical for the remaining SNPs, therefor the included SNPs had strong statistical effects (\u003cb\u003eAdditional file 1: Table S2\u003c/b\u003e). After multiple test adjustment based on the FDR method(\u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.05), the UVMR results showed a strong association between the immune cell HLA-DR on CD14\u0026thinsp;+\u0026thinsp;CD16- monocyte and influenza (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eIVW\u003c/sub\u003e=5.47E-05, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = 0.03) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In addition, the Cochran\u0026rsquo;s Q test (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eHeterogeneity\u003c/sub\u003e = 0.84) and MR-Egger intercept test (\u003cem\u003eP\u003c/em\u003e\u003csub\u003ePleiotropy\u003c/sub\u003e = 0.96) provided strong evidence for the absence of heterogeneity and pleiotropy (\u003cb\u003eAdditional file 1: Table S3\u003c/b\u003e). The scatter and leave-one-out plots indicated the stability of the results (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eScRNA-seq data analysis\u003c/h2\u003e \u003cp\u003eWe analysed scRNA-seq data from 9 samples, including peripheral blood mononuclear cells from 4 healthy controls, and 5 influenza patients. After preprocessing the data using stringent quality control metrics (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-C), a total of 39,245 cells were included in the analysis, and the cells were classified into 19 subpopulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD) and annotated to identifiable cell types using the \"SingleR\" package. The major cell types included B cells, monocytes, T cells, natural killer cells, and platelets (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-F). The proportion of monocytes was significantly higher in patients diagnosed with influenza(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG-H). Monocytes were isolated, downscaled, and clustered to identify their distinct cellular subpopulations. These subpopulations were annotated as monocytes: Classical monocytes (CMs), Non classical monocytes, Intermediate monocytes, Myeloid dendritic cells and Plasmacytoid dendritic cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Notably, the expression level of CMs was higher in monocyte subpopulations in influenza patients compared to healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Next, cellular communication was modelled using the \"CellChat\" function, which combines the interaction of ligand receptors and their cofactors using gene expression data as input. Inferred trajectories depicted the ability of one or more cells to differentiate into other cells, and bubble plots illustrated ligand receptor pairs signaling from specific cells to other cell populations. The results highlight the importance of monocyte developmental trajectories, with more significant communication between CMs and Non classical monocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-D), and bubble plots showing that ANXA1-FPR1 is significantly enriched in CMs, which regulate inflammatory mediators and immune responses by influencing the behavior of leukocytes in to exert anti-inflammatory effects(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).After clustering the cellular subpopulations, characteristic genes specific to this subpopulation that are associated with other subpopulations and cells were calculated. A total of 191 genes were crossed and plotted on a Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). These genes are essential for characterizing key cellular subpopulations associated with influenza(\u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e). Finally, enrichment analyses of these subpopulation-specific genes were performed to reveal the underlying biological mechanisms associated with them.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment analysis\u003c/h2\u003e \u003cp\u003eGO enrichment analysis showed that in BP function, the key genes were significantly correlated with regulation of inflammatory response, chemotaxis and taxis. In BP function, the key genes were significantly associated with secretory granule lumen, cytoplasmic vesicle lumen and vesicle lumen. In MF function, the key genes were significantly associated with cytokine binding and immune receptor activity, etc(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). KEGG pathway enrichment analysis revealed that the key genes were involved in Neutrophil extracellular trap formation, IL-17 signaling pathway and Hematopoietic cell lineage (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eMendelian randomization analysis of key cell subpopulations\u003c/h2\u003e \u003cp\u003eA total of 191 genes were analyzed as key marker genes for MR analysis. The F-statistics of all gene tools were higher than 10, indicating a strong tool strength. A total of 7 genes were potentially causally associated with the risk of influenza incidence (VIM, CTSA, UBE2D1, CSF3R, AHNAK, DPYD and FAM200B) through the use of the Wald ratio or IVW method (\u003cb\u003eAdditional file1: Table S5\u003c/b\u003e). The association volcano and frost plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B\u003cb\u003e)\u003c/b\u003e illustrates the relationship between key marker genes and influenza. According to gene prediction of MR analysis, elevated levels of VIM, CTSA, UBE2D1, AHNAK and FAM200B were negatively associated with influenza, and these 5 genes may be protective genes for the risk of developing influenza. In contrast, the other genes, CTSA and DPYD, were positively associated with influenza, i.e., elevated levels of the genes were associated with an increased risk of influenza, and these 2 genes may be risk genes for the risk of influenza (\u003cb\u003eAdditional file 1: Table S6\u003c/b\u003e). Of these 7 genes, 4 had associations consistent with the directionality observed by the weighted median method: CTSA, UBE2D1, CSF3R, AHNAK Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. In addition, sensitivity analyzed showed no evidence of heterogeneity (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) or pleiotropy (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) for any of the 7 genes (\u003cb\u003eAdditional file 1: Table S7\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eReverse causality detection and colocalization analysis\u003c/h2\u003e \u003cp\u003eTesting for reverse causality did not reveal any causal effect of influenza on these genes. Next, we performed colocalization analysis of the seven genes using the \"coloc\" R package.However, the results of the colocalization analysis indicated that there was insufficient evidence to support that these genes shared the same variant as influenza (PPH4\u0026thinsp;\u0026lt;\u0026thinsp;0.80) (\u003cb\u003eAdditional file 1: Table S7\u003c/b\u003e). The results of this analysis may be related to factors such as insufficient sample size or weak effects of genetic tools, genetic heterogeneity, confounders and bias, and complex biological pathways, which resulted in the failure of the analysis to detect colocalization signals. Thus, the genetic association with influenza may have been achieved through other pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003ePotential therapeutic drugs prediction\u003c/h2\u003e \u003cp\u003eWe identified 3 genes (VIM, CTSA and CSF3R) in the list of druggable genes that have been targeted for drug development (\u003cb\u003eAdditional file 1: Table S8-9\u003c/b\u003e). The content of the data is divided into 3 layers, and instructions on how to define them can be found further in the methods section of the main text [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The identified VIM and CSF3R genes both belong to Tier 1, which includes the efficacy targets of approved small molecule and biotherapeutic drugs, as well as the efficacy targets of clinical-stage drug candidates. CTSA belongs to Tier 2, a tier of genes encoding targets with known biologically active drug-like small molecule binding partners as well as \u0026ge;\u0026thinsp;50% identity (over \u0026ge;\u0026thinsp;75% of the sequence) to approved drug targets. Furthermore, we found that a number of drugs targeting the CSF3R have been developed for the treatment of neutropenia (e.g., PEGFILGRASTIM-JMDB and TBO-FILGRASTIM) and developed as anti-inflammatory and anti-tumor agents (e.g. RUXOLITINIB). TBO-FILGRASTIM is a recombinant human granulocyte colony-stimulating factor used to induce granulocyte production and reduce the risk of infection following myelosuppressive therapy, whereas PEGFILGRASTIM-JMDB possesses pharmacological activity comparable to that of TBO-FILGRASTIM and binds to the G-CSF receptor to stimulate the proliferation, differentiation and activation of neutrophils to increase the drug's effect[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. A number of drugs targeting VIM have been developed for their anti-inflammatory, analgesic, and cell-penetrating effects, such as DIMETHYL SULFOXIDE. The above-listed drugs are all approved by the U.S. Food and Drug Administration (FDA). However, no relevant approved drugs targeting the CTSA gene have been found in the DGIdb database.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eMolecular docking\u003c/h2\u003e \u003cp\u003eThe binding sites and interactions of the 3 drug candidates with the proteins encoded by the corresponding genes were obtained using Autodock, and binding energies for each interaction were generated, yielding a total of 2 effective docking results for the proteins and drugs. Each drug candidate was connected to the protein target through visible hydrogen bonding and strong electrostatic interactions. Of these, CSF3R and RUXOLITINIB exhibited the lowest binding capacity (-7.4 kcal/mol), indicating strong binding activity(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe WHO released the Global Influenza Prevention and Control Strategy (2019\u0026ndash;2030), and the importance of influenza prevention and control has attracted global attention[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In this study, the global influenza epidemic characteristics and trends were analyzed using descriptive epidemiology in conjunction with the GBD database from 1990 to 2019. The Joinpoint regression trend analysis shows that the overall trend of influenza DALYs rate and mortality rate is decreasing, which can be attributed to the following reasons: firstly, with the wider influenza vaccination and public acceptance of the vaccine, the number of influenza infections can be reduced, which can reduce the speed and severity of the disease; secondly, increased awareness of personal hygiene, effective dissemination of public health information, and better preventive measures during the influenza season can help reduce infection rates; thirdly, the availability of more rapid influenza tests, more effective antiviral therapeutic drugs on the market, and improvements in care and treatment regimens; fourth, improvements in disease surveillance systems globally have facilitated earlier identification and respond to influenza epidemics; and fifth, increased international collaboration in influenza virus surveillance and vaccine production has helped predict changes in influenza virus strains in advance, leading to the manufacture of more effective seasonal influenza vaccines. Our findings can raise awareness among doctors and patients, improve the health care system, and provide a basis for effective measures to prevent and control influenza.\u003c/p\u003e \u003cp\u003eNext, based on a large amount of publicly available genetic data, we explored causal relationships between 731 immune cells and influenza. To the best of our knowledge, this is the first MR analysis to explore causal relationships between multiple immune phenotypes and influenza. In this study, we found that HLA-DR on the monocyte cell group CD14\u0026thinsp;+\u0026thinsp;CD16- has been shown to be associated with an increased risk of influenza. HLA-DR is an important Major Histocompatibility Complex (MHC) class II molecule commonly known as Human Leukocyte Antigen in humans[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. HLA-DR surface molecules play an important role in the immune response, particularly in assisting in the activation of specific immune responses[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. CD14\u0026thinsp;+\u0026thinsp;CD16- monocytes are a typical class of inflammatory response monocytes that mainly phagocytose pathogens and present antigens in the immune response[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. CD14\u0026thinsp;+\u0026thinsp;CD16- monocytes with high HLA-DR expression play a crucial role in the development of influenza. In influenza infection, these monocytes may be involved in the role of antigen presentation. When influenza virus infects the organism, CD14\u0026thinsp;+\u0026thinsp;CD16- monocytes phagocytose viral particles, process and present peptides of the virus on their HLA-DR molecules[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. These peptides are presented to CD4\u0026thinsp;+\u0026thinsp;T cells, inducing activation and differentiation of the latter. These cells also produce and release a variety of pro-inflammatory factors, such as TNF-α and IL-1, which contribute to the immune system response and cause local and systemic inflammatory responses[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eScRNA-seq, a new technique for transcriptome sequencing of isolated single cells, is now being used to analyse virus-host interactions at the single-cell level. We verified at the single-cell level that the immune cells identified by UVMR HLA-DR on CD14\u0026thinsp;+\u0026thinsp;CD16- monocyte belong to the CMs subpopulation. And the expression level of CMs was higher in monocyte subpopulations from influenza patients compared to healthy controls. MR analysis of key genes in the cellular subpopulation identified 7 genes as causally associated with influenza, and 3 genes were identified as druggable genes by drug prediction, namely VIM, CTSA and CSF3R. Among them, the high binding activity of CSF3R and RUXOLITINIB by molecular docking demonstrated the strong potential of the CSF3R gene as a drug target.\u003c/p\u003e \u003cp\u003eThe protein encoded by the VIM gene is vimentin, a structural protein belonging to the intermediate fibronectin family. This protein supports the structural integrity of cells and is involved in a variety of cellular functions such as cell attachment, cell migration and signaling[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Although the VIM gene is not an immune gene that directly interacts with influenza viruses, some studies suggest that intermediate fibre proteins may play a role during viral infection, especially during the invasion and replication phases of the virus. For example, VIM may act as a non-specific viral attachment site during viral entry into cells, or during viral replication and assembly[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Some viruses are able to modulate host cell backbone proteins to aid in their replication and propagation[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. The CTSA gene encodes galactose neuraminidase 1 (Cathepsin A), a protective protein of the lysosomal enzyme class that is implicated in a variety of biochemical processes[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. It has been suggested that lysosomes may play a role in the life cycle of viral infections, where influenza virus replication may require lysosomal function. After the influenza virus invades a cell, its RNA must be released from the inner vesicles that encapsulate them in order to begin the process of viral replication, and the acidification process of this inner vesicle is associated with lysosomal function[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. On the other hand, lysosomal proteins, such as galactose neuraminidase 1, may also be involved in regulating the immune response of host cells to viral invasion. For example, they may influence the production of inflammatory mediators or activate other cellular pathways involved in viral defense[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCSF3R encodes a receptor whose primary function is to regulate the production of white blood cells (granulocytes)[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. This receptor is essential for the growth and differentiation of granulocytes (mainly neutrophils), which are one of the body's main white blood cells and play an important role in warding off infections[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. During infection, neutrophils act as a front-line defense for the body's immune system, and they respond rapidly to help fight the invasion of the influenza virus through the regulation of the CSF3R receptor. Several studies have suggested that differences in the immune system response to the virus in different individuals may be associated with variations in specific genes, which may include the CSF3R gene[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. CSF3R is associated with may indirectly affect the course, severity, and duration of influenza infection[\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Gene variants may affect an individual's susceptibility to viral infections as well as the ability to recover. Thus, individual differences in the CSF3R gene may affect their response to influenza[\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. However, the onset and progression of influenza is multifactorial, involving host genetic background, viral strain specificity, and other genetic and environmental factors. Genetic variation is only one part of the equation, and these interactions and effects are often very complex. In addition, influenza virus-host cell interactions are complex and often involve the synergistic action of multiple genes and metabolic pathways. Therefore, identifying the roles of the aforementioned genes in influenza infection will require studies that include virology, molecular biology and host genetics to more fully understand the potential functions of these genes in the influenza virus life cycle.\u003c/p\u003e \u003cp\u003eIn summary, this is the first time that descriptive epidemiology has been used to analyze the epidemiological characteristics and trends of influenza based on the GBD database. This study is also the first to identify potential causal associations between HLA-DR on CD14\u0026thinsp;+\u0026thinsp;CD16- monocyte and influenza by UVMR. Gene expression of different cell types in blood samples from influenza patients was also analyzed and annotated using scRNA-seq, and potential drug targets for influenza were initially identified from genetic insights. However, there are some limitations to this study. First, only global influenza DALYs rates and mortality rates were analyzed, and subgroup analyses of trends in influenza DALYs rates and mortality rates for different age groups were not conducted. In addition, the influenza data information for this study was derived from sources only up to 2019, and updated stream GBD information is not yet available. Additionally, the applicability of the study's findings is limited due to its focus on predominantly European-descended individuals. To extend these results to people of different ethnic backgrounds, further research and thorough validation are needed to confirm their universal relevance. Despite meticulous attempts to eradicate bias, MR analysis is still susceptible to confounding by unobserved variables or pleiotropy, which may distort the outcomes. Also, while enrichment analysis provides meaningful insights, it is not without its confines, as it depends on predetermined groups of genes or biological pathways that may not fully capture the entire spectrum of potential biological processes or their interplay. Precision in molecular docking analysis is contingent on the high quality of the protein structures and ligands involved. Although this method is instrumental in pinpointing likely drug candidates, it cannot ensure their success in a clinical environment. It is therefore imperative that further laboratory research and clinical trials be conducted to substantiate the medical viability of these proposed targets. Acknowledging and addressing these limitations will pave the way for future research to improve the understanding of influenza and its potential treatments, and will help to create a more holistic perspective and advance relevant research in the field in a meaningful way.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study encompasses a combination of multidisciplinary approaches ranging from epidemiology to genetics to bioinformatic analyses and genomics, which not only enables us to further understand the characteristics of the global influenza pandemic and its changing trends, but also provides a comprehensive understanding of the complex relationship between immune cell populations and influenza. In addition, observing and comparing the expression patterns of these immune cell-associated genes at the cellular level provides insights into their functions and regulatory mechanisms in the development of influenza, as well as potential possibilities for the prediction and development of drug targets for influenza in the future.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGBD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; The Global Burden of Disease Study\u003c/p\u003e\n\u003cp\u003eDALYs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Disability-adjusted life years\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAAPCs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;The average annual percent changes\u003c/p\u003e\n\u003cp\u003eAPC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Annual percentage change\u003c/p\u003e\n\u003cp\u003ePVE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Phenotypic variation explained\u003c/p\u003e\n\u003cp\u003eIV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Instrumental variables\u003c/p\u003e\n\u003cp\u003eSNP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Single Nucleotide Polymorphism\u003c/p\u003e\n\u003cp\u003eScRNA-Seq \u0026nbsp; \u0026nbsp; \u0026nbsp;Single-cell RNA-Sequencing\u003c/p\u003e\n\u003cp\u003ePCA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;principal component analysis\u003c/p\u003e\n\u003cp\u003eUMAP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Uniform Manifold Approximation and Projection\u003c/p\u003e\n\u003cp\u003eIVW \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Inverse variance weighted\u003c/p\u003e\n\u003cp\u003eUVMR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Univariable Mendelian randomization\u003c/p\u003e\n\u003cp\u003eFDR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;False discovery rate\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study were obtained from genome-wide association study summary statistics that were publicly released by genetic consortia. Interested parties may obtain the data by submitting a request to the corresponding author. Furthermore, all datasets generated for this study have been included in the article or additional files.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGZW has full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. GZW, WDJ, CXH, WJC and LWL conceived and designed the study. GZW wrote the first draft of the manuscript. WDJ undertook the statistical analyses. GZW, WDJ, CXH, WJC and LWL interpreted data, reviewed the paper, and made critical revision of the manuscript for important intellectual content. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Scientific and technological innovation project of China Academy of Chinese Medical Sciences (CI2021A00801 and CI2021A00806).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sharing statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data analyzed in this study can be obtained by a reasonable request to corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher’s Note\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eUyeki TM: \u003cstrong\u003eInfluenza\u003c/strong\u003e. \u003cem\u003eAnnals of internal medicine\u0026nbsp;\u003c/em\u003e2021, \u003cstrong\u003e174\u003c/strong\u003e(11):Itc161-itc176.\u003c/li\u003e\n \u003cli\u003eNeumann G, Kawaoka Y: \u003cstrong\u003eSeasonality of influenza and other respiratory viruses\u003c/strong\u003e. \u003cem\u003eEMBO molecular medicine\u0026nbsp;\u003c/em\u003e2022, \u003cstrong\u003e14\u003c/strong\u003e(4):e15352.\u003c/li\u003e\n \u003cli\u003eIuliano AD, Roguski KM, Chang HH, Muscatello DJ, Palekar R, Tempia 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diseases\u0026nbsp;\u003c/em\u003e2022, \u003cstrong\u003e22\u003c/strong\u003e(1):343.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Epidemiology, Single-cell RNA sequencing, Mendelian randomization, Genetic, Influenza","lastPublishedDoi":"10.21203/rs.3.rs-4276363/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4276363/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eThe relationship between immune cells and influenza is a battle between the host immune system and viral invaders, however, the causality and underlying mechanisms have not been fully elucidated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eThis study first analysed disability-adjusted life years (DALYs) and mortality of influenza using descriptive epidemiology based on the Global Burden of Disease (GBD) data from 1990 to 2019. Potential causal associations between 731 immune cells and influenza were then explored using univariate Mendelian randomization (UVMR), followed by validation of the cellular subpopulations to which the immune cells identified by UVMR belonged at the single-cell level, and then enrichment analysis has been performed. Finally, we also performed MR of key genes in cellular subpopulations, reverse MR analysis, colocalization analysis, potential drug prediction and molecular docking for genes satisfying causal associations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eJoinpoint regression trend analysis showed a general downward trend in the change of influenza DALYs rate and mortality rate, and then UVMR results showed a strong association between the immune cell HLA-DR on CD14+ CD16- monocyte and influenza (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eIVW \u003c/sub\u003e= 5.47E-05, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = 0.03). The single-cell sequencing (scRNA-Seq) results verified that the immune cell HLA-DR on CD14+ CD16- monocyte identified by UVMR belonged to the Classical monocytes (CMs) subpopulation. MR analysis of key genes in the cellular subpopulation identified a total of 7 genes as causally associated with influenza, and no reverse causal association was found. The 3 genes were identified as druggable by drug prediction, namely VIM, CTSA and CSF3R. Finally, molecular docking results demonstrated the strong potential of the CSF3R gene as a drug target.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eOur study provides new insights into future prevention and treatment strategies for influenza from epidemiology to genetics to bioinformatic analyses and genomic.\u003c/p\u003e","manuscriptTitle":"Combination of multidisciplinary approaches reveals potential causal associations between influenza and immune cells: Single-cell RNA sequencing and Mendelian randomization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-22 14:57:15","doi":"10.21203/rs.3.rs-4276363/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"557ebe0c-bb23-4e2b-8e9b-665b4761130d","owner":[],"postedDate":"April 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-22T14:57:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-22 14:57:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4276363","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4276363","identity":"rs-4276363","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}