Abstract Background: Organ weight change is widely accepted as a measure of toxicologic pathology, and we and other groups have shown that excessive alcohol exposure leads to decreased spleen weight in rodents. The present study explores the mechanisms underlying alcohol-induced splenic injury through a network meta-analysis. Methods: QIAGEN Ingenuity Pathway Analysis (IPA) and Mammalian Phenotype (MP) Ontology were used to identify alcohol-related molecules associated with the small spleen phenotype. Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and IPA bioinformatics tools were then used to analyze the biological processes and enriched signaling pathways engaging these molecules. In addition, the “downstream effects analysis” algorithm was used to quantify alcohol’s effect. Results: IPA identified 623 molecules affected by alcohol and Venn Diagram revealed 26 of these molecules overlapped with those associated with the MP Ontology of small spleen. These 26 molecules are Tgfb1, Casp8, Mtor, Esr1, Cxcr4, Camk4, Nfkbia, Drd2, Bcl2, Fas, Pebp1, Traf2, Atm, Ighm, Ednrb, Mdm2, Glra1, Prf1, Tlr7, Ifng, Alox5, Foxo1, Il15, Apoe, Ikbkg, and Rora. A portion of these 26 molecules was also associated with the MP Ontology of abnormal white pulp and red pulp morphology of the spleen, abnormal splenic cell ratio, decreased splenocyte number, abnormal spleen physiology, increased splenocyte apoptosis, and reduced splenocyte proliferation. STRING and IPA “Core Analysis” showed that these molecules were mainly involved in pathways related to cell apoptosis, proliferation, migration, and immune responses. IPA’s “Molecular Activity Predictor” (MAP) tool showed concurrent effects of activation and inhibition of these molecules led to decreased spleen size by modulating apoptosis, proliferation, and migration of splenocytes. Conclusions: Our network meta-analysis revealed that excessive alcohol exposure can damage the spleen through a variety of molecular mechanisms, thereby affecting immune function and human health. We found that alcohol-mediated spleen atrophy is largely mediated via increasing apoptosis signaling, migration of cells, and inhibiting the proliferation of splenocytes. Keywords: Ingenuity Pathway Analysis, Mammalian Phenotype Ontology, STRING, spleen atrophy, apoptosis, cell migration, cell proliferation Introduction The spleen is the body’s largest lymphatic organ, and it plays important roles in the blood and immune system through its two main components, the red pulp, and the white pulp. It does not only filter the blood by removing pathogens, aging red blood cells, and platelets but also participates in both innate and adaptive immune responses ([32]Lewis et al., 2019, [33]Cesta, 2006). The spleen is a reservoir of monocytes and lymphocytes that migrate to the circulatory system and enter other organs to participate in both local and systemic immunity ([34]Bronte and Pittet, 2013, [35]Swirski et al., 2009, [36]Cesta, 2006). As many physiological and pathological processes involve immune mechanisms, the spleen plays a key role in human health and diseases and has received more and more attention. Changes in the structure and function of the spleen have been associated with infection, inflammation, tumors, metabolic and cardiovascular diseases, autoimmune diseases, blood diseases, and even brain and psychiatric diseases ([37]Attina et al., 2021, [38]McKim et al., 2016, [39]Lori et al., 2017). Organ weight is widely accepted for the evaluation of toxicologic pathology, and spleen weight and spleen/body weight ratio are often used as indicators of immunotoxicity ([40]Sellers et al., 2007, [41]Flaherty, 2007). When the spleen atrophies, it decreases in size and weight, and cannot function properly. Splenic atrophy is often accompanied by a decrease in the number of cells and destruction of the histological structure and may reflect a variety of pathological conditions ([42]Liu et al., 2017). Smaller spleen volume has been suggested to predict poor prognosis in patients with bacteremia caused by encapsulated organisms ([43]Shimoyama et al., 2021). Alcohol drinking remains a public health issue of global concern. Sustained heavy drinking or occasional binge drinking can cause damage to many organs and systems including brain, heart, liver, pancreas, spleen, and immune system leading to serious health problems ([44]https://www.niaaa.nih.gov/alcohols-effects-health/alcohols-effects -body). Using rodent models, we and other groups have shown that excessive alcohol [ethanol (EtOH)] exposure causes atrophy of the spleen, accompanied by changes in histological structure, cell number and composition, and gene expression ([45]Budec et al., 2000, [46]Liu et al., 2011, [47]Liu et al., 2016, [48]Chadha et al., 1991), however, the exact mechanism resulting in these observed changes remains to be elucidated. QIAGEN Ingenuity Pathway Analysis (IPA) is an all-in-one, web-based software application for the analysis, integration, and interpretation of data derived from omics experiments, and can project downstream effects and identify new targets or candidate biomarkers ([49]https://www.qiagen.com/). STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) is a database of known and predicted protein-protein interactions. It can also perform enrichment analysis with user-provided lists of proteins using several functional classification systems such as Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and REACTOME ([50]https://string-db.org). The Mammalian Phenotype (MP) Ontology is a community effort to provide standard terms for annotating phenotypic data and can provide links to all genotypes annotated to a selected phenotype term ([51]http://www.informatics.jax.org/vocab/mp_ontology). In this study, we used IPA to capture 623 molecules affected by alcohol and found that 26 of these molecules overlapped with the molecules associated with the small spleen phenotype (MP:0000692). Through in-depth analysis of these 26 molecules using various bioinformatics tools, our network meta-analysis study has identified the pathways involved in apoptosis, migration, and proliferation as the possible mechanisms by which excess use of alcohol has damaged the spleen. Materials and methods Ingenuity pathway analysis (IPA) software The IPA bioinformatics tool (QIAGEN, Germantown, MD) employs computational algorithms to analyze the functional connectivity of molecules present in the QIAGEN Knowledge Base (QKB). QKB is a horizontally and vertically structured repository database including over 7 million individually designed relationships between diseases, drugs, biological entities like genes, proteins, and metabolites, biological processes like expression, molecular cleavage, and phosphorylation. It also includes published experiments from omics research including differential gene expression. All the information available on the QKB database is manually curated and extracted mainly from scientific journals, molecular content databases, and book chapters over the last 20 years. All the information provided in the present study was retrieved from QKB between December 10, 2021, and Jan 15, 2022. Mammalian Phenotype (MP) Ontology The MP Ontology ([52]http://www.informatics.jax.org/vocab/mp_ontology) is a structured vocabulary for describing mammalian phenotypes and serves as a critical tool for efficient annotation and comprehensive retrieval of phenotype data. Use of this ontology allows comparisons of data from diverse sources, can facilitate comparisons across mammalian species, assists in identifying appropriate experimental disease models, and aids in the discovery of candidate disease genes and molecular signaling pathways ([53]Smith and Eppig, 2009, [54]Smith and Eppig, 2012). All the information provided in the present study was retrieved from MP Ontology between December 26, 2021, and Jan 6, 2022. Identification of molecules associated with EtOH and small spleen. Using the IPA’s “My Pathway” feature the EtOH was added to a new pathway. The IPA’s “Build”-“Grow” tool was used to identify molecules including genes, proteins, transcription and translation regulators, and complexes associated with EtOH. The “Trim” tool was used to remove the molecules that are not naturally occurring in the biological system (such as chemical reagents, toxicants, and drugs). The molecules associated with small spleen were identified from the MP Ontology. Under the mammalian phenotype browser, we searched “small spleen” ([55]http://www.informatics.jax.org/mp/annotations/MP:0000692) to obtain these molecules. Identification of overlapping molecules associated with EtOH and those with small spleen phenotypes. The overlapping molecules associated with EtOH and small spleen were identified using Bioinformatics & Evolutionary Genomics Venn Diagram tool ([56]http://bioinformatics.psb.ugent.be/webtools/Venn/). This tool can show the similarities, differences, and relationships between two or more sets of items. Molecules associated with EtOH and those associated with small spleen were uploaded separately and the software will generate a textual output indicating the common molecules between the two data sets of molecules. GO enrichment analysis and pathway enrichment analysis using the STRING Database The STRING database ([57]https://string-db.org/) is an online resource dedicated to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. STRING can perform automated functional enrichment analysis on the user’s input, using several functional pathway classification frameworks such as Gene Ontology annotations, KEGG pathways, Reactome pathways, etc. (Szklarczyk et al. 2021; Szklarczyk et al. 2019). The overlapping molecules associated with EtOH, and small spleen obtained using Venn Diagram were uploaded to the STRING search tool and analyzed for the enrichment pathways mediated by the overlapping molecules. The entire STRING-based dataset provided in the present study was retrieved from STRING on Jan 14, 2022. IPA core analysis The overlapping set of molecules obtained from the Bioinformatics & Evolutionary Genomics Venn Diagram were uploaded into IPA for “core analysis”. The Core analysis is comprised of canonical pathway analysis, upstream regulator analysis, and Disease and function analysis. Canonical pathway analysis was performed to identify pathways affected by the overlapping molecules among the 705 canonical pathways stored in the QKB. The overlapping molecules were rated with a right-tailed p-value from the Benjamini–Hochberg Corrected Fisher’s Exact Test. This determines the probability of identifying a specific number of overlapping molecules within the input data set and canonical pathways stored in QKB. The top biological signaling and metabolic canonical pathways associated with the shared molecules between EtOH and spleen atrophy were revealed. Upstream regulator analysis was performed to identify the potential upstream regulators that may account for the changes in the expression level of the queried molecules by overlapping them with curated networks in QKB. Each upstream regulator was assigned an overlap p-value to measure the significance using Krämer et al.([58]Krämer et al., 2014). The disease and function analysis was used to identify the disease and function associated with the overlapping molecules associated with EtOH and small spleen. A right-tailed Fisher’s Exact Test was employed to determine the significance of the association between the overlapping molecules and the diseases and functions stored in the QKB. Connectivity mapping of EtOH with apoptosis, proliferation of splenocytes, and cell migration The overlapping molecules and EtOH were added to “my pathway” and a simulation of the effect of EtOH on apoptosis, proliferation of splenocytes, and cell migration was performed using the “Molecule Activity Predictor (MAP)” tool. The MAP tool predicts the effect of activation or inhibition of EtOH on the neighboring molecules. The connection between EtOH and apoptosis, proliferation of splenocytes, or cell migration with the set of overlapping molecules was identified using the “Connect” tool, according to the curated findings in the QKB. The MAP tool was employed to study the changes in the activity process which includes expression, transcription, activation, inhibition, and phosphorylation of the set overlapping molecules and cell proliferation/cell migration upon simulation exposure to EtOH. Quantitative analysis of the influence of EtOH on the overlapping molecules The “Downstream Effect Analysis” algorithm was used as described by Krämer et al. ([59]Krämer et al., 2014) to calculate the quantitative weight given to the expression changes of overlapping molecules predicted by the MAP tool in simulating how exposure to EtOH would affect the cell proliferation and cell migration. The algorithm utilized findings curated within the QKB as data points since the MAP tool within IPA also pulls its data from QKB to predict the activity changes. The algorithm calculates the Z score for each intermediary molecule upon exposure to EtOH and infers the change in apoptosis, cell proliferation or cell migration. The Z value ranges between −2 and +2, where −2 indicates inhibitory relationships, whereas +2 indicates activation relationships. The Krämer analysis calculates the magnitude of the overlapping molecules activity and cell proliferation/cell migration when exposed to EtOH. Results Molecules associated with EtOH and with small spleen phenotype A total of 623 molecules curated in the QKB were found to be associated with EtOH using IPAs “Build”-“Grow” tool. ([60]Fig. 1). These molecules included cytokines, enzymes, G-protein coupled receptors, kinases, nuclear receptors, peptidase, phosphatase, transcription and translation regulators, transmembrane receptors, transporters, etc. Additionally, a total of 421 molecules associated with small spleen size were identified from MP Ontology (MP:0000692) ([61]Fig. 2). Fig. 1: [62]Fig. 1: [63]Open in a new tab 623 Molecules associated with EtOH obtained from QKB. Fig. 2: [64]Fig. 2: [65]Open in a new tab 421 molecules associated with small spleen phenotype MGI database obtained from MP Ontology. Overlapping molecules between the molecules associated with EtOH and those with small spleen phenotype. There were 26 molecules that were found to be present among both the 623 molecules associated with EtOH and the 421 molecules associated with small spleen. These 26 molecules were Tgfb1, Casp8, Mtor, Esr1, Cxcr4, Camk4, Nfkbia, Drd2, Bcl2, Fas, Pebp1, Traf2, Atm, Ighm, Ednrb, Mdm2, Glra1, Prf1, Tlr7, Ifng, Alox5, Foxo1, Il15, Apoe, Ikbkg, and Rora ([66]Fig. 3). [67]Table 1 lists the portion of these 26 molecules that are associated with abnormal white pulp and red pulp morphology of the spleen, abnormal splenic cell ratio, decreased splenocyte number, abnormal spleen physiology, increased splenocyte apoptosis, or reduced splenocyte proliferation ([68]Table 1). Fig. 3: [69]Fig. 3: [70]Open in a new tab Identification of molecules. 623 molecules associated with EtOH were identified from QKB and 421 molecules associated with small spleen phenotype were obtained from MP Ontology. The Venn Diagram tool revealed 26 overlapping molecules between them. Simulation activation of EtOH upregulated 14 molecules, downregulated 7 molecules, and 5 molecules had no clear simulation results. Table 1. Genes associated with abnormal spleen phenotypes (based on MP Ontology) small spleen abnormal spleen white pulp morphology abnormal spleen red pulp morphology abnormal splenic cell ratio decreased splenocyte number abnormal spleen physiology increased splenocyte apoptosis decreased splenocyte proliferation Tgfb1 (transforming growth factor beta 1) Casp8 (caspase 8) Casp8 Mtor (mechanistic target of rapamycin kinase) Mtor Mtor Mtor Mtor Esr1 (estrogen receptor 1) Esr1 Esr1 Cxcr4 (C-X-C motif chemokine receptor 4) Cxcr4 Camk4 (calcium/calmodulin-dependent protein kinase IV) Nfkbia (NFKB inhibitor alpha) Nfkbia Drd2 (dopamine receptor D2) Bcl2 (BCL2 apoptosis regulator) Bcl2 Bcl2 Bcl2 Bcl2 Bcl2 Fas (Fas cell surface death receptor) Fas Fas Fas Pebp1 (phosphatidylethanolamine binding protein 1) Atm (ATM serine/threonine kinase) Atm Ighm (immunoglobulin heavy constant mu) Ighm Ighm Ednrb (endothelin receptor type B) Ednrb Ednrb Ednrb Glra1 (glycine receptor alpha 1) Prf1 (perforin 1) Prf1 Prf1 Tlr7 (toll-like receptor 7) Tlr7 Tlr7 Tlr7 Ifng (interferon gamma) Ifng Ifng Ifng Alox5 (arachidonate 5-lipoxygenase) Il15 (interleukin 15) Il15 Rora (RAR-related orphan receptor A) [71]Open in a new tab A network map that was constructed on these 26 overlapping molecules showed that in response to exposure to EtOH, four were upregulated, seven were downregulated, while the remaining five molecules showed no response to exposure to EtOH ([72]Fig. 3). GO enrichment analysis and pathway enrichment analysis of the 26 overlapping molecules using the STRING Database GO enrichment analysis also showed that the 26 overlapping molecules were significantly enriched in the 602 GO-terms involving multiple molecular biological processes including immune cells apoptosis, activation, proliferation, differentiation, migration, the structure of the spleen, immune response, cytokine secretion, oxidative stress, lipid metabolism, calcium ion transport, stress and glucocorticoid’s reaction, nutrition and protein metabolism, autophagy, and protein phosphorylation. KEGG, REACTOME, and WikiPathways enrichment analysis shows that these molecules are mainly involved in pathways related to cell apoptosis, proliferation, senescence and migration, immune responses (T cell, B cell, NK cell, cytokine, chemokine, pattern recognition receptors, etc), metabolism, oxygen homeostasis, and calcium signaling ([73]Table 1). These results were consistent with the IPA core analysis using either the 26 or 21 molecules (excluding 5 molecules- FOXO1, IKBKG, TRAF2, APOE, and MDM2 that did not show any expression change following EtOH simulation exposure) associated with EtOH and small spleen phenotype. With these data being noted, we removed these five molecules in subsequent analyses as shown in the workflow ([74]Supplementary Figure 1, [75]SF1). Core analysis for the 21 molecules associated with EtOH and small spleen phenotype. The list of 21 molecules showing altered expression in response to EtOH exposure was used as the basis for IPA’s core analysis to determine if there was any enrichment of these molecules in known canonical pathways. Among 292 pathways (with p<0.05), the top 3 canonical pathways identified were induction of apoptosis by HIV-1 (p=3.76E-13), MYC mediated apoptosis signaling (p=1.08E-11), and death receptor signaling (p=6.13E-10). Moreover, several immune pathways including the role of osteoblasts, osteoclasts, and chondrocytes in Rheumatoid arthritis, PEDF signaling, systemic lupus erythematosus in B cell signaling pathway, and crosstalk between dendritic cells and natural killer cells also showed enrichment for this group of 21 molecules revealing a significant association with the immune response ([76]Table 2). Table 2: Core analysis of the top canonical pathway. Core analysis of the 21 overlapping molecules associated with EtOH and small spleen phenotype identified the top 20 canonical pathways (P-value<0.05) associated with the molecules. Canonical pathways P-value Induction of apoptosis by HIV1 2.56E-09 Cross-talk between dendritic cells and natural killer cells 1.42E-08 Coronavirus pathogenesis pathway 1.75E-08 Systemic lupus erythematosus in B cell signaling pathway 1E-07 Systemic lupus erythematosus in B cell signaling pathway 2.78E-07 FAT10 cancer signaling pathway 1.01E-07 Neuroinflammation signaling pathway 2.45E-07 Erythropoietin Signaling Pathway 3.98E-07 Hepatic Cholestasis 5.8E-07 Hepatic Fibrosis / Hepatic Stellate Cell Activation 6.26E-07 PEDF Signaling 8.26E-07 Autophagy 1.04E-06 Tumoricidal Function of Hepatic Natural Killer Cells 1.19E-06 Role of Osteoblasts, Osteoclasts, and Chondrocytes in Rheumatoid Arthritis 1.27E-06 Death Receptor Signaling 1.41E-06 Molecular Mechanisms of Cancer 1.82E-06 Apoptosis Signaling 1.95E-06 Altered T Cell and B Cell Signaling in Rheumatoid Arthritis 3.16E-06 Airway Pathology in Chronic Obstructive Pulmonary Disease 3.22E-06 [77]Open in a new tab [78]Table 3 showed the upstream regulator of the 21 molecules upon core analysis ([79]Table 3). The top 10 upstream regulators were humoral immunity molecule Immunoglobulin E, T cell marker CD3, 4 cytokines (IL6, IFNG, IL1B, IFN-a), and 4 transcription factors (SP1, FOXO1, NFKB, STAT5A), showing an obvious association with immunity. Table 3: Upstream regulators. Core analysis of the 21 overlapping molecules associated with EtOH and small spleen phenotype identified the upstream regulators (P-value <0.05) associated with the molecules. Upstream regulators P-value Immunoglobulin E (Ige) 1.40E-13 SP1 (Transcription factor) 9.30E-13 Interleukin 6 (IL6) 1.05E-12 Interferon-gamma (IFNG) 3,86E-12 Forkhead Box O1 (FOXO1) 5.20E-12 Interleukin 1 beta (IL1B) 3.73E-11 CD3 8.31E-11 Nuclear factor kappa B (NFKB) 1.01E-10 Signal Transducer and Activator Of Transcription 5A (STAT5A) 1.13E-10 Interferon alpha 1.34E-10 Amyloid Precursor Protein (APP) 2.12E-10 CSF2 5.18E-10 Forkhead Box O3 (FOXO3) 5.56E-10 B-cell lymphoma 2 (BCL2) 5.69E-10 T Cell receptor (TCR) 9.56E-10 TNF Alpha Induced Protein 3 (TNFAIP3) 1.42E-09 Toll-like receptor 4 (TLR4) 1.65E-09 TGFB1 1.91E-09 TNF 2.24E-09 KITLG 2.240E-09 [80]Open in a new tab Diseases and Functions associated with EtOH, and small spleen were revealed via the core analysis of these 21 molecules were listed in [81]Supplementary Table 1, ([82]ST1). The top 10 diseases and functions were hematological system development and function, lymphoid tissue structure and development, tissue morphology, organ morphology, organismal development, cellular function and maintenance, cell death and survival, cellular development, cellular growth and proliferation, and hematopoiesis. Influence of EtOH on apoptosis As noted above, the core analysis on the 21 molecules has identified induction of apoptosis by HIV-1 (p=2.56E-09) as the top upstream canonical pathway. [83]Fig. 4A showed the association of the 21 overlapping molecules with the apoptosis node in QKB. All molecules except GLRA1 had a connection with apoptosis ([84]Fig. 4A). [85]Fig. 4B showed that simulation of exposure to EtOH, mimicking alcohol consumption, increased apoptosis via activating RORA, ALOX5, NFKBIA, ESR1, CASP8, MTOR, TLR7, ATM, CAMK4, FAS, CXCR4, FAS, EDNRB and inhibiting PRF1, IGHM, DRD2, IL15, BCL2, IFNG, respectively. Fig. 4. [86]Fig. 4. [87]Open in a new tab (A) Involvement of the overlapping molecules and upstream regulators associated with EtOH and small spleen phenotype with apoptosis. IPA connect tool revealed the association of the 21 overlapping molecules with apoptosis. All the molecules except GLRA1 showed association with apoptosis. (B) EtOH simulation activation increased the apoptosis via activating RORA, ALOX5, NFKBIA, ESR1, CASP8, MTOR, TLR7, ATM, CAMK4, FAS, CXCR4, FAS, EDNRB and inhibiting PRF1, IGHM, DRD2, IL15, BCL2, IFNG. (C) Influence of EtOH on upstream regulators and apoptosis. EtOH simulation activation increased the apoptosis via activating SP1, IL1B, TNF, TGFB1, and inhibiting the expression of IKBKB, and BCL2. (D) Quantitative illustration of the overlapping molecules in exposure to EtOH mediated activation of apoptosis showing the individual molecule changes to the overall change in apoptosis in response to EtOH exposure. The cumulative z-score of EtOH impact on apoptosis via the overlapping molecules was found to be 1.808, with a corresponding p-value of 0.0706 at a 95% confidence interval. (E) The z-scores for each of the upstream regulators involved in EtOH induced activation of apoptosis were also calculated. The overall z-score was found to be 3.61 corresponding to a p-value of 0.000306 in a two tailed distribution at a 95% confidence interval. In addition, to confirm the association of alcohol consumption in activation apoptosis signaling we employed the upstream regulators associated with the overlapping molecules. As shown in [88]Fig. 4C, the shortest paths between EtOH and apoptosis were constructed via the top 20 upstream regulators. Among the 20 upstream regulators, EtOH simulation exposure increased the expression of SP1, IL1B, TNF, and TGFB1, and inhibited the expression of IKBKB, and BCL2 leading to a concurrent increase in the apoptosis activation. Molecules including Il6, interferon alpha, NFKBIA, NFkB, and IFNG showed simulation exposure following EtOH exposure; however, findings suggesting their involvement in apoptosis were inconsistent with the state of the downstream molecules. Downstream Effect Analysis was then performed to quantify the contribution of each intermediary molecule in the above-mentioned networks. 11 molecules with a z-score between 0.3–0.9 and 8 molecules with −0.4- −0.8 were found to influence apoptosis. The cumulative z-score for the EtOH-induced activation of apoptosis was found to be 1.808 corresponding to a p-value of 0.0706 for a two-tailed distribution at a 95% confidence interval ([89]Fig. 4D). Similarly, the z-scores for each of the upstream regulators involved in EtOH induced activation of apoptosis was also calculated. The overall z-score was found to be 3.61 corresponding to a p-value of 0.000306 in a two-tailed distribution at a 95% confidence interval ([90]Fig. 4E). Quantitative characterization of the influence of EtOH exposure on proliferation of splenocytes The upstream regulators including TLR4, TGFB1, IL6, IFNG, STA5A, KITLG, CD3, APP, and TCR were found to be associated with proliferation of splenocytes ([91]Supplementary Figure 2A, [92]SF2A). The network map constructed between EtOH, upstream regulators, and splenocyte proliferation as shown in [93]Supplementary Figure 2B ([94]SF2B), showed the association between EtOH and splenocyte proliferation was mediated via IFNG, IL6, Interferon alpha, TGFB1, and TLR4 (SF2B). This was the premise leading to the investigation of the involvement of 21 overlapping molecules in EtOH use in related to the proliferation of splenocytes. MAP activation of EtOH mimicking exposure to EtOH showed that exposure to EtOH inhibited proliferation of splenocytes. [95]Fig. 5A showed that simulation of exposure to EtOH increasing expression of TGFB1 and decreasing expression of IGHM, IL15, and IFNG concurrently led to the inhibition of proliferation of splenocytes ([96]Fig. 5A). Fig. 5: [97]Fig. 5: [98]Open in a new tab (A) Influence of EtOH on 21 overlapping molecules associated with EtOH and small spleen phenotype and splenocyte proliferation. EtOH simulation activation increased the expression of TGFB1 and inhibited IGHM, IL15, and IFNG eventually leading to the inhibition of the proliferation of splenocytes. (B) Influence of EtOH on upstream regulators and proliferation of splenocytes. EtOH simulation exposure increased the expression of TGFB1 and inhibited the expression of STAT5B and interferon alpha leading inhibition of the proliferation of splenocytes. (C) Quantitative illustration of the overlapping molecules in exposure to EtOH mediated inhibition of proliferation of splenocytes. The cumulative z-score of EtOH impact on the proliferation of splenocytes was found to be −0.8148, with a corresponding p-value of 0.2075 at a 95% confidence interval. (D) The cumulative z-score for the contribution of upstream regulators in EtOH-mediated inhibition of the proliferation of splenocytes was −1.365 with a p-value of 0.172 in a two tailed distribution. Further, we also studied the association of upstream regulators in EtOH-mediated inhibition of proliferation of splenocytes. EtOH simulation exposure increased the expression of TGFB1 and inhibited the expression of STAT5B and interferon alpha leading to the overall inhibition of the proliferation of splenocytes ([99]Fig. 5B). The z-scores for the association of overlapping molecules and upstream regulators in EtOH-mediated inhibition of proliferation of splenocyte were performed. The cumulative Z-score for the overlapping molecules in EtOH induced inhibition of proliferation of splenocyte was found to be −0.8148, with a corresponding p-value of 0.2075 at a 95% confidence interval ([100]Fig. 5C). The z-score for the involvement of the upstream regulators was found to be −1.365 with a p-value 0.172 in a two-tailed distribution ([101]Fig. 5D). Quantitative characterization of the influence of exposure to EtOH on migration of cells. Core analysis of the overlapping molecules associated with EtOH, and small spleen phenotype revealed that cellular movement (p-value: 4.46E-17–1.86E-08) is one of the top 20 diseases and function associated ([102]Supplementary Table 1, [103]ST1). The top twenty upstream regulators were found to be associated with migration of cells ([104]Supplementary Figure 3A, [105]SF3A). A network map constructed between EtOH, the 20 upstream regulators, and migration of cells revealed the association between EtOH and cell migration ([106]Supplementary Figure 3B, [107]SF3B). Simulation of the effects of exposure to EtOH on migration of cells in the network map between EtOH, 21 overlapping molecules, and migration of cells showed that exposure to EtOH increased cell migration. [108]Fig. 6A showed that exposure to EtOH activated expression of FAS, CXCR4, NFKBIA, RORA, TGFB1, EDNRB, ALOX5, CASP8, MTOR, ESR1, ATM, CAMK4, TLR7, and PEBP1 and inhibited expression of PRF1, BCL2, IGHM, IFNG, IL15, DRD2 and GLRA1 leading to elevation of cell migration. In parallel, we also studied the influence of the upstream regulators associated with 21 overlapping molecules on the migration of cells. EtOH simulation exposure increased the expression of NFKBIA, IL6, TNF, TGFB1, NFKB, and IL1B and inhibited the expression of STAT5B leading to increased migration of cells ([109]Fig. 6B). Fig. 6. [110]Fig. 6 [111]Open in a new tab (A) Influence of EtOH on cell migration: EtOH simulation activation activated FAS, CXCR4, NFKBIA, RORA, TGFB1, EDNRB, ALOX5, CASP8, MTOR, ESR1, ATM, CAMK4, TLR7, and PEBP1, inhibited PRF1, BCL2, IGHM, IFNG, IL15, DRD2 and GLRA1 eventually leading to activation of cell migration. (B) Influence of EtOH on upstream regulators and migration of cells. EtOH simulation exposure increased the expression of NFKBIA, IL6, TNF, TGFB1, NFKB, and IL1B and inhibited the expression of STAT5B leading to increased migration of cells. (C) Quantitative illustration of the overlapping molecules in exposure to EtOH mediated cell migration. The cumulative z-score of EtOH impact on cell proliferation was found to be 10.057, with a corresponding p-value less than 0.00001 at a 95% confidence interval. (D) The cumulative z-score for the contribution of upstream regulators in EtOH-mediated activation of migration of cell was 3.372 with a P-value of .000746 in a two tailed distribution. Involvement of the overlapping molecules and upstream regulators was quantified using downstream effector analysis as described above. [112]Fig. 6C showed the cumulative z-score of EtOH impact on the migration of cells via the 21 overlapping molecules, the z-score was found to be 10.057, with a corresponding p-value less than 0.00001 in a two tailed distribution ([113]Fig. 6C). Similarly, the z-score for the involvement of upstream regulators in the EtOH modulation of migration of cells were quantified. The overall z-score was found to be 3.372 with a p-value of 0.000746 in a two tailed distribution ([114]Fig. 6D). The integrated network of decreased size of spleen mediated via apoptosis, proliferation of splenocytes, and migration of cells following EtOH exposure. The direct involvement of EtOH with small spleen was shown in [115]Fig. 7. EtOH Simulation of exposure to EtOH was shown to decrease the spleen size, mainly mediated via apoptosis pathway ([116]Fig. 4B), proliferation of splenocytes ([117]Fig. 5B) and migration of cells ([118]Fig. 6B). [119]Fig. 7 showed the involvement of the 21 molecules associated with EtOH and small spleen size in the network of apoptosis, proliferation of splenocytes and migration of cells. Simulation of exposure to EtOH in the network map decreased the size of the spleen via elevation of apoptosis, increased migration of cells, and decreased proliferation of splenocytes. Fig. 7: [120]Fig. 7: [121]Open in a new tab influence of EtOH on spleen atrophy: Simulation activation of EtOH using MAP tool has activated the spleen atrophy via inhibiting splenocyte proliferation, increasing apoptosis and cell migration, mediated through the 21 overlapping molecules. Discussion The present network meta-analysis studied how alcohol consumption may cause spleen atrophy. Our meta-analysis revealed alcohol consumption activated apoptosis signaling increases cell migration and inhibits the proliferation of splenocytes, which, in turn, leads to an increase in spleen atrophy. First, we found that 26 molecules with documented responses to EtOH were associated with the phenotype of small spleen. These molecules were also associated with abnormal white pulp and red pulp morphology of the spleen, abnormal splenic cell ratio, decreased splenocyte number, abnormal spleen physiology, increased splenocyte apoptosis, and reduced splenocyte proliferation ([122]Table 1). GO enrichment analysis showed that these molecules were enriched in multiple molecular biological processes including immune cells apoptosis, activation, proliferation, differentiation, migration, the structure of the spleen, immune response, cytokine secretion, oxidative stress, lipid metabolism, calcium ion transport, stress and glucocorticoid reaction, nutrition and protein metabolism, autophagy, protein phosphorylation. Results from KEGG, REACTOME, Wiki Pathways enrichment analysis, and IPA core analysis show that these molecules are mainly involved in pathways related to cell apoptosis, proliferation, senescence and migration, immune responses (T cell, B cell, NK cell, cytokine, chemokine, pattern recognition receptors, etc), metabolism, oxygen homeostasis, and calcium signaling ([123]Table 1). Using the Molecular Activity Predictor (MAP) tool of IPA to simulate the effect of increased exposure to EtOH on these 26 molecules, we found that increased exposure to EtOH elevated expression of Esr1, Casp8, Pebp1, Atm, Ednrb, Rora, Mtor, Fas, Alox5, Camk4, Cxcr4, Nfkbia, Tgfb1, and Tlr7 and inhibited expression of Prf1, Il15, Ifng, Bcl2, Glra1, Drd2, and Ighm. Concurrent effects of molecules activation and inhibition led to decreased spleen size by regulation of cell death (apoptosis), cell proliferation, and cell migration/emigration (immune cell redistribution) via multiple mechanisms including stress hormone, nutritional deficiency, oxidative stress, circadian rhythms, and epigenetics. The influence of alcohol on the key pathways was quantified using Krämer analysis, a positive z-score indicates alcohol-mediated activation of the pathway, and a negative z-score indicates inhibition. IPA’s core analysis of the overlapping molecules associated with EtOH, and small spleen phenotype revealed that apoptosis signaling is one among the top upregulated pathways. Previous studies have reported that chronic alcohol consumption renders cells more vulnerable to undergoing apoptosis. This is mainly due to increased levels of reactive oxygen species (ROS) following alcohol consumption, which plays a crucial role in apoptosis signal transduction ([124]Del Re et al., 2007). Alcohol can induce apoptosis of spleen cells directly or indirectly through other mechanisms. Slukvin and Jerrells have shown that alcohol at concentrations of 0.4%−2% in culture directly induces apoptosis of splenic T and B cells, with a more profound effect on B cells than on T cells ([125]Slukvin and Jerrells, 1995). The spleen is innervated with sympathetic nerves and controlled by the adrenomedullary system and is sensitive to stress ([126]Li et al., 2018). Alcohol abuse can induce a stress response that increases glucocorticoid levels, leading to a significant increase in the percentage of apoptotic cells in the spleen ([127]Collier et al., 1998), and norepinephrine level, which also can induce lymphocyte apoptosis ([128]Fitzgerald, 2013). The understanding of the significance of the overall z-score is critical; we have employed Downstream Effect Analysis to calculate the local z-score of the influence of EtOH on individual molecules and in turn how it affects the physiological function. The overall z-score for EtOH simulation exposure induced activation of apoptosis was 1.808 with corresponding to a p-value of 0.0706 at a 95% confidence interval ([129]Fig. 4). The molecules CASP8, ATM, TGFB1, PEBP1, TLR7, and CXCR4 showed strong positive z-scores, suggesting that alcohol mediated activation of these molecules contributes to activation of apoptosis signaling. Molecules including DRD2, IL15, IGHM, and BCL2 have shown strong negative z-score, indicating that alcohol-mediated inhibition of these molecules leads to activation of apoptosis. Our studies found that CASP8 was activated by EtOH exposure and influenced apoptosis with a -score of 0.8. CASP8 plays a central role in the execution phase of cell apoptosis. Activation of CASP8 further activates CASP3 and the death signaling pathway. Induction of apoptosis is one molecular mechanism underlying splenic atrophy. Previous studies have reported a close association between spleen atrophy and apoptosis ([130]Offner et al., 2006b, [131]Offner et al., 2006a). Chronic alcohol exposure has also been reported to induce apoptosis of spleen cells in white pulp and decrease in white pulp size, which is associated with P53 expression ([132]Eid et al., 2000), and induce splenic NK cell apoptosis, which is associated with decreased IL-15/IL-15R alpha signaling ([133]Zhang and Meadows, 2009). Although not obtained from the spleen, human T cells harvested from the peripheral blood of healthy subjects after a binge alcohol drinking show enhanced T cell apoptosis through the mitochondrial pathway, manifested by decreased bcl-2 expression and activation of caspase-3 ([134]Kapasi et al., 2003). In addition, alcohol can disrupt the splenic oxidative/antioxidant balance leading to oxidative stress, which has been shown to play a key role in immunotoxicity-induced splenic lymphocyte apoptosis ([135]Erukainure et al., 2012). An important characteristic of T and B cells is the proliferation response after activation. The stress response induced by alcohol can decrease the proliferation response of splenic cells to mitogens ([136]Jimenez-Ortega et al., 2011). Chronic alcohol consumption can lead to nutritional deficiencies, and malnourished animals show decreased splenic proliferation rates, with decreased IL-2 production and increased IL-10 production, and decreased STAT-1 expression and increased STAT-3 expression ([137]Mello et al., 2014). [138]Fig. 5 demonstrates the influence of EtOH simulation exposure on proliferation of splenocytes. Our result suggests that EtOH simulation exposure inhibited the proliferation of splenocytes. The overall z-score was found to be −0.8148 suggesting the inhibitory effect of EtOH on proliferation splenocytes. The structure of the organ is the basis of its function. The decrease in spleen size is often accompanied by a change in function. Excessive use of alcohol has an obvious influence on the physiological function of the spleen. In addition to the above-mentioned influence of immune cell proliferation, leading to the loss of immune cells, change the normal ratio of different immune cells and the distribution of systemic immune cells, alcohol, and its metabolite acetaldehyde also affect antigen presentation, T and B cell-mediated immune response, NK cytolytic activity, and production of a variety of cytokines and chemokines, resulting in local and systemic dysfunction of immune function ([139]Mikszta et al., 1995, [140]Zabrodskii et al., 2002, [141]Song et al., 2002, [142]Boyadjieva et al., 2004, [143]Boyadjieva et al., 2001, [144]Chen et al., 2006, [145]Dokur et al., 2003, [146]Liu et al., 2011). In addition, alcohol may affect the immune function of the spleen by altering circadian rhythms or through epigenetic mechanisms ([147]Sureshchandra et al., 2019, [148]Curtis et al., 2013). It is noteworthy that the adolescent spleen shows a relative sensitivity to alcohol ([149]Tonk et al., 2013). We also found a strong EtOH-mediated inhibition of IL15 ([150]Fig. 6A). IL15 is crucial for immune cell survival, differentiation, and proliferation ([151]Caligiuri, 2008). Anton et al., have reported that NK cell proliferation is induced by IL15. They also found that activation of inhibitory receptors including CD94-NKG2A and KIR2DL shows decrease in IL15-induced proliferation ([152]Anton et al., 2015). The naïve T and B cells produced from the thymus and bone marrow enter the bloodstream and migrate to peripheral lymphoid organs, where they can be recirculated in different lymphoid organs through the vascular and lymphatic systems. Since the spleen is in the blood circulation pathway, besides T and B cells, many other cells in the spleen can also be redistributed through the blood, thus affecting the total number of cells in the spleen. The effects of EtOH on the immune system are varied and include the loss of lymphoid cells from the central and peripheral organs, and glucocorticoid has been suggested to be responsible for most of the cell loss from the thymus, spleen, mesenteric lymph nodes, and Peyer’s patches in association with EtOH consumption ([153]Sibley and Jerrells, 2000, [154]Padgett et al., 2000). Extensive lymphocyte loss will result in reduced cell migration to the spleen. Our results were consistent with the above-mentioned findings. Simulation exposure of EtOH showed a potential increase in the migration of cells with a positive z-score of 10.057 with a corresponding p-value less than 0.00001 ([155]Fig. 6C). EtOH simulation exposure increased the expression of FAS, CXCR4, NFKBIA, RORA, TGFB1, EDNRB, ALOX5, CASP8, MTOR, ESR1, ATM, CAMK4, TLR7 and inhibited PRF1, BCL2, IGHM, IFNG, IL15, DRD2, and GLRA1 contributing to increased migration of cells. [156]Fig. 6C showed the individual z-score of each molecule, NKBIA, ALOX5, and PRF1 showed the highest z-score ≈1. ALOX5 and its activating protein FLAP are expressed in most of the B-cells. ALOX5 pathway plays a crucial role in the migration and adherence of the cells and contributes to the pathological characteristics of B-cell lymphoma ([157]Mahshid et al., 2009). Xia et al. have shown that CRISPR-Cas9 mediated knockdown of and pharmacological inhibition of the ALOX5 gene inhibits cell migration and adherence in the MCL cell line ([158]Xia et al., 2021). In addition, alcohol-induced splenic cell loss and ratio imbalance will lead to changes in the signaling network of chemokines, cytokines, and adhesion molecules produced by the spleen cells, thus affecting the normal recruitment and migration of cells, resulting in abnormal organization of splenic compartments ([159]Hermida et al., 2018). As an important immune organ, the close relationship between the spleen and other organs has been reflected by some new terms, such as the liver-spleen axis (Hepato-splenic axis), spleen-gut-microbiota axis, central nervous system (CNS)-spleen axis, and cardiosplenic axis ([160]Aoyama et al., 2017, [161]Wei et al., 2021, [162]Khan et al., 2021, [163]Keramida et al., 2018, [164]Dunford et al., 2017). The spleen has been considered the center of the blood defense system, an important player in lipid metabolism and endocrine functions, the reservoir of immune cells and the hub of inflammatory cells, the crucial hub allowing the interaction of different systems, and the key participant in the brain-gut-microbiota axis ([165]Lori et al., 2017, [166]Tarantino et al., 2013, [167]Ai et al., 2018, [168]Fernandez-Garcia et al., 2020, [169]Weiberg et al., 2018, [170]Wei et al., 2021, [171]Kashimura, 2020). In addition to its well-known role in increasing susceptibility to infection and interfering with its recovery, spleen dysfunction has been associated with the onset, progression, and prognosis of a variety of diseases and conditions, such as autoimmune disorder, nonpathogenic-driven chronic systemic inflammation and related diseases, digestive system diseases, cardiovascular and metabolic diseases, brain and mental disorders, and tumors, etc ([172]Di Sabatino et al., 2018, [173]Giuffrida et al., 2020, [174]Lori et al., 2017, [175]McKim et al., 2016, [176]Said et al., 2022, [177]Emami et al., 2015, [178]Bellinger and Lorton, 2018). Mehran Midia even suggests it is time to consider taking a fresh look at the spleen and envision novel splenic interventions in patients with cancer, metabolic syndrome, chronic liver disease, etc ([179]Midia, 2015). Therefore, considering that alcohol can cause pathological or pathophysiological changes in most organs of the human body ([180]Dguzeh et al., 2018), this meta-analysis not only helps to reveal the mechanism of alcohol damage to the spleen and the extensive harm of alcohol to human health but also provides a new research approach for alcohol-induced multi-organ damage. In summary, through in-depth bioinformatics analysis, our meta-analysis summarized the influence and possible mechanism of alcohol on spleen structure and function, as well as its consequences on health, thus providing data support for alcohol prohibition and clues for further alcohol toxicology research. Limitations Like other experimental methodologies, there are limitations and critical issues while performing network meta-analysis. Molecules associated with small spleen phenotype or spleen atrophy were not specifically curated in the QKB. Therefore, we have employed MP Ontology to collect these molecules. These molecules collected by MP Ontology were taken into QKB for further analysis. This approach enabled us to crosslink two datasets to expand the scope of our studies. Alcohol induction of spleen atrophy has been understudied. Wong et al., 2008 reported no significant change in the spleen size of chronic alcoholics (Wong, Arango-Viana et al. 2008). However, Kashani et al., 2015 reported significant reduction in spleen size of alcoholic cirrhosis patients (Kashani, Salehi et al. 2015). Similarly, our previous study using F344 rat model, we reported the differential decrease in the spleen size of these animals given binge treatment with EtOH ([181]Liu, Connaghan et al. 2016). Taken these together, it is essential to investigate the possible effects of alcohol on spleen size by employing multiples approaches including in-silico, in-vivo, in-vitro and their combinations. Our network meta-analysis study in-silico has identified the molecular mechanisms underlying the association of alcohol consumption with spleen atrophy to supplement our study in 2016 ([182]Liu, Connaghan et al. 2016). The information available in the databases is relatively minimal due to the lack of experimental findings in this area. However, understanding the significance of the molecules and signaling pathways as reported in this present meta-analysis has provided a systemic view of the alcohol’s impact on the spleen architecture. Our results show that alcohol consumption-induced spleen atrophy is mainly mediated through the signaling of apoptosis, proliferation, and migration. The present study only analyzed the overlapping molecules associated with EtOH and those with small spleen. In addition to the effects produced by overlapping molecules, molecules associated with EtOH may also exert their effects through other small spleen-related molecules besides overlapping molecules, which would be the target of our future studies. Conclusion The present study employed QIAGEN Ingenuity Pathway Analysis (IPA) and QIAGEN Knowledge Base (QKB) to conduct network meta-analyses to investigate mechanisms underlying alcohol-induced spleen atrophy. The findings suggest that EtOH consumption increases the spleen atrophy mediated through increasing apoptosis signaling, migration of cell, and inhibiting proliferation of splenocytes. Supplementary Material Supinfo [183]NIHMS1946905-supplement-Supinfo.pdf^ (451.8KB, pdf) Acknowledgement: