The binding of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein to its cellular receptor, the angiotensin-converting enzyme 2 (ACE2), causes its downregulation, which subsequently leads to the dysregulation of the renin–angiotensin system (RAS) in favor of the ACE–angiotensin II (Ang II)–angiotensin II type I receptor (AT1R) axis. AT1R has a major role in RAS by being involved in several physiological events including blood pressure control and electrolyte balance. Following SARS-CoV-2 infection, pathogenic episodes generated by the vasoconstriction, proinflammatory, profibrotic, and prooxidative consequences of the Ang II–AT1R axis activation are accompanied by a hyperinflammatory state (cytokine storm) and an acute respiratory distress syndrome (ARDS). AT1R, a member of the G protein-coupled receptor (GPCR) family, modulates Ang II deleterious effects through the activation of multiple downstream signaling pathways, among which are MAP kinases , receptor tyrosine kinases , and nonreceptor tyrosine kinases ), and nicotinamide adenine dinucleotide phosphate (NADPH) oxidase. COVID-19 is well known for generating respiratory symptoms, but because ACE2 is expressed in various body tissues, several extrapulmonary pathologies are also manifested, including neurologic disorders, vasculature and myocardial complications, kidney injury, gastrointestinal symptoms, hepatic injury, hyperglycemia, and dermatologic complications. Therefore, the development of drugs based on RAS blockers, such as angiotensin II receptor blockers (ARBs), that inhibit the damaging axis of the RAS cascade may become one of the most promising approaches for the treatment of COVID-19 in the near future. We herein review the general features of AT1R, with a special focus on the receptor-mediated activation of the different downstream signaling pathways leading to specific cellular responses. In addition, we provide the latest insights into the roles of AT1R in COVID-19 outcomes in different systems of the human body, as well as the role of ARBs as tentative pharmacological agents to treat COVID-19. In late autumn 2019, the global health and economic management were dominated by a novel virus, reported from the city of Wuhan (China), designated as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) . The nucThe membrane-bound angiotensin-converting enzyme 2 (ACE2), a significant component of the renin–angiotensin system (RAS), is the human site of SARS-CoV-2 entry into host cells ,7. ACE2 The RAS is a complex and dynamic two-axis-molecular cascade present in different organ systems, which has an essential function in neural, pulmonary, renal, cardiovascular, and immune homeostasis . The claThe physiological effects of Ang II are mediated mainly by AT1R and Ang II type 2 receptor (AT2R) ,13,14. AThe COVID-19 pandemic has prompted researchers to look for viable treatments, including vaccinations, antiviral medications that target the SARS-CoV-2 virus, and methods to manage disease pathology and complications caused by interactions with host components. Presently, 46.6% of the world population has been fully vaccinated . The mosOn that account, the present review article focuses on the distribution and function of AT1 receptors, with the aim to help determine the pathways involved in their function. Certainly, their activation in response to SARS-CoV-2 infection, as well as their negative consequences on several human body systems, is emphasized. Additionally, the AT1R blockers and their efficacy in the treatment of COVID-19 are discussed in this review.Until now, four types of Ang II receptors have been identified  among which the AT1R is the most well studied . In factThe extracellular part of AT1R consists of the N-terminal domain, ECL1 (Glu91-Phe96) linking helices II and III, ECL2 (His166 to Ile191) linking helices IV and V, and ECL3 (Ile270 to Cys274) linking helices VI and VII . This exThe intracellular part of AT1R contains ICL1  linking helices I and II, ICL2  linking helices III and IV, ICL3 (Leu222 to Asn235) linking helices V and VI, and the C-terminal helix VIII ,26. ModiAng II binds to a specific binding domain of AT1R. In fact, the carboxyl group of Phe8 in Ang II binds to Lys199 in the transmembrane domain (TM5) of the AT1R. The Ang II binding to AT1R is further stabilized by two other interactions; the first between His183 in ECL2 and Asp1 in Ang II, and the second between Asp281 in TM7 and Arg2 in Ang II. Moreover, interactions of His256, Val254, and Phe259 in TM6 with the aromatic side chain of Phe8 of Ang II, and interaction of Asn111 in TM3 with Tyr4 of Ang II have been postulated to contribute to Ang II docking with the receptor . As a reAlthough AT1R is typically activated by Ang II, additional ligands such as AT1R autoantibodies and mechanical stress have been reported to activate this receptor in a variety of clinical conditions such as hypertension, pre-eclampsia, and cardiac hypertrophy .Polymorphisms in GPCRs were shown to be associated with different phenotypes and to predispose to certain types of diseases . More thThe AT1R gene is polymorphic, and several variants have been discovered. The human AT1R gene is located on chromosome 3 (3q21–q25) and is constituted of four introns and five exons, with a >55 kb length ,30,31,32The study of Izmailova et al. on the Ukrainian population showed an association between COVID-19 severity and the A1166C polymorphism. When compared with A-allele carriers, C allele carriers had significant COVID-19 pathogenicity, as well as higher oxygen requirement due to cardiovascular problems . In factp = 0.008), as well as a significantly higher mortality rate (p = 0.008). Increased AT1R expression and receptor activity were seen in homozygous patients, which could explain the RAS’s amplified neurohormonal activation [Amir et al. examined the AT1R A1166C polymorphism in patients with systolic heart failure and its relation to clinical outcomes. It was shown that the CC genotype was associated with more advanced disease and severe abnormalities of the renal function  [2O2 [Activation of the AT1R by Ang II leads to G protein-mediated signaling pathways in a tissue-specific manner. Signal transduction occurs via a series of intracellular second messengers, including phospholipases, calcium signaling, and PKC ,32 Figu. After As “JAK”) . The caps “JAK”) . JAK/STAs “JAK”) . In facts “JAK”) . The JAKs “JAK”) . In addis “JAK”) . The NADK”) [2O2 . AT1R alK”) [2O2 . FurtherK”) [2O2   protective, in which the adaptive immune response plays an important role in eradicating the virus early in the disease and (2) damaging, in which immune responses can cause tissue damage by over-releasing proinflammatory cytokines . The sevIt is well known that these features are induced by the dysregulated RAS. Ang II has been shown to play a role in regulating the immune system . In factThe Ang II–AT1R axis activates the NF-κB/IL-6/STAT3 pathway, which can trigger the amplification of IL-6 via a positive feedback loop of NF-κB and STAT3 coactivation ,37. The It was discovered that inhibiting NF-κB reduced the release of proinflammatory cytokines and chemokines and interfered with their harmful effects, such as multiorgan tissue damage and ARDS . Ang II Liu et al. have studied, retrospectively, the ability of IL-6, CRP, and procalcitonin (PCT) to predict mild and severe cases in 140 patients diagnosed with COVID-19. They found that IL-6, CRP, and PCT levels increased by 67.9%, 65.0%, and 5.7%, respectively. The proportion of patients with elevated levels of these proteins in the severe group was significantly higher than in the mild group . TawinwuMoreover, Ang II increases ROS production through AT1R-dependent induction of NADPH oxidase (NOX), mostly Nox2 and Nox4 . ROS oveFinally, it is noteworthy to mention that obesity is considered a risk factor for COVID-19 severity, and this could be explained by the role of Ang II–AT1R. In fact, it has been shown that human-cultured adipocytes promote the production of IL-6 and IL-8 by the NF-κB-mediated pathway. In addition, it was demonstrated that plasma levels of IL-6 in obese individuals are closely correlated to body mass index (BMI) .SARS-CoV-2 is known to be transmitted mainly through the respiratory tract by direct or indirect contact . The higWith all of its proinflammatory, destructive, and profibrotic properties, Ang II–AT1R signaling in the lung increases vascular permeability ,10 by reARDS is the most severe form of acute lung injury, with a mortality ranging from 30% to 60% . ARDS isHuang et al. observed upper respiratory tract symptoms such as rhinorrhea, sneezing, and sore throat in patients with SARS-CoV-2. In addition, all of the 41 patients involved in the study had pneumonia, and the common complication was ARDS (12 of 41 patients). The chest CT scan images of 40 patients showed a bilateral involvement with multiple lobules and areas of consolidation. In four patients, invasive mechanical ventilation was required; two of them had refractory hypoxemia and received extracorporeal membrane oxygenation . Severe Many COVID-19 patients presented with one or more cardiovascular disorders or developed cardiac damage later in their infection period, making cardiovascular diseases account for the majority of the severe cases .Disseminated intravascular coagulation and pulmonary embolisms were detected in COVID-19 patients . In addiIn the cardiovascular system (CV) system, AT1R overactivity has been associated with the development of several pathological conditions, including hypertension, vascular inflammation, atherosclerosis, and heart failure ,56. FurtTaken together, overstimulation and imbalance of the RAS can be associated with CV responses observed in COVID-19 patients, including hypertension, inflammation atherosclerosis, thrombosis, and myocarditis. Both the CV and systemic ACE/ACE2 ratio could be crucial in the evolution of COVID-19 individuals, favoring either the cardiotoxic ACE–Ang-II–AT1R axis or the cardioprotective ACE2/Ang-(1–7)/MasR pathway . TherefoARBs effectively block AT1R, antagonizing the main Ang-II actions and exhibiting protective pleiotropic effects against hypertension and CV inflammation, fibrosis, and thrombosis . In COVIRegarding the complications of SARS-CoV-2, it is important to consider its effects on the brain. Loss of taste and smell was largely seen among COVID-19 patients. In fact, taste bud cells have an average life span of 10 days and renew continuously from a population of stem cells in the oral epithelium. Proinflammatory cytokines such as TNF-α, IFN-γ, and IL-6 activated through the AT1R, can impede stem cell proliferation, and may also reduce the lifespan of mature taste bud cells. Therefore, these proinflammatory cytokines may lead to dysgeusia. Anosmia may be caused by direct infection and disruption of the olfactory nerve by COVID-19. As a result, it is believed that infection of support cells and regenerative stem cells, rather than direct impacts on the olfactory nerve, may cause anosmia. The loss of supporting cells around olfactory neurons impairs their ability to function .The consequences of SARS-CoV-2 on CNS have been studied, and it has been discovered that this virus produces complications such as seizures and cerebral hemorrhage . IncreasIn this context, Ang II could be an important element in determining the link between SARS-CoV-2 and the brain difficulties it causes. After SARS-CoV-2 binds to ACE2, the virus causes endocytosis of this receptor .Low levels of ACE2 in the cells’ surface increase Ang II levels, as reported in patients with COVID-19 . NormallMoreover, this condition can explain SARS-CoV-2-induced cerebral hemorrhage in COVID-19 patients, as the association between early BBB breakdown and hemorrhage has been reported in patients treated with thrombolytic drugs . HoweverOn the other hand, increased levels of Ang II may contribute to neuronal loss in different parts of the brain. It has been revealed that Ang II causes dopaminergic neurons death and losartan, an AT1R antagonist, protects these neurons against apoptosis .Ang II–AT1R binding contributes to increased sympathetic tone . AT1R isACE2 overexpression in the hypothalamus has substantial antihypertensive and SNS dampening effects, raising Ang (1–7), compared with Ang II, and thus having therapeutic benefits in mice models of brain damage and stroke . The effp = 0.034, and 6.8% vs. 2.1%, p = 0.034, respectively) [The presence of SARS-CoV-2 RNA in stool samples of some patients and more frequently in those with diarrhea suggests that asymptomatic patients may have infectious viral particles in their stools, which could be a source of infection to others . The digctively) .The GI RAS regulates a number of physiological functions in the intestine, including electrolyte homeostasis, digestion, peptide transport, glucose, salt, and water absorption, GI motility, and secretion through the intestinal epithelium ,89,90. TGenerally, the activation of the ACE1–AT1R axis induces vasoconstriction and is involved in the induction of apoptosis, vascular remodeling, atherosclerosis, and inflammation ,99. MoreGut dysbiosis is an important issue that should also be taken into consideration in SARS-CoV-2 infection due to its potential link in the disease progression. This pathology is found in several COVID-19 risk groups such as diabetic patients, elderly, and immunocompromised patients .It has been shown that the colonic RAS induces colonic inflammation by stimulating TH17 activation. In fact, within the colonic mucosa, Ang II induces colitis via AT1R via the JAK2/STAT1/3 pathway. By increasing the release of TH17-polarizing cytokines (such as Il-6 and TGF-1) from colonic epithelial cells via the JAK/STAT pathway, Ang II promotes TH17 polarization both directly and indirectly . When SAMoreover, SARS-CoV-2 infection may lead to degeneration of the gut–blood barrier, driving to systemic propagation of bacteria and endotoxins and resulting in a septic shock . InteresFinally, SARS-CoV-2 may infect hepatocytes, causing an increased inflammatory response, as well as thromboembolic events that culminate in liver damage . In factmacula densa cells become activated in response to a lower sodium load in the distal convoluted tubule [The juxtaglomerular (JG) cells, afferent arterioles of the kidney, contain prorenin. While the latter is released constitutively in its inactive form, activation of JG cells causes the cleavage of prorenin to renin. Once these cells are active, blood pressure drops, beta-activation occurs, and d tubule ,103.In COVID-19 patients, kidney function abnormalities were observed, including hematuria (26.7%) and proteinuria (43.9%) . AcidosiAn evaluation of 12 autopsy cases revealed that 6 patients had viremia, and viral RNA was also detected in kidney tissues at concentrations exceeding viremia. As ACE2 is expressed in epithelial basal cells, glomerular parietal epithelial cells, and proximal tubules of the kidney, these findings show that SARS-CoV-2 travels through the bloodstream and directly injures the kidneys ,106.Increased activation of the RAS has been postulated to play a central role in the progression of chronic kidney disease (CKD) . In factFinally, it is noteworthy to mention that AT1R polymorphisms may influence the activity of Ang II detected in the kidneys. Healthy Caucasians carrying the A1166 C-allele (AC or CC) polymorphism showed an inferior basal glomerular filtration rate (GFR) and basal renal plasma flow and had increases in GFR following treatment with the AT1R blocker treatment .ARBs, a well-known antihypertensive drug group that blocks AT1R, have been postulated as tentative pharmacological agents to treat COVID-19-induced lung inflammation . TherefoThere is a debate on the use of RAS inhibitors such as ARBs and ACEi in SARS-CoV-2 infection . The rolSome researchers speculated that RAS inhibitors would contribute to a higher susceptibility and severity of SARS-CoV-2 infection due to observations that elderly patients with CVD, for whom ARBs and ACEi are routinely prescribed, were at a higher risk for more severe SARS-CoV-2 infection ,127. MorSoleimani et al. investigated the impact of ARBs on hospitalized hypertensive patients (48%) and found that they did not worsen clinical outcomes after COVID-19 infection . AdditioThe production of SARS-CoV-2 particles in supernatant of ARB-pretreated infected Vero E6 cells was evaluated at 24, 48, and 72 h.p.i. An increase in viral RNA expression in 24 h.p.i was significant for three of the ARBs used—azilsartan, eprosartan, and irbesartan. Interestingly, these compounds were likewise effective, positive modulators of the ACE2 protein expression. Moreover, the expression of viral proteins was evaluated inside of cells infected with SARS-CoV-2 after treatment with ARBs. These same three compounds were shown to induce the expression of S-protein, thus confirming the evidence that these compounds increase the multiplication of SARS-CoV-2. Results indicated that Vero E6 cells previously treated with ARBs for 72 h exhibit relative increases in ACE2 expression and SARS-CoV-2 production. Interestingly, scientists commented that Vero E6 cells were treated with ARBs without adding Ang II, which has been reported as a biomarker of severe COVID-19 . In addiConversano et al. sought to describe patient characteristics, ongoing pharmacological treatment at the time of admission, and any link between chronic usage of ACE inhibitors/ARBs and negative COVID-19 clinical outcomes in 191 patients. Data demonstrated that chronic treatment with ACE inhibitors/ARBs did not result in increased mortality, worse clinical presentation, or impairment of renal function in pneumonia patients who were hospitalized. Overall mortality was significant (22%), and age and comorbidities such as heart failure and chronic kidney disease were found as indicators of poor prognosis, all of which were consistent with previous reports from Wuhan, China .Data from retrospective studies from COVID-19 patients have provided some evidence to support that hypothesis ,145,146.n = 651). This design trial will provide valuable insights into the use of ARB in SARS-CoV-2 infected patients and may contribute to improved treatment recommendations for a large group of patients in this time of the global COVID-19 pandemic [Interestingly, the PRAETORIAN-COVID trial is a double-blind, placebo-controlled 1:1 randomized trial to assess the effect of ARB valsartan, compared with placebo on the occurrence of ICU admission, mechanical ventilation, and death within 14 days of randomization in hospitalized SARS-CoV-2–infected patients  showed high levels of binding affinity with RAS proteins, which could subsequently block RAS and its damaging effects induced by SARS-CoV-2 . Additio pathway .Two years have passed since the outbreak of SARS-CoV-2 and its variations, which have posed a threat to human health around the world. The virus, which targets RAS, dysregulates it, and causes hyperactivation of its classical deleterious axis (Ang II–AT1R), characterized mainly by vasoconstriction, hyperinflammation, oxidative stress, and cell proliferation, as well as proapoptotic, proangiogenic, prothrombotic, prohypertrophic, and profibrotic events. In reality, Ang II, a critical constituent of RAS, primarily coordinates its endocrine, autocrine, paracrine, and intracrine effects through AT1R, a GPCR that stimulates a number of signaling pathways, including MAPK, NF-B, ROS, tyrosine kinase, etc. This leads to a variety of disorders in many organs and tissues, such as the lungs, heart, gonads, skin, and brain. Finally, several research studies conducted so far have argued that, like vitamin D, RAS inhibitors  might be key elements to treat COVID-19, which affect renal, pulmonary, cardiovascular, immune, and brain functions.


Fibroblasts are important structural cells in synovium and play key roles in maintaining the synovial homeostasis. By single-cell RNA sequencing (scRNA-seq), subpopulation of synovium-resident cells has been reported to protect intra-articular structures from chronic inflammation and promote tissue repair. However, a significant number of researchers have concentrated on the role of fibroblasts in the progress of rheumatoid arthritis (RA) while few reports had described the contribution of distinct fibroblast subsets in the RA remission. It is helpful to understand the role of fibroblast subpopulations in the RA process to provide predictive biomarkers and address RA remission mechanisms. Here, we found HBEGF+ fibroblasts that contributed to RA remission by integrating scRNA-seq datasets and bulk RNA sequencing (bulk RNA-seq) datasets.Three single-cell RNA datasets of cells harvested from RA patients were processed and integrated by Seurat and Harmony R packages. After identifying cell types by classic marker genes, the integrated dataset was used to run CellChat for analysis of cell-cell communication. Specially, EGF signaling pathway was found and HBEGF+ fibroblasts were identified based on HBEGF expression. Differential expressed genes of HBEGF+ were shown in heatmap and volcano plot and used to run gene ontology (GO) enrichment analysis. Next, bulk RNA-seq datasets of synovium under different conditions , rheumatoid arthritis, before and after classical treatment) were compared to show expression change of HBEGF and gene markers that are mainly expressed by HBEGF+ fibroblasts such as CLIC5, PDGFD, BDH2, and ENPP1. Finally, two single-cell RNA sequencing datasets of synovial cells from mice were integrated to identify Hbegf+ fibroblasts and calculate the population of Hbegf+ fibroblasts under different joint conditions , and remission of STA).After integrating three single-cell RNA sequencing datasets, we identified 11 clusters of synovial cells, such as fibroblasts, mural cells, endothelial cells, CD4+ T cells, CD8+ T cells, natural killer cells, synovium macrophage, peripheral blood macrophages, plasma cells, B cells, and STMN1+ cells. We found fibroblasts had an extensive communication network with other clusters and interacted with synovial macrophages through EGF signaling pathway via analysis of cell-cell communication between synovial cells. HBEGF, ligand to EGF signaling pathway, was highly expressed by a subset of fibroblasts and macrophages, and EGFR, receptor to EGF signaling pathway, was highly expressed by fibroblasts and meniscus cells. Moreover, HBEGF was downregulated under RA state and it had an increase after classical treatment. We then defined fibroblasts with high expression of HBEGF as HBEGF+ fibroblasts. In addition, we also found that HBEGF+ fibroblasts highly expressed CRTAC1, ITGB8, SCARA5, THBS4, and ITGBL1, genes relative to encoding extracellular matrix proteins and engaged in positive regulation of cell migration and motility, cellular component movement, and cell growth by GO enrichment analysis. We eventually identified HBEGF+ fibroblasts specially expressed CLIC5, PDGFD, BDH2, and ENPP1, which positively correlated with the expression of HBEGF. Moreover, the expression of CLIC5, PDGFD, BDH2, and ENPP1 was downregulated under RA state and elevated by classical therapy. On the contrary, the HBEGF+ macrophages specially expressed SLAMF8, GK, L1RN, and JAK2, which negatively correlated with the expression of HBEGF. The expression was upregulated in SLAMF8, GK, L1RN, and JAK2 under the RA state, whereas it was decreased after classical treatment. In mice, the number of Hbegf+ fibroblasts was reduced in the RA synovium but increased in the RA remitting synovium.HBEGF+ fibroblasts play a role in the remission of rheumatoid arthritis, and HBEGF has potential to become a novel biomarker for prediction of RA progress. Moreover, MerTKposCD206pos synovial tissue macrophages showed the biofunction including mediator profile resolution and repair responses [Rheumatoid arthritis (RA) is a chronic and systemic autoimmune disease. It is characterized by chronic synovitis and progressive articular damage –3. Accuresponses . In miceesponses . These tposCD206pos synovial tissue macrophages and engage in tissue repair have not been reported in detail [Fibroblasts are important structural cells in the synovium and have key roles in maintaining the homeostasis. Previous literatures have reported that fibroblasts had an important role in RA pathological processes . Recent n detail . FurtherThe total of 13 datasets, including 7 single-cell transcriptomics datasets and 6 bulk RNA-seq datasets, were collected from public datasets Gene Expression Omnibus (GEO) and NIH IMMPOR , THY1, MCAM , CD34, VWF , CD2, CD4 (CD4+ T cells), CD8A, GNLY, GZMB (CD8+ T cells), LTB, CD3D , VSIG4, CD163 (synovium macrophages), CD68, LYZ , XBP1, CD27 (plasma cells), CD79A, CD37 (B cells), and STMN1 (STMN1+ cells). Gene expression of each cluster was visualized using Dotplot.The OA scRNA-seq datasets were integrated following the steps mentioned above with the same parameters. Total 6708 cells were involved for analysis after filtering and classified into distinct cell types referring to the source of cells. Expression of EGFR was showed using function FeaturePlot.After identifying cell types in RA synovium, cell-cell communication was analyzed by implementing the CellChat (V.1.1.3) pipeline . A new CWhen changing clustering resolution from 0.2 to 0.5 for visualizing the first two UMAP dimensions, fibroblasts that highly expressed HBEGF were divided into one group. Therefore, this group of cells was defined as HBEGF+ fibroblasts (average expression of HBEGF higher than 1.5) while fibroblasts lowly expressed HBEGF were defined as HBEGF− fibroblasts. Macrophages that highly expressed HBEGF were also defined as HBEGF+ macrophages and macrophages that lowly expressed HBEGF were defined as HBEGF− macrophages. Differential gene expression that HBEGF+ fibroblasts compared to HBEGF− fibroblasts was calculated by the function FindMarkers (Seurat R package) and showed in volcano plot. Gene expression of each cell type was computed using function FindAllMarkers (Seurat R package) and top 10 of which were showed in heatmap using function Doheatmap (Seurat R package). Expression of CLIC5, PDGFD, BDH2, ENPP1, SLAMF8, GK, L1RN, and JAK2 were displayed using function Vlnplot (Seurat R package). Differential expression gene markers of HBEGF+ fibroblasts and HBEGF− fibroblasts with the log2 fold change >1 were selected to generate a gene list for GO enrichment analysis using the clusterProfiler R package (V.4.2.2) , 32.The bulk RNA-seq datasets were consisted of 1 dataset of fibroblasts from human synovial tissue, 1 dataset of macrophages from human synovial tissue, 5 multicenter datasets of synovial tissue from 53 RA joints, 33 OA joints and 26 healthy joints, and 1 dataset of synovial tissue derived from 12 RA patients before and after classical treatment of combination of triple disease-modifying anti-rheumatic drugs . Datasets from cells and synovial tissue were integrated for analysis separately. Dataset from RA joint before and after drug treatment was analyzed individually.t test with a significance threshold of P < 0.05. Next, samples of fibroblasts were divided into three groups, high HBEGF group (fibroblasts with count of HBEGF higher than 1000), middle HBEGF group (fibroblasts with count of HBEGF between 100 and 1000), and low HBEGF group (fibroblasts with count of HBEGF between lower than 100). Differential gene expression analysis was performed using DESeq2 R package (V 1.36.0) between high and low HBEGF expression group and differential expression genes (log2 fold change >1 and p < 0.05) were showed in volcano plot.Datasets of fibroblasts and macrophages were merged and normalized before correcting batch effect. Sva R package (V. 3.42.0) was used to correct batch effect by running function ComBat. Then point plot was used to show the HBEGF expression in fibroblasts and macrophages. The HBEGF expression between fibroblasts and macrophages was tested by Student’s t test with a significance threshold of P < 0.05. Expression of HBEGF, CLIC5, PDGFD, BDH2, ENPP1, GK, IL1B, L1RN, and SLAMF8 before and after treatment was tested by paired Student’s t test and showed in box plot with a significance threshold of P < 0.05.Five multicenter bulk RNA-seq datasets were merged, normalized, and corrected following the steps mentioned above. Then the expression of HBEGF, AREG, BTC, EGF, EREG, and TGFA was showed in box plot. HBEGF and gene markers such as CLIC5, PDGFD, BDH2, ENPP1, GK, IL1B, L1RN, and SLAMF8 were tested by the Pearson correlation test and displayed in dot plot separately. Expression of HBEGF, CLIC5, PDGFD, BDH2, ENPP1, GK, IL1B, L1RN, and SLAMF8 between healthy joints and RA joints was tested by Student’s The mouse single-cell transcriptomics datasets were composed of 1 dataset of CD45+CD11b+Ly6G- synovial cells from mice with healthy joints and STA and 1 dataset of synovial cells from mice with healthy joints, STA, and STA in remission.Two mouse scRNA-seq datasets were integrated following the steps of integrating scRNA-seq datasets of cells from RA patients with the same parameters. After filtering, total 28983 cells were involved for analysis. Next, distinct cell types were identified by marker genes such as Prg4, Pdpn, Hbegf (Hbegf+ fibroblasts and Hbegf− fibroblasts), Thy1, Mcam , Cd34, Vwf , Cd2, Cd4 (T cells), Vsig4, Cd163 (synovium macrophages), Cd79A, Cd37 (B cells), and Stmn1 (Stmn1 + cells). Expression of Hbegf was showed using the function FeaturePlot. Differential gene expression was calculated for each cell type using the function FindAllMarkers. Next, differential expression gene markers of Hbegf+ fibroblasts were ordered by the log2 fold change to generate a gene list, which was then used as an input for GSEA analysis. The top 200 differential expression gene markers of each cell type from human were used as the gene sets when running fgsea (V.1.2.0). Finally, the percentage of Hbegf+ fibroblasts was calculated under different arthritis conditions.A total of 29,382 cells were included in the downstream analysis after three scRNA-seq datasets had been integrated and corrected for batch effects by Harmony. Eleven clusters in RA synovium were identified and showed in uniform manifold approximation and projection (UAMP) Fig. a and defNext, CellChat was employed to analyze the cell-cell communication between each cell type in the RA synovial microenvironment Fig. c. We fouP = 0.0003, Fig. P =0.05433, Fig. Among all the signaling pathways that fibroblasts were involved, we discovered the EGF signaling pathway that was the ligand-receptor interaction between fibroblasts and synovium macrophages Fig. a. HBEGF,As can be seen from Fig. By calculating the differential gene expression between HBEGF+ fibroblasts and HBEGF− fibroblasts, we detected 162 genes with distinct expression patterns between HBEGF+ fibroblasts and HBEGF− fibroblasts. Forty-four gene markers were highly expressed by HBEGF+ fibroblasts and 118 gene markers by HBEGF− fibroblasts. The main differential expressed gene of HBEGF+ fibroblasts was HBEGF whereas the differentially expressed gene in HBEGF− fibroblasts is MDK, which is related to the pathogenesis of RA Fig. b. Next, Finally, we used GO enrichment analysis to figure out the difference between HBEGF+ fibroblasts and HBEGF− fibroblasts in biofunction. We found that HBEGF+ fibroblasts involved in positive regulation of cell migration and motility, cellular component movement and cell growth whereas HBEGF− fibroblasts engaged in collagen metabolic and catabolic process and angiogenesis Fig. f, g.To evaluate the population changes of HBEGF+ fibroblasts in different states of synovium, we investigated gene markers that are specially expressed by HBEGF+ fibroblasts and HBEGF+ macrophages. We found that CLIC5, PDGFD, BDH2, and ENPP1 were mainly expressed by HBEGF+ fibroblasts, and SLAMF8, GK, L1RN, and JAK2 were mainly expressed by HBEGF+ macrophages Fig. a.Fig. 4PThen, we selected these gene markers for the Pearson correlation test in the integrated bulk RNA sequencing dataset and found that the expression of CLIC5, PDGFD, BDH2, and ENPP1 was positively correlated with expression of HBEGF while the expression of SLAMF8, GK, L1RN, and JAK2 was negatively correlated with expression of HBEGF Fig. b. ExpresNext, we analyzed single-cell RNA sequencing datasets from mouse with different states of arthritis . Total 28,983 cells included fibroblasts, mural cell, endothelial cells, T cells, synovium macrophages, B cells, and STMN1+ cells Fig. e. Hbegf+In rheumatoid arthritis (RA), synovial fibroblasts have been considered as the key roles in regulation of joint homeostasis , 17–20. This study subdivided fibroblasts into 2 clusters based on the expression of HBEGF. Fibroblasts that highly expressed HBEGF (average expression of HBEGF higher than 1.5) were defined as HBEGF+ fibroblasts while the population with low expression of HBEGF were regarded as HBEGF− fibroblast. HBEGF, heparin-binding EGF-like growth factor, is one of the ligands for the ErbB family of epidermal growth factor receptors (including EGFR) . It stimIn synovium, a fraction of synovial macrophages also highly expressed HBEGF. Previous research defined them as HBEGF+ macrophages and described their function in RA synovium . HBEGF+ This study has proven that expression of HBEGF was downregulated in RA state synovium. However, pervious literatures had pointed out that the importance of the ErbB family pathway in chronic pain and activating EGF singling pathway resulted in deterioration of RA. For instance, injecting HBEGF into the paw of mice caused painful mechanical hypersensitivity and severe pain and blocking the ErbB receptor could alleviate RA pain and joint inflammation , 40. In In summary, population of HBEGF+ fibroblasts and expression of HBEGF decreased in RA synovium and increased after treatment. To conclude, HBEGF+ fibroblasts play a role in the remission of rheumatoid arthritis and HBEGF has potential to become a novel biomarker for prediction of RA progress.


Lung Squamous Cell Carcinoma (LUSC) is an aggressive malignancy with limited therapeutic options. The response to immune therapy is a determining factor for the prognosis of LUSC patients. This study aimed to develop a reliable immune-related prognostic signature in LUSC. We extracted gene expression and clinical data of LUSC from The Cancer Genome Atlas (TCGA). A total of 502 patients enrolled and were divided into respond and non-responder groups by the TIDE algorithm. The CIBERSORT algorithm and the LM22 gene signature were used to analyze the distribution of immune cells in LUSC. Efficacy and response strength of immunotherapy are calculated by the tumor mutation burden (TMB) and ESTIMATE Score. Differentially expressed genes (DEGs) between the two groups were analyzed. The differential expression genes related to overall survival were pointed as hub DEGs, and a prognostic signature was constructed with lasso regression analysis. LUSC patients were divided into responder and non-responder groups based on the response to immunotherapy. The distribution of immune cells was significantly different between the two groups. Forty-four DGEs were considered as overall survival-related genes. A prognostic signature was constructed, consisting of 11 hub-DGEs, including MMP20, C18orf26, CASP14, FAM71E2, OPN4, CGB5, DIRC1, C9orf11, SPATA8, C9orf144B, and ZCCHC5. The signature can accurately distinguish LUSC patients into high and low-risk groups. Moreover, the high-risk group had a shorter survival time than the low-risk group. The area under the ROC curve was 0.67. The multivariate Cox regression showed that the risk score calculated by the constructed signature was an independent prognostic predictor for LUSC patients. In short, we established a novel immune-related prognostic signature in LUCS, which has significant sensitivity and accuracy in predicting the prognosis of patients. Our research can guide the evaluation of the prognosis of LUSC patients in clinical, and the discovered immune-related genes can provide a theoretical basis for the discovery of new therapeutic targets. LUSC is the second-largest subtype of non-small cell lung cancer. Due to the lack of distinct driver mutation and poor response to target therapy, the overall survival of LUSC patients is about 30% shorter than other NSCLC subtypes2. While, studies reported that tumor could monitor its surrounding environment to facilitate its proliferation, invasion, and metastasis3. The tumor microenvironment (TME), which including stromal cells, fibroblasts, endothelial cells, innate immune cells, and adaptive immune cells, has been reported to affect the prognosis of cancer patients. For instance, less T CD8+ infiltration was associated with a higher risk of brain metastases in NSCLC patients4. Tumor-associated macrophages (TAMs) are a well-known primary element of the tumor microenvironment and are also characterized as M2-like macrophages. Hwang et al. found that the elevated M2 ratio (CD163+/CD68+) was significantly related to poor overall survival in NSCLC, providing insight into TAM-based immunotherapy strategies5. Hence, systematic analyses of TME provide a novel strategy for tumor immunotherapy.More recently, immunotherapy has been applied in the treatment of lung cancer. Immune checkpoint inhibitors (ICIs) such as anti-PD-1/PDL-1 showed distinct efficacy (~ 30% response rate) and improved the overall survival of patients with metastatic NSCLC8. By analyzing information from large public datasets such as TCGA and GEO, we were able to explore potential mechanisms affecting the tumor microenvironment and immune responses in lung cancer patients. For example, Sun et al. developed an immune-related four-gene signature including ARNTL2, ECT2, PPIA, and TUBA4A, an independent prognostic factor for lung adenocarcinoma cancer (LUAD)9. Furthermore, Liu et al. built a prognostic model for NSCLC patients based on the expression profiles of autophagy-associated genes10. Therefore, it is indispensable to establish immune-related gene signatures and upgrade treatment strategies to improve patient survival.In the era of information explosion, bioinformatics can integrate multi-omics data, discover valuable features, and open up broader fields based on previous basic research. Currently, various bioinformatics algorithms are developed to predict TME and immunotherapy response, such as CIBERSORT, Timer, and ESTIMATEIn the current study, an 11 differential expressed hub genes (DEGs) prognostic signature was constructed, including MMP20, C18orf26, CASP14, FAM71E2, OPN4, CGB5, DIRC1, C9orf11, SPATA8, C9orf144B, and ZCCHC5. The signature showed moderate accuracy, and the risk score estimated by the signature is an independent prognostic indicator for LUSC.https://portal.gdc.cancer.gov/)11. A total of 502 LUSC patients were enrolled in this study. Next, the Tumor Immune Dysfunction and Exclusion (TIDE) (http://tide.dfci.harvard.edu/) was used to predict the response to immunotherapy based on the simulation of tumor immune escape mechanism12. The response of the TCGA-LUSC cohort to immunotherapy based on TIDE algorithm divided patients with LUSC into non-responder and responder groups.RNA-sequencing and corresponding clinical data of LUSC were extracted from the TCGA database (https://cibersort.stanford.edu/)6, and the LM22 gene signature was used. The distribution of immune cells was analyzed in LUSC patients between non-responder and responder groups with unpaired Student t-tests, including B cells naïve, B cells memory, plasma cells, T cells CD8, T cells CD4 naïve, T cells CD4 memory resting, T cells CD4 memory activated, T cells follicular helper, T cells regulatory (Tregs), T cells gamma delta, NK cells resting, NK cells activated, monocytes, macrophages M0, macrophages M1, macrophages M2, dendritic cells resting, dendritic cells activated, mast cells resting, mast cells activated, eosinophils, and neutrophils.The abundance of 22 immune cell types in LUSC was calculated by the CIBERSORT algorithm (https://bioinformatics.mdanderson.org/estimate/) was applied to predict tumor immune infiltration levels using gene expression data8. From the algorithm, three scores will obtain Immune Score (the infiltration of immune cells in tumor tissue), Stromal Score (the presence of stroma in tumor tissue), and ESTIMATE Score (the tumor purity). A higher immune score represents higher infiltration status. The ESTIMATE Score in LUSC patients between responder and non-responder was calculated by Estimate R package (version 3.5.1) with unpaired Student t-tests.Tumor mutation burden (TMB) was identified as the total somatic nonsynonymous mutation counts in coding regions. TMB score of LUSC patients between responder and non-responder was calculated by Maftools R package with unpaired Student t-tests. The ESTIMATE algorithm (https://www.bioconductor.org/packages/release/bioc/html/limma.html) was used to identify the DEGs in LUSC between responder and non-responder13. P-value < 0.05, false discovery rate (FDR) filter <  = 0.05, and Log (fold change) filter >  = 0.58 were considerate as the selected criteria of DEGs.The limma package (16. The significantly enriched pathways of those DEGs were clustered with CooLGeN (http://ci.smu.edu.cn/CooLGeN/Home.php).Gene Ontology (GO) and Kyoto Encylopedia of Genes and Genomes (KEGG) analysis Clusterprofiler software performed GO function enrichment analysis and KEGG pathway enrichment analysis in DEGsp < 0 0.05 corrected with Benjamini–Hochberg).Cytoscape ClueGo was used to explore the biological processes (BPs) and cellular components (CCs) enriched in selected DEGs 17. TF gene expressions from the TCGA database were matched with the Cistrome Cancer database. Cytoscape software visually presented the TF-DEGs network based on the standard of correlation coefficient filter > 0.4 and p-value filter < 0.05. Overall survival analysis of those DEGs in LUSC was constructed by Kaplan–Meier plotter (http://kmplot.com/private/index.php.p=home).TFs or sequence-specific DNA-binding factors were a cluster of proteins that could control the rate of transcription from DNA to mRNA, which can be obtained from the Cistrome Cancer database , age (aged <  = 65 and > 65), anatomic subdivision , follow-up outcome , number pack-years smoked (packs from 0.15 to 240), pathologic T , pathologic M , pathologic N , pathologic stage , person neoplasm cancer status (tumor or tumor-free), radiation therapy (no or yes), targeted molecular therapy (no or yes), and status . Clinic correlation between high-risk score group and low-risk score group was performed using heatmap R package. In addition, clinical characteristics  associated with overall survival were analyzed in lung cancer patients with univariate and multivariate Cox regression model.Further, the DEGs were selected to construct a prognosis-related signature. The lung cancer samples were divided into two groups according to the median value of risk score (high-risk score group and low-risk score group). The Kaplan–Meier method was used to evaluate the availability of a prognostic model between the high-risk score group and the low-risk score group. Principal component analysis (PCA) and receiver operating characteristic (ROC) curves were used to test the classification measurement based on the risk scorep-value was calculated with unpaired Student t-tests; and for non-normally distributed variables, the p-value was calculated with Mann–Whitney U tests , and statistical significance was set as p < 0.05. FDR and Benjamini–Hochberg for multiple testing were used for correction of the p-value in DEGs, GO, and pathway analyses. The Kaplan–Meier method was for the generation of survival curves. The Log-rank (Mantel-Cox) test was used to evaluate the statistical significance of differences, with a statistical significance of p < 0.05. The hazard ratio was calculated for univariate or and multivariate Cox proportional hazard regression models (p < 0.05).All the statistical analyses were performed using the R package (Vision3.5.1). For between-group comparisons, for normally distributed variables, the n = 147) and non-responder (n = 355) groups.  bring effective antitumor activity in lung cancer patients with KRAS G12C mutation20. Moreover, researchers suggested that the PD-L1 upregulation may be one of the main mechanisms of immune escape in KRAS-mutated NSCLC21. The combination of MAPK/ERK inhibitors and PD-1/PD-L1 inhibitors may be a novel strategy to overcome the EGFR-TKIs resistance with high PD-L1 expression. Further investigations of the DEGs and related pathways might contribute to therapeutic of LUSC patients.Then GO, and KEGG enrichment analyses were performed on these DEGs. We got 20 enrichments according to BP and 18 enrichments according to CC . including ZCCHC5 , FAM71E2 , DIRC1 , INSSR , C1orf68 , EFNA2 , ESkine , GUCA , HTR1B , KLHL34 , OBP2A , RXFP3 , and SPINK6  in LUSC  was between − 3 and − 4, 11 out of 44 DEGs were defined as an ideal element of the immune-related DEGs signature model, including MMP20, C18orf26, CASP14, FAM71E2, OPN4, CGB5, DIRC1, C9orf11, SPATA8, C9orf144B, and ZCCHC5 and low-risk score (n = 247) groups according to the mean value of risk scores  was found to be involved in pigmentation, cell death induction, and molecular clock modulation, thereby regulating the cellular response to UVA radiation which is the critical cause of melanoma28. Moreover, Chen et al. discovered the relationship between a woman's placental homologous CGB5 and her post-pregnancy breast cancer risk, with women carrying the variant C allele of CGB5 rs726002 earlier age at childbirth suffer a higher breast cancer risk29. Disrupted in Renal Cancer 1 (DIRC1) was related to tumor progression and poor prognosis in gastric cancer30. Chorionic gonadotrophin beta5 (CBG5) was also identified as a DEG in prognosis signatures for gastric cancer32. Dysregulation of RNA-binding proteins was associated with tumorigenesis. A previous study reported that ZCCHC5 is a prognosis-associated hub gene in LUSC, which is consistent with our findings33. Although some of these 11 genes have not been reported to be directly related to tumor development, our study has identified their potential as new tumor therapeutic targets.In this study, we identified a prognosis model that consisted of MMP20, C18orf26, CASP14, FAM71E2, OPN4, CGB5, DIRC1, C9orf11, SPATA8, C9orf144B, and ZCCHC5 between immune responder and non-responder groups. Aseervatham et al. reported that Matrix Metalloproteinase 20 (MMP20) played a tumorigenic role on Oral Squamous Cell Carcinomas (OSCCs) by upregulating the genes related to invasion, metastasis, angiogenesis, and epithelial-mesenchymal transition (EMT)34. Thus, the concept of multidrug combination shifts from one drug per GPCR target. The innate and adaptive immune responses depend on the dynamic control of leukocytes, and leukocytes receive a variety of molecular signals through GPCRs35. Recently, studies have shown that the effect on leukocyte migration is also thought to be the function in “Multiple GPCRs systems"36. Additionally, GPCR was functional as guidance for T cells to the target area36. From our enrichment analysis, the DEGs were significantly related to GPCRs . It suggested that GPCRs might be closely related to the immunotherapy response in LUSC patients. Furthermore, the Ras signaling pathway is also a frequent tumor-related signaling pathway. In summary, the immune-related gene signature is biologically significant in LUSC.In terms of immune-related pathways, DEGs between responder and the non-responder group were mainly enriched in including CDO in myogenesis, myogenesis, GPCR ligand binding, Class A/1 (Rhodopsin-like receptors), developmental biology, G alpha (i) signaling events, cell–cell junction organization, Ras signaling pathway, formation of the cornified envelope, cell junction organization, neuroactive ligand-receptor interaction, GPCR downstream signaling, signaling by GPCR, Keratinization, cell–cell communication, cell adhesion molecules (CAMs), axon guidance, tight junction, peptide ligand-binding receptors. G-protein–coupled receptors (GPCRs) is an extensive family of cell surface receptors in the human genome and play distinct roles in tumorigenesis. GPCRs are pleiotropic to the cell signal proteins they activate, and different ligands can induce specific receptor conformational states after activating. Several conformation changes in a single GPCR may produce discrete downstream signaling pathways37. Small molecule drugs could regulate the function of transcription factors by directly targeting their structurally and functionally domains38.It is of great significance to fully reveal the regulatory network of TFs-DEGs. Nineteen pairs of TFs-DEGs were identified based on co-expression analysis. The transcription factor Early B cell factor 1 (EBF1) is expressed in early B cells and recognized as an upstream transcription factor of potential oncogene PNO140. EBF1 inhibits cell proliferation and induces cell apoptosis in colorectal cancer (CRC) cells by inhibiting the activation of the PNO1-mediated p53/p21 signaling pathway40. GATA3 has conventionally been described as a T helper 2 cell differentiation driver and functions as an immune regulator41. Other identified transcription factors have also been reported to be associated with tumors, such as FOXA2 in oral cancer and endometrial cancers, HNF1B in prostate cancer44. In our results, EBF1 was positively corelated with HTR1B and ZCCHC5, GATA3 was positively related with CASP14 and FAM71E2, and FOXA2 was also positively related with ZCCHC5. These all suggested that identified DEGs and TFs could be latent biomarkers or therapeutic targets for LUSC.Dysregulation of transcription factors is a significant cause in malignant tumors, and therapeutic strategies targeting TFs have specific therapeutic effects on diseases related to immunotherapy45. Studies reported that neutrophils promote the antitumor immunity in CRC by enhancing the responsiveness of CD8 + T cells to TCR triggering46. Similarly, Eruslanov et al. pointed that the tumor-associated neutrophils (TANs) stimulate T cells responses in the earliest stages of lung cancer47. In this study, the higher proportion of neutrophils in the low-risk score group may also be related to the enhanced immune response of neutrophils.Moreover, infiltrating inflammatory cells such as neutrophils were identified as an important regulator in the tumor microenvironment and associated with tumor initiation, proliferation, and metastasisIn the diagnosis and treatment of cancer, increasing clinical data suggests that limited effect was obtained from the single biomarkers and target. The combination of multiple indicators and personalized treatment options leads to better outcomes for patients.48. And other two studies are both built immune-related signatures of LUSC. Fan et al. finally validated the predictive value of the signature by tissue sample and that's what we need to do with the rest of our study49. Additionally, Hou and his colleagues also discussed the potential upstream regulator of DEGs—transcription factors, but lacked functional enrichment and pathway analysis50. These studies, including ours, may provide a theoretical basis for more effective, individualized immunotherapy.Most recently, there are studies on immune-related genes identify and signatures construction in LUSC. Gu et al. verified the prognostic-related and immune checkpoints-related genes in LUSC patientsThe study needs to be improved. A large prospective clinical study is necessary to verify the signature. Next, in vitro and in vivo functional assays are needed to clarify the role of DEG further.In conclusion, the DEGs prognosis signature is biologically significant in LUSC. Moreover, the multivariate Cox regression showed that the risk score calculated by the 11 hub DEGs signature was an independent prognostic predictor for LUSC patients.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8.Supplementary Information 9.Supplementary Information 10.Supplementary Information 11.


Although numerous studies have explored the relationship between selenium intake and thyroid diseases, few epidemiological studies have investigated the association between selenium intake and thyroid hormones. Therefore, we conducted this analysis to investigate the association between dietary selenium intake and thyroid hormones. Our sample included 5,575 adults (age ≥ 20) years from the National Health and Nutrition Examination Survey (NHANES) 2007–2012. Thyroid hormones, including total triiodothyronine (T3), total thyroxine (T4), free T3 (FT3), free T4 (FT4), and thyroid-stimulating hormone (TSH), were detected. Multivariable linear regression models showed that log10-transformed selenium intake (LogSe) was negatively correlated with TT4  and TT4/TT3  in U.S. adults. Besides, additional stratified analyses by sex demonstrated that LogSe was negatively associated with TT4  and TT4/TT3  and positively associated with FT4/TT4  in male adults. Meanwhile, subgroup analysis by iodine status showed that LogSe was negatively associated with TT4 , FT4/FT3  and TT4/TT3  in iodine sufficiency but not in iodine deficiency adults. Our results demonstrated that the increased dietary selenium intake was negatively correlated with TT4 and TT4/TT3 in U.S. adults. Furthermore, the association between dietary selenium intake and thyroid hormones was more pronounced in males and iodine sufficiency adults. T4 and T3 exist in free and binding forms in peripheral blood, and the former enters target cells to exert their biological functions. In addition, T4 can be converted to T3 under the action of type 1 and 2 iodothyronine deiodinases   and thyroxine T4), are present in numerous tissues and play important roles in maintaining the normal development of the brain, heart, and other organs by controlling energy expenditure and thermogenesis, modulating lipid profiles, and maintaining the normal reproductive function, etc . The bio and thyr, are pre1, DIO2) .2O2 by thyroid follicular epithelial cells might damage the normal function of thyroid function .The National Health and Nutrition Examination Surveys (NHANES) was a nationwide and ongoing cross-sectional survey conducted among the non-institutionalized US population. To assemble a sample of participants who were representative of the civilian non-institutionalized U.S. population, a repeated 2-year cycle survey with a complex multistage probability sampling design was used. Detailed information about the survey design and methods has been described elsewhere . The NHAn = 1,758), at the age of 19 years or below , with the missing value of covariates . Also, pregnant women (n = 49) and participants with thyroid disease or thyroid cancer (n = 503) were excluded. Finally, a total of 5,575 participants were included in the present study. These participants represented a weighted population of 73.5 million non-institutionalized US adults. The flowchart of sample selection was presented in Two thousand seven to two thousand eight, 2009–2010, and 2011–2012 NHANES cycles were selected, and a total of 10,548 participants with complete thyroid function data constituted the study sample. We excluded participants under the minimum criteria on dietary recall status , total T3 (TT3), and free T3 (FT3) were determined using a competitive binding immunoenzymatic assay. Serum free T4 (FT4) was determined using a two-step enzyme immunoassay. Serum thyroid-stimulating hormone (TSH) was determined using a 3rd generation, two-site immunoenzymatic (“sandwich”) assay. Meanwhile, the ratios of FT4/FT3 and TT4/TT3 were calculated to reflect the metabolic level of peripheral T4, while the ratios of FT4/TT4 and FT3/TT3 were acquired to reveal the binding level of thyroid hormones with thyroid hormone-binding proteins .In NHANES, dietary intake information was assessed by two reliable 24-h dietary recall interviews. The first dietary recall interview was conducted in the mobile examination center (MEC) and the second interview was conducted by telephone 3–10 days later. The intakes of dietary selenium during the 24-h period prior to the interview were calculated based on the University of Texas Food Intake Analysis System and U.S. Department of Agriculture (USDA) Survey Nutrients Database. Then, the mean value of selenium intake from the two 24-h dietary recall interviews was adopted as the final dietary selenium intake.2)  , sex ma, educati2) , serum c2) , 29, dri2) , urine i2) , and fas2) .2. Serum cotinine was measured using isotope dilution-high performance liquid chromatography/atmospheric pressure chemical ionization tandem mass spectrometry (ID HPLC-APCI MS/MS). Drinking status was ascertained via questionnaires. Urine iodine concentration was detected using inductively coupled plasma dynamic reaction cell mass spectroscopy (ICP-DRC-MS). In addition, fasting time was acquired by questionnaires before blood collection.Demographic data such as age, sex, race, education level, household income, and marital status were collected in an in-home interview. The body weight (kg) and height (m) were measured during mobile physical examination, and the BMI was calculated as BMI = weight/heightIn order to generate nationally representative estimates, SDMVPSU, and SDMVSTRA procedures were used to interpret NHANES's complex survey design, and WTMEC2YR was used to provide weight for all data.The continuous variables were shown as means ± standard deviations, and the categorical variables were presented as counts (percentages). Initially, the Scott–Rao chi-square test was used to compare dietary selenium intake levels of different groups. Then, the one-way analysis of variance (ANOVA) was used to assess FT4, TT4, FT3, TT3, TSH, and ratios of thyroid hormones  differences among different dietary selenium intake groups. Whereafter, two multivariable linear regression models were used to explore the association of dietary selenium intake with levels of FT4, TT4, FT3, TT3, TSH, FT4/FT3, TT4/TT3, FT4/TT4, and FT3/TT3. Due to right skewness, we log10-transformed dietary selenium intake to approximate normality assumptions. Model 1 (unadjusted) did not include any covariates. Model 2 was adjusted for sex, age, race, education level, household income, marital status, BMI, serum cotinine level, drinking, urine iodine concentration, and fasting time. Considering the effects of sex and urine iodine condition on thyroid function indices, we performed subgroup analyses to explore whether the association between dietary selenium intake and thyroid hormones was modified by sex and urine iodine condition. In addition, we carried out several restricted cubic spline (RCS) analyses to explore the non-linear dose-response relationship between dietary selenium intake and thyroid hormones in the whole and subgroup adults, and five knots were placed at the 5th, 25th, 50th, 75th, and 95th percentiles.P-values and confidence intervals (CI) were reported two-sided without adjustment for multiple testing. The p < 0.05 was the significance criterion in the Scott–Rao chi-square test, ANOVA, and RCS analyses. Confidence intervals that do not contain 0 were considered to indicate statistical significance in multivariable linear regression models.All statistical analyses were performed using STATA software (version 16.0), and R software . As shown in In the unadjusted model , dietarySubgroup analyses by sex  showed tMoreover, subgroup analysis by iodine status  showed tP for non-linearity = 0.708) and TT4/TT3 (P for non-linearity = 0.670) of whole adults, TT4 (P for non-linearity = 0.203), TT4/TT3 (P for non-linearity = 0.796), and FT4/TT4 (P for non-linearity = 0.072) of male adults, and TT4 (P for non-linearity = 0.715), FT4/FT3 (P for non-linearity = 0.095), and TT4/TT3 (P for non-linearity =0.663) of iodine-sufficient adults.In addition, we further used the restricted cubic splines to estimate the dose-response relationship between LogSe and thyroid hormones . OverallBased on a nationally representative survey of non-institutionalized US adults, we found inverse correlations between LogSe and TT4 and TT4/TT3 in U.S. adults. LogSe was also negatively associated with FT4/FT3 and positively associated with FT3, FT4/TT4, and FT3/TT3 in U.S. adults. However, these correlations were no longer significant when the model was further adjusted for potential confounders. When the subgroup analysis was performed according to sex, we found that LogSe was negatively correlated with TT4 and TT4/TT3 while positively correlated with FT4/TT4 within male adults. When subgroup analysis was carried out according to urine iodine concentration, we found that LogSe was negatively associated with TT4, FT4/FT3, and TT4/TT3 within iodine sufficiency adults.in vivo and in vitro are needed in future studies.Previous research showed that low selenium status was associated with an increased risk of thyroid disease , 33. HowFurthermore, the present study also found sex differences in the relationship between dietary selenium intake and thyroid hormone levels. For male adults, LogSe was negatively associated with TT4 and TT4/TT3 while positively correlated with FT4/TT4. Previous studies have demonstrated that estrogens could increase iodine uptake, thyroperoxidase activity, thyroglobulin expression, and modulate TSH levels –38. MoreIodine is also an essential micronutrient for the thyroid gland to synthesize thyroid hormones . PreviouSelenium is an essential nutrient element, which is rich in organ meats and seafood, followed by grain, cereals, and dairy products . HoweverThe strength of the present study was that this was the first large population-based study to date to reveal the relationship between dietary selenium intake and thyroid hormones in U.S. adults to the best of our knowledge, and our findings might be a complement to the literature regarding the association between selenium and thyroid health. However, the study also had some limitations. First, as with any cross-sectional study, we cannot ascertain causality between dietary selenium and thyroid hormone. Second, dietary data were collected using two days of 24-h dietary recall survey, which might cause an underestimation or overestimation of diet selenium consumption. Third, the data on dietary selenium supplementation was not assessed due to substantial missing data. Lastly, we excluded 4,973 participants due to missing values for covariates, complete information about dietary selenium intake, or were under the minimum criteria on dietary recall status, which might decrease the generalizability of our results.In conclusion, this study demonstrated that the increased dietary selenium intake was negatively correlated with TT4 and TT4/TT3 in U.S. adults. Furthermore, the association between dietary selenium intake and thyroid hormones was more pronounced in males and iodine sufficiency adults. However, further large-scale prospective studies are needed to confirm these findings in different populations.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.FL and JN contributed to the conception and design of this study. FL, KW, and M-GD performed the statistical analysis. FL, QF, XL, and YY wrote the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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There have been no reports on mortality in patients with markedly elevated aspartate aminotransferase (AST) levels from non-hepatic causes to date. This study aimed to determine the etiologies of markedly elevated AST levels > 400 U/L due to non-hepatic causes and to investigate the factors associated with mortality in these cases. This retrospective study included 430 patients with AST levels > 400 U/L unrelated to liver disease at two centers between January 2010 and December 2021. Patients were classified into three groups according to etiology: skeletal muscle damage, cardiac muscle damage, and hematologic disorder. Binary logistic regression analysis was performed to evaluate the factors associated with 30-day mortality. The most common etiology for markedly elevated AST levels was skeletal muscle damage (54.2%), followed by cardiac muscle damage (39.1%) and hematologic disorder (6.7%). The 30-day mortality rates for the skeletal muscle damage, cardiac muscle damage, and hematologic disorder groups were 14.2%, 19.5%, and 65.5%, respectively. The magnitude of the peak AST level significantly correlated with 30-day mortality, with rates of 12.8%, 26.7%, and 50.0% for peak AST levels < 1000 U/L, <3000 U/L, and ≥3000 U/L, respectively. In the multivariate analysis, cardiac muscle damage , hematologic disorder , peak AST < 3000 U/L , and peak AST ≥ 3000 U/L  were associated with increased 30-day mortality. Our study revealed three etiologies of markedly elevated AST unrelated to liver disease and showed that etiology and peak AST level significantly affected the survival rate. Alanine aminotransferase (ALT) and aspartate aminotransferase (AST) are often referred to as liver function tests, but rather than being true measurements of hepatic function, the release of AST and ALT from hepatocytes into the bloodstream represents hepatocellular injury. ALT is more specific for hepatocellular injury because of its much higher concentration in the liver tissue than in other tissues. In contrast, AST is widely detected in various cells, such as hepatocytes, cardiac and skeletal myocytes, and erythrocytes. The relative activity of AST in various tissues and cells is as follows: 7800 in the heart, 7100 in the liver, 5000 in the skeletal muscle, 4500 in the kidney, 2500 in the brain, 1400 in the pancreas, 700 in the spleen, 500 in the lung, and 40 in erythrocytes .Elevated aminotransferase levels due to non-hepatic causes are frequently encountered in clinical practice. Skeletal muscle damage  is one of the most common non-hepatic causes of elevated aminotransferase levels. The mortality rate of rhabdomyolysis was reported to be 3–9% in non-intensive care unit (ICU) patients and 7–46% in ICU patients ,9,10. CaIt is well-known that marked elevation of aminotransferase levels is associated with higher mortality, as it usually occurs in the setting of severe hepatic injury, such as in ischemic hepatitis and septic shock in critically ill patients. However, there have been no reports on mortality in patients with non-hepatic causes of markedly elevated AST levels, such as skeletal muscle damage, cardiac muscle damage, or hematologic disorders. Therefore, our study aimed to determine the non-hepatic etiologies of markedly elevated AST levels and investigate the factors associated with mortality in these cases.n = 104), (2) loss to follow-up (n = 163), (3) lack of available data (n = 1008), (4) cardiopulmonary arrest on arrival (n = 54), and hepatobiliary causes (n = 4745). The participants of this study included 430 patients with at least one instance of markedly elevated AST > 400 U/L and no evidence of liver disease  due to non-hepatic causes were identified from two centers between January 2010 and December 2021. The exclusion criteria were as follows: (1) age < 18 years , hepatic encephalopathy, hepatorenal syndrome, ascites, or documented gastroesophageal variceal hemorrhage. Laboratory data included initial levels of AST, ALT, total bilirubin, direct bilirubin, albumin, alkaline phosphatase, creatine kinase (CK), lactate dehydrogenase, prothrombin time-international normalized ratio (PT-INR), and creatinine, as well as the peak levels of AST, ALT, and total bilirubin.The index date for the analysis of the initial laboratory data was defined as the first date wherein an AST level > 400 U/L was reached. Serial AST, ALT, and bilirubin levels were measured at least every 1–2 days. The highest AST level within 30 days of the index date was defined as the peak AST level. Three grades for the severity of AST elevation were applied based on the peak AST level: (1) peak AST levels < 1000 U/L (as 400 U/L ≤ AST < 1000 U/L), (2) peak AST levels < 3000 U/L (as 1000 U/L ≤ AST < 3000 U/L), and (3) peak AST levels ≥ 3000 U/L.Patients with markedly elevated AST levels due to hepatobiliary causes were excluded from the study. Hepatobiliary causes included the following: (1) pancreatobiliary diseases, including cholangitis, cholecystitis, and pancreatitis; (2) hepatocellular diseases, including drug-induced liver injury, viral hepatitis, autoimmune hepatitis, pregnancy-related liver disorder, alcoholic liver disease, liver involvement in systemic infection, and acute hepatitis of unknown origin; (3) liver surgery or liver trauma; (4) hepatobiliary malignancy; and (5) ischemic hepatitis. Patients with markedly elevated AST levels due to non-hepatobiliary causes were classified into three groups according to etiology: (1) skeletal muscle damage, (2) cardiac muscle damage, and (3) hematologic disorder .Skeletal muscle damage  was defined as a serum CK level > 1000 U/L in the presence of skeletal muscle injury. We excluded patients with an insufficient CK profile, peak CK < 1000 U/L, or CK elevation due to myocardial muscle damage. We also excluded patients with elevated CK and AST levels due to concurrent ischemic hepatitis, as ischemic hepatitis resulting from hepatocellular injury is considered to have a greater effect on mortality than skeletal muscle damage. Skeletal muscle damage was stratified into eight causes: (1) excessive physical exertion, including exercise and seizures; (2) direct muscle injury, including crush injuries and burn; (3) muscle ischemia, including generalized ischemia, shock, emboli, thrombus, vascular occlusion, immobilization, and compartment syndrome; (4) temperature extremes, including heat stroke and hypothermia; (5) drugs, toxins, and venom, including alcohol use, benzodiazepine use, statin use, and snake bite; (6) metabolic and endocrine disorders, including hypothyroidism, hypokalemia, hyponatremia, hypernatremia, and diabetic ketoacidosis; (7) infections, including viral, bacterial, and parasitic etiologies; and (8) autoimmune diseases, including polymyositis and dermatomyositis.Cardiac muscle damage was defined as a serum CK level > 500 U/L in the presence of cardiac muscle injury. We excluded patients with an insufficient CK profile, peak CK level < 500 U/L, CK elevation due to skeletal muscle damage, and concurrent ischemic hepatitis . Through a retrospective review, patients with cardiac muscle damage and markedly elevated AST were classified into three categories: (1) acute coronary syndrome, including STEMI and non-STEMI, and unstable angina, (2) inflammatory disorders of the heart, including endocarditis, myocarditis, and pericarditis, and (3) perioperative myocardial injury (within 5 days postoperatively).Patients with an elevation of AST levels associated with disorders of the blood and blood-forming organs were classified under the hematologic disorder group. We excluded patients with elevated AST levels due to concurrent ischemic hepatitis but included patients with hepatic involvement in hematologic malignancy. Hematologic disorders were classified as (1) malignant, such as leukemia, lymphoma, and multiple myeloma; and (2) non-malignant, such as paroxysmal nocturnal hemoglobinuria, thrombotic thrombocytopenic purpura, idiopathic myelofibrosis, myelodysplastic syndrome, and hemophagocytic lymphohistiocytosis.p value < 0.05 was considered statistically significant for all analyses. The PASW software  and R software  were used to process the data.Clinical and laboratory characteristics of patients are expressed as numbers (%) for categorical data and medians (interquartile range) for continuous data. For categorical variables, between-group differences were compared using the Chi-squared test or Fisher’s exact test. For continuous variables, the Mann–Whitney U test and Kruskal–Wallis test were used as appropriate. Survival curves for 30-day mortality were constructed using the Kaplan–Meier method with the log-rank test. Univariate and multivariate binary logistic regression analyses were performed to evaluate the odds ratios (ORs) with 95% confidence intervals (CIs) for 30-day mortality. The accuracy of the initial AST, initial ALT, peak AST, and peak ALT in correlating with 30-day mortality was assessed by the area under the receiver operating characteristic curve. A two-sided The present study protocol was reviewed and approved by the institutional review boards of Gyeongsang National University Changwon Hospital (IRB File No. 2021-06-032) and Gyeongsang National University Hospital (IRB File No. 2015-07-029). The requirement for informed consent was waived owing to the retrospective design of this study, as determined by the institutional review boards.The baseline characteristics of 430 subjects are provided in At enrollment, we identified 325 (75.6%) patients with initial AST levels < 1000 U/L, 94 (21.9%) with initial AST levels < 3000 U/L, and 11 (2.6%) with initial AST levels ≥ 3000 U/L. During the follow-up period, 274 (63.7%) patients had a peak AST level < 1000 U/L, 116 (27.0%) had a peak AST level < 3000 U/L, and 40 (9.3%) had a peak AST level ≥ 3000 U/L. A total of 81 (18.8%) patients with markedly elevated AST levels developed jaundice during the study period. Of the 430 patients, 112 (26.0%) were admitted to the ICU and 13 (3.0%) had hepatic decompensation at enrollment. In the entire study group, the 30-day mortality rate was 20%.n = 233, 54.2%), followed by the cardiac muscle damage group  and the hematologic disorder group . Patients in the skeletal muscle damage group had the highest initial and peak AST levels among the groups. The cardiac muscle damage group had the highest number of patients with diabetes (20.8%) and pre-existing congestive heart failure (8.9%), as well as patients admitted to the ICU (41.1%). The hematologic disorder group had the most patients with hepatic decompensation (13.8%), infection (48.3%), and hypotension (48.3%). The 30-day mortality rates were 14.2%, 19.6%, and 65.5% in the skeletal muscle damage, cardiac muscle damage, and hematologic disorder group, respectively (The three groups of patients are shown in ectively a.n = 73), followed by muscle ischemia (n = 49), direct muscle injury (n = 38), and drugs, toxins, and venom (n = 36)  . Among t(n = 36) b.n = 141), followed by perioperative myocardial injury (n = 14) and inflammatory disorders of the heart (n = 13). Among these, inflammatory disorders of the heart had the highest 30-day mortality rate (30.8%).The most common cause of cardiac muscle damage was acute coronary syndrome (n = 19), including leukemia (n = 4), lymphoma (n = 8), and multiple myeloma (n = 7), and non-malignant hematologic disorders (n = 10), including paroxysmal nocturnal hemoglobinuria (n = 2), thrombotic thrombocytopenic purpura (n = 1), idiopathic myelofibrosis (n = 1), myelodysplastic syndrome (n = 2), and hemophagocytic lymphohistiocytosis (n = 4). Malignant and non-malignant hematologic disorders had 30-day mortality rates of 68.4% and 60%, respectively.The hematologic disorder group was divided into malignant hematologic disorders (p < 0.001) and skeletal muscle damage  , 0.580 , 0.686 , and 0.674 , respectively . Peak AS< 0.001) b.The skeletal muscle group was used as a reference for the etiologic groups while peak AST levels < 1000 IU/L was used as a reference for peak AST levels in the logistic regression analyses. In the univariate analysis, age , hepatic decompensation , albumin level , bilirubin level , creatinine level , PT-INR , hematologic disorder , peak AST < 3000 U/L , and peak AST ≥ 3000 U/L  were associated with 30-day mortality. In the multivariate analysis, age , albumin level , creatinine level , cardiac muscle damage , hematologic disorder , peak AST < 3000 U/L , and peak AST ≥ 3000 U/L  were independently associated with 30-day mortality .In patients with markedly elevated AST levels unrelated to liver disease, we developed a simplified algorithm that incorporates etiology and peak AST levels . First, In this study, we found that the most common non-hepatic cause of markedly elevated AST levels was skeletal muscle damage (54.2%), followed by cardiac muscle damage (39.1%) and hematologic disorders (6.7%). Patients with markedly elevated AST levels unrelated to liver disease had a 30-day mortality rate of 20%. The magnitude of the peak AST levels and etiology of elevated AST levels were independent risk factors of 30-day mortality.AST activity has been widely detected in various human tissues. It is generally thought that increased levels of circulating AST may reflect direct tissue damage and apoptosis related to liver inflammation, septic shock, acute myocardial infarction, and skeletal muscle injury . In factWe identified the three main non-hepatic etiologies of markedly elevated AST levels and demonstrated that etiology affects patient survival. To the best of our knowledge, there have been no studies on the prognosis of patients with markedly elevated AST levels unrelated to liver disease. In previous studies, mortality rates in patients with rhabdomyolysis ranged from 3% to 46% depending on the study population, frequency of acute kidney injury, comorbidities, and cause of rhabdomyolysis ,8,9,10. In the present study, we arbitrarily used a cutoff level of 400 U/L for markedly elevated AST levels. Previous studies on patients with markedly elevated aminotransferase levels due to liver disease showed that all-cause mortality rate correlated with increasing aminotransferase levels. The reported all-cause mortality rates were 16–22%, 30–45%, and 31–55% in patients with aminotransferase levels > 400 U/L ,26,27, >In clinical practice, patients with markedly elevated aminotransferase levels with no evidence of liver disease present a significant challenge to physicians due to their high mortality. In this study, among 5644 patients with AST > 400 U/L, only 7.6% were found to be associated with non-hepatic causes. This suggests that markedly elevated AST levels unrelated to liver disease may not be encountered frequently in clinical practice. Therefore, we present a simplified schematic approach for patients with markedly elevated AST levels unrelated to liver disease, involving (1) determination of the etiology  and (2) measurement of the peak AST level during follow-up testing  .One limitation of this study is that the etiology of markedly elevated AST levels could not be determined prospectively. Another limitation is that additional liver damage, including ischemic injury, multiple organ failure, and drug-induced liver injury, may have contributed to the magnitude of AST levels during follow-up testing, although there was no evidence of liver disease at enrollment in all patients. In addition, we only focused on 30-day mortality in patients with markedly elevated AST. Therefore, further prospective study is needed to elucidate the factors that affect the long-term prognosis of patients who survived more than 30 days. Nevertheless, the strength of this study is that it is the first to examine the etiologies and prognoses of markedly elevated AST levels in patients without liver disease.Various etiologies affect the survival rate of patients with markedly elevated AST levels (>400 U/L) unrelated to liver disease. Etiology and peak AST levels, but not factors reflecting liver function , were independent factors associated with mortality in these patients. Large prospective studies are required to further elucidate the etiologies and prognoses of markedly elevated AST levels in this patient group.


NFKB1, CTNNB1, ALB, peroxides, NOS2, tumor necrosis factor (TNF), and interleukin-6 (IL-6) and that the network comprises interconnected immune-oxidative-nitrosative and Wnt/β-catenin subnetworks. Multiomics enrichment analysis shows that the CFAS-D network is highly significantly associated with cellular (antioxidant) detoxification, hydrogen peroxide metabolic process, peroxidase and oxidoreductase activity, interleukin-10 (IL-10) anti-inflammatory signaling and neurodegenerative canonical Wnt, the β-catenin complex, cadherin domains, cell–cell junctions and TLR2/4 pathways, and the transcription factors nuclear factor kappa B (NF-κB) and RELA. The top 10 DOID annotations of the CFAS-D network include four intestinal, three immune system disorders, cancer, and infectious disease. The custom Gene Ontology (GO) term annotation analysis revealed that the CFAS-D network is associated with a response to a toxic substance, lipopolysaccharides, bacterium, or virus. In conclusion, CFAS-D may be triggered by a variety of stimuli and their effects are mediated by aberrations in the cross-talks between redox, NF-κB, and Wnt/β-catenin signaling pathways leading to dysfunctions in multicellular organismal homeostatic processes.There is evidence that chronic fatigue spectrum disorders (CFAS-Ds), including myalgic encephalomyelitis (ME), chronic fatigue syndrome (CFS), and chronic fatigue with physiosomatic symptoms including when due to comorbid medical disease, are characterized by neuroimmune and neuro-oxidative biomarkers. This study was performed to delineate the protein–protein interaction (PPI) network of CFAS-D and to discover the pathways, molecular patterns, and domains enriched in their PPI network. We performed network, enrichment, and annotation analyses using differentially expressed proteins and metabolics, which were established in patients with CFAS-D. The PPI network analysis revealed that the backbone of the highly connective CFAS-D network comprises  Contemporary study of chronic fatigue spectrum disorders (CFAS-Ds), including myalgic encephalomyelitis (ME), chronic fatigue syndrome (CFS), and chronic fatigue (CF) and associated and associated physiosomatic (psychosomatic) symptoms, is plagued by a cacophony of controversies. These different approaches, sometimes even competing, comprise folk psychology  and the medical approach (the culprit is one specific virus or different viruses or bacteria). The dominant view, especially in Europe, is that of the cognitive-behavioral and the biopsychosocial schools . This viIt is incomprehensible that expert committees make consensus diagnostic criteria of ME/CFS , whereas, in fact, these new diagnostic classes were never validated, for example, by using machine learning techniques and there is no evidence that the case definitions have reliability and replicability validity . One diaIt was not until 2010–2013, when Maes et al.  launchedThe key AOPs of CFAS-D  are shown in All in all, these findings show that multiple differentially expressed proteins (DEPs) and metabolic pathways are involved in the pathophysiology of CFAS-D. However, no study has delineated the protein–protein interaction (PPI) and metabolic-protein interactions (MPIs) networks of CFAS-D and the biological processes, molecular functions and complexes, cellular components, pathways, transcriptional regulatory relationships, protein domains, and human disease annotations, which are associated with the PPI and MPI networks of CFAS-D.Hence, we have conducted network, enrichment, and annotation analyses to delineate the hotspots in the CFAS-D network and the top functions and paths enriched in the networks. This is important because the most influential genes, metabolics, and pathways may constitute new drug targets to treat CFAS-D. Moreover, such analyses may disclose the putative trigger factors of the CFAS-D interactome and its associations with comorbid medical disorders. As such, these enrichment and annotation analyses may help to explain the strong comorbidity of CFAS-D with immune, infectious, and neuropsychiatric disorders and the possible shared pathophysiological core, which may underpin CFAS-D.1 pathways, namely, C01290 (lactosylceramide), C02470 (xanthurenate), C10164 (picolinic acid), C03227 [3-OH-L-kynurenine (3OHK)], C11378 (coenzyme Q10), C00038 (zinc), C00070 (copper), C06428 [eicosapentaenoic acid (EPA)], C00027 [hydrogen peroxides (H2O2)], C19440 [malondialdehyde (MDA)], C004555 (DHEA), and C05926 (neopterin).“This study is a secondary data analysis on existing data using open, deidentified, and non-coded data sets and, therefore, this is non-human subject study, which is not subjected to Institutional Review Board approval” . In case2  and Cytoscape3. The second was constructed using OmicsNet4 using the abovementioned metabolites and examining the MPI based on the KEGG reactions. Consequently, the genes interacting with the metabolics in the different subnetworks were used in STRING to build a giant network based on our seed genes enlarged with the MPI-derived DEPs, which were, consequently, analyzed in OmicsNet to construct a composite network consisting of metabolites and DEPs first entering MPIs and then the PPIs 5 with a targeted incorporation of TF protein interactions (TFPIs) 6.The network, enrichment, and annotation analyses were conducted, as reviewed previously . In brieNetwork features were calculated using STRING and the Cytoscape plugin NetworkAnalyzer and comprise number of nodes, number of edges and expected number of edges, average node degree, network diameter and radius, characteristic path length, and network density and heterogeneity. The top hubs (high degree) and bottlenecks  were calculated and used to delineate the backbone of the network, i.e., the top 7 hubs and the top 2 non-hub bottlenecks. We used Markov Clustering (MCL) employing STRING to discover communalities of interconnected nodes, which display similar attributes and/or functions.7, OmicsNet, MetaScape8, inBio Discover9, and the R package ClusterProfiler. Heatmaps were produced using Appyter and MetaScape. Functional enrichments were established using the Gene Ontology (GO) biological processes, the GO molecular functions and the GO cellular components, STRING local network clusters, the KEGG pathways, Reactome pathways (the European Bioinformatics Institute Pathway Database)10, PANTHER biological processes 11, TTRUST transcriptional regulatory relationships, InterPro domains (InterPro)12, Wikipathways (WikiPathways - WikiPathways), and DOID annotations of human diseases 13. We also performed Molecular Complex Detection (MCODE) (using Metascape) to delineate small molecular complexes. In this study, all the results are shown as p (or q) values corrected for false discovery rate (FDR).We examined the different networks and MCL subnetworks for their enrichment scores and annotated terms and these analyses were also performed on the downregulate seed genes and the hotspots of the enlarged network. Enrichment/annotation analyses were performed using STRING, Enrichrn = 323) with a p-enrichment value of 1.0E-16. The network has an average local clustering coefficient of 0.683 and average node degree of 20.6, with a network diameter of 4, radius of 2, a characteristic path length = 1.978, network density = 0.229, and heterogeneity = 0.682. The top 7 hubs (highest degree) were in descending order of importance: TNF (degree = 63), IL-6 (61), IL-1β (58), ALB (54), TLR4 (47), IL-10 (47), and CTNNB1 (46). The top two non-hub bottlenecks were: EGFR (0.066) and TLR2 (0.0188). EGFR and TLR were the top 5 and 9 in the bottleneck list . The network of the seed proteins showed 42 nodes and 311 edges, thus, exceeding the expected number of edges (n = 70) with a p-enrichment value of 1.0E-16. The network has an average local clustering coefficient of 0.74 and an average node degree of 14.8.Markov Clustering analysis (inflation parameter of 2) resulted in two protein subnetworks, as shown in n = 413) with a p-enrichment value of 1.0E-16. The network has an average local clustering coefficient of 0.624 and average node degree of 19.1, with a network diameter of 5, radius of 3, characteristic path length = 2.278, network density = 0.131, and heterogeneity = 0.799. The top 7 hubs (highest degree) were in descending order of importance: TNF (degree = 82), IL-6 (79), ALB (75), IL-1β (74), catalase (CAT) (59), IL-10 (57), and TLR4 (57). The top 5 bottlenecks were CAT (0.1534), ALB (0.1212), CTNNB1 (0.0918), TNF (0.08867), and IL-6 (0.06755). The first non-hub bottlenecks were EGFR (0.04909) and NOS2 (0.04572).In order to construct a second giant network including proteins interconnecting with the metabolites, we entered the latter in OmicsNet analysis and examined the MPIs (using the KEGG and IntAct and using only the first-order MPIs). We found 5 subnetworks: one centered around hydroperoxides (46 nodes), another centered around EPA (6 nodes), 6 centered around 3OHK, 4 centered around nitric oxide (NO), and 3 centered around DHEA . The 59 nodes coupled with the nodes from the first network see  were conIO&NS/Wnt genes of the network given in IO&NS/Wnt genes. We found five significant molecular complexes, the first represents cytokine/IL-10 signaling and a response to LPS; the second comprises cellular oxidant detoxification and response to a toxic substance; the third comprises amine oxidase reactions, hydrogen peroxide metabolic process, and degradation of β-catenin; the fourth comprises peroxisomal protein import and a carboxylic acid catabolic response; and the fifth was the same as MCODE2, as given in Electronic IO&NS genes and a second centered around Wnt/β-catenin genes. The backbone of the master network comprises DEPs/metabolics, including NFKB1, CTNNB1, ALB, peroxides, NOS2, TNF, and IL-6, while in the giant network, many redox-related enzymes were predicted to be hubs, including endothelial NO synthase producing NO relaxing smooth muscle relaxation (NOS3), mitochondrial superoxide dismutase 2 (SOD2), and peroxiredoxin 6 (PRDX6) . It is important to note that without the delineation of the MPIs, one would have concluded that especially proinflammatory cytokine genes are the dominant forces in this network, whereas, in fact, the IO&NS subnetwork is more dominated by redox genes, NF-κB and IL-10.The first major finding of this study is that the PPI network of the DEPs and metabolics of CFAS-D show high connectivity and comprises two subnetworks, a first centered around CTNNB1, which belongs to the Wnt subnetwork, was another hotspot and additionally showed many interactions with genes in both the subnetworks indicating that this gene is a relevant switch linking both the subnetworks. Other relevant switches belonging to the immune subnetwork were ALB and IL-6, which showed many interactions with cluster 1 genes, but also with CTNNB1, DKK1, and AGRN. Hotspots and switches are new drugs targets because they govern and control the network and/or link the subnetworks . All in The second major finding of this study is that the top relevant functions and pathways in the first network revolve primarily around redox mechanisms, namely, cellular oxidant detoxification , the hydrogen peroxide metabolic process, antioxidant activity, amine oxidase reactions, oxidoreductase activity, peroxidase activity, and heme and glutathione peroxidase. These findings indicate that increased oxidative stress (hydrogen peroxides) probably as a response to a toxic substance coupled with reduced antioxidant defenses  are the major pathways in CFAS-D. Indeed, many of the seed DEPs and metabolics show antioxidant properties, including ALB, GPX1, HP, CoQ10, DHEA, zinc, and EPA and pro-oxidant properties, including NFKB1, NOS2, PTGS2, and hydroperoxides. Moreover, the giant network based on seed DEPs and MPI-derived genes indicates that many redox genes are expected to participate in the CFAS-D network, including CAT, GPX2-9, SOD1, SOD2, LPO, LOX, MPO, NOS1, NOS3, and PRDX6.Withania somnifera, quercetin and Hypericum perforatum L  and RELA (NF-κB p65 unit or transcription factor p65) were the most important transcription factors controlling the network and that the NF-κB signaling pathway was one of the most important paths enriched in the network. NF-κB is a major transcriptional factor involved in the response to a vast array of stimuli, including bacterial or viral antigens, cytokines, free radicals, oxidized epitopes, and glutamate and is a transcriptional inducer of many genes, including cellular adhesion molecules, proinflammatory cytokines, chemokines, and growth, apoptosis and coagulation factors, and antioxidants and pro-oxidants –29. RELASOD, HO1, GPX1, TRX1, and TRX2), which reduce ROS production, thereby promoting cell survival  and NF-κB signaling. Indeed, ROS may modulate the NF-κB response leading to transcriptional activation of NF-κB-target antioxidant genes  danger signal response pathway. LTF, an iron-binding glycoprotein, activates NF-κB via the TLR2/4 complexes and additionally RAGE and TREM-1 receptors . LTF funThe NF-κB signaling pathway not only regulates the transcription of proinflammatory genes, including IFNG, IL-1β, and TNF, but also IL-10, which protects against an overzealous inflammatory response . IL-10 hFinally, our network and enrichment analyses showed that the TRYCAT pathway may be involved in CFAS-D, although it is not a key component but rather a spin-off of the IO&NS response. Previously, it was reported that this pathway is highly strongly associated with somatization disorder, a psychiatric disease accompanied by psychosomatic symptoms but not necessarily by fatigue . The TRYOur enrichment analyses revealed that the CFAS-D network was highly significantly associated with two major interrelated functions/pathways/domains, namely, the canonical Wnt/β-catenin pathway, T-cell factor (TCF)-dependent signaling in response to Wnt, degradation of β-catenin by the destruction complex, the β-catenin complex and DIX domain, and adherens junctions, cadherin prodomain, and cell–cell junctions. The DIX domains and axin, GSK-3, and Disheveled (Dvl) are key players in the β-catenin destruction complex, thereby determining the interaction of β-catenin with the transcription factor TCF and, thus, the expression of the Wnt target genes .The β-catenin/E-cadherin complex is the key component of adherens junctions (AJs), which stabilizes cell–cell junctions, provides cell–cell adhesion, and binds β-catenin with the actin cytoskeleton , 49. MorPseudomonas aeruginosa, one of the bacteria involved in CFAS-D  pain and neuropathic pain with Wnt inhibition improving pain , 69; (b)Aberrations in the cross-talks among redox, NF-κB, and Wnt signaling are predicted to be key pathways in CFAS-D, which are, therefore, associated with dysfunctions in multicellular organism homeostatic processes (including negative regulation of energy homeostasis and tissue maintenance). Disorders in the intertwined interactions between these systems may explain the broad spectrum of organs and dysfunctions that participate in CFAS-D . An important spin-off is increased IL-10 production, which may contribute to immunosuppression and recurrent or protracted infections and increased TRYCAT production may aggravate the neurotoxic effects of oxidative stress. Increased translocation of Gram-negative bacteria with increased LPS load is probably a major trigger factor, but also other bacterial infections, toxoplasmosis, viral infections , cancer, and gastrointestinal, autoimmune, immune-inflammatory, neuroinflammatory, and neurodegenerative disorders appear to be associated with those pathways via activation of TLR/LTF/TWEAK signaling.Future study should scrutinize the specific role of the NF-κB, ROS, and Wnt axis in CFAS-D. The cross-talks between these three pathways may also constitute new drug targets to treat CFAS-D. Given that the Wnt pathway shows many complex, negative and positive feedback loops interacting with redox systems and NK-κB signaling, manipulations of Wnt signaling and β-catenin (despite being a hub and master switch) appear to be very challenging. It may be more promising to simultaneously target the cross-talk among redox and NF-κB pathways. New knowledge on the precise aberrations in the Wnt pathway in association with ROS and NF-κB signaling may lead to more effective treatments by specifically targeting β-catenin, the β-catenin destruction complex, or the TCF transcription factor.There are now many studies showing that targeting redox pathways with selected antioxidants may improve chronic fatigue-like symptoms in animal models of CFS and in chronic fatigue comorbid with neuroinflammatory disease , 80, 81.The new model of CFAS-D proposed here is shown in MM designed the study and performed the network, enrichment, and annotation analyses. All authors contributed to interpretation of the data, writing of the manuscript, and participated in the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.