Blocking of the CD80/86 axis as a therapeutic approach to prevent progression to more severe forms of COVID-19
Antonio Julià, Irene Bonafonte, Antonio Gómez, María López-Lasanta, Mireia López-Corbeto, Sergio H. Martínez-Mateu, Jordi Lladós, Iván Rodríguez-Nunez, Richard M. Myers, Sara Marsal
1 Blocking of the CD80/86 axis as a therapeutic approach to prevent progression to more severe forms of COVID-19
Antonio Julià , Irene Bonafonte , Antonio Gómez , María López-Lasanta , Mireia López-Corbeto , Sergio H. Martínez-Mateu , Jordi Lladós , Iván Rodríguez-Nunez , Richard M. Myers , Sara Marsal Rheumatology Department and Rheumatology Research Group, Vall d’Hebron Hospital Research Institute, Barcelona, Spain. HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, USA. *Corresponding author: [email protected] (AJ); [email protected] (SM)
Abstract
In its more severe forms, COVID-19 progresses towards an excessive immune response, leading to the systemic overexpression of proinflammatory cytokines like IL6, mostly from the infected lungs. This cytokine storm can cause multiple organ damage and death. Consequently, there is a pressing need to identify therapies to treat and prevent severe symptoms during COVID-19. Based on previous clinical evidence, we hypothesized that inhibiting T cell co-stimulation by blocking CD80/86 could be an effective therapeutic strategy against progression to severe proinflammatory states. To support this hypothesis, we performed an analysis integrating blood transcriptional data we generated from rheumatoid arthritis patients treated with abatacept -a CD80/86 co-stimulation inhibitor- with the pathological features associated with COVID-19, particularly in its more severe forms. We have found that many of the biological processes that have been consistently associated with COVID-19 pathology are reversed by CD80/86 co-stimulation inhibition, including the downregulation of IL6 production. Also, analysis of previous transcriptional data from blood of SARS-CoV-infected patients showed that the response to abatacept has a very high level of antagonism to that elicited by COVID-19. Finally, analyzing a recent single cell RNA-seq dataset from bronchoalveolar lavage fluid cells from COVID-19 patients, we found a significant correlation along the main elements of the C80/86 axis: CD86+/80+ antigen presenting cells, activated CD4+ T cells and IL6 production. Our in-silico study provides additional support to the hypothesis that blocking of the CD80/CD86 signaling axis may be protective of the excessive proinflammatory state associated with COVID-19 in the lungs. Introduction
Infection with SARS-CoV2 can lead to different degrees of symptomatology and severity, ranging from asymptomatic to an extreme immune response leading to patient death (1). The fatality rate of COVID-19 is estimated to be close to 1%, which is 10 times more than typical seasonal influenza (2). COVID-19 has been associated to the cytokine storm or cytokine release syndrome (CRS) (3), an overload of proinflammatory cytokines that leads to massive organ failure, predominantly uncontrollable lung inflammation that, even with the help of mechanical ventilation, can lead to organ collapse and death (4). Its high capacity of dissemination and the severity stages at which it can lead, have contributed to one of the largest pandemics since the Spanish flu in 1912 (5). There is currently a major international effort to develop vaccines against SARS-CoV2 (6). However, vaccine development is a lengthy process that needs to ensure both effective virus neutralization and patient safety(7). Therefore, while vaccines are being developed, there is a need to identify therapies that can help reduce the symptomatology associated with COVID-19 and, mostly, prevent infected individuals from progressing into life-threatening stages(8). Many different strategies are being contemplated to find drugs to treat COVID-19. These include in-silico docking strategies (9), protein-protein network interactions (10)(11), in vitro testing (12), and also evaluation of disease progression to severe forms in patients undergoing different therapies. In inflammatory rheumatic disease, many patients are treated with drugs targeting specific components of the immune system (e.g. Tumor Necrosis Factor-alpha (TNF) and Interleukin-6 (IL6) signaling, and CD80/86 T cell co-stimulation) to dampen the excess immune activation that characterizes these diseases. Closely evaluating these patients can provide invaluable information on how blocking certain elements of the immune response can be beneficial or detrimental towards COVID-19 severity. Using a cohort of 959 rheumatic patients under different targeted therapies, we have recently provided epidemiological evidence (13) that suggests that anti-IL6 receptor therapy (tocilizumab) and CD80/86 blockade with CTLA4-Ig (abatacept) are associated with a lower prevalence of COVID-19 associated symptomatology as defined by the world health organization. Given that severe COVID-19 patients express high levels of IL6 (14), targeting the signaling of this cytokine -either directly or through its receptor- has been contemplated and is currently being used off-label to attempt to rescue critically ill patients (15). CTLA4-Ig, however, has not been so far proposed as a therapeutic agent for COVID-19 severity. Abatacept is a fusion protein consisting of the extracellular domain of human cytotoxic T lymphocyte antigen 4 (CTLA4) linked to the modified Fc domain of human IgG1 (16). It binds to both costimulatory proteins CD80 and CD86 on professional antigen presenting cells -dendritic cells, macrophages and B cells- with higher affinity than CD28 on the surface of T cells, thereby preventing the necessary costimulatory signal required by T cells to progress to activation. It was originally developed to treat rheumatoid arthritis, where T cell activation is central to the disease pathology, but has currently been approved also for psoriatic arthritis and juvenile idiopathic arthritis (16). In severe COVID-19, macrophages in the lung activate and produce large amounts of IL6. Single cell analysis on bronchoalveolar lavage fluid (BALF) from COVID-19 patients suggest that monocyte-derived FCN1+ macrophages substitute alveolar macrophages in the lung during inflammation (17). In either case, how macrophages drastically increase the proinflammatory levels in COVID-19 is yet not known(8). High levels of T-cell mediated activation could be one of the possible causes. In turn, T cells could become hyperactivated due to excessive activation through the CD80/86 axis ( Figure 1 ). Therefore, dampening this activation signaling pathway could be an effective therapeutic approach to prevent immune system hyperactivation, and progress to more severe stages. To support this hypothesis raised by the findings of our previous epidemiological survey in rheumatic patients, here we performed an in-silico study using transcriptional data from abatacept-treated patients as well as from COVID-19 patients. Our data support a significant antagonism of abatacept on COVID-19-associated processes at the systemic level, and suggest that blocking the CD80/86 axis could be a useful therapeutic approach to reduce the level of macrophage-associated inflammatory response.
Methods
Patients and samples
A total of n=38 rheumatoid arthritis (RA) patients starting treatment with abatacept were recruited in the framework of the PACTABA project (BMS). All patients were treated with subcutaneous abatacept at the recommended weekly dosage of 125 mg. The PACTABA project is a multicentric Spanish pharmacogenomic study performed in a subset of patients from the ASCORE clinical trial (Bristol-Myers Squibb, ClinicalTrials.gov Identifier: NCT02090556). This observational and prospective study was designed to estimate the retention rate of subcutaneous abatacept over 24 months in the routine clinical practice of RA patients (18). The study was approved by the institutional review board and informed consent was obtained in all cases. Whole blood samples were obtained at the start of the therapy with abatacept and at week 12. Blood was collected using RNA-stabilizing PaxGene tubes (PreAnalytiX, Switzerland), which preserve total RNA from the time of venipuncture. Total RNA was extracted using the PaxGene blood isolation kit (Qiagen). All samples had a RIN index > 7 and were included for RNA-seq analysis. RNA-seq libraries were performed using with the KAPA RNA HyperPrep Kit, with RiboErase (HMR) Globin (globin and rRNA depletion protocol) for Illumina sequencing platforms and with the addition of Unique Molecule Identifiers (UMIs). Sequencing was performed using the NovaSeq platform (Illumina) at average of 250 paired-end reads. RNA data preprocessing
For our RA patient cohort, FASTQ files were aligned to the GRCh37 human reference genome assembly using STAR (19). After alignment, we performed deduplication of the PCR duplicates using UMItools (20) to mark and after removing with samtools (21). Gene-level read counts of the deduplicated bam files were obtained with featureCounts (22). The RA longitudinal dataset will be made available through the NCBI Gene Expression Omnibus database (GEO link available after data has been deposited). To characterize the gene expression profile associated with COVID-19, the RNA-seq data from Xiong et al on peripheral blood mononuclear cells (PBMCs) from three patients infected with COVID-19 and three healthy controls was used (23). The raw sequencing data was processed as described previously. In order to infer the purity and cell composition of this dataset, we used the ABIS immune cell deconvolution method which has been specifically developed for PBMC data (24). This analysis revealed potential neutrophil presence, particularly in one sample (
Supplementary Figure 1 ). To compensate for this potential confounding effect, the estimated percentage of granulocytes was used as a covariate in the differential expression analysis.
Differential expression and gene set enrichment analysis
Genes differentially expressed in COVID-19 patients were determined using edgeR (25). Raw counts were normalized by trimmed mean of mean values (TMM) normalization, and genes with low expression values (i.e. < 2 counts per million -CPM- in at least 2 samples). Differentially expressed genes were determined by fitting a quasi-likelihood negative binomial generalized linear model and adjusting for multiple testing by Benjamini & Hochberg (26). Genes with an adjusted p-value < 0.05 were considered significant. To test for gene expression changes induced by treatment with abatacept at a systemic level, the differential expression between baseline and week 12 was tested in RA patients using limma (27). Raw counts were normalized using TMM normalization for all genes with > 0.6 CPM in at least 20 samples. Normalized counts were log-transformed to log-counts using voom (28) and a linear model was fitted blocking by individual and adding sex, age and batch (i.e. library plate) as covariates. The percentage of granulocytes in each sample were estimated using DeconCell (29), and also included as a covariate in the model in order to make the results more directly comparable with the results obtained with the COVID-19 dataset. Biological processes affected either by SARS-CoV-2 infection or treatment with abatacept were determined using the Gene Set Enrichment Analysis (GSEA)(30) implemented in the fGSEA R package. To rank the genes based on their association to COVID-19 and abatacept, the minus log of the p-value of the differential expression analysis was calculated and multiplied by the sign of the log fold change as described previously (31). We included the sign of the log fold change in order to preserve the directionality of the enrichment and, therefore, to be able to determine if the biological process was either activated or repressed by the viral infection and by the treatment with abatacept. Biological processes with a FDR < 0.05 and a normalized enrichment score (NES) > 0 were considered to be up regulated, while biological processes with a FDR < 0.05 and a NES < 0 were considered to be down regulated.
Analysis of the antagonism of the abatacept signature to COVID-19 associated processes
To identify COVID-19 biological processes we used two alternative strategies. In the first, we identified a list of biological processes that have been associated to COVID-19 pathology through diverse clinical and biological studies. In particular, biological processes that have been associated with COVID-19 severity were prioritized. Those pathological processes that were unlikely to be captured at the blood transcriptional level (e.g. lung fibrosis) were excluded. A total of 22 processes were finally selected, which are divided in a group of 6 processes related to viral immune sensing and anti-viral response (i.e. first stage of COVID-19)(5), and a group of 16 processes related to hyperinflammation and severity (i.e. second stage)(32)(3)(33)(34).
Table 1 describes the selected processes and the most relevant bibliographical evidence. To evaluate their modulation by abatacept, we selected a representative gene set from the Biological Process (BP) database from the Gene Ontology (GO)(35) and tested their association to treatment with GSEA as described previously. In the second approach, we used the available PBMC transcriptional data generated from COVID-19 patients and controls (14) to determine the biological processes associated with COVID-19. Similarly, biological processes regulated by abatacept at the systemic level were determined by comparing the baseline gene expression of RA patients to the expression after 12 weeks of treatment with abatacept. To avoid testing BPs represented by very low gene numbers or excessive gene content (too broad process annotation), we selected only GO terms with > 10 genes and < 300 genes. To account for the large number of GO terms tested, a false-discovery rate (FDR) adjustment was performed. GO terms with FDR < 0.05 were considered significant. Since many GO terms can be very similar in their gene composition, we reduced the redundancy in the analyzed gene sets using a distance measure based on the Jaccard index and hierarchical clustering. Within each cluster, the GO term showing the highest association to COVID was chosen to represent the corresponding biological process. Single cell RNA-seq analysis of COVID-19 BALF cells
In order to evaluate the potential utility of CTLA4-Ig blockade of CD80/86 T cell signaling in COVID-19-mediated lung inflammation, we analyzed single cell RNA-seq data from bronchoalveolar lavage fluid (BALF) cells. Single-cell RNA-seq raw data from BALF samples from 9 COVID-19 patients (n=6 severe and n=3 mild) and 3 controls were downloaded from the GEO database (accession GSE145926). This dataset was generated using the 10x genomics platform and includes samples used in a publication demonstrating the predominant influx of monocyte-derived macrophages in COVID-19 patients (17). The raw data processing and analysis -including, normalization, scaling, and clustering of cells- was performed using Seurat (v3)(36)(37) and scTransform (38). Cells with <200 or >6,000 unique feature counts, >10% mitochondrial counts or < 1,000 UMIs were filtered out. Samples were log-normalized and scaled for the number of genes, number of UMIs and percentage of mitochondrial reads. Cell type clustering was performed using the “FindClusters” function from Seurat. In brief, this method uses a shared nearest neighbour (SNN) modularity optimization-based clustering algorithm to identify clusters of cells based on their PCs. Before constructing the SNN graph, this function calculates the k-nearest neighbours. The number of PCs used for each clustering round (k=50) was estimated by the evaluation of the elbow of the PCA scree plot. Results
The transcriptional changes induced by CD80/86 blocking antagonize COVID-19 associated processes
In our first approach, we evaluated how COVID-19 pathological processes were affected by treatment with CTLA4-Ig at the transcriptional level. Of the 22 curated processes, we found that abatacept induced transcriptional changes in 16 of them (72.7%, binomial test P=8.5e-17). All significant changes occurred in the opposite direction to that described by previous clinical and experimental studies on COVID-19
Figures 2A and 2B show the observed changes induced by CTLA4-Ig in the anti-viral primary response and hyperinflammation biological processes, respectively.
Figures 3A and 3B show a detailed visualization of the differential gene expression for the selected pathological processes representing anti-viral immune response and hyperinflammation-mediated severity, respectively. In our second approach, we identified a total of 260 pathways differentially activated in COVID-19 patients compared to controls (FDR < 0.05) (
Supplementary Table 1 ). These pathways were mostly associated to phagocytosis, endocytosis and lysosome function, immunoglobulin mediated immunity, antigen processing and presentation, acute inflammatory response and cytokine cascades (i.e. IL1, IL6 and IL8 production, TNF signaling, type I and type II interferon signaling, NF-kB signaling and macrophage activation), metabolic processes, mitochondrial activity, cell cycle, response to reactive oxygen species and apoptosis. Other interesting pathways included the down-regulation of T cell receptor signaling, the over-activation of the cellular response to angiotensin, the up regulation of transferrin transport and the regulation of blood pressure. In the RA patient cohort, comparing week 12 to baseline gene expression profiles, we found 109 pathways associated with CTLA4-Ig therapy (FDR < 0.05,
Supplementary Table 2 ). These pathways were associated predominantly to cell cycle and cell division, myeloid cell differentiation, antigen processing and presentation, immunoglobulin mediated immunity, acute inflammatory response, cytokine cascades (IL7 and IL6), phagocytosis, endocytosis and lysosome function, ribosomal RNA related processes and apoptosis. Comparing the two datasets, we found a total of 49 overlapping significant pathways (
Figure 4 ). From these, 47 (96%) were found to be antagonistically activated by abatacept compared to COVID-19. Among the significant antagonistic processes, we found 16 pathways related to immune system processes including processes related to viral defense (viral transcription, GO:0019083), innate immune system activation (Fc receptor signaling pathway, GO:0038093; myeloid leukocyte differentiation: GO:0002573) and acquired immune response (regulation of B cell activation, GO:0050864; humoral immune response mediated by circulating immunoglobulin, GO:0002455) and cytokine production (interleukin-6 production; GO:0032635). Other abundant significant processes included those related with cell cycle and RNA transcription (n=18) and with endocytosis (n=5). Only one pathway (mitochondrial translational elongation, GO:0070125) was activated and one process (mucosal immune response: GO:0002385) was inactivated by both exposures. The probability that the observed antagonism occurred by chance, is very low (P value < 2.5e-18, binomial test). Figure 5 shows the most significant processes antagonized by CD80/86 blocking with abatacept. The complete list of overlapping -antagonistic and agonistic- pathways is included in
Supplementary Table 3 . scRNA-seq analysis of CD80/86 axis in BALF In order to evaluate the relevance of CD80/86 co-stimulation in COVID-19 pathology we analyzed the correlation of three key cell types participating in this axis: CD80+ or CD86+ antigen-presenting cells, activated CD4+ T cells and IL6-producing macrophages. For this objective we analyzed a recent scRNA-seq dataset generated from 12 BALF samples (17).This dataset consists on samples from six severe and three moderate COVID-19 patients, as well as a set of CD45+ selected BALF cells from healthy controls. In total, 54,420 cells passed our quality control analysis and were analyzed using the scTransform algorithm (38). With this approach a total of 34 cell clusters were identified (
Figure 6 ). Most of the cell clusters aggregated into four major regions representing i) T and NK cells (8,014 cells, 14.7%, clusters 9, 11, 5, 25 and 18), ii) two regions of FABP4+ alveolar macrophage cells (17,399 cells, 32%, clusters 1, 2, 3, 30, 7, 32 and 12), and iii) a large cluster aggregate of active, IL6-expressing macrophages both from FABP4+ alveolar and monocyte-derived FCN1+ macrophages (24,742 cells, 45.5%, clusters 6, 20, 13, 22, 0, 8, 4, 15, 10, 16, 14, 26). Both alveolar macrophage-only clusters belong to the three healthy controls (
Supplementary Figure 2 ). As described by the original study(17), there is a marked transition from FABP4+ alveolar macrophages to FCN1+ monocyte-derived macrophages from healthy to infected, and as COVID-19 severity increases (
Figure 7 A ). When looking at the co-stimulation proteins that are the target of CTLA4-Ig -CD80 and CD86- we found that healthy controls had a significantly lower expression of both proteins (
Figure 7 B ). This difference was high for CD86 (26.5% CD86+ in controls vs 45.4% in COVID-19 infected patients, P<1e-16) and drastically different for CD80+ cells (1.7% CD86+ in controls vs 19.6% in COVID-19 infected patients, P<1e-16). Next, we identified the set of cells showing production of IL6, the key cytokine in COVID-19 severity and the cytokine storm. Among the different BALF cell clusters, we found IL6-expressing cells almost exclusively in the activated macrophage cluster derived from COVID19 patients (
Figure 7 C ). Finally, we identified the cluster containing the CD4+ T cell element of the CD80/86 axis. Cluster 5 was found to aggregate markers of different types of CD4+ T cells including CCR7 (naïve TCD4), IL2RA (Treg), FOXP3 (Treg), IL7R (naive), LTB (naive), CXCL13(T peripheral helper) (
Figure 7D ). This cell cluster also was characterized by the expression of CTLA4, the protein that is recombined in abatacept and also a marker that is highly upregulated in active T cells and Treg cells (39), and was therefore used to represent the CD80/CD86 activated T CD4+ element. We next tested for association between the simultaneous presence of each element, of the CD80/86 axis. First, we found a significant correlation between CD80+ or CD86+ APCs and active CD4+ T cells (r =0.87, P=0.00036; and r =0.73, P=0.0069) ( Figure 8A and Figure 8B ). Second, we also found evidence of correlation (r =0.79, P=0.0024) between the number of activated CD4+ T cells and the number of IL6 producing cells ( Figure 8C ). Finally, we found a significant correlation between the number of CD80+ or CD86+ cells and IL6+ production (r =0.93, P=1.5e-7, and r =0.82, P=0.0011, respectively) ( Figure 8D ). Discussion
Since the beginning of the pandemic, SarsCov-2 has infected >4 million people and caused at least 280,000 deaths world-wide as of May 11 th patients, we have also found that this antagonism extends significantly to many other biological processes induced by SARS-CoV2 virus infection. Finally, analyzing single cell data from BALF samples, we have found evidence that the CD80/86 axis is closely linked to IL6 expression in the BALF macrophage compartment in COVID-19. Our results suggest that blocking the CD80/86 axis could be useful to dampen the hyperactivation of the immune response in severe COVID-19, and subsequently reduce the damage to the lungs and the excessive systemic cytokine production. Once SARS-CoV2 enters the lung through attachment to ACE2 expressed on alveolar epithelial cells (40)(41), an immune response is subsequently mounted. Central to this response, phagocytic cells, initially alveolar macrophages that are eventually replaced by massively infiltrating monocytes (17), are highly activated, releasing a large number of cytokines. Macrophages act as powerful antigen-presenting cells (APCs) on activated CD4+ T cells to further amplify the level of immune response. For this objective, the co-stimulatory signal in the form of CD80 and CD86 expression in the surface of APCs is essential, otherwise an anergic program is initiated (42). From all organs, the lung is the tissue with the highest levels of CD80 mRNA, and the second tissue after the spleen with the highest levels of CD86 mRNA expression ( Supplementary Figure 3 ). Even CTLA4 itself is highly expressed in the lung compared to the rest of tissues. This high expression of the elements of the CD80/86 axis might reflect the need of the lung to respond rapidly to potential immunological insults in a tissue that is constantly exposed to environmental cues (43). However, having this natively high potential for antigen presentation and T cell activation could be a detrimental factor in a rapidly spreading virus like SARS-CoV2. A larger cell mass capable of activating T cells would in turn lead to a more pronounced stimulation of IL6 producing programs (44). This high signal amplification potential could therefore explain the sudden transition into the extremely high cytokine production stage known as cytokine storm (45). In our analysis approach, treatment with CTLA4-Ig showed a regulation at the transcriptional level that is highly antagonistic to that induced by COVID-19. We have shown this by two complementary strategies. In the first strategy we have found that, from the most well-established pathological processes associated with COVID-19 to date, abatacept induces a transcriptional change that is contrary to the one induced by the virus in most of them. In a first group of processes related to innate immune system detection of SARS-CoV2 we found that treatment with abatacept downregulates TLR-signaling pathway and endosomal transport. TLRs are key to detect the presence of the virus, leading to the activation of proinflammatory transcription factors and the expression of IFN and other cytokines(46). While viral detection is essential to trigger the immune response in the first stages of the infection, later its utility might be superseded by the need to adequately control excessive inflammatory response (5). To this regard, our analysis did not detect a strong type I IFN inhibition, thereby suggesting that this basic anti-viral mechanism is protected. Of interest, we found a significant downregulation of endosomal transport, a biological process that has been linked to the possible protective of hydroxychloroquine in COVID-19 (33). A reduction endosomal development and maturation would keep at stake viral load. However, the confirmation of this mechanism in relation to SARS-CoV2 infection and the clinical protection are still controversial and are in need of robust evidence (47). Abatacept inhibits many biological pathways that are key to the hyperinflammatory stage. As described previously, the immune response to SARS-CoV2 can be divided into two phases, one first phase where the antiviral response is deployed and a second phase of excessive inflammatory response (27). The latter might not activate in many infected patients, but likely occurs in most of those who progress to more severe symptoms, particularly those with increasing lung inflammation. This stage is characterized by the activation of myeloid cells, mostly monocyte-derived macrophages that massively infiltrate the lung (8). We have found that treatment with CTLA4-Ig significantly down-regulates myeloid leukocyte differentiation and macrophage activation. This specific anti-inflammatory role could be a useful property to avoid entering into the life-threatening massive cytokine release stage. To this regard, we have found that abatacept downregulated both the production and the response to several key cytokines of the severe stage in COVID-19 including TNF (48), IL1 (33), IL8 (49), IL7 (48) and IL6 (48). Importantly, we have found that downregulation is strong for IL6, the cytokine that is more abundantly produced in patients progressing to severity both in serum and in the lungs (45). Therapies aimed at IL6 signaling, tocilizumab and sarilumab, are currently being evaluated in at least three clinical trials by 11 th May 2020, and there is increasing reporting of beneficial effects in off-label use in severe patients (50). In line with this, our previous epidemiological study, both CD80/86 blocking and anti-IL6R therapies showed the lowest incidence of COVID-19 suspected cases among rheumatic patients (13). Consequently, the potential beneficial effect of CTLA4-Ig could be due to the upstream regulatory effect of this central cytokine for COVID-19 pathogenesis. Abatacept also strongly downregulated two additional immune processes: complement activation and B cell mediated immunity. Complement over-activation has been identified in the lungs of COVID-19 and antibody therapies against elements of the complement system are being currently being evaluated to treat severe cases (32). The downregulation of the B cell mediated response, could also be beneficial to avoid overactivation of the myeloid compartment. While neutralizing antibodies to SARS-CoV are associated to a reduction in the viral load (51), there is also the possibility that high titers antibodies could lead to a more aggressive immunological response. During the SARS-CoV1 epidemic, neutralizing antibody levels were higher in deceased patients (52), which raised the possibility of antibody-dependent enhancement (ADE) contributing to disease exacerbation. In this situation, non-neutralizing antibodies facilitate the phagocytosis of the virus into macrophage through the Fc-receptor leading to their activation into proinflammatory phenotypes (53). ADE has been previously identified in other coronaviruses (54)(55), and experimental evidence in rhesus macaques infected with SARS-CoV1 found that anti-spike IgG antibodies contributed to massive influx of monocytes in the lung and subsequent severe acute injury (56). In this context, the effect of reducing B cell immunity by abatacept might be beneficial by reducing the likelihood of low-affinity antibodies that would contribute to ADE-induction (7). To this regard, evidence showing that antibodies raised against SARS-Cov2 can lead to ADE would be of high relevance, particularly in the development of safe vaccines. The results of this study are entirely in the in-silico domain and therefore they must be considered as additional evidence on top of increasing evidence from the clinical domain. Consequently, additional independent data from large patient cohorts will be useful to confirm this hypothesis. For example, the Global Rheumatology Alliance (57), an international collaborative initiative that is currently aggregating data from patients around the world, will likely become an ideal resource to confirm the observed lower at-risk incidence in abatacept-treated rheumatic patients (13). Among the limitations of this study is that our analysis of transcriptional changes due to abatacept could have been confounded by the specific immune activation that is due to RA. Like SARS, however, RA is a disease where macrophage IL6 production is central to the pathology (58). Of relevance, there is increasing evidence that abatacept could be an efficacious therapy to treat interstitial lung disease in RA (ILD) (59). ILD is a comorbidity that occurs in RA patients and is characterized by the inflammation and subsequent fibrotic development of the lung tissue. Treatment with abatacept was motivated by the finding that blocking T cell co-stimulation through CTLA4-Ig is effective in a mouse model of hypersensitivity pneumonitis (60), a disease characterized by a massive influx of activated T cells in the lungs and where alveolar macrophages express high levels of B7 (CD80/CD86) molecules. This evidence is in line with our hypothesis that the CD80/86 axis is a key factor for lung hyperinflammatory response. Additionally, while immunosuppressive agents are generally associated with an increase in the risk of infectious diseases in rheumatic patients (61), abatacept has shown to be the therapy that least increases this risk. There is substantial epidemiological evidence supporting that abatacept-treated patients do not increase the risk of serious infections (62)(63)(64). Our finding that abatacept-treated patients have a lower percentage of COVID-19 symptoms, prompted us to provide additional evidence of the benefit of this therapeutic approach. After characterizing the transcriptional changes induced by the therapy in a cohort of RA patients, we have seen that it is highly antagonistic to that induced by COVID-19, both to several of the most well-known pathological processes associated with the disease, and to the biological processes that are differentially activated in samples of patients. Together, this evidence identifies blocking of the CD80/86 axis as a candidate therapy for COVID-19. To this regard, Belatacept, a CTLA4-Ig drug derived from abatacept that binds more avidly to CD80/86 and is used to prevent renal transplant rejection (65), could be a therapeutic alternative. However, there’s yet no epidemiological evidence evaluating this drug in relation to COVID-19, with only sporadic reporting (66). While the development of vaccines and the questions like permanency of immunization are still being investigated, there is a need to find therapies that can lower the risk of progressing to severe stages of COVID-19 and reduce the number of fatalities during the next months or years. Additional clinical and experimental evidence gathered in the next months will be crucial to confirm if blocking of the CD80/86 axis is a useful therapeutic approach to prevent progression to severe stages of the disease. References
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The PACTABA project was funded Bristol-Myers Squibb. We thank all participants from the PACTABA study for their collaboration. AJ and SM are supported by the DoCTIS project funded by the European Union’s H2020 programme (grant
Author Contributions
AJ directed the project, conceived, designed and analyzed data and wrote the manuscript; IB developed computational approaches, implemented bioinformatic analyses and wrote the manuscript; AG performed various computational analyses and wrote the manuscript; MLL and MLC contributed to patient recruitment, clinical data collection; SHMM performed statistical analyses; JLL contributed to data processing; INR performed sample sequencing experiments and revised the manuscript; RMM contributed to data generation and revised the manuscript; SM directed the RA project, conceived the project and wrote the manuscript.
Declaration of Interests
SM and RMM are co-founders of IMIDomics, a biotech company focused in bringing precision medicine to immune-mediated inflammatory diseases. Data and code availability
The whole code used to analyze the PBMC RNA-Seq data from COVID-19 infected patients, blood RNA-Seq data from RA patients treated with abatacept and the BALF single-cell RNA-Seq dataset is freely available at https://github.com/Rheumatology-Research-Group/COVID-19_Abatacept. The dataset generated during this study will be deposited in the Gene Expression Omnibus (GEO) repository.
Figure Legends Figure 1. CTLA4-Ig (abatacept) dampens T cell co-stimulation and excessive cytokine production in COVID-19.
Schematics depicting the model for abatacept-mediated inhibition of excess cytokine production. Once lungs become infected with SARS-CoV2 virus, an immune response is mounted. Antigen presenting cells (APCs) in the lung present antigen to activated CD4+ T cells thereby stimulating their production of proinflammatory cytokines (e.g. TNF, IL6). Resident alveolar macrophages and, mostly, large numbers of infiltrating monocyte-derived macrophages respond to the T cell stimulation by producing large quantities of interleukins, mainly IL6. Large numbers of activated macrophages present in the lung can lead to systemic secretion of IL6, forming the cytokine storm. Treatment with CTLA4-Ig recombinant fusion protein could dampen APC activation of T cells and, therefore, reduce the level of hyperinflammatory response observed in severe COVID-16 patients.
Figure 2. Longitudinal transcriptional changes induced by CTLA4-Ig therapy in COVID-19 associated processes. A.
Changes in biological processes associated with immune viral response. B. Changes in biological processes associated with hyperinflammation and severity in COVID-19. The first column on the left indicates the direction of change of the biological process by COVID19 as described in different clinical and experimental studies (blue: up-regulation, red: down-regulation). As a reference, we include the transcriptional changes observed for the same set of processes in the COVID-19 PBMC samples (middle column). Finally, the third column shows the change induced by treatment with abatacept in RA patients. Up-regulation (blue) and down-regulation (red) is depicted according to effect size (diameter of circle). Statistical significance is reflected as color intensity and p-value levels according to *<0.05, **<0.005, ***<0.0005, ****<0.00005 and *****<0.000005. Figure 3. Gene-level differential expression of the COVID-19 related biological processes induced by abatacept. A.
Immune sensing and response to viral infection. B. Immune processes associated with COVID-19 severity stages. Volcano-plots showing the statistical significance (-log10(p-value), y-axis) against the effect size (log fold change, x-axis) for all genes. In color are highlighted the genes from the specific biological processes, red for genes down-regulated by abatacept, and blue for genes up-regulated by abatacept.
Figure 4. Venn plot of the biological processes regulated by COVID-19 and by abatacept.
Numbers represent the biological processes that are specific and those that are commonly changed by both exposures. From the latter, 47 processes modified by COVID-19 are antagonistically modified by abatacept, compared to only 2 processes that are agonistically induced by co-stimulation inhibition.
Figure 5. Most significant COVID-19 transcriptional processes antagonized by abatacept.
The 20 top biological processes more significantly down or up-regulated by COVID-19 at the transcriptional level and also more significantly antagonized by abatacept are shown in this diagram. Up-regulation (blue) and down-regulation (red) is depicted according to effect size (diameter of circle). Statistical significance is reflected as color intensity and p-value levels according to *<0.05, **<0.005, ***<0.0005, ****<0.00005 and *****<0.000005.
Figure 6. UMAP of the BALF samples from COVID-19 and healthy control individuals.
Total cell BALF samples from 6 severe and 3 mild COVID-19 infected individuals and 3 CD45+ sorted BALF samples from 3 healthy controls were analyzed for cell cluster identification. Each number depicts each of the 34 cell clusters identified using the SNN approach implemented in Seurat v3.
Figure 7. Expression of cell type specific markers in BALF samples from COVID-19 and healthy controls. A. Previously described markers FCN1+ and FABP4+ defining monocyte-derived macrophages and resident alveolar macrophages, respectively. The two cluster aggregates that entirely express FABP4 correspond of cells from healthy controls. B. Expression of co-stimulating protein genes CD80 and CD86, which are the target of abatacept. While CD86 has a more ubiquitous expression (including both FCN1+ and FABP4+ macrophages), CD80 is specifically expressed in FCN1+ monocyte-derived macrophages. C. IL6 expression is circumscribed to the activated macrophage cluster. D. Selected T cell markers representing activated T cells (CTLA4, IL2RA), CD8+ lymphocytes (CD8A) and Tregs (FOXP3 and IL2RA). CTLA4 expression is enriched in the CD4+ T cell cluster 5.
Figure 8. CD80/86 axis cell type abundance and correlation.
Scatter plots showing the cell type abundance correlation between the key elements of the CD80/86 axis targeted by CTLA4-Ig therapy. All pairwise correlation analyses were found to be positive and significant (r > 0.7, P<0.005). A CD86+ cells vs active CD4+ T cells. B CD80+ cells vs active CD4+ T cells. C active CD4+ T cells vs IL6+ cells. D CD86+ cells vs IL6+ cells. E CD80+ cells vs IL6+ cells.
Supplementary Figure Legends Supplementary Figure 1. Evidence of neutrophil presence in the COVID-19 PBMC dataset.
Using the cell type deconvolution approach in ABIS, we identified a potential presence of neutrophils in the PBMC samples. In order to correct for this potential confounder, we used the estimated neutrophil percentage as covariate in the differential expression analysis. N1-3: control samples; P1-P3: patient samples.
Supplementary Figure 2. UMAP depicting the individual origin of the cell clusters.
Cell coloring is based on the 12 individuals simultaneously analyzed in the BALF scRNA-Seq study. Samples C51, C52 and C100 correspond to the three controls, which clearly show distinct clusters of FABP4+ alveolar macrophage cells. The remaining samples are from COVID-19 patients with 3 samples from mild patients (C141, C142 and C144) and 6 samples from patients with severe disease (C143, C145, C146, C148, C149, C152).
Supplementary Figure 3. Tissue-level gene expression distribution for CD80, CD86 and CTLA4 genes from GTex database.
The distribution of gene expression levels for the three key genes from 53 human tissues from nearly 1,000 individuals are shown (GTex database, version 8). All three key genes from the CD80/86 axis are highly expressed in the lung, being the first tissue for
CD80 , the second for
CD86 and the third for
CTLA4 mRNAs. Fig. 1 Fig. 2 A Fig. 2 B Fig. 3. A Fig. 3. B Fig. 4 Fig. 5 Fig. 6 Fig. 7 A Fig. 7 B Fig. 7 C Fig. 7 D Fig. 8 A Fig. 8 B Fig. 8 C Fig. 8 D
Fig. 8 E Table 1. List of biological processes associated with COVID-19 pathology
Biological process GO term Implication in SARS-CoV-2 References
Viral entry into cell endosomal transport Mechanism of viral entry into cell. (33)(67) Virus sensing toll−like receptor signaling pathway Dificultates cell infection. Evaded by CoVs. (33) Virus sensing cytoplasmic pattern recognition receptor signaling pathway in response to virus Dificultates cell infection. Evaded by CoVs. (33) Virus sensing type I interferon signaling pathway Unclear. Impaired in severe. (68)(5)(69) Natural killer mediated immunity natural killer cell chemotaxis Over-activation (17)(15) Natural killer mediated immunity natural killer cell mediated cytotoxicity Down-regulated (70)(71) Blood coagulation regulation of blood coagulation Up regulated in severe. (72)(73)
Biological process GO term Implication in SARS-CoV-2 References
T cells T cell mediated immunity Blood lymphopenia, altered function, activated and exhausted in severe. Increased in lung. (8)(74)(74)(70)(75)(76)(77)(78)(79)(80)(17)(81) T cells T cell cytokine production Over activation. Up in severe. (8)(74)(80)(17)(81) T cell interaction with myeloid cells antigen processing and presentation Up in severe (44) T cell interaction with myeloid cells response to interferon−gamma Over activation (48)(8)(82) T cell interaction with myeloid cells cellular response to tumor necrosis factor Over activation (8) Myeloid cell activation myeloid leukocyte differentiation Over activation. Up in severe. (8)(74)(83)(76)(84)(17) Myeloid cell activation macrophage activation Over activation. Up in severe. (8)(74)(83)(76)(17)(73) Cytokine production interleukin−1 production Over activation (48)(76)(81) Cytokine production interleukin−6 production Over activation. Up in severe. (49)(8)(75)(69)(83)(84)(77)(73)(82) Cytokine production tumor necrosis factor production Over activation. Up in severe. (48)(49)(8)(75)(69)(83)(22)(73) Cytokine production interleukin−8 production Over activation. Up in severe. (48)(49)(75)(73) Cytokine production response to interleukin−7 Over activation. Up in severe. (48)(8) Cytokine production interleukin−10 production Over activation. Up in severe. (48)(49)(75)(77)(82) Complement pathway complement activation Over activation in lung. Up in severe. (32) Ig production by B cells B cell mediated immunity Over activation. Up in severe. (33)(51)(52)(80) Suppl. Fig. 1
Suppl. Fig. 2 E s t i m a t ed neu t r oph il s Group
HealthyCOVID − Suppl. Fig. 3
Lung S p l een T h y r o i d S m a ll I n t e s t i ne - T e r m i na l I l eu m A r t e r y - A o r t a A r t e r y - C o r ona r y K i dne y - M edu ll a M i no r S a li v a r y G l and A d i po s e - V i sc e r a l ( O m en t u m ) T e s t i s A d i po s e - S ub c u t aneou s C o l on - T r an sv e r s e N e r v e - T i b i a l C e r v i x - E ndo c e r v i x B r ea s t - M a mm a r y T i ss ue U t e r u s P r o s t a t e A d r ena l G l and P i t u i t a r y P an c r ea s W ho l e B l ood A r t e r y - T i b i a l B r a i n - S p i na l c o r d ( c e r v i c a l c - ) E s ophagu s - M u c o s a V ag i na E s ophagu s - G a s t r oe s ophagea l J un c t i on E s ophagu s - M u sc u l a r i s S t o m a c hL i v e r C e r v i x - E c t o c e r v i x B l adde r C o l on - S i g m o i d K i dne y - C o r t e x S k i n - N o t S un E x po s ed ( S up r apub i c ) H ea r t - A t r i a l A ppendage F a ll op i an T ube S k i n - S un E x po s ed ( Lo w e r l eg ) O v a r y B r a i n - C e r ebe ll u m B r a i n - S ub s t an t i a n i g r a B r a i n - C e r ebe ll a r H e m i s phe r e M u sc l e - S k e l e t a l H ea r t - Le ft V en t r i c l e B r a i n - H y po t ha l a m u s B r a i n - C auda t e ( ba s a l gang li a ) B r a i n - A m y gda l a B r a i n - H i ppo c a m pu s B r a i n - P u t a m en ( ba s a l gang li a ) B r a i n - N u c l eu s a cc u m ben s ( ba s a l gang li a ) B r a i n - C o r t e x B r a i n - A n t e r i o r c i ngu l a t e c o r t e x ( BA ) B r a i n - F r on t a l C o r t e x ( BA ) C e ll s - C u l t u r ed fi b r ob l a s t s T P M Gene expression for CD80 (ENSG00000121594.11) S p l eenLung W ho l e B l ood B r a i n - S p i na l c o r d ( c e r v i c a l c - ) A r t e r y - A o r t a A d i po s e - V i sc e r a l ( O m en t u m ) A r t e r y - C o r ona r y N e r v e - T i b i a l S m a ll I n t e s t i ne - T e r m i na l I l eu m A d i po s e - S ub c u t aneou s A d r ena l G l and B r a i n - S ub s t an t i a n i g r a C o l on - T r an sv e r s e A r t e r y - T i b i a l B r ea s t - M a mm a r y T i ss ue B l adde r C o l on - S i g m o i d F a ll op i an T ube C e r v i x - E ndo c e r v i x B r a i n - H y po t ha l a m u s P r o s t a t e M i no r S a li v a r y G l and T e s t i s E s ophagu s - G a s t r oe s ophagea l J un c t i on E s ophagu s - M u sc u l a r i s B r a i n - H i ppo c a m pu s B r a i n - C auda t e ( ba s a l gang li a ) S k i n - N o t S un E x po s ed ( S up r apub i c ) P i t u i t a r y T h y r o i d B r a i n - A m y gda l a V ag i na U t e r u s S k i n - S un E x po s ed ( Lo w e r l eg ) S t o m a c h E s ophagu s - M u c o s a K i dne y - M edu ll a B r a i n - N u c l eu s a cc u m ben s ( ba s a l gang li a ) O v a r y H ea r t - A t r i a l A ppendage C e r v i x - E c t o c e r v i x B r a i n - P u t a m en ( ba s a l gang li a ) B r a i n - F r on t a l C o r t e x ( BA ) B r a i n - A n t e r i o r c i ngu l a t e c o r t e x ( BA ) K i dne y - C o r t e x L i v e r B r a i n - C o r t e x B r a i n - C e r ebe ll a r H e m i s phe r e P an c r ea s H ea r t - Le ft V en t r i c l e B r a i n - C e r ebe ll u mM u sc l e - S k e l e t a l C e ll s - C u l t u r ed fi b r ob l a s t s T P M Gene expression for CD86 (ENSG00000114013.15) S p l een S m a ll I n t e s t i ne - T e r m i na l I l eu m Lung T e s t i s W ho l e B l ood F a ll op i an T ube A d i po s e - V i sc e r a l ( O m en t u m ) C o l on - T r an sv e r s e E s ophagu s - M u c o s a V ag i na B r ea s t - M a mm a r y T i ss ue A r t e r y - C o r ona r y P r o s t a t e T h y r o i d C e r v i x - E ndo c e r v i x K i dne y - M edu ll a S k i n - N o t S un E x po s ed ( S up r apub i c ) N e r v e - T i b i a l A r t e r y - A o r t a A d i po s e - S ub c u t aneou s B l adde r C e r v i x - E c t o c e r v i x S t o m a c h M i no r S a li v a r y G l and S k i n - S un E x po s ed ( Lo w e r l eg ) P i t u i t a r y C o l on - S i g m o i d K i dne y - C o r t e x U t e r u s A r t e r y - T i b i a l B r a i n - S p i na l c o r d ( c e r v i c a l c - ) E s ophagu s - G a s t r oe s ophagea l J un c t i on B r a i n - H y po t ha l a m u s O v a r y E s ophagu s - M u sc u l a r i s B r a i n - C o r t e x B r a i n - C e r ebe ll u m H ea r t - Le ft V en t r i c l e B r a i n - F r on t a l C o r t e x ( BA ) L i v e r H ea r t - A t r i a l A ppendage B r a i n - C e r ebe ll a r H e m i s phe r e B r a i n - S ub s t an t i a n i g r a A d r ena l G l and B r a i n - H i ppo c a m pu s B r a i n - A n t e r i o r c i ngu l a t e c o r t e x ( BA ) B r a i n - C auda t e ( ba s a l gang li a ) B r a i n - A m y gda l a B r a i n - N u c l eu s a cc u m ben s ( ba s a l gang li a ) P an c r ea s B r a i n - P u t a m en ( ba s a l gang li a ) M u sc l e - S k e l e t a l C e ll s - C u l t u r ed fi b r ob l a s t s T P M Gene expression for CTLA4 (ENSG00000163599.14) upplementary Table 1: List of biological processes associated to COVID-19 infection.ID Description Size NES pvalue p.adjust ratio
GO:0016236 macroautophagy 295 2.152 1.96E-06 1.08E-04 0.432GO:0038093 Fc receptor signaling pathway 241 2.466 1.96E-06 1.08E-04 0.524GO:0002758 innate immune response-activating signal transduction 298 1.895 1.96E-06 1.08E-04 0.414GO:0015980 energy derivation by oxidation of organic compounds 285 2.125 1.96E-06 1.08E-04 0.330GO:0002440 production of molecular mediator of immune response 286 1.918 1.96E-06 1.08E-04 0.442GO:0072593 reactive oxygen species metabolic process 284 1.975 1.97E-06 1.08E-04 0.455GO:0019882 antigen processing and presentation 226 2.494 1.97E-06 1.08E-04 0.475GO:0006733 oxidoreduction coenzyme metabolic process 207 2.138 1.97E-06 1.08E-04 0.468GO:0002455 humoral immune response mediated by circulating immunoglobulin 150 3.043 1.97E-06 1.08E-04 0.739GO:0030100 regulation of endocytosis 281 1.998 1.97E-06 1.08E-04 0.477GO:0036294 cellular response to decreased oxygen levels 217 1.995 1.97E-06 1.08E-04 0.439GO:0046434 organophosphate catabolic process 248 2.072 1.97E-06 1.08E-04 0.351GO:0016052 carbohydrate catabolic process 199 2.129 1.97E-06 1.08E-04 0.393GO:0010324 membrane invagination 135 2.441 1.97E-06 1.08E-04 0.701GO:0022900 electron transport chain 186 2.139 1.97E-06 1.08E-04 0.577GO:0016054 organic acid catabolic process 275 1.950 1.97E-06 1.08E-04 0.465GO:0033209 tumor necrosis factor-mediated signaling pathway 167 1.998 1.97E-06 1.08E-04 0.463GO:0038094 Fc-gamma receptor signaling pathway 142 2.607 1.97E-06 1.08E-04 0.642GO:0002526 acute inflammatory response 220 2.600 1.97E-06 1.08E-04 0.558GO:1901136 carbohydrate derivative catabolic process 192 2.362 1.97E-06 1.08E-04 0.421GO:0061025 membrane fusion 154 2.299 1.97E-06 1.08E-04 0.505GO:0043281 regulation of cysteine-type endopeptidase activity involved in apoptotic process 215 1.926 1.97E-06 1.08E-04 0.400GO:0000070 mitotic sister chromatid segregation 151 2.028 1.97E-06 1.08E-04 0.273GO:0038061 NIK/NF-kappaB signaling 183 1.940 1.97E-06 1.08E-04 0.489GO:0050864 regulation of B cell activation 184 1.949 1.97E-06 1.08E-04 0.500GO:0007033 vacuole organization 163 2.177 1.97E-06 1.08E-04 0.459GO:0044106 cellular amine metabolic process 129 2.214 1.97E-06 1.08E-04 0.571GO:0055067 monovalent inorganic cation homeostasis 154 2.237 1.97E-06 1.08E-04 0.528GO:1902750 negative regulation of cell cycle G2/M phase transition 105 2.142 1.97E-06 1.08E-04 0.455GO:0002474 antigen processing and presentation of peptide antigen via MHC class I 96 2.509 1.97E-06 1.08E-04 0.505O:0070498 interleukin-1-mediated signaling pathway 100 2.216 1.97E-06 1.08E-04 0.553GO:0031145 anaphase-promoting complex-dependent catabolic process 81 2.425 1.97E-06 1.08E-04 0.494GO:0009060 aerobic respiration 87 2.366 1.97E-06 1.08E-04 0.493GO:0032612 interleukin-1 production 115 2.113 1.97E-06 1.08E-04 0.557GO:0043648 dicarboxylic acid metabolic process 99 2.163 1.97E-06 1.08E-04 0.545GO:0061418 regulation of transcription from RNA polymerase II promoter in response to hypoxia 77 2.189 1.97E-06 1.08E-04 0.559GO:0045454 cell redox homeostasis 76 2.361 1.97E-06 1.08E-04 0.607GO:0045851 pH reduction 53 2.280 1.97E-06 1.08E-04 0.625GO:1901658 glycosyl compound catabolic process 43 2.264 1.98E-06 1.08E-04 0.457GO:0006614 SRP-dependent cotranslational protein targeting to membrane 105 -2.113 2.03E-06 1.09E-04 0.611GO:0140014 mitotic nuclear division 264 1.766 3.93E-06 1.76E-04 0.227GO:0005996 monosaccharide metabolic process 292 1.833 3.93E-06 1.76E-04 0.384GO:0010466 negative regulation of peptidase activity 262 1.999 3.94E-06 1.76E-04 0.485GO:0008037 cell recognition 215 2.040 3.94E-06 1.76E-04 0.535GO:0016999 antibiotic metabolic process 151 2.091 3.94E-06 1.76E-04 0.480GO:0019682 glyceraldehyde-3-phosphate metabolic process 21 2.271 3.95E-06 1.76E-04 0.643GO:0006739 NADP metabolic process 32 2.271 3.95E-06 1.76E-04 0.542GO:0007040 lysosome organization 61 2.170 3.95E-06 1.76E-04 0.611GO:0031668 cellular response to extracellular stimulus 268 1.787 5.90E-06 2.50E-04 0.457GO:0009152 purine ribonucleotide biosynthetic process 280 1.768 5.90E-06 2.50E-04 0.303GO:0042180 cellular ketone metabolic process 248 1.838 5.90E-06 2.50E-04 0.481GO:0006376 mRNA splice site selection 53 -2.321 6.08E-06 2.54E-04 0.323GO:0044839 cell cycle G2/M phase transition 266 1.740 7.85E-06 3.08E-04 0.379GO:0097164 ammonium ion metabolic process 205 1.884 7.87E-06 3.08E-04 0.472GO:0016051 carbohydrate biosynthetic process 214 1.864 7.87E-06 3.08E-04 0.444GO:0006900 vesicle budding from membrane 102 1.990 7.88E-06 3.08E-04 0.452GO:0000395 mRNA 5'-splice site recognition 29 -2.217 8.11E-06 3.13E-04 0.583GO:1901988 negative regulation of cell cycle phase transition 267 1.757 9.82E-06 3.65E-04 0.432GO:0050701 interleukin-1 secretion 61 2.137 9.87E-06 3.65E-04 0.556GO:0034341 response to interferon-gamma 199 1.834 1.18E-05 4.15E-04 0.410GO:0007032 endosome organization 79 2.034 1.18E-05 4.15E-04 0.528GO:0006140 regulation of nucleotide metabolic process 146 1.924 1.38E-05 4.61E-04 0.368O:0044242 cellular lipid catabolic process 217 1.835 1.38E-05 4.61E-04 0.456GO:0072350 tricarboxylic acid metabolic process 39 2.150 1.38E-05 4.61E-04 0.606GO:0032635 interleukin-6 production 161 1.859 1.57E-05 4.99E-04 0.505GO:1902850 microtubule cytoskeleton organization involved in mitosis 131 1.891 1.57E-05 4.99E-04 0.330GO:0033572 transferrin transport 36 2.163 1.58E-05 4.99E-04 0.643GO:0002532 production of molecular mediator involved in inflammatory response 72 2.091 1.58E-05 4.99E-04 0.608GO:0034381 plasma lipoprotein particle clearance 69 2.122 1.78E-05 5.44E-04 0.629GO:0048771 tissue remodeling 179 1.894 1.97E-05 5.85E-04 0.505GO:0051304 chromosome separation 90 1.965 1.97E-05 5.85E-04 0.267GO:0018196 peptidyl-asparagine modification 34 2.160 1.97E-05 5.85E-04 0.484GO:0009595 detection of biotic stimulus 23 2.171 1.98E-05 5.85E-04 0.800GO:0046466 membrane lipid catabolic process 35 2.157 2.76E-05 7.49E-04 0.692GO:0071216 cellular response to biotic stimulus 236 1.756 3.15E-05 8.42E-04 0.421GO:0050663 cytokine secretion 240 1.742 3.35E-05 8.87E-04 0.425GO:0042116 macrophage activation 95 1.970 3.95E-05 1.01E-03 0.529GO:0007584 response to nutrient 219 1.765 4.33E-05 1.09E-03 0.517GO:0000041 transition metal ion transport 112 1.924 4.34E-05 1.09E-03 0.467GO:0051262 protein tetramerization 172 1.812 4.53E-05 1.12E-03 0.311GO:0006364 rRNA processing 214 -1.717 4.69E-05 1.13E-03 0.420GO:0060337 type I interferon signaling pathway 95 1.933 4.93E-05 1.16E-03 0.500GO:0006643 membrane lipid metabolic process 207 1.749 5.32E-05 1.24E-03 0.403GO:0019083 viral transcription 177 -1.732 5.69E-05 1.32E-03 0.465GO:0042737 drug catabolic process 140 1.906 5.92E-05 1.37E-03 0.427GO:0000184 nuclear-transcribed mRNA catabolic process, nonsense-mediated decay 120 -1.808 6.51E-05 1.48E-03 0.576GO:0006081 cellular aldehyde metabolic process 74 2.030 6.52E-05 1.48E-03 0.568GO:0010499 proteasomal ubiquitin-independent protein catabolic process 23 2.118 6.70E-05 1.52E-03 0.524GO:0051701 interaction with host 209 1.707 7.47E-05 1.64E-03 0.416GO:0002220 innate immune response activating cell surface receptor signaling pathway 116 1.835 8.07E-05 1.74E-03 0.580GO:0051225 spindle assembly 108 1.833 8.87E-05 1.87E-03 0.284GO:0050764 regulation of phagocytosis 98 1.880 8.88E-05 1.87E-03 0.547GO:0032418 lysosome localization 74 1.947 9.28E-05 1.93E-03 0.632GO:0070646 protein modification by small protein removal 299 1.607 1.04E-04 2.16E-03 0.440GO:0006479 protein methylation 179 -1.744 1.12E-04 2.28E-03 0.421GO:0016197 endosomal transport 224 1.670 1.12E-04 2.28E-03 0.415GO:0090288 negative regulation of cellular response to growth factor stimulus 166 -1.858 1.18E-04 2.38E-03 0.415GO:0043112 receptor metabolic process 192 1.733 1.24E-04 2.49E-03 0.455GO:0032868 response to insulin 272 1.641 1.51E-04 2.93E-03 0.402O:1905330 regulation of morphogenesis of an epithelium 180 1.752 1.77E-04 3.35E-03 0.496GO:0046365 monosaccharide catabolic process 67 1.941 1.80E-04 3.39E-03 0.408GO:0097205 renal filtration 22 2.059 1.83E-04 3.45E-03 0.615GO:0090175 regulation of establishment of planar polarity 110 1.799 2.21E-04 4.05E-03 0.512GO:0032722 positive regulation of chemokine production 58 1.937 2.45E-04 4.42E-03 0.561GO:1902600 proton transmembrane transport 163 1.771 2.52E-04 4.52E-03 0.547GO:0030193 regulation of blood coagulation 79 1.915 2.62E-04 4.67E-03 0.511GO:0006890 retrograde vesicle-mediated transport, Golgi to ER 86 1.818 2.66E-04 4.72E-03 0.455GO:0002573 myeloid leukocyte differentiation 204 1.656 2.85E-04 4.97E-03 0.497GO:0002793 positive regulation of peptide secretion 288 1.608 2.97E-04 5.13E-03 0.361GO:0060759 regulation of response to cytokine stimulus 190 1.678 3.05E-04 5.25E-03 0.430GO:1901568 fatty acid derivative metabolic process 167 1.764 3.11E-04 5.32E-03 0.355GO:0042133 neurotransmitter metabolic process 153 1.777 3.11E-04 5.32E-03 0.494GO:0031638 zymogen activation 53 1.988 3.12E-04 5.32E-03 0.567GO:0036109 alpha-linolenic acid metabolic process 13 2.011 3.40E-04 5.74E-03 0.727GO:0034976 response to endoplasmic reticulum stress 285 1.564 3.59E-04 5.95E-03 0.416GO:0031639 plasminogen activation 25 2.011 3.73E-04 6.09E-03 0.538GO:0046364 monosaccharide biosynthetic process 98 1.813 3.77E-04 6.14E-03 0.443GO:1902476 chloride transmembrane transport 88 1.956 3.93E-04 6.31E-03 0.424GO:0032637 interleukin-8 production 82 1.841 3.97E-04 6.35E-03 0.533GO:0009566 fertilization 182 1.803 4.20E-04 6.69E-03 0.451GO:1902036 regulation of hematopoietic stem cell differentiation 72 1.812 4.73E-04 7.48E-03 0.379GO:0006801 superoxide metabolic process 73 1.860 4.92E-04 7.66E-03 0.588GO:0006575 cellular modified amino acid metabolic process 202 1.666 4.92E-04 7.66E-03 0.484GO:0043174 nucleoside salvage 16 1.991 4.93E-04 7.66E-03 0.462GO:0006027 glycosaminoglycan catabolic process 61 1.949 5.19E-04 7.99E-03 0.586GO:1903008 organelle disassembly 101 1.730 5.54E-04 8.49E-03 0.467GO:0090092 regulation of transmembrane receptor protein serine/threonine kinase signaling pathway 241 -1.668 5.73E-04 8.77E-03 0.366GO:0008217 regulation of blood pressure 182 1.756 5.80E-04 8.84E-03 0.325GO:0006888 ER to Golgi vesicle-mediated transport 212 1.585 5.96E-04 9.05E-03 0.420GO:0043154 negative regulation of cysteine-type endopeptidase activity involved in apoptotic process 84 1.802 6.10E-04 9.20E-03 0.500GO:2000379 positive regulation of reactive oxygen species metabolic process 102 1.768 6.32E-04 9.50E-03 0.542GO:0045730 respiratory burst 37 1.930 6.46E-04 9.65E-03 0.552GO:0007029 endoplasmic reticulum organization 57 1.873 6.47E-04 9.65E-03 0.575GO:0071772 response to BMP 170 -1.754 6.67E-04 9.80E-03 0.403GO:0002755 MyD88-dependent toll-like receptor signaling pathway 36 1.901 6.84E-04 9.94E-03 0.471GO:1903557 positive regulation of tumor necrosis factor superfamily cytokine production 88 1.760 7.18E-04 1.03E-02 0.486O:0006672 ceramide metabolic process 92 1.780 7.34E-04 1.04E-02 0.530GO:1903034 regulation of response to wounding 179 1.671 7.51E-04 1.06E-02 0.448GO:0007034 vacuolar transport 142 1.629 7.74E-04 1.08E-02 0.414GO:0090263 positive regulation of canonical Wnt signaling pathway 147 1.666 7.79E-04 1.08E-02 0.327GO:1901216 positive regulation of neuron death 94 1.742 7.81E-04 1.08E-02 0.533GO:0048260 positive regulation of receptor-mediated endocytosis 51 1.895 7.82E-04 1.08E-02 0.576GO:0033865 nucleoside bisphosphate metabolic process 140 1.670 8.09E-04 1.11E-02 0.346GO:1901605 alpha-amino acid metabolic process 222 1.612 8.46E-04 1.15E-02 0.449GO:0010821 regulation of mitochondrion organization 182 1.587 8.53E-04 1.16E-02 0.290GO:0016125 sterol metabolic process 166 1.657 8.81E-04 1.19E-02 0.352GO:2001234 negative regulation of apoptotic signaling pathway 230 1.568 8.97E-04 1.21E-02 0.395GO:0098754 detoxification 131 1.717 9.41E-04 1.25E-02 0.506GO:0006919 activation of cysteine-type endopeptidase activity involved in apoptotic process 86 1.753 9.63E-04 1.27E-02 0.426GO:0046470 phosphatidylcholine metabolic process 83 1.804 9.73E-04 1.28E-02 0.481GO:0051321 meiotic cell cycle 249 1.596 9.94E-04 1.30E-02 0.275GO:0097352 autophagosome maturation 38 1.873 1.02E-03 1.33E-02 0.424GO:0001510 RNA methylation 81 -1.737 1.17E-03 1.48E-02 0.279GO:0032799 low-density lipoprotein receptor particle metabolic process 23 1.916 1.21E-03 1.52E-02 0.684GO:0045471 response to ethanol 125 1.733 1.22E-03 1.53E-02 0.471GO:0031060 regulation of histone methylation 65 -1.774 1.23E-03 1.53E-02 0.509GO:1903531 negative regulation of secretion by cell 211 1.615 1.28E-03 1.57E-02 0.410GO:0019321 pentose metabolic process 15 1.912 1.30E-03 1.59E-02 0.500GO:0048259 regulation of receptor-mediated endocytosis 103 1.743 1.30E-03 1.60E-02 0.452GO:0061647 histone H3-K9 modification 48 -1.819 1.38E-03 1.67E-02 0.564GO:0072329 monocarboxylic acid catabolic process 132 1.650 1.47E-03 1.77E-02 0.411GO:0017001 antibiotic catabolic process 58 1.825 1.48E-03 1.78E-02 0.500GO:0030041 actin filament polymerization 182 1.576 1.56E-03 1.86E-02 0.493GO:0042053 regulation of dopamine metabolic process 20 1.901 1.56E-03 1.86E-02 0.818GO:0042058 regulation of epidermal growth factor receptor signaling pathway 86 1.714 1.59E-03 1.88E-02 0.576GO:0140029 exocytic process 80 1.732 1.70E-03 1.98E-02 0.526GO:0051984 positive regulation of chromosome segregation 28 1.864 1.75E-03 2.02E-02 0.240GO:0061640 cytoskeleton-dependent cytokinesis 100 1.688 1.76E-03 2.03E-02 0.417GO:0006123 mitochondrial electron transport, cytochrome c to oxygen 21 1.890 1.77E-03 2.03E-02 0.706GO:1904668 positive regulation of ubiquitin protein ligase activity 12 1.889 1.79E-03 2.05E-02 0.545GO:0042471 ear morphogenesis 118 -1.775 1.84E-03 2.10E-02 0.409GO:0031146 SCF-dependent proteasomal ubiquitin-dependent protein catabolic process 95 1.658 1.86E-03 2.12E-02 0.373GO:0033077 T cell differentiation in thymus 70 -1.737 1.87E-03 2.12E-02 0.455O:1903509 liposaccharide metabolic process 111 1.654 1.89E-03 2.14E-02 0.405GO:0002576 platelet degranulation 128 1.636 1.90E-03 2.15E-02 0.516GO:0050862 positive regulation of T cell receptor signaling pathway 14 -1.897 2.02E-03 2.25E-02 0.750GO:0050820 positive regulation of coagulation 27 1.872 2.07E-03 2.31E-02 0.579GO:0043277 apoptotic cell clearance 46 1.813 2.12E-03 2.34E-02 0.750GO:0022618 ribonucleoprotein complex assembly 277 -1.471 2.14E-03 2.36E-02 0.294GO:0045410 positive regulation of interleukin-6 biosynthetic process 15 1.878 2.17E-03 2.39E-02 0.733GO:0036258 multivesicular body assembly 30 1.828 2.19E-03 2.40E-02 0.607GO:0010823 negative regulation of mitochondrion organization 55 1.758 2.20E-03 2.41E-02 0.409GO:0036124 histone H3-K9 trimethylation 16 -1.887 2.23E-03 2.43E-02 0.615GO:0006907 pinocytosis 21 1.874 2.25E-03 2.44E-02 0.563GO:0090148 membrane fission 12 1.869 2.26E-03 2.45E-02 0.700GO:0007229 integrin-mediated signaling pathway 103 1.659 2.34E-03 2.52E-02 0.459GO:0010257 NADH dehydrogenase complex assembly 64 1.698 2.35E-03 2.52E-02 0.383GO:1900407 regulation of cellular response to oxidative stress 88 1.700 2.38E-03 2.54E-02 0.441GO:0032506 cytokinetic process 39 1.779 2.42E-03 2.58E-02 0.459GO:0001845 phagolysosome assembly 13 1.863 2.43E-03 2.58E-02 0.600GO:1904385 cellular response to angiotensin 25 1.867 2.45E-03 2.59E-02 0.400GO:0045132 meiotic chromosome segregation 90 1.738 2.48E-03 2.60E-02 0.234GO:0060395 SMAD protein signal transduction 70 -1.787 2.51E-03 2.63E-02 0.412GO:0021700 developmental maturation 284 1.515 2.56E-03 2.66E-02 0.442GO:0015718 monocarboxylic acid transport 162 1.632 2.64E-03 2.72E-02 0.537GO:0090596 sensory organ morphogenesis 256 -1.590 2.65E-03 2.72E-02 0.419GO:0043094 cellular metabolic compound salvage 34 1.821 2.65E-03 2.72E-02 0.385GO:0072523 purine-containing compound catabolic process 51 1.772 2.66E-03 2.73E-02 0.278GO:0089718 amino acid import across plasma membrane 23 1.860 2.68E-03 2.74E-02 0.750GO:0033194 response to hydroperoxide 20 1.854 2.68E-03 2.74E-02 0.647GO:0017157 regulation of exocytosis 217 1.548 2.73E-03 2.77E-02 0.394GO:0030510 regulation of BMP signaling pathway 91 -1.744 2.76E-03 2.79E-02 0.381GO:0043627 response to estrogen 73 1.734 2.87E-03 2.89E-02 0.477GO:0003071 renal system process involved in regulation of systemic arterial blood pressure 25 1.849 2.87E-03 2.89E-02 0.600GO:1903076 regulation of protein localization to plasma membrane 95 1.664 2.96E-03 2.96E-02 0.477GO:0097066 response to thyroid hormone 26 1.841 2.99E-03 2.98E-02 0.556GO:0043300 regulation of leukocyte degranulation 46 1.742 3.06E-03 3.04E-02 0.550GO:2000482 regulation of interleukin-8 secretion 25 1.828 3.06E-03 3.04E-02 0.700GO:0002082 regulation of oxidative phosphorylation 31 1.824 3.08E-03 3.05E-02 0.455GO:0043624 cellular protein complex disassembly 217 1.483 3.10E-03 3.07E-02 0.447O:0098656 anion transmembrane transport 288 1.497 3.20E-03 3.16E-02 0.469GO:0019674 NAD metabolic process 76 1.687 3.25E-03 3.20E-02 0.519GO:0046835 carbohydrate phosphorylation 24 1.832 3.26E-03 3.20E-02 0.556GO:2001057 reactive nitrogen species metabolic process 85 1.681 3.30E-03 3.23E-02 0.509GO:0046683 response to organophosphorus 134 1.611 3.36E-03 3.28E-02 0.354GO:0009451 RNA modification 163 -1.523 3.39E-03 3.29E-02 0.264GO:0070125 mitochondrial translational elongation 88 1.613 3.43E-03 3.33E-02 0.481GO:0051650 establishment of vesicle localization 290 1.454 3.54E-03 3.42E-02 0.429GO:0031348 negative regulation of defense response 239 1.504 3.61E-03 3.47E-02 0.388GO:0018023 peptidyl-lysine trimethylation 44 -1.751 3.61E-03 3.47E-02 0.500GO:0006767 water-soluble vitamin metabolic process 88 1.633 3.68E-03 3.53E-02 0.449GO:0006836 neurotransmitter transport 269 1.524 3.69E-03 3.53E-02 0.426GO:0010507 negative regulation of autophagy 84 1.640 3.87E-03 3.67E-02 0.438GO:0051307 meiotic chromosome separation 25 1.825 3.92E-03 3.71E-02 0.357GO:0060986 endocrine hormone secretion 47 -1.818 3.96E-03 3.73E-02 0.333GO:0032928 regulation of superoxide anion generation 22 1.814 4.04E-03 3.79E-02 0.824GO:0034446 substrate adhesion-dependent cell spreading 100 1.639 4.05E-03 3.79E-02 0.508GO:0060042 retina morphogenesis in camera-type eye 49 -1.819 4.13E-03 3.84E-02 0.412GO:0072665 protein localization to vacuole 65 1.657 4.21E-03 3.91E-02 0.536GO:0008347 glial cell migration 49 1.738 4.39E-03 4.04E-02 0.455GO:0051931 regulation of sensory perception 40 1.815 4.39E-03 4.04E-02 0.583GO:0097028 dendritic cell differentiation 42 1.741 4.47E-03 4.08E-02 0.531GO:0006607 NLS-bearing protein import into nucleus 18 -1.804 4.54E-03 4.14E-02 0.389GO:0016573 histone acetylation 156 -1.507 4.58E-03 4.17E-02 0.386GO:0060021 roof of mouth development 89 -1.674 4.59E-03 4.17E-02 0.313GO:0006730 one-carbon metabolic process 27 1.778 4.63E-03 4.20E-02 0.609GO:0051187 cofactor catabolic process 65 1.726 4.66E-03 4.21E-02 0.324GO:0051785 positive regulation of nuclear division 66 1.696 4.72E-03 4.27E-02 0.317GO:0043032 positive regulation of macrophage activation 27 1.803 4.75E-03 4.28E-02 0.533GO:0034142 toll-like receptor 4 signaling pathway 35 1.765 4.77E-03 4.28E-02 0.720GO:0006691 leukotriene metabolic process 31 1.805 4.79E-03 4.30E-02 0.429GO:0044275 cellular carbohydrate catabolic process 45 1.722 4.84E-03 4.32E-02 0.441GO:0030316 osteoclast differentiation 97 1.599 4.86E-03 4.33E-02 0.542GO:0008637 apoptotic mitochondrial changes 124 1.548 4.90E-03 4.35E-02 0.367GO:0051881 regulation of mitochondrial membrane potential 72 1.651 4.91E-03 4.35E-02 0.434GO:0051965 positive regulation of synapse assembly 68 -1.788 4.93E-03 4.36E-02 0.650GO:0051988 regulation of attachment of spindle microtubules to kinetochore 12 1.803 5.05E-03 4.44E-02 0.750O:0031056 regulation of histone modification 143 -1.518 5.06E-03 4.44E-02 0.387GO:0043902 positive regulation of multi-organism process 188 1.475 5.16E-03 4.53E-02 0.376GO:0050729 positive regulation of inflammatory response 153 1.549 5.19E-03 4.53E-02 0.389GO:0097191 extrinsic apoptotic signaling pathway 224 1.456 5.25E-03 4.58E-02 0.405GO:0001774 microglial cell activation 48 1.705 5.30E-03 4.60E-02 0.417GO:0034058 endosomal vesicle fusion 10 1.791 5.36E-03 4.63E-02 0.700GO:0035635 entry of bacterium into host cell 15 1.791 5.36E-03 4.63E-02 0.600GO:0002385 mucosal immune response 36 -1.784 5.43E-03 4.68E-02 0.444GO:0006022 aminoglycan metabolic process 170 1.539 5.51E-03 4.71E-02 0.402GO:2000756 regulation of peptidyl-lysine acetylation 59 -1.657 5.67E-03 4.84E-02 0.383GO:1905819 negative regulation of chromosome separation 40 1.704 5.76E-03 4.89E-02 0.324GO:0019432 triglyceride biosynthetic process 41 1.747 5.84E-03 4.91E-02 0.500GO:0001659 temperature homeostasis 173 1.508 5.93E-03 4.96E-02 0.316
LegendSize
Number of genes defining the biological process in the GO database
NES
Normalize Enrichment Score, indicating the level of enrichment and its directionality pvalue
Empirical statistical significance value for enrichment (n=1e6 permutations) p.adjust
FDR-corrected p-value ratio*
Ratio between upplementary Table 2: List of biological processes associated to treatment with abataceptID Description Size NES pvalue p.adjust ratio
GO:0140014 mitotic nuclear division 264 -2.222 1.75E-06 1.28E-04 0.394GO:0038093 Fc receptor signaling pathway 241 -3.021 1.76E-06 1.28E-04 0.371GO:1901988 negative regulation of cell cycle phase transition 267 -1.811 1.76E-06 1.28E-04 0.295GO:0002440 production of molecular mediator of immune response 286 -2.758 1.76E-06 1.28E-04 0.216GO:0045637 regulation of myeloid cell differentiation 251 -1.959 1.76E-06 1.28E-04 0.388GO:0019882 antigen processing and presentation 226 -1.972 1.77E-06 1.28E-04 0.369GO:0050864 regulation of B cell activation 184 -2.765 1.81E-06 1.28E-04 0.326GO:0002526 acute inflammatory response 220 -3.092 1.81E-06 1.28E-04 0.379GO:0031497 chromatin assembly 165 -2.643 1.81E-06 1.28E-04 0.401GO:0000070 mitotic sister chromatid segregation 151 -2.243 1.81E-06 1.28E-04 0.382GO:2001251 negative regulation of chromosome organization 146 -2.297 1.82E-06 1.28E-04 0.380GO:0038094 Fc-gamma receptor signaling pathway 142 -3.182 1.82E-06 1.28E-04 0.531GO:0008037 cell recognition 215 -2.611 1.82E-06 1.28E-04 0.328GO:0045814 negative regulation of gene expression, epigenetic 136 -2.335 1.83E-06 1.28E-04 0.441GO:1902850 microtubule cytoskeleton organization involved in mitosis 131 -2.213 1.83E-06 1.28E-04 0.461GO:0002455 humoral immune response mediated by circulating immunoglobulin 150 -3.253 1.85E-06 1.28E-04 0.612GO:0010324 membrane invagination 135 -2.837 1.85E-06 1.28E-04 0.418GO:0051225 spindle assembly 108 -2.041 1.85E-06 1.28E-04 0.443GO:0045652 regulation of megakaryocyte differentiation 79 -2.168 1.87E-06 1.28E-04 0.432GO:0051304 chromosome separation 90 -2.215 1.87E-06 1.28E-04 0.297GO:0006335 DNA replication-dependent nucleosome assembly 32 -2.217 1.94E-06 1.28E-04 0.531GO:0019731 antibacterial humoral response 46 -2.234 1.95E-06 1.28E-04 0.655GO:0042274 ribosomal small subunit biogenesis 67 2.163 2.13E-06 1.28E-04 0.569GO:0042273 ribosomal large subunit biogenesis 71 2.214 2.14E-06 1.28E-04 0.500GO:0002181 cytoplasmic translation 100 2.017 2.18E-06 1.28E-04 0.404GO:0006614 SRP-dependent cotranslational protein targeting to membrane 105 2.746 2.18E-06 1.28E-04 0.704GO:0000184 nuclear-transcribed mRNA catabolic process, nonsense-mediated decay 120 2.663 2.21E-06 1.28E-04 0.563GO:0019083 viral transcription 177 2.439 2.27E-06 1.28E-04 0.494GO:0006413 translational initiation 193 2.229 2.28E-06 1.28E-04 0.421GO:0006364 rRNA processing 214 2.183 2.31E-06 1.28E-04 0.419O:0051290 protein heterotetramerization 54 -2.125 3.82E-06 1.92E-04 0.396GO:1905819 negative regulation of chromosome separation 40 -2.197 3.87E-06 1.92E-04 0.382GO:0007093 mitotic cell cycle checkpoint 165 -1.849 5.41E-06 2.58E-04 0.300GO:0060147 regulation of posttranscriptional gene silencing 117 -2.000 5.51E-06 2.58E-04 0.306GO:0009451 RNA modification 163 1.829 6.75E-06 2.97E-04 0.294GO:0038111 interleukin-7-mediated signaling pathway 30 -2.135 7.81E-06 3.37E-04 0.571GO:0000028 ribosomal small subunit assembly 19 2.131 8.03E-06 3.44E-04 0.706GO:0019730 antimicrobial humoral response 122 -2.052 1.13E-05 4.74E-04 0.450GO:0090224 regulation of spindle organization 41 -2.110 1.35E-05 5.51E-04 0.568GO:0030261 chromosome condensation 47 -2.099 1.35E-05 5.51E-04 0.667GO:0000027 ribosomal large subunit assembly 30 2.124 1.64E-05 6.33E-04 0.679GO:0001510 RNA methylation 81 1.953 2.15E-05 8.25E-04 0.359GO:0006890 retrograde vesicle-mediated transport, Golgi to ER 86 -1.947 2.99E-05 1.13E-03 0.418GO:0008033 tRNA processing 130 1.803 3.11E-05 1.16E-03 0.250GO:0006338 chromatin remodeling 182 -1.771 3.23E-05 1.19E-03 0.369GO:0090068 positive regulation of cell cycle process 298 -1.670 3.33E-05 1.22E-03 0.295GO:0051310 metaphase plate congression 57 -2.011 3.43E-05 1.25E-03 0.431GO:1901216 positive regulation of neuron death 94 -1.930 4.30E-05 1.54E-03 0.382GO:0070125 mitochondrial translational elongation 88 1.892 4.55E-05 1.60E-03 0.322GO:0051321 meiotic cell cycle 249 -1.721 1.06E-04 3.60E-03 0.188GO:0044839 cell cycle G2/M phase transition 266 -1.618 1.15E-04 3.86E-03 0.238GO:0022618 ribonucleoprotein complex assembly 277 1.576 1.54E-04 5.06E-03 0.399GO:0016236 macroautophagy 295 -1.572 1.56E-04 5.07E-03 0.365GO:0032886 regulation of microtubule-based process 218 -1.659 1.59E-04 5.07E-03 0.270GO:0050830 defense response to Gram-positive bacterium 101 -1.909 1.59E-04 5.07E-03 0.400GO:0034976 response to endoplasmic reticulum stress 285 -1.592 1.64E-04 5.17E-03 0.243GO:0045132 meiotic chromosome segregation 90 -1.902 2.15E-04 6.60E-03 0.235GO:0000082 G1/S transition of mitotic cell cycle 279 -1.597 2.20E-04 6.69E-03 0.256GO:0060969 negative regulation of gene silencing 39 -1.957 2.32E-04 7.02E-03 0.433GO:0051262 protein tetramerization 172 -1.699 2.50E-04 7.51E-03 0.248GO:0002474 antigen processing and presentation of peptide antigen via MHC class I 96 -1.780 2.52E-04 7.53E-03 0.333GO:0032635 interleukin-6 production 161 -1.728 2.62E-04 7.78E-03 0.427O:1905268 negative regulation of chromatin organization 62 -1.878 2.90E-04 8.46E-03 0.412GO:0036498 IRE1-mediated unfolded protein response 64 -1.856 3.02E-04 8.77E-03 0.483GO:0031167 rRNA methylation 27 1.942 3.04E-04 8.77E-03 0.440GO:0043388 positive regulation of DNA binding 59 -1.897 3.14E-04 9.03E-03 0.455GO:0000470 maturation of LSU-rRNA 21 1.916 4.73E-04 1.33E-02 0.571GO:0061640 cytoskeleton-dependent cytokinesis 100 -1.763 4.92E-04 1.37E-02 0.314GO:0000727 double-strand break repair via break-induced replication 11 -1.888 5.04E-04 1.40E-02 0.500GO:0008608 attachment of spindle microtubules to kinetochore 32 -1.904 5.29E-04 1.45E-02 0.429GO:0032715 negative regulation of interleukin-6 production 53 -1.874 5.31E-04 1.45E-02 0.425GO:0051701 interaction with host 209 -1.602 5.64E-04 1.52E-02 0.297GO:0032868 response to insulin 272 -1.559 6.21E-04 1.65E-02 0.335GO:0016052 carbohydrate catabolic process 199 -1.605 6.91E-04 1.83E-02 0.304GO:0007062 sister chromatid cohesion 63 -1.807 7.24E-04 1.91E-02 0.232GO:1902969 mitotic DNA replication 15 -1.894 7.48E-04 1.96E-02 0.571GO:0007033 vacuole organization 163 -1.619 7.52E-04 1.96E-02 0.355GO:0050764 regulation of phagocytosis 98 -1.748 8.13E-04 2.09E-02 0.468GO:0002701 negative regulation of production of molecular mediator of immune response 35 -1.884 8.17E-04 2.09E-02 0.269GO:0042770 signal transduction in response to DNA damage 133 -1.662 8.28E-04 2.10E-02 0.294GO:0030100 regulation of endocytosis 281 -1.544 8.78E-04 2.21E-02 0.393GO:0000018 regulation of DNA recombination 101 -1.719 8.80E-04 2.21E-02 0.333GO:0033260 nuclear DNA replication 60 -1.770 1.11E-03 2.70E-02 0.345GO:1900026 positive regulation of substrate adhesion-dependent cell spreading 37 -1.846 1.22E-03 2.97E-02 0.552GO:2001235 positive regulation of apoptotic signaling pathway 179 -1.588 1.25E-03 3.03E-02 0.291GO:0032465 regulation of cytokinesis 89 -1.726 1.40E-03 3.33E-02 0.279GO:1902115 regulation of organelle assembly 194 -1.566 1.41E-03 3.34E-02 0.400GO:0031935 regulation of chromatin silencing 37 -1.831 1.44E-03 3.38E-02 0.367GO:0031099 regeneration 198 -1.622 1.49E-03 3.47E-02 0.413GO:0045730 respiratory burst 37 -1.824 1.51E-03 3.50E-02 0.469GO:0051099 positive regulation of binding 179 -1.583 1.53E-03 3.53E-02 0.276GO:0018107 peptidyl-threonine phosphorylation 126 -1.639 1.57E-03 3.60E-02 0.264GO:1904668 positive regulation of ubiquitin protein ligase activity 12 -1.835 1.57E-03 3.60E-02 0.273GO:0051965 positive regulation of synapse assembly 68 1.794 1.62E-03 3.69E-02 0.364GO:0016482 cytosolic transport 158 -1.576 1.67E-03 3.78E-02 0.324GO:0002385 mucosal immune response 36 -1.837 1.71E-03 3.85E-02 0.542GO:0071216 cellular response to biotic stimulus 236 -1.529 1.72E-03 3.86E-02 0.385GO:0044003 modification by symbiont of host morphology or physiology 46 -1.786 1.81E-03 3.99E-02 0.359GO:0051984 positive regulation of chromosome segregation 28 -1.825 1.85E-03 4.01E-02 0.346O:0043044 ATP-dependent chromatin remodeling 88 -1.685 1.86E-03 4.01E-02 0.368GO:0000083 regulation of transcription involved in G1/S transition of mitotic cell cycle 29 -1.827 1.88E-03 4.05E-02 0.280GO:0051053 negative regulation of DNA metabolic process 156 -1.588 1.94E-03 4.16E-02 0.277GO:0031330 negative regulation of cellular catabolic process 264 -1.495 2.09E-03 4.39E-02 0.336GO:0048771 tissue remodeling 179 -1.613 2.11E-03 4.39E-02 0.367GO:0002573 myeloid leukocyte differentiation 204 -1.541 2.16E-03 4.46E-02 0.344GO:0003014 renal system process 120 -1.691 2.17E-03 4.46E-02 0.232GO:0030262 apoptotic nuclear changes 35 -1.802 2.21E-03 4.50E-02 0.483GO:0046653 tetrahydrofolate metabolic process 19 -1.829 2.23E-03 4.53E-02 0.438GO:0050777 negative regulation of immune response 150 -1.596 2.41E-03 4.81E-02 0.248
LegendSize
Number of genes defining the biological process in the GO database
NES
Normalize Enrichment Score, indicating the level of enrichment and its directionality pvalue
Empirical statistical significance value for enrichment (n=1e6 permutations) p.adjust
FDR-corrected p-value ratio*
Ratio between upplementary Table 3: List of overlapping biologial processes between COVID19 and abatacept exposuresID Description Size NES_COVID pCOVID ratio_COVID NES_ABT pABT ratio_ABT
GO:0000070 mitotic sister chromatid segregation 151 2.028 1.08E-04 0.273 -2.243 1.28E-04 0.382GO:0000184 nuclear-transcribed mRNA catabolic process, nonsense-mediated decay 120 -1.808 1.48E-03 0.576 2.663 1.28E-04 0.563GO:0001510 RNA methylation 81 -1.737 1.48E-02 0.279 1.953 8.25E-04 0.359GO:0002385 mucosal immune response 36 -1.784 4.68E-02 0.444 -1.837 3.85E-02 0.542GO:0002440 production of molecular mediator of immune response 286 1.918 1.08E-04 0.442 -2.758 1.28E-04 0.216GO:0002455 humoral immune response mediated by circulating immunoglobulin 150 3.043 1.08E-04 0.739 -3.253 1.28E-04 0.612GO:0002474 antigen processing and presentation of peptide antigen via MHC class I 96 2.509 1.08E-04 0.505 -1.780 7.53E-03 0.333GO:0002526 acute inflammatory response 220 2.600 1.08E-04 0.558 -3.092 1.28E-04 0.379GO:0002573 myeloid leukocyte differentiation 204 1.656 4.97E-03 0.497 -1.541 4.46E-02 0.344GO:0006364 rRNA processing 214 -1.717 1.13E-03 0.420 2.183 1.28E-04 0.419GO:0006614 SRP-dependent cotranslational protein targeting to membrane 105 -2.113 1.09E-04 0.611 2.746 1.28E-04 0.704GO:0006890 retrograde vesicle-mediated transport, Golgi to ER 86 1.818 4.72E-03 0.455 -1.947 1.13E-03 0.418GO:0007033 vacuole organization 163 2.177 1.08E-04 0.459 -1.619 1.96E-02 0.355GO:0008037 cell recognition 215 2.040 1.76E-04 0.535 -2.611 1.28E-04 0.328GO:0009451 RNA modification 163 -1.523 3.29E-02 0.264 1.829 2.97E-04 0.294GO:0010324 membrane invagination 135 2.441 1.08E-04 0.701 -2.837 1.28E-04 0.418GO:0016052 carbohydrate catabolic process 199 2.129 1.08E-04 0.393 -1.605 1.83E-02 0.304GO:0016236 macroautophagy 295 2.152 1.08E-04 0.432 -1.572 5.07E-03 0.365GO:0019083 viral transcription 177 -1.732 1.32E-03 0.465 2.439 1.28E-04 0.494GO:0019882 antigen processing and presentation 226 2.494 1.08E-04 0.475 -1.972 1.28E-04 0.369GO:0022618 ribonucleoprotein complex assembly 277 -1.471 2.36E-02 0.294 1.576 5.06E-03 0.399GO:0030100 regulation of endocytosis 281 1.998 1.08E-04 0.477 -1.544 2.21E-02 0.393GO:0032635 interleukin-6 production 161 1.859 4.99E-04 0.505 -1.728 7.78E-03 0.427GO:0032868 response to insulin 272 1.641 2.93E-03 0.402 -1.559 1.65E-02 0.335GO:0034976 response to endoplasmic reticulum stress 285 1.564 5.95E-03 0.416 -1.592 5.17E-03 0.243GO:0038093 Fc receptor signaling pathway 241 2.466 1.08E-04 0.524 -3.021 1.28E-04 0.371GO:0038094 Fc-gamma receptor signaling pathway 142 2.607 1.08E-04 0.642 -3.182 1.28E-04 0.531GO:0044839 cell cycle G2/M phase transition 266 1.740 3.08E-04 0.379 -1.618 3.86E-03 0.238O:0045132 meiotic chromosome segregation 90 1.738 2.60E-02 0.234 -1.902 6.60E-03 0.235GO:0045730 respiratory burst 37 1.930 9.65E-03 0.552 -1.824 3.50E-02 0.469GO:0048771 tissue remodeling 179 1.894 5.85E-04 0.505 -1.613 4.39E-02 0.367GO:0050764 regulation of phagocytosis 98 1.880 1.87E-03 0.547 -1.748 2.09E-02 0.468GO:0050864 regulation of B cell activation 184 1.949 1.08E-04 0.500 -2.765 1.28E-04 0.326GO:0051225 spindle assembly 108 1.833 1.87E-03 0.284 -2.041 1.28E-04 0.443GO:0051262 protein tetramerization 172 1.812 1.12E-03 0.311 -1.699 7.51E-03 0.248GO:0051304 chromosome separation 90 1.965 5.85E-04 0.267 -2.215 1.28E-04 0.297GO:0051321 meiotic cell cycle 249 1.596 1.30E-02 0.275 -1.721 3.60E-03 0.188GO:0051701 interaction with host 209 1.707 1.64E-03 0.416 -1.602 1.52E-02 0.297GO:0051965 positive regulation of synapse assembly 68 -1.788 4.36E-02 0.650 1.794 3.69E-02 0.364GO:0051984 positive regulation of chromosome segregation 28 1.864 2.02E-02 0.240 -1.825 4.01E-02 0.346GO:0061640 cytoskeleton-dependent cytokinesis 100 1.688 2.03E-02 0.417 -1.763 1.37E-02 0.314GO:0070125 mitochondrial translational elongation 88 1.613 3.33E-02 0.481 1.892 1.60E-03 0.322GO:0071216 cellular response to biotic stimulus 236 1.756 8.42E-04 0.421 -1.529 3.86E-02 0.385GO:0140014 mitotic nuclear division 264 1.766 1.76E-04 0.227 -2.222 1.28E-04 0.394GO:1901216 positive regulation of neuron death 94 1.742 1.08E-02 0.533 -1.930 1.54E-03 0.382GO:1901988 negative regulation of cell cycle phase transition 267 1.757 3.65E-04 0.432 -1.811 1.28E-04 0.295GO:1902850 microtubule cytoskeleton organization involved in mitosis 131 1.891 4.99E-04 0.330 -2.213 1.28E-04 0.461GO:1904668 positive regulation of ubiquitin protein ligase activity 12 1.889 2.05E-02 0.545 -1.835 3.60E-02 0.273GO:1905819 negative regulation of chromosome separation 40 1.704 4.89E-02 0.324 -2.197 1.92E-04 0.382
LegendID
GO term ID
Size
Number of genes defining the biological process in the GO database
NES COVID
Normalize Enrichment Score for the COVID19 dataset pCOVID
FDR-corrected p-value ratio COVID
Ratio between
NES RA Abatacept
Normalize Enrichment Score for the abatacept dataset pABT
FDR-corrected p-value atio ABTatio ABT