V600 mutant metastatic melanoma. Since the RAS/RAF/MEK/ERK-pathway is crucial for the function of different immune cells, we postulated an effect on their function and thus interference with anti-tumor immunity. Therefore, we examined the influence of BRAFi/MEKi, either as single agent or in combination, on the maturation of monocyte-derived dendritic cells (moDCs) and their interaction with T cells. DCs matured in the presence of vemurafenib or vemurafenib/cobimetinib altered their cytokine secretion and surface marker expression profile. Upon the antigen-specific stimulation of CD8+ and CD4+ T cells with these DCs or with T2.A1 cells in the presence of BRAFi/MEKi, we detected a lower expression of activation markers on and a lower cytokine secretion by these T cells. However, treatment with any of the inhibitors alone or in combination did not change the avidity of CD8+ T cells in peptide titration assays with T2.A1 cells. T-helper cell/DC interaction is a bi-directional process that normally results in DC activation. Vemurafenib and vemurafenib/cobimetinib completely abolished the helper T-cell-mediated upregulation of CD70, CD80, and CD86 but not CD25 on the DCs. The combination of dabrafenib/trametinib affected DC maturation and activation as well as T-cell activation less than combined vemurafenib/cobimetinib did. Hence, for a potential combination with immunotherapy, our data indicate the superiority of dabrafenib/trametinib treatment.BRAF and MEK inhibitor (BRAFi/MEKi) combinations are currently the standard treatment for patients with BRAF V600E; most commonly), lysine (BRAFV600K), or aspartic acid (BRAFV600D) [During the last decade, next generation sequencing allowed to uncover the mutational landscape of tumors. Melanoma was upon the first, for which this knowledge was translated into clinical applications. Virtually all cutaneous melanomas harbor driver mutations that activate the RAS/RAF/MEK/ERK MAPK pathway , which fAFV600D) , resultiAFV600D) ,8. The sAFV600D) ,8,9.V600 can be targeted with specific kinase inhibitors (BRAFi), i.e., vemurafenib , i.e., cobimetinib (cobi), trametinib (tram) [BRAFreatment ), dabrafreatment ,10,11,12reatment . To preereatment , the BRAb (tram) , or binib (tram) ,15,16. Mb (tram) . Neverthb (tram) ,19), notb (tram) ,22. SincTherapeutic cancer vaccination is defined as the attempt to actively immunize a tumor-bearing individual against the tumor, thus inducing a host versus tumor reaction, which can support cancer therapy. Dendritic cells (DCs) are most commonly utilized as antigen-presenting cells, either by in vivo targeting ,24 or byTherefore, we here intend to evaluate the effects of BRAFi/MEKi on the immunogenic key functions of DCs in an in vitro model studying DC/T-cell interactions. For that, we focused on the inhibitors dabra, tram, vemu, and cobi, and their combinations dabra/tram (D + T) and vemu/cobi (V + C). These agents have also recently been tested in clinical trials in combination with PD-1 checkpoint inhibition ,22,27 asThis study identified the critical effects of BRAFi/MEKi on DC function and T-cell stimulation, which should be considered in possible future combination therapies using BRAFi/MEKi and DC vaccination.To assess the effects of BRAF and MEK inhibitor (BRAFi/MEKi) treatment on moDC maturation, we applied vemurafenib , dabrafenib , trametinib , and cobimetinib  either alone or in combination during the moDC maturation process . The con2, and IL-1β [First, we analyzed whether BRAFi/MEKi application affects the cytokine secretion profile of the DCs . Therefond IL-1β . ConcomiUpon vemu treatment, IL-8, IL-12p70, and low quantities of IL-10 were secreted. The differences to the solvent control DMSO were highly significant. In combination with cobi, vemu also increased the secretion of IL-8 and IL-10. Neither dabra nor tram affected the cytokine secretion patterns of the DCs . Hence, To address whether BRAFi and MEKi also affect DC maturation in terms of inducing changes in the maturation marker profile, we again generated moDCs and added BRAFi and MEKi alone or in combination during the maturation process. Cells were harvested after 24 h and were stained for the indicated maturation markers .A life gate was defined by FSC/SSC to determine the fraction of living cells a. Vemu tAnalyzing the expression pattern of surface molecules after co-incubation with BRAFi/MEKi, we detected a significant reduction of CD25, CD80, CD83, CD86, CD70, and CCR7 expression by vemu treatment b. Dabra Even though tram and cobi alone had an impact on the expression of CD80, CD86, and CD70, they did not affect the expression of CD25 and CD83. In contrast, vemu and V + C not only changed the cytokine secretion pattern of the DCs, but also influenced viability and inhibited the upregulation of maturation markers during the moDC maturation process. Thus, the combination of V + C had a more negative impact on moDCs during their maturation process.+ T cells, which had been equipped with a gp100-specific TCR. We used varying concentrations of the gp100-peptide, ranging from 106 pg/mL to 1 pg/mL, to pulse the T2.A1 cells. Non-peptide-loaded T2.A1 cells (0 pg/mL) and T2.A1 cells loaded with a control peptide (ctrl pep) served as negative controls. Additionally, T2.A1 cells and CD8+ T cells were incubated alone. Afterwards, the expression of CD25 and CD69 on T cells .+ or CD4+ T cells equipped with a gp100-specific TCR and non-peptide-loaded  in co-cultures with both CD8 T cells . This ef T cells . The sec T cells . Vemu alaffected . In contreatment . Also hereatment .+ and CD4+ T-cell/DC co-cultures . For TNF, reduction was significantly higher in co-cultures with CD4+ T cells . Accordingly, we prepared co-cultures of either non-peptide-loaded (to detect the spontaneous proliferation) or peptide-loaded moDCs (to assess antigen-specific proliferation) and CD8+  or CD4+  T cells . The T c+ T cells was only reduced by combined V + C treatment  of vemu for BRAFV600E is 31 nM. Additionally, vemu has a similar IC50  for CRAF inhibition. This is not the case for dabra  [V600E than vemu, as indicated by the ratio of IC50 for BRAFV600E vs. BRAFwt, which is 0.3 for vemu [V600E and BRAFV600K [Both vemu and dabra are selective type 1 BRAF, adenosine triphosphate-competitive inhibitors, which are chemically similar but not identical . As desctively) ,34. Furtfor vemu  and 0.05RAFV600K .21H21F3IN3O2, tram: C26H23FIN5O4 [Likewise, both cobi and tram are reversible inhibitors of MEK1 and MEK2, blocking both their activation and kinase activity, which are chemically similar but not identical . The ch23FIN5O4 ).Especially the differences in RAF kinase inhibition between vemu and dabra and the stronger impact of the former on the wild-type BRAF, which may result in a stronger impact on the immune cells, can explain the differential observations we made in our in vitro study.V600 inhibitor PLX4720 did not affect T-cell function [We have assessed the effects of BRAFi and MEKi in single treatment or in combination on T-cell stimulation in our already validated in vitro model system, which consists of moDCs and TCR-transfected T cells to observe antigen-specific interaction . Other gfunction , althoug+ and CD8+ T-cell proliferation and were able to assess a negative impact of vemu, tram, cobi, and V + C but not of D + T on the spontaneous proliferation of CD4+ T cells. CD4+ T-cell proliferation upon antigen-specific stimulation was exclusively inhibited by V + C. Spontaneous CD8+ T-cell proliferation was only affected by V + C treatment, while antigen-specific proliferation was also affected by cobi. In contrast to Gargett et al., we did not detect a negative impact of D + T on T-cell proliferation. Nevertheless, in line with their data, we also detected a reduction in CD25 expression. However, this was limited to vemu, cobi, and V + C for CD8+ T cells and vemu and V + C for CD4+ T cells. We did not see an effects by tram or D + T. We only detected changes in CD69 expression after the treatment of the CD4+ and CD8+ T-cell stimulations with vemu, tram, cobi, V + C, and D + T. These differences could have been caused by the fact that we used moDCs to stimulate the TCR-transfected T cells, whereas Gargett et al. used CAR-T-cells or CTLs expanded from PBMCs.Others detected an inhibition of stimulated CAR-transduced T cells only when vemu, dabra, or tram were applied at high concentrations in vitro . CD25 ex+ T-cell proliferation upon CD3/CD28 stimulation by the exposure to tram and D + T at concentrations above 0.1 µM [+ T cells were first activated [+ T-cell co-cultures, on TNF secretion in CD8+ and CD4+ co-cultures, and of tram on IFNγ production in CD8+ and CD4+ co-cultures. In contrast to us, Liu et al. did not find an effect of tram or D + T on CD69 expression but detected the inhibition of CD25 expression by tram before activation, findings that were also subsequently determined by D + T [Liu et al. reported a reduced CD4e 0.1 µM . We did e 0.1 µM . After Dctivated . We alsoby D + T .Vella et al. investigated the effects on T lymphocyte function and on moDC surface marker expression . Dabra h+ monocytes upon treatment with tram and D + T [2, and IL-1β, we did not observe an induction of DC maturation but an inhibition of distinct maturation markers upon D + T treatment  and an induction of CCR7 expression. Again, the observed inhibitory effects were more enhanced by V + C treatment.Regarding the effects on moDCs, Vella et al. detected an induction of DC maturation, which could be measured by CD83 and CD86 expression, when using LPS-matured moDCs generated from CD14nd D + T . ProbablOther groups have also tested the effect of BRAFi and MEKi on DCs (reviewed in ). Hajek Ott et al. investigated the effects of vemu and a MEKi called U0126 on human monocyte-derived, polyI:C-matured DCs . They obTel et al. showed that ex vivo-isolated plasmacytoid DCs (pDCs) and myeloid DCs (mDCs) of healthy donors, which were matured with R848, had a reduced expression of CD80 (pDCs and mDCs) and CD86 (pDCs) after treatment with vemu . This isV600 mut, BRAFwt, and CRAFwt) and tram on human LPS-matured moDCs [+ T cells induced by antigen-unspecific stimulation with anti-CD3 and anti-CD28 mAbs was clearly inhibited by tram [Finally, Riegel et al. studied the effects of a panRAF inhibitor , many studies combine BRAFi/MEKi with checkpoint inhibitors (CPI), such as pembrolizumab, nivolumab (both targeting PD-1), ipilimumab (targeting CTLA-4), or avelumab and atezolizumab (both targeting PD-L1) . The application of these CPI results in “releasing the brakes” on T cells for their effective priming against certain tumor antigens [In our opinion, the findings presented in this study are of eminent importance for potential combination therapies with BRAFi/MEKi and checkpoint inhibitor therapy. Any combination attempt using cell-based therapies plus targeted therapy, including DC vaccination in the treatment of melanoma, needs to carefully address the potential immunosuppressive effects of these drugs. However, these findings can have an even wider impact. With respect to clinical trials for melanoma patients, as listed in antigens . In thisantigens . It was Another example is the combined use of BRAFi/MEKi and oncolytic viruses such as Talimogene laherparepvec . These oncolytic viruses are injected directly into the tumor, causing the local destruction of virus-infected tumor cells, subsequently resulting in a systemic immune response against other metastases induced by DCs and other immune cells . The choThe SARS-CoV-2 pandemic impressively showed the effectiveness of mRNA-vaccine strategies. In fact, mRNA-vaccine development was initiated to treat cancer. After the success with different mRNA-based COVID-19 vaccines, many companies now have mRNA-based therapeutic cancer vaccines in their development pipelines once again, which probably will enter late phase clinical trials soon. With respect to these novel approved therapies, the observations presented in this study are of importance. Combination therapies of BRAFi/MEKi with mRNA-vaccines are an obvious way to go. However, the effectiveness of mRNA-based vaccines largely depends again on DCs, and therefore, the effectiveness of a combination therapy may be subject to the correct choice of BRAFi/MEKi.In summary, the choice of BRAFi/MEKi agents should be carefully made when combined with other immunotherapies since it can have a detrimental consequence on the effectiveness of the anti-cancer immune response.Vemurafenib was acquired from Adooq Bioscience , dabrafenib from AbMole BioScience , trametinib from Selleckchem , and cobimetinib from Roche . BRAF and MEK inhibitors were diluted according to the manufacturers’ instructions with DMSO . Final concentrations of the inhibitors are shown in 2 .Monocyte-derived DCs were generated from the fresh blood of healthy volunteers following informed consent and approval by the institutional review board  as described previously . PBMCs w+ and CD8+) were isolated from the non-adherent fraction (NAF) using MACS beads , according to the manufacturer’s instructions. Subsequently, T cells were cultured in MLPC medium consisting of RPMI 1640 , 10% human AB serum , 2 mM L-glutamine , 20 mg/L gentamycin , 10 mM HEPES , 1 mM sodium pyruvate , and 1% MEM nonessential aa , supplemented with 10 ng/mL IL-7  and 5 ng/mL IL-15  (CD4+ T cells) or 10 ng/mL IL-7 (CD8+ T cells).T cells (CD4T2.A1 cells (TAP-deficient TxB cell hybrid) were cultured in R10 medium consisting of RPMI 1640  supplemented with 2 mM L-glutamine , penicillin–streptomycin , 10% fetal calf serum , 2 mM HEPES , and 2 mM 2-mercaptoethanol .moDCs were generated and matured as described above. Concomitantly with the maturation cocktail, cells were either treated with vemurafenib (vemu), dabrafenib (dabra), trametinib (tram), cobimetinib (cobi), the combination of vemu and cobi (V + C), the combination of dabra and tram (D + T), DMSO solvent control, or were left untreated. Inhibitor concentrations are described in + or CD8+ T cells as previously described [RNA encoding the gp100/HLA-A2-specific TCR was generated using the in vitro transcription kit with T7 RNA polymerase , according to the manufacturer’s instructions as described before . RNA codescribed ,46.2). moDCs and T2.A1 cells were loaded for 1 h at 37 °C with the indicated peptides (10 μg/mL) or were left untreated. CD4+ and CD8+ T cells were electroporated with a gp100-specific TCR as described above. Four hours after electroporation, T cells were either co-cultured with moDCs or T2.A1 cells at a 1:1 ratio. Concomitantly, BRAFi and/or MEKi were applied at the indicated concentration  using anhydrous DMSO. Co-cultures between T cells and peptide-loaded or non-peptide-loaded moDCs was performed as described above at a 1:1 ratio. BRAF and/or MEK inhibitors were applied at the indicated concentration (+ or CD4+ T cells either after antigen-specific (gp100 peptide) or unspecific (no peptide) stimulation, respectively.CD4ntration . After tCytokine concentrations in the co-culture supernatants were analyzed after 17−20 h with the Inflammatory Cytometric Bead Array  and a Th1/Th2 Cytometric Bead Array  following the manufacturer’s instructions.Extracellular surface marker staining was performed with IgG1-FITC, IgG2a-FITC, IgG1-PE, αCD25-FITC, αCD40-PE, αCD69-PE, αCD70-PE, αCD80-FITC, αCD83-PE, αCD86-FITC, and PD-L1-PE ; IgG3-PE ; and αCCR7-FITC  for 30 min at 4 °C in PBS supplemented with 1% FCS and 0.02% sodium azide . The cells were analyzed using a FACScan cytofluorometer equipped with CellQuest software . Analysis was performed with the FCS Express software . Specific MFIs were calculated by subtracting the background MFI obtained with the isotype controls.p-values were determined by 1-way ANOVA without greenhouse correction. In Dunnett’s multiple comparisons test, all conditions were tested against the solvent control DMSO. To compare unspecific with antigen-specific surface marker expression . pression  or the ppression , p-valuep-value between the whole curves * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001, **** p ≤ 0.0001, ns: p > 0.05.To compare surface expression or cytokine secretion kinetics between non-peptide-loaded and peptide-loaded conditions , a two-fTaken together, we present here a broad analysis of effects of BRAFi/MEKi on monocyte-derived DCs, their stimulatory capacity, and their bi-directional interaction with T-helper cells using clinically relevant concentrations  and combinations of these inhibitors. This study shows that BRAFi/MEKi influence immune function. Since these influences are highly dependent on the type of inhibitor, one must carefully consider the differential effects in the choice of combination trials. Considering the data presented above, we suggest that DC vaccination therapy, and other therapies involving DCs, for that matter, should be combined with D + T rather than with V + C.


Background: Cancer is considered one of the most lethal diseases worldwide. Venous thromboembolism (VTE) is the second leading cause of death in cancer patients. As one of the most reproducible predictors of thromboembolism, the D-dimer level is commonly considered by oncologists. Previous studies have demonstrated that the most correlated genes at the D-dimer level are F3, F5 and FGA.Methods: Using data from TCGA and multiple webtools, including GEPIA2, UALCAN, TIMER2.0, Kaplan-Meier Plotter and CIBERSORTx, we analyzed the tumor mutation burden (TMB), microsatellite instability (MSI) and functions of D-dimer-related genes in cancer. Validation was conducted via quantitative real-time polymerase chain reaction (qRT-PCR) and independent GEO + GTEx cohort. All statistical analyses were performed in R software and GraphPad Prism 9.Results: F3, F5 and FGA were expressed differently in multiple cancer types. TMB, MSI and anti-PD1/PDL1 therapy responses were correlated with D-dimer-related gene expression. D-Dimer-related genes expression affect the survival of cancer patients. F3 and F5 functioned in TGF-beta signaling. F3 and F5 were related to immunity and affected the fraction of CD8+ T cells by upregulating the TGF-beta signaling pathway, forming an F3, F5/TGF-beta signaling/CD8+ T cell axis.Conclusion: F3, F5 and FGA serve as satisfactory GC multibiomarkers and potentially influence the immune microenvironment and survival of cancer patients by influencing TGF-beta signaling. Cancer is considered a fatal disease worldwide. In 2020, approximately 1,806,590 new cancer cases and 606,520 cancer deaths were expected in the United States . AlthougD-Dimer is a fibrin degradation product comprising a small protein fragment in blood after blood clots are dissolved and degraded by plasmin . It contF3, or tissue factor, is a cell surface glycoprotein . This faF5, or coagulation factor V, is an essential cofactor in the blood coagulation cascade . This faFGA, or fibrinogen alpha chain, is the alpha component of fibrinogen . Fibrino+ T cells via TGF-beta signaling and have the potential to become cancer biomarkers, particularly for gastric cancer.In this study, we aimed to explore the potential of the D-dimer-related genes F3, F5 and FGA as biomarkers for various cancer types. After using several bioinformatic tools to reveal D-dimer-related gene survival relationships, we focused on the immune functions of F3 and F5. The novel CIBERSORTx helped us link D-dimer-related genes with immune cell types. Our results demonstrated that D-dimer-related genes influence CD8The RNA-seq data of 33 tumors were downloaded from The Cancer Genome Atlas (TCGA). TMB data, MSI data and clinical data, including age, sex, tumor grade, clinical stage, TNM stage, and survival time, were also downloaded. Gene Expression Omnibus (GEO) datasets of GSE84437, GSE78220, GSE67501 and IMvigor210 were also downloaded . Data exhttp://timer.cistrome.org). We used TIMER2.0 to analyze the differential expression  in a pan-cancer prospective study   is an enhanced web server that conducts large-scale expression profiling and interactive analysis (-pku.cn) . Here, whttp://ualcan.path.uab.edu)  . In thiswww.kmplot.com)  . In the http://bioinfo.henu.edu.cn/DatabaseList.jsp) (Long-term Outcome and Gene Expression Profiling database of pan-cancers (LOGpc) included 209 expression datasets and 13 survival analyses of 27 different malignancies involving 31,310 patients (ist.jsp) . We utilGastric cancer cDNA microarray from 30 GC patients (cDNA-HStmA060CS01), including 30 paired cancer and non-cancerous gastric tissues, were purchased from Shanghai Outdo Biotech Co., Ltd. . The access date of the microarray is 3.21.2017. This study was completed under the guidance of the International Ethical Guidelines for Biomedical Research Involving Human Subjects (CIOMS). The Clinical Research Ethics Committee of Shenyang First People's Hospital approved the research protocols. The characteristics of the GC patients’ data can be found in -△△CT, where △CT = [CT (target gene)—CT (B-actin)]. The expression level was normalized against endogenous B-actin. The specific primers were as follows: human F3 forward 5′-GGAACCCAAACCCGTCAATC-3′ and reverse 5′-AGGAGAAGACCCGTGCCAAG-3′; human F5, forward 5′-TGACCTTCTCGCCTTATGA-3′ and reverse 5′-CTCTGTATTCCTCGCCTGT-3'; human B-actin, forward 5′-GAAGAGCTACGAGCTGCCTGA-3′ and reverse 5′- CAGACAGCACTGTGTTGGCG-3'.The cDNA used in this study was synthesized by RNA reverse transcription using Thermo Fisher’s SuperScript IV Reverse transcriptase (18090010). It was then used to detect the mRNA levels of F3, F5, and B-actin using Takara’s SYBR® Premix Ex Taq™ II (Tli RNaseH Plus) (RR820Q). The relative mRNA expression was determined using the cycle threshold (CT) formula 2http://bioinformatics.psb.ugent.be/webtools/Venn/).Venn plots of this study were generated using an online tool (p < 0.05 were considered significant (Gene Ontology (GO) enrichment analyses using 82 upregulated and 3 downregulated DEGs were performed using the R packages “clusterProfiler”3.16.1, “enrichplot”1.8.1, and “ggplot2”3.3.2. Terms with nificant .p < 0.05 and a false discovery rate (FDR) q < 0.06 were considered significant.Gene set enrichment analysis (GSEA) was performed to reveal correlated pathways of D-dimer-related genes . Hallmarp > 0.05 were eliminated from further study (The novel CIBERSORTx webtool was used to estimate the tumor-infiltrating immune cell (TIC) profile in tumor samples, and samples with er study .p < 0.05. The log rank test was used to evaluate the significance of Kaplan-Meier survival analysis.All statistical analyses were conducted using R software (version 4.1.0) and GraphPad Prism 9. The statistical significance threshold of all statistical tests was + T cells. The analytical workflow for the study is shown in To explore the value of the D-dimer-related genes F3, F5 and FGA, we conducted a pan-cancer study of these three genes and found a correlation among them and TMB, MSI and immune-related genes. According to the survival Kaplan-Meier plots grouped by the expression level of the three genes combined, we focused on a particular cancer type, gastric cancer, and explored the expression pattern within tumor and normal gastric samples along with their survival. F3 and F5, which showed higher expression in gastric cancer in qRT-PCR, were selected for further study. We conducted clinicopathology, gene set enrichment analysis and Gene Ontology-based studies and found that F3 and F5 affect TGF-beta signaling. Next, we performed immune cell analysis using CIBERSORTx and found a correlation between F3 and F5 with CD8We first explored the differential expression of D-dimer-related genes between tumor and normal samples using TIMER2.0. The expression differentiation within the 33 cancer types was demonstrated via boxplots . The namStudies were conducted on the relationship between the gene expression levels and TMB and the MSI levels. TMB, or tumor mutation burden, represents the number of somatic mutations in the coding region of the tumor cell genome at an average size of 1 MB . MSI, orTo explore the relationship between D-dimer-related genes and genes correlated with immunity, we drew heatmaps that displayed the correlation factor and P value of D-dimer genes with each immune-related gene . The hea+ T cells.In order to explore whether D-dimer-related genes are capable of influencing the efficacy of anti-PD1/PDL1 therapy, we downloaded the responsiveness of anti-PD1/PDL1 therapy in three datasets, GSE78220 and GSE67501 of GEO, and also IMvigor210 dataset. The three datasets represent three types of cancer, namely melanoma, renal cell carcinoma and urothelial carcinoma. The transcriptome data included within the three datasets derived from pre-treatment tumors. We generated bar plots of the three genes, and we saw positive results in F3  and FGA  . The resWe next examined the survival correlations of D-dimer-related genes via Kaplan-Meier plots . We usedAfter focusing on STAD, we conducted studies on the differential expression and survival correlation using UALCAN and KM Plotter. A differential expression boxplot showed that F3 and F5 were overexpressed in tumor samples compared with normal samples, while FGA expression differentiation was not significant . We then-△△CT data, can be found in -△△CT can be found in . Demonstrated in In hope of verifying D-dimer-related genes F3 and F5’s upregulation in GC and their survival correlation, we first conducted quantitative real-time polymerase chain reaction on the two genes. Patients’ data, including clinicopathological data and 2To explore the functions of D-dimer-related genes, we performed gene set enrichment analysis (GSEA) of both F3 and F5. The multi-GSEA plots are shown in To identify genes that are expressed correlatively with D-dimer-related genes, we conducted differential expression analysis between the high and low F3 and F5 expression groups. Volcano plots and heatmaps were constructed , and Ven+ T cells overlapped in all four tests, including the difference test and correlation test of F3 and the difference test and correlation test of F5 . Because cancer patients are at a higher risk of developing VTE, VTE has become a crucial influencer of the prognosis of cancer patients. Previous studies have concluded that VTE is the second leading cause of death in cancer patients . Thus, sIn the present study, we first evaluated D-dimer-related genes using a pan-cancer method. We found that the D-dimer-related genes F3, F5 and FGA were differentially expressed in cancer tissues and normal tissues in multiple cancer types, such as STAD and PAAD. The expression differentiation between cancer and normal tissues enabled the three D-dimer-related genes to be used as cancer biomarkers. Tissue factor and factor V, the proteins encoded by F3 and F5, were proved by previous studies to be linked with cancer-associated thrombosis and survival of cancer patients . Our finThe tumor mutation burden (TMB) and microsatellite instability (MSI) are important factors to classify cancer phenotypes and predict the survival of cancer patients. F3 had the most positive correlation with TMB in THYM and the most negative correlation with TMB in LIHC. F5 had the most positive correlation with TMB in THYM, ESCA and PRAD and the most negative correlation with TMB in LGG. FGA had the most positive correlation with TMB in SARC and the most negative correlation with TMB in STAD. F3 had the most positive correlation with MSI in TGCT and COAD and the most negative correlation with MSI in GBM. F5 had the most positive correlation with MSI in UVM and the most negative correlation with MSI in DLBC. FGA had the most positive correlation with MSI in SARC and the most negative correlation with MSI in PAAD. Our results suggest that D-dimer-related genes are related to TMB and MSI.Anti-PD1/PDL1 therapy is by far one of the most well-known immunotherapies for cancer treatment . By blocPrognostic biomarkers have been used to predict the survival of cancer patients in recent years. Although biomarkers such as CA125 are successful for survival prediction, most single biomarkers have failed to provide optimal testing results for cancer patients . PreviouConsidering that the prognostic value of D-dimer-related genes is most satisfactory in gastric cancer, we assessed the independent prognostic value of F3, F5 and FGA. Although all three genes exhibited survival correlation via Kaplan-Meier plots, only F3 and F5 showed expression differentiation between tumor and normal tissues in STAD. The results ruled out the possibility of FGA being a satisfactory independent GC biomarker. Independent GEO cohort and qRT-PCR results verified F3 and F5’s biomarker potential. Further exploration into the correlation between D-dimer-related gene expression and GC clinicopathological characteristics showed that D-dimer-related gene F3 and F5 expression is correlated with GC clinicopathological characteristics such as N .Previously developed bioinformatic methods such as Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) have allowed scientists to explore the functions of selected gene sets. These analytical methods advanced the studies of multiple diseases, including cancer. Our team first used GSEA to examine the related functions of F3 and F5. Consistent with our common knowledge, the coagulation pathway is upregulated in both the F3 and F5 gene sets. Additionally, other pathways that were found to be upregulated enabled the identification of these D-dimer-related genes. Pathways that are commonly regarded as cancer-related were found among the upregulated pathways, such as hypoxia . TGF-bet+ T cells and macrophages within STAD, which are immune cell types that we previously studied  in LIHC and MESO; both F3 and F5 were positively correlated with CTLA4 in COAD, GBM, LGG, LIHC, READ, SARC, TGCT and THCA. These findings suggest that F3 and F5 are well immunocorrelated with cancers such as LIHC. Inspired by previous reports of the potential mechanism that expression of F3 to be influenced by T infiltrating lymphocytes and, in turn, influences VTE, we propose that genes related to T cell infiltration  might be risk factors of VTE. Additional correlation tests were conducted between F3 and F5 and immune cell markers of CD8 studied . We founell axis . Our stu+ T cells. Our results demonstrated that the three D-dimer-related genes are potential GC multibiomarkers that correlate with TMB and MSI and have immune-related functions, regulating CD8+ T cells. Further study of D-dimer-related genes will help improve the survival of cancers such as GC.Our group conducted a pan-cancer study of three D-dimer-related genes—F3, F5 and FGA—and revealed their potential as multibiomarkers as well as single markers for cancer, particularly gastric cancer. Additional studies were conducted on the functions of D-dimer-related genes, and upregulated pathways, such as coagulation and protein secretion, were identified. Immune-related characteristics were explored, and F3 and F5 were identified as potential inhibitors of CD8


GLE1 gene. The LOF mutation-induced disruption of RNA metabolism through the haploinsufficiency mechanism is implicated in ALS pathogenesis. A total of 628 ALS patients and 522 individuals without neurodegenerative disorders were enrolled in this study to explore the GLE1 gene contribution to ALS in the Chinese population. All 16 exons and the flanking intron of GLE1 were screened by Sanger sequencing. In total, we identified seven rare GLE1 coding variants, including one novel nonsense mutation and six rare missense mutations in 628 ALS patients. The frequency of GLE1 LOF mutations was 0.16% (1/628) among Chinese sALS patients, implying that it is an uncommon genetic determinant of ALS in Chinese patients. Additionally, the rare missense variants in the hCG1-binding domain of GLE1 impairing the distribution of the hGle1B isoform at the nuclear pore complex (NPC) region may be involved in the pathogenesis of ALS.Amyotrophic lateral sclerosis (ALS) is a fatal progressive neurodegenerative disease involving the upper and lower motor neurons of the spinal cord, brainstem, and cerebral cortex. At least 30 genes have been implicated in familial ALS (fALS) and sporadic ALS (sALS).  Patient studies .GLE1 gene encodes a highly conserved 75-kDa protein with a leucine-rich nuclear export sequence that is essential for the export of mRNAs from the nucleus to the cytoplasm and the regulation of distinct stages of translation  . Recentlnslation . Kaneb en = 368) and Xuanwu Hospital of Capital Medical University (n = 260) from December 2010 to October 2019. Patients were examined by two neurologists and diagnosed as possible, probable, and definite ALS based on the Awaji criteria (n = 522) without neurodegenerative disorders were included. The study was supported by the ethics committee of the Chinese PLA General Hospital and Xuanwu Hospital of Capital Medical University. The participants consented to the publication of clinical information and provided informed consent.The Chinese patient cohort screened in the study was recruited at the Department of Neurology, Chinese PLA General Hospital  according to a standard procedure. All 16 exons of GLE1 (NM_001003722.1) were amplified using polymerase chain reaction (PCR) with the published extension primer sequences listed in XhoI (5′) and KpnI (3′) sites of pGV230 (CMV-hGle1B/G20C/Y24C/A658V-EGFP-SV40-Neomycin). To generate the pom121 [nuclear pore complex (NPC)-associated integral membrane protein] overexpression plasmid, the ORF of pom121 (NM_001387691) was amplified using the PrimeStar HS DNA polymerase kit  as the template. The cDNAs encoding pom121 were directionally cloned into the NheI (5′) and XhoI (3′) sites of pCV296 (CMV-pom121-mCherry-SV40-Neomycin). HeLa cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM)  with 10% fetal bovine serum (FBS)  at 37°C in 5% CO2. HeLa cells were co-transfected with plasmids expressing Pom121-mCherry and either hGle1B-WT (Wild-Type)-EGFP, hGle1B-G20C-EGFP, hGle1B-Y24C-EGFP, or hGle1B-A658V-EGFP using LipofectamineTM 3000 . Twelve hours after transfection, the location of the EGFP-tagged protein and mCherry-tagged protein was observed on the Zeiss LSM 700 confocal microscope .To generate the GLE1 rare variant overexpression plasmids, the open reading frame (ORF) of human Gle1 (NM_001003722) was amplified using the PrimeStar HS DNA polymerase kit  as the template. The cDNAs encoding Gle1B wild protein, G20C, Y24C, and A658V were directionally cloned into the Seven fALS patients and 621 sporadic patients were included. In total, we identified seven non-synonymous variants (one nonsense mutation and six rare missense mutations) in 628 ALS patients and one missense variant in 522 controls . All theThe nonsense mutation p.Y25X (c.75C > A), not reported in the ALS and population databases mentioned above, is classified as pathogenic variants based on the American College of Medical Genetics guidelines  of ALS cases. The p.P2R, p.G20C, p.Y24C, p.V35F, p.R235W, and p.A658V detected exclusively in the ALS cases and not in the controls fall into the category of variants of uncertain significance according to the ACMG guidelines . The p.PTherefore, we generated the hGle1B-EGFP and the EGFP-tagged hGle1B variants, including hGle1B-G20C-EGFP, hGle1B-Y24C-EGFP, and hGle1B-A658V-EGFP. When the GFP-tagged hGle1B constructs and the mCherry-tagged pom121  were co-transfected in HeLa cells, we observed that the hGle1B-A658V protein was nearly absent from the nucleus, without apparent co-localization with pom121. Instead, the distribution of hGle1B-G20C and hGle1B-Y24C proteins was similar to the wild-type hGle1B protein, located at the nucleus, cytoplasm, and nuclear envelope with co-localization of pom121 , 4.GLE1 gene were identified, including seven variations exclusive to ALS patients and one mutation exclusive to controls. We identified the first LOF mutation  in the Chinese population, with a frequency of 0.16%. To date, two LOF variants comprising the nonsense mutation  and splicing site mutations  have been identified in Caucasian ALS patients. Co-segregation of GLE1 LOF variants has only been confirmed for one splicing site mutation (c.1965-2A > C) in a Finnish pedigree  non-synonymous variations in the pedigree . Zhang eThe human Gle1 protein (hGle1) plays an indispensable role in the export of mRNAs from the nucleus to the cytoplasm, regulating distinct stages of translation . There eGLE1 gene in Chinese ALS patients and did not identify LOF mutations among control subjects. We only identified one LOF mutation  of the in silico analysis. Therefore, we generated the hGle1B-G20C, hGle1B-Y24C, and hGle1B-A658V constructs. The p.G20C and p.Y24C variants, localized to the hNup155-binding domain  developed limbs weakness at the age of 61 years and died 2 years later after symptom onset and a fALS proband with the splice site mutation hGle1-IVS14-2A > C (c.1965-2A > C) diagnosed as bulbar onset ALS at the age of 64.5 years and passed away with a disease duration of 1.5 years  in GLE1 also carried another mutation p.E273X (c.817G > T) in OPTN. .5 years . The pat.5 years .In conclusion, we identified a total of seven rare GLE1 coding variants, including one novel nonsense mutation and six rare missense mutations in 628 ALS patients. The frequency of GLE1 LOF mutations is 0.16% (1/628) among Chinese ALS patients, implying that it is an uncommon genetic determinant of ALS in Chinese patients. Additionally, the rare missense variants in the hCG1-binding domain of GLE1 impairing the distribution of the hGle1B isoform at the NPC region may be involved in the pathogenesis of ALS.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Ethics Committee of the Chinese PLA General Hospital and Xuanwu Hospital of Capital Medical University.XH and CW devised the study and provided fund support. YL and BS completed the study and the manuscript. ZW, ZH, FY, HW, and FC collected the clinical information and blood samples of patients. ZC and LL performed the electromyography examination for the patients. All authors contributed to the article 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.


Escherichia coli and proinflammatory cytokines in the ileum, and ameliorate the blood biochemical indices of yellow-feathered chickens at 21 days of age. This study indicates that the antimicrobial peptide plectasin has beneficial effects on the growth performance, intestinal health and immune function of yellow-feathered chickens.The goal of the study was to test the effects of an antibiotic substitute, plectasin, on the growth performance, immune function, intestinal morphology and structure, intestinal microflora, ileal mucosal layer construction and tight junctions, ileal immune-related cytokines, and blood biochemical indices of yellow-feathered chickens. A total of 1,500 one-day-old yellow-feathered chicks were randomly divided into four dietary treatment groups with five replicates in each group and 75 yellow-feathered chicks in each replication, as follows: basal diet (group A); basal diet supplemented with 10 mg enramycin/kg of diet (group B), basal diet supplemented with 100 mg plectasin/kg of diet (group C), and basal diet supplemented with 200 mg plectasin/kg of diet (group D). It was found that the dietary antimicrobial peptide plectasin could improve the ADG and had better F/G for the overall period of 1–63 days. Dietary plectasin can enhance H9N2 avian influenza virus (AIV) and Newcastle disease virus (NDV) antibody levels of yellow-feathered chickens at 21, and 35 days of age. Dietary plectasin can enhance the intestine structure, inhibit  Antibiotics have been used by the poultry industry to enhance the health and productivity of flocks and treat diseases , 2. HoweAntimicrobial peptides (AMPs) are small molecules (12–15 amino acids) containing positive charges and amphipathic structures, which have broad-spectrum antibacterial, fungal, and protozoan activity as well as a certain level of virus activity . Due to Defensins are cationic antibacterial active peptides that are found in antibacterial peptides, which are widely present in animals and plants , 21. Ple6K50_A) used in this study was purchased from Guangdong Hinabiotech Co., Ltd. . The method for producing the plectasin was as follows according to previous reported study with some modifications , average daily gain (ADG), feed to gain ratio (F/G). and the survival rate of chickens at 1–21, 22–42, 43–63 and 1–63 days of age were evaluated.The yellow-feathered chickens were vaccinated with the commercial inactivated vaccines for H9N2 avian influenza virus (AIV)  and Newcastle disease virus (NDV)  via spray immunization at 1-day-old, and then they were revaccinated with the same vaccine via subcutaneous neck injection at 10 days of age. There were 25 birds in each treatment group with five chickens were randomly selected from each replicate  at 7, 21, 35, and 42 days old. Five milliliters of blood was transferred into the centrifuge tube by a 10 mL syringe, left to stand for 30 min at room temperature, and centrifuged for 10 min at 3,000 rpm. Then, the supernatant was collected to detect the antibody levels of H9N2 AIV and NDV by HI assay as described previously .Chickens at 21 days of age were selected for testing the intestinal morphology . There wTM II kit  was 5 μL, the reaction performed on an CFX96 Touch™ real-time PCR detection system . The specific primers used for the mucosal layer structure and the tight-junction-related factors and inflammation-related cytokines of ileum were designed using Primer Express 3.0 (Applied Biosystems) in accordance with the sequences published in GenBank for qRT-PCR and are shown in GAPDH. The 2−ΔΔCt method was used to calculate the relative expression of each gene  Co. Ltd.]. Primers  were used to amplify the fragment inserts of E. coli (243 bp) and Lactobacilli (200 bp) by PCR. DNA standards were prepared from E. coli or Lactobacilli strains carrying plasmids with E. coli or Lactobacilli fragment inserts which were ligated into a PMD19-T vector . The DNA concentration was calculated by measuring the absorbance at 260 nm. Using the DNA concentration, the copy number of the plasmid was calculated using the following formula: copy/μL = 6.02 × 1023(copy/mol) × DNA concentration (g/μL)/MW (g/mol). Serial 10 fold dilutions from 104 to 1010 copies of the purified plasmid were prepared in duplicate to produce a standard curve. The quantitative Real-time PCR (qRT-PCR) was used to evaluate the copy number of E. coli and Lactobacilli in ileum and cecum with following reaction system: DNA (1.0 μL), 10.0 μL of SYBR Green qPCR Mix , each primer was 0.5 μL, and 8 μL of H2O. Samples were amplified under the following conditions: 94°C for 2 min , 40 cycles of 94°C for 10 s (heat denaturation), 60°C for 40 s . Fluorescence signal acquisition was set at 60°C. The primers for the amplification of E. coli by qRT-PCR were: 5′-GTTAATACCTTTGCTCATTGA-3′ and 5′-ACCAGGGTATCTTAATCCTGTT-3′. The primers for Lactobacilli amplification by qRT-PCR were as follows: 5′- CCCTTGTCATTAGTTGCCAGCATTAAG-3′ and 5′- CCTCCTCGTTGTACTGTCCATTGTAGC-3′.Chickens at 21 days of age were selected for testing the intestinal flora . There wChickens at 21 days of age were selected for testing the blood biochemical indices . Venous P < 0.05 was considered significant.SPSS 20.0 software was used for the one-way ANOVA analysis and Tukey's test was used for multiple comparisons. The experimental data are expressed as the mean ± standard error of the mean (SEM). The average daily feed intake (ADFI), average daily gain (ADG), feed to gain ratio (F/G) and survival rate of yellow-feathered chickens were determined by different treatments on days 1–21, 22–42, 43–63 and 1–63 among group A (control group), group B (10 mg enramycin/kg of diet), and group C (100 mg plectasin/kg of diet) and group D (200 mg plectasin/kg of diet). The results are shown in P < 0.05), the ADFI in group D and group C was significantly lower than that in group A (P < 0.05), the F/G in group D and group C was significantly lower than that in group A (P < 0.05). Additionally, the ADG of group D was significantly higher than that in group C (P < 0.05), while the F/G of group D was significantly lower than that in group C (P < 0.05), indicating that administration of 200 mg plectasin/kg of diet is better than 100 mg plectasin/kg of diet. On days 22–42, the ADG of groups B, C, and D was significantly higher than that in group A (P < 0.05), while the F/G of groups B, C, and D was significantly lower than that in group A (P < 0.05). On days 43–63, the ADG of group D was significantly higher than that in groups A, B, and C (P < 0.05) and the ADFI in groups B, C, and D was significantly lower than that in group A (P < 0.05). Furthermore, the F/G was significantly lower in groups C and D than in groups A and B (P < 0.05). For the overall 63-day period, the ADG was significantly higher in groups C and D than in groups A and B, while the ADFI was significantly lower in groups C and D than in group A (P < 0.05). Furthermore, the F/G was significantly lower in groups C and D than in group A (P < 0.05).As shown in P < 0.05). Besides, the serum antibody level of H9N2 AIV was significantly higher in group D than in groups A, B, and C at 42 days of age (P < 0.05).As shown in P < 0.05). Additionally, the serum antibody level of NDV was significantly higher in group D than in group C at 21 and 35 days of age (P < 0.05).As shown in The villus lengths, crypt depths, and villus length/crypt depth ratio of the ileum, jejunum and duodenum of 21-day-old yellow-feathered chickens in group A (control group), group B (10 mg enramycin/kg of diet), group C (100 mg plectasin/kg of diet), and group D (200 mg plectasin/kg of diet) are shown in P < 0.05); the crypt depths of the ileum, jejunum and duodenum were significantly lower in group D than in groups A, B, and C (P < 0.05), and the villus length/crypt depth of the ileum, jejunum, and duodenum was significantly greater in group D than in groups A, B, and C (P < 0.05). Tissue sections of the ileum, jejunum, and duodenum are shown in As shown in The effects of plectasin on the intestinal microflora of 21-day-old yellow-feathered chickens from group A (control group), group B (10 mg enramycin/kg of diet), group C (100 mg plectasin/kg of diet), and group D (200 mg plectasin/kg of diet) are shown in E. coli in the ileums of chickens from groups B, C, and D significantly decreased (P < 0.05). In addition, the content of E. coli in the ceca of chickens in groups B, C, and D was not significantly changed compared with that of group A (P > 0.05) . As shown in Lactobacilli and E. coli was significantly higher in groups C and D than in groups A and B (P < 0.05), while the ratio between Lactobacilli and E. coli was significantly higher in the ceca of chickens in group B than in groups A, C, and D (E. coli in the ileum while increasing the ratio of between Lactobacilli and E. coli in the ileum.As can be seen from  > 0.05) . As showC, and D . The aboThe effects of plectasin on the mucosal layer construction and tight junctions of ileum of 21-day-old yellow-feathered chickens from group A (control group), group B (10 mg enramycin/kg of diet), group C (100 mg plectasin/kg of diet), and group D (200 mg plectasin/kg of diet) are shown in TFF2 was significantly higher in group D than in other groups (P < 0.05). As shown in MUC2 was significantly higher in groups A, C, and D than in group B (P < 0.05). As shown in ZO-1 in groups C and D was significantly higher than in groups A and B (P < 0.05). As shown in Claudin-3 was significantly higher in group D than in group A (P < 0.05).As shown in IL-17A, IL-22, IFN-α, IFN-γ, IL-1β, and IL-6 in 21-day-old yellow-feathered chickens from group A (control group), group B (10 mg enramycin/kg of diet), group C (100 mg plectasin/kg of diet), and group D (200 mg plectasin/kg of diet) are shown in The effects of plectasin on the ileal immune factors IL-17A expression in the ileum was significantly decreased in groups B, C, and D compared with group A (P < 0.05). As shown in IL-22 in the ileum was significantly decreased in groups B, C, and D compared with group A (P < 0.05). As shown in IFN-α expression in the ileum was significantly decreased in groups B, C, and D compared with group A (P < 0.05). Additionally, IFN-α expression in the ileum was significantly lower in group D than in group B (P < 0.05). As shown in IFN-γ expression in the ileum was significantly decreased in groups B, C, and D compared with group A (P < 0.05). As shown in IL-1β expression in the ileum was significantly lower in groups B, C, and D compared with group A (P < 0.05). Furthermore, IL-1β expression in the ileum was significantly lower in group D than in group B (P < 0.05). As shown in IL-6 expression in the ileum was significantly lower in groups B, C, and D than in group A (P < 0.05). Moreover, IL-6 expression in the ileum was significantly lower in group D than in group B (P < 0.05).As shown in The effects of plectasin on blood biochemical indices in 21-day-old yellow-feathered chickens in chickens from group A (control group), group B (10 mg enramycin/kg of diet), group C (100 mg plectasin/kg of diet), and group D (200 mg plectasin/kg of diet) are shown in P < 0.05). The triglyceride concentration was significantly higher in groups B, C, and D than in group A (P < 0.05). The total cholesterol concentration was significantly higher in groups B, C, and D than in group A (P < 0.05). Meanwhile, the total cholesterol was significantly higher in group D than in groups A, B, and C (P < 0.05). However, there was no significant difference in the high-density cholesterol concentration among the different groups (P > 0.05). The albumin concentration was significantly higher in group B than in groups A, C, and D (P < 0.05). The total protein content was significantly higher in group D than in groups A, B, and C (P < 0.05). Additionally, the total protein content was significantly higher in group D than in group C (P < 0.05).As shown in in vivo challenging E. coli. A previous study showed that plectasin elicits similar improvements in performance and intestinal mucosa growth and activity in weaned pigs when used as an antibiotic  likely can escape more from intestinal digestion and can direct antibacterial effects on E. coli. May be the peptide have more antibacterial effects on Gram negative bacteria vs. Gram positive bacteria for yellow-feathered chickens.The intestinal microecological flora of chickens plays an important role in immune regulation and disease control. Intestinal microbes can be divided into intestinal symbiotic bacteria, conditional pathogenic bacteria, and pathogenic bacteria regarding to the relationship between the intestinal tract and host . The intisorders . In thisIL-17A and IL-22 are representative cytokines secreted by Th17 cells. IL-17A is an inflammatory cytokine that can promote the activation of T cells and stimulate epithelial cells to produce a variety of inflammatory factors (such as IL-6 etc.). Both IFN-α and IFN-γ play important anti-infection and immune regulation roles. Our study showed that the expression of interferon was positively correlated with the degree of inflammation in vivo of the intestine, and dietary plectasin can significantly decrease the mRNA expression levels of IFN-α and IFN-γ in the ileums of yellow-feathered chickens at 21 days of age. In this study, plectasin may maintain intestinal homeostasis by inhibiting the production of pro-inflammatory cytokines, reducing inflammatory cell infiltration, repairing intestinal barrier damage to a certain extent, enhancing intestinal epithelial barrier function, thereby reducing intestinal inflammation and achieving a protective effect on the intestinal tract.Cytokines play an important role in the immune response. Some studies indicated that AMPs have an important effect on NF-kB pathway regarding immunomodulatory activities of AMPs , 55. BesEpinephelus coioides, weaned piglets, and chickens .QX and FenC conceived and designed the study. ZYao, ZYan, FeiC, and ZX collected the samples. CW, LW, RL, and LC conducted laboratory analysis and acquired the data. XZ and QZ conducted bioinformatics, data analysis, and drafted the manuscript. LW analyzed the data and provided suggestions on the design of the experiment. All authors contributed to the article, critically revised the manuscript, and approved the submitted version.LW was employed by Guangdong Hinabiotech Co., Ltd. ZYan was employed by Wen's Foodstuff Group Co., Ltd. The remaining 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.


SARS-CoV-2 continues to evolve through emerging variants, more frequently observed with higher transmissibility. Despite the wide application of vaccines and antibodies, the selection pressure on the Spike protein may lead to further evolution of variants that include mutations that can evade immune response. To catch up with the virus’s evolution, we introduced a deep learning approach to redesign the complementarity-determining regions (CDRs) to target multiple virus variants and obtained an antibody that broadly neutralizes SARS-CoV-2 variants. The ability of viruses to mutate and evade the human immune system and neutralizing antibodies remains an obstacle to antiviral and vaccine development. Many neutralizing antibodies, including some approved for emergency use authorization (EUA), reduced or lost activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. Here, we introduce a geometric deep learning algorithm that efficiently enhances antibody affinity to achieve broader and more potent neutralizing activity against such variants. We demonstrate the utility of our approach on a human antibody P36-5D2, which is effective against SARS-CoV-2 Alpha, Beta, and Gamma but not Delta. We show that our geometric neural network model optimizes this antibody’s complementarity-determining region (CDR) sequences to improve its binding affinity against multiple SARS-CoV-2 variants. Through iterative optimization of the CDR regions and experimental measurements, we enable expanded antibody breadth and improved potency by ∼10- to 600-fold against SARS-CoV-2 variants, including Delta. We have also demonstrated that our approach can identify CDR changes that alleviate the impact of two Omicron mutations on the epitope. These results highlight the power of our deep learning approach in antibody optimization and its potential application to engineering other protein molecules. Our optimized antibodies can potentially be developed into antibody drug candidates for current and emerging SARS-CoV-2 variants. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide over the past 2 y, causing hundreds of millions of confirmed infections and millions of deaths . The rec2711However, reduced sensitivity of SARS-CoV-2 variants to antibody and serum neutralization has been widely observed –21. For 23Experimental methods to improve antibody binding and neutralization have been developed. In vitro affinity maturation methods, such as random mutagenesis with display technologies, has been shown to improve antibody binding against target proteins, but such approaches are time consuming and labor intensive –32. TargHere, we develop and apply a deep learning framework to efficiently optimize antibodies to achieve broader and more potent neutralizing activity against SARS-CoV-2 variants. Based on a large collection of antibody–antigen complex structures and binding affinity data, we trained a geometric neural network model, recently developed in computer vision, that effectively extracts interresidue interaction features and makes predictions of changes in binding affinity due to single or multiple amino acid substitutions to the antigen. To search for favorable complementarity-determining region (CDR) mutations that potentially improve antibody binding, we also simulate an in silico ensemble of predicted complex structures with CDR mutations to obtain a robust estimation of the free energy change, also known as ΔΔG. Compared to traditional approaches, the deep learning search space is theoretically much larger and is also easily applicable in targeting multiple variants simultaneously via multiobjective optimization.To demonstrate the utility of our approach, we sought to optimize a human neutralizing antibody P36-5D2, which was initially isolated from a convalescent patient, and demonstrated reasonably strong potency and breadth against Alpha, Beta, and Gamma  but not We tapped neural networks because they are known as universal approximators for learning complex nonlinear mappings from high-dimensional data with generalizability. Among many successful neural network architectures, attention-based networks are extremely powerful for modeling complicated interactions between entities, making them suitable for modeling proteins whose functions are largely determined by interactions between amino acids –37.SI Appendix, Table S1. An additional subset consisting of multipoint mutations (M1707)   was also (M1707) , the cur (M1707) –43, in oWe sought to experiment with an antibody that has an epitope with highly conserved regions, which makes it a good candidate to become potent against multiple SARS-CoV-2 variants. We focused on a broad neutralizing antibody P36-5D2, isolated from convalescent patients, known to neutralize Alpha, Beta, and Gamma . HoweverTo improve the neutralizing activity of P36-5D2 against the Delta variant, we applied our deep learning method to predict and identify optimized antibody mutations. While the initial structure of P36-5D2 bound with the RBD, the neutralizing activity of P36-5D2 against SARS-CoV-2 WT, Beta, Gamma, and Delta was then utilized to analyze and select CDR mutations. Based on those data, our method generated an in silico mutation library of antibody CDRs, ranked by trained geometric neutral networks in such a way that they should not only improve antibody binding to the Delta RBD but also maintain binding to the RBD of other variants of concern (VOC). The predicted top-ranked mutations were selected for experimental verification via pseudotyped virus neutralization assays , an ultrWe first sought to narrow down which optimized antibodies to experimentally test, the number determined by our experimental capacity. According to the model’s predictions, 12 top-ranked single mutations were selected and introduced into the original antibody P36-5D2. These mutations included T30P, T31W, and Y32P in HCDR1; N52D, A53F, N55L, and N57L in HCDR2; G100P, R103M, L104F, and Q105F in HCDR3; and S30Y in LCDR1. Out of the 12 single-mutation sites, only 4  were positioned on the paratope of P36-5D2 bound to the RBD, while the remaining 8 were outside and did not directly interact with the RBD. To evaluate the neutralizing breadth and potency of the optimized antibodies, we used an established panel of 10 pseudotyped viruses, including SARS-CoV-2 WT 614G, four VOCs—Alpha, Beta, Gamma, Delta—as well as Delta Plus, two VOIs—Kappa, Epsilon which carried L452R, and Eta which carried E484K—and the variant N439K which effected reference antibody REGN10987.50) reaching 0.038 μg/mL. Four mutations, T31W in HCDR1, A53F, and N57L in HCDR2, and L104F in HCDR3, also improved the overall neutralizing activity, with average IC50 of 0.479 μg/mL, 0.547 μg/mL, 0.025 μg/mL, and 0.104 μg/mL, respectively. Three mutations, N52D, N55L in HCDR2, and G100P in HCDR3, obtained enhanced neutralization against Delta but reduced neutralization against WT. The four mutations T30P, Y32P in HCDR1, Q105F in HCDR3, and S30Y in LCDR1 had negligible impact on the neutralizing activity of P36-5D2.In the first-round neutralization shown in 50 values increased to 0.007μg/mL and 0.013μg/mL, respectively  and HX001-015 (R103M and T31W), for which average ICectively . HX001-050 of 0.006 μg/mL, 0.006μg/mL, and 0.010μg/mL, respectively , HX001-024 , and HX001-033  with average ICectively . HX001-050 of 0.017μg/mL, yet was slightly weaker than three mutation combinations: HX001-020, HX001-024, and HX001-033  of the optimized antibodies to the SARS-CoV-2 WT RBD was 1.2 nM to 0.42 nM, which is 20- to 50-fold stronger than the original P36-5D2 antibody. While the off-rate (kd) value of the original antibody against WT RBD was around 10−2, the off-rate (kd) values of the six optimized antibodies all reached 10−3, signifying a longer half-life binding period and higher binding stability. Antibodies HX001-020, HX001-024, HX001-033, and HX001-034 with three or four mutations were also stronger than HX001-013 with only two mutations. The increase in binding affinity may contribute to the increased neutralizing activity of these antibodies against SARS-CoV-2 WT and variants. Notably, the dissociation constant of optimized antibodies with the SARS-CoV-2 Delta RBD was 6.22 nM to 26.4 nM, which is twofold stronger than or similar to P36-5D2, while the KD of optimized antibodies with the SARS-CoV-2 Delta RBD is still slightly weaker than with WT RBD.To experimentally test our predicted mutations, we compared the binding affinity of the original antibody and optimized ones against the SARS-CoV-2 WT RBD monomer and Delta RBD monomer by surface plasmon resonance (SPR) . The disTo help explain why our predicted mutations could improve the neutralizing activity from a structural perspective, we used Rosetta to predict the complex structures of WT/Delta RBD with antibodies carrying four critical single mutations: T31W, N57L, R103M, or L104F. As shown in A new SARS-CoV-2 variant Omicron emerged in South Africa at the end of November 2021. Omicron carries 36 total mutations in the spike protein and 15 mutations in the RBD, which greatly changed the immunogenicity of the RBD and caused many neutralizing antibodies to lose their neutralization –47. Thro50 between 0.006 μg/mL and 0.017 μg/mL, indicating the capability of our deep learning method to predict mutations with improved neutralizing ability of antibodies against WT or other variants.Collectively, the deep learning platform presented here efficiently optimized the original antibody to obtain broader and more potent neutralizing activity against SARS-CoV-2 variants in only 2 wk for each single-round optimization. Optimized antibodies with single mutations or their combinations can achieve 10- to 600-fold improvement in neutralizing activity against SARS-CoV-2 variants, including Delta. In all optimized antibodies, six antibodies  had the most potent and broad neutralizing activity, with an ICIn this study, we have reported the efficient optimization of a single original antibody P36-5D2 to obtain broader and more potent neutralizing ability against multiple SARS-CoV-2 variants via a cutting-edge, deep learning guided approach. Based on the crystal structure of the interaction of the RBD and antibodies, and neutralizing data, our geometric neural network model created libraries of CDR mutations and ranked each mutation according to its contribution to binding affinity and structural stability. Through an iterative optimization procedure, we found that optimized antibodies exhibited broader and much more potent neutralizing activity compared to the original antibody. More detailed neutralization assays and binding affinity analyses by SPR further proved that optimized antibodies had hundredfold improvements against specific viral strains.By deep learning and computational modeling of optimized antibodies of SARS-CoV-2 RBD variants, we observed that predicted optimized antibody candidates could successfully abrogate preexisting steric clashes with RBD at the interface. Some other mutations significantly increase the number of interaction sites, expanding antibody epitopes to more regions on the surface of RBD. Interestingly, a few good mutations were located away from the antibody–RBD interface, which we speculate improve antibody stability and increase CDRs conformational exposure to enhance the neutralization. These results strongly suggested that our model can successfully predict potential modifications of antibodies that are critical to bind to an antigen, with increased structural stability and outstanding neutralizing activity. Surprisingly, peripheral sites not on the binding interface have also been picked up by our deep learning model which seem to be a challenge for conventional structural analysis. Our deep learning model exhibits superior advantages in antibody prediction and optimization for more broader and potent neutralization.As the virus continues to evolve, more and more diverse variants will emerge in the infected population with greater immune pressure. Many such variants, including Delta and the most recent Omicron, have adopted different mutation strategies to evade immune recognition, leading to the failure of neutralizing antibodies and vaccines , 45–48. 45Based on our platform, it would even be feasible to train a pool of antibodies that target different potential variants. It is also critical to develop a system to predict virus variants with potential immune escape capabilities such as in Hie et al. , even beVariant-specific amino acid substitutions in the RBD were first created to build complexes with different antigen variants. For each antigen variant, single-point mutations in CDR of the antibody were enumerated. The structures of mutated complexes were sampled using the Rosetta relax and Cartesian ΔΔG programs . Point miu1, iu2 are the orthogonal vectors. Each residue was also assigned an initial feature Each residue in the complex structure was encoded into a vector representation using a geometric attention network  designedFinally, message vectors Residues in the WT complex and the mutant complex were encoded using the aforementioned geometric neural network. As a complement to the features output by the network, physical energy values of a complex calculated by Rosetta were also included as a feature shared across residues in the complex. Let The training and validation datasets were constructed from the SKEMPI V2.0 dataset , the larSI Appendix, Table S1. Figure plots of ΔΔG predicted by the model and experimental ΔΔG are reported in SI Appendix, Table S1. Overall, our model's predictions were highly correlated with experimental results.The model was evaluated by split-by-complex fivefold cross-validation over the entire SKEMPI V2.0 dataset. A subset consisting of 1,131 single-point mutations (S1131)  was usedMutants in CDRs were scored using the average predicted ΔΔG from an ensemble of five trained models, as the models were trained in a fivefold cross-validation manner. Mutations were grouped by antigen variants and sorted in ascending order; those with lower ΔΔG values were more favorable. We also used GeoPPI as another ΔΔG predictor for ranking mutations in PPI complexes based on a pretrained graph neural network and a random forest regressor . The ΔΔGThe initial single-point mutation library was built as follows: 1) All single-point mutations in HCDRs were enumerated and scored; 2) all double-point mutations from the top single-point mutations were enumerated; and 3) all single and double mutations were ranked, and the top mutations on three CDR-H loops were selected as the initial mutations, including T30P, T31W, Y32O, N52D, N53F, N55L, N57L, G100P, R103M, L104F, and Q105F.2 in Dulbecco’s minimal essential medium containing 10% (vol/vol) heat-inactivated fetal bovine serum and 100 U/mL of penicillin–streptomycin. FreeStyle 293F cells  were maintained at 37 °C in 5% CO2.HEK293T cells (ATCC), HeLa cells expressing hACE2 orthologs  were maintained at 37 °C in 5% COAntibodies and Fab production were conducted as previously described , 52. Ref53The WT pseudotyped virus used throughout the analysis was the prototype strain (GenBank: MN908947.3) with a D614G mutation (WT D614G). Variants were extracted from the Global Initiative on Sharing All Influenza Data (GISAID) website. The Alpha variant (GISAID: EPI_ISL_601443) was constructed with a total of nine mutations . The Beta variant (GISAID: EPI_ISL_700450) was constructed with 10 mutations: L18F, D80A, D215G, 242-244del, S305T, K417N, E484K, N501Y, D614G, and A701V. The Gamma variant (GISAID: EPI_ISL_792681) was constructed with 12 mutations: L18F, T20N, P26S, D138Y, R190S, K417T, E484K, N501Y, D614G, H655Y, T1027I, and V1176F. The Delta variant (GISAID: EPI_ISL_1534938) was constructed with 10 mutations: T19R, G142D, 156-157del, R158G, A222V, L452R, T478K, D614G, P681R, and D950N. The variant Delta Plus (GISAID: EPI_ISL_3019629) was constructed with 11 mutations: 19R, G142D, 156-157del, R158G, A222V, K417N, L452R, T478K, D614G, P681R, and D950N. The Kappa variant (GISAID: EPI_ISL_1384866) was constructed with eight mutations: T95I, G142D, E154L, L452R, E484Q, D614G, P681R, and N1071H. The Eta variant (GISAID: EPI_ISL_2885901) was constructed with eight mutations: Q52R, A67V, 69-70del, 144del, E484K, D614G, Q677H, and F888L. The Epsilon variant (GISAID: EPI_ISL_2922315) was constructed with four mutations: S13I, W152C, L452R, and D614G. The single mutation N439K was introduced into the pcDNA3.1 vector encoding WT D614G using QuikChange site-directed mutagenesis (Agilent 210519). The gene of variants were synthesized in Genewiz, Inc. WT, and mutated SARS-CoV-2 pseudotyped viruses were generated by cotransfection of human immunodeficiency virus backbones expressing fireﬂy luciferase (pNL43R-E-luciferase) and pcDNA3.1 (Invitrogen) expression vectors encoding the respective spike proteins into 293T cells (ATCC) (4 per well) were then added into the mixture and incubated at 37 °C for 48 h to 60 h. Then the luciferase activity was measured after cell lysis. The percent of neutralization was determined by comparing with the virus control.Neutralization assays were conducted as previously described . Serial The binding kinetics of mAbs to SARS-CoV-2 WT or Delta RBD monomer were analyzed using SPR . Specifically, recombinant protein A  was immobilized to a CM5 sensor chip. The mAbs (2 μg/mL) were captured by recombinant protein A, and then serial dilutions of SARS-CoV-2 WT/Delta RBD with highest concentration of 200 nM were run at a flow rate of 30 μL/min in PBST buffer (1×PBS and 0.05% [vol/vol] Tween-20). The resulting data were fitted to a 1:1 binding model using the Biacore 8K Evaluation software .50 of mAbs were calculated by the four-parameter dose inhibition equation in using Graphpad Prism 9.0.The technical and independent experiment replicates are indicated in the figure legends. ICSupplementary File


CS-SC) preconditions. The simulations confirmed the major role of the unique trans-GJ SC pattern comprising conserved  and non-conserved (57Q) residues of the apposed EL1 loops in the stabilization of the GJC complex. Importantly, clusters of SC patterns residing close to the GJ interface domain appear to orient the interface formation via the numerous SCs between EL1 and EL2. These include central C54S-SC198 or C61S-SC192 contacts with residues 53R, 54C, 55N, 197D, 199F or 64V, 191P, respectively. In addition, we revealed that GJC interface formation is favoured when the psi dihedral angle of the nearby 193P residue is stable around 180° and the interface SCs disappear when this angle moves to the 0° to −45° range. The potential of the association of non-conserved residues with SC motifs in connexon-connexon coupling makes the development of Cx subtype-specific inhibitors viable.Connexin (Cx) proteins establish intercellular gap junction channels (Cx GJCs) through coupling of two apposed hexameric Cx hemichannels . Pre- and post-GJ interfaces consist of extracellular EL1 and EL2 loops, each with three conserved cysteines. Previously, we reported that known peptide inhibitors, mimicking a variety of Cx43 sequences, appear non-selective when binding to homomeric Cx43 vs. Cx36 GJC homology model subtypes. In pursuit of finding potentially Cx subtype-specific inhibitors of connexon-connexon coupling, we aimed at to understand better how the GJ interface is formed. Here we report on the discovery of Cx GJC subtype-specific protein stabilization centers (SCs) featuring GJ interface architecture. First, the Cx43 GJC homology model, embedded in two opposed membrane bilayers, has been devised. Next, we endorsed the fluctuation dynamics of SCs of the interface domain of Cx43 GJC by applying standard molecular dynamics under open and closed cystine disulfide bond ( Vertebrate cells perform dominant remodelling of intercellular adhesion and communication via specific subunit combinations of hexameric connexin (Cx) hemichannels  that couple to form dodecameric gap junction channels (Cx GJCs) ,5,6,7,8.cf. Results section). Based on mapping mutation-function relationships, Foote and co-workers [CS-SC bonds take part in anti-parallel beta sheets with the first C(1) cysteine in one loop connected to the C(3) of the other. Distances of cysteines C(1), C(2) and C(3) in EL1 and EL2 loop motifs may also anticipate a critical role for cystine disulfide C(2)S-SC(2) bond formation.Canonical Cx proteins present three conserved cysteines in their extracellular loops EL1 and EL2, except Cx23 subtype identified in mammals and zebrafish. The Cx23a isoform has only two conserved cysteines in its extracellular EL1 and EL2 loops and is characterized by less efficient GJC coupling . Pannexi-workers  suggestePreviously, we reported that known peptide inhibitors, mimicking a variety EL1 or EL2 sequences of Cx43 subtype, appear non-selective when binding to Cx43 versus Cx36 GJC subtypes . In searTo this end, we seek unique EL1 and/or EL2 patterns of Cx GJC by selecting the homomeric Cx43 GJC as prototype. Cx43 GJCs are widespread in the brain, connecting the brain-specific astrocytic cells ,20. Alsocf. Methods). Our selection criteria were to provide high resolution data on residues participating in junctional coupling. As first described, the Cx26 X-Ray structure may well serve as a reference when connexon channel structures are compared. Initial inspection of the extracellular domain of the Cx43 GJC model [cf. Methods) highlighted pre- and post-junctional cystine disulfide interfaces (cf.D-48E-49Q-50S-51A-52F-53R-54C-55N-56T-57Q-58Q-59P-60G-61C-62E-63N-64V-65C-66Y-67D-68K-69S-70F-71P-72I-73S) and EL2 (177Y-178G-179F-180S-181L-182S-183A-184V-185Y-186T-187C-188K-189R-190D-191P-192C-193P-194H-195Q-196V-197D-198C-199F-200L-201S-202R-203P) (residues conserved or partly conserved between the ELs are marked in bold).In Cx43 GJC model building, we used the X-ray structure of the homomeric Cx26 GJC dodecamer as a template  simulation with standard t intact .N, 56T) and non-conserved (57Q) EL1 residues as it was postulated earlier [N, 56T and 57Q along with cystine disulfides C54S-SC198 has been disclosed  also provide an opportunity to induce local conformation changes [P, 191P and 193P in the closed disulfide configuration. These angles were found to be confined to 180°, indicating the stable presence of the trans-proline configuration. However, ψ (psi) torsion angles were observed to be largely fluctuating in all subunits. Moreover, we found that the values of ψ torsion angles of the 193P residues significantly correlated to the presence of GJ interface SCs  determines plaque stability . It may Building Cx26-based homology model of Cx43 GJC. Cx43 GJC was built up as described previously [https://swissmodel.expasy.org/interactive, accessed on 1 July 2018). Notably, the Cx26 GJC lacks the long and disordered CT domain appearing in the Cx43 GJC. Then, Swiss-Model generated an alignment between the target and the template  requires coordinate files (PDB) and structure files (psf) for all types of calculations. While PDB files are used generally, the psf files are specific to VMD, comprising names of atoms, coordinates, capping residues for N and C termini, topology files for lipids and proteins and others, such as the specification of cystine disulfide bond residues. First, to generate the psf file, the Cx43 GJC dodecamer model should be disconnected into the twelve individual subunits as the “psfgen” program expects non-covalently linked segments being in separate pdb files. In order to accomplish the condition of 100 ns MD of Cx43 GJC at 300 K with “close S-S bonds” precondition, protein subunits were prepared by setting disulfide bonds between residues 54C-198C, 61C-192C and 65C-187C in the psf file of each subunit. For the condition of the Cx43 GJC (100 ns) at 300 K with the “open S-S bonds” precondition, or the “partially open S-S bonds” precondition, where 61C-192C was opened, the Cx43 GJC subunits were also prepared individually, but without setting the S-S bonds. For the wild type runs we started from the structure obtained from OPM (see previous section), while for the mutant N55H, D197T we introduced the mutations in the OPM generated structure by Pymol. Subsequently, we followed the same protocol for both Cx43 GJC structures with and without setting extracellular S-S bonds. The 12 subunits were re-combined into a single file once again and the “Membrane builder plugin” of VMD was used to generate two POPC membranes (150 × 150 Å each). Membranes were aligned manually relative to the protein by translation along with “z” direction, leaving the extracellular E1 and E2 loops  membrane-free. In order to handle two different membranes, segment IDs of the lipid residues  and water  are to be renamed before switching over the second membrane. After combining the two membranes with the protein we used the CHARMM 27-derived [+ and Cl− to a physiological ionic concentration of 0.15 mol/L using the “Autoionize module” of VMD. Thereafter, the system was ready for minimization and MD running.For the reason that VMD allows the application of more than one membrane per protein, we prepared Cx43 GJC structures using the VMD 1.9.3 tutorial  as descrMD runs were performed as described  applyingIn order to identify stabilization centers (SCs), MD trajectories from NAMD or Desmond were imported into VMD and individual frames at 10 ps interval were exported as .pdb files. After adding sequence residues (SEQRES) data by Maestro, SCs were identified in these .pdb files by the stabilizing residues (SRide) server . ExtracePhi and psi dihedral angles for proline residues 59P, 191P and 193P were calculated by VMD. The omega angle for the residue on the N-terminal side of these prolines were also calculated, since rotation around this C-N bond may induce major structural changes .The Uniprot database was searched for keywords “name: gap junction” and “organism: human”, which resulted in 21 human Cx subtypes. These sequences were downloaded to a common fasta file and opened in Jalview . The ali