African swine fever (ASF) is an acute, highly contagious, and deadly infectious disease caused by the African swine fever virus (ASFV) and has a huge impact on the pig industry. A lack of vaccines and effective therapeutic drugs has brought great challenges to the prevention and control of ASF. In this study, insect baculovirus expression system was used to express ASFV B602L protein (B602L) alone and the IgG FC-fused B602L protein (B602L-Fc), and evaluate the immune effect of B602L-Fc in mice model. To be specific, the ASFV B602L protein and B602L-Fc fusion protein were successfully expressed by the insect baculovirus expression system. Then, Functional analysis in vitro revealed that the B602L-Fc fusion protein bound and interacted with the FcRI receptor of antigen-presenting cells and significantly promoted the expression of proteins involved in antigen presentation and various cytokines at mRNA levels in porcine alveolar macrophages. Additionally, immunization using B602L-Fc fusion protein remarkably promoted the Th1-biased cellular immune response and humoral immune response in mice. In conclusion, The B602L-Fc fusion protein could up-regulate the expression of molecules involved in antigen presentation in APCs and enhance the humoral and cellular immune responses in mice. These results suggest that ASFV B602L-Fc recombinant fusion protein may be a promising candidate for subunit vaccine. This study provided useful data for the development of subunit vaccines for ASF. African swine fever (ASF) is a highly infectious hemorrhagic and infectious viral disease of swine. There is currently no commercial vaccine to prevent ASF. Since the disease was first notified in 1921 in Kenya, ASF has spread to many African countries, affecting up to 35 African countries . At presThe cause of ASF is African Swine Fever Virus (ASFV), a large enveloped double-stranded DNA virus . The virSo far, there are no commercially effective vaccines or drugs to prevent, control, and treat ASFV . ASFV vaTherefore, the ASFV subunit vaccine has potential, but its immune protection effect need to be enhanced by some methods. On the other hand, besides neutralizing antibodies, cell-mediated immune response in the prevention of ASFV is also very important , 22. OurSaccharomyces cerevisiae in IPEC-J2 cells or porcine alveolar macrophages (PAM) cells were also significantly enhanced (The Fc receptors (FcγRs) of immunoglobulin G (IgG) belong to the immune receptor family and exist on the surface of various immune effector cells . FcRⅠ plenhanced . All theIn this study, ASFV B602L protein and a novel Fc refusion protein were expressed by the baculovirus expression system. We further examined whether ASFV B602L-Fc fusion protein can promote the immune response in mice. This study provides the basis and strategy for future research on ASFV subunit vaccine.ASFV-SY18 ((GenBank: MH766894.1)) B602L gene and the porcine IgG Fc gene synthesis and codon optimization were all outsourced by Tsingke Biotechnology , and cloned into pUC57 and verified by gene sequencing. Synthetic porcine IgG Fc fragment included hinge, extended CH2 and CH3 domains. The HBM signal peptide sequence was designed at the N-terminus of the B602L protein to promote protein expression (The expression of ASFV B602L and B602L-Fc recombinant proteins was detected using indirect Immunofluorescence (IFA). Sf9 cells were infected with recombinant baculovirus rBac-B602L and rBac-B602L-Fc and placed at 28°C for 48 h in an incubator. The cells were fixed using 4% PFA at room temperature for 30 min, incubated with 5% bovine serum albumin for 30 min, and permeabilized with NP 40 solution for 15 min. Subsequently, the cell samples were incubated with ASFV-positive serum  or anti-His monoclonal antibody . Then, cells were treated with fluorescein isothiocyanate (FITC)-conjugated goat anti-pig antibody  or FITC-conjugated goat anti-mouse antibody . The nuclei were stained with DAPI . Lastly, the images results were taken under an inverted fluorescence microscope (Nikon).ASFV B602L and ASFV B602L-Fc proteins were detected by western blotting. Sf9 cells were infected with B602L and B602L-Fc recombinant baculovirus and placed at 28°C for 72 h in an incubator, and then collected for western blot analysis. Protein samples were separated using 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred onto NC membranes. The NC membranes (nitrocellulose filter membrane) were incubated overnight at 4 °C with the ASFV positive serum  and anti-His monoclonal antibody  at 4 °C overnight respectively. The secondary antibody, the horseradish peroxidase (HRP)-labelled goat anti-pig antibody  and HRP-labelled goat anti-mouse antibody , were incubated at room temperature for 1 h and the signals were visualized using the ECL  chemiluminescence system.To purify B602L and B602L-Fc proteins, a large number of Sf9 cells were infected with recombinant baculovirus. After infection for 72 h, the cells were collected by centrifugation. ASFV B602L and B602L-Fc proteins were purified by BioLogic DuoFlow Chromatography System  through a nickel affinity chromatography column. Lastly, the purified ASFV B602L and B602L-Fc proteins were identified by resolving them using 10% SDS-PAGE.Preliminary verification of the Fc fusion protein function by western blot analysis and Immunofluorescence assay. For western blot analysis, we took the purified B602L and B602L-Fc proteins to prepare for gel SDS electrophoresis, transferred them onto NC membranes, blocked overnight at 4°C. The membranes were incubated with HRP-labelled goat anti-pig antibody  and then with HRP-labelled goat anti-mouse antibody  at room temperature for 1 h, and ETL  was added for color development.For the immunofluorescence assay, the Sf9 cells were infected with the recombinant baculovirus. The cells were fixed, blocked, punched and then directly incubated at room temperature for 1 h with FITC-conjugated goat anti-pig antibody  or FITC-conjugated goat anti-mouse antibody . The images were photographed under inverted fluorescence microscope (Nikon).The binding ability of B602L-Fc fusion protein to Fc receptor (FcRI) on APCs was determined. Therefore, the porcine alveolar macrophages (PAMs) were cultured in RPMI-1640 containing 10% fetal bovine serum in 24-well plates. When about 60-80% of the cells were adherent, B602L or B602L-Fc fusion proteins were added into the wells. After incubating at 37 °C for 12 h, the cells were fixed, blocked, and punched. The cells were incubated with anti-pig CD64 (FcRI) polyclonal antibody  and anti-His monoclonal antibody  for 60 min, sequentially. After that, the PAMs were incubated with FITC-conjugated goat anti-rabbit antibody  and FITC-conjugated goat anti-mouse antibody  for 60 min, respectively. The image results were taken under a laser scanning confocal microscope .-ΔΔCT method was used to analyze the differences between the B602L group and the B602L-Fc group using GAPDH as the reference gene.Porcine PAMs were prepared in a 24-well plate in a 37 °C cell incubator with 5% CO2. After the cells were adherent, purified B602L protein and B602L-Fc fusion protein were added, respectively and the cells were placed for about 12 h in the cell incubator; the wells supplemented with phosphate buffer saline (PBS) were set as a negative control. The medium supernatant was discarded, and cell samples were collected. The total RNA was extracted from the porcine PAMs cell samples and then analyzed by quantitative real-time polymerase chain reaction (q-PCR). p <0.05 and p <0.01 were considered significant, * and ** respectively represent p <0.05, p < 0.01.Data statistics and analysis: GraphPad Prism 8 was used to make a statistical chart, and the two-tailed T-test was used to analyze the significance of the difference between the B602L and B602L-Fc experimental groups. The female Balb/c mice aged 4 weeks were bought from the JSJ  and randomly categorized into three groups (n = 10/group). Afterwards, the mice were subcutaneously immunized with the same dose (200 pM) of B602L (Group A), B602L-Fc (Group B), and (200 μL) PBS (Group C). For the first immunization, the purified proteins were emulsified in a 1:1 (w/w) ratio using Freund’s complete adjuvant . Each mouse received subcutaneous multipoint injection of immune protin. After three weeks of the first vaccination, each mouse was injected with the same dose (200 pM) of protein at subcutaneous multipoint injection. The proteins were emulsified with Freund’s incomplete adjuvant  in a ratio of 1:1 (w/w). Body-weight changes of mice immunized with B602L and B602L-Fc protein were monitored for 49 days. The blood samples were collected from orbital veins at 0, 7, 14, 21, 28, 35, 42, and 49 days after the primary immunization, and serum was then isolated for the detection of specific antibodies. On day 49 of immunization, three mice in each group were randomly sacrificed to isolate spleen lymphocytes aseptically to detect lymphocyte proliferation. The immunization procedure and regimen of mice is shown in ASFV B602L specific antibody was detected by enzyme linked-immunosorbent assay (ELISA) in each group at 0, 7, 14, 21, 28, 35, 42, and 49 days after the first immunization . IndirecAccording to the above method, 14 -, 28 -, and 42-day mouse serum was added with a two-fold serial dilution starting at 1: 100 up to 1:51200 was used. Finally, the absorbance was measured at OD450mm on a microplate reader.6 cells/mL) were cultured with 100 μL RPMI-1640 containing 10% fetal bovine serum in 96-well plates. To stimulate these lymphocytes, RPMI-1640, concanavalin A (5 μg/mL), and purified pB602L protein (10 μg/mL) were added to each well. After culturing at 37 °C for 72 h, 20 μL CCK-8 solution was added to each well. The incubation was then continued at 37°C for 3 h to calculate the stimulation index for each group. The stimulus index (SI) was calculated as follows: SI = /.After 49 days of first immunization, three mice in each group were randomly selected, and the spleens of mice were collected aseptically. Then, the spleen lymphocytes of immunized mice were isolated according to the mouse spleen lymphocyte isolation kit , and lymphocytes  were used for the detection of interferon (IFN-γ) and interleukin (IL-4) levels. Finally, the concentrations of different cytokines were measured according to the standard curve.The spleen lymphocytes of immunized mice were isolated according to the mouse spleen lymphocyte isolation kit. Isolated mouse lymphocytes were specifically stimulated p < 0.05 was considered statistically significant.All data were analyzed using GraphPad Prism 8 software . The two-tailed Student’s t-test was used to analyze all data. The expression of ASFV B602L and B602L-Fc recombinant proteins was detected by immunofluorescence assay (IFA). Green fluorescence signal was detected in Sf9 cells infected with B602L and B602L-Fc recombinant baculovirus but not in normal cells when ASFV-positive serum or anti-His monoclonal antibody were used as primary antibodies  to the FcRI receptor on APCs. B602L-Fc fusion recombinant protein could bind to FcRI on porcine macrophages (p <0.01), and that porcine alveolar macrophages of CD86 were significantly different (p <0.05) (p <0.01), and that of IL-6, IL-12 (P40), IFN-α, IFN-β, and TNF-α were also significantly different (p <0.05)   of the two protein immunization groups was significantly different from that of the PBS group. Moreover, the SI of the B602L-Fc group was significantly higher than that of the B602L group, with a statistically significant difference  and performed in compliance with the Guidelines on the Humane Treatment of Laboratory Animals .YY: Formal analysis, investigation, and validation. QX: Formal analysis. Methodology and writing–review and editing. YZ: Formal analysis and investigation. ZG, and JZ: Investigation and validation. Validation. BL and YQ: Validation and writing–review and editing. KL: Validation. DS: Methodology. ZM: Conceptualization, funding acquisition, and project administration. JW: Conceptualization, formal analysis, funding acquisition, investigation, methodology, and writing– original draft. All authors contributed to the article and approved the submitted version.


Major histocompatibility complex class II (MHC-II) antigen presentation underlies a wide range of immune responses in health and disease. However, how MHC-II antigen presentation is regulated by the peptide-loading catalyst HLA-DM (DM), its associated modulator, HLA-DO (DO), is incompletely understood. This is due largely to technical limitations: model antigen-presenting cell (APC) systems that express these MHC-II peptidome regulators at physiologically variable levels have not been described. Likewise, computational prediction tools that account for DO and DM activities are not presently available. To address these gaps, we created a panel of single MHC-II allele, HLA-DR4-expressing APC lines that cover a wide range of DO:DM ratio states. Using a combined immunopeptidomic and proteomic discovery strategy, we measured the effects DO:DM ratios have on peptide presentation by surveying over 10,000 unique DR4-presented peptides. The resulting data provide insight into peptide characteristics that influence their presentation with increasing DO:DM ratios. These include DM sensitivity, peptide abundance, binding affinity and motif, peptide length, and choice of binding register along the source protein. These findings have implications for designing improved HLA-II prediction algorithms and research strategies for dissecting the variety of functions that different APCs serve in the body.  •A new cell line panel with varied DO:DM ratios informs MHC-II presentation.•Tuning DO:DM ratios corresponds with strikingly divergent MHC-II immunopeptidomes.•Our study bridges a gap in understanding and predicting MHC-II antigen presentation. Immunopeptide presentation by MHC-II regulates adaptive immunity. The noncanonical MHC molecules HLA-DM and HLA-DO cooperatively regulate MHC-II function, and their relative abundances vary across APCs and cellular contexts. How this variation influences immunopeptide repertoires remains unclear. We addressed this by creating cell lines expressing HLA-DM and HLA-DO, spanning several relative abundances and measuring their immunopeptide repertoires. We found that immunopeptides clustered according to their presentation levels across different DO:DM ratios. Predicted MHC-II binding affinity substantially contributed to but did not fully account for these results. The immune system develops effector T cell repertoires that are both tolerant of self-proteins and reactive to foreign antigens through selection steps that remove T cell clones bearing high-affinity, self-reactive antigen receptors , 2. Profneg dendritic cells diminished the presentation of certain pMHC-II  analysis on the peptidomes associated with an MHC-II human leucocyte antigen allele, HLA-DR4, expressed by model antigen-presenting cell lines, by jointly considering the related proteomic information. These cell lines cover a wider range of DO:DM stoichiometry than the previously used DO+ nullDOnullDMnull T x B hybrid), T2DR4 (DOnullDMnull), T2DR4DM, and two clonal cell lines, T2DR4DMDO++ (1C3) and T2DR4DMDO+++ (2D7) (M) and T2DR4DMDO+++ H) clonal lines were constructed by fluorescence-activated cell sorting (FACS) of the parental, polyclonal T2DR4DMDO transfectant for single cells with low  or high  levels of surface CLIP/DR4 complexes (L), a plasmid pBudCE4.1-DOA/DOB_Gly8-linker_FLAG-tag was constructed similarly to the construction of pBudCE4.1-DOA/DOB. After transfection of T2DR4DM with the plasmid by nucleofection, two independent human promoters in the plasmid drove the expression of DOα and DOβ with a C-terminal FLAG-tag, respectively. Amaxa nucleofector kit C (Lonza) designed for nucleofection of T2 cells was used. Transfected cells were cultured in complete Iscove’s modified Dulbecco’s medium  with 50 to 100 μg/ml zeocin for 3 to 5 weeks to eliminate parent nontransfected cells and to construct a stable cell line, T2DR4DMDO-G8. To construct the DOKO cell line, we used the CRISPR gene-editing strategy (DO in T2DR4DMDO-2D7 ((DO:DM)H) cells. Two single guided RNA (sgRNA) primers, GGCCACCAAGGCTGACCACATGG flanking the DOA exon 1/2 and GGGGAGAAAAGTGCAACCAGAGG flanking the DOB exon 2/3, were designed using the CRISPOR website (http://crispor.tefor.net/and) and synthesized from Synthego. The Cas9-encoding plasmid was a gift from Dr Matthew Porteus at Stanford University. sgRNA and Cas9 were introduced into T2DR4DMDO-2D7 by nucleofection using Amaxa nucleofector kit C. The nucleofection condition: 106 cells in 100 μl total nucleofection solution with 8 μg of each of the two sgRNA primers and 4 μg of Cas9-encoding plasmid. Transfected cells were further scaled up and analyzed for DO expression by flow cytometry as described (The previously described cell lines included in this work are T2 (MHC-II++ (2D7) , 21. Celomplexes . FACS-soomplexes . To consstrategy  to knockescribed , 21 , 1 μg/ml puromycin, or 100 μg/ml zeocin, respectively. All cell cultures in this study were maintained in a 37 °C incubator constantly supplied with 5% COTo measure total protein levels by flow cytometry, T2 and its derived cell lines were fixed and permeabilized using the Cytofix/Cytoperm kit . Washed cells were then resuspended in 1× PermWash buffer (BD Biosciences) at a density of 1 million cells per 100 μl and stained on ice with fluorophore-conjugated mAbs. These mAbs included fluorescein isothiocyanate (FITC)-conjugated anti-human CLIP mAb (BD Biosciences), PE (R-phycoerythrin)-conjugated anti-HLA-DR mAb (BD Biosciences), Alexa Fluor 568-conjugated anti-human DO mAb (MagsDO5), Alexa Fluor 647- or Alexa Fluor 700-conjugated anti-human DM mAb (MapDM1). Alexa Fluor 568-MagsDO5, Alexa Fluor 647-MapDM1, and Alexa Fluor 700-MapDM1 were generated previously , 21. To 7 cells were collected . To extract total protein contents for MS analysis, each replicate was lysed in 8 M urea, 150 mM NaCl, 5 mM DTT, 50 mM Tris-Cl (pH 8) supplemented with Complete Protease Inhibitor Cocktail tablet (Roche) and 1× Halt Protease and Phosphatase Inhibitor Cocktail (Thermo Fisher Scientific). The lysate was then centrifuged at 13,200 rpm for 15 min, and the supernatant was transferred to a fresh test tube for a second round of centrifugation. The resulting clarified supernatant was reduced with 5 mM DTT for 30 min at 37 °C, then alkylated with 14 mM iodoacetamide for 45 min at room temperature in the dark and then quenched with 5 mM DTT for 20 min at room temperature. In order to clean the proteins extracted from the lysate, a methanol–chloroform precipitation was performed, and the protein pellet was washed twice with acetone. The pellet was resuspended in 300 μl of 8 M urea, 50 mM Tris-Cl (pH 8), and the concentration of total proteins extracted from a cell line was determined using the Pierce BCA Protein Assay Kit (Pierce).Each T2-derived cell line was cultured as described above, from which three aliquots of 1 × 10C).Extracted proteins from each sample or replicate were diluted to 1 M urea, 50 mM Tris-Cl (pH 8) prior to digestion with Trypsin/Lys-C Mix (Promega) at a ratio of 1:25 . The reaction was quenched with the addition of formic acid to a final concentration of 5%. Digested peptides were desalted using a Sep-Pak C18 1 cc Vac Cartridge, 50 mg (Waters). Peptides were further labeled using tandem mass tag (TMT) reagents (Pierce) as previously described . In brieFig. 12O, pH 10) using an Agilent 1200 HPLC (Agilent Technologies). In total, 84 fractions were collected, concatenated, and combined , resuspended in 10 mM ammonium formate (pH 10), and fractionated using the high-pH reverse phase (Hp-RP) fractionation approach as described , 36. Form/z scan range 400 to 1500. The automatic gain control (AGC) targets were 4 × 105, and the maximum injection time was 50 ms. The most intense ions were then selected in top speed mode for sequencing using collision-induced dissociation (CID) and the fragments were analyzed in the ion trap. The normalized collision energy for CID was 35% at 0.25 activation Q. For tandem mass (MS2) scans, the AGC targets were 1 × 104 and the maximum injection time was 30 ms. Monoisotopic precursor selection and charge state rejection were enabled. Singly charged ion species and ions with no unassigned charge states were excluded from MS2 analysis. Ions within ±10 ppm m/z window around ions selected for MS2 were excluded from further selection for fragmentation for 90 s. Following each MS2 analysis, five most intense fragment ions were selected simultaneously for higher energy collisional dissociation (HCD) MS3 analysis with isolation width of 1.2 m/z, normalized collision energy of 65% at resolution of 60,000, AGC target of 1 × 105, and maximum injection time of 90 ms.Proteome-wide MS analyses were carried out as follows: TMT-labeled and Hp-RP-fractionated peptides described above were resuspended in 20 μl 0.1% formic acid, of which 10% of the material was injected onto the Dionex Ultimate 3000 autosampler and LC-system (Thermo Fisher Scientific). Peptides were separated on a 20-cm reversed phase column  over 120 min using a four-step linear gradient: 97% A (and 3% B) to 96% A for 15 min, to 75% A for 135 min, to 55% A for 15 min, and then to 5% A for 15 min (buffer A is 0.1% formic acid in water and buffer B is 0.1% formic acid in acetonitrile). Mass spectra acquisition was performed in a data-dependent mode on a Lumos Orbitrap mass spectrometer (Thermo Fischer Scientific) with precursor (MS1) scans acquired in the Orbitrap mass analyzer with a resolution of 120,000 and 154,513 entries), 41 common contaminants, and reversed decoys of the above  and contaminants were excluded from further analysis steps. Differentially expressed proteins were identified, statistically evaluated, and visualized from log-transformed quantitative data using R, Rstudio, and Qlucore Omics Explorer v3.2 (Qlucore AB). Unless otherwise noted in the text, differentially expressed proteins were selected based on a q-value threshold <0.05 following Benjamini–Hochberg correction for multiple hypothesis testing  and done as described . In briehe above  using thhe above . The Perhe above  was used8 cells per replicate were harvested. Cells were then lysed for 20 min on ice in 20 mM Tris-HCl (pH 8), 150 mM NaCl, 1 % (w/v) CHAPS, 0.2 mM PMSF, supplemented with 1× Halt Protease and Phosphatase Inhibitor Cocktail (Thermo Fisher Scientific) and Complete Protease Inhibitor Cocktail (Roche). The lysate was centrifuged , and the resulting supernatant was precleared for 30 min using rProtein A Sepharose fast-flow beads . The precleared lysate was incubated with HLA-DR-specific antibody L243 . The ions were fragmented with a normalized collision energy of 35% and an activation time of 5 ms for CID and 30 ms for HCD. Dynamic exclusion was enabled with repeat count of 2, repeat duration of 30 s, and exclusion duration of 30 s. The minimal signal threshold was set to 500 counts.Peptides eluted from DR4 proteins as described above were reconstituted in 12 μl 0.1% formic acid and analyzed on an LTQ Orbitrap Elite mass spectrometer (Thermo Fischer Scientific). Samples were then injected onto the Eksigent ekspert nanoLC-425 system (SCIEX), and peptides were separated by capillary reverse phase chromatography on a 20-cm reversed phase column  over a total run time of 160 min, including a two-step linear gradient with 4% to 25% buffer B (0.2% (v/v) formic acid, 5% dimethyl sulfoxide, and 94.8% (v/v) acetonitrile) for 120 min followed by 25% to 40 % buffer B for 30 min. Each cell condition was prepared in parallel as two biological replicates, and each biological replicate was injected three times, each using a different complementary instrumental method, resulting in six raw data files per cell condition. The three instrumental methods are HCD, CID including single-charged species, and CID excluding single-charged species . Its scoring models apply artificial neural networks trained on multiple extensive datasets that measured in vitro binding affinity (BA) and MS-derived eluted ligands (ELs). Optionally, NetMHCIIpan-4.0 can score binding against a model trained only on BA and not EL data. As MS data were drawn from untargeted MS studies rather than from discrete binding measurements, the two models should be expected to produce different results in some cases. We therefore designate the former prediction approach “EL” and the latter prediction approach “BA.” For both EL and BA prediction approaches, we applied rank-based thresholds of 2% and 10% to separate strong, weak, and nonbinders. See main texts and figures for details. To evaluate prevalent motifs among peptides in a more untargeted fashion we applied Gibbs cluster analyses  and a lHochberg , 50. Net0 Server  with theThis study was designed to include proteomic and immunopeptidomic components, which differed in the numbers of replicates and data collection modalities. Differences between these experiments were due to technical considerations, since cell input for immunopeptidome assays needed to be approximately 100-fold more than proteomic assays. Proteomic data were measured as preparative triplicates, such that each cell culture was divided into three aliquots, and each was lysed, digested, and labeled with TMT reagents in parallel as described above. Within each replicate set, all samples were processed in a single batch. LC-MS/MS analysis of HPRP-fractionated peptides proceeded according to the sequential order of the concatenated fractions, as this was not deemed to be a significant source of potentially confounding variation. LC-MS analyses were collected as single injections.Immunopeptidomic data were collected from biological duplicates, which were processed from lysis, immunoprecipitation, and desalting at different times and in a randomized order. LC-MS analyses proceeded in a sequential order based on the nomenclature of the cell lines, one replicate set at a time. Technical triplicate LC-MS analyses were acquired per sample to increase coverage and overall signal using different experimental methods as described above. Carryover between different samples was minimized by acquiring blank analyses between each.e.g., B cells) prior to pMHC-II presentation for CD4+ T cell scanning (A). In B cells and several other APCs described above, this process is further modulated by DO, likely via the competition between DO and MHC-II for binding to DM (A). Acid-driven DO denaturation from DMDO complexes elevates free DM levels, thereby promoting the formation of stable pMHC-II . To generate the cell lines with varied DO:DM, we used a polyclonal DO transfectant of T2.DR4.DM cells and two clonal lines expanded from FACS-sorted single cells, based on levels of surface CLIP/DR4 complexes . This strategy allowed us to measure how DO:DM influences DR4 peptidomes, while accounting for the role protein abundance may have in peptide selection, and any subtle proteome-wide alterations that might exist between each cell line.In this study, we sought to establish a tractable system to explore the peptide repertoires under varied DO:DM conditions and to elucidate the impact of DO:DM on MHC-II peptidomic landscapes. We used a series of human T x B hybrid cell lines  suggest that DM:DR ratios ranging from ∼1:2 ((DO:DM)H cells) to ∼1:1 ((DO:DM)L-M cells) to ∼2:1 ((DO:DM)0 cells) were captured by our cell model. Levels of the α and β components of each protein, compared across cell lines, were consistent with those of the corresponding total αβ heterodimeric protein detected by flow cytometry . Flow cytometry analysis further showed that DM+ lines have comparable levels of total DR and a hierarchy of total DM or DO H lines were selected based on CLIP/DR4 at the cell surface. This resulted in a step-wise increase of DO:DM across these cell lines. As we observed previously H, whereas CLIP level was only slightly increased in (DO:DM)L and (DO:DM)M compared with (DO:DM)0.We confirmed the expression of DR4, DM, and DO molecules in the full panel of T2-derived stable cell lines using MS and flow cytometry. Both techniques aimed at comparing a particular protein across cell lines, rather than comparing different proteins within a given cell line. MS-facilitated proteomic analysis validated the expression of α and β chains of DR, DM, and DO Fig. 2A,  and S2. eviously , 21, DM H displayed significantly lower levels of non-CLIP/DR4 than the levels in the other DM+ lines. The high level of invariant chain protein  found in (DO:DM)L and (DO:DM)M concurrent with low levels of CLIP/DR4  suggests that CLIP/DR4 levels directly depend on DO:DM stoichiometry and not on CD74 protein abundance. Of interest, we found that non-CLIP/DR4 levels, as detected by NFLD.D11, were increased in (DO:DM)L and (DO:DM)M compared with (DO:DM)0 and with (DO:DM)H. This observation of discordant CLIP/DR4 and non-CLIP/DR4 abundances  is consistent with the high likelihood that NFLD.D11 stains a limited subset of non-CLIP/DR4 complexes. One possible explanation for these data is that DR4 peptide ligands in NFLD.D11-stained complexes require enough DM activity for CLIP exchange but not so much as to facilitate replacement by other, non-NFLD.D11-reactive peptides that are more DM-resistant L and (DO:DM)Mversus (DO:DM)0 cells). Thus, these data suggest that some DR4-presented peptides require intermediate levels of DO:DM and therefore are repressed when DO:DM is excessively low or excessively high.Using NFLD.D11, an antibody specific for non-CLIP/DR4 , we furt0 cells and decreases as the DO:DM ratio increases. It is fully absent in the DOnullDMnull cells that simulate a DM-negative condition to evaluate DM-independent peptidomes, like the ones eluted from DM-resistant MHC-II alleles  alleles , 54, 55.A), an appropriate average length for MHC-II-associated peptides. Peptides with no quantified signal were excluded from further consideration, resulting in 7390 quantified unique sequences. Of these, 4224 (4528 if considering different residue modifications) were quantified in both bioreplicates.To evaluate how DO:DM affects DR4-presented peptide repertoires, we eluted peptide ligands from DR4 in each cell line and analyzed them by MS . We idenFig. 3A and Most MHC-II-eluted peptides we identified could be grouped into nested sets of overlapping peptides. This is a common observation, resulting from the MHC-II open-ended binding groove that permits peptides of multiple lengths to bind . This peFig. 3B). The DR4 binding motifs generated from the entire peptidomic dataset and from the condensed cores  corresponded well with the reference motifs generated using the NetMHCIIpan-4.0 motif viewer , the NetMHCIIpan-4.0 algorithm proved to be a useful tool with appreciable accuracy and throughput advantages , 50. NetFig. 3f viewer . CollectnullDMnull or (DO:DM)H, DM+ lines with zero to medium DO:DM showed a tendency toward longer peptides or cores . These lines, which have modest DO:DM, also yielded a greater proportion of predicted strong DR4-binding cores  and similar motifs to one another . The amino acid residue preferences at the four dominant anchor positions  were most similar among the (DO:DM)0-M lines, suggesting a DM-mediated preference for Y over F at P1. We note, however, that the top two most frequent residues at each of the anchor positions were conserved among all five lines . The (DO:DM)H line generated the greatest number of cores (656), compared with (DO:DM)0-M (E and F); this indicates that the presence of DO enables a larger number and an increased diversity of peptides to be presented by DR4, as also observed by Nanaware et al., (0-M than (DO:DM)H, with consequent selection for high affinity, stable pMHC-II complexes with decreased sequence diversity. Equivalent results between peptide and core sequences H with (DO:DM)0-M (A and B). We found smaller differences between each of the three (DO:DM)0-M cell line pairings and larger differences between (DO:DM)0-M and DOnullDMnull, as compared with the differences observed between the other pairs of cell lines . This analysis confirmed the greater similarity between DR4 peptidomes derived from the three (DO:DM)0-M lines, as mentioned above . This grouping is surprising considering that (DO:DM)L and (DO:DM)M expressed DO, whereas (DO:DM)0 did not. This suggests that many cores did not simply increase or decrease monotonically with tuning the DO level itself; rather they might depend on a synergistic tuning of DO:DM instead. Particular DO:DM thresholds might therefore promote some peptides’ efficient loading onto DR4 while inhibiting others.From a global perspective, differences between each cell line were most apparent relative to DOO:DM)0-M . Within O:DM)0-M , A and BC). We found that most 0-L) < (DO:DM)H; type II: (DO:DM)0-H < DOnullDMnull; type III: (DO:DM)0-M < ((DO:DM)H, DOnullDMnull); type IV: DOnullDMnull < (DO:DM)0-H; type V: H) < (DO:DM)0-M; type VI: M-H) < (DO:DM)0-L. The majorities of types II and III (up to 89.9%) were predicted by NetMHCIIpan-4.0 as weak or nonbinders, whereas the majorities of types IV and V (up to 84.3%) were predicted as strong binders, regardless of prediction approaches (El versus BA) used (D). The deduced motif types associated with these core types were related to the canonical DR4 binding motifs  used D. The deg motifs .C and supplemental Fig. S2A) further suggest cores fall into two primary categories: DM-sensitive (types I–III), which showed low abundance in the (DO:DM)0-M lines, and DM-resistant (types IV–VI), which showed high abundance in (DO:DM)0-M lines, as compared with DOnullDMnull cells. DM-sensitive cores may spontaneously bind DR in the absence of DM, notably in DOnullDMnull cells. These cores may be readily displaced or competed off by DM-resistant cores if DM is present. This appears to be the case particularly for types II and III, half of which were predicted by NetMHCIIpan-4.0 to be weak binders . For others such as types I and III, binding to DR4 in the presence of DM can be protected with high DO:DM, which effectively opposes DM activity. In contrast, DM-resistant cores were presented almost exclusively in the presence of DM, and many were predicted to be strong DR4 binders (C and D). These data suggest that some cores, such as those of type IV can be selected for DR4 presentation at higher DO:DM condition when there is less, but apparently adequate, free DM activity. In contrast, other cores like those of type VI may not be presented until DO:DM reaches very low levels.These hierarchical clusters  were shorter, on average, than the other types  and further diminished in length compared with all cores observed from DOnullDMnull cells . Conversely, type V cores H cells) were longer on average than the other types , which is substantially longer than the average core length recorded from any single cell line (maximum mean length = 16.23 for (DO:DM)0; Fig. 3D). We further note that type II cores and type VI cores 0-L cells) showed an unusual register shift of the anticipated dominant anchor residues . This shift implies longer N-terminal extensions among the underlying peptides. We cannot rule out the possibility that this apparent register shift was due to algorithmic limitations and suboptimal input amount (<100 core sequences) 0 cells, which yielded the most abundant type VI cores . Despite the similar numbers of cores clustered into each of the three DM-sensitive core types (C), types I and II showed substantially lower abundances than type III (A). Thus, types I and II account for a small contribution to the overall peptidome in any of the cell lines. In contrast, the abundances associated with the DM-resistant cores of types IV–VI appeared to scale approximately with the number of cores represented in each group (C and 5A). Of these, type IV was the most abundant core type in the three cell lines with substantial DM activity ((DO:DM)0-M) (C and 5A), but notably, a strong binding type IV subset was enriched when DM activity was most limited by DO (C).The intracore Z-score normalization we used to generate Figure 4ll lines A. Despitre types C, types type III A. Thus, ch group C and 5A.:DM)0-M) C and 5A,ed by DO C.Fig. 5AMRMATPLLMQA (binding register underlined), was dominant, with 28.8% normalized intensity in DOnullDMnull and 6.66% in (DO:DM)H (B). This core represents the most abundant overlapping peptide sequences in our entire dataset . The trend was consistent with the flow cytometric measurements of CLIP/DR4 (B) and confirms the known role DO and DM have on catalyzing peptide exchange for CLIP. The second most abundant DM-sensitive type III core (RPAGDGTFQKWAAVVVPSGQEQR), with 7.8% normalized intensity in DOnullDMnull (B and C), was derived from the HLA-A class I protein. It also followed a similar decrease in abundance  across the (DO:DM0-M gradient) with a substantial increase in (DO:DM)H . Two HLA-B-derived cores demonstrated similar DM-sensitive profiles across the five cell lines, albeit with 10-fold lower intensities . A majority of DO:DM-dependent reductions in peptide abundance did not reflect any underlying protein abundance differences, and protein abundances largely were consistent across the five cell lines H B. This c dataset . The CLICLIP/DR4 B and conll lines . ExceptiA) tended to be dominated by a small number of cores  in DOnullDMnull and (DO:DM)H cells (B). In contrast, the greater abundance of DM-resistant cores of types IV-VI among (DO:DM)0-M cells (A) can be attributed to a more diverse set of cores, each with moderately high abundance, for example, cores derived from DSG2, IGKC, LAPTM5, NDFIP1, STX6, and TFRC (B). These were mostly concentrated among type IV cores, whereas types V and VI core abundances were more restricted to single protein sources . As with the DM-resistant cores, these patterns relative to DOnullDMnull cells cannot simply be attributed to abundance differences at the protein level H cells B. In con-M cells A can be and TFRC B. These in level . InsteadD): in agreement with the aggregated quantifications of DM-sensitive peptides (A), the type III core, FAEKVANAESNAIG, was approximately 10-fold more abundant than the type II core, NSVIIVDKNGRLV, in DOnullDMnull and (DO:DM)H cells, but neither were robustly presented in (DO:DM)0-M (D). In contrast, the type IV DM-resistant core, GQFLYQDSNWASKVE, decreased with increasing (DO:DM)0-H (D). This most abundant core from TFRC (up to 6% relative intensity in (DO:DM)0-L) was predicted to be a strong binder by both EL and BA models (D). It is noteworthy that the type V DM-resistant TFRC core, CPSDWKTDSTCRMVTSE, also decreased with increasing (DO:DM)0-H (D). Of the 40 peptides containing this core that were mapped in the (DO:DM)0 line, 35 were cysteinylated on the N-terminal cysteine residue, whereas only two peptides containing this core were mapped in the (DO:DM)H line for this core (B).These data further demonstrate that single proteins can harbor cores of multiple types. For example, we identified multiple cores across the length of TFRC with moderate to high abundance belonging to four core types (II–V) D: in agrpeptides A, the tyO:DM)0-M D. In conO:DM)0-H D. This mA models D. It is O:DM)0-H D. Of thehis core . These dhis core B.H cells (DOKO) and measured the corresponding DR4 peptidomes and proteomes using the aforementioned strategy . Of note, using CRISPR to edit the DO gene preserved a similar degree of cell heterogeneity for DO expression . Notable exceptions included the DO α and β chains (both p < 0.01 and log2 fold change >2) that we specifically targeted by CRISPR.DO is downregulated in naïve and memory B cells that enter GC for antigen presentation and affinity maturation of their antigen receptors. To mimic this regulation and to verify our model, we used CRISPR  to knockpression  and S11.H and (DO:DM)0-M, increased free DM in the DOKO resulted in longer peptides and longer cores, a larger proportion of which were predicted strong binders (C). The sequence motif generated from the DOKO peptidomic data (D) was more homologous to that generated using the (DO:DM)0-M peptidomic data (F) than either DOnullDMnull or (DO:DM)H cells, consistent with free DM activity. As expected, a drastic reduction in the dominant CLIP containing core was observed in the knockout (E). In keeping with their lower DO:DM ratio, pair-wise volcano plot comparisons indicated that DR4-presented cores in DOKO cells were qualitatively more similar to those presented in (DO:DM)M cells than in (DO:DM)H cells (F), consistent with the overall expected peptidomic outcome after DO:DM was reduced (A). Considering all cores mentioned earlier (C and D), abundances in DOKO changed from the level in (DO:DM)H to a level that was similar to (DO:DM)0-M (B and C and Consistent with the aforementioned difference between cores presented in (DO:DM) binders C. The semic data D was mormic data F than eiknockout E. In kee)H cells F, consis reduced A. Consid earlier , C and Dex vivo human tonsillar B cells  versus the technical challenge of precisely recapitulating widely ranging DO and DM expression levels spanning many different types of antigen-presenting cells. We therefore developed a model system in which DM and DO levels were varied with respect to one another, resulting in a graded range of DO:DM stoichiometries. We report here five new cell lines that span three DO+ states and two DO- states. We used these cell lines to approximate “infinitely” opposed DM , and an increasing gradient of DM activity, based on high, medium, low, and zero DO:DM ratios. MS-facilitated peptidomic and proteomic measurements demonstrated the critical tuning function DO:DM can have in modulating HLA-DR4-presented peptidomes.We sought an experimental compromise between the limited information that can be gleaned by comparing just two DO expression states (DO+ C) and those that depended on or can tolerate at least some DM activity for their presentation and detection . In particular, we found that decreasing DO:DM yielded increasingly varied peptide subsets . Concurrently, a large proportion of DM-sensitive peptides were measurable when DM activity was effectively opposed by DO in (DO:DM)H cells  or when DM was “infinitely” opposed in (DO:DM)null/null cells . Knocking down DO relative to (DO:DM)H cells (highest DO level) resulted in a DO:DM state similar to that of (DO:DM)M cells (F). Together, these data predict that, upon antigen exposure, DO downregulation in DO-expressing APCs would favor increased MHC-II loading of DM-resistant peptides and diminished loading of DM-sensitive ones.Two major categories of peptides, as represented by their PLAtEAU-deduced cores, emerged from these data: those that depended on there being minimal, if any, DM activity M cells F. TogethD). It should be stressed, however, that binding predictions alone were poor predictors of DM sensitivity and resistance (D). Many self or foreign peptides hold the potential to bind DR with high affinity and should satisfy the models employed by computational prediction tools such as NetMHCIIpan-4.0. However, the peptides that survive DO:DM-tuned DM editing can be selected based on multiple factors that are difficult to model. These include the abundance of source proteins and their sequence susceptibility to proteolysis; inherent allelic characteristics and intracellular modifications of MHC-II that influence binding specificities; intracellular modifications of source proteins or their peptide derivatives; and endolysosomal locations where proteins are degraded or protected in a pH-dependent fashion. Our MS-based peptidomic and proteomic analyses across seven cell lines with varying DO:DM ratio states empirically revealed several pMHC-II presentation characteristics related to these factors, in addition to DO:DM tuning, as discussed below.We note that the increased repertoire of DM-resistant peptides we found with decreasing DO:DM roughly correspond with predicted binding affinities D. It shosistance D. Many si.e., (DO:DM)M or even (DO:DM)H, Fig. 5D and H. In contrast, other cores with low predicted binding affinities (BA model) were either included (FAEKVANAESLNAIG from TFRC) or excluded (CPSDWKTDSTCRMVTSE from TFRC) among cores presented by (DO:DM)H cells. Although these data point to nuanced HLA-DR4 presentation patterns (C), we note that the specific patterns observed here likely are not universal. Other MHC-II alleles can have wide-ranging thresholds at which DO:DM or free DM levels promote CLIP removal and peptide exchange. To an extreme extent, some alleles, e.g., HLA-DQ2 and HLA-DQ8, have evolved to require very high DM expression to overcome their intrinsically low DM susceptibility (nullDMnull and (DO:DM)H, respectively, and likely occurred across all DO:DM states in these DR4-positive cells.First, a spectrum of DO:DM can correspond with different free DM activity levels, and consequently different types of peptides that survive DM editing. Although DO can block DM-catalyzed CLIP removal from DR4 , our modpatterns C, we nottibility , 54, 55.tibility . As a reD). Prominent examples include the CLIP peptides derived from CD74 and RPAGDGTFQKWAAVVVPSGQEQR from HLA-A (B). Conversely, a large proportion of cores among DM-resistant types IV–VI from (DO:DM)0-M were predicted to be strong binders  (D and 5B). Therefore, we propose that APCs can be regulated by DO:DM tuning, allowing more peptides with wider affinity ranges to be presented when DO:DM is high and the degree of DM editing is low. One caveat to this notion is that our analysis was based on isolating total DR4 rather than surface-expressed molecules; thus in (DO:DM)0-M cells, pMHC-II with weaker binding peptides might represent intermediates along the presentation pathway.Second, in addition to the underlying DO:DM context, a peptide’s abundance, binding affinity, and DM sensitivity influence the likelihood it will measurably contribute to pMHC-II repertoires. Our assessment of binding strength was based on scoring with predictive algorithms (NetMHCIIpan-4.0’s BA and EL models) rather than direct measurement. Nonetheless, it is likely true that eluted peptides from each DO:DM cell state and across all six types of cores include both strong and weak binders. However, a large proportion of DM-sensitive cores were predicted to be weak or nonbinders  D and 5B.versus low DO:DM. For example, we observed a swap of the P1 dominant residue (F->Y) between cells with highly or “infinitely” opposed DM ((DO:DM)H and DOnull:DMnull) versus cells with appreciable DM activity ((DO:DM)0-M (F)). A similar P1 residue swap was also found when comparing the EL- versus BA-based motif models  data, it could be useful to account for DO:DM and its correlation with abundant peptides that might otherwise be deemed weak binders. Such a model could, for example, be appropriately applied to naïve or memory B cells’ repertoires as opposed to activated B cells, which have high and low DO:DM, respectively. Relatedly, motif prediction, which is integral to binding prediction, also should consider the possible contribution of high DO:DM O:DM)0-M F). A simf models . These d0-M as compared with DOnullDMnull and (DO:DM)H cells (E). A DO:DM influence on peptide length can affect T cell recognition and peptide immunogenicity )H cells E. A DO:Dgenicity , 60, 61.genicity , 63. Altgenicity , additiogenicity , 64. Longenicity , 65. Ourgenicity  did not ndosomes , where pnullDMnull and (DO:DM)H cells as compared with in (DO:DM)0-M cells, corresponding with these cells’ limited and appreciable amounts of free DM activity, respectively. These differences were independent of TFRC protein’s abundance across these cell states and mostly consistent with the intrinsic DM sensitivity, DR4 binding affinity, and abundance characteristics of each pMHC-II mentioned above. This observation leads us to speculate that, upon antigen stimulation, DO-expressing APCs could experience similar DO:DM tuning state progressions over the course of their maturation until DM activity reaches a point that promotes stable and abundant epitope presentation. In this way, a range of pMHC-II could be made available for T cell inspection over the course of APC maturation.Last, DO:DM tuning in APCs may affect immunodominant T cell epitope selection from across the sequence of a given protein. One such source protein, TFRC, generated multiple DR4-binding cores belonging to four different major types (II–V). We identified multiple cores derived from the TFRC protein with substantially different abundances in DOversus tolerance of self. Accounting for multiple DO:DM states in the context of DO regulation will guide future investigations, such as comparing pMHC-II repertoires derived from multiple HLA-II alleles with respect to varying DO:DM and from primary cells that are sorted into different DO:DM states. In addition, further exploration of scenarios in which DO expression is limited  is warranted, because it is likely that we have also underestimated the graded effects that varied levels of DM can have on pMHC-II presentation. Experiments like these stand to promote improved models of MHC-II antigen presentation that can be generalized to a wider array of APCs with important relevance to human health.In conclusion, our study points to DO:DM tuning as an important factor that modulates pMHC-II repertoires. Our observations were made possible by a model system designed to recapitulate the variable levels of DO and DM seen in different kinds of APCs with different differentiation states—states that regulate the balance between vigilance against pathogens www.ebi.ac.uk/pride/archive under accession number PXD024392.All peptide data and mass spectrometry raw data files have been deposited in the PRIDE Archive at This article contains The authors declare no competing interests.


EGFR) T790M mutation (micro-T790M), which is detected using droplet digital PCR (ddPCR) but not conventional PCR, in formalin-fixed and paraffin-embedded (FFPE) samples have been investigated as a predictive factor for the efficacy of EGFR-tyrosine kinase inhibitors (TKIs). However, the predictive value of micro-T790M remains controversial, possibly owing to the failure to examine artificial T790M in FFPE specimens. Therefore, we examined the predictive value of micro-T790M in first-generation (1G), second-generation (2G), and third-generation (3G) EGFR-TKI efficacy using a new method to exclude FFPE-derived artificial mutations in our retrospective cohort. The primary objective was time to treatment failure (TTF) of 1G, 2G, and 3G EGFR-TKIs according to micro-T790M status. In total, 315 patients with EGFR-positive non-small cell lung cancer treated with 1G, 2G, and 3G EGFR-TKIs were included in this study. The proportion of patients positive for micro-T790M in the 1G, 2G, and 3G EGFR-TKI groups was 48.2%, 47.1%, and 47.6%, respectively. In the micro-T790M-positive group, the TTF was significantly longer in the 2G and 3G EGFR-TKI groups than in the 1G TKI group. No differences in the micro-T790M-negative group were observed. Micro-T790M status detected using ddPCR, eliminating false positives, may be a valuable predictor of EGFR-TKI efficacy.Small amounts of epidermal growth factor receptor ( EGFR) gene mutations are the most common driver mutations in patients with lung adenocarcinoma1. A third-generation (3G) EGFR tyrosine kinase inhibitor (TKI), osimertinib, is the preferred first-line treatment, although 1st and 2nd generations  EGFR-TKIs have also been recommended3. The strategy for selecting appropriate first-line treatments for patients with EGFR-mutated non-small cell lung cancer (NSCLC) using concomitant genetic alterations or mutation status remains the major challenge in clinical practice.Epidermal growth factor receptor  methods are conventional standard qualitative methods with a low detection limit (sensitivity) of 1–10%. Conventional PCR methods, including the Cobas EGFR Mutation Test v2, Therascreen EGFR assay, and PNA LNA PCR-Clamp, were validated for commercial use and used as diagnostic tools in clinical practice7. In addition, droplet digital PCR (ddPCR) was developed for the quantitative evaluation of gene mutations with a lower detection limit (< 0.1%) than conventional PCR (1–10%)8. Today, several targeted therapeutic drugs for driver mutations are clinically available in addition to EGFR-TKI. Therefore, next-generation sequencing (NGS) is increasingly used to detect driver mutations.EGFR exon20 T790M mutation11. In vitro, compared with that of 3G EGFR-TKIs, the 50% inhibitory concentration for T790M-positive cells of 1G and 2G EGFR-TKIs is high and moderate, respectively12. In addition, de novo EGFR T790M mutations are detected in 0.5–5.8% of EGFR-TKI naïve patients using conventional PCR14. These previous studies showed that 1G EGFR-TKI is ineffective, whereas 3G EGFR-TKI is effective for patients with de novo T790M mutation detected using conventional PCR15.The most common mechanism underlying the acquired resistance in patients treated with 1G and 2G EGFR-TKIs is EGFR T790M mutation with a variant allele frequency (VAF) of less than 1.0% (micro-T790M mutation), which is detected using droplet digital PCR (ddPCR) but not conventional PCR, on EGFR-TKI efficacy has been investigated17. Similar to the data using conventional PCR, a previous report has shown that a small amount of T790M (micro-T790M mutation) detected using ddPCR is a negative predictive factor for 1G and 2G EGFR-TKIs17. Another study has reported better efficacy for 1G EGFR-TKI in patients with micro-T790M mutation than without16. Considering these reports, the clinical significance of pretreatment micro-EGFR T790M subclones on 1G and 2G EGFR-TKI efficacy has not been clarified.The significance of very small amounts of 18. We newly defined micro-T790M mutation by excluding artificial T790M mutations due to hydrolytic deamination19. The aim of this retrospective study was to investigate the significance of newly defined micro-T790M mutation in 1G, 2G, and 3G EGFR-TKI efficacy using the large EGFR mutation-positive NSCLC cohort.We hypothesize that the failure to assess the presence of an artificial T790M mutation caused by hydrolytic deamination over time in formalin-fixed and paraffin-embedded (FFPE) specimens may be the cause of these conflicting resultsEGFR T790M-positive and -negative patients are listed in Table EGFR T790M-positive and -negative patients with the Del19 or L858 mutations are shown in Tables S1 and S2.A total of 382 patients were recruited. A flowchart of the patient selection process is shown in Fig. EGFR T790M ratio in the T790M-positive group detected using the Cobas method was significantly higher than that in the T790M-negative group  [interquartile range]) , [95% confidence interval (CI)]: 0.625, [0.400–0.975], p = 0.038) or 3G ]: 0.495, [0.296–0.829], p = 0.007) EGFR-TKI treatment was an independent predictive factor based on the 1G EGFR-TKI. The following results in the Del19 subgroup are listed in Table S3. In the 3G EGFR-TKI administration group, micro-EGFR T790M positivity was an independent predictor ]: 0.388, [0.125–0.990], p = 0.048). Furthermore, in the micro-EGFR T790M-positive group, 3G EGFR-TKI was an independent factor based on the 1G ]: 0.231 [0.097–0.549], p = 0.001) and 2G ]: 0.337 [0.142–0.800], p = 0.014) EGFR-TKIs. In contrast, in the L858R subgroup  [interquartile range], was 0.061 [0.016–0.39], and that in T790M-negative cases was 0.016 [0.016–0.022] 23. The false-positive T790M allele derived from the FFPE-artificial mutation and the different cut-off points for micro-EGFR T790M positivity might have affected these data. We propose that the cut-off value and positive criteria for micro-EGFR T790M positivity in our study, but the appropriate criteria for microT790M positivity to predict the EGFR-TKI efficacy needs further prospective study.Using our study’s micro-EGFR T790M-positive criteria, 40–50% of cases were micro-EGFR T790M in Del19 but not in the L858R subgroup. TTF in patients treated with 3G EGFR-TKI was significantly longer than that in patients treated with 1G and 2G EGFR-TKI in the Del19 but not L858R and micro-EGFR T790M mutation-positive groups. These results may be strongly influenced by the presence of co-mutation (tumor suppressor mutations), which is detected in EGFR-mutated NSCLC. The presence of co-mutation has been reported to attenuate the effect of EGFR-TKI in L858R but not Del19-positive cases24. Furthermore, a previous study using NGS has shown that more compound mutations that can affect the therapeutic effect of EGFR-TKI is detected in L858R than in Del1925. We believe that the presence of co-mutation and compound mutation affecting the efficacy of EGFR-TKI is the reasons why we could not detect the significance of mT790M in the L858R subgroup in our study. We are conducting a phase III trial to evaluate the superiority of erlotinib plus ramucirumab over osimertinib in EGFR L858R-positive patients, and we plan to investigate the significance of micro-T790M in this trial26.Our study showed the significance of micro-27. The sensitivity of NGS for T790M detection is lower than that of PCR28, and it is assumed that the micro T790M clones shown in the present study are difficult to detect by NGS. Whether micro T790M detected by NGS can help in the selection of treatment for EGFR mutated lung cancer needs to be further investigated.In this study, we report the significance of treatment selection based on the micro T790M clone, which is detected by ddPCR and not the PCR methods used in clinical practice. As the number of driver gene mutations increases, NGS-based multi-detection systems have become mainstream in practical clinical practice29. There are a few caveats to detect microT790M by the ddPCR method used in this study. First, tumor tissues properly fixed and stored for as short a time as possible were needed to minimize DNA degradation30. Second, large biopsy tumor samples with a tumor content percentage of 10% or greater or surgically resected samples would be needed to perform ddPCR analysis30. Further studies are needed to determine the appropriate quality and quantity of tumor tissue required for micro-T790M analysis to adapt this method to real-world practice.Presently, it is important to collect tumor specimens of sufficient quality and quantity and to evaluate adequate driver mutations33. To determine which theory is associated with our results, we compared the micro-EGFR T790M ratio in T790M-positive and -negative cases detected using the Cobas method and found that the micro-EGFR T790M ratio was not significantly different between the two groups. We believe that this result supports the “clonal evolution theory.” However, the small size and heterogeneity of the tumors34 might have contributed to this result. The results in the present study indicate that it is difficult to estimate the acquired T790M mutation after 1G or 2G EGFR-TKI treatment using the information about the presence of pretreatment micro-EGFR T790M.EGFR T790M mutation clones are generated through two theories: clonal selection and evolution35. Our study did not examine the effects of these factors on EGFR-TKI efficacy.This study has some limitations. First, this was a retrospective study. Second, concurrent genetic alterations, including TP53 and BIM polymorphism, have been reported as factors affecting the efficacy of EGFR-TKIsEGFR T790M mutations influence EGFR-TKI efficacy in patients with NSCLC. Detection of micro-EGFR T790M mutations using ddPCR and eliminating FFPE-derived false positive mutation will be helpful in selecting optimal EGFR-TKI regimens for patients with EGFR-mutated NSCLC.In conclusion, this study shows that micro-EGFR Del19 or L858R without the T790M mutation, which was verified using conventional PCR methods. Patients from 31 Japanese institutions who received 1G or 2G EGFR-TKIs until June 2018, or 3G EGFR-TKIs until June 2019, as first- or second-line treatment were consecutively included. The exclusion criteria were patients with EGFR Del19 or L858R with other EGFR mutations and those whose EGFR-TKI treatment was changed from one generation to another owing to the lack of tumor response. This retrospective study was approved by the institutional review board of Hiroshima University (No. E939) and the ethics committee of each participating institution. The opt-out method of patient consent for participation was applied in this study and written informed consent was obtained from all patients as per the guidance of the ethics committee of several institutions. The registration number of the study is UMIN000040474. All methods were performed following the relevant guidelines and regulations.We enrolled patients with unresectable NSCLC who harbored We collected the following data from medical records: age, sex, smoking history, clinical stage, Eastern Cooperative Oncology Group performance status, histologic type, presence or absence of brain metastasis, line of treatment, the status of EGFR mutation, and type of EGFR-TKIs. The primary outcome of this study was TTF, defined as the time from EGFR-TKI treatment initiation to death, disease progression, or treatment discontinuation.ddPCR was performed using pre-EGFR-TKI treatment FFPE tumor tissue specimens obtained after April 2015 , as described in the EGFR mutations were excluded. The criteria for amplification are described in Our ddPCR using the normal genomic DNA (Horizon) and DNA derived from the peripheral blood of healthy volunteers showed one T790M-positive (FAM+ and HEX−) drop in one DNA sample Table . Therefo10. This cytosine within the CpG site is converted to 5-methylcytosine by methylation36. The 5-methylcytosine is converted to thymidine through hydrolytic deamination in the FFPE sample over time. Finally, a false-positive C-to-T change is detected as T790M using PCR. Therefore, to estimate this false-positive T790M mutation, we used the following new method19. We considered that cytosine and guanine (CG) were changed to thymine and guanine (TG) in T790; CG in F795, located within the CpG site, was also changed to TG. The primers and probes for mutant F795F and wild-type (WT) F795F  − (VAF of F795F) > 0. Furthermore, to exclude allele frequency of the WT EGFR gene in normal epithelial cells, we determined the final T790M ratio as follows: (VAF of T790M − VAF of F795F)/VAF of active mutation (Del19 or L858R). The lowest value was set to 0.The cytosine to thymidine (C-to-T) base-pair change within the CpG site at position 790 results in a transition from threonine to methionine (T790M)T) F795F . In addiPatients without follow-up information or submission of the pretreatment specimen were excluded from the analysis. Baseline demographic and clinical characteristic data were expressed as numbers (percentages) for categorical variables and mean ± standard deviation for continuous variables. TTF was depicted using the Kaplan–Meier curves. Differences in TTF among micro-T790M-positive/-negative and treatment generations were assessed using log-rank tests. To evaluate the treatment effects based on the micro-T790M result for each treatment generation with adjustment of known prognostic factors, multivariable Cox proportional hazards analyses were performed. The following variables were included in the models of hazards for treatment failure: performance status, sex, smoking status, cancer type, brain metastasis, indicators of an EGFR exon 19 deletion-positive or EGFR exon 21 L858R-positive result, micro-T790M result, and treatment generation. Analyses were also performed for predefined subgroups of patients positive for EGFR exon 19 deletion and patients positive for EGFR exon 21 L858R. The relationship between the results obtained using the Cobas method and the T790M mutation ratio via ddPCR using samples obtained after acquired resistance to 1G or 2G EGFR-TKIs were examined using the Wilcoxon test. Similarly, the relationship between the results obtained by the Cobas method using samples obtained after acquired resistance to 1G or 2G EGFR-TKIs and the T790M mutation ratio via ddPCR using pretreatment samples were examined using the Wilcoxon test.https://www.r-project.org/).The sensitivity analysis for differences in TTF among treatment generations was conducted using T790M ratio cut-offs of 0.001 and 0.003. We performed an exploratory analysis to assess the relationship between micro-T790M values and hazards for treatment failure by Cox proportional hazards analyses. Micro-T790M values were included in the models through the restricted cubic spline function with three knots. The estimated relative hazards for treatment failure were plotted against the micro-T790M values for each treatment generation. Statistical significance for all tests was defined as a two-tailed p-value of ≤ 0.05. Data analyses were performed using R, version 4.1.0 (R Foundation for Statistical Computing, Vienna, Austria, Supplementary Information.


CD147 rs8259T>A single nucleotide variant (SNV) on SARS-CoV-2 susceptibility remains poorly investigated. Objective: To investigate the impact of rs8259T>A on the CD147 gene in individuals from Mexico with COVID-19 disease. Methods: We genotyped the CD147 rs8359T>A SNV in 195 patients with COVID-19 and 185 healthy controls from Mexico. In addition, we also measured the expression levels of CD147 and TNF mRNA and miR-492 from whole blood of patients with COVID-19 through RT-q-PCR. Results: We observed a significant association between the CD147 rs8259T>A SNV and susceptibility to COVID-19: T vs. A; OR 1.36, 95% CI 1.02–1.81; p = 0.037; and TT vs. AA; OR 1.77, 95% CI 1.01–3.09; p = 0.046. On the other hand, we did not find differences in CD147, TNF or miR-492 expression levels when considering the genotypes of the CD147 rs8259T>A SNV. Conclusions: Our results suggest that the CD147 rs8259T>A variant is a risk factor for COVID-19.Background: Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Clinical manifestations of COVID-19 range from mild flu-like symptoms to severe respiratory failure. Nowadays, extracellular matrix metalloproteinase inducer (EMMPRIN), also known as cluster of differentiation 147 (CD147) or BASIGIN, has been studied as enabling viral entry and replication within host cells. However, the impact of the  Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in over 6.8 million deaths worldwide . The abiCD147, a highly glycosylated transmembrane glycoprotein belonging to the immunoglobulin superfamily, is expressed in diverse immune cells and plays a role in triggering inflammation with the release of cytokines such as interleukin (IL)-6 and tumor necrosis factor (TNF), induction of matrix metalloproteinase 2 (MMP)-2, IL-9, interferon (IFN)-gamma, and immune cell activation . This inIFITM3) variants and susceptibility to COVID-19. Notably, they identified an association between the IFITM3 rs12252A>G SNV and COVID-19 infection [CD147 rs8259T>A SNV in patients with COVID-19 [CD147 rs8259T>A, ACE2 rs4240157T>C, TMPRSS2 rs12329760C>T, and TMPRSS11A rs353163C/T variants and COVID-19 severity in the Ukrainian population. They analyzed these variants among cases divided into three groups: without oxygen therapy, non-invasive oxygen therapy, and invasive oxygen therapy. Interestingly, they only found frequency differences for the TMPRSS2 rs12329760C>T variant in the group receiving invasive oxygen therapy, but they did not identify an association between CD147 rs8259T>A and COVID-19 susceptibility or severity [CD147 rs8259T>A variant is in the 3′-untranslated region (3′ UTR) of CD147 on chromosome 19:582927 and has been evolutionarily conserved in several eutherian mammals , and they were free from respiratory diseases. Additionally, individuals in the control group tested negative for COVID-19 infection based on RT-qPCR. Our study was conducted in accordance with the principles outlined in the Declaration of Helsinki and received approval from the Ethics, Biosecurity, and Research Committees of the Hospital Juárez de México (project number HJM 024/22-I). All patients or their relatives provided informed consent by signing the institutional consent letter.Whole blood samples (5 mL) were collected in EDTA tubes following WHO recommendations for sampling and processing biological samples from COVID-19 cases . GenomicK-EDTA whole blood samples (400 μL) were isolated using Tripure reagent and subsequently treated with DNAse I . For CD147, TNF, and GAPDH mRNA levels, 50 ng of total RNA was amplified by one-step RT-qPCR with the QuantiNova Probe RT-PCR Kit .For miR-492 and U6 (used as endogenous control) levels, 20 ng of total RNA was retrotranscribed using the TaqMan miRNA RT kit . For qPCR, 1 μL of cDNA was used along with the QuantiNova Probe PCR Master Mix (Qiagen), following the manufacturer’s instructions.CD147 rs8259T>A from 25 randomized samples of patients with COVID-19, maintaining the same percentages of genotypes identified in all patients with this infectious disease.CD147 mRNA levels were compared with whole blood from 25 patients and 25 controls. In addition, we evaluated the levels of miR-492, CD147, and TNF mRNA considering the three genotypes of −∆Ct) method.The qPCR was performed using the Oppus CFX96 system . The cycling conditions consisted of an initial denaturation at 95 °C for 10 min, followed by 45 cycles at 95 °C for 15 s, 60 °C for 60 s, and 72 °C for 1 s. Expression levels were measured in duplicate and normalized using glyceraldehyde-3-phosphate dehydrogenase (GAPDH) sequence 5′-AGCCACATCGCTCAGACAC-3′ and 5′-GCCCAATACGACCAAATCC-3′ as the reference. The following NCBI assay genes and TaqMan catalog numbers were used: CD147: Hs00936295_m1, Catalog #4331182; TNF: Hs00174128_m1, Catalog #4331182; hsa-miR-492: 001039, U6: Catalog #4427975 . Relative quantification was performed using the 2 −delta Ct  was observed, and 122 (62.6%) patients were male. Mechanical ventilation was required for 60 (30.8%) patients during hospitalization. Clinical improvement was observed in 68.2% of the cases, while 31.8% ultimately died. The main clinical and laboratory data are summarized in CD147 rs8259T>A variant in controls. The distribution of allele and genotype frequencies of the CD147 rs8259T>A variant in cases and controls is presented in CD147 rs8259T>A SNV and COVID-19 infection; T vs. A  and TT vs. AA   . Howevers8259T>A . Our datp = 0.04)  a. Next,  = 0.04) b, miR-49 = 0.04) c, and TN = 0.04) d consideCD147, a transmembrane glycoprotein belonging to the immunoglobulin superfamily, has been implicated in viral infections. Like the human ACE2 receptor, CD147 has been reported to be another human receptor for the S protein of SARS-CoV-2 and to be involved in viral entry ,15. In aCD147 gene could be important to the susceptibility and/or severity of COVID-19 [CD147 rs8259T>A variant is associated with COVID-19 infection in the Mexican population. To the best of our knowledge, only one study has previously reported on the role of the rs8259T>A variant in COVID-19 susceptibility and severity in the Ukrainian population; however, they did not find any association [Some authors have suggested that certain variants in the COVID-19 ,17,18, gociation . In contociation . These dCD147 rs8259T>A variant, which we identified as a risk factor for COVID-19, has previously been reported as a risk factor for chronic heart failure (CHF) in Chinese patients [CD147 and matrix metalloproteinase-9 (MMP-9) in the susceptibility and severity of coronary artery disease (CAD). They genotyped the rs8259T>A, rs28915400G>T, rs4919859G>C, rs6758G>A, and rs8637G>A CD147 variants and rs3918242C>T MMP-9 variant in 812 patients and 258 controls and found associations between rs8259T>A and rs3918242C>T variants and CAD, suggesting their involvement in the pathological process of the disease [It is worth noting that the A allele or AA genotype of the patients . Moreove disease .CD147 rs8259T>A variant in controls, this being one of the limitations of our study.Finally, we investigated the expression levels of CD147 mRNA between patients with COVID-19 and controls, as well as whether the three genotypes of rs8259T>A were associated with abnormal levels of CD147, miR-492, and TNF mRNA expression in patients with COVID-19. We observed significantly higher expression levels of CD147 mRNA in patients with COVID-19 compared to controls. This finding suggests that CD147 may play a role in the pathogenesis or progression of COVID-19. The elevated expression of CD147 in patients with COVID-19 is in line with previous studies that have reported an increase of CD147 mRNA in some inflammatory and autoimmune diseases ,22,23. TCD147 rs8259T>A variant have been associated with higher expression levels of CD147 mRNA or protein in PBMCs from patients with ACS but not in patients with stable angina [CD147 rs8259T>A variant; meanwhile, the A allele destroys the binding site for this miRNA, which generates an increase in CD147 protein expression [On the other hand, the A/A and T/A genotypes of the e angina . Thus, tpression . In thispression ,25. In lpression ,25. Althpression .CD147 rs8259T>A variant in relation to COVID-19, laboratory parameters, and gene expression, our findings are based on a specific population and may not be generalizable to other ethnicities or regions.It is important to acknowledge the limitations of our study. Although we conducted a comprehensive analysis of the CD147 rs8259T>A variant is a risk factor for COVID-19 in the Mexican population. These findings highlight the potential involvement of CD147 in the pathogenesis of COVID-19 and suggest the importance of genetic factors in disease susceptibility. Understanding the role of CD147 and its variants in COVID-19 may contribute to the development of personalized medicine and targeted therapeutic interventions.Our data suggest that this


IL17A+ T-cells and 95% of IL17F+ T-cells in psoriasis skin. The expression of IL23A in DC subsets was also downregulated by IL-17A blockade. The expression of IL-17-driven inflammatory mediators  in suprabasal keratinocytes was correlated with psoriasis severity and was downregulated by IL-17A blockade. For the regulatory DC transcriptome, the proportion of regulatory semimature DCs expressing regulatory DC markers of BDCA-3 (THBD) and DCIR (CLEC4A) was increased in posttreatment psoriasis lesional skin compared to pretreatment psoriasis lesional skin. In addition, IL-17A blockade induced higher expression of CD1C and CD14, which are markers of CD1c+ CD14+ dendritic cell (DC) subset that suppresses antigen-specific T-cell responses, in posttreatment regulatory semimature DCs compared to pretreatment regulatory semimature DCs. In conclusion, systemic IL-17A inhibition not only blocks the entire IL-23/T17 cell axis but also promotes regulatory gene expression in regulatory DCs in human psoriasis skin.Durable psoriasis improvement has been reported in a subset of psoriasis patients after treatment withdrawal of biologics blocking IL-23/Type 17 T-cell (T17) autoimmune axis. However, it is not well understood if systemic blockade of the IL-23/T17 axis promotes immune tolerance in psoriasis skin. The purpose of the study was to find translational evidence that systemic IL-17A blockade promotes regulatory transcriptome modification in human psoriasis skin immune cell subsets. We analyzed human psoriasis lesional skin 6 mm punch biopsy tissues before and after systemic IL-17A blockade using the muti-genomics approach integrating immune cell-enriched scRNA-seq (n = 18), microarray (n = 61), and immunohistochemistry (n = 61) with repository normal control skin immune cell-enriched scRNA-seq (n = 10) and microarray (n = 8) data. For the T17 axis transcriptome, systemic IL-17A blockade depleted 100% of  Psoriasis is one of the most common organ-specific autoimmune diseases in the human population affecting 3.2% of the adult population –3. PsoriPsoriasis persists as a lifelong disease that rarely improves without treatment, but systemic administration of recent monoclonal antibodies targeting the IL-23/Type 17 T-cell (T17) autoimmune axis is highly effective for psoriasis treatment : systemic administration of an anti-IL-17A monoclonal antibody increased the expression of genes involved in skin homeostasis promotion and KC stem cell activation in psoriasis skin . (2) sysWe recently used RT-PCR analyses of total skin from a randomized placebo-controlled clinical trial  to study psoriasis skin transcriptome modifications induced by systemic IL-17A blockade . Here, w23 adult psoriasis patients received anti-IL-17A monoclonal antibody (secukinumab) injections for more than 12 weeks in the phase II clinical trial . Patients received the anti-IL-17A monoclonal antibody at a dose of 300 mg with injections administered once weekly at baseline and at weeks 1, 2, 3, and 4 and then every 4 weeks. The clinical responses of patients and RT-PCR analyses of psoriasis skin were previously published .For this study, psoriasis skin biopsy samples obtained from the clinical trial  were furvs.) control, and timepoint of biopsy is summarized in Lesional biopsy specimens were harvested with a 6 mm punch biopsy from areas with representative psoriasis lesions. The individual skin sample information, including age, gender, psoriasis versus  experiments. Another half of the bisected skin tissue was snap-frozen and embedded in the Optimal Cutting Temperature (OCT) compound  for microarray and immunohistochemistry experiments.2 for 3 hours. Then, the epidermis and dermis were separated with forceps and sliced into small pieces with #10 blade scalpels. The epidermis and dermis were separately incubated in RPMI-1640 medium with L-glutamine  supplemented with 10% human albumin serum  in a humidified incubator at 37°C and 5% CO2. Nonplastic adherent cells that had emigrated out of the epidermis and dermis were harvested after 48 hours. The harvested cells from the epidermis and dermis were filtered through a 40-µm cell strainer  and stored on ice. The cell numbers and viability were determined using a Countess automated cell counter  and trypan blue staining .Skin biopsy samples were incubated in culture medium for harvesting emigrating cells . To splihttps://support.10xgenomics.com) was used to prepare the single-cell suspension. The appropriate volume of each sample was diluted to recover 10,000 cells. Subsequently, the single-cell suspension, gel beads, and oils were added to the 10X Genomics single-cell chip. After droplet generation, samples were transferred into PCR tubes and we performed reverse transcription using a ProFlex PCR Thermocycler System . After reverse transcription, cDNA was recovered using a recovery agent, provided by 10X Genomics, followed by silane DynaBead clean-up as outlined in the user guide.The 10x Genomics Chromium Single Cell 3′ Reagents Kit user guide  installed in R (version 4.0.2) for the downstream single-cell analyses  were selected for Uniform Manifold Approximation and Projection for Dimension Reduction. With a resolution of 0.8, cells were clustered by the FindClusters function. The average gene expression of psoriasis When 10,000 cells are submitted to a microfluidic platform for droplet-based single-cell library construction, 2.3% to 4.6% of single-cell data could be technical artifacts caused by cell doublets formed during cell capture . To remove clusters with possible doublets for downstream single-cell analysis, we excluded clusters expressing gene signatures of more than two different immune cell types  according to the manufacturer’s protocol. A total of 100 ng of biotinylated cDNA was hybridized to the GeneChip Human Genome U133 Plus 2.0 Array (Affymetrix). The expression values were obtained using the GCRMA algorithm , while nTotal RNA was extracted from frozen skin biopsies by the Qiagen miNeasy Mini Kit . DNA was removed with on-column DNase digestion by the Qiagen RNase-free DNase Set . The quality of extracted RNA was examined using Agilent Bioanalyzer 2100 . Real-time PCR was performed on QuantStudio 7 Flex Real-Time PCR System  with TaqMan Array Cards . All primers are listed in For immunostaining, vertical sections were cut to a thickness of 6 μm (+/- 1 μm) from the frozen skin biopsy tissues embedded in the OCT compound. Frozen sections were dried at room temperature and then fixed for 2 minutes in acetone. Next, the samples were blocked with 10% normal serum of the species in which the secondary antibody was made, and then the samples were incubated overnight at 4°C with the appropriate primary antibody. Primary antibodies used in this study were all mouse antibodies: KRT16 , CD3 , and CD11c . Biotin-labeled horse anti-mouse antibodies  were used to detect the primary antibodies. The staining signal was amplified with avidin-biotin complex  and developed using chromogen 3-amino-9-ethyl carbazole . Section images were acquired at ×10 magnification. The number of immunostaining-positive cell counts per section was manually counted using computer-assisted image analysis software . 61 sections were quantified for CD3 immunostaining (n = 40 for pretreatment sections and n = 21 for posttreatment sections) and 60 sections were quantified for CD11c immunostaining (n = 40 for pretreatment sections and n = 20 for posttreatment sections).p < 0.05 was considered statistically significant.Statistical analyses of scRNA-seq data at the level of total T-cells, DCs, or KCs were performed under the general framework of the Seurat R package –19. We up-value that has been adjusted for the False Discovery rate < 0.05 was considered statistically significant  and calibration with a reference sample Control01 under the general framework of pcr_analyze R package  method for relative quantification to a housekeeping gene  package  as we de package . Two-sidp < 0.05 was considered statistically significant.For statistical analyses of immunohistochemistry data, we used a Wilcoxon rank sum test to compare measures between groups. A value of The study protocol and informed consent were approved by the Institutional Review Board of the Rockefeller University, New York, NY, USA. The study was conducted in accordance with Good Clinical Practice and the Declaration of Helsinki, and all subjects provided written informed consent before entering the study.p < 0.05).We first studied how the immune cell numbers are changed in psoriasis skin after systemic IL-17A blockade, and how the immune cell number changes are reflected in total skin transcriptome. KC hyperproliferation [KRT16 staining ], T-cellIL17A, IL26, IL12B, and IL20) (IL24 and CTLA4) (ITGAE (CD103)] (FOXP3) (p < 0.05).Reflecting the decreased T-cell numbers by systemic IL-17A blockade, total skin transcriptome of T17 responses (nd IL20) , 28, T-cd CTLA4) , 30, res(CD103)]  and regu (FOXP3)  was decrIL23A) (LAMP3 (DC-LAMP), CD274 (PD-L1) and IDO1) (p < 0.05).Reflecting the decreased DC numbers by systemic IL-17A blockade, total skin transcriptome of T17 cell-stimulating DCs IL2A (3) andnd IDO1) , 33, 34 S100A8 and IL36G) (p < 0.05).Reflecting the decreased KC hyperproliferation by systemic IL-17A blockade, total skin transcriptome of KC cytokines that stimulate DCs and T-cells in feed-forward mechanisms (IL34 (IL37 (KRT15 (p < 0.05).In contrast, total skin transcriptome of Treg-specific cytokine that mediates immune tolerance IL34 –37], an , an IL3434  corneum, S. granulosum, S. spinosum, S. basale, endothelial cells, and fibroblasts without subclustering  clusters in the scRNA-seq data and tested if psoriasis T-cells express more T17 cell genes than control T-cells. The expression of T17 cell genes , a (CXCL13) –47, and IL17A and/or IL17F, the majority of T17 cells were within the CD161+ T-cell cluster. 5.5% of total T-cells were CD161+ T-cell cluster cells and 15.0% of CD161+ T-cell cluster cells were T17 cells . Although anti-IL-17A monoclonal antibody does not directly target IL-17F, the expression of IL17F was also decreased in the CD161+ T-cell cluster in psoriasis lesional skin after IL-17A blockade (p < 0.05). When all T-cell cluster cells were considered, IL-17A blockade decreased 95% of IL17F+ T-cells , the expression of HLA-DRA and HLA-DPB1) and upregulated maturation markers  16) ( (HLA-DR(FCGR1A) ]  , 52 (FiBDCA-3 (THBD) (DCIR (CLEC4A) .Reflecting the decreased DC numbers after systemic IL-17A blockade in the immunohistochemistry experiment (3 (THBD)  and DCIR(CLEC4A)  was incresponses , 54, wasIL36G, S100A8, DEFB4A, and DEFB4B (p < 0.05). After systemic IL-17A blockade, the expression of those inflammatory mediators  was decreased in posttreatment psoriasis lesional skin KCs compared to pretreatment psoriasis lesional skin KCs (p < 0.05).We next studied how systemic IL-17A blockade modified transcriptome of KCs in different layers of epidermis. We segregated KC clusters representing different layers of the epidermis  and tested if psoriasis KCs express more IL-17-driven inflammatory mediators than control KCs. The expression of inflammatory mediators induced by IL-17 in keratinocytes  was localized to suprabasal layers  in pretreatment psoriasis lesional skin . Reflecting that inflammation in the upper layers of the epidermis determines psoriasis disease severity ), KC expression of IL36G, DEFB4A, and DEFB4B in S. corneum and S. granulosum was correlated with PASI (p < 0.05). After IL-17A blockade, KC expression of IL36G, S100A8, DEFB4A, and DEFB4B in S. corneum and S. granulosum decreased in posttreatment psoriasis lesional skin compared to pretreatment psoriasis lesional skin (p < 0.05). The number of IL36G and/or DEFB4A expressing KCs in S. corneum and S. granulosum was decreased by 3 times from pretreatment psoriasis lesional skin to posttreatment psoriasis lesional skin  was decreased in pretreatment psoriasis lesional skin KCs compared to control skin KCs (p < 0.05). After systemic IL-17A blockade, the expression of KRT15 was increased in posttreatment psoriasis lesional skin KCs compared to pretreatment psoriasis lesional skin KCs (p < 0.05). The expression of KRT15 in pretreatment psoriasis lesional skin was localized to the basal layer in pretreatment psoriasis lesional skin .In contrast to the inflammatory mediators induced by IL-17, the expression of the KC stem cell marker  was decreased in posttreatment psoriasis lesional skin compared to pretreatment psoriasis lesional skin  and the transcriptome of skin immune cell subsets (scRNA-seq) generated from the same tissues with different techniques. When skin transcriptome changes by systemic IL-17A blockade were compared between total skin and skin immune cell subsets, IL-23/T17 axis cytokine expression was downregulated at both total skin levels and skin immune cell subset levels. The quantity of overall immune cells, including both pathogenic and regulatory subsets, was decreased in posttreatment psoriasis lesional skin compared to pretreatment psoriasis lesional skin  CD161ll paper Figure 3 studies   severity –47  in KCs was localized to suprabasal KCs  The expression of BDCA-3 (THBD) (DCIR (CLEC4A) (LILRB1 (LILRB2 (ILT4) (LILRB4 (ILT3) (CD1C and CD14 (BDCA-3 (THBD) (DCIR (CLEC4A) (CD1C and CD14  wasClinically, when moderate-to-severe psoriasis patients were treated with systemic anti-IL-17A monoclonal antibody (secukinumab) administration for 52 weeks and then stopped the treatment, 16.0% of them maintained response after treatment withdrawal over the next 52 weeks without need for retreatment , 13. OurOur study has limitations: 1) Our single-cell analysis approach relied on immune cell emigration from the skin biopsies in culture. Whilst this is a recognized approach to isolate immune cells from the skin , some skIL17A+ T17 cells and 95% of IL17F+ T17 cells at single-cell levels  can be found below: GSE183047 and GSE226244 (GEO).The study protocol and informed consent were approved by the Institutional Review Board of the Rockefeller University, New York, NY, USA. The study was conducted in accordance with Good Clinical Practice and the Declaration of Helsinki, and all subjects provided written informed consent before entering the study. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.All the authors have made substantial contributions to the acquisition, analysis, or interpretation of the data. In addition, all the authors have contributed to the drafting or editing of the manuscript and gave their approval for the version submitted. JK and JGK designed the clinical trial, experiments, and laboratory protocols. JK, NK, IC, KK, and VC contributed to patient consent and care. JL and XL conducted single-cell and microarray studies. DR conducted immunohistochemistry studies. JK, YK, SG, WZ, JC, and JGK interpreted the data. JK and JGK verified all the underlying data. All authors contributed to the article and approved the submitted version.