Visual Abstract

Genetic analysis of human inborn errors of immunity has defined the contribution of specific cell populations and molecular pathways in the host defense against infection. The STAT family of transcription factors orchestrate hematopoietic cell differentiation. Patients with de novo activating mutations of STAT3 present with multiorgan autoimmunity, lymphoproliferation, and recurrent infections. We conducted a detailed characterization of the blood monocyte and dendritic cell (DC) subsets in patients with gain-of-function (GOF) mutations across the gene. We found a selective deficiency in circulating nonclassical CD16+ and intermediate CD16+CD14+ monocytes and a significant increase in the percentage of classical CD14+ monocytes. This suggests a role for STAT3 in the transition of classical CD14+ monocytes into the CD16+ nonclassical subset. Developmentally, ex vivo–isolated STAT3GOF CD14+ monocytes fail to differentiate into CD1a+ monocyte-derived DCs. Moreover, patients with STAT3GOF mutations display reduced circulating CD34+ hematopoietic progenitors and frequency of myeloid DCs. Specifically, we observed a reduction in the CD141+ DC population, with no difference in the frequencies of CD1c+ and plasmacytoid DCs. CD34+ hematopoietic progenitor cells from patients were found to differentiate into CD1c+ DCs, but failed to differentiate into CD141+ DCs indicating an intrinsic role for STAT3 in this process. STAT3GOF-differentiated DCs produced lower amounts of CCL22 than healthy DCs, which could further explain some of the patient pathological phenotypes. Thus, our findings provide evidence that, in humans, STAT3 serves to regulate development and differentiation of nonclassical CD16+ monocytes and a subset of myeloid DCs.

Inborn errors of immunity are heterogeneous disorders in which a component of the immune system is impaired leading to clinical disease (1). Genetic analysis of human primary immunodeficiencies has defined the contribution of specific cell populations and molecular pathways in the host defense against infection. The mononuclear phagocyte system contains monocytes and macrophages, which are critical effectors and regulators of innate immune responses, and dendritic cells (DCs), which are central to the initiation and regulation of disease-specific adaptive immune responses. Recent descriptions of human DC and monocyte deficiencies in patients with haploinsufficiency of GATA2 (2) and IRF8 deficiency (3) have shed light on the importance of these cells in human disease and provided a unique opportunity to understand specific genes and pathways critical to human DC and monocyte development.

Human blood monocytes can be subdivided into three populations based on their expression of CD14 and CD16. CD14high classical monocytes (hereafter CD14+) are the major population and CD14dimCD16high nonclassical monocytes (CD16+) are the minor population, whereas an intermediate population expresses both CD14 and CD16 (CD14+CD16+). These three subsets display functional differences in inflammatory, migratory, and phagocytic capacity (46).

Human blood DCs are a heterogeneous population composed of a myeloid and a plasmacytoid arm. The myeloid DC subset consists of the cross-presenting CD141+ DCs (“DC1”) and the heterogeneous CD1c+ population (“DC2”). Plasmacytoid DCs are marked by CD11cCD123+ and are a major source of IFN-α in the blood. Monocytes can give rise to DCs or macrophages in the presence of GM-CSF or M-CSF, respectively. Terminally differentiated DCs acquire CD1c and CD1a expression, and the latter is seen in vivo in peripheral tissues such as in the skin (7). Retention of CD14 expression has been used as a de facto marker of likely monocyte origin, as well as macrophages.

The STAT family of transcription factors, which are widely expressed in hematologic cell types and other cells, orchestrates hematopoietic cell differentiation. Dominant-negative mutations or germline, heterozygous mutations causing loss or gain of STAT activity cause human immune disease (8, 9), indicating that tightly regulated STAT3 function is critical for normal human immune function (1013). STAT3 signaling has also been shown to play a role in myeloid differentiation in mice (1416).

We hypothesized that STAT3 gain-of-function (STAT3GOF) mutations may lead to aberrant DC and/or monocyte differentiation in patients, which may shed light on the unexplained pathogenesis of infectious disease and autoimmunity seen in these patients (17). We evaluated nine STAT3GOF patients with mutations in the DNA binding domain, transcription activation domain, and coiled-coil domain [p.R278H (18), p.V353F (13), p.G421R (13), p.C468R, p.P715L (13, 19), p.T716M (13), p.R103W (20), p.R152W (13, 21), p.F174S (22)] of STAT3 in comparison with healthy individuals. In this study, we report, to our knowledge, the first known genetic variants in humans that lead to selective deficiency in nonclassical monocytes and a subset of classical DCs as well as a role for STAT3 in the transition of classical monocytes into nonclassical monocytes.

STAT3GOF patients were enrolled in Institutional Review Board (IRB)-approved research studies at the Washington University School of Medicine in St. Louis, The University of South Florida, and the National Institutes of Health (USA); patient details are shown in Table I. Blood from healthy donors was purchased from the Mississippi Valley Regional Blood Center or from healthy donors enrolled in IRB-approved research study. Cord blood from healthy donors was purchased from the St. Louis Cord Blood Bank. Samples were processed according to protocols approved by the IRB at the Washington University School of Medicine in St. Louis.

Human PBMC were isolated from whole blood immediately on arrival. Blood was diluted with an equal volume of PBS. This solution was then layered onto a Ficoll-Paque PLUS (GE Healthcare Life Sciences, Marlborough, MA) density gradient and centrifuged at 800 × g for 30 min (no brake applied). Buffy coats at the interface were then collected and treated with RBC lysis buffer. The single cell suspension was used for further analysis or DC differentiation. Mononuclear cell fractions were analyzed from all patients from frozen PBMCs, with a couple of rare exceptions where fresh blood was obtained from p.G421R STAT3GOF and PBMCs or CD34+ hematopoietic progenitor cells (HPCs) were analyzed as specified below.

Cord blood samples were purchased from the St. Louis Cord Blood Bank. Blood from the p.G421R STAT3GOF patient was received with parental consent. Immediately after receipt, blood samples were incubated with RosetteSep human HPC enrichment cocktail (StemCell Technologies, Cambridge, MA) to deplete CD2, CD3, CD14, CD16, CD19, CD24, CD56, CD61, and CD66b cells. Mononuclear cells were then layered onto a Ficoll-Paque PLUS (GE Healthcare Life Sciences) density gradient and centrifuged at 800 × g for 30 min (no brake applied). CD34+ HPCs were isolated from either fresh PBMCs or frozen PBMCs through positive selection using the EasySep Human CD34 Positive Selection Kit (StemCell Technologies) or using CD34+ UltraPure microbeads, (Miltenyi Biotec, Auburn, CA), or were labeled with an Ab mix (Supplemental Table I) and sorted using a BD FACSAriaII. CD34+ HPCs were sorted as live Lin (CD1c, CD141, BDCA2, CD14) CD10 that were also CD34+CD123CD117+. Progenitor cells were then cultured as described previously (23). Briefly, MS-5 stromal cells (a gift from Kang Liu, Columbia University, New York, NY) were maintained in complete α-MEM supplemented with l-glutamine, 10% heat-inactivated FCS (GemCell) and 1% penicillin/streptomycin (Invitrogen, Waltham, MA), but without ribonucleosides or deoxyribonucleosides (Invitrogen). At 24 h prior to coculture with HPCs, stromal cells were treated with 10 μg/ml mitomycin C (Millipore Sigma, St. Louis, MO) for 3 h at 37°C and plated at 2.5 × 104 cells per 100 μl in a 96-well flat-bottom tissue culture plate. HPCs (1 × 103 to 1 × 104) and cytokines were added in 100 μl complete α-MEM. FLT3-L (R&D Systems) was used at 200 ng/ml, stem cell factor (SCF; R&D Systems, Minneapolis, MN) at 40 ng/ml, and GM-CSF (Sanofi) at 50 ng/ml. IL-6, IL-10, TGF-β, or IL-4 (all from R&D) or M-CSF (100 ng/ml; PeproTech, Rocky Hill, NJ) were added at indicated concentrations. Cells were cultured for 7 d as previously described (24). All cytokines were replenished at the full dose on day 5, except FLT3-L, which was replenished at 100 ng/ml. For the analysis of STAT3 and STAT5 phosphorylation, CD34+ HPCs were differentiated for 10–12 d to increase cell numbers. Progenitors were differentiated with GM-CSF, SCF, and FLT3-L and with or without IL-6 (100 ng/ml) (Supplemental Fig. 2A). Alternatively, with SCF and GM-CSF, or SCF and FLT3-L (Supplemental Fig. 2B). Cytokines were replenished on day 5 and on day 7. Phosphorylation was assessed following stimulation, as detailed below.

For PhosphoFlow cytometric analysis of CD34+-derived DCs, cells were stimulated with IL-6 (200 ng/ml) or GM-CSF (500 ng/ml) in serum-free TexMACS (Miltenyi Biotec) for 15 min at 37°C. Subsequently, cells were fixed in 2% paraformaldehyde (Cytofix; BD Biosciences, Franklin Lakes, NJ) for 15 min at 37°C or at room temperature and then washed and resuspended in True-Phos Perm Buffer (BioLegend, San Diego, CA) according to the manufacturer’s instructions. For flow cytometric analysis, cells were washed and stained simultaneously with fluorochrome-conjugated Abs against DC surface proteins, as well as phospho-STAT3 (BioLegend) and phospho-STAT5 and total STAT3 (BD Biosciences), at room temperature for 30 min.

Monocytes from STAT3GOF patients and PBMCs from healthy donors were generated by adherence of PBMCs as previously described (25). Briefly, PBMCs isolated by gradient density centrifugation were seeded at 7 × 106 cells per well in a 6-well plate in RPMI 1640 (Life Technologies, Waltham, MA) supplemented with 1% human serum albumin (25% Albutein; Grifols, Los Angeles, CA) and incubated for 1 h at 37°C. Nonadherent cells were then removed by extensive washing. The adherent cell population was shown to contain both subsets of monocytes (CD14+ and CD16+) and were detected predifferentiation (26). In our hands, the adherent cells expressed HLA-DR and the majority of the cells (∼85%) expressed CD14, although the CD16+ fraction was barely detected among these cells (Supplemental Fig. 1). Nevertheless, the classical monocytes and not the CD16+ monocytes were shown to differentiate into monocyte-derived DCs (moDCs) (26). Adherent cells were then cultured for 3–6 d in RPMI 1640 containing 10% FBS (GemCell). To generate IFN-α–primed moDCs, 500 U/ml IFN-α (Schering Corporation, Kenilworth, NJ) and 100 ng/ml GM-CSF (Leukine; Sanofi, Paris, France) were added to the culture. Cytokines were replenished on day 1 and cells were harvested on day 3. To generate IL-4–primed moDCs, 100 ng/ml GM-CSF (Leukine; Sanofi) and 25 ng/ml IL-4 (R&D Systems) were added to the culture. Cytokines were replenished on days 3 and 5. Cells were analyzed on day 6. For some experiments, monocytes were cultured with M-CSF (PeproTech) at 100 ng/ml for 5 d.

PBMCs, moDCs, monocyte-derived macrophages, or CD34+-derived DCs from healthy donors and STAT3GOF patients were labeled using fluorochrome-conjugated Abs against the indicated receptors and analyzed using a BD LSR Fortessa X-20 or BD FACSymphony. Details of the Abs against myeloid and lymphoid markers are shown in Supplemental Table I. Data were analyzed with FlowJo software. Cytokine production (CCL22/macrophage-derived chemokine, IL-10, TNF-α, TNF-β, IL-6, IL-1β, GM-CSF, IFN-α2, IL-12p40, IL-15, and IL-12p70) was measured in the CD34+-derived DC supernatants using a multiplex bead assay (Luminex, Milliplex; Millipore Sigma).

Healthy donors were sex-matched with the STAT3GOF patients. Data represent the mean ± SEM of at least two independent experiments calculated using GraphPad Prism software. For pairwise comparisons, t tests were used. The data obtained for patients and healthy controls at the same time were analyzed by paired t test. For comparisons between more than two groups, one-way ANOVA was performed unless otherwise noted. The p values < 0.05 were considered to indicate statistical significance. Comparisons found to be statistically insignificant are not shown.

Because of the critical role of signaling via JAK2/STAT3 in myeloid differentiation, we set out to investigate this compartment in patients with STAT3GOF mutations (Table I). We initially profiled the frequency of blood mononuclear cells in nine patients with a STAT3GOF mutation (p.T716M, p.R152W, p.G421R, p.P715L, p.V353F, p.R103W, p.C468R, p.F174S, and p.R278H) compared with healthy adults by flow cytometry. We found that all nine STAT3GOF patients displayed a selective deletion in the frequency of Lineageneg(CD3negCD19negCD56neg)CD14negHLA-DR+CD11c+ myeloid cell population, which includes DCs and nonclassical monocytes (Fig. 1A). Detailed characterization revealed a selective deficiency of nonclassical (CD16+) monocytes (Fig. 1B and 1C; p < 0.0001) and intermediate (CD14+CD16+) monocytes (Fig. 1B and 1D; p = 0.02). It has been shown that nonclassical CD16+ monocytes develop from the CD14+ by transitioning into the CD14+CD16+ state in both mice and humans (24). In accordance with this, we observed a significant increase in the percentage of CD14+ monocytes (Fig. 1B and 1E; p = 0.005). Overall, our results suggested that STAT3GOF inhibits the transition of CD14+ classical monocytes into the CD14+CD16+ state (24).

FIGURE 1.

Selective depletion of nonclassical monocytes in STAT3GOF patients. (A) Flow cytometric analysis of the frequency of Lineageneg(CD3CD19CD56)CD14HLA-DR+CD11c+ cells within the hematopoietic cell populations of nine healthy donors and nine patients with a STAT3GOF mutation. Healthy control individuals and patients that were analyzed on the same day are shown on the top and bottom rows, respectively. (B) Flow cytometric analysis of the frequency of monocyte subsets (classical: CD14+CD16; intermediate: CD14+CD16+; and nonclassical: CD14CD16+) within the Lineageneg(CD3CD19CD56)HLA-DR+ cell population of eight healthy donors and eight patients with a STAT3GOF mutation. Healthy control individuals and patients that were analyzed on the same day are shown on the top and bottom rows, respectively. (CE) Flow cytometric analysis of the frequency of monocyte subsets in the blood of STAT3GOF patients and healthy individuals. Data represent the mean ± SEM. Some of the patients were measured independently more than one time: p.T716M (n = 3), p.R152W (n = 3), p.G421R (n = 1), p.P715L (n = 1), p.V353F (n = 2), p.R103W (n = 1), p.F174S (n = 1), and p.R278S (n = 1). Control healthy donors were included each time. The data for the healthy controls were obtained from 16 different individuals. The mean percentage of all the healthy donors analyzed in each occasion was plotted. (C) shows CD14CD16+ monocytes in healthy controls, 9.3 ± 0.9%, or STAT3GOF patients, 1.9 ± 0.4% (n = 13; p < 0.0001). (D) shows intermediate CD14+CD16+ monocytes from healthy controls, 10.5 ± 1.12% (n = 13), or STAT3GOF patients, 5.7 ± 1.7 (n = 13; p = 0.02), and (E) shows CD14+CD16 monocytes from healthy controls, 57.6 ± 3.8%, or STAT3GOF patients, 71.4 ± 5.4% (n = 13; p = 0.005). Box and whisker plots: mean and range of several independent experiments.

FIGURE 1.

Selective depletion of nonclassical monocytes in STAT3GOF patients. (A) Flow cytometric analysis of the frequency of Lineageneg(CD3CD19CD56)CD14HLA-DR+CD11c+ cells within the hematopoietic cell populations of nine healthy donors and nine patients with a STAT3GOF mutation. Healthy control individuals and patients that were analyzed on the same day are shown on the top and bottom rows, respectively. (B) Flow cytometric analysis of the frequency of monocyte subsets (classical: CD14+CD16; intermediate: CD14+CD16+; and nonclassical: CD14CD16+) within the Lineageneg(CD3CD19CD56)HLA-DR+ cell population of eight healthy donors and eight patients with a STAT3GOF mutation. Healthy control individuals and patients that were analyzed on the same day are shown on the top and bottom rows, respectively. (CE) Flow cytometric analysis of the frequency of monocyte subsets in the blood of STAT3GOF patients and healthy individuals. Data represent the mean ± SEM. Some of the patients were measured independently more than one time: p.T716M (n = 3), p.R152W (n = 3), p.G421R (n = 1), p.P715L (n = 1), p.V353F (n = 2), p.R103W (n = 1), p.F174S (n = 1), and p.R278S (n = 1). Control healthy donors were included each time. The data for the healthy controls were obtained from 16 different individuals. The mean percentage of all the healthy donors analyzed in each occasion was plotted. (C) shows CD14CD16+ monocytes in healthy controls, 9.3 ± 0.9%, or STAT3GOF patients, 1.9 ± 0.4% (n = 13; p < 0.0001). (D) shows intermediate CD14+CD16+ monocytes from healthy controls, 10.5 ± 1.12% (n = 13), or STAT3GOF patients, 5.7 ± 1.7 (n = 13; p = 0.02), and (E) shows CD14+CD16 monocytes from healthy controls, 57.6 ± 3.8%, or STAT3GOF patients, 71.4 ± 5.4% (n = 13; p = 0.005). Box and whisker plots: mean and range of several independent experiments.

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Table I.

Information on STAT3GOF patients and control donors

STAT3GOF VariantAge (y)/SexMajor Clinical SymptomsImmune Modulatory MedicationsReference
 p.G421R 7–12/M AIHA, arthritis, scleroderma, hepatitis IVIG, tocilizumab, mercaptopurine (13) 
 p.F174S 45/F
14/F 
Mother: lymproliferation (splenomegaly), lymphadenopathy, lymphoproliferation in bone marrow, psoriasis, growth failure, enteropathy, hypogammaglobulinemia, lymphopenia
Child: autoimmune hepatitis, growth failure, hypogammaglobulinemia, lymphopenia 
None (22
 p.C468R 9/M History of autoimmune enteropathy, hepatosplenomegaly, abnormal liver function None Unpublished observations, GOF variant validated (22) 
 p.P715L 12/F Diffuse LAD, splenomegaly, enteropathy, eczematous dermatitis MMF (19) 
 p.V353F 34/M AIHA, AITP, AIN, inflammatory lung disease None (13) 
 p.T716M 15/M AIHA, AITP, AIN, enteropathy, short stature MMF and IVIG 
(13) 
 p.R152W 34/M AIHA, AITP, IDDM, alopecia, lung nodules, LAD, hepatosplenomegaly MMF and IVIG (13, 21) 
 p.R103W 37/M Chronic lung disease, chronic sinusitis, Evans Syndrome with refractory ITP, non-mycobacterial pulmonary infection, lymphoproliferative disorder None (20
 p.R278H 17/F Chronic lung disease, enteropathy, Evans Syndrome, arthritis, scleroderma None  
Control samples     
 soJIA patient 9/F Fever, arthritis Tocilizumab  
 Healthy child 5/M    
 Healthy control adults 28–63/M and F None   
STAT3GOF VariantAge (y)/SexMajor Clinical SymptomsImmune Modulatory MedicationsReference
 p.G421R 7–12/M AIHA, arthritis, scleroderma, hepatitis IVIG, tocilizumab, mercaptopurine (13) 
 p.F174S 45/F
14/F 
Mother: lymproliferation (splenomegaly), lymphadenopathy, lymphoproliferation in bone marrow, psoriasis, growth failure, enteropathy, hypogammaglobulinemia, lymphopenia
Child: autoimmune hepatitis, growth failure, hypogammaglobulinemia, lymphopenia 
None (22
 p.C468R 9/M History of autoimmune enteropathy, hepatosplenomegaly, abnormal liver function None Unpublished observations, GOF variant validated (22) 
 p.P715L 12/F Diffuse LAD, splenomegaly, enteropathy, eczematous dermatitis MMF (19) 
 p.V353F 34/M AIHA, AITP, AIN, inflammatory lung disease None (13) 
 p.T716M 15/M AIHA, AITP, AIN, enteropathy, short stature MMF and IVIG 
(13) 
 p.R152W 34/M AIHA, AITP, IDDM, alopecia, lung nodules, LAD, hepatosplenomegaly MMF and IVIG (13, 21) 
 p.R103W 37/M Chronic lung disease, chronic sinusitis, Evans Syndrome with refractory ITP, non-mycobacterial pulmonary infection, lymphoproliferative disorder None (20
 p.R278H 17/F Chronic lung disease, enteropathy, Evans Syndrome, arthritis, scleroderma None  
Control samples     
 soJIA patient 9/F Fever, arthritis Tocilizumab  
 Healthy child 5/M    
 Healthy control adults 28–63/M and F None   

AIHA, autoimmune hemolytic anemia; AIN, autoimmune neutropenia; AITP, autoimmune thrombocytopenia; IDDM, insulin-dependent diabetes mellitus; ITP immune thrombocytopenia; LAD, lymphadenopathy; MMF, mycophenolate mofetil; SoJIA, systemic-onset juvenile idiopathic arthritis.

We next tested the capacity of monocytes to develop into macrophages and DCs. We found that following differentiation with M-CSF for 5 d, but not prior to differentiation (Fig. 2A; left panel), monocytes from STAT3GOF patients expressed on average 4.9-fold higher levels of CD14 compared with healthy monocytes (geometric mean expression ± SEM; 60,591 ± 11,608 versus 14,804 ± 3,758; p = 0.04) (Fig. 2A; right panel). Next, we measured the capacity of healthy or STAT3GOF (p.T716M) CD34+ HPCs to differentiate into macrophages and DCs (Fig. 2B). As seen with the monocytes, CD34+ HPCs from STAT3GOF patients yielded a higher percentage of CD14+CD11b+ myeloid cells relative to the CD34+ HPCs from healthy donors, suggesting the increased emergence of macrophages in STAT3GOF patients, relative to healthy controls (Fig. 2C). To mimic STAT3GOF during DC differentiation, we differentiated CD34+ HPCs from healthy donors into DCs with GM-CSF, SCF, and FLT3-L and in the presence or absence of STAT3-activating or control cytokines (27) (Fig. 2B). IL-6 and IL-10 are direct activators of STAT3 and IL-6 inhibition, in particular, has been shown to benefit patients in the clinic and was therefore selected for use in this study (13, 28). TGF-β has been shown to facilitate STAT3 activation (29), whereas IL-4, a STAT6 activator (30), has been used as a control. We found that in the presence of IL-6, IL-10, or TGF-β, CD34+ HPCs failed to differentiate efficiently into CD1c+ DCs and retained high levels of CD14 and these effects were dose dependent (Fig. 2D and 2E; upper panels). In contrast, IL-4 conditioned cultures yielded mainly CD1c+ DCs (mean ± SEM; 49.5 ± 16.7% compared with 2.4 ± 1.3% in the presence of IL-6 or 8.9 ± 5.4% with GM-CSF, SCF, and FLT3-L alone; (Fig. 2D and 2E; lower panels).

FIGURE 2.

STAT3GOF prevents DC differentiation from monocytes. (A) Monocytes from healthy controls or STAT3GOF patients (p.F174S, p.P715L, p.T716M, p.V353F, and p.R103W) were cultured for 5 d in the presence of M-CSF. Left panel: histograms show CD14 expression on STAT3GOF (black line) or healthy (filled gray) monocytes pre- and postdifferentiation with M-CSF. Right panel: graph shows the fluorescence of CD14 on HLA-DR+CD11c+ cells at the end of the culture. Data represent the mean ± SEM of three independent experiments (p = 0.04) performed with four STAT3GOF and four healthy donors. (B) Schematic diagram of the protocol used to induce differentiation of CD34+ HPCs into DCs or macrophages shown in (CE). (C) Blood CD34+CD123CD117+ HPCs sorted from STAT3GOF patient p.T716M and a healthy donor were cultured in the presence of GM-CSF, SCF, FLT3-L, and M-CSF. Flow cytometry plots, gated on live CD45+HLA-DR+CD11c+ cells, show the culture output of CD14+CD11b+ cells on day 7. (D) CD34+CD123CD117+ HPCs sorted from two healthy cord blood donors #036 (square symbol) and #039 (round symbol) were cultured for 7 d in the presence of GM-CSF, SCF, and FLT3-L (GM/FL/SC) and GM/FL/SC with either IL-6 (100 ng/ml), IL-4 (25 ng/ml), IL-10 (5 ng/ml), or TGF-β (5 ng/ml). Output data of two experiments showing the frequency CD45+HLA-DR+CD11c+CD14+ cells (upper panel) and CD1c+ DCs (lower panel) for each condition were analyzed by flow cytometry. (E) CD34+CD123CD117+ HPCs from healthy cord blood donor #032 were cultured in the presence of GM-CSF, SCF, and FLT3-L, and descending concentrations of IL-6 (4, 20, 100, 200, and 400 ng/ml), IL-4 (1, 5, 25, 75, and 225 ng/ml), IL-10 (0.5, 1.5, 5, 15, and 45 ng/ml), or TGF-β (0.5, 1.5, 5, 15, and 45 ng/ml). Output of CD45+HLA-DR+CD11c+CD14+ cells (upper panel) and CD1c+ DCs (lower panel) for each condition. An overall significance model indicated statistical significance between IL-6– and IL-4–induced CD14+ cell differentiation (p = 00003) and indicated statistical significance between IL-6– and IL-4–induced CD1c+ DCs (p < 0.0001). (F) Monocytes from healthy control or STAT3GOF patient p.G421R were cultured for 3 d in the presence of GM-CSF and IFN-α or for 5 d with GM-CSF and IL-4. CD1a and CD40 expression on HLA-DR+CD11c+ cells were analyzed by flow cytometry. (G) Monocytes from healthy control or STAT3GOF patient p.R152W were cultured for 5 d in the presence of GM-CSF and IL-4. CD1a, CD1c, and CD40 expression on HLA-DR+CD11c+ cells were analyzed by flow cytometry. (H) Graph shows the percentage of CD1a+ DCs among moDCs determined in three different experiments performed as in (F) and (G). Two dots represent CD1a output from two GM-CSF and IFN-α independent cultures (filled circles) and three dots represents CD1a output from two GM-CSF and IL-4 independent cultures (empty squares) (n = 5; p = 0.02).

FIGURE 2.

STAT3GOF prevents DC differentiation from monocytes. (A) Monocytes from healthy controls or STAT3GOF patients (p.F174S, p.P715L, p.T716M, p.V353F, and p.R103W) were cultured for 5 d in the presence of M-CSF. Left panel: histograms show CD14 expression on STAT3GOF (black line) or healthy (filled gray) monocytes pre- and postdifferentiation with M-CSF. Right panel: graph shows the fluorescence of CD14 on HLA-DR+CD11c+ cells at the end of the culture. Data represent the mean ± SEM of three independent experiments (p = 0.04) performed with four STAT3GOF and four healthy donors. (B) Schematic diagram of the protocol used to induce differentiation of CD34+ HPCs into DCs or macrophages shown in (CE). (C) Blood CD34+CD123CD117+ HPCs sorted from STAT3GOF patient p.T716M and a healthy donor were cultured in the presence of GM-CSF, SCF, FLT3-L, and M-CSF. Flow cytometry plots, gated on live CD45+HLA-DR+CD11c+ cells, show the culture output of CD14+CD11b+ cells on day 7. (D) CD34+CD123CD117+ HPCs sorted from two healthy cord blood donors #036 (square symbol) and #039 (round symbol) were cultured for 7 d in the presence of GM-CSF, SCF, and FLT3-L (GM/FL/SC) and GM/FL/SC with either IL-6 (100 ng/ml), IL-4 (25 ng/ml), IL-10 (5 ng/ml), or TGF-β (5 ng/ml). Output data of two experiments showing the frequency CD45+HLA-DR+CD11c+CD14+ cells (upper panel) and CD1c+ DCs (lower panel) for each condition were analyzed by flow cytometry. (E) CD34+CD123CD117+ HPCs from healthy cord blood donor #032 were cultured in the presence of GM-CSF, SCF, and FLT3-L, and descending concentrations of IL-6 (4, 20, 100, 200, and 400 ng/ml), IL-4 (1, 5, 25, 75, and 225 ng/ml), IL-10 (0.5, 1.5, 5, 15, and 45 ng/ml), or TGF-β (0.5, 1.5, 5, 15, and 45 ng/ml). Output of CD45+HLA-DR+CD11c+CD14+ cells (upper panel) and CD1c+ DCs (lower panel) for each condition. An overall significance model indicated statistical significance between IL-6– and IL-4–induced CD14+ cell differentiation (p = 00003) and indicated statistical significance between IL-6– and IL-4–induced CD1c+ DCs (p < 0.0001). (F) Monocytes from healthy control or STAT3GOF patient p.G421R were cultured for 3 d in the presence of GM-CSF and IFN-α or for 5 d with GM-CSF and IL-4. CD1a and CD40 expression on HLA-DR+CD11c+ cells were analyzed by flow cytometry. (G) Monocytes from healthy control or STAT3GOF patient p.R152W were cultured for 5 d in the presence of GM-CSF and IL-4. CD1a, CD1c, and CD40 expression on HLA-DR+CD11c+ cells were analyzed by flow cytometry. (H) Graph shows the percentage of CD1a+ DCs among moDCs determined in three different experiments performed as in (F) and (G). Two dots represent CD1a output from two GM-CSF and IFN-α independent cultures (filled circles) and three dots represents CD1a output from two GM-CSF and IL-4 independent cultures (empty squares) (n = 5; p = 0.02).

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We also evaluated the capacity of monocytes from STAT3GOF patients to differentiate into DCs (moDCs). Healthy p.G421R or p.R152W monocytes were cultured in the presence of GM-CSF and IL-4 or IFN-α to induce differentiation (Fig. 2F and 2G). Similar to IL-4, IFN-α is used in conjugation with GM-CSF to differentiate monocytes into DCs, yielding IL-4 moDCs or IFN-α moDCs, respectively; however, compared with IL-4 DCs, IFN-α DCs show greater capacity to activate cytotoxic T lymphocyte responses (31). IFN-α enhanced the differentiation of mature DCs expressing CD40 on monocytes from both STAT3GOF patients and healthy donors (Fig. 2F; right panel). Under both conditions, the expression of CD1a, a well-established DC marker, was reduced on STAT3GOF patient HLA-DR+CD11c+ moDCs relative to that on healthy donor moDCs (Fig. 2F–H; p = 0.02). Thus, our findings indicated that STAT3 signaling promotes the differentiation of M-CSF–induced macrophages and inhibits the differentiation of CD1a+ DCs.

The reduced capacity of monocytes from patients with STAT3GOF mutations to differentiate into DCs prompted our analysis of blood DCs from these patients relative to those from healthy individuals. We found that the frequency of DCs (LineagenegHLA-DR+) was significantly reduced in STAT3GOF patient p.G421R (mean ± SEM; 3.2 ± 1.6% versus 8.8 ± 1.89%; p = 0.05; (Fig. 3A and 3B). As shown in (Fig. 3C, STAT3GOF patient p.G421R had lower numbers of CD14negHLA-DR+CD11c+ myeloid cells compared with a patient with systemic-onset juvenile idiopathic arthritis (soJIA), although both patients were receiving the same anti-IL-6R treatment (tocilizumab). The reduction in myeloid DC frequency was not unique to STAT3GOF patient p.G421R, indeed, we observed a significant reduction in the frequency of myeloid DCs in eight additional patients (p = 0.01; (Fig. 3D) relative to healthy individuals, whereas there was no significant reduction in plasmacytoid DCs (Fig. 3E). A thorough examination of the myeloid DCs revealed a significant reduction in the CD141+ “DC1” subset (p < 0.0001; (Fig. 3F), but not the CD1c+ “DC2” subset (Fig. 3G). Interestingly, there was variability in the frequency of CD141+ DCs among the different STAT3GOF patients. Patients with p.G421R, p.P715L, p.V353F, p.C468R, and p.F174S mutations had lower frequencies of CD141+ DCs compared with patients with p.T716M, p.R152W, or p.R103W and the soJIA patient treated with tocilizumab (anti-IL-6R therapy) or healthy donors (Fig. 3H).

FIGURE 3.

Selective depletion of DCs in patients with STAT3GOF mutations. (A) Flow cytometric evaluation of myeloid fractions expressed as a percentage of mononuclear cells in STAT3GOF patient p.G421R and a healthy control. Plots show gating strategy for the Lineageneg(CD3CD19CD56CD16CD14)HLA-DR+ subset, containing myeloid DCs (mDCs) and plasmacytoid DCs (pDCs), or the Lineage+(CD3+CD19+CD56+CD16+CD14+)HLA-DR+ subset, containing monocytes and B cells. One representative analysis out of six performed. (B) Quantification of peripheral blood Lineageneg(CD3CD19CD56CD16CD14)HLA-DR+ cells from STAT3GOF patient p.G421R (9 y of age), examined multiple times compared with six normal adults. Box and whisker plots: mean and range; p = 0.005). (C) Representative flow cytometry contour plots showing the frequency of Lineageneg(CD3CD19CD56)CD14HLA-DR+CD11c+ cells in blood of STAT3GOF patient p.G421R, a healthy control, and a soJIA patient receiving a similar treatment (anti-IL-6R therapy) to that received by the STAT3GOF patient. One of two independently analyzed samples is shown. (D) Graph shows the frequency of mDCs, gated as Lineageneg(CD3CD19CD56CD16CD14)HLA-DR+CD11c+ cells, in nine STAT3GOF patients tested at least once [p.T716M (n = 2), p.R152W (n = 3), p.G421R (n = 5), p.P715L (n = 1), p.V353F (n = 2), p.R103W (n = 1), p.F174S (n = 3), p.C468R (n = 1), and p.R278H (n = 1)] compared with healthy controls (n = 19). The data obtained for patients and healthy controls at the same time were analyzed by paired t test (p = 0.01). Graph shows mean ± SEM. (E) Graph shows the frequency of pDCs, gated as Lineageneg(CD3CD19CD56CD16CD14)HLA-DR+CD11cCD123+ cells, in nine STAT3GOF patients tested at least once [p.T716M (n = 2), p.R152W (n = 3), p.G421R (n = 4), p.P715L (n = 1), p.V353F (n = 2), p.R103W (n = 1), p.F174S (n = 3), p.C468R (n = 1), and p.R278H (n = 1)] compared with healthy controls (n = 18). Graph shows mean ± SEM. (F) Quantification of peripheral blood CD141+ DC1s, gated as Lineageneg(CD3CD19CD56CD16CD14)HLA-DR+CD11c+CD141+ cells, in nine STAT3GOF patients tested at least once [p.T716M (n = 3), p.R152W (n = 4), p.G421R (n = 4), p.P715L (n = 1), p.V353F (n = 2), p.R103W (n = 1), p.F174S (n = 4), p.C468R (n = 2), and p.R278H (n = 1)] compared with healthy controls (n = 22; p < 0.0001). Graph shows mean ± SEM. (G) Quantification of peripheral blood CD1c+ DC2s, gated as Lineageneg(CD3CD19CD56CD16CD14)HLA-DR+CD11c+CD1c+ cells, in nine STAT3GOF patients tested at least once [p.T716M (n = 3), p.R152W (n = 4), p.G421R (n = 5), p.P715L (n = 1), p.V353F (n = 2), p.R103W (n = 1), p.F174S (n = 4), p.C468R (n = 2), and p.R278H (n = 1)], compared with healthy controls (n = 23). Graph shows mean ± SEM. (H) Blood CD141+ DC1s were gated on Lineageneg(CD3CD19CD56CD16CD14)HLA-DR+CD11c+ cells, and expression of CD141+ was determined. Percentage of LineagenegHLA-DR+CD11c+ cells that are positive for CD141+ is shown for eight STAT3GOF patients, one soJIA control patient, and 30 healthy controls, with mean of three healthy control donors plotted each time (n = 10). Numbers on plots represent the mean of all analyzed independent samples.

FIGURE 3.

Selective depletion of DCs in patients with STAT3GOF mutations. (A) Flow cytometric evaluation of myeloid fractions expressed as a percentage of mononuclear cells in STAT3GOF patient p.G421R and a healthy control. Plots show gating strategy for the Lineageneg(CD3CD19CD56CD16CD14)HLA-DR+ subset, containing myeloid DCs (mDCs) and plasmacytoid DCs (pDCs), or the Lineage+(CD3+CD19+CD56+CD16+CD14+)HLA-DR+ subset, containing monocytes and B cells. One representative analysis out of six performed. (B) Quantification of peripheral blood Lineageneg(CD3CD19CD56CD16CD14)HLA-DR+ cells from STAT3GOF patient p.G421R (9 y of age), examined multiple times compared with six normal adults. Box and whisker plots: mean and range; p = 0.005). (C) Representative flow cytometry contour plots showing the frequency of Lineageneg(CD3CD19CD56)CD14HLA-DR+CD11c+ cells in blood of STAT3GOF patient p.G421R, a healthy control, and a soJIA patient receiving a similar treatment (anti-IL-6R therapy) to that received by the STAT3GOF patient. One of two independently analyzed samples is shown. (D) Graph shows the frequency of mDCs, gated as Lineageneg(CD3CD19CD56CD16CD14)HLA-DR+CD11c+ cells, in nine STAT3GOF patients tested at least once [p.T716M (n = 2), p.R152W (n = 3), p.G421R (n = 5), p.P715L (n = 1), p.V353F (n = 2), p.R103W (n = 1), p.F174S (n = 3), p.C468R (n = 1), and p.R278H (n = 1)] compared with healthy controls (n = 19). The data obtained for patients and healthy controls at the same time were analyzed by paired t test (p = 0.01). Graph shows mean ± SEM. (E) Graph shows the frequency of pDCs, gated as Lineageneg(CD3CD19CD56CD16CD14)HLA-DR+CD11cCD123+ cells, in nine STAT3GOF patients tested at least once [p.T716M (n = 2), p.R152W (n = 3), p.G421R (n = 4), p.P715L (n = 1), p.V353F (n = 2), p.R103W (n = 1), p.F174S (n = 3), p.C468R (n = 1), and p.R278H (n = 1)] compared with healthy controls (n = 18). Graph shows mean ± SEM. (F) Quantification of peripheral blood CD141+ DC1s, gated as Lineageneg(CD3CD19CD56CD16CD14)HLA-DR+CD11c+CD141+ cells, in nine STAT3GOF patients tested at least once [p.T716M (n = 3), p.R152W (n = 4), p.G421R (n = 4), p.P715L (n = 1), p.V353F (n = 2), p.R103W (n = 1), p.F174S (n = 4), p.C468R (n = 2), and p.R278H (n = 1)] compared with healthy controls (n = 22; p < 0.0001). Graph shows mean ± SEM. (G) Quantification of peripheral blood CD1c+ DC2s, gated as Lineageneg(CD3CD19CD56CD16CD14)HLA-DR+CD11c+CD1c+ cells, in nine STAT3GOF patients tested at least once [p.T716M (n = 3), p.R152W (n = 4), p.G421R (n = 5), p.P715L (n = 1), p.V353F (n = 2), p.R103W (n = 1), p.F174S (n = 4), p.C468R (n = 2), and p.R278H (n = 1)], compared with healthy controls (n = 23). Graph shows mean ± SEM. (H) Blood CD141+ DC1s were gated on Lineageneg(CD3CD19CD56CD16CD14)HLA-DR+CD11c+ cells, and expression of CD141+ was determined. Percentage of LineagenegHLA-DR+CD11c+ cells that are positive for CD141+ is shown for eight STAT3GOF patients, one soJIA control patient, and 30 healthy controls, with mean of three healthy control donors plotted each time (n = 10). Numbers on plots represent the mean of all analyzed independent samples.

Close modal

To determine whether endogenous STAT3GOF affects DC differentiation, we evaluated the potential of CD34+ HPCs from STAT3GOF patients to differentiate into DCs compared with the corresponding cells from healthy control donors. We found that the frequency of CD34+ HPCs was reduced compared with healthy control individuals (p = 0.019; (Fig. 4A). Successful isolation of CD34+ HPCs from STAT3GOF patient p.G421R (Fig. 4B), permitted us to analyze their capacity to differentiate into DCs according to the protocol shown in (Fig. 2B. Cells were analyzed for the expression of HLA-DR+CD11c+CD1c+ or HLA-DR+CD11c+CD141+ DCs after 7 d of culture (23). As observed in the ex vivo analysis of patient PBMCs, STAT3GOF CD34+ HPCs differentiated into CD1c+ DCs (74.7% of HLA-DR+CD11c+ cells), and the frequencies of both CD1a+ and CD141+ DCs were reduced in STAT3GOF patient–derived cultures relative to healthy controls (14.2% versus 28.6% and 0.57% versus 9.63%, respectively) (Fig. 4C). Culture supernatants of CD34+ HPCs differentiated DCs from STAT3GOF p.G421R or p.F174S patient or healthy controls were analyzed for the presence of cytokines. Out of all the tested cytokines, we found that CCL22 was detected in STAT3GOF DC cultures in reduced levels compared with healthy donor–derived DC cultures (Fig. 4D). Consistent with that, CCL22 levels were reduced when IL-6 was present during healthy CD34+-derived DC differentiation (Fig. 4D). Overall, our findings established an intrinsic role for STAT3GOF in the overall production of DC CCL22 and the development of CD141+ DCs.

FIGURE 4.

STAT3GOF prevents CD141+ DC differentiation from CD34+ HPCs. (A) Plots show the frequency of blood CD34+ HPCs, measured by flow cytometry at least once in STAT3GOF patients [p.R278S (n = 1), p.G421R (n = 2), p.T716M (n = 4), p.R152W (n = 3), p.P715L (n = 2), and p.V353F (n = 4)] or in healthy controls (n = 16). n refers to repeated measurements of individual patients. Mean ± SEM, 0.21 ± 0.12% and 0.48 ± 0.22%, for patients and controls, respectively (p = 0.019). (B) Gating strategy for sorting CD34+ HPCs from patient p.G421R fresh blood. (C) Sorted blood CD34+CD123CD117+ HPCs from STAT3GOF patient p.G421R or a healthy donor were cultured for 7 d in the presence of GM-CSF, SCF, and FLT3-L (see (Fig. 2B). Flow cytometry contour plots show the frequency of CD1c+CD1a+ and CD141+ DCs among live CD45+HLA-DR+CD11c+ cells. One of two experiments is shown. (D) Amounts of CCL22 in cultures of CD34+-derived DCs that were differentiated from CD34+ HPCs of two healthy or two STAT3GOF patients p.G421R or p.F174S with GM-CSF, SCF, and FLT3-L and with or without IL-6 for 7 d. CCL22 was analyzed in the supernatant by Luminex bead array.

FIGURE 4.

STAT3GOF prevents CD141+ DC differentiation from CD34+ HPCs. (A) Plots show the frequency of blood CD34+ HPCs, measured by flow cytometry at least once in STAT3GOF patients [p.R278S (n = 1), p.G421R (n = 2), p.T716M (n = 4), p.R152W (n = 3), p.P715L (n = 2), and p.V353F (n = 4)] or in healthy controls (n = 16). n refers to repeated measurements of individual patients. Mean ± SEM, 0.21 ± 0.12% and 0.48 ± 0.22%, for patients and controls, respectively (p = 0.019). (B) Gating strategy for sorting CD34+ HPCs from patient p.G421R fresh blood. (C) Sorted blood CD34+CD123CD117+ HPCs from STAT3GOF patient p.G421R or a healthy donor were cultured for 7 d in the presence of GM-CSF, SCF, and FLT3-L (see (Fig. 2B). Flow cytometry contour plots show the frequency of CD1c+CD1a+ and CD141+ DCs among live CD45+HLA-DR+CD11c+ cells. One of two experiments is shown. (D) Amounts of CCL22 in cultures of CD34+-derived DCs that were differentiated from CD34+ HPCs of two healthy or two STAT3GOF patients p.G421R or p.F174S with GM-CSF, SCF, and FLT3-L and with or without IL-6 for 7 d. CCL22 was analyzed in the supernatant by Luminex bead array.

Close modal

This study reports the role for STAT3 activation in human monocyte and DC specification. We examined nine patients with STAT3GOF mutations, and based on their ex vivo cellular phenotypes and relevant in vitro cultures of patient cells, we identified STAT3 activation as a checkpoint event in the transitioning of classical CD14+ monocytes into nonclassical monocytes (CD16+ monocytes), as well as in the specification of a subset of myeloid DCs (CD141+ DCs) and CD1a+ moDCs.

It was shown that compared with classical monocytes, nonclassical monocytes (CD16+) express higher levels of Ag presentation–related molecules and produce more TNF-α. Furthermore, these nonclassical monocytes play important roles in facilitating transendothelial migration and complement- and FcR-mediated phagocytosis, functions that are critical for host defense against pathogens. Thus, the reduced frequency of these cells, and therefore these functions, in STAT3GOF patients could explain the repeated infections, including pathogens such as mycobacteria, often seen in these patients (12, 13, 17). Moreover, the reduced capacity of patient monocytes to differentiate into CD1a+ moDCs and produce lower amounts of CCL22 can explain the patient susceptibility to infections (32, 33). Indeed, CCL22 possesses an antimicrobial activity, and also serves as a chemoattractant for CCR4-expressing T cells, thereby promoting the interaction of these DCs with T cells (34). CCL22 was also shown to facilitate regulatory T cell (Treg) interaction with DCs in lymph nodes, thus its absence might explain the Treg dysregulation and autoimmune manifestations seen in STAT3GOF patients (12, 35, 36). CD1a, a member of the lipid-presenting proteins, is expressed on tissue DCs and acts to activate T cells specific to microbial lipid Ags. In the skin most of these T cells produce IL-22, which play a role in host defense against infections and maintaining epithelial homeostasis (3739). Whether CD1a+ DCs and CD1a-restricted T cells are reduced in STAT3GOF patient peripheral tissues such as the skin and mucosa is yet to be defined, but should have important therapeutic implications.

Furthermore, the observed reduction in CD141+ DCs suggests a mechanism for the lack of immune tolerance in the STAT3GOF patients. Indeed, a regulatory DC population characterized as CD141+CD14+CD16+ (DC10) was recently identified in human blood as a poor stimulator of allogeneic naive CD4+ T cells and inducers of alloantigen-specific anergic Tr1 cells (40). Additionally, the reduced levels of CD141+ DCs in patient blood shifts the balance in DC subsets such that CD1c+ DCs become the predominant DC population in these patients. CD1c+ are the main drivers of Th1 and Th17 responses (41), which accounts for the increased number of Th17 cells with a concomitant decrease in Tregs causing high sensitivity to multiorgan autoimmunity in STAT3GOF patients. It is intriguing that the frequency of CD141+ DCs in the patients seems variable. Patients with p.G421R, p.F174S, p.V353F, p.C468R, and p.P715L mutations had lower frequencies compared with patients with p.T716M, p.R152W, or p.R103W, in which CD141+ DC expression was detected at higher levels. Different STAT3 mutations between the patients may affect STAT3 activity (binding, dimerization, cell survival) and can offer some explanation for this difference. Finally, the reduced levels of CD34+ HPCs in STAT3GOF patients could be relevant in cases where that specific lymphopenia drives lymphoproliferation. Under these conditions, the progenies are stimulated to proliferate to fill the space in this compartment left by the reduced levels of CD34+ HPCs, leading to dysregulation in secondary lymphoid organs as well as other organs (e.g., the gastrointestinal tract).

The observed decrease in the frequency of the nonclassical monocytes was associated with an increase in the classical CD14+ monocytes, suggesting that STAT3GOF mutations control the transition of classical to nonclassical monocytes. STAT3GOF patients have a decrease in both STAT5 and STAT1 phosphorylation (13). The partial decrease in STAT1 phosphorylation likely contributes to the immune deficiency of STAT3GOF patients (9), as does the partial decrease in STAT5 phosphorylation, which is important for induction of genes during monocyte/macrophage differentiation (42). Indeed, we found that STAT5 phosphorylation is elevated in GM-CSF–containing CD34+ HPC–differentiated cultures, compared with resting PBMCs (Supplemental Fig. 2A; right panel), even in the presence of IL-6, which, as predicted, resulted in elevated STAT3 levels (Supplemental Fig. 2A; left and middle panels). Moreover, in the absence of GM-CSF, pSTAT5 expression was lower on CD34+-derived DCs, compared with GM-CSF–containing cultures (Supplemental Fig. 2B). Overall, this indicates the therapeutic potential of GM-CSF in promoting STAT5 phosphorylation and enhancing nonclassical monocyte differentiation in patients. However, whether STAT5 phosphorylation could be efficiently manipulated in STAT3GOF HPCs is yet to be determined. Of note, the increase in frequency of CD14+ monocytes in STAT3GOF patients compared with healthy donors was not as robust as the decrease in frequency of nonclassical (CD16+) monocytes, suggesting that classical monocytes, aside from their transition into nonclassical monocytes, differentiate also into other various resident myeloid cells (43), possibly by increasing responsiveness to M-CSF (44, 45).

It is likely that the effect of STAT3 on myeloid cells depends on the stage of cell development. It is intriguing that fetal and adult CD14+ monocytes are distinct in terms of STAT1, STAT5, and STAT3 activation in response to stimuli, suggesting that this population in particular is required in fetal development (46). Because the mutations in STAT3GOF patients are germline, it is plausible that in patients with STAT3GOF with reduced STAT1 phosphorylation, monocytes may not undergo complete specification into the CD16+ subset (46). We hypothesize that STAT3 activation affects the development of progenitors committed to CD141+ DCs. Indeed, relative to healthy controls, we found reduced frequency of CD34+CD117+ progenitors in STAT3GOF patient blood (Fig. 4A). However, whether this specific deficiency arises from a defect in the monocyte DC progenitors, common DC progenitors, or CD141+ DC (DC1) progenitors in the bone marrow of these patients is not known. Alternatively, it can be speculated that STAT3 activation diverts the development of CD141+ DC progenitors into the CD1c+ lineage. Batf3 is required for Irf8 autoactivation and commitment to the DC1 lineage in mice and, in its absence, the progenitors divert into the IRF4+ DC2 lineage. Whether STAT3 activation inhibits Batf3 or controls Irf8 activation remains to be determined. Finally, although a factor that specifically controls the differentiation of nonclassical monocytes has not yet been identified, our study highlights a role for STAT3 in this process.

On a final note, our study is particularly relevant in the current COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2. Indeed, IL-6–activating and STAT3-activating pathways are profoundly induced in infected patients, which, based on our findings, would impact on DC and monocyte development and ultimately, we surmise, on antiviral responses.

We thank Dr. Erica Maria Lantelme, Dorjan Brinja, Dr. Laurent Gorvel, Vince Garin, Adriana Parra-Gonzalez, and Kairav Shah at the Washington University School of Medicine in St. Louis Department of Pathology and Immunology for their help.

This work was supported by funding from the Washington University School of Medicine in St. Louis Department of Pathology and Immunology, Siteman Cancer Center, the National Psoriasis Foundation (Translational Research Grant), and by National Institute of Biomedical Imaging and Bioengineering Grant 5R21EB024767-02, National Institute of Arthritis and Musculoskeletal and Skin Diseases Grant 1R01AR075959-01, and National Cancer Institute Grant 1R01CA245277-01A1 to E.K.

D.K. and K.R. conducted experiments, analyzed data, and participated in writing the manuscript. S.D. conducted experiments. M.C., T.P.V., H.P., J.L., J.W.L., and J.M. provided patient specimens and clinical data/expertise. E.K. designed the study, performed and supervised experiments, analyzed data, and wrote the manuscript.

The online version of this article contains supplemental material.

Abbreviations used in this article

DC

dendritic cell

GOF

gain-of-function

HPC

hematopoietic progenitor cell

moDC

monocyte-derived DC

SCF

stem cell factor

soJIA

systemic-onset juvenile idiopathic arthritis

Treg

regulatory T cell

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The authors have no financial conflicts of interest.

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