Induction of antitumor cytotoxic lymphocytes using engineered human primary blood dendritic cells

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Abstract

The dendritic cell (DC) is the master regulator of host immunity. The results of our study bring significant technical improvements in DC methodology. First, a method was developed to expand primary blood DCs at unlimited amounts. Second, the established DCs are constitutively activated and readily available to prime naïve T cells. Third, the DCs can be genetically modified to deliver given tumor antigens in high efficiency and to express activating molecules in driving simultaneous production of antigen-specific T cells and natural killer (NK) cells. Fourth, introducing two allogeneic DRB1 molecules into the DCs improves generation of tumor antigen-specific T cells. Further, the DC-activated cytotoxic T lymphocytes and NK cells potently suppress tumor growth and metastasis in human lung cancer mouse models. Dendritic cell (DC)-based cancer immunotherapy has achieved modest clinical benefits, but several technical hurdles in DC preparation, activation, and cancer/testis antigen (CTA) delivery limit its broad applications. Here, we report the development of immortalized and constitutively activated human primary blood dendritic cell lines (ihv-DCs). The ihv-DCs are a subset of CD11c +/CD205 + DCs that constitutively display costimulatory molecules. The ihv-DCs can be genetically modified to express human telomerase reverse transcriptase (hTERT) or the testis antigen MAGEA3 in generating CTA-specific cytotoxic T lymphocytes (CTLs). In an autologous setting, the HLA-A2 + ihv-DCs that present hTERT antigen prime autologous T cells to generate hTERT-specific CTLs, inducing cytolysis of hTERT-expressing target cells in an HLA-A2–restricted manner. Remarkably, ihv-DCs that carry two allogeneic HLA-DRB1 alleles are able to prime autologous T cells to proliferate robustly in generating HLA-A2–restricted, hTERT-specific CTLs. The ihv-DCs, which are engineered to express MAGEA3 and high levels of 4-1BBL and MICA, induce simultaneous production of both HLA-A2–restricted, MAGEA3-specific CTLs and NK cells from HLA-A2 + donor peripheral blood mononuclear cells. These cytotoxic lymphocytes suppress lung metastasis of A549/A2.1 lung cancer cells in NSG mice. Both CTLs and NK cells are found to infiltrate lung as well as lymphoid tissues, mimicking the in vivo trafficking patterns of cytotoxic lymphocytes. This approach should facilitate the development of cell-based immunotherapy for human lung cancer.

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Superior antigen cross-presentation and XCR1 expression define human CD11c+CD141+ cells as homologues of mouse CD8+ dendritic cells

Annabell Bachem, Steffen Güttler, Evelyn Hartung … (2010)

In recent years, human dendritic cells (DCs) could be subdivided into CD304+ plasmacytoid DCs (pDCs) and conventional DCs (cDCs), the latter encompassing the CD1c+, CD16+, and CD141+ DC subsets. To date, the low frequency of these DCs in human blood has essentially prevented functional studies defining their specific contribution to antigen presentation. We have established a protocol for an effective isolation of pDC and cDC subsets to high purity. Using this approach, we show that CD141+ DCs are the only cells in human blood that express the chemokine receptor XCR1 and respond to the specific ligand XCL1 by Ca2+ mobilization and potent chemotaxis. More importantly, we demonstrate that CD141+ DCs excel in cross-presentation of soluble or cell-associated antigen to CD8+ T cells when directly compared with CD1c+ DCs, CD16+ DCs, and pDCs from the same donors. Both in their functional XCR1 expression and their effective processing and presentation of exogenous antigen in the context of major histocompatibility complex class I, human CD141+ DCs correspond to mouse CD8+ DCs, a subset known for superior antigen cross-presentation in vivo. These data define CD141+ DCs as professional antigen cross-presenting DCs in the human.

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Novel insights into the relationships between dendritic cell subsets in human and mouse revealed by genome-wide expression profiling

Scott H. Robbins, Thierry Walzer, Doulaye Dembele … (2008)

Background Dendritic cells (DCs) were initially identified by their unique ability to present antigen for the priming of naïve CD4 and CD8 T lymphocytes [1]. DCs have more recently been shown to be key sentinel immune cells able to sense, and respond to, danger very early in the course of an infection due to their expression of a broad array of pattern recognition receptors [2]. Indeed, DCs have been shown to play a major role in the early production of effector antimicrobial molecules such as interferon (IFN)-α and IFN-β [3] or inducible nitric oxide synthase [4] and it has been demonstrated that DCs can also activate other innate effector cells such as natural killer (NK) cells [5]. In light of these properties, it has been clearly established that DCs are critical for defense against infections, as they are specially suited for the early detection of pathogens, the rapid development of effector functions, and the triggering of downstream responses in other innate and adaptive immune cells. DCs can be divided into several subsets that differ in their tissue distribution, their phenotype, their functions and their ontogeny [6]. Lymph node-resident DCs (LN-DCs) encompass conventional DCs (cDCs) and plasmacytoid DCs (pDCs) in both humans and mice. LN-cDCs can be subdivided into two populations in both mouse (CD8α and CD11b cDCs) [6] and in human (BDCA1 and BDCA3 cDCs) [7]. In mouse, CD8α cDCs express many scavenger receptors and may be especially efficient for cross-presenting antigen to CD8 T cells [8] whereas CD11b cDCs have been suggested [9,10], and recently shown [11], to be specialized in the activation of CD4 T cells. As human cDC functions are generally studied with cells derived in vitro from monocytes or from CD34+ hematopoietic progenitors, which may differ considerably from the naturally occurring DCs present in vivo, much less is known of the eventual functional specialization of human cDC subsets. Due to differences in the markers used for identifying DC subsets between human and mouse and to differences in the expression of pattern recognition receptors between DC subsets, it has been extremely difficult to address whether there are functional equivalences between mouse and human cDC subsets [6]. pDCs, a cell type discovered recently in both human and mouse, appear broadly different from the other DC subsets to the point that their place within the DC family is debated [3]. Some common characteristics between human and mouse pDCs that distinguish them from cDCs [3] include: their ability to produce very large amounts of IFN-α/β upon activation, their limited ability to prime naïve CD4 and CD8 T cells under steady state conditions, and their expression of several genes generally associated with the lymphocyte lineage and not found in cDCs [12]. Several differences have also been reported between human and mouse pDCs, which include the unique ability of mouse pDCs to produce high levels of IL-12 upon triggering of various toll-like receptors (TLRs) or stimulation with viruses [13,14]. Adding to the complexity of accurately classifying pDCs within leukocyte subsets are recent reports describing cell types bearing mixed phenotypic and functional characteristics of NK cells and pDCs in the mouse [15,16]. Collectively, these findings raise the question of how closely related human and mouse pDCs are to one another or to cDCs as compared to other leukocyte populations. Global transcriptomic analysis has recently been shown to be a powerful approach to yield new insights into the biology of specific cellular subsets or tissues through their specific gene expression programs [17-21]. Likewise, genome-wide comparative gene expression profiling between mouse and man has recently been demonstrated as a powerful approach to uncover conserved molecular pathways involved in the development of various cancers [22-27]. However, to the best of our knowledge, this approach has not yet been applied to study normal leukocyte subsets. Moreover, DC subsets have not yet been scrutinized through the prism of gene expression patterns within the context of other leukocyte populations. In this report, we assembled compendia comprising various DC and other leukocyte subtypes, both from mouse and man. Using intra- and inter-species comparisons, we define the common and specific core genetic programs of DC subsets. Results Generation/assembly and validation of the datasets for the gene expression profiling of LN-DC subsets We used pan-genomic Affymetrix Mouse Genome 430 2.0 arrays to generate gene expression profiles of murine splenic CD8α (n = 2) and CD11b (n = 2) cDCs, pDCs (n = 2), B cells (n = 3), NK cells (n = 2), and CD8 T cells (n = 2). To generate a compendium of 18 mouse leukocyte profiles, these data were complemented with published data retrieved from public databases, for conventional CD4 T cells (n = 2) [28] and splenic macrophages (n = 3) [29]. We used Affymetrix Human Genome U133 Plus 2.0 arrays to generate gene expression profiles of blood monocytes, neutrophils, B cells, NK cells, and CD4 or CD8 T cells [30]. These data were complemented with published data on human blood DC subsets (pDCs, BDCA1 cDCs, BDCA3 cDCs, and lin-CD16+HLA-DR+ cells) retrieved from public databases [31]. All of the human samples were done in independent triplicates. Information regarding the original sources and the public accessibility of the datasets analyzed in the paper are given in Table 1. Table 1 Information on the sources and public access for the datasets analyzed in the paper Figures‡ Dataset Population* Laboratory† Public repository Accession number 1a,c; 2a 1b,d; 2b 1e 3 4a 4b 5a 5b Affymetrix Mouse Genome 430 2.0 data Spleen CD8 DCs (2) MD/SCPK GEO [95] GSE9810 X X X X X Spleen CD11b DCs (2) MD/SCPK GEO GSE9810 X X X X X Spleen pDCs (2) MD/SCPK GEO GSE9810 X X X X X Spleen NK cells (2) MD/SCPK GEO GSE9810 X X X Spleen CD8 T cells (2) MD/SCPK GEO GSE9810 X X Spleen B cells (3) MD/SCPK GEO GSE9810 X X X Spleen CD4 T cells (2) AYR GEO GSM44979; GSM44982 X X X Spleen monocytes (3) SB NCI caArray [96] NA X X X Spleen monocytes (2) BP GEO GSM224733; GSM224735 X Peritoneal MΦ (1) SA GEO GSM218300 X BM-MΦ (2) RM GEO GSM177078; GSM177081 X BM-MΦ (1) CK GEO GSM232005 X BM-DCs (2) RM GEO GSM40053; GSM40056 X BM-DCs (2) MH GEO GSM101418; GSM101419 X Affymetrix Mouse U74Av2 data Spleen CD4 T cells (3) CB/DM GEO GSM66901; GSM66902; GSM66903 X Spleen B2 cells (2) CB/DM GEO GSM66913; GSM66914 X Spleen B1 cells (2) CB/DM GEO GSM66915; GSM66916 X Spleen NK cells (2) FT EBI ArrayExpress [97] E-MEXP-354 X Spleen CD4 DCs (2) CRES GEO GSM4697; GSM4707 X Spleen CD8 DCs (2) CRES GEO GSM4708; GSM4709 X Spleen DN DCs (2) CRES GEO GSM4710; GSM4711 X Spleen IKDCs (2) FH GEO GSM85329; GSM85330 X Spleen cDCs (2) FH GEO GSM85331; GSM85332 X Spleen pDCs (2) FH GEO GSM85333; GSM85334 X Affymetrix Human Genome U133 Plus 2.0 data Blood monocytes (3) FRS Authors' webpage [86] NA X X X X Blood CD4 T cells (3) FRS Authors' webpage NA X X X Blood CD8 T cells (3) FRS Authors' webpage NA X X X Blood B cells (3) FRS Authors' webpage NA X X X Blood NK cells (3) FRS Authors' webpage NA X X X Blood neutrophils (3) FRS Authors' webpage NA X X X Blood pDCs (3) CAKB EBI ArrayExpress E-TABM-34 X X X X X Blood BDCA1 DCs (3) CAKB EBI ArrayExpress E-TABM-34 X X X X X Blood BDCA3 DCs (3) CAKB EBI ArrayExpress E-TABM-34 X X X X X Blood CD16 DCs (3) CAKB EBI ArrayExpress E-TABM-34 X X PBMC-derived MΦ (2) SYH GEO GSM109788; GSM109789 X Monocyte-derived MΦ LZH GEO GSM213500 X Monocyte-derived DCs (3) MVD GEO GSM181931; GSM181933; GSM181971 X *The number of replicates is shown in parentheses. †MD/SCPK, M Dalod, S Chan, P Kastner; AYR, AY Rudensky; SB, S Bondada; BP, B Pulendran; SA, S Akira; RM, R Medzhitov; CK, C Kim; MH, M Hikida; CB/DM, C Benoist, D Mathis; FT, F Takei; CRES, C Reis e Sousa; FH, F Housseau; FRS, FR Sharp; CAKB, CAK Borrebaeck; SYH, S Yla-Herttuala; LZH, L Ziegler-Heitbrock; MVD, MV Dhodapkar. ‡Shown in the indicated figure in this study. BM-DC, mouse bone-marrow derived GM-CSF DCs; BM-MΦ, mouse bone marrow-derived M-CSF macrophages; monocyte-derived MΦ, monocyte-derived M-CSF macrophages; NA, not applicable; PBMC-derived MΦ, human peripheral blood mononuclear cell-derived M-CSF macrophages; peritoneal MΦ, peritoneal mouse macrophages. To verify the quality of the datasets mentioned above, we analyzed signal intensities for control genes whose expression profiles are well documented across the cell populations under consideration. Expression of signature markers were confirmed to be detected only in each corresponding population (see Table 2 for mouse data and Table 3 for human data). For example, Cd3 genes were detected primarily in T cells and often to a lower extent in NK cells; the mouse Klrb1c (nk1.1) gene or the human KIR genes in NK cells; Cd19 in B cells; the mouse Siglech and Bst2 genes or the human LILRA4 (ILT7) and IL3RA (CD123) genes in pDCs; and Cd14 in myeloid cells. As expected, many markers were expressed in more than a single cell population. For example, in the mouse, Itgax (Cd11c) was found expressed to high levels in NK cells and all DC subsets; Itgam (Cd11b) in myeloid cells, NK cells, and CD11b cDCs; Ly6c at the highest level in pDCs but also strongly in many other leukocyte populations; and Cd8a in pDCs and CD8α cDCs. However, the analysis of combinations of these markers confirmed the lack of detectable cross-contaminations between DC subsets: only pDCs expressed high levels of Klra17 (Ly49q) and Ly6c together, while Cd8a, ly75 (Dec205, Cd205), and Tlr3 were expressed together at high levels only in CD8α cDCs, and Itgam (Cd11b) with Tlr1 and high levels of Itgax (Cd11c) only in CD11b cDCs. Thus, each cell sample studied harbors the expected pattern of expression of control genes and our data will truly reflect the gene expression profile of each population analyzed, without any detectable cross-contamination. Table 2 Expression of control genes in mouse cells Dendritic cells Lymphocytes Probe set ID Gene Myeloid cells pDC CD8α DC CD11b DC NK CD8 T CD4 T B 1419178_at Cd3g 40 ± 10 4 3-4 2-3 1-2 0,4-1 pDC Pacsin1; Sla2; 2210020M01Rik - Epha2; Sh3bgr; Ets1; Cobll1; Blnk; Myb; Sit1; Zfp521; Nucb2; Igj; Stambpl1; Ptprcap; Spib; Glcci1; Syne2; Ahi1; Atp13a2; Tcf4; Lair1 Runx2; LOC637870; Hs3st1; Asph; L3mbtl3; Tex2; Nrp1; Npc1; Maged1; Tm7sf2; Igh-6 ; Csf2rb2; Ccr2; Cdk5r1; Fcrla; Rnasel; Arid3a; Rassf8; Tgfbr3; Tlr7 ; Trp53i11; Ltb4dh; Arhgap24; Creb3l2; Itpr2; Bcl11a; Usp11; Gpm6b; Snx9; Hivep1; Irf7 ; Cnp1; Cybasc3; Pcyox1; Aacs Ifnar2; Ugcg; Kmo; Tspan31; Xbp1; Alg2; Txndc5; Abca5; Carhsp1; Ptp4a3; Lypla3; Cxxc5; Sema4c; Vamp1; Klhl9; BC031353; Cybb; Scarb2; Card11; Cdkn2d; 4931406C07Rik; Gimap8; Plxdc1; Lman1; 4631426J05Rik; Tcta; Mgat5; Ern1; Atp8b2; Lrrc16; Cln5; Rexo2; Atp2a3; Tspyl4; Anks3; Slc23a2; Gata2; Trp53i13; Slc44a2; Tmem63a; Dnajc7; Rhoh; Daam1; Lancl1; Aff3; Chst12; Unc5cl; Rwdd2; Armcx3; Vps13a; Mcoln2; Tm7sf3; Stch; Glt8d1; Pscd4; Ormdl3; 1110028C15Rik; Snag1; Prkcbp1; Klhl6; Cbx4; Pcmtd1; Bet1; Ccs; Tceal8; Dpy19l3; Pcnx; LOC672274; Sec11l3; Ctsb; Slc38a1; Ostm1; Acad11; Zbtb20; 1110032A03Rik; Ralgps2; Dtx3; Pls3; Ptprs; Zdhhc8; Rdh11; Bcl7a; Tbc1d2b cDC - 9130211I03Rik; Hnrpll; Fgl2; Id2; Slamf8 Chn2; Ddef1; Havcr2; A530088I07Rik; Rab32; Adam8; 2610034B18Rik; Dusp2; Btbd4; Pak1; Bzrap1; Anpep; Apob48r; Aif1 Arrb1; H2-Ob ; Arhgap22; Aytl1; 2810417H13Rik; Pik3cb; Nav1; Acp2; Tnfaip2; Tspan33; Ralb; Marcks; Epb4.1l2; Rab31; Aim1; Cias1; Cd86 ; Cdca7; Rin3; Hk2; Actn1; Snx8; Cd1d1; Cxcl9; Sestd1; Anxa1; Il15; Ahr; Myo1f; Avpi1; Pde8a; Stom; Spint1; Kit; 1100001H23Rik; Specc1; Bcl6; Tpi1; Kcnk6; Efhd2; Cxcl16; Ddb2; C2ta ; Tgif; Pfkfb3; Ptpn12; Pitpnm1; Rtn1; Maff; Sgk; BB220380; Tes; Elmo1; Tm6sf1; Mast2; Stx11; Dhrs3; Tlr2 Il18; Vasp; Ppfibp2; Itfg3; Wdfy3; Atad2; Hck; Cnn2; BC039210; Lima1; Fhod1; Klhl5; Flna; Egr1; Mrps27; Gas2l3; Atp2b1; Gypc; Lst1; 8430427H17Rik; Lmnb1; Junb; Irf2; Soat1; Cd83 ; Spg21; Nab2; Rbpsuh; Tiam1; Spfh1; Gemin6; Entpd1; Lzp-s; Lyzs; Slc8a1; Dusp16; Plscr1; Ptcd2; Slc19a2; Mthfd1l; Copg2; Dym; Limd2; Bag3; Csrp1; Ppa1; Nr4a2; Snx10; Hmgb3; Plekhq1; Oat; Rgs12; Numb; Hars2; Pacs1; Gtdc1; Ezh2; Swap70; Rasgrp4; Asahl; Susd3; Lrrk2; Sec14l1; Asb2; Txnrd2; E330036I19Rik; Sla; Fscn1; Nr4a1; Inpp1; Tdrd7; 4933406E20Rik; Usp6nl mCD8 and hBDCA3 - Clnk Gcet2; BC028528; Igsf4a sept3; Sema4f; Fkbp1b; Tlr3 ; Lima1; Dbn1; Plekha5; Fuca1; Fgd6; Snx22; Gfod1 Rasgrp3; Btla; Asahl; 4930506M07Rik; Lrrc1; 1700025G04Rik; Tspan33; Fnbp1; Itga6; Zbed3; 9030625A04Rik; Rab32; Ptcd2; Gas2l3; Rab11a; Ptplb; Cbr3; Pqlc2; Slamf8; St3gal5; 4930431B09Rik; Dock7; Stx3; Csrp1; Nbeal2; Gnpnat1; Slc9a9; Ncoa7 mCD11b and hBDCA1 - - Il1rn; Papss2; Pram1 Il1r2; Oas3; Rin2; Ptgs2; Csf1r; Tlr5; Centa1; Pygl; Igsf6; Csf3r; Tesc; Ncf2; S100a4; Rtn1; Cst7; Car2; Ifitm1; 1810033B17Rik; Lrp1; Dennd3; Ifitm3 Gbp2; Oas2; Ccl5; Pilra; Sirpa; Pla2g7; Ifitm2; Ms4a7; Cdcp1; Nfam1; BC013672; Slc7a7; Ripk2; Map3k3; Ripk5; Lactb; Rsad2; Parp14; D930015E06Rik; Gyk; Ank; Atp8b4; Emilin2; Arrdc2; Slc16a3; Fcgr3; Clec4a2; Ksr1; Itgax; Sqrdl; Hdac4; Rel; Pou2f2; Chka; Lyst; Ubxd5; Jak2; Cd300a; Lst1; Ssh1; Casp1; D12Ertd553e; Ogfrl1; Rin3; Cd302; Pira2 *Ratio expressed as Minimum expression among the cell types selected/Maximum expression among all other cell types. Genes already known to be preferentially expressed in the cell types selected are shown in boldface. The functional annotations associated with the genes selectively expressed in specific DC subsets when compared to the others are listed in Table 7. The most significant clusters of functional annotations in pDCs point to the specific expression in these cells of many genes expressed at the cell surface or in intracellular compartments, including the endoplasmic reticulum, the Golgi stack, and the lysosome. A cluster of genes involved in endocytosis/vesicle-mediated transport is also observed. This suggests that pDCs have developed an exquisitely complex set of molecules to sense, and interact with, their environment and to regulate the intracellular trafficking of endocytosed molecules, which may be consistent with the recent reports describing different intracellular localization and retention time of endocytosed CpG oligonucleotides in pDCs compared to cDCs [62,63]. The most significant clusters of functional annotations in cDCs concerns the response to pest, pathogens or parasites and the activation of lymphocytes, which include genes encoding TLR2, costimulatory molecules (CD83, CD86), proinflammatory cytokines (IL15, IL18), and chemokines (CXCL9, CXCL16), consistent with the specialization of cDCs in T cell priming and recruitment. Clusters of genes involved in inflammatory responses are found in both pDCs and cDCs. However, their precise analysis highlights the differences in the class of pathogens recognized, and in the nature of the cytokines produced, by these two cell types: IFN-α/β production in response to viruses by pDCs through mechanisms involving IRF7 and eventually TLR7; and recognition and killing of bacteria and production of IL15 or IL18 by cDCs through mechanisms eventually involving TLR2 or lysozymes. Many genes selectively expressed in cDCs are involved in cell organization and biogenesis, cell motility, or cytoskeleton/actin binding, consistent with the particular morphology of DCs linked to the development of a high membrane surface for sampling of their antigenic environment and for the establishment of interactions with lymphocytes. pDCs and cDCs also appear to express different arrays of genes involved in signal transduction/cell communication, transcription regulation and apotosis. A statistically significant association with lupus erythematosus highlights the proposed harmful role of pDCs in this autoimmune disease [64]. Table 7 Selected annotations for the conserved transcriptomic signatures identified for DC subsets when compared to one another Cell type Annotation Genes pDC Endoplasmic reticulum Ern1, Lman1, Txndc5, Rdh11, Tm7sf2, Asph, Ormdl3, Stch, Nucb2, Ugcg, Itpr2, Bet1, Sec11l3, Atp2a3 Golgi stack BET1, HS3ST1, CHST12, SNAG1, LMAN1, MGAT5, GLCCI1, Pacsin1 Lysosome Lypla3, Npc1, Scarb2, Ctsb, Pcyox1, Cln5 Endocytosis/vesicle-mediated transport Bet1; Gata2; Igh-6; Lman1; Npc1; Pacsin1; Vamp1 Integral to plasma membrane EPHA2, SCARB2, CSF2RB, SIT1, ATP2A3, IFNAR2, VAMP1, PTPRS, SLC23A2, PTPRCAP, LANCL1, TM7SF2, CCR2, TSPAN31 Inflammatory response TLR7, CYBB, IRF7, CCR2, BLNK Intracellular signaling cascade/I-κB kinase/NF-κB cascade SNAG1, SLC44A2, TMEPAI, CARD11, ERN1, SLA2, IFNAR2, CARHSP1, SNX9, RALGPS2, CXXC5, CCR2, BLNK, RHOH Regulation of transcription, DNA-dependent/DNA binding/transcription regulator activity/RNA polymerase II transcription factor activity/IPR004827: Basic-leucine zipper (bzip) transcription factor 1110028C15Rik; Aff3; Anks3; Arid3a; Bcl11a; Carhsp1; Cbx4; Cdkn2d; Creb3l2; Cxxc5; Ern1; Ets1; Gata2; Hivep1; Ifnar2; Irf7; Maged1; Myb; Nucb2; Prkcbp1; Runx2; Sla2; Spib; Tcf4; Tspyl4; Xbp1; Zbtb20 Systemic lupus erythematosus LMAN1, CCR2, ETS1 Regulation of apoptosis CDK5R1, CARD11, ERN1, CBX4, TXNDC5, CTSB cDC Response to pest, pathogen or parasite/defense response/immune response/response to stress/inflammatory response/cytokine biosynthesis/response to bacteria/lymphocyte activation ANXA1; NR4A2; CIAS1; TLR2; CD83; CD86; IL18; CXCL16; MAST2; AIF1; CIITA; SNFT; Lzp-s, Lyzs; ENTPD1; CXCL9; PLSCR1; BCL6; SGK; TXNRD2; DDB2; AHR; IRF2; LST1; SOAT1; HLA-DOB; CD1D; IL15; Rbpsuh; Swap70; Hmgb3; Egr1 Cytoskeleton/actin binding/filopodium/cell motility FLNA; FHOD1; CNN2; MYO1F; ACTN1; VASP; EPB41L2; FSCN1; KLHL5; MARCKS; Epb4,1l2; Mast2; Aif1; Csrp1; Elmo1; LIMA1; LMNB1; STOM; Nav1, CXCL16, ANXA1 Morphogenesis/cell organization and biogenesis/neurogenesis Rasgrp4; Myo1f; Aif1; Pak1; Pacs1; Vasp; Tiam1; Lst1; Cnn2; Numb; Csrp1; Fhod1; Nav1; Rab32; Stx11; Ezh2; Epb4,1l2; Flna; Acp2; Elmo1; Ralb; Rab31; Id2; Tnfaip2; Txnrd2; Anpep; Il18; Rbpsuh, Nr4a2; Spint1 Signal transduction/cell communication/MMU04010:MAPK signaling pathway/regulation of MAPK activity/GTPase regulator activity/small GTPase mediated signal transduction/IPR003579:Ras small GTPase, Rab type ADAM8; AHR; ANXA1; ARRB1; Asb2; Avpi1; CD83; CD86; Chn2; CIAS1; CXCL9; Dusp16; DUSP2; Elmo1; ENTPD1; FLNA; Hck; IL15; IL18; INPP1; Kit; Lrrk2; Mast2; NR4A1; NR4A2; PAK1; PDE8A; PIK3CB; PPFIBP2; Rab31; Rab32; Ralb; Rasgrp4; RBPSUH; RGS12; Rin3; RTN1; Sla; SLC8A1; Snx10; Snx8; Tiam1; TLR2; Arhgap22; Ddef1; Rgs12; Usp6nl Transcription regulator activity Junb, Id2, Asb2, Ddef1, Irf2, Nr4a2, C2ta, Nab2, Egr1, Nr4a1, Ahr, 9130211I03Rik, Tgif, Rbpsuh, Bcl6 Apoptosis Ahr, Nr4a1, Il18, Bag3, Cias1, Elmo1, Cd1d1, Sgk, Bcl6 mCD8 and hBDCA3 Cell organization and biogenesis DBN1, RAB32, ITGA6, FGD6, RAB11A, SEMA4F Intracellular signaling cascade/small GTPase mediated signal MIST, TLR3, SNX22; DOCK7; FGD6; RAB11A; RAB32; RASGRP3; sep3 mCD11b and hBDCA1 Immune response/defense response/inflammatory response/positive regulation of cytokine production/response to pest, pathogen or parasite/antimicrobial humoral response/IPR006117:2-5-oligoadenylate synthetase IFITM3, PTGS2, POU2F2, LST1, GBP2, CCL5, OAS2, FCGR2A, NCF2, CSF1R, TLR5, CSF3R, IL1R2, CST7, IL1RN, NFAM1, IFITM2, IFITM1, LILRB2, OAS3, LYST, CLEC4A, IGSF6, HDAC4, PLA2G7, RIPK2, OAS2, OAS3; Rel; Fcgr3 Signal transduction/cell communication/signal transducer activity/positive regulation of I-κB kinase/NF-κB cascade/protein-tyrosine kinase activity/IPR003123:Vacuolar sorting protein 9; vesicle-mediated transport; endocytosis CASP1; CCL5; CD300A; CD302; CENTA1; CHKA; CLEC4A; CSF1R; CSF3R; FCGR2A; IFITM1; IGSF6; IL1R2; IL1RN; ITGAX; JAK2; KSR1; LILRB2; LRP1; LYST; MAP3K3; MS4A7; NFAM1; OGFRL1; REL; RIN2; RIN3; RIPK2; RIPK5; RTN1; TLR5; Fcgr3 Chemotaxis/cell adhesion ITGAX, CD300A, CSF3R, EMILIN2, CLEC4A, CCL5, Fcgr3 HSA04640:hematopoietic cell lineage CSF1R, CSF3R, IL1R2 Asthma. Atopy PLA2G7, CCL5, The mCD11b/hBDCA1 cDC cluster of genes comprises many genes involved in inflammatory responses and the positive regulation of the I-kappaB kinase/NF-kappaB cascade. A statistically significant association with asthma also highlights the proinflammatory potential of this cell type. Recently, it has been reported that the mouse CD11b cDC subset is specialized in MHC class II mediated antigen presentation in vivo [11]. In support of our findings here that mouse CD11b cDCs are equivalent to human BDCA1 cDCs, we found that many of the genes involved in the MHC class II antigen presentation pathway that were reported to be expressed to higher levels in mouse CD11b cDCs over CD8α cDCs [11] are also preferentially expressed in the human BDCA1 cDC subset over the BDCA3 one. These genes include five members of the cathepsin family (Ctsb, Ctsd, Ctsh, Ctss, and Ctsw) as well as Ifi30 and Lamp1 and Lamp2 (see Additional data file 2 for expression values). Thus, it is possible that, like the mouse CD11b cDC subset, human BDCA1 cDCs serve as a subset of DCs that are specialized in presenting antigen via MHC class II molecules. It is also noteworthy that mCD11b and hBDCA1 cDCs express high constitutive levels of genes that are known to be induced by IFN-α/β and that can contribute to cellular antiviral defense (Oas2, Oas3, Ifitm1, Ifitm2, Ifitm3). No significant informative functional annotations are found for the mCD8α/hBDCA3 cDC gene cluster. However, groups of genes involved in cell organization and biogenesis or in small GTPase regulator activity are found and the study of these genes may increase our understanding of the specific functions of these cells. Mouse CD8α cDCs have been proposed to be specialized for a default tolerogenic function but to be endowed with the unique ability to cross-present antigen for the activation of naïve CD8 T cells within the context of viral infection [65]. It will be important to determine whether this is also the case for hBDCA3 cDCs. From this point of view, it is noteworthy that hBDCA3 cDCs selectively express TLR3, lack TLR7 and TLR9, and exhibit the highest ratio of IRF8 (ICSBP)/TYROBP (DAP12) expression, all of which have been shown to participate in the regulation of the balance between tolerance and cross-presentation by mouse CD8α cDCs [65,66]. Use of leukocyte gene expression compendia to classify cell types of ambiguous phenotype or function Interferon-producing killer dendritic cells A novel cell type has been recently reported in the mouse that presents mixed phenotypic and functional characteristics of pDCs and NK cells, IKDCs [15,16]. A strong genetic relationship between IKDCs and other DC populations was suggested. However, this analysis was based solely on comparison of the transcriptional profile of IKDCs to DCs and not to other cell populations [15]. As IKDCs were also reported to be endowed with antigen presentation capabilities [15] and to be present in mice deficient for the expression of RAG2 and the common γ chain of the cytokine receptors [16], they have been proposed to belong to the DC family rather than to be a subset of NK cells in a particular state of differentiation or activation. However, IKDCs have been reported to express many mRNA specific for NK cells and many of their phenotypic characteristics that were claimed to discriminate IKDCs from NK cells [16] are in fact consistent with classical NK cell features as recently reviewed [67], including the expression of B220 [68] and CD11c [69,70] (BD/Pharmingen technical datasheet of the CD11c antibody) [71]. To clarify the genetic nature of IKDCs, we reanalyzed the published gene chip data on the comparison of these cells with other DC subsets [15], together with available datasets on other leukocyte populations. We thus assembled published data generated on the same type of microarrays (Affymetrix U74Av2 chips) to build a second mouse compendium, allowing us to compare the transcriptomic profile published for the IKDCs (n = 2) with that of pDCs (n = 2), cDCs (n = 2) [15], CD8α+ (n = 2), CD4+ (n = 2) or double-negative (n = 2) cDC subsets [56], NK cells [72], CD4 T cells (n = 2), and B1 (n = 2) and B2 (n = 2) cells [18]. Information regarding the original sources and the public accessibility of the corresponding datasets are given in Table 1. As depicted in Figure 4a, the hierarchical clustering with complete linkage results of these data sets, together with our novel 430 2.0 data, clearly show that IKDCs cluster with NK cells, close to other lymphocytes, and not with DCs. Indeed, IKDCs express the conserved genetic signature of NK cells but not of DCs (Table 8 and Additional data file 4). Thus, these results strongly support the hypothesis that the cells described as IKDCs feature a specific subset of mouse NK cells that are in a particular differentiation or activation status, rather than a new DC subset. Figure 4 Clustering of mouse IKDCs and human CD16 cells. Hierarchical clustering with complete linkage was performed on the indicated cell populations isolated from: (a) mouse and (b) human. Mono, monocytes; neu, neutrophils. Table 8 Expression of APC, DC and NK signature genes in IKDCs Ratio Probe set ID Gene CD8 DC DN DC CD4 DC pDC cDC IKDC NK IKDC/DC NK/DC IKDC/NK APC signature genes 98035_g_at H2-DMb1 2,701* 3,416 4,281 1,105 2,722 179 36 0.2 1.7 >1.5 >4.7 1552256_a_at SCARB1 151 8.98 6.58 7.21 - 2.33 1.70 5.30 cDC signature genes 206298_at ARHGAP22 - >5.8 >6.5 >3.1 - >6.2 >4.6 - 227329_at BTBD4 - >1.6 >2.8 >5.8 - >9.3 >8.7 - 219386_s_at SLAMF8 98 24.75 38.66 23.99 0.51 15.48 5.30 0.51 220358_at SNFT 148 0.62 0.34 8.62 5.66 16.01 4.82 0.34 224772_at NAV1 64 2.01 3.25 1.40 2.00 23.87 10.50 1.62 205101_at CIITA 481 0.29 0.12 1.09 0.48 4.51 5.28 1.43 218631_at AVPI1 - >18.7 >31.3 >64.8 >1.6 >3.2 >7.0 - 202826_at SPINT1 84 4.65 7.15 8.79 0.90 2.59 2.92 0.68 208660_at CS 1,848 1.24 0.99 1.04 0.84 1.70 1.63 0.89 APC signature genes 203932_at HLA-DMB 5,137 1.28 0.64 1.37 0.44 1.45 1.62 1.14 219574_at MARCH1 1,133 0.42 0.89 0.73 0.62 0.64 0.44 0.46 201425_at ALDH2 8,782 0.51 0.54 0.34 0.18 0.84 1.01 0.69 222891_s_at BCL11A 310 0.98 0.34 0.50 0.74 2.40 1.73 14.23 205504_at BTK 1,372 0.29 0.47 0.64 1.13 0.58 0.75 0.81 202295_s_at CTSH 3,755 1.76 2.37 2.09 0.56 0.63 1.57 0.31 209312_x_at HLA-DRB1 12,737 1.02 0.57 1.34 1.11 1.12 1.11 1.00 209619_at CD74 8,540 1.49 0.86 2.12 0.73 1.33 1.34 1.11 210042_s_at CTSZ 369 0.76 1.13 17.00 1.66 2.13 2.17 1.83 201560_at CLIC4 2,828 0.87 0.88 1.00 0.12 0.10 0.28 0.22 217388_s_at KYNU 3,429 1.50 1.95 0.90 0.94 0.30 0.65 0.63 217118_s_at C22orf9 1,617 3.33 3.46 2.77 1.43 1.85 1.79 1.04 203927_at NFKBIE 173 3.30 9.96 3.13 1.45 1.39 2.60 1.25 220998_s_at UNC93B1 847 0.60 1.31 0.97 1.31 0.99 1.06 2.27 Non-DC signature genes 219243_at GIMAP4 4,384 0.15 0.11 0.19 0.27 - - - 221704_s_at VPS37B 559 0.26 0.90 0.47 0.80 - - - 204891_s_at LCK 96 1.48 0.52 0.52 0.59 - - - 214582_at PDE3B 144 2.82 2.99 2.43 0.76 - 0.51 - *Average expression across replicates. Genes for which expression between monocyte-derived DCs and blood DCs or blood cDCs varies more than two-fold are shown in bold. mo-DC, monocyte-derived GM-CSF DC; mo-MΦ, monocyte-derived M-CSF macrophages; mono, monocyte; PBMC-MΦ, human peripheral blood mononuclear cell-derived M-CSF macrophages. Table 11 Comparison of the transcriptome of mouse GM-CSF BM-derived DCs to that of spleen DCs Ratio to monocytes Probe set ID Name Mono Mono(2) MΦ BM-MΦ BM-DC pDC CD8 DC CD11b DC Myeloid signature genes 1420804_s_at Clec4d 4,934 0.65 0.49 0.75 0.41 - - - 1420330_at Clec4e 5,511 0.11 0.22 0.23 0.11 - - - 1450808_at Fpr1 119 1.91 - 5.55 2.41 - - - 1452001_at Nfe2 139 1.44 - - 3.31 - - - 1450919_at Mpp1 1,888 0.15 2.05 1.75 0.52 0.23 0.09 0.07 1419609_at Ccr1 403 1.27 4.04 0.53 3.98 0.2 - - 1417061_at Slc40a1 2,588 0.68 - 0.56 0.07 0.01 0.01 0.02 1448756_at S100a9 8,664 1.2 - 0.01 0.99 0 0 - 1417268_at Cd14 6,745 0.1 0.3 0.6 0.19 0.02 0.01 0.01 1418163_at Tlr4 464 0.1 0.36 0.93 0.66 - 0.07 0.06 1448620_at Fcgr3 1,471 2.02 3.56 2.15 2.46 - 0.02 0.07 1422953_at Fpr-rs2 839 2.04 0.12 0.85 1.58 - - 0.05 1419132_at Tlr2 1,763 0.11 0.42 0.24 0.48 0.04 0.1 0.14 1417566_at Abhd5 170 0.19 0.72 0.86 2.2 0.18 0.45 0.25 1415814_at Atp6v1b2 1,556 0.22 2.75 1.57 1.43 0.18 0.27 0.24 1427327_at Pilra 434 1.53 0.16 0.47 2.29 0.1 - 0.21 1418888_a_at Sepx1 4,416 0.48 0.34 0.31 0.56 0.03 0.04 0.05 1438928_x_at Ninj1 5,574 0.03 1.3 0.46 0.36 0.03 0.02 0.02 1448881_at Hp 400 3.19 0.14 0.06 3.09 - - - 1449453_at Bst1 340 1.08 4.97 0.58 1.61 0.21 0.51 - 1419394_s_at S100a8 10,190 1.37 0.01 0.01 0.66 - 0 - 1437200_at Fcho2 311 1.09 1.32 1.06 0.76 0.28 0.2 0.33 1418806_at Csf3r 2,598 0.2 0.14 0.19 0.11 - - 0.03 1439902_at C5ar1 317 8.21 0.19 1.63 0.37 - - - 1456046_at Cd93 1,559 0.1 0.49 1.18 0.33 0.02 - - 1418901_at Cebpb 3,797 0.14 0.7 0.22 0.42 0.02 0.01 0.02 1420699_at Clec7a 2,748 0.83 2.62 0.44 1.71 0.08 0.06 0.54 Pan-DC signature genes 1419538_at Flt3 51 0.74 - - 0.7 16.2 25.32 17.78 1427619_a_at Sh3tc1 - >1.1 >6.8 >2.8 >4.9 >5.2 >6.5 >4.6 1424489_a_at Trit1 54 7.28 0.44 0.76 1.23 9.03 11.53 8.63 1428744_s_at Bri3bp 161 0.84 0.6 1.44 3.28 6.09 7.24 5.98 1448923_at Prkra 72 1.28 0.77 2.89 2.57 4.45 7.88 3.63 1434880_at Etv6 140 5.39 1.52 0.74 1.75 5.79 6.02 7.78 1416108_a_at Tmed3 154 0.81 3.74 2.63 4.65 10.17 4.48 3 1436633_at Bahcc1 41 1.77 - 0.83 - 1.8 3.88 2.35 1437378_x_at Scarb1 97 5.02 1.25 2.61 3.17 7.41 8.27 4.05 cDC signature genes 1435108_at Arhgap22 63 - - 2.37 0.57 0.59 10.65 4.43 1429168_at Btbd4 129 0.19 0.27 - 0.47 0.81 3.89 3.8 1425294_at Slamf8 146 1.06 39.89 1.83 1.77 0.39 8.48 5.27 1453076_at 9130211I03Rik 36 1.61 2.85 1.03 13.11 0.62 30.94 25.64 1436907_at Nav1 102 1.59 0.74 2.63 1.96 1.21 6.08 13.14 1421210_at C2ta 125 0.17 1.79 0.19 0.93 1.46 5.94 5.43 1423122_at Avpi1 150 0.32 - 0.2 0.86 0.61 2.47 7.62 1416627_at Spint1 - >1.5 >1.1 - >22.9 >1.6 >25.7 >30.6 1450667_a_at Cs 396 2.47 0.9 1.19 3.54 2.83 4.64 4.5 APC signature genes 1419744_at H2-DMb2 451 0.12 0.1 0.08 1.47 0.45 0.48 1.69 1443687_x_at H2-DMb1 547 0.56 0.13 0.11 1.56 1.06 0.82 3.13 1434955_at March1 80 32.64 0.83 1.51 3.48 3.73 13.4 8.57 1448143_at Aldh2 867 0.47 2.14 2.07 1.32 0.95 0.65 0.45 1419406_a_at Bcl11a 60 1.47 0.34 - 0.71 20.41 7.63 9.19 1422755_at Btk 416 0.56 0.76 1.3 1.15 0.88 1.45 1.17 1418365_at Ctsh 1,393 0.81 3.9 2.19 2.15 3.69 1.24 2.16 1417025_at H2-Eb1 6,385 0.13 0.39 0.04 0.8 0.9 1.31 1.33 1425519_a_at Cd74 8,377 0.36 0.95 0.2 0.9 0.83 0.97 0.98 1417868_a_at Ctsz 7,061 0.05 1.16 0.95 0.85 0.5 0.3 0.49 1423393_at Clic4 2,807 0.07 2.04 0.84 0.57 0.69 0.72 0.67 1430570_at Kynu 31 1.23 - - 3.21 12.87 5.16 11.56 1435745_at 5031439G07Rik 356 0.95 0.73 2.76 2.51 3.23 3.14 4.28 1458299_s_at Nfkbie 767 0.4 0.62 0.1 0.44 1.25 0.65 1.27 1423768_at Unc93b1 663 0.1 2.27 2.69 1.46 1.2 0.93 0.91 Non-DC signature genes 1424375_s_at Gimap4 362 0.14 0.29 - 0.1 0.11 - 0.11 1424380_at Vps37b 313 0.44 0.46 0.45 0.26 0.28 0.28 0.27 1425396_a_at Lck 118 - 0.57 0.2 0.32 0.21 - 0.17 1433694_at Pde3b 352 0.69 0.15 0.16 0.42 - 0.65 0.35 *Average expression across replicates. Genes for which expression between mouse bone-marrow derived GM-CSF DCs (BM-DCs) and spleen DCs or spleen cDCs varies more than two-fold are shown in bold. BM-MΦ, mouse bone marrow-derived M-CSF macrophages; MΦ, peritoneal mouse macrophages; mono, mouse spleen monocytes from the SB laboratory; mono(2), mouse spleen monocytes from the BP laboratory, as listed in Table 1. Figure 5 Clustering of in vitro GM-CSF derived DCs with monocytes, macrophages and LN-resident DCs. Hierarchical clustering with complete linkage was performed on the indicated cell populations isolated from: (a) mouse, (b) human, and (c) both. The heat maps used for illustration were selected as the two clusters of genes encompassing either Flt3 or Mafb, with a correlation cut-off for similarity of gene expression within each cluster at 0.8. Discussion By performing meta-analyses of various datasets describing global gene expression of mouse spleen and human blood leukocyte subsets, we have been able to identify for the first time conserved genetic programs common to human and mouse LN-DC subsets. All the LN-DC subsets examined here are shown to share selective expression of several genes, while harboring only low levels of other transcripts present in all other leukocytes. These analyses indicate that LN-DCs, including pDCs, constitute a specific family of leukocytes, distinct from those of classic lymphoid or myeloid cells. Furthermore, we demonstrate a striking genetic proximity between mouse and human pDCs, which are shown for the first time to harbor a very distinct transcriptional signature as large and specific as that observed for NK cells or T cells. In contrast, a higher genetic distance is observed between mouse and human conventional DC subsets, although a partial functional equivalence is suggested between mCD8α and hBDCA3 cDCs on the one hand versus mCD11b and hBDCA1 cDCs on the other hand. Our finding that LN-DCs constitute a distinct entity within immune cells raises the question of whether these cells form a distinct lineage in terms of ontogeny, or whether their shared gene expression profile (notably that between cDCs and pDCs) reflects a functional rather than a developmental similarity. To date, the place of both cDCs and pDCs in the hematopoietic tree is not clear [78,79]. A BM progenitor, named macrophage and dendritic cell progenitor (MDP), has been recently identified that specifically gives rise to monocytes/macrophages and to cDCs, but not to polymorphonuclear cells or to lymphoïd cells [80,81]. Under the experimental conditions used in the corresponding report, pDCs were not detected in the progeny of MDPs. Here, we show that the transcriptome programs of mouse spleen and human blood cDCs exhibit only a very limited overlap with that of monocytes/macrophages (Figure 2). This is consistent with the recent observation that monocytes can give rise to mucosal, but not splenic, cDCs, suggesting that splenic cDCs develop from MDPs without a monocytic intermediate [81]. While mouse pDCs have been argued to arise from both lymphoid or myeloid progenitors, their gene expression overlaps with lymphoid or myeloid cells are limited. Interestingly, a murine progenitor cell line that exhibits both cDC and pDC differentiation potential has been described recently [82], suggesting that putative pan-DC progenitors might also exist in vivo, which would be consistent with the gene profiling analyses presented here. Our study identifies transcriptional signatures conserved between mouse and human, common to all LN-DC subsets examined, or specific to pDCs, cDCs, or individual cDC subsets. A genetic equivalence is suggested between mouse CD8α cDCs and human BDCA3 cDCs, and between mouse CD11b cDCs and human BDCA1 cDCs. In contrast to the genes selectively expressed in subsets of myeloid or lymphoid cells in a conserved manner between mouse and human, most of the genes specifically increased in all LN-DC subsets or in individual LN-DC subsets are currently uncharacterized. As a consequence, the functional annotations of the LN-DC transcriptional signatures appear much less informative than those for myeloid cells, lymphocytes or APCs. This highlights how much has already been deciphered regarding the molecular regulation of antigen presentation or lymphocyte biology, as opposed to how little we know about the genetic programs that determine the specific features of LN-DCs. We believe that our study provides a unique database resource for future investigation of the evolutionarily conserved molecular pathways governing specific aspects of the ontogeny and functions of leukocyte subsets, especially DCs. It should be noted that many genes are found to be expressed to very high levels in specific subsets of either mouse or man while no orthologous gene has been identified in the other species. This could be due to a true absence of orthologous genes between these two vertebrate species, or to a lack of identification of an existing orthology relationship. It is also possible that some of the genes expressed only in mouse DCs or only in human DCs, and not conserved between the two species, might represent functional homologs, similar to what is observed for human KIR and mouse Ly49 NK cell receptors. This may be the case for the human LILRA4 (ILT7) and the mouse SIGLECH molecules, as both of them signal through immunoreceptor tyrosine-based activation motif (ITAM)-bearing adaptors to downmodulate IFN-α/β production by human and mouse pDCs, respectively, upon triggering of TLRs [83,84]. Thus, understanding the role in LN-DCs of genes identified here only in mouse or human might be important. The transcriptional signatures identified for mouse LN-DC subsets in this study have been confirmed by analyses of independent data recently published by others on mouse cDC subsets, B cells and T cells [11] or on cDCs and pDCs [15]. Most of the data for the mouse 430 2.0 compendium were generated in-house, with the exceptions being CD4 T cells and myeloid cells. In humans, we generated the data for non-DC populations, whereas data for DC subsets and CD16 cells were all generated by another group and retrieved from a public database. It is well known that datasets for the same cell type can vary considerably between laboratories. However, many of the genes identified as specific for each mouse LN-DC subset using our own data were confirmed by the analysis of other data independently generated by the groups of M Nussenzweig and R Steinman [11]. These data are given in Additional data file 5. Our clustering analyses and PCA also showed relatively little dataset-dependent biases, and generally grouped related cell populations together, even if they were from different origins (see, for instance, the PCA clustering of in vitro derived GM-CSF DC samples, which originated from two independent datasets in Additional data file 6). In addition, we analyzed by real-time PCR the expression profile of 27 genes across mouse leukocyte subsets from biological samples independent of those used in the gene chips analysis. All the results were consistent with the gene chip data (Additional data file 7). We also confirmed specific expression of PACSIN1 in human pDCs at both the mRNA and protein levels (Additional data file 8). Finally, we believe that our approach validates the gene expression profile identified for leukocyte subsets in the strongest way possible, by demonstrating the evolutionary conservation between mouse and human. Indeed, the gene signatures that we describe here are based on genes found specifically expressed in putatively homologous subsets of mouse and human leukocytes compared to several other types of leukocytes. This approach does not rely solely on the use of independent biological samples of similar origin and on different techniques for measurement of the expression of mRNA. It actually shows that orthologous genes share the same specific expression pattern in putatively homologous immune cell subsets from two different species, under conditions where the markers used to purify the human and mouse cell populations, and the probes used to check the expression of the orthologous genes, differ considerably. Thus, we believe that the analyses presented here are extremely robust even though they were, in part, performed by creating compendia regrouping data generated by different laboratories for different cell types. In addition to our discovery of transcriptional signatures specific to all LN-DCs or to LN-DC subsets, we demonstrate that, once identified, the transcriptional signatures of multiple cell types can be effectively used to help determine the nature of newly identified cell types of ambiguous phenotype or functions. In our attempt to appropriately place IKDCs and CD16 cells within the leukocyte family, we used the microarray data from the original reports aimed at characterizing these cells and compared them to the data from several other leukocyte populations. The conclusions of this analysis are in sharp contrast to those originally reported [15,31]. We believe that these opposing conclusions arise from the difference in the contextual framework within which our data and that of the previously mentioned studies were analyzed. Thus, the results of our analysis of the transcriptional signature of both IKDCs and CD16 cells emphasize the need to study the transcriptional signatures of individual cell populations in the context of multiple cell types of various phenotypes and functions. Finally, this approach also allowed us to confirm a very recent report that demonstrated that in vitro derived GM-CSF mouse DCs likely correspond to inflammatory DCs and greatly differ from LN-DCs, based on ontogenic and functional studies [75]. Thus, extrapolation to LN-DCs of the results of the cell biology and functional studies performed with in vitro derived GM-CSF DCs should only be made with extreme caution. Conclusion This study comparing whole genome expression profiling of human and mouse leukocytes has identified for the first time conserved genetic programs common to all LN-DCs or specific to the plasmacytoid versus conventional subsets. In depth studies of these genetic signatures should provide novel insights on the developmental program and the specific functions of LN-DC subsets. The study in the mouse of the novel, cDC-specific genes identified here should accelerate the understanding of the mysteries of the biology of these cells in both mouse and human. This should help to more effectively translate fundamental immunological discoveries in the mouse to applied immunology research aimed at improving human health in multiple disease settings. Materials and methods Sorting of cell subsets Duplicates of pDCs (Lin-CD11c+120G8high), CD8α cDCs (Lin-CD11chighCD8α+120G8-/low), CD11b cDCs (Lin-CD11chighCD11b+120G8-/low) and NK cells (NK1.1+TCRβ-) were sorted during two independent experiments from pooled spleens of untreated C57BL/6 mice. Splenic CD19+ B lymphocytes, CD4 T cells and CD8 T cells were sorted in other independent experiments. Purity of sorted cell populations was over 98% as checked by flow cytometry (not shown). Processing of cell samples for the Affymetrix GeneChip assays RNA was extracted from between 7.5 × 105 and 1.5 × 106 cells for each leukocyte subset with the Qiagen (Courtaboeuf, France) micro RNAeasy kit, yielding between 200 and 700 ng of total RNA for each sample. Quality and absence of genomic DNA contamination were assessed with a Bioanalyser (Agilent, Massy, France). RNA (100 ng) from each sample was used to synthesize probes, using two successive rounds of cRNA amplification with appropriate quality control to ensure full length synthesis according to standard Affymetrix protocols, and hybridized to mouse 430 2.0 chips (Affymetrix, Santa Clara, CA, USA). Raw data were transformed with the Mas5 algorithm, which yields a normalized expression value, and 'absent' and 'present' calls. Target intensity was set to 100 for all chips. Individual analysis of the mouse 430 2.0 or human U133 Plus 2.0 compendia For each compendium, all datasets were normalized with the invariant rank method and only one representative dataset was kept for redundant ProbeSets targeting the same gene. The datasets were further filtered to eliminate genes with similar expression in all samples, by selecting only the genes expressed above 50 (respectively 100) in all the replicates of at least one population for the mouse (respectively human) datasets and whose expression across all samples harbored a coefficient of variation above the median of the coefficient of variation of all ProbeSets. The final dataset consisted of 7,298 (respectively 11,507) ProbeSets for the mouse 430 2.0 compendium (respectively human U133 Plus 2.0), representing individual genes with differential expression between ex vivo isolated cell subsets. The final dataset consisted of 12,857 (respectively 6,724) ProbeSets for the mouse 430 2.0 compendium (respectively human U133 Plus 2.0), representing individual genes with differential expression between LN-DCs, monocytes/macrophages and in vitro derived GM-CSF DCs. These datasets for ex vivo isolated cells are accessible as Excel workbooks in Additional data files 1 and 2. The software Cluster and Treeview were used to classify cell subsets according to the proximity of their gene expression pattern as assessed by hierarchical clustering with complete linkage. We implemented a function in the Matlab software to perform PCA. This function computes the eigenvalues and eigenvectors of the dataset using the correlation matrix. The eigenvalues were then ordered from highest to lowest, indicating their relative contribution to the structure of the data. For both mouse and human datasets, the first principal component accounted for most of the information (54% and 68% for mouse and human, respectively) and was associated with a similar coordinate for all samples. This component thus reflected the common gene expression among the samples. Second and third components together represented 24% and 21%, respectively, of the information for mouse and human datasets, and thus accounted for a large part of the variability. The projection of each sample on the planes defined by these components was represented as a dot plot to generate the PCA figures. Partitional clustering was performed using the FCM algorithm, which links each gene to all clusters via a vector of membership indexes, each comprised between 0 and 1 [34]. For both mouse and human datasets, we heuristically set the number of clusters to 30, and the fuzziness parameter m was taken as 1.2 (see [34] for the determination of m). Ten independent runs of the algorithm were performed, and the one minimizing the inertia criterion was selected [34]. A threshold value of 0.9 was taken to select probe sets most closely associated with a given cluster. This selection retained 4,062 and 4,751 probe sets from mouse and human datasets, respectively. Probe set clusters were then manually ordered to provide coherent pictures, which were visualized with Treeview. Meta-analysis of aggregated mouse and human datasets We identified 2,227 orthologous genes that showed significant variation of expression in both the mouse 430 2.0 and U133 Plus 2.0 human datasets. This dataset is accessible as an Excel workbook in Additional data file 3. In order to compare the expression patterns of these genes between human and mouse, the log signal values for each of these genes were first normalized to a mean equal to zero and a variance equal to 1, independently in the mouse and human datasets, as previously described for comparing the gene expression program of human and mouse tumors [22,27]. The two normalized datasets were then pooled and a hierarchical clustering with complete linkage was performed. A similar analysis was performed for the comparison of human and mouse LN-DCs, monocytes, macrophages and in vitro derived GM-CSF DCs. Meta-analysis of mouse 430 2.0 and U74Av2 datasets In order to classify the IKDCs based on the optimal gene signatures of the different cell subsets examined, with only minimal impact of differences in the experimental protocols used to prepare the cells and to perform the gene chips assays, the clustering of the cell populations was performed as a meta-analysis of our own mouse 430 2.0 dataset together with the published U74Av2 datasets. The Array Comparison support information of the NetAffyx™ analysis center (Affymetrix) was used to identify matched ProbSets between the two types of microarrays. Only one representative dataset was kept for redundant ProbeSets targeting the same gene. This yielded a set of 2,251 genes whose expression could be compared between the two datasets, using the same normalization method as described above. This dataset is accessible as Excel workbooks in Additional data file 4. As expected, this meta-analysis led to co-clustering of all the samples derived from identical cell types whether their gene expression had been measured by us on 430 2.0 microarrays or by others on U74Av2 microarrays, with the exception of the cDC population from [15], which segregated with pDCs rather than with the cDC subsets from the other datasets. Data mining Gene lists were analyzed using the DAVID 'functional annotation chart' tool accessible on the NIAID website [52,53]. Different databases were used for these annotations: gene ontology (Amigo), knowledge pathways (KEGG), interactions (BIND), interprotein domains (INTERPRO), and disease (OMIM/OMIA). The annotations shown in Tables 5 and 7 were selected as the most highly significant terms retrieved by performing an over-representation study. To this end, a modified Fisher exact P value called the 'EASE score' was calculated to measure the enrichment in gene-annotation terms between the gene signature specific to the leukocyte subpopulation examined ('List') and the complete set of all the genes selected for the compendium analyzed ('Background'). The significance threshold was set at an EASE score below 0.05 in most instances, or below 0.1 for DC signatures that did not yield many highly significant terms as discussed in Results. Individual significant annotations encompassing many common genes or similar biological processes were regrouped using the 'Functional annotation clustering' tool of the DAVID software. More information on this type of analysis is available on the DAVID website [85]. Public access to the raw data for the datasets analyzed in the paper Our datasets for mouse DC subsets, NK cells, CD8 T cells, and B lymphocytes have been deposited in the Gene Expression Omnibus (GEO) database under reference number GSE9810. The references for download of the public data used from the original websites where they were first made available are given in Table 1. In addition, all raw transcriptomic data analyzed here have been regrouped on our website [86] and are available for public download. Abbreviations APC, antigen-presenting cell; BM, bone marrow; cDC, conventional dendritic cell; CDP, common dendritic progenitor; DC, dendritic cell; FCM, fuzzy c-means; GEO, Gene Expression Omnibus; GM-CSF, granulocyte-macrophage colony stimulating factor; IFN, interferon; IKDC, interferon-producing killer dendritic cell; ITAM, immunoreceptor tyrosine-based activation motif; LN-DC, lymph node-resident DC; M-CSF, macrophage colony-stimulating factor; MDP, macrophage and dendritic cell progenitor; MHC, major histocompatibility; NK, natural killer; PCA, principal component analysis; pDC, plasmacytoid dendritic cell; TLR, toll-like receptor. Authors' contributions SHR, TW, SC, PK, and MD designed the research; SHR, TW, CT, HX, MS, GB, AD and MD performed the research; EV and PP contributed new reagents/analytical tools; SHR, TW, CT, HX, DD, MS, FRS, SC, PK, and MD analyzed data; and SHR, TW, and MD wrote the paper. Note added in proof During the review process of this paper, two reports were published in Nature Immunology that identified a common progenitor characterized as FLT3+M-CSF+ for mouse LN-DCs (pDCs, CD8α cDCs and CD11b cDCs), devoid of any capability to generate lymphoid cells or monocytes/macrophages, and named common dendritic progenitor (CDP) [87,88]. This observation is thus consistent with our gene profiling analysis of human and mouse leukocytes. The question whether this pathway for LN-DCs is the major one, or just one possibility among others, including differentiation from monocytes, has been raised [89]. Our gene profiling data would suggest that most mouse LN-DCs derive from the recently identified CDP or MDP in vivo, without a monocytic intermediate, consistent with a recent report [81]. It also implies that a similar pathway must exist in humans. The relationship between the CDP and the MDP still remains to be established. Three reports have been published very recently in the Journal of Experimental Medicine that showed that IKDCs are a specific subset of NK cells, based on functional and ontogenic approaches comparing these cells to DCs and NK cells [90-92]. This is consistent with the results of our clustering analysis of IKDCs with other leukocyte subsets. Finally, two recent reports have identified a new transduction pathway in human pDCs involving a B cell receptor-like ITAM-signaling pathway [93,94]. This pathway involves the BLNK transduction molecule, which we have identified here as expressed to very high levels in mouse and human pDCs compared to the other LN-DCs (Table 6) and many other leukocytes. We believe that the conserved transcriptional signatures identified here for mouse and human LN-DC subsets will lead to many more discoveries for the understanding of the specialized functions of these cells. Additional data files The following additional data are available. Additional data file 1 is a Microsoft Excel workbook with raw data for the mouse gene chip compendium. Additional data file 2 is a Microsoft Excel workbook with raw data for the human gene chip compendium. Additional data file 3 is a Microsoft Excel workbook with raw data for the human/mouse gene chip compendium. Additional data file 4 is a Microsoft Excel workbook with raw data for the IKDC gene chip compendium. Additional data file 5 is a Microsoft Excel workbook giving the mouse DC subset gene signatures according to our datasets with confirmation from two other independent datasets (one for pDCs and one for cDC subsets). Additional data file 6 is a figure showing the results of PCA for investigation of the relationships between in vitro derived GM-CSF DCs and LN-DCs in mouse and human. Additional data file 7 is a table giving real-time PCR data for the pattern of expression of 27 genes across mouse leukocyte subsets. Additional data file 8 is a figure illustrating PACSIN1 expression in human pDCs versus PBMCs by RT-PCR and western blotting. Supplementary Material Additional file 1 Raw data for the mouse gene chip compendium. Click here for file Additional file 2 Raw data for the human gene chip compendium. Click here for file Additional file 3 Raw data for the human/mouse gene chip compendium. Click here for file Additional file 4 Raw data for the IKDC gene chip compendium. Click here for file Additional file 5 Mouse DC subset gene signatures according to our datasets with confirmation from two other independent datasets (one for pDCs and one for cDC subsets). Click here for file Additional file 6 Results of PCA for investigation of the relationships between in vitro derived GM-CSF DCs and LN-DCs in mouse and human. Click here for file Additional file 7 Real-time PCR data for the pattern of expression of 27 genes across mouse leukocyte subsets. Click here for file Additional file 8 PACSIN1 expression in human pDCs versus PBMCs by RT-PCR and western blotting. Click here for file

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Tumor necrosis factor receptor-associated factors (TRAFs).

Jordan Pober, J. Bradley (2001)

Tumor necrosis factor receptor-associated factors (TRAFS) were initially discovered as adaptor proteins that couple the tumor necrosis factor receptor family to signaling pathways. More recently they have also been shown to be signal transducers of Toll/interleukin-1 family members. Six members of the TRAF family have been identified. All TRAF proteins share a C-terminal homology region termed the TRAF domain that is capable of binding to the cytoplasmic domain of receptors, and to other TRAF proteins. In addition, TRAFs 2-6 have RING and zinc finger motifs that are important for signaling downstream events. TRAF proteins are thought to be important regulators of cell death and cellular responses to stress, and TRAF2, TRAF5 and TRAF6 have been demonstrated to mediate activation of NF-kappaB and JNK. TRAF proteins are expressed in normal and diseased tissue in a regulated fashion, suggesting that they play an important role in physiological and pathological processes.