Genomic expression profiling of NK cells in health and disease.

Genomic expression profiling of NK cells in health and disease Fuyan Wang1,2, Zhigang Tian1, and Haiming Wei1

1 Institute of Immunology, School of Life Sciences and Hefei National Laboratory for Physical Sciences at the Microscale,

University of Science and Technology of China, Hefei, China

2 Diabetes Center, School of Medicine, Ningbo University, Ningbo, China

Correspondence: Haiming Wei and Zhigang Tian, Institute of Immunology, School of Life Sciences and Hefei National

Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, 230027, China;

Phone: +86-551-6360-7379, Fax: +86-551-6360-6783; e-mail: [email protected]; [email protected]

Received: 04-Jul-2014; Revised: 01-Oct-2014; Accepted: 01-Oct-2014 This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/eji.201444998. This article is protected by copyright. All rights reserved.

1

Abstract Natural killer (NK) cells are important components of innate and adaptive immunity. Functionally, they play key roles in host defense against tumors and infectious pathogens. Within the past few years, genomic-scale experiments have provided us with a plethora of gene expression data that reveal an extensive molecular and biological map underlying gene expression programs. In order to better explore and take advantage of existing data sets, we review here the genomic expression profiles of NK cells and their subpopulations in resting or stimulated states, in diseases and in different organs; moreover, we contrast these expression data to those of other lymphocytes. We have also compiled a comprehensive list of genomic profiling studies of both human and murine NK cells in this review.

Keywords: Natural killer cell; Microarray; Gene expression; Genomic profiling; Transcriptome

Introduction

Natural killer (NK) cells perform important functions in both innate and adaptive immune responses. They play key roles in the early host defense against viruses and other pathogenic infections as well as in killing tumor cells by releasing cytokines and by cell-mediated cytotoxicity [1-3]. Additionally, NK cells can also develop antigen-specific immunologic memory [4]. The progress already made in understanding NK-cell biology and function has allowed for the use of adoptive NK-cell transfer as a promising cancer immunotherapy tool in recent years [5-7]. Autologous and allogeneic NK cells, genetically modified NK cells, and NK-92 cells (a peripheral blood–derived human NK-cell line) have been used as tumor immunotherapies for solid tumors (such as advanced non-small cell lung, recurrent ovarian and breast cancers) or hematological malignancies (such as acute myelocytic leukemia (AML) and lymphoma) and have been shown to achieve moderate success [5, 8-11]. However, despite this understanding of the powerful functions of NK cells and their current therapeutic applications within the clinic, much remains to be learned. A comprehensive understanding of NK-cell transcription signatures in different subpopulations and under various conditions is essential to achieving an even greater understanding of these cells. Currently, studies revealing NK-cell signatures remain relatively limited in mice and even more so in humans.

This article is protected by copyright. All rights reserved.

2

Genome-wide systems biology approaches aim to view the complete picture of a biological process while maintaining molecular precision. Using parallel microarray technology that can handle massive amounts of data, tens of thousands of transcripts can be measured simultaneously. Thus, these methods are increasingly accepted as powerful and reductionist approaches to study the complex systems within immune cells [12]. For example, recent large-scale microarray analysis of immune cells, including NK cells, T cells, invariant natural killer T (iNKT) cells, and dendritic cells (DCs), shows that lymphocyte differentiation, activation and function are accompanied by simultaneous changes in hundreds of genes [13-15]. Moreover, transcriptional changes were identified in malignant and immune disorders, including lymphoma, leukemia, rheumatoid arthritis, systemic lupus erythematosus and many others [16-20]. Another advantage of gene expression profiling is its potential to reveal novel physiological roles of molecules in various signaling pathways. As an example in NK-cell biology, analysis of a cDNA microarray of all genes involved in the NF- pathway demonstrated that the glucocorticoid-induced tumor necrosis factor receptor (GITR, also known as TNFRSF18) primarily suppresses activation of the NF-B pathway and upregulates the anti-inflammatory genes Hmox1 and Il10 [21]. Likewise, gene expression profiling of mice deficient in transcription factors (TFs) has been helpful in identifying transcription-factor regulated genes [22, 23]. As an example, genomic profiling of Runx3–/– NK cells compared with that of WT NK cells revealed that four members of the nectin-binding receptor family (Crtam, Cd96, Cd226, Tigit), three chemokine receptors (Cxcr3, Cxcr4, Cx3cr1) and two integrins (Itgαx, Itgβ7) are Runx3-regulated genes [22]. For NK cells in particular, a series of recent publications using gene expression profiling have provided detailed molecular insights into NK-cell activation, development and diversity as well as the function of NK-cell lineages and the distinct NK-cell subpopulations in both humans and mice (Tables 1 and 2). Most studies comparing gene expression between resting and activated NK cells induced by cytokines (including IL-2, IL-12, IL-15, IL-18 and IFN-α) and infection (including parasites and viruses) are listed in the Tables. NK-cell precursors and subpopulations as well as NK cells in different locations have different genetic profiles, which enrich our understanding of NK-cell molecular signatures far more than repertoire diversity. Although the recent gene expression data provide an extensive molecular definition of NK cells, there are ways to further capitalize on these data; for instance, integrative analyses can help to transform these data into valuable and novel information on NK cells. In this review, the major findings from genomic profiling analyses of human and mouse NK cells are summarized, including most of the microarray-based transcriptomes obtained for NK cells and their subpopulations to date. The key findings from these studies are discussed here with a focus on highlighting how our understanding of NK cells from an immunological perspective can be expanded by data from bioinformatics and multiscale


3

biological investigations. This integrative strategy can ultimately help to accelerate progress toward a more comprehensive understanding of NK cells.

Integrative genomic expression profiling analysis

Transcriptional profiling by microarray is an important systematic approach to examine how transcriptional changes within cells correlate with their diverse states and with various states of the immune system in general. In addition to mRNA microarray, many high-throughput profiling technologies (e.g., miRNA and DNA microarray; mass cytometry; RNA- and ChIP-seq) can be used to investigate NK cells and other immune cells in complex immune states [24]. The Immunological Genome Project has provided gene expression profiles for >200 mouse immune cell types, allowing for the identification of valuable genes to distinguish each cell type or group as well as to study co-expressed genes and their predicted regulators [25]. The Human Immunology Project Consortium (HIPC) is creating a new public data resource of different cell types that characterize diverse states of the human immune system [26]. Network analysis tools (e.g., WGCNA, GeneMANIA, Inferelator) have the potential to place a given molecule in the context of molecular interactions, pathways and/or even an unanticipated tissue or disease [27, 28]. We have taken advantage of this integrative genomic profiling in our own studies. We first used whole-genome microarray analyses with independent verification to provide comprehensive molecular signatures of human decidual NK (dNK), cord-blood NK (cNK) and peripheral blood NK (pNK) cells [29]. According to functional classification of the Gene Ontology (GO) project [30], we selected several highly expressed genes within five different categories, including membrane receptors, TFs, growth factors & cytokines, chemokines and signal-transduction molecules, in either dNK, cNK or pNK cells. By integrating the data generated from the genomic profiling with information from published reports and bioinformatic databases (e.g., STRING, Gene Network Central, Transcriptional Regulatory Element Database) , we were able to determine that the genes highly expressed in NK cells formed a complex network, which was analyzed and visualized using the network analysis tool GeneMANIA [31] and the visualization software Cytoscape [32] (Fig. 1). Additionally, by combining these data with information available from published reports, bioinformatic databases and network analysis tools (such as STRING [33]), we were able to predict putative target genes of the selected TFs and finally describe the transcriptional regulatory networks of NK cells (Fig. 2). TFs including Ikaros, PU.1, Ets-1, Nfil3, Id2, T-bet and Eomes are key regulators that have a major effect on NK-cell fate, differentiation and function. The target genes for all TFs examined in


4

[60] were identified or predicted by searching published reports and online bioinformatic databases, including STRING [33], GeneMANIA [31] and TRED [34] (Fig. 3). The interaction network was visualized by Cytoscape software [32] (Fig. 3). In addition to Cytoscape, other visualization software including 3Dscape [35], Circos [36] and Gephi [37] are also available to integrate, analyze and visualize the network data, complex systems, dynamics and hierarchical graphs. Overall, we think that integrating different analysis methods takes full advantage of what can be learned from the enormous amount of data generated from gene expression profiles. Many databases, software and online tools are available and useful for searching and predicting the function of gene sets and particular genes-of-interest. Moreover, we provide here a list of the databases, software and online tools useful for this endeavor and include information on how the network biology tools and integrative informatics can be applied to large microarray datasets (Fig. 3 and Table 3). Finally, we illustrate how this strategy can be successfully applied to a large genomic expression profile dataset in our own studies in order to make further investigations into NK-cell biology.

Gene expression profiling of NK-cell subpopulations in different locations within the body

NK-cell subpopulations have a remarkable degree of repertoire and functional diversity. In humans, these diverse subpopulations include tolerant, cytotoxic and regulatory NK cells [38]. Recent studies suggest an even more complex view of NK-cell diversity, as different functional and tissue-localizing capabilities seem to preferentially segregate with distinct NK-cell subpopulations. Genomic profiling can be used as a powerful tool to identify novel differences and separate out these subpopulations in a more detailed manner.


5

NK-cell subpopulations during development The early stages of human lymphopoiesis are poorly characterized. Common lymphoid progenitors (CLPs) commit to either the NK-cell or the B/T-cell lineages. Two subsets of CD34+ hematopoietic progenitor cells (HPCs) have been proposed as candidate CLPs: CD45RA+CD38–CD7+ cells from the umbilical cord blood (UCB) and CD45RAhiLin–CD10+ cells from the bone marrow (BM) [39, 40]. In vitro experiments showed that UCB-derived CD34+CD45RAhiCD7+ HPCs skew towards generating T/NK lineages in vitro, while CD34+CD45RAhiLin–CD10+ BM-derived HPCs predominantly exhibit a B-cell potential [39]. Gene expression profiling by DNA microarrays confirmed that CD34 +CD45RAhiCD7+ HPCs selectively express NK and T lineage–committed genes while retaining expression of genes related to the granulomonocytic lineage, whereas CD34+CD45RAhiLin–CD10+ HPCs exhibit a typical pro-B cell transcriptional profile and generally lack genes unrelated to the B-cell lineage [41].

Circulating NK-cell subpopulations Human NK cells account for a small fraction of total lymphocytes (~10%) in the peripheral blood and are comprised of two different subpopulations: the predominant CD56dimCD16+ mature subset (~95%) and the much smaller CD56 brightCD16– immature subset (~5%) [29]. CD56dim and CD56bright pNK cells have differential expression patterns for cell receptors, adhesion molecules, cytokines, chemokines, transcription factors and cytolytic molecules [29, 42, 43]; three studies to date have characterized these two NK-cell subpopulations using genomic profiling (Table 4). All three studies revealed that, compared with CD56bright pNK cells, CD56dim pNK cells upregulate killer cell Ig-like receptors (KIRs) (including Kir2dl1 and Kir2d2), cytolytic molecules (including Prf1, Gzma and Gzmb) and chemokines (including Cxcl8, Mip-1b and Mip-1b) [42-44]. Additionally, Koopman et al. [43] compared CD56bright dNK cells with CD56bright or CD56dim pNK cells and found that CD56bright pNK cells were more similar to the CD56dim pNK–cell subset than they were to the CD56bright dNK cells. Hanna et al. [42] analyzed ~20,000 genes among purified CD56brightCD16+, CD56dimCD16– and in vitro–activated CD16+ pNK cells to find that overexpression of certain tetraspanin family receptors (CD9, CD53, CD81) on activated NK cells might enhance or alter their migration to, and retention in, inflamed tissues. Wendt et al. [44] analyzed ~33,000 genes in resting CD56bright and CD56dim pNK cells, and verified the observed changes in cytokine and chemokine genes at the protein level using CBA and protein arrays. While GM-CSF, TARC and TGF-β3 were exclusively expressed in CD56bright pNK-cell supernatants, CD56dim pNK cells were the main producers of IGF-1 and IGFBP-3. GDNF, IGFBP-1, EGF and TIMP-2 were detected in both CD56bright and CD56dim pNK subsets [44]. Despite the diversity in human NK cells, the mostly similar gene


6

profiling results (e.g., distribution of receptors, cytolytic molecules, secreted soluble factors) found among the three studies were consistent with each other, confirming the reliability of gene arrays [42-44].

NK-cell subpopulations in the decidua During the early stages of pregnancy, NK cells are the dominant lymphocyte in the decidua, constituting ~70% of the total lymphocytes. Approximately 90% of dNK cells belong to the CD56brightCD16– immature phenotype [29], called immature dNK (idNK) cells, which are specialized NK cells remarkably distinct from the mature pNK (mpNK) cell subpopulation. These idNK cells function to regulate key developmental processes, including trophoblast invasion and vascular growth [29, 45]. In a previous study we found that both human and mice dNK cells play a key regulatory role at the maternal–fetal interface by suppressing Th17-mediated local inflammation, thus promoting immune tolerance [46]. Compared with mpNK cells, idNK cells show increased expression of several genes, including inhibitory receptors, growth factors, cytokines, chemokines and cell cycle– or proliferation-related proteins [43]. On the other hand, mpNK cells were shown to have increased expression of genes related to activating receptors, co-stimulatory factors and chemokines compared with idNK cells. Additionally, we showed that idNK and mpNK cells had different TF profiles; idNK cells are enriched in homeobox TFs, which may contribute to their immaturity; while mpNK cells are enriched in zinc-finger proteins, which may contribute to their cytotoxic function [29]. Hanna et al. performed a microarray analysis on purified dNK cells in order to generate a transcriptional profile in terms of secreted cytokines, growth factors and chemokines thought to be crucial for placental development [45]. Several growth factor transcripts known to stimulate angiogenesis and act as endothelial mitogens, including vascular endothelial growth factor (VEGF) and placental growth factor (PLGF), were highly expressed in dNK cells [45]. These data highlight the superior ability of the dNK over the pNK subpopulation to secrete various mediators important for trophoblast invasion and vascular growth. Additionally, several chemokine transcripts, including Cxcl8, Ccl5 and Cxcl10, were also highly expressed in dNK cells [29, 45]. Reduced trophoblast invasion and vascular growth in the decidua are thought to be a primary defect in pregnancy [47, 48]. These situations can manifest in several different ways including fetal growth restriction, miscarriage and preeclampsia. Genetic studies suggest that these conditions are linked to the particular combination of KIR receptors expressed on maternal dNK cells and the HLA-C genes expressed by the fetal trophoblast [45, 49]. Both the activating KIR2DS1 and inhibitory KIR2DL1 receptors have been shown to bind to HLA-C2 but serve to confer either protection or increased risk for pregnancy disorders respectively [49] ; the mechanisms underlying these genetic associations with opposing outcomes


7

remain unknown. To identify previously unrecognized responses triggered by KIR2DS1 or KIR2DL1 binding to HLA-C2, Xiong et al. performed microarray-based genomic profiling of the following four dNK subpopulations: KIR2DS1+, KIR2DL1+, KIR2DS1+KIR2DL1+ and KIR2DS1–KIR2DL1– [49]. KIR2DS1+KIR2DL1+ dNK cells exhibited different responses than the KIR2DL1+ single-positive dNK cells, whereas only HLA-C2–activated KIR2DS1+ dNK cells produced several soluble products, such as GM-CSF, that enhanced the migration of primary trophoblast and JEG-3 trophoblast cells in vitro [49]. These findings provide a possible molecular mechanism for the fact that expression of activating KIR receptors on maternal dNK cells can be beneficial for placentation.

Liver-resident NK-cell subpopulations The liver is an immunotolerant organ containing a large proportion of innate immune cells such as NK cells, NKT cells, γδT cells and macrophages [50]. These immune cells play an important role in inhibiting autoimmune diseases as well as in maintaining immunotolerance and homeostasis [51]. In humans, 30% – 50% of intrahepatic lymphocytes are NK cells [52]. In mice, NK cells account for approximately 10% – 15% of intrahepatic lymphocytes and can be divided into two distinct subpopulations: CD49a+DX5– and CD49a–DX5+ NK cells [51, 53]. We performed gene expression microarray analysis of ~22,000 genes to explore the differences in the transcriptional signatures of hepatic DX5– and DX5+ NK cells in mice [53]. Although nearly half of the tested genes were identically expressed between the DX5– and DX5+ NK-cell subpopulations, these two subpopulations were distinct from each other in the following ways: among the 1,507 genes found to be significantly different between the subpopulations, 566 genes enriched in DX5– NK cells were associated with negative regulation and immune tolerance, while the 941 genes enriched in DX5+ NK cells were instead associated with migration, proliferation, immune responses and cell maturation [53]. DX5– NK cells expressed relatively high numbers of genes related to IL-17 production and Th17-cell development (including Il21r, Rora and Ahr) [54] as well as genes preferentially expressed by Treg cells (including LAG-3, Helios and Egr-2) [55, 56], raising the possibility that DX5– NK cells might exert negative regulatory control within the liver.

The relationships among NK-cell subpopulations and with other lymphocytes

Microarray data sets are not only used to find previously unrecognized gene changes under various conditions but also to


8

establish a molecular definition of cell identity. Clustering and other classical techniques, such as principal-component analysis (PCA), are useful methods for analysis of gene expression data [41, 57]. The relatedness of NK-cell subpopulations to each other and to other leukocyte populations have been investigated using hierarchical clustering or PCA.

Relationships among NK-cell subpopulations A hierarchical clustering algorithm of variable genes within the NK-cell subpopulations tested in our previous study [29] indicated that human pNK cells clustered far more closely with cNK cells than with dNK cells and that these three NK-cell populations exhibited a closer transcriptional relationship with each other than with CD56 +CD3+ T and naïve CD56–CD3+ T cells. Moreover, dNK and endometrial NK (eNK) cells within the uterus were found to be closely related [58], and CD56bright pNK cells and CD56dim pNK cells from peripheral blood had a closer transcriptional relationship to each other than with CD56bright dNK cells [43] (Fig. 4A). We therefore infer that close ontogenetic relationships among NK-cell subpopulations correlate to their maturity level and local microenvironment. Overall, by comparing the expression profile of NK-cell subpopulations, a new heterogeneous molecular basis for developmental and functional differences has been revealed. Microarray-based gene expression profiling analyses have also been used to identify the evolutionary relationship among different lineages within NK-cell subpopulations, as well as between NK cells and other immune cells, as will be discussed below in “Relationships between NK cells and other immune cells” [58-61]. An example of transcriptome-based analysis of ontogenetic relationships among NK cells is from Kopcow et al. [58], who identified that human dNK cells from gravid uteri and eNK cells from cycling endometrium are distinct NK-cell populations, and Guimont-Desrochers et al. [59], who redefined IFN-producing killer DCs as a novel intermediate in NK-cell differentiation.

Relationships between NK cells and other immune cells Expression profiles of several human immune cell populations including NK cells, CD4+ T cells, CD8+ T cells, B cells, monocytes, myeloid DCs (mDCs), plasmacytoid DCs (pDCs), neutrophils and eosinophils form a comprehensive resource for a transcriptome database [62, 63]. This profiling can also help to elucidate the key molecules important during the establishment of immune cell identity and to identify cell-type specific microRNAs (miRNAs) and mRNAs [62, 63]. In the mouse system, lymphoid cells including B cells, NK cells, γδ T cells, iNKT cells and αβ T-cell subsets were shown to form groups distantly related to macrophages by PCA plot analysis of all genes expressed by these populations [41, 57, 64]. In


9

these studies, based on relatedness in the expression profile of genes, murine NK cells were shown to cluster far more closely with T cells than with any other lymphocyte population or any myeloid population, such as macrophages, conventional DCs and pDCs (Fig. 4B) [41, 57, 64]. Resting NK cells and cytotoxic CD8+ T cells are known to express many molecules in common [41, 65]. Transcriptome-wide analysis indicates that these commonalities extend to hundreds of genes, many of which encode molecules with unknown function. In contrast to naïve T cells, however, resting NK cells display a ‘pre-primed’ state containing abundant mRNAs for granzyme A, granzyme B and perforin, which allow NK cells to respond more rapidly to viral infection [41, 65].

Gene expression profiling of NK cells in resting and activated states

Typically, NK cells are activated by interaction with ligands (such as Fas and MICA) on target cells or by soluble mediators such as growth factors, cytokines and chemokines [29, 66-68]. Many studies have compared gene expression between resting and activated NK cells using microarray analysis.

Cytokine-mediated NK-cell activation Several cytokines including IL-2, IL-8, IL-12, IL-21 and IFN-α can activate NK cells and alter multiple cellular responses, such as proliferation, cytotoxicity and cytokine/chemokine production [69]. Microarray analysis of cytokine-induced variations in gene expression has led to a better understanding of the molecular mechanisms underlying these responses in NK cells [6, 7, 70-72]. Microarray analysis revealed that IL-2–activated human NK cells rapidly downregulate quiescence-associated genes (FOXO3A, CDKN1B) proliferation

and upregulate genes associated with cell-cycle progression and

(cyclins, CDKs, E2f TFs, and PCNA) [73]. Moreover, numerous genes that enhance immune responses were

upregulated, including activating receptors (KLRC1, KLRC3), death receptor ligands (FasL, TRAIL), cytokine receptors (IL2RG, IL18RAB, IL27RA), chemokine receptors (CX3CR1, CCR5, CCR7), members of secretory pathways (DEGS1, FKBP11, SLC3A2) and the transcription factor T-bet [73]. Furthermore, systematic analysis showed that IL-2–activated CD16+ pNK cells overexpress several genes (including OX40 ligand, CD86, Tim3 and galectins) that have been shown to enable NK cells to directly crosstalk with other innate and adaptive immune effector cells, such as DCs and T cells [42].


10

Moreover, these activated CD16+ pNK cells acquired immunoregulatory functions, secreted more immune effector molecules (such as granzyme A, granzyme B and CTLA1) and displayed enhanced cell cytotoxicity [42]. Another study by Hodge et al. compared the gene expression patterns between resting and cytokine-stimulated NK-92 cells, and the comparison included stimulation by IL-2 alone, IL-2 plus IL-18, and IL-2 plus IL-12 [74]. Interestingly, the majority of these altered transcripts were cytokines, chemokines and chemokine receptors. The authors showed that activated NK-92 cells upregulate immune effectors (including perforin, IFN-γ and IL-10). Meanwhile, after activation, NK-92 cells downregulate expression of the CXCR3 chemokine receptor and thus significantly reduced chemotaxis in the presence of its ligand, IFN-γ–inducible protein 10 (CXCL10, also known as IP-10) [74].

Receptor-mediated activation NK cells are also activated through stimulation of their activating NK receptors, which can be modeled experimentally by cross-linking these receptors with soluble agonist monoclonal antibodies (mAbs). The Ly49 receptors are type II C-type lectin-like membrane glycoproteins that recognize MHC class I and MHC class I–like proteins on target cells in mice [75]. Gene expression microarray analysis of Ly49D-activated mouse liver NK cells revealed a central role for Ly49D in cytokine and chemokine production in which cross-linking Ly49D strongly induced mRNA transcripts for cytokines (Ifng) and chemokines (Xcl1, Mip-1a, Mip-1b), which were also measurable at the protein level as assessed by ELISA. These results suggest that a primary function of the activating NK receptors in immune regulation is to control NK-cell production of immunomodulatory factors [76].

The human KIRs, which recognize HLA class I molecules as ligands, are functional homologs to the Ly49 receptors in mice [75]. KIR2DL4 is the human homolog of Ly49D in mice, therefore the genetic changes observed in KIR-activated human NK cells and in Ly49D-activated mouse NK cells are mostly the same [75]. KIR2DL4 (CD158d) resides in endosomes within NK cells and binds to its soluble ligand, HLA-G, which is produced by fetal trophoblast cells during early pregnancy [66]. KIR2DL4 is an unusual member of the polymorphic KIR family because it possesses an NK-cell–activating function despite harboring an inhibitory immunoreceptor tyrosine-based inhibitory motif (ITIM) [77]. Microarray analysis of human NK cells undergoing sustained activation after treatment with a soluble anti-KIR2DL4 agonist mAb revealed upregulated genes typical of a senescent signature (such as Il6, Il8, IL1B and p21), and the supernatants from KIR2DL4-activated NK cells could increase vascular permeability and promote angiogenesis [66]. Thus, sustained


11

activation of NK cells induces senescence in response to soluble HLA-G in the microenvironment, and may contribute to remodeling the maternal vasculature in early pregnancy [66]. An independent study using a human cytokine array to evaluate mRNA expression of 114 common human cytokine genes also showed that activation of human dNK cells by anti-KIR2DL4 mAb or HLA-G homodimer upregulates pro-inflammatory cytokines including IFN-γ, IL-6, IL-8 and TNF-α as well as proangiogenic protein VEGF, which are essential for a successful pregnancy [77].

Infection-mediated activation Parasite activation. Malaria infection has been shown to trigger early activation and expansion of NK cells [78]. Microarray analysis of early blood responses in mice infected with erythrocytic-stage Plasmodium chabaudi revealed that NK-cell–associated transcripts (such as lectin-like killer cell receptors, Prf1 and GzmA) in the blood increase dramatically, which was confirmed by the observations of increased NK-cell numbers and frequency in both the blood and spleen 72 hours after infection [79]. At the molecular level within these P. chabaudi infection–induced pNK cells, subsequent microarray analysis revealed a cell proliferation signature consistent with the above findings [79]. Viral activation. NK cells are essential for controlling certain viral infections in the host. Murine cytomegalovirus (MCMV) infection induces NK-cell activation and expansion, and thus serves as an ideal model for physiological NK-cell activation [41, 80, 81]. Bezman et al. generated a kinetic profile of gene expression featuring Ly49H+ NK cells before and after infection with mouse MCMV to explore the molecular changes within NK cells during viral pathogen–specific activation [41]. The most considerable changes occurred early after infection (day 1.5) and waned during late infection (day 7) [41]. At the early time point (day 1.5), NK cells were activated, and genes encoding inflammatory (Cd69, Ifih1, Ifitm3), proliferation (Il2ra) and effector (Ifng, GzmB) function were upregulated [41]. Meanwhile, genes encoding the suppressors of cytokine signaling Socs1 and Socs3 were also highly expressed at this early time point to avoid uncontrolled inflammation. At the late stage of the infection (day 7), Ly49H+ NK cells achieved the peak of clonal expansion with higher expression of genes encoding cell cycle– or proliferation-related genes (including cell-division cycle (CDC) genes and MKI67). A contraction phase then occurs in which most effector Ly49H+ NK cells undergo cell death and leave behind long-lived memory NK cells (day 27) that persist for months [41]. These memory NK cells are able to mount a robust secondary response against previously encountered pathogens and have higher IFN-γ transcripts than naïve NK cells [82]. At day 27 after infection, genes including


12

Ly6c1, Fasl and Casp1 were more highly expressed in memory than in naïve NK cells [41]. Thus, profiling the transcriptional dynamics within NK cells during MCMV infection has shed light on the potential cellular processes that may be involved in the differentiation of naïve NK cells into effector and memory cells. Effects of activation on NK cell–mediated cytotoxicity upon infection. Resting NK cells have minimal cytotoxic function; upon activation, NK cells gain the ability to kill target cells using the granule exocytosis pathway immediately upon recognition of transformed or infected cells through the interactions between receptors and ligands. At the molecular level, resting human CD56bright and CD56dim NK cell subpopulations as well as mouse NK cells are in a persistently ‘alerted’ state containing abundant granzyme A, granzyme B and perforin at the mRNA level, but contain only granzyme A at the protein level [29, 41, 43, 72]. Upon cytokine activation in vitro, NK cells drastically increase their granzyme B and perforin protein levels without major changes in the abundance of their respective mRNA [41, 72]. The same pattern of regulation occurred in NK cells in vivo after MCMV infection [72]. These data suggest that resting NK cells have minimal cytotoxic function due to a block in perforin and granzyme B mRNA translation and that NK-cell activation functions to release this block, although the specific mechanism is unknown [72]. Overall, the genes overexpressed in activated NK cells confer not only potent cytotoxic ability but also immunomodulatory function to these activated NK cells [42]. The gene expression profiling of NK cells in resting and stimulated states provide us with a better understanding of NK-cell function and improve our understanding of the molecular mediators underlying NK-cell activation.


13

Gene expression profiling of NK cells in virus-infected states or the tumor microenvironment

Since NK cells are a first line of defense in certain viral infections (such as MCMV, HIV and HBV) and in tumor suppression, high-mutating viruses and tumor cells have developed ways of subverting NK-cell function as a mechanism to evade antiviral or antitumor immune responses [41, 83-85]. Genomic profiling of NK cells either after viral infection or from the tumor microenvironment has shed light on some of these suppression mechanisms. Moreover, genomic profiling has led to further understanding of NK cell–derived leukemias/lymphomas as well as why functional NK cells are useful as an adoptive immunotherapy against some tumors [16, 17, 86].

Virus-mediated NK-cell suppression NK cells have been shown to lose functionality in HIV-infected individuals when these individuals become viremic [87]. To investigate the effect of HIV-envelope glycoproteins (gp120) on physiologic NK-cell functions, DNA microarray analyses were performed on freshly isolated human NK cells in the presence or absence of R5- or X4-subtype HIV gp120 envelopes [85]. A profound number of cellular abnormalities was shown to occur at the level of gene expression upon treatment of NK cells with HIV gp120, including upregulation of apoptosis-related genes (Casp3) and downregulation of genes important for cell proliferation (Nmyc) and innate immune defense (Ncr3) [84]. The microarray data was further validated by phenotypic and functional characterization, showing that both the X4 and R5 subtypes of gp120 suppress NK-cell cytotoxicity, proliferation and the ability to secrete IFN-γ [84]. These findings suggest that antiretroviral therapy may decrease HIV envelope–induced suppressive effects on NK cells and contribute to partially restoring NK-cell function during HIV infection [85, 88].


14

NK cells in the tumor microenvironment Tumor-mediated NK-cell suppression. NK cells are a major component of the antitumor immune response and function to suppress tumor progression [5, 89]. However, the effect of the tumor microenvironment on NK cells remains controversial. Our group investigated the phenotypic profile of tumor-infiltrating NK (TINK) cells in non–small-cell lung carcinoma (NSCLC), and we found that tumor tissues harbor a substantial CD11b–CD27– NK-cell population displaying an immature and inactive phenotype with low CD16, CD57, CD226 and NKp30 expression [90]. The tumor microenvironment may thus induce a specific gene expression signature that renders TINK cells less tumoricidal, thereby contributing to cancer progression [90, 91]. By comparative microarray analysis of purified human NK cells isolated from tumoral and non-tumoral lung tissues from 12 NSCLC patients, Gillard-Bocquet et al. characterized the transcriptional profile of TINK cells and confirmed that the tumor microenvironment induced specific gene expression modifications in these cells [19]. They found that TINK cells expressed higher mRNA levels of the NKG2A inhibitory receptor, granzymes A and K, Fas, CXCR5 and CXCR6 compared to non-tumoral NK cells, but had lower expression of CX3CR1 and S1PR1 [19]. In another study, gene expression profiling of circulating NK cells in head and neck squamous cell carcinoma (HNSCC) patients revealed that NK cells from HNSCC patients who had not received any therapy exhibited some downregulated receptors (CCR7, CXCR3, IL-7R) compared with expression in healthy subjects; these receptors were then upregulated upon treatment [18]. These data indicate that TINK cells exhibit a specific modulation of the expression of chemokine receptors involved in cell migration within the tumor microenvironment. The precise identification of the molecular modulations in NK cells within the tumor microenvironment can help to understand how to control NK-cell antitumoral functions during tumor immunosurveillance [19]. Using NK cells for anti-tumor therapy. The adoptive infusion of NK cells is a promising immunotherapy for patients with advanced malignancies [5]. Using gene and microRNA expression microarrays, Park et al. provided distinct expression profiles of ex vivo–expanded NK cells and freshly isolated NK cells from cancer patients [17]. Among approximately 25,100 genes evaluated, the expanded NK cells overexpressed 1,098 genes and 28 microRNAs when compared with freshly isolated human NK cells [17]. Genes related to crosstalk between DCs and NK cells as well as those for mitochondrial dysfunction were upregulated, while some genes related to immune function pathways were downregulated, including IFN, IL-10 and CXCR4 signaling. These differences may ultimately have an effect on the clinical outcomes when using adoptive transfer of NK cells as an immunotherapeutic strategy [17].


15

NK cell–derived tumors. The outgrowth of CD3–CD56+CD16+ NK cells causes NK cell–type lymphoproliferative disease of granular lymphocytes (LDGLs), which can be further subdivided into two distinct categories: aggressive NK-cell leukemia (ANKL) and chronic NK lymphocytosis (CNKL) [16]. A comparison between purified pNK cells in healthy and CNKL individuals identified a total of 15 LDGL-associated genes, such as Bmi1, Zfr and Optn, which may potentially serve as candidate genes for diagnosing NK-cell disorders; additionally, this data provided new insights into the molecular pathogenesis of NK cell–type LDGLs [16]. Extranodal nasal-type NK/T-cell lymphoma (NKTL) is characterized by a clonal proliferation of NK or T cells with a cytotoxic phenotype [92]. Comprehensive genome-wide gene expression profiling revealed that human NKTL (including HANK-1) and NK-cell lines (including NK-92 and NK-YS) are enriched in several cell cycle–related genes (including Plk1, Cdk1 and Myc) as compared with pNK cells from healthy donors. Almost all cases of NKTL expressed high p53 and survivin levels, which were not expressed in pNK cells from healthy humans. Thus, genomic profiling of NKTL provides further understanding into its pathogenesis and oncogenic pathways, and suggests that survivin is a potential novel therapeutic target for NKTL [92]. Overall, gene expression profiling of NK cells in virus-infected states or in the tumor microenvironment provides insights into gene expression changes in NK cells and can thus provide several candidate genes suitable for further investigation into more effective strategies to either harness or dampen NK-cell function in disease for therapeutic purposes.

Conclusions

Painting a transcriptional landscape of NK cells is a significant step towards understanding their activation, development and functional heterogeneity. This not only provides us with a global view of what occurs under these conditions in various cell types, but also potentially reveals new genes with important immunological function. These valuable resources impart crucial clues for further investigations into NK cells that will facilitate and accelerate research into multiple areas of NK-cell biology and into NK cell–mediated clinical immunotherapy.

Acknowledgments: We thank Yonggang Zhou for helping to export the network map into the manuscript. This work was supported by grants from the Natural Science Foundation of China (#81330071, #31021061).


16

Conflict of interest: The authors declare no financial or commercial conflict of interest.

References

1

Vivier, E., Raulet, D. H., Moretta, A., Caligiuri, M. A., Zitvogel, L., Lanier, L. L., Yokoyama, W. M. et al, Innate or adaptive immunity? The example of natural killer cells. Science 2011. 331: 44-49.

2

Biron, C. A. and Brossay, L., NK cells and NKT cells in innate defense against viral infections. Curr Opin Immunol 2001. 13: 458-464.

3

Yokoyama, W. M., Kim, S. and French, A. R., The dynamic life of natural killer cells. Annu Rev Immunol 2004. 22: 405-429.

4

Sun, J. C., Ugolini, S. and Vivier, E., Immunological memory within the innate immune system. EMBO J 2014. 33: 1295-1303.

5

Cheng, M., Chen, Y., Xiao, W., Sun, R. and Tian, Z., NK cell-based immunotherapy for malignant diseases. Cell Mol Immunol 2013. 10: 230-252.

6

Fujisaki, H., Kakuda, H., Shimasaki, N., Imai, C., Ma, J., Lockey, T., Eldridge, P. et al, Expansion of highly cytotoxic human natural killer cells for cancer cell therapy. Cancer Res 2009. 69: 4010-4017.

7

Garg, T. K., Szmania, S. M., Khan, J. A., Hoering, A., Malbrough, P. A., Moreno-Bost, A., Greenway, A. D. et al, Highly activated and expanded natural killer cells for multiple myeloma immunotherapy. Haematologica 2012. 97: 1348-1356.

8

Iliopoulou, E. G., Kountourakis, P., Karamouzis, M. V., Doufexis, D., Ardavanis, A., Baxevanis, C. N., Rigatos, G. et al, A phase I trial of adoptive transfer of allogeneic natural killer cells in patients with advanced non-small cell lung cancer. Cancer Immunol Immunother 2010. 59: 1781-1789.

9

Geller, M. A., Cooley, S., Judson, P. L., Ghebre, R., Carson, L. F., Argenta, P. A., Jonson, A. L. et al, A phase II study of allogeneic natural killer cell therapy to treat patients with recurrent ovarian and breast cancer. Cytotherapy 2011. 13: 98-107.

10

Rizzieri, D. A., Storms, R., Chen, D. F., Long, G., Yang, Y., Nikcevich, D. A., Gasparetto, C. et al, Natural killer cell-enriched donor lymphocyte infusions from A 3-6/6 HLA matched family member following nonmyeloablative allogeneic stem cell transplantation. Biol Blood Marrow Transplant 2010. 16: 1107-1114.

11

Bachanova, V. and Miller, J. S., NK cells in therapy of cancer. Crit Rev Oncog 2014. 19: 133-141.

12

Staudt, L. M. and Brown, P. O., Genomic views of the immune system*. Annu Rev Immunol 2000. 18: 829-859.

13

Kim, C. C. and Lanier, L. L., Beyond the transcriptome: completion of act one of the Immunological Genome Project. Curr Opin Immunol 2013. 25: 593-597.

14

Germain, R. N., Meier-Schellersheim, M., Nita-Lazar, A. and Fraser, I. D., Systems biology in immunology: a computational modeling perspective. Annu Rev Immunol 2011. 29: 527-585.

15

Benoist, C., Germain, R. N. and Mathis, D., A plaidoyer for 'systems immunology'. Immunol Rev 2006. 210: 229-234.

16

Choi, Y. L., Makishima, H., Ohashi, J., Yamashita, Y., Ohki, R., Koinuma, K., Ota, J. et al, DNA microarray analysis of natural killer cell-type lymphoproliferative disease of granular lymphocytes with purified CD3-CD56+


17

fractions. Leukemia 2004. 18: 556-565. 17

Park, K. U., Jin, P., Sabatino, M., Feng, J., Civini, S., Khuu, H., Berg, M. et al, Gene expression analysis of ex vivo expanded and freshly isolated NK cells from cancer patients. J Immunother 2010. 33: 945-955.

18

Bottcher, A., Ostwald, J., Koczan, D., Knecht, R., Kramp, B. and Dommerich, S., Gene expression profiling of circulating natural killer cells in head and neck squamous cell carcinoma. Cancer Genomics Proteomics 2013. 10: 197-207.

19

Gillard-Bocquet, M., Caer, C., Cagnard, N., Crozet, L., Perez, M., Fridman, W. H., Sautes-Fridman, C. et al, Lung tumor microenvironment induces specific gene expression signature in intratumoral NK cells. Front Immunol 2013. 4: 19.

20

Du, X., Tang, Y., Xu, H., Lit, L., Walker, W., Ashwood, P., Gregg, J. P. et al, Genomic profiles for human peripheral blood T cells, B cells, natural killer cells, monocytes, and polymorphonuclear cells: comparisons to ischemic stroke, migraine, and Tourette syndrome. Genomics 2006. 87: 693-703.

21

Liu, B., Li, Z., Mahesh, S. P., Pantanelli, S., Hwang, F. S., Siu, W. O. and Nussenblatt, R. B., Glucocorticoid-induced tumor necrosis factor receptor negatively regulates activation of human primary natural killer (NK) cells by blocking proliferative signals and increasing NK cell apoptosis. J Biol Chem 2008. 283: 8202-8210.

22

Levanon, D., Negreanu, V., Lotem, J., Bone, K. R., Brenner, O., Leshkowitz, D. and Groner, Y., Transcription factor Runx3 regulates interleukin-15-dependent natural killer cell activation. Mol Cell Biol 2014. 34: 1158-1169.

23

Daussy, C., Faure, F., Mayol, K., Viel, S., Gasteiger, G., Charrier, E., Bienvenu, J. et al, T-bet and Eomes instruct the development of two distinct natural killer cell lineages in the liver and in the bone marrow. J Exp Med 2014. 211: 563-577.

24

Kidd, B. A., Peters, L. A., Schadt, E. E. and Dudley, J. T., Unifying immunology with informatics and multiscale biology. Nat Immunol 2014. 15: 118-127.

25

Shay, T. and Kang, J., Immunological Genome Project and systems immunology. Trends Immunol 2013. 34: 602-609.

26

Benoist, C., Lanier, L., Merad, M., Mathis, D. and Immunological Genome, P., Consortium biology in immunology: the perspective from the Immunological Genome Project. Nat Rev Immunol 2012. 12: 734-740.

27

Zhang, B. and Horvath, S., A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 2005. 4: Article17.

28

Bonneau, R., Reiss, D. J., Shannon, P., Facciotti, M., Hood, L., Baliga, N. S. and Thorsson, V., The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol 2006. 7: R36.

29

Wang, F., Zhou, Y., Fu, B., Wu, Y., Zhang, R., Sun, R., Tian, Z. et al, Molecular signatures and transcriptional regulatory networks of human immature decidual NK and mature peripheral NK cells. Eur J Immunol 2014.

30

Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P. et al, Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000. 25: 25-29.

31

Zuberi, K., Franz, M., Rodriguez, H., Montojo, J., Lopes, C. T., Bader, G. D. and Morris, Q., GeneMANIA prediction server 2013 update. Nucleic Acids Res 2013. 41: W115-122.

32

Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N. et al, Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003. 13: 2498-2504.

33

Franceschini, A., Szklarczyk, D., Frankild, S., Kuhn, M., Simonovic, M., Roth, A., Lin, J. et al, STRING v9.1:


18

protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res 2013. 41: D808-815. 34

Jiang, C., Xuan, Z., Zhao, F. and Zhang, M. Q., TRED: a transcriptional regulatory element database, new entries and other development. Nucleic Acids Res 2007. 35: D137-140.

35

Wang, Q., 3DScape: three dimensional visualization plug-in for Cytoscape, Available from Nature Precedings 2011.

36

Krzywinski, M., Schein, J., Birol, I., Connors, J., Gascoyne, R., Horsman, D., Jones, S. J. et al, Circos: an information aesthetic for comparative genomics. Genome Res 2009. 19: 1639-1645.

37

Jacomy, M., Venturini, T., Heymann, S. and Bastian, M., ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software. PLoS One 2014. 9: e98679.

38

Fu, B., Tian, Z. and Wei, H., Subsets of human natural killer cells and their regulatory effects. Immunology 2014. 141: 483-489.

39

Haddad, R., Guardiola, P., Izac, B., Thibault, C., Radich, J., Delezoide, A. L., Baillou, C. et al, Molecular characterization of early human T/NK and B-lymphoid progenitor cells in umbilical cord blood. Blood 2004. 104: 3918-3926.

40

Hao, Q. L., Zhu, J., Price, M. A., Payne, K. J., Barsky, L. W. and Crooks, G. M., Identification of a novel, human multilymphoid progenitor in cord blood. Blood 2001. 97: 3683-3690.

41

Bezman, N. A., Kim, C. C., Sun, J. C., Min-Oo, G., Hendricks, D. W., Kamimura, Y., Best, J. A. et al, Molecular definition of the identity and activation of natural killer cells. Nat Immunol 2012. 13: 1000-1009.

42

Hanna, J., Bechtel, P., Zhai, Y., Youssef, F., McLachlan, K. and Mandelboim, O., Novel insights on human NK cells' immunological modalities revealed by gene expression profiling. J Immunol 2004. 173: 6547-6563.

43

Koopman, L. A., Kopcow, H. D., Rybalov, B., Boyson, J. E., Orange, J. S., Schatz, F., Masch, R. et al, Human decidual natural killer cells are a unique NK cell subset with immunomodulatory potential. J Exp Med 2003. 198: 1201-1212.

44

Wendt, K., Wilk, E., Buyny, S., Buer, J., Schmidt, R. E. and Jacobs, R., Gene and protein characteristics reflect functional diversity of CD56dim and CD56bright NK cells. J Leukoc Biol 2006. 80: 1529-1541.

45

Hanna, J., Goldman-Wohl, D., Hamani, Y., Avraham, I., Greenfield, C., Natanson-Yaron, S., Prus, D. et al, Decidual NK cells regulate key developmental processes at the human fetal-maternal interface. Nat Med 2006. 12: 1065-1074.

46

Fu, B., Li, X., Sun, R., Tong, X., Ling, B., Tian, Z. and Wei, H., Natural killer cells promote immune tolerance by regulating inflammatory TH17 cells at the human maternal-fetal interface. Proc Natl Acad Sci U S A 2013. 110: E231-240.

47

Moffett, A. and Loke, C., Immunology of placentation in eutherian mammals. Nat Rev Immunol 2006. 6: 584-594.

48

Brosens, I., Pijnenborg, R., Vercruysse, L. and Romero, R., The "Great Obstetrical Syndromes" are associated with disorders of deep placentation. Am J Obstet Gynecol 2011. 204: 193-201.

49

Xiong, S., Sharkey, A. M., Kennedy, P. R., Gardner, L., Farrell, L. E., Chazara, O., Bauer, J. et al, Maternal uterine NK cell-activating receptor KIR2DS1 enhances placentation. J Clin Invest 2013. 123: 4264-4272.

50

Gao, B., Jeong, W. I. and Tian, Z., Liver: An organ with predominant innate immunity. Hepatology 2008. 47: 729-736.

51

Sun, H., Sun, C., Tian, Z. and Xiao, W., NK cells in immunotolerant organs. Cell Mol Immunol 2013. 10: 202-212.


19

52

Yamagiwa, S., Kamimura, H. and Ichida, T., Natural killer cell receptors and their ligands in liver diseases. Med Mol Morphol 2009. 42: 1-8.

53

Peng, H., Jiang, X., Chen, Y., Sojka, D. K., Wei, H., Gao, X., Sun, R. et al, Liver-resident NK cells confer adaptive immunity in skin-contact inflammation. J Clin Invest 2013. 123: 1444-1456.

54

Korn, T., Bettelli, E., Oukka, M. and Kuchroo, V. K., IL-17 and Th17 Cells. Annu Rev Immunol 2009. 27: 485-517.

55

Okamura, T., Fujio, K., Shibuya, M., Sumitomo, S., Shoda, H., Sakaguchi, S. and Yamamoto, K., CD4+CD25-LAG3+ regulatory T cells controlled by the transcription factor Egr-2. Proc Natl Acad Sci U S A 2009. 106: 13974-13979.

56

Thornton, A. M., Korty, P. E., Tran, D. Q., Wohlfert, E. A., Murray, P. E., Belkaid, Y. and Shevach, E. M., Expression of Helios, an Ikaros transcription factor family member, differentiates thymic-derived from peripherally induced Foxp3+ T regulatory cells. J Immunol 2010. 184: 3433-3441.

57

Gautier, E. L., Shay, T., Miller, J., Greter, M., Jakubzick, C., Ivanov, S., Helft, J. et al, Gene-expression profiles and transcriptional regulatory pathways that underlie the identity and diversity of mouse tissue macrophages. Nat Immunol 2012. 13: 1118-1128.

58

Kopcow, H. D., Eriksson, M., Mselle, T. F., Damrauer, S. M., Wira, C. R., Sentman, C. L. and Strominger, J. L., Human decidual NK cells from gravid uteri and NK cells from cycling endometrium are distinct NK cell subsets. Placenta 2010. 31: 334-338.

59

Guimont-Desrochers, F., Boucher, G., Dong, Z., Dupuis, M., Veillette, A. and Lesage, S., Redefining interferon-producing killer dendritic cells as a novel intermediate in NK-cell differentiation. Blood 2012. 119: 4349-4357.

60

Lopez-Verges, S., Milush, J. M., Pandey, S., York, V. A., Arakawa-Hoyt, J., Pircher, H., Norris, P. J. et al, CD57 defines a functionally distinct population of mature NK cells in the human CD56dimCD16+ NK-cell subset. Blood 2010. 116: 3865-3874.

61

Menssen, A., Edinger, G., Grun, J. R., Haase, U., Baumgrass, R., Grutzkau, A., Radbruch, A. et al, SiPaGene: A new repository for instant online retrieval, sharing and meta-analyses of GeneChip expression data. BMC Genomics 2009. 10: 98.

62

Allantaz, F., Cheng, D. T., Bergauer, T., Ravindran, P., Rossier, M. F., Ebeling, M., Badi, L. et al, Expression profiling of human immune cell subsets identifies miRNA-mRNA regulatory relationships correlated with cell type specific expression. PLoS One 2012. 7: e29979.

63

Abbas, A. R., Baldwin, D., Ma, Y., Ouyang, W., Gurney, A., Martin, F., Fong, S. et al, Immune response in silico (IRIS): immune-specific genes identified from a compendium of microarray expression data. Genes Immun 2005. 6: 319-331.

64

Jojic, V., Shay, T., Sylvia, K., Zuk, O., Sun, X., Kang, J., Regev, A. et al, Identification of transcriptional regulators in the mouse immune system. Nat Immunol 2013. 14: 633-643.

65

Hidalgo, L. G., Einecke, G., Allanach, K. and Halloran, P. F., The transcriptome of human cytotoxic T cells: similarities and disparities among allostimulated CD4(+) CTL, CD8(+) CTL and NK cells. Am J Transplant 2008. 8: 627-636.

66

Rajagopalan, S. and Long, E. O., Cellular senescence induced by CD158d reprograms natural killer cells to promote vascular remodeling. Proc Natl Acad Sci U S A 2012. 109: 20596-20601.

67

Long, E. O., Kim, H. S., Liu, D., Peterson, M. E. and Rajagopalan, S., Controlling natural killer cell responses: integration of signals for activation and inhibition. Annu Rev Immunol 2013. 31: 227-258.


20

68

Narni-Mancinelli, E., Ugolini, S. and Vivier, E., Tuning the threshold of natural killer cell responses. Curr Opin Immunol 2013. 25: 53-58.

69

Marcais, A., Viel, S., Grau, M., Henry, T., Marvel, J. and Walzer, T., Regulation of Mouse NK Cell Development and Function by Cytokines. Front Immunol 2013. 4: 450.

70

Apps, R., Sharkey, A., Gardner, L., Male, V., Trotter, M., Miller, N., North, R. et al, Genome-wide expression profile of first trimester villous and extravillous human trophoblast cells. Placenta 2011. 32: 33-43.

71

Stegmann, K. A., Bjorkstrom, N. K., Veber, H., Ciesek, S., Riese, P., Wiegand, J., Hadem, J. et al, Interferon-alpha-induced TRAIL on natural killer cells is associated with control of hepatitis C virus infection. Gastroenterology 2010. 138: 1885-1897.

72

Fehniger, T. A., Cai, S. F., Cao, X., Bredemeyer, A. J., Presti, R. M., French, A. R. and Ley, T. J., Acquisition of murine NK cell cytotoxicity requires the translation of a pre-existing pool of granzyme B and perforin mRNAs. Immunity 2007. 26: 798-811.

73

Dybkaer, K., Iqbal, J., Zhou, G., Geng, H., Xiao, L., Schmitz, A., d'Amore, F. et al, Genome wide transcriptional analysis of resting and IL2 activated human natural killer cells: gene expression signatures indicative of novel molecular signaling pathways. BMC Genomics 2007. 8: 230.

74

Hodge, D. L., Schill, W. B., Wang, J. M., Blanca, I., Reynolds, D. A., Ortaldo, J. R. and Young, H. A., IL-2 and IL-12 alter NK cell responsiveness to IFN-gamma-inducible protein 10 by down-regulating CXCR3 expression. J Immunol 2002. 168: 6090-6098.

75

Rahim, M. M., Tu, M. M., Mahmoud, A. B., Wight, A., Abou-Samra, E., Lima, P. D. and Makrigiannis, A. P., Ly49 receptors: innate and adaptive immune paradigms. Front Immunol 2014. 5: 145.

76

Ortaldo, J. R., Bere, E. W., Hodge, D. and Young, H. A., Activating Ly-49 NK receptors: central role in cytokine and chemokine production. J Immunol 2001. 166: 4994-4999.

77

Li, C., Houser, B. L., Nicotra, M. L. and Strominger, J. L., HLA-G homodimer-induced cytokine secretion through HLA-G receptors on human decidual macrophages and natural killer cells. Proc Natl Acad Sci U S A 2009. 106: 5767-5772.

78

Roetynck, S., Baratin, M., Johansson, S., Lemmers, C., Vivier, E. and Ugolini, S., Natural killer cells and malaria. Immunol Rev 2006. 214: 251-263.

79

Kim, C. C., Parikh, S., Sun, J. C., Myrick, A., Lanier, L. L., Rosenthal, P. J. and DeRisi, J. L., Experimental malaria infection triggers early expansion of natural killer cells. Infect Immun 2008. 76: 5873-5882.

80

Miletic, A., Krmpotic, A. and Jonjic, S., The evolutionary arms race between NK cells and viruses: who gets the short end of the stick? Eur J Immunol 2013. 43: 867-877.

81

Min-Oo, G., Bezman, N. A., Madera, S., Sun, J. C. and Lanier, L. L., Proapoptotic Bim regulates antigen-specific NK cell contraction and the generation of the memory NK cell pool after cytomegalovirus infection. J Exp Med 2014. 211: 1289-1296.

82

Miller, J. S., Therapeutic applications: natural killer cells in the clinic. Hematology Am Soc Hematol Educ Program 2013. 2013: 247-253.

83

Lugli, E., Marcenaro, E. and Mavilio, D., NK Cell Subset Redistribution during the Course of Viral Infections. Front Immunol 2014. 5: 390.

84

Sun, C., Fu, B., Gao, Y., Liao, X., Sun, R., Tian, Z. and Wei, H., TGF-beta1 down-regulation of NKG2D/DAP10 and 2B4/SAP expression on human NK cells contributes to HBV persistence. PLoS Pathog 2012. 8: e1002594.

85

Kottilil, S., Shin, K., Jackson, J. O., Reitano, K. N., O'Shea, M. A., Yang, J., Hallahan, C. W. et al, Innate


21

immune dysfunction in HIV infection: effect of HIV envelope-NK cell interactions. J Immunol 2006. 176: 1107-1114. 86

Iqbal, J., Weisenburger, D. D., Chowdhury, A., Tsai, M. Y., Srivastava, G., Greiner, T. C., Kucuk, C. et al, Natural killer cell lymphoma shares strikingly similar molecular features with a group of non-hepatosplenic gammadelta T-cell lymphoma and is highly sensitive to a novel aurora kinase A inhibitor in vitro. Leukemia 2011. 25: 348-358.

87

Mavilio, D., Lombardo, G., Benjamin, J., Kim, D., Follman, D., Marcenaro, E., O'Shea, M. A. et al, Characterization of CD56-/CD16+ natural killer (NK) cells: a highly dysfunctional NK subset expanded in HIV-infected viremic individuals. Proc Natl Acad Sci U S A 2005. 102: 2886-2891.

88

Mavilio, D., Benjamin, J., Daucher, M., Lombardo, G., Kottilil, S., Planta, M. A., Marcenaro, E. et al, Natural killer cells in HIV-1 infection: dichotomous effects of viremia on inhibitory and activating receptors and their functional correlates. Proc Natl Acad Sci U S A 2003. 100: 15011-15016.

89

Imai, K., Matsuyama, S., Miyake, S., Suga, K. and Nakachi, K., Natural cytotoxic activity of peripheral-blood lymphocytes and cancer incidence: an 11-year follow-up study of a general population. Lancet 2000. 356: 1795-1799.

90

Jin, J., Fu, B., Mei, X., Yue, T., Sun, R., Tian, Z. and Wei, H., CD11b(-)CD27(-) NK cells are associated with the progression of lung carcinoma. PLoS One 2013. 8: e61024.

91

Vitale, M., Cantoni, C., Pietra, G., Mingari, M. C. and Moretta, L., Effect of tumor cells and tumor microenvironment on NK-cell function. Eur J Immunol 2014. 44: 1582-1592.

92

Ng, S. B., Selvarajan, V., Huang, G., Zhou, J., Feldman, A. L., Law, M., Kwong, Y. L. et al, Activated oncogenic pathways and therapeutic targets in extranodal nasal-type NK/T cell lymphoma revealed by gene expression profiling. J Pathol 2011. 223: 496-510.


22

Figure 1. Network of highly expressed genes in human dNK, cNK or pNK cells categorized into five major groups based on general function. Genes were grouped into those that encoded transcription factors (green), membrane receptors (yellow), signal transduction (blue), chemokines (lilac) and growth factors/cytokines (pink).

Data were obtained from whole-genome

microarray datasets under GEO accession number GSE24268. The network was visualized by Cytoscape software, and the interaction relationships within the network were analyzed by GeneMANIA.


23

Figure 2. Putative combinatorial interaction network of the core TFs and regulated genes in NK cells. TFs (light green) including Ikaros, PU.1, Ets-1, Runx3, Nfil3, Id2, Gata3, and IRF-2 mainly control the early NK-cell developmental process. TFs including Mef, T-bet, Eomes, MITF, CEBP-, and Blimp-1 mainly control NK-cell maturation or function. The target genes for all TFs were identified or predicted by searching published reports and online bioinformatic databases, including STRING, GeneMANIA, and the Transcriptional Regulatory Element Database. The interaction network was visualized by Cytoscape software.


24

Figure 3. Schematic of the workflow in obtaining gene expression profiles and microarray data analysis. RNA is extracted from immune cells and is subjected to transcriptome microarray, and raw data need to perform computational analysis are obtained. Most researchers deposit their raw and processed data into the National Center for Biotechnology Information (NCBI) GEO repository for data sharing. Another such database is Array Express.

Several software packages perform

differential gene expression analysis of microarray data (Database or online tools, pink list). Valuable genes screened out from expression profiles are then used for network analysis and visualization (green list).


25

Figure 4. Dendogram of the relationships among NK-cell subpopulations and between NK cells and other immune cells. (A) The inter-relatedness among human NK-cell subpopulations, as evaluated by transcriptome data. dNK and eNK cells are closely related. CD56bright pNK cells and CD56dim pNK cell cluster far more closely than with activated CD16+ pNK, eNK and dNK cells. NK-cell subpopulations have a closer transcriptional relationship with each other than with CD56 + T and conventional T cells. Data correspond to microarray datasets in previous studies [29, 42, 43, 58]. (B) The inter-relatedness among murine lymphocytes and myeloid populations. Murine NK cells cluster far more closely with T cells than with any other lymphocytes, including B cells and myeloid populations. Data correspond to microarray datasets in previous studies [41, 57, 64]. DC, dendritic cells; MF, macrophage.


26

Table 1. Available resources for expression profiling by microarray of human NK cells.

Name

Phenotype

Location

State/stimulation

GEO NO./ PMID/ Reference

PB

---

GSE24268; PMID: 24838931; [28]

PB

---

GSE8059; PMID: 17623099; [67]

PB

---

PB

---

PB

---

GSE26163; PMID: 22253695; [88]

Decidua

---

GSE24268; PMID: 24838931; [28]

Decidua

---

GSE20499; PMID: 20172608; [47]

Resting populations CD3–CD56+

pNK cells –

–

+

–

CD16 pNK cells

CD3 CD56 CD16

CD56dim pNK cells

CD3–CD56dimCD16+

GSE28492; PMID: 22276136; [43]

CD56

bright

pNK cells

pNK cells from Caucasoid

–

bright

–

+

CD3 CD56 CD3 CD56

CD16

–

GSE1511; PMID: 15557145; [29]

individuals dNK cells –

CD16 dNK cells

CD3–CD56+ –

+

–

–

+

CD3 CD56 CD16

GSE5172; PMID: 16892062; [32] eNK cells Colonic NK cells

+

CD45 CD3 CD56 +

+

–

CD56 NKp46 CD117 CD127

–

Endometrium

---

GSE20499; PMID: 20172608; [47]

Colon

---

GSE41469; PMID: 23181063; [89]

PB

IL-2 for 0, 2, 8 and 24 h

GSE8059; PMID: 17623099; [67]

PB

IL-2, IL-12 and IL-18 for 24

GSE22919; PMID: 20944005; [90]

Activation pNK cells pNK cells

CD3–CD56+CD16– –

+

–

+

PB

poly(I:C) (25 μg/mL) for 6 h

–

+

PB

poly(I:C)

CD3 CD56

h pNK cells pNK cells

CD3 CD56 CD3 CD56

(25

[21]

μg/mL),

anti-GITR (10 μg/mL) for 6 h pNK cells pNK cells

–

CD3 CD56+ –

+

–

+

CD3 CD56

PB

IFN-α-2b for 6 h

GSE15743; PMID: 20334827; [65]

PB

X4-subtype HIV gp120 for

[79]

60 min pNK cells

CD3 CD56


PB

R5-subtype HIV gp120 for 27

60 min –

pNK cells

+

CD3 CD56 CD16

+

PB

IL-2, PHA, irradiated

GSE1511; PMID: 15557145; [29]

nonautologous PBLs, and classical NK target cells (irradiated RPMI 8866 cells) stimuli for 2 weeks K562 responding pNK cells K562 non-responding pNK

–

+

+

–

+

–

CD3 CD56 CD107 CD3 CD56 CD107

PB

K562 for 4 h with GolgiStop

PB

K562 for 4 h with GolgiStop

PB

Geldanamycin (Hsp90

GSE55977

cells Hsp90 inhibitor-treated NK

CD3–CD56+

cells

inhibitor), 1 mM for 24 h

Expanded NK cells isolated

–

+

–

+

–

+

CD3 CD56

PB

from healthy donors CD3 CD56

PB

from myeloma patients cells

isolated

Co-cultured with irradiated

GSE27838; PMID: 22419581; [7]

K562-mb15-41BBL

Expanded NK cells isolated

NK

GSE42253; PMID: 23293352; [91]

Co-cultured with irradiated K562-mb15-41BBL

from

CD3 CD56

PB

---

from

CD3–CD56+

PB

---

CD3–CD56+

PB

K562-mb15-41BBL cells and

healthy donors NK

cells

isolated

myeloma patients NKAES-derived NK cells

GSE12198; PMID: 19383914; [6]

10 IU/mL IL-2 for 7 days pNK cells stimulated by

–

CD3 CD56

+

PB

200 IU/mL IL-2 for 7 days

CD3–CD56+

PB

6,000 IU/mL IL-2, for 7 days

CD3–CD56+

Decidua

Cross-linking

low dose IL-2 pNK cells stimulated by high dose IL-2 dNK cells


28

[60]

with anti-LILRB1 dNK cells

–

+

–

+

Decidua

cIg (MOPC21)

–

+

Decidua

CD158d mAb for 4, 16

CD3 CD56

Decidua

Cross-linking with anti- CD158d

dNK cells

CD3 CD56

dNK cells

CD3 CD56

GSE35330; PMID: 23184984; [60]

or 64 h. NK cells from patients pNK cells from patients

CD3–CD56+

PB

---

CD3–CD56+

PB

co-cultured with IL-2 and

[17]

with metastatic cancer Expanded pNK cell from patients

with

metastatic

irradiated

cancer

EBV-TM-LCL

cells for 14 to 16 days

Non-Tum-NK from patients

–

+

–

+

–

+

CD3 CD56

with NSCLC

non-tumoral

---

[19]

tissues

Tum-NK from patients with

CD3 CD56

NSCLC

tumoral

---

tissues

pNK cells from patients

CD3 CD56

PB

Treated

[18]

PB

Non-treated

ANKL

PB

---

CNKL

PB

---

NKTL

PB

---

ANKL

PB

---

with HNSCC pNK cells from patients

CD3–CD56+

with HNSCC Lymphoma/NK cell lines

NK cell

lines

HANK,


[16]

GSE19067; PMID: 21052088; [80]

---

29

IMC-1,

KAI3,

NK-92,

NKYS,

KHYG1, SNK1,

SNK6, SNK10 NK-92 cells

---

[68]

NK-92 cells

IL-2

NK-92 cells

IL-2 plus IL-12

NK-92 cells

IL-2 plus IL-18

NK-92 cells

0, 6, 12 and 24 h after

GSE26876; PMID: 22018162; [92]

co-culture with RBC NK-92 cells

0, 6, 12 and 24 h after co-culture with Plasmodium falciparum–infected RBC

Receptor subsets CD57+CD3–CD56dimCD16+

CD57+ pNK cells –

CD57 pNK cells –

CD3 CD56

bright

PB

---

PB

---

CD62L

PB

---

CD3–CD56dimCD62L+

PB

---

CD3–CD56dimCD62L–

PB

---

UCB

UCB HSCs cultured with

–

–

CD57 CD3 CD56 +

CD62L

–

CD3 CD56

bright

dim

CD16 +

+

GSE23695; PMID: 20733159; [49]

GSE21774; PMID: 19265543; [50]

pNK cells CD3–CD56dimCD62L+ pNK cells CD3–CD56dimCD62L– pNK cells Other HSC-derived NK cells

GSE47521

IL-15 for 1, 7 or 14 days pNK cells before exercise pNK cells after exercise

–

+

PB

---

–

+

PB

Healthy men performed ten

CD3 CD56 CD3 CD56


GSE41914; PMID: 23288554; [93]

30

2-min

bouts

ergometer

of

exercise

cycle with

1-min rest. Most expression profiling data are available at the National Center for Biotechnology Information (NCBI) GEO repository. PMID, PubMed-Indexed for MEDLINE; Ref, Reference; pNK, peripheral NK cells; dNK, decidual NK cells; eNK, endometrial NK cells; NKTL, extranodal nasal-type natural killer/T-cell lymphoma; FFPE, formalin-fixed, paraffin-embedded tissues; ANKL, aggressive NK cell leukemia; CNKL, chronic NK lymphocytosis; PB, peripheral blood; IFN-α, interferon-alpha; HNSCC, head and neck squamous cell carcinoma; Non-Tum-NK, non-tumoral NK; Tum-NK, tumoral NK; NSCLC, non–small cell lung cancer; HNSCC, head and neck squamous cell carcinoma; PBLs, peripheral blood lymphocytes; UCB, umbilical cord blood; K562-mb15-41BBL, K562 cells transfected with 4-1BBL and membrane-bound IL15; NKAES, NK cell activation and expansion system; ITNK, induced T-to-natural killer; RBC, red blood cell; PHA, phytohemagglutinin; poly(I:C), polyinosinic:polycytidylic acid


31

Table 2. Available resources for expression profiling by microarray of murine NK cells. Name

Phenotype

Origin

Location

State/stimulation

GEO accession /PMID or Ref

Basic populations CD3–NK1.1+

Spl NK cells Liver NK cells

–

*

CD3 NK1.1

+

B6

Spl

---

GSE15907

B6

Liver

---

GSE15907

Precursors BM preNK cells *

CD3–NK1.1+Mac1loCD43lo

B6

BM

---

GSE15907

Neonatal iNK cells *

CD3–NK1.1+

B6

Spl

---

GSE15907

CD3–NK1.1+

B6

Spl

IL-15 for 0 and 24

GSE7764;

h

PMID: 17540585; [66]

---

GSE33627;

Activation Spl NK cells +

Conventional NK1.1 c-Kit

–

–

+

–

+

–

+

+

–

CD3 NK1.1 c-Kit CD11b

B6

Spl

Spl NK cells

PMID: 22733969; [94]

IL-18–generated +

CD3 NK1.1 c-Kit CD11b

B6

Spl

+

NK1.1 c-Kit Spl NK cells

rhIL-2

(300

IU/mL)

and

rmIL-18

(25

ng/mL) overnight –

Spl NK cells

+

CD3 NK1.1 DX5

+

ICR

Spl

1000 IU/mL IL-2 or IL-15 for 7 d

GSE50122；GSE50123； PMID: 24236182; [95]

Spl NK cells

–

+

+

–

+

+

–

+

+

–

+

–

+

CD3 NK1.1 DX5

Runx3

–/–

Spl

1000 IU/mL IL-2 or IL-15 for 7 d

Spl NK cells

CD3 NK1.1 DX5

Spl NK cells

CD3 NK1.1 DX5

Spl NK cells

CD3 NK1.1

B6 Runx3

–/–

B6

Spl

---

Spl

---

Spl

1 and 7 days after

GSE15907

MCMV infection +

+

Ly49H Spl NK cells

CD3 NK1.1 Ly49H

B6

Spl

Ly49H– Spl NK cells *

CD3–NK1.1+Ly49H–

B6

Spl

1 and 7 days after

GSE15907

MCMV infection 1 and 7 days after

GSE15907

MCMV infection WT memory NK cells

*

–

+

+

+

–

+

+

+

TCRb CD45.1 NK1.1 Ly49H

B6

Spl

27

days

after

GSE15907

MCMV infection –/–

Bim

memory NK cells

*

TCRb CD45.2 NK1.1 Ly49H

–/–

Bim

Spl

27

days

after

GSE15907

MCMV infection Spl NK cells

–

CD3 NK1.1

+

B6

Spl

0, 1.5, 7, 14 and

GSE25672;

30

PMID: 21289313; [96]

days

after

MCMV infection Spl NK cells

–

+

–

+

CD3 NK1.1

B6.CD45.1

Spl

---

GSE39555; PMID: 23084923; [97]

Spl NK cells

CD3 NK1.1

B6.CD45.1

Spl

1.5

days

after

MCMV infection This article is protected by copyright. All rights reserved.

32

CD3–NK1.1+

Spl NK cells

B6.CD45.2 IFNAR

–/–

Spl

---

Spl

1.5



CD45.1 WT → WT mixed BMC –

Spl NK cells

CD3 NK1.1

+

B6.CD45.2 IFNAR

–/–



days

after

MCMV infection

CD45.1 WT → WT mixed BMC –

+

Spl NK cells

CD3 NK1.1

Spl NK cells

CD3–NK1.1+

B6

Spl

Anti–Ly49D, 6 h

B6

Spl

Ptaquiloside

[70] +

selenium, 14 days Spl NK cells

–

+

–

+

B6

Spl

Selenium, 14 days

–

+

B6

PB

P.

–

+

–

+

–

+

–

+

CD3 NK1.1

B6

Spl

Ptaquiloside,

GSE30629; PMID: 23274088; [98]

14

days Spl NK cells

CD3 NK1.1

pNK cells

CD3 NK1.1

chabaudi

infection, 72 h Spl NK cells

CD3 NK1.1

B6

Spl

P.

GSE12727; PMID: 18824529; [73]

chabaudi

infection, 72 h pNK cells

CD3 NK1.1

B6

PB

mock-infected, 72 h

Spl NK cells

CD3 NK1.1

B6

Spl

mock-infected, 72 h

Spl NK cells

CD3 NK1.1

B6.IFNAR

–/–

Spl

GSE39556; poly(I:C)

by

PMID: 23084923; [97]

intravenous injection, 3 h after MCMV infection –

Spl NK cells

CD3 NK1.1

+

B6.IFNAR

–/–

Spl

---

B6

Spl

---

GSE15907

B6

Spl

---

GSE15907

B6

Spl

---

GSE15907

B6

Spl

---

GSE15907

B6

Liver

Receptor subsets Spl NK cells, Ly49H+ subset –

Spl NK cells, Ly49H subset +

Spl NK cells, Ly49C/I subset –

Spl NK cells, Ly49C/I subset –

Liver DX5 NK cells

CD3–NK1.1+Ly49H+ –

+

–

–

+

+

–

+

–

–

+

–

–

+

+

CD3 NK1.1 Ly49H

CD3 NK1.1 Ly49C/I CD3 NK1.1 Ly49C/I CD3 NK1.1 DX5

GSE43339; PMID: 23524967; [39]

+

Liver DX5 NK cells Spl mNK cells, Rag2

CD3 NK1.1 DX5

–/–

B6

–

+

+

–/–

–

+

+

+

Lin CD122 DX5 Rag2 Ets1

+/+

129/SvJ Rag2

Liver –/–

Spl

---

GSE37301; PMID: 22608498; [99]

–/–

Spl mNK cells, Rag2 Ets1 kit+ Spl NK cells

–/–

Lin CD122 DX5 NK1.1

129/SvJ Rag2

Rag2–/–Ets1–/–

Ets1–/–

CD3–NK1.1+CD117+

B6

–/–

Spl

---

Spl

--

GSE9431; PMID: 22427351; [100]


33

kit– Spl NK cells

CD3–NK1.1+CD117–

+

low

B220 Spl NK cells

B6

+

CD11c B220 CD122

–/–

+

B6.Rag

Spl

--

Spl

---

GSE34237; PMID: 22353997; [48]

+

CD27 Spl NK cells

–/low

–

+

+

–/low

–

+

–

CD11c

–

CD27 Spl NK cells

CD11c

+

CD27 Spl NK cells

B220 CD122 CD27 B220 CD122 CD27

+

+

–

+

+

+

+

–

+

NK1.1 CD27 CD11b

–/–

B6.Rag

–/–

B6.Rag

–/–

B6.Rag

Spl

---

Spl

---

Spl

---

GSE13229; PMID: 22267813; [101]

+

+

–

+

CD27 CD11b Spl NK cells CD27 CD11b Spl NK cells

NK1.1 CD27 CD11b NK1.1 CD27 CD11b

–/–

B6.Rag

Spl

---

B6.Rag

Spl

---

Spl

---

GSE15907

Spl

---

GSE15907

Spl

---

GSE15907

Spl

---

GSE15907

Spl

---

GSE15907

Spl

---

GSE15907

Spl

IL-12 and

[102]

–/–

Genetic variants Spl NK cells, b2m–/–

CD3–NK1.1+

B6

Spl NK cells, DAP10–/–

CD3–NK1.1+

B6.DAP10–/–

Spl NK cells, DAP12

–/– –/–

Spl NK cells, DAP10 12

–

+

–

+

CD3 NK1.1 –/– *

CD3 NK1.1

B6.DAP12

–/–

B6.DAP10 DAP 12

Spl NK cells, Tbet Spl NK cells, Tbet

+*

+

–

+

–

+

CD3 NK1.1

–/– *

Spl NK cells, Nfkbiz

–

+/−

CD3 NK1.1 Tbet CD3 NK1.1

–/–

–/–

B6 7W –/–

B6.Tbet

–/–

B6.Nfkbiz

+/−

IL-18, 24 h Spl NK cells, Nfkbiz–/–

CD3–NK1.1+

B6.Nfkbiz–/–

Spl

IL-12 and IL-18, 24 h

–

Eomes NK cells

–

+

–

–

+

+

–

+

CD3 NK1.1 Eomes

B6

Liver

---

GSE53486; PMID: 24516120; [23]

+

Eomes NK cells +

–

CD27 CD11b iNK cells.

CD3 NK1.1 Eomes

B6

+

CD3 NK1.1 CD27 CD11b

–

Runx3

–/–

Liver

---

Spl

--GSE50120； PMID: 24421391; [22]

+

–

CD27 CD11b iNK cells.

–

+

+


–

IL15/Ra–injected

Spl

---

Runx3–/–

Spl

---

IL15/Ra–injected

Spl

---

WT DP mNK cells DP mNK cells

CD3–NK1.1+CD27+CD11b+ –

+

+

+

–

+

–

+

Runx3–/–

Spl

---

–

+

–

+

IL15/Ra–injected

Spl

---

–

+

Spl

---

–

+


WT –

+

–

+

CD27 CD11b mNK cells CD27 CD11b mNK cells

CD3 NK1.1 CD27 CD11b CD3 NK1.1 CD27 CD11b

WT IL-15–Tg Spl mNK cells

CD3 NK1.1

B6

GSE10365; PMID: 18664585; [103]

NKD–IL-15–Tg Spl iNK cells

CD3 NK1.1

DN3-derived ITNKs

NKp46+CD3±

B6

Spl/BM

---

B6.Cre-ERT2;

Thymus

Bcl11b–/–

Bcl11b

flox/flox

thymocytes co-cultured


DN

GSE21016; PMID: 20538915; [104]

with

34

OP9-DL1

in

T

cell culture media, muIL-15

or

huIL-2 IL-2–expanded

NK

cells

–

CD3 NK1.1

+

B6

Spl

---

(LAK cells) Most expression profiling data are available at the National Center for Biotechnology Information (NCBI) GEO repository. GSE15907 is a series of data provided by Immunological Genome Project, and * indicates that the data is not currently available for browsing. PMID, PubMed-Indexed for MEDLINE; Ref, Reference; Spl, splenic; mNK, mature NK cells; iNK, immature NK; preNK, precursor NK; B6, C57Bl/6; Tg, transgenic; BM, bone marrow; BMC, bone marrow chimera; LN, lymph nodes; DN, double negative; DP, double positive; LAK cells, lymphokine-activated killer cells; WT, wild-type; ITNK, induced T-to-natural killer; MCMV, murine cytomegalovirus; poly (I:C), polyinosinic:polycytidylic acid


35

Table 3. Resource and analysis tools for microarray-based expression profiling of immune cells.

Data types or tools

Program name

Weblink

WGCNA

http://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/Rpackages/WGCNA/

GeneMANIA

http://www.genemania.org/

Inferelator

http://bonneaulab.bio.nyu.edu/networks.html

ARACNE

http://wiki.c2b2.columbia.edu/workbench/index.php/ARACNe/

RimbaNET

http://icahn.mssm.edu/departments-and-institutes/genomics/about/software/rimbanet/

STRING

http://string-db.org/

BioGRID

http://thebiogrid.org/

coXpress

http://coxpress.sourceforge.net/

Networks

Network Visualization 3Dscape

http://scape3d.sourceforge.net/3DScape.html

cytoscape

http://www.cytoscape.org/

Circos

http://circos.ca/

Gephi

https://gephi.org/

Gene Ontology and Annotation Gene Ontology

http://beta.geneontology.org.

GSEA

http://www.broadinstitute.org/gsea/index.jsp

Signaling Gateway

http://www.signaling-gateway.org/molecule/search

KEGG

http://www.genome.jp/kegg/

Pathway

http://www.pathwaycommons.org/about/

Pathways

Commons WikiPathways

http://wikipathways.org/index.php/WikiPathways

Databases or online tools ImmGen

http://www.immgen.org/

ImMunoGeneTics

http://www.imgt.org/

ImmPort

https://immport.niaid.nih.gov/

MsigDB

http://www.broadinstitute.org/gsea/msigdb/index.jsp

DAVID

http://david.abcc.ncifcrf.gov/

GenePattern

http://www.broadinstitute.org/cancer/software/genepattern/

Transfac

http://www.gene-regulation.com/pub/databases.html

TRED

http://rulai.cshl.edu/TRED

GWASdb

http://jjwanglab.org/gwasdb/


36

Table 4. Gene expression of human pNK–cell subpopulations. Koopman et al. (2003) Gene

CD56

bright

–

CD16 vs.

Hanna et al. (2004) CD56brightCD16– vs.

Wendt et al. (2006) CD56bright vs. CD56dim

CD56dimCD16+

CD56dimCD16+

Kir2dl1 (CD158a)

down

down

down

Kir2dl2 (CD158b)

down

down

down

Kir2dl4 (CD158d)

ns

up

up

Klrc2 (NKG2C)

up

up

ns

Klrc3 (NKG2E)

ns

ns

ns

Cd53 (CD53)

down

down

---

Hladr (HLADR)

up

up

---

Icam2 (CD102)

---

down

down

Cd31 (PECAM1)

ns

ns

up

Cd62l (CD62L)

up

up

up

Il7r (CD117)

up

up

ns

Cd44 (CD44)

up

up

ns

Prf1 (Perforin)

down

down

down

Gzma (Granzyme A)

down

ns

down

Gzmb (Granzyme B)

down

down

ns

Gzmm (Granzyme M)

ns

ns

ns

Gzmk (Granzyme K)

ns

up

ns

Cxcl8 (CXCL8)

down

down

down

Mip-1b (CCL4)

down

down

---

Mip-1a (CCL3)

down

down

---

Membrane receptors

Cytolytic molecules

Chemokines


37

Genomic expression profiling and bioinformatics analysis of pancreatic cancer.

Human NK cells in health and disease: clinical, functional, phenotypic and DNA genotypic characteristics.

Functional Significance of CD57 Expression on Human NK Cells and Relevance to Disease.

Circulating NK cells and their subsets in Behçet's disease.

NFIL3 Expression Distinguishes Tissue-Resident NK Cells and Conventional NK-like Cells in the Mouse Submandibular Glands.

Human NK cells and NK receptors.

Genomic profiling of liver cancer.

Genomic profiling of breast cancers.

Expression of MICA in oral squamous carcinoma cells and its effect on NK cells.

High variability of genomic instability and gene expression profiling in different HeLa clones.

Comprehensive genomic analysis and expression profiling of diacylglycerol kinase gene family in Malus prunifolia (Willd.) Borkh.

Expansion of NK cells and reduction of NKG2D expression in chronic lymphocytic leukemia. Correlation with progressive disease.

NK cells and interferons.

Gene expression profiling in PC12 cells infected with an oncolytic Newcastle disease virus strain.

Genomic analysis of drug resistant gastric cancer cell lines by combining mRNA and microRNA expression profiling.

Genomic profiling in triple-negative breast cancer.

Genomic profiling in luminal breast cancer.

Genomic expression profiling and bioinformatics analysis on diabetic nephrology with ginsenoside Rg3.

Genomic profiling of collecting duct renal carcinoma.

NK cells and T cells: mirror images?

NK Cells and Cancer Immunoediting.

Coordinated expression of DNAM-1 and LFA-1 in educated NK cells.

NK Cells during Dengue Disease and Their Recognition of Dengue Virus-Infected cells.

Expression and Functional Role of α7 Nicotinic Receptor in Human Cytokine-stimulated Natural Killer (NK) Cells.