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CD14 is a monocytic differentiation antigen that regulates innate immune responses to pathogens. Here, we show that murine Cd14 SNPs regulate the length of Cd14 mRNA and CD14 protein translation efficiency, and consequently the basal level of soluble CD14 (sCD14) and type I IFN production by murine macrophages. This has substantial downstream consequences for the innate immune response; the level of expression of at least 40 IFN-responsive murine genes was altered by this mechanism. We also observed that there was substantial variation in the length of human CD14 mRNAs and in their translation efficiency. sCD14 increased cytokine production by human dendritic cells (DCs), and sCD14-primed DCs augmented human CD4 T cell proliferation. These findings may provide a mechanism for exploring the complex relationship between CD14 SNPs, serum sCD14 levels, and susceptibility to human infectious and allergic diseases.
Since only ~1% of the human genome is located within exons (Venter et al., 2001), the vast majority of polymorphisms are single nucleotide polymorphisms (SNPs) located within non-coding regions. Approximately 40% of the ~1200 SNPs identified in human genome-wide disease association studies are not located within exons and thus are regulatory SNPs (Visel et al., 2009). Several regulatory SNPs (rSNPs) have been identified that cause human genetic diseases by known mechanisms, including: including: thalassaemias, preaxial polydactyly, and Hirschsprung’s disease [reviewed in (Brem et al., 2002; Buckland, 2004; Visel et al., 2009)]. A small number of rSNPs are located within transcription factor binding sites, and may affect disease susceptibility through a cis-genetic effect on gene expression (Helms et al., 2003; Knight et al., 1999; Ozaki et al., 2002; Prokunina et al., 2002; Tokuhiro et al., 2003). However, the functional effects of most rSNPs have not been characterized, including their effects on gene regulation. Genetic analyses in organisms ranging from yeast (Brem et al., 2005; Yvert et al., 2003) to mice (Schadt et al., 2003) have demonstrated that most gene expression differences arise from genetic variation located outside of a pre-selected interval containing the differentially expressed gene, which is referred to as a trans-acting genetic effect. Moreover, the levels of expression of most mRNAs are regulated by more than one genetic locus (Brem et al., 2005), and each locus may have a small individual tissue or context-specific effect (Dimas et al., 2009; Knight, 2005).
In order to understand how the vast majority of our genetic differences contribute to phenotypic differences, we must identify and characterize the effects that trans-acting rSNPs have on gene expression, but this is currently a complex and time-consuming undertaking (Buckland, 2006; Knight, 2005). We previously demonstrated that haplotype-based computational genetic mapping (HBCGM) (Liao et al., 2004; Wang and Peltz, 2005) could be used to analyze microarray-generated gene expression data obtained from a panel of inbred mouse strains, and a novel cis-acting enhancer element contributing to the tissue-specific pattern of differential H2-Ea mRNA expression was identified (Liao et al., 2004). Other analysis methods have been used to analyze gene expression differences in rodents (Tesson and Jansen, 2009), humans (Franke and Jansen, 2009) or plants (Jansen et al., 2009). Genetic changes exerting a local effect on target gene expression (in cis) often have a strong influence that is replicable. However, distal (trans) genetic associations that were identified by these methods have more subtle effects, which have proven to be more difficult to validate (Majewski and Pastinen, 2010). Therefore, we explored whether a different approach for the identification of genetic factors with a trans effect on gene expression would be productive. We wanted to determine if we could identify groups of genes with a common pattern of differential expression in lymphocytes, and then use HBCGM to investigate whether there was a common genetic basis for the differential expression of a gene cluster. This approach was used because a genetic factor affecting the expression of a cluster of downstream genes is more likely to impact phenotypes related to immunological diseases. Since B cells play an important role in immune responses, the genetic basis for an expression cluster of 40 genes with a similar expression profile in murine B cells was investigated. rSNPs located near the Cd14 transcriptional start site were shown to alter basal CD14 protein secretion and macrophage type I IFN production by a novel mechanism. Because the variable length of human CD14 transcripts also affected human CD14 protein translation efficiency, we investigated the impact that sCD14 has on cytokine production by human DCs and on human CD4 T cell responses.
Mice were obtained from Jackson Labs, and were used in the experiments at 8–10 weeks of age. Spleen cells were independently prepared from three female mice of the following 11 inbred strains: 129/SvJ, A/J, AKR/J, BALB/cJ, C3H/HeJ, C57BL/6J, DBA/2J, MRL/MpJ, NZB/BinJ, NZW/LaC, SMJ. Single-cell suspensions of spleen cells were prepared by lysis in ACK buffer (Cambrex, East Rutherford, N.J.). B cells were positively selected using B220 magnetic beads (Miltenyi Biotec, Auburn, CA) on LS cell separation columns (Miltenyi Biotec, Auburn, CA), and then plated at 4 × 106 cells/ml in complete RPMI-1640 medium prior to freezing. Murine costal chondrocytes were isolated from rib cages obtained from 3 day old newborn mice using published methods (Gosset et al., 2008) from the following strains: A/J, AKR/J, BALB/cJ, BALB/cByJ, C3H/HeJ, C57BL/6J, and DBA/2J. The isolated cells were frozen from 3 independent preparations for each strain, and stored at −80 °C before use.
RNA was purified and oligonucleotide microarray data was generated using the Affymetrix GeneChip Mouse Genome 430 2.0 Array (~39,000 transcripts) for all samples using previously described methods (Guo et al., 2007). There were 69 B cell samples, obtained from 11 mouse strains (129/SvJ, A/J, AKR/J, BALB/cJ, C3H/HeJ, C57BL/6J, DBA/2J, MRL/MpJ, NZB/BinJ, NZW/LaC, SMJ) that were exposed to 2 treatment conditions: 1) control and 2) Anti-IgM and CD40 stimulation. There were 3–4 independent samples analyzed for each strain and treatment condition. The same microarrays were used to analyze gene expression in chondrocyte cultures, and 3 independent preparations were analyzed for each strain.
The probe intensity data generated from all 69 arrays were read into the R software environment (http://www.R-project.org) directly from the .CEL files using the R/affy package (Gautier et al., 2004), which was also used to extract and manipulate probe level data to assess data quality and to create expression summary measures. The array data were also checked for quality using GCOS (Gene Chip Operating Software) from Affymetrix. Normalization was carried out using the robust multiarray average (RMA) method (Irizarry et al., 2003) to generate one expression measure for each probe set on each array.
The arrays have 45,100 probesets correspond to ~39,000 transcripts. The following analyses were applied to each probeset to identify probesets that are differentially expressed among strains with large fold change. A one-way ANOVA (Analysis of Variance) model was applied to test the whether a gene was differentially expressed among the mouse strains; the basal and stimulated B cell conditions were analyzed separately. The average expression level for each probeset was calculated for each strain. Since RMA signals are on a log2 scale, the fold change was defined as 2 to the power of the maximum average expression level minus the minimum expression level. Probesets with an ANOVA p-value<10−10 and a fold change greater than 10 were identified as genes that were highly significantly differentially expressed. There were 257 and 243 such probesets that corresponded to 183 and 179 genes for the basal and stimulated B cells, respectively. The differentially expressed probesets were selected for further cluster analysis.
K-mean cluster analysis was used to group the differentially expressed probesets into groups with similar expression profiles. Each probeset is associated with an 11 dimensional vector that corresponds to the average expression levels in the 11 mouse strains. The distance between each pair of probesets is measured by the correlation coefficient of the two vectors. When two probesets have a strong positive correlation, they are considered to have similar profiles. The K-mean clustering algorithm is an unsupervised classification algorithm that separates the probesets into a predefined number of groups; and each group contains probesets with similar profiles. Since the number of clusters must be pre-specified (before the analysis is complete), different numbers were tested. The number of genes within a cluster with a representative profile should be large enough to allow identification of genes that are regulated by a common factor, yet not so large that genes with distinct profiles are clustered together. Through empirical testing, we found that specification of 40 clusters each for the B cell basal and stimulated conditions, met this criteria. The cluster analysis was performed using Spotfire DecisionSite 8.2.1 (http://www.spotfire.com/) software.
The average gene expression profile for genes within cluster 24 was used as the input data. Then, genes with a haplotypic pattern that matched this gene expression were identified using the previously described haplotype mapping method (Wang and Peltz, 2005). To cover the entire genome, the haplotype blocks were produced by analysis of 8.3 million SNPs among 16 inbred mouse strains that were identified in the NIEHS database (Frazer et al., 2007). Of note, CAST/EiJ, MOLF/EiJ, PWD/PhJ, WSB/EiJ are wild-derived strains, which we could not productively incorporate into a haplotype map structure that is useful for computational mapping (Wang et al., 2005). Therefore, the genome-wide haplotype map was constructed using the 3.4M SNPs that are polymorphic among the 12 other strains (Frazer et al., 2007). Within a haplotypic block, the SNPs display a limited level of variation that can be quantified by measuring the linkage-disequilibrium (LD) among the SNPs. In brief, our previously described methods (Liao et al., 2004; Wang et al., 2005) were used to partition a chromosome into a set of haplotype blocks that maximize the within-block LD measure and minimize the between-block LD measure. The average pair-wise LD measure among the component SNPs was used to represent the degree of within-block LD, and times the average LD measure was used as the score for a candidate block; where n is the number of SNPs in that block. For partitioning a chromosome into a set of haplotype blocks, the total score for a candidate partition was the sum of the scores of the individual blocks within the partition. The optimal partition was identified through maximizing the score. The haplotype blocks in this optimal partition were those that maximize the within-block LD measure and minimize the between-block LD measure. There were 228,885 haplotype blocks produced by this method. The average number of SNPs and haplotypes per block was 12.02 and 2.86, respectively. According to our previous method (Liao et al., 2004), only the 141,014 (61.6%) blocks that had more than 3 SNPs were used for genetic mapping. Available phenotypic datasets (Liao et al., 2004) (MHC, aromatic hydrocarbon response, H2-Ea gene expression) were used to assess computational mapping results generated using this extended haplotype map. In all cases, the genetic loci that were known to be responsible for the inter-strain differences were identified with the expanded database.
First, we determined which of the 2222 correlated genes were expressed in chondrocytes, which were used as a surrogate for macrophages. For this analysis, we used a gene expression dataset prepared from chondrocytes isolated from 7 different strains. Chondrocytes and macrophages are derived from a common mesenchymal stem cell (Caplan, 1991); and chondrocytes have been shown to have a common pattern of chemokine production (Borzi et al., 1999), antigen expression (Summers et al., 1995) and important functional properties (class II histocompatibility antigen expression, antigen presentation to lymphocytes, induction of mixed and autologous lymphocyte stimulation, production of reactive oxygen intermediates) that are specifically associated with macrophages (Rathakrishnan and Tiku, 1993; Tiku et al., 1985). Moreover, chondrocytes have been shown to respond to IFNβ, and their response is of importance to inflammatory arthritis (Corr et al., 2011; Palmer et al., 2004). To do this, the MAS5 calls for the chondrocyte gene expression data were analyzed using the R/affy package (Gautier et al., 2004). If a probe set was determined to be “present” for all 3 replicates of that strain, the probe set was labeled as expressed in this strain. For this analysis, a probe set was labeled as expressed if it was present in at least one of the 7 strains analyzed. Secondly, we identified the genes with a unique pattern of expression in C3H/HeJ chondrocytes. To do this, we used an ANOVA model to identify genes whose absolute expression level was at least 2-fold different in C3H/HeJ chondrocytes relative to the average level of expression in the other 6 strains. This ANOVA model had a nested feature that was written as: gene expression ~ isC3H + strain(isC3H), where the primary variable of interest is a variable to indicate whether the strain is C3H and different mouse strains are nested within the primary variable. To identify the genes that were commonly found in the different gene lists, all gene symbols were converted to Entrez Gene ID using the IDconverter (Alibes et al., 2007) program. Then, the genes that were expressed in both lists with common gene identifiers were selected using R (www.r-project.org).
B cells were purified from splenic cells using a Miltenyi Biotec B Cell Isolation Kit (Cat. # 130-090-862). The cell density was adjusted using complete media (RPMI-1640 with 10% heat-inactivated FBS, P/S, non-essential amino acids, pyruvate, and L-Gln). Cells were incubated at 37°C, 5% CO2. After purification, cells were adjusted to 0.8 × 106/ml. 1 hour later, cells were stimulated with indicated concentrations of recombinant mouse interferon-β (R&D, Cat. # 12400-1). At indicated time points, cells were harvested for either real-time PCR analyses or Western Blot. For real-time PCR analysis, total RNA was extracted using the Qiagen RNeasy Mini Kit (Valencia, CA, Cat. # 74104) and QIAshredder (Cat. # 79654). 2 µg total RNA from each sample was used for 1st-strand cDNA synthesis that was performed using Invitrogen SuperScript III (Carlsbad, CA Cat. # 18080-051). 0.5 µl cDNA each sample was used for realtime PCR (Qiagen SYBR GreenER, Cat. # 56465). The data were collected and analyzed using an Applied Biosystems 7900HT, and all values were normalized relative to the expression level of β-Actin. The following primer sequences were used: Isg20 5’-TCC CTG AGG CTG CTG TGT AAG-3’, 5’-TGG GGG AGT GTT CTT GGT TTT-3’; Zbp1 5’-GTA GCC CCC AGA CCA CAG AAC-3’, 5’-GCA-AGG-TCG-GTT-CCA-CTT-CTT-3’; Mx1 5’-GCC AGG ACC AGG TTT ACA AGG-3’, 5’-TCC AGG AAC CAG CTG CAC TTA-3’; Irf7 5’-CAC CCC CAT CTT CGA CTT CAG-3’, 5’-GAC CCA GGT CCA TGA GGA AGT-3’; Bst2 5’-GCT GGA GAA TCT GAG GAT CCA A-3’, 5’-AAG CAG AAC TCC CTC CCC ACT-3’; β-Actin 5’-TGA CGT TGA CAT CCG TAA AGA CC-3’, 5’-AAG GGT GTA AAA CGC AGC TCA-3’. For western blotting, the cells were lysed in 1× SDS loading buffer, boiled, and the viscosity was reduced by incubation with Benzonase (Novagen, Gibbstown N.J., Cat. # 70664) at 4°C for 1 hour before further analyses.
IRF7 (Santa Cruz Biotechnology, Santa Cruz CA, clone H-246), pSTAT1 (Cell Signaling, clone Tyr701), β-Actin (Cell Signaling, Boston MA, clone 13E5), CD14 (BD, clone rmC5-3). Alkaline-phosphatase-conjugated secondary antibodies were Promega or Santa-Cruz Biotechnology.
CD11b+ peritoneal macrophages were purified from C3H/HeJ or C57BL/6 mice using Miltenyi Biotec CD11b MicroBeads (Auburn CA, Cat #. 130-049-601)., and the cells were adjusted to a final density of 3.75 × 105 cells per ml in a 96-well plate. Each well contained 60,000 cells that were stimulated in the presence or absence of recombinant soluble CD14 (Cell Sciences, Canton MA, Cat. # CRCC03) and the following TLR ligands for 24 hr: Pam3CSK4 for TLR1/2; HKLM (heat killed Listeria monocytogenes) for TLR2; polyI:C for TLR3; LPS-EK for TLR4; ST-FLA (flagellin from Salmonella typhimurium) for TLR5; FSL1 (Pam2CGDPKHPKSF) for TLR6/2; ssRNA40 for TLR7; CpG ODN1826 for TLR9. After 24 hours, media were collected for either Western blot or EIA analyses. Poly I:C was obtained from Amersham Biosciences (Pittsburg PA) and the other Toll-like receptor ligands were from Invivogen (Cat. # tlrl-kit1m).
Mouse interferon-β was measured using the PBL VeriKine kit (Piscataway, NJ, Cat. #, 42400-1), and IL-6 was quantified using the R&D Systems kit (Cat. # M6000B). For analysis of sCD14 analysis, 3 ml of serum from the following strains were purchased from Jackson Laboratory: A/J (12 weeks); AKR/J (9 weeks); C3H/HeJ (12 weeks); and C57BB6 (12 weeks). The sera were analyzed using a CD14 EIA kit (Cell Sciences cat. #: CKM034).
Cd14 RACE was performed using the Clontech (Mountain View, CA) SMARTer RACE cDNA Amplification Kit (Cat. # 634923). Primer sequences were as following: 5’-outer 5’-CGC ACC GTA AGC CGC TTT AAG GAC-3’, 5’-inner 5’-CTT CCG TGT CCA CAC GCT TTA-3’; 3’-outer 5’-AGC CAG ATT GGT CCA GCG CTT TC-3’, 3-inner 5’-GGC AGA TGT GGA ATT GTA CGG-3’.
Cd14 RNA mutants were prepared using T7 polymerase (Thermo Scientific, Cat. # 88856) acting on 200 ng of agarose-fractionated DNA templates, which were PCR-amplified from strain-specific cDNA clones using purified forward primers (sequences below) and a common reverse primer (5’-TTA AAC AAA GAG GCG ATC TCC-3’): C57BL/6 (5’-GGA AGG AAG GAA GAG ATA ATA CGA CTC ACT ATA G AGA GAA CAC CAC CGC TGT AAA G-3’ with a C57BL/6 clone); C3H-L (5’-GGA AGG AAG GAA GAG ATA ATA CGA CTC ACT ATA GAG ACG CAA TTA GAA TTC ACA GAG with a C3H clone); C3H-S (5’-GGA AGG AAG GAA GAG ATA ATA CGA CTC ACT ATA GAA CAA GCC CGT GGA ACC TG-3’ with a C3H clone); and 5’-UU-3’ (5’-GGA AGG AAG GAA GAG ATA ATA CGA CTC ACT ATA GAG AGA ACA CCA TCG CTGTAA AG-3’ with a C3H clone). The resulting RNA was precipitated with ammonium acetate, and re-suspended with nuclease-free water; 2 µg RNA was used for in vitro translation (Thermo Scientific, Cat. # 88856); and at the indicated time points, a fixed portion of each reaction was analyzed by immunoblotting with anti-CD14 antibodies. Reactions from 1 µg of pCFE-GFP (Thermo Scientific, Cat. # 88856) were used as a positive control for in vitro transcription, and as a negative control for immunoblotting after in vitro translation.
Blood was obtained from anonymous donors at the Stanford University Blood Bank after informed consent was obtained. 100 ul of whole blood was placed in 1 ml PBS/2mM EDTA, and the solution was centrifuged and re-suspended in 1 ml 1× RBC lysis buffer (Biolegend, San Diego, CA, catalogue #420301). The solution was divided in half, and used for either total RNA or genomic DNA extraction. Total RNA was prepared using the QIAGEN RNeasy Mini kit (cat. # 74104). Genomic DNA was obtained by lysing the cells in 500ul SDS buffer (100mM Tris pH 7.4, 5mM EDTA, 200mM NaCl, 0.2% SDS, 100 ug/ml Proteinase K) at 55C for 10 min. The DNA was precipitated with 500 ul isopropanol, washed with 70% Ethanol, and genomic DNA was re-suspended in 100 ul TE1/10 buffer and stored at 4°C before use.
The genomic region spanning SNP rs2569190 was amplified (primers: 5'-TCC TGG GGA GAG AGC AGA GGT-3' and 5'-TTT GGT GGC AGG AGA TCA ACA-3') using the following PCR conditions: 95°C 5 min, 35 cycles of 95°C 30 sec, 60°C 30 sec, 72°C 1 min, plus a final 72°C 10 min cycle. The amplified products were subject to Ava II digestion; which digests amplicons containing the A (but not the G) allele. The allelic determinations were confirmed by A- or G-allele specific PCR performed using the following primers: 5'-TCC TGG GGA GAG AGC AGA GGT-3' and 5'-CAG AAT CCT TCC TGT TAC GGT-3' for A allele; 5'-TCC TGG GGA GAG AGC AGA GGT-3' and 5'-CAG AAT CCT TCC TGT TAC GGC-3' for G allele. The PCR amplifications were performed as follows: 95°C 5 min, 5 cycles of 95°C 30 sec, 68°C 30 sec, 72°C 30 sec, another 5 cycles of 95°C 30 sec, 65°C 30 sec, 72°C 30 sec, and 25 cycles of 95°C 30 sec, 63°C 30 sec, 72°C 30 sec, plus the final 72°C 10 min.
First strand cDNA from total RNA was synthesized using the Clontech SMARTer RACE Kit (cat. # 634923) and human CD14-specific primers. 5'-AAG GTT CTG GCG TGG TCG CAG AG-3' and 5'-CGG GTG CCG CTG TGT AGG AAA G-3' were used for 5'-RACE and 3'-RACE, respectively, according to the manufacturer's recommended PCR conditions. The 5'-RACE product was subject to a second round of PCR amplification (primer pair: 5'-ACT GAT GAG CTC AAG CAG TGG TAT CAA CGC AGA GT-3'/5'-AGC AGC AGC AGC AAC AAG CAG-3') under the following PCR parameters: 95°C for 5 min, 35 cycles of 95°C for 30 sec, 65°C for 30 sec, 72°C for 2 min, and a final 72°C for 10 min. Purified DNA was subject to Sac I digestion and ligated onto pcDNA 3.1+ (Invitrogen) for sequencing. At least 8 independent clones were analyzed for each blood sample.
A full length human CD14 construct was produced by ligating the longest 5'-RACE fragment from each of the above donors onto a 3'-RACE human CD14 clone at the Sac I site (located 25 bp 5’ of the initiator ATG) in pcDNA 3.1+ (Invitrogen) vector. The structure of all resulting constructs was confirmed by full-length sequencing. The DNA templates for T7-driven human CD14 transcripts were PCR-amplified from the full-length clone using a common reverse primer (5'-ACT GAT GTT TAA ACT GGG GCA AAG GGT TGA ATT GGT C-3' in one experiment and 5'-TTA CTT GTC GTC ATC GTC TTT GTA GTC GGC AAA GCC CCG GGC CCC TTG G-3' in the other experiment) and the following forward primers: 5'-GGA AGG AAG GAA GAG ATA ATA CGA CTC ACT ATA GAG GAT TAC ATA AAC TGT CAG AGG CAG-3' for −142 from ATG, 5'-GGA AGG AAG GAA GAG ATA ATA CGA CTC ACT ATA GCA GCC GAA GAG TTC ACA AGT GTG AAG-3' for −119 from ATG, 5'-GGA AGG AAG GAA GAG ATA ATA CGA CTC ACT ATA GTC ACA AGT GTG AAG CCT GGA AGC CGG-3' for −107 from ATG, and 5'-GGA AGG AAG GAA GAG ATA ATA CGA CTC ACT ATA GAC AAG TGT GAA GCC TGG AAG CCG GCG-3' for −105 from ATG. The in vitro transcription and translation experiments were performed as described for the murine CD14 experiments. The amount of in vitro translated human CD14 was measured by enzyme immunoassay (R&D, cat. # DC140) according to the manufacturer's instructions. The capture step was performed with an overnight incubation at 4°C, and the color was developed for 1 hour. Two independent experiments produced similar results.
After informed consent was provided, peripheral blood was obtained from healthy donors the Stanford Blood Center and monocytes were enriched using RosetteSep® Human Monocyte Enrichment Cocktail (STEMCELL Technologies, cat# 15028) followed by ficoll-hypaque density gradient centrifugation. Monocytes were further purified using MACS® CD14 microbeads (Miltenyi Biotec, cat# 130-050-201), and cultured at 1 × 106 cells/ml in DC media (Iscove’s Modified Dulbecco’s Medium (Gibco) containing 10% human AB serum, 100ug/ml Penicillin-Streptomycin, 2mM L-glutamine, and 50uM 2-ME) supplemented with recombinant human cytokines GM-CSF (100ng/ml, Berlex Laboratories Inc) and IL-4 (20ng/ml, Peprotech) for 6 days at 37°C/10% CO2. These cytokines were replenished on days 2 and 5 of culture. On day 6, the monocyte-derived DCs were assessed for conventional morphology by light microscopy and harvested via gentle scraping with cold 5mM EDTA in PBS. DCs or freshly isolated monocytes were stained with DAPI (Invitrogen), fluorescently conjugated mAbs against CD14, HLA-DR (Biolegend), CD209 (BD Biosciences), or appropriate isotype control mAbs. Their cell surface phenotype was assessed using a BD LSRII (BD Biosciences) insturment, and the data was analyzed using FlowJo software (TreeStar, Inc).
Viable DCs were re-suspended in DC stimulation media in the presence or absence of 1–5 ug/ml sCD14 (R&D Systems, cat# 383-CD-050-CF) at 1 × 106 cells/ml overnight at 37°C/10%CO2. The DC stimulation media contained different TLR agonists (Invitrogen), including LPS (TLR4), PolyI:C (TLR3), Flagellin (TLR5), or Pam3Csk4-(TLR1/2) at a range of concentrations. Supernatants and media only controls were collected 24 hr post stimulation, centrifuged to remove cellular debris, and stored in 96-well plates at −80°C for EIA. DCs were prepared from at least 4 healthy donors, and were used in two independent experiments that produced similar results. Human TNF-α, IL-10, IL-12, and IL-6 were analyzed using R&D Systems EIA kits (Cat. # DTA00C, D1000B, D1200, D6050). Human IFN-α and IFN-β were measured using PBL VeriKine kits (Cat. # 41100, 41410). For statistical analysis, the background-corrected IL-10 measurements were log-transformed, and an ANOVA model was used to assess the effect of sCD14. A fixed effect variable representing the presence or absence of sCD14 in the media as well as two random effect variables representing the donors and dosage of LPS, respectively, were included in the model. The model was evaluated and the significance of the effect of sCD14 was calculated using SAS 9.1 (Cary, NC).
DC were isolated as described above. On day 6 of culture, DC were harvested and stimulated with LPS in the absence or presence of 1ug/ml sCD14 for 24h and washed 4× with PBS to remove residual stimulants. Bulk CD4 T cells were enriched from peripheral blood from an allogeneic healthy donor using Rosette Sep cocktail (StemCell Technologies), and naïve CD4 T cells were further purified using MACs negative selection kit (Miltenyi Biotec). Naïve CD4 T cells were labeled with CFSE or CellTrace Violet tracking dyes (Invitrogen) prior to addition to washed DC for 7 days (2T and 4T to 1DC ratios). The T cells were re-suspended in fresh media and re-stimulated with PMA (200ng/ml) and Ionomycin (1ug/ml) in the presence of Brefeldin A for 15h and stained with Live Dead Aqua Fixable Dead cell stain for viability (Invitrogen), anti-human mAbs CD3 FITC, TNFalpha PE-CY7, IL-10 APC (BD Biosciences), CD3 APC-CY7, IL-17A PerCP Cy5.5, IFNgamma AF700, IL-13 PE, FOXP3 Pacific Blue, T-bet PerCP Cy5.5 (Biolegend), RORg(t) APC (eBiosciences) according to manufacturer’s protocols. For data analysis: the p-values were calculated using a standard ANOVA model. When the effect of adding sCD14 with different LPS concentrations was assessed, a variable representing the donor and another variable representing whether sCD14 was added and their interaction was included in the ANOVA model. When data for all concentrations of LPS were combined, another variable representing the LPS concentration as well as its interaction with the other variables was added to the model. In both cases, the p values for the main effect of adding sCD14 were reported.
Microarrays were used to measure the level of mRNA expression in un-stimulated splenic B cells purified from 11 different inbred mouse strains that were cultured for 24 hr. Using highly stringent criteria (ANOVA p-value<10−10 and a fold change >10 across the 11 strains analyzed), we identified 183 differentially expressed genes that could be separated into 40 groups with similar expression profiles across the inbred strains using K-mean cluster analysis, which is as an un-supervised (non-biased) analysis method (Fig. 1A). Although several clusters had interesting expression patterns, cluster 24 (Fig. 1B) was particularly interesting for several reasons. (i) It had the largest number of genes (n=40) of any cluster. (ii) The level of expression of each of the 40 cluster 24 genes in C3H/HeJ B cells was at least 10-fold below that in the other 10 strains. (iii) All of the genes in cluster 24 were known to be induced by type I interferons (Table S1). All of these features suggested that there could be common genetic basis for this expression pattern.
A missense (ProHis712) mutation within the 3rd exon of the Tlr4 gene is uniquely found in C3H/HeJ mice, which causes defective TLR4 signaling and LPS unresponsiveness (Hoshino et al., 1999). Proline is highly conserved at this position throughout the Toll-like receptor (TLR) protein family; the His712 allele is not found in other inbred strains, including the closely related C3H/OuJ and C3H/HeN sub-strains (Poltorak et al., 1998). Since this mutation could be responsible for the decreased expression in C3H/HeJ B cells, we also examined the level of expression of 5 of these mRNAs (Irf7, Mx1, Zpb1, Isg20, and Bst2) in B cells purified from C3H/OuJ mice, which have a normal Tlr4 allele on a genetic background that is otherwise identical to C3H/HeJ. C3H/OuJ B cells had reduced basal levels of expression of these mRNAs (not shown); but stimulation with increasing concentrations of IFNβ increased their expression to a level that was comparable to that in other strains (Figure 2A). Moreover, C3H/HeJ and C3HOuJ B cells, regardless of Tlr4 mutation status, had similarly reduced levels of IRF7 protein, which was significantly below that in C57B6 B cells (Figure 2B). It was also possible that a genetic difference within a cluster 24 gene could be causative. For example, Irf7 is a transcription factor, which is a member of the interferon regulatory factor gene family, that plays a major role in virally induced IFNα/β production and in immune cell development [reviewed in (Chau et al., 2008; Zhang and Pagano, 2002)]. However, neither Irf7 nor any other cluster 24 gene was amongst those with a C3H/HeJ-specific allele (see Table S2). Thus, neither the Tlr4 mutation nor a cis-genetic difference was responsible for the cluster 24-gene expression pattern. We also investigated the possibility that C3H/HeJ B cells had a defect in the proximal IFNα/β signaling pathway by measuring the amount of phosphorylated STAT1 protein in cultured B cells after IFNβ stimulation. Splenic B cells purified from C3H/HeJ, C3H/OuJ, C57B6 and DBA/2J mice exhibited comparable STAT1 phosphorylation after IFNβ stimulation (Figure 2C).
These analyses demonstrated that: 1) neither the Tlr4 mutation, nor a cis-genetic difference within a cluster 24 gene were causative; 2) the proximal (STAT1 phosphorylation) and distal segments (mRNA induction) of the type I IFN signaling pathway in C3H/HeJ B cells were intact; and 3) exogenous IFNβ increased cluster 24 gene expression in C3H/HeJ B cells to the same level as in the other strains. This indicated that the causative factor was extrinsic to B cells, and suggested that the decreased type I interferon production by C3H/HeJ mice could be responsible for the differences in expression. Therefore, we measured the amount of IFNβ produced by purified CD11b+ peritoneal macrophages after exposure to poly(IC), a synthetic mimic of viral dsRNA used to induce IFN α/β production (Majde, 2000). C3H/HeJ macrophages produced substantially less (2–2.5 fold) IFNβ than C57BL/6 macrophages after stimulation with a range of poly(IC) concentrations (1 – 10 µg/ml), but produced comparable amounts of IL-6 after stimulation with poly(IC) or multiple other agents that act via TLR receptors, except for lipopolysaccharide (LPS), which acts via Tlr4 (Fig. 2D, see legend for the TLR used by each agent). Consistent with their inactivating His712 Tlr4 mutation, C3H/HeJ macrophages produced much less IL-6 in response to a TLR4 agonist (LPS) than C57BL/6 macrophages. Decreased IFNβ production by C3H/HeJ macrophages could account for the cluster 24 gene expression pattern in B cells.
Haplotype-based computational genetic mapping (Liao et al., 2004; Wang and Peltz, 2005) with an expanded genetic database that covered all genes in the mouse genome was used to search for the causative factor. This analysis identified 2222 different genes whose pattern of genetic variation correlated with the cluster 24 gene expression pattern (Table S2). The large number of genetically correlated genes was not surprising, since this is the first time that a large (3.2 million SNPs) database that covers all genes in the mouse genome was used and C3H/HeJ mice had a unique phenotype (reduced gene expression), so that the computational analysis identified many genomic regions where C3H/HeJ mice have unique alleles. We have previously demonstrated (Li et al., 2010; Sato et al., 2004; Zhang et al., 2011) that gene expression data can be used to reduce the number of genetically correlated genes for further consideration. Therefore, we used a two-tiered gene expression-based strategy to identify the likely causative genes. Since C3H/HeJ macrophages produced less IFNα/β, we first selected 455 correlated genes that were expressed in macrophage-like cells (Table S2), and then further limited the number of candidates to the genes that were differentially expressed in C3H/HeJ macrophage-like cells (≥2-fold absolute difference from the average value of the other strains). Only 4 genes met this expression criterion (Cd14, Thbs1, Wtap, and 1110018M03Rik) and had a C3H/HeJ-specific haplotype (Fig. 3A).
Because of its known biological role in the innate immune response to viral and other pathogens, Cd14 was an obvious candidate. CD14 is a monocytic differentiation antigen that is an important regulator of innate immune responses to pathogens through an effect on TLR function [reviewed in (Akashi-Takamura and Miyake, 2008)]. CD14 is a high affinity receptor that can bind bacterial LPS (Labeta et al., 1993), viruses (Compton et al., 2003; Kurt-Jones et al., 2000; Pauligk et al., 2004) or poly(IC); thereby facilitating its internalization and enhancing intracellular activation and cytokine production (Lee et al., 2006). Re-sequencing of genomic DNA identified 7 rSNPs with alleles uniquely found in C3H/HeJ mice that are located near the previously identified Cd14 transcriptional start site (Table S3 and Fig. 3B). These were the only SNPs present in CD14 where C3H/HeJ had a unique allele among the classical inbred strains, and there were no non-synonymous coding changes found. Therefore, we analyzed the 5’ ends of Cd14 transcripts in C3H/HeJ and C57BL/6 macrophages. C3H/HeJ macrophages expressed 2 different Cd14 transcripts; in contrast, C57BL/6 macrophages, whose genotype is shared with the 9 other strains analyzed, had only one transcript. The shorter Cd14 C3H/HeJ transcript (C3H-S) had 83 bp at its 5’ end truncated relative to the larger transcript (C3H-L), and the C57BL/6 transcript was 26 bp smaller than the C3H-S transcript (Figs. 3B and S3). Cd14 transcripts did not have codon changes, and there were no other C3H/HeJ-unique SNPs within this gene.
CD14 is a 56-kDa glycosylphosphatidylinositol (GPI)-anchored receptor that is expressed on the surface of monocytic cells. Purified C3H/HeJ macrophages had the same amount of cellular CD14 as macrophages obtained from 3 other strains (Fig. 4A). However, there are at least two soluble forms of CD14 (sCD14) that are present in sera, which are produced by different mechanisms. In response to various activating stimuli, sCD14 is released by matrix metalloproteinase-mediated cleavage of the GPI-linked membrane form (Bazil and Strominger, 1991; Bufler et al., 1995; Mira et al., 2004). sCD14 can also be produced by direct secretion of CD14 with an intact COOH-terminal sequence that is not linked to a GPI moiety (Bufler et al., 1995). Irrespective of the method of production, sCD14 can bind ligand, and act as a co-ligand to alter cytokine production by endothelial, epithelial, vascular smooth muscle or glial cells (Cauwels et al., 1999; Yin et al., 2009). Sera obtained from C3H/HeJ mice had significantly less sCD14 than sera from other strains (Fig. 4B). To investigate the basis for this difference, purified peritoneal macrophages were cultured for 24 hr in the presence or absence of poly(IC), and the amounts of membrane CD14 and sCD14 in the supernatant were measured. Under basal conditions, C3H/HeJ macrophages reproducibly generated a minimal amount of sCD14, which was below that produced by C57BL/6 macrophages (Fig. 4A, lower panel). However, after poly(IC) activation, C3H/HeJ macrophages produced a similar level of sCD14 as C57BL/6 macrophages.
Since sCD14 was decreased in C3H/HeJ sera, and C3H/HeJ B cells had decreased expression of an IFN-responsive gene cluster, we investigated whether sCD14 affected type I IFN production. The effect of sCD14 on poly(IC)-stimulated IFNβ production by macrophages was examined. Although exogenous sCD14 did not increase IFNβ production by C57BL/6 macrophages; it increased IFNβ production by C3H/HeJ macrophages up to the same level as produced by C57BL/6 macrophages (Fig. 4C). Thus, under experimental conditions that mimic viral stimulation, exogenous sCD14 restored IFNβ production by C3H/HeJ macrophages to a normal level.
As noted above, the C3H-L and C3H-S transcripts are 109 bp and 26 bp longer, respectively, than the C57BL/6 transcript. Interestingly, there are have two SNPs located near (49 and 75 bp 5’ of) the AUG translation start site (Figs 5A and S1). Since the 5’UTR plays an important role in regulating protein translation (Gray and Wickens, 1998; van der Velden and Thomas, 1999), we used a T7-dependent in vitro transcription-translation system to investigate whether the different transcripts exhibit differences in protein translation. Both C3H-L and C3H-S transcripts had lower translation efficiency than the C57BL/6 transcript (Fig. 5B); quantitative densitometry indicated that they had ~50% of the translation efficiency of the C57/BL6 transcript (data not shown). Since this could be caused by a length-dependent or SNP-dependent mechanism, the in vitro translation efficiency of length-altered mutant forms of Cd14 transcripts was examined. Truncation to the same length as the C57BL/6 transcript (5’U-U 3’ construct) (see Fig. 5A) increased the translation efficiency of the C3H/HeJ transcripts (Fig. 5C). Thus, the Cd14 rSNPs affected the length of the 5’ UTR of Cd14 mRNA, and there was a transcript length-dependent effect on CD14 protein translation efficiency.
It was possible that a genetic mechanism affecting soluble CD14 production that was similar to that identified in mouse could be operative in humans. Therefore, we analyzed the 5' UTR region (−599 to the initiating ATG) of the human CD14 gene (from 139,992,841–139,993,439 on Chromosome 5, NCBI B36) using data obtained from the International HapMap1 and 1000 Genomes Project2 (June 2011). Of note, there was only a single SNP (C/T, rs2569190) in this region with a minor allele frequency (MAF) of >1%. SNP rs2569190, which is located 260 bp 5’ of the human CD14 translation initiation (ATG) codon, had MAFs of 48%, 50%, 49% and 32% in the CEU (n=174 individuals), CHB (n=86), JPT (n=89) and YRI (n=176) populations, respectively, in the International HapMap Project data. This SNP has been widely investigated; alleles have been associated with alterations in transcription (LeVan et al., 2001; Mertens et al., 2009) and in serum sCD14 levels (Kabesch et al., 2004; LeVan et al., 2006; Levan et al., 2008). Thus, a survey of over >500 individuals revealed that there is only one significant SNP in the human population in the 5’ UTR region of CD14.
Next, the 5’ UTR of human CD14 mRNAs, which were isolated from the peripheral blood of 14 normal donors that were genotyped at SNP rs2569190, were analyzed by PCR amplification and sequencing. CD14 mRNAs from all donors utilized more than one transcriptional start site, with a total of 19 different transcription start sites (TSS) being identified (Figure 6). The TSS were best matched to reference CD14 mRNA variant 1 (NM_000591.3), while the many of the others were matched with other known CD14 variants (NM_001040021.2, NM_001174104.1, NM_001174105.1). The dominant TSS was located at −104 bp (relative to the ATG initiation codon); but the TSS ranged from −353 to −79 bp, indicating that there is substantial variability (>274 bp) in the length of the 5’ UTR of human CD14 mRNA. Most of the TSS of the CD14 mRNAs were located between the −141 bp and −105 bp positions. No polymorphisms were identified within the 5’ UTR region of the CD14 mRNAs analyzed from these 14 donors, and there was no correlation between the TSS and SNP rs2569190 genotype in these donors.
To investigate whether CD14 transcript length affected the efficiency of protein translation, the 5’ UTR of CD14 transcripts with 3 different TSS (−141, −118, and −104 bp) were ligated into the same full-length human CD14 clone. The amount of human CD14 produced by each construct was measured using the T7-dependent in vitro transcription-translation system (Figure 6B). Surprisingly, the longest CD14 transcript (−141 bp) had the highest translation efficiency; statistically significant differences (p=0.003 or less) were noted at the 3 and 5-hour time points in the amount of CD14 protein produced relative to the other two transcripts, whose translational efficiency was ~50% of that of the −141 bp variant. Although the longer human CD14 transcript had a higher translational efficiency, these results indicate that there is variability in the length of the 5’ UTR of CD14 mRNAs in mouse and man, and the length of the 5’ UTR affects the efficiency of CD14 protein translation in both species.
Since DCs initiate the innate immune response to a nascent infection, we examined the effect that physiologic concentrations of sCD14 (1–5 ug/ml) had on cytokine production by human DCs in vitro. Human myeloid DCs isolated from peripheral blood and cultured in the presence of IL4 and GM-CSF express conventional DC markers CD209 and HLA-DR, but ~60-fold less membrane CD14 than freshly isolated monocytes (Fig. 7A). Since sCD14 might have a greater impact on cells with less membrane CD14, we examined IL-12, IL-10, IL-6, TNF-α, IFNα and IFNβ production by DCs after they were stimulated with different concentrations of microbial-derived TLR agonists, including: flagellin (TLR5), LPS (TLR4), poly(IC) (TLR3), and Pam3csk4 (TLR1/2). These ligands did not stimulate DCs to produce bioactive IL-12p70, IFNα, or IFNβ under multiple experimental conditions tested (data not shown). In the presence of the highest LPS concentration tested, sCD14 did not alter the amount of TNFα produced by DCs; while at the lowest (0.5 ng/ml) LPS concentration tested, sCD14 induced a 4-fold increase in TNFα production (Fig. 7B). At 5.0 and 0.5 ng/ml LPS, sCD14 caused a statistically significant increase in TNF-α (ANOVA model p-value= 0.0058) and IL-10 production (Fig. 7A). This effect was particularly pronounced at the 5.0 and 0.5 ng/ml LPS concentrations, where the aggregate effect of exogenous sCD14 on IL-10 production in all tested donors was statistically significant (p value = 0.0017). Unexpectedly, physiologic concentrations of sCD14 potently stimulated IL-6 production by DCs (Fig. 7C); the amount of sCD14-induced IL-6 production was comparable to the maximal amounts induced by the microbial-derived TLR5, TLR4, and TLR1/2 agonists (Fig. 7C). In contrast, under the same experimental conditions, these 3 microbial-derived TLR agonists (tested across a 3-log range of concentrations) induced much more TNFα production than did sCD14 (Fig. 7D). Of note, since human DCs were cultured in the presence of human serum, which contains sCD14, the effect of sCD14 could be underestimated in these in vitro experiments. Indeed, we observed that the effects of sCD14 on cytokine production were increased when the DCs from some donors were cultured for a short period without human serum (Fig. 7E). Thus, sCD14 has a potent and relatively specific ability to stimulate IL-6 production by DCs.
We wanted to determine if the effects of sCD14 on DCs could impact lymphocyte responses. Since it was previously demonstrated that proliferation and cytokine production by human T cells was inhibited when they were incubated with sCD14 (Rey Nores et al., 1999), we used a modified protocol, where the DCs were incubated with CD14 prior to co-culture with lymphocytes, to investigate to investigate this possibility. Purified human DCs were initially cultured in the presence or absence of sCD14, and 0, 0.5 or 5.0 ng/ml LPS for 24 hr. After washing, these DCs were used to stimulate mixed lymphocyte reactions with purified human CD4+ T cells. Under all of the conditions tested, the sCD14-treated DCs stimulated a (adjusted ANOVA p value = 0.0000003) 3-fold increase in CD4+ T cell proliferation relative to that induced by control DCs (adjusted ANOVA p value = 0.0000003). The % of proliferating T cells increased from 1.09±0.12% in the control DC cultures to 3.06±0.43% in the sCD14-stimulated cultures, and the sCD14-stimulated increases were statistically significant at all 3 LPS concentrations tested (Fig. 8). Thus, through an effect on DCs, sCD14 can augment the human T cell response.
The genetic and experimental analyses in mice demonstrate that Cd14 rSNPs affect basal sCD14 secretion through a coupled effect on the Cd14 transcript length and protein translation efficiency. This novel genetic effecter mechanism has substantial downstream consequences for the innate immune response; it altered type I IFN production by macrophages, and consequently, the level of expression of at least 40 IFN-responsive mRNAs in B cells, many of which (e.g. Irf7, Mx1, and Mx2) play important roles in the innate immune response. Although CD14 was initially identified as an LPS receptor (Wright et al., 1990) that initiated the immune response to bacterial infections, CD14 also plays a significant role in host responses to viruses (Pauligk et al., 2004) (Compton et al., 2003) (Kurt-Jones et al., 2000) and fungi (Yauch et al., 2004). Given the importance of this innate immune response pathway, it is likely that these Cd14 rSNPs affect other phenotypes of biomedical importance. The availability of transgenic mice expressing high levels of sCD14 (Higuchi et al., 2002) (Tamura et al., 1999) (Jacque et al., 2006) should enable the impact that changes in sCD14 levels have on biomedical traits to be evaluated.
We propose a novel mechanism to explain how Cd14 rSNPs might alter basal sCD14 production. Cells utilize a complex enzymatic process to produce GPI-anchored cell membrane proteins [reviewed in (Fujita and Kinoshita, 2010)], with at least eleven steps being required to synthesize the GPI precursor. A membrane-bound multi-subunit enzyme (the GPI transamidase) must recognize nascent proteins bearing the COOH-terminal GPI attachment signal, cleave this signal sequence, and link it with the GPI precursor (Ramalingam et al., 1996). Of note, the pattern of genetic variation within the genes involved in GPI synthesis and protein linkage would not explain the selective decrease in sCD14 in C3H/HeJ, nor would it explain the cluster 24-gene expression pattern. Since any enzyme-mediated process can be saturated, a genetic factor altering the translational efficiency of a nascent substrate protein could affect the efficiency of the GPI linkage pathway. By this proposed mechanism (‘overload hypothesis’), nascent CD14 protein that is not incorporated into the GPI-linkage pathway will be directly secreted. This model could explain why: C3H/HeJ mice have decreased basal levels of serum sCD14; C3H/HeJ macrophages have normal levels of membrane CD14 but produce less sCD14 under basal conditions, and release normal amounts of sCD14 after stimulation (via proteolytic cleavage of membrane CD14). Since there are at least 161 GPI-linked proteins in mammalian cells3 and soluble forms of many GPI proteins are present in serum, it is conceivable that rSNPs in other GPI-linked proteins could impact other biomedical phenotypes through this mechanism.
We did not identify a human allelic difference that altered sCD14 production. However, the analysis of cytokine production by human DCs and alloantigen-induced human CD4 T cell proliferation demonstrates how a subtle genetic alteration in basal sCD14 secretion can have a significant downstream impact on the innate immune response. DCs function as sentinels that initiate the innate immune response to pathogens (Liu, 2005). Physiologic concentrations of human sCD14 had a dual effect on DCs. First, sCD14 functioned as a signal-amplifier, enabling DCs to respond to low concentrations of microbial products, which is consistent with similar findings by other investigators (Cauwels et al., 1999; Chase and Bosio, 2010; de Buhr et al., 2009). Second, sCD14 stimulated IL-6 production by human DCs. IL-6 has pleotropic effects on virtually all organ systems, including an important role in qualitatively regulating T cell responses (Kimura and Kishimoto, 2010). IL-6 promotes the differentiation of Th17 cells (when combined with TGFβ), which are crucial for immune defense against multiple pathogens, it suppresses Treg cell development (Kimura et al., 2007; Zhou et al., 2007), and can stimulate cytolytic T cells when small amounts of antigen presenting cells are present (Ming et al., 1992). It is likely that sCD14 acts at least in part via TLR2, as sCD14 has been shown to be an agonist of TLR2-mediated cytokine production (Bsibsi et al., 2007; Iwaki et al., 2005; Kirschning et al., 1998; Nakata et al., 2006). sCD14 also promoted IL-10 production by DCs, which can limit the extent and qualitatively shape the host immune response to a pathogen [reviewed in (Saraiva and O'Garra, 2010)]. This dual effect may explain the discrepant results obtained when exogenous sCD14 was administered in two experimental murine models of infection. Interestingly, when exogenous sCD14 was administered in the context of Streptococcus (meningitis) infection, there was increased cytokine production within the infected organ even though the bacterial load was not diminished (Cauwels et al., 1999), nor did exogenous sCD14 improve survival after Franciscella tularensis infection (Chase and Bosio, 2010). The signal amplifying effect would explain why sCD14 caused a localized increase in cytokine production. However, if sCD14 promoted IL-10 and IL-6 production by DCs, this would alter the immune response to these pathogens, which explains why it failed to alter the outcome after these infections.
Over 500 publications have measured the amount of sCD14 in human serum in relation to a disease condition, and 281 publications have investigated an association between a CD14 5’ rSNP and susceptibility to asthma, allergy, sarcoidosis, biliary atresia, or acute otitis media4. Moreover, a rSNP (C/T, rs2569190) located 260 bp 5’ of the human CD14 translational start site has been shown to affect serum sCD14 levels in multiple human populations (Baldini et al., 1999; Inoue et al., 2007; Kabesch et al., 2004; LeVan et al., 2006; Levan et al., 2008), and is associated with infant susceptibility to respiratory syncytial virus (RSV)-induced bronchiolitis (Inoue et al., 2007). This is of interest since CD14 plays an essential role in the innate immune responses to RSV (Kurt-Jones et al., 2000) and other viruses (Compton et al., 2003; Pauligk et al., 2004). It is noteworthy that the allelic effect on sCD14 levels was seen in infants (LeVan et al., 2006) and in adults under basal conditions, but was eliminated in human subjects after experimental LPS exposure (Levan et al., 2008). These findings are strikingly similar to our characterization of sCD14 production by murine macrophages. IFNβ production by C3H/HeJ macrophage in response to an experimental viral stimulus was selectively restored after addition of exogenous sCD14 at concentrations (1–5 ug/ml) equivalent to those present in human serum (Baldini et al., 1999; Kabesch et al., 2004; LeVan et al., 2006). The selective impact of murine Cd14 rSNPs on basal sCD14 secretion may explain the discrepant results obtained in different human populations, and the variable outcomes emerging from association studies for CD14 rSNPs in atopic diseases that develop after infancy (Baldini et al., 1999; Campos et al., 2007; Kabesch et al., 2004). These associations may be explained, at least in part, by the selective impact of allelic differences on basal sCD14 levels and by the dual effects that sCD14 exerts on human DCs. Although the length of the 5’ UTR of CD14 mRNA was not determined by the SNP rs2569190 allele in the small number of individuals analyzed here, it is conceivable that other SNPs could alter the CD14 transcriptional start site or protein translation efficiency. Further studies are also required to determine if human sCD14 secretion is regulated by a mechanism that is similar to that observed in the mouse.
We thank Dr. Robert Lewis for helpful discussions. G.P. was partially supported by funding from a transformative RO1 award (1R01DK090992-01) from the NIDDK
This work was also supported in part by the Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health (R.S. and W.J.L.)
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3SwissProt database as of 5/11/10
4Pubmed search as of 5/10/10.