Expression profile of immune response genes in patients with Severe Acute Respiratory Syndrome
- Renji Reghunathan†1,
- Manikandan Jayapal†1,
- Li-Yang Hsu2,
- Hiok-Hee Chng3,
- Dessmon Tai4,
- Bernard P Leung1 and
- Alirio J Melendez1Email author
© Reghunathan et al; licensee BioMed Central Ltd. 2005
Received: 10 September 2004
Accepted: 18 January 2005
Published: 18 January 2005
Severe acute respiratory syndrome (SARS) emerged in later February 2003, as a new epidemic form of life-threatening infection caused by a novel coronavirus. However, the immune-pathogenesis of SARS is poorly understood. To understand the host response to this pathogen, we investigated the gene expression profiles of peripheral blood mononuclear cells (PBMCs) derived from SARS patients, and compared with healthy controls.
The number of differentially expressed genes was found to be 186 under stringent filtering criteria of microarray data analysis. Several genes were highly up-regulated in patients with SARS, such as, the genes coding for Lactoferrin, S100A9 and Lipocalin 2. The real-time PCR method verified the results of the gene array analysis and showed that those genes that were up-regulated as determined by microarray analysis were also found to be comparatively up-regulated by real-time PCR analysis.
This differential gene expression profiling of PBMCs from patients with SARS strongly suggests that the response of SARS affected patients seems to be mainly an innate inflammatory response, rather than a specific immune response against a viral infection, as we observed a complete lack of cytokine genes usually triggered during a viral infection. Our study shows for the first time how the immune system responds to the SARS infection, and opens new possibilities for designing new diagnostics and treatments for this new life-threatening disease.
Severe acute respiratory syndrome (SARS) emerged in 2003, as a new epidemic form of life-threatening infection . As of September 2003, there were 8098 cases of SARS from 29 countries with 774 deaths (WHO). SARS is characterized by high fever, malaise, rigors, headache, dry cough, and progression to interstitial infiltration in lungs with eventual mortality of greater than 10% in many countries . SARS has been shown to be caused by a novel coronavirus; SARS-CoV, with genome sequences recently published [3–7]. However, the pathogenesis of SARS is poorly understood. Major hematological features of this disease are lymphopenia, transient thrombocytopenia, and normal neutrophil and monocyte counts . It has been shown that SARS coronavirus infects and replicates in a wide variety of host cells, including PBMCs, in susceptible animals and human beings [9, 10]. Hence, to understand the host response to this pathogen, we profiled the gene expression patterns of peripheral blood mononuclear cells (PBMC) from SARS patients, compared to healthy controls using oligo nucleotide microarrays. We found that in the PBMC from SARS patients a number of genes were differentially expressed, as compared to healthy controls, including immune-related genes and these genes are not the typical ones expected in a viral infection.
During a viral infection, most cell types in the body respond by secreting high levels of type 1 interferons (IFN-α and IFN-β) . IFN-α/β can directly induce antiviral activities in neighboring cells, preventing viral spread by increasing the resistance of uninfected cells toward the virus. Moreover, these IFNs can activate Natural Killer (NK) cells mediated cytotoxity toward virus-infected cells [11, 12], and there is accumulating evidence that IFN-α/β contribute to driving the adaptive-immune response in the T helper cell type 1 (Th1) direction, via stimulation of IFN-γ expression . NK cells can produce IFN-γ , which activates leukocytes, such as monocytes/macrophages, that, in turn, participate in the antiviral responses by producing free radicals and proinflammatory cytokines such as TNF-α . During the response to viral infections, a key role is played by the expansion and activation of CD4+ and CD8+ T cells, which are central to the antiviral immunity, including their capability to inhibit replication and clear the infection. CD8+ cells have a direct effector role through cytotoxic T lymphocyte mediated lysis, and cytokine and chemokine production . The role of CD4+ T cells in antiviral immunity is highly dependent on production of cytokines, notably IFN-γ , and the cytolytic activity exerted by a subset of CD4+ T cells . Activation, coordination, and regulation of the above-described antiviral responses are mediated by complex mechanisms, where cytokines play important roles. However, to our surprise, we found that the patients' response of SARS appears to be mainly an innate inflammatory response, rather than a specific immune response against a viral infection. There is no significant level of up-regulation of MHC-I genes, neither for major cytokines including IFNs, nor for genes involved in complement mediated cytolysis, suggesting that the immune response against SARS-CoV may be different from other viral infections.
Results and discussion
To study the differential immune-gene expression patterns induced by SARS coronavirus, PBMCs from patients with SARS and normal subjects were examined using microarray technology. We shooed to use PBMCs as these cells are more easily obtained from patients compared to other infected tissues, and the SARS-CoV has recently been shown to infect PBMCs [9, 10]. The method of global gene expression analysis using oligonucleotide microarrays has proven to be a sensitive method to develop and refine the molecular determinants of several human disorders, including cancer and autoimmune diseases, and has provided us with signatures of the immune response . Using this technology, complemented with powerful analytical methods, we compared the gene expression profiles of PBMC from a series of SARS patients with those of healthy donors. To ensure the reliability and reproducibility of the microarray analysis, Pearson Correlation factors using the signal from all the normal samples were calculated. All four control arrays have Pearson correlation coefficients (r) of >0.95, which suggests an excellent reproducibility among individual arrays in the same experiment and between normal control experiments.
Immune-response related genes which were found to be significantly up-regulated in PBMCs of SARS patients. Level of expression is expressed in Fold change (average of fold changes of ten patients, S1-S10) as compared to that of control samples from normal human subjects (C1-C4).
Gen Bank ID
Fold change (S1-S10)
Carcinoembryonic antigen-related cell adhesion molecule 8
S100 calcium binding protein A9
Bactericidal permeability-increasing protein
Peptidoglycan recognition protein
Defensin alpha 4
Antimicrobial LPS-binding protein CAP18
S100 calcium-binding protein P
Defensin alpha 1
Leukocyte immunoglobulin-like receptor, subfamily A, member 3
RAS oncogene family (RAB13)
Secretory leukocyte protease inhibitor
Neutrophil cytosolic factor 1
Interleukin 8 receptor alpha
Rag D protein
integrin, alpha 2b (antigen CD41B)
Charot-Leyden crystal protein
Nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha
S100 calcium-binding protein A12
Tissue inhibitor of metalloproteinase 2
Formyl peptide receptor 1
Nuclear factor, interleukin 3 regulated
Complement cytolysis inhibitor (CLI)
S100 calcium-binding protein A9
Inositol (myo)-1(or 4)-monophosphatase 2
T-cell acute lymphocytic leukemia 1
Alanyl (membrane) aminopeptidase
RAS oncogene family
CD9 antigen (p24)
C-type lectin, superfamily member 9
RAS oncogene family member (RAB32)
Leukocyte IgG receptor (Fc-gamma-R)
Lleukocyte immunoglobulin-like receptor, subfamily A2
Leukotriene b4 receptor
Integrin, alpha M
Phosphoinositide-3-kinase, catalytic, beta polypeptide
Myeloid cell nuclear differentiation antigen
Toll-like receptor 2
Immediate early response 3
Serine (or cysteine) proteinase inhibitor alpha-1
Platelet derived growth factor C
Frequently rearranged in advanced T-cell lymphomas
Colony stimulating factor 2 receptor beta
T-cell, immune regulator 1
Leukotriene A-4 hydrolase
Tubulin, beta, 2
Tissue inhibitor of metalloproteinase 1
Similar to vesicle-associated membrane protein 3
Lipocalins are a family of small, secreted proteins, which have little amino acid sequence homology (20–30%) but share a common three-dimensional structure [21, 22]. Tissue distribution studies have revealed that a member of this family 24p3 lipocalin (24p3) is mainly expressed in the liver during an acute phase response . It has also been detected in spleen, lung and the uterus. In the latter location, its expression has been found to be coincident with parturition, a time of major tissue remodeling and inflammation . 24p3 has also been detected in the conditioned media of LPS stimulated murine PU5.1.8 macrophages and therefore it has been suggested to function in defense against infectious agents . However, recent evidence proposed that the lipocalins may trigger apoptosis in immune cells via an unknown cell surface receptor . SARS patients with respiratory distress fulfill criteria for Acute Respiratory Distress Syndrome (ARDS) and diffuse alveolar damage is seen in the lungs on histological examination of postmortem . We speculate that upregulation of lipocalins are part of the host response in limiting unwanted tissue damage and in reducing inflammation and lung fibrosis. In addition, enhanced expression of lipocalins may play a role in the potential cause of lymphopenia observed in the majority of patients.
There is also up-regulation of expression of genes, such us, Bactericidal Permeability Increasing Protein (BPI) and Carcino Embryonic Antigen related Cell adhesion Molecule 8 (CEACAM 8 or CD66b). BPI is released from activated neutrophils and is an endogenous antibiotic, which rapidly kills Gram negative bacteria by high affinity binding to the LPS component of the cell wall . CEACAM 8 is expressed by activated monocytes and granulocytes, and significant up-regulation of this gene indicates the involvement of innate immune cells in SARS. Other genes which encode proteins like Leukotrien-B4 receptor (LTB4R), Leukotrien-A4 hydrolase, IL-8 receptor (IL-8RA), anaphylatoxin C3a receptor-1 (C3aR1), Neutrophil Cytosolic Factor 1 (NCF 1), S100 calcium binding protein A9, Defensin, (DEFA 1/4), LPS binding protein CAP18 (CAMP), and Peptidoglycan Recognition Protein (PGLYRP) are involved in chemotaxis, inflammatory reaction and superoxide metabolism . Similarly, Formyl Peptidase Receptor (FPR) genes are expressed by activated neutrophils. Regulation of expression of FPR on neutrophils plays a key role in neutrophil polarization and chemotaxis. Chemotaxis is effected by a neutrophil membrane mesh, via remodeling of the actin component of the membrane . Up-regulation of CD24 and FcγR3A indicates some degree of neutrophil, B cell and NK cell activity. FcγR3a, a low affinity receptor for IgG, expressed in activated macrophages and NK cells, facilitates Antibody Dependent Cell mediated Cytotoxicity (ADCC) by NK cells .
There is moderate up-regulation of the kappa light chain of the Nuclear Factor (NFκB 1A) and the B-cell lymphoma 3-encoded protein (BCL3) (Table 1). The major form of NFκB is a heterodimer of p65/p50 subunits, which regulates expression of many proteins in activated immune cells by binding to DNA. It interacts with NFκB through its ankyrin repeats and it favors p50 dimerization by recruiting p50 monomers from the cytoplasmic pool of p105/p50 dimers, and thereby enhancing nuclear translocation and DNA binding of p50 dimers . BLC3 is expressed by activated B cells and T cells on mitotic stimuli, playing an important role in transcription activation .
Immune-response related genes which were found to be significantly down-regulated in PBMCs of SARS patients. Level of expression is expressed in Fold change (average of fold changes of ten patients, S1-S10) as compared to that of control samples from normal human subjects (C1-C4).
Fold change (S1-S10)
IL2-inducible T-cell kinase
Lymphoid enhancer factor-1
Lymphocyte-specific protein tyrosine kinase
Signal transducer and activator of transcription 4
ADP-ribosylation factor-like 7
T cell receptor zeta-chain
Alpha 4 subunit of VLA-4 receptor
Chemokine (C-C motif) receptor 7
Interleukin 10 receptor, alpha
Epsilon polypeptide of CD3
Interferon consensus sequence binding protein 1
Lymphocyte adaptor protein
Ras-GTPase activating protein SH3 domain-binding protein 2
Small inducible cytokine subfamily E, member 1
Genes involved in homeostasis and cell growth, which were found to be significantly up-regulated in PBMCs of SARS patients. Level of expression is expressed in Fold change (average of fold changes of ten patients, S1-S10) as compared to that of control samples from normal human subjects (C1-C4).
Gen Bank ID
Fold change (S1-S10)
Eukaryotic translation initiation factor 1A
Ribosomal protein S4, Y-linked
transcobalamin I (vitamin B12 binding protein, R binder family)
MAX dimerization protein
Peptidyl arginine deiminase, type V
Hemoglobin, gamma G
Hemoglobin, gamma A
SMC (mouse) homolog, Y chromosome
Ribonuclease, RNase A family, 2
Homeo box 1
B-cell CLLlymphoma 3
Early growth response 1
Solute carrier family 2, member 3
Fatty-acid-Coenzyme A ligase, long-chain 2
Nuclear factor (erythroid-derived) 2
H2A histone family, member O
H1 histone family, member 2
insulin receptor substrate-2
hemoglobin, epsilon 1
Cathepsin X precursor
syntaxin binding protein 2
H2B histone family, member S
Secreted protein, acidic, cysteine-rich (osteonectin)
Two genes for novel histone 1
Pyridoxal (pyridoxine, vitamin B6) kinase
Ribonucleotide reductase M2 polypeptide
Sjogren syndrome antigen A2
CCAATenhancer binding protein
H2B histone family, member Q
Similar to metaxin 1
Histone family, member A
H2B histone family, member B
Pituitary tumor-transforming 1
Pilin-like transcription factor
Methionine sulfoxide reductase A
CCAAT enhancer binding protein
Putative Rab5-interacting protein
Thyroid Hormone Receptor Associated protein
Glutathione-S-transferase like; glutathione transferase omega
Solute carrier family 21 (organic anion transporter)
Expression of microarray analysis compared with Real-time RT-PCR analysis
In this study we have performed extensive analysis of gene expression of PBMCs of SARS patients using a microarray platform that includes more than 8000 gene sequences. However, one potential drawback in our current experimental design is that the gene expression levels were compared between patient samples and normal controls using PBMCs, rather than purified individual cell types. While it would be of interest to determine the exact proportions of monocytes, lymphocytes and contaminating granulocytes in our PBMC preparations, unfortunately, given the highly infective nature of patient samples and lack of suitable flowcytometry facility with appropriate bio-safety control, FACS analysis was, however, not possible.
Our results suggest that the response of SARS affected patients seems to be mainly an innate inflammatory response, rather than a specific immune response against a viral infection. There is no significant level of up-regulation of MHC-I genes or major cytokines including IFNs (α,β, and γ), or genes involved in complement mediated cytolysis, suggesting that the immune response against the SARS-CoV may be different from other viral infections, or that the virus may be using a unusual strategy to evade the host immune system and cause the pathogenesis and mortality.
This differential gene expression profiling of PBMCs from patients with SARS, strongly suggests that the response of SARS affected patients seems to be mainly an innate inflammatory response, rather than a specific immune response against a viral infection. There is no significant level of up-regulation of MHC-I genes or major cytokines, including IFNs, or complement mediated cytolysis, suggesting that the immune response against the SARS-CoV may be different from other viral infections or that the virus may be using a different strategy to evade the host immune system and cause the pathogenesis and mortality. The severity of the disease does not seem to change the genetic profile of the PBMCs: 5 of our patients were in intensive care, whereas the other 5 patients did not require intensive care, at the time the blood was taken. However, no substantial gene-expression differences were observed that could correlate to disease severity. Moreover, we confirmed (by real-time-PCR) that cytokines usually associated with viral infections (such as interferons and other cytokines) were not detected in the SARS samples, but were triggered in another viral infection (influenza patients). We also showed that genes triggered in the SARS patients were not triggered in the influenza patients.
Our study shows, for the first time, how the immune system responds to the SARS infection and opens new possibilities for designing new diagnostics and treatments for this new life-threatening disease.
Patients and peripheral blood RNA extraction
Our study included ten adult patients (S1-S10) who were diagnosed with SARS according to the World Health Organization (WHO) SARS criteria and admitted to the Tan Tock Seng Hospital, Singapore, for treatment and four additional normal human subjects (C1-C4). Our protocol was approved by the Tan Tock Seng Hospital Research Ethics committee. Peripheral blood was collected by vein-puncture of the brachial vein; mononuclear cells (PBMC) were prepared using histopaque (Sigma- Aldrich, St. Louis, MO) according to manufacturer's recommendations. Sample preparation and processing procedures were carried out as described in the Affymetrix Gene Chip Expression Analysis Manual (Affymetrix Inc., Santa Clara, CA). Briefly, total RNA was extracted from PBMCs, using Trizole method (Invitrogen, Carlsbad, CA) and further purified using RNeasy columns according to manufacturer's instructions (Qiagen, Valencia, CA). Integrity of total RNA was confirmed by formamide gel electrophoresis and quantification was carried out by measuring the A260 nm.
Generation of cDNA and labeled cRNA
5 μg of total RNA was used to synthesize double stranded cDNA using T7-(dT24) oligonucleotide primer and Superscript reverse transcriptase (Invitrogen). The resultant cDNA was purified by phenol:chloroform extraction and ethanol precipitation in presence of 7.5 M ammonium acetate. 1 μg of purified cDNA was subsequently used to synthesize biotin labeled cRNA by in vitro transcription (IVT) using T7 RNA polymerase at 37°C for 5-6 hr as per manufacturers' instructions (ENZO labeling kit, Ambion, USA). Labeled cRNA obtained after IVT was purified using RNeasy columns (Qiagen). Purified cRNA was fragmented using fragmentation buffer (40 mmol/L Tris acetate, pH 8.1, 100 mmol/L Potassium acetate, 30 mmol/L Magnesium acetate) at 94°C for 35 min.
Microarray hybridization and scanning
Fragmented cRNA (10 to11μg/ probe array) was used to hybridize to human focus array (HG-Focus Array) at 45°C for 16 hr with constant rotation of 60 rpm in a Gene chip hybridization oven 640 (Affymetrix). The chips were washed and stained using Gene chip fluidics Station 400 (Affymetrix). Staining was performed using streptavidine phycoerythrin conjugate (SAPE, Molecular Probes, Eugene, OR), followed by the addition of biotinylated antibody to streptavidine (Vector Laboratories, CA), and finally with streptavidine phycoerythrin conjugate. Probe arrays were scanned using Agilent Gene Array Scanner Series US 74900593 (Agilent technologies, USA).
Data filtering and analysis
An absolute expression analysis was performed using Microarray Suite Software 5.0 (Affymetrix) and relative mRNA expression levels were expressed as plus or minus fold changes compared to normal controls. Each chip was scaled to an overall intensity of 500 to correct for minor differences in overall chip hybridization intensity, and to allow comparison between chips. The data from ~8700 genes was imported in to MicroDB 3.0 and Data Mining Tool 3.0 (Affymetrix) for further analysis. Pearson Correlation coefficient (r) was used to ensure the reproducibility of the data using signal from normal samples. T-test was performed to identify genes that were differentially expressed in SARS patients over normal samples. The statistical significance of the differential expression of any gene was assessed by computing P value for each gene. Any gene for which this P value was < 0.01 was considered to be differentially expressed. We selected 186 genes for further analysis that met the following criteria:
(i) Changes in expression of at least 2 fold higher or lower comparing the normal
(ii) Signal >500
(iii) Detection P < 0.01 and
(iv) Genes which met the above criteria in at least 30% of samples.
Finally, data representing 10 SARS patients fold changes over control samples were averaged.
Genes were annotated according to biological process using the Gene ontology: tool for the unification of biology from the Gene Ontology Consortium . The complete set of raw data was deposited into the NCBIs' Gene Expression Omnibus (GEO) and it can be accessed through the GEO accession 'GSE1739' http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1739.
Unweighted average linkage Hierarchical clustering was applied for samples using the 'Genesis' software . Genes with 'Present' call (P < 0.01) and significantly changes in expression of at least 2 fold higher or lower comparing the normal were selected. Finally 248 genes which are passing this filter criteria in at least 15% of samples and above were selected for clustering.
Real-time quantitative PCR
Real-time PCR was performed for ten genes, namely: Lactoferrin (Primers: 5' tcg tcc tgc tgt tcc tcg ggg 3' and 5' tcc agc ggt cct gcg aag gcc 3'); Lipocalin (Primers: 5' aag ccc ctg ctc ctg gcc atc agc 3' and 5' cga cct gat gct gta tgc cac gtg 3'); S100P (Primers: 5' cat gat cat aga cgt ctt ttc 3' and 5' aca cga tga act cac tga agt 3'); TLR2 (Primers: 5' gta tct gca agg gca gct cag gat 3' and 5' ttc ctc aag gaa ggt aag tcc agc 3'); FCGR3A (Primers: 5' ctc cgg ata tct ttg gtg act 3' and 5' tgc aga gca gtg ttc ttc cag 3'); IFNA (Primers: 5'atg gcc ttg acc ttt gct tt 3' and 5'tgg aag att tcc tca tag c 3'); IFNB (Primers: 5'atg acc aac aag tgt ctc ctc caa a 3' and 5' ttc ttc cag gac tgt ctt ca 3'); IL-12 p40 (Primers: 5'atg tgt cac cag cag ttg gtc atc 3' and 5'ctg aat gtc aaa tca gta ct 3'); TNFA (Primers: 5'gag tga caa gcc tgt agc cca tgt tgt agc 3' and 5'gca atg atc cc a aag tag acc tgc cca gac 3'); GAPDH (Primers: 5'acc aca gtc cat gcc atc ac 3' and 5'tcc acc acc ctg ttg ctg ta 3'). For amplicon detection, the Light Cycler RNA Master SYBR Green Kit (Roche) was used as described by the manufacturer. PCRs were performed in a LightCycler® instrument (Roche) as follows: reverse transcription at 61°C for 20 min, initial denaturation at 95°C for 2 min; amplification for 45–65 cycles of denaturation (95°C, 5s, ramp rate 2°C /s), annealing (optimal temperature, 5s, ramp rate 2°C /s) and extension (72°C, product length [bp]/25 s, ramp rate 2°C /s). A single online fluorescence reading for each sample was taken at the end of extension step. Quantitative results were expressed by identification of the second derivative maximum points, which marked the cycles where the second derivatives of the fluorescence signal curves are at maximum. These points were expressed as fractional cycle numbers. Then, these cycle numbers were plotted against the logarithm of the concentrations of serially 2-fold diluted standard samples to obtain a standard curve. The concentrations of unknown samples were calculated by extrapolation from this standard curve. Positive sample specificity was confirmed by determining the melting curve (95°C, 5s, ramp rate 20°C /s; 68°C, 15s, ramp rate 20°C /s; 95 °C, 0s, ramp rate 0.1°C /s, continuous measurement).
This work was supported by Biomedical Research Council-Young Investigator Award grant R-185-000-044-305. We thank A-K Fraser-Andrews for proofreading the manuscript.
- Peiris JS, Chu CM, Cheng VC, Chan KS, Hung IF, Poon LL, Law KI, Tang BS, Hon TY, Chan CS, Chan KH, Ng JS, Zheng BJ, Ng WL, Lai RW, Guan Y, Yuen KY: Clinical progression and viral load in a community outbreak of coronavirus-associated SARS pneumonia: a prospective study. Lancet . 2003, 361: 1767-1772. 10.1016/S0140-6736(03)13412-5.View ArticlePubMedGoogle Scholar
- Lee N, Hui D, Wu A, Chan P, Cameron P, Joynt GM, Ahuja A, Yung MY, Leung CB, To KF, Lui SF, Szeto CC, Chung S, Sung JJ: A Major Outbreak of Severe Acute Respiratory Syndrome in Hong Kong. N Engl J Med . 2003, 348: 1986-1994. 10.1056/NEJMoa030685.View ArticlePubMedGoogle Scholar
- Peiris JS, Lai ST, Poon LL, Guan Y, Yam LY, Lim W, Nicholls J, Yee WK, Yan WW, Cheung MT, Cheng VC, Chan KH, Tsang DN, Yung RW, Ng TK, Yuen KY: Coronavirus as a possible cause of severe acute respiratory syndrome. Lancet. 2003, 361: 1319-1325. 10.1016/S0140-6736(03)13077-2.View ArticlePubMedGoogle Scholar
- Drosten C, Gunther S, Preiser W, van der Werf S, Brodt HR, Becker S, Rabenau H, Panning M, Kolesnikova L, Fouchier RA, Berger A, Burguiere AM, Cinatl J, Eickmann M, Escriou N, Grywna K, Kramme S, Manuguerra JC, Muller S, Rickerts V, Sturmer M, Vieth S, Klenk HD, Osterhaus AD, Schmitz H, Doerr HW: Identification of a novel coronavirus in patients with severe acute respiratory syndrome. N Engl J Med. 2003, 348: 1967-1976. 10.1056/NEJMoa030747.View ArticlePubMedGoogle Scholar
- Ksiazek TG, Erdman D, Goldsmith CS, Zaki SR, Peret T, Emery S, Tong S, Urbani C, Comer JA, Lim W, Rollin PE, Dowell SF, Ling AE, Humphrey CD, Shieh WJ, Guarner J, Paddock CD, Rota P, Fields B, DeRisi J, Yang JY, Cox N, Hughes JM, LeDuc JW, Bellini WJ, Anderson LJ: A novel coronavirus associated with severe acute respiratory syndrome. N Engl J Med . 2003, 348: 1953-1966. 10.1056/NEJMoa030781.View ArticlePubMedGoogle Scholar
- Rota PA, Oberste MS, Monroe SS, Nix WA, Campagnoli R, Icenogle JP, Penaranda S, Bankamp B, Maher K, Chen MH, Tong S, Tamin A, Lowe L, Frace M, DeRisi JL, Chen Q, Wang D, Erdman DD, Peret TC, Burns C, Ksiazek TG, Rollin PE, Sanchez A, Liffick S, Holloway B, Limor J, McCaustland K, Olsen-Rasmussen M, Fouchier R, Gunther S, Osterhaus AD, Drosten C, Pallansch MA, Anderson LJ, Bellini WJ: Characterization of a novel coronavirus associated with severe acute respiratory syndrome. Science. 2003, 300: 1394-1399. 10.1126/science.1085952.View ArticlePubMedGoogle Scholar
- Marra MA, Jones SJ, Astell CR, Holt RA, Brooks-Wilson A, Butterfield YS, Khattra J, Asano JK, Barber SA, Chan SY, Cloutier A, Coughlin SM, Freeman D, Girn N, Griffith OL, Leach SR, Mayo M, McDonald H, Montgomery SB, Pandoh PK, Petrescu AS, Robertson AG, Schein JE, Siddiqui A, Smailus DE, Stott JM, Yang GS, Plummer F, Andonov A, Artsob H, Bastien N, Bernard K, Booth TF, Bowness D, Czub M, Drebot M, Fernando L, Flick R, Garbutt M, Gray M, Grolla A, Jones S, Feldmann H, Meyers A, Kabani A, Li Y, Normand S, Stroher U, Tipples GA, Tyler S, Vogrig R, Ward D, Watson B, Brunham RC, Krajden M, Petric M, Skowronski DM, Upton C, Roper RL: The genome sequence of the SARS-associated coronavirus. Science. 2003, 300: 1399-1404. 10.1126/science.1085953.View ArticlePubMedGoogle Scholar
- Ruan YJ, Wei CL, Ee AL, Vega VB, Thoreau H, Su ST, Chia JM, Ng P, Chiu KP, Lim L, Zhang T, Peng CK, Lin EO, Lee NM, Yee SL, Ng LF, Chee RE, Stanton LW, Long PM, Liu ET: Comparative full-length genome sequence analysis of 14 SARS coronavirus isolates and common mutations associated with putative origins of infection. Lancet. 2003, 361: 1779-1785. 10.1016/S0140-6736(03)13414-9.View ArticlePubMedGoogle Scholar
- Gillim-Ross L, Taylor L, Scholl DR, Ridenour J, Paul S, Masters PS, Wentworth DE: Discovery of Novel Human and Animal Cells Infected by the Severe Acute Respiratory Syndrome Coronavirus by Replication-Specific Multiplex Reverse Transcription-PCR. JCM. 2004, 42: 3196-3206.View ArticleGoogle Scholar
- Li L, Wo J, Shao J, Zhu H, Wu N, Li M, Yao H, Hu M, Dennin RH: SARS-coronavirus replicates in mononuclear cells of peripheral blood (PBMCs) from SARS patients. J Clin Virol. 2003, 28: 239-44. 10.1016/S1386-6532(03)00195-1.View ArticlePubMedGoogle Scholar
- Guidotti LG, Chisari FV: Cytokine-mediated control of viral infections. Virology. 2000, 273: 221-227. 10.1006/viro.2000.0442.View ArticlePubMedGoogle Scholar
- Biron CA: Role of early cytokines, including α and β interferons (IFN-α/β), in innate and adaptive immune responses to viral infections. Semin Immunol. 1998, 10: 383-390. 10.1006/smim.1998.0138.View ArticlePubMedGoogle Scholar
- Biron CA, Nguyen KB, Pien GC, Cousens LP, Salazar-Mather TP: Natural killer cells in antiviral defence: function and regulation by innate cytokines. Annu Rev Immunol. 1999, 17: 189-220. 10.1146/annurev.immunol.17.1.189.View ArticlePubMedGoogle Scholar
- Harty JT, Tvinnereim AR, White DW: CD8+ T cell effector mechanisms in resistance to infection. Annu Rev Immunol. 2000, 18: 275-308. 10.1146/annurev.immunol.18.1.275.View ArticlePubMedGoogle Scholar
- Guidotti LG, Chisari FV: Noncytolytic control of viral infections by the innate and adaptive immune response. Annu Rev Immunol. 2001, 19: 65-91. 10.1146/annurev.immunol.19.1.65.View ArticlePubMedGoogle Scholar
- Price DA, Klenerman P, Booth BL, Phillips RE, Sewel AKl: Cytotoxic T lymphocytes, chemokines and antiviral immunity. Immunol Today. 1999, 20: 212-216. 10.1016/S0167-5699(99)01447-4.View ArticlePubMedGoogle Scholar
- Shaffer AL, Rosenwald A, Hurt EM, Giltnane JM, Lam LT, Pickeral OK, Staudt LM: Signatures of the Immune Response. Immunity . 2001, 15: 375-385. 10.1016/S1074-7613(01)00194-7.View ArticlePubMedGoogle Scholar
- Baveye S, Elass E, Mazurier J, Spik G, Legrand D: Lactoferrin: a multifunctional glycoprotein involved in the modulation of the inflammatory process. Clin Chem Lab Med. 1999, 37: 281-286. 10.1515/CCLM.1999.049.View ArticlePubMedGoogle Scholar
- Yang Z, Tao T, Raftery MJ, Youssef P, Di Girolamo N, Geczy CL: Proinflammatory properties of the human S100 protein S100A12. J Leukoc Biol. 2001, 69: 986-994.PubMedGoogle Scholar
- Frosch M, Strey A, Vogl T, Wulffraat NM, Kuis W, Sunderkotter C, Harms E, Sorg C, Roth J: Myeloid-related proteins 8 and 14 are specifically secreted during interaction of phagocytes and activated endothelium and are useful markers for monitoring disease activity in pauciarticular-onset juvenile rheumatoid arthritis. Arthritis Rheum . 2000, 43: 628-637. 10.1002/1529-0131(200003)43:3<628::AID-ANR20>3.0.CO;2-X.View ArticlePubMedGoogle Scholar
- Flower DR, North ACT, Attwood TK: Mouse oncogene protein 24p3 is a member of the lipocalin protein family. Biochem Biophys Res Commun. 1991, 180: 69-74.View ArticlePubMedGoogle Scholar
- Liu QS, Nilsen-Hamilton M: Identification of a new acute phase protein. J Biol Chem. 1995, 270: 22565-22570. 10.1074/jbc.270.38.22565.View ArticlePubMedGoogle Scholar
- Devireddy LR, Teodoro JG, Richard FA, Green MR: Induction of apoptosis by a secreted lipocalin that is transcriptionally regulated by IL-3 deprivation. Science. 2001, 293: 829-834. 10.1126/science.1061075.View ArticlePubMedGoogle Scholar
- Lew TW, Kwek TK, Tai D, Earnest A, Loo S, Singh K, Kwan KM, Chan Y, Yim CF, Bek SL, Kor AC, Yap WS, Chelliah YR, Lai YC, Goh SK: Acute respiratory distress syndrome in critically ill patients with severe acute respiratory syndrome. J A M A. 2003, 290: 374-380. 10.1001/jama.290.3.374.View ArticlePubMedGoogle Scholar
- Marra MN, Wilde CG, Collins MS, Snable JL, Thornton MB, Scott RW: The role of BPI protein a natural inhibitor of bacterial endotoxin. J Immunol. 1992, 148: 532-537.PubMedGoogle Scholar
- Melchjorsen J, Sorensen LN, Paludan SR: Expression and function of chemokines during viral infections: from molecular mechanisms to in vivo function. J Leukoc Biol. 2003, 74: 331-343. 10.1189/jlb.1102577.View ArticlePubMedGoogle Scholar
- Jesaitis AJ, Klotz KN: Cytoskeletal regulation of chemotactic receptors: Molecular complexation of N-formyl peptide receptors with G proteins and actin. Eur J Haematol. 1993, 51: 288-293.View ArticlePubMedGoogle Scholar
- May MJ, Ghosh S: Signal transduction through NF-κB. Immunol Today. 1998, 19: 80-88. 10.1016/S0167-5699(97)01197-3.View ArticlePubMedGoogle Scholar
- Na SY, Choi JE, Kim HJ, Jhun BH, Lee YC, Lee JW: Bcl3, an IkappaB protein, stimulates activating protein-1 transactivation and cellular proliferation. J Biol Chem. 1999, 274: 28491-28496. 10.1074/jbc.274.40.28491.View ArticlePubMedGoogle Scholar
- Heidecke CD, Hensler T, Weighardt H, Zantl N, Wagner H, Siewert JR, Holzmann B: Selective defects of T lymphocyte function in patients with lethal intra-abdominal infection. Am J Surg. 1999, 178: 288-292. 10.1016/S0002-9610(99)00183-X.View ArticlePubMedGoogle Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G: Gene ontology: tool for the unification of biology from the Gene Ontology Consortium. Nat Genet. 2000, 25: 25-29. 10.1038/75556.PubMed CentralView ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.