Molecular signature of clinical severity in recovering patients with severe acute respiratory syndrome coronavirus (SARS-CoV)

Abstract

Background: Severe acute respiratory syndrome (SARS), a recent epidemic human disease, is caused by a novel coronavirus (SARS-CoV). First reported in Asia, SARS quickly spread worldwide through international travelling. As of July 2003, the World Health Organization reported a total of 8,437 people afflicted with SARS with a 9.6% mortality rate. Although immunopathological damages may account for the severity of respiratory distress, little is known about how the genome-wide gene expression of the host changes under the attack of SARS-CoV.

Results: Based on changes in gene expression of peripheral blood, we identified 52 signature genes that accurately discriminated acute SARS patients from non-SARS controls. While a general suppression of gene expression predominated in SARS-infected blood, several genes including those involved in innate immunity, such as defensins and eosinophil-derived neurotoxin, were upregulated. Instead of employing clustering methods, we ranked the severity of recovering SARS patients by generalized associate plots (GAP) according to the expression profiles of 52 signature genes. Through this method, we discovered a smooth transition pattern of severity from normal controls to acute SARS patients. The rank of SARS severity was significantly correlated with the recovery period (in days) and with the clinical pulmonary infection score.

Conclusion: The use of the GAP approach has proved useful in analyzing the complexity and continuity of biological systems. The severity rank derived from the global expression profile of significantly regulated genes in patients may be useful for further elucidating the pathophysiology of their disease.

Figure 1

Significant differences in gene expression profiles in patients with SARS or bacterial infection. Using a probe set with 6,525 annotated genes, global gene expression was analyzed by (A) variation distribution in peripheral blood specimens from patients with acute SARS (AS), recovering SARS (RS), bacterial infections (IN), and normal controls (NC). (B) In the hierarchical clustering of relative change in gene expression using a probe set of 885 filtered genes (gene vector >0.5 SD), red indicates upregulation and green indicates downregulation in gene expression relative to a common reference that was the pooled amplified RNA from 11 normal controls. (C) Using the 885-gene set, singular value decomposition (SVD) analysis by two eigenvectors showed three distinguished clusters of AS (red ●), IN (green ▲), and NC (blue ▮) groups, with the RS (red ○) specimens scattering among AS and IN.

Figure 2

The top 40 discriminating genes with the highest distinction values for AS or NC groups. Twenty genes that were specifically (A) upregulated or (B) downregulated in patients with SARS. Another twenty genes that were non-specifically (C) upregulated or (D) downregulated by both bacterial infection and SARS. Each column represents an individual sample and each row represents a gene. The color range reflected relative change according to the scale shown. NC, normal control; IN, bacterial infection; AS, acute SARS. GMRCL clone numbers of some ESTs are also included in the parentheses.

Figure 3

Generalized associated plots (GAP) analysis of SARS patients samples. (A) Pair-wise Euclidean distance matrix that was sorted by a GAP using 52 genes with the highest discriminating power for AS groups revealed the minimum anti-Robinson events in the matrix, resulting in a smooth transition order of the AS and RS specimens from severely diseased to healthy states. AS (red ●); RS (red ○); NC (blue ▮). (B) Gene expression profile for the 52 discriminating genes displayed in the order obtained from the GAP method.

Figure 4

Correlations between the GAP-derived rank for SARS severity and clinical parameters. (A) The scatter plot of all SARS specimens with the order obtained from the GAP method and the days after the onset of disease showed a significant correlation (P < 5 × 10^-7). (B) Sixteen out of 17 SARS patients who submitted multiple blood specimens showed a similar trend of changes in the GAP-derived severity rank along with the recovery from the disease. Patients with 2 (n = 15) and 3 specimens (n = 2) were labeled with blue and green lines, respectively. (C) The scatter plot of all AS and RS specimens with the order obtained from the GAP method and clinical pulmonary infection score (CPIS) showed a significant correlation (P < 0.001).