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


CONCLUSIONS
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.
Lee
Yun-Shien
YS
Genomic Medicine Research Core Laboratory, Chang Gung Memorial Hospital, Tao-Yuan, Taiwan. bojack@mcu.edu.tw
Chen
Chun-Houh
CH
Chao
Angel
A
Chen
En-Shih
ES
Wei
Min-Li
ML
Chen
Lung-Kun
LK
Yang
Kuender D
KD
Lin
Meng-Chih
MC
Wang
Yi-Hsi
YH
Liu
Jien-Wei
JW
Eng
Hock-Liew
HL
Chiang
Ping-Cherng
PC
Wu
Ting-Shu
TS
Tsao
Kuo-Chein
KC
Huang
Chung-Guei
CG
Tien
Yin-Jing
YJ
Wang
Tzu-Hao
TH
Wang
Hsing-Shih
HS
Lee
Ying-Shiung
YS
D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology; D018076:DNA, Complementary / Q000378:metabolism; D004797:Enzyme-Linked Immunosorbent Assay; D015870:Gene Expression; D015967:Gene Expression Regulation, Viral; D005814:Genes, Viral; D016678:Genome; D006801:Humans; D007113:Immunity, Innate; D008171:Lung Diseases / Q000378:metabolism / Q000821:virology; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D020133:Reverse Transcriptase Polymerase Chain Reaction; D045473:Severe acute respiratory syndrome-related coronavirus / Q000235:genetics*; D045169:Severe Acute Respiratory Syndrome / Q000097:blood / Q000821:virology*; D015854:Up-Regulation
D018076:DNA, Complementary