Adapting Microarray Gene Expression Signatures for Early Melioidosis Diagnosis

被引:7
|
作者
Sangwichian, Ornuma [1 ,2 ]
Whistler, Toni [2 ,3 ]
Nithichanon, Arnone [1 ]
Kewcharoenwong, Chidchamai [1 ]
Sein, Myint Myint [1 ]
Arayanuphum, Chawitar [4 ]
Chantratita, Narisara [4 ,5 ]
Lertmemongkolchai, Ganjana [1 ]
机构
[1] Khon Kaen Univ, Fac Associated Med Sci, Ctr Res & Dev Med Diagnost Labs, Khon Kaen, Thailand
[2] Thailand Minist Publ Hlth, US Ctr Dis Control & Prevent Collaborat TUC, Nonthaburi, Thailand
[3] Ctr Dis Control & Prevent, Ctr Global Hlth, Div Global Hlth Protect, Atlanta, GA USA
[4] Mahidol Univ, Fac Trop Med, Dept Microbiol & Immunol, Bangkok, Thailand
[5] Mahidol Univ, Fac Trop Med, Mahidol Oxford Trop Med Res Unit, Bangkok, Thailand
关键词
Burkholderia; diagnostics; gene expression; melioidosis; sepsis; TIME PCR ASSAYS; BURKHOLDERIA-PSEUDOMALLEI; AIM2; INFLAMMASOME; RAPID DIAGNOSIS; IDENTIFICATION; DNA; ANTIBODIES; ACTIVATION; CANCER;
D O I
10.1128/JCM.01906-19
中图分类号
Q93 [微生物学];
学科分类号
071005 ; 100705 ;
摘要
Melioidosis is caused by Burkholderia pseudomallei and is predominantly seen in tropical regions. The clinical signs and symptoms of the disease are nonspecific and often result in misdiagnosis, failure of treatment, and poor clinical outcome. Septicemia with septic shock is the most common cause of death, with mortality rates above 40%. Bacterial culture is the gold standard for diagnosis, but it has low sensitivity and takes days to produce definitive results. Early laboratory diagnosis can help guide physicians to provide treatment specific to B. pseudomallei. In our study, we adapted host gene expression signatures obtained from microarray data of B. pseudomallei-infected cases to develop a real-time PCR diagnostic test using two differentially expressed genes, AIM2 (absent in melanoma 2) and FAM26F (family with sequence similarity 16, member f). We tested blood from 33 patients with B. pseudomallei infections and 29 patients with other bacterial infections to validate the test and determine cutoff values for use in a cascading diagnostic algorithm. Differentiation of septicemic melioidosis from other sepsis cases had a sensitivity of 82%, specificity of 93%, and negative and positive predictive values (NPV and PPV) of 82% and 93%, respectively. Separation of cases likely to be melioidosis from those unlikely to be melioidosis in nonbacteremic situations showed a sensitivity of 40%, specificity of 54%, and NPV and PPV of 44% and 50%, respectively. We suggest that our AIM2 and FAM26F expression combination algorithm could be beneficial for early melioidosis diagnosis, offering a result within 24 h of admission.
引用
收藏
页数:12
相关论文
共 50 条
  • [1] Gene-expression profiles in murine melioidosis
    Wiersinga, W. Joost
    Dessing, Mark C.
    van der Poll, Tom
    MICROBES AND INFECTION, 2008, 10 (08) : 868 - 877
  • [2] A novel gene expression index (GEI) with software support for comparing microarray gene signatures
    Khan, Haseeb Ahmad
    GENE, 2013, 512 (01) : 82 - 88
  • [3] Evaluation of the Active Melioidosis Detect™ test as a point-of-care tool for the early diagnosis of melioidosis: a comparison with culture in Laos
    Rizzi, Maria Chiara
    Rattanavong, Sayaphet
    Bouthasavong, Latsaniphone
    Seubsanith, Amphayvanh
    Vongsouvath, Manivanh
    Davong, Viengmon
    De Silvestri, Annalisa
    Manciulli, Tommaso
    Newton, Paul N.
    Dance, David A. B.
    TRANSACTIONS OF THE ROYAL SOCIETY OF TROPICAL MEDICINE AND HYGIENE, 2019, 113 (12) : 757 - 763
  • [4] A snapshot of microarray-generated gene expression signatures associated with ovarian carcinoma
    Gyoerffy, B.
    Dietel, M.
    Fekete, T.
    Lage, H.
    INTERNATIONAL JOURNAL OF GYNECOLOGICAL CANCER, 2008, 18 (06) : 1215 - 1233
  • [5] Quantitative comparison of microarray experiments with published leukemia related gene expression signatures
    Klein, Hans-Ulrich
    Ruckert, Christian
    Kohlmann, Alexander
    Bullinger, Lars
    Thiede, Christian
    Haferlach, Torsten
    Dugas, Martin
    BMC BIOINFORMATICS, 2009, 10
  • [6] Improved Wavelet Neural Network for Early Diagnosis of Cancer Patients Using Microarray Gene Expression Data
    Zainuddin, Zarita
    Pauline, Ong
    IJCNN: 2009 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1- 6, 2009, : 2663 - 2670
  • [7] Specific gene expression signatures of low grade meningiomas
    Tsitsikov, Erdyni N. N.
    Hameed, Sanaa
    Tavakol, Sherwin A. A.
    Stephens, Tressie M. M.
    Tsytsykova, Alla V. V.
    Garman, Lori
    Bi, Wenya Linda
    Dunn, Ian F. F.
    FRONTIERS IN ONCOLOGY, 2023, 13
  • [8] Microarray meta-analysis defines global angiogenesis-related gene expression signatures in human carcinomas
    Anders, Mario
    Fehlker, Marion
    Wang, Qing
    Wissmann, Christoph
    Pilarsky, Christian
    Kemmner, Wolfgang
    Hoecker, Michael
    MOLECULAR CARCINOGENESIS, 2013, 52 (01) : 29 - 38
  • [9] DNA Microarray Gene Expression Analysis for Diagnosis of Oral Dysplasia and Squamous-Cell Carcinoma
    Zarzar, Mouayad
    Razak, Eliza
    Htike, Zaw Zaw
    Yusof, Faridah
    ADVANCED SCIENCE LETTERS, 2015, 21 (11) : 3468 - 3471
  • [10] Leukocyte glucocorticoid receptor expression and related transcriptomic gene signatures during early sepsis
    Li, Jiabao
    Xie, Miaorong
    Yu, Yanan
    Tang, Ziren
    Hang, Chenchen
    Li, Chunsheng
    CLINICAL IMMUNOLOGY, 2021, 223