A Burkholderia pseudomallei protein microarray reveals serodiagnostic and cross-reactive antigens.

Understanding the way in which the immune system responds to infection is central to the development of vaccines and many diagnostics. To provide insight into this area, we fabricated a protein microarray containing 1,205 Burkholderia pseudomallei proteins, probed it with 88 melioidosis patient sera, and identified 170 reactive antigens. This subset of antigens was printed on a smaller array and probed with a collection of 747 individual sera derived from 10 patient groups including melioidosis patients from Northeast Thailand and Singapore, patients with different infections, healthy individuals from the USA, and from endemic and nonendemic regions of Thailand. We identified 49 antigens that are significantly more reactive in melioidosis patients than healthy people and patients with other types of bacterial infections. We also identified 59 cross-reactive antigens that are equally reactive among all groups, including healthy controls from the USA. Using these results we were able to devise a test that can classify melioidosis positive and negative individuals with sensitivity and specificity of 95% and 83%, respectively, a significant improvement over currently available diagnostic assays. Half of the reactive antigens contained a predicted signal peptide sequence and were classified as outer membrane, surface structures or secreted molecules, and an additional 20% were associated with pathogenicity, adaptation or chaperones. These results show that microarrays allow a more comprehensive analysis of the immune response on an antigen-specific, patient-specific, and population-specific basis, can identify serodiagnostic antigens, and contribute to a more detailed understanding of immunogenicity to this pathogen.

Presence of Burkholderia pseudomallei in Soil and Paddy Rice Water in a Rice Field in Northeast Thailand, but Not in Air and Rainwater.

Environmental Burkholderia pseudomallei has been postulated to be aerosolized during ploughing and heavy rain, and could result in inhalational melioidosis. Here, we determined the presence of B. pseudomallei in soil, paddy field water (PFW), air, and rainwater samples in a single rice paddy field in Ubon Ratchathani, northeast Thailand. In 2012, we collected 100 soil samples during the dry season, 10 PFW samples during the monsoon season, 77 air samples during ploughing (N = 31) and heavy rains (N = 46), and 60 rainwater samples during 12 rain events. We found that 32 soil samples (32%), six PFW samples (60%), and none of the air and rainwater samples were culture positive for B. pseudomallei. Other soil bacteria were isolated from air and rainwater samples. Mean quantitative count of B. pseudomallei estimated from two culture-positive PFW samples was 200 colony forming units/mL. Our findings suggest that the risk of melioidosis acquisition by inhalation in Thailand might be low.

A clinical diagnostic model for predicting influenza among young adult military personnel with febrile respiratory illness in Singapore.

INTRODUCTION: Influenza infections present with wide-ranging clinical features. We aim to compare the differences in presentation between influenza and non-influenza cases among those with febrile respiratory illness (FRI) to determine predictors of influenza infection. METHODS: Personnel with FRI (defined as fever ≥ 37.5 °C, with cough or sore throat) were recruited from the sentinel surveillance system in the Singapore military. Nasal washes were collected, and tested using the Resplex II and additional PCR assays for etiological determination. Interviewer-administered questionnaires collected information on patient demographics and clinical features. Univariate comparison of the various parameters was conducted, with statistically significant parameters entered into a multivariate logistic regression model. The final multivariate model for influenza versus non-influenza cases was used to build a predictive probability clinical diagnostic model. RESULTS: 821 out of 2858 subjects recruited from 11 May 2009 to 25 Jun 2010 had influenza, of which 434 (52.9%) had 2009 influenza A (H1N1), 58 (7.1%) seasonal influenza A (H3N2) and 269 (32.8%) influenza B. Influenza-positive cases were significantly more likely to present with running nose, chills and rigors, ocular symptoms and higher temperature, and less likely with sore throat, photophobia, injected pharynx, and nausea/vomiting. Our clinical diagnostic model had a sensitivity of 65% (95% CI: 58%, 72%), specificity of 69% (95% CI: 62%, 75%), and overall accuracy of 68% (95% CI: 64%, 71%), performing significantly better than conventional influenza-like illness (ILI) criteria. CONCLUSIONS: Use of a clinical diagnostic model may help predict influenza better than the conventional ILI definition among young adults with FRI.