Dataset Information

A Biochemiluminescent Sialidase Assay for Diagnosis of Bacterial Vaginosis.

ABSTRACT: Bacterial vaginosis (BV) is a common condition among women of reproductive age. A sensitive, quantitative and rapid assay is needed for the diagnosis of and, particularly, therapy monitoring for BV. Bacterial sialidase appears to play an important role in bacterial biofilms on vaginal epithelium, a condition closely associated with BV. Here, we report a biochemiluminescent sialidase assay that uses a substrate derivatized with firefly luciferin. In the presence of sialidase in the reaction, the substrate is cleaved to release luciferin, which is subsequently oxidized by firefly luciferase to generate a light signal. Thus, the light signal intensity can be used to detect and measure the relative concentration of sialidase in a vaginal sample as a means of BV diagnosis. All reagents are present in a reagent bead and sample buffer, enabling essentially a one-step assay. The assay is highly sensitive and quantitative, with a sensitivity and specificity of 95.40% and 94.94%, respectively, compared to the Amsel method. Interestingly, only 27.6% of those with BV had high levels of sialidase activity with a signal to cutoff ratio of 10 or more. The assay may be used for diagnosis of BV, risk assessment of BV patients in terms of sialidase activity levels, and monitoring antibiotic therapy.

SUBMITTER: Wu S

PROVIDER: S-EPMC6934538 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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A Biochemiluminescent Sialidase Assay for Diagnosis of Bacterial Vaginosis.

Wu Shengjun S Lin Xuexiang X Hui Kwok Min KM Yang Su S Wu Xuanlan X Tan Yichen Y Li Meimei M Qin Ai-Qing AQ Wang Qingxi Q Zhao Qi Q Ding Pengfei P Shi Kaisheng K Li X James XJ

Scientific reports 20191227 1

Bacterial vaginosis (BV) is a common condition among women of reproductive age. A sensitive, quantitative and rapid assay is needed for the diagnosis of and, particularly, therapy monitoring for BV. Bacterial sialidase appears to play an important role in bacterial biofilms on vaginal epithelium, a condition closely associated with BV. Here, we report a biochemiluminescent sialidase assay that uses a substrate derivatized with firefly luciferin. In the presence of sialidase in the reaction, the ...[more]

PMID: 31882933

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Project description:Bacterial vaginosis (BV) is caused by the excessive and imbalanced growth of bacteria in vagina, affecting 30 to 50% of women. Gram staining followed by Nugent scoring based on bacterial morphotypes under the microscope is considered the gold standard for BV diagnosis; this method is often labor-intensive and time-consuming, and results vary from person to person. We developed and optimized a convolutional neural network (CNN) model and evaluated its ability to automatically identify and classify three categories of Nugent scores from microscope images. The CNN model was first established with a panel of microscopic images with Nugent scores determined by experts. The model was trained by minimizing the cross-entropy loss function and optimized by using a momentum optimizer. The separate test sets of images collected from three hospitals were evaluated by the CNN model. The CNN model consisted of 25 convolutional layers, 2 pooling layers, and a fully connected layer. The model obtained 82.4% sensitivity and 96.6% specificity with the 5,815 validation images when altered vaginal flora and BV were considered the positive samples, which was better than the rates achieved by top-level technologists and obstetricians in China. The capability of our model for generalization was so strong that it exhibited 75.1% accuracy in three categories of Nugent scores on the independent test set of 1,082 images, which was 6.6% higher than the average of three technologists, who are hold bachelor's degrees in medicine and are qualified to make diagnostic decisions. When three technologists ran one specimen in triplicate, the precision of three categories of Nugent scores was 54.0%. One hundred three samples diagnosed by two technologists on different days showed a repeatability of 90.3%. The CNN model outperformed human health care practitioners in terms of accuracy and stability for three categories of Nugent score diagnosis. The deep learning model may offer translational applications in automating diagnosis of bacterial vaginosis with proper supporting hardware.

Project description:UNLABELLED:Bacterial vaginosis (BV) is characterized by shifts in the vaginal microbiota from Lactobacillus dominant to a microbiota with diverse anaerobic bacteria. Few studies have linked specific metabolites with bacteria found in the human vagina. Here, we report dramatic differences in metabolite compositions and concentrations associated with BV using a global metabolomics approach. We further validated important metabolites using samples from a second cohort of women and a different platform to measure metabolites. In the primary study, we compared metabolite profiles in cervicovaginal lavage fluid from 40 women with BV and 20 women without BV. Vaginal bacterial representation was determined using broad-range PCR with pyrosequencing and concentrations of bacteria by quantitative PCR. We detected 279 named biochemicals; levels of 62% of metabolites were significantly different in women with BV. Unsupervised clustering of metabolites separated women with and without BV. Women with BV have metabolite profiles marked by lower concentrations of amino acids and dipeptides, concomitant with higher levels of amino acid catabolites and polyamines. Higher levels of the signaling eicosanoid 12-hydroxyeicosatetraenoic acid (12-HETE), a biomarker for inflammation, were noted in BV. Lactobacillus crispatus and Lactobacillus jensenii exhibited similar metabolite correlation patterns, which were distinct from correlation patterns exhibited by BV-associated bacteria. Several metabolites were significantly associated with clinical signs and symptoms (Amsel criteria) used to diagnose BV, and no metabolite was associated with all four clinical criteria. BV has strong metabolic signatures across multiple metabolic pathways, and these signatures are associated with the presence and concentrations of particular bacteria. IMPORTANCE:Bacterial vaginosis (BV) is a common but highly enigmatic condition that is associated with adverse outcomes for women and their neonates. Small molecule metabolites in the vagina may influence host physiology, affect microbial community composition, and impact risk of adverse health outcomes, but few studies have comprehensively studied the metabolomics profile of BV. Here, we used mass spectrometry to link specific metabolites with particular bacteria detected in the human vagina by PCR. BV was associated with strong metabolic signatures across multiple pathways affecting amino acid, carbohydrate, and lipid metabolism, highlighting the profound metabolic changes in BV. These signatures were associated with the presence and concentrations of particular vaginal bacteria, including some bacteria yet to be cultivated, thereby providing clues as to the microbial origin of many metabolites. Insights from this study provide opportunities for developing new diagnostic markers of BV and novel approaches for treatment or prevention of BV.

Dataset Information

A Biochemiluminescent Sialidase Assay for Diagnosis of Bacterial Vaginosis.

Publications

A Biochemiluminescent Sialidase Assay for Diagnosis of Bacterial Vaginosis.

Similar Datasets

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