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. 2021 Oct 1;16(10):e0257708.
doi: 10.1371/journal.pone.0257708. eCollection 2021.

High-throughput low-cost nl-qPCR for enteropathogen detection: A proof-of-concept among hospitalized patients in Bangladesh

Katelyn E Flaherty  1   2 , Jessica A Grembi  3   4 , Vasavi V Ramachandran  5 , Farhana Haque  6   7 , Selina Khatun  6 , Mahmudu Rahman  6   8 , Stace Maples  9 , Torben K Becker  1   2 , Alfred M Spormann  3 , Gary K Schoolnik  4 , Andrew J Hryckowian  10   11 , Eric J Nelson  1   4   12
Affiliations

High-throughput low-cost nl-qPCR for enteropathogen detection: A proof-of-concept among hospitalized patients in Bangladesh

Katelyn E Flaherty et al. PLoS One. .

Abstract

Background: Diarrheal disease is a leading cause of morbidity and mortality globally, especially in low- and middle-income countries. High-throughput and low-cost approaches to identify etiologic agents are needed to guide public health mitigation. Nanoliter-qPCR (nl-qPCR) is an attractive alternative to more expensive methods yet is nascent in application and without a proof-of-concept among hospitalized patients.

Methods: A census-based study was conducted among diarrheal patients admitted at two government hospitals in rural Bangladesh during a diarrheal outbreak period. DNA was extracted from stool samples and assayed by nl-qPCR for common bacterial, protozoan, and helminth enteropathogens as the primary outcome.

Results: A total of 961 patients were enrolled; stool samples were collected from 827 patients. Enteropathogens were detected in 69% of patient samples; More than one enteropathogen was detected in 32%. Enteropathogens most commonly detected were enteroaggregative Escherichia coli (26.0%), Shiga toxin-producing E.coli (18.3%), enterotoxigenic E. coli (15.5% heat stable toxin positive, 2.2% heat labile toxin positive), Shigella spp. (14.8%), and Vibrio cholerae (9.0%). Geospatial analysis revealed that the median number of pathogens per patient and the proportion of cases presenting with severe dehydration were greatest amongst patients residing closest to the study hospitals."

Conclusions: This study demonstrates a proof-of-concept for nl-qPCR as a high-throughput low-cost method for enteropathogen detection among hospitalized patients.

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Conflict of interest statement

The authors have read the journal’s policy and have the following competing interests: EJN is associated with a patent on data collection software (Outbreak Responder, A Method, Apparatus and Computer Program Product for Personal Identification and Electronic Data Management) used in this study (Patent Publication Number 2020/0082921). The software development was funded by the National Institutes of Health (DP5OD019893), the University of Florida and Stanford University. EJN has no financial interest in the development and deployment of the software. This does not alter our adherence to PLOS ONE policies on sharing data and materials. There are no other patents, products in development or marketed products associated with this research to declare. Development or marketed products associated with this research to declare.

Figures

Fig 1
Fig 1. Geospatial distribution of 827 patients by administrative unit (union) and proximity to district hospitals.
Basemap: World Topographic Map [29].
Fig 2
Fig 2. Enteropathogens detected by nl-qPCR.
Red lines represent the median copy number per gram of stool. Vertical gray dotted line demarcates analysis of total Fungi, total Archaea, and Bacteria. Genetic targets are provided in brackets. Abbreviations are heat-labile and heat-stable enteropathogenic Escherichia coli (LT-ETEC and ST-ETEC), Vibrio cholerae (VCHOL), Shigella spp. (SHIGE), typical enteroaggregative Escherichia coli (EAEC), Salmonella spp. (SALMO), Campylobacter jejuni/coli (CAMPY), enteropathogenic Escherichia coli (EPEC), Giardia lamblia (GIARD), Shiga-toxin producing Escherichia coli (STEC), Entamoeba histolytica (ENTAM), Ascaris lumbricoides (ASCAR), Cryptosporidium supp (CRYPTO), Trichuris trichiura (TRICH), Helicobacter pylori (HPYLO), and Yersinia enterocolitica (YERSI); see Methods and S1 Table. NEG = number.
Fig 3
Fig 3. Distribution of single-and multi-pathogen infections among hospitalized patients with diarrheal disease.
Distributions are presented as an aggregate (A) or stratified by age group (B). The percentages are based on the total number of samples in which a stool sample was tested (n = 827). Pathogens under 1 percent are grouped as ‘other’. Abbreviations are defined in Fig 2.
Fig 4
Fig 4. Distribution of co-infections.
Data along the diagonal axis enumerate single pathogen detections. Shading represents the number of positive infections. Abbreviations are defined in Fig 2. Asterisks demarcate non-etiologic pathogens.
Fig 5
Fig 5. Geospatial distribution of diarrheal disease.
A. Median number of pathogens per patient per administrative unit (union). B. Hotspot Analysis: Median number of pathogens per patient per union hot and cold spots. C. Proportion of cases presenting with severe dehydration per administrative unit (union) among subset of patients enrolled after November.15th. Data are derived from patients in which a stool sample was collected. D. Hotspot Analysis: Proportion of cases presenting with severe dehydration per union ‘hot’ and ‘cold’ spots. Basemap: World Topographic Map [29].
See this image and copyright information in PMC

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