Phebe Verbrugghe1, Olivier Van Aken2, Frida Hållenius3, Anne Nilsson3. 1. Food Technology, Engineering and Nutrition, Lund University, PO Box 124, 221 00, Lund, Sweden. vphebe4@yahoo.com. 2. Department of Biology, Lund University, Lund University Plant Sciences, Sölvegatan 35, 223 62, Lund, Sweden. 3. Food Technology, Engineering and Nutrition, Lund University, PO Box 124, 221 00, Lund, Sweden.
Abstract
BACKGROUND: Since its discovery in 2007, the importance of the human gut bacterium Prevotella copri (P. copri) has been widely recognized with its links to diet and health status and potential as next generation probiotic. Therefore, precise, convenient and cost-effective diagnostic tools for the detection and quantification of P. copri from clinical and environmental samples are needed. RESULTS: In this study, a Sybr Green qPCR protocol for P. copri detection and quantification was developed and tested on P. copri-spiked murine faeces samples targeting both the 16S rRNA gene and P. copri genome specific genes. The use of one 16S rRNA primer pair and 2 genome specific primer pairs resulted in at least 10x higher specificity and sensitivity than the primer-only PCR currently cited in the literature, reaching a sensitivity of 103 CFU/mL. Furthermore, we showed that the new 16S rRNA primer set provided the best balance of detection of a wide range of P. copri strains, while avoiding off-target detection of other Prevotella genus species. The quantification of P. copri in human stool samples using the new 16S rRNA primers also correlated well with 16S rRNA high throughput MiSeq sequencing data (r2 = 0.6604, p = 0.0074). The two genome specific primer pairs on the other hand uniquely detect the DSM18205 reference strain, allowing differential detection of indigenous and experimentally administered P. copri populations. Finally, it was shown that SYBR green qPCR mixes have an influence on sensitivity and specificity, with Biorad SsoAdvanced Universal SYBR Green Supermix performing the best under our test conditions of six commercially available SYBR green master mixes. CONCLUSIONS: This improved qPCR-based method will allow accurate P. copri identification and quantification. Moreover, this methodology can also be applied to identify other bacterial species in complex samples.
BACKGROUND: Since its discovery in 2007, the importance of the human gut bacterium Prevotella copri (P. copri) has been widely recognized with its links to diet and health status and potential as next generation probiotic. Therefore, precise, convenient and cost-effective diagnostic tools for the detection and quantification of P. copri from clinical and environmental samples are needed. RESULTS: In this study, a Sybr Green qPCR protocol for P. copri detection and quantification was developed and tested on P. copri-spiked murine faeces samples targeting both the 16S rRNA gene and P. copri genome specific genes. The use of one 16S rRNA primer pair and 2 genome specific primer pairs resulted in at least 10x higher specificity and sensitivity than the primer-only PCR currently cited in the literature, reaching a sensitivity of 103 CFU/mL. Furthermore, we showed that the new 16S rRNA primer set provided the best balance of detection of a wide range of P. copri strains, while avoiding off-target detection of other Prevotella genus species. The quantification of P. copri in human stool samples using the new 16S rRNA primers also correlated well with 16S rRNA high throughput MiSeq sequencing data (r2 = 0.6604, p = 0.0074). The two genome specific primer pairs on the other hand uniquely detect the DSM18205 reference strain, allowing differential detection of indigenous and experimentally administered P. copri populations. Finally, it was shown that SYBR green qPCR mixes have an influence on sensitivity and specificity, with Biorad SsoAdvanced Universal SYBR Green Supermix performing the best under our test conditions of six commercially available SYBR green master mixes. CONCLUSIONS: This improved qPCR-based method will allow accurate P. copri identification and quantification. Moreover, this methodology can also be applied to identify other bacterial species in complex samples.
Authors: Inés Martínez; James C Stegen; Maria X Maldonado-Gómez; A Murat Eren; Peter M Siba; Andrew R Greenhill; Jens Walter Journal: Cell Rep Date: 2015-04-16 Impact factor: 9.423
Authors: Mahboobeh Mahdavinia; Heather E Rasmussen; Maresa Botha; Thi Dong Binh Tran; Jolice P Van den Berg; Erica Sodergren; Erika Davis; Krista Engen; Claudia Gray; Nonhlanhla Lunjani; Carol Hlela; Nailliw Z Preite; Wisdom Basera; Lelani Hobane; Alexandra Watkins; Phillip Engen; Avumile Mankahla; Ben Gaunt; Facey Thomas; Mary C Tobin; Alan Landay; George M Weinstock; Ali Keshavarzian; Michael E Levin Journal: J Allergy Clin Immunol Date: 2018-12-19 Impact factor: 10.793
Authors: Carlotta De Filippo; Duccio Cavalieri; Monica Di Paola; Matteo Ramazzotti; Jean Baptiste Poullet; Sebastien Massart; Silvia Collini; Giuseppe Pieraccini; Paolo Lionetti Journal: Proc Natl Acad Sci U S A Date: 2010-08-02 Impact factor: 11.205