Identification of the Causative Bacteria in Musculoskeletal Infections Using 16s rDNA - Denaturing Gradient Gel Electrophoresis Analysis

Musculoskeletal infections are infections of the bone and surrounding tissues. They are currently diagnosed based on culture analysis, which is the gold standard for pathogen identification. However, these clinical laboratory methods are frequently inadequate for the identification of the causative agents, because a large percentage (25-50%) of confirmed musculoskeletal infections are false negatives in which no pathogen is identified in culture. My data supports these results. The goal of this project was to use PCR amplification of a portion of the 16S rRNA gene to test an alternative approach for the identification of these pathogens and to assess the diversity of the bacteria involved. The advantages of this alternative method are that it should increase sample sensitivity and the speed of detection. In addition, bacteria that are non-culturable or in low abundance can be detected using this molecular technique. However, a complication of this approach is that the majority of musculoskeletal infections are polymicrobial, which prohibits direct identification from the infected tissue by DNA sequencing of the initial 16S rDNA amplification products. One way to solve this problem is to use denaturing gradient gel electrophoresis (DGGE) to separate the PCR products before DNA sequencing. Denaturing gradient gel electrophoresis (DGGE) separates DNA molecules based on their melting point, which is determined by their DNA sequence. This analytical technique allows a mixture of PCR products of the same length that electrophoreses through agarose gels as one band, to be separated into different bands and then used for DNA sequence analysis. In this way, the DGGE allows for the identification of individual bacterial species in polymicrobial-infected tissue, which is critical for improving clinical outcomes. By combining the 16S rDNA amplification and the DGGE techniques together, an alternative approach for identification has been used. The 16S rRNA gene PCR-DGGE method includes several critical steps: DNA extraction from tissue biopsies, amplification of the bacterial DNA, PCR product separation by DGGE, amplification of the gel-extracted DNA, and DNA sequencing and analysis. Each step of the method was optimized to increase its sensitivity and for rapid detection of the bacteria present in human tissue samples. The limit of detection for the DNA extraction from tissue was at least 20 Staphylococcus aureus cells and the limit of detection for PCR was at least 0.05 pg of template DNA. The conditions for DGGE electrophoreses were optimized by using a double gradient of acrylamide (6 – 10%) and denaturant (30-70%), which increased the separation between distinct PCR products. The use of GelRed (Biotium) improved the DNA visualization in the DGGE gel. To recover the DNA from the DGGE gels the gel slices were excised, shredded in a bead beater, and the DNA was allowed to diffuse into sterile water overnight. The use of primers containing specific linkers allowed the entire amplified PCR product to be sequenced and then analyzed. The optimized 16S rRNA gene PCR-DGGE method was used to analyze 50 tissue biopsy samples chosen randomly from our collection. The results were compared to those of the Memorial Hermann Hospital Clinical Microbiology Laboratory for the same samples. The molecular method was congruent for 10 of the 17 (59%) culture negative tissue samples. In 7 of the 17 (41%) culture negative the molecular method identified a bacterium. The molecular method was congruent with the culture identification for 7 of the 33 (21%) positive cultured tissue samples. However, in 8 of the 33 (24%) the molecular method identified more organisms. In 13 of the 15 (87%) polymicrobial cultured tissue samples the molecular method identified at least one organism that was also identified by culture techniques. Overall, the DGGE analysis of 16S rDNA is an effective method to identify bacteria not identified by culture analysis.

New methods for quantification and analysis of quantitative real-time polymerase chain reaction data

Quantitative real-time polymerase chain reaction (qPCR) is a sensitive gene quantitation method that has been widely used in the biological and biomedical fields. The currently used methods for PCR data analysis, including the threshold cycle (CT) method, linear and non-linear model fitting methods, all require subtracting background fluorescence. However, the removal of background fluorescence is usually inaccurate, and therefore can distort results. Here, we propose a new method, the taking-difference linear regression method, to overcome this limitation. Briefly, for each two consecutive PCR cycles, we subtracted the fluorescence in the former cycle from that in the later cycle, transforming the n cycle raw data into n-1 cycle data. Then linear regression was applied to the natural logarithm of the transformed data. Finally, amplification efficiencies and the initial DNA molecular numbers were calculated for each PCR run. To evaluate this new method, we compared it in terms of accuracy and precision with the original linear regression method with three background corrections, being the mean of cycles 1-3, the mean of cycles 3-7, and the minimum. Three criteria, including threshold identification, max R2, and max slope, were employed to search for target data points. Considering that PCR data are time series data, we also applied linear mixed models. Collectively, when the threshold identification criterion was applied and when the linear mixed model was adopted, the taking-difference linear regression method was superior as it gave an accurate estimation of initial DNA amount and a reasonable estimation of PCR amplification efficiencies. When the criteria of max R2 and max slope were used, the original linear regression method gave an accurate estimation of initial DNA amount. Overall, the taking-difference linear regression method avoids the error in subtracting an unknown background and thus it is theoretically more accurate and reliable. This method is easy to perform and the taking-difference strategy can be extended to all current methods for qPCR data analysis.^