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.

A METAGENOMIC STUDY OF THE TICK MIDGUT

A Metagenomic Study of the Tick Midgut Daniel Yuan, B.S. Supervisory Professor : Steven J. Norris, Ph.D. Southern tick–associated rash illness (STARI) or Master’s disease is a Lyme-like illness that occurs following bites by Amblyomma americanum, the lone-star tick. Clinical symptoms include a bull’s eye rash similar to the erythema migrans lesions of Lyme disease, as well as fever and joint pains. Lyme disease is caused by Borrelia burgdorferi and related spirochetes. However, B. burgdorferi has not been detected in STARI patients, or in ticks in the South Central U.S. The causative agent of STARI has not been identified, although it was once thought to be caused by another Borrelia species, Borrelia lonestari. Furthermore, while adult A. americanum have up to a 5.6% Borrelia lonestari infection rate, the prevalence of all Borrelia species in Texas ticks as a whole is not known. Previous studies indicate that 6%-30% of Northern Ixodes scapularis ticks are infected by Borrelia burgdorferi while only 10% of Northern A. americanum and I. scapularis ticks are infected by Borrelia species. The first specific aim of this project was to determine the bacterial community that inhabits the midgut of Texas and Northeastern ticks by using high throughput metagenomic sequencing to sequence bacterial 16S rDNA. Through the use of massively parallel 454 sequencing, we were able to individually sequence hundreds of thousands of 16S rDNA regions of the bacterial flora from 133 ticks from the New York, Missouri and Texas. The presence of previously confirmed endosymbionts, specifically the Rickettsia spp. and Coxiella spp., that are commonly found in ticks were confirmed, as well as some highly prevalent genera that were previously undocumented. Furthermore, multiple pathogenic genera sequences were often found in the same tick, suggesting the possibility of co-infection of multiple pathogenic species. The second specific aim was to use Borrelia specific primers to screen 344 individual ticks from Missouri, Texas and the Northeast to determine the prevalence of Borrelia species in ticks. To screen for Borrelia species, two housekeeping genes, uvrA and recG, were selected as well as the 16S-23S rDNA intergenic spacer. Ticks from Missouri, Texas and New York were screened. None of the Missouri or Texas ticks tested positive for Borrelia spp. The rate of I. scapularis infection by B.burgdorferi is dependent on tick feeding activity as well as reservoir availability. B. burgdorferi is endemic in the Northeast, sometimes reported as highly present in over 50% of all I. scapularis ticks. 11.6% of all New York ticks were positive for a species of Borrelia, however only 6.9% of all New York ticks were positive for B. burgdorferi. Despite being significantly lower than 50%, the results still fall in line with previous reports of about the prevalence of B. burgdorferi. 1.5% of all Texas ticks were positive for a Borrelia species, specifically B. lonestari. While this study was unable to identify the causative agent for STARI, 454 sequencing was able to provide a tremendous insight into the bacterial flora and possible pathogenic species of both the I. scapularis and the A. americanum tick.

The sequence comparison index: A novel method for comparing proteins and proteomes

Historically morphological features were used as the primary means to classify organisms. However, the age of molecular genetics has allowed us to approach this field from the perspective of the organism's genetic code. Early work used highly conserved sequences, such as ribosomal RNA. The increasing number of complete genomes in the public data repositories provides the opportunity to look not only at a single gene, but at organisms' entire parts list. ^ Here the Sequence Comparison Index (SCI) and the Organism Comparison Index (OCI), algorithms and methods to compare proteins and proteomes, are presented. The complete proteomes of 104 sequenced organisms were compared. Over 280 million full Smith-Waterman alignments were performed on sequence pairs which had a reasonable expectation of being related. From these alignments a whole proteome phylogenetic tree was constructed. This method was also used to compare the small subunit (SSU) rRNA from each organism and a tree constructed from these results. The SSU rRNA tree by the SCI/OCI method looks very much like accepted SSU rRNA trees from sources such as the Ribosomal Database Project, thus validating the method. The SCI/OCI proteome tree showed a number of small but significant differences when compared to the SSU rRNA tree and proteome trees constructed by other methods. Horizontal gene transfer does not appear to affect the SCI/OCI trees until the transferred genes make up a large portion of the proteome. ^ As part of this work, the Database of Related Local Alignments (DaRLA) was created and contains over 81 million rows of sequence alignment information. DaRLA, while primarily used to build the whole proteome trees, can also be applied shared gene content analysis, gene order analysis, and creating individual protein trees. ^ Finally, the standard BLAST method for analyzing shared gene content was compared to the SCI method using 4 spirochetes. The SCI system performed flawlessly, finding all proteins from one organism against itself and finding all the ribosomal proteins between organisms. The BLAST system missed some proteins from its respective organism and failed to detect small ribosomal proteins between organisms. ^