Use of pyrosequencing to identify streptococci and to detect mutations causing antimicrobial resistance

Rapid identification and resistance determination of pathogens in clinical specimens is vital for accurate treatment and monitoring of infectious diseases. Antimicrobial drug resistance is increasing globally and healthcare settings are facing this cost-intensive and even life-threatening problem. The incidence of resistant pathogens in Finland has remained relatively steady and manageable at least for the time being. DNA sequencing is the gold standard method for genotyping, mutation analysis, and identification of bacteria. Due to significant cost decrease in recent years, this technique is available to many research and clinical laboratories. Pyrosequencing technique, a rapid real-time DNA sequencing method especially suitable for analyzing fairly short stretches of DNA, was used in this study. Due to its robustness and versatility, pyrosequencing was applied in this study for identification of streptococci and detection of certain mutations causing antimicrobial resistance in different bacteria. Certain streptococcal species such as S. pneumoniae and S. pyogenes are significantly important clinical pathogens. S. pneumoniae causes e.g. pneumonia and otitis media and is one of the most important community-acquired pathogens. S. pyogenes, also known as group A streptococcus, causes e.g. angina and erysipelas. In contrast, the socalled alpha-haemolytic streptococci, such as S. mitis and S. oralis, belong to the normal microbiota, which are regarded to be non-pathogenic and are nearly impossible to identify by phenotypic methods. In this thesis, a pyrosequencing method was developed for identification of streptococcal species based on the 16S rRNA sequences. Almost all streptococcal species could be differentiated from one another by the developed method, including S. pneumoniae from its close relatives S. mitis and S. oralis . New resistance genes and their variants are constantly discovered and reported. In this study, new methods for detecting certain mutations causing macrolide resistance or extended spectrum beta-lactamase (ESBL) phenotype were developed. These resistance detection approaches are not only suitable for surveillance of mechanisms causing antimicrobial resistance but also for routine analysis of clinical samples particularly in epidemic settings. In conclusion, pyrosequencing was found to be an accurate, versatile, cost-effective, and rapid DNA sequencing method that is especially suitable for mutation analysis of short DNA fragments and identification of certain bacteria.

Suun alueen pigmentoivat Prevotel/a-Iajit äideillä

Suuren osan hampaiston ja suun limakalvopintojen anaerobimikrobistosta muodostaa gram- negatiiviset Prevotella-suvun lajit. Viime vuosina Prevotella-ryhmässä on tapahtunut laajoja taksonomisia muutoksia ja Prevotella-sukuun on liitetty useita uusia lajeja. Näiden lisäksi on kantoja, jotka eivät asetu mihinkään jo tunnistettuun lajiin (Mario ym., 2011). Tässä tutkimuksessa käytimme materiaalina kahdesta eri väitösaineistosta peräisin olevia bakteeri-isolaatteja (Könönen 1994, Gürsoy 2012). Suun bakteeri-isolaatit oli eristetty synnyttäneiltä naisilta. Tutkimuksen päätavoitteena oli aiemmin biokemiallisesti tunnistettujen Prevotel/a-isolaattien lajitason tunnistus nykyaikaisin tunnistusmenetelmin. Tämän lisäksi tavoitteena oli vertailla niiden esiintyvyyttä äitien sylki- ja plakkinäytteissä. Fenotyyppisen tunnistuksen luotettavuuden arvioimiseksi tunnistusmenetelmänä käytettiin 16S rRNA osasekvenointia (n. 500 emäsparia) valikoiduille bakteeri-isolaateille pigmentoivien Prevotel/a-Iajien sekä mahdollisesti kokonaan uusien aiemmin tunnistamattomien lajien identifiointiin.

Data analysis tools and methods for DNA microarray and high-throughput sequencing data

The recent rapid development of biotechnological approaches has enabled the production of large whole genome level biological data sets. In order to handle thesedata sets, reliable and efficient automated tools and methods for data processingand result interpretation are required. Bioinformatics, as the field of studying andprocessing biological data, tries to answer this need by combining methods and approaches across computer science, statistics, mathematics and engineering to studyand process biological data. The need is also increasing for tools that can be used by the biological researchers themselves who may not have a strong statistical or computational background, which requires creating tools and pipelines with intuitive user interfaces, robust analysis workflows and strong emphasis on result reportingand visualization. Within this thesis, several data analysis tools and methods have been developed for analyzing high-throughput biological data sets. These approaches, coveringseveral aspects of high-throughput data analysis, are specifically aimed for gene expression and genotyping data although in principle they are suitable for analyzing other data types as well. Coherent handling of the data across the various data analysis steps is highly important in order to ensure robust and reliable results. Thus,robust data analysis workflows are also described, putting the developed tools andmethods into a wider context. The choice of the correct analysis method may also depend on the properties of the specific data setandthereforeguidelinesforchoosing an optimal method are given. The data analysis tools, methods and workflows developed within this thesis have been applied to several research studies, of which two representative examplesare included in the thesis. The first study focuses on spermatogenesis in murinetestis and the second one examines cell lineage specification in mouse embryonicstem cells.