Cyanobacteria are one of the oldest forms of photosynthetic life and may have contributed significantly to the evolution of oxygen into the then anoxic environment. Cyanobacteria are also one of the best sources of natural secondary metabolites (cyanopeptides) some of which have harmful effects on the ecosystem, while others may be beneficial. It is known that these secondary metabolites are continuously produced during growth, however, it is not known whether the producing cyanobacteria actually benefit from these metabolites. The overarching aim of this study was to answer the question ‘Why do cyanobacteria produce secondary metabolites?’. With this aim in mind, preliminary work focused on understanding the growth and secondary metabolite production characteristics of Microcystis aeruginosa PCC7806. The technique of labelling secondary metabolites with 15N was successfully employed in differentiation and quantification of ex-novo and de-novo metabolites. The effect of exogenous cyanopeptides such as microcystins, aerucyclamides, anabaenopeptins, aeruginosamide, cyanopeptolin and aeruginosin on M. aeruginosa PCC7806 was evaluated using a rapid bioassay approach along with an automated cell enumeration technique. The results indicate that at least some cyanopeptides (microcystins-LR, microcystin-LF, aeruginosamide, anabaenopeptin B and aerucyclamide A) induce significant changes to cell division and metabolite production rate. In an ecological scenario, the release of such secondary metabolites by lysing cells (such as when blooms collapse), may be perceived as an alarm signal by surrounding live cells, which may in turn slow cell division and prepare for re-invasion. This may be a strategy for species survival and dominance. While the results from this study do not confirm a role for cyanopeptides, it is thought that the results are clearly indicative of the role played by cyanopeptides for the producing organism. In order to confirm a role, it is recommended that monitoring ribosomally synthesised metabolites (e.g. aerucyclamides) along with chlorophyll-a gene expression, with sophisticated techniques such as qPCR are used.

Det finns många biotekniska analys- och detekteringsmetoder. Metoderna används för identifiering och kvantifiering av biomarkörer. Denna studie har analyserat detekteringsmetoder i de fall där två hjärtspecifika biomarkörer används, troponin och kreatinkinas. Studien avsåg att först identifiera tillämpningsfrekvensen av detekteringsmetoder i Sverige samt internationellt. Vidare identifieras sambandet mellan avgörande parametrar i val av detekteringsmetod. Metoden gick ut på att först bestämma den mest frekventa detekteringsmetoden i Sverige med hjälp av en enkät som skickades till olika laboratorier, sedan studerades tidigare studier publicerade på olika internationella databaser. Studierna som tillämpades var på hjärtspecifika troponin och kreatinkinas för att identifiera val av detekteringsmetod, detekteringskaraktäristika och användarvänlighetsparametrar. Studiens resultat visade att nationellt finns det tre detekteringsmetoder som är de mest använda för identifiering av kreatinkinas: masspektrometri, elektrokemisk luminescence och spektrometri. Internationellt är den dominerande metoden däremot elektrokemisk luminescence. För troponin är den dominerande metoden nationellt: elektrokemisk luminescence och flödescytometri, medan internationellt: elektrokemisk luminescence. Elektrokemisk luminescence är i många fall en stark vinnare i tillämpningen. Ytterligare iakttogs korrelationskoefficienter mellan parametern för att identifiera det starkaste respektive svagaste sambandet. Avgörande parametrar i val av elektrokemisk luminescence, visar på flera samband. Elektrokemisk luminescence och kreatinkinas tilldelas en korrelationskoefficient nära ett för parametrar som volym och känslighet och en korrelationskoefficient nära minus ett för linjärt mätområde och volym, samt kostnad och minimummängd. Medan för troponin och elektrokemisk luminescence erhålls en korrelationskoefficient nära ett för parametrar som känslighet och kostnad och en koefficient nära minus ett för kostnad och tid. / There are many biotechnological analysis- and detection methods. The methods are used for identification and quantification of biomarkers. This study has analyzed detection methods incases where two heart-specific biomarkers are used, troponin and creatine kinase. The study was intended to first identify the application frequency of detection methods in Sweden and internationally. Then identify the relationship between crucial parameters in the choice of detection method. The method consisted of first determining the most frequent detection method in Sweden with the help of a questionnaire that was sent to different laboratories, then previous studies published on various international databases were observed. The studies applied were on topics regarding cardiac-specific troponin and creatine kinase to identify choice of detection method, detection characteristics, and ease of use parameters. The results of the study showed that nationally, the detection methods most used for creatine kinase are mass spectrometry, electrochemical luminescence, and spectrometry. Internationally, however, the dominant method is electrochemical luminescence. For troponin, on a national level the dominant methods are electrochemical luminescence and flow cytometry, while internationally: electrochemical luminescence. Electrochemical luminescence is in many cases a strong winner in application. In addition, correlation coefficients are observed between the decisive parameters for a detection method, to identify the strongest and weakest relationships. Electrochemical luminescence and creatine kinase are assigned a correlation coefficient close to one for parameters such as volume and sensitivity and a correlation coefficient when minus one for measurement range and volume, as well as cost and minimum amount. While for troponin and electrochemical luminescence, a correlation coefficient close to one is obtained for parameters such as sensitivity and cost and a coefficient close to minus one for cost and time.

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