Einleitung: Ruminal werden Futterpflanzen zu kurzkettigen Fettsäuren (SCFAs) abgebaut. Diese bilden die Hauptenergiequelle für den Wiederkäuerorganismus. Da diese Fettsäuren jedoch auch maßgeblich die pH-Homöostase der Vormagenschleimhaut beeinflussen, muss das Pansenepithel in der Lage sein, Änderungen im Substrat- und Protonenangebot festzustellen und anschließend regulative Prozesse anzupassen, um Stoffwechselentgleisungen und so auch einer Pansenazidose vorzubeugen. In anderen Spezies erwiesen sich sogenannte „Freie Fettsäurerezeptoren“ (FFARs) als potenzielle Sensoren veränderter SCFA-Mengen im Darmlumen, die u. a. durch Modulation der intrazellulären Spiegel an zyklischem Adenosinmonophosphat (cAMP) ihre Wirkung vermitteln. Ziele der Untersuchungen: Es sollte in der vorliegenden Arbeit untersucht werden, ob FFARs im Pansenepithel des Schafes vorkommen und durch SCFAs aktiviert sowie intrazelluläre Signalwege über cAMP moduliert werden können. Im Anschluss sollte erarbeitet werden, inwiefern der nachgewiesene Einfluss von Butyrat auf die epithelialen cAMP-Spiegel Auswirkungen auf die epitheliale pH-Modulation infolge einer veränderten Aktivität von Monocarboxylattransportern (MCTs) und Na+/H+-Austauschern (NHEs) hat. Tiere, Material und Methoden: Sämtliche Untersuchungen wurden an Geweben des Vormagens von Schafen (Ovis gmelini aries) durchgeführt. Mittels Reverse-Transkriptase-Polymerase-Kettenreaktion (RT-PCR) und immunhistochemischer Färbungen wurde das Vorliegen verschiedener FFARs in nativem Pansengewebe untersucht. Zur funktionellen Charakterisierung wurden Epithelstücke aus dem ventralen Pansensack in Ussing-Kammern inkubiert und anschließend die cAMP-Spiegel im Epithel mittels einer quantitativen, kompetitiven Analyse bestimmt. Dabei wurde der Einfluss von Forskolin (ein Stimulator der cAMP-synthetisierenden Adenylylzyklasen), von Butyrat sowie von Niacin (ein FFAR-Agonist) betrachtet. Mithilfe von radioaktiv markiertem Azetat wurde der Effekt variierender cAMP-Spiegel auf die Transportaktivität von MCTs unter Zuhilfenahme von zwei verschiedenen MCT-Hemmstoffen (Cyanohydroxyzimtsäure und p-Hydroxymercuribenzoesäure) in Ussing-Kammern evaluiert. Die Aktivität der NHEs wurde an kultivierten Pansenepithelzellen durch Messung des intrazellulären pH-Wertes mittels Spektrofluorometrie unter Einfluss des NHE-Inhibitors 5-N-Ethyl-N-Isopropyl Amilorid ermittelt. Auch hierbei wurden in den Zellen unterschiedliche cAMP-Spiegel durch Forskolin-Applikation induziert. Die Daten der verschiedenen Untersuchungen wurden an 5-8 Tieren je Versuchsansatz erhoben. Die Normalverteilung wurde mittels Kolmogorov–Smirnov-Test ermittelt. Ein Friedman-Test mit anschließendem Dunn-Test wurde für die Analyse der cAMP-Experimente genutzt. MCT und NHE Experimente wurden mithilfe einer einfachen, geblockten Varianzanalyse und anschließendem Tukey-Test ausgewertet. Ergebnisse: Die FFARs GPR109A und FFAR2 konnten an allen untersuchten Lokalisationen (Netzmagen, Pansenvorhof, dorsaler und ventraler Pansensack, Psalter) über RT-PCR bzw. im ventralen Pansensack auch über die immunhistochemischen Färbungen detektiert werden, wohingegen FFAR3 lediglich als mRNA im Vorhof nachweisbar war. Dies lässt die beiden Rezeptoren GPR109A und FFAR2 als mögliche Strukturen zur Detektion von SCFAs im Pansenepithel erscheinen. Die Analyse der intrazellulären cAMP-Spiegel in Epithelien aus dem ventralen Pansensack konnte einen hemmenden Einfluss von Butyrat auf diesen Botenstoff darlegen, was auf eine Beteiligung der genannten FFARs hindeutet. Die Applikation des GPR109A-Agonisten Niacin hatte jedoch keinen Effekt auf die cAMP-Spiegel, sodass eine Wirkungsvermittlung von Butyrat über diesen Rezeptor unwahrscheinlich scheint. Mit Blick auf die funktionellen Auswirkungen dieser cAMP-Modulation hatten variierende cAMP-Level im Kontrast zu Erkenntnissen aus Nicht-Wiederkäuerspezies keinen Einfluss auf die Transportaktivität des ruminalen MCT1 unter den gewählten in vitro-Versuchsbedingungen. Andererseits konnte die Regulation des intrazellulären pH-Wertes von kultivierten Pansenepithelzellen tendenziell durch erhöhte cAMP-Spiegel gehemmt werden, was auf einer Hemmung von NHEs durch den second messenger beruhen könnte. Schlussfolgerungen: Die Expression von GPR109A und FFAR2 lassen diese zwei FFARs als potenzielle Sensoren der intraruminalen bzw. intraepithelialen Nährstoffkonditionen erscheinen. Dabei deuten die vorliegenden Untersuchungen auf eine Aktivierung des FFAR2 durch Butyrat und dessen Metaboliten in den basalen Schichten des Pansenepithels hin. Infolge der Rezeptoraktivierung kommt es vermutlich zu einer Verminderung der intraepithelialen cAMP-Spiegel, welche wiederum einen (schwachen) Einfluss auf die Regulation des intrazellulären pH-Wertes mithilfe von NHEs zu haben scheinen. Entgegen unserer Ausgangshypothese scheinen aber die FFARs des ovinen Pansenepithels die pH-Homöostase des Epithels nur geringfügig zu beeinflussen. Ihre genaue physiologische Bedeutung – insbesondere des GPR109A – bleibt somit noch spekulativ.:1 Einleitung 1 2 Literaturübersicht 3 2.1 Bedeutung kurzkettiger Fettsäuren für den Wiederkäuer 3 2.2 Transport kurzkettiger Fettsäuren über das Pansenepithel 3 2.2.1 Apikale Aufnahme in das Pansenepithel 4 2.2.2 Basolaterale Ausschleusung in den Blutstrom 6 2.3 Metabolisierung kurzkettiger Fettsäuren im Pansenepithel 8 2.4 pH-Homöostase 9 2.4.1 pH-Regulation des Pansenlumens 9 2.4.2 pH-Regulation des Pansenepithels 10 2.5 Anpassungsmechanismen des Pansenepithels 12 2.6 Rolle des Butyrats 15 2.7 Fettsäurerezeptoren 16 2.7.1 G-Protein-gekoppelte Rezeptoren 17 2.7.2 GPRs für SCFAs 17 2.7.2.1 FFAR2 17 2.7.2.2 FFAR3 18 2.7.2.3 GPR109A 19 2.7.3 FFARs im Wiederkäuerorganismus 20 2.8 Monocarboxylattransporter 22 2.8.1 Die Familie der MCTs 22 2.8.2 Regulation der MCTs 23 2.8.3 MCTs im Pansenepithel 24 2.9 Natrium-Protonen-Austauscher 26 2.9.1 Die Familie der NHEs 26 2.9.2 Regulation der NHEs 27 2.9.3 NHEs im Pansenepithel 28 2.10 Fragestellungen der vorliegenden Arbeit 30 3 Publikationen 32 3.1 Publikation 1 32 3.2 Publikation 2 41 3.2.1 Supporting Information 56 4 Diskussion 57 4.1 Nachweis von FFARs im Pansenepithel 57 4.1.1 Regulation intrazellulärer Signalwege durch FFARs 59 4.1.2 GPR109A als potenzieller Butyrat-Rezeptor im Pansenepithel 62 4.1.3 FFAR2 als potenzieller Rezeptor für Butyrat 63 4.2 Seitenabhängigkeit der Butyrat-Effekte 64 4.3 pH-Abhängigkeit der cAMP-Spiegel 66 4.4 Einfluss von cAMP auf die Aktivität der MCTs 68 4.5 Einfluss von cAMP auf die NHE-Aktivität 70 4.6 Schlussfolgerungen 73 5 Zusammenfassung 75 6 Summary 77 7 Literaturverzeichnis 79 8 Anhang 101 8.1 Im Rahmen dieser Dissertation gehaltene Präsentationen 101 Danksagung 103 / Introduction: Forage plants are ruminally degraded to short chain fatty acids (SCFAs). These serve as the main energy source for ruminants. As SCFAs also influence the pH-homeostasis of the ruminal mucosa, the epithelium must be able to detect changes of both substrate and proton accumulation and adapt transport processes accordingly, in order to prevent metabolic dysfunction and thus the risk of ruminal acidosis. Studies in non-ruminant species detected so-called ‘free fatty acid receptors’ (FFARs) as potential SCFA-sensors in the gut lumen. It has been shown that these receptors transduce their information by modulation of intracellular levels of cyclic adenosine monophosphate (cAMP). Aim: This study intended to investigate if FFARs are located in the ovine ruminal epithelium. It should further be evaluated if FFARs can be stimulated by SCFAs leading to a modulation of intracellular pathways via cAMP. Finally, the study aimed to elucidate the influence of low epithelial cAMP-levels after butyrate application on the regulation of pH-homeostasis in the ruminal epithelium by modulating the activity of transport proteins such as monocarboxylate transporters (MCTs) and Na+/H+ exchangers (NHEs). Animals, material, and methods: All experiments were conducted with ovine (Ovis aries) ruminal tissues. The expression of different FFARs was investigated in native tissues using a reverse transcription polymerase chain reaction (RT-PCR) and immunohistochemical staining. For functional analysis, epithelial cAMP levels were determined by a quantitative and competitive assay after incubation of epithelia of the ruminal ventral sac in Ussing chambers. The influence of forskolin (a stimulator of the adenylyl cyclases), butyrate, as well as niacin (an FFAR agonist) was evaluated. Further, the effect of varying cAMP levels on transport activity of MCTs was characterised on Ussing chamber-mounted epithelia with radioactively labelled acetate and two MCT inhibitors (cyano-hydroxycinnamic acid and p-hydroxymercuribenzoic acid). Finally, the activity of NHEs was assessed in cultured ruminal epithelial cells. The intracellular pH was evaluated by spectrofluorometry while the cells were incubated with forskolin (to modify intracellular cAMP levels) or the NHE inhibitor 5-(N-ethyl-N-isopropyl)-amiloride. The data for the different set-ups were acquired from 5-8 animals each. Kolmogorov–Smirnov test was used for testing normality. For cAMP level analyses, the Friedman test followed by Dunn's test was performed. MCT and NHE measurements were analysed using one-way randomized block analysis of variance followed by Tukey's test. Results: GPR109A and FFAR2 were detected in all ovine ruminal epithelia examined (reticulum, atrium ruminis, ruminal ventral and dorsal sac, omasum) by RT-PCR and in ruminal ventral sac also by immunohistochemical staining. FFAR3, however, was detected solely on mRNA level in tissues of the ovine atrium ruminis. Thus, the two immunohistochemically detected receptors may serve as potential sensors for SCFAs in the ruminal epithelium. The analysis of intraepithelial cAMP levels revealed an inhibiting influence of butyrate application on cAMP pointing to an activation of FFARs by this SCFA. Nonetheless, the incubation with the GPR109A agonist niacin did not show any effect on cAMP levels. This finding contradicts the theory of an activation of GPR109A by butyrate. Looking at functional consequences of varying cAMP levels, in contrast to studies on non-ruminant species ruminal MCT1 activity was not influenced by different cAMP levels, at least under the conditions chosen in this in vitro study. However, regulation of intracellular pH in cultured ruminal epithelial cells tended to decrease with augmented cAMP levels. This might be mediated by an inhibition of NHEs. Conclusions: The expression of GPR109A and FFAR2 points at a participation of these receptors in sensing intraruminal and intraepithelial energy status. The present data hint at an activation of FFAR2 by butyrate or its metabolites in the basal layers of the epithelium. Activation of the receptor leads to decreased cAMP levels. This in turn seems to slightly modify the regulation of intracellular pH via NHEs. Contradicting our initial hypothesis, ovine ruminal FFARs seem to play only a minor role in modulation of epithelial pH homeostasis. The main physiological role of ruminal FFARs – especially of GPR109A – remains to be clarified.:1 Einleitung 1 2 Literaturübersicht 3 2.1 Bedeutung kurzkettiger Fettsäuren für den Wiederkäuer 3 2.2 Transport kurzkettiger Fettsäuren über das Pansenepithel 3 2.2.1 Apikale Aufnahme in das Pansenepithel 4 2.2.2 Basolaterale Ausschleusung in den Blutstrom 6 2.3 Metabolisierung kurzkettiger Fettsäuren im Pansenepithel 8 2.4 pH-Homöostase 9 2.4.1 pH-Regulation des Pansenlumens 9 2.4.2 pH-Regulation des Pansenepithels 10 2.5 Anpassungsmechanismen des Pansenepithels 12 2.6 Rolle des Butyrats 15 2.7 Fettsäurerezeptoren 16 2.7.1 G-Protein-gekoppelte Rezeptoren 17 2.7.2 GPRs für SCFAs 17 2.7.2.1 FFAR2 17 2.7.2.2 FFAR3 18 2.7.2.3 GPR109A 19 2.7.3 FFARs im Wiederkäuerorganismus 20 2.8 Monocarboxylattransporter 22 2.8.1 Die Familie der MCTs 22 2.8.2 Regulation der MCTs 23 2.8.3 MCTs im Pansenepithel 24 2.9 Natrium-Protonen-Austauscher 26 2.9.1 Die Familie der NHEs 26 2.9.2 Regulation der NHEs 27 2.9.3 NHEs im Pansenepithel 28 2.10 Fragestellungen der vorliegenden Arbeit 30 3 Publikationen 32 3.1 Publikation 1 32 3.2 Publikation 2 41 3.2.1 Supporting Information 56 4 Diskussion 57 4.1 Nachweis von FFARs im Pansenepithel 57 4.1.1 Regulation intrazellulärer Signalwege durch FFARs 59 4.1.2 GPR109A als potenzieller Butyrat-Rezeptor im Pansenepithel 62 4.1.3 FFAR2 als potenzieller Rezeptor für Butyrat 63 4.2 Seitenabhängigkeit der Butyrat-Effekte 64 4.3 pH-Abhängigkeit der cAMP-Spiegel 66 4.4 Einfluss von cAMP auf die Aktivität der MCTs 68 4.5 Einfluss von cAMP auf die NHE-Aktivität 70 4.6 Schlussfolgerungen 73 5 Zusammenfassung 75 6 Summary 77 7 Literaturverzeichnis 79 8 Anhang 101 8.1 Im Rahmen dieser Dissertation gehaltene Präsentationen 101 Danksagung 103

A characteristic feature of species of Trichoderma is the production of concentric rings of conidia in response to alternating light-dark conditions. In response to a single burst of light, a single ring of conidia forms at what was the colony perimeter. On the basis of these observations, competency to photoconidiate has been proposed to be due to the age and metabolic rate of the hyphal cell. In this study, conidiation was investigated in five biocontrol isolates (T. hamatum, T. atroviride, T. asperellum, T. virens and T. harzianum) using both a morphological and molecular approach. All five isolates produced concentric conidial rings under alternating light-dark conditions on potato-dextrose agar (PDA), however, in response to a 15 min burst of blue light, only T. asperellum and T. virens produced a clearly, defined conidial ring which correlated with the colony margin at the time of light exposure. Both T. harzianum and T. hamatum photoconidiated in a disk-like fashion and T. atroviride produced a broken ring with a partially filled in appearance. On the basis of these results, it was postulated that competency to photoconidiate is a factor of the metabolic state of the hyphal cell rather than chronological age or metabolic rate. The influence of the source of nitrogen on photoconidiation was assessed on pH-buffered (pH 5.4) minimal medium (MM) amended with glutamine, urea or KNO₃. In the presence of glutamine or urea, T. asperellum and T. harzianum conidiated in a disk, whereas, when KNO₃ was the sole nitrogen source, a ring of conidia was produced. Further, in the presence of increasing amounts of glutamine, the clearly defined photoconidial ring produced on PDA by T. asperellum became disk-like. These results clearly demonstrated that primary nitrogen promotes photoconidiation in these isolates and strongly suggests that competency of a hyphal cell to conidiate in response to light is dependent on the nitrogen catabolite repression state of the cell. The experiments were repeated for all five isolates on unbuffered MM. Differences were apparent between the buffered and unbuffered experiments for T. atroviride. No photoconidiation was observed in T. atroviride on buffered medium whereas on unbuffered medium, rings of conidia were produced on both primary and secondary nitrogen. These results show that photoconidiation in T. atroviride is influenced by the buffering capacity of the medium. Conidiation in response to light by T. hamatum and T. virens was absent in all nitrogen experiments, regardless of the nitrogen source and buffering capacity, whereas both isolates conidiated in response to light on PDA. These results imply that either both sources of nitrogen are required for photoconidiation, or a factor essential for conidiation in these two isolates was absent in the minimal medium. Mycelial injury was also investigated in five biocontrol isolates of Trichoderma. On PDA, all isolates except T. hamatum conidiated in response to injury. On nitrogen amended MM, conidiation in response to injury was again observed in all isolates except for T. hamatum. In T. atroviride, injury-induced conidiation was observed on all medium combinations except the pH-buffered MM amended with glutamine or urea and T. virens conidiated in response to injury on primary nitrogen only, regardless of the buffering capacity. These results have revealed conidiation in response to injury to be differentially regulated between isolates/species of Trichoderma. On unbuffered MM amended with glutamine or urea, conidiation in response to injury occurred at the colony perimeter only in T. atroviride. It was hypothesised that the restriction of conidiation to the perimeter may be due to changes in the pH of the agar. The experiment was repeated and the pH values of the agar under the growing colony measured at the time of light induction (48 h) or injury (72 h). The areas under the hyphal fronts were acidified to below the starting value of the medium (pH 5.4) and the centres of the plates were alkalinised. The region of acidification at the time of stimuli correlated with the production of conidia, which implicates a role for crossregulation of conidiation by the ambient pH. The influence of the ambient pH on injury-induced conidiation was investigated in T. hamatum and T. atroviride on MM amended with glutamine and PDA, pH-buffered from pH 2.8 to 5.6. Thickening of the hyphae around the injury site was observed at the lowest pH values on MM in both T. atroviride and T. hamatum, however no conidia were produced, whereas both Trichoderma species conidiated on pH-buffered PDA in a strictly low pH-dependent fashion. This is the first observation of injury-induced conidiation in T. hamatum. The influence of the ambient pH on photoconidiation was assessed in T. hamatum, T. atroviride and T. harzianum using both buffered and unbuffered PDA from pH 2.8 to 5.2. On buffered PDA, no conidiation in response to light was observed above pH 3.2 in T. hamatum, above 4.0 in T. atroviride and above 4.4 in T. harzianum, whereas on unbuffered PDA it occurred at all pH values tested. It was postulated that conidiation at pH values above 4.4 on unbuffered PDA was due to acidification of the agar. The pH values of the agar under the growing colony were measured at the time of light exposure and in contrast to the MM with glutamine experiments, alkalisation of the agar had occurred in both T. atroviride and T. hamatum. No change in medium pH was recorded under the growing T. harzianum colony. These results indicate that low pH-dependence of photoconidiation is directly related to the buffering capacity of the medium. Recent studies have linked regulation of conidiation in T. harzianum to Pac1, the PacC orthologue. In fungi, PacC regulates gene expression in response to the ambient pH. In these studies pH-dependent photoconidiation occurred only on buffered PDA and on unbuffered PDA conidiation occurred at significantly higher ambient pH levels. It is proposed that the influence of ambient pH on conidiation in the isolates used in this study is not due to direct Pac1 regulation. The T. harzianum isolate used in this study produced profuse amounts of the yellow anthraquinone pachybasin. Production of this secondary metabolite was strictly pH-dependent, irrespective of the buffering capacity of the medium. Studies in T. harzianum have linked Pac1 regulation to production of an antifungal α-pyrone. pH-dependence on both buffered and unbuffered media strongly suggests that pachybasin production may also be under the control of Pac1. Photoconidiation studies on broth-soaked filter paper, revealed rhythmic conidiation in the pachybasin producing T. harzianum isolate. Diffuse rings of conidia were produced in dark-grown cultures and, in cultures exposed to light for 15 min at 48 h, the rings were clearly defined. These results show that conidiation is under the control of an endogenous rhythm in T. harzianum and represent the first report of circadian conidiation in a wild-type Trichoderma. A Free-Running Rhythm (FRR) assay was used to investigate rhythmic gene expression in T. atroviride IMI206040 and a mutant derivative, in which the wc-2 orthologue, blr-2, was disrupted. Over a 3 d period, expression of gpd, which encodes the glycolytic enzyme glyceraldehyde-3-phosphate dehydrogenase, oscillated with a period of about 48 h. In the Δblr-2 mutant, the gpd rhythm was absent. These results revealed that in T. atroviride, gpd expression is under the control of an endogenous clock and that clock-regulated expression of gpd is associated with a functional BLR complex. Using degenerate primers, a portion of frq, which encodes the N. crassa clock oscillator FREQUENCY, was isolated from T. atroviride and used to probe the FRR assay northern blots. No frq expression was detected at any time point, which suggests that the circadian clock in Trichoderma does not involve FREQUENCY. In a concurrent study, orthologues of rco-1 (rcoT) were isolated and sequenced from T. atroviride and T. hamatum using a combination of degenerate, inverse and specific PCR. RcoT is an orthologue of the yeast global co-repressor Tup1 and in the filamentous fungi, RcoT orthologues have been demonstrated to negatively regulate conidiation. Genomic analysis of all available rcoT orthologues revealed the conservation of erg3, a major ergosterol biosynthesis gene, upstream from rcoT in ascomycetous filamentous fungi, but not in the ascomycetous yeast or in the basidiomycetes. These studies have significantly contributed to our understanding of the regulatory factors controlling conidiation in Trichoderma and have multiple implications for Trichoderma biocontrol; most notable the promotion of conidiation by primary nitrogen and low pH. Incubation conditions can be altered to suit the nitrogen and pH preferences of a biocontrol strain in order to promote cost effective conidial production, however this is not easily achieved in the soil, where the biocontrol strain must perform in a highly buffered environment optimised for plant growth. Successful use of Trichoderma biocontrol strains may involve the screening and targeting of strains to the appropriate pH conditions or the selection of new strains on the basis of capacity to perform under a given range of conditions.

pH is a key parameter in the optimization of animal cell processes, and has be linked to specific patterns of consumption and production of extracellular metabolites. However, the effect of extracellular pH on intracellular metabolism has not been fully elucidated. Metabolic flux analysis is a mathematical method that can be used to generate the intracellular flux distributions in cells, e.g. as a function of some environmental parameter. In this work, the overall objective was to develop a culture system and experimental protocol for cultivation of CHO cells, which can be used to generate data for analysis of the relationship between extracellular pH and intracellular fluxes in CHO cells by metabolic flux analysis. First, shake-flask culture of an IgG-producing cell line was performed to select an academic and chemically-defined medium with known composition. This was followed by subsequent adaptation of the cells. It was found that the originally selected medium had to be supplemented with a commercial medium to produce acceptable growth and viability. Shake-flask culture was also performed to evaluate the effect of the biological buffer HEPES on cell growth and viability, and the pH-stability during culture. HEPES-concentrations in the investigated range (7.5-45 mM) did not show an apparent effect on cell growth or viability. The higher concentrations gave slightly better buffering capacity at inoculation, however were not sufficient to keep pH stable during culture. As a result, the idea of using shake flask culture and similar techniques for cultivation of cells at various pH set-points was dismissed. Instead, a culture system and protocol based on a 100 mL Spinner flask with pH-regulation was custom-designed for the project. Features of the final design included continuous monitoring of pH and DO, stable temperature at 37 °C, adjustable agitation rate, as well as the option to incorporate inflow of air, O2 and CO2. In addition, the possibility to disconnect the flask unit to perform medium exchange and sample collection away from the reactor site (i.e. in a laminar flow workbench) was integrated into the design and protocol. The system was demonstrated for pseudo-perfusion culture with the adapted IgG-producing cell line at pH 7.0 during 24 days. Optimized regulation settings were identified. It was shown that the system could support viable cell densities of up to 11 MVC/mL and high viability (> 90 %). During the final phase of culture, stable growth, at specific growth rates of approximately 0.7 Day-1, was achieved. The specific rates of consumption and production of the key metabolites glucose, glutamine, lactate and NH4+, as well as 20 amino acids were analyzed. A majority of the rates were in accordance with CHO cell metabolism. The expected consumption of a majority of the essential amino acids and main carbon sources glucose and glutamine were confirmed, as well as the associated production of by-products lactate and NH4+. The system and protocol developed in this work can be used in future experiments to generate data describing metabolic profiles as a function of various pH-set points. This data may then be used in metabolic flux analysis to further elucidate the metabolism behind pH effects in CHO cells. / pH är en viktig parameter i optimeringen av animalcellsprocesser och har sammankopplats med specifika konsumtions- och produktionsmönster rörande extracellulära metaboliter. Det extracellulära pH-värdets effekt på den intracellulära metabolismen är dock inte fullt klarlagd. Metabolisk flux analys är en matematisk metod som kan användas för att generera intracellulära fluxfördelningar i celler, exempelvis som en funktion av någon yttre parameter. Det övergripande målet i detta arbete var att utveckla ett odlingssystem och experimentellt protokoll för odling av CHO-celler som kan användas för att generera data för metabolisk flux analys där målet är att studera effekten av pH på den intracellulära cellmetabolismen. En IgG-producerande CHO-cellslinje odlades först i skakkolv för att välja ut ett akademiskt kemiskt definierat medium med känd sammansättning. Därefter följde försök att anpassa cellerna till det valda mediet. Det visade sig att ett kommersiellt medium behövde tillsättas för att ge godtagbar tillväxt och viabilitet. Effekten av den biologiska bufferten HEPES på cellernas tillväxt och viabilitet, samt pH-stabiliteten under odling, undersöktes också genom odling i skakkolv. HEPES-koncentrationer i det undersökta intervallet (7.5 – 45 mM) hade ingen större effekt på tillväxt och viabilitet. För de högre koncentrationerna var buffertkapaciteten något bättre precis vid inokulering. Dessa koncentrationer var dock ej tillräckliga för att ge stabilt pH under odlingen. Baserat på dessa resultat övergavs tanken på att använda skakkolvsodling för att odla celler vid olika pH-värden. Ett odlingssystem och ett protokoll baserat på en 100 mL Spinnerflaska med pH-reglering specialdesignades istället för projektet. I det färdiga systemet fanns lösningar för kontinuerlig övervakning av pH och DO, stabil temperatur vid 37 °C, justerbar omrörningshastighet, samt valmöjligheten att flöda in luft, O2 och CO2. Dessutom infördes möjligheten att koppla loss flaskenheten från reglersystemet för byte av medium och provtagning. För att demonstrera systemet genomfördes en odling med den anpassade IgG-producerande cellinjen enligt principen för pseudo-perfusion vid pH 7.0. Odlingen pågick under 24 dagar och optimerade reglerinställningar identifierades. Det visades att systemet kunde understödja cellkoncentrationer upp till 11 miljoner celler per milliliter, samt hög viabilitet (> 90 %). Under den senare delen av odlingen uppnåddes stabil tillväxt, vid specifika tillväxthastigheter omkring 0.7 per dygn. Den specifika konsumtions- och produktionshastigheten för metaboliterna glukos, glutamin, laktat och NH4+, samt 20 aminosyror analyserades. Majoriteten av hastigheterna stämde överens med typisk CHO-cellsmetabolism. Den förväntade konsumtionen av majoriteten av de essentiella aminosyrorna och huvudsakliga kolkällorna glukos och glutamin konfirmerades, såväl som den associerade produktionen av bi-produkterna laktat och NH4+. Odlingssystemet och det experimentella protokollet som utvecklades i detta arbete kan användas i framtida experiment för att generera data som beskriver metaboliska profiler som funktion av extracellulärt pH. Dessa data kan sedan användas i metabolisk flux analys för att dra slutsatser om pH-effekter i CHO-celler.

Trace component analysis in gasification processes are important part of elemental component balances in order to understand the fate of these participating compounds in the feedstock. Residual traces in the raw synthesis gas after quench could bring about the poisoning of catalysts and corrosion effects on plant facilities. The objective of this work is to investigate the effects of quenching operation on the trace components during test campaigns of the autothermal non-catalytic reforming of natural gas (Gas-POX) mode in the HP POX (high pressure partial oxidation) test plant. In order to achieve this, Aspen Plus simulation model of the quench chamber of the HP POX test plant was developed to re-calculate the quench chamber input amount of different trace compounds from their output amount measured during test points of the Gas-POX campaigns. Variation in quench water temperatures from 130 °C to 220 °C and pH value of quench water as well as the resulting variation in Henry´s and Dissociation constant of the traces (CO2, H2S, NH3 and HCN) changed the distribution of traces calculated in the quench water. The formation of traces of organic acid (formic acid and acetic acid) and traces of BTEX, PAHs and soot in the quench water effluent were discussed. The discrepancies between equilibrium constant and reaction quotient (non-equilibrium or real) for the formation of NH3 and HCN at the exit of the gasifier were discussed. The assessment of the results in this work should lead to the improvement in the understanding of trace components and concepts that could be employed to influence their formation and reduction.

Trace component analysis in gasification processes are important part of elemental component balances in order to understand the fate of these participating compounds in the feedstock. Residual traces in the raw synthesis gas after quench could bring about the poisoning of catalysts and corrosion effects on plant facilities. The objective of this work is to investigate the effects of quenching operation on the trace components during test campaigns of the autothermal non-catalytic reforming of natural gas (Gas-POX) mode in the HP POX (high pressure partial oxidation) test plant. In order to achieve this, Aspen Plus simulation model of the quench chamber of the HP POX test plant was developed to re-calculate the quench chamber input amount of different trace compounds from their output amount measured during test points of the Gas-POX campaigns. Variation in quench water temperatures from 130 °C to 220 °C and pH value of quench water as well as the resulting variation in Henry´s and Dissociation constant of the traces (CO2, H2S, NH3 and HCN) changed the distribution of traces calculated in the quench water. The formation of traces of organic acid (formic acid and acetic acid) and traces of BTEX, PAHs and soot in the quench water effluent were discussed. The discrepancies between equilibrium constant and reaction quotient (non-equilibrium or real) for the formation of NH3 and HCN at the exit of the gasifier were discussed. The assessment of the results in this work should lead to the improvement in the understanding of trace components and concepts that could be employed to influence their formation and reduction.:List of Figures vii List of Tables xii List of Abbreviations and Symbols xiii 1 Introduction 1 1.1 Background 1 1.2 Objective of the Work 4 1.3 Overview of the Work 5 2 Process and test conditions 6 2.1 HP POX test plant 6 2.2 Test campaign procedure 8 2.2.1 Gas-POX operating parameter range 8 2.2.2 Gas-POX experiments 9 2.2.3 Net reactions of partial oxidation 9 2.3 Gaseous feedstock characterization 11 2.3.1 Natural gas feedstock composition 11 2.4 Analytical methods for gaseous products 12 2.4.1 Hot gas sampling 12 2.4.2 Raw synthesis gas analysis after quench 13 2.5 Aqueous phase product analysis 14 2.5.1 Molecularly dissolved trace compounds and their ions trace analysis 14 2.5.2 Other trace analysis 15 2.6 Limit of accuracy in measurement systems 15 2.7 Summary 17 3 Simulation and methods 18 3.1 Test points calculation of the HP POX test campaign 18 3.1.1 Aspen Plus model for HP POX quench water system 19 3.2 Gas-POX 201 VP1 quench water system model simulation by Aspen Plus 23 3.2.1 Measured and calculated input parameters 23 3.2.2 Calculated sensitivity studies of species and their distribution for test point (VP1) 24 3.3 Used calculation tools related to the work 25 3.3.1 VBA in Excel 25 3.3.2 Python as interface between Aspen Plus and Microsoft Excel 26 3.3.3 Aspen Simulation Workbook 27 3.4 Summary 29 4 Trace components in quench water system 30 4.1 Physico-chemical parameters of quench water 31 4.1.1 Quench water pH adjustment 32 4.1.2 Henry constant 34 4.1.3 Dissociation constant 35 4.1.4 Organic acids in quench water 38 4.2 Carbon dioxide (CO2) 39 4.2.1 Results of sensitivity study: quench water temperature variation effects on CO2 41 4.2.2 Results of sensitivity study: quench water pH variation influence on CO2 42 4.3 Nitrogen compounds 43 4.3.1 Ammonia (NH3) 44 4.3.2 Results of sensitivity study: quench water temperature variation effects on NH3 46 4.3.3 Results of sensitivity study: quench water pH variation influence on NH3 47 4.3.4 Hydrogen Cyanide (HCN) 48 4.3.5 Results of sensitivity study: quench water temperature variation effects on HCN 50 4.3.6 Results of sensitivity study: quench water pH variation influence on HCN 50 4.4 Sulphur compounds: H2S 51 4.4.1 Results of sensitivity study: quench water temperature variation effects on H2S 53 4.4.2 Results of sensitivity study: quench water pH variation influence on H2S 54 4.5 Summary 55 5 Organic acids trace studies in quench water 57 5.1 Organic acids interaction with ammonia compounds in the quench water 57 5.2 Formic acid 62 5.2.1 Trace of formic acid in quench water 64 5.3 Acetic acid 67 5.3.1 Trace of acetic acid in quench water 69 5.4 Summary 72 6 Temperature approach studies for NH3 and HCN formation in gasifier 74 6.1 Nitrogen compounds: NH3 and HCN 74 6.2 Ammonia (NH3) formation in the gasifer 77 6.3 Hydrogen cyanide (HCN) formation in the gasifier 79 6.4 Discrepancies between back-calculated reaction quotients and equilibrium constants of the NH3 formation 81 6.4.1 Case 1: calculated equilibrium distribution between N2, NH3 and HCN 81 6.4.2 Case 2: calculated equilibrium distribution between NH3 and HCN 83 6.5 Summary 84 7 Traces of BTEX, PAHs and soot in quench water 86 7.1 Quench water behaviour 87 7.2 BTEX compounds 88 7.2.1 BTEX in quench water effluent 90 7.3 PAH compounds 93 7.3.1 PAHs in quench water effluent 95 7.4 Soot formation 99 7.4.1 Soots in quench water effluent 101 7.5 Summary 102 8 Summary and outlook 103 Bibliography 106 9 Appendix 135 List of Figures Figure 2.1: HP POX test plant main facility components and material flow courtesy of [Lurgi GmbH, 2008] 6 Figure 2.2: Simplified scheme of HP POX plant (including quench system) [Lurgi GmbH, 2008] 7 Figure 2.3: Overview of reactions of methane 10 Figure 3.1: Simplified scheme for HP POX quench water system 18 Figure 3.2: Aspen Plus flow diagrams of simulated HP POX quench water system 19 Figure 3.3: Integration of information and functions in VBA via Microsoft Excel to Aspen Plus model 25 Figure 3.4: Integration of information and functions in Python via Microsoft Excel to Aspen Plus model 26 Figure 3.5: ASW enables Excel users to rapidly run scenarios using the underlying rigorous models to analyze plant data, monitor performance, and make better decisions. 27 Figure 4.1: Vapour-liquid equilibria system of CO2, H2S, NH3, HCN and organic acids in the quench water and extended mechanisms according to [Kamps et al., 2001], [Alvaro et al., 2000], [Kuranov et al., 1996], [Xia et al., 1999] and [Edwards et al., 1978]. 30 Figure 4.2: HP POX quench water system with pH regulator for sensitivity studies 34 Figure 4.3: Henry´s constant for CO2, H2S, NH3 and HCN derived from [Edwards et al., 1978] for CO2, [Alvaro et al., 2000] for NH3, [Kamps et al., 2001] for H2S, and [Rumpf et al., 1992] for HCN 35 Figure 4.4: Dissociation constants for CO2, H2S, NH3, HCN and H2O derived from [Alvaro et al., 2000], [Kamps et al., 2001], and [Edwards et al., 1978] 37 Figure 4.5: The flow of CO2 in the quench water cycle (test point VP1). 40 Figure 4.6: Calculated quench water temperature variation and effects on CO2 distribution 42 Figure 4.7: Calculated influence of pH regulation and effects on CO2 distribution 43 Figure 4.8: The flow of NH3 in the quench water cycle (test point VP1). 46 Figure 4.9: Calculated quench water temperature variation and effects on NH3 distribution 47 Figure 4.10: Calculated influence of pH regulation and effects on NH3 distribution 48 Figure 4.11: The flow of HCN in the quench water cycle (test point VP1). 49 Figure 4.12: Calculated quench water temperature variation and effects on HCN distribution 50 Figure 4.13: Calculated influence of pH regulation and effects on HCN distribution 51 Figure 4.14: The flow of H2S in the quench water cycle (test point VP1) 53 Figure 4.15: Calculated quench water temperature variation and effects on H2S distribution 54 Figure 4.16: Calculated influence of pH regulation and effects on H2S distribution 55 Figure 5.1: Aspen Plus back-calculated (real) formic acid concentration, quench water temperature and the calculated equilibrium formic acid concentration against back-calculated (real) ammonia concentration for the 47 test points (using amongst others sampled HCOO- and NH4+ values according to Table 2.6). 59 Figure 5.2: Aspen plus back-calculated (real) formic acid concentration, back-calculated (real) ammonia concentration and the calculated equilibrium formic acid concentration against quench water temperature for the 47 test points (using amongst others sampled HCOO- and NH4+ values according to Table 2.6). 60 Figure 5.3: Aspen plus back-calculated (real) acetic acid concentration, quench water temperature and the calculated equilibrium acetic acid concentration against back-calculated (real) ammonia concentration for the 47 test points. 61 Figure 5.4: Aspen plus back-calculated (real) acetic acid concentration, back-calculated (real) ammonia concentration and the calculated equilibrium acetic acid concentration against quench water temperature for the 47 test points. 62 Figure 5.5: Concentration of formic acid (Aspen plus calculated m_eq and back-calculted m_real) formation in the quench and quench water temperature for the 47 test points. 64 Figure 5.6: Concentration of formic acid (Aspen plus calculated m_eq and back-calculted m_real) in the quench against quench water temperature for the 47 test points (as in Fig.5.2). 65 Figure 5.7: Comparison between formic acid equilibrium constant (Keq), reaction quotient (Kreal) and the quench water temperature for the 47 test points. 66 Figure 5.8: Comparison between formic acid equilibrium constant (Keq) and reaction quotient (Kreal) against quench water temperatures for the 47 test points. 67 Figure 5.9: Concentration of acetic acid (Aspen plus calculated m_eq and back-calculted m_real) in the quench and quench water temperature for the 47 test points. 69 Figure 5.10: Concentration of acetic acid (Aspen plus calculated m_eq and back-calculted m_real) in the quench against quench water temperature for the 47 test points (as in Fig.5.4). 70 Figure 5.11: Comparison between acetic acid equilibrium constant (Keq), reaction quotient (Kreal) and the quench water temperature for the 47 test points. 71 Figure 5.12: Comparison between acetic acid equilibrium constant (Keq) and reaction quotient (Kreal) against quench water temperatures for the 47 test points. 72 Figure 6.1: Mole fraction of gas compoents in the hot gas outlet out of gasifier against hot gas temperature for the 47 test points 76 Figure 6.2: Calculated reaction quotient (Q) and equlibrium constant (Keq) for NH3 against hot gas temperature for the 47 test points (see Fig. 9.10 in Appendix) 77 Figure 6.3: NH3 temperature approach against hot gas temperature for the 47 test points (see Fig. 9.11 in Appendix) 78 Figure 6.4: Calculated reaction quotient (Q) and equlibrium constant (Keq) for HCN against hot gas temperature for the 47 test points (see Fig. 9.13 in Appendix) 79 Figure 6.5: HCN temperature approach against hot gas temperature for the 47 test points (see Fig. 9.14 in Appendix) 80 Figure 6.6: Comparison between calculated real and equilibrium hot gas N2, NH3 and HCN mol fractions against their respective hot gas temperature (case 1). 82 Figure 6.7: Relations between back-calculated real and equilibrium hot gas N2, NH3 and HCN mol fractions (for chemical equilibrium according to equations (6.1) and (6.4)) against their respective hot gas temperature (see Case 1, Section 6.4.1, and Fig. 6.6) 82 Figure 6.8: Comparison between calculated real and equilibrium hot gas HCN mol fraction against their respective hot gas temperature (case 2). 83 Figure 6.9: Relations between back-calculated real and equilibrium hot gas HCN mol fractions, and change in NH3 mol fractions (for chemical equilibrium according to equation (6.4)), against their respective hot gas temperature (see. Case 2, Section 6.4.2 and Fig. 6.7) 84 Figure 6.10 Comparison between NH3 and HCN formation (mole fraction) calculated equilibrium constant (Keq) and calculated reaction quotient (Q), N2 consumption and hot gas temperatures for the 47 test points (case 1 and case 2). 85 Figure 7.1: HP POX test plant quench water system 88 Figure 7.2: Traces of BTEX measured in the Gas-POX 203 – 207 quench water effluent sample. 91 Figure 7.3: Individual component of BTEX measured in the Gas-POX 203 – 207 quench water effluent sample. 92 Figure 7.4: (a) Alkyl radical decomposition and (b) C1 and C2 hydrocarbons oxidation mechanism [Warnatz et al., 2000] 93 Figure 7.5: Recombination of C3H3 to form benzene 94 Figure 7.6: The Diels - Alder reaction for the formation of PAHs 95 Figure 7.7: Amount of PAHs that were detected in Gas-POX 203 – 207 test points quench water effluent samples. 97 Figure 7.8: Distribution of PAH compounds in Gas-POX 203 – 207 quench water effluent samples. 98 Figure 7.9: Some steps in soot formation [McEnally et al., 2006]. 99 Figure 7.10: Illustration of soot formation path in homogenous mixture [Bockhorn et al., 1994] 100 Figure 9.1: Aspen flow sheet set up for HP POX quench system GasPOX 201 VP1 (simplified and extension of Fig. 3.2, organic acids not taken into account). Tabulated values are given in Table 9.11. 135 Figure 9.2: Comparison between the Henry´s constant profiles: Aspen Plus (markers) and Literatures (solid lines) ([Edwards et al., 1978] for CO2, [Alvaro et al., 2000] for NH3, [Kamps et al., 2001] for H2S, and [Rumpf et al., 1992] for HCN as it can be seen in Fig. 4.3) 137 Figure 9.3: Henry´s constant profiles derived from literatures ([Edwards et al., 1978] for CO2, [Alvaro Pérez-Salado et al., 2000] for NH3, [Kamps et al., 2001] for H2S, and [Rumpf et al., 1992] for HCN as it can be seen in Fig. 4.3) 137 Figure 9.4: Comparison between the dissociation constant profiles: Aspen Plus (markers) and Literatures (solid or dashed lines) [Alvaro et al., 2000], [Kamps et al., 2001], and [Edwards et al., 1978] as in Fig.4.4. 138 Figure 9.5: Dissociation constant profiles derived from literatures [Kamps et al., 2001], and [Edwards et al., 1978] as in Fig.4.4. 138 Figure 9.6: Calculated pH values, temperature range and species 139 Figure 9.7: Aspen Plus flow sheet setup for organic acid compounds calculations (GasPOX 201 VP1, see also Table 9.12) 142 Figure 9.8: Aspen Plus flow sheet setup for nitrogen compounds calculations (GasPOX 201 VP1, see also Table 9.12, organic acids are taken into account in the aqueous streams of the quench system) 145 Figure 9.9: Yield of ammonia in gasifier (calculated real) and hot gas temperature against the 47 test points 146 Figure 9.10: Kreal or reaction quotient for ammonia formation in the gasifier against the 47 test points. 146 Figure 9.11: Temperature approach studies for ammonia and the 47 test points 147 Figure 9.12: Yield of HCN from the gasifier (calculated real and equilibrium) and hot gas temperature and the 47 test points 147 Figure 9.13: Comparison between equilibrium constant and reaction quotient for HCN and 47 test points 148 Figure 9.14: Temperature approach studies for HCN and the 47 test points 148 Figure 9.15: Comparison among equilibrium constants of reactions against temperature, T [°C] 149 Figure 9.16: Comparison among equilibrium constants of reactions against temperature, 1/T [1/K] 150 List of Tables Table 2.1: Outline of Gas-POX mode operating parameter range 8 Table 2.2: Outline of test runs operating mode and parameters of chosen test campaigns 9 Table 2.3: Natural gas feedstock compositions 12 Table 2.4: Product synthesis gas analysis method (hot gas before quench) [Brüggemann, 2010] 12 Table 2.5: Analysis methods for raw synthesis gas [Brüggemann, 2010] 13 Table 2.6: Analysis methods for aqueous phase products [Brüggemann, 2010] 14 Table 2.7: Relative accuracy for the measured value for temperature, pressure and flow of each feed and product stream [Meyer, 2007] and [Brüggemann, 2010] 17 Table 3.1: Description of blocks used in Aspen Plus simulation. 20 Table 3.2: HP POX test plant quench water cycle parameters Gas-POX 201 VP1* 23 Table 3.3: pH regulator parameters 24 Table 4.1: Organic acids distribution in streams for VP1 based on calculation from Aspen Plus. 38 Table 4.2: The distribution of CO2 and its ions in all the streams 40 Table 4.3: The distribution of NH3 and its ions in all the streams 45 Table 4.4: The distribution of HCN and its ions in all the streams 49 Table 4.5: The distribution of H2S and its ions in all the streams 52 Table 7.1: Relative sooting tendency [Tesner et al., 2010] 101 Table 9.1: Natural gas feed analysis method [Brüggemann, 2010] 135 Table 9.2: pH scale with examples of solution [NALCO 2008] 136 Table 9.3: Gas-POX test campaigns and with designated serial numbers 140 Table 9.4: Summary of correlation coefficient (r) from Figures in Chapter 5 144 Table 9.5: Comparison among reactions temperatures and heat of reactions 149 Table 9.6: Content of BTEX compounds in Gas-POX quench water samples 151 Table 9.7: BTEX in quench water effluent samples results 152 Table 9.8: Content of PAH compounds in Gas-POX quench water samples 157 Table 9.9: PAHs in quench water effluent samples results 160 Table 9.10: Soot in quench water effluent samples results 169 Table 9.11: Aspen Plus flow sheet setup stream details (GasPOX 201 VP1, according to Fig.3.2 and Fig.9.1, organic acids not taken into account) 170 Table 9.12: Aspen Plus flow sheet setup for organic acid and nitrogen compounds calculations for GasPOX 201 VP1 (according to Figures 9.7 and 9.8, organic acids are taken into account) 174