Systems biology of the central metabolism of Streptococcus pyogenes

Streptococcus pyogenes gehört zu den häufigsten Erregern von Haut- und Atemwegserkrankungen beim Menschen und verursacht verschiedene Krankheiten, von leichten Hautinfektionen bis hin zu schweren immunologisch bedingten Folgeerkrankungen der Streptokokkeninfektion, beispielsweise rheumatisches Fieber. Wie alle Milchsäurebakterien gewinnt S. pyogenes die zum Wachstum benötigte Energie mittels Substratkettenphosphorylierung in der Glykolyse. Das dabei gebildete Pyruvat wird hauptsächlich zu Lactat reduziert. Bisher ist die Regulation einiger glykolytischer Enzyme von S. pyogenes untersucht worden, ein dynamisches Modell der Fermentation ist aber noch nicht aufgestellt worden. Um die Glykolyse von S. pyogenes verstehen und mit der anderer Milchsäurebakterien vergleichen zu können, entwickeln wir ein quantitatives Modell des zentralen Stoffwechsels. Die Konstruktion dieses ersten glykolytischen Modells für S. pyogenes erfolgt in enger Zusammenarbeit mit experimentellen und theoretischen Gruppen innerhalb eines SysMO-Konsortiums. Zur Modellentwicklung werden die von unseren Partnern bereitgestellten Daten benutzt. Fehlende Parameter und Regulationen werden von verwandten Organismen, insbesondere von Lactococcus lactis, übernommen. Aufgrund unbekannter Enzymmechanismen werden „Convenience Kinetics“ genutzt. Um eine gute Übereinstimmung zwischen unseren experimentellen Daten und dem Modell zu erhalten, wird eine Parameterschätzung durchgeführt. Unsere Glukosepulsexperimente zeigen, dass mit der extrazellulären Phosphatkonzentration das FBP-Level und die Rate der Glukoseaufnahme steigen. Mit der Erweiterung um ein Phosphataufnahme-System kann unser kinetisches Modell beide Effekte beschreiben. Zur Aufnahme von Phosphat aus dem Medium besitzt S. pyogenes zwei Systeme. Zum einen besitzt es einen ATP-abhängigen Uniporter, zum anderen wurde ein Natrium-Phosphat-Symporter vorhergesagt. In bisher veröffentlichten glykolytischen Modellen wurde die Phosphatkonzentration nicht als freie Variable implementiert, der Einfluss von Phosphat auf die Glykolyse wurde bislang unterschätzt. Weiterhin wurde ein genomweites Modell zur Simulation von Wachstum und Reproduktion von S. pyogenes konstruiert. Diese Rekonstruktion basiert auf der Genomsequenz und bereits existierenden metabolischen Netzwerken von Escherichia coli, Bacillus subtilis, Lactobacillus plantarum und Lactococcus lactis. Zur Auswertung und Simulation wird „Flux Balance Analysis“ angewandt. Gemessene Aminosäure- und Produktflüsse begrenzen die Flüsse des genomweiten Modells. Um Wachstum und Reproduktion zu simulieren, wurde die Produktion von Biomasse als Zielfunktion gewählt. Die Validierung des rekonstruierten Netzwerks erfolgt mit experimentellen Daten. Dazu haben wir das Wachstum auf einem chemisch definierten Medium mit dem Fehlen von ausgesuchten Aminosäuren untersucht. Außerdem haben wir das Substratspektrum von S. pyogenes studiert. In Übereinstimmung mit den experimentellen Daten simuliert das Modell Wachstum auf Trehalose, Sucrose, Maltose und Mannose. Das Modell erleichtert die Unteruschung des Verhaltens von S. pyogenes auf Veränderungen in seiner Umgebung. Basierend auf dem Modell haben wir essentielle Aminosäuren und ein Minimalmedium für das Wachstum von S. pyogenes bestimmt. Das kinetische und das genomweite Modell erleichtern das Bestimmen und Verstehen der Ähnlichkeiten und Unterschiede zwischen S. pyogenes und eng verwandten Milchsäurebakterien, insbesondere L. lactis. Diese Modelle erleichtern die Entwicklung von Strategien zur Kontrolle oder Reduktion des Wachstums des Krankheitserregers S. pyogenes

Integrated Regulatory and Metabolic Networks of the Marine Diatom Phaeodactylum tricornutum Predict the Response to Rising CO2 Levels.

Diatoms are eukaryotic microalgae that are responsible for up to 40% of the ocean's primary productivity. How diatoms respond to environmental perturbations such as elevated carbon concentrations in the atmosphere is currently poorly understood. We developed a transcriptional regulatory network based on various transcriptome sequencing expression libraries for different environmental responses to gain insight into the marine diatom's metabolic and regulatory interactions and provide a comprehensive framework of responses to increasing atmospheric carbon levels. This transcriptional regulatory network was integrated with a recently published genome-scale metabolic model of Phaeodactylum tricornutum to explore the connectivity of the regulatory network and shared metabolites. The integrated regulatory and metabolic model revealed highly connected modules within carbon and nitrogen metabolism. P. tricornutum's response to rising carbon levels was analyzed by using the recent genome-scale metabolic model with cross comparison to experimental manipulations of carbon dioxide. IMPORTANCE Using a systems biology approach, we studied the response of the marine diatom Phaeodactylum tricornutum to changing atmospheric carbon concentrations on an ocean-wide scale. By integrating an available genome-scale metabolic model and a newly developed transcriptional regulatory network inferred from transcriptome sequencing expression data, we demonstrate that carbon metabolism and nitrogen metabolism are strongly connected and the genes involved are coregulated in this model diatom. These tight regulatory constraints could play a major role during the adaptation of P. tricornutum to increasing carbon levels. The transcriptional regulatory network developed can be further used to study the effects of different environmental perturbations on P. tricornutum's metabolism

Genome-Scale Model Reveals Metabolic Basis of Biomass Partitioning in a Model Diatom

Diatoms are eukaryotic microalgae that contain genes from various sources, including bacteria and the secondary endosymbiotic host. Due to this unique combination of genes, diatoms are taxonomically and functionally distinct from other algae and vascular plants and confer novel metabolic capabilities. Based on the genome annotation, we performed a genome-scale metabolic network reconstruction for the marine diatom Phaeodactylum tricornutum. Due to their endosymbiotic origin, diatoms possess a complex chloroplast structure which complicates the prediction of subcellular protein localization. Based on previous work we implemented a pipeline that exploits a series of bioinformatics tools to predict protein localization. The manually curated reconstructed metabolic network iLB1027_lipid accounts for 1,027 genes associated with 4,456 reactions and 2,172 metabolites distributed across six compartments. To constrain the genome-scale model, we determined the organism specific biomass composition in terms of lipids, carbohydrates, and proteins using Fourier transform infrared spectrometry. Our simulations indicate the presence of a yet unknown glutamine-ornithine shunt that could be used to transfer reducing equivalents generated by photosynthesis to the mitochondria. The model reflects the known biochemical composition of P. tricornutum in defined culture conditions and enables metabolic engineering strategies to improve the use of P. tricornutum for biotechnological applications

GABA-modulating bacteria of the human gut microbiota.

The gut microbiota affects many important host functions, including the immune response and the nervous system1. However, while substantial progress has been made in growing diverse microorganisms of the microbiota2, 23-65% of species residing in the human gut remain uncultured3,4, which is an obstacle for understanding their biological roles. A likely reason for this unculturability is the absence in artificial media of key growth factors that are provided by neighbouring bacteria in situ5,6. In the present study, we used co-culture to isolate KLE1738, which required the presence of Bacteroides fragilis to grow. Bioassay-driven purification of B. fragilis supernatant led to the isolation of the growth factor, which, surprisingly, is the major inhibitory neurotransmitter GABA (γ-aminobutyric acid). GABA was the only tested nutrient that supported the growth of KLE1738, and a genome analysis supported a GABA-dependent metabolism mechanism. Using growth of KLE1738 as an indicator, we isolated a variety of GABA-producing bacteria, and found that Bacteroides ssp. produced large quantities of GABA. Genome-based metabolic modelling of the human gut microbiota revealed multiple genera with the predicted capability to produce or consume GABA. A transcriptome analysis of human stool samples from healthy individuals showed that GABA-producing pathways are actively expressed by Bacteroides, Parabacteroides and Escherichia species. By coupling 16S ribosmal RNA sequencing with functional magentic resonance imaging in patients with major depressive disorder, a disease associated with an altered GABA-mediated response, we found that the relative abundance levels of faecal Bacteroides are negatively correlated with brain signatures associated with depression

A qualitative study of fraternity leaders and alcohol abuse

A basic qualitative study was conducted using data collected from a series of semi structured interviews for fraternity leaders on the Ball State campus. The purpose of this qualitative study was to examine the role alcohol played in the lives of fraternity leaders.Eight fraternity leaders were interviewed to determine the role alcohol played in their lives as active leaders within the Greek community. Five themes emerged from the data: (1) fraternity leaders abuse alcohol, (2) fraternity leaders drink for social interaction, (3) fraternity leaders avoid high-risk situations, (4) fraternity leaders make decisions related to alcohol they regret, and (5) fraternity environments encourage drinking. Emerging themes coincided with the designed interview questions, which were used in all eight interviews. All questions in a combined form contributed to each resulting theme.Thesis (M.A.)Department of Educational Leadershi

Randomized Interdependent Group Contingencies: Group Reinforcement With a Twist

This investigation examined the effects of randomizing components of an interdependent group contingency procedure on the target behavior of 12 students in a second-grade classroom in a rural southeastern school district. Specifically, using a multiphase time-series design (i.e., A-B-A-C-B-C design) levels of disruptive behavior were compared across baseline, an intervention phase with only randomized reinforcers (the RR+ phase), and an intervention phase with all components randomized (R-ALL phase). Results suggest that both interventions were successful in decreasing levels of disruptive behavior, with the R-ALL phase resulting in lower mean, and more stable, percentages of disruptive behavior. The advantages to randomizing components within a group contingency procedure are discussed, because this procedure not only incorporates the strengths of an interdependent group contingency, but also addresses the limitations. (C) 2000 John Wiley & Sons, Inc

Integrated Regulatory and Metabolic Networks of the Marine Diatom Phaeodactylum tricornutum Predict the Response to Rising CO2 Levels.

Diatoms are eukaryotic microalgae that are responsible for up to 40% of the ocean's primary productivity. How diatoms respond to environmental perturbations such as elevated carbon concentrations in the atmosphere is currently poorly understood. We developed a transcriptional regulatory network based on various transcriptome sequencing expression libraries for different environmental responses to gain insight into the marine diatom's metabolic and regulatory interactions and provide a comprehensive framework of responses to increasing atmospheric carbon levels. This transcriptional regulatory network was integrated with a recently published genome-scale metabolic model of Phaeodactylum tricornutum to explore the connectivity of the regulatory network and shared metabolites. The integrated regulatory and metabolic model revealed highly connected modules within carbon and nitrogen metabolism. P. tricornutum's response to rising carbon levels was analyzed by using the recent genome-scale metabolic model with cross comparison to experimental manipulations of carbon dioxide. IMPORTANCE Using a systems biology approach, we studied the response of the marine diatom Phaeodactylum tricornutum to changing atmospheric carbon concentrations on an ocean-wide scale. By integrating an available genome-scale metabolic model and a newly developed transcriptional regulatory network inferred from transcriptome sequencing expression data, we demonstrate that carbon metabolism and nitrogen metabolism are strongly connected and the genes involved are coregulated in this model diatom. These tight regulatory constraints could play a major role during the adaptation of P. tricornutum to increasing carbon levels. The transcriptional regulatory network developed can be further used to study the effects of different environmental perturbations on P. tricornutum's metabolism