Urease is an Essential Component of the Acid Response Network of Staphylococcus Aureus and is Required for a Persistent Murine Kidney Infection

Staphylococcus aureus causes acute and chronic infections resulting in significant morbidity. Urease, an enzyme that generates NH3 and CO2 from urea, is key to pH homeostasis in bacterial pathogens under acidic stress and nitrogen limitation. However, the function of urease in S. aureus niche colonization and nitrogen metabolism has not been extensively studied. We discovered that urease is essential for pH homeostasis and viability in urea-rich environments under weak acid stress. The regulation of urease transcription by CcpA, Agr, and CodY was identified in this study, implying a complex network that controls urease expression in response to changes in metabolic flux. In addition, it was determined that the endogenous urea derived from arginine is not a significant contributor to the intracellular nitrogen pool in non-acidic conditions. Furthermore, we found that during a murine chronic renal infection, urease facilitates S. aureus persistence by promoting bacterial fitness in the low-pH, urea-rich kidney. Overall, our study establishes that urease in S. aureus is not only a primary component of the acid response network but also an important factor required for persistent murine renal infections

Urease is an essential component of the acid response network of \u3ci\u3eStaphylococcus\u3c/i\u3e aureus and is required for a persistent murine kidney infection

Staphylococcus aureus causes acute and chronic infections resulting in significant morbidity. Urease, an enzyme that generates NH3 and CO2 from urea, is key to pH homeostasis in bacterial pathogens under acidic stress and nitrogen limitation. However, the function of urease in S. aureus niche colonization and nitrogen metabolism has not been extensively studied. We discovered that urease is essential for pH homeostasis and viability in urea-rich environments under weak acid stress. The regulation of urease transcription by CcpA, Agr, and CodY was identified in this study, implying a complex network that controls urease expression in response to changes in metabolic flux. In addition, it was determined that the endogenous urea derived from arginine is not a significant contributor to the intracellular nitrogen pool in non-acidic conditions. Furthermore, we found that during a murine chronic renal infection, urease facilitates S. aureus persistence by promoting bacterial fitness in the low-pH, urea-rich kidney. Overall, our study establishes that urease in S. aureus is not only a primary component of the acid response network but also an important factor required for persistent murine renal infections

Targeting spike glycans to inhibit SARS-CoV2 viral entry

SARS-CoV-2 spike harbors glycans which function as ligands for lectins. Therefore, it should be possible to exploit lectins to target SARS-CoV-2 and inhibit cellular entry by binding glycans on the spike protein. Burkholderia oklahomensis agglutinin (BOA) is an antiviral lectin that interacts with viral glycoproteins via N-linked high mannose glycans. Here, we show that BOA binds to the spike protein and is a potent inhibitor of SARS-CoV-2 viral entry at nanomolar concentrations. Using a variety of biophysical approaches, we demonstrate that the interaction is avidity driven and that BOA cross-links the spike protein into soluble aggregates. Furthermore, using virus neutralization assays, we demonstrate that BOA effectively inhibits all tested variants of concern as well as SARS-CoV 2003, establishing that multivalent glycan-targeting molecules have the potential to act as pan-coronavirus inhibitors

Systems Biology and Chemometric Analyses of Cellular Chemistry

At the turn of the 20th century started the scientific chase to explain cell function, processes, controls, communication, and their regulation. The parallel improvements in available technologies made tools like nuclear magnetic resonance (NMR), mass spectrometry (MS), and high throughput sample handling available methods to characterize cell composition, communication, and metabolism. Genomics ushered us into an era driven by vast amount of data modeled to explain why cells and thus organism behave the way they do. All OMICs techniques function by extrapolating the central dogma of biology, where genetic material acts upon proteins that then regulate an array of small molecules – metabolites. In concept chemometrics can be used to examine any complex mixture, and when this mixture is of biological origin, we call it metabolomics. This dissertation focuses on three key details that enable better systems biology through chemometrics – (i) expanding the detectable metabolome, (ii) robust and potentially automated metabolite assignment (iii) implementation of intricate metabolomics experiments to explain cellular chemistry. In pursuit of the expansion of the detectable metabolome this dissertation evaluates other NMR active nuclei, namely, 15N and 31P for stable isotope labeled monitoring (SIRM) approaches. Multi-SIRM are evaluated in bacterial cells using a 15N isotope tracer and the 100% naturally abundant 31P isotope. We establish that concurrently acquiring GC-MS and NMR data for a metabolomics experiment leads to confident metabolite annotation. As a consequence, the analyst is rewarded with an increase in observed metabolites and thus improved understanding of the cellular processes. Automated metabolite assignment tool is an unmet need in metabolomics. Inclusion of 2D 1 H13C Heteronuclear MultiBond Coherence (HMBC) experiment with 1D 1 H NMR and a 1 H13C Heteronuclear Single Quantum Coherence experiment (HSQC) will invariably improve metabolite assignments and assist in implementation of automated metabolite assignments by weighted-graph matching. Using some of the existing methods and newer ones discussed this dissertation we explain chemistries in various biological systems, i.e. bacterial infections of Staphylococcus aureus, drug resistant cancers using gemcitabine resistance in pancreatic cancer, and two-cell communication platform using tumor and stromal cells. These examples give a larger picture of the utility of chemometrics and metabolomics in understanding cell chemistry while barely impressing upon the obvious entanglement of metabolic pathways

Systems Biology and Chemometric Analyses of Cellular Chemistry

At the turn of the 20th century started the scientific chase to explain cell function, processes, controls, communication, and their regulation. The parallel improvements in available technologies made tools like nuclear magnetic resonance (NMR), mass spectrometry (MS), and high throughput sample handling available methods to characterize cell composition, communication, and metabolism. Genomics ushered us into an era driven by vast amount of data modeled to explain why cells and thus organism behave the way they do. All OMICs techniques function by extrapolating the central dogma of biology, where genetic material acts upon proteins that then regulate an array of small molecules – metabolites. In concept chemometrics can be used to examine any complex mixture, and when this mixture is of biological origin, we call it metabolomics. This dissertation focuses on three key details that enable better systems biology through chemometrics – (i) expanding the detectable metabolome, (ii) robust and potentially automated metabolite assignment (iii) implementation of intricate metabolomics experiments to explain cellular chemistry. In pursuit of the expansion of the detectable metabolome this dissertation evaluates other NMR active nuclei, namely, 15N and 31P for stable isotope labeled monitoring (SIRM) approaches. Multi-SIRM are evaluated in bacterial cells using a 15N isotope tracer and the 100% naturally abundant 31P isotope. We establish that concurrently acquiring GC-MS and NMR data for a metabolomics experiment leads to confident metabolite annotation. As a consequence, the analyst is rewarded with an increase in observed metabolites and thus improved understanding of the cellular processes. Automated metabolite assignment tool is an unmet need in metabolomics. Inclusion of 2D 1 H13C Heteronuclear MultiBond Coherence (HMBC) experiment with 1D 1 H NMR and a 1 H13C Heteronuclear Single Quantum Coherence experiment (HSQC) will invariably improve metabolite assignments and assist in implementation of automated metabolite assignments by weighted-graph matching. Using some of the existing methods and newer ones discussed this dissertation we explain chemistries in various biological systems, i.e. bacterial infections of Staphylococcus aureus, drug resistant cancers using gemcitabine resistance in pancreatic cancer, and two-cell communication platform using tumor and stromal cells. These examples give a larger picture of the utility of chemometrics and metabolomics in understanding cell chemistry while barely impressing upon the obvious entanglement of metabolic pathways

Phosphorus NMR and its application to metabolomics

Stable isotopes are routinely employed by NMR metabolomics to highlight specific metabolic processes and to monitor pathway flux. 13C-carbon and 15N-nitrogen labeled nutrients are convenient sources of isotope tracers and are commonly added as supplements to a variety of biological systems ranging from cell cultures to animal models. Unlike 13C and 15N, 31P-phosphourous is a naturally abundant and NMR active isotope that doesn’t require an external supplemental source. To date, 31P NMR has seen limited usage in metabolomics because of a lack of reference spectra, difficulties in sample preparation, and an absence of two-dimensional (2D) NMR experiments. But, 31P NMR has the potential of expanding the coverage of the metabolome by detecting phosphorous-containing metabolites. Phosphorylated metabolites regulate key cellular processes, serve as a surrogate for intracellular pH conditions, and provides a measure of a cell’s metabolic energy and redox state, among other processes. Thus, incorporating 31P NMR into a metabolomics investigation will enable the detection of these key cellular processes. To facilitate the application of 31P NMR in metabolomics, we present a unified protocol that allows for the simultaneous and efficient detection of 1H-, 13C-, 15N- and 31P-labeled metabolites. The protocol includes the application of a 2D 1H-31P HSQC-TOCSY experiment to detect 31P-labeled metabolites from heterogeneous biological mixtures, methods for sample preparation to detect 1H-, 13C-, 15N- and 31P-labeled metabolites from a single NMR sample, and a dataset of one-dimensional (1D) 31P NMR and 2D 1H-31P HSQC-TOCSY spectra of 38 common phosphorus-containing metabolites to assist in metabolite assignments

Combining Mass Spectrometry and NMR Improves Metabolite Detection and Annotation

Despite inherent complementarity, nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are routinely separately employed to characterize metabolomics samples. More troubling is the erroneous view that metabolomics is better served by exclusively utilizing MS. Instead, we demonstrate the importance of combining NMR and MS for metabolomics by using small chemical compound-treatments of Chlamydomonas reinhardtii as an illustrative example. A total of 102 metabolites were detected (82 by GC-MS, 20 by NMR and 22 by both techniques). Out of these 47 metabolites of interest were identified, where 14 metabolites were uniquely identified by NMR and 16 metabolites were uniquely identified by GC-MS. A total of 17 metabolites were identified by both NMR and GC-MS. In general, metabolites identified by both techniques exhibited similar changes upon compound treatment. In effect, NMR identified key metabolites that were missed by MS and enhanced the overall coverage of the oxidative pentose phosphate pathway, Calvin cycle, tricarboxylic acid cycle and amino acid biosynthetic pathways that informed on pathway activity in central carbon metabolism leading to fatty acid and complex lipid synthesis. Our study emphasizes a prime advantage of combining multiple analytical techniques - an improved detection and annotation of metabolites

Metabolic profiling of historical and modern wheat cultivars using proton nuclear magnetic resonance spectroscopy

To determine changes in the grain components between historical and modern wheat (Triticum aestivum L.) cultivars, wholemeal flours from 19 wheat cultivars and 2 landraces released or introduced between 1870 and 2013 and grown over two crop years were extracted using hydroalcoholic solution and analyzed using one dimensional 1H NMR spectral profiling. Grain yield, grain volume weight (GVW), and grain protein concentration were also measured. Grain yield increased while protein concentration decreased by release year (p \u3c 0.001). Increasing trends (p \u3c 0.01) were observed for tryptophan, sum of the measured amino acids, chlorogenic acid, ferulic acid, vanillic acid, and sum of the measured phenolic acids. Grain yield, phenolic acids, and tryptophan were mainly associated with modern cultivars, whereas grain protein concentration and GVW were associated with historical cultivars. The findings from this study showed changes in concentration of grain components over a century of breeding that may have implications for grain quality and human health

Combining Mass Spectrometry and NMR Improves Metabolite Detection and Annotation

Despite inherent complementarity, nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are routinely separately employed to characterize metabolomics samples. More troubling is the erroneous view that metabolomics is better served by exclusively utilizing MS. Instead, we demonstrate the importance of combining NMR and MS for metabolomics by using small chemical compound-treatments of Chlamydomonas reinhardtii as an illustrative example. A total of 102 metabolites were detected (82 by GC-MS, 20 by NMR and 22 by both techniques). Out of these 47 metabolites of interest were identified, where 14 metabolites were uniquely identified by NMR and 16 metabolites were uniquely identified by GC-MS. A total of 17 metabolites were identified by both NMR and GC-MS. In general, metabolites identified by both techniques exhibited similar changes upon compound treatment. In effect, NMR identified key metabolites that were missed by MS and enhanced the overall coverage of the oxidative pentose phosphate pathway, Calvin cycle, tricarboxylic acid cycle and amino acid biosynthetic pathways that informed on pathway activity in central carbon metabolism leading to fatty acid and complex lipid synthesis. Our study emphasizes a prime advantage of combining multiple analytical techniques - an improved detection and annotation of metabolites

A Quality Pattern Based Approach for the Analysis and Design of Information Systems

Les modèles conceptuels (MC) jouent un rôle crucial qui est celui de servir de base à l’ensemble du processus de développement d’un système d’information (SI) mais aussi de moyen de communication à la fois au sein de l’équipe de développement et avec les utilisateurs durant les premières étapes de validation. Leur qualité joue par conséquent un rôle déterminant dans le succès du système final. Des études ont montré que la majeure partie des changements que subit un SI concerne des manques ou des défaillances liés aux fonctionnalités attendues. Sachant que la définition de ses fonctionnalités incombe à la phase de l’analyse et conception dont les MC constituent les livrables, il apparaît indispensable pour une méthode de conception de veiller à la qualité des MC qu’elle produit. Notre approche vise les problèmes liés à la qualité de la modélisation conceptuelle en proposant une solution intégrée au processus de développement qui à l’avantage d’être complète puisqu’elle adresse à la fois la mesure de la qualité ainsi que son amélioration. La proposition couvre les aspects suivants: i. Formulation de critères de qualité en fédérant dans un premier temps les travaux existant sur la qualité des MC. En effet, un des manques constaté dans le domaine de la qualité des MC est l’absence de consensus sur les concepts et leurs définitions. Ce travail a été validé par une étude empirique. Ce travail a également permis d’identifier les parties non couverte par la littérature et de les compléter en proposant de nouveaux concepts ou en précisant ceux dont la définition n’était complète. ii. Définition d’un concept (pattern de qualité) permettant de capitaliser les bonnes pratiques dans le domaine de la mesure et de l’amélioration de la qualité des MC. Un pattern de qualité sert à aider un concepteur de SI dans l’identification des critères de qualité applicables à sa spécification, puis de le guider progressivement dans la mesure de la qualité ainsi que dans son amélioration. Sachant que la plupart des approches existantes s’intéresse à la mesure de la qualité et néglige les moyens de la corriger. La définition de ce concept est motivée par la difficulté et le degré d’expertise important qu’exige la gestion de la qualité surtout au niveau conceptuel où le logiciel fini n’est pas encore disponible et face à la diversité des concepts de qualité (critères et métriques) pouvant s’appliquer. iii. Formulation d’une méthode orientée qualité incluant à la fois des concepts, des guides et des techniques permettant de définir les concepts de qualité souhaités, leur mesure et l’amélioration de la qualité des MC. Cette méthode propose comme point d’entrée le besoin de qualité que doit formuler le concepteur. Il est ensuite guidée de manière flexible dans le choix des critères de qualité adaptés jusqu’à la mesure et la proposition de recommandations aidant à l’amélioration de la qualité du MC initial conformément au besoin formulé. iv. Développement d'un prototype "CM-Quality". Notre prototype met en œuvre la méthode proposée et offre ainsi une aide outillé à son application. Nous avons enfin mené deux expérimentations ; la première avait comme objectif de valider les concepts de qualité utilisés et de les retenir. La deuxième visait à valider la méthode de conception guidée par la qualité proposéeConceptual models (CM) serve as the blueprints of information systems and their quality plays decisive role in the success of the end system. It has been witnessed that majority of the IS change-requests result due to deficient functionalities in the information systems. Therefore, a good analysis and design method should ensure that CM are correct and complete, as they are the communicating mediator between the users and the development team. Our approach targets the problems related to conceptual modeling quality by proposing a comprehensive solution. We designed multiple artifacts for different aspects of CM quality. These artifacts include the following: i. Formulation of comprehensive quality criteria (quality attributes, metrics, etc.) by federating the existing quality frameworks and identifying the quality criteria for gray areas. Most of the existing literature on CM quality evaluation represents disparate and autonomous quality frameworks proposing non-converging solutions. Thus, we synthesized (existing concepts proposed by researchers) and added the new concepts to formulate a comprehensive quality approach for conceptual models that also resulted in federating the existing quality frameworks. ii. Formulation of quality patterns to encapsulate past-experiences and good practices as the selection of relevant quality criteria (including quality attributes and metrics) with respect to a particular requirement (or goal) remains trickier for a non-expert user. These quality patterns encapsulate valuable knowledge in the form of established and better solutions to resolve quality problems in CM. iii. Designing of the guided quality driven process encompassing methods and techniques to evaluate and improve the conceptual models with respect to a specific user requirement or goal. Our process guides the user in formulating the desired quality goal, helps him/her in identifying the relevant quality patterns or quality attributes with respect to the quality goal and finally the process helps in evaluating the quality of the model and propose relevant recommendations for improvement. iv. Development of a software prototype “CM-Quality”. Our prototype implements all the above mentioned artifacts and proposes a workflow enabling its users to evaluate and improve CMs efficiently and effectively. We conducted a survey to validate the selection of the quality attributes through the above mentioned federating activity and also conducted three step detailed experiment to evaluate the efficacy and efficiency of our overall approach and proposed artifacts