Flux analysis in central carbon metabolism in plants: 13C NMR experiments and analysis

Metabolic flux analysis is crucial in metabolic engineering. This research concentrated on improvements in 13C labeling-based flux analysis, a powerful flux quantification method, particularly oriented toward application to plants. Furthermore, systemic 13C flux analyses were performed on two model plant systems: Glycine max (soybean) embryos, and Catharanthus roseus hairy roots.;The concepts \u27bond integrity\u27, \u27bondomer\u27 and the algorithm \u27Boolean function mapping\u27 were introduced, to facilitate efficient flux evaluation from carbon bond labeling experiments, and easier flux identifiability analysis.;13C labeling experiments were performed on developing soybean (Glycine max) embryos and C. roseus hairy roots. A computer program, NMR2Flux, was developed to automatically calculate fluxes from the labeling data. This program accepts a user-defined metabolic network model, and incorporates recent mathematical advances toward accurate and efficient evaluation of fluxes and their standard deviations. Several physiological insights were obtained from the flux results. For instance, in soybean embryos, the reductive pentose phosphate pathway was active in the plastid and negligible in the cytosol. Also, unknown fluxes (such as plastidic fructose-1,6-bisphosphatase) could be identified and quantified. To the best of the author\u27s knowledge, this is the most comprehensive flux analysis of a plant system to date.;Investigations on flux identifiability were carried out for the soybean embryo system. Using these, optimal labeling experiments were designed, that utilize judicious combinations of labeled varieties of two substrates (sucrose and glutamine), to maximize the statistical quality of the evaluated fluxes.;The identity of four intense peaks observed in the 2-D [13C, 1H] spectra of protein isolated from soybean embryos, was investigated. These peaks were identified as levulinic acid and 5-hydroxymethyl furfural, and were degradation products of glycosylating sugars associated with soybean embryo protein. A 2-D NMR study was conducted on them, and it was shown that the metabolic information in the degradation products can be used toward metabolic flux or pathway analysis.;In addition, the elemental make-up and composition of the biomass of C. roseus hairy roots (crucial toward flux analysis) is reported. 89.2% (+/-9.7%) of the biomass was accounted for.*;*This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation). The CD requires the following system requirements: Adobe Acrobat

SAFIUS - A secure and accountable filesystem over untrusted storage

We describe SAFIUS, a secure accountable file system that resides over an untrusted storage. SAFIUS provides strong security guarantees like confidentiality, integrity, prevention from rollback attacks, and accountability. SAFIUS also enables read/write sharing of data and provides the standard UNIX-like interface for applications. To achieve accountability with good performance, it uses asynchronous signatures; to reduce the space required for storing these signatures, a novel signature pruning mechanism is used. SAFIUS has been implemented on a GNU/Linux based system modifying OpenGFS. Preliminary performance studies show that SAFIUS has a tolerable overhead for providing secure storage: while it has an overhead of about 50% of OpenGFS in data intensive workloads (due to the overhead of performing encryption/decryption in software), it is comparable (or better in some cases) to OpenGFS in metadata intensive workloads.Comment: 11pt, 12 pages, 16 figure

Systems and Methods for Diagnosis and Treatment of Psychiatric Disorders

A device for diagnosing and treating psychiatric disorders is described. The device may be configured to provide a graphical user interface that enables a user to select at least one of: entering information related to a diagnosis of the psychiatric disorder and alleviating symptoms caused by the psychiatric disorder. Upon a user selecting entering information related to the diagnosis of a psychiatric disorder, the device may receive information related to the diagnosis of the psychiatric disorder. The device may determine the severity of a user\u27s condition based at least in part on the received information. The device may provide a treatment based on the determined severity of the user\u27s condition. A treatment may include providing feedback to a user

Meteoric effect of meropenem: an unrevealed case report on Jarisch Herxheimer reaction

Jarisch Herxheimer Reaction is an immune mediated, self- limited reaction that releases endotoxins from the spirochetes. It occurs due to an acute inflammatory response when lipoproteins, owing to their entry into the patient's bloodstream, cause an increase in inflammatory cytokines during the period of exacerbation, resulting in body aches, fevers, rashes, nausea and vomiting, and flushing, along with other symptoms. These symptoms usually begin within 2 hours after the administration of the antibiotics. We represent a 76-year-old male patient who has had a known case of recurrent urinary tract infections since 2017 and was recently diagnosed with urosepsis and syphilis after being administered an injection of Meropenem, wherein he developed a Jarisch Herxheimer Reaction. The causality assessment revealed a Naranjo score of 7, indicating a probable adverse drug reaction. This patient was treated with intravenous antihistamines and corticosteroids for its management. Benzathine penicillin was avoided owing to the previous suspected Jarisch Herxheimer Reaction. None of the studies reported that Meropenem could contribute to such a reaction. All healthcare professionals should maintain a high alert of suspicion and be aware of antibiotic induced Jarisch Herxheimer Reaction symptoms and their management to avoid life threatening conditions

Network component analysis provides quantitative insights on an Arabidopsis transcription factor-gene regulatory network

Gene regulatory networks (GRNs) are models of molecule-gene interactions instrumental in the coordination of gene expression. Transcription factor (TF)-GRNs are an important subset of GRNs that characterize gene expression as the effect of TFs acting on their target genes. Although such networks can qualitatively summarize TF-gene interactions, it is highly desirable to quantitatively determine the strengths of the interactions in a TF-GRN as well as the magnitudes of TF activities. To our knowledge, such analysis is rare in plant biology. A computational methodology developed for this purpose is network component analysis (NCA), which has been used for studying large-scale microbial TF-GRNs to obtain nontrivial, mechanistic insights. In this work, we employed NCA to quantitatively analyze a plant TF-GRN important in floral development using available regulatory information from AGRIS, by processing previously reported gene expression data from four shoot apical meristem cell types. The NCA model satisfactorily accounted for gene expression measurements in a TF-GRN of seven TFs (LFY, AG, SEPALLATA3 [SEP3], AP2, AGL15, HY5 and AP3/PI) and 55 genes. NCA found strong interactions between certain TF-gene pairs including LFY → MYB17, AG → CRC, AP2 → RD20, AGL15 → RAV2 and HY5 → HLH1, and the direction of the interaction (activation or repression) for some AGL15 targets for which this information was not previously available. The activity trends of four TFs - LFY, AG, HY5 and AP3/PI as deduced by NCA correlated well with the changes in expression levels of the genes encoding these TFs across all four cell types; such a correlation was not observed for SEP3, AP2 and AGL15. For the first time, we have reported the use of NCA to quantitatively analyze a plant TF-GRN important in floral development for obtaining nontrivial information about connectivity strengths between TFs and their target genes as well as TF activity. However, since NCA relies on documented connectivity information about the underlying TF-GRN, it is currently limited in its application to larger plant networks because of the lack of documented connectivities. In the future, the identification of interactions between plant TFs and their target genes on a genome scale would allow the use of NCA to provide quantitative regulatory information about plant TF-GRNs, leading to improved insights on cellular regulatory programs.https://doi.org/10.1186/1752-0509-7-12

NetFlow: A tool for isolating carbon flows in genome-scale metabolic networks

Partial funding for Open Access provided by the UMD Libraries' Open Access Publishing Fund.Genome-scale stoichiometric models (GSMs) have been widely utilized to predict and understand cellular metabolism. GSMs and the flux predictions resulting from them have proven indispensable to fields ranging from metabolic engineering to human disease. Nonetheless, it is challenging to parse these flux predictions due to the inherent size and complexity of the GSMs. Several previous approaches have reduced this complexity by identifying key pathways contained within the genome-scale flux predictions. However, a reduction method that overlays carbon atom transitions on stoichiometry and flux predictions is lacking. To fill this gap, we developed NetFlow, an algorithm that leverages genome-scale carbon mapping to extract and quantitatively distinguish biologically relevant metabolic pathways from a given genome-scale flux prediction. NetFlow extends prior approaches by utilizing both full carbon mapping and context-specific flux predictions. Thus, NetFlow is uniquely able to quantitatively distinguish between biologically relevant pathways of carbon flow within the given flux map. NetFlow simulates 13C isotope labeling experiments to calculate the extent of carbon exchange, or carbon yield, between every metabolite in the given GSM. Based on the carbon yield, the carbon flow to or from any metabolite or between any pair of metabolites of interest can be isolated and readily visualized. The resulting pathways are much easier to interpret, which enables an in-depth mechanistic understanding of the metabolic phenotype of interest. Here, we first demonstrate NetFlow with a simple network. We then depict the utility of NetFlow on a model of central carbon metabolism in E. coli. Specifically, we isolated the production pathway for succinate synthesis in this model and the metabolic mechanism driving the predicted increase in succinate yield in a double knockout of E. coli. Finally, we describe the application of NetFlow to a GSM of lycopene-producing E. coli, which enabled the rapid identification of the mechanisms behind the measured increases in lycopene production following single, double, and triple knockouts.https://doi.org/10.1016/j.mec.2020.e0015