Bridging The Gap Between Signalling And Metabolism Through Functional And Mechanistic Analysis Of Multi Omic Data

DOWNLOAD

Download Bridging The Gap Between Signalling And Metabolism Through Functional And Mechanistic Analysis Of Multi Omic Data PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Bridging The Gap Between Signalling And Metabolism Through Functional And Mechanistic Analysis Of Multi Omic Data book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page

Bridging The Gap Between Signalling And Metabolism Through Functional And Mechanistic Analysis Of Multi Omic Data

DOWNLOAD
Author : Aurélien Dugourd
language : en
Publisher:
Release Date : 2022*

Bridging The Gap Between Signalling And Metabolism Through Functional And Mechanistic Analysis Of Multi Omic Data written by Aurélien Dugourd and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022* with categories.

Metabolism Meets Function Untangling The Cross Talk Between Signalling And Metabolism

DOWNLOAD
Author : Alessandra Castegna
language : en
Publisher: Frontiers Media SA
Release Date : 2020-12-18

Metabolism Meets Function Untangling The Cross Talk Between Signalling And Metabolism written by Alessandra Castegna and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-18 with Medical categories.

This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.

Integrated Modeling Of Phototrophic Metabolism Leveraging Multi Omics Datasets

DOWNLOAD
Author : Debolina Sarkar
language : en
Publisher:
Release Date : 2022

Integrated Modeling Of Phototrophic Metabolism Leveraging Multi Omics Datasets written by Debolina Sarkar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.

Rapid progress in high-throughput experimental technologies has enabled generation of large-scale systems biology datasets. These span all biological hierarchies from genomics describing the genetic make-up, transcriptomics and proteomics at the gene and enzyme expression level, metabolomics that helps quantify the amount and nature of resultant biomolecules, to finally phenomics that describes the overall traits of an individual. This veritable data deluge necessitates algorithmic and computational advances that can leverage multi-omics integration, in order to facilitate the analysis of complex systems and extract meaningful insights. Flux balance analysis (FBA) using genome-scale metabolic (GSM) models provide an advantageous platform for doing so as these models are (relatively) parameter-free, can be constructed using the annotated genome alone and simulated in linear time offering scale-up benefits. GSMs model a network view of metabolism, wherein metabolites are cast as nodes in a graph linked via edges representing all possible biochemical conversions occurring within an organism. In Chapter 1, we present an overview of constraints-based analysis of metabolic networks, including the reconstruction of GSM models, their use within an optimization-based scheme such as FBA, and the various applications of such models. Next, we describe the extension of metabolic modeling frameworks, originally designed for microbial systems, to the study of plants. This is accompanied by its own set of challenges, such as accurately capturing the division of roles between the various tissue and organ systems and dealing with systematic biases that are typically associated with poorly annotated non-model systems. Finally, we explore how the incorporation of new data types, modeling schemes, and computational tools have impacted FBA by helping increase its predictive power and scope. FBA has proven to be quite adept at describing aggregated metabolite flows, i.e., providing a snapshot of metabolism as averaged over the entire growth cycle. However, it is also time invariant, and thus does not accommodate temporally varying cell processes such as sequestering different biomass components at various time points in a growth cycle However, we know from experiments that many organisms including cyanobacteria have a lifestyle that is heavily tailored around light availability and thus show metabolic oscillations. In Chapter 2, we present a framework called CycleSyn that augments FBA by accounting for such temporal trends. CycleSyn discretizes a growth cycle into individual time periods (called Time Point Models or TPMs), each described by its own GSM model. The flow of metabolites across TPMs is allowed while inventorying metabolite levels and only allowing for the utilization of currently or previously produced compounds. Additional time-dependent constraints can also be imposed to capture the cyclic nature of cellular processes. CycleSyn was used to develop a diurnal FBA model of Synechocystis sp. PCC 6803 metabolism. Predicted flux and metabolite pools were in line with published studies, paving the way for constructing time-resolved GSM models. Additionally, the metabolic reorganization that would be required to enable Synechocystis PCC 6803 to fix nitrogen by temporally separating it from photosynthesis was also explored. Similar to modeling multiple metabolic models at once in CycleSyn, in Chapter 3 we extend this to modeling multiple organisms together as in a community, so as to discern the underlying interactions. This community comprised a genetically streamlined unicellular cyanobacterium called Candidatus Atelocyanobacterium thalassa (or UCYN-A) living in a symbiosis with a phototrophic microalga. We used metabolic modeling to glean insights into UCYN-A's unique physiology and metabolic processes governing the symbiotic association. To this end, we developed an optimization-based framework that infers all possible trophic scenarios consistent with the observed data. Possible mechanisms employed by UCYN-A to accommodate diazotrophy with daytime carbon fixation by the host (i.e., two mutually incompatible processes) were also elucidated. We found that the metabolic functions of the two constituents, and UCYN-A's streamlined genome is optimized to support maximal nitrogen fixation flux, alluding that this symbiosis is as close to being a functional 'nitroplast' as any observed till date. We envision that the developed framework using UCYN-A and its algal host will be used as a roadmap and motivate the study of similarly unique microbial systems in the future. Understanding how genomic mutations impact the overall phenotype of an organism has been a focus of efforts aimed at improving growth yield, determining genetic markers governing a trait, and understanding adaptive processes. This has been performed conventionally using genome-wide association studies, which seek to identify the genetic background behind a trait by examining associations between phenotypes and single-nucleotide polymorphisms (SNPs). Although such studies are common, biological interpretation of the results remains a challenge; especially due to the confounding nature of population structure and the systematic biases thus introduced. In Chapter 4, we propose a complementary tool called SNPeffect that offers putative genotype-to-phenotype mechanistic interpretations by integrating biochemical knowledge encoded in metabolic models. SNPeffect was used to explain differential growth rate and metabolite accumulation in Arabidopsis and poplar as the outcome of SNPs in enzyme-coding genes. To this end, we also constructed a genome-scale metabolic model for Populus trichocarpa, the first for a perennial woody tree. As expected, our results indicated that growth is a complex polygenic trait governed by carbon and energy partitioning. The predicted set of functional SNPs in both species are associated with experimentally-characterized growth-determining genes and also suggest putative ones. Functional SNPs were found in pathways such as amino-acid metabolism, nucleotide biosynthesis, and cellulose and lignin biosynthesis, in line with breeding strategies that target pathways governing carbon and energy partition. Thus far, we have developed computational frameworks that examine how the metabolism of an organism dictates its total phenotype and interactions with other organisms in a community. In Chapter 5, we take the next step by examining ways in which an organism can impact its host, specifically how the infant gut microbiome is shaped. Fecal samples from newborn infants showed that gut bacteria is detectable by 16 h after birth. However, analysis of the microbiome, proteome, and metabolome data did not suggest a single genomic signature for neonatal gut colonization. Using flux balance modeling, we found E. coli to be the most common early colonizer. The appearance of bacteria was associated with decreased levels of free amino acids and an increase in products of bacterial fermentation, primarily acetate and succinate. Among all the microbial species found, these observations were only consistent with E. coli growing under anaerobic conditions using amino acid fermentation to support maximal ATP yield. These results provide a deep characterization of the first microbes in the human gut and show how the biochemical environment is altered by their appearance. Finally, in Chapter 6, we conclude with our efforts to develop computational frameworks enabling the integration of heterogeneous datasets within constraints-based optimization. We discuss current challenges associated with such modeling frameworks and their uses, and finally present future perspectives for augmenting these models with the incorporation of diverse data types, multi-scale modeling, cross-cutting applications.

Omics Of Disease

DOWNLOAD
Author : Morris Aguilar
language : en
Publisher:
Release Date : 2024

Omics Of Disease written by Morris Aguilar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with categories.

The complex relationship between genetics, metabolites, microvesicles RNAs, and disease progression is central to biomedical research. This dissertation navigates through the intricate layers of human diseases, highlighting the crucial demand for new, innovative methods and tools to decode the fundamental mechanisms vital for advancing multi-omic analyses in the field. Throughout its chapters, it takes a deep dive into various realms - from developing automated metabolomics pipelines and analyzing intricate cardiovascular networks to examining microvesicle RNAs - each revealing distinct facets of our multi-omic journey and significantly enriching our comprehension of human health and illness. While metabolomics analysis has shed light on the metabolic alterations in CVD and PD, the field still grapples with identifying a comprehensive set of metabolites that could serve as disease biomarkers. These metabolic changes offer potential diagnostic and therapeutic targets, emphasizing the importance of metabolic profiling in understanding disease mechanisms. However, automation tools with standardized methodologies are needed for the field to get closer to quantifying a comprehensive set of metabolites that might be biomarkers for disease. This dissertation documents an attempt to create a semi-automated metabolomics pipeline (SAMP) to provide an alternative to Chenomx, a commercial computer-assisted manual quantification software. We putatively identified 79 metabolites previously unreported in the dataset with our approach. However, a follow-up concordance analysis between SAMP and Chenomx revealed major inaccuracies in SAMP and supported Chenomx as the superior tool. We discuss SAMP's shortcomings and provide guidance for future attempts to assemble an automated metabolomics pipeline. Future automation of the manual task of metabolite quantification will aid in the broad application of NMR metabolomics to metabolic profile individuals with a disease and enable its incorporation in future multi-omic analyses. As we gather more data for other -omic domains, the field encounters the challenge of organizing these domains in a manner that aids in identifying biomolecular features that are likely to play a larger role in disease pathogenesis. Investigations that have considered a single--omic domain, such as genome-wide association studies, have successfully identified genetic markers of disease; however, the genetic signatures need to be in the context of other biological domains. This added context can aid the field in generating new hypotheses on how interdomain features function for a given disease. Multi-omic association networks can intertwine biomolecular features such as genomic, metabolomic, drug treatment, and disease features to capture cardiovascular disease's multifaceted nature. This network simplifies the vast connectivity of -omic domains in disease states, streamlining the identification of multi-omic features in complex diseases. We ran nine regression analyses cumulatively yielded 3,254 statistically significant results (total tests: 183,520,500) and these results were used to construct the network. The analysis of the network revealed that dyslipidemia plays a pivotal role in cardiovascular disease, exhibiting connections throughout genomic, metabolic, and pharmacological domains. Newly identified associations between genome and metabolite, particularly concerning collagen synthesis and cell growth, could potentially explain the structural alterations in the coronary and vascular systems seen in cardiovascular disease. Utilizing a multi-omic network that integrates genomic, metabolomic, and microvesicle domains can significantly enhance the selection of features for disease risk prediction models, providing a more comprehensive and accurate representation of underlying biological mechanisms, thereby enhancing precision medicine. Such an approach is instrumental in understanding the interconnected changes across -omic features, laying a solid foundation for subsequent molecular investigations. Focusing on neuronally derived microvesicle small RNAs (smRNAs), these non-coding RNAs are critical players in cellular communication and disease manifestation for neurodegenerative diseases such as PD. Encased within microvesicles, these RNAs bridge cellular interactions; however, the challenge is that not all microvesicles found in blood serum have neuronal origins. Our study focused on determining if small RNA (smRNA) profiles in neuron-specific serum exosomes and microvesicles differ between Parkinson's disease (PD) patients and healthy individuals. Using a proven neuronal marker (CD171), we extracted and isolated exosomes and microvesicles from these samples. Our findings revealed that in serum, CD171-enriched exosomes and microvesicles displayed 29 smRNAs with significantly different expression levels between PD patients and controls, with 23 smRNAs being upregulated and 6 downregulated in PD cases. Pathway analysis indicated these smRNAs are involved in regulating cell proliferation and signaling pathways. Univariate logistic regression models identified four smRNAs with an Area Under Curve (AUC) of at least 0.74, effectively distinguishing PD subjects from controls. Furthermore, a random forest model using the 29 smRNA panel achieved high predictive accuracy, with an AUC of 0.942. The microvesicle RNAs were established to have potential as biomarkers for PD according to their capacity to predict the PD phenotype. This sets the stage for future multi-omic endeavors to incorporate this domain to achieve non-invasive early diagnoses and precise therapeutic strategies.

Metabolome Analyses

DOWNLOAD
Author : Seetharaman Vaidyanathan
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-04-28

Metabolome Analyses written by Seetharaman Vaidyanathan and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-04-28 with Science categories.

Metabolome analysis is now recognized as a crucial component of functional genomic and systems biology investigations. Innovative approaches to the study of metabolic regulation in microbial, plant and animal systems are increasingly facilitating the emergence of systems approaches in biology. This book highlights analytical and bioinformatics strategies now available for investigating metabolic networks in microbial, plant and animal systems. The contributing authors are world leaders in this field and they present an unambiguous case for pursuing metabolome analysis as a means to attain a systems level understanding of complex biological systems.

On The Systems Characterization Of Metabolic Networks

DOWNLOAD
Author : Neema Jamshidi
language : en
Publisher:
Release Date : 2009

On The Systems Characterization Of Metabolic Networks written by Neema Jamshidi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.

Significant progress has been made in the development of genome-scale models of metabolism in the past 15 years. The majority of the efforts have focused on the analysis of the right null space. While the steady state solution space is very important since homeostatic mechanisms are continuously trying to push the system back to a steady state, there are three other subspaces which also have significance. This dissertation approaches metabolic networks with the goal of investigating all four fundamental subspaces of linear systems, particularly the row and column spaces, which determine the dynamic capabilities of networks. First, the key matrices are defined and their general properties and characteristics are identified. The subspaces are analyzed for a simple metabolic network. Next the right null space is explored in a targeted manner using real networks, the cardiomyocyte mitochondria and a genome-scale model of Mycobacterium tuberculosis, focusing on perfectly correlated reaction sets and the biologically interesting implications they may hold. This is followed by the development of analytical methods to define the time scale hierarchy in the column space of the human red cell, human folate metabolism, and yeast glycolysis. These studies are further pursued by investigating various decompositions of the stoichiometric and gradient matrices that are determined by the underlying physico-chemical characteristics. These investigations lead to the identification of key properties of metabolic networks, such as the duality between fluxes and concentrations in dynamic networks. Having established how to decompose these networks, a middle-out integration approach is described for building or reconstructing kinetic networks using '-omic' data streams that are increasingly available. This approach is applied to construct a dynamic model of human red cell metabolism with and without mechanistic integration of allosteric regulatory functions of enzymes, dynamic models of E. coli, as well as a general hepatocyte mitochondria model which can be used for application of these methods, when the appropriate data becomes available. Simulations with the regulated erythrocyte model highlight the importance of active versus inactive states of enzymes and how the binding state of the enzyme exerts control of the flux through competing pathways. The subsequent chapter investigates perturbational analyses and how they can inform functional states and identify pathophysiological conditions. This dissertation culminates in a description of a conceptually new approach to modeling, 'delta' networks, that relaxes data requirements and is focused on identifying the functional differences between different data sets in an effort to understand as much of the four subspaces as possible, not just the right null space. The studies carried out in this dissertation also further support the use of models as data interrogation tools; for data integration, analysis, as well as for the evaluation of data consistency. The developments and advancements made herein take steps towards achieving genome-scale dynamic regulated networks of metabolism, which can in principle be applied to any biological network.

Integration Of Metabolism Energetics And Signal Transduction

DOWNLOAD
Author : Robert K. Ockner
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-05-08

Integration Of Metabolism Energetics And Signal Transduction written by Robert K. Ockner and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-05-08 with Medical categories.

Complex and unexplained phenomena tend to foster unorthodox perspectives. This publication is an example, as is a prior publication that emphasized the concept that intermediary metabolism might play a significant and determining role in hepatocyte proliferation and 1 tumorigenesis. Formulation of this hypothesis was based on an attempt to clarify several poorly understood phenomena; including the observations: 1) that xenobiotic peroxisome proliferators such as the fibrate hypolipidemic agents induce hepatocyte proliferation and carcinogenesis in rodents; 2) that benign and malignant liver tumors complicate the human syndrome of glycogen storage disease type I (glucose-6-phosphatase deficiency); and 3) that in this same syndrome, administration of glucose exerts an anti-tumor effect. Fatty acid and glucose metabolism are tightly linked in a we- established and profoundly inportant interplay. This connection, together with the fact that peroxisome proliferator-induced hepatocyte proliferation and carcinogenesis reflects inhibition of mitochondrial carnitine palmitoyltransferase-I and fatty acid oxidation, suggested the possibility that regulation of fatty acid metabolism could prove to be a pivotal determinant in the control of cell growth. In 1993, the year in which the paper cited above was published, insight into the importance of growth factors and signal transduction pathways in cell cycle regulation was increasing rapidly, but metabolic and energetic aspects of cell proliferation had attracted relatively little attention. Despite this, the concept seemed inescapable that the two seemingly distinct and unrelated determinants — signal transduction and metabolism — were integrally linked.

Nutritional Signaling Pathway Activities In Obesity And Diabetes

DOWNLOAD
Author : Zhiyong Cheng
language : en
Publisher: Royal Society of Chemistry
Release Date : 2020-08-24

Nutritional Signaling Pathway Activities In Obesity And Diabetes written by Zhiyong Cheng and has been published by Royal Society of Chemistry this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-24 with Medical categories.

Nutrients can act as signalling molecules to initiate or mediate signalling transduction that regulates cell function and homeostasis. As such, altered nutrient status has been linked to dysregulated transcripts and protein expression, which affects mitochondrial function, autophagy, inflammation, metabolism and even gut microbiota. This book disseminates the cutting-edge knowledge pertaining to nutritional signalling activities in metabolism and metabolic derangements (e.g., obesity and diabetes), which covers the regulatory mechanisms and dietary interventions for disease prevention. This book represents current nutritional and metabolic research. From the basic (molecular science) perspective, it covers metabolomics, proteomics, nutrigenomics, nuclear receptors and transcription factors, inflammatory pathways, autophagy, mitochondrial health and gut microbiota. From the clinical (translational science) perspective, this book covers clinical trials, precision nutrition, maternal nutrition and transgenerational health, and allometric scaling of dietary bioactives in translational metabolic research. It brings to the reader in-depth understanding of the nutritional aspect, cellular and molecular biology, as well as pathophysiology of obesity and diabetes. In addition, each chapter in this book includes a component of future direction or intervention perspective, making the new knowledge transformative and translational. Aimed at researchers and professionals interested in nutrition, dietetics and metabolic disorders, this book will also appeal to health science researchers.

Metabolic Profiling Its Role In Biomarker Discovery And Gene Function Analysis

DOWNLOAD
Author : George G. Harrigan
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-01-31

Metabolic Profiling Its Role In Biomarker Discovery And Gene Function Analysis written by George G. Harrigan and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-01-31 with Medical categories.

It is evident that biochemical control is not strictly hierarchical and that intermediary metabolism can contribute to control of regulatory pathways. Metabolic studies are therefore increasingly important in gene function analyses, and an increased interest in metabolites as biomarkers for disease progression or response to therapeutic intervention is also evident in the pharmaceutical industry. This book offers guidelines to currently available technology and bioinformatics and database strategies now being developed. Evidence is presented that metabolic profiling is a valuable addition to genomics and proteomics strategies devoted to drug discovery and development, and that metabolic profiling offers numerous advantages.

Metabolic Signaling

DOWNLOAD
Author : Sarah-Maria Fendt
language : en
Publisher: Humana
Release Date : 2019-12-10

Metabolic Signaling written by Sarah-Maria Fendt and has been published by Humana this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-10 with Science categories.

This book provides protocols to quantify metabolism, to identify metabolic crosstalk, and to setup and develop tools and models to gain insight into metabolic signaling using experimental and computational approaches. Chapters detail protocols to quantify metabolism, identify metabolic crosstalk, and develop tools and models to gain a systems-level insight into metabolic signaling. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Metabolic Signaling: Methods and Protocols aims to provide researchers with methods to study, perturb, and functionally interpret metabolism and metabolic signaling from the sub-cellular to the whole-body level.