On functional module detection in metabolic networks

Functional modules of metabolic networks are essential for understanding the metabolism of an organism as a whole. With the vast amount of experimental data and the construction of complex and large-scale, often genome-wide, models, the computer-aided identification of functional modules becomes more and more important. Since steady states play a key role in biology, many methods have been developed in that context, for example, elementary flux modes, extreme pathways, transition invariants and place invariants. Metabolic networks can be studied also from the point of view of graph theory, and algorithms for graph decomposition have been applied for the identification of functional modules. A prominent and currently intensively discussed field of methods in graph theory addresses the Q-modularity. In this paper, we recall known concepts of module detection based on the steady-state assumption, focusing on transition-invariants (elementary modes) and their computation as minimal solutions of systems of Diophantine equations. We present the Fourier-Motzkin algorithm in detail. Afterwards, we introduce the Q-modularity as an example for a useful non-steady-state method and its application to metabolic networks. To illustrate and discuss the concepts of invariants and Q-modularity, we apply a part of the central carbon metabolism in potato tubers (Solanum tuberosum) as running example. The intention of the paper is to give a compact presentation of known steady-state concepts from a graph-theoretical viewpoint in the context of network decomposition and reduction and to introduce the application of Q-modularity to metabolic Petri net models

A Graphical and Computational Modelling Platform for Biological Pathways

A major endeavor of systems biology is the construction of graphical and computational models of biological pathways as a means to better understand their structure and function. Here, we present a protocol for a biologist-friendly graphical modeling scheme that facilitates the construction of detailed network diagrams, summarizing the components of a biological pathway (such as proteins and biochemicals) and illustrating how they interact. These diagrams can then be used to simulate activity flow through a pathway, thereby modeling its dynamic behavior. The protocol is divided into four sections: (i) assembly of network diagrams using the modified Edinburgh Pathway Notation (mEPN) scheme and yEd network editing software with pathway information obtained from published literature and databases of molecular interaction data; (ii) parameterization of the pathway model within yEd through the placement of 'tokens' on the basis of the known or imputed amount or activity of a component; (iii) model testing through visualization and quantitative analysis of the movement of tokens through the pathway, using the network analysis tool Graphia Professional and (iv) optimization of model parameterization and experimentation. This is the first modeling approach that combines a sophisticated notation scheme for depicting biological events at the molecular level with a Petri net–based flow simulation algorithm and a powerful visualization engine with which to observe the dynamics of the system being modeled. Unlike many mathematical approaches to modeling pathways, it does not require the construction of a series of equations or rate constants for model parameterization. Depending on a model's complexity and the availability of information, its construction can take days to months, and, with refinement, possibly years. However, once assembled and parameterized, a simulation run, even on a large model, typically takes only seconds. Models constructed using this approach provide a means of knowledge management, information exchange and, through the computation simulation of their dynamic activity, generation and testing of hypotheses, as well as prediction of a system's behavior when perturbed

Modelling the structure and dynamics of biological pathways

There is a need for formalised diagrams that both summarise current biological pathway knowledge and support modelling approaches that explain and predict their behaviour. Here, we present a new, freely available modelling framework that includes a biologist-friendly pathway modelling language (mEPN), a simple but sophisticated method to support model parameterisation using available biological information; a stochastic flow algorithm that simulates the dynamics of pathway activity; and a 3-D visualisation engine that aids understanding of the complexities of a system's dynamics. We present example pathway models that illustrate of the power of approach to depict a diverse range of systems

Ultrafast Microfluidic Immunoassays Towards Real-time Intervention of Cytokine Storms

Biomarker-guided precision medicine holds great promise to provide personalized therapy with a good understanding of the molecular or cellular data of an individual patient. However, implementing this approach in critical care uniquely faces enormous challenges as it requires obtaining “real-time” data with high sensitivity, reliability, and multiplex capacity near the patient’s bedside in the quickly evolving illness. Current immunodiagnostic platforms generally compromise assay sensitivity and specificity for speed or face significantly increased complexity and cost for highly multiplexed detection with low sample volume. This thesis introduces two novel ultrafast immunoassay platforms: one is a machine learning-based digital molecular counting assay, and the other is a label-free nano-plasmonic sensor integrated with an electrokinetic mixer. Both of them incorporate microfluidic approaches to pave the way for near-real-time interventions of cytokine storms. In the first part of the thesis, we present an innovative concept and the theoretical study that enables ultrafast measurement of multiple protein biomarkers (<1 min assay incubation) with comparable sensitivity to the gold standard ELISA method. The approach, which we term “pre-equilibrium digital enzyme-linked immunosorbent assay” (PEdELISA) incorporates the single-molecular counting of proteins at the early, pre-equilibrium state to achieve the combination of high speed and sensitivity. We experimentally demonstrated the assay’s application in near-real-time monitoring of patients receiving chimeric antigen receptor (CAR) T-cell therapy and for longitudinal serum cytokine measurements in a mouse sepsis model. In the second part, we report the further development of a machine learning-based PEdELISA microarray data analysis approach with a significantly extended multiplex capacity using the spatial-spectral microfluidic encoding technique. This unique approach, together with a convolutional neural network-based image analysis algorithm, remarkably reduced errors faced by the highly multiplexed digital immunoassay at low analyte concentrations. As a result, we demonstrated the longitudinal data collection of 14 serum cytokines in human patients receiving CAR-T cell therapy at concentrations < 10pg/mL with a sample volume < 10 µL and 5-min assay incubation. In the third part, we demonstrate the clinical application of a machine learning-based digital protein microarray platform for rapid multiplex quantification of cytokines from critically ill COVID-19 patients admitted to the intensive care unit. The platform comprises two low-cost modules: (i) a semi-automated fluidic dispensing module that can be operated inside a biosafety cabinet to minimize the exposure of technician to the virus infection and (ii) a compact fluorescence optical scanner for the potential near-bedside readout. The automated system has achieved high interassay precision (~10% CV) with high sensitivity (<0.4pg/mL). Our data revealed large subject-to-subject variability in patient responses to anti-inflammatory treatment for COVID-19, reaffirming the need for a personalized strategy guided by rapid cytokine assays. Lastly, an AC electroosmosis-enhanced localized surface plasmon resonance (ACE-LSPR) biosensing device was presented for rapid analysis of cytokine IL-1β among sepsis patients. The ACE-LSPR device is constructed using both bottom-up and top-down sensor fabrication methods, allowing the seamless integration of antibody-conjugated gold nanorod (AuNR) biosensor arrays with microelectrodes on the same microfluidic platform. Applying an AC voltage to microelectrodes while scanning the scattering light intensity variation of the AuNR biosensors results in significantly enhanced biosensing performance. The technologies developed have enabled new capabilities with broad application to advance precision medicine of life-threatening acute illnesses in critical care, which potentially will allow the clinical team to make individualized treatment decisions based on a set of time-resolved biomarker signatures.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163129/1/yujing_1.pd

UVR8 mediated spatial differences as a prerequisite for UV-B induced inflorescence phototropism

In Arabidopsis hypocotyls, phototropins are the dominant photoreceptors for the positive phototropism response towards unilateral ultraviolet-B (UV-B) radiation. We report a stark contrast of response mechanism with inflorescence stems with a central role for UV RESISTANCE LOCUS 8 (UVR8). The perception of UV-B occurs mainly in the epidermis and cortex with a lesser contribution of the endodermis. Unilateral UV-B exposure does not lead to a spatial difference in UVR8 protein levels but does cause differential UVR8 signal throughout the stem with at the irradiated side 1) increase of the transcription factor ELONGATED HYPOCOTYL 5 (HY5), 2) an associated strong activation of flavonoid biosynthesis genes and flavonoid accumulation, 3) increased GA2oxidase expression, diminished gibberellin1 levels and accumulation of DELLA protein REPRESSOR OF GA1 (RGA) and, 4) increased expression of the auxin transport regulator, PINOID, contributing to local diminished auxin signalling. Our molecular findings are in support of the Blaauw theory (1919), suggesting that differential growth occurs trough unilateral photomorphogenic growth inhibition. Together the data indicate phototropin independent inflorescence phototropism through multiple locally UVR8-regulated hormone pathways

Tree Peony Species Are a Novel Resource for Production of α-Linolenic Acid

Tree peony is known worldwide for its excellent ornamental and medical values, but recent reports that their seeds contain over 40% α-linolenic acid (ALA), an essential fatty acid for humans drew additional interest of biochemists. To understand the key factors that contribute to this rich accumulation of ALA, we carried out a comprehensive study of oil accumulation in developing seeds of nine wild tree peony species. The fatty acid content and composition was highly variable among the nine species; however, we selected a high- (P. rockii) and low-oil (P. lutea) accumulating species for a comparative transcriptome analysis. Similar to other oilseed transcriptomic studies, upregulation of select genes involved in plastidial fatty acid synthesis, and acyl editing, desaturation and triacylglycerol assembly in the endoplasmic reticulum was noted in seeds of P. rockii relative to P. lutea. Also, in association with the ALA content, transcript levels for fatty acid desaturases (SAD, FAD2 and FAD3), which encode for enzymes necessary for polyunsaturated fatty acid synthesis were higher in P. rockii compared to P. lutea. We further showed that the overexpression of PrFAD2 and PrFAD3 in Arabidopsis increased linoleic and α-linolenic acid content, respectively and modulated their final ratio in the seed oil. In conclusion, we identified the key steps that contribute to efficient ALA synthesis and validated the necessary desaturases in P. rockii that are responsible for not only increasing oil content but also modulating 18:2/18:3 ratio in seeds. Together, these results will aid to improve essential fatty acid content in seeds of tree peonies and other crops of agronomic interest

Annual General Assembly of the International Association of Maritime Universities

978-84-947311-7-

Advances in Food Processing (Food Preservation, Food Safety, Quality and Manufacturing Processes)

This e-book aims to compile advances in the area of food manufacturing including packaging to address issues of food safety, quality, fraud, and how these processes (new or old) could affect the organoleptic characteristics of foods, with the aim to promote consumers’ satisfaction. Moreover, food supply issues are explored. New and improved technologies are employed in the area of food manufacturing to address consumer needs in terms of quality and safety. The issues of research and development should be taken into account seriously before launching a new product onto the market. Finally, food fraud and authenticity are very important issues, and the food industry should focus on addressing them

Dissection of pleiotropic effects in genome-wide association studies of phenotypes related to cardiometabolic health

In the past seven years, Genome-Wide Association Studies (GWAS) have identified hundreds of variants associated with cardiometabolic quantitative traits and diseases. Many genetic loci appear to harbour variants associated with multiple phenotypes (cross-phenotype associations, CP). CP associations highlight that phenotypes may share common underlying genetic mechanisms that might, or might not, be consistent with epidemiological expectations and, therefore, add complexity to the relationships between human phenotypes. Pleiotropy occurs when the same genetic causal element affects more than one phenotype “in parallel” and can explain the presence of CP associations. It can appear at a single variant level, where a single causal variant is related to multiple phenotypes, or at a locus level, that is when multiple variants in the same gene or locus are associated with different phenotypes by affecting the same functional element. However, other potential genetic mechanisms, that can explain CP associations, exist. Among them, mediation occurs when a genetic variant is directly associated with a phenotype and that phenotype is itself causal for a second phenotype or more phenotypes; multi-phenotype allelic heterogeneity is a phenomenon which involves independent uncorrelated variants within the same locus which cause changes in multiple phenotypes, by affecting them through independent pathways related to distinct functional elements. The identification and characterisation of CP associations across the genome may help uncovering the mechanistic basis of physiological processes that underlie variability of cardiometabolic quantitative traits, and of pathogenetic processes leading to metabolic disorders. The definition of specific patterns of effect combinations on cardiometabolic phenotypes will highlight novel biological pathways, targets for translational research, for therapeutic intervention, and for the understanding of the pathophysiology of human metabolism. Based on this hypothesis, and in collaboration with the Cross-Consortia pleiotropy group and with the European Network for Genetic and Genomic Epidemiology (ENGAGE) consortium, my PhD project focused on dissection of CP effects, pleiotropy in particular, at common variants across the genome in association with cardiometabolic phenotypes. The objective was to improve our understanding of the extent of shared genetics between cardiometabolic phenotypes and of the influences of DNA sequence variation on risk of metabolic diseases, considering phenotypes as a range of inter-related manifestations of biological mechanisms rather than as isolated events. My research has been divided into three sub-projects: Project 1: Clustering and pathway analysis of univariate GWAS results for the detection of pleiotropic effects. We explored multi-phenotype effects at hundreds of established cardiometabolic genetic variants from published univariate GWAS meta-analyses on more than 20 respective phenotypes, by defining clusters of loci with similar multiple effects, comparing them to known epidemiological expectations, and identifying enriched biological networks within the most interesting groups of loci. Our results highlighted that many variants at cardiometabolic loci have multiple associations that characterise different aspects of metabolism. Cardiometabolic loci can be grouped according to their shared multi-phenotype effects and metabolic syndrome represents just one possible combination; in fact, several other unexpected combinations might be observed, for example healthy obesity/unhealthy leanness. We also highlighted that genetic loci with similar cardiometabolic effects are involved in shared biological pathways. Some of these may be expected, for instance, regulation of lipids metabolism or cholesterol transport for groups of loci with strong effects on lipids, and circulatory system processes for genes near blood pressure-association signals. Sometimes groups of loci affected fundamental cell functions, such as regulation of cellular processes, for the loci with effects on obesity and anthropometric traits. The enriched connectivity within pathway networks revealed new potential candidate genes and tissues of action that are more likely to have causal effect on phenotypes. Project 2: Validating pleiotropy and analysis of locus architecture in potential pleiotropic regions. We aimed to dissect the architecture of established cardiometabolic loci showing multiple associations for a better definition of the underlying mechanisms of multi-phenotype effects and for the discernment of potential pleiotropy from allelic heterogeneity. To this aim, we applied an approximate conditional analysis, based on observed linkage disequilibrium patterns, which led us to the discovery of multiple associations at adjacent variants that underlie the same genetic cause for variability of different phenotypes. Our results also highlighted that a substantial proportion of metabolic loci incorporate complex patterns of multi-phenotype allelic heterogeneity, thus suggesting an important contribution of this mechanism into cross-phenotype effects. Project 3: Application of a multivariate statistical approach for the study of pleiotropy within cardiometabolic phenotypes. We developed and applied a statistical strategy for joint multivariate analysis of multiple correlated phenotypes using individual genetic data from the ENGAGE consortium to discover new uncovered multiple associations and to follow-up GWAS meta-analysis at two loci, FTO and FADS1. Using this approach we were able to take into account correlation between phenotypes, and we achieved a boost in power; moreover, we improved precision of parameter estimates and of the identification of novel candidate genes. Our results allowed us to identify several variants jointly associated with multiple lipid traits and body mass index. Our approach was useful for the identification of mediation: we, in fact, confirmed mediation underlying causal relationship between adiposity and other cardiometabolic phenotypes at the FTO locus. Additionally, we demonstrated that multiple effects on cardiometabolic phenotypes attributable to the FADS1 locus are mediated by its independent, thus pleiotropic, effect on total cholesterol and triglycerides. In conclusion, we applied several statistical approaches which allowed dissecting suggestive CP effects and their mechanisms, including pleiotropy, mediation and allelic heterogeneity. Our analyses have demonstrated the complexity of the relationships between cardiometabolic phenotypes related to the variability of both, underlying genetic mechanisms and genetic loci architecture