Expansive evolution of the TREHALOSE-6-PHOSPHATE PHOSPHATASE gene family in Arabidopsis

Trehalose is a nonreducing sugar used as a reserve carbohydrate and stress protectant in a variety of organisms. While higher plants typically do not accumulate high levels of trehalose, they encode large families of putative trehalose biosynthesis genes. Trehalose biosynthesis in plants involves a two-step reaction in which trehalose-6-phosphate (T6P) is synthesized from UDPglucose and glucose-6-phosphate (catalyzed by T6P synthase [TPS]), and subsequently dephosphorylated to produce the disaccharide trehalose (catalyzed by T6P phosphatase [TPP]). In Arabidopsis (Arabidopsis thaliana), 11 genes encode proteins with both TPS- and TPP-like domains but only one of these (AtTPS1) appears to be an active (TPS) enzyme. In addition, plants contain a large family of smaller proteins with a conserved TPP domain. Here, we present an in-depth analysis of the 10 TPP genes and gene products in Arabidopsis (TPPA-TPPJ). Collinearity analysis revealed that all of these genes originate from whole-genome duplication events. Heterologous expression in yeast (Saccharomyces cerevisiae) showed that all encode active TPP enzymes with an essential role for some conserved residues in the catalytic domain. These results suggest that the TPP genes function in the regulation of T6P levels, with T6P emerging as a novel key regulator of growth and development in higher plants. Extensive gene expression analyses using a complete set of promoter-beta-glucuronidase/green fluorescent protein reporter lines further uncovered cell- and tissue-specific expression patterns, conferring spatiotemporal control of trehalose metabolism. Consistently, phenotypic characterization of knockdown and overexpression lines of a single TPP, AtTPPG, points to unique properties of individual TPPs in Arabidopsis, and underlines the intimate connection between trehalose metabolism and abscisic acid signaling

The regulatory and transcriptional landscape associated with carbon utilization in a filamentous fungus.

Filamentous fungi, such as Neurospora crassa, are very efficient in deconstructing plant biomass by the secretion of an arsenal of plant cell wall-degrading enzymes, by remodeling metabolism to accommodate production of secreted enzymes, and by enabling transport and intracellular utilization of plant biomass components. Although a number of enzymes and transcriptional regulators involved in plant biomass utilization have been identified, how filamentous fungi sense and integrate nutritional information encoded in the plant cell wall into a regulatory hierarchy for optimal utilization of complex carbon sources is not understood. Here, we performed transcriptional profiling of N. crassa on 40 different carbon sources, including plant biomass, to provide data on how fungi sense simple to complex carbohydrates. From these data, we identified regulatory factors in N. crassa and characterized one (PDR-2) associated with pectin utilization and one with pectin/hemicellulose utilization (ARA-1). Using in vitro DNA affinity purification sequencing (DAP-seq), we identified direct targets of transcription factors involved in regulating genes encoding plant cell wall-degrading enzymes. In particular, our data clarified the role of the transcription factor VIB-1 in the regulation of genes encoding plant cell wall-degrading enzymes and nutrient scavenging and revealed a major role of the carbon catabolite repressor CRE-1 in regulating the expression of major facilitator transporter genes. These data contribute to a more complete understanding of cross talk between transcription factors and their target genes, which are involved in regulating nutrient sensing and plant biomass utilization on a global level

Graph Theory and Networks in Biology

In this paper, we present a survey of the use of graph theoretical techniques in Biology. In particular, we discuss recent work on identifying and modelling the structure of bio-molecular networks, as well as the application of centrality measures to interaction networks and research on the hierarchical structure of such networks and network motifs. Work on the link between structural network properties and dynamics is also described, with emphasis on synchronization and disease propagation.Comment: 52 pages, 5 figures, Survey Pape

Transcriptional regulation and steady-state modeling of metabolic networks

Biologiske systemer er karakteriseret ved en høj grad af kompleksitet, hvori de individuelle komponenter (f.eks. proteiner) er indbyrdes forbundet på en måde, der fører til en opførsel, der er vanskelig at forstå i detaljer. Udredning af systemets kompleksitet kræver i det mindste svar på følgende tre spørgsmål: hvad er komponenterne af systemerne, hvordan er de forskellige komponenter sammenkoblet, og hvordan udfører disse netværk de funktioner, der resulterer i systemernes adfærd? Moderne analytiske teknologier giver os mulighed for at optrævle de bestanddele og interaktioner der findes i et givet system, men det tredje spørgsmål er den ultimative udfordring for systembiologi. Nærværende afhandling behandler dette spørgsmål systematisk i forbindelse med metaboliske netværk, som velsagtens er de mest velbeskrevne biologiske netværk hvad angår komponenter og samspillet mellem dem. Desuden er der stor interesse for at forstå og manipulere cellestofskiftet ud fra såvel sundhedsmæssige som bioteknologiske perspektiver. Fundamentalt forskellige biologiske spørgsmål undersøges i forskellige centrale kapitler i afhandlingen, selv om de alle er forbundet af det fælles tema omkring, hvordan det cellulære stofskifte fungerer. De tre vigtigste emner, der behandles, er: i) Transkriptionel regulering af metabolit-koncentrationer, ii) transkriptionel dys-regulering af skeletmuskulaturens stofskifte i type-2 diabetes, og iii) metaboliske interaktioner i mikrobielle økosystemer. Det overordnede mål er at opnå ny forståelse bag de operationelle principper for metaboliske netværk.Cellers reaktioner på forstyrrelser i vækstvilkår og genetiske/epigenetiske ændringer styres i høj grad gennem transkription, som er en af de grundlæggende mekanismer for cellulær regulering. Et vigtigt spørgsmål er, i hvilket omfang genekspression kan forklare metaboliske fænotyper; med andre ord, hvor godt kan ændringer i metabolitkoncentrationer forklares med ændringer i mængderne af mRNA kodende for de ansvarlige enzymer? Forsøg på at forudsige ændringer i metabolomet ud fra genekspressionsdata har hidtil ikke ladet sig gøre. Her udfordrer jeg dette spørgsmål ved at foreslå en mekanistisk forklaring af samspillet mellem metabolitkoncentrationer, transkripter og flux baseret på Michaelis-Menten kinetik på netværks-skala. Dette arbejde viser, at i steady-state systemer er ændringer i intracellulære metabolit-koncentrationer forbundet med ændringer i genekspression af både reaktioner, der producerer, og reaktioner, der forbruger en bestemt metabolit. I modsætning til tidligere tænkning tyder analyse af en stor samling af genekspressionsdata endvidere på, at transkriptionel regulering ved metaboliske forgreningspunkter er meget plastisk, og i flere tilfælde synes den selektive fordel ved reguleringen at være metabolit-orienteret snarere end pathway-orienteret. Undersøgelsen giver således et fundamentalt og nyt syn på metabolisk netværksregulering i Saccharomyces cerevisiae.Metabolisme er et i høj grad bevaret system på tværs af hele biologien. I dag er stofskifte blevet et centralt punkt i diagnosticering og behandling af sygdomme såsom diabetes og kræft. Type 2-diabetes mellitus er en kompleks metabolisk sygdom, der er anerkendt som en af de største trusler mod menneskers sundhed i det 21. århundrede. Nylige undersøgelser af genekspressionsniveauer i humane vævsprøver har vist, at flere metaboliske veje er dysreguleret i diabetes og hos personer med risiko for diabetes; hvilke af disse veje der er primære og/eller centrale for patogenesen, er fortsat et centralt spørgsmål. Cellulære metaboliske netværk er meget tæt forbundne og ofte stramt regulerede; eventuelle forstyrrelser ved et enkelt forbindelsespunkt kan således hurtigt udbrede sig til resten af netværket. En sådan kompleksitet udgør en betydelig udfordring i at indkredse de vigtigste molekylære mekanismer og kendetegn, der er forbundet med insulinresistens og type 2 diabetes. Det foreliggende arbejde løser dette problem ved at bruge en metode, der integrerer genekspressionsdata med det humane cellulære metaboliske netværk. Denne fremgangsmåde demonstreres ved analyse af to datasæt fra skeletmusklers genekspression. Den foreslåede metode identificerede transkriptionsfaktorer og metabolitter, der udgør potentielle mål for farmaka og fremtidig klinisk diagnose for type 2-diabetes og forringet glukosemetabolisme. I en bredere sammenhæng frembyder undersøgelsen en ramme for analyse af genekspression-data indsamlet ved komplekse heterogene sygdomme, genetiske og miljømæssige perturbationer, der afspejles i og/eller er medieret via ændringer i stofskiftet.I naturen eksisterer mikroorganismer normalt ikke som rene kulturer, men udvikler sig og sameksisterer med andre arter. Mikrobielle samfund har en bred vifte af mulige anvendelser, herunder behandling af metaboliske sygdomme og bioteknologi. Eksempelvis kan mikrobielle konsortier bestående af forskellige bakterier og svampe udføre biologisk nedbrydning bedre end rene kulturer, hvilket gør dem attraktive at udforske. Det er almindeligt antaget, at ernæring spiller en afgørende rolle i udformningen af mikrobielle samfund, og indbyrdes udveksling og udnyttelse af metabolitter kan give flere fordele for samfundet som helhed. For eksempel kan en mere effektiv og fuldstændig anvendelse af de tilgængelige næringsstoffer, eller en forbedret evne til at tilpasse sig skiftende ernæringsforhold, føre til forbedret overlevelse af individerne. Det tredje emne i denneafhandling undersøger de metaboliske interaktioners rolle i blandede mikrobielle samfund. Formålet med undersøgelsen er at identificere de egenskaber ved metabolismen, der er bestemmende for strukturerne af de blandede samfund. Analysen er baseret på et globalt metagenomisk datasæt, og metaboliske modeller i genom-skala pegede på, at arter inden for sameksisterende samfund har et større potentiale for metabolisk samarbejde i forhold til tilfældigt sammensatte samfund. Dette arbejde førte til en ny metode (kaldet species metabolic coupling analysis) for at studere metaboliskinteraktion og indbyrdes afhængighed inden for mikrobielle samfund. Metoden har en vifte af konkrete anvendelser, herunder undersøgelse af metaboliske interaktioner i menneskets mikrobiom, værtspatogene interaktioner og udvikling af stabile mikrobielle samfund.Samlet set bidrager dette arbejde med nye indsigter, værktøjer og metoder til at studere hvordan cellulært stofskifte fungerer.Biological systems are characterized by a high degree of complexity wherein the individual components (e.g. proteins) are inter-linked in a way that leads to emergent behaviors that are difficult to decipher. Uncovering system complexity requires, at least, answers to the following three questions: what are the components of the systems, how are the different components interconnected and how do these networks perform the functions that make the resulting system behavior? Modern analytical technologies allow us to unravel the constituents and interactions happening in a given system; however, the third question is the ultimate challenge for systems biology. The work of this thesis systematically addresses this question in the context of metabolic networks, which are arguably the most well characterized cellular networks in terms of their constituting components and interactions among them. Furthermore, there is large interest in understanding and manipulating cellular metabolism from health as well as biotechnological perspectives. Fundamentally different biological questions are investigated in different core chapters of the thesis, though all are linked by the common thread of the functioning of cellular metabolism. The three main topics addressed are: i) transcriptional regulation of metabolite concentration, ii) transcriptional dys-regulation of skeletal muscle metabolism in type 2 diabetes, and iii) metabolic interactions in microbial ecosystems. The overall objective is to obtain novel understanding underlying the operating principles of metabolic networks. Cellular responses to environmental perturbations and genetic/epigenetic modifications are to a large extent controlled through transcription, which is one of the fundamental mechanism/means of cellular regulation. An important question is to what extent gene expression can explain metabolic phenotype, in other words, how well changes in metabolite concentrations can be explained by the changes in related enzyme-coding transcripts? Attempts to predict changes in the metabolome from gene expression data have so far remained unsolved. Here, I challenge this question by proposing a mechanistic explanation of the interplay between metabolite concentrations, transcripts and fluxes based on Michaelis-Menten kinetics at the network-scale. The work demonstrates that in steadystate systems, changes of intracellular metabolites concentrations are linked with the changes in gene expression of both reactions that produce and reactions that consume a given metabolite. Analysis of a large compendium of gene expression data further suggested that, contrary to previous thinking, transcriptional regulation at metabolic branch points is highly plastic and, in several cases, the objective of the regulation appears to be metabolite-oriented as opposed to pathway-oriented. The study thus provides a fundamental and novel view of metabolic network regulation in Saccharomyces cerevisiae. Metabolism is a conserved system across all domains of life. Nowadays, metabolism has become a focal point in diagnosing and treating diseases such as diabetes and cancer. Type 2 diabetes mellitus is a complex metabolic disease which is recognized as one of the largest threats to human health in the 21st century. Recent studies of gene expression levels in human tissue samples have indicated that multiple metabolic pathways are dys-regulated in diabetes and in individuals at risk for diabetes; which of these are primary, or central to disease pathogenesis, remains a key question. Cellular metabolic networks are highly interconnected and often tightly regulated; any perturbations at a single node can thus rapidly diffuse to the rest of the network. Such complexity presents a considerable challenge in pinpointing key molecular mechanisms and signatures associated with insulin resistance and type 2 diabetes. The present work addresses this problem by using a methodology that integrates gene expression data with the human cellular metabolic network. The approach is demonstrated by analysis of two skeletal muscle gene expression datasets. The proposed methodology identified transcription factors and metabolites that represent potential targets for therapeutic agents and future clinical diagnostics for type 2 diabetes and impaired glucose metabolism. In a broader context, the study provides a framework for analysis of gene expression datasets from complex heterogeneous diseases, genetic, and environmental perturbations that are reflected in and/or mediated through changes in metabolism.In nature, microorganisms do not exist as pure cultures, but evolve and co-exist with other species. Microbial communities have a variety of potential applications, including metabolic disease therapies and biotechnology. For example, microbial consortia consisting of various bacteria and fungi are known to exhibit a biodegradation performance superior to pure cultures, making them attractive research targets. It is believed that nutrition plays a crucial role in shaping microbial communities. Interspecies metabolite cross-feeding can confer several advantages to the community as a whole. For example, more efficient and complete use of available nutrients, or increased ability to survive under diverse/changing nutrition availability potentially induces fitness of individuals. The third topic of this thesis investigates the role of metabolic interaction in co-occurring microbial communities. The study aims to identify metabolic properties that shape the community structures. The analysis based on a global metagenomic dataset and genome-scale metabolic models suggested that species within coexisting communities have higher potential of metabolic cooperation compared to random controls. This work yielded a novel methodology (termed species metabolic coupling analysis) for studying metabolic interaction and interdependencies within microbial communities. Species metabolic coupling analysis has a spectrum of applications to real-world problems, including investigation of metabolic interactions within the human microbiome, host -pathogen interactions and development of stable microbial communities. Overall, this work contributes with novel insights, tools and methodologies to study the operation of cellular metabolism

Systems analysis of host-parasite interactions.

Parasitic diseases caused by protozoan pathogens lead to hundreds of thousands of deaths per year in addition to substantial suffering and socioeconomic decline for millions of people worldwide. The lack of effective vaccines coupled with the widespread emergence of drug-resistant parasites necessitates that the research community take an active role in understanding host-parasite infection biology in order to develop improved therapeutics. Recent advances in next-generation sequencing and the rapid development of publicly accessible genomic databases for many human pathogens have facilitated the application of systems biology to the study of host-parasite interactions. Over the past decade, these technologies have led to the discovery of many important biological processes governing parasitic disease. The integration and interpretation of high-throughput -omic data will undoubtedly generate extraordinary insight into host-parasite interaction networks essential to navigate the intricacies of these complex systems. As systems analysis continues to build the foundation for our understanding of host-parasite biology, this will provide the framework necessary to drive drug discovery research forward and accelerate the development of new antiparasitic therapies

Semantic systems biology of prokaryotes : heterogeneous data integration to understand bacterial metabolism

The goal of this thesis is to improve the prediction of genotype to phenotypeassociations with a focus on metabolic phenotypes of prokaryotes. This goal isachieved through data integration, which in turn required the development ofsupporting solutions based on semantic web technologies. Chapter 1 providesan introduction to the challenges associated to data integration. Semantic webtechnologies provide solutions to some of these challenges and the basics ofthese technologies are explained in the Introduction. Furthermore, the ba-sics of constraint based metabolic modeling and construction of genome scalemodels (GEM) are also provided. The chapters in the thesis are separated inthree related topics: chapters 2, 3 and 4 focus on data integration based onheterogeneous networks and their application to the human pathogen M. tu-berculosis; chapters 5, 6, 7, 8 and 9 focus on the semantic web based solutionsto genome annotation and applications thereof; and chapter 10 focus on thefinal goal to associate genotypes to phenotypes using GEMs. Chapter 2 provides the prototype of a workflow to efficiently analyze in-formation generated by different inference and prediction methods. This me-thod relies on providing the user the means to simultaneously visualize andanalyze the coexisting networks generated by different algorithms, heteroge-neous data sets, and a suite of analysis tools. As a show case, we have ana-lyzed the gene co-expression networks of M. tuberculosis generated using over600 expression experiments. Hereby we gained new knowledge about theregulation of the DNA repair, dormancy, iron uptake and zinc uptake sys-tems. Furthermore, it enabled us to develop a pipeline to integrate ChIP-seqdat and a tool to uncover multiple regulatory layers. In chapter 3 the prototype presented in chapter 2 is further developedinto the Synchronous Network Data Integration (SyNDI) framework, whichis based on Cytoscape and Galaxy. The functionality and usability of theframework is highlighted with three biological examples. We analyzed thedistinct connectivity of plasma metabolites in networks associated with highor low latent cardiovascular disease risk. We obtained deeper insights froma few similar inflammatory response pathways in Staphylococcus aureus infec-tion common to human and mouse. We identified not yet reported regulatorymotifs associated with transcriptional adaptations of M. tuberculosis.In chapter 4 we present a review providing a systems level overview ofthe molecular and cellular components involved in divalent metal homeosta-sis and their role in regulating the three main virulence strategies of M. tu-berculosis: immune modulation, dormancy and phagosome escape. With theuse of the tools presented in chapter 2 and 3 we identified a single regulatorycascade for these three virulence strategies that respond to limited availabilityof divalent metals in the phagosome. The tools presented in chapter 2 and 3 achieve data integration throughthe use of multiple similarity, coexistence, coexpression and interaction geneand protein networks. However, the presented tools cannot store additional(genome) annotations. Therefore, we applied semantic web technologies tostore and integrate heterogeneous annotation data sets. An increasing num-ber of widely used biological resources are already available in the RDF datamodel. There are however, no tools available that provide structural overviewsof these resources. Such structural overviews are essential to efficiently querythese resources and to assess their structural integrity and design. There-fore, in chapter 5, I present RDF2Graph, a tool that automatically recoversthe structure of an RDF resource. The generated overview enables users tocreate complex queries on these resources and to structurally validate newlycreated resources. Direct functional comparison support genotype to phenotype predictions.A prerequisite for a direct functional comparison is consistent annotation ofthe genetic elements with evidence statements. However, the standard struc-tured formats used by the public sequence databases to present genome an-notations provide limited support for data mining, hampering comparativeanalyses at large scale. To enable interoperability of genome annotations fordata mining application, we have developed the Genome Biology OntologyLanguage (GBOL) and associated infrastructure (GBOL stack), which is pre-sented in chapter 6. GBOL is provenance aware and thus provides a consistentrepresentation of functional genome annotations linked to the provenance.The provenance of a genome annotation describes the contextual details andderivation history of the process that resulted in the annotation. GBOL is mod-ular in design, extensible and linked to existing ontologies. The GBOL stackof supporting tools enforces consistency within and between the GBOL defi-nitions in the ontology. Based on GBOL, we developed the genome annotation pipeline SAPP (Se-mantic Annotation Platform with Provenance) presented in chapter 7. SAPPautomatically predicts, tracks and stores structural and functional annotationsand associated dataset- and element-wise provenance in a Linked Data for-mat, thereby enabling information mining and retrieval with Semantic Webtechnologies. This greatly reduces the administrative burden of handling mul-tiple analysis tools and versions thereof and facilitates multi-level large scalecomparative analysis. In turn this can be used to make genotype to phenotypepredictions. The development of GBOL and SAPP was done simultaneously. Duringthe development we realized that we had to constantly validated the data ex-ported to RDF to ensure coherence with the ontology. This was an extremelytime consuming process and prone to error, therefore we developed the Em-pusa code generator. Empusa is presented in chapter 8. SAPP has been successfully used to annotate 432 sequenced Pseudomonas strains and integrate the resulting annotation in a large scale functional com-parison using protein domains. This comparison is presented in chapter 9.Additionally, data from six metabolic models, nearly a thousand transcrip-tome measurements and four large scale transposon mutagenesis experimentswere integrated with the genome annotations. In this way, we linked gene es-sentiality, persistence and expression variability. This gave us insight into thediversity, versatility and evolutionary history of the Pseudomonas genus, whichcontains some important pathogens as well some useful species for bioengi-neering and bioremediation purposes. Genome annotation can be used to create GEM, which can be used to betterlink genotypes to phenotypes. Bio-Growmatch, presented in chapter 10, istool that can automatically suggest modification to improve a GEM based onphenotype data. Thereby integrating growth data into the complete processof modelling the metabolism of an organism. Chapter 11 presents a general discussion on how the chapters contributedthe central goal. After which I discuss provenance requirements for data reuseand integration. I further discuss how this can be used to further improveknowledge generation. The acquired knowledge could, in turn, be used to de-sign new experiments. The principles of the dry-lab cycle and how semantictechnologies can contribute to establish these cycles are discussed in chapter11. Finally a discussion is presented on how to apply these principles to im-prove the creation and usability of GEM’s.</p