Evolution and diversity of secretome genes in the apicomplexan parasite Theileria annulata

<b>BACKGROUND</b>: Little is known about how apicomplexan parasites have evolved to infect different host species and cell types. Theileria annulata and Theileria parva invade and transform bovine leukocytes but each species favours a different host cell lineage. Parasite-encoded proteins secreted from the intracellular macroschizont stage within the leukocyte represent a critical interface between host and pathogen systems. Genome sequencing has revealed that several Theileria-specific gene families encoding secreted proteins are positively selected at the inter-species level, indicating diversification between the species. We extend this analysis to the intra-species level, focusing on allelic diversity of two major secretome families. These families represent a well-characterised group of genes implicated in control of the host cell phenotype and a gene family of unknown function. To gain further insight into their evolution and function, this study investigates whether representative genes of these two families are diversifying or constrained within the T. annulata population. <b>RESULTS</b>: Strong evidence is provided that the sub-telomerically encoded SVSP family and the host-nucleus targeted TashAT family have evolved under contrasting pressures within natural T. annulata populations. SVSP genes were found to possess atypical codon usage and be evolving neutrally, with high levels of nucleotide substitutions and multiple indels. No evidence of geographical sub-structuring of allelic sequences was found. In contrast, TashAT family genes, implicated in control of host cell gene expression, are strongly conserved at the protein level and geographically sub-structured allelic sequences were identified among Tunisian and Turkish isolates. Although different copy numbers of DNA binding motifs were identified in alleles of TashAT proteins, motif periodicity was strongly maintained, implying conserved functional activity of these sites. <b>CONCLUSIONS</b>: This analysis provides evidence that two distinct secretome genes families have evolved under contrasting selective pressures. The data supports current hypotheses regarding the biological role of TashAT family proteins in the management of host cell phenotype that may have evolved to allow adaptation of T. annulata to a specific host cell lineage. We provide new evidence of extensive allelic diversity in representative members of the enigmatic SVSP gene family, which supports a putative role for the encoded products in subversion of the host immune response

Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience

This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power law scaling, self-similarity, and self-organized criticality. As regards criticality, I will document that it has become a pivotal reference point in Neurodynamics. Furthermore, I will emphasize the not yet fully appreciated significance of allometric control processes. For dynamic fractals, I will assemble reasons for attributing to them the capacity to adapt task execution to contextual changes across a range of scales. The final Section consists of general reflections on the implications of the reviewed data, and identifies what appear to be issues of fundamental importance for future research in the rapidly evolving topic of this review

Topology dependence of PPM-based Internet Protocol traceback schemes

Multiple schemes that utilize probabilistic packet marking (PPM) have been proposed to deal with Distributed Denial of Service (DDoS) attacks by reconstructing their attack graphs and identifying the attack sources. In the first part of this dissertation, we present our contribution to the family of PPM-based schemes for Internet Protocol (IP) traceback. Our proposed approach, Prediction-Based Scheme (PBS), consists of marking and traceback algorithms that reduce scheme convergence times by dealing with the problems of data loss and incomplete attack graphs exhibited by previous PPM-based schemes. Compared to previous PPM-based schemes, the PBS marking algorithm ensures that traceback is possible with about 54% as many total network packets, while the traceback algorithm takes about 33% as many marked packets for complete attack path construction. In the second part of this dissertation, we tackle the problem of scheme evaluation and comparison across discrepant network topologies. Previous research in this area has overlooked the influence of network topology on scheme performance and often utilized disparate and simplistic network abstractions to evaluate and compare these schemes. Our approach to this problem involves the evaluation of selected PPM-based schemes across a set of 60 Internet-like topologies and the adaptation of the network motif approach to provide a common ground for comparing the schemes\u27 performances in different network topologies. This approach allows us to determine the level of structural similarity between network topologies and consequently enables the comparison of scheme performance even when the schemes are implemented on different topologies. Furthermore, we identify three network-dependent factors that affect different PPM-based schemes uniquely causing a variation in, and discrepancy between, scheme performance from one network to another. Results indicate that scheme performance is dependent on the network upon which it is implemented, i.e. the value of the PPM-based schemes\u27 convergence times and their rankings vary depending on the underlying network topology. We show how the identified network factors contribute, individually and collectively, to the scheme performance in large-scale networks. Additionally, we identify five superfamilies from the 60 considered networks and find that networks within a superfamily also exhibit similar PPM-based scheme performance. To complement our results, we present an analytical model showing a link between scheme performance in any superfamily, and the motifs exhibited by the networks in that superfamily. Our work highlights a need for multiple network evaluation of network protocols. To this end, we demonstrate a method of identifying structurally similar network topologies among which protocol performance is potentially comparable. Our work also presents an effective way of comparing general network protocol performance in which the protocol is evaluated on specific representative networks instead of an entire set of networks

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

Virus-Host Coevolution: Common Patterns of Nucleotide Motif Usage in Flaviviridae and Their Hosts

Virus-host biological interaction is a continuous coevolutionary process involving both host immune system and viral escape mechanisms. Flaviviridae family is composed of fast evolving RNA viruses that infects vertebrate (mammals and birds) and/or invertebrate (ticks and mosquitoes) organisms. These host groups are very distinct life forms separated by a long evolutionary time, so lineage-specific anti-viral mechanisms are likely to have evolved. Flaviviridae viruses which infect a single host lineage would be subjected to specific host-induced pressures and, therefore, selected by them. In this work we compare the genomic evolutionary patterns of Flaviviridae viruses and their hosts in an attempt to uncover coevolutionary processes inducing common features in such disparate groups. Especially, we have analyzed dinucleotide and codon usage patterns in the coding regions of vertebrate and invertebrate organisms as well as in Flaviviridae viruses which specifically infect one or both host types. The two host groups possess very distinctive dinucleotide and codon usage patterns. A pronounced CpG under-representation was found in the vertebrate group, possibly induced by the methylation-deamination process, as well as a prominent TpA decrease. The invertebrate group displayed only a TpA frequency reduction bias. Flaviviridae viruses mimicked host nucleotide motif usage in a host-specific manner. Vertebrate-infecting viruses possessed under-representation of CpG and TpA, and insect-only viruses displayed only a TpA under-representation bias. Single-host Flaviviridae members which persistently infect mammals or insect hosts (Hepacivirus and insect-only Flavivirus, respectively) were found to posses a codon usage profile more similar to that of their hosts than to related Flaviviridae. We demonstrated that vertebrates and mosquitoes genomes are under very distinct lineage-specific constraints, and Flaviviridae viruses which specifically infect these lineages appear to be subject to the same evolutionary pressures that shaped their host coding regions, evidencing the lineage-specific coevolutionary processes between the viral and host groups