Icosahedral dsDNA viruses utilize a single portal for genome delivery and packaging. The structural similarity of these portals in diverse viruses, as well as their invariable positioning at a unique icosahedral vertex, led to the consensus that a highly conserved vertex-portal architecture is essential for viral DNA translocations. By using high resolution imaging techniques we found the first exception to this paradigm. We demonstrate that genome delivery and packaging in the largest virus heretofore identified, the Acanthamoeba polyphaga Mimivirus, occur through two distinct portals that allow for highly effective translocation of the huge viral genome. Moreover, we show that the replication and assembly of the Mimivirus proceed exclusively in the host cytoplasm within highly ordered viral factories. These observations provide new insights on the enormous complexity that characterizes the infection cycles of large viruses.
While having already successfully helped at solving various bioinformatics tasks, Markov chains are still challenging state-of-the-art statistics when optimal choices of the order of the chain are sought. This problem is actually two-fold, involving the choice of a set of models under consideration as well as the definition of an optimality criterion. This talk will first introduce increasingly refined variants of Markov models that have been introduced in the past, which allow the modeler to draw a finer-grained compromise between model complexity and the amount of information carried by the data. Bayesian model selection is a privileged framework to achieve such a compromise, yet the choice of priors is hindering its practical implementation: principled solutions ensuring that models are compared on an equal footing are indeed still missing. A solution to this issue that is currently investigated in collaboration with the group of I. Grosse (Halle-Wittenberg university) will be presented.
The processing of the replication termination region is crucial for the segregation of circular bacterial chromosomes. It includes the removal of catenation links by TopoIV and the resolution of dimeric chromosomes to monomers by XerCD/dif recombination. The FtsK DNA-translocase controls these last steps of chromosome segregation. It is associated with the cell division septum and translocates sister chromosomes using the KOPS DNA motifs to orient its activity. KOPS are specific octameric motifs over-represented on the chromosome and whose orientation is highly biased from the replication origin to the opposite dif site, following the two chromosome replichores. FtsK is thought to control TopoIV activity and finally reaches the dif site to induce XerCD/dif recombination. We will report our recent findings of how these activities are coordinated during the cell cycle.
We have investigated the effects of important asymmetry of the two replichores in E. coli. We show that large chromosome inversions from the dif position disturbs the ongoing post-replicative events resulting in inhibition of both cell division and cell elongation. This is accompanied by alterations of the segregation pattern of loci located at the inversion endpoints, particularly of the new replichore junction. None of these defects is suppressed by restoration of termination of replication opposite oriC, indicating that they are more likely due to the asymmetry of replichore polarity than to asymmetric replication. Strikingly, DNA translocation by FtsK, which processes the terminal junction of the replichores during cell division, becomes essential in inversion-carrying strains. Inactivation of the FtsK translocation activity leads to aberrant cell morphology, strongly suggesting that it controls membrane synthesis at the division septum. Our results reveal that FtsK mediates a reciprocal control between processing of the replichore polarity junction and cell division.
We have used XerCD/dif recombination to probe the interaction of chromosome loci with translocating FtsK. We show that FtsK acts in a ~400 kb region around the dif position. Measurement of the relative times at which different loci segregate shows that this region is subjected to an elevated level of post-replicative cohesion between sister-chromosomes. Both cohesion and interaction with FtsK can be uncoupled from the zone where replication terminates. Cohesion is controlled by FtsK and TopoIV, strongly suggesting that these two proteins collaborate to the establishment and release of cohesion during segregation of this specific chromosome region.
An increasing amount of experimental data on global properties of genome organization across various species and phyla is becoming available, suggesting general principles as, e.g., scaling relationships or spatial regularities of gene distribution on DNA. A second level of information is accessible with gene regulatory networks, that control the space-time pattern of gene expression; here, similar (statistical) patterns of conserved regularities are observed. In many models of gene regulatory networks, however, the tight connection between genome architecture and -evolution on one side, and network structure and -dynamics on the other side is ignored, limiting their predictive power. I will introduce and discuss an artificial genome model that allows an integrative approach to model both levels of genome information. In particular, the following questions will be addressed: (1) Which types of network properties could be explained from combinatorial/statistical properties of genomes (random genome model), (2) how do they change in evolving genomes, in particular when (3) selective pressure is present, e.g. stabilizing selection for certain patterns of gene activity (phenotypes). I will also outline how particular questions, e.g. the evolution of a genetic switch for coordinated switching of functional groups of genes, can be addressed in this framework. Last, an outlook will be provided on questions and challenges posed by novel modes of gene regulation present in eukaryotic cells, the complexity of which by far exceeds what is found in Prokaryotes.
Systems biology exploits networks implying virtual and physical interactions of macromolecules. In a cell, significant interactions are plethoric and may never allow projection of a comprehensive model. This holds true for DNA- and RNA-protein interactions controlling gene expression: if there are up to 500.000 proteins (encoded in 20-30.000 genomic domains), as many mRNAs must be controlled in the cellular space and in time. To simplify matters, one might single out specific control networks at individual levels of the Cascade of Regulation (1), excluding NA-interactions with cellular structure (e.g. nuclear matrix, cytoskeleton), or other identifiable secondary networks. The Genon concept (2) allows identification of specific cis-programs at DNA and RNA level controlling gene expression under the influence of trans-acting factors. The basic unit-program is the cis-genon an ensemble of signals added and super-imposed onto the coding sequence of an mRNA, forming an mRNA-protein complex (mRNP) under the influence of factors from the transgenon. Systematically going through all levels of genomic and cellular organisation from the DNA down to individual proteins a Holistic Model of genome and gene expression may be proposed. - Surprisingly, one of the simplest pertinent networks that might be analysed is constituted by the genome itself. The - largely forgotten - phenomenon of ectopic pairing shows that DNA is organised into a 3D network (cf. 3). Indeed, in the light microscope cables can be observed, at interband level, between the 4 polytene chromosomes of Drosophila which link specific genomic domains with each other: this network holds every part of the genome in a specific 3D position. It was established by 1948 that these cables are heritable and hence genetically determined. Recent data imply that such inter-chromosomal links are present in normal cells as well and important in gene regulation. According to actual genomics data, the 4 drosophila chromosomes are subdivided into about 5000 observable bands, representing genomic domains as units of transcription and meiotic recombination. About 200 ectopic cables can be directly observed and mapped; for the entire fly genome we may thus assume the actual number to about 500 ectopic cables. In analogy, the human genome might thus contain about 2500 such links, assuming 25.000 genomic domains divided among the 46 chromosomes. It is obvious that this network must be based on accessible euchromatin. Since the organisation of hetero- versus euchromatin changes in cell differentiation, this 3D network must be variable, most likely controlled by trans-acting factors acting at DNA/chromatin level. This might thus provide a greatly simplified model to apply systems biology and information theoretic analysis at DNA level, the first step that controls gene expression. - Within the Holistic model one might expand this analysis to downstream levels of regulation: (1) the DNP networks of chromatin organisation of individual genomic domains controlled by the protogenon (cf. 2), (2) the 3D pattern of pre-mRNAs at individual steps of processing, conditioned by their pre-genon and pre-transgenon, down to (3) the 3D structure of an mRNA forming an mRNP complex by interaction of factors from the transgenon with its individual genon. These mRNPs, in turn, are basic elements of the entire network of the ensemble of mRNPs operating in a cell under the influence of trans-acting factors. Possibly, the systems approach may allow one day to integrate all these networks and make feasible their analysis by information theory.
(1) Klaus Scherrer (1980) In Kolodny (ed.), Eukaryotic Gene Regulation. CRC press Inc., Boca Raton, Florida, Vol. 1, pp. 57-129.(2) Klaus Scherrer and Jürgen Jost (2007) Mol Syst Biol. 3:87. Epub 2007 Mar 13. (EMBO and Nature Publishing Group.), (doi:10.1038/msb4100123) (3) Scherrer, K. (1989) Bioscience Reports, 9, 157-188
In search of an efficient method for gene regulatory network inference we have recently proposed a new framework for the systematic representation of a genome using probability profiles. These profiles are derived from empirical and functional genomcs data, model predictions, and the genome sequence itself. Some insights into the applicability and the mathematical nature of such a representation are discussed.
Game theory can be considered as an extension of the theory of optimization. In biological appications, it can describe situations where two or more organisms (or more generally, units of replication) tend to optimize their properties in an interdependent way. Thus, the outcome of the strategy adopted by one species depends on the strategy of the other species. In this talk, the use of evolutionary game theory for analysing intracellular (e.g. metabolic) networks is outlined. The presentation is illustrated by a number of instructive examples such as the competition between micro-organisms using different metabolic pathways for synthesizing ATP and the secretion of extracellular enzymes or toxins. These examples show that, due to conflicts of interest, the global optimum (in the sense of being the best solution for the entire system) is not always obtained. For example, some yeast species use metabolic pathways that waste the nutrient. A particularly interesting game is the Prisoner’s Dilemma, in which cooperation is unstable due to the tendency of the “players” to increase their fitness, which leads to the counter-intuitive result of a decrease in the fitness of all “players”. Such situations of “arms race” are indeed found in biology, even at the level of biochemical pathways. In other situations or for other parameter values, also other types of game can be relevant, such as the snowdrift, rock-scissors-paper or harmony games. Since game theory takes into account the systemic interactions and can describe emergent properties, it is very promising for Systems Biology. It also has important applications in biotechnology. For example, it can be used for analysing whether exoenzyme producing strains will be outcompeted by cheater mutants.
Given a growth medium, only certain sets of reactions (defining genotypes) will be able to produce pre-specified target biomass components. We investigate this question insilico using reactions in KEGG database and in an E. coli metabolism model. By sampling the space of all possible sets of reactions, we determine the characteristics of genotypes which grow on different minimal media. In particular we find that mutational robustness has a narrow distribution, and that environmental robustness is only weakly associated with mutational robustness.
This work has been done in collaboration with Jürgen Jost, Olivier C. Martin and Andreas Wagner.
Halophilic archaea share both a common environment environment and a close phylogenetic relation. However, they differ significantly in their nutritional demands and metabolic capabilities. This diversity, together with the marked differences between archaea and the better studied domains: Bacteria and Eukarya, calls for novel approaches to achieve an overview of haloarchaeal metabolism.
The integration of experimental information from the literature, predictions from genomic analysis and comprehensive databases like KEGG have yielded accurate large-scale metabolic networks for two halophilic archaea: Halobacterium salinarum and Natronomonas pharaonis. Using Flux Balance Analysis together with a dynamic layer, these networks have been used as the basis to design and interpret experiments. Such studies have proved to be an effective way to link the physiology of these organisms, severely constrained by a harsh habitat, with its underlying molecular mechanisms. Altogether, the systems approach offers a systematic and comprehensive way to better understand the adaptive strategies adopted by halophilic archaea.
Lots of individual site-specific histone modifications are described in the literature. Tough, the mechanism and functional role of chromatin regulation is substantially less well understood. Some of the open fundamental questions are: Is there a complex chromatin code? How much information can chromatin hold on top of the genetic information? How does transcriptional regulation by chromatin and transcription factors conceptually differ?
To address these questions, we studied chromatin regulation in the light of evolution. We, therefore, considered the phylogenetic distribution of functional domains involved in chromatin regulation. We could derive the hypothesis that the functional role of chromatin is general regulation and specification of genomic states that correspond to phenotypic states. Variants of chromosomal architectural proteins serve this function in all domains of life. Only Archaea and Eukaryotes seem to modify their DNA associated proteins to change their chemical properties and affinity to DNA in response to differential regulation. The major innovation in chromatin regulation, however, is the appearance of 8 structurally distinct domain families that bind specific modifications. With the emergence of these "reader" molecules, modifications start to serve as signal in an information theoretic sense.
In my talk I will present our hypothesis on chromatin evolution and discuss the conceptual and functional consequences that can serve as a framework to pose and answer biological questions and to explain regulatory phenomena that have already been observed.
Scientific Organizers
Jürgen Jost
Max-Planck-Institut für Mathematik in den Naturwissenschaften
Victor Norris
Université de Rouen
Administrative Contact
Antje Vandenberg
Max-Planck-Institut für Mathematik in den Naturwissenschaften
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