Roles of different forms of lipopolysaccharides in Ralstonia solanacearum pathogenesis.

Lipopolysaccharides (LPS) are critical components for the fitness of most gram-negative bacteria. Ralstonia solanacearum causes a deadly wilting disease in many crops; however, the pathogenic roles of different forms of LPS and their pathways of biogenesis remain unknown. By screening for phage-resistant mutants of R. solanacearum Pss4, whose genome sequence is unavailable, mutants with various types of structural defects in LPS were isolated. Pathogenesis assays of the mutants revealed that production of rough LPS (R-LPS), which does not contain O-polysaccharides, was sufficient to cause necrosis on Nicotiana benthamiana and induce the hypersensitive response on N. tabacum. However, biosynthesis of smooth LPS (S-LPS), which contains O-polysaccharides, was required for bacterial proliferation at infection sites on N. benthamiana leaves and for proliferation and causing wilt on tomato. Complementation tests confirmed the involvement of the previously unidentified cluster RSc2201 to RSc2204 in the formation of R. solanacearum S-LPS. With these data and the availability of the annotated genomic sequence of strain GMI1000, certain loci involved in key steps of R. solanacearum LPS biosynthesis were identified. The strategy of this work could be useful for similar studies in other bacteria without available genome sequences.

RNA with iron(II) as a cofactor catalyses electron transfer.

Mg(2+) is essential for RNA folding and catalysis. However, for the first 1.5 billion years of life on Earth RNA inhabited an anoxic Earth with abundant and benign Fe(2+). We hypothesize that Fe(2+) was an RNA cofactor when iron was abundant, and was substantially replaced by Mg(2+) during a period known as the 'great oxidation', brought on by photosynthesis. Here, we demonstrate that reversing this putative metal substitution in an anoxic environment, by removing Mg(2+) and replacing it with Fe(2+), expands the catalytic repertoire of RNA. Fe(2+) can confer on some RNAs a previously uncharacterized ability to catalyse single-electron transfer. We propose that RNA function, in analogy with protein function, can be understood fully only in the context of association with a range of possible metals. The catalysis of electron transfer, requisite for metabolic activity, may have been attenuated in RNA by photosynthesis and the rise of O2.

Estimation of dynamic flux profiles from metabolic time series data.

BACKGROUND: Advances in modern high-throughput techniques of molecular biology have enabled top-down approaches for the estimation of parameter values in metabolic systems, based on time series data. Special among them is the recent method of dynamic flux estimation (DFE), which uses such data not only for parameter estimation but also for the identification of functional forms of the processes governing a metabolic system. DFE furthermore provides diagnostic tools for the evaluation of model validity and of the quality of a model fit beyond residual errors. Unfortunately, DFE works only when the data are more or less complete and the system contains as many independent fluxes as metabolites. These drawbacks may be ameliorated with other types of estimation and information. However, such supplementations incur their own limitations. In particular, assumptions must be made regarding the functional forms of some processes and detailed kinetic information must be available, in addition to the time series data. RESULTS: The authors propose here a systematic approach that supplements DFE and overcomes some of its shortcomings. Like DFE, the approach is model-free and requires only minimal assumptions. If sufficient time series data are available, the approach allows the determination of a subset of fluxes that enables the subsequent applicability of DFE to the rest of the flux system. The authors demonstrate the procedure with three artificial pathway systems exhibiting distinct characteristics and with actual data of the trehalose pathway in Saccharomyces cerevisiae. CONCLUSIONS: The results demonstrate that the proposed method successfully complements DFE under various situations and without a priori assumptions regarding the model representation. The proposed method also permits an examination of whether at all, to what degree, or within what range the available time series data can be validly represented in a particular functional format of a flux within a pathway system. Based on these results, further experiments may be designed to generate data points that genuinely add new information to the structure identification and parameter estimation tasks at hand.

Recent developments in parameter estimation and structure identification of biochemical and genomic systems.

The organization, regulation and dynamical responses of biological systems are in many cases too complex to allow intuitive predictions and require the support of mathematical modeling for quantitative assessments and a reliable understanding of system functioning. All steps of constructing mathematical models for biological systems are challenging, but arguably the most difficult task among them is the estimation of model parameters and the identification of the structure and regulation of the underlying biological networks. Recent advancements in modern high-throughput techniques have been allowing the generation of time series data that characterize the dynamics of genomic, proteomic, metabolic, and physiological responses and enable us, at least in principle, to tackle estimation and identification tasks using 'top-down' or 'inverse' approaches. While the rewards of a successful inverse estimation or identification are great, the process of extracting structural and regulatory information is technically difficult. The challenges can generally be categorized into four areas, namely, issues related to the data, the model, the mathematical structure of the system, and the optimization and support algorithms. Many recent articles have addressed inverse problems within the modeling framework of Biochemical Systems Theory (BST). BST was chosen for these tasks because of its unique structural flexibility and the fact that the structure and regulation of a biological system are mapped essentially one-to-one onto the parameters of the describing model. The proposed methods mainly focused on various optimization algorithms, but also on support techniques, including methods for circumventing the time consuming numerical integration of systems of differential equations, smoothing overly noisy data, estimating slopes of time series, reducing the complexity of the inference task, and constraining the parameter search space. Other methods targeted issues of data preprocessing, detection and amelioration of model redundancy, and model-free or model-based structure identification. The total number of proposed methods and their applications has by now exceeded one hundred, which makes it difficult for the newcomer, as well as the expert, to gain a comprehensive overview of available algorithmic options and limitations. To facilitate the entry into the field of inverse modeling within BST and related modeling areas, the article presented here reviews the field and proposes an operational 'work-flow' that guides the user through the estimation process, identifies possibly problematic steps, and suggests corresponding solutions based on the specific characteristics of the various available algorithms. The article concludes with a discussion of the present state of the art and with a description of open questions.

System estimation from metabolic time-series data.

MOTIVATION: At the center of computational systems biology are mathematical models that capture the dynamics of biological systems and offer novel insights. The bottleneck in the construction of these models is presently the identification of model parameters that make the model consistent with observed data. Dynamic flux estimation (DFE) is a novel methodological framework for estimating parameters for models of metabolic systems from time-series data. DFE consists of two distinct phases, an entirely model-free and assumption-free data analysis and a model-based mathematical characterization of process representations. The model-free phase reveals inconsistencies within the data, and between data and the alleged system topology, while the model-based phase allows quantitative diagnostics of whether--or to what degree--the assumed mathematical formulations are appropriate or in need of improvement. Hallmarks of DFE are the facility to: diagnose data and model consistency; circumvent undue compensation of errors; determine functional representations of fluxes uncontaminated by errors in other fluxes and pinpoint sources of remaining errors. Our results suggest that the proposed approach is more effective and robust than presently available methods for deriving metabolic models from time-series data. Its avoidance of error compensation among process descriptions promises significantly improved extrapolability toward new data or experimental conditions.

Transposon mutagenesis reveals differential pathogenesis of Ralstonia solanacearum on tomato and Arabidopsis.

Ralstonia solanacearum causes a deadly wilting disease on a wide range of crops. To elucidate pathogenesis of this bacterium in different host plants, we set out to identify R. solanacearum genes involved in pathogenesis by screening random transposon insertion mutants of a highly virulent strain, Pss190, on tomato and Arabidopsis thaliana. Mutants exhibiting various decreased virulence levels on these two hosts were identified. Sequence analysis showed that most, but not all, of the identified pathogenesis genes are conserved among distinct R. solanacearum strains. A few of the disrupted loci were not reported previously as being involved in R. solanacearum pathogenesis. Notably, a group of mutants exhibited differential pathogenesis on tomato and Arabidopsis. These results were confirmed by characterizing allelic mutants in one other R. solanacearum strain of the same phylotype. The significantly decreased mutants' colonization in Arabidopsis was found to be correlated with differential pathogenesis on these two plants. Differential requirement of virulence genes suggests adaptation of this bacterium in different host environments. Together, this study reveals commonalities and differences of R. solanacearum pathogenesis on single solanaceous and nonsolanaceous hosts, and provides important new insights into interactions between R. solanacearum and different host plants.

Parameter optimization in S-system models.

BACKGROUND: The inverse problem of identifying the topology of biological networks from their time series responses is a cornerstone challenge in systems biology. We tackle this challenge here through the parameterization of S-system models. It was previously shown that parameter identification can be performed as an optimization based on the decoupling of the differential S-system equations, which results in a set of algebraic equations. RESULTS: A novel parameterization solution is proposed for the identification of S-system models from time series when no information about the network topology is known. The method is based on eigenvector optimization of a matrix formed from multiple regression equations of the linearized decoupled S-system. Furthermore, the algorithm is extended to the optimization of network topologies with constraints on metabolites and fluxes. These constraints rejoin the system in cases where it had been fragmented by decoupling. We demonstrate with synthetic time series why the algorithm can be expected to converge in most cases. CONCLUSION: A procedure was developed that facilitates automated reverse engineering tasks for biological networks using S-systems. The proposed method of eigenvector optimization constitutes an advancement over S-system parameter identification from time series using a recent method called Alternating Regression. The proposed method overcomes convergence issues encountered in alternate regression by identifying nonlinear constraints that restrict the search space to computationally feasible solutions. Because the parameter identification is still performed for each metabolite separately, the modularity and linear time characteristics of the alternating regression method are preserved. Simulation studies illustrate how the proposed algorithm identifies the correct network topology out of a collection of models which all fit the dynamical time series essentially equally well.

Biological systems modeling and analysis: a biomolecular technique of the twenty-first century.

It is proposed that computational systems biology should be considered a biomolecular technique of the twenty-first century, because it complements experimental biology and bioinformatics in unique ways that will eventually lead to insights and a depth of understanding not achievable without systems approaches. This article begins with a summary of traditional and novel modeling techniques. In the second part, it proposes concept map modeling as a useful link between experimental biology and biological systems modeling and analysis. Concept map modeling requires the collaboration between biologist and modeler. The biologist designs a regulated connectivity diagram of processes comprising a biological system and also provides semi-quantitative information on stimuli and measured or expected responses of the system. The modeler converts this information through methods of forward and inverse modeling into a mathematical construct that can be used for simulations and to generate and test new hypotheses. The biologist and the modeler collaboratively interpret the results and devise improved concept maps. The third part of the article describes software, BST-Box, supporting the various modeling activities.

Parameter estimation in biochemical systems models with alternating regression.

BACKGROUND: The estimation of parameter values continues to be the bottleneck of the computational analysis of biological systems. It is therefore necessary to develop improved methods that are effective, fast, and scalable. RESULTS: We show here that alternating regression (AR), applied to S-system models and combined with methods for decoupling systems of differential equations, provides a fast new tool for identifying parameter values from time series data. The key feature of AR is that it dissects the nonlinear inverse problem of estimating parameter values into iterative steps of linear regression. We show with several artificial examples that the method works well in many cases. In cases of no convergence, it is feasible to dedicate some computational effort to identifying suitable start values and search settings, because the method is fast in comparison to conventional methods that the search for suitable initial values is easily recouped. Because parameter estimation and the identification of system structure are closely related in S-system modeling, the AR method is beneficial for the latter as well. Specifically, we show with an example from the literature that AR is three to five orders of magnitudes faster than direct structure identifications in systems of nonlinear differential equations. CONCLUSION: Alternating regression provides a strategy for the estimation of parameter values and the identification of structure and regulation in S-systems that is genuinely different from all existing methods. Alternating regression is usually very fast, but its convergence patterns are complex and will require further investigation. In cases where convergence is an issue, the enormous speed of the method renders it feasible to select several initial guesses and search settings as an effective countermeasure.

The genomic sequence of the accidental pathogen Legionella pneumophila.

We present the genomic sequence of Legionella pneumophila, the bacterial agent of Legionnaires' disease, a potentially fatal pneumonia acquired from aerosolized contaminated fresh water. The genome includes a 45-kilobase pair element that can exist in chromosomal and episomal forms, selective expansions of important gene families, genes for unexpected metabolic pathways, and previously unknown candidate virulence determinants. We highlight the genes that may account for Legionella's ability to survive in protozoa, mammalian macrophages, and inhospitable environmental niches and that may define new therapeutic targets.

DNA sequence-specific recognition of peptides incorporating the HPRK and polyamide motifs.

Three peptide amides, HPRK(Py)(4)HPRK-NH(2) (PyH-12), HPRK(Py)(3)HPRK-NH(2) (PyH-11) and HPRK(Py)(2)HPRK-NH(2) (PyH-10), incorporating two HPRK motifs and various 4-amino-1-methylpyrrole-2-carboxylic acid residues (Py) were synthesized by solid-phase peptide methodology. The binding of these three peptides to a 5'-32P-labeled 158-mer DNA duplex (Watson fragment) and to a 5'-32P-labeled 135-mer DNA duplex (complementary Crick fragment) was investigated by quantitative DNase I footprinting. On the 158-mer Watson strand, the most distinctive DNase I blockages seen with all three peptides occur around positions 105-112 and 76-79, corresponding to the sequences 5'-GAGAAAAT-3' and 5'-CGGT-3', respectively. However, on the complementary Crick strand, only PyH-12 strongly discriminates the 5'-TTT-3' site around positions 108-110 whereas both PyH-11 and PyH-10 have moderate binding around positions 102-112 comprising the sequence 5'-ATTTTCTCCTT-3'. Possible bidentate and single interactions of the side-chain functions and alpha-amino protons of the peptides with DNA bases are discussed.

Preferential binding to DNA sequences of peptides related to a novel XPRK motif.

Two dodecapeptide amines: (WPRK)(3)NH(2)[WR-12] and (YPRK)(3)NH(2)[YR-12], and a 30-mer polypeptide amide (SP-30) were synthesized by solid-phase peptide methodology. DNase I footprinting studies on a 117-mer DNA showed that WR-12 and YR-12 bind selectively to DNA sequences in a manner similar to SP-30 which has a repeating SPK(R)K sequence. The most distinctive blockages seen with all three peptides occur at positions 26-30, 21-24 and 38-45 around sequences 5'-GAATT-3', 5'-TAAT-3' and 5'-AAAACGAC-3', respectively. However, it appears that YR-12 is better able to extend its recognition site to include CG pairs than is SP-30. At low concentrations YR-12 was able to induce enhanced rates of DNase I cleavage at regions surrounding some of its binding sites. To obtain further quantitative data supplementary to the footprinting work, equilibrium binding experiments were performed in which the binding of the two peptides to six decanucleotide duplexes was compared. Scatchard analyses indicated that WR-12 may be more selective for oligomers containing runs of consecutive purines or pyrimidines. On the other hand, YR-12 binds better to d(CTTAGACGTC)- d(GACGTCTAAG) than to the other oligomer duplexes, denoting selectivity for evenly distributed C/G and A/T sequences.