Meningococcal ACWYX Conjugate Vaccine in 2-to-29-Year-Olds in Mali and Gambia.

BACKGROUND: An effective, affordable, multivalent meningococcal conjugate vaccine is needed to prevent epidemic meningitis in the African meningitis belt. Data on the safety and immunogenicity of NmCV-5, a pentavalent vaccine targeting the A, C, W, Y, and X serogroups, have been limited. METHODS: We conducted a phase 3, noninferiority trial involving healthy 2-to-29-year-olds in Mali and Gambia. Participants were randomly assigned in a 2:1 ratio to receive a single intramuscular dose of NmCV-5 or the quadrivalent vaccine MenACWY-D. Immunogenicity was assessed at day 28. The noninferiority of NmCV-5 to MenACWY-D was assessed on the basis of the difference in the percentage of participants with a seroresponse (defined as prespecified changes in titer; margin, lower limit of the 96% confidence interval [CI] above -10 percentage points) or geometric mean titer (GMT) ratios (margin, lower limit of the 98.98% CI >0.5). Serogroup X responses in the NmCV-5 group were compared with the lowest response among the MenACWY-D serogroups. Safety was also assessed. RESULTS: A total of 1800 participants received NmCV-5 or MenACWY-D. In the NmCV-5 group, the percentage of participants with a seroresponse ranged from 70.5% (95% CI, 67.8 to 73.2) for serogroup A to 98.5% (95% CI, 97.6 to 99.2) for serogroup W; the percentage with a serogroup X response was 97.2% (95% CI, 96.0 to 98.1). The overall difference between the two vaccines in seroresponse for the four shared serogroups ranged from 1.2 percentage points (96% CI, -0.3 to 3.1) for serogroup W to 20.5 percentage points (96% CI, 15.4 to 25.6) for serogroup A. The overall GMT ratios for the four shared serogroups ranged from 1.7 (98.98% CI, 1.5 to 1.9) for serogroup A to 2.8 (98.98% CI, 2.3 to 3.5) for serogroup C. The serogroup X component of the NmCV-5 vaccine generated seroresponses and GMTs that met the prespecified noninferiority criteria. The incidence of systemic adverse events was similar in the two groups (11.1% in the NmCV-5 group and 9.2% in the MenACWY-D group). CONCLUSIONS: For all four serotypes in common with the MenACWY-D vaccine, the NmCV-5 vaccine elicited immune responses that were noninferior to those elicited by the MenACWY-D vaccine. NmCV-5 also elicited immune responses to serogroup X. No safety concerns were evident. (Funded by the U.K. Foreign, Commonwealth, and Development Office and others; ClinicalTrials.gov number, NCT03964012.).

ProbAnnoWeb and ProbAnnoPy: probabilistic annotation and gap-filling of metabolic reconstructions.

Summary: Gap-filling is a necessary step to produce quality genome-scale metabolic reconstructions capable of flux-balance simulation. Most available gap-filling tools use an organism-agnostic approach, where reactions are selected from a database to fill gaps without consideration of the target organism. Conversely, our likelihood based gap-filling with probabilistic annotations selects candidate reactions based on a likelihood score derived specifically from the target organism's genome. Here, we present two new implementations of probabilistic annotation and likelihood based gap-filling: a web service called ProbAnnoWeb, and a standalone python package called ProbAnnoPy. Availability and implementation: Our tools are available as a web service with no installation needed (ProbAnnoWeb) at probannoweb.systemsbiology.net, and as a local python package implementation (ProbAnnoPy) at github.com/PriceLab/probannopy. Contact: evangelos.simeonidis@systemsbiology.org or nathan.price@systemsbiology.org. Supplementary information: Supplementary data are available at Bioinformatics online.

Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast.

Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAM's enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.

The clinical impact of platelet-rich plasma on tendinopathy compared to placebo or dry needling injections: A meta-analysis.

OBJECTIVE: The purpose of this meta-analysis was to compare the impact of platelet-rich plasma with that of placebo or dry needling injections on tendinopathy. METHODS: The databases of PubMed, CENTRAL, Scopus, Web of Science, and trial registries, reference lists, and conference abstract books were searched up to December 2014. Adults with tendinopathy in randomized controlled trials were enrolled. The trials compared effect of platelet-rich plasma with that of placebo or dry needling. We used subgroup analysis linked to the anatomical location of the tendinopathy. The primary outcome was pain intensity at two or three and six months after intervention. The secondary outcome was functional disability at three months after treatment. RESULTS: Five trials were included. There was a statistically significant difference in favor of the platelet-rich plasma intervention at the second primary outcome time point (SMD -0.48, 95%CIs -0.86 to -0.10, I(2) = 0%, p = 0.01) and at the secondary outcome time point (SMD -0.47, 95%CIs -0.85 to -0.09, I(2) = 0%, p=0.01). CONCLUSIONS: Platelet-rich plasma did not provide significantly greater clinical benefit versus placebo or dry needling for the treatment of tendinopathy at a six-month follow-up. However, there was a marginal clinical difference in favor of platelet-rich plasma injections on rotator cuff tendinopathy.

Genome-scale modeling for metabolic engineering.

We focus on the application of constraint-based methodologies and, more specifically, flux balance analysis in the field of metabolic engineering, and enumerate recent developments and successes of the field. We also review computational frameworks that have been developed with the express purpose of automatically selecting optimal gene deletions for achieving improved production of a chemical of interest. The application of flux balance analysis methods in rational metabolic engineering requires a metabolic network reconstruction and a corresponding in silico metabolic model for the microorganism in question. For this reason, we additionally present a brief overview of automated reconstruction techniques. Finally, we emphasize the importance of integrating metabolic networks with regulatory information-an area which we expect will become increasingly important for metabolic engineering-and present recent developments in the field of metabolic and regulatory integration.

Macromolecular networks and intelligence in microorganisms.

Living organisms persist by virtue of complex interactions among many components organized into dynamic, environment-responsive networks that span multiple scales and dimensions. Biological networks constitute a type of information and communication technology (ICT): they receive information from the outside and inside of cells, integrate and interpret this information, and then activate a response. Biological networks enable molecules within cells, and even cells themselves, to communicate with each other and their environment. We have become accustomed to associating brain activity - particularly activity of the human brain - with a phenomenon we call "intelligence." Yet, four billion years of evolution could have selected networks with topologies and dynamics that confer traits analogous to this intelligence, even though they were outside the intercellular networks of the brain. Here, we explore how macromolecular networks in microbes confer intelligent characteristics, such as memory, anticipation, adaptation and reflection and we review current understanding of how network organization reflects the type of intelligence required for the environments in which they were selected. We propose that, if we were to leave terms such as "human" and "brain" out of the defining features of "intelligence," all forms of life - from microbes to humans - exhibit some or all characteristics consistent with "intelligence." We then review advances in genome-wide data production and analysis, especially in microbes, that provide a lens into microbial intelligence and propose how the insights derived from quantitatively characterizing biomolecular networks may enable synthetic biologists to create intelligent molecular networks for biotechnology, possibly generating new forms of intelligence, first in silico and then in vivo.

MediaDB: a database of microbial growth conditions in defined media.

Isolating pure microbial cultures and cultivating them in the laboratory on defined media is used to more fully characterize the metabolism and physiology of organisms. However, identifying an appropriate growth medium for a novel isolate remains a challenging task. Even organisms with sequenced and annotated genomes can be difficult to grow, despite our ability to build genome-scale metabolic networks that connect genomic data with metabolic function. The scientific literature is scattered with information about defined growth media used successfully for cultivating a wide variety of organisms, but to date there exists no centralized repository to inform efforts to cultivate less characterized organisms by bridging the gap between genomic data and compound composition for growth media. Here we present MediaDB, a manually curated database of defined media that have been used for cultivating organisms with sequenced genomes, with an emphasis on organisms with metabolic network models. The database is accessible online, can be queried by keyword searches or downloaded in its entirety, and can generate exportable individual media formulation files. The data assembled in MediaDB facilitate comparative studies of organism growth media, serve as a starting point for formulating novel growth media, and contribute to formulating media for in silico investigation of metabolic networks. MediaDB is freely available for public use at https://mediadb.systemsbiology.net.

A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes.

We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a "cycle of knowledge" strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought.

A community-driven global reconstruction of human metabolism.

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.

A guide to integrating transcriptional regulatory and metabolic networks using PROM (probabilistic regulation of metabolism).

The integration of transcriptional regulatory and metabolic networks is a crucial step in the process of predicting metabolic behaviors that emerge from either genetic or environmental changes. Here, we present a guide to PROM (probabilistic regulation of metabolism), an automated method for the construction and simulation of integrated metabolic and transcriptional regulatory networks that enables large-scale phenotypic predictions for a wide range of model organisms.

Computing life: Add logos to biology and bios to physics.

This paper discusses the interrelations between physics and biology. Particularly, we analyse the approaches for reconstructing the emergent properties of physical or biological systems. We propose approaches to scale emergence according to the degree of state-dependency of the system's component properties. Since the component properties of biological systems are state-dependent to a high extent, biological emergence should be considered as very strong emergence - i.e. its reconstruction would require a lot of information about state-dependency of its component properties. However, due to its complexity and volume, this information cannot be handled in the naked human brain, or on the back of an envelope. To solve this problem, biological emergence can be reconstructed in silico based on experimentally determined rate laws and parameter values of the living cell. According to some rough calculations, the silicon human might comprise the mathematical descriptions of around 10(5) interactions. This is not a small number, but taking into account the exponentially increase of computational power, it should not prove to be our principal limitation. The bigger challenges will be located in different areas. For example they may be related to the observer effect - the limitation to measuring a system's component properties without affecting the system. Another obstacle may be hidden in the tradition of "shaving away" all "unnecessary" assumptions (the so-called Occam's razor) that, in fact, reflects the intention to model the system as simply as possible and thus to deem the emergence to be less strong than it possibly is. We argue here that that Occam's razor should be replaced with the law of completeness.

Mechanistic modeling of aberrant energy metabolism in human disease.

Dysfunction in energy metabolism-including in pathways localized to the mitochondria-has been implicated in the pathogenesis of a wide array of disorders, ranging from cancer to neurodegenerative diseases to type II diabetes. The inherent complexities of energy and mitochondrial metabolism present a significant obstacle in the effort to understand the role that these molecular processes play in the development of disease. To help unravel these complexities, systems biology methods have been applied to develop an array of computational metabolic models, ranging from mitochondria-specific processes to genome-scale cellular networks. These constraint-based (CB) models can efficiently simulate aspects of normal and aberrant metabolism in various genetic and environmental conditions. Development of these models leverages-and also provides a powerful means to integrate and interpret-information from a wide range of sources including genomics, proteomics, metabolomics, and enzyme kinetics. Here, we review a variety of mechanistic modeling studies that explore metabolic functions, deficiency disorders, and aberrant biochemical pathways in mitochondria and related regions in the cell.

Understanding complexity in neurodegenerative diseases: in silico reconstruction of emergence.

Healthy functioning is an emergent property of the network of interacting biomolecules that comprise an organism. It follows that disease (a network shift that causes malfunction) is also an emergent property, emerging from a perturbation of the network. On the one hand, the biomolecular network of every individual is unique and this is evident when similar disease-producing agents cause different individual pathologies. Consequently, a personalized model and approach for every patient may be required for therapies to become effective across mankind. On the other hand, diverse combinations of internal and external perturbation factors may cause a similar shift in network functioning. We offer this as an explanation for the multi-factorial nature of most diseases: they are "systems biology diseases," or "network diseases." Here we use neurodegenerative diseases, like Parkinson's disease (PD), as an example to show that due to the inherent complexity of these networks, it is difficult to understand multi-factorial diseases with simply our "naked brain." When describing interactions between biomolecules through mathematical equations and integrating those equations into a mathematical model, we try to reconstruct the emergent properties of the system in silico. The reconstruction of emergence from interactions between huge numbers of macromolecules is one of the aims of systems biology. Systems biology approaches enable us to break through the limitation of the human brain to perceive the extraordinarily large number of interactions, but this also means that we delegate the understanding of reality to the computer. We no longer recognize all those essences in the system's design crucial for important physiological behavior (the so-called "design principles" of the system). In this paper we review evidence that by using more abstract approaches and by experimenting in silico, one may still be able to discover and understand the design principles that govern behavioral emergence.

Building a kinetic model of trehalose biosynthesis in Saccharomyces cerevisiae.

In this chapter, we describe the steps needed to create a kinetic model of a metabolic pathway based on kinetic data from experimental measurements and literature review. Our methodology is presented by utilizing the example of trehalose metabolism in yeast. The biology of the trehalose cycle is briefly reviewed and discussed.

Systematic integration of experimental data and models in systems biology.

BACKGROUND: The behaviour of biological systems can be deduced from their mathematical models. However, multiple sources of data in diverse forms are required in the construction of a model in order to define its components and their biochemical reactions, and corresponding parameters. Automating the assembly and use of systems biology models is dependent upon data integration processes involving the interoperation of data and analytical resources. RESULTS: Taverna workflows have been developed for the automated assembly of quantitative parameterised metabolic networks in the Systems Biology Markup Language (SBML). A SBML model is built in a systematic fashion by the workflows which starts with the construction of a qualitative network using data from a MIRIAM-compliant genome-scale model of yeast metabolism. This is followed by parameterisation of the SBML model with experimental data from two repositories, the SABIO-RK enzyme kinetics database and a database of quantitative experimental results. The models are then calibrated and simulated in workflows that call out to COPASIWS, the web service interface to the COPASI software application for analysing biochemical networks. These systems biology workflows were evaluated for their ability to construct a parameterised model of yeast glycolysis. CONCLUSIONS: Distributed information about metabolic reactions that have been described to MIRIAM standards enables the automated assembly of quantitative systems biology models of metabolic networks based on user-defined criteria. Such data integration processes can be implemented as Taverna workflows to provide a rapid overview of the components and their relationships within a biochemical system.

Further developments towards a genome-scale metabolic model of yeast.

BACKGROUND: To date, several genome-scale network reconstructions have been used to describe the metabolism of the yeast Saccharomyces cerevisiae, each differing in scope and content. The recent community-driven reconstruction, while rigorously evidenced and well annotated, under-represented metabolite transport, lipid metabolism and other pathways, and was not amenable to constraint-based analyses because of lack of pathway connectivity. RESULTS: We have expanded the yeast network reconstruction to incorporate many new reactions from the literature and represented these in a well-annotated and standards-compliant manner. The new reconstruction comprises 1102 unique metabolic reactions involving 924 unique metabolites--significantly larger in scope than any previous reconstruction. The representation of lipid metabolism in particular has improved, with 234 out of 268 enzymes linked to lipid metabolism now present in at least one reaction. Connectivity is emphatically improved, with more than 90% of metabolites now reachable from the growth medium constituents. The present updates allow constraint-based analyses to be performed; viability predictions of single knockouts are comparable to results from in vivo experiments and to those of previous reconstructions. CONCLUSIONS: We report the development of the most complete reconstruction of yeast metabolism to date that is based upon reliable literature evidence and richly annotated according to MIRIAM standards. The reconstruction is available in the Systems Biology Markup Language (SBML) and via a publicly accessible database http://www.comp-sys-bio.org/yeastnet/.

Systems biochemistry in practice: experimenting with modelling and understanding, with regulation and control.

Biology and medicine have become 'big science', even though we may not always like this: genomics and the subsequent analysis of what the genomes encode has shown that interesting living organisms require many more than 300 gene products to interact. We once thought that somewhere in this jungle of interacting macromolecules was hidden the molecule that constitutes the secret of Life, and therewith of health and disease. Now we know that, somehow, the secret of Life is the jungle of interactions. Consequently, we need to find the Rosetta Stones, i.e. interpretations of this jungle of systems biology. We need to find, perhaps convoluted, paths of understanding and intervention. Systems biochemistry is a good place to start, as it has the foothold that what goes in must come out. In the present paper, we review two strategies, which look at control and regulation. We discuss the difference between control and regulation and prove a relationship between them.

Why does yeast ferment? A flux balance analysis study.

Advances in biological techniques have led to the availability of genome-scale metabolic reconstructions for yeast. The size and complexity of such networks impose limits on what types of analyses one can perform. Constraint-based modelling overcomes some of these restrictions by using physicochemical constraints to describe the potential behaviour of an organism. FBA (flux balance analysis) highlights flux patterns through a network that serves to achieve a particular objective and requires a minimal amount of data to make quantitative inferences about network behaviour. Even though FBA is a powerful tool for system predictions, its general formulation sometimes results in unrealistic flux patterns. A typical example is fermentation in yeast: ethanol is produced during aerobic growth in excess glucose, but this pattern is not present in a typical FBA solution. In the present paper, we examine the issue of yeast fermentation against respiration during growth. We have studied a number of hypotheses from the modelling perspective, and novel formulations of the FBA approach have been tested. By making the observation that more respiration requires the synthesis of more mitochondria, an energy cost related to mitochondrial synthesis is added to the FBA formulation. Results, although still approximate, are closer to experimental observations than earlier FBA analyses, at least on the issue of fermentation.

Towards a genome-scale kinetic model of cellular metabolism.

BACKGROUND: Advances in bioinformatic techniques and analyses have led to the availability of genome-scale metabolic reconstructions. The size and complexity of such networks often means that their potential behaviour can only be analysed with constraint-based methods. Whilst requiring minimal experimental data, such methods are unable to give insight into cellular substrate concentrations. Instead, the long-term goal of systems biology is to use kinetic modelling to characterize fully the mechanics of each enzymatic reaction, and to combine such knowledge to predict system behaviour. RESULTS: We describe a method for building a parameterized genome-scale kinetic model of a metabolic network. Simplified linlog kinetics are used and the parameters are extracted from a kinetic model repository. We demonstrate our methodology by applying it to yeast metabolism. The resultant model has 956 metabolic reactions involving 820 metabolites, and, whilst approximative, has considerably broader remit than any existing models of its type. Control analysis is used to identify key steps within the system. CONCLUSIONS: Our modelling framework may be considered a stepping-stone toward the long-term goal of a fully-parameterized model of yeast metabolism. The model is available in SBML format from the BioModels database (BioModels ID: MODEL1001200000) and at http://www.mcisb.org/resources/genomescale/.