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Jennifer L Reed

Publications and source records attributed to Jennifer L Reed.

16 recordsLinked to original sources
PubMed2006

Systems approach to refining genome annotation.

Genome-scale models of Escherichia coli K-12 MG1655 metabolism have been able to predict growth phenotypes in most, but not all, defined growth environments. Here we introduce the use of an optimization-based algorithm that predicts the missing reactions that are required to reconcile computation and experiment when they disagree. The computer-generated hypotheses for missing reactions were verified experimentally in five cases, leading to the functional assignment of eight ORFs (yjjLMN, yeaTU, dctA, idnT, and putP) with two new enzymatic activities and four transport functions. This study thus demonstrates the use of systems analysis to discover metabolic and transport functions and their genetic basis by a combination of experimental and computational approaches.

Biological Transport↗
PubMed2006

Experimental and computational assessment of conditionally essential genes in Escherichia coli.

Genome-wide gene essentiality data sets are becoming available for Escherichia coli, but these data sets have yet to be analyzed in the context of a genome scale model. Here, we present an integrative model-driven analysis of the Keio E. coli mutant collection screened in this study on glycerol-supplemented minimal medium. Out of 3,888 single-deletion mutants tested, 119 mutants were unable to grow on glycerol minimal medium. These conditionally essential genes were then evaluated using a genome scale metabolic and transcriptional-regulatory model of E. coli, and it was found that the model made the correct prediction in approximately 91% of the cases. The discrepancies between model predictions and experimental results were analyzed in detail to indicate where model improvements could be made or where the current literature lacks an explanation for the observed phenotypes. The identified set of essential genes and their model-based analysis indicates that our current understanding of the roles these essential genes play is relatively clear and complete. Furthermore, by analyzing the data set in terms of metabolic subsystems across multiple genomes, we can project which metabolic pathways are likely to play equally important roles in other organisms. Overall, this work establishes a paradigm that will drive model enhancement while simultaneously generating hypotheses that will ultimately lead to a better understanding of the organism.

Bacterial Proteins↗
PubMed2006

Mast cells are essential intermediaries in regulatory T-cell tolerance.

Contrary to the proinflammatory role of mast cells in allergic disorders, the results obtained in this study establish that mast cells are essential in CD4+CD25+Foxp3+ regulatory T (T(Reg))-cell-dependent peripheral tolerance. Here we confirm that tolerant allografts, which are sustained owing to the immunosuppressive effects of T(Reg) cells, acquire a unique genetic signature dominated by the expression of mast-cell-gene products. We also show that mast cells are crucial for allograft tolerance, through the inability to induce tolerance in mast-cell-deficient mice. High levels of interleukin (IL)-9--a mast cell growth and activation factor--are produced by activated T(Reg) cells, and IL-9 production seems important in mast cell recruitment to, and activation in, tolerant tissue. Our data indicate that IL-9 represents the functional link through which activated T(Reg) cells recruit and activate mast cells to mediate regional immune suppression, because neutralization of IL-9 greatly accelerates allograft rejection in tolerant mice. Finally, immunohistochemical analysis clearly demonstrates the existence of this novel T(Reg)-IL-9-mast cell relationship within tolerant allografts.

Animals↗
PubMed2006

Towards multidimensional genome annotation.

Our information about the gene content of organisms continues to grow as more genomes are sequenced and gene products are characterized. Sequence-based annotation efforts have led to a list of cellular components, which can be thought of as a one-dimensional annotation. With growing information about component interactions, facilitated by the advancement of various high-throughput technologies, systemic, or two-dimensional, annotations can be generated. Knowledge about the physical arrangement of chromosomes will lead to a three-dimensional spatial annotation of the genome and a fourth dimension of annotation will arise from the study of changes in genome sequences that occur during adaptive evolution. Here we discuss all four levels of genome annotation, with specific emphasis on two-dimensional annotation methods.

Archaeal Proteins↗
PubMed2006

Isolation and characterization of monoclonal antibodies which neutralize human metapneumovirus in vitro and in vivo.

Human metapneumovirus (hMPV) is a recently described member of the Paramyxoviridae family/Pneumovirinae subfamily and shares many common features with respiratory syncytial virus (RSV), another member of the same subfamily. hMPV causes respiratory tract illnesses that, similar to human RSV, occur predominantly during the winter months and have symptoms that range from mild to severe cough, bronchiolitis, and pneumonia. Like RSV, the hMPV virus can be subdivided into two genetic subgroups, A and B. With RSV, a single monoclonal antibody directed at the fusion (F) protein can prevent severe lower respiratory tract RSV infection. Because of the high level of sequence conservation of the F protein across all the hMPV subgroups, this protein is likely to be the preferred antigenic target for the generation of cross-subgroup neutralizing antibodies. Here we describe the generation of a panel of neutralizing monoclonal antibodies that bind to the hMPV F protein. A subset of these antibodies has the ability to neutralize prototypic strains of both the A and B hMPV subgroups in vitro. Two of these antibodies exhibited high-affinity binding to the F protein and were shown to protect hamsters against infection with hMPV. The data suggest that a monoclonal antibody could be used prophylactically to prevent lower respiratory tract disease caused by hMPV.

Antibodies, Monoclonal↗
PubMed2005

The global transcriptional regulatory network for metabolism in Escherichia coli exhibits few dominant functional states.

A principal aim of systems biology is to develop in silico models of whole cells or cellular processes that explain and predict observable cellular phenotypes. Here, we use a model of a genome-scale reconstruction of the integrated metabolic and transcriptional regulatory networks for Escherichia coli, composed of 1,010 gene products, to assess the properties of all functional states computed in 15,580 different growth environments. The set of all functional states of the integrated network exhibits a discernable structure that can be visualized in 3-dimensional space, showing that the transcriptional regulatory network governing metabolism in E. coli responds primarily to the available electron acceptor and the presence of glucose as the carbon source. This result is consistent with recently published experimental data. The observation that a complex network composed of 1,010 genes is organized to achieve few dominant modes demonstrates the utility of the systems approach for consolidating large amounts of genome-scale molecular information about a genome and its regulation to elucidate an organism's preferred environments and functional capabilities.

Bacterial Proteins↗
PubMed2004

Integrating high-throughput and computational data elucidates bacterial networks.

The flood of high-throughput biological data has led to the expectation that computational (or in silico) models can be used to direct biological discovery, enabling biologists to reconcile heterogeneous data types, find inconsistencies and systematically generate hypotheses. Such a process is fundamentally iterative, where each iteration involves making model predictions, obtaining experimental data, reconciling the predicted outcomes with experimental ones, and using discrepancies to update the in silico model. Here we have reconstructed, on the basis of information derived from literature and databases, the first integrated genome-scale computational model of a transcriptional regulatory and metabolic network. The model accounts for 1,010 genes in Escherichia coli, including 104 regulatory genes whose products together with other stimuli regulate the expression of 479 of the 906 genes in the reconstructed metabolic network. This model is able not only to predict the outcomes of high-throughput growth phenotyping and gene expression experiments, but also to indicate knowledge gaps and identify previously unknown components and interactions in the regulatory and metabolic networks. We find that a systems biology approach that combines genome-scale experimentation and computation can systematically generate hypotheses on the basis of disparate data sources.

Aerobiosis↗
PubMed2004

Hierarchical thinking in network biology: the unbiased modularization of biochemical networks.

As reconstructed biochemical reaction networks continue to grow in size and scope, there is a growing need to describe the functional modules within them. Such modules facilitate the study of biological processes by deconstructing complex biological networks into conceptually simple entities. The definition of network modules is often based on intuitive reasoning. As an alternative, methods are being developed for defining biochemical network modules in an unbiased fashion. These unbiased network modules are mathematically derived from the structure of the whole network under consideration.

Animals↗
PubMed2004

Genome-scale models of microbial cells: evaluating the consequences of constraints.

Microbial cells operate under governing constraints that limit their range of possible functions. With the availability of annotated genome sequences, it has become possible to reconstruct genome-scale biochemical reaction networks for microorganisms. The imposition of governing constraints on a reconstructed biochemical network leads to the definition of achievable cellular functions. In recent years, a substantial and growing toolbox of computational analysis methods has been developed to study the characteristics and capabilities of microorganisms using a constraint-based reconstruction and analysis (COBRA) approach. This approach provides a biochemically and genetically consistent framework for the generation of hypotheses and the testing of functions of microbial cells.

Bacterial Physiological Phenomena↗
PubMed2004

Genome-scale in silico models of E. coli have multiple equivalent phenotypic states: assessment of correlated reaction subsets that comprise network states.

The constraint-based analysis of genome-scale metabolic and regulatory networks has been successful in predicting phenotypes and useful for analyzing high-throughput data sets. Within this modeling framework, linear optimization has been used to study genome-scale metabolic models, resulting in the enumeration of single optimal solutions describing the best use of the network to support growth. Here mixed-integer linear programming was used to calculate and study a subset of the alternate optimal solutions for a genome-scale metabolic model of Escherichia coli (iJR904) under a wide variety of environmental conditions. Analysis of the calculated sets of optimal solutions found that: (1) only a small subset of reactions in the network have variable fluxes across optima; (2) sets of reactions that are always used together in optimal solutions, correlated reaction sets, showed moderate agreement with the currently known transcriptional regulatory structure in E. coli and available expression data, and (3) reactions that are used under certain environmental conditions can provide clues about network regulatory needs. In addition, calculation of suboptimal flux distributions, using flux variability analysis, identified reactions which are used under significantly more environmental conditions suboptimally than optimally. Together these results demonstrate the utilization of reactions in genome-scale models under a variety of different growth conditions.

Aerobiosis↗
PubMed2003

Network-based analysis of metabolic regulation in the human red blood cell.

Reconstruction of cell-scale metabolic networks is now possible. A description of allowable metabolic network functions can be obtained using extreme pathways, which are the convex basis vectors of the solution space containing all steady state flux distributions. However, only a portion of these allowable network functions are physiologically possible due to kinetic and regulatory constraints. Methods are now needed that enable us to take a defined metabolic network and deduce candidate regulatory structures that control the selection of these physiologically relevant states. One such approach is the singular value decomposition (SVD) of extreme pathway matrices (P), which allows for the characterization of steady state solution spaces. Eigenpathways, which are the left singular vectors from the SVD of P, can be described and categorized by their biochemical function. SVD of P for the human red blood cell showed that the first five eigenpathways, out of a total of 23, effectively characterize all the relevant physiological states of red blood cell metabolism calculated with a detailed kinetic model. Thus, with five degrees of freedom the magnitude and nature of the regulatory needs are defined. Additionally, the dominant features of these first five eigenpathways described key metabolic splits that are indeed regulated in the human red blood cell. The extreme pathway matrix is derived directly from network topology and only knowledge of Vmax values is needed to reach these conclusions. Thus, we have implemented a network-based analysis of regulation that complements the study of individual regulatory events. This topological approach may provide candidate regulatory structures for metabolic networks with known stoichiometry but poorly characterized regulation.

Erythrocytes↗
PubMed2003

An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR).

BACKGROUND: Diverse datasets, including genomic, transcriptomic, proteomic and metabolomic data, are becoming readily available for specific organisms. There is currently a need to integrate these datasets within an in silico modeling framework. Constraint-based models of Escherichia coli K-12 MG1655 have been developed and used to study the bacterium's metabolism and phenotypic behavior. The most comprehensive E. coli model to date (E. coli iJE660a GSM) accounts for 660 genes and includes 627 unique biochemical reactions. RESULTS: An expanded genome-scale metabolic model of E. coli (iJR904 GSM/GPR) has been reconstructed which includes 904 genes and 931 unique biochemical reactions. The reactions in the expanded model are both elementally and charge balanced. Network gap analysis led to putative assignments for 55 open reading frames (ORFs). Gene to protein to reaction associations (GPR) are now directly included in the model. Comparisons between predictions made by iJR904 and iJE660a models show that they are generally similar but differ under certain circumstances. Analysis of genome-scale proton balancing shows how the flux of protons into and out of the medium is important for maximizing cellular growth. CONCLUSIONS: E. coli iJR904 has improved capabilities over iJE660a. iJR904 is a more complete and chemically accurate description of E. coli metabolism than iJE660a. Perhaps most importantly, iJR904 can be used for analyzing and integrating the diverse datasets. iJR904 will help to outline the genotype-phenotype relationship for E. coli K-12, as it can account for genomic, transcriptomic, proteomic and fluxomic data simultaneously.

Citric Acid Cycle↗
PubMed2003

Safety and immunogenicity of a recombinant parvovirus B19 vaccine formulated with MF59C.1.

A recombinant human parvovirus B19 vaccine (MEDI-491; MedImmune) composed of the VP1 and VP2 capsid proteins and formulated with MF59C.1 adjuvant was evaluated in a randomized, double-blind, phase 1 trial. Parvovirus B19-seronegative adults (n=24) received either 2.5 or 25 microg MEDI-491 at 0, 1, and 6 months. MEDI-491 was safe and immunogenic. All volunteers developed neutralizing antibody titers that peaked after the third immunization and were sustained through study day 364.

Adjuvants, Immunologic↗
PubMed2003

Analysis of metabolic capabilities using singular value decomposition of extreme pathway matrices.

It is now possible to construct genome-scale metabolic networks for particular microorganisms. Extreme pathway analysis is a useful method for analyzing the phenotypic capabilities of these networks. Many extreme pathways are needed to fully describe the functional capabilities of genome-scale metabolic networks, and therefore, a need exists to develop methods to study these large sets of extreme pathways. Singular value decomposition (SVD) of matrices of extreme pathways was used to develop a conceptual framework for the interpretation of large sets of extreme pathways and the steady-state flux solution space they define. The key results of this study were: 1), convex steady-state solution cones describing the potential functions of biochemical networks can be studied using the modes generated by SVD; 2), Helicobacter pylori has a more rigid metabolic network (i.e., a lower dimensional solution space and a more dominant first singular value) than Haemophilus influenzae for the production of amino acids; and 3), SVD allows for direct comparison of different solution cones resulting from the production of different amino acids. SVD was used to identify key network branch points that may identify key control points for regulation. Therefore, SVD of matrices of extreme pathways has proved to be a useful method for analyzing the steady-state solution space of genome-scale metabolic networks.

Algorithms↗