A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information

An updated genome-scale reconstruction of the metabolic network in Escherichia coli K-12 MG1655 is presented. This updated metabolic reconstruction includes: (1) an alignment with the latest genome annotation and the metabolic content of EcoCyc leading to the inclusion of the activities of 1260 ORFs, (2) characterization and quantification of the biomass components and maintenance requirements associated with growth of E. coli and (3) thermodynamic information for the included chemical reactions. The conversion of this metabolic network reconstruction into an in silico model is detailed. A new step in the metabolic reconstruction process, termed thermodynamic consistency analysis, is introduced, in which reactions were checked for consistency with thermodynamic reversibility estimates. Applications demonstrating the capabilities of the genome-scale metabolic model to predict high-throughput experimental growth and gene deletion phenotypic screens are presented. The increased scope and computational capability using this new reconstruction is expected to broaden the spectrum of both basic biology and applied systems biology studies of E. coli metabolism

The RR Lyrae Distance Scale

We review seven methods of measuring the absolute magnitude M_V of RR Lyrae stars in light of the Hipparcos mission and other recent developments. We focus on identifying possible systematic errors and rank the methods by relative immunity to such errors. For the three most robust methods, statistical parallax, trigonometric parallax, and cluster kinematics, we find M_V (at [Fe/H] = -1.6) of 0.77 +/- 0.13, 0.71 +/- 0.15, 0.67 +/- 0.10. These methods cluster consistently around 0.71 +/- 0.07. We find that Baade-Wesselink and theoretical models both yield a broad range of possible values (0.45-0.70 and 0.45-0.65) due to systematic uncertainties in the temperature scale and input physics. Main-sequence fitting gives a much brighter M_V = 0.45 +/- 0.04 but this may be due to a difference in the metallicity scales of the cluster giants and the calibrating subdwarfs. White-dwarf cooling-sequence fitting gives 0.67 +/- 0.13 and is potentially very robust, but at present is too new to be fully tested for systematics. If the three most robust methods are combined with Walker's mean measurement for 6 LMC clusters, V_{0,LMC} = 18.98 +/- 0.03 at [Fe/H] = -1.9, then mu_{LMC} = 18.33 +/- 0.08.Comment: Invited review article to appear in: `Post-Hipparcos Cosmic Candles', A. Heck & F. Caputo (Eds), Kluwer Academic Publ., Dordrecht, in press. 21 pages including 1 table; uses Kluwer's crckapb.sty LaTeX style file, enclose

Inferring predominant pathways in cellular models of breast cancer using limited sample proteomic profiling

<p>Abstract</p> <p>Background</p> <p>Molecularly targeted drugs inhibit aberrant signaling within oncogenic pathways. Identifying the predominant pathways at work within a tumor is a key step towards tailoring therapies to the patient. Clinical samples pose significant challenges for proteomic profiling, an attractive approach for identifying predominant pathways. The objective of this study was to determine if information obtained from a limited sample (i.e., a single gel replicate) can provide insight into the predominant pathways in two well-characterized breast cancer models.</p> <p>Methods</p> <p>A comparative proteomic analysis of total cell lysates was obtained from two cellular models of breast cancer, BT474 (HER2+/ER+) and SKBR3 (HER2+/ER-), using two-dimensional electrophoresis and MALDI-TOF mass spectrometry. Protein interaction networks and canonical pathways were extracted from the Ingenuity Pathway Knowledgebase (IPK) based on association with the observed pattern of differentially expressed proteins.</p> <p>Results</p> <p>Of the 304 spots that were picked, 167 protein spots were identified. A threshold of 1.5-fold was used to select 62 proteins used in the analysis. IPK analysis suggested that metabolic pathways were highly associated with protein expression in SKBR3 cells while cell motility pathways were highly associated with BT474 cells. Inferred protein networks were confirmed by observing an up-regulation of IGF-1R and profilin in BT474 and up-regulation of Ras and enolase in SKBR3 using western blot.</p> <p>Conclusion</p> <p>When interpreted in the context of prior information, our results suggest that the overall patterns of differential protein expression obtained from limited samples can still aid in clinical decision making by providing an estimate of the predominant pathways that underpin cellular phenotype.</p

Continuum Molecular Simulation of Large Conformational Changes during Ion–Channel Gating

A modeling framework was developed to simulate large and gradual conformational changes within a macromolecule (protein) when its low amplitude high frequency vibrations are not concerned. Governing equations were derived as alternative to Langevin and Smoluchowski equations and used to simulate gating conformational changes of the Kv7.1 ion-channel over the time scale of its gating process (tens of milliseconds). The alternative equations predict the statistical properties of the motion trajectories with good accuracy and do not require the force field to be constant over the diffusion length, as assumed in Langevin equation. The open probability of the ion–channel was determined considering cooperativity of four subunits and solving their concerted transition to the open state analytically. The simulated open probabilities for a series of voltage clamp tests produced current traces that were similar to experimentally recorded currents

Paradoxes in carcinogenesis: New opportunities for research directions

<p>Abstract</p> <p>Background</p> <p>The prevailing paradigm in cancer research is the somatic mutation theory that posits that cancer begins with a single mutation in a somatic cell followed by successive mutations. Much cancer research involves refining the somatic mutation theory with an ever increasing catalog of genetic changes. The problem is that such research may miss paradoxical aspects of carcinogenesis for which there is no likely explanation under the somatic mutation theory. These paradoxical aspects offer opportunities for new research directions that should not be ignored.</p> <p>Discussion</p> <p>Various paradoxes related to the somatic mutation theory of carcinogenesis are discussed: (1) the presence of large numbers of spatially distinct precancerous lesions at the onset of promotion, (2) the large number of genetic instabilities found in hyperplastic polyps not considered cancer, (3) spontaneous regression, (4) higher incidence of cancer in patients with xeroderma pigmentosa but not in patients with other comparable defects in DNA repair, (5) lower incidence of many cancers except leukemia and testicular cancer in patients with Down's syndrome, (6) cancer developing after normal tissue is transplanted to other parts of the body or next to stroma previously exposed to carcinogens, (7) the lack of tumors when epithelial cells exposed to a carcinogen were transplanted next to normal stroma, (8) the development of cancers when Millipore filters of various pore sizes were was inserted under the skin of rats, but only if the holes were sufficiently small. For the latter paradox, a microarray experiment is proposed to try to better understand the phenomena.</p> <p>Summary</p> <p>The famous physicist Niels Bohr said "How wonderful that we have met with a paradox. Now we have some hope of making progress." The same viewpoint should apply to cancer research. It is easy to ignore this piece of wisdom about the means to advance knowledge, but we do so at our peril.</p

Moving toward a system genetics view of disease

Testing hundreds of thousands of DNA markers in human, mouse, and other species for association to complex traits like disease is now a reality. However, information on how variations in DNA impact complex physiologic processes flows through transcriptional and other molecular networks. In other words, DNA variations impact complex diseases through the perturbations they cause to transcriptional and other biological networks, and these molecular phenotypes are intermediate to clinically defined disease. Because it is also now possible to monitor transcript levels in a comprehensive fashion, integrating DNA variation, transcription, and phenotypic data has the potential to enhance identification of the associations between DNA variation and diseases like obesity and diabetes, as well as characterize those parts of the molecular networks that drive these diseases. Toward that end, we review methods for integrating expression quantitative trait loci (eQTLs), gene expression, and clinical data to infer causal relationships among gene expression traits and between expression and clinical traits. We further describe methods to integrate these data in a more comprehensive manner by constructing coexpression gene networks that leverage pairwise gene interaction data to represent more general relationships. To infer gene networks that capture causal information, we describe a Bayesian algorithm that further integrates eQTLs, expression, and clinical phenotype data to reconstruct whole-gene networks capable of representing causal relationships among genes and traits in the network. These emerging network approaches, aimed at processing high-dimensional biological data by integrating data from multiple sources, represent some of the first steps in statistical genetics to identify multiple genetic perturbations that alter the states of molecular networks and that in turn push systems into disease states. Evolving statistical procedures that operate on networks will be critical to extracting information related to complex phenotypes like disease, as research goes beyond a single-gene focus. The early successes achieved with the methods described herein suggest that these more integrative genomics approaches to dissecting disease traits will significantly enhance the identification of key drivers of disease beyond what could be achieved by genetic association studies alone

Community Justice and Public Safety: Assessing Criminal Justice Policy Through the Lens of the Social Contract

A reconceptualization of the idea of “community justice” is framed in the logic of the social contract and emphasizes the responsibility of the justice system for the provision of public safety. First, we illustrate the ways in which the criminal justice system has hindered the efforts of community residents to participate in the production of public safety by disrupting informal social networks. Then we turn to an examination of the compositional dynamics of California prison populations over time to demonstrate that the American justice system has failed to meet their obligations to provide public safety by incapacitating dangerous offenders. We argue that these policy failures represent a breach of the social contract and advocate for more effective collaboration between communities and the formal criminal justice system so that all parties can fulfill their obligations under the contract

Genome-Wide Association Data Reveal a Global Map of Genetic Interactions among Protein Complexes

This work demonstrates how gene association studies can be analyzed to map a global landscape of genetic interactions among protein complexes and pathways. Despite the immense potential of gene association studies, they have been challenging to analyze because most traits are complex, involving the combined effect of mutations at many different genes. Due to lack of statistical power, only the strongest single markers are typically identified. Here, we present an integrative approach that greatly increases power through marker clustering and projection of marker interactions within and across protein complexes. Applied to a recent gene association study in yeast, this approach identifies 2,023 genetic interactions which map to 208 functional interactions among protein complexes. We show that such interactions are analogous to interactions derived through reverse genetic screens and that they provide coverage in areas not yet tested by reverse genetic analysis. This work has the potential to transform gene association studies, by elevating the analysis from the level of individual markers to global maps of genetic interactions. As proof of principle, we use synthetic genetic screens to confirm numerous novel genetic interactions for the INO80 chromatin remodeling complex

Timed sequential chemotherapy with concomitant Granulocyte Colony-Stimulating Factor for high-risk acute myelogenous leukemia: a single arm clinical trial

BACKGROUND: The timed-sequential chemotherapy regimen consisting of etoposide, mitoxantrone and cytarabine (EMA) is an effective therapy for relapsed or refractory acute myelogenous leukemia (AML). We postulated that granulocyte colony-stimulating factor (G-CSF) might enhance the cytotoxicity of EMA by increasing the proportion of leukemic blasts in S-phase. We added G-CSF to EMA (EMA-G) for therapy of advanced high-risk AML patients. METHODS: High-risk AML was defined as refractory, relapsed or secondary to either an antecedent hematologic disorder or exposure to cytotoxic agents. The patients were treated with one course of EMA-G consisting of mitoxantrone and cytarabine on days 1–3, and etoposide and cytarabine on days 8–10. G-CSF was started on day 4 and continued until absolute neutrophil count recovered. RESULTS: Thirty patients were enrolled. The median age was 51 years (range, 25–75). Seventeen (61%) patients had unfavorable cytogenetic karyotypes. Twenty (69%) patients had secondary AML. Ten (34%) had relapsed disease. Four (14%) had refractory AML. Three (10%) patients died from febrile neutropenia and sepsis. Major non-hematologic toxicity included hyperbilirubimenia, renal insufficiency, mucositis, diarrhea, nausea and vomiting, skin rash. A complete remission was achieved in 13 (46%) patients. Median overall survival was 9 months (range, 0.5–66). Median relapse-free survival (RFS) for those who had a CR was 3 months (range, 0.5–63) with RFS censored at the time of allogeneic bone marrow transplantation or peripheral stem cell transplantation for 6 of the patients. CONCLUSIONS: EMA-G is a safe and efficacious option for induction chemotherapy in advanced, high-risk AML patients. The activity of EMA may be increased if applied in patients with less advanced disease

High-Resolution Mapping of Gene Expression Using Association in an Outbred Mouse Stock

Quantitative trait locus (QTL) analysis is a powerful tool for mapping genes for complex traits in mice, but its utility is limited by poor resolution. A promising mapping approach is association analysis in outbred stocks or different inbred strains. As a proof of concept for the association approach, we applied whole-genome association analysis to hepatic gene expression traits in an outbred mouse population, the MF1 stock, and replicated expression QTL (eQTL) identified in previous studies of F2 intercross mice. We found that the mapping resolution of these eQTL was significantly greater in the outbred population. Through an example, we also showed how this precise mapping can be used to resolve previously identified loci (in intercross studies), which affect many different transcript levels (known as eQTL “hotspots”), into distinct regions. Our results also highlight the importance of correcting for population structure in whole-genome association studies in the outbred stock