Development and analysis of an in vivo-compatible metabolic network of Mycobacterium tuberculosis

<p>Abstract</p> <p>Background</p> <p>During infection, <it>Mycobacterium tuberculosis </it>confronts a generally hostile and nutrient-poor <it>in vivo </it>host environment. Existing models and analyses of <it>M. tuberculosis </it>metabolic networks are able to reproduce experimentally measured cellular growth rates and identify genes required for growth in a range of different <it>in vitro </it>media. However, these models, under <it>in vitro </it>conditions, do not provide an adequate description of the metabolic processes required by the pathogen to infect and persist in a host.</p> <p>Results</p> <p>To better account for the metabolic activity of <it>M. tuberculosis </it>in the host environment, we developed a set of procedures to systematically modify an existing <it>in vitro </it>metabolic network by enhancing the agreement between calculated and <it>in vivo-</it>measured gene essentiality data. After our modifications, the new <it>in vivo </it>network contained 663 genes, 838 metabolites, and 1,049 reactions and had a significantly increased sensitivity (0.81) in predicted gene essentiality than the <it>in vitro </it>network (0.31). We verified the modifications generated from the purely computational analysis through a review of the literature and found, for example, that, as the analysis suggested, lipids are used as the main source for carbon metabolism and oxygen must be available for the pathogen under <it>in vivo </it>conditions. Moreover, we used the developed <it>in vivo </it>network to predict the effects of double-gene deletions on <it>M. tuberculosis </it>growth in the host environment, explore metabolic adaptations to life in an acidic environment, highlight the importance of different enzymes in the tricarboxylic acid-cycle under different limiting nutrient conditions, investigate the effects of inhibiting multiple reactions, and look at the importance of both aerobic and anaerobic cellular respiration during infection.</p> <p>Conclusions</p> <p>The network modifications we implemented suggest a distinctive set of metabolic conditions and requirements faced by <it>M. tuberculosis </it>during host infection compared with <it>in vitro </it>growth. Likewise, the double-gene deletion calculations highlight the importance of specific metabolic pathways used by the pathogen in the host environment. The newly constructed network provides a quantitative model to study the metabolism and associated drug targets of <it>M. tuberculosis </it>under <it>in vivo </it>conditions.</p

SNIT: SNP identification for strain typing

With ever-increasing numbers of microbial genomes being sequenced, efficient tools are needed to perform strain-level identification of any newly sequenced genome. Here, we present the SNP identification for strain typing (SNIT) pipeline, a fast and accurate software system that compares a newly sequenced bacterial genome with other genomes of the same species to identify single nucleotide polymorphisms (SNPs) and small insertions/deletions (indels). Based on this information, the pipeline analyzes the polymorphic loci present in all input genomes to identify the genome that has the fewest differences with the newly sequenced genome. Similarly, for each of the other genomes, SNIT identifies the input genome with the fewest differences. Results from five bacterial species show that the SNIT pipeline identifies the correct closest neighbor with 75% to 100% accuracy. The SNIT pipeline is available for download at http://www.bhsai.org/snit.htm

Neural networks and their application to nuclear power plant diagnosis

The authors present a survey of artificial neural network-based computer systems that have been proposed over the last decade for the detection and identification of component faults in thermal-hydraulic systems of nuclear power plants. The capabilities and advantages of applying neural networks as decision support systems for nuclear power plant operators and their inherent characteristics are discussed along with their limitations and drawbacks. The types of neural network structures used and their applications are described and the issues of process diagnosis and neural network-based diagnostic systems are identified. A total of thirty-four publications are reviewed

The Impact of the Rise in the Price of Crude Oil on the World Economy: Prognosis and Policy Options

This report outlines the major effects of OPEC countries raising the effective oil prices from $3.45 a barrel to$9 a barrel in 1973. The main economic problems the author mentions are inflation, domestic demand, and acute balance-of-payments. In addition, this report outlines policy options for the United States and other industrial countries

Real Jurors\u27 Understanding of the Law in Real Cases

A survey of 224 Michigan citizens called for jury duty over a 2-month period was conducted to assess the jurors\u27 comprehension of the law they had been given in the judges\u27 instructions. Citizens who served as jurors were compared with a base line of those who were called for duty but not selected to serve, and with those who served on different kinds of cases. Consistent with previous studies of mock jurors, this study found that actual jurors understand fewer than half of the instructions they receive at trial. Subjects who received judges\u27 instructions performed significantly better than uninstructed subjects on questions about the procedural law, but no better on questions about the substantive (criminal) law. Additionally, jurors who asked for help from the judge understood the instructions better than other jurors. Since the results replicate previous research using simulated trials, this study provides evidence for the generalizability of earlier work to actual trials

The Case for and Against an Import Surcharge

The United States is now running a deficit of over $100 billion in its foreign trade and the Federal budget is in the red by roughly$200 billion. To deal with these two deficits, Congress is considering a temporary import surcharge. This brief examines the case for and against such a surcharge as well as its use against Japan

Influence of Protein Abundance on High-Throughput Protein-Protein Interaction Detection

Experimental protein-protein interaction (PPI) networks are increasingly being exploited in diverse ways for biological discovery. Accordingly, it is vital to discern their underlying natures by identifying and classifying the various types of deterministic (specific) and probabilistic (nonspecific) interactions detected. To this end, we have analyzed PPI networks determined using a range of high-throughput experimental techniques with the aim of systematically quantifying any biases that arise from the varying cellular abundances of the proteins. We confirm that PPI networks determined using affinity purification methods for yeast and Eschericia coli incorporate a correlation between protein degree, or number of interactions, and cellular abundance. The observed correlations are small but statistically significant and occur in both unprocessed (raw) and processed (high-confidence) data sets. In contrast, the yeast two-hybrid system yields networks that contain no such relationship. While previously commented based on mRNA abundance, our more extensive analysis based on protein abundance confirms a systematic difference between PPI networks determined from the two technologies. We additionally demonstrate that the centrality-lethality rule, which implies that higher-degree proteins are more likely to be essential, may be misleading, as protein abundance measurements identify essential proteins to be more prevalent than nonessential proteins. In fact, we generally find that when there is a degree/abundance correlation, the degree distributions of nonessential and essential proteins are also disparate. Conversely, when there is no degree/abundance correlation, the degree distributions of nonessential and essential proteins are not different. However, we show that essentiality manifests itself as a biological property in all of the yeast PPI networks investigated here via enrichments of interactions between essential proteins. These findings provide valuable insights into the underlying natures of the various high-throughput technologies utilized to detect PPIs and should lead to more effective strategies for the inference and analysis of high-quality PPI data sets

Probing the Extent of Randomness in Protein Interaction Networks

Protein–protein interaction (PPI) networks are commonly explored for the identification of distinctive biological traits, such as pathways, modules, and functional motifs. In this respect, understanding the underlying network structure is vital to assess the significance of any discovered features. We recently demonstrated that PPI networks show degree-weighted behavior, whereby the probability of interaction between two proteins is generally proportional to the product of their numbers of interacting partners or degrees. It was surmised that degree-weighted behavior is a characteristic of randomness. We expand upon these findings by developing a random, degree-weighted, network model and show that eight PPI networks determined from single high-throughput (HT) experiments have global and local properties that are consistent with this model. The apparent random connectivity in HT PPI networks is counter-intuitive with respect to their observed degree distributions; however, we resolve this discrepancy by introducing a non-network-based model for the evolution of protein degrees or “binding affinities.” This mechanism is based on duplication and random mutation, for which the degree distribution converges to a steady state that is identical to one obtained by averaging over the eight HT PPI networks. The results imply that the degrees and connectivities incorporated in HT PPI networks are characteristic of unbiased interactions between proteins that have varying individual binding affinities. These findings corroborate the observation that curated and high-confidence PPI networks are distinct from HT PPI networks and not consistent with a random connectivity. These results provide an avenue to discern indiscriminate organizations in biological networks and suggest caution in the analysis of curated and high-confidence networks