走近生物武器

生物武器是由生物战剂和施放装置构成的一种作战武器。美国“九一”事件之后发生的炭疽事件,敲响了生物战的警钟,由于生物武器被视为廉价的原子弹,生物恐怖再次引起人们的广泛关注。     

Enzymes for the homeland defense: optimizing phosphotriesterase for the hydrolysis of organophosphate nerve agents

Phosphotriesterase (PTE) from soil bacteria is known for its ability to catalyze the detoxification of organophosphate pesticides and chemical warfare agents. Most of the organophosphate chemical warfare agents are a mixture of two stereoisomers at the phosphorus center, and the S(P)-enantiomers are significantly more toxic than the R(P)-enantiomers. In previous investigations, PTE variants were created through the manipulation of the substrate binding pockets and these mutants were shown to have greater catalytic activities for the detoxification of the more toxic S(P)-enantiomers of nerve agent analogues for GB, GD, GF, VX, and VR than the less toxic R(P)-enantiomers. In this investigation, alternate strategies were employed to discover additional PTE variants with significant improvements in catalytic activities relative to that of the wild-type enzyme. Screening and selection techniques were utilized to isolate PTE variants from randomized libraries and site specific modifications. The catalytic activities of these newly identified PTE variants toward the S(P)-enantiomers of chromophoric analogues of GB, GD, GF, VX, and VR have been improved up to 15000-fold relative to that of the wild-type enzyme. The X-ray crystal structures of the best PTE variants were determined. Characterization of these mutants with the authentic G-type nerve agents has confirmed the expected improvements in catalytic activity against the most toxic enantiomers of GB, GD, and GF. The values of k(cat)/K(m) for the H257Y/L303T (YT) mutant for the hydrolysis of GB, GD, and GF were determined to be 2 × 10(6), 5 × 10(5), and 8 × 10(5) M(-1) s(-1), respectively. The YT mutant is the most proficient enzyme reported thus far for the detoxification of G-type nerve agents. These results support a combinatorial strategy of rational design and directed evolution as a powerful tool for the discovery of more efficient enzymes for the detoxification of organophosphate nerve agents.     

Scientific progress and new biological weapons

The biological weapons are different from conventional weapons, because living germs hold an extraordinary and predictable potential for multiplication, propagation and genetic variation during their dissemination in a susceptible population. Only natural pathogens (1rst generation weapons) have been used in the past (smallpox virus, plague, anthrax, toxins...). However, new threats are emerging, due to the rapid progress of scientific knowledge and its exponential worldwide diffusion. It is possible to synthesize microorganisms from in silico sequences widely diffused on Internet (poliovirus, influenza...), thus resulting in the accessibility of very dangerous virus confined today in high-security laboratories (virus Ebola...). It is possible also to "improve" pathogens by genetic manipulations, becoming more resistant or virulent (2nd generation weapons). Finally, one can now create de novo new pathogens by molecular breeding (DNA shuffling), potentially highly dangerous for naive populations (3rd generation weapons). Making biological weapons does not require too much technological resources and appears accessible to terrorists, due to low cost and easy use. Although the destructive consequences are difficult to predict, the psychological and social damages should be considerable, because of the highly emotional burden in the population associated to the transgression by man of a taboo of life.     

Automated data integration for developmental biological research

In an era exploding with genome-scale data, a major challenge for developmental biologists is how to extract significant clues from these publicly available data to benefit our studies of individual genes, and how to use them to improve our understanding of development at a systems level. Several studies have successfully demonstrated new approaches to classic developmental questions by computationally integrating various genome-wide data sets. Such computational approaches have shown great potential for facilitating research: instead of testing 20,000 genes, researchers might test 200 to the same effect. We discuss the nature and state of this art as it applies to developmental research.     

Automated screening of neurite outgrowth

Outgrowth of neurites in culture is used for assessing neurotrophic activity. Neurite measurements have been performed very slowly using manual methods or more efficiently with interactive image analysis systems. In contrast, medium-throughput and noninteractive image analysis of neurite screens has not been well described. The authors report the performance of an automated image acquisition and analysis system (IN Cell Analyzer 1000) in the neurite assay. Neuro-2a (N2a) cells were plated in 96-well plates and were exposed to 6 conditions of retinoic acid. Immunofluorescence labeling of the cytoskeleton was used to detect neurites and cell bodies. Acquisition of the images was automatic. The image set was then analyzed by both manual tracing and automated algorithms. On 5 relevant parameters (number of neurites, neurite length, total cell area, number of cells, neurite length per cell), the authors did not observe a difference between the automated analysis and the manual analysis done by tracing. These data suggest that the automated system addresses the same biology as human scorers and with the same measurement precision for treatment effects. However, throughput of the automated system is orders of magnitude higher than with manual methods.     

Facing the global challenges posed by biological weapons

This review article examines the growing concern about the threat posed by the use of biological weapons by States or terrorist groups. The article analyzes the nature of the perceived risk from bioweapons, the historical attempts to control them, and the emerging policy and legal framework designed to deal with the bioweapon threat.     

Automated force volume image processing for biological samples

Atomic force microscopy (AFM) has now become a powerful technique for investigating on a molecular level, surface forces, nanomechanical properties of deformable particles, biomolecular interactions, kinetics, and dynamic processes. This paper specifically focuses on the analysis of AFM force curves collected on biological systems, in particular, bacteria. The goal is to provide fully automated tools to achieve theoretical interpretation of force curves on the basis of adequate, available physical models. In this respect, we propose two algorithms, one for the processing of approach force curves and another for the quantitative analysis of retraction force curves. In the former, electrostatic interactions prior to contact between AFM probe and bacterium are accounted for and mechanical interactions operating after contact are described in terms of Hertz-Hooke formalism. Retraction force curves are analyzed on the basis of the Freely Jointed Chain model. For both algorithms, the quantitative reconstruction of force curves is based on the robust detection of critical points (jumps, changes of slope or changes of curvature) which mark the transitions between the various relevant interactions taking place between the AFM tip and the studied sample during approach and retraction. Once the key regions of separation distance and indentation are detected, the physical parameters describing the relevant interactions operating in these regions are extracted making use of regression procedure for fitting experiments to theory. The flexibility, accuracy and strength of the algorithms are illustrated with the processing of two force-volume images, which collect a large set of approach and retraction curves measured on a single biological surface. For each force-volume image, several maps are generated, representing the spatial distribution of the searched physical parameters as estimated for each pixel of the force-volume image.     

Automated generation of heuristics for biological sequence comparison

Background  

Exhaustive methods of sequence alignment are accurate but slow, whereas heuristic approaches run quickly, but their complexity makes them more difficult to implement. We introduce bounded sparse dynamic programming (BSDP) to allow rapid approximation to exhaustive alignment. This is used within a framework whereby the alignment algorithms are described in terms of their underlying model, to allow automated development of efficient heuristic implementations which may be applied to a general set of sequence comparison problems.     

Automated screening procedure for high-throughput generation of antibody fragments

In the emerging field of proteomics, there is an urgent need for catcher molecules such as antibodies for detecting the proteome or parts of the proteome in a microarray format. A suitable source for providing a large diversity of binders is obtained by combinatorial libraries, such as phage display libraries of single chain antibody fragments (scFv) or Fab fragments. To find novel binders from the n-CoDeR libraries with a high throughput, we have automated the screening process with robotics. The automated system is configured to screen tens of thousands of clones per day to target antigens in various formats, including peptides and soluble proteins, as well as cell-bound targets; thus, it is well designed to meet demands from the proteomics area.     

Automated screening for mutants affecting dopaminergic-neuron specification in C. elegans

We describe an automated method to isolate mutant Caenorhabditis elegans that do not appropriately execute cellular differentiation programs. We used a fluorescence-activated sorting mechanism implemented in the COPAS Biosort machine to isolate mutants with subtle alterations in the cellular specificity of GFP expression. This methodology is considerably more efficient than comparable manual screens and enabled us to isolate mutants in which dopamine neurons do not differentiate appropriately.     

Automated Bayesian model development for frequency detection in biological time series

Background

The ability to perform quantitative studies using isotope tracers and metabolic flux analysis (MFA) is critical for detecting pathway bottlenecks and elucidating network regulation in biological systems, especially those that have been engineered to alter their native metabolic capacities. Mathematically, MFA models are traditionally formulated using separate state variables for reaction fluxes and isotopomer abundances. Analysis of isotope labeling experiments using this set of variables results in a non-convex optimization problem that suffers from both implementation complexity and convergence problems.

Results

This article addresses the mathematical and computational formulation of ¹³C MFA models using a new set of variables referred to as fluxomers. These composite variables combine both fluxes and isotopomer abundances, which results in a simply-posed formulation and an improved error model that is insensitive to isotopomer measurement normalization. A powerful fluxomer iterative algorithm (FIA) is developed and applied to solve the MFA optimization problem. For moderate-sized networks, the algorithm is shown to outperform the commonly used 13CFLUX cumomer-based algorithm and the more recently introduced OpenFLUX software that relies upon an elementary metabolite unit (EMU) network decomposition, both in terms of convergence time and output variability.

Conclusions

Substantial improvements in convergence time and statistical quality of results can be achieved by applying fluxomer variables and the FIA algorithm to compute best-fit solutions to MFA models. We expect that the fluxomer formulation will provide a more suitable basis for future algorithms that analyze very large scale networks and design optimal isotope labeling experiments.     

Evolutionary increases in defense during a biological invasion

Invasive plants generally escape from specialist herbivores of their native ranges but may experience serious damage from generalists. As a result, invasive plants may evolve increased resistance to generalists and tolerance to damage. To test these hypotheses, we carried out a common garden experiment comparing 15 invasive populations with 13 native populations of Chromolaena odorata, including putative source populations identified with molecular methods and binary choice feeding experiments using three generalist herbivores. Plants from invasive populations of C. odorata had both higher resistance to three generalists and higher tolerance to simulated herbivory (shoot removal) than plants from native populations. The higher resistance of plants from invasive populations was associated with higher leaf C content and densities of leaf trichomes and glandular scales, and lower leaf N and water contents. Growth costs were detected for tolerance but not for resistance, and plants from invasive populations of C. odorata showed lower growth costs of tolerance. Our results suggest that invasive plants may evolve to increase both resistance to generalists and tolerance to damage in introduced ranges, especially when the defense traits have low or no fitness costs. Greater defenses in invasive populations may facilitate invasion by C. odorata by reducing generalist impacts and increasing compensatory growth after damage has occurred.