Progress in realizing the promise of microarrays in systems neurobiology

The power of microarrays in neuroscience has been challenged by the cellular heterogeneity and complexity of the central nervous system. In this issue of Neuron, Arlotta, Molyneaux, and colleagues have developed a technique combining retrograde labeling, flow cytometry, and microarrays to purify and molecularly characterize a specific population of neurons from the brain, focusing here on cortical projection neurons. We discuss these findings and the implications of this development for both systems and molecular neuroscience.     

Aerosolization as novel sanitizer delivery system to reduce food-borne pathogens

AIMS: As a preliminary experiment on new sanitizer delivery tools, the efficacy of aerosolized sanitizer on food-borne pathogens was investigated in larger model chamber system. METHODS: Peroxyacetic acid and hydrogen peroxide were aerosolized in a model system against artificially inoculated target micro-organisms on laboratory media. Cultures of four different food-borne pathogens were inoculated and affixed onto three different heights (bottom, wall and ceiling), and three different orientations (face-down, vertical and face-down) inside a commercial semi-trailer cabinet (14.6 x 2.6 x 2.8 m). Sanitizer was aerosolized into 2 microm droplet size fog and treated for 1 h at ambient temperature. RESULTS: Populations of Bacillus cereus, Listeria innocua, Staphylococcus aureus, and Salmonella typhimurium were reduced by an average of 3.09, 7.69, 6.93 and 8.18 log units per plate respectively. Interestingly, L. innocua, Staph. aureus, and Salm. typhimurium showed statistically not different (P >/= 0.05) reduction patterns relative to height and orientation that were never expected in a spraying system. CONCLUSIONS: Aerosolized sanitizers diffuse like gaseous sanitizers. SIGNIFICANCE AND IMPACT OF THE STUDY: Aerosolization has great potential for use in commercial applications.     

Identification of 113 conserved essential genes using a high-throughput gene disruption system in Streptococcus pneumoniae

The recent availability of bacterial genome sequence information permits the identification of conserved genes that are potential targets for novel antibiotic drug discovery. Using a coupled bioinformatic/experimental approach, a list of candidate conserved genes was generated using a Microbial Concordance bioinformatics tool followed by a targeted disruption campaign. Pneumococcal sequence data allowed for the design of precise PCR primers to clone the desired gene target fragments into the pEVP3 ‘suicide vector’. An insertion–duplication approach was employed that used the pEVP3 constructs and resulted in the introduction of a selectable chloramphenicol resistance marker into the chromosome. In the case of non-essential genes, cells can survive the disruption and form chloramphenicol-resistant colonies. A total of 347 candidate reading frames were subjected to disruption analysis, with 113 presumed to be essential due to lack of recovery of antibiotic-resistant colonies. In addition to essentiality determination, the same high-throughput methodology was used to overexpress gene products and to examine possible polarity effects for all essential genes.     

Subtraction-coupled custom microarray analysis for gene discovery and gene expression studies in the CNS

The revolution in our knowledge about the genomes of organisms gives rise to the question, what do we do with this information? The development of techniques allowing high throughput analysis of RNA and protein expression, such as cDNA microarrays, provide for genome-wide analysis of gene expression. These analyses will help bridge the gap between systems and molecular neuroscience. This review discusses the advantages of using a subtractive hybridization technique, such as a representational difference analysis, to generate a custom cDNA microarray enriched for genes relevant to investigating complex, heterogeneous tissues such as those involved in the chemical senses. Real and hypothetical examples of these experiments are discussed. Benefits of this approach over traditional microarray techniques include having a more relevant clone set, the potential for gene discovery and the creation of a new tool to investigate similar systems. Potential pitfalls may include PCR artifacts and the need for sequencing. However, these disadvantages can be overcome so that the coupling of subtraction techniques to microarray screening can be a fruitful approach to a variety of experimental systems.     

Inhibitory effects of high pressure and heat on Alicyclobacillus acidoterrestris spores in apple juice

The effectiveness of combined high pressure and heat treatment for reducing spore levels of Alicyclobacillus acidoterrestris, a thermoacidophilic spore-forming bacterium, in commercial pasteurized apple juice was investigated. Spores suspended in apple juice were successfully destroyed by combining high pressure with a mild or high temperature (45, 71, or 90 degrees C).     

Corrected small-sample estimation of the Bayes error

MOTIVATION: A major problem of pattern classification is estimation of the Bayes error when only small samples are available. One way to estimate the Bayes error is to design a classifier based on some classification rule applied to sample data, estimate the error of the designed classifier, and then use this estimate as an estimate of the Bayes error. Relative to the Bayes error, the expected error of the designed classifier is biased high, and this bias can be severe with small samples. RESULTS: This paper provides a correction for the bias by subtracting a term derived from the representation of the estimation error. It does so for Boolean classifiers, these being defined on binary features. Although the general theory applies to any Boolean classifier, a model is introduced to reduce the number of parameters. A key point is that the expected correction is conservative. Properties of the corrected estimate are studied via simulation. The correction applies to binary predictors because they are mathematically identical to Boolean classifiers. In this context the correction is adapted to the coefficient of determination, which has been used to measure nonlinear multivariate relations between genes and design genetic regulatory networks. An application using gene-expression data from a microarray experiment is provided on the website http://gspsnap.tamu.edu/smallsample/ (user:'smallsample', password:'smallsample)').     

Gene selection: a Bayesian variable selection approach

Selection of significant genes via expression patterns is an important problem in microarray experiments. Owing to small sample size and the large number of variables (genes), the selection process can be unstable. This paper proposes a hierarchical Bayesian model for gene (variable) selection. We employ latent variables to specialize the model to a regression setting and uses a Bayesian mixture prior to perform the variable selection. We control the size of the model by assigning a prior distribution over the dimension (number of significant genes) of the model. The posterior distributions of the parameters are not in explicit form and we need to use a combination of truncated sampling and Markov Chain Monte Carlo (MCMC) based computation techniques to simulate the parameters from the posteriors. The Bayesian model is flexible enough to identify significant genes as well as to perform future predictions. The method is applied to cancer classification via cDNA microarrays where the genes BRCA1 and BRCA2 are associated with a hereditary disposition to breast cancer, and the method is used to identify a set of significant genes. The method is also applied successfully to the leukemia data. SUPPLEMENTARY INFORMATION: http://stat.tamu.edu/people/faculty/bmallick.html.     

Probabilistic Boolean Networks: a rule-based uncertainty model for gene regulatory networks

MOTIVATION: Our goal is to construct a model for genetic regulatory networks such that the model class: (i) incorporates rule-based dependencies between genes; (ii) allows the systematic study of global network dynamics; (iii) is able to cope with uncertainty, both in the data and the model selection; and (iv) permits the quantification of the relative influence and sensitivity of genes in their interactions with other genes. RESULTS: We introduce Probabilistic Boolean Networks (PBN) that share the appealing rule-based properties of Boolean networks, but are robust in the face of uncertainty. We show how the dynamics of these networks can be studied in the probabilistic context of Markov chains, with standard Boolean networks being special cases. Then, we discuss the relationship between PBNs and Bayesian networks--a family of graphical models that explicitly represent probabilistic relationships between variables. We show how probabilistic dependencies between a gene and its parent genes, constituting the basic building blocks of Bayesian networks, can be obtained from PBNs. Finally, we present methods for quantifying the influence of genes on other genes, within the context of PBNs. Examples illustrating the above concepts are presented throughout the paper.