Raman spectroscopy detects phenotypic differences among Escherichia coli enriched for 1‐butanol tolerance using a metagenomic DNA library

Advances in Raman spectroscopy are enabling more comprehensive measurement of microbial cell chemical composition. Advantages include results returned in near real‐time and minimal sample preparation. In this research, Raman spectroscopy is used to analyze E. coli with engineered solvent tolerance, which is a multi‐genic trait associated with complex and uncharacterized phenotypes that are of value to industrial microbiology. To generate solvent tolerant phenotypes, E. coli transformed with DNA libraries are serially enriched in the presence of 0.9% (v/v) and 1.1% (v/v) 1‐butanol. DNA libraries are created using degenerate oligonucleotide primed PCR (DOP‐PCR) from the genomic DNA of E. coli, Clostridium acetobutylicum ATCC 824, and the metagenome of a stream bank soil sample, which contained DNA from 72 different phyla. DOP‐PCR enabled high efficiency library cloning (with no DNA shearing or end‐polishing) and the inclusion un‐culturable organisms. Nine strains with improved tolerance are analyzed by Raman spectroscopy and vastly different solvent‐tolerant phenotypes are characterized. Common among these are improved membrane rigidity from increasing the fraction of unsaturated fatty acids at the expense of cyclopropane fatty acids. Raman spectroscopy offers the ability to monitor cell phenotype changes in near real‐time and is adaptable to high‐throughput screening, making it relevant to metabolic engineering.     

Effects of genetic mutations and chemical exposures on Caenorhabditis elegans feeding: evaluation of a novel, high-throughput screening assay

Background

Government agencies have defined a need to reduce, refine or replace current mammalian-based bioassays with testing methods that use alternative species. Invertebrate species, such as Caenorhabditis elegans, provide an attractive option because of their short life cycles, inexpensive maintenance, and high degree of evolutionary conservation with higher eukaryotes. The C. elegans pharynx is a favorable model for studying neuromuscular function, and the effects of chemicals on neuromuscular activity, i.e., feeding. Current feeding methodologies, however, are labor intensive and only semi-quantitative.

Methodology/Principal Findings

Here a high-throughput assay is described that uses flow cytometry to measure C. elegans feeding by determining the size and intestinal fluorescence of hundreds of nematodes after exposure to fluorescent-labeled microspheres. This assay was validated by quantifying fluorescence in feeding-defective C. elegans (eat mutants), and by exposing wild-type nematodes to the neuroactive compounds, serotonin and arecoline. The eat mutations previously determined to cause slow pumping rates exhibited the lowest feeding levels with our assay. Concentration-dependent increases in feeding levels after serotonin exposures were dependent on food availability, while feeding levels decreased in arecoline-exposed nematodes regardless of the presence of food. The effects of the environmental contaminants, cadmium chloride and chlorpyrifos, on wild-type C. elegans feeding were then used to demonstrate an application of the feeding assay. Cadmium exposures above 200 µM led to a sharp drop in feeding levels. Feeding of chlorpyrifos-exposed nematodes decreased in a concentration-dependent fashion with an EC₅₀ of 2 µM.

Conclusions/Significance

The C. elegans fluorescence microsphere feeding assay is a rapid, reliable method for the assessment of neurotoxic effects of pharmaceutical drugs, industrial chemicals or environmental agents. This assay may also be applicable to large scale genetic or RNAi screens used to identify genes that are necessary for the development or function of the pharynx or other neuromuscular systems.     

Biological effects of power frequency magnetic fields: Neurochemical and toxicological changes in developing chick embryos

BACKGROUND: There are several reports that indicate a linkage between exposure to power frequency (50 - 60 Hz) magnetic fields with abnormalities in the early embryonic development of the chicken. The present study was designed to understand whether power frequency electromagnetic fields could act as an environmental insult and invoke any neurochemical or toxicological changes in developing chick embryo model. METHODS: Fertilized chicken eggs were subjected to continuous exposure to magnetic fields (50 Hz) of varying intensities (5, 50 or 100 microT) for a period of up to 15 days. The embryos were taken out of the eggs on day 5, day 10 and day 15. Neurochemical (norepinephrine and 5-hydroxytryptamine) and amino acid (tyrosine, glutamine and tryptophan) contents were measured, along with an assay of the enzyme glutamine synthetase in the brain. Preliminary toxicological investigations were carried out based on aminotransferases (AST and ALT) and lactate dehydrogenase activities in the whole embryo as well as in the liver. RESULTS: The study revealed that there was a significant increase (p < 0.01 and p < 0.001) in the level of norepinephrine accompanied by a significant decrease (p < 0.01 and p < 0.001) in the tyrosine content in the brain on day 15 following exposure to 5, 50 and 100 microT magnetic fields. There was a significant increase (p < 0.001) in glutamine synthetase activity resulting in the significantly enhanced (p < 0.001) level of glutamine in the brain on day 15 (for 100 microT only). The possible mechanisms for these alterations are discussed. Further, magnetic fields had no effect on the levels of tryptophan and 5-hydroxytryptamine in the brain. Similarly, there was no effect on the activity of either aminotransferases or lactate dehydrogenase in the whole embryo or liver due to magnetic field exposure. CONCLUSIONS: Based on these studies we conclude that magnetic field-induced changes in norepinephrine levels might help explain alterations in the circadian rhythm, observed during magnetic field stress. Also, the enhanced level of glutamine can act as a contributing factor for developmental abnormalities.     

Modeling and E-M estimation of haplotype-specific relative risks from genotype data for a case-control study of unrelated individuals

The US National Cancer Institute has recently sponsored the formation of a Cohort Consortium (http://2002.cancer.gov/scpgenes.htm) to facilitate the pooling of data on very large numbers of people, concerning the effects of genes and environment on cancer incidence. One likely goal of these efforts will be generate a large population-based case-control series for which a number of candidate genes will be investigated using SNP haplotype as well as genotype analysis. The goal of this paper is to outline the issues involved in choosing a method of estimating haplotype-specific risk estimates for such data that is technically appropriate and yet attractive to epidemiologists who are already comfortable with odds ratios and logistic regression. Our interest is to develop and evaluate extensions of methods, based on haplotype imputation, that have been recently described (Schaid et al., Am J Hum Genet, 2002, and Zaykin et al., Hum Hered, 2002) as providing score tests of the null hypothesis of no effect of SNP haplotypes upon risk, which may be used for more complex tasks, such as providing confidence intervals, and tests of equivalence of haplotype-specific risks in two or more separate populations. In order to do so we (1) develop a cohort approach towards odds ratio analysis by expanding the E-M algorithm to provide maximum likelihood estimates of haplotype-specific odds ratios as well as genotype frequencies; (2) show how to correct the cohort approach, to give essentially unbiased estimates for population-based or nested case-control studies by incorporating the probability of selection as a case or control into the likelihood, based on a simplified model of case and control selection, and (3) finally, in an example data set (CYP17 and breast cancer, from the Multiethnic Cohort Study) we compare likelihood-based confidence interval estimates from the two methods with each other, and with the use of the single-imputation approach of Zaykin et al. applied under both null and alternative hypotheses. We conclude that so long as haplotypes are well predicted by SNP genotypes (we use the Rh2 criteria of Stram et al. [1]) the differences between the three methods are very small and in particular that the single imputation method may be expected to work extremely well.