M1 and m2 muscarinic receptor subtypes regulate antidepressant-like effects of the rapidly acting antidepressant scopolamine.

Scopolamine produces rapid and significant symptom improvement in patients with depression, and most notably in patients who do not respond to current antidepressant treatments. Scopolamine is a nonselective muscarinic acetylcholine receptor antagonist, and it is not known which one or more of the five receptor subtypes in the muscarinic family are mediating these therapeutic effects. We used the mouse forced-swim test, an antidepressant detecting assay, in wild-type and transgenic mice in which each muscarinic receptor subtype had been genetically deleted to define the relevant receptor subtypes. Only the M1 and M2 knockout (KO) mice had a blunted response to scopolamine in the forced-swim assay. In contrast, the effects of the tricyclic antidepressant imipramine were not significantly altered by gene deletion of any of the five muscarinic receptors. The muscarinic antagonists biperiden, pirenzepine, and VU0255035 (N-[3-oxo-3-[4-(4-pyridinyl)-1-piper azinyl]propyl]-2,1,3-benzothiadiazole-4-sulfonamide) with selectivity for M1 over M2 receptors also demonstrated activity in the forced-swim test, which was attenuated in M1 but not M2 receptor KO mice. An antagonist with selectivity of M2 over M1 receptors (SCH226206 [(2-amino-3-methyl-phenyl)-[4-[4-[[4-(3 chlorophenyl)sulfonylphenyl]methyl]-1-piperidyl]-1-piperidyl]methanone]) was also active in the forced-swim assay, and the effects were deleted in M2 (-/-) mice. Brain exposure and locomotor activity in the KO mice demonstrated that these behavioral effects of scopolamine are pharmacodynamic in nature. These data establish muscarinic M1 and M2 receptors as sufficient to generate behavioral effects consistent with an antidepressant phenotype and therefore as potential targets in the antidepressant effects of scopolamine.

Rapid method to estimate the presence of secondary metabolites in microbial extracts.

Screening microbial secondary metabolites is an established method to identify novel biologically active molecules. Preparation of biological screening samples from microbial fermentation extracts requires growth conditions that promote synthesis of secondary metabolites and extraction procedures that capture the secondary metabolites produced. High-performance liquid chromatography (HPLC) analysis of fermentation extracts can be used to estimate the number of secondary metabolites produced by microorganisms under various growth conditions but is slow. In this study we report on a rapid (approximately 1 min per assay) surrogate measure of secondary metabolite production based on a metabolite productivity index computed from the electrospray mass spectra of samples injected directly into a spectrometer. This surrogate measure of productivity was shown to correlate with an HPLC measure of productivity with a coefficient of 0.78 for a test set of extracts from 43 actinomycetes. This rapid measure of secondary metabolite productivity may be used to identify improved cultivation and extraction conditions by analyzing and ranking large sets of extracts. The same methods may also be used to survey large collections of extracts to identify subsets of highly productive organisms for biological screening or additional study.

Use of direct-infusion electrospray mass spectrometry to guide empirical development of improved conditions for expression of secondary metabolites from actinomycetes.

A major barrier in the discovery of new secondary metabolites from microorganisms is the difficulty of distinguishing the minor fraction of productive cultures from the majority of unproductive cultures and growth conditions. In this study, a rapid, direct-infusion electrospray mass spectrometry (ES-MS) technique was used to identify chemical differences that occurred in the expression of secondary metabolites by 44 actinomycetes cultivated under six different fermentation conditions. Samples from actinomycete fermentations were prepared by solid-phase extraction, analyzed by ES-MS, and ranked according to a chemical productivity index based on the total number and relative intensity of ions present in each sample. The actinomycete cultures were tested for chemical productivity following treatments that included nutritional manipulations, autoregulator additions, and different agitation speeds and incubation temperatures. Evaluation of the ES-MS data from submerged and solid-state fermentations by paired t test analyses showed that solid-state growth significantly altered the chemical profiles of extracts from 75% of the actinomycetes evaluated. Parallel analysis of the same extracts by high-performance liquid chromatography-ES-MS-evaporative light scattering showed that the chemical differences detected by the ES-MS method were associated with growth condition-dependent changes in the yield of secondary metabolites. Our results indicate that the high-throughput ES-MS method is useful for identification of fermentation conditions that enhance expression of secondary metabolites from actinomycetes.

Assessing the relative accuracies of two screening tests in the presence of verification bias.

Epidemiological studies of dementia often use two-stage designs because of the relatively low prevalence of the disease and the high cost of ascertaining a diagnosis. The first stage of a two-stage design assesses a large sample with a screening instrument. Then, the subjects are grouped according to their performance on the screening instrument, such as poor, intermediate and good performers. The second stage involves a more extensive diagnostic procedure, such as a clinical assessment, for a particular subset of the study sample selected from each of these groups. However, not all selected subjects have the clinical diagnosis because some subjects may refuse and others are unable to be clinically assessed. Thus, some subjects screened do not have a clinical diagnosis. Furthermore, whether a subject has a clinical diagnosis depends not only on the screening test result but also on other factors, and the sampling fractions for the diagnosis are unknown and have to be estimated. One of the goals in these studies is to assess the relative accuracies of two screening tests. Any analysis using only verified cases may result in verification bias. In this paper, we propose the use of two bootstrap methods to construct confidence intervals for the difference in the accuracies of two screening tests in the presence of verification bias. We illustrate the application of the proposed methods to a simulated data set from a real two-stage study of dementia that has motivated this research.

COMPROC and CHECKNORM: computer programs for comparing accuracies of diagnostic tests using ROC curves in the presence of verification bias.

To assess relative accuracies of two diagnostic tests, we often compare the areas under the receiver operating characteristic (ROC) curves of these two tests in a paired design. Standard methods for analyzing data from a paired design require that every patient tested has the known disease status. In practice, however, some of the patients with test results may not have verified disease status. Any analysis using only verified cases may result in verification bias. COMPROC is an easy to use program for comparing the effectiveness of two diagnostic tests based on the area under the ROC curve in the presence of verification bias. COMPROC compensates for verification bias by implementing the maximum likelihood (ML) estimation of the areas and covariance matrix of two ROC curves under the missing at random (MAR) assumption as described by Zhou (Biometrics 54 (1998) 349-366). This method assumes normality of the difference of the two ROC curve area estimators. We also describe a program CHECKNORM that does a bootstrap analysis to test this normality assumption (B. Efron, R.J. Tibshirani, An Introduction to the Bootstrap, Chapman and Hall, London, 1993). COMPROC allows for the inclusion of observed covariates that may influence the decision to verify the disease status of a patient. The program computes the estimates of the area under the ROC curve for the two diagnostic tests along with the variance of each area, the covariance between the two areas, a two-sided p-value, and a confidence interval for the difference of the areas. The programs COMPROC and CHECKNORM require the scripting language Perl and the statistical software SAS and can be run on both UNIX machines as well as PCs. The use of COMPROC and CHECKNORM is illustrated in a clinical study designed to compare relative accuracies of MRI and CT in detecting pancreatic cancer.

A method for quantitatively differentiating crude natural extracts using high-performance liquid chromatography-electrospray mass spectrometry.

This paper describes a method for quantitatively differentiating crude natural extracts using high-performance liquid chromatography-electrospray mass spectrometry (HPLC-ESI-MS). The method involves performing an HPLC-MS analysis using standard reversed-phase C18 gradient separation on the crude extract. The HPLC system used in this study was a dual-column system designed to optimize throughput. Using image analysis techniques, the data are reduced to a list containing the m/z value and retention time of each ion. The ion lists are then compared in a pairwise fashion to compute a sample similarity index between two samples. The similarity index is based on the number of ions common to both and is scaled from 0 to 1. Extract controls were analyzed throughout a run of 88 unknown fungal extracts. The controls provided information about column and spectrometer stability and overall sensitivity. Pairwise comparison of all control samples indicates that the similarity index is high (0.8) for replicate samples. Comparison between the unknown extract samples produces a distribution of similarities ranging from replicates (0.8) to very dissimilar (0.1). This information can be used to judge the chemical diversity of natural extract samples, which is one approach to determining the quality of libraries being used for drug discovery via high-throughput screening.

Application of automatic image segmentation to tibiae and vertebrae from ovariectomized rats.

Automatic contextual segmentation algorithms were developed to objectively identify bone compartments in pQCT images of tibiae, femora, and vertebrae. Principal advantages of this approach over existing techniques such as histomorphometry are as follows: (a) the algorithms can be implemented in a fast, uniform, nonsubjective manner across many images, allowing unbiased comparisons of therapeutic efficacy; (b) much larger volumes in the region of interest can be analyzed to derive true volumetric parameters for trabecular and cortical bone compartments; and (c) pQCT can be used to quantitate bone effects longitudinally in vivo. An automatic contextual segmentation algorithm was used to analyze over 600 scans of proximal tibiae, distal femora, and L-4 vertebrae from studies with ovariectomized rats. Accuracy and precision analyses were performed, and correlation to histomorphometry parameters showed that pQCT trabecular bone density correlates to Tb.N with r = 0.93, while BV/TV correlates to Tb.N with r = 0.95. In other words, pQCT correlates as well to histomorphometry as histomorphometry does to itself. We conclude that the developed automatic segmentation algorithm provides fast, precise, and objective quantitation of bone compartments that are highly correlated with histomorphometry measurements.