An iterative method for extracting energy-like quantities from protein structures.

We present a method (ENERGI) for extracting energy-like quantities from a data base of protein structures. In this paper, we use the method to generate pairwise additive amino acid "energy" scores. These scores are obtained by iteration until they correctly discriminate a set of known protein folds from decoy conformations. The method succeeds in lattice model tests and in the gapless threading problem as defined by Maiorov and Crippen [Maiorov, V. N. & Crippen, G. M. (1992) J. Mol. Biol. 227, 876-888]. A more challenging test of threading a larger set of test proteins derived from the representative set of Hobohm and Sander [Hobohm, U. & Sander, C. (1994) Protein Sci. 3, 522-524] is used as a "workbench" for exploring how the ENERGI scores depend on their parameter sets.

Serotonin receptor 1A knockout: An animal model of anxiety-related disorder

To investigate the contribution of individual serotonin (5-hydroxytryptamine; 5-HT) receptors to mood control, we have used homologous recombination to generate mice lacking specific serotonergic receptor subtypes. In the present report, we demonstrate that mice without 5-HT1A receptors display decreased exploratory activity and increased fear of aversive environments (open or elevated spaces). 5-HT1A knockout mice also exhibited a decreased immobility in the forced swim test, an effect commonly associated with antidepressant treatment. Although 5-HT1A receptors are involved in controlling the activity of serotonergic neurons, 5-HT1A knockout mice had normal levels of 5-HT and 5-hydroxyindoleacetic acid, possibly because of an up-regulation of 5-HT1B autoreceptors. Heterozygote 5-HT1A mutants expressed approximately one-half of wild-type receptor density and displayed intermediate phenotypes in most behavioral tests. These results demonstrate that 5-HT1A receptors are involved in the modulation of exploratory and fear-related behaviors and suggest that reductions in 5-HT1A receptor density due to genetic defects or environmental stressors might result in heightened anxiety.

Ts1Cje, a partial trisomy 16 mouse model for Down syndrome, exhibits learning and behavioral abnormalities

A mouse model for Down syndrome, Ts1Cje, has been developed. This model has made possible a step in the genetic dissection of the learning, behavioral, and neurological abnormalities associated with segmental trisomy for the region of mouse chromosome 16 homologous with the so-called “Down syndrome region” of human chromosome segment 21q22. Tests of learning in the Morris water maze and assessment of spontaneous locomotor activity reveal distinct learning and behavioral abnormalities, some of which are indicative of hippocampal dysfunction. The triplicated region in Ts1Cje, from Sod1 to Mx1, is smaller than that in Ts65Dn, another segmental trisomy 16 mouse, and the learning deficits in Ts1Cje are less severe than those in Ts65Dn. In addition, degeneration of basal forebrain cholinergic neurons, which was observed in Ts65Dn, was absent in Ts1Cje.

Trials of the beta model for complex inheritance.

Theoretical advantages of nonparametric logarithm of odds to map polygenic diseases are supported by tests of the beta model that depends on a single logistic parameter and is the only model under which paternal and maternal transmissions to sibs of specified phenotypes are independent. Although it does not precisely describe recurrence risks in monozygous twins, the beta model has greater power to detect family resemblance or linkage than the more general delta model which describes the probability of 0, 1, or 2 alleles identical by descent (ibd) with two parameters. Available data on ibd in sibs are consistent with the beta model, but not with the equally parsimonious but less powerful gamma model that assumes a fixed probability of 1/2 for 1 allele ibd. Additivity of loci on the liability scale is not disproven. A simple equivalence extends the beta model to multipoint analysis.

Adaptive mutations in Escherichia coli as a model for the multiple mutational origins of tumors.

The cells in most tumors are found to carry multiple mutations; however, based upon mutation rates determined by fluctuation tests, the frequency of such multiple mutations should be so low that tumors are never detected within human populations. Fluctuation tests, which determine the cell-division-dependent mutation rate per cell generation in growing cells, may not be appropriate for estimating mutation rates in nondividing or very slowly dividing cells. Recent studies of time-dependent, "adaptive" mutations in nondividing populations of microorganisms suggest that similar measurements may be more appropriate to understanding the mutation origins of tumors. Here I use the ebgR and ebgA genes of Escherichia coli to measure adaptive mutation rates where multiple mutations are required for rapid growth. Mutations in either ebgA or ebgR allow very slow growth on lactulose (4-O-beta-D-galactosyl-D-fructose), with doubling times of 3.2 and 17.3 days, respectively. However, when both mutations are present, cells can grow rapidly with doubling times of 2.7 hr. I show that during prolonged (28-day) selection for growth on lactulose, the number of lactulose-utilizing mutants that accumulate is 40,000 times greater than can be accounted for on the basis of mutation rates measured by fluctuation tests, but is entirely consistent with the time-dependent adaptive mutation rates measured under the same conditions of prolonged selection.