Antiobesity and Antidiabetic β-agonists: Lessons Learned and Questions to be Answered

In several species of obese animals, a group of phenethanolamine β-agonists stimulates lipolysis and thermogenesis, resulting in the loss of body fat and weight. Brown adipose tissue is considered to be the major target tissue for the antiobesity activity of these compounds. Independent of this antiobesity activity, some of these compounds are also antidiabetic and increase muscle mass. Based on the pharmacological profile of these com-pounds, a npeceptor was proposed and character-ized in mouse, rat, and humans. The 133-receptor in brown adipose tissue has been suggested to mediate the antiobesity activity of these 13-agonists. Whether this receptor is responsible for the antidiabetic activ-ity and whether there is a linkage between the antiobesity/antidiabetic activity and the nutrient par-titioning activity is not clear. Clinical trials with these mixed 13-agonists showed marginal antiobesity effects when caloric intake of subjects was restricted. Insulin sensitivity was also improved in some of the trials designed to test the antidiabetic activity of these compounds. Side effects included tachycardia and tremor. To eliminate these side effects, a second generation of compounds was selected for its agonist activity on rat D3-receptors. Clinical trials with these compounds have shown lit-tle increase of energy expenditure even at high doses. Successful development of an antiohesity and antidi-abetic drug from this class of compounds will require the elucidation of the physiological role of the human 133-receptor and the regulatory mecha-nism between fuel efficiency and feeding behavior.     

Selection of dieldrin-resistant strains of Lucilia cuprina (Diptera: Calliphoridae) after ethyl methanesulfonate mutagenesis of a susceptible strain.

After ethyl methanesulfonate (EMS) mutagenesis of a susceptible strain (SWT), selective screening of Lucilia cuprina (Wiedemann) resulted in four strains that were resistant to the insecticide dieldrin. Concentrations used for selection were greater than LC99 of susceptible phenotypes. No resistant variants were screened from the standard laboratory strain (SWT) not treated with EMS. The resistance phenotypes of the four resistant strains were similar to each other and to that of a field-selected resistant strain. The genetic basis of resistance is monogenic in all strains and the data are consistent with the same locus, Rdl, determining resistance status in each strain. The Rdl locus maps to chromosome V, approximately 3.5 map units distal to the Sut locus. Dieldrin resistance may be caused by less effective blocking of insect neuronal GABA receptors by the chemical in resistant strains. The data indicate that the evolution of resistance to an insecticide in the field may be constrained by a limited number of genetical and biochemical options if a monogenic response is selected for and that the spontaneous mutation rate to the Rdl allele is less than 1 in 10(6) in the laboratory.     

Peptide accumulation during growth of peptidase deficient mutants

Mutant strains of Salmonella typhimurium simultaneously lacking peptidases N, A, B and D accumulate a heterogeneous mixture of small, trichloroacetic acid-soluble peptides during growth in minimal medium. Approximately 20% of the labelled leucine supplied to a growing culture of the mutant strain is converted to peptides. These peptides accumulate inside the cells before being released into the growth medium. Although the origin of these peptides has not been established, there are several processes that might contribute peptides to this pool. These include (1) turnover of signal sequences, (2) turnover of attenuator peptides, and (3) degradation of prematurely terminated proteins. These results indicate that the same family of peptidases that catabolizes exogenously supplied peptides and functions in carbon-starvation-induced protein turnover also hydrolyzes peptides generated during normal exponential growth.     

Guidelines for model adaptation: A study of the transferability of a general seagrass ecosystem Dynamic Bayesian Networks model

In general, it is not feasible to collect enough empirical data to capture the entire range of processes that define a complex system, either intrinsically or when viewing the system from a different geographical or temporal perspective. In this context, an alternative approach is to consider model transferability, which is the act of translating a model built for one environment to another less well‐known situation. Model transferability and adaptability may be extremely beneficial—approaches that aid in the reuse and adaption of models, particularly for sites with limited data, would benefit from widespread model uptake. Besides the reduced effort required to develop a model, data collection can be simplified when transferring a model to a different application context. The research presented in this paper focused on a case study to identify and implement guidelines for model adaptation. Our study adapted a general Dynamic Bayesian Networks (DBN) of a seagrass ecosystem to a new location where nodes were similar, but the conditional probability tables varied. We focused on two species of seagrass (Zostera noltei and Zostera marina) located in Arcachon Bay, France. Expert knowledge was used to complement peer‐reviewed literature to identify which components needed adjustment including parameterization and quantification of the model and desired outcomes. We adopted both linguistic labels and scenario‐based elicitation to elicit from experts the conditional probabilities used to quantify the DBN. Following the proposed guidelines, the model structure of the general DBN was retained, but the conditional probability tables were adapted for nodes that characterized the growth dynamics in Zostera spp. population located in Arcachon Bay, as well as the seasonal variation on their reproduction. Particular attention was paid to the light variable as it is a crucial driver of growth and physiology for seagrasses. Our guidelines provide a way to adapt a general DBN to specific ecosystems to maximize model reuse and minimize re‐development effort. Especially important from a transferability perspective are guidelines for ecosystems with limited data, and how simulation and prior predictive approaches can be used in these contexts.