The Gene Encoding the Catalytic Subunit Cα of cAMP-Dependent Protein Kinase (Locus PRKACA) Localizes to Human Chromosome Region 19p13.1

We have determined the chromosomal localization of the gene for the catalytic subunit Cα of cAMP-dependent protein kinase (locus PRKACA) to human chromosome 19 using polymerase chain reaction (PCR) and Southern blot analysis of two different somatic cell hybrid mapping panels. In addition, PCR analysis of a chromosome 19 mapping panel revealed the presence of a human Cα-specific amplification product only in cell lines containing the region 19p13.1 to 19q12. Finally, two-color fluorescencein situhybridization to metaphase chromosomes using the human Cα cDNA and human chromosome 19 inter-Alu-PCR product as probes localized the human Cα gene to chromosome region 19p13.1.     

Synthesis, molecular modeling studies and biological evaluation of fluorine substituted analogs of GW 501516

(±)-2-Fluoro-2-(2-methyl-4-(((4-methyl-2-(4-(trifluoromethyl)phenyl)thiazol-5-yl)methyl)thio)phenoxy)acetic acid (2a) has been prepared and subjected to biological testing against all three subtypes of the PPARs. This compound exhibited agonist effects with EC(50) values of 560 and 55 nM against PPARα and PPARδ, respectively, in a luciferase assay. Moreover, compound (±)-2a also exhibited potent ability to induce oleic acid oxidation in a human myotube cell assay with EC(50)=3.7 nM. Compound (±)-2a can be classified as a dual PPARα/δ agonist with a 10-fold higher potency against the PPARδ receptor than against the PPARα receptor. Molecular modeling studies revealed that both enantiomers of 2a bind to the PPARδ receptor with similar binding energies.     

Assignment of the gene encoding the catalytic subunit Cβ of CAMP-dependent protein kinase to the p36 band on chromosome

Summary A cDNA for the human catalytic subunit (C) of cAMP-dependent protein kinase (PKA) has been cloned from a testis cDNA library. In the present study, we have determined the chromosomal localization of this gene using a cDNA for C as a probe. Southern blot analysis of genomic DNA from human/mouse cell hybrids revealed that the presence or absence of a 20-kbXbaI fragment, which hybridized with the C probe, was concordant with the presence of human chromosome 1.In situ hybridization to metaphase chromosome confirmed the somatic cell hybrid data and regionally mapped the C gene of PKA to the p36 band on chromosome 1.     

Isatin 1,2,3-triazoles as potent inhibitors against caspase-3

Sixteen disubstituted 1,2,3-triazoles were prepared using the Huisgen cycloaddition reaction and evaluated as inhibitors against caspase-3. The two most potent inhibitors were found to be (S)-1-((1-(2,3-dihydrobenzo[b][1,4]dioxin-6-yl)-1H-1,2,3-triazol-4-yl)methyl)-5-((2-(methoxymethyl)pyrrolidin-1-yl)sulfonyl)indoline-2,3-dione (7f) and (S)-1-((1-benzyl-1H-1,2,3-triazol-5-yl)methyl)-5-((2-(methoxymethyl)pyrrolidin-1-yl)sulfonyl)indoline-2,3-dione (8g) with IC₅₀-values of 17 and 9 nM, respectively. Lineweaver-Burk plots revealed that these two triazoles show competitive inhibitory mechanism against caspase-3.     

The extended Moran effect and large-scale synchronous fluctuations in the size of great tit and blue tit populations

1. Synchronous fluctuations of geographically separated populations are in general explained by the Moran effect, i.e. a common influence on the local population dynamics of environmental variables that are correlated in space. Empirical support for such a Moran effect has been difficult to provide, mainly due to problems separating out effects of local population dynamics, demographic stochasticity and dispersal that also influence the spatial scaling of population processes. Here we generalize the Moran effect by decomposing the spatial autocorrelation function for fluctuations in the size of great tit Parus major and blue tit Cyanistes caeruleus populations into components due to spatial correlations in the environmental noise, local differences in the strength of density regulation and the effects of demographic stochasticity. 2. Differences between localities in the strength of density dependence and nonlinearity in the density regulation had a small effect on population synchrony, whereas demographic stochasticity reduced the effects of the spatial correlation in environmental noise on the spatial correlations in population size by 21.7% and 23.3% in the great tit and blue tit, respectively. 3. Different environmental variables, such as beech mast and climate, induce a common environmental forcing on the dynamics of central European great and blue tit populations. This generates synchronous fluctuations in the size of populations located several hundred kilometres apart. 4. Although these environmental variables were autocorrelated over large areas, their contribution to the spatial synchrony in the population fluctuations differed, dependent on the spatial scaling of their effects on the local population dynamics. We also demonstrate that this effect can lead to the paradoxical result that a common environmental variable can induce spatial desynchronization of the population fluctuations. 5. This demonstrates that a proper understanding of the ecological consequences of environmental changes, especially those that occur simultaneously over large areas, will require information about the spatial scaling of their effects on local population dynamics.     

Contrasting consequences of climate change for migratory geese: Predation,density dependence and carryover effects offset benefits of high‐arctic warming

Climate change is most rapid in the Arctic, posing both benefits and challenges for migratory herbivores. However, population‐dynamic responses to climate change are generally difficult to predict, due to concurrent changes in other trophic levels. Migratory species are also exposed to contrasting climate trends and density regimes over the annual cycle. Thus, determining how climate change impacts their population dynamics requires an understanding of how weather directly or indirectly (through trophic interactions and carryover effects) affects reproduction and survival across migratory stages, while accounting for density dependence. Here, we analyse the overall implications of climate change for a local non‐hunted population of high‐arctic Svalbard barnacle geese, Branta leucopsis, using 28 years of individual‐based data. By identifying the main drivers of reproductive stages (egg production, hatching and fledging) and age‐specific survival rates, we quantify their impact on population growth. Recent climate change in Svalbard enhanced egg production and hatching success through positive effects of advanced spring onset (snow melt) and warmer summers (i.e. earlier vegetation green‐up) respectively. Contrastingly, there was a strong temporal decline in fledging probability due to increased local abundance of the Arctic fox, the main predator. While weather during the non‐breeding season influenced geese through a positive effect of temperature (UK wintering grounds) on adult survival and a positive carryover effect of rainfall (spring stopover site in Norway) on egg production, these covariates showed no temporal trends. However, density‐dependent effects occurred throughout the annual cycle, and the steadily increasing total flyway population size caused negative trends in overwinter survival and carryover effects on egg production. The combination of density‐dependent processes and direct and indirect climate change effects across life history stages appeared to stabilize local population size. Our study emphasizes the need for holistic approaches when studying population‐dynamic responses to global change in migratory species.     

Ha-ras-1 alleles in Norwegian lung cancer patients

Summary We have examined DNA restriction fragment length polymorphisms (RFLP) of the Ha-ras-1 gene in DNA from 118 lung cancer patients and 123 unaffected controls. When DNA samples were digested with MspI/ HpaII restriction endonucleases. Southern blot analysis demonstrated 4 common, 4 intermediate and 7 different rare alleles in the combined population after hybridization to the pGDa1 probe. Six of the rare alleles were unique for the lung cancer group and 1 rare allele for the control group. The frequency of rare alleles in lung cancer patients (10/236) was significantly different (P<0.01) from the control group (1/246). The lung cancer group also had a significantly lower frequency of the common 2.57 kb fragment than the controls (P<0.02). The results thus indicate that Ha-ras genotyping may be of value in lung cancer risk assessment.