Disease-associated pathophysiologic structures in pediatric rheumatic diseases show characteristics of scale-free networks seen in physiologic systems: implications for pathogenesis and treatment

Background

Tamoxifen (TAM) is a well characterized breast cancer drug and selective estrogen receptor modulator (SERM) which also has been associated with a small increase in risk for uterine cancers. TAM's partial agonist activation of estrogen receptor has been characterized for specific gene promoters but not at the genomic level in vivo.Furthermore, reducing uncertainties associated with cross-species extrapolations of pharmaco- and toxicogenomic data remains a formidable challenge.

Results

A comparative ligand and species analysis approach was conducted to systematically assess the physiological, morphological and uterine gene expression alterations elicited across time by TAM and ethynylestradiol (EE) in immature ovariectomized Sprague-Dawley rats and C57BL/6 mice. Differential gene expression was evaluated using custom cDNA microarrays, and the data was compared to identify conserved and divergent responses. 902 genes were differentially regulated in all four studies, 398 of which exhibit identical temporal expression patterns.

Conclusion

Comparative analysis of EE and TAM differentially expressed gene lists suggest TAM regulates no unique uterine genes that are conserved in the rat and mouse. This demonstrates that the partial agonist activities of TAM extend to molecular targets in regulating only a subset of EE-responsive genes. Ligand-conserved, species-divergent expression of carbonic anhydrase 2 was observed in the microarray data and confirmed by real time PCR. The identification of comparable temporal phenotypic responses linked to related gene expression profiles demonstrates that systematic comparative genomic assessments can elucidate important conserved and divergent mechanisms in rodent estrogen signalling during uterine proliferation.     

Genetic Network Analyzer: qualitative simulation of genetic regulatory networks

MOTIVATION: The study of genetic regulatory networks has received a major impetus from the recent development of experimental techniques allowing the measurement of patterns of gene expression in a massively parallel way. This experimental progress calls for the development of appropriate computer tools for the modeling and simulation of gene regulation processes. RESULTS: We present Genetic Network Analyzer (GNA), a computer tool for the modeling and simulation of genetic regulatory networks. The tool is based on a qualitative simulation method that employs coarse-grained models of regulatory networks. The use of GNA is illustrated by a case study of the network of genes and interactions regulating the initiation of sporulation in Bacillus subtilis. AVAILABILITY: GNA and the model of the sporulation network are available at http://www-helix.inrialpes.fr/gna.     

The molecular basis of spinocerebellar ataxia type 48 caused by a de novo mutation in the ubiquitin ligase CHIP

The spinocerebellar ataxias (SCAs) are a class of incurable diseases characterized by degeneration of the cerebellum that results in movement disorder. Recently, a new heritable form of SCA, spinocerebellar ataxia type 48 (SCA48), was attributed to dominant mutations in STIP1 homology and U box-containing 1 (STUB1); however, little is known about how these mutations cause SCA48. STUB1 encodes for the protein C terminus of Hsc70 interacting protein (CHIP), an E3 ubiquitin ligase. CHIP is known to regulate proteostasis by recruiting chaperones via a N-terminal tetratricopeptide repeat domain and recruiting E2 ubiquitin-conjugating enzymes via a C-terminal U-box domain. These interactions allow CHIP to mediate the ubiquitination of chaperone-bound, misfolded proteins to promote their degradation via the proteasome. Here we have identified a novel, de novo mutation in STUB1 in a patient with SCA48 encoding for an A52G point mutation in the tetratricopeptide repeat domain of CHIP. Utilizing an array of biophysical, biochemical, and cellular assays, we demonstrate that the CHIP^A52G point mutant retains E3-ligase activity but has decreased affinity for chaperones. We further show that this mutant decreases cellular fitness in response to certain cellular stressors and induces neurodegeneration in a transgenic Caenorhabditis elegans model of SCA48. Together, our data identify the A52G mutant as a cause of SCA48 and provide molecular insight into how mutations in STUB1 cause SCA48.     

Lipid peroxidation in purified plasma membrane fractions of rat liver in relation to the hepatoxicity of carbon tetrachloride

Preparations of rat liver sinusoidal plasma membrane have been tested for their ability to metabolize the hepatotoxin carbon tetrachloride (CCl4) to reactive free radicals in vitro and compared in this respect with standard preparations of rat liver microsomes. The sinusoidal plasma membranes were relatively free of endoplasmic reticulum-associated activities such as the enzymes of the cytochrome P450 system and glucose-6-phosphatase. CCl4 metabolism was measured as (i) covalent binding of [14C]-CCl4 to membrane protein, (ii) electron spin resonance spin-trapping of CCl3. radicals and (iii) CCl4-induced lipid peroxidation. By all of these tests, purified sinusoidal plasma membranes were found unable to metabolize CCl4. The fatty acid composition of the plasma membranes was almost identical to that of the microsomal preparation and both membrane fractions exhibited similar rates of the lipid peroxidation that was stimulated non-enzymically by gamma-radiation or incubation with ascorbate and iron. The absence of CCl4-induced lipid peroxidation in the plasma membranes seems to be due, therefore, to an absence of CCl4 activation rather than an inherent resistance to lipid peroxidation. We conclude that damage to the hepatocyte plasma membrane during CCl4 intoxication is not due to a significant local activation of CCl4 to CCl3. within that membrane.     

Sampling Issues Affecting Accuracy of Likelihood-based Classification Using Genetical Data

We demonstrate the effectiveness of a genetic algorithm for discovering multi-locus combinations that provide accurate individual assignment decisions and estimates of mixture composition based on likelihood classification. Using simulated data representing different levels of inter-population differentiation (F_st～ 0.01 and 0.10), genetic diversities (four or eight alleles per locus), and population sizes (20, 40, 100 individuals in baseline populations), we show that subsets of loci can be identified that provide comparable levels of accuracy in classification decisions relative to entire multi-locus data sets, where 5, 10, or 20 loci were considered. Microsatellite data sets from hatchery strains of lake trout, Salvelinus namaycush, representing a comparable range of inter-population levels of differentiation in allele frequencies confirmed simulation results. For both simulated and empirical data sets, assignment accuracy was achieved using fewer loci (e.g., three or four loci out of eight for empirical lake trout studies). Simulation results were used to investigate properties of the ‘leave-one-out’ (L1O) method for estimating assignment error rates. Accuracy of population assignments based on L1O methods should be viewed with caution under certain conditions, particularly when baseline population sample sizes are low (<50).