High resolution maps of chromatin reorganization through mouse meiosis reveal novel features of the 3D meiotic structure.

When germ cells transition from the mitotic cycle into meiotic prophase I (MPI), chromosomes condense into an array of chromatin loops that are required to promote homolog pairing and genetic recombination. To identify the changes in chromosomal conformation, we isolated nuclei on a trajectory from spermatogonia to the end of MPI. At each stage along this trajectory, we built genomic interaction maps with the highest temporal and spatial resolution to date. The changes in chromatin folding coincided with a concurrent decline in mitotic cohesion and a rise in meiotic cohesin complexes. We found that the stereotypical large-scale A and B compartmentalization was lost during meiotic prophase I alongside the loss of topological associating domains (TADs). Still, local subcompartments were detected and maintained throughout meiosis. The enhanced Micro-C resolution revealed that, despite the loss of TADs, higher frequency contact sites between two loci were detectable during meiotic prophase I coinciding with CTCF bound sites. The pattern of interactions around these CTCF sites with their neighboring loci showed that CTCF sites were often anchoring the meiotic loops. Additionally, the localization of CTCF to the meiotic axes indicated that these anchors were at the base of loops. Strikingly, even in the face of the dramatic reconfiguration of interphase chromatin into a condensed loop-array, the interactions between regulatory elements remained well preserved. This establishes a potential mechanism for how the meiotic chromatin maintains active transcription within a highly structured genome. In summary, the high temporal and spatial resolution of these data revealed previously unappreciated aspects of mammalian meiotic chromatin organization.

Quantitative RT-PCR methods for evaluating toxicant-induced effects on steroidogenesis using the H295R cell line.

Gene expression profiles show considerable promise for the evaluation of the toxic potential of environmental contaminants. For example, any alterations in the pathways of steroid synthesis or breakdown have the potential to Cause endocrine disruption. Therefore monitoring these pathways can provide information relative to a chemical's ability to impact endocrine function. One approach to monitoring these pathways has been to use a human adrenocortical carcinoma cell line (H295R) that expresses all the key enzymes necessary for steroidogenesis. In this study we have further developed these methods using accurate and specific quantification methods utilizing molecular beacon-based quantitative RT-PCR (Q-RT-PCR). The assay system was used to analyze the expression patterns of 11 steroidogenic genes in H295R cells. The expression of gene transcripts was measured using a real-time PCR system and quantified based on both a standard curve method using a dilution series of RNA standards and a comparative Ct method. To validate the optimized method, cells were exposed to specific and nonspecific model compounds (inducers and inhibitors of various steroidogenic enzymes) for gene expression profiling. Similar gene expression profiles were exhibited by cells treated with chemicals acting through common mechanisms of action. Overall, our findings demonstrated that the present assay can facilitate the development of compound-specific response profiles, and will provide a sensitive and integrative screen for the effects of chemicals on steroidogenesis.

A multiple regression model for urban traffic noise in Hong Kong.

This article describes the roadside traffic noise surveys conducted in heavily built-up urban areas in Hong Kong. Noise measurements were carried out along 18 major roads in 1999. The measurement data included L10, L50, L90, Leq, Lmax, the number of light vehicles, the number of heavy vehicles, the total traffic flow, and the average speed of vehicles. Statistical analysis using the analysis of variance (ANOVA) and Tukey test (p<0.05) reveals that the total traffic flow and the number of heavy vehicles are the most significant factors of urban traffic noise. Multiple regression was used to derive a set of empirical formulas for predicting L10 noise level due to road traffic. The accuracy of these empirical formulas is quantified and compared to that of another widely used prediction model in Hong Kong--the Calculation of Road Traffic Noise. The applicability of the selected multiple regression model is validated by the noise measurements performed in the winter of 2000.