RESUMO
Proper chromosome alignment and segregation during mitosis depend on cohesion between sister chromatids. Cohesion is thought to occur through the entrapment of DNA within the tripartite ring (Smc1, Smc3 and Rad21) with enforcement from a fourth subunit (SA1/SA2). Surprisingly, cohesin rings do not play a major role in sister telomere cohesion. Instead, this role is replaced by SA1 and telomere binding proteins (TRF1 and TIN2). Neither the DNA binding property of SA1 nor this unique telomere cohesion mechanism is understood. Here, using single-molecule fluorescence imaging, we discover that SA1 displays two-state binding on DNA: searching by one-dimensional (1D) free diffusion versus recognition through subdiffusive sliding at telomeric regions. The AT-hook motif in SA1 plays dual roles in modulating non-specific DNA binding and subdiffusive dynamics over telomeric regions. TRF1 tethers SA1 within telomeric regions that SA1 transiently interacts with. SA1 and TRF1 together form longer DNA-DNA pairing tracts than with TRF1 alone, as revealed by atomic force microscopy imaging. These results suggest that at telomeres cohesion relies on the molecular interplay between TRF1 and SA1 to promote DNA-DNA pairing, while along chromosomal arms the core cohesin assembly might also depend on SA1 1D diffusion on DNA and sequence-specific DNA binding.
Assuntos
Segregação de Cromossomos/genética , Proteínas Nucleares/genética , Proteínas de Ligação a Telômeros/genética , Telômero/genética , Proteína 1 de Ligação a Repetições Teloméricas/genética , Motivos AT-Hook/genética , Cromátides/genética , Cromátides/ultraestrutura , Proteínas de Ligação a DNA/genética , Humanos , Microscopia de Força Atômica , Mitose/genética , Proteínas Nucleares/metabolismo , Telômero/ultraestrutura , Proteínas de Ligação a Telômeros/metabolismo , Proteína 1 de Ligação a Repetições Teloméricas/metabolismoRESUMO
Although most genetic association studies are performed with the intention of detecting nucleotide polymorphisms that are correlated with a complex trait, transcript abundance should also be expected to associate with diseases or phenotypes. We performed a scan for such quantitative trait transcripts in adult female heads of the fruit fly (Drosophila melanogaster) that might explain variation for nicotine resistance. The strongest association was seen for abundance of ornithine aminotransferase transcripts, implicating detoxification and neurotransmitter biosynthesis as mediators of the quantitative response to the drug. Subsequently, genetic analysis and metabolite profiling confirmed a complex role for ornithine and GABA levels in modification of survival time upon chronic nicotine exposure. Differences between populations from North Carolina and California suggest that the resistance mechanism may be an evolved response to environmental exposure.
Assuntos
Drosophila melanogaster/genética , Resistência a Medicamentos , Variação Genética , Longevidade , Nicotina/farmacologia , Animais , California , Drosophila melanogaster/efeitos dos fármacos , Feminino , Ligação Genética , Glutamato Descarboxilase/metabolismo , Isoenzimas/metabolismo , Camundongos , North Carolina , Ornitina-Oxo-Ácido Transaminase/metabolismo , Locos de Características Quantitativas , Ácido gama-Aminobutírico/metabolismoRESUMO
BACKGROUND: Populations diverge in genotype and phenotype under the influence of such evolutionary processes as genetic drift, mutation accumulation, and natural selection. Because genotype maps onto phenotype by way of transcription, it is of interest to evaluate how these evolutionary factors influence the structure of variation at the level of transcription. Here, we explore the distributions of cis-acting and trans-acting factors and their relative contributions to expression of transcripts that exhibit two or more classes of abundance among individuals within populations. RESULTS: Expression profiling using cDNA microarrays was conducted in Drosophila melanogaster adult female heads for 58 nearly isogenic lines from a North Carolina population and 50 from a California population. Using a mixture modeling approach, transcripts were identified that exhibit more than one mode of transcript abundance across the samples. Power studies indicate that sample sizes of 50 individuals will generally be sufficient to detect divergent transcript abundance classes. The distribution of transcript abundance classes is skewed toward low frequency minor classes, which is reminiscent of the typical skew in genotype frequencies. Similar results are observed in reported data on gene expression in human lymphoblast cell lines, in which analysis of association with linked polymorphisms implies that cis-acting single nucleotide polymorphisms make only a modest contribution to bimodal distributions of transcript abundance. CONCLUSION: Population surveys of gene expression may complement genetical genomics as a general approach to quantifying sources of transcriptional variation. Differential expression of transcripts among individuals is due to a complex interplay of cis-acting and trans-acting factors.