Redistribution of Actin during Assembly and Reassembly of the Contractile Ring in Grasshopper Spermatocytes

Cytokinesis in animal cells requires the assembly of an actomyosin contractile ring to cleave the cell. The ring is highly dynamic; it assembles and disassembles during each cell cleavage, resulting in the recurrent redistribution of actin. To investigate this process in grasshopper spermatocytes, we mechanically manipulated the spindle to induce actin redistribution into ectopic contractile rings, around reassembled lateral spindles. To enhance visualization of actin, we folded the spindle at its equator to convert the remnants of the partially assembled ring into a concentrated source of actin. Filaments from the disintegrating ring aligned along reorganizing spindle microtubules, suggesting that their incorporation into the new ring was mediated by microtubules. We tracked incorporation by speckling actin filaments with Qdots and/or labeling them with Alexa 488-phalloidin. The pattern of movement implied that actin was transported along spindle microtubules, before entering the ring. By double-labeling dividing cells, we imaged actin filaments moving along microtubules near the contractile ring. Together, our findings indicate that in one mechanism of actin redistribution, actin filaments are transported along spindle microtubule tracks in a plus-end–directed fashion. After reaching the spindle midzone, the filaments could be transported laterally to the ring. Notably, actin filaments undergo a dramatic trajectory change as they enter the ring, implying the existence of a pulling force. Two other mechanisms of actin redistribution, cortical flow and de novo assembly, are also present in grasshopper, suggesting that actin converges at the nascent contractile ring from diffuse sources within the cytoplasm and cortex, mediated by spindle microtubules

TGF-β1 genotype and phenotype in breast cancer and their associations with IGFs and patient survival

Transforming growth factor-β (TGF-β)-mediated signals play complicated roles in the development and progression of breast tumour. The purposes of this study were to analyse the genotype of TGF-β1 at T29C and TGF-β1 phenotype in breast tumours, and to evaluate their associations with IGFs and clinical characteristics of breast cancer. Fresh tumour samples were collected from 348 breast cancer patients. TGF-β1 genotype and phenotype were analysed with TaqMan® and ELISA, respectively. Members of the IGF family in tumour tissue were measured with ELISA. Cox proportional hazards regression analysis was performed to assess the association of TGF-β1 and disease outcomes. Patients with the T/T (29%) genotype at T29C had the highest TGF-β1, 707.9 pg mg−1, followed by the T/C (49%), 657.8 pg mg−1, and C/C (22%) genotypes, 640.8 pg mg−1, (P=0.210, T/T vs C/C and C/T). TGF-β1 concentrations were positively correlated with levels of oestrogen receptor, IGF-I, IGF-II and IGFBP-3. Survival analysis showed TGF-β1 associated with disease progression, but the association differed by disease stage. For early-stage disease, patients with the T/T genotype or high TGF-β1 had shorter overall survival compared to those without T/T or with low TGF-β1; the hazard ratios (HR) were 3.54 (95% CI: 1.21–10.40) for genotype and 2.54 (95% CI: 1.10–5.89) for phenotype after adjusting for age, grade, histotype and receptor status. For late-stage disease, however, the association was different. The T/T genotype was associated with lower risk of disease recurrence (HR=0.13, 95% CI: 0.02–1.00), whereas no association was found between TGF-β1 phenotype and survival outcomes. The study suggests a complex role of TGF-β1 in breast cancer progression, which supports the finding of in vitro studies that TGF-β1 has conflicting effects on tumour growth and metastasis

SNP-SNP interactions in breast cancer susceptibility

BACKGROUND: Breast cancer predisposition genes identified to date (e.g., BRCA1 and BRCA2) are responsible for less than 5% of all breast cancer cases. Many studies have shown that the cancer risks associated with individual commonly occurring single nucleotide polymorphisms (SNPs) are incremental. However, polygenic models suggest that multiple commonly occurring low to modestly penetrant SNPs of cancer related genes might have a greater effect on a disease when considered in combination. METHODS: In an attempt to identify the breast cancer risk conferred by SNP interactions, we have studied 19 SNPs from genes involved in major cancer related pathways. All SNPs were genotyped by TaqMan 5'nuclease assay. The association between the case-control status and each individual SNP, measured by the odds ratio and its corresponding 95% confidence interval, was estimated using unconditional logistic regression models. At the second stage, two-way interactions were investigated using multivariate logistic models. The robustness of the interactions, which were observed among SNPs with stronger functional evidence, was assessed using a bootstrap approach, and correction for multiple testing based on the false discovery rate (FDR) principle. RESULTS: None of these SNPs contributed to breast cancer risk individually. However, we have demonstrated evidence for gene-gene (SNP-SNP) interaction among these SNPs, which were associated with increased breast cancer risk. Our study suggests cross talk between the SNPs of the DNA repair and immune system (XPD-[Lys751Gln] and IL10-[G(-1082)A]), cell cycle and estrogen metabolism (CCND1-[Pro241Pro] and COMT-[Met108/158Val]), cell cycle and DNA repair (BARD1-[Pro24Ser] and XPD-[Lys751Gln]), and within carcinogen metabolism (GSTP1-[Ile105Val] and COMT-[Met108/158Val]) pathways. CONCLUSION: The importance of these pathways and their communication in breast cancer predisposition has been emphasized previously, but their biological interactions through SNPs have not been described. The strategy used here has the potential to identify complex biological links among breast cancer genes and processes. This will provide novel biological information, which will ultimately improve breast cancer risk management