S100A2 is a BRCA1/p63 coregulated tumour suppressor gene with roles in the regulation of mutant p53 stability

Here, we show for the first time that the familial breast/ovarian cancer susceptibility gene, BRCA1, along with interacting ΔNp63 proteins, transcriptionally upregulate the putative tumour suppressor protein, S100A2. Both BRCA1 and ΔNp63 proteins are required for S100A2 expression. BRCA1 requires ΔNp63 proteins for recruitment to the S100A2 proximal promoter region, while exogenous expression of individual ΔNp63 proteins cannot activate S100A2 transcription in the absence of a functional BRCA1. Consequently, mutation of the ΔNp63/p53 response element within the S100A2 promoter completely abrogates the ability of BRCA1 to upregulate S100A2. S100A2 shows growth control features in a range of cell models. Transient or stable exogenous S100A2 expression inhibits the growth of BRCA1 mutant and basal-like breast cancer cell lines, while short interfering RNA (siRNA) knockdown of S100A2 in non-tumorigenic cells results in enhanced proliferation. S100A2 modulates binding of mutant p53 to HSP90, which is required for efficient folding of mutant p53 proteins, by competing for binding to HSP70/HSP90 organising protein (HOP). HOP is a cochaperone that is required for the efficient transfer of proteins from HSP70 to HSP90. Loss of S100A2 leads to an HSP90-dependent stabilisation of mutant p53 with a concomitant loss of p63. Accordingly, S100A2-deficient cells are more sensitive to the HSP-90 inhibitor, 17-N-allylamino-17-demethoxygeldanamycin, potentially representing a novel therapeutic strategy for S100A2- and BRCA1-deficient cancers. Taken together, these data demonstrate the importance of S100A2 downstream of the BRCA1/ΔNp63 signalling axis in modulating transcriptional responses and enforcing growth control mechanisms through destabilisation of mutant p53.

Small leucine rich proteoglycan family regulates multiple signalling pathways in neural development and maintenance

The small leucine-rich repeat proteoglycan (SLRPs) family of proteins currently consists of five classes, based on their structural composition and chromosomal location. As biologically active components of the extracellular matrix (ECM), SLRPs were known to bind to various collagens, having a role in regulating fibril assembly, organization and degradation. More recently, as a function of their diverse proteins cores and glycosaminoglycan side chains, SLRPs have been shown to be able to bind various cell surface receptors, growth factors, cytokines and other ECM components resulting in the ability to influence various cellular functions. Their involvement in several signaling pathways such as Wnt, transforming growth factor-β and epidermal growth factor receptor also highlights their role as matricellular proteins. SLRP family members are expressed during neural development and in adult neural tissues, including ocular tissues. This review focuses on describing SLRP family members involvement in neural development with a brief summary of their role in non-neural ocular tissues and in response to neural injury.

Automated tumour annotation and analysis for molecular pathology using TissueMark (R)

Objective: Molecular pathology relies on identifying anomalies using PCR or analysis of DNA/RNA. This is important in solid tumours where molecular stratification of patients define targeted treatment. These molecular biomarkers rely on examination of tumour, annotation for possible macro dissection/tumour cell enrichment and the estimation of % tumour. Manually marking up tumour is error prone. Method: We have developed a method for automated tumour mark-up and % cell calculations using image analysis called TissueMark® based on texture analysis for lung, colorectal and breast (cases=245, 100, 100 respectively). Pathologists marked slides for tumour and reviewed the automated analysis. A subset of slides was manually counted for tumour cells to provide a benchmark for automated image analysis. Results: There was a strong concordance between pathological and automated mark-up (100 % acceptance rate for macro-dissection). We also showed a strong concordance between manually/automatic drawn boundaries (median exclusion/inclusion error of 91.70 %/89 %). EGFR mutation analysis was precisely the same for manual and automated annotation-based macrodissection. The annotation accuracy rates in breast and colorectal cancer were 83 and 80 % respectively. Finally, region-based estimations of tumour percentage using image analysis showed significant correlation with actual cell counts. Conclusion: Image analysis can be used for macro-dissection to (i) annotate tissue for tumour and (ii) estimate the % tumour cells and represents an approach to standardising/improving molecular diagnostics.

Neural basis of language switching in the brain: fMRI evidence from Korean-Chinese early bilinguals

Using fMRI, we conducted two types of property generation task that involved language switching, with early bilingual speakers of Korean and Chinese. The first is a more conventional task in which a single language (L1 or L2) was used within each trial, but switched randomly from trial to trial. The other consists of a novel experimental design where language switching happens within each trial, alternating in the direction of the L1/L2 translation required. Our findings support a recently introduced cognitive model, the 'hodological' view of language switching proposed by Moritz-Gasser and Duffau. The nodes of a distributed neural network that this model proposes are consistent with the informative regions that we extracted in this study, using both GLM methods and Multivariate Pattern Analyses: the supplementary motor area, caudate, supramarginal gyrus and fusiform gyrus and other cortical areas.