Autophagic lysosome reformation dysfunction in glucocerebrosidase deficient cells: relevance to Parkinson disease.

Glucocerebrosidase (GBA1) gene mutations increase the risk of Parkinson disease (PD). While the cellular mechanisms associating GBA1 mutations and PD are unknown, loss of the glucocerebrosidase enzyme (GCase) activity, inhibition of autophagy and increased α-synuclein levels have been implicated. Here we show that autophagy lysosomal reformation (ALR) is compromised in cells lacking functional GCase. ALR is a cellular process controlled by mTOR which regenerates functional lysosomes from autolysosomes formed during macroautophagy. A decrease in phopho-S6K levels, a marker of mTOR activity, was observed in models of GCase deficiency, including primary mouse neurons and the PD patient derived fibroblasts with GBA1 mutations, suggesting that ALR is compromised. Importantly Rab7, a GTPase crucial for endosome-lysosome trafficking and ALR, accumulated in GCase deficient cells, supporting the notion that lysosomal recycling is impaired. Recombinant GCase treatment reversed ALR inhibition and lysosomal dysfunction. Moreover, ALR dysfunction was accompanied by impairment of macroautophagy and chaperone-mediated autophagy, increased levels of total and phosphorylated (S129) monomeric α-synuclein, evidence of amyloid oligomers and increased α-synuclein release. Concurrently, we found increased cholesterol and altered glucosylceramide homeostasis which could compromise ALR. We propose that GCase deficiency in PD inhibits lysosomal recycling. Consequently neurons are unable to maintain the pool of mature and functional lysosomes required for the autophagic clearance of α-synuclein, leading to the accumulation and spread of pathogenic α-synuclein species in the brain. Since GCase deficiency and lysosomal dysfunction occur with ageing and sporadic PD pathology, the decrease in lysosomal reformation may be a common feature in PD

Hunting and Shooting:The Ambiguities of ‘Country Sports’

An introduction to sports shooting as an aspect of animal abuse and the criminal, social and environmental harms - direct, indirect and collateral - associated with the field sports shooting industry

An integration of complementary strategies for gene-expression analysis to reveal novel therapeutic opportunities for breast cancer

INTRODUCTION. Perhaps the major challenge in developing more effective therapeutic strategies for the treatment of breast cancer patients is confronting the heterogeneity of the disease, recognizing that breast cancer is not one disease but multiple disorders with distinct underlying mechanisms. Gene-expression profiling studies have been used to dissect this complexity, and our previous studies identified a series of intrinsic subtypes of breast cancer that define distinct populations of patients with respect to survival. Additional work has also used signatures of oncogenic pathway deregulation to dissect breast cancer heterogeneity as well as to suggest therapeutic opportunities linked to pathway activation. METHODS. We used genomic analyses to identify relations between breast cancer subtypes, pathway deregulation, and drug sensitivity. For these studies, we use three independent breast cancer gene-expression data sets to measure an individual tumor phenotype. Correlation between pathway status and subtype are examined and linked to predictions for response to conventional chemotherapies. RESULTS. We reveal patterns of pathway activation characteristic of each molecular breast cancer subtype, including within the more aggressive subtypes in which novel therapeutic opportunities are critically needed. Whereas some oncogenic pathways have high correlations to breast cancer subtype (RAS, CTNNB1, p53, HER1), others have high variability of activity within a specific subtype (MYC, E2F3, SRC), reflecting biology independent of common clinical factors. Additionally, we combined these analyses with predictions of sensitivity to commonly used cytotoxic chemotherapies to provide additional opportunities for therapeutics specific to the intrinsic subtype that might be better aligned with the characteristics of the individual patient. CONCLUSIONS. Genomic analyses can be used to dissect the heterogeneity of breast cancer. We use an integrated analysis of breast cancer that combines independent methods of genomic analyses to highlight the complexity of signaling pathways underlying different breast cancer phenotypes and to identify optimal therapeutic opportunities.V Foundation for Cancer Research (Partners in Excellence grant

Multiple Oncogenic Pathway Signatures Show Coordinate Expression Patterns in Human Prostate Tumors

BACKGROUND: Gene transcription patterns associated with activation of oncogenes Myc, c-Src, beta-catenin, E2F3, H-Ras, HER2, EGFR, MEK, Raf, MAPK, Akt, and cyclin D1, as well as of the cell cycle and of androgen signaling have been generated in previous studies using experimental models. It was not clear whether genes in these "oncogenic signatures" would show coordinate expression patterns in human prostate tumors, particularly as most of the signatures were derived from cell types other than prostate. PRINCIPAL FINDINGS: The above oncogenic pathway signatures were examined in four different gene expression profile datasets of human prostate tumors (representing approximately 250 patients in all), using both Q1-Q2 and one-sided Fisher's exact enrichment analysis methods. A significant fraction (approximately 5%) of genes up-regulated experimentally by Myc, c-Src, HER2, Akt, or androgen were co-expressed in human tumors with the oncogene or biomarker corresponding to the pathway signature. Genes down-regulated experimentally, however, did not show anticipated patterns of anti-enrichment in the human tumors. CONCLUSIONS: Significant subsets of the genes in these experimentally-derived oncogenic signatures are relevant to the study of human prostate cancer. Both molecular biologists and clinical researchers could focus attention on the relatively small number of genes identified here as having coordinate patterns that arise from both the experimental system and the human disease system

Both habitat change and local lek structure influence patterns of spatial loss and recovery in a black grouse population

The final publication is available at Springer via http://dx.doi.org/10.1007/s10144-015-0484-3Land use change is a major driver of declines in wildlife populations. Where human economic or recreational interests and wildlife share landscapes this problem is exacerbated. Changes in UK black grouse Tetrao tetrix populations are thought to have been strongly influenced by upland land use change. In a long-studied population within Perthshire, lek persistence is positively correlated with lek size, and remaining leks clustered most strongly within the landscape when the population is lowest, suggesting that there may be a demographic and/or spatial context to the reaction of the population to habitat changes. Hierarchical cluster analysis of lek locations revealed that patterns of lek occupancy when the population was declining were different to those during the later recovery period. Response curves from lek-habitat models developed using MaxEnt for periods with a declining population, low population, and recovering population were consistent across years for most habitat measures. We found evidence linking lek persistence with habitat quality changes and more leks which appeared between 1994 and 2008 were in improving habitat than those which disappeared during the same period. Generalised additive models (GAMs) identified changes in woodland and starting lek size as being important indicators of lek survival between declining and low/recovery periods. There may also have been a role for local densities in explaining recovery since the population low point. Persistence of black grouse leks was influenced by habitat, but changes in this alone did not fully account for black grouse declines. Even when surrounded by good quality habitat, leks can be susceptible to extirpation due to isolation

A Signature Inferred from Drosophila Mitotic Genes Predicts Survival of Breast Cancer Patients

Introduction: The classification of breast cancer patients into risk groups provides a powerful tool for the identification of patients who will benefit from aggressive systemic therapy. The analysis of microarray data has generated several gene expression signatures that improve diagnosis and allow risk assessment. There is also evidence that cell proliferation-related genes have a high predictive power within these signatures. Methods: We thus constructed a gene expression signature (the DM signature) using the human orthologues of 108 Drosophila melanogaster genes required for either the maintenance of chromosome integrity (36 genes) or mitotic division (72 genes). Results: The DM signature has minimal overlap with the extant signatures and is highly predictive of survival in 5 large breast cancer datasets. In addition, we show that the DM signature outperforms many widely used breast cancer signatures in predictive power, and performs comparably to other proliferation-based signatures. For most genes of the DM signature, an increased expression is negatively correlated with patient survival. The genes that provide the highest contribution to the predictive power of the DM signature are those involved in cytokinesis. Conclusion: This finding highlights cytokinesis as an important marker in breast cancer prognosis and as a possible targe

Evaluation of biological pathways involved in chemotherapy response in breast cancer

INTRODUCTION: Our goal was to examine the association between biological pathways and response to chemotherapy in estrogen receptor-positive (ER+) and ER-negative (ER-) breast tumors separately. METHODS: Gene set enrichment analysis including 852 predefined gene sets was applied to gene expression data from 51 ER- and 82 ER+ breast tumors that were all treated with a preoperative paclitaxel, 5-fluoruracil, doxorubicin, and cyclophosphamide chemotherapy. RESULTS: Twenty-seven (53%) ER- and 7 (9%) ER+ patients had pathologic complete response (pCR) to therapy. Among the ER- tumors, a proliferation gene signature (false discovery rate [FDR] q = 0.1), the genomic grade index (FDR q = 0.044), and the E2F3 pathway signature (FDR q = 0.22, P = 0.07) were enriched in the pCR group. Among the ER+ tumors, the proliferation signature (FDR q = 0.001) and the genomic grade index (FDR q = 0.015) were also significantly enriched in cases with pCR. Ki67 expression, as single gene marker of proliferation, did not provide the same information as the entire proliferation signature. An ER-associated gene set (FDR q = 0.03) and a mutant p53 gene signature (FDR q = 0.0019) were enriched in ER+ tumors with residual cancer. CONCLUSION: Proliferation- and genomic grade-related gene signatures are associated with chemotherapy sensitivity in both ER- and ER+ breast tumors. Genes involved in the E2F3 pathway are associated with chemotherapy sensitivity among ER- tumors. The mutant p53 signature and expression of ER-related genes were associated with lower sensitivity to chemotherapy in ER+ breast tumors only.Journal ArticleResearch Support, N.I.H. ExtramuralResearch Support, Non-U.S. Gov'tSCOPUS: ar.jinfo:eu-repo/semantics/publishe

A systematic, large-scale comparison of transcription factor binding site models

Background The modelling of gene regulation is a major challenge in biomedical research. This process is dominated by transcription factors (TFs) and mutations in their binding sites (TFBSs) may cause the misregulation of genes, eventually leading to disease. The consequences of DNA variants on TF binding are modelled in silico using binding matrices, but it remains unclear whether these are capable of accurately representing in vivo binding. In this study, we present a systematic comparison of binding models for 82 human TFs from three freely available sources: JASPAR matrices, HT-SELEX-generated models and matrices derived from protein binding microarrays (PBMs). We determined their ability to detect experimentally verified “real” in vivo TFBSs derived from ENCODE ChIP-seq data. As negative controls we chose random downstream exonic sequences, which are unlikely to harbour TFBS. All models were assessed by receiver operating characteristics (ROC) analysis. Results While the area- under-curve was low for most of the tested models with only 47 % reaching a score of 0.7 or higher, we noticed strong differences between the various position-specific scoring matrices with JASPAR and HT-SELEX models showing higher success rates than PBM-derived models. In addition, we found that while TFBS sequences showed a higher degree of conservation than randomly chosen sequences, there was a high variability between individual TFBSs. Conclusions Our results show that only few of the matrix-based models used to predict potential TFBS are able to reliably detect experimentally confirmed TFBS. We compiled our findings in a freely accessible web application called ePOSSUM (http:/mutationtaster.charite.de/ePOSSUM/) which uses a Bayes classifier to assess the impact of genetic alterations on TF binding in user-defined sequences. Additionally, ePOSSUM provides information on the reliability of the prediction using our test set of experimentally confirmed binding sites

Data-driven model order reduction using orthonormal vector fitting

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Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures

INTRODUCTION: Breast cancer subtyping and prognosis have been studied extensively by gene expression profiling, resulting in disparate signatures with little overlap in their constituent genes. Although a previous study demonstrated a prognostic concordance among gene expression signatures, it was limited to only one dataset and did not fully elucidate how the different genes were related to one another nor did it examine the contribution of well-known biological processes of breast cancer tumorigenesis to their prognostic performance. METHOD: To address the above issues and to further validate these initial findings, we performed the largest meta-analysis of publicly available breast cancer gene expression and clinical data, which are comprised of 2,833 breast tumors. Gene coexpression modules of three key biological processes in breast cancer (namely, proliferation, estrogen receptor [ER], and HER2 signaling) were used to dissect the role of constituent genes of nine prognostic signatures. RESULTS: Using a meta-analytical approach, we consolidated the signatures associated with ER signaling, ERBB2 amplification, and proliferation. Previously published expression-based nomenclature of breast cancer 'intrinsic' subtypes can be mapped to the three modules, namely, the ER-/HER2- (basal-like), the HER2+ (HER2-like), and the low- and high-proliferation ER+/HER2- subtypes (luminal A and B). We showed that all nine prognostic signatures exhibited a similar prognostic performance in the entire dataset. Their prognostic abilities are due mostly to the detection of proliferation activity. Although ER- status (basal-like) and ERBB2+ expression status correspond to bad outcome, they seem to act through elevated expression of proliferation genes and thus contain only indirect information about prognosis. Clinical variables measuring the extent of tumor progression, such as tumor size and nodal status, still add independent prognostic information to proliferation genes. CONCLUSION: This meta-analysis unifies various results of previous gene expression studies in breast cancer. It reveals connections between traditional prognostic factors, expression-based subtyping, and prognostic signatures, highlighting the important role of proliferation in breast cancer prognosis.Journal ArticleMeta-AnalysisResearch Support, Non-U.S. Gov'tSCOPUS: ar.jinfo:eu-repo/semantics/publishe