Intracellular APP Domain Regulates Serine-Palmitoyl-CoA Transferase Expression and Is Affected in Alzheimer's Disease

Lipids play an important role as risk or protective factors in Alzheimer's disease (AD), a disease biochemically characterized by the accumulation of amyloid beta peptides (Aβ), released by proteolytic processing of the amyloid precursor protein (APP). Changes in sphingolipid metabolism have been associated to the development of AD. The key enzyme in sphingolipid de novo synthesis is serine-palmitoyl-CoA transferase (SPT). In the present study we identified a new physiological function of APP in sphingolipid synthesis. The APP intracellular domain (AICD) was found to decrease the expression of the SPT subunit SPTLC2, the catalytic subunit of the SPT heterodimer, resulting in that decreased SPT activity. AICD function was dependent on Fe65 and SPTLC2 levels are increased in APP knock-in mice missing a functional AICD domain. SPTLC2 levels are also increased in familial and sporadic AD postmortem brains, suggesting that SPT is involved in AD pathology

Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice

Introducing Nature 4.0: A sensor network for environmental monitoring in the Marburg Open Forest

Successful conservation strategies require frequent observations and assessments of the landscape. Although expert surveys provide a great level of detail, the trade-off is the limited spatial coverage and repetition with which they are executed. Remote sensing technology can partially resolve these issues; nevertheless, it still requires experts’ experience to create conservation planning and reaction options. Nature 4.0 seeks to address these shortcomings by developing a prototype of a modular environmental monitoring system for high-resolution observation of species, habitats, and processes. The project combines expert surveys by nature conservationists, remote sensing, and a network of environmental sensors, which are integrated into stationary units as well as attached to unmanned aerial vehicles, rovers, or animals. By utilizing powerful data integration and analysis methods, Nature 4.0 will enable researchers to effectively observe landscapes through a set of diverse lenses. Time series data from the project will also inform the development of early warning indicators. Following the open-source principle, as much of the project as possible will be made publicly available, including, for instance, schematics for sensor units, algorithms for data integration or information on species occurrence. In summary, Nature 4.0 will establish new methods and protocols in the field of comprehensive environmental monitoring by combining traditional sampling, remote sensing, and automated measurement stations. The prototype system is being developed in the Marburg Open Forest, an open research, education, and development platform for environmental monitoring methods. The Marburg Open Forest brings a cross-disciplinary group of scientists together with nature conservation experts from the private sector and the state government, as well as local schools and private citizens to collaborate and bridge the gap between basic and applied environmental research. After one year, we will present the results of the initial phase and share our experience with developing Nature 4.0

NetRank feature selection outperforms standard feature selection methods.

<p>(<b>A</b>) The accuracy of different feature selection methods for predicting patient outcome was tested on the screening dataset. The NetRank feature selection using a transcription factor network is shown in red. For smaller training set sizes, our method is superior to all other feature selection methods, reaching an accuracy of 72% in a Monte Carlo cross-validation. (<b>B</b>) Markers found with NetRank are more accurate than markers described in literature.</p

Signature to predict risk in patients with and without adjuvant therapy.

<p>(<b>A</b>) Signature to predict risk in patients with adjuvant therapy. The signature was developed with patients receiving adjuvant therapy separated by their median survival into two groups, a high risk group with shorter survival and a low risk group with longer survival. A classifier trained with the signature using leave-one-out cross-validation shows a significant difference between the predicted low and high risk group (, logrank test). (<b>B</b>) Signature to predict risk in patients without adjuvant therapy. The signature was developed with patients not receiving adjuvant therapy separated by their median survival into two groups, a high risk group with shorter survival and a low risk group with longer survival. A classifier trained with the signature using leave-one-out cross-validation shows a significant difference between the predicted low and high risk group (, logrank test).</p

Clinical characteristics of patients used in this study.

<p>The screening dataset (genome-wide gene expression profiling) comprises 30 samples of surgically resected pancreatic ductal adenocarcinoma from patients without adjuvant chemotherapy. The validation dataset (immunohistochemistry of seven marker candidates) comprises samples from 412 patients, of which 172 had received adjuvant therapy and 240 had not. Significant differences between the adjuvant and no adjuvant therapy subgroups were found for regional lymph nodes status (, Fisher's exact test) and for the stage groupings (, Fisher's exact test). Differences in all other variables were not significant.</p>†<p>Based on postsurgical histopathological assessment (indicated by the p prefix).</p>‡<p>Stage was assessed by the American Joint Committee on Cancer 2006 guidelines.</p

Monte Carlo cross-validation workflow to evaluate the accuracy of methods for ranking genes for outcome prediction.

<p>The full dataset is a gene expression matrix with 8,000 features (the genes) as rows and 30 samples (the patients) as columns. For each patient, the outcome (poor or good) is given (1). The dataset is randomly divided into a training and a test set (2). Within the training set, genes are ranked by how different they are between patients with poor and good outcome (3). The most different genes are selected (4). They are used to train a machine learning classifier on the training set (5). After training, the classifier is asked to predict the outcome of the test set patients (6). The predicted outcome is compared with the true outcome and the number of correctly classified patients is noted (7). Steps 2–7 are repeated 1,000 times, and the resulting final accuracy is obtained by averaging over the 1,000 accuracies of step 7.</p