Cataloguing of Potential HIV Susceptibility Factors during the Menstrual Cycle of Pig-Tailed Macaques by Using a Systems Biology Approach.

UNLABELLED: Our earlier studies with pig-tailed macaques demonstrated various simian-human immunodeficiency virus (SHIV) susceptibilities during the menstrual cycle, likely caused by cyclic variations in immune responses in the female genital tract. There is concern that high-dose, long-lasting, injectable progestin-based contraception could mimic the high-progesterone luteal phase and predispose women to human immunodeficiency type 1 (HIV-1) acquisition and transmission. In this study, we adopted a systems biology approach employing proteomics (tandem mass spectrometry), transcriptomics (RNA microarray hybridization), and other specific protein assays (enzyme-linked immunosorbent assays and multiplex chemokine and cytokine measurements) to characterize the effects of hormonal changes on the expression of innate factors and secreted proteins in the macaque vagina. Several antiviral factors and pathways (including acute-phase response signaling and complement system) were overexpressed in the follicular phase. Conversely, during the luteal phase there were factors overexpressed (including moesins, syndecans, and integrins, among others) that could play direct or indirect roles in enhancing HIV-1 infection. Thus, our study showed that specific pathways and proteins or genes might work in tandem to regulate innate immunity, thus fostering further investigation and future design of approaches to help counter HIV-1 acquisition in the female genital tract. IMPORTANCE: HIV infection in women is poorly understood. High levels of the hormone progesterone may make women more vulnerable to infection. This could be the case during the menstrual cycle, when using hormone-based birth control, or during pregnancy. The biological basis for increased HIV vulnerability is not known. We used an animal model with high risk for infection during periods of high progesterone. Genital secretions and tissues during the menstrual cycle were studied. Our goal was to identify biological factors upregulated at high progesterone levels, and we indeed show an upregulation of genes and proteins which enhance the ability of HIV to infect when progesterone is high. In contrast, during low-progesterone periods, we found more HIV inhibitory factors. This study contributes to our understanding of mechanisms that may regulate HIV infection in females under hormonal influences. Such knowledge is needed for the development of novel prevention strategies.

Successful management of multi-focal hepatic infantile hemangioendothelioma using TACE/surgery followed by maintenance metronomic therapy.

Infantile hepatic hemangioendothelioma (IHE) is a rare angiogenic tumour with diverse clinical presentations and varied course ranging from spontaneous regression to life-threatening complications. The authors report a 2-year boy who presented with respiratory distress and was identified as a case of inoperable multi-focal hepatic IHE. He showed a transient response to trans-arterial-chemo-embolisation and liver resection but had progressive disease despite chemotherapy (prednisolone/vicristine/ifosfamide/cisplatin). Thereafter, he was successfully managed with metronomic therapy using cyclophosphamide and tamoxifen.

Graph kernels for disease outcome prediction from protein-protein interaction networks.

It is widely believed that comparing discrepancies in the protein-protein interaction (PPI) networks of individuals will become an important tool in understanding and preventing diseases. Currently PPI networks for individuals are not available, but gene expression data is becoming easier to obtain and allows us to represent individuals by a co-integrated gene expression/protein interaction network. Two major problems hamper the application of graph kernels - state-of-the-art methods for whole-graph comparison - to compare PPI networks. First, these methods do not scale to graphs of the size of a PPI network. Second, missing edges in these interaction networks are biologically relevant for detecting discrepancies, yet, these methods do not take this into account. In this article we present graph kernels for biological network comparison that are fast to compute and take into account missing interactions. We evaluate their practical performance on two datasets of co-integrated gene expression/PPI networks.

Class prediction from time series gene expression profiles using dynamical systems kernels.

We present a kernel-based approach to the classification of time series of gene expression profiles. Our method takes into account the dynamic evolution over time as well as the temporal characteristics of the data. More specifically, we model the evolution of the gene expression profiles as a Linear Time Invariant (LTI) dynamical system and estimate its model parameters. A kernel on dynamical systems is then used to classify these time series. We successfully test our approach on a published dataset to predict response to drug therapy in Multiple Sclerosis patients. For pharmacogenomics, our method offers a huge potential for advanced computational tools in disease diagnosis, and disease and drug therapy outcome prognosis.

Protein function prediction via graph kernels.

MOTIVATION: Computational approaches to protein function prediction infer protein function by finding proteins with similar sequence, structure, surface clefts, chemical properties, amino acid motifs, interaction partners or phylogenetic profiles. We present a new approach that combines sequential, structural and chemical information into one graph model of proteins. We predict functional class membership of enzymes and non-enzymes using graph kernels and support vector machine classification on these protein graphs. RESULTS: Our graph model, derivable from protein sequence and structure only, is competitive with vector models that require additional protein information, such as the size of surface pockets. If we include this extra information into our graph model, our classifier yields significantly higher accuracy levels than the vector models. Hyperkernels allow us to select and to optimally combine the most relevant node attributes in our protein graphs. We have laid the foundation for a protein function prediction system that integrates protein information from various sources efficiently and effectively. AVAILABILITY: More information available via www.dbs.ifi.lmu.de/Mitarbeiter/borgwardt.html.