Immediate T-Helper 17 Polarization Upon Triggering CD11b/c on HIV-Exposed Dendritic Cells.

Early on in human immunodeficiency virus (HIV) type 1 infection, gut T-helper (Th) 17 cells are massively depleted leading eventually to compromised intestinal barrier function and excessive immune activation. In contrast, the functional Th17 cell compartment of the gut is well-maintained in nonpathogenic simian immunodeficiency virus infection as well as HIV-1 long-term nonprogressors. Here, we show that dendritic cells (DCs) loaded with HIV-1 bearing high surface complement levels after incubation in plasma from HIV-infected individuals secreted significantly higher concentrations of Th17-polarizing cytokines than DCs exposed to nonopsonized HIV-1. The enhanced Th17-polarizing capacity of in vitro-generated and BDCA-1(+) DCs directly isolated from blood was linked to activation of ERK. In addition, C3a produced from DCs exposed to complement-opsonized HIV was associated with the higher Th17 polarization. Our in vitro and ex vivo data, therefore, indicate that complement opsonization of HIV-1 strengthens DC-mediated antiviral immune functions by simultaneously triggering Th17 expansion and intrinsic C3 formation via DC activation.

Bioinformatics for cancer immunology and immunotherapy.

Recent mechanistic insights obtained from preclinical studies and the approval of the first immunotherapies has motivated increasing number of academic investigators and pharmaceutical/biotech companies to further elucidate the role of immunity in tumor pathogenesis and to reconsider the role of immunotherapy. Additionally, technological advances (e.g., next-generation sequencing) are providing unprecedented opportunities to draw a comprehensive picture of the tumor genomics landscape and ultimately enable individualized treatment. However, the increasing complexity of the generated data and the plethora of bioinformatics methods and tools pose considerable challenges to both tumor immunologists and clinical oncologists. In this review, we describe current concepts and future challenges for the management and analysis of data for cancer immunology and immunotherapy. We first highlight publicly available databases with specific focus on cancer immunology including databases for somatic mutations and epitope databases. We then give an overview of the bioinformatics methods for the analysis of next-generation sequencing data (whole-genome and exome sequencing), epitope prediction tools as well as methods for integrative data analysis and network modeling. Mathematical models are powerful tools that can predict and explain important patterns in the genetic and clinical progression of cancer. Therefore, a survey of mathematical models for tumor evolution and tumor-immune cell interaction is included. Finally, we discuss future challenges for individualized immunotherapy and suggest how a combined computational/experimental approaches can lead to new insights into the molecular mechanisms of cancer, improved diagnosis, and prognosis of the disease and pinpoint novel therapeutic targets.

Mathematical models for translational and clinical oncology.

In the context of translational and clinical oncology, mathematical models can provide novel insights into tumor-related processes and can support clinical oncologists in the design of the treatment regime, dosage, schedule, toxicity and drug-sensitivity. In this review we present an overview of mathematical models in this field beginning with carcinogenesis and proceeding to the different cancer treatments. By doing so we intended to highlight recent developments and emphasize the power of such theoretical work.We first highlight mathematical models for translational oncology comprising epidemiologic and statistical models, mechanistic models for carcinogenesis and tumor growth, as well as evolutionary dynamics models which can help to describe and overcome a major problem in the clinic: therapy resistance. Next we review models for clinical oncology with a special emphasis on therapy including chemotherapy, targeted therapy, radiotherapy, immunotherapy and interaction of cancer cells with the immune system.As evident from the published studies, mathematical modeling and computational simulation provided valuable insights into the molecular mechanisms of cancer, and can help to improve diagnosis and prognosis of the disease, and pinpoint novel therapeutic targets.

[COMMODE] a large-scale database of molecular descriptors using compounds from PubChem.

BACKGROUND: Molecular descriptors have been extensively used in the field of structure-oriented drug design and structural chemistry. They have been applied in QSPR and QSAR models to predict ADME-Tox properties, which specify essential features for drugs. Molecular descriptors capture chemical and structural information, but investigating their interpretation and meaning remains very challenging. RESULTS: This paper introduces a large-scale database of molecular descriptors called COMMODE containing more than 25 million compounds originated from PubChem. About 2500 DRAGON-descriptors have been calculated for all compounds and integrated into this database, which is accessible through a web interface at http://commode.i-med.ac.at.