Differential gene expression profile of glucocorticoids, testosterone, and dehydroepiandrosterone in human cells.

Glucocorticoids are the major immunomodulating hormones in the human body. Recently, increasing interest in androgens as immunomodulators has emerged. In particular, Dehydroepiandrosterone (DHEA) has been suggested as beneficial in the treatment of some autoimmune disorders. However, the action and role of testicular and adrenal androgens on human immune cells remains unclear. This is the first study to provide large-scale gene expression data on the action of different steroids (DHEA, glucocorticoids, and testosterone) on human peripheral blood mononuclear cells using the recently developed genomic-scale technology of microarrays. Novel computational tools and techniques such as Principal Component Analysis (PCA) were used for analysis, clustering and visualization. We have demonstrated that each steroid has its distinct gene expression profile, although DHEA and testosterone co-regulated most genes in a similar direction while glucocorticoids frequently regulated the same genes in an opposite direction. Our data suggest an important and a complex regulatory role for androgens on human immune cells that should be considered in androgen replacement or treatment strategies.     

Serum Autoantibodies in Chronic Prostate Inflammation in Prostate Cancer Patients

Background

Chronic inflammation is frequently observed on histological analysis of malignant and non-malignant prostate specimens. It is a suspected supporting factor for prostate diseases and their progression and a main cause of false positive PSA tests in cancer screening. We hypothesized that inflammation induces autoantibodies, which may be useful biomarkers. We aimed to identify and validate prostate inflammation associated serum autoantibodies in prostate cancer patients and evaluate the expression of corresponding autoantigens.

Methods

Radical prostatectomy specimens of prostate cancer patients (N = 70) were classified into high and low inflammation groups according to the amount of tissue infiltrating lymphocytes. The corresponding pre-surgery blood serum samples were scrutinized for autoantibodies using a low-density protein array. Selected autoantigens were identified in prostate tissue and their expression pattern analyzed by immunohistochemistry and qPCR. The identified autoantibody profile was cross-checked in an independent sample set (N = 63) using the Luminex-bead protein array technology.

Results

Protein array screening identified 165 autoantibodies differentially abundant in the serum of high compared to low inflammation patients. The expression pattern of three corresponding antigens were established in benign and cancer tissue by immunohistochemistry and qPCR: SPAST (Spastin), STX18 (Syntaxin 18) and SPOP (speckle-type POZ protein). Of these, SPAST was significantly increased in prostate tissue with high inflammation. All three autoantigens were differentially expressed in primary and/or castration resistant prostate tumors when analyzed in an inflammation-independent tissue microarray. Cross-validation of the inflammation autoantibody profile on an independent sample set using a Luminex-bead protein array, retrieved 51 of the significantly discriminating autoantibodies. Three autoantibodies were significantly upregulated in both screens, MUT, RAB11B and CSRP2 (p>0.05), two, SPOP and ZNF671, close to statistical significance (p = 0.051 and 0.076).

Conclusions

We provide evidence of an inflammation-specific autoantibody profile and confirm the expression of corresponding autoantigens in prostate tissue. This supports evaluation of autoantibodies as non-invasive markers for prostate inflammation.     

Association of the FAM46A Gene VNTRs and BAG6 rs3117582 SNP with Non Small Cell Lung Cancer (NSCLC) in Croatian and Norwegian Populations

We analyzed for associations between a variable number of tandem repeat (VNTR) polymorphism in the Family with sequence similarity 46, member A (FAM46A) gene and a single nucleotide polymorphism (rs3117582) in the BCL2-Associated Athanogene 6 (BAG6) with non small cell lung cancer in Croatian and Norwegian subjects. A total of 503 (262 Croatian and 241Norwegian) non small cell lung cancer patients and 897 controls (568 Croatian and 329 Norwegian) were analyzed. We found that the frequency of allele b (three VNTR repeats) of FAM46A gene was significantly increased in the patients compared to the healthy controls in the Croatian and the combined Croatian and Norwegian subjects. Genotype frequencies of cd (four and five VNTR repeats) and cc (four VNTR repeats homozygote) of the FAM46A gene were significantly decreased in the patients compared to the healthy controls in the Croatian and Norwegian subjects, respectively. Logistic regression analyses revealed FAM46A genotype cc to be an independent predictive factor for non small cell lung cancer risk in the Norwegian subjects after adjustment for age, gender and smoking status. This is the first study to suggest an association between the FAM46A gene VNTR polymorphisms and non small cell lung cancer. We found also that BAG6 rs3117582 SNP was associated with non small cell lung cancer in the Norwegian subjects and the combined Croatian-Norwegian subjects corroborating the earlier finding that BAG6 rs3117582 SNP was associated with lung cancer in Europeans. Logistic regression analyses revealed that genotypes and alleles of BAG6 were independent predictive factor for non small cell lung cancer risk in the Norwegian and combined Croatian-Norwegian subjects, after adjustment for age and gender.     

Link between epigenetic diversity and invasive status of south-eastern European populations of phytopathogenic fungus Cryphonectria parasitica

Epigenetic modifications may play an important role in invasion and adaptation of clonal and invasive populations to different environments. The aim of this study was to analyse epigenetic diversity and structure within and among populations of invasive pathogenic fungus Cryphonectria parasitica from south-eastern Europe, where one haplotype S12 dominates. The highest level of epigenetic diversity was found in haplotype S1, followed by S2, while the lowest level of epigenetic diversity was found in haplotype S12. Similar pattern of epigenetic diversity was detected in the control, genetically diverse Croatian population where S1 haplotype dominates. In four south-eastern European populations, the highest level of epigenetic diversity was observed in the Italian population, the oldest population in the studied area, while the lowest diversity was found in most recently established Bulgarian population. This relationship between epigenetic diversity and population age implies the important role of epigenetic modifications on the process of invasion. Our data suggest that epigenetic differences might affect the success of expansion of certain haplotype into new regions. Understanding the role of epigenetic processes in expansion and (pre)adaptation of fungal plant pathogens, besides fundamental knowledge, can contribute to development of strategies for control of fungal spread and pathogenesis.     

Contrasting Zooplankton Assemblages in Two Oxbow Lakes with Low Transparencies and Narrow Emergent Macrophyte Belts (Krapina River,Croatia)

The aim of this study was to examine the combined effect of water transparency and narrow macrophyte belts on zooplankton assemblages in two oxbow lakes (Krapina River, Croatia). Samples were collected in open water and among helophytes in the littoral zone from April until September 2008. Rotifers were the most abundant group of zooplankton in both lakes, and dominated in the Krapina oxbow lake 1 (KO1). Lake KO1 had significantly lower transparency, lower percentage macrophyte cover and higher chlorophyll a concentration than Krapina oxbow lake 2 (KO2). In lake KO1, variation in the horizontal distribution of cladocerans and rotifers in terms of their abundance seemed to be determined by competition between Bosmina longirostris and Keratella cochlearis, initiated by oscillation in transparency and detritus availability. In lake KO2, with higher transparency and higher percentage macrophyte cover, the abundance of small‐ and large‐bodied cladocerans increased in the littoral zone simultaneously with higher transparency, suggesting fish predation. Results of this study indicated that small differences in transparencies between the two lakes caused significant differences in horizontal distribution of the zooplankton assemblage. Even narrow helophyte belts offered a refuge to zooplankton, although lower transparencies reduced the effectiveness of macrophytes as a refuge from predators. (© 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

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.     

SIMPLEX: Cloud-Enabled Pipeline for the Comprehensive Analysis of Exome Sequencing Data

In recent studies, exome sequencing has proven to be a successful screening tool for the identification of candidate genes causing rare genetic diseases. Although underlying targeted sequencing methods are well established, necessary data handling and focused, structured analysis still remain demanding tasks. Here, we present a cloud-enabled autonomous analysis pipeline, which comprises the complete exome analysis workflow. The pipeline combines several in-house developed and published applications to perform the following steps: (a) initial quality control, (b) intelligent data filtering and pre-processing, (c) sequence alignment to a reference genome, (d) SNP and DIP detection, (e) functional annotation of variants using different approaches, and (f) detailed report generation during various stages of the workflow. The pipeline connects the selected analysis steps, exposes all available parameters for customized usage, performs required data handling, and distributes computationally expensive tasks either on a dedicated high-performance computing infrastructure or on the Amazon cloud environment (EC2). The presented application has already been used in several research projects including studies to elucidate the role of rare genetic diseases. The pipeline is continuously tested and is publicly available under the GPL as a VirtualBox or Cloud image at http://simplex.i-med.ac.at; additional supplementary data is provided at http://www.icbi.at/exome.