High MIG (CXCL9) plasma levels favours response to peginterferon and ribavirin in HCV-infected patients regardless of DPP4 activity

Sustained virological response (SVR) following peginterferon (pegIFN) and ribavirin (RBV) treatment in hepatitis C virus (HCV) infected patients has been linked with the IL28B genotype and lower peripheral levels of the CXCR3-binding chemokine IP-10 (CXCL10). To further improve the understanding of these biomarkers we investigated plasma levels of the other CXCR3-binding chemokines and activity of the dipeptidyl peptidase IV (DPP4, CD26) protease, which cleaves IP-10, in relation to treatment response

Plasma levels of growth-related oncogene (CXCL1-3) associated with fibrosis and platelet counts in HCV-infected patients

Fibrosis progression in hepatitis C virus (HCV)-infected patients varies greatly between individuals. Chemokines recruit immune cells to the infected liver and may thus play a role in the fibrosis process

Knowledge-based gene expression classification via matrix factorization

Motivation: Modern machine learning methods based on matrix decomposition techniques, like independent component analysis (ICA) or non-negative matrix factorization (NMF), provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield expression modes (ICA) or metagenes (NMF). These extracted features are considered indicative of underlying regulatory processes. They can as well be applied to the classification of gene expression datasets by grouping samples into different categories for diagnostic purposes or group genes into functional categories for further investigation of related metabolic pathways and regulatory networks. Results: In this study we focus on unsupervised matrix factorization techniques and apply ICA and sparse NMF to microarray datasets. The latter monitor the gene expression levels of human peripheral blood cells during differentiation from monocytes to macrophages. We show that these tools are able to identify relevant signatures in the deduced component matrices and extract informative sets of marker genes from these gene expression profiles. The methods rely on the joint discriminative power of a set of marker genes rather than on single marker genes. With these sets of marker genes, corroborated by leave-one-out or random forest cross-validation, the datasets could easily be classified into related diagnostic categories. The latter correspond to either monocytes versus macrophages or healthy vs Niemann Pick C disease patients.Siemens AG, MunichDFG (Graduate College 638)DAAD (PPP Luso - Alem˜a and PPP Hispano - Alemanas

cn.FARMS: a latent variable model to detect copy number variations in microarray data with a low false discovery rate

Cost-effective oligonucleotide genotyping arrays like the Affymetrix SNP 6.0 are still the predominant technique to measure DNA copy number variations (CNVs). However, CNV detection methods for microarrays overestimate both the number and the size of CNV regions and, consequently, suffer from a high false discovery rate (FDR). A high FDR means that many CNVs are wrongly detected and therefore not associated with a disease in a clinical study, though correction for multiple testing takes them into account and thereby decreases the study's discovery power. For controlling the FDR, we propose a probabilistic latent variable model, ‘cn.FARMS’, which is optimized by a Bayesian maximum a posteriori approach. cn.FARMS controls the FDR through the information gain of the posterior over the prior. The prior represents the null hypothesis of copy number 2 for all samples from which the posterior can only deviate by strong and consistent signals in the data. On HapMap data, cn.FARMS clearly outperformed the two most prevalent methods with respect to sensitivity and FDR. The software cn.FARMS is publicly available as a R package at http://www.bioinf.jku.at/software/cnfarms/cnfarms.html

FABIA: factor analysis for bicluster acquisition

Motivation: Biclustering of transcriptomic data groups genes and samples simultaneously. It is emerging as a standard tool for extracting knowledge from gene expression measurements. We propose a novel generative approach for biclustering called ‘FABIA: Factor Analysis for Bicluster Acquisition’. FABIA is based on a multiplicative model, which accounts for linear dependencies between gene expression and conditions, and also captures heavy-tailed distributions as observed in real-world transcriptomic data. The generative framework allows to utilize well-founded model selection methods and to apply Bayesian techniques

Interacting effects of climate change and habitat fragmentation on drought-sensitive butterflies

Climate change is expected to increase the frequency of some climatic extremes. These may have drastic impacts on biodiversity, particularly if meteorological thresholds are crossed, leading to population collapses. Should this occur repeatedly, populations may be unable to recover, resulting in local extinctions. Comprehensive time series data on butterflies in Great Britain provide a rare opportunity to quantify population responses to both past severe drought and the interaction with habitat area and fragmentation. Here, we combine this knowledge with future projections from multiple climate models, for different Representative Concentration Pathways (RCPs), and for simultaneous modelled responses to different landscape characteristics. Under RCP8.5, which is associated with ‘business as usual’ emissions, widespread drought-sensitive butterfly population extinctions could occur as early as 2050. However, by managing landscapes and particularly reducing habitat fragmentation, the probability of persistence until mid-century improves from around zero to between 6 and 42% (95% confidence interval). Achieving persistence with a greater than 50% chance and right through to 2100 is possible only under both low climate change (RCP2.6) and semi-natural habitat restoration. Our data show that, for these drought-sensitive butterflies, persistence is achieved more effectively by restoring semi-natural landscapes to reduce fragmentation, rather than simply focusing on increasing habitat area, but this will only be successful in combination with substantial emission reductions

Systemic virus distribution and host responses in brain and intestine of chickens infected with low pathogenic or high pathogenic avian influenza virus

<p>Abstract</p> <p>Background</p> <p>Avian influenza virus (AIV) is classified into two pathotypes, low pathogenic (LP) and high pathogenic (HP), based on virulence in chickens.</p> <p>Differences in pathogenicity between HPAIV and LPAIV might eventually be related to specific characteristics of strains, tissue tropism and host responses.</p> <p>Methods</p> <p>To study differences in disease development between HPAIV and LPAIV, we examined the first appearance and eventual load of viral RNA in multiple organs as well as host responses in brain and intestine of chickens infected with two closely related H7N1 HPAIV or LPAIV strains.</p> <p>Results</p> <p>Both H7N1 HPAIV and LPAIV spread systemically in chickens after a combined intranasal/intratracheal inoculation. In brain, large differences in viral RNA load and host gene expression were found between H7N1 HPAIV and LPAIV infected chickens. Chicken embryo brain cell culture studies revealed that both HPAIV and LPAIV could infect cultivated embryonic brain cells, but in accordance with the absence of the necessary proteases, replication of LPAIV was limited. Furthermore, TUNEL assay indicated apoptosis in brain of HPAIV infected chickens only. In intestine, where endoproteases that cleave HA of LPAIV are available, we found minimal differences in the amount of viral RNA and a large overlap in the transcriptional responses between HPAIV and LPAIV infected chickens. Interestingly, brain and ileum differed clearly in the cellular pathways that were regulated upon an AI infection.</p> <p>Conclusions</p> <p>Although both H7N1 HPAIV and LPAIV RNA was detected in a broad range of tissues beyond the respiratory and gastrointestinal tract, our observations indicate that differences in pathogenicity and mortality between HPAIV and LPAIV could originate from differences in virus replication and the resulting host responses in vital organs like the brain.</p

Quantitation of Pretreatment Serum IP-10 Improves the Predictive Value of an IL28B Gene Polymorphism for Hepatitis C Treatment Response

Polymorphisms of IL28B gene are highly associated with sustained virological response (SVR) in patients with chronic hepatitis C treated with peginterferon and ribavirin. Quantitation of Interferon-γ Inducible Protein-10 (IP-10) may also differentiate antiviral response. We evaluated IP-10 levels in pretreatment serum from 115 non-responders and 157 sustained responders in the VIRAHEP-C cohort, including African Americans (AA) and Caucasian Americans (CA). Mean IP-10 was lower in sustained responders compared to non-responders (460 ± 37 pg/ml vs 697 ± 49 pg/ml, p600 pg/ml) was 67%. We assessed the combination of pretreatment IP-10 levels with IL28B genotype as predictors of treatment response. The IL28B polymorphism rs12979860 was tested in 210 participants. CC, CT, or TT genotypes were found in 30%, 49%, and 21%, respectively, with corresponding SVR rates of 87%, 50%, and 39% (p<0.0001). Serum IP-10 levels within the IL28B genotype groups provided additional information regarding the likelihood of SVR (p< 0.0001). CT carriers with low IP-10 had 64% SVR versus 24% with high IP-10. Similarly, a higher SVR rate was identified for TT and CC carriers with low versus high IP-10 (TT: 48% versus 20%, CC: 89% versus 79%). IL28B genotype and baseline IP-10 levels were additive but independent when predicting SVR in both AA and CA

A feature selection method for classification within functional genomics experiments based on the proportional overlapping score

Background: Microarray technology, as well as other functional genomics experiments, allow simultaneous measurements of thousands of genes within each sample. Both the prediction accuracy and interpretability of a classifier could be enhanced by performing the classification based only on selected discriminative genes. We propose a statistical method for selecting genes based on overlapping analysis of expression data across classes. This method results in a novel measure, called proportional overlapping score (POS), of a feature's relevance to a classification task.Results: We apply POS, along-with four widely used gene selection methods, to several benchmark gene expression datasets. The experimental results of classification error rates computed using the Random Forest, k Nearest Neighbor and Support Vector Machine classifiers show that POS achieves a better performance.Conclusions: A novel gene selection method, POS, is proposed. POS analyzes the expressions overlap across classes taking into account the proportions of overlapping samples. It robustly defines a mask for each gene that allows it to minimize the effect of expression outliers. The constructed masks along-with a novel gene score are exploited to produce the selected subset of genes

Developmental Stability Covaries with Genome-Wide and Single-Locus Heterozygosity in House Sparrows

Fluctuating asymmetry (FA), a measure of developmental instability, has been hypothesized to increase with genetic stress. Despite numerous studies providing empirical evidence for associations between FA and genome-wide properties such as multi-locus heterozygosity, support for single-locus effects remains scant. Here we test if, and to what extent, FA co-varies with single- and multilocus markers of genetic diversity in house sparrow (Passer domesticus) populations along an urban gradient. In line with theoretical expectations, FA was inversely correlated with genetic diversity estimated at genome level. However, this relationship was largely driven by variation at a single key locus. Contrary to our expectations, relationships between FA and genetic diversity were not stronger in individuals from urban populations that experience higher nutritional stress. We conclude that loss of genetic diversity adversely affects developmental stability in P. domesticus, and more generally, that the molecular basis of developmental stability may involve complex interactions between local and genome-wide effects. Further study on the relative effects of single-locus and genome-wide effects on the developmental stability of populations with different genetic properties is therefore needed