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

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

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

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

Causal Modeling Using Network Ensemble Simulations of Genetic and Gene Expression Data Predicts Genes Involved in Rheumatoid Arthritis

Tumor necrosis factor α (TNF-α) is a key regulator of inflammation and rheumatoid arthritis (RA). TNF-α blocker therapies can be very effective for a substantial number of patients, but fail to work in one third of patients who show no or minimal response. It is therefore necessary to discover new molecular intervention points involved in TNF-α blocker treatment of rheumatoid arthritis patients. We describe a data analysis strategy for predicting gene expression measures that are critical for rheumatoid arthritis using a combination of comprehensive genotyping, whole blood gene expression profiles and the component clinical measures of the arthritis Disease Activity Score 28 (DAS28) score. Two separate network ensembles, each comprised of 1024 networks, were built from molecular measures from subjects before and 14 weeks after treatment with TNF-α blocker. The network ensemble built from pre-treated data captures TNF-α dependent mechanistic information, while the ensemble built from data collected under TNF-α blocker treatment captures TNF-α independent mechanisms. In silico simulations of targeted, personalized perturbations of gene expression measures from both network ensembles identify transcripts in three broad categories. Firstly, 22 transcripts are identified to have new roles in modulating the DAS28 score; secondly, there are 6 transcripts that could be alternative targets to TNF-α blocker therapies, including CD86 - a component of the signaling axis targeted by Abatacept (CTLA4-Ig), and finally, 59 transcripts that are predicted to modulate the count of tender or swollen joints but not sufficiently enough to have a significant impact on DAS28

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

Pigmentation plasticity enhances crypsis in larval newts: Associated metabolic cost and background choice behaviour

In heterogeneous environments, the capacity for colour change can be a valuable adaptation enhancing crypsis against predators. Alternatively, organisms might achieve concealment by evolving preferences for backgrounds that match their visual traits, thus avoiding the costs of plasticity. Here we examined the degree of plasticity in pigmentation of newt larvae (Lissotriton boscai) in relation to predation risk. Furthermore, we tested for associated metabolic costs and pigmentation-dependent background choice behaviour. Newt larvae expressed substantial changes in pigmentation so that light, high-reflecting environment induced depigmentation whereas dark, low-reflecting environment induced pigmentation in just three days of exposure. Induced pigmentation was completely reversible upon switching microhabitats. Predator cues, however, did not enhance cryptic phenotypes, suggesting that environmental albedo induces changes in pigmentation improving concealment regardless of the perceived predation risk. Metabolic rate was higher in heavily pigmented individuals from dark environments, indicating a high energetic requirement of pigmentation that could impose a constraint to larval camouflage in dim habitats. Finally, we found partial evidence for larvae selecting backgrounds matching their induced phenotypes. However, in the presence of predator cues, larvae increased the time spent in light environments, which may reflect a escape response towards shallow waters rather than an attempt at increasing crypsisFinancial support was provided by the Spanish Ministry of Science and Innovation (MICINN), Grant CGL2012-40044 to IGM, and by the Universidad Autónoma de Madrid, Short Stay Grant to NPC. Additional financial support was provided by the MICINN, Grant CGL2015-68670-R to NP

A strategy to discover new organizers identifies a putative heart organizer

Organizers are regions of the embryo that can both induce new fates and impart pattern on other regions. So far, surprisingly few organizers have been discovered, considering the number of patterned tissue types generated during development. This may be because their discovery has relied on transplantation and ablation experiments. Here we describe a new approach, using chick embryos, to discover organizers based on a common gene expression signature, and use it to uncover the anterior intestinal portal (AIP) endoderm as a putative heart organizer. We show that the AIP can induce cardiac identity from non-cardiac mesoderm and that it can pattern this by specifying ventricular and suppressing atrial regional identity. We also uncover some of the signals responsible. The method holds promise as a tool to discover other novel organizers acting during development

Sequential analysis of global gene expression profiles in immature and in vitro matured bovine oocytes: potential molecular markers of oocyte maturation

Abstract Background Without intensive selection, the majority of bovine oocytes submitted to in vitro embryo production (IVP) fail to develop to the blastocyst stage. This is attributed partly to their maturation status and competences. Using the Affymetrix GeneChip Bovine Genome Array, global mRNA expression analysis of immature (GV) and in vitro matured (IVM) bovine oocytes was carried out to characterize the transcriptome of bovine oocytes and then use a variety of approaches to determine whether the observed transcriptional changes during IVM was real or an artifact of the techniques used during analysis. Results 8489 transcripts were detected across the two oocyte groups, of which ~25.0% (2117 transcripts) were differentially expressed (p < 0.001); corresponding to 589 over-expressed and 1528 under-expressed transcripts in the IVM oocytes compared to their immature counterparts. Over expression of transcripts by IVM oocytes is particularly interesting, therefore, a variety of approaches were employed to determine whether the observed transcriptional changes during IVM were real or an artifact of the techniques used during analysis, including the analysis of transcript abundance in oocytes in vitro matured in the presence of α-amanitin. Subsets of the differentially expressed genes were also validated by quantitative real-time PCR (qPCR) and the gene expression data was classified according to gene ontology and pathway enrichment. Numerous cell cycle linked (CDC2, CDK5, CDK8, HSPA2, MAPK14, TXNL4B), molecular transport (STX5, STX17, SEC22A, SEC22B), and differentiation (NACA) related genes were found to be among the several over-expressed transcripts in GV oocytes compared to the matured counterparts, while ANXA1, PLAU, STC1and LUM were among the over-expressed genes after oocyte maturation. Conclusion Using sequential experiments, we have shown and confirmed transcriptional changes during oocyte maturation. This dataset provides a unique reference resource for studies concerned with the molecular mechanisms controlling oocyte meiotic maturation in cattle, addresses the existing conflicting issue of transcription during meiotic maturation and contributes to the global goal of improving assisted reproductive technology