Functional analysis and transcriptional output of the Göttingen minipig genome

In the past decade the Göttingen minipig has gained increasing recognition as animal model in pharmaceutical and safety research because it recapitulates many aspects of human physiology and metabolism. Genome-based comparison of drug targets together with quantitative tissue expression analysis allows rational prediction of pharmacology and cross-reactivity of human drugs in animal models thereby improving drug attrition which is an important challenge in the process of drug development.; Here we present a new chromosome level based version of the Göttingen minipig genome together with a comparative transcriptional analysis of tissues with pharmaceutical relevance as basis for translational research. We relied on mapping and assembly of WGS (whole-genome-shotgun sequencing) derived reads to the reference genome of the Duroc pig and predict 19,228 human orthologous protein-coding genes. Genome-based prediction of the sequence of human drug targets enables the prediction of drug cross-reactivity based on conservation of binding sites. We further support the finding that the genome of Sus scrofa contains about ten-times less pseudogenized genes compared to other vertebrates. Among the functional human orthologs of these minipig pseudogenes we found HEPN1, a putative tumor suppressor gene. The genomes of Sus scrofa, the Tibetan boar, the African Bushpig, and the Warthog show sequence conservation of all inactivating HEPN1 mutations suggesting disruption before the evolutionary split of these pig species. We identify 133 Sus scrofa specific, conserved long non-coding RNAs (lncRNAs) in the minipig genome and show that these transcripts are highly conserved in the African pigs and the Tibetan boar suggesting functional significance. Using a new minipig specific microarray we show high conservation of gene expression signatures in 13 tissues with biomedical relevance between humans and adult minipigs. We underline this relationship for minipig and human liver where we could demonstrate similar expression levels for most phase I drug-metabolizing enzymes. Higher expression levels and metabolic activities were found for FMO1, AKR/CRs and for phase II drug metabolizing enzymes in minipig as compared to human. The variability of gene expression in equivalent human and minipig tissues is considerably higher in minipig organs, which is important for study design in case a human target belongs to this variable category in the minipig. The first analysis of gene expression in multiple tissues during development from young to adult shows that the majority of transcriptional programs are concluded four weeks after birth. This finding is in line with the advanced state of human postnatal organ development at comparative age categories and further supports the minipig as model for pediatric drug safety studies.; Genome based assessment of sequence conservation combined with gene expression data in several tissues improves the translational value of the minipig for human drug development. The genome and gene expression data presented here are important resources for researchers using the minipig as model for biomedical research or commercial breeding. Potential impact of our data for comparative genomics, translational research, and experimental medicine are discussed

Sodium Iodate-Induced Degeneration Results in Local Complement Changes and Inflammatory Processes in Murine Retina

Age-related macular degeneration (AMD), one of the leading causes of blindness worldwide, causes personal suffering and high socioeconomic costs. While there has been progress in the treatments for the neovascular form of AMD, no therapy is yet available for the more common dry form, also known as geographic atrophy. We analysed the retinal tissue in a mouse model of retinal degeneration caused by sodium iodate (NaIO3)-induced retinal pigment epithelium (RPE) atrophy to understand the underlying pathology. RNA sequencing (RNA-seq), qRT-PCR, Western blot, immunohistochemistry of the retinas and multiplex ELISA of the mouse serum were applied to find the pathways involved in the degeneration. NaIO3 caused patchy RPE loss and thinning of the photoreceptor layer. This was accompanied by the increased retinal expression of complement components c1s, c3, c4, cfb and cfh. C1s, C3, CFH and CFB were complement proteins, with enhanced deposition at day 3. C4 was upregulated in retinal degeneration at day 10. Consistently, the transcript levels of proinflammatory ccl-2, -3, -5, il-1β, il-33 and tgf-β were increased in the retinas of NaIO3 mice, but vegf-a mRNA was reduced. Macrophages, microglia and gliotic Müller cells could be a cellular source for local retinal inflammatory changes in the NaIO3 retina. Systemic complement and cytokines/chemokines remained unaltered in this model of NaIO3-dependent retinal degeneration. In conclusion, systemically administered NaIO3 promotes degenerative and inflammatory processes in the retina, which can mimic the hallmarks of geographic atrophy

Adipose tissue-derived WNT5A regulates vascular redox signaling in obesity via USP17//RAC1-mediated activation of NADPH oxidases

Obesity is associated with changes in the secretome of adipose tissue (AT), which affects the vasculature through endocrine and paracrine mechanisms. Wingless-related integration site 5A (WNT5A) and secreted frizzled-related protein 5 (SFRP5), adipokines that regulate noncanonical Wnt signaling, are dysregulated in obesity. We hypothesized that WNT5A released from AT exerts endocrine and paracrine effects on the arterial wall through noncanonical RAC1-mediated Wnt signaling. In a cohort of 1004 humans with atherosclerosis, obesity was associated with increased WNT5A bioavailability in the circulation and the AT, higher expression of WNT5A receptors Frizzled 2 and Frizzled 5 in the human arterial wall, and increased vascular oxidative stress due to activation of NADPH oxidases. Plasma concentration of WNT5A was elevated in patients with coronary artery disease compared to matched controls and was independently associated with calcified coronary plaque progression. We further demonstrated that WNT5A induces arterial oxidative stress and redox-sensitive migration of vascular smooth muscle cells via Frizzled 2–mediated activation of a previously uncharacterized pathway involving the deubiquitinating enzyme ubiquitin-specific protease 17 (USP17) and the GTPase RAC1. Our study identifies WNT5A and its downstream vascular signaling as a link between obesity and vascular disease pathogenesis, with translational implications in humans

Double-modified glycopolymers from thiolactones to modulate lectin selectivity and affinity

Multivalent glycomaterials show high affinity toward lectins but are often nonselective as they lack the precise 3-D presentation found in native glycans. Here, thiolactone chemistry is exploited to enable the synthesis of glycopolymers with both a primary binding (galactose) and a variable secondary binding unit in close proximity to each other on the linker. These polymers are used to target the Cholera toxin B subunit, CTxB, inspired by its native branched glycan target, GM-1. The secondary, nonbinding unit was shown to dramatically modulate affinity and selectivity toward the Cholera toxin. These increasingly complex glycopolymers, assembled using accessible chemistry, can help breach the synthetic/biological divide to obtain future glycomimetics

Qualitative Biclustering with Bioconductor Package rqubic

Biclustering has been suggested and found very useful to discover gene regulation patterns from gene expression microarrays. Several quantitative algorithms, among others CC and BIMAX, have been implemented in R, mainly by the biclust package. To our best knowledge, there have been so far no qualitative biclustering methods implemented. Therefore we introduce rqubic, a Bioconductor package implementing the qualitative biclustering (QUBIC) algorithm. Compared to quantitative alternatives, this algorithm is less sensitive to outliers and heavy tails of microarray data. In addition, it is straightforward to plug other discretized data types in the algorithm, for example differentially expressed transcripts in NGS experiments, for which several R packages (e.g. edgeR and DESeq) have been available. The vignette introduces the functionalities provided by the rqubic package. And we demonstrate the usage of the package by implementing a biclustering software pipeline.

Measurement Error in Panel Data

Measurement error is a pervasive problem in economics and other social and behavioral sciences. Estimators that ignore the issue are generally inconsistent, and more information or stronger assumptions are necessary to correct for measurement error. Panel data are a source of more information, but consistent estimators still require some additional information or assumptions. This chapter gives an overview of the measurement error problem, with an emphasis on the linear regression model for panel data, and shows various ways in which panel data can be helpful to obtain consistent estimators. More advanced topics, such as non linear models and nonclassical measurement error are also discussed

Advanced signal recognition methods applied to seismo-volcanic events from Planchon Peteroa Volcanic Complex: deep neural network classifier

Advanced techniques in the recognition and classification of seismo-volcanic events are transcendental when studying active volcanoes, not only for their importance as an accurate real time seismic monitoring procedure but also for the use of their results in modeling the dynamics of the volcanic environment. It is well known that real time seismic monitoring deals with such a large amount of data that it would become an overwhelming job for an operator to do manually. Therefore the use of automatic detection and classification techniques based on the Machine Learning approach are suitable in meeting such a challenge. The aim of this work is to test the capability of the Deep Neural Network (DNN) by using different event parametrization as a confident classifier tool that could permit a reliable seismic catalog to be built in a new and un-analyzed volcanic scenario. We tested different configurations in order to build an approach that was as simple as possible to use this classifier with a limited number of events. In this regard, the feature space was explored in order to select the most significant parameters of the seismic signals. The data used for this analysis corresponds to the Planchon Peteroa Volcanic Complex (PPVC) located in the Transitional Southern Volcanic Zone (TSVZ) between Chile and Argentina, South America. The most significant result of this work was not only that it provided an analysis in terms of performance of this algorithm, especially when the training, validation and test dataset is reliable although definitely reduced, but it also gave an insight of into how an optimal event parametrization can significantly improve the automatic detection and classification of seismo-volcanic events.Fil: Martinez, Veronica Laura. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de la Plata. Facultad de Cs.astronómicas y Geofísicas. Departamento de Sismología E Información Meteorologica; ArgentinaFil: Titos, Manuel. Universidad de Granada; EspañaFil: Benítez, Carmen. Universidad de Granada; EspañaFil: Badi, Gabriela Badi. Universidad Nacional de la Plata. Facultad de Cs.astronómicas y Geofísicas. Departamento de Sismología E Información Meteorologica; Argentina. Secretaría de Industria y Minería. Servicio Geológico Minero Argentino; ArgentinaFil: Casas, José Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Secretaría de Industria y Minería. Servicio Geológico Minero Argentino; ArgentinaFil: Olivera Craig, Victoria H.. Secretaría de Industria y Minería. Servicio Geológico Minero Argentino; ArgentinaFil: Ibáñez, Jesús M.. Universidad de Granada; Españ

Using OWL reasoning to support the generation of novel gene sets for enrichment analysis

Abstract Background The Gene Ontology (GO) consists of over 40,000 terms for biological processes, cell components and gene product activities linked into a graph structure by over 90,000 relationships. It has been used to annotate the functions and cellular locations of several million gene products. The graph structure is used by a variety of tools to group annotated genes into sets whose products share function or location. These gene sets are widely used to interpret the results of genomics experiments by assessing which sets are significantly over- or under-represented in results lists. F Hoffmann-La Roche Ltd. has developed a bespoke, manually maintained controlled vocabulary (RCV) for use in over-representation analysis. Many terms in this vocabulary group GO terms in novel ways that cannot easily be derived using the graph structure of the GO. For example, some RCV terms group GO terms by the cell, chemical or tissue type they refer to. Recent improvements in the content and formal structure of the GO make it possible to use logical queries in Web Ontology Language (OWL) to automatically map these cross-cutting classifications to sets of GO terms. We used this approach to automate mapping between RCV and GO, largely replacing the increasingly unsustainable manual mapping process. We then tested the utility of the resulting groupings for over-representation analysis. Results We successfully mapped 85% of RCV terms to logical OWL definitions and showed that these could be used to recapitulate and extend manual mappings between RCV terms and the sets of GO terms subsumed by them. We also show that gene sets derived from the resulting GO terms sets can be used to detect the signatures of cell and tissue types in whole genome expression data. Conclusions The rich formal structure of the GO makes it possible to use reasoning to dynamically generate novel, biologically relevant groupings of GO terms. GO term groupings generated with this approach can be used in. over-representation analysis to detect cell and tissue type signatures in whole genome expression data