An Information-Theoretic Approach to Detect the Associations of GPS-Tracked Heifers in Pasture

Sensor technologies, such as the Global Navigation Satellite System (GNSS), produce huge amounts of data by tracking animal locations with high temporal resolution. Due to this high resolution, all animals show at least some co-occurrences, and the pure presence or absence of co-occurrences is not satisfactory for social network construction. Further, tracked animal contacts contain noise due to measurement errors or random co-occurrences. To identify significant associations, null models are commonly used, but the determination of an appropriate null model for GNSS data by maintaining the autocorrelation of tracks is challenging, and the construction is time and memory consuming. Bioinformaticians encounter phylogenetic background and random noise on sequencing data. They estimate this noise directly on the data by using the average product correction procedure, a method applied to information-theoretic measures. Using Global Positioning System (GPS) data of heifers in a pasture, we performed a proof of concept that this approach can be transferred to animal science for social network construction. The approach outputs stable results for up to 30% missing data points, and the predicted associations were in line with those of the null models. The effect of different distance thresholds for contact definition was marginal, but animal activity strongly affected the network structure

Removing Background Co-occurrences of Transcription Factor Binding Sites Greatly Improves the Prediction of Specific Transcription Factor Cooperations

Today, it is well-known that in eukaryotic cells the complex interplay of transcription factors (TFs) bound to the DNA of promoters and enhancers is the basis for precise and specific control of transcription. Computational methods have been developed for the identification of potentially cooperating TFs through the co-occurrence of their binding sites (TFBSs). One challenge of these methods is the differentiation of TFBS pairs that are specific for a given sequence set from those that are ubiquitously appearing, rendering the results highly dependent on the choice of a proper background set. Here, we present an extension of our previous PC-TraFF approach that estimates the background co-occurrence of any TF pair by preserving the (oligo-) nucleotide composition and, thus, the core of TFBSs in the sequences of interest. Applying our approach to a simulated data set with implanted TFBS pairs, we could successfully identify them as sequence-set specific under a variety of conditions. When we analyzed the gene expression data sets of five breast cancer associated subtypes, the number of overlapping pairs could be dramatically reduced in comparison to our previous approach. As a result, we could identify potentially cooperating transcriptional regulators that are characteristic for each of the five breast cancer subtypes. This indicates that our approach is able to discriminate specific potential TF cooperations against ubiquitously occurring combinations. The results obtained with our method may help to understand the genetic programs governing specific biological processes such as the development of different tumor types

A Novel Sequence-Based Feature for the Identification of DNA-Binding Sites in Proteins Using Jensen–Shannon Divergence

The knowledge of protein-DNA interactions is essential to fully understand the molecular activities of life. Many research groups have developed various tools which are either structure- or sequence-based approaches to predict the DNA-binding residues in proteins. The structure-based methods usually achieve good results, but require the knowledge of the 3D structure of protein; while sequence-based methods can be applied to high-throughput of proteins, but require good features. In this study, we present a new information theoretic feature derived from Jensen–Shannon Divergence (JSD) between amino acid distribution of a site and the background distribution of non-binding sites. Our new feature indicates the difference of a certain site from a non-binding site, thus it is informative for detecting binding sites in proteins. We conduct the study with a five-fold cross validation of 263 proteins utilizing the Random Forest classifier. We evaluate the functionality of our new features by combining them with other popular existing features such as position-specific scoring matrix (PSSM), orthogonal binary vector (OBV), and secondary structure (SS). We notice that by adding our features, we can significantly boost the performance of Random Forest classifier, with a clear increment of sensitivity and Matthews correlation coefficient (MCC)

Computational identification of tissue-specific transcription factor cooperation in ten cattle tissues.

Transcription factors (TFs) are a special class of DNA-binding proteins that orchestrate gene transcription by recruiting other TFs, co-activators or co-repressors. Their combinatorial interplay in higher organisms maintains homeostasis and governs cell identity by finely controlling and regulating tissue-specific gene expression. Despite the rich literature on the importance of cooperative TFs for deciphering the mechanisms of individual regulatory programs that control tissue specificity in several organisms such as human, mouse, or Drosophila melanogaster, to date, there is still need for a comprehensive study to detect specific TF cooperations in regulatory processes of cattle tissues. To address the needs of knowledge about specific combinatorial gene regulation in cattle tissues, we made use of three publicly available RNA-seq datasets and obtained tissue-specific gene (TSG) sets for ten tissues (heart, lung, liver, kidney, duodenum, muscle tissue, adipose tissue, colon, spleen and testis). By analyzing these TSG-sets, tissue-specific TF cooperations of each tissue have been identified. The results reveal that similar to the combinatorial regulatory events of model organisms, TFs change their partners depending on their biological functions in different tissues. Particularly with regard to preferential partner choice of the transcription factors STAT3 and NR2C2, this phenomenon has been highlighted with their five different specific cooperation partners in multiple tissues. The information about cooperative TFs could be promising: i) to understand the molecular mechanisms of regulating processes; and ii) to extend the existing knowledge on the importance of single TFs in cattle tissues

Boletín de Segovia: Número 146 - 1923 diciembre 5

Copia digital. Madrid : Ministerio de Cultura. Subdirección General de Coordinación Bibliotecaria, 200

EXITOX-II: Development of an animal free testing strategy for the risk assessment of inhalable compounds

In human risk assessment there is a paradigm shift toward mechanistic risk assessment with the aim to replace as far as possible in vivo animal testing by new approach methodologies (NAMs). These NAMs include different techniques based on in silico (QSAR, grouping and PBPK), in vitro, and ex vivo approaches. In this study we hypothesize that adverse outcomes of chronic respiratory diseases are shared between similar substances and change signaling pathways in single cells, tissues, and organisms. We aim to develop an integrated approach for testing and assessment (IATA) to replace animal studies with repeated inhalational exposure. EXITOX-II, explain inhalation toxicity II, is a public funded project and started in January 2017 as a follow up project of EXITOX. In this project we selected five groups of structurally similar compounds for testing findings/adverse outcomes in repeated dose toxicity because of (RDT)shared toxicological studies with inhalation exposure. The RDT studies were taken from the FhG database RepDose. Three groups induce inflammation, one hyperplasia and one pulmonary fibrosis. Based on their generic physico-chemical properties these compounds are tested in vitroin i) alveolar epithelial cells A549 and ii) in fresh human lung tissue. So far we exposed cells and tissues to chemicals at air-liquid interface or submerse. Cellular readouts such as cytotoxicity and chemokine release are currently measured. Based on these results, samples are collected for omic analyses with the TempoSeqTM technology (mRNA) and Affymetrix arrays (miRNA). By dose dependent testing, we will investigate the onset of gene changes and miRNA regulation. Further we aim to better differentiate between group-specific toxicological changes and general stress responses e.g. related to high dosing. Finally, we will confirm gene changes by RTqPCR testing. In this presentation we will show the current results from in vitro testing, QSAR models for ADME parameters such as ppb (plasma protein binding), and PBPK models for lung uptake. Furthermore, we will present our concept for biomarker discovery based on e.g. differentially expressed genes and the unique upstream analysis available from the geneXplain platform. A first indication how we see these building blocks in the context of an IATA will be provided