This repository contains the various scripts and files used to identify differential histone post-translational modifications (DHPTMs). The complete details about the various scripts and files in this repository, as well as the set of commands used to run the analysis, are included in the file README.txt
This repository contains the positive and negative training sets generated from the ChIP-seq transcription factor and histone post-translational modification data. Such sets of data were run as input to the RFECS software. RFECS is a tailor-made random forest model for making cis-regulatory module (CRM) predictions. The random forest model used for making the predictions is also available in this repository as a binary MATLAB file. The references below explain specifically how to (1) access a MATLAB file and (2), how to use the current model file to predict the CRMs. For more details about the RFECS algorithm itself, take a look at reference (3).
Summary Human pluripotent stem cell derived muscle models show great potential for translational research. Here, we describe developmentally inspired methods for derivation of skeletal muscle cells and their utility in three-dimensional skeletal muscle organoid formation as well as skeletal muscle tissue engineering. Key steps include the directed differentiation of human pluripotent stem cells to embryonic muscle progenitors of hypaxial origin followed by primary and secondary fetal myogenesis into hypaxial muscle with development of a satellite cell pool and evidence for innervation in vitro . Skeletal muscle organoids faithfully recapitulate all steps of embryonic myogenesis in 3D Tissue engineered muscle exhibits organotypic maturation and function, advanced by thyroid hormone. Regenerative competence was demonstrated in a cardiotoxin injury model with evidence of satellite cell activation as underlying mechanism. Collectively, we introduce a hypaxial muscle model with canonical properties of bona fide skeletal muscle in vivo to study muscle development, maturation, disease, and repair.
DESCRIPTION qPCR was used to assess the enrichment of ChIP and MeDIP experiments and to validate the integrity of sequencing libraries
FILES Validation of ChIP-seq library enrichment: 20130426-CA1-1h-K27ac-K79me3-K4me3-H3-lib_qPCR.xlsx – this file contains tables with qPCR values from validations of ChIP-seq libraries for samples from CA1 neuronal or non-neuronal cells (+ or -, respectively) taken 1 hour (1h) after naïve (N), context (Ct) or context-shock (FC) and immunoprecipitated for H3K27ac, H3K79me3, H3K4me3 or H3. The target genes were NeuN, Chd4, GAPDH, InterG and cFOS. 20130715-ACCnaiveK76me3K27ac_qPCR.xlsx – this file contains tables with qPCR values from validations of ChIP-seq libraries for naïve (N) samples from ACC neuronal or non-neuronal cells (+ or -, respectively) and immunoprecipitated for H3K79me3 or H3K27ac. The target genes were NeuN, Chd4 and GAPDH. 20131125-CA1-Naive_qPCR.xlsx – this file contains tables with qPCR values from validations of ChIP-seq libraries for naïve (N) samples from CA1 neuronal or non-neuronal cells (+ or -, respectively) and immunoprecipitated for H3K27ac, H3K27me3, H3K79me3, H3K4me3, H3 or H3K4me1. The target genes were TFF1, GAPDH, InterG and NeuN. 20150122-CA1-K9ac_qPCR.xlsx – this file contains tables with qPCR values from validations of ChIP-seq libraries for samples from ACC neuronal or non-neuronal cells (+ or -, respectively) taken 1 hour (1h) after naïve (N), context (Ct) or context-shock (FC) and immunoprecipitated for H3K9ac. The target genes were cFosF2R2, cFosF3R3, GAPDH, InterG and NeuN.
Validation of MedIP-seq library enrichment: 20150729-MeDIP-LIB-ACC_qPCR.xlsx – this file contains tables with qPCR values from validations of MedIP-seq libraries for samples from ACC taken 1 hour (1h) after naïve (N), context (Ct) or context-shock (CS). The target genes were Cobl, Loxhd1, Vrk1 and GAPDHenh (-). 20150802-ACC-lib_qPCR.xlsx – this file contains tables with qPCR values from validations of MedIP-seq libraries for samples from ACC taken 1 hour (1h) or 4 weeks (4w) after naïve (N), context (C) or context-shock (CS). The target genes were Fgfr2, Nova1 and Reelin.
DATA GENERATION qPCR experiments were run on Roche LightCycler 480, and the machine was used to export the Ct values. The resulting output was imported into pyQPCR (version 0.10dev) using dilutions of inputs as standards, along with the following parameters: Calculation type: Absolute quantification Confidence interval: 90.00% Machine: Roche LightCycler 480 Maximum E(Ct): 0.30 Minimum Ct: 35.00
This repository contains the scripts and files used to generate the alignment files used for downstream analyses, the bigWig files used to visualise the data and various Quality Control (QC) results used to address the quality of the various samples.
The complete details about the various scripts and files in this repository, as well as the set of commands used to run the analysis, are included in the file README.txt
DESCRIPTION qPCR was used to assess the enrichment of ChIP and MeDIP experiments and to validate the integrity of sequencing libraries
FILES Validation of ChIP-seq library enrichment: 20130426-CA1-1h-K27ac-K79me3-K4me3-H3-lib_qPCR.xlsx – this file contains tables with qPCR values from validations of ChIP-seq libraries for samples from CA1 neuronal or non-neuronal cells (+ or -, respectively) taken 1 hour (1h) after naïve (N), context (Ct) or context-shock (FC) and immunoprecipitated for H3K27ac, H3K79me3, H3K4me3 or H3. The target genes were NeuN, Chd4, GAPDH, InterG and cFOS. 20130715-ACCnaiveK76me3K27ac_qPCR.xlsx – this file contains tables with qPCR values from validations of ChIP-seq libraries for naïve (N) samples from ACC neuronal or non-neuronal cells (+ or -, respectively) and immunoprecipitated for H3K79me3 or H3K27ac. The target genes were NeuN, Chd4 and GAPDH. 20131125-CA1-Naive_qPCR.xlsx – this file contains tables with qPCR values from validations of ChIP-seq libraries for naïve (N) samples from CA1 neuronal or non-neuronal cells (+ or -, respectively) and immunoprecipitated for H3K27ac, H3K27me3, H3K79me3, H3K4me3, H3 or H3K4me1. The target genes were TFF1, GAPDH, InterG and NeuN. 20150122-CA1-K9ac_qPCR.xlsx – this file contains tables with qPCR values from validations of ChIP-seq libraries for samples from ACC neuronal or non-neuronal cells (+ or -, respectively) taken 1 hour (1h) after naïve (N), context (Ct) or context-shock (FC) and immunoprecipitated for H3K9ac. The target genes were cFosF2R2, cFosF3R3, GAPDH, InterG and NeuN.
Validation of MedIP-seq library enrichment: 20150729-MeDIP-LIB-ACC_qPCR.xlsx – this file contains tables with qPCR values from validations of MedIP-seq libraries for samples from ACC taken 1 hour (1h) after naïve (N), context (Ct) or context-shock (CS). The target genes were Cobl, Loxhd1, Vrk1 and GAPDHenh (-). 20150802-ACC-lib_qPCR.xlsx – this file contains tables with qPCR values from validations of MedIP-seq libraries for samples from ACC taken 1 hour (1h) or 4 weeks (4w) after naïve (N), context (C) or context-shock (CS). The target genes were Fgfr2, Nova1 and Reelin.
DATA GENERATION qPCR experiments were run on Roche LightCycler 480, and the machine was used to export the Ct values. The resulting output was imported into pyQPCR (version 0.10dev) using dilutions of inputs as standards, along with the following parameters: Calculation type: Absolute quantification Confidence interval: 90.00% Machine: Roche LightCycler 480 Maximum E(Ct): 0.30 Minimum Ct: 35.00
Unraveling genetic and epigenetic mechanisms behind various biological processes is possible with Next generation sequencing (NGS) methodologies, with a multitude of tools developed to analyze such data. Nevertheless, automated, robust and flexible workflows that analyze NGS data quickly and efficiently have been lacking. In addition, given that many NGS studies today involve integration of results from multiple resources in order to better understand complex biological mechanisms, the quick generation of primary results from separate NGS studies will allow researchers to focus on the result integration. As such, the development of such automated workflows is essential in order to analyse multiple datasets of the same type quickly and efficiently. In addition to the implementation of analysis workflows, the lack of an efficient tool for fragment size estimation and enrichment testing of chromatin immunoprecipitation sequencing (ChIP-seq) data brought the necessity to develop such a tool, and so the R package chequeR was implemented and integrated into the \gls{chip-seq} workflow. The workflows developed for ChIP-seq, methylated DNA immunoprecipitation sequencing (MedIP-seq) and RNA-sequencing (RNA-seq) data were generated as automated scripts to integrate various analysis tools together in order to analyze datasets and return primary results. Having such workflows may allow users to generate said results with relative ease and use them in an integrative manner to establish regulatory networks between multiple genomic and epigenomic elements. This point is demonstrated in Chapters 5 and 6, where the former chapter discusses a study on the effect of short- and long-term memory on the epigenetic and genetic mechanisms in the mouse brain, while the latter chapter explains how the role of p73 in multiciliogenesis regulation was determined. With those workflows used in two particular case studies involving integration of various NGS data types, the importance of having reproducible, automated workflows to generate primary results quickly and simply, while allowing researchers to focus on the main integrative aspects of the studies, is displayed.
Active substances of pesticides, biocides or pharmaceuticals can induce adverse side effects in the aquatic ecosystem, necessitating environmental hazard and risk assessment prior to substance registration. The freshwater crustacean Daphnia magna is a model organism for acute and chronic toxicity assessment representing aquatic invertebrates. However, standardized tests involving daphnia are restricted to the endpoints immobility and reproduction and thus provide only limited insights into the underlying modes-of-action. Here, we applied transcriptome profiling to a modified D. magna Acute Immobilization test to analyze and compare gene expression profiles induced by the GABA-gated chloride channel blocker fipronil and the nicotinic acetylcholine receptor (nAChR) agonist imidacloprid. Daphnids were expose to two low effect concentrations of each substance followed by RNA sequencing and functional classification of affected gene ontologies and pathways. For both insecticides, we observed a concentration-dependent increase in the number of differentially expressed genes, whose expression changes were highly significantly positively correlated when comparing both test concentrations. These gene expression fingerprints showed virtually no overlap between the test substances and they related well to previous data of diazepam and carbaryl, two substances targeting similar molecular key events. While, based on our results, fipronil predominantly interfered with molecular functions involved in ATPase-coupled transmembrane transport and transcription regulation, imidacloprid primarily affected oxidase and oxidoreductase activity. These findings provide evidence that systems biology approaches can be utilized to identify and differentiate modes-of-action of chemical stressors in D. magna as an invertebrate aquatic non-target organism. The mechanistic knowledge extracted from such data will in future contribute to the development of Adverse Outcome Pathways (AOPs) for read-across and prediction of population effects.
