The kinematics of swimming and relocation jumps in copepod nauplii

Copepod nauplii move in a world dominated by viscosity. Their swimming-by-jumping propulsion mode, with alternating power and recovery strokes of three pairs of cephalic appendages, is fundamentally different from the way other microplankters move. Protozoans move using cilia or flagella, and copepodites are equipped with highly specialized swimming legs. In some species the nauplius may also propel itself more slowly through the water by beating and rotating the appendages in a different, more complex pattern. We use high-speed video to describe jumping and swimming in nauplii of three species of pelagic copepods: Temora longicornis, Oithona davisae and Acartia tonsa. The kinematics of jumping is similar between the three species. Jumps result in a very erratic translation with no phase of passive coasting and the nauplii move backwards during recovery strokes. This is due to poorly synchronized recovery strokes and a low beat frequency relative to the coasting time scale. For the same reason, the propulsion efficiency of the nauplii is low. Given the universality of the nauplius body plan, it is surprising that they seem to be inefficient when jumping, which is different from the very efficient larger copepodites. A slow-swimming mode is only displayed by T. longicornis. In this mode, beating of the appendages results in the creation of a strong feeding current that is about 10 times faster than the average translation speed of the nauplius. The nauplius is thus essentially hovering when feeding, which results in a higher feeding efficiency than that of a nauplius cruising through the water

Aufnahme von Fettsäuren in Spermatozoenlipide von Sus scrofa domestica und physiologische Auswirkungen

Die vorliegende Arbeit beschäftigt sich mit den physiologischen Veränderungen porciner Spermatozoen, die durch einen metabolischen Einbau von Fettsäuren in Spermatozoenlipide hervorgerufen werden. Ziel dieser Arbeit war die Untersuchung der metabolischen Aufnahme von Fettsäuren in die Spermatozoenlipide und die Bewertung des physiologischen Zustandes porciner Spermatozoen mit Hinblick auf die Niedrigtemperaturlagerung. Alle in den porcinen Spermatozoen vorkommenden Lipide wurden mittels GC und MALDI-TOF-MS analysiert. Hauptvertreter der polaren Lipidklassen sind Glycerophospholipide (GPC, GPE). Der Hauptvertreter der neutralen Lipidklassen ist Diacylglycerol (DAG). Die metabolische Aufnahme von Fettsäuren in die Lipide wurde durch die Supplementierung des Flüssigkonservierungsmediums mit [14C]-Octadecadiensäure radiochemisch untersucht. Anhand dieser Experimente wurde gezeigt, dass die Temperatur und die Inkubationsdauer wichtige Faktoren für die metabolische Aufnahme dieser Radiochemikalie in die Spermatozoenlipide sind. Die zugesetzten Fettsäuren werden sowohl in die neutralen (DAG) als auch in die polaren Lipide (diacyl-GPC) der Spermatozoen eingebaut. Nach Supplementierung mit 13C-markierter Octadecadiensäure wurden die Lipide mittels MALDI- und Q-TOF-MS als DAG (18:2/18:2), GPC (16:0/18:2) und GPC (18:2/18:2) charakterisiert. Die gleichen Ergebnisse wurden auch für die in den Spermatozoenlipiden vorkommenden Hexadecen-, Octadecen-, und Octadecatriensäure erhalten. Bei der Untersuchung des physiologischen Zustandes von Spermatozoen wurde gezeigt, dass insbesondere Supplementierungsvarianten mit endogen vorkommenden Fettsäuren zu einer besseren Spermatozoenvitalität und Motilität bei Niedrigtemperaturlagerung führten. Gleichzeitig wurde eine Verminderung des Auftretens von akrosomalen Schäden festgestellt. Damit stellt eine Supplementierung der Spermatozoen mit ausgewählten Fettsäuren eine effektive Maßnahme zur Lagerung von Spermatozoen bei 4 bis 6°C dar.This study examines the metabolic incorporation of selected fatty acids into the lipids of porcine spermatozoa and evaluates the physiological state of spermatozoa subsequent to low temperature storage supplementation with selected free fatty acids. The aim was to understand the role of fatty acids in relation to the (cryo-)preservation of spermatozoa and successful reproduction in more detail. All lipids present in porcine spermatozoa were analysed using gas chromatography (GC) and mass spectrometry (MALDI-TOF-MS). The main representatives of the polar lipid classes are glycerophospholipids (in particular GPC and GPE). The main representatives of the neutral lipid classes are diacylglycerols (DAG). Metabolic incorporation of fatty acids into lipids was radiochemically monitored using [14C]-octadecadienoic acid in the supplied spermatozoa-preservation medium. Temperature and incubation time were shown to be particularly important determinants. The added fatty acids were incorporated into both the spermatozoas’ neutral (DAG) and polar lipids (diacyl-GPC). The affected lipids were characterised by means of MALDI- and Q-TOF-MS subsequent to the supplementation of uniformly 13C-labelled octadecadienoic acid. DAG (18:2/18:2), GPC (16:0/18:2) and GPC (18:2/18:2) could be identified and a de-novo biosynthesis of DAG (18:2/18:2) could be proven. The same results were obtained when spermatozoa were supplemented with hexadecenoic, octadecenoic and octadecatrienoic acids. Finally, it was shown that the physiological state of the spermatozoa, especially those supplemented with endogeneously present fatty acids, led to an enhanced vitality and motility in spermatozoa subsequent to low temperature storage. It was also observed that acrosomal damage was reduced and that hexadecenoic acid significantly stabilised all the vitality parameters. In conclusion, supplementing spermatozoa with selected fatty acids is an effective solution for the storage of spermatozoa at 4 to 6°C

IDENTIFICATION OF CIS-REGULATORY MODULES AND NON-CODING VARIATION USING MACHINE LEARNING METHODS

Table of Contents CHAPTER 1: INTRODUCTION 1 1. Transcriptional regulation 1 1.1 Classes of cis-regulatory modules 2 1.1.1 CRM architecture 3 1.2 Chromatin signatures of CRMs 4 1.3 Motif and evolutionary constraint in noncoding regions 5 1.4 Detecting regulatory regions using experimental methods 7 1.4.1 Genome-wide identification of TF binding with ChIP and DamID 7 1.4.2 Identification of enhancers using open chromatin profiling. 7 1.4.3 Functional validation of enhancers 8 1.4.3.1 Massively parallel reporter assay 8 1.4.2.2 STARR-seq 9 1.4.2.3 Assays using genomic integration 9 2. Computational identification of regulatory elements in the genome 9 2.1 Motif-based approaches 9 2.2 Comparative genomics approaches to identify functional binding sites 10 2.3 CRM detection using motif clustering 11 2.4 Machine learning approaches to find CRMs 12 2.4.1 Unsupervised learning methods 12 2.4.1.1 Hidden Markov Models 12 2.4.2 Supervised methods 13 2.4.2.1 Evaluation of model performance 13 2.4.2.2 Regularized linear models 14 2.4.2.3 SVM for CRM prediction 14 2.4.2.4 Ensemble of decision trees 15 2.4.2.4.1 Algorithms to train a decision tree classifier 16 2.4.2.4.2 Parameters of the Random Forest 17 2.4.2.5 Feature selection methods 17 2.4.2.5.1 Filter methods 18 2.4.2.5.2 Wrapper methods 18 2.4.2.5.3 Embedded methods 19 2.4.2.6 Deep learning methods 19 2.4.2.6.1 Convolutional Neural Networks 19 2.4.2.6.2 Overfitting in the CNN 21 2.4.2.6.3 CNNs for computational identification of CRMs 21 3. Transcriptional regulation and cancer 22 3.1 Role of TP53 in cancer 23 3.2 Role of non-coding mutations in cancer 23 CHAPTER II: Objectives 27 CHAPTER III: Results 29 PAPER 1: Identification of High-Impact cis-Regulatory Mutations Using Transcription Factor Specific Random Forest Models 31 PAPER 2: Multiplex enhancer-reporter assays uncover unsophisticated TP53 enhancer logic 83 CHAPTER IV: DISCUSSION 141 5.1 Computational models to identify TF-specific enhancers 141 5.2 Prediction of high-impact cis-regulatory mutations with enhancer models 142 5.3 Deciphering p53 enhancer logic using high-throughput enhancer reporter assays coupled with machine learning 143 5.4 General conclusion 146 5.5 Future perspectives 147 BIBLIOGRAPHY 153nrpages: 172status: publishe

A novel High-throughput Enhancer reporter assay reveals unsophisticated p53 enhancer logic

Deciphering the cis-regulatory logic encoded in enhancer sequences requires large-scale reporter assays to experimentally validate candidate enhancers predicted by genomic approaches such as chromatin accessibility and ChIP-seq. Here, we propose a novel high-throughput enhancer-reporter assay called CHEQ-Seq (Captured High-throughput Enhancer testing by Quantitative Sequencing). A set of candidate enhancers are pre-selected as regions of 0.5-1 kb and enriched from genomic, sheared DNA using custom-designed capturing baits. They are subsequently cloned into a reporter library and randomly combined with unique barcodes, before being tested under various conditions in cell culture. The relationship between each enhancer and its reporter-barcode is determined by PacBio long-read sequencing of the entire library; while the barcode expression level is determined by Illumina short-read cDNA sequencing. We have applied Cheq-seq to test the enhancer activity of 1526 p53 ChIP-seq peaks under p53 knock-down and p53 over-activating conditions. We obtained reproducible reporter expression for 1060 captured enhancers, of which 397 showed a significant p53-dependent activation. Strikingly, the large majority (99%) of p53 target enhancers can be characterized and distinguished from negative sequences by the occurrence of a single p53 binding site. Thus, the p53 enhancer logic represents a new ancestral class of enhancers, distinct from developmental enhancers that adhere to the billboard and enhanceosome models. The p53 enhancers do not contain obvious combinatorial complexity of binding sites for multiple transcription factors. This suggests that p53 acts alone on its target enhancers, and that context-dependent regulation of target genes is not encoded in the p53 enhancer sequences, but at different upstream or downstream layers of the cell’s gene regulatory network.status: accepte

Identification of High-Impact cis-Regulatory Mutations Using Transcription Factor Specific Random Forest Models

Cancer genomes contain vast amounts of somatic mutations, many of which are passenger mutations not involved in oncogenesis. Whereas driver mutations in protein-coding genes can be distinguished from passenger mutations based on their recurrence, non-coding mutations are usually not recurrent at the same position. Therefore, it is still unclear how to identify cis-regulatory driver mutations, particularly when chromatin data from the same patient is not available, thus relying only on sequence and expression information. Here we use machine-learning methods to predict functional regulatory regions using sequence information alone, and compare the predicted activity of the mutated region with the reference sequence. This way we define the Predicted Regulatory Impact of a Mutation in an Enhancer (PRIME). We find that the recently identified driver mutation in the TAL1 enhancer has a high PRIME score, representing a "gain-of-target" for MYB, whereas the highly recurrent TERT promoter mutation has a surprisingly low PRIME score. We trained Random Forest models for 45 cancer-related transcription factors, and used these to score variations in the HeLa genome and somatic mutations across more than five hundred cancer genomes. Each model predicts only a small fraction of non-coding mutations with a potential impact on the function of the encompassing regulatory region. Nevertheless, as these few candidate driver mutations are often linked to gains in chromatin activity and gene expression, they may contribute to the oncogenic program by altering the expression levels of specific oncogenes and tumor suppressor genes.status: publishe

Identification of High-Impact cis-Regulatory Mutations Using Transcription Factor Specific Random Forest Models.

Cancer genomes contain vast amounts of somatic mutations, many of which are passenger mutations not involved in oncogenesis. Whereas driver mutations in protein-coding genes can be distinguished from passenger mutations based on their recurrence, non-coding mutations are usually not recurrent at the same position. Therefore, it is still unclear how to identify cis-regulatory driver mutations, particularly when chromatin data from the same patient is not available, thus relying only on sequence and expression information. Here we use machine-learning methods to predict functional regulatory regions using sequence information alone, and compare the predicted activity of the mutated region with the reference sequence. This way we define the Predicted Regulatory Impact of a Mutation in an Enhancer (PRIME). We find that the recently identified driver mutation in the TAL1 enhancer has a high PRIME score, representing a "gain-of-target" for MYB, whereas the highly recurrent TERT promoter mutation has a surprisingly low PRIME score. We trained Random Forest models for 45 cancer-related transcription factors, and used these to score variations in the HeLa genome and somatic mutations across more than five hundred cancer genomes. Each model predicts only a small fraction of non-coding mutations with a potential impact on the function of the encompassing regulatory region. Nevertheless, as these few candidate driver mutations are often linked to gains in chromatin activity and gene expression, they may contribute to the oncogenic program by altering the expression levels of specific oncogenes and tumor suppressor genes

Identification of cis-regulatory mutations generating de novo edges in personalized cancer gene regulatory networks

The identification of functional non-coding mutations is a key challenge in the field of genomics. Here we introduce μ-cisTarget to filter, annotate and prioritize cis-regulatory mutations based on their putative effect on the underlying "personal" gene regulatory network. We validated μ-cisTarget by re-analyzing the TAL1 and LMO1 enhancer mutations in T-ALL, and the TERT promoter mutation in melanoma. Next, we re-sequenced the full genomes of ten cancer cell lines and used matched transcriptome data and motif discovery to identify master regulators with de novo binding sites that result in the up-regulation of nearby oncogenic drivers. μ-cisTarget is available from http://mucistarget.aertslab.org .status: publishe