Computational analysis of alternative splicing in human and mice

Im ersten Teil wurden Transkript-Spleißstellen untersucht, mit dem Ziel, alternative und Referenzspleißstellen zu unterscheiden. Die Ergebnisse belegen, dass sich beide Klassen von Spleißstellen durch einen Spleißstellen-Score und vermehrtes Auftreten von Spleißfaktor-Bindemotiven in Umgebung der Spleißstellen abgrenzen lassen. Zusätzlich konnte eine positive Korrelation zwischen der Häufigkeit der Nutzung bestimmter Spleißstellen und dem Spleißstellen-Score in beiden Vergleichsklassen nachgewiesen werden. Diese Abhängigkeit impliziert, dass die Genauigkeit der Annotation alternativer Spleißvarianten mit der Anzahl beobachteter Transkripte steigt. Im zweiten Teil wurde das Spleißsignalmotiv GYNNGY untersucht, welches mehr als 40% aller überlappenden Donor-Spleißsignale ausmacht. Mittels in silico Analysen und experimenteller Validierung wurde die Plausibilität dieses subtilen Spleißmusters bestätigt. Der Vergleich mit anderen humanen Spleißvarianten sowie mit Tandem Donoren in Maus-Transkripten zeigte zudem ausgeprägte Unterschiede bezüglich des Spleißstellen-Scores, der Konservierung, sowie dem Vorkommen von Spleißfaktoren-Bindemotiven. Die Verschiebung des Leserasters durch alternatives Spleißen an GYNNGY-Donoren lässt auf eine komplexe Rolle im RNA-Reifungsprozess schließen. Im dritten Teil wurden Reaktionen des spleißosomalen Makrokomples aus publizierten, experimentellen Daten zusammengestellt und mit Hilfe der Petri-Netz-Theorie in einem qualitativen Modell dargestellt. Unter Annahme eines Steady-State Systems wurden minimale, semipositive T-Invarianten berechnet und zur Validierung des Modells herangezogen. Auf Grundlage der vollständigen Abdeckung des Reaktionsnetzwerks mit T-Invarianten konnten weitere Strukturmerkmale, wie Maximal-Gemeinsame Transitions.Mengen und T-Cluster berechnet werden, welche wichtige Stadien des Spleißosomaufbaus widerspiegeln

Combinatorial biological complexity: a study of amino acid side chains and alternative splicing

Both, laymen and experts have always been intrigued by nature’s vast complexity and variety. Often, these phenomena arise from combination of parts, as for example, cell types of the human body, or the diverse proteins of a cell. In this thesis I investigate three instances of combinatorial complexity: combinations of aliphatic amino acid side chains, alternative mRNA splicing in fungi, and mutually exclusively spliced exons in human and mouse. In the first part the number of aliphatic amino acid side chains is studied. Structural combinations yield a vast theoretical number, yet we find that only a fraction of them is realized in nature. Reasons especially with respect to restrictions by the genetic code are discussed. Moreover, strategies for the need for increased diversity are examined. In the second part, the extent of alternative splicing (AS) in fungi is investigated. A genome-wide, comparative multi-species study is conducted. I find that AS is common in fungi, but with lower frequency compared to plants and animals. AS is more common in more complex fungi, and is over-represented in pathogens. It is hypothesized that AS contributes to multi-cellular complexity in fungi. In the third part, mutually exclusive exons (MXEs) of mouse and human are detected and characterized. Rather unexpected patterns arose: the majority of MXEs originate from non-adjacent exons and frequently appear in clusters. Known regulatory mechanisms of MXE splicing are unsuitable for these MXEs, and thus, new mechanisms have to be sought. Summarizing it is hypothesized that complexity from combinations constitutes a universal principle in biology. However, there seems to be a need to restrict the combinatorial potential. This is highlighted by the interdependence of MXEs and the low number of realized amino acids in the genetic code. Combinatorial complexity and its restriction are discussed with respect to other biological systems to further substantiate the hypotheses

Structural modelling and robustness analysis of complex metabolic networks and signal transduction cascades

The dissertation covers the topic of structural robustness of metabolic networks on the basis of the concept of elementary flux modes (EFMs). It is shown that the number of EFMs does not reflect the topology of a network sufficiently. Thus, new methods are developed to determine the structural robustness of metabolic networks. These methods are based on systematic in-silico knockouts and the subsequent calculation of dropped out EFMs. Thereby, together with single knockouts also double and multiple knockouts can be used. After evaluation of these methods they are applied to metabolic networks of human erythrocyte and hepatocyte as well as to a metabolic network of Escherichia coli (E. coli). It is found that the erythrocyte has the lowest structural robustness, followed by the hepatocyte and E. coli. These results coincide very well with the circumstance that human erythrocyte and hepatocyte and E. coli are able to adapt to conditions with increasing diversity. In a further part of the dissertation the concept of EFMs is expanded to signal transduction pathways consisting of kinase cascades. The concept of EFMs is based on the steady-state condition for metabolic pathways. It is shown that under certain circumstances this steady-state condition also holds for signalling cascades. Furthermore, it is shown that it is possible to deduce minimal conditions for signal transduction without knowledge about the kinetics involved. On the basis of these assumptions it is possible to calculate EFMs for signalling cascades. But due to the fact that these EFMs do no longer just have mass flux but also information flux, they are now called elementary signalling modes (ESMs).Die Dissertation behandelt die strukturelle Robustheit von metabolischen Netzwerken auf der Basis des Konzepts der elementaren Flussmoden (EFMen). Es wird gezeigt, dass die Anzahl der EFMen die Topologie eines metabolischen Netzes nicht ausreichend widerspiegelt. Darauf aufbauend werden neue Methoden entwickelt, um die strukturelle Robustheit metabolischer Netze zu bestimmen. Diese Methoden beruhen auf systematischen in-silico-Knockouts und der anschließenden Bestimmung des Anteils an weggefallenen EFMen. Dabei können neben Einfach-Knockouts auch Doppel- oder Mehrfach-Knockouts verwendet werden. Nach der Evaluierung werden diese Methoden auf metabolische Netzwerke des menschlichen Erythrozyten und Hepatozyten, sowie des Bakteriums Escherichia coli (E. coli) angewendet. Es zeigt sich, dass der Erythrozyt die im Vergleich geringste strukturelle Robustheit besitzt, gefolgt vom Hepatozyten und E. coli. Diese Ergebnisse stimmen sehr gut mit der Beobachtung überein, dass sich die menschlichen Erythrozyten und Hepatozyten, sowie E. coli an zunehmend verschiedene Bedingungen anpassen können. In einem weiteren Teil der Dissertation wird das Konzept der EFMen auf Signaltransduktionswege bestehend aus Kinase-Kaskaden erweitert. Das Konzept der EFMen beruht auf der Annahme eines quasi-stationären Zustands für metabolische Netzwerke. Es wird gezeigt, dass dieser quasi-stationäre Zustand unter bestimmten Bedingungen auch in Signal-Kaskaden angenommen werden kann. Weiterhin wird gezeigt, dass man ohne Kenntnis der beteiligten Kinetiken Minimalbedingungen für die Signalweiterleitung ableiten kann. Auf Basis dieser Annahmen lassen sich für Signal-Kaskaden EFMen berechnen. Aber aufgrund der Tatsache, dass sie nicht mehr nur Masse-, sondern auch Informationsfluss beschreiben, werden sie nun als elementare Signalmoden (ESMen) bezeichnet

Identification of Amyotrophic Lateral Sclerosis Disease Mechanisms by Cerebrospinal Fluid Proteomic Profiling

Amyotrophic lateral sclerosis (ALS) is the most common form of adult-onset motor neuron disease. Heterogeneity in clinical, genetic, and pathological features of ALS suggest the disease is a spectrum of disorders each resulting in motor neuron degeneration. Molecular profiling of ALS patients is, therefore, a useful means of characterizing and stratifying the ALS population. To this end, mass spectrometric proteomic profiling was performed on cerebrospinal fluid (CSF) from ALS, healthy control (HC), and other neurological disease (OND) subjects. This resulted in the identification of 1,712 CSF proteins, 123 of which exhibited altered relative abundance in ALS CSF. Biological processes related to these 123 proteins included synaptic activity, extracellular matrix, and inflammation. The application of feature selection and machine learning methods to these CSF proteomic profiles resulted in a classifier that used relative levels of WDR63, APLP1, SPARCL1, and CADM3 to predict independent ALS, HC, and OND samples with 83% sensitivity and 100% specificity. To aid in the validation of selected CSF proteins, a Western blot loading control method was developed and validated using a reversible, iodine-based total protein stain. This method improves the accuracy and sensitivity of the relative quantification of CSF proteins via Western blot. As RNA binding protein (RBP) pathology/dysfunction is common to several forms of ALS, the largest CSF RBP alteration, that of RNA binding motif 45 (RBM45) protein, was validated externally. The results demonstrated that RBM45 pathology is common to several forms of ALS, frontotemporal lobar degeneration (FTLD), and Alzheimer’s disease. To further understand the biological functions of RBM45, immunoprecipitation coupled to mass spectrometry was performed to identify RBM45 protein-protein interactions (PPIs). RBM45 PPIs and associated pathways were most strongly associated with hnRNP proteins, RNA processing, and cytoplasmic translation. RBM45 also participates in the general cellular response to stress via association with nuclear stress bodies. This association is dependent on RNA binding, is upregulated in ALS/FTLD, and is sufficient to induce the aggregation of the protein. Collectively, these results illustrate the utility of CSF proteomic profiling for characterizing mechanisms of neurological disease and provide new insights into the contributions of RNA binding protein dysregulation to ALS/FTLD

Dichotomic role of NAADP/two-pore channel 2/Ca2+ signaling in regulating neural differentiation of mouse embryonic stem cells

Poster Presentation - Stem Cells and Pluripotency: abstract no. 1866The mobilization of intracellular Ca2+stores is involved in diverse cellular functions, including cell proliferation and differentiation. At least three endogenous Ca2+mobilizing messengers have been identified, including inositol trisphosphate (IP3), cyclic adenosine diphosphoribose (cADPR), and nicotinic adenine acid dinucleotide phosphate (NAADP). Similar to IP3, NAADP can mobilize calcium release in a wide variety of cell types and species, from plants to animals. Moreover, it has been previously shown that NAADP but not IP3-mediated Ca2+increases can potently induce neuronal differentiation in PC12 cells. Recently, two pore channels (TPCs) have been identified as a novel family of NAADP-gated calcium release channels in endolysosome. Therefore, it is of great interest to examine the role of TPC2 in the neural differentiation of mouse ES cells. We found that the expression of TPC2 is markedly decreased during the initial ES cell entry into neural progenitors, and the levels of TPC2 gradually rebound during the late stages of neurogenesis. Correspondingly, perturbing the NAADP signaling by TPC2 knockdown accelerates mouse ES cell differentiation into neural progenitors but inhibits these neural progenitors from committing to the final neural lineage. Interestingly, TPC2 knockdown has no effect on the differentiation of astrocytes and oligodendrocytes of mouse ES cells. Overexpression of TPC2, on the other hand, inhibits mouse ES cell from entering the neural lineage. Taken together, our data indicate that the NAADP/TPC2-mediated Ca2+signaling pathway plays a temporal and dichotomic role in modulating the neural lineage entry of ES cells; in that NAADP signaling antagonizes ES cell entry to early neural progenitors, but promotes late neural differentiation.postprin

Current Frontiers and Perspectives in Cell Biology

A numerous internationally renowned authors in the pages of this book present the views of the fields of cell biology and their own research results or review of current knowledge. Chapters are divided into five sections that are dedicated to cell structures and functions, genetic material, regulatory mechanisms, cellular biomedicine and new methods in cell biology. Multidisciplinary and often quite versatile approach by many authors have imposed restrictions of this classification, so it is certain that many chapters could belong to the other sections of this book. The current frontiers, on the manner in which they described in the book, can be a good inspiration to many readers for further improving, and perspectives which are highlighted can be seen in many areas of fundamental biology, biomedicine, biotechnology and other applications of knowledge of cell biology. The book will be very useful for beginners to gain insight into new area, as well as experts to find new facts and expanding horizons

Computational Methods for Analysing Long-run Dynamics of Large Biological Networks

Systems biology combines developments in the fields of computer science, mathematics, engineering, statistics, and biology to study biological networks from a holistic point of view in order to provide a comprehensive, system level understanding of the underlying system. Recent developments in biological laboratory techniques have led to a slew of increasingly complex and large biological networks. This poses a challenge for formal representation and analysis of those large networks efficiently. To understand biology at the system level, the focus should be on understanding the structure and dynamics of cellular and organismal function, rather than on the characteristics of isolated parts of a cell or organism. One of the most important focuses is the long-run dynamics of a network, as they often correspond to the functional states, such as proliferation, apoptosis, and differentiation. In this thesis, we concentrate on how to analyse long-run dynamics of biological networks. In particular, we examine situations where the networks in question are very large. In the literature, quite a few mathematical models, such as ordinary differential equations, Petri nets, and Boolean networks (BNs), have been proposed for representing biological networks. These models provide different levels of details and have different advantages. Since we are interested in large networks and their long-run dynamics, we need to use ``coarse-grained" level models that focus on the system behaviour of the network while neglecting molecular details. In particular, we use probabilistic Boolean networks (PBNs) to describe biological networks. By focusing on the wiring of a network, a PBN not only simplifies the representation of the network, but it also captures the important characteristics of the dynamics of the network. Within the framework of PBNs, the analysis of long-run dynamics of a biological network can be performed with regard to two aspects. The first aspect lies in the identification of the so-called attractors of the constituent BNs of a PBN. An attractor of a BN is a set of states, inside which the network will stay forever once it goes in; thus capturing the network's long-term behaviour. A few methods have been discussed for computing attractors in the literature. For example, the binary decision diagram based approach and the satisfiability based approach. These methods, however, are either restricted by the network size, or can only be applied to synchronous networks where all the elements in the network are updated synchronously at each time step. To overcome these issues, we propose a decomposition-based method. The method works in three steps: we decompose a large network into small sub-networks, detect attractors in sub-networks, and recover the attractors of the original network using the attractors of the sub-networks. Our methods can be applied to both asynchronous networks, where only one element in the network is updated at each time step, and synchronous networks. Experimental results show that our proposed method is significantly faster than the state-of-the-art methods. The second aspect lies in the computation of steady-state probabilities of a PBN with perturbations. The perturbations of a PBN allow for a random, with a small probability, alteration of the current state of the PBN. In a PBN with perturbations, the long-run dynamics is characterised by the steady-state probability of being in a certain set of states. Various methods for computing steady-state probabilities can be applied to small networks. However, for large networks, the simulation-based statistical methods remain the only viable choice. A crucial issue for such methods is the efficiency. The long-run analysis of large networks requires the computation of steady-state probabilities to be finished as soon as possible. To reach this goal, we apply various techniques. First, we revive an efficient Monte Carlo simulation method called the two-state Markov chain approach for making the computations. We identify an initialisation problem, which may lead to biased results of this method, and propose several heuristics to avoid this problem. Secondly, we develop several techniques to speed up the simulation of PBNs. These techniques include the multiple central processing unit based parallelisation, the multiple graphic processing unit based parallelisation, and the structure-based parallelisation. Experimental results show that these techniques can lead to speedups from ten times to several hundreds of times. Lastly, we have implemented the above mentioned techniques for identification of attractors and the computation of steady-state probabilities in a tool called ASSA-PBN. A case-study for analysing an apoptosis network with this tool is provided

A proteomics investigation into the role of zDHHC23 and MROH6 in neuroblastoma

Neuroblastoma (NB) is the most common malignant solid tumour diagnosed in infants, accounting for ~15% of all childhood cancer-related deaths. Current patient risk stratification criteria are heavily reliant on the presence of a MYCN amplification, albeit only accounting for ~25% of patients. The inadequate prognostic risk stratification of patients results in children receiving either inefficient or excessive treatment with a myriad of severe lifelong side effects for survivors. Therefore, the identification and characterisation of novel biomarkers could not only identify new therapeutic targets but could also improve risk stratification and treatment planning. A comparative transcriptomic analysis of NB tumours (obtained from the chick embryo model) grown under normal oxygen tensions (normoxia, 21% O2) or hypoxia (1% O2), a model for aggressive NB tumours that correlates with poor patient prognosis, identified multiple significantly upregulated genes in aggressive (hypoxic) tumours specifically, with Zinc Finger DHHC-Type Palmitoyltransferase 23 (zDHHC23) and Maestro Heat Like Repeat Family Member 6 (MROH6) exhibiting the best correlation with poor prognosis. This thesis sought to validate these expressed gene products as potential biomarkers in NB. I also investigated the molecular function of these two proteins under normoxic and hypoxic conditions, supplementing the currently limited available knowledge. Commercially available antibodies for these two proteins were unsuccessful for use in either immunostaining, a procedure currently used as the ‘gold-standard’ of clinical biomarker screening, or for immunoblotting of endogenous protein, with all of the antibodies evaluated lacking specificity. Although targeted mass spectrometry assays were successfully developed, they lacked the sensitivity to detect endogenous proteins, likely due to low levels of protein expression. Therefore, I focused on the biochemical characterisation of these two proteins, cloning dual reporter HA-mCherry-protein and protein-mCherry-HA plasmids to facilitate immunoprecipitation of exogenously expressed protein and evaluation of sub-cellular localisation. I developed and optimised a HA-tag based immunoprecipitation protocol for liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis to allow identification of post-translational modifications (PTMs) and protein interaction networks. These experiments revealed extensive hypoxia-induced regulation of protein binding partners, with ~70% of the interactome (from a total of 262 and 253 co-immunoprecipitated proteins for zDHHC23 and MROH6 respectively) changing as a function of O2 tension. GOterm analysis of these interactomes suggests that zDHHC23 is a component of several potentially important malignancy pathways, including cytoskeletal reorganisation and adhesion. Label free quantification analysis of MROH6 identifies high stoichiometric binding to Breast Cancer Anti-oestrogen Resistance protein 1 (BCAR1), inferring potential roles in telomere maintenance and genetic stability. Additionally, PTM analysis identified one and three phosphorylation sites on MROH6 and zDHHC23 respectively, with zDHHC23 S252 predicted to be regulated by Cyclin dependent kinases. Finally, I developed, to my knowledge, the first reported click-chemistry based high-throughput LC-MS/MS pipeline for the unbiased identification of zDHHC23 palmitoylated substrates, concluding that the ‘palmitome’ is much more complex than currently understood and likely regulates localisation to membrane bound organelles and extracellular vesicles, as well as its established role in plasma membrane localisation. Overall, using LC-MS/MS approaches, I explore and discuss how zDHHC23 and MROH6 overexpression may contribute to aggressive NB development and poor patient prognosis