28 research outputs found

    Quasi-cellular Systems: Stochastic Simulation Analysis at Nanoscale Range

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    I complessi sistemi di reazioni biochimiche all’interno della cellula sono altamente compartimentalizzati, conseguenza di un importante fenomeno di macromolecolar crowding (sovraffollamento molecolare). E’ dunque importante determinare il comportamento e le proprietà di un sistema di reazioni in piccoli volumi. Sono stati riprodotti con successo diversi sistemi di semplici reazioni all’interno di vescicole lipidiche (liposomi) nell’ordine del micro/nanometro di diametro, osservando in molti casi una risposta cinetica diversa dalle reazioni in esame rispetto al comportamento in sistemi di grandi volumi. Questo fenomeno di divergenza tra piccoli e grandi volumi è in gran parte dipendente da fenomeni non completamente chiariti, quali l’incapsulamento delle specie e il crowding molecolare, aspetti sempre più importanti man mano che l’attenzione si sposta verso i piccoli volumi. Recenti dati sperimentali dimostrano che il fenomeno dell'intrappolamento sembra non seguire un andamento casuale squisitamente probabilistico, ma un comportamento di tipo power-law (a legge di potenza), in cui solo pochissime vescicole intrappolano tante specie, mentre la maggior parte resta completamente vuota. A tal proposito è stato intrapreso uno studio sui meccanismi generativi delle distribuzioni a legge di potenza calate nel contesto dell’incapsulamento (entrapment) delle specie all'interno di vescicole lipidiche. Utilizzando un sistema cell-free di trascrizione/traduzione (PURESYSTEM™), volto alla produzione di EGFP all’interno di liposomi di POPC, è possibile monitorare la produzione di proteina fluorescente in liposomi di differente grandezza. Tuttavia, è molto difficile osservare la produzione di molecole fluorescenti in singole vescicole di 100 nm di diametro; diventa così importante poter studiare in silico la di produzione di proteina in singole vescicole virtuali, utilizzando un modello formalmente valido del complesso sistema di reazioni del PURESYSTEM™. QDC (Quick Direct-Method Controlled), è un software di simulazione stocastico precedentemente sviluppato in laboratorio, basato sull’algoritmo di simulazione SSA Direct-Method di Gillespie, tra i più usati in biologia computazionale/systems biology. L’argomento della tesi riguarda l’uso di questo software nello studio delle oltre 100 reazioni biochimiche del PURESYSTEM™, comparando i risultati ottenuti in diverse condizioni (volume totale di reazione, concentrazioni delle specie, costanti cinetiche delle singole reazioni). Dopo aver affinato il modello in silico di Trascrizione/traduzione coupled (accoppiato), sono state effettuate delle simulazioni variando alcune variabili macroscopiche (concentrazioni delle specie e costanti cinetiche), mostrando un'importante dipendenza della traduzione dalla trascrizione, soprattutto considerando il grande limite energetico di un sistema che non produce al suo interno nucleotidi trifosfato

    Quasi-cellular systems: Stochastic simulation analysis at nanoscale range

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    Background: The wet-lab synthesis of the simplest forms of life (minimal cells) is a challenging aspect in modern synthetic biology. Quasi-cellular systems able to produce proteins directly from DNA can be obtained by encapsulating the cell-free transcription/translation system PURESYSTEM™(PS) in liposomes. It is possible to detect the intra-vesicle protein production using DNA encoding for GFP and monitoring the fluorescence emission over time. The entrapment of solutes in small-volume liposomes is a fundamental open problem. Stochastic simulation is a valuable tool in the study of biochemical reaction at nanoscale range. QDC (Quick Direct-Method Controlled), a stochastic simulation software based on the well-known Gillespie's SSA algorithm, was used. A suitable model formally describing the PS reactions network was developed, to predict, from inner species concentrations (very difficult to measure in small-volumes), the resulting fluorescence signal (experimentally observable).Results: Thanks to suitable features specific of QDC, we successfully formalized the dynamical coupling between the transcription and translation processes that occurs in the real PS, thus bypassing the concurrent-only environment of Gillespie's algorithm. Simulations were firstly performed for large liposomes (2.67μm of diameter) entrapping the PS to synthetize GFP. By varying the initial concentrations of the three main classes of molecules involved in the PS (DNA, enzymes, consumables), we were able to stochastically simulate the time-course of GFP-production. The sigmoid fit of the GFP-production curves allowed us to extract three quantitative parameters which are significantly dependent on the various initial states. Then we extended this study for small-volume liposomes (575 nm of diameter), where it is more complex to infer the intra-vesicle composition, due to the expected anomalous entrapment phenomena. We identified almost two extreme states that are forecasted to give rise to significantly different experimental observables.Conclusions: The present work is the first one describing in the detail the stochastic behavior of the PS. Thanks to our results, an experimental approach is now possible, aimed at recording the GFP production kinetics in very small micro-emulsion droplets or liposomes, and inferring, by using the simulation as a reverse-engineering procedure, the internal solutes distribution, and shed light on the still unknown forces driving the entrapment phenomenon. © 2013 Calviello et al.; licensee BioMed Central Ltd

    Linking disaster risk reduction, climate change, and the sustainable development goals

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    PURPOSE: The purpose of this paper is to better link the parallel processes yielding international agreements on climate change, disaster risk reduction, and sustainable development. DESIGN/METHODOLOGY/APPROACH: This paper explores how the Paris Agreement for climate change relates to disaster risk reduction and sustainable development, demonstrating too much separation amongst the topics. A resolution is provided through placing climate change within wider disaster risk reduction and sustainable development contexts. FINDINGS: No reason exists for climate change to be separated from wider disaster risk reduction and sustainable development processes. RESEARCH LIMITATIONS/IMPLICATIONS: Based on the research, a conceptual approach for policy and practice is provided. Due to entrenched territory, the research approach is unlikely to be implemented. ORIGINALITY/VALUE: Using a scientific basis to propose an ending for the silos separating international processes for climate change, disaster risk reduction, and sustainable development

    Why High-Performance Modelling and Simulation for Big Data Applications Matters

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    Modelling and Simulation (M&S) offer adequate abstractions to manage the complexity of analysing big data in scientific and engineering domains. Unfortunately, big data problems are often not easily amenable to efficient and effective use of High Performance Computing (HPC) facilities and technologies. Furthermore, M&S communities typically lack the detailed expertise required to exploit the full potential of HPC solutions while HPC specialists may not be fully aware of specific modelling and simulation requirements and applications. The COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications has created a strategic framework to foster interaction between M&S experts from various application domains on the one hand and HPC experts on the other hand to develop effective solutions for big data applications. One of the tangible outcomes of the COST Action is a collection of case studies from various computing domains. Each case study brought together both HPC and M&S experts, giving witness of the effective cross-pollination facilitated by the COST Action. In this introductory article we argue why joining forces between M&S and HPC communities is both timely in the big data era and crucial for success in many application domains. Moreover, we provide an overview on the state of the art in the various research areas concerned

    A SARS-CoV-2 protein interaction map reveals targets for drug repurposing

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    The novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 2.3 million people, killed over 160,000, and caused worldwide social and economic disruption1,2. There are currently no antiviral drugs with proven clinical efficacy, nor are there vaccines for its prevention, and these efforts are hampered by limited knowledge of the molecular details of SARS-CoV-2 infection. To address this, we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), identifying 332 high-confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (29 FDA-approved drugs, 12 drugs in clinical trials, and 28 preclinical compounds). Screening a subset of these in multiple viral assays identified two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the Sigma1 and Sigma2 receptors. Further studies of these host factor targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19

    Detecting and quantifying the translated transcriptome with Ribo-seq data

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    Die Untersuchung der posttranskriptionellen Genregulation erfordert eine eingehende Kenntnis vieler molekularer Prozesse, die auf RNA wirken, von der Prozessierung im Nukleus bis zur Translation und der Degradation im Zytoplasma. Mit dem Aufkommen von RNA-seq-Technologien können wir nun jeden dieser Schritte mit hohem Durchsatz und Auflösung verfolgen. Ribosome Profiling (Ribo-seq) ist eine RNA-seq-Technik, die darauf abzielt, die präzise Position von Millionen translatierender Ribosomen zu detektieren, was sich als ein wesentliches Instrument für die Untersuchung der Genregulation erweist. Allerdings ist die Interpretation von Ribo-seq-Profilen über das Transkriptom aufgrund der verrauschten Daten und unserer unvollständigen Kenntnis des translatierten Transkriptoms eine Herausforderung. In dieser Arbeit präsentiere ich eine Methode, um translatierte Regionen in Ribo-seq-Daten zu erkennen, wobei ein Spektralanalyse verwendet wird, die darauf abzielt, die ribosomale Translokation über die übersetzten Regionen zu erkennen. Die hohe Sensibilität und Spezifität unseres Ansatzes ermöglichten es uns, eine umfassende Darstellung der Translation über das menschlichen und pflanzlichen (Arabidopsis thaliana) Transkriptom zu zeichnen und die Anwesenheit bekannter und neu-identifizierter translatierter Regionen aufzudecken. Evolutionäre Konservierungsanalysen zusammen mit Hinweisen auf Proteinebene lieferten Einblicke in ihre Funktionen, von der Synthese von bisher unbekannter Proteinen einerseits, zu möglichen regulatorischen Rollen andererseits. Darüber hinaus zeigte die Quantifizierung des Ribo-seq-Signals über annotierte Genemodelle die Translation mehrerer Transkripte pro Gen, was die Verbindung zwischen Translations- und RNA-Überwachungsmechanismen offenbarte. Zusammen mit einem Vergleich verschiedener Ribo-seq-Datensätze in menschlichen und planzlichen Zellen umfasst diese Arbeit eine Reihe von Analysestrategien für Ribo-seq-Daten als Fenster in die vielfältigen Funktionen des exprimierten Transkriptoms.The study of post-transcriptional gene regulation requires in-depth knowledge of multiple molecular processes acting on RNA, from its nuclear processing to translation and decay in the cytoplasm. With the advent of RNA-seq technologies we can now follow each of these steps with high throughput and resolution. Ribosome profiling (Ribo-seq) is a popular RNA-seq technique, which aims at monitoring the precise positions of millions of translating ribosomes, proving to be an essential tool in studying gene regulation. However, the interpretation of Ribo-seq profiles over the transcriptome is challenging, due to noisy data and to our incomplete knowledge of the translated transcriptome. In this Thesis, I present a strategy to detect translated regions from Ribo-seq data, using a spectral analysis approach aimed at detecting ribosomal translocation over the translated regions. The high sensitivity and specificity of our approach enabled us to draw a comprehensive map of translation over the human and Arabidopsis thaliana transcriptomes, uncovering the presence of known and novel translated regions. Evolutionary conservation analysis, together with large-scale proteomics evidence, provided insights on their functions, between the synthesis of previously unknown proteins to other possible regulatory roles. Moreover, quantification of Ribo-seq signal over annotated transcript structures exposed translation of multiple transcripts per gene, revealing the link between translation and RNA-surveillance mechanisms. Together with a comparison of different Ribo-seq datasets in human cells and in Arabidopsis thaliana, this work comprises a set of analysis strategies for Ribo-seq data, as a window into the manifold functions of the expressed transcriptome

    DDX3X and DDX3Y are redundant in protein synthesis

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    DDX3 is a DEAD-box RNA helicase that regulates translation and is encoded by the X- and Y-linked paralogs DDX3X and DDX3Y While DDX3X is ubiquitously expressed in human tissues and essential for viability, DDX3Y is male-specific and shows lower and more variable expression than DDX3X in somatic tissues. Heterozygous genetic lesions in DDX3X mediate a class of developmental disorders called DDX3X syndrome, while loss of DDX3Y is implicated in male infertility. One possible explanation for female-bias in DDX3X syndrome is that DDX3Y encodes a polypeptide with different biochemical activity. In this study, we use ribosome profiling and in vitro translation to demonstrate that the X- and Y-linked paralogs of DDX3 play functionally redundant roles in translation. We find that transcripts that are sensitive to DDX3X depletion or mutation are rescued by complementation with DDX3Y. Our data indicate that DDX3X and DDX3Y proteins can functionally complement each other in the context of mRNA translation in human cells. DDX3Y is not expressed in a large fraction of the central nervous system. These findings suggest that expression differences, not differences in paralog-dependent protein synthesis, underlie the sex-bias of DDX3X-associated diseases

    Data from: Super-resolution ribosome profiling reveals unannotated translation events in Arabidopsis

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    Deep sequencing of ribosome footprints (ribosome profiling) maps and quantifies mRNA translation. Because ribosomes decode mRNA every 3 nt, the periodic property of ribosome footprints could be used to identify novel translated ORFs. However, due to the limited resolution of existing methods, the 3-nt periodicity is observed mostly in a global analysis, but not in individual transcripts. Here, we report a protocol applied to Arabidopsis that maps over 90% of the footprints to the main reading frame and thus offers super-resolution profiles for individual transcripts to precisely define translated regions. The resulting data not only support many annotated and predicted noncanonical translation events but also uncover small ORFs in annotated noncoding RNAs and pseudogenes. A substantial number of these unannotated ORFs are evolutionarily conserved, and some produce stable proteins. Thus, our study provides a valuable resource for plant genomics and an efficient optimization strategy for ribosome profiling in other organisms
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